*Proceeding Paper* **Development of the Smart Jacket Featured with Medical, Sports, and Defense Attributes using Conductive Thread and Thermoelectric Fabric †**

**Aman Ul Azam Khan 1,\*, Aurghya Kumar Saha 1, Zarin Tasnim Bristy 2, Tasnima Tazrin 1, Abdul Baqui 1,‡ and Barshan Dev 1**

**Abstract:** The exigency of humans is boosting the necessity of Smart Textiles in this modern era. A decade ago, envisioning sophisticated outerwear with several uses were considered a challenge. This study aims to a jacket with 15 features; divided into 7 groups, including defense, sports, health, medical, women, and children safety mechanisms, 4 out of these 15 functions can be controlled by an Android app, "Smart Jacket BUFT". To avoid nonrenewable energy sources, solar power and energy harvesting technology to produce electricity from body heat and foot-powered energy were used, Smart jacket has embedded circuits and sensors alone with AD8232, MAX30100, NEO6m GPS, and ESP32 microcontrollers & voice and app-control. It is hopping that; his initial stage of growth and improvement will pave the way for subsequent activities.

**Keywords:** smart jacket; 15 features; android application; medical; defense; conductive thread; energy harvesting technology

#### **1. Introduction**

The invention of textiles roughly 27,000 years ago could be regarded as the first time humans created a useful material [1]. Smart Textile can detect mechanical, thermal, magnetic, chemical, electrical, or other environmental variables and respond in a programmed manner (stimuli) [2]. Electronics are made from conductive threads and fabrics, whose limits and potential are determined by textile materials and production procedures [3]. In the late 1990s, MIT and the Georgia Institute of Technology conducted a series of studies on E-textiles in academia [4]. In the field of Textile Technology, it was hard to imagine the concept of Smart Jackets with all sorts of features, including health and medicinal functionalities, sports flexibilities, women's safety options, children's safety options, defense options, and a lot more, in just one jacket. The wearable electronic jacket is nowadays playing an important role in the medical world. New monitoring system support resources have been developed because of recent technological advances in mobile devices and wireless communications. A few years ago, the thought of a jacket that could seamlessly communicate and converse with a personal assistant and deliver practical solutions to everyday difficulties was simply a concept. This research paper brought the notion to life by designing a practical smart jacket with a total of fifteen features. To assure the jacket's flexibility and efficiency, we developed our own conductive thread and fabric flexible circuit. The Wearable MotherboardTM envisioned clothing that could monitor important signals discretely [5]. Cooseman et al. described a wireless charging garment with a patient

**Citation:** Khan, A.U.A.; Saha, A.K.; Bristy, Z.T.; Tazrin, T.; Baqui, A.; Dev, B. Development of the Smart Jacket Featured with Medical, Sports, and Defense Attributes using Conductive Thread and Thermoelectric Fabric. *Eng. Proc.* **2023**, *30*, 18. https:// doi.org/10.3390/engproc2023030018

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 7 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

monitoring system [6]. The use of carbon nanotubes to convert cotton thread for use in E-textiles was described in a 2008 study [7]. MyHeart, a 2009 EU (FP 6)-funded project, created ECG- and breathing-sensitive smart textiles [8]. For energy textile solution, in 2007, Qin et al. described energy harvesting utilizing Piezoelectric Zinc Oxide nanowires grown around textile fibers [9]. Design Research Lab at the Berlin University of the Arts and Telekom Innovation Laboratories designed the Smart Maintenance Jacket, which uses wearable technology for industrial maintenance work and predicts the future role of networked wearables in these contexts for telecommunications carriers [10]. Muhammad Arsalan et al. developed a next-generation tactical system for monitoring military health data. The suit's internal CPU receives and sends sensor data using LoRaWAN technology. The report analyzes the suit's system design and functionality [11]. Anurag Sharma et al. developed an IoT-based smart jacket in 2022 with ECG, heart rate, body temperature, air temperature, and oximeter sensors [12]. In the same year, Manibabu A and colleagues designed a Smart Thermal Jacket with Wearable Sensors using IoT [13]. In addition, Paolo Visconti et al. demonstrated a smart garment, in 2022, that monitors environmental factors and vital signs to monitor workers in hazardous industries [14]. The University of Engineering and Technology, Pakistan designed a wearable solar energy harvesting jacket for vital health monitoring systems in the current year [15]. Dilber Uzun Ozsahin et al. used multiple devices in one smart jacket [16].

#### **2. Objectives**

#### **3. Materials & Methods**

#### *3.1. Materials*

The jacket was made with 100% polyester fabric of 300 GSM.

Trims: Plastic Zipper, Metal Zipper; Sewing Thread: 20/2, 40/3 and Fabric Glue was also used. Conductive thread and fabric flexible circuits maintain conductivity and add flexibility. To make it eco-friendly and cost-effective, waste materials were utilized. Besides, developed thermoelectric fabric was used which body heat into electrical energy. Table 1 shows the waste materials used in Smart Jacket.

**Table 1.** Waste Material Used in Smart Jacket.

#### *3.2. Methodology*

Figure 1 shows Jacket's methodology according to units. The smart jacket's heart and brain work simultaneously. It collects data from NEO6M GPS module, MAX30100 Oxi-Pulse sensor, and AD8232 sensor circuit. An Android app was developed for Smart Jacket's brain and heart. Before starting the app, ESP32 was connected to a mobile hotspot. The NEO6M GPS module sent a signal to satellites. GPS started when satellites sent data to NEO6M GPS module. Firebase displays the user's location, heart rate, oxygen level, and

ECG via a mobile app after receiving ESP32 data. (Figure 2b) Despite using two 1000 mAh 3.7 V DC batteries for a total voltage of 7.2 V and an LM7805 MOSFET as a 5.0 V voltage regulator, they were all turned on by 5.0 V. This 7.2-volt battery was charged by a 5.0-volt, two-piece, 1-watt solar panel on the back of the jacket.

**Figure 1.** Flowchart of Jacket's methodology according to units.

**Figure 2.** (**a**) Final Smart Jacket (**b**) Dual Display of automatic heat-warm system; (**c**) Jacket Defense Unit (**d**) The Android application's data of the smart jacket.

The body unit was equipped with an automated heating system, automatic light on/off, a smart screen on the cuff, and a device charging system. Automatic heating used a W1219 temperature controller circuit and an NTC sensor module. It was a heating/cooling controller. First, a W1219 circuit was connected to a 14.8-volt power bank charged by a shoe's piezo element generated electricity. Three heating pads were on W1219. As the user walked, mechanical energy was transferred to piezo components and converted to electrical energy. The potential difference's energy was then converted with a DC-to-DC converter and sent to a 14.8-volt power source. Nichrome wire and cotton made a heating pad formed a solenoid. The heating pad warmed. W1219 has two screens. The two displays showed the current and set temperatures. The user could set the temperature to their personal preferences. Moreover, the automated light on/off system constructed by a flexible fabric circuit with a resistor, transistor, and LDR. The battery was 3.7 V, 800 mAh DC which was charged by thermoelectric fabric. Thermoelectric fabric was formed from electronic and textile waste. When light-dependent resistors detect more light, their resistance rises, deactivating the circuit. When the LDR sensed no light, its resistance dropped, triggering the circuit. This turned on the jacket's hoodie LEDs.

Electronic device charging system contained by a 3.7 volt, 3600 mAh Lithium-ion battery with a voltage boost module. A 5-volt, 1-watt solar panel charged this battery. A USB voltage booster was attached to the battery in the welt pocket. This study featured a smart screen on the cuff of the smart jacket which was created with a fabric flexible circuit using Arduino pro mini, HC-05 Bluetooth module, 0.96" OLED display, 3.7-volt 2800 mAh battery (which can be charged by1 watt, 5.0-volt solar panel), and polyester fabric and conductive thread. Figure 2c exhibits the methods of the smart screen the on cuff.

The jacket's voice unit consisted of an old speaker PCB circuit, 3.7 volts and 1000 mAh a lithium-ion battery (which can be charged by 1 watt, 5.0-volt solar panel), a speaker (3 w) and a microphone. A Bluetooth circuit and an mp3 circuit were combined to from this circuit whose connection was formed with the smartphone upon activation. Personal assistants such as Google, Siri, and Alexa can be linked into smartphones.

In the jacket's right-glove there is a shocking element. For that, a high-voltage transformer whose input was 3.7 to 6 volts and output were 400,000 DC volt, a metal shank button, a 3.7-volt DC lithium-ion battery, and a 5.0-volt 1-watt mini solar panel. A high voltage transformer was connected to shank button and placed on the glove's two-finger. The glove had to be waterproof and for extra safety we used synthetic rubber on the shank button area. Upon pressing the switch shank button, 400,000 DC volts were produced, allowing them to defend themselves easily. The left glove has a toxic gas spreader system with an ultrasonic MIST module, a switch, and a 1000 mAh, 3.7-volt lithium polymer battery, which was charged by a 5-volt 1-watt solar panel. For MIST, we made a liquid reserve tank from plastic waste. MIST module had a piezo atomizer disc. When activated by battery, the MIST module main circuit of IC sent data to the disc, which turned liquid into gas. As a self-defense glove, chloroform (CHCl3) was used. So, users can easily spread toxic gas (CHCl3) for defense or protection purposes.

#### **4. Results**

All this jacket's functionalities were executed excellently. Figure 2a–c show the practical visualization of the jacket. An Android mobile application was developed and given the name "SMART JACKET BUFT" for these four functions (GPS with emergency switch, Oximeter, pulse meter, ECG). Figure 2d shows the Android application of the smart jacket. These features can be accessed by multiple users who are using the same mobile applications as the wearer. the power unit uses solar system (which covers most functions), piezoelectric shoe, and thermoelectric fabric (which is still under testing). However, during this research we were unable to find any kind of flexible battery, so we used tiny-sized lithium polymer and lithium-ion batteries.

#### *Formatting of Mathematical Components*

For the solar system,

Estimate voltage and current from the mini solar panel [17].

$$\text{Charging time} = \frac{\text{battery capacity (watt hours)}}{\text{solar power (in watt)}} \times 2 \tag{1}$$

On the back part of this jacket are five small solar panels that serve as the power output's charging mechanism. Each solar panel has dimensions of 90 mm by 90 mm (l × w) and voltage of 5.00 volts. Table 2 estimated charging time according to unit.

