**4. Methodology**

### *4.1. Traffic Data Processing*

A traffic network is comprised of a set of links and a set of junctions or intersections in the area of a typical urban transportation network. The network-wide congestion state of links is expressed as a 2 × 2 matrix indexed in time (the number of time steps *T*) and space (the number of links *N*). Given the intersection and link topological characteristics, the intent is to develop a bottleneck identification for each intersection in the network using GPS vehicles as the input data. The initial step is to identify the traffic congestion state of each link using GPS data. The GPS data are collected with varying time resolutions (1 s, 5 s, 10 s, 15 s, 20 s, 25 s, 30 s, 35 s, 40 s, 45 s, 50 s, 55 s, 60 s) to represent the congestion state of each upstream link *u* and downstream link *d*, for each of the intersection areas. Herein, let *e* denote an arbitrary link index for upstream link *u* or downstream link *d*. The mean link speed *Vte* of link *e* at time *t* is computed from the arithmetic mean of the speed values of all GPS vehicles on that link *e* at time *t*. In the matrix, the mean link speed *Vte*values for all links *e* and time *t* is expressed as:

$$\begin{bmatrix} V\_1^1 & V\_1^2 & \dots & V\_1^T \\\\ V\_2^1 & V\_2^2 & \dots & V\_2^T \\ \vdots & \vdots & \ddots & \vdots \\ V\_N^1 & V\_N^2 & \dots & V\_N^T \end{bmatrix}$$

### *4.2. Description of Bottleneck and Gridlock Identification*

In this study, the congestion state for each upstream link *u* and downstream link *d* is defined in terms of the low mean link speed indicator *Cte* at time *t* of an arbitrary link *e*, as shown in Equation (1). The traffic congestion is set with the cut-off mean link speed threshold (5 km/h). If the mean link speed is less than or equal to the threshold, then the low mean link speed indicator *Cte* of link *e* at time *t* is 1 (congested); otherwise, it is 0 (not congested).

$$\mathbf{C}\_{\mathfrak{e}}^{t} = \begin{cases} 1, & \text{when } V\_{\mathfrak{e}}^{t} < = 5 \text{ } km/h\\ 0, & \text{otherwise} \end{cases} \tag{1}$$

However, the low mean link speed for both the upstream and downstream can be affected by the red traffic signal light. To filter out such a red traffic signal light effect, therefore, if the low mean link speed indicator *Cte* alerts that there is congestion for a long enough time interval of length *w*, then this congestion state is said to be persistent for the corresponding link. For instance, the time interval *w* is 10 min herein, which is long enough to filter out the usual red time intervals in practical scenarios in the Chula-SSS dataset [7]. The persistently low mean link speed indicator *C* ˜*t e* of link *e* at time *t* can then be expressed as:

$$\mathbf{C}\_{\varepsilon}^{t} = \prod\_{\tau=0}^{w-1} \mathbf{C}\_{\varepsilon}^{t-\tau} \tag{2}$$

A bottleneck occurrence at an intersection can be determined by considering two locations, upstream link *u* and downstream link *d* at the intersection. This study considers the direction of the upstream and downstream only on the conflicting approaches in the loop, as shown in Figure 1. A bottleneck is said to occur if and only if the low mean link speed indicator for any possible pair of upstream and downstream links at that intersection drops below a cut-off speed threshold. Let *P* be the number of upstream and downstream link pairs at intersection *I*, and (*uj*, *dj*) denotes the *j*th pair of upstream and downstream links, (*j* = 1, 2, ... , *P*). For intersection *I*, define the bottleneck indicator *BtI* at time *t* as Equation (4) based on the low mean link speed indicators *Ctu* and *Ctd* for any upstream and downstream link pair (*u*, *d*) in *I*. If all pairs of upstream and downstream links of the intersection *I* are congested at *t*, then we say that there is a bottleneck in this intersection *I* at time *t*.

$$\mathbb{C}^t\_{(\iota,d)} = \mathbb{C}^t\_{\iota} \cdot \mathbb{C}^t\_d \tag{3}$$

$$B\_I^t = \prod\_{\forall (u,d) \in \{ (u\_1, d\_1), (u\_2, d\_2), \dots, (u\_P, d\_P) \}} C\_{(u,d)}^t \tag{4}$$

A temporary low-speed intersection does not necessarily represent a bottleneck, but an active bottleneck always results in a low-speed intersection. Therefore, the persistent bottleneck indicator *B* ˜*t I* is used to confirm an active bottleneck for every time step *τ* within a time interval of length *w*, as shown in Equation (6).

$$
\vec{\mathcal{C}}\_{\left(u,d\right)}^{t} = \vec{\mathcal{C}}\_{u}^{t} \cdot \vec{\mathcal{C}}\_{d}^{t} \tag{5}
$$

$$\tilde{B}\_I^t = \prod\_{\forall (u,d) \in \{ (u\_1, d\_1), (u\_2, d\_2), \dots, (u\_P, dp) \}} \prod\_{\tau=0}^{w-1} \tilde{C}\_{(u,d)}^{t-\tau} \tag{6}$$

$$G\_L^t = \sum\_{I \in L} B\_I^t \tag{7}$$

$$G\_L^t = \sum\_{I \in L} \mathcal{B}\_I^t \tag{8}$$

With the bottleneck intersection identification, gridlock can be defined as the severe congestion state of a critical region that forms a loop of adjacent intersections in an urban road network. Let *L* be a set of potentially gridlock-looped intersections and *L* = {*I*1, *I*2, ... , *IJ*}. For the Chula-SSS dataset, a recurrent gridlock loop is shown in Figure 5 with intersections within yellow dotted circles. The gridlock indicator for loop *L*, *GtL*, is triggered at time *t* if the bottleneck indicator *BtI* for every intersection *I* ∈ *L* at time *t* is triggered, as shown in Equation (7). Consequently, the persistent gridlock is triggered due to the active bottleneck of all intersections, as shown in Equation (8). The computed gridlock labels quantizing the overall effect of the bottleneck indicators *B* ˜*t I* in loop *L* for all intersections are as shown in Figure 5. The computed gridlock labels are 0, 1, 2, 3, 4, and 5 based on the percentage of congested intersections in loop *L*. For example, if all intersections are not congested, then the computed gridlock label is 0; if 20% of intersections are congested, then the computed gridlock label is 1; if 40% of intersections are congested, then the computed gridlock label is 2, and so on, as shown in Table 1.

**Figure 5.** Gridlock-looped intersections in the Sathorn area.

**Table 1.** Gridlock labels.

### *4.3. Effect of Sample Size on Gridlock Detection*

The sample size selection is fundamental to any traffic engineering analysis, including bottleneck and gridlock analysis. Firstly, this section attempts to determine the time resolution based on the vehicles' mean speed on the links in the interested area. The GPS data collection is simulated in the Chula-SSS dataset with different time resolutions (1 s, 5 s, 10 s, 15 s, 20 s, 25 s, 30 s, 35 s, 40 s, 45 s, 50 s, 55 s, 60 s) for the simulation time interval of three hours from 6 am to 9 am. Figure 6 shows the mean speed of all GPS vehicles on the upstream links (Sathorn\_Thai\_1, Charoen\_Rat, Surasak) and downstream link (Sathorn\_Thai\_2) at the Surasak intersection. In this Figure 6, the mean speed of each upstream and downstream link reaches the congested conditions after the low mean speed. The standard deviation is the measure of the variability of the data distribution. Herein, the standard deviation of the mean speed of GPS vehicles on each upstream/downstream link for the sample size of 30% in each time resolution is shown in Figure 7. The reported results show that the standard deviation of the mean speed of all GPS vehicles on each upstream/downstream link obtained from 1 s at a high resolution is not statistically different from that obtained from 60 s at a low resolution. This suggests that any time resolution in the range of 1 s to 60 s can be used for bottleneck and gridlock analysis. Therefore, this study selected, for subsequent experimental investigations, the value of 60 s, which is currently available using GPS vehicles in practice for Bangkok's targeted area in this research study.

