**1. Introduction**

Endometriosis is a common gynecological disorder affecting ca. 10% women of reproductive age. The disease is related to the endometrial-like tissue (endometrial glands and stroma) located outside the uterine cavity, mainly on the pelvic viscera and/or ovaries. Endometriosis is associated with chronic pelvic inflammation and manifests with dysmenorrhea, dyspareunia or chronic pelvic pain. It also accounts for ca. 50% of women's infertility. Endometriosis is a debilitating disorder having a significant impact on patients' quality of life. Nevertheless, the etiopathogenesis of this disease is still poorly understood [1–4].

There are several theories on the origin of the endometriosis; however, the most accepted cause of this disease is retrograde menstrual blood flow [5]. In this mechanism shed endometrial cells enter the peritoneal cavity, where they survive and form ectopic foci of the endometriotic tissue. This may be possible owing to the resistance of endometriotic cells to apoptosis and their increased adhesiveness and invasiveness [6–9]. It is also plausible that the formation of ectopic endometriotic lesions may also be facilitated by a permissive local peritoneal milieu as well as abrogated elimination of endometriotic cells by the cells of the local immune system, e.g., NK cells and macrophages [10,11].

Due to chronic pelvic inflammation and the elevated production of a variety of autoantibodies such as anti-nuclear, anti-phospholipid, and anti-endometrial antibodies, endometriosis may be considered as an autoimmune/autoinflammatory disorder [11–14]. The disease manifests with the local and systemic abnormal lymphocyte responses and abrogated NK cell cytotoxicity [10,13,15–17]. Pelvic inflammation includes peritoneal infiltration with a variety of immune cells including various subsets of lymphocytes, activated macrophages and granulocytes [18,19]. The endometriotic peritoneal milieu is also characterized by a local excessive production and accumulation of a bulk of proinflammatory and regulatory cytokines [20,21].

The role of the peritoneal milieu and the peritoneal fluid (PF) in the immunopathogenesis of endometriosis still remains obscure. Although endometriosis is considered to be an inflammatory disorder there is a growing body of evidence that the local peritoneal milieu may display, rather, an immunosuppressive character. Indeed, it has been reported that endometriosis is characterized by increased numbers of the peritoneal Treg cells displaying immunosuppressive and anti-inflammatory activity [22–25]. Furthermore, the PF from women with endometriosis also contains increased levels of suppressive anti-inflammatory cytokines such as TGF-β and IL-10 [20,26]. Thus, it is plausible that the PF from women with endometriosis may display some immunoregulatory properties. These properties, however, are still poorly characterized. Therefore, the present study was aimed at testing the immunomodulatory effects of the PF from women with endometriosis in comparison to control women without the disease. We investigated the effects of the PF on the immunoregulatory cytokine and chemokine production by the isolated CD4+ T cells as well as on the differentiation of CD4+ T cells into Treg and Th17 cells. Finally, we also tested the effect of PF on the cytotoxic activity of the peripheral blood natural killer (NK) cells.


2.2.1. Cytokines and Cytokine Receptors

A significant number of RA-associated genetic variants have been identified in the interleukins (ILs) and their receptor genes. Interleukins are cytokines that stimulate hematopoietic cell development and T- and B-lymphocyte differentiation. There are seven interleukin families characterised by significant variability in both ligand forms and receptors. The key mediator of inflammation, IL1, is represented by two isoforms and is combined with another nine interleukins in one family. Its expression is affected by the –511A/G (rs16944) polymorphism in the promoter (associated with RA in Caucasians) and the +3953C/T polymorphism in exon 5 (RA-associated in some Asian populations). According to meta-analyses, RA-associated alleles in Caucasians include –174G/C and –572G/C of the *IL6* gene, rs1800896 in the *IL10* gene, rs13151961 and rs6822844 located near the *IL2* and *IL21* genes, rs7530511 and rs11209026 in the *IL23R* gene, and some others. A significant number of RA-associated polymorphic loci included in the meta-analyses are unique to Asian populations. The polymorphism found at rs1946518 of the *IL18* gene is found in Egyptian populations, and the polymorphism found at rs549908 is found in the Taiwanese population. Thus the candidate genes for assessing genetic RA predisposition need to be thoroughly selected [19–22].

Another candidate gene is *IL2RA*, which encodes the interleukin 2 (IL2) receptor α-subunit. The rs12722495 polymorphism of this gene correlates with its mRNA and protein expression in stimulated monocytes, naive T-cells, and memory T-cells [6]. Additionally, according to a meta-analysis of 37 studies, a minor allele A of the rs1343151 polymorphism in the IL23R gene, which encodes the receptor for interleukin 23, is associated with a risk of developing RA [23].

#### 2.2.2. Chemokines and Chemokine Receptors

Changes in the *CCR6* gene can affect the development of RA. This gene encodes the chemokine receptor, which is the surface marker of Th17 cells. Dinucleotide polymorphism rs3093024 in the 5 regulatory sequence of CCR6 binds nuclear proteins. This polymorphism is associated with expression levels of the chemokine receptor and increased plasma concentration of IL17 cytokine in RA patients. The role of the CCR6 receptor in RA development appears to be more significant than previously known due to its expression on different subtypes of T-cells involved in the regulation of inflammation. According to a meta-analysis, rs3093024 and several other SNPs (single nucleotide polymorphism) in *CCR6* are associated with RA in European and Asian populations [6,24]. At the same time, a 32-bp deletion in another chemokine receptor gene, *CCR5,* has a protective effect, according to a case–control study in Brazil with samples of 740 RA patients and 676 controls [25].

The *CCL2* gene encodes the chemoattractant protein, which recruits monocytes and T-cells during inflammation. A meta-analysis of the functionally significant 2518A/G polymorphism of this gene promoter on chromosome 17 was performed. This polymorphism was studied for associations with various autoimmune diseases (systemic lupus erythematosus, RA, systemic sclerosis, nephropathy with Ig-A, Crohn's disease) in different populations. The unfavourable allele A showed the most pronounced association with RA in Asian populations, as well as with Crohn's disease in Europeans [26].


*Article* **Novel App-Based Portable Spirometer for the Early Detection of COPD**

**Ching-Hsiung Lin 1,2,3, Shih-Lung Cheng 4,5,\*,†, Hao-Chien Wang 6, Wu-Huei Hsu 7, Kang-Yun Lee 8, Diahn-Warng Perng 9, Hen-I. Lin 10, Ming-Shian Lin 11, Jong-Rung Tsai 12, Chin-Chou Wang 13, Sheng-Hao Lin 1, Cheng-Yi Wang 9, Chiung-Zuei Chen 14, Tsung-Ming Yang 15, Ching-Lung Liu 16, Tsai-Yu Wang 17 and Meng-Chih Lin 13,\*,†**

**Abstract:** Chronic obstructive pulmonary disease (COPD) is preventable and treatable. However, many patients remain undiagnosed and untreated due to the underutilization or unavailability of spirometers. Accordingly, we used Spirobank Smart, an app-based spirometer, for facilitating the early detection of COPD in outpatient clinics. This prospective study recruited individuals who were at risk of COPD (i.e., with age of ≥40 years, ≥10 pack-years of smoking, and at least one respiratory symptoms) but had no previous COPD diagnosis. Eligible participants were examined with Spirobank Smart and then underwent confirmatory spirometry (performed using a diagnostic spirometer), regardless of their Spirobank Smart test results. COPD was defined and confirmed using the postbronchodilator forced expiratory volume in 1 s/forced vital capacity values of <0.70 as measured by confirmatory spirometry. A total of 767 participants were enrolled and examined using Spirobank Smart; 370 participants (94.3% men, mean age of 60.9 years and mean 42.6 pack-years