**Table 2.** Estimated charging time according to unit.

#### **5. Features**

The jacket includes 15 features.

#### **6. Discussion & Conclusions**

*6.1. Discussion*

This study sought to improve the adaptability of textiles and electronic components. Conductive thread was utilized to connect both flexible fabric circuits and fabric-printed circuit boards. To manufacture the most popular conductive thread, however, carbon nanotubes, graphene, and silver were utilized, which was relatively expensive [18]. For this study, a conductive thread was developed with a meager resistance (0.2 Ω/cm to 0.0164 Ω/cm). Therefore, many circuits in one jacket will not be a problem. The jacket research encompassed all human requirements. This garment can be employed to construct seven jackets in the future. Medical, Military, Women's, Children's, Tech-Tex, Cyberia Survival, and Sports Jackets. E-textile research also requires Flexible Nanochips and Nano electric particles which were not available in BD. In addition, the required chemical composition for this experiment was not available in BD. Moreover, the financial crisis was the most crucial truth about this research. Despite showing that entire jackets can be developed with Fabric PCB shown in Figure 3 and Fabric Flexible Circuits. A piezo-shoe

jacket attachment was also created for charging reasons. This feature aimed to construct wireless electrical charging technology; however, it failed due to lack of resources, time, and knowledge. This investigation was concluded despite a few flaws. Any researcher working on e-textiles or intelligent textiles might benefit from this study.

**Figure 3.** Fabric PCB.

#### *6.2. Future Outlook*

Regarding the characteristics, this investigation discovered further revolutionary concepts. Some of the jacket's attributes may not have existed because of economic slump. Among other innovations, there was power generated from human hair, a small, flexible tab on apparel, a 20× magnifying camera, a poisonous gas detector, an air oxygen level meter, and a button projector. In addition, there were several medical features, such as an automatic blood pressure measuring system, a body temperature meter, and galvanic skin resistance, and others. In addition to defensive functions, such as a web shooter, laser burning system on the glove, CO2 spreading system, heads-up translating display, 100× magnification camera glasses, and several others, the smart jacket would be outfitted with an assortment of extra functionality. This investigation also offers some original ideas for the defense industry. As additions to robotics, two micro robots resembling Mr. Doctor and Mr. Commander were proposed. These intelligent micro robots will be concealed within the flap pockets. Importantly, jacket advancements include anti-bacterial fabric, ECG electrode fabric, fire-prevention fabric, and bulletproof fabric. Energy-harvesting textiles, such as solar fabric, would boost the value of clothing. The next generation of smart jackets would be powered by artificial intelligence. In the medical and military industries, this jacket improvement would be incredibly useful. Micro- and nanotechnology would enhance the jacket's performance, reduce its weight, and make it more comfortable.

**Author Contributions:** Conceptualization, A.U.A.K.; A.K.S.; A.B.; Z.T.B.; methodology, A.U.A.K.; A.B.; software, A.K.S.; validation, A.B.; Z.T.B.; T.T.; formal analysis, A.B.; A.U.A.K.; investigation, A.B.; B.D.; resources, A.K.S.; A.U.A.K.; data curation, A.U.A.K.; Z.T.B.; A.K.S.; writing—original draft preparation Z.T.B.; A.U.A.K.; writing—review and editing, A.B.; Z.T.B.; B.D.; visualization, A.U.A.K.; Z.T.B.; supervision, A.B.; project administration, A.B.; B.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to acknowledge financial support from the BGMEA University of Fashion and Technology, Dhaka 1230, Bangladesh.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available within the article and there is presented in every graph. There is no more data apart from the presented.

**Acknowledgments:** Erin Jahan Meem, Department of Textile Engineering, BGMEA University of Fashion & Technology. Taslima Ahmed Tamanna, Department of Textile Engineering, BGMEA University of Fashion & Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Proceeding Paper* **The Machine-Learning-Empowered Gesture Recognition Glove †**

**Jun Luo 1, Yuze Qian 1, Zhenyu Gao 2, Lei Zhang 2,\*, Qinliang Zhuang 1,\* and Kun Zhang 1,\***

**Abstract:** Recently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainstream trend of gesture recognition technology. Therefore, this article designs a machinelearning-empowered gesture recognition glove. We fabricate a flexible strain sensor with a sandwich structure, which has high sensitivity and good cycle stability. After the sensors are configured in the knitted gloves, the smart gloves can respond to different gestures. Additionally, according to the representation characteristics and recognition targets of sampled signal data, we explore a segmented processing method of dynamic gesture recognition based on Logit Adaboost algorithm. After classification training, the recognition accuracy of smart gloves can reach 97%.

**Keywords:** gesture recognition; flexible sensor; machine learning; Logit Adaboost algorithm

#### **1. Introduction**

As a method of nonverbal communication [1], standardized gestures play an important role in the transmission of message on specific occasions, such as communication between auditory-impaired individuals [2] and information interaction in AR/VR environments [3] or military fields [4]. However, without long-term systematic learning, it is difficult for most people to understand the meaning of gestures [5]. At present, gesture recognition technology offers a feasible solution to this communication barrier, which can effectively judge the specific movements of the hand.

Machine-vision-based image taking and processing has received extensive attention as a means of gesture recognition due to simple equipment requirements [6]. However, it is troubled by a series of problems in the process of application, including complex operation mechanism and variable lighting condition [7]. Accelerometers and gyroscopes are widely used in commercial gesture recognition devices, which can track the direction of the hand movement in three-dimensional space [8]. However, rigid materials and excessive volume limit the application scenarios of these devices, which bring inconvenience to users. Benefiting from the rapid advancement of flexible electronics and artificial intelligence, wearable flexible data gloves based on flexible sensors have been developed. Common flexible sensors for gesture recognition include piezoresistive sensors [9], capacitive sensors [10], piezoelectric sensors [11], triboelectric sensors [12] and EMG sensor arrays [13]. Through the electromechanical performance of flexible sensors, the deformation degree of the hand can be reflected correspondingly.

Furthermore, the classification of sensor signals and the extraction of gesture features are the key factors to determine the accuracy of gesture recognition. It generally requires intelligent algorithms to train sensor signals and generate appropriate classifiers [14]. However, due to the differences in gesture behavior patterns of individuals, the generalization

**Citation:** Luo, J.; Qian, Y.; Gao, Z.; Zhang, L.; Zhuang, Q.; Zhang, K. The Machine-Learning-Empowered Gesture Recognition Glove. *Eng. Proc.* **2023**, *30*, 19. https://doi.org/ 10.3390/engproc2023030019

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 21 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ability of the trained classifiers is low if the sample capacity is limited [15]. A common approach is to increase the number of people participating in algorithm training, which is time- and energy-consuming [16]. Therefore, there is a new idea to remove the irrelevant factors of sensor signals as much as possible through special designs.

In this study, we report on the machine-learning-empowered gesture recognition glove. Flexible piezoresistive strain sensors with high sensitivity and cycle stability were configured in knitted gloves. Then, in order to enhance the generalization ability of gesture recognition model, a multi segment classification model of dynamic gesture recognition based on Logit AdaBoost algorithm was explored. Static signals collected at intervals of gesture changes are usually considered invalid signals because of unobvious characteristics and uncertain duration. So, the model distinguished static signals from effective dynamic signals due to finger movements. Additionally, subsequent training and classification was only related to dynamic signals, avoiding misjudgment caused by static signals. With sufficient training for the defined gestures, the recognition accuracy reached 97%, showing great classification performance.

#### **2. Materials and Methods**

#### *2.1. Materials*

Reduced graphene oxide (r-GO) was supplied by Qingdao University. Polydimethylsiloxane (PDMS) was purchased from Microflu Co., Ltd. (Changzhou, China). *N*,*N*-Dimethylformamide (DMF, 99.5%) was bought from Aladdin Co., Ltd. (Shanghai, China).

#### *2.2. Fabrication of the Flexible Strain Sensor*

The r-Go dispersion (6 wt%) was prepared by two steps. The reduced graphene oxide powders were firstly dispersed in *N*,*N*-Dimethylformamide by mechanical shearing for 1 h at 3500 r/min, and, secondly, it was necessary to remove air bubbles from the solution using ultrasonic for 5 min. Then, the solution was poured onto the PDMS substrate (the volume ratio of PDMS matrix to curing agent was 10:1 and the curing condition was 70 ◦C for 1 h) and heated at 50 ◦C for 3 h using a hot plate. After the solvent evaporates completely, there was a composite film containing the r-Go layer and PDMS substrate. Finally, two copper wires were adhered to both sides of the r-Go layer with silver paste, which used as the electrodes of the strain sensor. Additionally, PDMS was poured and cured on the top of the composite film, resulting in a sandwich structure. The size of the final sample is about 35 mm × 4 mm × 1 mm.

#### *2.3. Characterization*

A scanning electron microscope (SEM, S-4800, Hitachi, Japan) was used to characterize the surface morphology of the R-Go film. Additionally, in order to test the sensing performance of the strain sensors, the flexible strain sensors were fixed between the metallic clamps of a stretching machine (ZQ-CI701G, 1000 N, ZHIQU, Dongguan, China), and a digital source-meter (Keithley 2400, USA) was used to measure the resistance of the sensors continuously in the process of stretching. The stretching speed was maintained at 30 mm/min.

#### *2.4. The Preparation of Data Gloves and Data Acquisition*

The configuration of strain sensors in data glove is shown in Figure 1a. To monitor the bending degree of fingers, the sensors were attached to the corresponding finger joint position of knitted gloves. Figure 1b shows the gesture library of this article. When making these gestures, the resistance signal acquisition of sensors was realized by the data acquisition card (Ni-6211). In the detection circuit, sensors were connected in parallel, and divider resistors were connected in series with the sensor on each branch. Based on the differential mode of the data acquisition card, the voltage on both sides of the sensors were collected completely and displayed in a laptop program designed with LabVIEW. The resistance of the sensor can be converted by Ohm's law.