**Figure 6.** Mean speed of the upstream links (Sathorn\_Thai\_1, Charoen\_Rat, Surasak) and downstream link (Sathorn\_Thai\_2) at the Surasak intersection.

**Figure 7.** Standard deviation of the mean speed of GPS vehicles on each upstream/downstream link for a sample size of 30% for each time resolution.

Besides, this study investigated the sample size with which to experiment to analyze the status of gridlock with the multiple categories of gridlock labels based on bottleneck intersections. These gridlock labels are based on the identification of gridlock, as defined in Section 4.2. In particular, from the simulated movements of all GPS vehicles, we can compute the Detection Rate (DR) and the False Alarm Rate (FAR) of detected gridlock labels. DR and FAR are based on the True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) by:

$$DR = \left(\frac{TP}{TP + FN}\right) \tag{9}$$

$$FAR = \left(\frac{TN}{TN + FP}\right) \tag{10}$$

where TP, TN, FP, and FN measure the number of time steps for which corresponding results occur for the detection of gridlock label values. The detection of a gridlock label is said to be positive if, and only if, Equation (8) returns that gridlock label as the output. Otherwise, the detection is said to be negative for that gridlock label. The actual value of the gridlock label is computed by Equation (8) when 100% of simulated vehicles are assumed to have GPS data. In the case of less than 100% of available GPS data, the positive (or negative) detection results are said to be true or false according to whether or not the detection results are the same as those obtainable by the actual value of the gridlock label.

This section explores the effect of sample size on the identification of gridlock labels to understand the detection rate and false alarm rate. The detection rate and false alarm rate resulting from the settings of different sample percentages of GPS vehicles' penetration ratio (1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%) for the identification of gridlock labels are plotted in Figures 8 and 9. Here, DR\_1, DR\_2, DR\_3, DR\_4, and DR\_5 are indicated for the detection rate of the gridlock labels 1, 2, 3, 4, and 5. Furthermore, FAR\_1, FAR\_2, FAR\_3, FAR\_4, and FAR\_5 are indicated for the false alarm rate of the gridlock labels 1, 2, 3, 4, and 5. These diagrams depict the detection rate and false alarm rate in each sample size with the time resolution of 60 s. In general, DR denotes the percentage of correctly detected gridlock. The algorithm works well in identifying gridlock labels if the DR is close to 100%. Notably, increasing the number of GPS vehicles will dramatically affect the detection rate and false alarm rate.

**Figure 8.** Detection Rate (DR) for all gridlock labels.

The data coverage of the road network is helpful for bottleneck and gridlock analysis. The higher the penetration ratio of vehicles with GPS in the road network is, the better the data coverage capability, and consequently, the detection rate increases and false alarm rate decreases, as shown in Figures 8 and 9 for each gridlock label, except the sample size of 1% for low data coverage. Furthermore, it should be noted that the identification is most accurate for the gridlock label 5 (the network has been locked) in terms of both DR and FAR because the traffic flow of the gridlock-lopped intersections reaches almost the halted state. With the same logic for the explanation, on the contrary, identifying low gridlock labels (where the network is lightly or moderately loaded) is less accurate. However, such identification for lower gridlock labels poses less concern than when the whole network is gridlocked. Thus, a significant benefit of studying the detection rate and false alarm rate is that they allow for a means of gridlock prediction under the circumstances of the GPS vehicle penetration ratio. At the gridlock label 5, which is the most critical one for gridlock management, it is finally noted that to achieve the practical target of at least 80% DR and at most 10% FAR, Figures 8 and 9 indicate that 30% of GPS vehicles are needed.

### **5. Urban Gridlock Prediction Based on Intersection Bottlenecks with Long Short-Term Memory**

Time series modeling and prediction have been an active area of research due to the wide variety of applications [32]. Deep learning has been applied in time series analysis for both classification and prediction [33]. Prediction for time series is usually based on historical measurements. The better the learning of the inter-dependencies among past observations is modeled, the more accurate the prediction can be. Long Short-Term Memory (LSTM) [23] has been developed as an extension of the recurrent neural network with the advantages of learning long-range time dependencies.

### *5.1. Time Series Construction for Gridlock Prediction*

The time series prediction methods have been developed over the years, advancing from simple regression techniques to statistical and intelligent algorithms. Short-term traffic flow prediction is a time series analysis that aims to predict traffic flow for a particular time based on historical traffic flow, as shown in Figure 10. This research adopted this concept to predict the severe road traffic congestion based on the historical time series congestion status of the intersections and gridlocks in the urban road network.

**Figure 10.** Time series prediction.

In our formulation, firstly, the time series observations of bottleneck indicators *BtI*1 , ..., *BtIJ* and computed gridlock indicator *GtL* for each *t* are calculated. The values of all such indicators are then transformed to a multivariate time series of persistent bottleneck indicators for all intersections *B* ˜*t I*1 , ..., *B* ˜*t IJ* and computed persistent gridlock indicator *G* ˜*t L*, as defined earlier in Section 4.2. A multivariate time series is denoted as *X* = *B*˜*TI*1 , ..., *B*˜*TIJ* , *G*˜ *TL* with time stamp *T*. To take into

account all the intersections that can form a potential gridlock loop, define *xt* as the vector of all persistent bottleneck indicators of *B* ˜*t I*1 , ..., *B* ˜*t IJ* and persistent gridlock indicator *G* ˜*t L* for each *t*, as shown in Table 2. As indicated in Figure 10, to distinguish the time instances used in simulating detailed system dynamics and in obtaining the time series observations with the potentially more extended time sampling period, we change the previously used time indices from *t* to *k* for the following gridlock prediction formulation.

**Instance Bottleneck Gridlock** *x*1 *B*˜1*I*1 *B*˜1*I*2 ... *B*˜1*IJ G*˜ 1*L x*2 *B*˜2*I*1 *B*˜2*I*2 ... *B*˜2*IJ G*˜ 2*L x*3 *B*˜3*I*1 *B*˜3*I*2 ...  *B*˜*TIJ G*˜ *TL*

**Table 2.** Observation instances of observed and computed time series.

Define the *k*-th observation value as the original time series value sampled at time *t* = *kTs* where *Ts* denotes the length of the time sampling interval. The basic idea in this study is to predict the *k*-th observation *yk* fed to produce *G*˜ *kTs L* from a finite set of *s* time-lagged observations; we aim at predicting in a rolling forecasting fashion, herein defined as:

$$z^k = \{ \mathbf{x}^{(k-1)T\_s}, \dots, \mathbf{x}^{(k-s)T\_s} \} \tag{11}$$

where *x*(*k*−1)*Ts*, ... , *x*(*k*−*s*)*Ts* denote the corresponding time sampled values for the *k*-th observation. The input historical bottleneck and computed gridlock sequence are used to predict gridlock at the *k*-th observation. Figure 11 shows the unrolled network of time-lagged observation to predict *yk* over the set of time-lagged observations *zk*. In the experiments, we consider *zk* = *x*(*k*−1)*Ts*, i.e., *s* = 1 for the case of using one time-lagged observation, as well as trying to increase *s*, the number of time-lagged observations.