**Citation:** Lin, C.-H.; Cheng, S.-L.; Wang, H.-C.; Hsu, W.-H.; Lee, K.-Y.; Perng, D.-W.; Lin, H.-I.; Lin, M.-S.; Tsai, J.-R.; Wang, C.-C.; et al. Novel App-Based Portable Spirometer for the Early Detection of COPD. *Diagnostics* **2021**, *11*, 785. https:// doi.org/10.3390/diagnostics11050785

Academic Editor: Koichi Nishimura

Received: 16 March 2021 Accepted: 21 April 2021 Published: 27 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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/).

of smoking) underwent confirmatory spirometry. Confirmatory spirometry identified COPD in 103 participants (27.8%). At the optimal cutoff point of 0.74 that was determined using Spirobank Smart for COPD diagnosis, the area under the receiver operating characteristic was 0.903 (95% confidence interval (CI) = 0.860–0.947). Multivariate logistic regression revealed that participants who have an FEV1/FVC ratio of <74% that was determined using Spirobank Smart (odds ratio (OR) = 58.58, 95% CI = 27.29–125.75) and old age (OR = 3.23, 95% CI = 1.04–10.07 for 60 ≤ age < 65; OR = 5.82, 95% CI = 2.22–15.27 for age ≥ 65) had a higher risk of COPD. The Spirobank Smart is a simple and adequate tool for early COPD detection in outpatient clinics. Early diagnosis and appropriate therapy based on GOLD guidelines can positively influence respiratory symptoms and quality of life.

**Keywords:** COPD; underdiagnosis; early detection; app-based spirometer

#### **1. Introduction**

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide [1,2]. In Taiwan, COPD is the seventh leading cause of death, with the corresponding age-adjusted mortality rates for men and women being 19.67 and 5.70 deaths per 100,000, respectively [3]. In general, COPD is treatable and preventable when identified in the early stage; nevertheless, the underdiagnosis of COPD remains a common challenge and imposes a considerable burden on healthcare systems and patients [4,5].

Spirometry is the gold standard for diagnosing COPD and monitoring treatment response [6]; however, it is still underused or unavailable in primary care settings or nonspecialized areas [4,7]. This may be attributed to various factors; for example, spirometry entails labor-intensive and time-consuming procedures and requires well-trained professionals for its execution [5,8]. Underuse of spirometry is considered a critical factor that is related to COPD underdiagnosis and results in unnecessary specialty referrals for diagnostic testing, creating barriers in COPD screening through spirometry and increasing the cost of diagnostic tests [4,9,10]. However, a report by the U.S. Preventive Services Task Force (USPSTF) revealed that the use of spirometry to screen for COPD in asymptomatic cases has no net benefit [11,12]. The early detection of COPD through a case-finding approach has been heavily advocated, and such an initiative provides opportunities for implementing interventions in the early stages, thus preventing disease progression [13,14]. Moreover, the USPSTF report suggested that COPD screening in at-risk populations has relatively high cost-effectiveness [15]. Hence, developing a feasible case-finding model that can be a suitable alternative to spirometry for identifying undiagnosed at-risk patients with COPD is urgently required.

A portable spirometer is a small and cheap device that can be a valid alternative to spirometry. Spirometers are advantageous for their ease of use, the requirement of less patient effort, and time-saving features, which render them useful for COPD screening [16–19]. Recently, the trend of integrating smart devices and portable medical devices has grown considerably and has affected the market and medical diagnosis field, including spirometry [20,21]. Systems integrating portable spirometers with smartphones can save processing power and reduce interface components, reduce medical device size and cost, and afford effective data communications [20–22]. However, only a few such systems have been validated for COPD screening, with their performance varying with the diagnostic testing procedure applied.

Spirobank Smart is an FDA-approved, app-based ultraportable device that connects to a smartphone app through Bluetooth for seamless recording of various lung function parameters, including the forced expiratory volume in one second (FEV1), PEF, forced vital capacity (FVC), FEV1/FVC, FEV6, and FEF25–75. Spirobank Smart can provide real-time feedback on the test quality, provide systematic and numeric visualizations of spirometer tests on the smartphone app, and afford effective data communication. A study conducted a correlation analysis between measurements obtained from Spirobank Smart and those obtained from confirmatory spirometry; the analysis indicated that this device exhibited acceptable validity and that it meets the latest ATS/ERS standards for accuracy [23]. However, the performance of Spirobank Smart in COPD screening remains unknown.

Accordingly, to fill the aforementioned knowledge gap, we conducted this study to investigate the feasibility of Spirobank Smart for the early detection of COPD. Specifically, we applied Spirobank Smart to assist in the early detection of COPD using case-finding in outpatient clinics. We determined the correlation between FEV1/FVC values obtained by Spirobank Smart and postbronchodilator (post-BD) FEV1/FVC ratio obtained through confirmatory spirometry (executed using a diagnostic spirometer). Furthermore, we determined the cutoff point for the FEV1/FVC ratio and the corresponding predictive performance of Spirobank Smart in identifying COPD in high-risk populations; subsequently, we evaluated whether the FEV1 obtained using Spirobank Smart was useful for classifying patient disease severity according to the GOLD (Global Initiative for Chronic Obstructive Lung Disease) severity stratification system.

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

#### *2.1. Study Design*

This prospective multicenter validation study was conducted for the period from April 2018 to December 2019, and the study protocol was approved by the Institutional Review Board of Changhua Christian Hospital (IRB number 190806); Institutional Review Board of Changhua Christian Hospital reviewed the project and deemed it to be exempt research because the evaluation was for a public service program and all data were deidentified. The requirement for written consent was waived. Participants were enrolled from 26 outpatient clinics in Taiwan, from which their demographic information, CAT (COPD Assessment Test) questionnaires, Spirobank Smart measurements, and diagnostic spirometer measurements were obtained.

#### *2.2. Study Participants*

The Taiwan Society of Pulmonary and Critical Care Medicine recruited participants and implemented a study, commissioned by the Taiwan Ministry of Health and Welfare, on early diagnosis of COPD. The eligibility criteria were as follows: having an age of ≥40 years, having ≥10 pack-years of smoking, exhibiting chronic respiratory symptoms (cough, phlegm, or dyspnea, or combination thereof), and not having a confirmed COPD diagnosis within the prior year. We excluded individuals who did not undergo post-BD spirometry because we could not confirm whether a given individual had a COPD diagnosis; we also excluded those who were unable to correctly operate the Spirobank Smart device. Accordingly, a total of 370 participants completed all the tests, and their data were analyzed in this study. A flowchart of the participant enrollment procedure is presented in Figure 1.