**Figure 1.** (**a**) Configuration of flexible sensor in data glove. (**b**) Gesture library.

#### *2.5. The Design of Machine-Learning Algorithm*

A flow diagram of the machine-learning process is shown in Figure 2. During the training process, the change in voltage due to predetermined gestures was firstly merged into a matrix as the input of mRMR algorithm to discard useless information and retain relevant signals, which improves the generalization ability of the model and reduces the complexity of the algorithm. Subsequently, the multi-segment classification model was applied to classify gestures by using the extracted features with Logit AdaBoost algorithm. Based on the training results of the previous weak classifier, the Logit AdaBoost algorithm evaluated and redefined the parameter weights to update the classifier. Additionally, the Logit AdaBoost algorithm was not sensitive to noise and outliers, which prevented the model from over fitting to a certain extent. The classification process included two steps. First, static signals and dynamic signals were effectively distinguished. Then, combined with the sample characteristics and distribution, the motion trajectory of the finger was further subdivided. Finally, 10-fold cross-validation method was used to evaluate the model. It can make full use of the samples in the training process, which avoids over fitting.

**Figure 2.** The process of a multi-segment classification model.

#### **3. Results and Discussions**

#### *3.1. Morphology Characterization*

As shown in Figure 3a, based on a sandwich structure composed of a r-GO layer and two PDMS layers, the overlapping state of graphene sheets can change during the deformation of the sensor and recover under the constraint of elastic material (PDMS), resulting in the electromechanical response of sensors.

**Figure 3.** Topography characterization of the sensor. (**a**) Cross-section of the sensor. (**b**) SEM images of the r-GO film.

Figure 3b shows the that reduced graphene oxide is uniformly distributed in the r-GO layer, which is the basis of ensuring the stable electrical performance of the sensor.

#### *3.2. Piezoresistive Sensing Response*

Figure 4a shows the relationship between the sensor resistance and applied strains, from which the sensitivity of the strain sensor can be obtained. The sensitivity of the strain sensor can be calculated by the following equation:

$$GF = \frac{\Delta R / R\_0}{\varepsilon} \tag{1}$$

$$
\Delta R = R - R\_{\text{0}} \tag{2}
$$

where *ε* is the strain of the strain sensor, *R* represents the resistance of sensor after stretching, *R*0 is the resistance of sensors at *ε* = 0%. Figure 4a shows the strain sensor provides high sensitivity for detecting hand movements (GF~2.69 at strains below 12%, GF~5.05 at the strain ranges from 12% to 36%). Furthermore, the strain sensors also show a stability response over a large number of stretch–release cycles (above 1500 cycles), as shown in Figure 4b.

**Figure 4.** (**a**) The relationship between the sensor resistance and the applied strain. (**b**) Sensor stability under 1500 stretch−release cycles at 10% strain.

#### *3.3. The Selection of Classification Algorithm and Feature*

In order to find the data correlation, we extracted 10 characteristics as signal features, which are related to indicators of time domain, frequency domain and entropy. Then, we used the mRMR algorithm to check the importance of the features in the two stages. As shown in Table 1, waveform factors and skewness have the great correlation with the classification results of the first stage. Skewness and waviness are of significance for the classification of the second stage.

**Table 1.** Feature importance evaluation based on mRMR algorithm.

RUSBoost, Logit AdaBoost and Bagging are the common machine learning algorithms in data classification. Based on selected features through the mRMR algorithm, the classification results of the three algorithms on gesture data are shown in Figure 5. Area under curve (AUC) is a statistical concept. It represents the probability that the predicted value of positive cases is greater than the predicted value of negative cases. Whether it is the first stage or the second stage, the AUC of Logit AdaBoost algorithm is maximum. It shows that the model generated by Logit AdaBoost algorithm is most effective for gesture classification.

**Figure 5.** Comparison of model performance by AUC for RUSBoost, Logit AdaBoost, Bagging. (**a**) Performance of different algorithm models in the first stage. (**b**) Performance of different algorithm models in the second stage.

#### *3.4. The Gesture Recognition Modeling*

In the first stage, dynamic signals are separated from all samples for subsequent training. As shown in Figure 6a, the classification model can correctly classify most of the static gesture data and dynamic gesture data. Table 2 further shows the classification results of static signals and dynamic signals. Positive predictive value (PPV) is an index to evaluate the level of correct classification. The lower PPV of dynamic signals, the more sample capacity is lost in the second stage, resulting in unobvious and incomplete features as gesture output. Additionally, false discovery rate (FDR) represents the probability of misclassification, which affects the interference degree of data directly. High PPV level (96.9%) and low FDR level (3.1%) illustrate that effective gesture features are completely preserved.

The ROC curve after 10-fold cross-validation is shown in Figure 6b. Assuming that the dynamic signal is positive, the AUC value of the model in the first stage is 0.99 and the accuracy reaches 98.4%, indicating that the model can further eliminate invalid static signals.

Based on defined gestures, the movement of the hand is divided into the bending and straightening of fingers. Figure 6c and Table 3 shows the accuracy of classification remains at a high level. Additionally, Figure 6d illustrates the AUC value of the model is 0.97, which only has subtle decreases compared with the first stage. The good classification results of two stages indicates that the multi-segment classification model has the generalization ability for gesture recognition.

**Figure 6.** Test results for classification model of two phases. (**a**) Confusion matrix of dynamic gesture and static gesture. (**b**) ROC curve of classification model assuming dynamic gesture is positive. (**c**) Confusion matrix of straightening fingers and bending fingers. (**d**) ROC curve of classification model assuming straightening fingers is positive.

**Table 2.** Evaluation of classification model to distinguish static signals and dynamic signals.

**Table 3.** Evaluation of classification model to judge finger motion trajectory.

#### **4. Conclusions**

This article reported a type of machine-learning empowered gesture recognition glove. Combined with the electrical characteristics of flexible strain sensors and the signal processing ability of machine learning algorithms, data gloves judged the specific actions of hands accurately, which showed many commendable features, such as low weight and good comfortableness. At the same time, we paid attention to the interference of invalid static signals in the training and classification process. The model successfully extracted effective dynamic signals from all signals through multi-segment classification based on Logit AdaBoost algorithm. It is of significance to enhance the generalization ability of the recognition model and increase the accuracy of gesture recognition. However, not all irrelevant factors have been excluded for this model, which requires further research.

**Author Contributions:** Conceptualization, K.Z.; methodology, J.L., Y.Q. and Z.G.; writing—original draft preparation, J.L., Y.Q. and Z.G.; writing—review and editing, J.L., Y.Q., Z.G., L.Z., Q.Z. and K.Z.; supervision, L.Z., Q.Z. and K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (2232022G-01 and 19D110106), the National Natural Science Foundation of China (NO. 51973034).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Proceeding Paper* **Zinc-Ion Battery on a Polyester-Cotton Textile †**

**Sheng Yong 1,\*, Nick Hillier 1,2 and Steven Beeby 1**

**Abstract:** This work presents a simple, scalable and flexible zinc-ion secondary battery, fabricated on top of a textile substrate via standard fabrication processes. The proposed zinc-ion battery was fabricated on top of a polyester-cotton textile using solution-based processes and inexpensive cathode, anode and electrolyte materials. This battery achieved an area capacity of 19.1 μAh·cm−2 between 1.9 and 0.9 V.

**Keywords:** e-textile; flexible battery; zinc-ion battery

#### **1. Introduction**

Wearable electronics also known as e-textile are the integration of flexible electrical devices in clothing and accessories. In these systems, sufficient electrical energy is required for devices such as sensor nodes [1], microprocessors and transceivers [2]. At present, such systems are typically powered using an external electrical connection, battery or supercapacitor [3] which require frequent replacement [4] and/or need to be physically removed before washing. Therefore, a flexible energy-storage device such as a zinc-ion battery (ZIB) integrated on top of or inside a textile is important for e-textile applications.

ZIBs are rechargeable secondary electrical energy-storage devices. In comparison to ordinary secondary batteries such as lithium-ion and aluminium air batteries. This type of battery demonstrates distinct advantages, such as improving device safety by not using aggressive or environmentally unfriendly electrolytes (in lithium-ion batteries) and can be fully sealed without the requirement of oxygen access such as in an aluminium air battery. In addition, the assembly of ZIBs can be performed in the air, offering reduced fabrication complexity. Previously Yong et al. [5] presented a zinc-ion secondary textile battery. The manganese oxide cathode, zinc anode and polymer separator were integrated into a single polyester cotton textile layer. After vacuum impregnating the battery with an aqueous electrolyte, the zinc-ion battery device achieved an areal capacity of 35.6 μAh·cm−2 between 1.9 V and 0.9 V. The use of the polyester cotton textile as the separator layer holder increased the device thickness, introducing extra encapsulation challenges. Xu et al. [6] reported flexible vanadium (V) oxide carbon-fiber cloth electrodes for potential zinc-ion battery application. This electrode demonstrated an areal capacity of 154 mAh·g−1. These examples demonstrate the capability of fabricated ZIBs within textile material. However, the electrode material vanadium (V) oxide is corrosive and required extra consideration during the device-encapsulation process. It can increase the device thickness and mechanical inflexibility, introducing extra encapsulation challenges for real-world power source units in e-textile systems.

This paper builds upon the previous works [5], the proposed zinc-ion battery was fabricated on top of a polyester-cotton textile using solution-based processes and inexpensive materials. The battery's anode was a flexible zinc polymer film prepared via spray coating,

**Citation:** Yong, S.; Hillier, N.; Beeby, S. Zinc-Ion Battery on a Polyester-Cotton Textile. *Eng. Proc.* **2023**, *30*, 20. https://doi.org/ 10.3390/engproc2023030020

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 16 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and the battery's cathode was a spray-coated manganese (II, III)-oxide (Mn3O4) polymer layer on a polyester-cotton textile. The separator layers were implemented on top of the battery's cathode with doctor blading or screen printing followed by a phase inversion process. The battery was tested with an aqueous electrolyte to study its discharge performance.