**Figure 11.** Unrolled LSTM network.

### *5.2. Formulation for the Prediction of the Time Sampled Persistent Gridlock Indicator*

The LSTM has the advantage of capturing temporal information and is approved to be adopted in time series modeling. It is a special kind of recurrent neural network with a purpose-built memory cell representing long-term dependencies in the time series data.

The memory cell has three units, namely the input gate (*ik*), the forget gate (*f k*), and the output gate (*ok*), to maintain the information of a long period. The forget gate determines what information is required to be rejected from the input *zk*. The input gate *ik* determines what information *zk* is required to be kept, and the output gate *ok* produces the output based on *zk*. They are composed of a sigmoid neural network layer (*σ*), pointwise multiplication operation, and tanh function, as depicted in Figure 12. *wf* , *bf* , *wi*, *bi*, *wo*, and *bo* are the weight matrices and bias vectors of each gate. The gates are updated in LSTM by [23]:

$$\begin{aligned} f^k &= \sigma(w\_f \cdot [z^k, y^{k-1}] + b\_f) \\ \dot{x}^k &= \sigma(w\_i \cdot [z^k, y^{k-1}] + b\_i) \\ \sigma^k &= \sigma(w\_o \cdot [z^k, y^{k-1}] + b\_o) \end{aligned} \tag{12}$$

**Figure 12.** LSTM memory cell for time series prediction of the persistent gridlock indicator with the time sampling interval of *Ts*.

After updating the gates, the updated values from the input and forget gates are used for updating the current state *ck* of the LSTM unit based on *c k* and *ck*−1 · *wc* and *bc* are the weight metrics and bias vector, and tanh is the activation function. The output *yk* is triggered by the output gate *ok* as defined in Equation (13), which yields the activated output to the next LSTM unit.

$$\begin{aligned} \hat{c}^k &= \tanh(w\_{\mathcal{E}} \cdot [z^k, y^{k-1}] + b\_{\mathcal{E}})\\ c^k &= f^k \cdot c^{k-1} + i^k \cdot \hat{c}^k\\ y^k &= o^k \cdot \tanh(c^k) \end{aligned} \tag{13}$$

Additionally, the activation sigmoid function *σ* is used to map the range of [0,1], and the tanh function is applied to map the range of [−1,1].

### **6. Evaluation of the LSTM Based Prediction Experiment on the Chula-SSS Dataset**

This study used a dataset that has been collected from the GPS vehicles sampled from the calibrated Chula-SSS dataset in SUMO using different random seed numbers for a total of 100 simulated days (each from 6 am to 9 am). The GPS data collection was periodically sampled every 60 s during the simulation time interval. To develop and evaluate the LSTM model, the dataset was divided into training data containing 80 simulated days and testing data containing the remaining 20 simulated days from a total of 100 simulated days. Each simulated day has 181 time steps (every 60 s within 6 am to 9 am) and 6 features by combining the 5 persistent bottleneck indicators of 5 intersections and 1 persistent gridlock indicator. The important step is to select the model hyperparameters. In this investigation, the hyperparameters of LSTM include the number of neurons to improve the network's learning capacity, the number of epochs, and the batch size, as shown in Table 3. The number of epochs defines the number of times that the learning algorithm will work through the all training data. The models were developed with the standard Keras library [34]. Additionally, the standard gradient descent algorithm, ADAM [35], was used to update the weight values given the batch size value.

**Table 3.** Description of the LSTM model setup.

To evaluate the effectiveness of the gridlock prediction model, we calculate the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) [36] in estimating the time sampled values of the persistent gridlock indicator. These metrics are calculated by comparing the time series's target values and the corresponding time series for the concatenation of all the testing data, each lasting for three hours. The proposed approach for predicting time series is applied to predict gridlock based on the bottleneck state for each intersection in the experiments with different sample sizes by varying the GPS vehicle penetration ratio in percentage and with various settings on the time-lagged observation period (*sTs*) and the time sampling interval (*Ts*). Figures 13–15 report the obtained results, i.e., the RMSE and the MAE, as well as the required computation time of LSTM.

**Figure 13.** RMSE with varied time-lagged observation period.

**Figure 14.** MAE with varied time-lagged observation period.

**Figure 15.** Computation time with varied time-lagged observation period.

### *6.1. Effect of Time-Lagged Observation Period*

Firstly, the time-lagged observation period (*sTs*) was varied as 1, 5, 10, 15, 30, and 60 min, as shown in Figures 13–15. Herein, the time sampling interval (*Ts*) for every 60 s was used. Figure 13 shows the RMSE values for different time-lagged observation periods using 10 neurons of LSTM outputs linearly combined with the final dense layer with one prediction output, 10 epochs, and a batch size of 50. Figure 14 shows the MAE values for different time-lagged observation periods using the same parameters. These hyperparameter values were searched by brute force and finally selected to show the achieved performance of LSTM in our considered case studies. To compare the prediction error values, this study considered as a baseline the lowest 1 min time-lagged observation period, i.e., LSTM is allowed to look back into the past for only 1 min. Figures 13 and 14 show that the RMSE and MAE eventually decrease with the increase of the past observation interval length allowable for LSTM based prediction. When the time-lagged observation is 1 min, there is a big gap between the RMSE values and the other time-lagged observations of 5 min to 60 min. It is understandable that the short historical observation interval cannot provide adequate information for accurate prediction. However, the gap of the RMSE and MAE values between 5 min and other time-lagged observation periods from 10 min to 60 min is relatively not as much as that from 1 min to 5 min.

Although a short historical observation interval does not generally provide accurate prediction, Figures 13 and 14 suggest that the RMSE and MAE values are still acceptable even at the 1 min time-lagged observation, i.e., less than 0.25 for RMSE and 0.05 for MAE from the presumed linear scale of five. On the other hand, if LSTM has more information on the extended time-lagged observation, the RMSE value is reducible to as low as 0.02, and the MAE value is as low as 0.001 with the 60 min time-lagged observation. Therefore, the result confirms that LSTM has good performance over the whole practical range of time-lagged observations. The performance improves further when the input time-lagged observations are sufficiently long.

Although prediction accuracy is improved, if the number of time-lagged observations is increased, the computation is unavoidably increased, as shown in Figure 15. The computation time reported here is based on the currently used hardware of NVIDIA GeForce GTX 1080 Ti with 11 GB memory. At the 60 min time-lagged observation period, it is found that the computation time is up to 700 s or almost 12 min. Such a computation time can be further reduced if the hardware capacity is upgraded. In practice, engineers can design the needed hardware so that the computation can be completed within the time sampling interval, resulting in a real-time prediction effect.

Figure 16 shows the predicted and actual gridlock labels with 1 min time-lagged observation using the 5% sample size. Figure 17 shows the detection rate for all gridlock labels after prediction with LSTM. In Section 4.3, this study presents the effect of sample size to detect gridlock, and the detection rate is 80% on the sample size of 30% GPS vehicles. However, after prediction with LSTM, our proposed LSTM model can predict more gridlock labels, 1, 2, and 3, as low as the 1% sample, but the 1% sample cannot predict the gridlock labels 4 and 5 using the insufficient information of the loop. In Figure 17, the reported DR is 93% for the 5% sample size. This reported detection rate after LSTM confirms that LSTM has good performance using the time-lagged observation in the rolling forecast fashion.