#### *2.3. Study Procedures*

All tests undertaken in the 26 outpatient settings were conducted on the same day. The participates were assessed using Spirobank Smart and required to complete a CAT questionnaire. Regarding the operation of the Spirobank Smart device, each nurse and physician who contributed to this study were adequately trained on the use of the device. The participants were also required to inhale maximally and then exhale forcibly into the mouthpiece of the device for at least 6 s until the chronometer was switched on and changed its color to green. Measurements of acceptable quality, that is those with the highest summed value (FEV1 + FVC), were subsequently recorded; individual ratios were then calculated. Cross-contamination was minimized using disposable plastic mouthpieces. Spirometry with BD reversibility was performed independently by trained operators in accordance with the guidelines proposed by the American Thoracic Society. Lung function parameters were measured before and after 20 min of BD inhalation. At least three

adequate baseline and post-BD FVC maneuvers were performed to measure the highest FEV1 and FVC values. COPD diagnosis and COPD severity classification were conducted in accordance with GOLD definitions. The reference criterion for COPD was defined as a post-BD FEV1/FVC value of <70%, and COPD severity was classified according to the post-BD FEV1 percentages of the predicted values.

**Figure 1.** Flowchart of participant selection for the early detection of chronic obstructive pulmonary disease (COPD).

#### *2.4. Devices and System*

The Spirobank Smart device (MIR, Rome, Italy) used in this study can connect to smartphone apps through Bluetooth for the seamless recording of lung function parameters; it makes measurements through a bidirectional digital turbine. The turbine sensor operates based on the infrared interruption principle, which can ensure appropriate and repeatable measurement procedures. The device does not require calibration and applies a disposable turbine flow meter for its measurements. In this study, each participant's demographic information, including age, gender, height, and weight, was manually inputted into the device before the spirometry could be performed. To obtain measurements using this spirometer, each participant was required to exhale into the turbine, thus activating the motor inside the spirometer; concurrently, the speed of the rotor was recorded, and the recorded data were then adapted and transmitted to the app on the participant's smartphone. The exhalation process switched on the chronometer, and the color of the chronometer changed from orange to green after 6 s of exhalation. Accordingly, data on several parameters, including FEV1, PEF, FVC, FEV1/FVC, FEV6, and FEF25–75, were instantly displayed on the app. When an error occurred, the device detected and indicated the error description. Each participant was required to perform three cycles of inhalation and exhalation, and the best FEV1 and FVC values were selected. GLI-2012-predicted values were used as the reference values (as reported in our previous study [24]) for the executed spirometry. Such predicted values can be expressed as follows: predicted value = ea × Hb × Ac × ed×group × espline. In the preceding equation, "group" represents Southeast Asian.

#### *2.5. Statistical Analysis*

Data are expressed as a frequency with a percentage and as mean ± standard deviation for categorical and continuous variables, respectively. The distributions of the variables between COPD and non-COPD were compared using Student's *t*-test and the chi-square test. The agreement between the post-BD FEV1/FVC values measured through confirmatory spirometry and pre-BD FEV1/FVC values measured using Spirobank Smart was assessed using Bland–Altman plots. The optimal cutoff point for the FEV1/FVC ratio determined by Spirobank Smart for identifying COPD in high-risk participants was determined using the Youden index derived from receiver operating characteristic (ROC) analysis. Sensitivity,

specificity, positive predictive values (PPVs), negative predictive values (NPVs), and ROC curve values were then used to assess the effectiveness of the aforementioned screening tools in differentiating between COPD and non-COPD. Logistic regression analyses were performed to determine the association between potential risk factors and COPD incidence. All statistical analyses were performed using IBM SPSS 22 (IBM Corp., Armonk, NY, USA). In all analyses, two-tailed *p*-values < 0.05 were considered statistically significant.

#### **3. Results**

#### *3.1. Demographic Characteristics of Study Participants*

This study included data from 26 hospitals and clinics in Taiwan, including medical centers, regional hospitals, district hospitals, and clinics. The demographic characteristics of the enrolled participants are listed in Table 1. Among the participants, 103 had COPD (COPD group) and 267 did not have COPD (non-COPD group); of the participants, 94.3% were men. Participants with COPD appeared to be older, have higher pack-years of smoking, and have lower body mass index (BMI) values. The pre-BD FEV1/FVC ratio that was determined using Spirobank Smart was significantly lower in the COPD group (63.57 ± 13.37) than it was in the non-COPD group (81.78 ± 7.44; *p* < 0.001). The COPD group had a significantly higher CAT score (12 ± 7) and lower post-BD FEV1/FVC ratio (59.06 ± 9.04) than did the non-COPD group (CAT score: 9 ± 6; post-BD FEV1/FVC ratio: 81.28 ± 7.19; all *p* < 0.001). These variables demonstrated statistically significant differences between the two groups.

**Table 1.** Demographic characteristics of eligible participants for the early detection of chronic obstructive pulmonary disease (COPD).

**Table 1.** *Cont.*

*3.2. Agreement between Post-BD FEV1/FVC Ratios Measured Using the Confirmatory Spirometry and Pre-BD FEV1/FVC Ratios Measured Using Spirobank Smart*

Bland–Altman plots (Figure 2) are useful for determining the relationship between differences and averages, which can help researchers to explore any systematic biases and identify possible outliers. In this study, we derived Bland–Altman plots to evaluate the agreement between the pre-BD FEV1/FVC ratios measured using Spirobank Smart and the post-BD FEV1/FVC ratios measured using the confirmatory spirometry. The plots revealed that few values (5.67%) fell outside the 95% confidence interval. The mean difference between the post-BD values derived using the confirmatory spirometry and the FEV1/FVC values derived using Spirobank Smart was 1.6%. Additionally, the plots demonstrated that the limits of agreement were superior (narrower) for the mean of FEV1/FVC values of <70% than they were for the mean of FEV1/FVC values of ≥70%.

**Figure 2.** Bland–Altman plots illustrating the differences between the prebronchodilator (pre-BD) forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) values obtained using Spirobank Smart and the post-BD FEV1/FVC values obtained using the diagnostic spirometer as a percentage of the mean difference (vertical axis) versus the mean of the two FEV1/FVC ratios (horizontal axis).

#### *3.3. ROC Curves and Diagnostic Accuracy for the Pre-BD FEV1/FVC Ratios Measured Using Spirobank Smart*

We constructed ROC curves (Figure 3) to assess the suitability of FEV1/FVC measured using Spirobank Smart as a prescreening measure for COPD identification. Our results indicated that FEV1/FVC measured by Spirobank Smart could significantly predict COPD, with the corresponding area under the ROC curve (AUROC) being 0.903 (95% CI = 0.860– 0.947). According to the Youden index derived from our ROC analysis, Spirobank-Smartmeasured FEV1/FVC values of < 74% can serve as high-risk indicators of COPD, and the highest predictive ability was observed at an AUROC value of 0.873 (95% CI = 0.827–0.920).

**Figure 3.** Receiver operating characteristic (ROC) curves for the forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio measured using Spirobank Smart.

The cutoff values for FEV1/FVC and the corresponding predictive performance of Spirobank Smart are presented in Table 2. As indicated in this table, at the optimal cutoff point of 0.74, the sensitivity, specificity, PPV, and NPV were 82.50%, 92.13%, 80.20%, and 93.20%, respectively. Compared with the sensitivity of Spirobank Smart with an FEV1/FVC ratio of <70, the sensitivity of the diagnostic spirometer was as low as 70.90%; thus, most patients with COPD were underdiagnosed, despite the AUROC being 0.834 (95% CI = 0.779–0.889). The portable spirometer achieved a balance between sensitivity and specificity when the FEV1/FVC ratio was <74%; accordingly, it exhibited the best predictive ability. Our findings revealed that Spirobank Smart could be used as a diagnostic tool in general population screening procedures for identifying patients that are at high risk of COPD.