#### **2. Material and Methods**

Figure 1 shows the fabrication process of the zinc-ion battery. Firstly, silver paste (TC-C4001, Smart Fabric Inks Ltd., Southampton, UK) was screen printed on the hotmelt PEVA encapsulation film that dried at room temperature for 24 h and polyester-cotton textile where the silver paste soaked through the textile, forming the current collector after cured in a box oven at 100 ◦C for 30 min. A thin film of nickel metal was sputter coated on top of silver textile forming an inert layer. A Mn3O4/polymer (85:15 by wt.%) cathode was spray coated onto the top of the nickel film. The coated textile was then dried in a box oven at 120 ◦C for 30 min. The current collector and cathode layer were screen-printed with a co-polymer solution and treated with a phase-inversion process to form a porous membrane published previously [5]. This co-polymer membrane acts as the separator of the supercapacitor that prevents electrical short circuitry but allows ions to transfer between the cathode and anode electrodes. The anode layer used to evaluate the battery performance was a spray-coated zinc/polymer (95:5 by wt.%) on top of silver that printed on the encapsulation film. The current collector, cathode, separator combined textiles and the zinc metal anode foil shown in Figure 2a were punched into circular shapes with a diameter of 1 cm, added with an aqueous electrolyte containing 1 M ZnSO4 + 0.1 M MnSO4 through the hole before being heat pressed together to form the full device.

**Figure 1.** Schematic, illustration of the fabrication process.

**Figure 2.** (**a**) Photograph of manganese oxide cathode textile, cathode textile with membrane coating and zinc anode layer with encapsulation film, (**b**) battery initial voltage reading under 90 degrees bending.

#### **3. Results**

The manganese-oxide cathode textile, cathode textile with membrane coating and zinc anode layer with encapsulation film are shown in Figure 2a. The device is assessed using a potentiostat Autolab pgsatat101(Metrohm Autolab, Utrecht, The Netherlands). Its electrochemical performance was obtained from galvanostatic cycling (GC) at 1 mA·cm−2 and cyclic voltammetry (CV) at 10 mV·s−1 between 0.9 and 1.9 V.

The cycling test in Figure 3a was derived from the GC test at 1; this battery achieved an area capacity of 19.1 μAh·cm−2 between 1.9 and 0.9 V after the initial test cycle. Figure 3b shows the CV test results after the first test cycle for the zinc-ion battery on the textile. The oxidation (charging) peak occurred at 1.72 V; it also demonstrated a reduction (discharging) peak at 1.26 V. These are typical voltage peaks for the redox reactions in a zinc-ion secondary battery with manganese-oxide cathodes.

**Figure 3.** (**a**) GC-derived voltage charge and discharge result of the zinc-ion battery at 1 mA cm−2 tested between 0.9 and 1.9 V, (**b**) CV tests between 0.9 V to 1.9 V at the scan rate of 10 mV·s−1.

#### **4. Conclusions**

This paper presents an encapsulated, flexible zinc-ion battery on a single piece of polyester cotton. The operating voltage for this textile battery in this work was between 0.9–1.9 V and achieves an area-specific capacity of 19.1 μAh·cm−2 and demonstrates good bending durability. In comparison with the previous devices [6], the proposed zinc-ion battery is encapsulated and tested without tube fitting. Future work will include optimizing the formulation and fabrication method of the polymer membrane in the textiles for better electrochemical performances and durability. The final device can be applied in a wide range in the e-textile system.

**Author Contributions:** Conceptualization, S.Y.; methodology, S.Y. and N.H.; validation, S.Y. and N.H.; formal analysis, S.Y.; investigation, S.Y.; resources, S.B.; data curation, S.Y.; writing—original draft preparation, S.Y.; writing—review and editing, N.H. and S.B.; visualization, S.Y.; supervision, S.B.; project administration, S.B.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors thank the EPSRC for supporting this research with grant references EP/P010164/1 and EP/I005323/1.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data for this paper can be found at DOI: https//doi.org/10.5258/ SOTON/D1127.

**Acknowledgments:** This work was also supported by the Royal Academy of Engineering under the Chairs in Emerging Technologies scheme.

**Conflicts of Interest:** Steven Beeby is the director of Smart Fabric Inks.

#### **References**

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*3.3. Antioxidant Compounds, Oxidation and Color Stability of LTL under Storage Conditions*

The content of antioxidant compounds observed in LTL samples stored at two retail display conditions is shown in Table 4. No significant interactions were observed between the main effects.

**Table 4.** Content of antioxidant vitamins and antioxidant stability in LTL muscle at the end of each storage and retail display condition.

1 D, Dietary treatment: 0DG, control; 15DG, 15% DG; 30DG, 30% DG and 45DG, 45% DG (%DM basis); R, retail treatment: R1, aerobic exposure for seven days; R2, vacuum-packed conditions for 25 days, plus aerobic exposure for three days. 2 SEM: standard error of the mean. 3 D\*R, dietary treatment x retail treatment interaction. Means in the same row having different letters are significant at the *p* ≤ 0.05 level. NS: no significant.

The content of α-tocopherol was not affected either by the feeding diet or the storage conditions, and the average concentration was 0.58 μg/g meat. Regarding γ-tocopherol, a higher content was observed in meat from steers fed with DG diets, while it was not affected by the storage conditions. However, in all cases, the values of α- and γ-tocopherol observed were lower than those observed at 72 h post-mortem. Previously, it has been reported that vitamin E affects lipid and color stability after refrigerated storage [13]. In the present study, during storage, α-tocopherol levels dropped around 34% in meat from all diets, except in those from 45DG, in which it decreased by 64%, whereas γ-tocopherol levels dropped around 20% in meat from all diets, except in those from 45DG, in which it decreased by 48%. Indeed, the FRAP assay showed that the total reducing activity in meat was greater for R1 than for R2, but also that it was not affected by the feeding diets. Other authors have reported that, after meat aging and aerobic exposure, both the FRAP values and the content of antioxidant vitamins decreased [34]. In our study, the effect of storage on FRAP was not observed on vitamin content.

Regarding retinol content, the meat from animals fed 30DG had greater content than that from animals fed 15DG and 45DG, while the meat from 0DG had the lowest content (Table 4). Retinol content was not affected by the retail display conditions.

Together with α-tocopherol, other compounds like the minor forms of vitamin E, carotenoids, and their retinoid derivatives, protect tissues against free radicals and prevent oxidation reactions [13]. All these compounds could be administered in the feeding diets, as stated before. However, pro-oxidant compounds, like unsaturated fatty acids, could also be administered in the diets. It has been reported that, during refrigerated storage, oxidative processes increase exponentially [13], since factors like temperature, the presence of light, and oxygen act as catalysts [6,9]. In our study, the values of TBARS measured at the end of the storage period in LTL samples showed a tendency (*p* = 0.08) to interact between dietary treatment and retail display conditions (Figure 2). Meat from DG diets showed similar TBARS values, independently of the storage condition. Interestingly, the meat from 0DG showed the highest TBARS values when exposed to R2 conditions. In this sense, greater TBARS values have been reported in meat samples from 0% DG diets, compared to meat samples from 50% DG diets, after 21 days of aging [38]. However, de Mello et al. [17] observed greater TBARS values in meat from animals fed 30% DG than in meat from animals fed 0 and 15% DG and aged for 42 days. These authors observed meat surface discoloration after aging followed by a retail display and related these events with a higher content of PUFAs in meat when feeding cattle with 30% DG.

**Figure 2.** Lipid oxidation (mean ± SEM) measured as thiobarbituric acid reactive substances (TBARS) in LTL at the end of storage and retail display conditions for each dietary treatment (0, 0%DG control; 15, 15% DG; 30, 30%DG; 45, 45% DG, DM basis).

The TBARS assay is used as a marker of lipid oxidation and as a predictor of the perception of rancidity in meat. In different studies, values of 2–2.5 mg MDA/kg have been established as the accepted limit at which meat shows no rancidity [6]. In our study, all the TBARS values observed were below that threshold.

As stated above, anti- and pro-oxidants can be provided in the feeding diets, and their balance would be a determinant for the oxidative stability of meat. In our study, the feeding diets supplied liposoluble vitamins to meat, mostly γ-tocopherol. However, α-tocopherol is the isomer with the greatest antioxidant action and at least 3.0 to 3.5 μg/g of meat is needed to preserve tissues from oxidation and/or to maintain color stability [7,23,39]. Interestingly, it has been proposed that a dosage level of γ-tocopherol and a similar dosage level of α-tocopherol are approximately equally effective in preventing oxidation in red meat [39]. In our study, the sum of the content of antioxidant vitamins (1.39 μg/g meat on average) was below the optimal levels proposed to preserve tissues from oxidation, but the extent of oxidative damage was far below the threshold for rancidity. Therefore, it could be assumed that the antioxidant compounds present in meat samples were exerting a cooperative antioxidant activity.

It is known that the nutritional strategies that modify the fatty acid profile of meat could increase the susceptibility of meat to lipid oxidation by increasing n-3 PUFAs [7,13]. We showed that the content of PUFAs and n-6 PUFAs did not change with the DG level, while that of n-3 PUFAs decreased numerically [40]. This effect was statistically seen in the n-6/n-3 PUFA ratio, which increased as the DG level increased due to the decrease in n-3 PUFAs. In the present study, we observed that the inclusion of DG in the feeding diets supplied both more antioxidant compounds and fewer pro-oxidant compounds.

In this sense, it has been reported that the supplementation of DG diets with vitamin E does not improve the effect against lipid oxidation, since the meat from 30DG-diets, with and without vitamin E supplementation, had similar values of TBARS after 7 days of retail display [41].

The meat color parameters found at the end of the retail display conditions are presented in Table 5. In the case of the R2 storage condition, an extra measurement was done at the end of the vacuum-packed storage, prior to display conditions (R2a). The color parameters analyzed showed no interaction between the main effects.

**Table 5.** Color parameters (CIELab) in LTL muscle at the end of each storage and retail display condition.

1 D, Dietary treatment: 0DG, control; 15DG, 15% DG; 30DG, 30% DG and 45DG, 45% DG (%DM basis); R, retail treatment: R1, aerobic exposure; R2a, end of vacuum-packed storage (25 days); R2b, end of aerobic exposure of R2. 2 SEM: standard error of the mean. 3 D\*R, dietary treatment x retail treatment interaction. Means in the same row having different letters are significant at the *p* ≤ 0.05 level. NS: no significant.