**Figure 16.** Example of actual and predicted gridlock labels with the 1 min time-lagged observation using the 5% sample size.

**Figure 17.** Detection rate for all gridlock labels after prediction with LSTM.

In Figures 18 and 19, the reported RMSE and MAE values are decreased to as low as 0.03 and 0.001 with the 60 min time-lagged observation using the 3% sample. Therefore, the results confirm that the required minimum sample size of 3% GPS vehicles traveling on each link of the loop is enough to predict the gridlock. Using the mean speed of this penetration ratio of GPS vehicles, in practice, can successfully and effectively detect gridlock.

**Figure 18.** RMSE of sample sizes 3% to 5% with varied time-lagged observation period.

**Figure 19.** MAE of sample sizes 3% to 5% with varied time-lagged observation period.

### *6.2. Effect of Time-Sampling*

A short time sampling interval of data collection refers to a high sampling rate, while a long time sampling interval of data collection refers to a low sampling rate. The higher sampling rate of GPS vehicle trajectories gives more sampling points to form the trajectory path. However, because of the strict limits of battery storage for hand-held GPS-embedded devices carried inside moving vehicles, they are often unable to collect high sampling rate data for a long period [37]. Therefore, the time sampling rate interval of GPS vehicle data is an important issue. To study the effect of the sampling interval in gridlock prediction, this study used the time sampling interval *Ts* of the GPS vehicle data in Figures 20–22 at 5, 10, and 15 min. Like in Figures 13–15, we selected the LSTM hyperparameter values of 10 neurons, 10 epochs, and a batch size of 50.

**Figure 21.** MAE with varied time sampling.

**Figure 22.** Computation time with varied time sampling.

Our findings suggest that the time sampling interval directly affects the achievable accuracy of LSTM prediction for persistent gridlock indicators. In Figure 20, the reported prediction error increases as the time sampling interval increases. The prediction error obtained from the time sampling interval of 5 min is at most 0.4 for RMSE for the minimally required 5% GPS vehicle sample. However, a long time sampling interval results in a large RMSE value, for example up to 0.9 for RMSE for the 15 min time sampling. From the computation time point of view, the longer the time sampling interval, the less the computation time, as shown in Figure 22. For example, the 15 min time sampling lasts only around 6 s or 7 s.

In practice, one can evaluate the quantized gridlock label by rounding off the predicted gridlock label value first. In this case, an RMSE less than 0.5 should be considered as acceptable. Under such practical concerns, based on Figures 20–22, the proposed LSTM based model can predict the gridlock label for effectively 5 min into the future and at the affordable up to 15 s computation time. That lead time of 5 min is comparable to a reasonable cycle length of adjusting traffic signal lights. Therefore, by foreseeing such gridlock occurrences by one control cycle, at least, the operational traffic signal controller can adaptively try to correct its signal interval settings to either mitigate the occurring gridlock effects or prevent the gridlock from happening in the first place.


**1. Introduction**

The application goal of geonets is mainly for planar drainage, and it is used as a medium to safely discharge the leachate generated from the landfill to the outside of the landfill. The two strands are cross-bonded to form a network structure in geonet, which serves as a drainage passage for the liquid. Geonet products of various structures are used and drainage capacity of geonet is very important because the landfill service life is determined by geonets drainage capacity when designing landfill.-

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fannin [1] investigated the factors influencing the drainage capacity of geonets, and confirmed that the flow rate depends on the structure of the geonet and the drainage capacity is smaller when the strand channel and the crossing angle of the geonet are large.

Also, Zhao [2] analyzed the factors affecting the drainage performance of geonets and confirmed that the drainage capacity decreases as the hydraulic gradient and the compressive strength increase.

To investigate the behavior of geosynthetics under high normal stresses, Narejo and Rad et al. conducted a transmissivity test with normal stresses up to 2000 kPa [3–5]. From the results showed the variation of geonet transmissivity with normal stresses. Geonets behaved similarly up to 200 kPa regardless of weight. However, in the case of geonet with a small strand crosssection area thickness, the drainage performance decreases when the compressive strength increases, but the drainage performance does not decrease much even when the maximum confining load is 2000kPa for thicker geonets. This is probably due to the fact that as the thickness- of the strand cross-section layer constituting the geonet becomes smaller and the compressive strength becomes larger, the roll-over of the strand intersection of the geonet occurs more often.

Koerner [6] analyzed the roll-over phenomenon of bi-planar geonet. According to this, since the upper and lower strands constituting the geonet are not vertically bonded to each other at the cross-section areas, when the compressive strength is applied to the upper and lower layers of the geonet, the drainage capacity is large at the initial stage, the deformation of the strand joint starts to occur. This roll-over phenomenon causes a change in the drainage capacity in the upper and lower layers of the geonet, and the drainage capacity of the geonet is decreased and the drainage performance is lowered with time.

Allen [7] analyzed the effect of orientation of strand constituting geonet on drainage performance. In the case of the lower strand with small inclination angle at the cross-section area of the upper and lower strands, the roll-over phenomenon occurs more than the upper strand, and the drainage performance deteriorates.

Kopp reported that geonet drainage performance was affected by the construction site temperature when the geonet was constructed at the landfill site [8]. Pegg indicates that the drainage performance of the geonet is most affected under the circumstance at 80 or 85°C in the landfill site [9].

Therefore, when the environmental temperature is increased, the mobility of the molecular chain of the polyethylene, which is the raw material of geonet, becomes larger, and the drainage performance may be decreased due to the structural change of the geonet to the compressive strength.

In this chapter, the variation of drainage performance of geonets used for horizontal drainage is analyzed in relation to the influence of constraint load related factors.-


**5. Clinical manifestation**

The clinical presentation of strongyloidiasis varies with the status of the host´s immune system, and the infection is classified as acute, chronic or severe. Acute infections of strongyloidiasis manifest as a wide spectrum of clinical features ranging from asymptomatic disease to cutaneous (larva currens and urticaria), pulmonary (cough and tracheal irritation), and gastrointestinal symptoms (diarrhoea and constipation), although the majority of *S. stercoralis* infections are resolved (Mahmoud, 1996).

The ability of *S. stercoralis* to establish a cycle of autoinfection within the host results in chronic infections that can persist in an individual for decades. Chronic infections are often asymptomatic, but when symptoms occur they are usually mild, episodic and prolonged, including nausea, vomiting, diarrhoea, constipation, weight loss or cutaneous reactions (Grove, 1989).

Uncontrolled autoinfection of *S. stercoralis* is more likely to occur in immunosuppressed patients, leading to hyperinfection syndrome. The pulmonary phase of hyperinfection due to migrating larvae resembles Löffler syndrome with coughing and wheezing, asthma-like symptoms, haemorrhaging and pneumonia. In the intestine, symptoms include diarrhoea, nausea, vomiting, abdominal pain, and weight loss (Concha et al., 2005; Viney & Lok, 2007). Bacteremia is a common complication of hyperinfection syndrome and is caused by filariform larvae that may lead bacteria from the bowel to the bloodstream with subsequent secretion into the host circulation (Bamias et al., 2010). Pathogens such as *Streptococcus bovis, Escherichia coli, Klebsiella pneumonia* or *Enterobacter cloacae* are found during fatal complications of strongyloidiasis (Link & Orenstein, 1999). The mortality rate of dissemination associated with bacterial infections can reach approximately 90% (Igra-Siegman et al., 1981).