Figure 4 illustrates the diagnostic performance, as assessed with respect to COPD severity, that was observed at the optimal cutoff point for FEV1/FVC (<74%). Some of the participants without COPD were recorded as having COPD, resulting in false-positive results; this thus led to moderate PPV estimates (80.20%). In addition, relatively few participants with COPD were recorded as not having COPD, resulting in false-negative results; this thus led to high NPV estimates (93.20%). Of the participants for whom falsenegative results were recorded, 80% had mild or only moderate COPD.

**Table 2.** Diagnostic accuracy of Spirobank Smart across different cutoff points.

**Figure 4.** Scatter plots of post-bronchodilator (post-BD) forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratios that were determined using the diagnostic spirometer against pre-BD FEV1/FVC ratios that were determined using Spirobank Smart for eligible participants. The quadrants are defined by the GOLD spirometer criteria for obstruction (post-BD FEV1/FVC < 70%) and the Spirobank-Smart-measured FEV1/FVC ratios that yielded optimal performance characteristics (FEV1//FVC < 74%).

#### *3.4. GOLD Classification and CAT Score of Participants Based on the FEV1 Values Obtained Using Confirmatory Spirometry and Spirobank Smart*

To evaluate whether the FEV1 determined using Spirobank Smart could be a reliable parameter for classifying obstruction severity, we analyzed the obstruction severity by using the FEV1 values obtained through confirmatory spirometry and those determined using Spirobank Smart; the analysis results are summarized in Table 3. The FEV1 values obtained using the diagnostic spirometer revealed that 24.3% and 51.5% of the participants exhibited mild and moderate COPD (GOLD I and II), respectively. The classification results obtained using the FEV1 values determined using Spirobank Smart were comparable to those obtained using the values determined using the diagnostic spirometer (i.e., 15.5% and 57.3% of the participants were classified as having GOLD I and II severity levels, respectively). Moreover, the CAT scores for grades III and V were higher than those for grades I and II.

**Table 3.** GOLD classifications and CAT scores of the patients. The scores depended on the forced expiratory volume in 1 s (FEV1) values obtained using a diagnostic spirometer and Spirobank Smart. The characteristics of patients with chronic obstructive pulmonary disease (COPD) were defined according to the GOLD classification of disease severity.

*3.5. Associations of FEV1/FVC Determined Using Spirobank Smart and the Participant Characteristic Variables with the COPD Incidence*

Univariate logistic regression analysis revealed that factors such as an FEV1/FVC ratio of <74% that was determined using Spirobank Smart, ≥50 pack-years of smoking, age, and lower BMI were positively associated with the incidence of COPD. After the multivariate adjustments, we observed that an FEV1/FVC ratio of <74% that was determined using Spirobank Smart (odds ratio (OR) = 58.58, 95% CI = 27.29–125.75) and old age (OR = 3.23, 95% CI = 1.04–10.07 for 60 ≤ age < 65; OR = 5.82, 95% CI = 2.22–15.27 for age ≥ 65) remained significantly associated with the incidence of COPD (Table 4).

**Table 4.** Multivariate logistic regression analysis used to evaluate the associations between the forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) that was determined using Spirobank Smart, patients characteristic variables, and chronic obstructive pulmonary disease (COPD) incidence.

#### **4. Discussion**

Earlier detection of COPD in patients can improve the short- and long-term patient outcomes when treated using current therapies. Although spirometry is the gold standard for COPD diagnosis, it is often perceived as an expensive and time-consuming process, leading to the underdiagnosis or misdiagnosis of COPD. Furthermore, spirometry is not cost-effective for screening for COPD in a population of truly asymptomatic smokers. To

the best of our knowledge, few efficacious strategies have been designed for identifying patients with undiagnosed COPD who are most likely to benefit from current therapies, especially in Taiwan [19]. Accordingly, to bridge this gap, we used an app-based portable spirometer, namely, Spirobank Smart, to identify undiagnosed COPD in at-risk populations as the first step in determining which patients should be referred for further COPD diagnostic evaluation.

We validated the feasibility and suitability of Spirobank Smart for deriving FEV1/FVC values for screening undiagnosed patients who were at risk of COPD. Based on our knowledge, this is the first study in Taiwan to evaluate the accuracy and feasibility of an app-based spirometer for early COPD detection. The principal findings of this study regarding at-risk population outcomes are outlined as follows. First, we found that 27.8% of the participants were newly diagnosed with COPD using confirmatory spirometry at tertiary hospitals, and nearly 70% of the newly diagnosed patients had mild or moderate COPD. Second, the AUROC value for COPD identification using Spirobank-Smart-derived was 0.903. Third, patients who have an FEV1/FVC ratio of <74% that was determined using Spirobank Smart (odds ratio (OR) = 60.70, 95% CI = 27.95–131.83) and old age (OR = 3.23, 95% CI = 1.04–10.07 for 60 ≤ age < 65; OR = 5.82, 95% CI = 2.22–15.27 for age ≥ 65) exhibited a higher risk of COPD; this indicates that COPD incidence was associated with increased age and FEV1/FVC ratio < 74% determined by Spirobank-Smart. These findings support the claim that Spirobank Smart is an acceptable and feasible screening tool for the early detection of COPD.

A previous study reported a COPD prevalence of 18.9% when respiratory symptoms were not considered in the inclusion criteria [25]. When respiratory symptoms were considered in the inclusion criteria, the prevalence of COPD increased to 25–52.9% [16,18]. In Taiwan, a nationwide survey of the general population revealed an estimated COPD prevalence of 6% [26]; however, a case-finding study reported that the COPD prevalence in an at-risk population was 48.8% [27]. The present study indicated a COPD prevalence of 27.85% in smokers who were aged >40 years and had ≥10 pack-years of smoking and at least one symptom. Kjeldgaard et al. used a similar study design and similar inclusion criteria to those of the present study; they determined the COPD prevalence to be 32% in an at-risk population (aged ≥40 years with smoking history and at least one respiratory symptom) [18]. Kim et al. measured the COPD prevalence in smokers who had 10 packyears of smoking and were aged >40 years in a primary care setting; they reported that the prevalence of COPD was 23.7% [28]. These findings suggest that the prevalence would be significantly influenced by the inclusion criteria that are used to define COPD and that screening for high-risk individuals can detect high proportions of patients that are undiagnosed as having COPD. Furthermore, our findings demonstrate that Spirobank Smart could be used for early detection of COPD in an outpatient clinical setting, as evidenced by a follow-up diagnostic spirometer test used for confirmation; the concomitant presence of respiratory symptoms increased the likelihood of identifying COPD.

In the present study, the level of agreement between the pre-BD FEV1/FVC values that were determined using Spirobank Smart and the post-BD FEV1/FVC values that were determined using the diagnostic spirometer was satisfactory; this thus demonstrates that Spirobank Smart is an acceptable alternative screening tool for early detection of COPD. Notably, the limits of agreement for the FEV1/FVC values of <70% were narrower than those for FEV1/FVC values of ≥70%, implying that FEV1/FVC values of ≥70% may lead to slight underestimations. In other words, the underdiagnosis of obstruction may occur in younger adults or those with mild COPD. Therefore, we suggest that FEV1/FVC values of <0.74, instead of FEV1/FVC values of <0.70 (the conventional GOLD definition), be used along with Spirobank Smart for optimal screening; additionally, this cutoff resulted in the achievement of a balance between false-positive and false-negative results in our study population, thereby reducing the possibility of underdiagnosis or misdiagnosis.