Meat color did not differ statistically with the inclusion of increasing levels of DG in the diets. These results are in agreement with those presented by de Mello et al. [17], who compared beef from DG diets with control diets (0% only corn, 15% and 30% DG). On the other hand, Depenbusch et al. [19] reported a linear decrease in redness (a\* value), after 7 days of retail display conditions, in steaks from heifers fed increasing levels of DG (0% to 75%). Moreover, to analyze differences in beef color, delta color change (ΔE) was calculated, following AMSA procedures, between steaks from DG diets and steaks from the control diet. The values of ΔE obtained for 15DG, 30DG, and 45 DG were 1.58, 1.69, and 2.13, respectively. Some authors reported that values of ΔE from 2 to 10 indicate that differences in color are perceptible by the human eye at a quick look [42] while other authors reported that values between 1.5 to 3.0 in beef could be evident to consumers [43].

Meat color stability is the result of the balance between anti- and pro-oxidant compounds [44]. It could be assumed that meat with an increase in PUFA content, due to the inclusion of DG in the diet, would be more susceptible to lipid oxidation and discoloration. Thus, as previously reviewed, it is difficult to define a general effect on meat color due to the inclusion of DG in the diet [3].

During storage, the formation of metmyoglobin (MMb) modifies meat color with a decrease in both a\* and C\* parameters [45]. Besides, the rate at which MMb develops could be affected by the conditions of packaging and storage. In the present study, storage conditions influenced color parameters. Samples from R2 conditions were brighter and had higher values of a\* and C\* than steaks from R1 conditions. Indeed, samples from R1 and R2 showed a value of ΔE of 2.93, which indicated that differences could be evident to the human eye [42,43]. Additionally, a decrease in a\* and C\* values was observed in R2b with respect to R2a.

Meat from R1 had lower values of color parameter than meat from R2b (Table 5). These results are in accordance with those of Jose and McGilchrist [46], who proposed that in low pH beef, aging increased bloom depth and made meat appear brighter and redder in color. However, other authors reported that aging time before retail display did not affect the redness (a\* parameter) of meat samples from DG diets, while retail display conditions decreased the values of a\* [41]. Similarly, in our study, a decrease in a\* and C\* values were observed when changing from vacuum to aerobic packaging in R2 (R2b with respect to R2a). This decrease could be associated with an increase in MMb content when samples were exposed to aerobic conditions.

In this sense, it has been reported that during refrigeration storage, lipid oxidation occurs more slowly than discoloration [14]. McKenna et al. [47] reported that color-stable muscles had lower TBARS than less color-stable ones. These authors found values up to 0.35 mg/kg for *M. longissimus lumborum* and *M. longissimus thoracis* after 5 days of a retail display while preserving color traits. In agreement, in our study, the values of TBARS observed were lower than 0.35 mg/kg, except for meat from the 0DG treatment at R2 storage conditions. Furthermore, the values of a\* measured for all samples in our study were above 14.5, the value considered as a threshold for beef color acceptability [48].

Moreover, a PCA was performed to depict the relationship between the oxidant/antioxidant status and the color parameters, at each storage condition (Figure 3). The new components identify where the maximum variance of the data occurs in a multidimensional space. For the R1 condition, the first component (PC1) accounted for 29.5% of the variance, the second component (PC2) accounted for 21.0% of the variance, and the third component (PC3) accounted for 19.3% of the variance. For R2 conditions, the principal components accounted for 36.3% (PC1), 28.2% (PC2), and 16.7% (PC3) of the variance. Interestingly, it was observed that the relationship between the variables differed with the type of storage. In R1 storage, retinol and FRAP showed a positive correlation with a\* and C\* and a negative correlation with TBARS. In R2 storage, γ-tocopherol and FRAP showed a positive correlation with a\* and C\* and a negative correlation with TBARS. For both storage conditions, a\* and C\* parameters showed a negative correlation with TBARS.

**Figure 3.** Principal component analysis (PCA) depicting the relationship between the oxidant/antioxidant status and the color parameters, at (**A**) R1 conditions (aerobic exposure for seven days) and at (**B**) R2 conditions (vacuum-packed storage for 25 days, plus aerobic exposure for three days).

#### **4. Conclusions**

The inclusion of DG in the feeding diets of beef steers increased the content of antioxidant compounds in the diets. This was reflected in a greater content of γ-tocopherol in beef from DG diets, and a numerical increase in retinol content. Although the values of α-tocopherol were below those indicated as optimal for oxidation delay, the values of TBARS observed were far lower than the ones associated with meat rancidity. Therefore, it could be assumed that the antioxidant compounds present in the meat from animals fed with DG exerted a cooperative antioxidant activity.

The meat exposed to refrigerated storage was affected by the conditions of retail display since FRAP and color parameters decreased. The inclusion of DG affected the extent of lipid oxidation. In this regard, the samples from DG-diets showed similar values of TBARS for both storage conditions, and these values were lower than those from 0DG and extended storage. Thus, DG inclusion in finishing diets positively impacts the balance between anti- and pro-oxidant compounds and meat oxidative stability during the display conditions evaluated.

**Author Contributions:** Conceptualization, M.M., D.P. and G.G.; methodology, M.M., S.A.R., L.R., D.P. and G.G.; software, M.M., S.A.R. and L.R.; validation, M.M., S.A.R., L.R., D.P. and G.G.; formal analysis, M.M., L.R. and G.G.; investigation, M.M., S.A.R., L.R., D.P. and G.G.; resources, L.R., D.P. and G.G.; data curation, G.G. and M.M.; writing—original draft preparation, M.M., D.P. and G.G.; writing—review and editing, M.M., S.A.R., L.R., D.P. and G.G.; visualization, M.M.; supervision, D.P. and G.G.; project administration, M.M.; funding acquisition, M.M., L.R., D.P. and G.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Instituto Nacional de Tecnología Agropecuaria (INTA PEI#517), by Instituto Promoción Carne Vacuna Argentina (Convenio de Cooperación Técnica IPCVA- INTA, 2017) and by Consejo Nacional de Investigaciones Científicas y Técnicas, Beca Doctoral Temas Estratégicos 2016–2021 (Res DN#5040).

**Institutional Review Board Statement:** Experimental procedures were approved by the Ethics Committee of the School of Veterinary Sciences of the University of Buenos Aires (Comité Institucional de Cuidado y Uso de Animales de Laboratorio, Facultad de Ciencias Veterinarias, protocol number 2016/42), Argentina. The assay was carried out as part of common zootechnical procedures and the animals did not suffer any intervention beyond those typical in such farming conditions. Muscle samples were collected for the analysis during the routine carcass cutting procedure at the meat processing plant.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from corresponding author.

**Acknowledgments:** The authors express their gratitude to Karina Moreno, Marta Signorelli and Cecilia Barreto for their technical assistance. The authors are members of the Healthy Meat network funded by CYTED (119RT0568).

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


**3. Cellular trafficking and toxicity of polycationic nanostructures**

For achievement of an efficient systemic delivery of gene-based nanomedicines, various factors appear to play crucial role, including: 1) the physicochemical characteristics of the gene-based therapies, 2) the effects of biological environment, 3) the functionality of membranes and barriers, and 4) the biological impacts of cellular microenvironment.

Toxicogenomics of Nonviral Cationic Gene Delivery Nanosystems 551

to cells by means of one or both of two types of cell binding interaction machineries, i.e. receptor and non-receptor mediated bindings (Medina-Kauwe et al., 2005). At cellular level, trafficking of the gene-based nanomedicines is basically performed through vesicular transportation pathways, in which they may engineer their own escape from demise in the lysosome. Endocytosis of macromolecular nanomedicines occurs through various cellular pathways, including clathrin coated pits, caveolae membranes and lipid rafts (Conner & Schmid, 2003; Spang, 2008). More likely, these complexes enter cells through nonspecific exploitation of these endocytic machineries, presumably mainly involving clathrin-mediated endocytic pathway. This route initiates and stabilizes membrane curvature formation, in which the adaptor proteins bind to clathrin pits and augment the inward pull of the

It has been evidenced that the N-1(-(2,3-dioleoyloxy)propyl)-N,N,Ntrimethylammoniummethylsulphate (DOTAP) lipoplexes are internalized by cells solely via clathrin-mediated endocytosis, however PEI polyplexes were shown to be internalized both by clathrin-mediated and caveolae-mediated endocytosis (Rejman et al., 2005). Once inside the cytoplasm, DNA is released from vesicular compartment upon physicochemical properties of the genomedicine. The endosomal escape of DNA at an early stage of endocytosis is deemed to be critical for cytosolic DNA delivery and determination of overall transfection efficiency. Among CPs and CLs, fusogenic lipid dioleoylphosphatidylethanolamine (DOPE) as a helper lipid for liposome-based DNA delivery were reported to induce membrane fusion between the endosome and the liposome and result in membrane destabilization and release of DNA into the cytoplasm (Farhood et al., 1995). Such destabilization of the vesicular membrane further highlights the interaction of cationic lipids with cellular compartments. This inadvertent nonspecific interaction may be exacerbated for *in vivo* systemic gene, which requires high and potentially toxic doses of nonviral vectors. Utilization of the cell-specific ligands or antibodies were reported to lower the cytotoxicity, while facilitating tissue targeting (Rawat et al., 2007), in which the ligand choice is largely dictated by whether or not the target receptor undergoes vesicular trafficking and the endocytic pathway used by the vector is dependent upon the targeting ligand as well as cell type. The structural architecture of the gene delivery nanosystems was shown to be important from gene expression changes viewpoints (Omidi et al., 2005b), which is also largely dependent upon cell type, in particular the membrane lipid composition and membrane phase state (Kabanov, 2006). Adsorption of polycations such as poly(N -ethyl-4-vinylpyridinium) salts (PEVP) in liposomic biomembranes was shown to induce flip-flop of negatively charged lipids (e.g., cardiolipin, phosphatidylserine, and phosphatidic acid) from the inner to the outer leaflet of the liquid liposomal membrane, but not in solid membranes (Yaroslavov et al., 1994; Yaroslavov et al., 2006). Among polycations, starburst PAMAM dendrimers and PEI appeared to elicit the most dramatic increase in membrane permeability by interacting the membranous biomolecules and forming holes in lipid membranes (Hong et al., 2006; Leroueil et al., 2007). Such structures could function as gates, through which the lipid molecules can be transported across the biomembranes (Kabanov, 2006). Fig. 2 represents cytotoxicity of linear and branched PEI in

Upon differences in cell types, the polyions can bind to the cellular compartments and accordingly induce compartmentalization within certain areas of the membranes and inadvertently trigger various signaling paths. Furthermore, nanoscaled defects were shown to be induced by PAMAM dendrimers through removing lipid from the fluid domains at a

membrane towards the cytoplasm leading to vesicle formation (Young, 2007).