Dissemination occurs upon larval migration to organs beyond the range of the pulmonary phase, such as the liver, heart, lymph nodes, gallbladder, kidneys, pancreas, and brain (Keiser & Nutman, 2004). Petechiae and purpura have also been reported in disseminated cases as a result of larval migration through vessel walls, which promotes haemorrhage (Basile et al., 2010). Others complications of disseminated strongyloidiasis can occur and include syndromes such as cholecystitis, pancreatitis, paralytic ileus, intestinal perforation

Hyperinfection Syndrome in Strongyloidiasis 381

Hyperinfection cases following organ transplant principally occur during the initial months after transplantation, but the infection was acquired before transplantation in the majority of cases (Roxby et al., 2009). Higher mortality rates occur from extraintestinal strongyloidiasis, which in most of these cases are related to corticosteroid therapy to treat rejection (Keiser &

Renal transplants are most commonly associated with hyperinfection, which is related to immunosuppressive treatments (Devault et al., 1990; Rajapurkar et al., 2007; Valar et al., 2007).  Hyperinfection in hematopoietic stem cell transplant patients may be due to

An important cause of immunodeficiency that is related to hyperinfection is malnutrition, particularly in developing countries. Malnutrition promotes disruption of the intestinal mucosa, impairing the host's ability to expel the parasite from the gut (Olsen et al., 2009).

Patients with immunodeficient conditions, such as hypogammaglobulinemia, may develop fatal hyperinfection. Case reports show that hypogammaglobulinemia is refractory to

Although HIV infection predisposes a patient to hyperinfection due to immunosuppression, few cases of *S. stercoralis* and AIDS have been described (Marcos et al., 2008). The association between *S. stercoralis* and HIV principally occurs in endemic areas (Siddiqui & Berk, 2001). The hyperinfection syndrome can occur in patient with HIV with immune reconstruction syndromes increased after starting of highly active antiretroviral therapy (Brown et al., 2006). On the other hand, the infection with *Strongyloides* may contribute to serious nutritional deciencies in HIV-infected individuals, such as anorexia and malabsorption (Lindo et al., 1998). However, the immunobiological and immunoregulatory mechanisms

HTLV-1 is a virus that infects T cells and induces lymphocyte proliferation with the production of a Th1-type immune response in humans. The genome of the HTLV-1virus is diploid and, following interaction with the immune system, HTLV-1 enables the transcription of the viral DNA by integrating into the host genome effectively evading immune surveillance without killing the host (Iriemenan et al., 2010). Strongyloidiasis is strongly associated with HTLV-1, which predisposes patients to severe infections by depressing cell-mediated immunity or IgE responses (Grove, 1996; Carvalho & Da Fonseca Porto, 2004). *Strongyloides* and HTLV-1 may promote the Th1-type response in patients, increasing interferon levels and decreasing Th2-type responses, such as interleukin 4 (IL-4), IL-5, IL-13, and IgE, important host defences against helminths, and a decrease in this response allows not only an increasing in autoinfection but also decreased parasite killing.

prolonged anthelmintic therapy (Brandt de Oliveira et al., 1981; Seet et al., 2005).

immunosuppressive therapies (Dulley et al., 2008; Wirk & Wingard, 2008).

Nutman, 2004).

**6.4 Malnutrition**

**6.6 HIV**

**6.7 HTLV-1**

**6.5 Hypogammaglobulinemia**

involving HIV and strongyloidiasis remain unclear.

or infarction, peritonitis, and sepsis (Krishnan et al., 2006). Although unusual, brain involvement can occur in disseminated infections, with symptoms including headache, focal seizures, altered mental state, secondary bacterial meningitis and coma (Dutcher et al., 1990).


**Acknowledgements**

This study was financially supported by the National Research Foundation (NRF) of the Republic of South Africa (project name: Modelling of intake, feeding behaviour and kinetics of digestion and passage of digesta in ruminants; grant unique number: 112905) and the University of KwaZulu-Natal (competitive grant number: P029).


*4.3. Scaled-Up Composites Results*

From the lab-scale data, 30 wt.% seems the most promising HSPs amount granting both a high fiber content and acceptable mechanical properties. The results of the scaled-up composites are summarized in Table 5.

**Table 4.** DSC first heating results for H0210 and HM200 PLA-based composites.

**Table 5.** Mechanical and MFR results of the scaled-up HSPs composites.

\* Blends extruded with a semi-industrial COMAC twin-screw extruder (up-scaled).

All scaled-up formulations show a lower MFR, respect to their corresponding lab-scale formulations. This MFR decrement is also reflected in the injection pressure increment during the injection molding process (Table 2). The marked viscosity decrement observed during the lab-scale step is limited, thanks to the coupling of the low extruder residence time and the presence of the venting system connected to a vacuum pump that guarantees the humidity stripping during the melt extrusion, avoiding or limiting any eventual PLA degradation [49,50]. The mechanical results are noteworthy. In fact, it can be observed that the scaled-up composites show an increment of elastic modulus and tensile strength. In particular, the reached tensile stress is very similar, confirming the efficiency of the venting system in removing the fillers humidity.

The thermal properties (Table 6) of the scaled-up composite remains almost unchanged, confirming the nucleation effect of HSPs.

**Table 6.** DSC first heating results of scaled-up HSPs composites.

\* Blends extruded with a semi-industrial COMAC twin-screw extruder (up-scaled).


**Meet the editor**

Olena Ivanivna Lutsenko studied the peculiarities of the cardiovascular system in elderly and old people for two years and received her master's degree in biology from Cherkasy National University named after Bohdan Khmelnytskyy. In 2011, she entered postgraduate studies in Cherkasy National University named after Bohdan Khmelnytskyy and began to work on the

features of menstrual cycle and its relationship to hemodynamics. She now works on research topics at the Hlukhiv National Pedagogical University of Alexander Dovzhenko and teaches medical and biological disciplines. She has published over 74 scientific works including five textbooks and two monographs. Recent studies focus on women's menstrual cycle and its relationship with body typology, the period of puberty, and healthcare of the younger generation.

Contents

**Preface VII**

Olena Lutsenko

Chapter 2 **Normal Menstrual Cycle 15**

Chapter 3 **Pre Menstrual Syndrome 43**

Chapter 4 **Secretory Phase and Implantation 55**

N. Taylor and Monique Monard

Chapter 5 **Menstrual Cycle and Physical Effort 67**

Chapter 6 **Premature Ovarian Insufficiency 97**

**Intensive Longitudinal Data 121** Kayla M. Joyce and Sherry H. Stewart

Magdalena Wiecek

Chapter 1 **Introductory Chapter: Regulation of Ovarian-Menstrual Cycle as a Systemic Problem of Physiology of Humans 1**

Barriga-Pooley Patricio and Brantes-Glavic Sergio

Preye Fiebai, Avwebo Ochuko Ukueku and Rosemary Ogu

Abdelhamid Benmachiche and Amel Dammene Debbih

Chapter 7 **Standardization of Menstrual Cycle Data for the Analysis of**

Courtney Marsh, Katelyn Schumacher, Warren B. Nothnick, Robert


**1. Introduction**

Nature has substantially participated in the discovery of drugs for human remedial treatments since the beginning of mankind [1]. The marine environment, with more than 70% of the surface of the Earth, represents the largest ecosystem and is characterized by quite variable physicochemical parameters (e.g., limited light access, low temperature, high pressure, and high salinity) [2]. Among the various marine microbes, fungi are a superabundant and ecologically substantial component of marine microbiota [3]. Fungi are one of nature's treasures that inhabit various environments on the earth's surface, including the marine environment [4–7]. They play a growing relevant role in drug development and biomedicine research, either directly as drugs or indirectly as lead structures for bio-inspired drug synthesis [8–12]. In the last decades, natural product chemists and pharmacologists have turned their research interests to marine-derived fungi, which are renowned as a vast unexploited reservoir of metabolic diverseness and found to have the capability to produce structurally unique bio-metabolites [6,7,12–16]. Furthermore, research on fungiderived metabolites has tremendously increased because of the need for compounds with potential economical values and pharmaceutical applications. Sesquiterpenes belonging to various classes, including hirsutane, alliacane, tremulane, bergamotane, drimane, etc.,