Few reports are available on the feasibility and reliability of portable devices for COPD screening in clinical settings. Frith et al. evaluated the feasibility of piko-6 in the early

detection of COPD without including a BD test [29]. Two other studies have also used COPD-6 and piko-6 screening devices. These three studies have indicated that COPD-6 and piko-6 implemented with FEV1/FEV6 for COPD screening in at-risk populations had AUROC values of 0.75 and 0.86, respectively, validating these screening devices as acceptable tools for COPD screening in at-risk populations [16–19,29]. These findings are similar to those of the present study. Although FEV6 is an acceptable surrogate for FVC in spirometry, FEV6 has some drawbacks, such as the underestimation of mild airway obstruction [30–32]. Hernández et al. reported that FEV1/FVC values of <0.7 determined using a smartphone-based spirometer, namely Air-Smart, for detecting obstructive airway diseases had a sensitivity of 94.0% and a specificity of 97.2% [20]. Although the use of portable devices to diagnose COPD or obstructive airway diseases has been validated, the results cannot be compared with those of the current study due to the differences in experimental design and spirometry procedures between the studies.

ROC analysis for the FEV1/FVC values that were determined using Spirobank Smart demonstrated that this parameter could differentiate between the COPD and non-COPD groups with moderate to good AUROC values (0.903 (95% CI = 0.860–0.947)). The optimal cutoff point (FEV1/FVC value of <74.0%) with a score of 0.757 was determined according to the Youden index. A screening method with perfect sensitivity, specificity, PPV, and NPV is not usually available. In COPD screening, high sensitivity, rather than high specificity, is prioritized because identifying more potential patients with COPD-like conditions is crucial. The PPVs derived in this study revealed that approximately 20% of patients had post-BD FEV1/FEV values below the optimal cutoff point of <0.7; therefore, the PPV is useful for avoiding unnecessary, excessive examination of participants due to false positives. Additionally, high NPVs were observed in this study, indicating that 90% of patients had FEV1/FVC values of ≥74.0%, as determined using Spirobank Smart; therefore, we could feasibly determine whether a participant could have COPD and require further testing. Our results are similar to those of a previous study that examined the COPD screening accuracy of a handheld spirometer according to the AUROC, sensitivity, specificity, PPV, and NPV at a specified cutoff point. Additional studies are warranted to determine the clinically sensible cutoff point of the parameter in Spirobank Smart in other cohorts and thereby confirm its clinical usefulness for COPD screening. The use of a spirometer alongside a BD is the gold standard for COPD diagnosis. However, a BD is not sufficiently safe for implementation in primary clinics or nonspecialist areas due to insufficient monitoring and emergency procedures [28]. Therefore, we suggest that for individuals who are at risk of COPD and have abnormal testing results from Spirobank Smart, another diagnostic procedure executed using a spirometer alongside a BD should be used. Accordingly, Spirobank Smart may reduce inappropriate BD use in COPD diagnoses and provide a feasible screening process.

The conventional GOLD definition may lead to the underdiagnosis of obstruction in younger adults and overdiagnosis of obstruction in older adults [33,34]. Therefore, scholars have suggested the use of a lower limit of normal (LLN)-based diagnosis of COPD [33–35]. However, a previous study reported that LLN-based definitions tend to underdiagnose COPD in symptomatic patients [4]. In our study design, we used a case-finding strategy based on symptom screening. The conventional GOLD definition may, therefore, be the appropriate choice for reducing the underdiagnosis of COPD in our screening strategy. In addition, previous studies have indicated that the LLN-based diagnosis of COPD generated fewer false positives and more false negatives compared with the conventional GOLD definition. False negatives lead to the undertreatment of patients with COPD during disease stages (e.g., GOLD I and II) when they are likely to benefit most. Moreover, LLN tends to categorize elderly adults with mild obstruction into a non-COPD category (due to its high specificity and low sensitivity) [36]. In our study population, the mean age was 60.9 years, and a large proportion of the participants had COPD in GOLD I and II; thus, the use of the conventional GOLD definition may help to avoid potential false-negative results. A previous study revealed that compared with diagnoses executed using fixed

cutoff values, diagnoses based on the LLN involved slightly higher NPVs [37]. Considering the potential false positives that can result from using the conventional GOLD definition and considering the evolving understanding of strategies for airflow obstruction detection, the use of the LLN-based definitions as a gold standard in spirometry to validate the FEV1/FVC values that were determined using Spirobank Smart could be valuable and warrants further study.

A CAT score of ≥10 points is used to classify patients with COPD as highly symptomatic. In the present study, 41.7% of patients that were newly diagnosed as having COPD had CAT scores of <10, which indicated that the presence of fewer symptoms may contribute to diagnostic confusion and clinical neglect. Moreover, studies have reported that handheld spirometers are useful for determining obstruction severity. In our study, we assessed the feasibility of evaluating COPD severity in an at-risk population by using FEV1. We determined that nearly 70% of the newly diagnosed COPD cases were classified as GOLD I or II based on the FEV1 values that were measured using Spirobank Smart. The frequency of GOLD classifications was similar to that of the diagnostic spirometer. Our findings also demonstrate that the prevalence of COPD in GOLD I was 24.3%, which is similar to that (27.2%) observed in a previous study conducted at a single medical center in Taiwan [23]; this suggests that Spirobank Smart is useful for identifying undiagnosed COPD with mild or moderate airway limitations in at-risk populations and can help to raise awareness of current perceptions of COPD. Based on our study findings, to provide the appropriate intervention, we propose the following clinical procedure for the triage of participants who are at risk of COPD: (1) if the FEV1/FVC ratio is <0.74, the patient should be referred to a pulmonologist for further diagnosis; (2) if the FEV1/FVC ratio is ≥0.74, a diagnosis alternative to COPD should be considered, and smoking cessation should be recommended to the patient; (3) if a patient is newly diagnosed as having COPD, they should be invited to join the COPD pay-for-performance program that is implemented by the National Health Insurance Administration to encourage physicians to provide patient-centered care plans; (4) if an individual is diagnosed as not having COPD, they should be advised to cease smoking (Figure 5). Furthermore, our findings indicated that patients with known smoking history who are old age had a higher risk of COPD. The English Longitudinal Study of Ageing (ELSA) demonstrated that nearly all smoking-related deaths (99%) were due to the occurrence of COPD in people aged >50 years, and the mean duration of smoking for current smokers was 42 years [38]. These findings suggest that an age of >40 years and a smoking history of ≥10 pack-years are adequate criteria for early COPD detection. In our study, we believe that symptomatic individuals who are aged >60 years with smoking history have greater risk of COPD and deserve closely routine COPD screening.

Limitations regarding the implementation of Spirobank Smart should be acknowledged. The data were collected from only a limited number of participants enrolled in hospital-based facilities, including medical centers and regional hospitals, which may not reflect the entire COPD population, especially those who have never visited hospitals for COPD testing. Additional studies including more types of medical facilities, such as primary care facilities, are warranted to assess the validity of Spirobank Smart. Moreover, the FEV1 measurements that were obtained using Spirobank Smart were underestimated by up to 5%, and the FEV1/FVC ratios were underestimated by 3–4%. These underestimations may cause by the inaccuracy of the portable device. However, Spirobank Smart complies with the latest documented ATS/ERS standards for accuracy, and our ROC analysis indicated that Spirobank Smart was sufficiently accurate for COPD identification through the case-finding strategy. Finally, the interday repeatability of the lung function test, even in the clinical trial population, was determined to be considerable, which may be a limitation of our study. A previous study highlighted the obscurity of lung function values from a single spirometry procedure. The results indicated that approximately 1% of the participants' values changed from the lowest to highest quintiles (and vice versa), and only approximately half of the participants allocated to different lung function quintiles

at screening were grouped into the same quintile at baseline. A possible explanation for this is the methodological variability of lung function measurements and the physiological variability of the airway caliber [39].