A431 cells (Kafil & Omidi, 2011).

Fig. 1. Schematic representation of various polymer based gene delivery nanosystems. To prepare gene medicine nanosystems (NS) nucleic acids (e.g., antisense, siRNA, and aptamer) are generally entrapped, encapsulated or conjugated with polymers. Genes can be conjugated to magnetic nanoparticles (MNP) and quantum dots for concurrent detection and therapy.

Within the circulation system, blood cells, proteins, enzymes and serum components may bind to the genomedicines and cause instability and lowered transfection efficiency (Konopka et al., 2005). In addition, the circulating gene therapies must circumvent the immune system clearance and cross the capillary endothelial cells to reach the target cells/tissue. Once inside the target cells (normally via receptor-mediated endocytosis pathway), the genomedicine must overcome the subcellular and/or biomolecular impacts. In fact, the amphipathic sheet like lipid bilayer architecture of the biological membranes along with the integrated proteins separate cells from their environment and form the boundaries of different organelles inside the cells, at which exchange of materials among the different parts of a cell is controlled (Omidi & Gumbleton, 2005). Nonviral vectors may bind

Fig. 1. Schematic representation of various polymer based gene delivery nanosystems. To prepare gene medicine nanosystems (NS) nucleic acids (e.g., antisense, siRNA, and aptamer)

Within the circulation system, blood cells, proteins, enzymes and serum components may bind to the genomedicines and cause instability and lowered transfection efficiency (Konopka et al., 2005). In addition, the circulating gene therapies must circumvent the immune system clearance and cross the capillary endothelial cells to reach the target cells/tissue. Once inside the target cells (normally via receptor-mediated endocytosis pathway), the genomedicine must overcome the subcellular and/or biomolecular impacts. In fact, the amphipathic sheet like lipid bilayer architecture of the biological membranes along with the integrated proteins separate cells from their environment and form the boundaries of different organelles inside the cells, at which exchange of materials among the different parts of a cell is controlled (Omidi & Gumbleton, 2005). Nonviral vectors may bind

are generally entrapped, encapsulated or conjugated with polymers. Genes can be conjugated to magnetic nanoparticles (MNP) and quantum dots for concurrent detection

and therapy.

to cells by means of one or both of two types of cell binding interaction machineries, i.e. receptor and non-receptor mediated bindings (Medina-Kauwe et al., 2005). At cellular level, trafficking of the gene-based nanomedicines is basically performed through vesicular transportation pathways, in which they may engineer their own escape from demise in the lysosome. Endocytosis of macromolecular nanomedicines occurs through various cellular pathways, including clathrin coated pits, caveolae membranes and lipid rafts (Conner & Schmid, 2003; Spang, 2008). More likely, these complexes enter cells through nonspecific exploitation of these endocytic machineries, presumably mainly involving clathrin-mediated endocytic pathway. This route initiates and stabilizes membrane curvature formation, in which the adaptor proteins bind to clathrin pits and augment the inward pull of the membrane towards the cytoplasm leading to vesicle formation (Young, 2007).

It has been evidenced that the N-1(-(2,3-dioleoyloxy)propyl)-N,N,Ntrimethylammoniummethylsulphate (DOTAP) lipoplexes are internalized by cells solely via clathrin-mediated endocytosis, however PEI polyplexes were shown to be internalized both by clathrin-mediated and caveolae-mediated endocytosis (Rejman et al., 2005). Once inside the cytoplasm, DNA is released from vesicular compartment upon physicochemical properties of the genomedicine. The endosomal escape of DNA at an early stage of endocytosis is deemed to be critical for cytosolic DNA delivery and determination of overall transfection efficiency. Among CPs and CLs, fusogenic lipid dioleoylphosphatidylethanolamine (DOPE) as a helper lipid for liposome-based DNA delivery were reported to induce membrane fusion between the endosome and the liposome and result in membrane destabilization and release of DNA into the cytoplasm (Farhood et al., 1995). Such destabilization of the vesicular membrane further highlights the interaction of cationic lipids with cellular compartments. This inadvertent nonspecific interaction may be exacerbated for *in vivo* systemic gene, which requires high and potentially toxic doses of nonviral vectors. Utilization of the cell-specific ligands or antibodies were reported to lower the cytotoxicity, while facilitating tissue targeting (Rawat et al., 2007), in which the ligand choice is largely dictated by whether or not the target receptor undergoes vesicular trafficking and the endocytic pathway used by the vector is dependent upon the targeting ligand as well as cell type. The structural architecture of the gene delivery nanosystems was shown to be important from gene expression changes viewpoints (Omidi et al., 2005b), which is also largely dependent upon cell type, in particular the membrane lipid composition and membrane phase state (Kabanov, 2006). Adsorption of polycations such as poly(N -ethyl-4-vinylpyridinium) salts (PEVP) in liposomic biomembranes was shown to induce flip-flop of negatively charged lipids (e.g., cardiolipin, phosphatidylserine, and phosphatidic acid) from the inner to the outer leaflet of the liquid liposomal membrane, but not in solid membranes (Yaroslavov et al., 1994; Yaroslavov et al., 2006). Among polycations, starburst PAMAM dendrimers and PEI appeared to elicit the most dramatic increase in membrane permeability by interacting the membranous biomolecules and forming holes in lipid membranes (Hong et al., 2006; Leroueil et al., 2007). Such structures could function as gates, through which the lipid molecules can be transported across the biomembranes (Kabanov, 2006). Fig. 2 represents cytotoxicity of linear and branched PEI in A431 cells (Kafil & Omidi, 2011).

Upon differences in cell types, the polyions can bind to the cellular compartments and accordingly induce compartmentalization within certain areas of the membranes and inadvertently trigger various signaling paths. Furthermore, nanoscaled defects were shown to be induced by PAMAM dendrimers through removing lipid from the fluid domains at a

Toxicogenomics of Nonviral Cationic Gene Delivery Nanosystems 553

Once complexed with nucleic acid (e.g., antisense oligonucleotide or plasmid vector), lipoplexes revealed marginally reduced toxicity towards macrophages (Filion & Phillips, 1997b). Furthermore, since cationic lipids display intrinsic anti-inflammatory activity, they should be cautiously utilized as a gene delivery system to transfer nucleic acids for

DNA microarray technology has advanced and accelerated the identification process for mechanistic toxicology to illuminate genomic aspects of toxicology that could consequently postulate early effect within targets cells/tissues upon exposure to the toxicants (de et al., 2004). Recently, an interesting study was performed to compare different commercially available cationic liposome–DNA lipoplexes (Masotti et al., 2009), and it was reported that the lipoplex size and cationic lipid to DNA ratio are the two main parameters affecting the transfection efficiency of lipoplexes. The lipofection efficiency was determined mainly by lipoplex size, but not by the extent of lipoplex–cell interactions including binding, uptake or fusion. In the presence or absence of serum, lipoplex size was found to be a major factor determining lipofection efficiency. These researchers concluded that, by controlling lipoplex size, an efficient lipid delivery system may be

Florea et al. (2002) evaluated PEIs with different molecular weights for their efficiency in transfecting undifferentiated COS-1 and well-differentiated human submucosal airway epithelial Calu-3 cells and showed that transfection efficiency was dependent upon the cell types, but not molecular weights. These researchers reported that gene transfer by PEI was 3 orders of magnitude more effective in COS-1 than in Calu-3 cells, perhaps because of secretion of mucins by Calu-3 cells (Florea et al., 2002). However, the larger molecular weights of PEI were also shown to yield the highest transfection efficiency in EA.hy 926 cell line derived from a fusion of the human A549 cell line with human umbilical vein endothelial cells, HUVEC (Godbey et al., 2001). Two types of cytotoxicities in process of PEI -mediated cell transfection have been reported: 1) an immediate toxicity associated with free PEI, 2) a delayed toxicity associated with cellular processing of PEI/DNA complexes (Godbey et al., 1999; Godbey et al., 2001). The immediate toxicity seems to occur upon interaction of the free PEIs with negatively charged serum proteins (e.g., albumin) and red blood cells (cytotoxic effects), while the delayed toxicity by PEI/DNA complex appeared to be closely related to the release of DNA (genomic effects). In cell culture, free PEI interacts with cellular components and inhibits normal cellular process. It causes several changes to cells, which include cell shrinking, reduced number of mitoses and vacuolization of the cytoplasm. We have observed significant genotoxicity impacts induced by PEI in A431 cells

Toxicity impacts of nanostructured materials have been recently reviewed (Nel et al., 2006), while many aspects of this issue (in particular at genomics/protemics levels) still remains unresolved. As a result, necessity of analysis of toxicogenomics of the nanoscaled advanced biomaterials is very clear. It will direct us towards development of safe pharmaceutical formulations with maximal efficiency and wide therapeutic index yet displaying minimal toxicity profiles since the conventional assessment of toxicity solely provide preliminary information with little devotion to the global genomic/proteomic impacts (Hollins et al., 2007; Kabanov et al., 2005; Kabanov, 2006; Omidi et al., 2005a). If this is the case, then the gene and drug delivery paradigms are going to stumble upon new era to deal with

gene therapy *in vivo*.

achieved for *in vitro* and *in vivo* gene therapy.

(Kafil & Omidi, 2011) and xenografted mice (our unpublished data).

"functionalized excipients".

significantly greater rate than for the gel domains (Erickson et al., 2008). This reinforces a possibility of compartmentalization of synthetic polymers within different membrane domains as well as a differential effect of polymers on functional systems in the membranes that consecutively provoke inadvertent cytoplasmic/nucleic consequences directly and indirectly via secondary messengers such as G proteins.