**Citation:** Khayat, M.T.; Mohammad, K.A.; Omar, A.M.; Mohamed, G.A.; Ibrahim, S.R.M. Fungal Bergamotane Sesquiterpenoids—Potential Metabolites: Sources, Bioactivities, and Biosynthesis. *Mar. Drugs* **2022**, *20*, 771. https://doi.org/10.3390/ md20120771

Academic Editor: Dehai Li

Received: 5 November 2022 Accepted: 5 December 2022 Published: 8 December 2022

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

**Copyright:** © 2022 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/).

are reported from fungi [17–19]. The biosynthesis of their C15 skeleton from FPP (farnesyl pyrophosphate) was catalyzed by sesquiterpene synthases [19,20].

Among these metabolites, the bergamotane family represents an uncommon class of natural sesquiterpenes that includes bi-, tri-, or tetracyclic derivatives [19]. Bergamotane sesquiterpenoids having a bridged 6/4 bicyclic skeleton involved in an isopentyl unit are biosynthesized by fungi and plants [21,22]. Interestingly, polyoxygenated derivatives featuring a 6/4/5/5 tetracyclic framework represent a rare class of natural metabolites, and all polycyclic bergamotanes are mainly encountered in fungi [23–26]. Bergamotane sesquiterpenoids have been reported from various marine sources such as sponges, sea mud, deep-sea hydrothermal sulfide deposits, and sea sediments. These metabolites could gain the interest of chemists and biologists because of their unusual structural features and diversified activities, such as phytotoxicity, plant growth regulation, antimicrobial, anti-HIV, cytotoxic, pancreatic lipase inhibition, immunosuppressive, antidiabetic, and anti-inflammatory properties. It is noteworthy that no available work has addressed this class of sesquiterpenes in term of their sources, bioactivities, and biosynthesis. In the current work, the reported fungal bergamotane sesquiterpenoids ranging from 1958 to June 2022 have been listed. They have been classified according to their ring system, i.e., into bi-, tri-, or tetracyclic derivatives (Table 1). Additionally, their fungal sources, structural characterization, biosynthesis, and biological relevance have been provided. Moreover, some of their reported structural characteristics and methods of separation and characterization, as well as their structure–activity relation, are discussed.

**Table 1.** Naturally occurring fungal bergamotane sesquiterpenoids (name, source, extract/fraction, molecular weights and formulae, and location).

#### **Compound Name Fungal Source/Host Extract/Fraction Mol. Wt. Mol. Formula Location Ref.** Brasilamide B (**46**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) Acetone extract 265 C15H23NO3 Dongling Mountain, Beijing, China [43] Brasilamide C (**47**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) Acetone extract 279 C15H21NO4 Dongling Mountain, Beijing, China [43,44] Brasilamide D (**48**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) Acetone extract 321 C17H23NO5 Dongling Mountain, Beijing, China [43] Brasilamide K (**49**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) EtOAc extract 279 C15H21NO4 Dongling Mountain, Beijing, China [44] Brasilamide L (**50**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) EtOAc extract 265 C15H23NO3 Dongling Mountain, Beijing, China [44] Brasilamide M (**51**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) EtOAc extract 293 C15H19NO5 Dongling Mountain, Beijing, China, [44] Brasilamide N (**52**) *Paraconiothyrium brasiliense* Verkley (M3–3341) (Leptosphaeriaceae)/ *Acer truncatum* Bunge (branches, Sapindaceae) EtOAc extract 279 C15H21NO4 Dongling Mountain, Beijing, China [44] Craterodoratin I (**53**) *Craterellus odoratus* (Cantharellaceae) EtOAc extract 250 C15H22O3 Southern part of the Gaoligong-Mountains, Yunnan, China [30] Craterodoratin J (**54**) *Craterellus odoratus* (Cantharellaceae) EtOAc extract 282 C15H22O5 Southern part of the Gaoligong-Mountains, Yunnan, China [30] Craterodoratin K (**55**) *Craterellus odoratus* (Cantharellaceae) EtOAc extract 282 C15H22O5 Southern part of the Gaoligong-Mountains, Yunnan, China [30] Craterodoratin L (**56**) *Craterellus odoratus* (Cantharellaceae) EtOAc extract 278 C15H18O5 Southern part of the Gaoligong-Mountains, Yunnan, China [30]

Surveying their bioactivities may open a new research area for the synthesis of new agents from these metabolites by synthetic and medicinal chemists. The literature search for the reported data was performed using diverse databases and publishers, including Web of Science, Google Scholar, PubMed, Scopus, SciFinder, Wiley, SpringerLink, and ACS Publications, using specific keywords (bergamotane, marine, fungi, biosynthesis, and biological activities).

#### **2. Structural Assignment and Stereochemistry Determination**

A total of 97 metabolites have been separated from various fungal source extracts using different chromatographic techniques and characterized by NMR, MS, and IR spectral analyses as well as chemical derivatization. The relative configuration of these metabolites was established using NOESY or ROESY spectral analyses. Various studies reported the assigning of their absolute stereochemistry using total synthesis [53,54], Mosher's method [26], X-ray diffraction, chemical conversion [34,43,55], and ECD analyses [31]. The reported metabolites have been categorized into bi-, tri-, and tetracyclic derivatives.

#### **3. Biological Activities of Bergamotane Sesquiterpenoids**

Various reported studies revealed the assessment of bergamotane sesquiterpenoids for diverse bioactivities, including plant growth regulation, phototoxic, antimicrobial, anti-HIV, cytotoxic, pancreatic lipase inhibition, antidiabetic, anti-inflammatory, and immunosuppressive, which were summarized in this work (Table 2). Additionally, the reported structure–activity relation was included.

### *3.1. Anti-Inflammatory Activity*

NO (nitric oxide) is a substantial pro-inflammatory mediator, and its excessive production is accompanied with various inflammatory illnesses; therefore, it possesses a remarkable role for regulating immune responses and inflammation [56]. NO production inhibitors may represent the potential capacity for treating various inflammatory disorders. Thus, further research for fungal metabolites must be conducted to discover novel anti-inflammation agents.

The epigenetic chemical manipulation of *Eutypella* sp. derived from deep-sea hydrothermal sulfide deposit by co-treatment with SBHA (histonedeacetylase inhibitor, suberohydroxamic acid) and 5-Aza (DNA methyltransferase inhibitor, 5-azacytidine) was shown to activate a biosynthetic sesquiterpene-linked gene cluster [35]. From elicitortreated cultures EtOAc extract, eutypeterpenes A–F (**18**–**22** and **28**) along with xylariterpenoids A (**16**) and B (**17**) were purified using SiO2/RP-18/HPLC that were identified by spectral analyses, as well as by using chemical conversion, X-ray diffraction, ECD, and calculated NMR for configuration assignments.