**Figure 5.** Clinical procedure for the triage of participants who are at risk of chronic obstructive pulmonary disease (COPD) based on the results obtained in this study using a lung function test and Spirobank Smart.

In conclusion, Spirobank Smart was determined to be a simple and feasible device for screening undiagnosed COPD in at-risk populations in outpatient clinical settings. We observed that measurements obtained from the device had a moderate correlation with those obtained from a diagnostic spirometer and that the device had acceptable accuracy in identifying undiagnosed COPD in our at-risk population. The use of an app-based spirometer is a potential strategy for improving the early detection of COPD. We suggest that individuals who are aged >40 years and have >10 pack-years of smoking and at least one respiratory symptom should receive a lung function evaluation using an appbased spirometer. Furthermore, we suggest that participants who have abnormal test results, as determined using Spirobank Smart with FEV1/FVC values of <0.74, should undergo diagnostic spirometrer to confirm the COPD diagnosis and initiate an appropriate intervention in the early stages of the disease.

**Author Contributions:** Task, C.-H.L., S.-L.C., and H.-C.W.; formal analysis, C.-H.L., S.-L.C., and C.- Z.C.; investigation, D.-W.P., H.-I.L., M.-S.L., J.-R.T., C.-C.W., S.-H.L., H.-C.W., C.-Z.C., T.-M.Y., C.-L.L., and T.-Y.W.; resources, D.-W.P., H.-I.L., M.-S.L., J.-R.T., C.-C.W., S.-H.L., H.-C.W., C.-Z.C., T.-M.Y., C.- L.L., and T.-Y.W.; data curation, C.-H.L., S.-L.C., K.-Y.L., C.-Y.W.; writing—original draft preparation, C.-H.L. and S.-L.C.; writing—review and editing, H.-C.W.; visualization, C.-H.L.; supervision, H.- C.W. and M.-C.L.; project administration, W.-H.H. and M.-C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Health Promotion Administration of Taiwan, grant number D1070206-108; The APC was funded by 109-CCH-MST-175.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Changhua Christian Hospital (protocol code 190806 and date 10-08-2019).

**Informed Consent Statement:** Institutional Review Board of Changhua Christian Hospital reviewed the project and deemed it to be exempt research because the evaluation was for a public service program and all data were de-identified. The requirement for written consent was waived.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons.

**Conflicts of Interest:** All authors declare no conflict of interest related to the data collected and procedures in this study.

### **Abbreviations**

### **References**


**1. Introduction**

Understanding and recognizing human emotions has been identified as a main interest area in smart systems [1–5]. Such systems are being applied in many fields like well-being and healthcare [6–11], safe driving [12], smart cities [13] and smart environments [14,15], among others. Pleasure, arousal and dominance are three independent emotional dimensions to describe people's state of feeling [16,17]. Arousal was conceived as a mental activity describing the state of feeling along a single dimension ranging from sleep to frantic excitement and linked to adjectives such as stimulated–relaxed, excited–calm and wide awake–sleepy to define arousal [18].

The arousal level changes constantly, and it has a profound influence on performance during everyday activities [19]. Fluctuations in arousal are regulated by the autonomic nervous system, which is mainly controlled by the balanced activity of the parasympathetic and sympathetic systems [20]. Electrodermal activity (EDA; or skin conductance) has also frequently been used as a measure of arousal. The advantage of EDA is that it is unambiguous, given that it is innervated entirely by the sympathetic nervous system (SNS) [21]. Within the domain of music emotion research, physiological measures such as EDA, heart rate, respiration, and body temperature have been frequently used as correlates of emotional arousal. Among these, EDA is generally a preferred measure as it is highly sensitive and under strict control of the sympathetic nervous system and is therefore largely involuntary. Furthermore, a relationship between EDA as indicator of emotional arousal and experienced pleasure in response to music has previously been demonstrated [22]. At the same time, in their studies with volunteers the participants' feelings have been obtained by questionnaires in the form of Likert scales, self-assessment manikins (SAM) and free text [23–26].

This paper introduces arousal detection from EDA signals using musical stimuli. Several studies have reported that using music to elicit emotions is one of the most effective methods of emotion induction [27–30]. Music plays a key role in most people's lives, frequently being used to explore and regulate emotions. The proposal is linked to our current research elicitation of emotions in elderly people to trigger processes of emotional self-regulation [31–33]. Those processes should help elderly people to improve their mood and mental state. The importance of emotional self-regulation is related to the fact that older people, especially when living alone, are at high risk of suffering from diseases such as depression and anxiety [34,35].

Specifically, people over 60 years old from the region of Murcia, Spain, were recruited as participants to listen to a series of musical pieces similar to those played in their younger years in order to study the level of arousal produced by each musical genre. Although many protocols have investigated physiological responses to music, the present work explores the physiological responses to pieces of music composed specifically for this experiment. The use of original pieces of music, which had not been heard by the listener before, is a novel research technique that has yielded interesting results so far [27–30]. The use of this type of music fragments provides a high level of experimental control and allows knowledge of the influence of the independent variables on the dependent ones. Experimental control is especially important when analyzing physiological responses like EDA [36]. The signals collected during the experiment were used in conjunction with a SAM questionnaire to undergo a couple of studies oriented towards discriminating the arousal. One study analyzed some EDA features only, and the second, based on classifiers, examined possible correlations between the objective detection of the arousal level from processed physiological EDA signals and the level of arousal subjectively perceived by participants when answering the SAM questionnaire.

The remainder of the paper is as follows. Section 2 shows the materials and methods needed to perform the experiment successfully, as well as the investigation methods and metrics used. In Section 3, the results obtained are shown and a discussion about the results obtained in the context of the experiment is provided. Finally, in Section 4 the results obtained in this study are presented.

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

This section describes the methodology and materials required to carry out the proposed experiment. First, an introduction is made about the electrodermal activity as a biomarker of activation detection. Then, a description of the material used, and the processes required to detect the level of activation is made. Next, the methods of data collection (SAM questionnaires) and how they are used within the experiment are explained. Afterwards, a detailed explanation of the experiment is given. Finally, the process of data segmentation and feature extraction for further analysis is explained.

#### *2.1. Electrodermal Activity*

Electrodermal activity (EDA) reflects the output of the attentional and affective and motivational processes integrated within the central nervous system that act on the body [37]. When emotional arousal increases, the accompanying activation of the SNS results in increased sweat gland activity and skin conductance. The validity of EDA as a measure of emotional arousal has been established in studies showing that EDA varies linearly with self-reported arousal when viewing emotional pictures [38]. Therefore, EDA is outstanding in behavioral medicine as a biomarker of individual characteristics of emotional response. EDA monitoring has been used for multiple applications, including assessment of anxiety and stress, detection of orientation response, providing neurofeedback for epilepsy, recognition of emotional state, and many others. In addition, EDA can be very effective in discriminating patients with depression from healthy controls [39]. Specific patterns of electrodermal hypoactivity may be a reliable marker of a depressive state at population level, but they should be carefully combined with other physiological and non-physiological indicators when used for preventive and diagnostic purposes.