Fig. 2. Cytotoxicity of polyethylenimine (25 kDa) polymers in A431 cells evaluated by MTT assay. A) Cytotoxicity of B PEI with IC50=37 μg. B) Cytotoxicity of LPEI with IC50=74 μg. BPEI: Branched polyethylenimine; LPEI: linear polyethylenimine; adapted with permission from (Kafil & Omidi, 2011).

Fischer et al. (2003) monitored cytotoxicity of various polycationic gene delivery systems in L929 mouse fibroblasts using MTT assay and the release of the cytosolic enzyme lactate dehydrogenase (LDH). They showed a pattern for cellular toxicity as follow, poly(ethylenimine)=poly(L-lysine)>poly(diallyl-dimethyl-ammonium chloride)>diethylaminoethyl-dextran>poly(vinyl pyridinium bromide)>Starburst dendrimer>cationized albumin>native albumin. These researchers, interestingly, confirmed the molecular weight and the cationic charge density of the polycations as key parameters for the interaction with the cell membranes and accordingly the cell damage (Fischer et al., 2003). Besides, interaction of dendrimers with erythrocyte membrane proteins was shown to trigger echinocytosis (Domanski et al., 2004), while the cationic liposomes are less cytotoxic than dendrimers. The toxicity by CLs appeared to be dependent upon the type of cationic lipid macromolecule, concentration, molecular weight and the presence of DNA, where complexation of the polycations with DNA resulted in reduced tissue damage. However, Gebhart et al. (2001) showed increased cytotoxicity in the cos-7 cells upon complexation of various polymers with DNA (Gebhart & Kabanov, 2001).

Filion et al. (1997) have performed an important body of work by evaluating the toxicity of liposomes, formulated with various cationic lipids, towards murine macrophages and T lymphocytes and the human monocyte-like U937 cell line. They reported occurrence of pronounced toxicity by cationic liposomes formulated from DOPE and cationic lipids based on diacyltrimethylammonium propane (dioleoyl-, dimyristoyl-, dipalmitoyl-, disteroyl-: DOTAP, DMTAP, DPTAP, DSTAP) or dimethyldioctadecylammonium bromide (DDAB) in the phagocytic cells (macrophages and U937 cells), but not within non-phagocytic T lymphocytes. They also showed the rank order of toxicity as follows: DOPE/DDAB > DOPE/DOTAP > DOPE/DMTAP > DOPE/DPTAP > DOPE/DSTAP.

significantly greater rate than for the gel domains (Erickson et al., 2008). This reinforces a possibility of compartmentalization of synthetic polymers within different membrane domains as well as a differential effect of polymers on functional systems in the membranes that consecutively provoke inadvertent cytoplasmic/nucleic consequences directly and

Fig. 2. Cytotoxicity of polyethylenimine (25 kDa) polymers in A431 cells evaluated by MTT assay. A) Cytotoxicity of B PEI with IC50=37 μg. B) Cytotoxicity of LPEI with IC50=74 μg. BPEI: Branched polyethylenimine; LPEI: linear polyethylenimine; adapted with permission

Fischer et al. (2003) monitored cytotoxicity of various polycationic gene delivery systems in L929 mouse fibroblasts using MTT assay and the release of the cytosolic enzyme lactate dehydrogenase (LDH). They showed a pattern for cellular toxicity as follow,

chloride)>diethylaminoethyl-dextran>poly(vinyl pyridinium bromide)>Starburst dendrimer>cationized albumin>native albumin. These researchers, interestingly, confirmed the molecular weight and the cationic charge density of the polycations as key parameters for the interaction with the cell membranes and accordingly the cell damage (Fischer et al., 2003). Besides, interaction of dendrimers with erythrocyte membrane proteins was shown to trigger echinocytosis (Domanski et al., 2004), while the cationic liposomes are less cytotoxic than dendrimers. The toxicity by CLs appeared to be dependent upon the type of cationic lipid macromolecule, concentration, molecular weight and the presence of DNA, where complexation of the polycations with DNA resulted in reduced tissue damage. However, Gebhart et al. (2001) showed increased cytotoxicity in the cos-7 cells upon complexation of

Filion et al. (1997) have performed an important body of work by evaluating the toxicity of liposomes, formulated with various cationic lipids, towards murine macrophages and T lymphocytes and the human monocyte-like U937 cell line. They reported occurrence of pronounced toxicity by cationic liposomes formulated from DOPE and cationic lipids based on diacyltrimethylammonium propane (dioleoyl-, dimyristoyl-, dipalmitoyl-, disteroyl-: DOTAP, DMTAP, DPTAP, DSTAP) or dimethyldioctadecylammonium bromide (DDAB) in the phagocytic cells (macrophages and U937 cells), but not within non-phagocytic T lymphocytes. They also showed the rank order of toxicity as follows: DOPE/DDAB > DOPE/DOTAP > DOPE/DMTAP > DOPE/DPTAP > DOPE/DSTAP.

poly(ethylenimine)=poly(L-lysine)>poly(diallyl-dimethyl-ammonium

various polymers with DNA (Gebhart & Kabanov, 2001).

indirectly via secondary messengers such as G proteins.

from (Kafil & Omidi, 2011).

Once complexed with nucleic acid (e.g., antisense oligonucleotide or plasmid vector), lipoplexes revealed marginally reduced toxicity towards macrophages (Filion & Phillips, 1997b). Furthermore, since cationic lipids display intrinsic anti-inflammatory activity, they should be cautiously utilized as a gene delivery system to transfer nucleic acids for gene therapy *in vivo*.

DNA microarray technology has advanced and accelerated the identification process for mechanistic toxicology to illuminate genomic aspects of toxicology that could consequently postulate early effect within targets cells/tissues upon exposure to the toxicants (de et al., 2004). Recently, an interesting study was performed to compare different commercially available cationic liposome–DNA lipoplexes (Masotti et al., 2009), and it was reported that the lipoplex size and cationic lipid to DNA ratio are the two main parameters affecting the transfection efficiency of lipoplexes. The lipofection efficiency was determined mainly by lipoplex size, but not by the extent of lipoplex–cell interactions including binding, uptake or fusion. In the presence or absence of serum, lipoplex size was found to be a major factor determining lipofection efficiency. These researchers concluded that, by controlling lipoplex size, an efficient lipid delivery system may be achieved for *in vitro* and *in vivo* gene therapy.

Florea et al. (2002) evaluated PEIs with different molecular weights for their efficiency in transfecting undifferentiated COS-1 and well-differentiated human submucosal airway epithelial Calu-3 cells and showed that transfection efficiency was dependent upon the cell types, but not molecular weights. These researchers reported that gene transfer by PEI was 3 orders of magnitude more effective in COS-1 than in Calu-3 cells, perhaps because of secretion of mucins by Calu-3 cells (Florea et al., 2002). However, the larger molecular weights of PEI were also shown to yield the highest transfection efficiency in EA.hy 926 cell line derived from a fusion of the human A549 cell line with human umbilical vein endothelial cells, HUVEC (Godbey et al., 2001). Two types of cytotoxicities in process of PEI -mediated cell transfection have been reported: 1) an immediate toxicity associated with free PEI, 2) a delayed toxicity associated with cellular processing of PEI/DNA complexes (Godbey et al., 1999; Godbey et al., 2001). The immediate toxicity seems to occur upon interaction of the free PEIs with negatively charged serum proteins (e.g., albumin) and red blood cells (cytotoxic effects), while the delayed toxicity by PEI/DNA complex appeared to be closely related to the release of DNA (genomic effects). In cell culture, free PEI interacts with cellular components and inhibits normal cellular process. It causes several changes to cells, which include cell shrinking, reduced number of mitoses and vacuolization of the cytoplasm. We have observed significant genotoxicity impacts induced by PEI in A431 cells (Kafil & Omidi, 2011) and xenografted mice (our unpublished data).

Toxicity impacts of nanostructured materials have been recently reviewed (Nel et al., 2006), while many aspects of this issue (in particular at genomics/protemics levels) still remains unresolved. As a result, necessity of analysis of toxicogenomics of the nanoscaled advanced biomaterials is very clear. It will direct us towards development of safe pharmaceutical formulations with maximal efficiency and wide therapeutic index yet displaying minimal toxicity profiles since the conventional assessment of toxicity solely provide preliminary information with little devotion to the global genomic/proteomic impacts (Hollins et al., 2007; Kabanov et al., 2005; Kabanov, 2006; Omidi et al., 2005a). If this is the case, then the gene and drug delivery paradigms are going to stumble upon new era to deal with "functionalized excipients".

Toxicogenomics of Nonviral Cationic Gene Delivery Nanosystems 555

microarray technology. However, for accomplishment of a significant correlation between the gene expression profiles and their functionality expression, it is important to implement substantial complementary investigations to verify the results at the molecular level and as a result extend our understanding of gene expression patterns and molecular pathways. Microarray technology can be exploited to attain a wealth of data that can be used to develop a more complete understanding of gene expression, which can be used for transcriptional regulation and interactions as well as functional genomics. Despite its successful *in vitro* cell-based implementation, application of this technology for *in vivo*  investigations is deemed to be more sophisticated because of complexity of cytotoxicity and genotoxicity studies, which can be confounded by a number of variables such as type of target organ, effect of pharmacokinetics and/or pharmacodynamics parameters (Lobenhofer et al., 2001). Since its advent and application in life sciences, microarray has been widely applied for molecular/biological studies. In fact, a large number of indexed articles in various data banks (e.g., MEDLINE/PubMed) highlight the importance of microarray

Fig. 3 shows a schematic illustration of step-wise processes of the DNA microarray