Eutypeterpene A (**28**) is the first bergamotene sesquiterpene incorporating a dioxolanone moiety. These metabolites were assessed for their NO production inhibitory capacity induced by LPS-(lipopolysaccharide) in RAW 264.7 macrophages [35]. The results indicated thatcompound **18** and **19** (IC50 13.4 and 16.8 μM, respectively) displayed more effectiveness than quercetin (IC50 of 17.0 μM), whereas other metabolites had noticeable potentials (IC50 values ranged from 18.7 to 24.3 μM) with weak cytotoxic capacities (IC50 > 100 μM). A structure–activity study revealed that the analog with a triol unit (**18**) at the side chain was more effective than compound **16**, **17**, and **19** with a diol unit, which were more potent than compound **20**, **21**, and **28** with one hydroxy group. Furthermore, the α,β-unsaturated ketone unit (as in compound **21** and **22**) and the OH-linked carbon configuration also affected the activities (**16** versus **17**) [35] (Figure 1).

**Figure 1.** Structures of bicyclic bergamotane sesquiterpenoids (**1**–**17**).

Biogenetically, compounds **18**–**22** are derived from FPP that performs a 1,6-cyclization to produce bisabolane (**A**). The 4,7-cyclization of **A** generates bergamotane (**B**), which further generates **18**–**22** via diverse oxidation and reduction processes. Additionally, compound **28** is formed from **18** by carbonate incorporation [35] (Scheme 1).

The deep-sea-isolated *Graphostroma* sp. MCCC3A00421 associated with the Atlantic Ocean hydrothermal sulfide deposits biosynthesized new bergamotane sesquiterpenoids: (10S)-xylariterpenoid A (**23**), (10R)-xylariterpenoid B (**24**), xylariterpenoid E (**25**), xylariterpenoid F (**26**), and xylariterpenoid G (**27**), which were purified using SiO2/OSD/Sephadex LH-20/RP-18 CC and preparative TLC. They were characterized by extensive spectral data, and their absolute configuration was established by ECD, Cu-Ka-single-crystal X-ray diffraction, and modified Mosher's method analyses. Compound **25** is trinor-bergamotane. Compounds **23**, **26**, and **27** revealed moderate inhibition potentials (IC50s of 86, 85, and

85 μM, respectively) of NO production in LPS-stimulated RAW264.7 macrophages compared with aminoguanidine (IC50 of 23 μM). It was noted that bergamotane moiety's 10S configuration obviously boosted the activity as in compound **23** (10S, IC50 of 85 μM) versus compound **24** (10R, of IC50 230 μM) (Figure 2) [34].

**Scheme 1.** Biosynthetic pathway of eutypeterpenes A–F (compounds **18**–**22** and **28**) [35].


**11. Epidemiology**

More than 30% of worldwide deaths are thought to be cardiovascular based and the frequency tends to increase due to changes in lifestyle and prolonged life. According to AHA 2016 statistics, in the United States, one in every 42 seconds loses his/her life due to cardiovascular reasons [19]. In Europe, the cardiovascular mortality rate is 4.1 million a year. A total of 1.8 million deaths, in other words 20% of all deaths, are due to ischemic heart disease. This is followed by cerebrovascular events with an annual death of 1.1 million. According to ESC data, 1.5 million deaths before the age of 75 and 710,000 deaths before the age of 65 are cardiovascular sources; half is due to coronary artery disease [20]. Deaths in all age groups, 51% of women and 42% of men are cardiovascular.

## **12. Coronary artery disease and cholesterol**

Acute coronary syndrome is a clinical event that occurs when the coronary blood flow is reduced by thrombus on the rupture plaque and the myocardial oxygen requirement cannot be met. Acute coronary syndrome is broad spectrum which contains STEMI, nonSTEMI, unstable angina pectoris, and sudden cardiac death. In many cases, the thrombosis process begins with plaque rupture. Up to 25% of cases of acute coronary syndromes can begin with plaque erosion. Lymphocyte and macrophage activation and the inflammatory response is accompanied by atherothrombosis. There are clinical differences according to coronary collateral reserve and obstruction severity. This process occurs after a plaque rupture and is called Type 1 MI.

Atherosclerotic plaques that play an essential role in acute coronary syndrome are divided according to their structural characteristics: Plaque structure is with thin fibrous cap, dense necrotic core, high inflammatory cell density, and low smooth muscle content; it is called vulnerable plaque. Vulnerable plaque increases with hypertension, diabetes mellitus, elevated LDL, decreased HDL, and elevated ACE. Conversely, stabilized plaques with thick fibrous caps, poor necrotic cells, and dense extracellular matrix with low inflammatory content are observed in individuals with low risk factors (**Figure 1**).

**Figure 1.** Structural differences between vulnerable and stable plaques.

In a meta-analysis involving 90,056 patients, a reduction of 38.6 mg/dl in LDL was shown to be associated with a 20% reduction in major cardiovascular events [21]. In MIRACLE study: increased plaque stability with statin therapy reduces death, incidence of acute coronary syndrome and frequency of recurrent coronary ischemia [13]. In the PROVE IT TIMI-22 trial, atorvastatin 80 mg and pravastatin 40 mg were compared and it was determined that highdose statin therapy was more effective than low-dose statin therapy in reducing cardiovascular events. The 2017 ESC STEMI guidelines recommended that high-dose statin therapy was independent of cholesterol level. In the FOURIER study, it has been shown that the addition of the evolocumab in patient with LDL level ≥ 70 mg/dl, despite the use of high dose statin, is associated with a decrease in cardiovascular deaths [22].


*Article* **Lithium-Ion Battery Thermal Management Systems: A Survey and New CFD Results**

**Morena Falcone 1, Eleonora Palka Bayard De Volo 1, Ali Hellany 2, Claudio Rossi 3 and Beatrice Pulvirenti 1,\***

**Abstract:** The environment has gained significant importance in recent years, and companies involved in several technology fields are moving in the direction of eco-friendly solutions. One of the most discussed topics in the automotive field is lithium-ion battery packs for electric vehicles and their battery thermal management systems (BTMSs). This work aims to show the most used lithium-ion battery pack cooling methods and technologies with best working temperature ranges together with the best performances. Different cooling methods are presented and discussed, with a focus on the comparison between air-cooling systems and liquid-cooling systems. In this context, a BTMS for cylindrical cells is presented, where the cells are arranged in staggered lines embedded in a solid structure and cooled through forced convection within channels. The thermal behavior of this BTMS is simulated by employing a computational fluid dynamics (CFD) approach. The effect of the geometry of the BTMS on the cell temperature distribution is obtained. It is shown that the use of materials with additives for the solid structure enhances the performance of the system, giving lower temperatures to the cells. The system is tested with air-cooling and water-cooling, showing that the best performances are obtained with water-cooling in terms of cell packing density and lowest cell temperatures.

**Keywords:** lithium-ion cells; battery thermal management systems; CFD simulations; liquid cooling


*4.2. Aqueous Humor*

AH is primarily composed of water (99.9%) and small amounts of carbohydrates, vitamins, proteins, and other nutrients, as well as growth factors and cytokines. It is responsible for maintaining eye pressure among other things for the support of ocular health. It is important to know that AH is drain from the eye through one of two passive pathways: the traditional trabecular meshwork (TM) pathway and the unconventional one, which is the uveoscleral pathway [79,80].