EDA covers the electrical variations that occur on the surface of the skin due to changes in sweat secretion. EDA signals are obtained by measuring the potential when a small constant current is applied between two metal electrodes (for example, chrome-silver electrodes). The skin usually responds to stress by producing an increase in sweat. Consequently, the skin's conductivity increases. On the other hand, sweat production stops and skin conductivity is reduced when a person is subjected to a calm or neutral induction. In this study, EDA is measured at the wrist, bearing in mind that wrist biosensors are being widely adopted in conventional and commercial devices. The bracelets provide excellent surfaces for attaching the electrodes to the skin. Ideally, the proposed system should be further miniaturized to record EDA in the areas of the palm where the activity of the skin conduction response (SCR) is most pronounced, without being intrusive or interfering with daily activities.

#### *2.2. Data Acquisition and Empatica E4 Device*

The commercial Empatica E4 wristband has been used to carry out our experiment. The Empatica E4 bracelet is a device that allows the collection and measurement of physiological signals such as EDA, blood volume pressure, temperature and acceleration. This device has been used with good results in some previous works [14,40,41]. In this work, we have used only the EDA signals to study the possibility of determining whether significant differences occur when a participant is subjected to different musical stimuli.

An essential component of our proposal is to acquire, process and obtain a set of data that will be used for identification of the listener's arousal. The Empatica E4 device must be firmly attached to the wrist so that the electrodes touch the skin correctly. Otherwise, if the device is not properly connected, the captured data are not valid due to manifold artefacts.


5.1.1. Effect of Varying Reforming Temperature

The variation of H2 molar flow rate at the outlet of the steam methane and steam methanol reformers in thermodynamic equilibrium with varying reforming temperature and S/C ratio was derived to understand the system behavior. As can be observed in Figure 10a, the methane steam reformer shows the tendency that at S/C ratios below 3.5, the hydrogen flow rate increases when the reforming temperature increases up to 750 ◦C. After then, the H2 molar flow rate decreases as the reforming temperature increases. At S/C ratios above 3.5, the H2 molar flow rate continuously decreases as the reforming temperature increases. The steam methanol reformer shows a trend that as the reforming temperature increases from 180 to 260 ◦C, the molar flow rate of H2 continuously decreases, although the decrease rates are different, as shown in Figure 10b. Figure 10a,b represent the behavior of each reformer and can be used for interpretation of each system in the next section. The effects of varying the reforming temperature on the efficiencies and molar flow rate of CO2 in the methane-based and methanol-based systems were studied and illustrated in Figure 11.

As can be observed in Figure 11a, for the methane-based system, as the reforming temperature increases from 700 to 950 ◦C, the electrical, cogeneration, and exergy efficiency continuously decrease, from 39.53%, 63%, and 41.14% to 37.92%, 58.03%, and 38.47%, respectively. This trend occurs because as the reforming temperature increases, the amount of additional fuel needed for the reformer and combustor increases by 5.8% and leads to a decrease in efficiencies of the system. It can be noticed that the slope of the efficiency curves between 700 and 750 ◦C is less steep than that at other temperature ranges. The reason for this is that the amount of hydrogen produced increases when the temperature is increased from 700 to 750 ◦C; after then, the amount of hydrogen produced starts to decrease, as shown in Figure 10a. In addition, the slope of the cogeneration efficiency curve is a little steeper than that of the electrical and exergy efficiencies as the amount of the produced CO2 increases; accordingly, the heat required to capture CO2 in the CCU increases. The methanol-based system shows a similar behavior to that of the methane-based system, as shown in Figure 11b. As the reforming temperature increases from 180 to 260 ◦C, the electrical and exergy efficiencies decrease from 47.85% and 43.25% to 46.27% and 42.23%, respectively, whereas the cogeneration efficiency stays almost constant. Noteworthy, unlike the methane-based system, as the reforming temperature increases above 200 ◦C, the amount of produced CO2 decreases. This happens because an increase in the reforming temperature above 200 ◦C will favor the CO formation and the increase in CO content will decrease the production of H2 and CO2. Therefore, the heat required for CO2 capture increases and this results in a slight increase in cogeneration efficiency as the reforming temperature increases.

(**a**) Steam methane reformer: Methane supply of 1 kmole/h.

(**b**) Steam methanol reformer: Methanol supply of 1 kmole/h.

**Figure 10.** Variation of H2 molar flow rates of the exit gases from the reformer as a function of the S/C ratio and reforming temperature.

**Figure 11.** Influence of reforming temperature on system efficiencies and variation of molar flow rates of CO2 and fuel.


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## Meet the editors

Syed Abdul Rehman Khan is a teacher of supply chain and logistics management. Dr Khan achieved his Certified Supply Chain Professional (CSCP) certificate from the United States and successfully completed his Ph.D. in China. Since 2018, Dr Khan has been affiliated with Tsinghua University as a postdoctoral researcher. He has more than 9 years' core experience of supply chain and logistics at industry and academic levels. He

has attended several international conferences and also has been invited as a keynote speaker in different countries. He has published 70+ scientific research papers in different well-renowned international journals and conferences. He is a regular contributor to conferences and workshops around the world. In addition, Dr Khan has received two scientific innovation awards, consecutively, from the Education Department of Shaanxi Provincial Government, China. Also, Dr Khan holds memberships in the following well renowned institutions and supply chain bodies/ associations: APCIS, USA; Production and Operations Management Society, India; Council of Supply Chain Management Professionals, USA; Supply Chain Association of Pakistan; and Global Supply Chain Council, China.

Dr Selay Ilgaz Sümer is an Assistant Professor at the Department of Management in the Faculty of Economics and Administrative Sciences at the Baskent University, Ankara, Turkey. She received her PhD in Business Administration from Gazi University, Ankara, Turkey in 2011. She has published several articles, conference proceedings, and book chapters. Dr Ilgaz Sümer is a member of various referee boards of national and international journals. Her

primary research interests are consumer behavior, marketing strategies, international marketing, sales management, digital marketing, and services marketing.

Contents

**Section 1**

**Section 2**

Housing Rental Market

*by Selay Ilgaz Sümer*

*by Hesham O. Dinana*

**Section 3**

*by Takeshi So and Chihiro Shimizu*

Prologue: Marketing in the Modern Age

Insight-Driven Sales Management

**Preface XI**

Introduction **1**

**Chapter 1 3**

Supply Management Practices and Strategies **7**

**Chapter 2 9**

**Chapter 3 23**

**Chapter 4 41**

Sales Management in the New Age **63**

**Chapter 5 65**

**Chapter 6 69**

Introductory Chapter: Purchasing and Supply Management

*by Zulkaif Ahmed Saqib, Khubaib Ahmed Saqib and Jin Ou*

Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing Equipment in the Tokyo

*by Syed Abdul Rehman Khan and Zhang Yu*

Role of Visibility in Supply Chain Management

Sourcing and Manufacturing in the Market Region *by David J. Newlands and Fawaz Baddar Al Hussan*

## Contents

### **Chapter 7 87**

Organizational Capabilities, Value Cocreation, and Marketing Innovation: How Well Are We Prepared to Face Future Challenges? *by Leslier Valenzuela-Fernández and Natacha Peñaloza-Briones*