Technically, DNA microarray can be generated in two different types including printing pre-synthesized cDNAs (500–2000 bp) or synthesizing short oligonucleotides (20–50 bases) onto glass microscope slides, in which gene spots include either fully sequenced genes of known function or collections of partially sequenced cDNA derived from expressed sequence tags (ESTs) corresponding to the messenger RNAs of unknown genes. For example in practice, one may compare two different cells/tissues from untreated (UT) versus treated (T). For gene expression profiling, normally total RNA is extracted from the untreated and treated samples. Using an indirect labeling methodology, they are converted to labeled cDNA (e.g., with aminoallyle-dUTP). The aminoallyle-dUTP-cDNA is then labeled with cyanine dye (e.g., Cy3 or Cy5). The Cy3 and Cy5 labeled aminoallyle-dUTPcDNA from UT and T samples are hybridized on a single glass array, which is subjected to several washing steps, scanning with an appropriate scanner (e.g., using RS Reloaded™, TECAN, Switzerland) and data mining (e.g., using GeneMath™ software; Applied Maths, Sint-Martens-Lathem, Belgium); for detailed information reader is directed to see (Hegde et

For microarray analysis, significantly upregulated and/or downregulated genes can be identified using traditional method (gene expression changes with a fixed cutoff threshold usually in 2 fold) to infer significance differences (i.e., the so called "fold change method"). The resultant data are normally presented as scatter plots of treated (T) versus untreated (UT) control. To reach this stage, data need to undergo a number of processes called as "transformation" and "normalization" to minimize the experimental erroneousness (i.e., the so called "data mining"). Since a scatter plot of T versus UT genes would cluster along a straight line, normalization of this type of data is equivalent to calculating the best-fit slope using regression techniques and adjusting the intensities so that the calculated slope is one. In many experiments, the intensities are nonlinear, and local regression techniques are more suitable, such as Locally WEighted Scatterplot Smoothing (LOWESS) regression (Berger et

In our studies, we have successfully exploited both approaches to study the impacts of the nonviral vectors (CPs and CLs based formulation) on global gene expression experiments. To get the significant alterations in gene expression, we rejected the arrays showing non-

technology in post-genomics era.

al., 2000; Omidi et al., 2005b; Omidi et al., 2008).

al., 2004; Chen et al., 2003).

technology.


**Acknowledgements**

We want to thank Professor Lorenzo Derchi (IRCCS A.O.U San Martino – IST, Radiology Unit, Genova) for the original Figures of scrotal ultrasounds performed on our CF patients.

We want to thank Dr. Mauro Costa (Ospedale Evangelico Internazionale, Reproduction Medicine Unit, Genova) for the collaboration.


**3.1 Demand and supply position of medicinal plants**

India has very strong traditional health care practices that are represented by the classical systems of medicine like Ayurveda, Siddha, Unani, and Swa-rigpa. Besides these traditional heath care practices, there exists a very diverse area-specific and community-specific folk healthcare practices. Both the Indian classical system of medicine and the folk health care systems are highly dependent on the raw herbal drug material derived from a diverse species of medicinal plants, which is estimated to be about 6500 in number [16].

NMPB, during 2001–2002, commissioned a study through Centre for Research, Planning and Action (CERPA) to understand annual trade levels of selected 162 medicinal plant species. The NMPB, then in 2006–2007 commissioned a national study that was carried out by Foundation for Revitalisation of Local Health Traditions (FRLHT) to assess demand and supply of medicinal plants in India which for the first time highlighted various drawbacks in the herbal drug sector and added more to the existing knowledge and understanding of the subject related to the diversity of raw drugs in trade, their botanical correlation, volume of annually trade drugs, their supply sources and others [16].

**Figure 1** below shows various Industrial uses of Medicinal Plants [17]. Each of these groups can contain a wide range of products like herbal medicinal products, food supplements or dietary supplements, foodstuffs, Cosmetics, etc.

Countries where the highest uses of traditional medicines are practiced include [18]:

A major percentage of raw drugs are used in making plant extracts. This is carried out either by the end product manufacturers or by extract companies. In addition to this, there is an exorbitant demand in developed countries for plant based products including health supplements, herbal health drinks, herbal cosmetics and various other health and personal care products. The current worth of the global market for herbal products is estimated around US\$60 billion per year with a growth rate of 7% [16].

In India, trade in medicinal plants accounts for a turnover of Rs. 2300 crores of ayurvedic and herbal products with a contribution of about Rs. 1200 crores of overthe-counter products. Currently Covid-19 pandemic has opened door to explore more in the ayurvedic treasures. This would help in enabling rapid access of these herbal drugs into developed country markets as well as in Indian market [16].

There are major challenges in tapping the substantial potential for utilizing medicinal, aromatic and natural dyes plants (MADPs) nationally in India, as well as in export markets. At the forefront of these problems is ensuring consistent and acceptable quality which always suffers against making money out of it. As this tradition has now moved to industries which was earlier a part of a local community's culture and health practices, quality is not manageable and has suffered a

**179**

*Herbal Drugs Forensic*

*DOI: http://dx.doi.org/10.5772/intechopen.98253*

treating one or the other disease [16].

**4. Constraints to the development of trade**

final packaging and trade of the herbal product.

and no exploitation of biodiversity occurs.

July 2004.

lot. In case of medicinal plants, quality means the correct identity of the medicinal herb, purity of the composition and safety and efficacy of the final drug. However,

• Firstly, unlike allopathic drugs that are composed of a single or a fixed number of chemical compounds that are easy to be standardized, plants have a diverse range of phytoconstituents contributing to their bioactivity and it is very difficult to isolate, purify and standardize that bioactive compound.

• Secondly, bioactivity is not constant but varies according to time (day, season,

• Thirdly, there are also differences in the bioactivity exists depending on the

There are many more factors that contribute to the varied behavior of plant based drugs besides the ones stated above. Phytoconstituents that are responsible for the specific medicinal properties of a plant are not present in uniform quality or quantity under different edaphic (soil) conditions. The medicinal and toxic property of a plant may also vary according to the kind of processing through which it had undergone. Traditional, ancient systems of medicine used to take care of all these parameters and thus it had specific recommendations and procedures as to how a plant should be collected and processed in order to get its optimum efficacy. Currently, there are modern analytical techniques like chromatography, spectroscopy and bioassay methods that can be used to determine the chemical profile of the selected medicinal plants and can compare their bioactivity and efficacy in

Note: Information in the above paragraph has been taken from the country studies prepared for the Workshop on Medicinal Plants held in Bangalore, India, 22–26

While developing countries like India has huge potential in the marketing and trade of herbal drugs owing to their ancient knowledge and experience with the Ayurveda but they face a number of challenges and limitations that need to be addressed to meet the growing demands in the developed country markets [16].

a.**Quality assurance:** the herbal drugs need to be consistent in quality and should be free from contaminants. Unwanted ingredients like dirt, soil particles, plant parts, or any other chemical adulterant need to be addressed before

b.**Consistent volume:** the herbal drugs that are high in demand must be available in consistent volume so that there should be no pressure on natural resources

c.**Strict regulatory laws:** to regulate the collection, processing, storage, manufacturing and trade in herbal products, there should be strict norms laid by the

d.**Pre- and post-harvesting practises:** the herbal plants that are harvested for the use in different products need to be monitored and their pre- and

concerned authority at local, national and international level.

quality standardization of MADPs suffers a lot of challenges as:

constellar position) and to region (arid, marshy).

way a plant has been collected, processed and stored [16].

**Figure 1.** *Industrial uses of medicinal plants. Source: De Silva [17].*

lot. In case of medicinal plants, quality means the correct identity of the medicinal herb, purity of the composition and safety and efficacy of the final drug. However, quality standardization of MADPs suffers a lot of challenges as:

There are many more factors that contribute to the varied behavior of plant based drugs besides the ones stated above. Phytoconstituents that are responsible for the specific medicinal properties of a plant are not present in uniform quality or quantity under different edaphic (soil) conditions. The medicinal and toxic property of a plant may also vary according to the kind of processing through which it had undergone. Traditional, ancient systems of medicine used to take care of all these parameters and thus it had specific recommendations and procedures as to how a plant should be collected and processed in order to get its optimum efficacy.

Currently, there are modern analytical techniques like chromatography, spectroscopy and bioassay methods that can be used to determine the chemical profile of the selected medicinal plants and can compare their bioactivity and efficacy in treating one or the other disease [16].

Note: Information in the above paragraph has been taken from the country studies prepared for the Workshop on Medicinal Plants held in Bangalore, India, 22–26 July 2004.


**Solid Organ Transplantation in the Era of COVID-19**

Editors

**Mac ´e M. Schuurmans Ren ´e Hage**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Mace M. Schuurmans ´ University Hospital Zurich Switzerland

Rene Hage ´ University Hospital Zurich Switzerland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Transplantology* (ISSN 2673-3943) (available at: https://www.mdpi.com/journal/transplantology/ special issues/Solid Organ Transplantation COVID).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-6513-2 (Hbk) ISBN 978-3-0365-6514-9 (PDF)**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**5. Conclusions**

The culturally adapted Brazilian Portuguese version of the RIFL-P measure proved to be a valid and reliable instrument for a brief assessment of responsive caregiving in parent–child interactions in Brazil. Efficient and valid measuring instruments of responsive caregiving appropriate to primary care settings are essential tools to advance ECD policies and programs. This study is one of the first of its kind, designed to provide a culturally adapted, reliable, and valid strengths-based measure of responsive parenting interactions with children under two years of age in Brazil.

**Author Contributions:** Conceptualization: A.S., A.J.D.B., J.M.J., M.R.; methodology: A.S., O.F., J.M.J.; sample: T.N.M., A.J.D.B., J.M., M.R.D.; software: A.S., O.F.; formal analysis: A.S., O.F.; resources: O.F., T.N.M., A.J.D.B., J.M., M.R.D.; data curation: A.S.; writing—original draft preparation: A.S., A.J.D.B., J.M.J., M.R., T.N.M., J.M., M.R.D.; supervision: A.J.D.B., O.F., J.M.J.; funding acquisition: A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)–Finance Code 001.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Board of the UNIVERSITY OF TORONTO (protocol code 33024 of June 27, 2016) and the Ethics Committee of the Medical School of the FEDERAL UNIVERSITY OF PELOTAS (protocol code 1.717.977 of 8 September 2016).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request.

**Acknowledgments:** We are grateful to the families who gave their time so generously and have contributed to knowledge of parental interactions in Brazil. We thank the Postgraduate Program in Epidemiology at the Federal University of Pelotas, Brazil for the partnership.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. RIFL-P Coding Sheet (Brazilian Portuguese Version)**