### **Chapter 8 107**

Empowerment of the Sales Forces in 2000s *by Leslier Maureen Valenzuela Fernández and Francisco Javier Villegas Pinuer*

Preface

Along with developments in technology and globalization movements in recent years, significant changes have occurred in the activities of businesses. These developments have also led to the diversification of consumers' demands and expectations. This situation required the needs of the consumers to be met faster than the competitors. In addition, it has become necessary for companies to attract the attention of consumers in a highly competitive environment. As a result, the importance of sales management and supply management has

In the context of marketing, businesses should plan their activities carefully. At this point, sales management activities and coordination of sales forces are very important. Besides, some of the concepts such as being customer-oriented and creating value, which have become very important in the planning of activities related to sales management, should be taken into consideration. Within the scope of sales management, in addition to organizing sales-related activities, some points related to the training, motivation, and planning of sales forces should be arranged

For most of its history, procurement has been focused primarily on transactional processing, with virtually no strategic decision-making responsibilities. The typical purchasing routine has been simple: receive a requisition from the user, place the purchase order with the supplier, expedite when necessary, and resolve invoice discrepancies. In this process, there was very little room for decision-making such

Today, this is certainly no longer the case, procurement and supply management has evolved into a strategic element in the competitive arsenal of most organizations. There is now a justifiable movement toward centralized control of corporate

within the framework of today's modern perspectives.

spending for compliance, risk management, and cost reduction.

This book is unique! Until now, purchasing and supply management books have had a primarily domestic outlook. However, in this book, important issues related to sales management and supply management are handled with a modern perspective. This book has global vision tied into management principles based on an understanding of the sales management and basic job of purchasing and supply management, as all authors have held high-level positions directing the effort. Distinguished researchers from prestigious universities have written chapters and case studies from real-world events that challenge the

In the end, we would like to thank the contributors/authors of the book for their extraordinary hard work to submit their valuable and innovative work in the form

as supply sourcing and even less discretion.

increased.

brightest minds.


*Article* **Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification**

**Bin Pan 1,2,3, Zhenwei Shi 1,2,3,\*, Xia Xu 1,2,3 and Yi Yang 4**

Received: 24 September 2017; Accepted: 24 October 2017; Published: 27 October 2017

**Abstract:** Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and propose an ensemble based feature representation method for hyperspectral imagery classification, which aims at generating a hierarchical feature representation for the original hyperspectral data. The proposed method is composed of three cascaded layers: firstly, multiple features, including local, global and spectral, are extracted from the hyperspectral data. Next, a new hashing based feature representation method is proposed and conducted on the features obtained in the first layer. Finally, a simple but efficient extreme learning machine classifier is employed to get the classification results. To some extent, the proposed method is a combination of MFF and FE: instead of feature fusion or single feature extraction, we use an ensemble strategy to provide a hierarchical feature representation for the hyperspectral data. In the experiments, we select two popular and one challenging hyperspectral data sets for evaluation, and six recently proposed methods are compared. The proposed method achieves respectively 89.55%, 99.36% and 77.90% overall accuracies in the three data sets with 20 training samples per class. The results prove that the performance of the proposed method is superior to some MFF and FE based ones.

**Keywords:** hashing ensemble; hierarchical feature; hyperspectral classification


*4.2. Human Apo D Purification*

Human Apo D (hApo D) was purified from BCF samples provide by the Pathology Unit of the Hospital Universitario Central de Asturias (HUCA). First, a cell fractionation was performed by differential centrifugation. Then, Amicon® Ultra-15, 100 kDa, centrifugal filter units (Z740211, Sigma-Aldrich, St. Louis, MO, USA) were used. The solution containing the protein was flow through two consecutively ion-exchange chromatographic columns (HiTrap® Q Fast Flow, GE Healthcare, Chicago, IL, USA) with 25 mM Tris pH 8.0, followed by a size-exclusion chromatography (HiLoad® 16/60 Superdex® 200 prep grade, GE Healthcare, Chicago, IL, USA ) in 50 mM Tris pH 8.0, 75 mM NaCl. Elution fractions with the protein of interest can be further concentrated using an appropriate 30 kDa cut-off Amicon® centrifuge filter (Z717185, Sigma-Aldrich, St. Louis, MO, USA). The presence of hApo D in these fractions was checked by Western blot, the amount of this apolipoprotein (concentration in the fraction) was quantified and its functionality was tested.


**5. Use of polscope**

120 Enhancing Success of Assisted Reproduction

does not improve the overall results.

**3. Use of PICSI**

drops).

**4. Use of IMSI**

pushing the needle tip close to the membrane opposite the puncture site, aspirating the cytoplasm at this point and releasing the sperm in the centre of oocyte [5]. This modification improves fertilization in oocyte-dependent activation failure, but its routine application

The cell surface hyaluronic acid (HA) binding glycoprotein is present in spermatozoa of different species including rat, mice, bull, and human [15]. The formation of hyaluronic-acid (HA)-binding sites on the sperm plasma membrane is one of the signs of sperm maturity. Various biochemical sperm markers indicate that human sperm bound to HA exhibit attributes similar to that of zona pellucida-bound sperm, including minimal DNA

PICSI Sperm Selection Device (Biocoat, Inc. Horsham, PA, USA) offers advantage in selecting sperm for ICSI. The PICSI device, a dish similar to ICSI dish, contains 3 microdots of hyaluronan hydrogel which need to be hydrated by media before ICSI. The prepared sperm sample is placed at the edge of the microdrop of PICSI dish. Mature, biochemically competent sperm bind to the hyaluronan where they can be isolated by the embryologist and used for ICSI **(Figure 3)**. The research supports that hyaluronan-bound PICSI-selected sperm are, in the vast majority of cases, more mature, exhibit less DNA damage, and have fewer chromosomal aneuploidies [17]. Further studies are needed to prove that use of PICSI

fragmentation, normal shape, and low frequency of chromosomal aneuploidies [16].

technique improves pregnancy rates and reduce the number of IVF miscarriages.

**Figure 3.** A is a graphical presentation of a PICSI dish. Each arrow is pointing to a dot containing hyaluronan. B and C are suggested arrangements for oocyte washing, PVP and ICSI drops (A, B and C are oocyte washing drops, P is PVP drop, a, b and c are hyaluronan dots and1, 2, 3, and 4 are ICSI

Intracytoplasmic morphologically selected sperm injection (IMSI) is examination of unstained spermatozoa at 6000 or higher magnification to select sperm with best morphology. It is based on a method of high magnification motile sperm organelle morphology examination (MSOME) [18]. It requires an inverted light microscope equipped The oocyte spindle can be imaged non-invasively based on the birefringence, an inherent optical property of highly ordered molecules, such as microtubules, as they are illuminated with polarized light. Polarized light microscopy has been applied to embryology for decades. A digital, orientation-independent polarized light microscope, the Polscope, has demonstrated the exquisite sensitivity needed to image the low levels of birefringence exhibited by mammalian spindles [26]. The Polscope, is used to protect the meiotic spindle from damage during ICSI. The oocytes having Polscope visualised spindle have higher fertilization rate. When the spindle is located at 0°-30° in relation to the first polar body, ICSI achieves highest fertilization rate [27]. The use of Polscope is still not widely practiced and further improvements are needed. Morphometric evaluation of the spindle through the Polscope is not consistent with confocal analysis. This suggests that the Polscope may still be a rather inefficient method for assessing the metaphase II spindle [28].