The samples of AH are collected by aqueous route through a paracenthesis in patients undergoing cataract surgery, trabeculectomy, phacoemulsification, or post-mortem eyes. Collection volumes are small and range from 100 to 150 μL [81,82]. After aqueous humor samples are obtained they are usually stored undiluted directly at −80 ◦C; they can be aliquoted according to the volume obtained and no more than one freeze–thaw cycle is recommended prior to analysis [83]. Potentially valuable information can be obtained from the analysis of these samples, however, there are potential risks associated with their collection. The methods are highly invasive and can create a risk of additional damage to the cornea and lens [73]. Post-mortem eye samples will have di fferent profiles than those collected from living patients due to the accumulation of metabolic waste and other uncontrolled post-mortem processes. This is why samples collected from living patients are considered more useful.

## *4.3. Vitreous Humor*

The vitreous is located in the posterior segment between the lens and the retina. As stated earlier, it is mainly composed of water and a mesh of fine collagen fibrils embedded with dissolved hyaluronan molecules, inorganic salts and lipids [84]. In addition, the vitreous contains other proteins such as albumin, globulins, clotting proteins, and complement factors that have accumulated from blood filtration or the spread of surrounding tissue and vasculature [85]. The anatomical position of this matrix in contact and close with the retina makes it an ideal compartment for taking samples that reflect biochemical and pathophysiological changes when there are states of retinal or vitreoretinal disease, including PDR [73,86].

Vitreous samples in vivo are generally collected through vitreous taps from vitrectomy patients [87]. Samples obtained from vitreous humor (900–920 uL) are generally stored undiluted at −80 ◦C as well as aqueous humor, it is recommended to separate them in aliquots to avoid freeze–thaw cycles. For its analysis, the samples can be centrifuged to eliminate cellular components and debris [88]. Given the invasiveness of vitreous sampling, recently has been evaluated the possibility of collecting vitreous reflux after intravitreal injections, being the appropriate time and without the need to enter the operating room, however, the possible risks should be better assessed [89]. In a study by Srividya and colleagues, they used Schirmer's tear strips to collect vitreous reflux from patients with diabetic macular edema (DME) and PDR, where they compared their total protein concentration with undiluted vitrectomy specimens. The results showed similar total protein concentrations between the vitreous reflux and the vitrectomy samples ( *P* < 0.05) [89]. The most common method of analysis for biomarkers in this samples are enzyme-linked immunosorbent assays (ELISAs) and enzyme-colorimetric assays. Recently, multiplex bead array assays are now commonly used to maximize the usefulness of the vitreous sample, like in AH because they have the advantage that they can measure multiple analytes simultaneously in small volumes [90]. Proteomic and genomic analysis techniques are also used to analyze biomarkers of di fferent nature in vitreous samples.

#### **5. Measurement of Biomarkers**

A biomarker provide a powerful and dynamic approach to improve our understanding of the mechanisms underlying eye disease, supporting information for diagnosis, disease control, or to predict clinical response to treatment [73]. Biomarkers can help to understand DR and contribute to the development of new treatments or new clinical strategies to prevent vision loss [91]. The FDA-NIH Joint Leadership Council updated the BEST Resource (Biomarkers, Endpoints, and Other Tools), modify the original definition of biomarker to "a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions" [92]. New biomarkers are generally considered of interest, as they are associated to predict the disease or its response to treatments, however they are not proven, nor are they widely accepted or used in clinical practice as a decision-making tool [91]. The development of new biomarkers is a complex process that requires high refinement, and depends on the etiological disclosure that is presented. On the other hand, in clinical practice individual biomarkers but also a set or panel of biomarkers can be used, these can include biomarkers of di fferent types, such as exposure, effect, and susceptibility biomarkers. Multiplex immunoassays are ideal for analyzing small volumes of samples, such as tears and now also in AH where they have been shown to be e ffective in measuring cytokine levels. With Next Generation Sequencing Techniques (NGS), small-sample miRNome analysis can be performed that avoids some of the limitations of hybridization-based detection methods [93]. Genetic matrix analysis is an additional powerful technique for comparing gene expression profiles in AH [94].

#### *5.1. Oxidative Stress: ROS and Relevance in DR*

The ocular surface is always subjected to intense exposure to light and solar ultraviolet radiation, also high metabolic activity increases the production of ROS and OS. This is a common adaptation secondary to inflammation and diabetes that produce more mediators to ROS in ocular surface as xanthine oxidase. It is hypothesized that the increase in antioxidant markers may reflect a local, compensatory response in the eye against OS [73]. Therefore, adequate levels of antioxidant enzymes responsible for free radical scavenging are essential for oxidation and reduction (redox) homeostasis [95]. Nezzar et al. analyzed the expression of glutathione peroxidase (GPx1–GPx8), catalase and SOD1 in human tissues of meibomian glands and conjunctiva and they found that both tissues express GPx2, GPx4, and GPx7 to control peroxide concentration in the lipid layer to protects the ocular surface from OS damage. Moreover, SOD1 and catalase were found expressed in conjunctiva and meibomian gland this might suggest their role in recycling ROS and their interaction with inflammation caused by OS [96]. In addition, products such as GPx, SOD and malondialdehyde (MDA) have been observed at irregular levels in the AH of patients with PDR. SOD is a key antioxidant enzyme involved in the metabolism of oxygen free radicals and prevents the formation of other ROS [97].

GPXs are a family of selenium-dependent isoenzymes that catalyze the reduction of H2O2 or organic hydroperoxides in water and alcohols through oxidation of GSH to GSSG. Its activity can be measured using cumene hydroperoxide and GSH as substrates in a coupled reaction with glutathione reductase (GR) [98,99]. GPx messenger RNA express mainly in the ocular ciliary epithelium suggests that in AH glutathione peroxidase originates from these cells [100], its activity in the AH of glaucoma patients has been reported to be three times higher than in cataract patients, the mean value of GPx in AH of glaucoma patients was 18.4 ± 2.5 U/mL, and the mean value of the cataract group was 6.1 ± 0.6 U/mL (*p* < 0.001) [101] (see Table 1). Some antioxidant mechanisms, such as glutathione-related enzymes, are likely to work in normal and vitreous lenses to keep proteins in a reduced state. Unstable pro-oxidant molecules in the vitreous can play a role in cataract pathogenesis along with retinal disease. They can spread damage to lens membranes and vitreous proteins [102,103]. The study by Altomare et al. said specific activity of the GPx could not be detected in the vitreous humor; it is probably due to the high dilution of the vitreous humor used for the extraction procedure [103].

**Table 1.** Biomarker levels in tear, aqueous, and vitreous humor. Levels of the markers of oxidative stress, inflammation, and apoptosis measured in tear, aqueous, and vitreous humor in different ocular conditions, in addition to PDR, differentiating the different methodologies used. PDR: proliferative diabetic retinopathy, DED: dry eye disease, TAC: total antioxidant capacity, LPO: lipoperoxides, MDA: malondialdehyde, GSH: glutathione, GPx: glutathione peroxidase, SOD: superoxide dismutase, 8-OHdG: 8-hidroxi-2-deoxiguanosine, 8-IPGF: 8-isoprostaglandins, NO: nitric oxide, IL-6: interleukin-6, IL-8: interleukin-8, IL-10: interleukin-10, TNF-α: Tumour necrosis factor alpha, VEGF: vascular endothelial growth factor, EGF: epidermal grow factor, MMP-9: metalloproteinase-9, FN-kβ: Nuclear factor kβ, T1DM: type 1 diabetes mellitus, T2DM: type 2 diabetes mellitus.

\* Data are expressed as the mean ± SD. \*\* Median values and 5th and 95th percentile values in pg/mL.

SODs are a family of enzymes that catalyze the dismutation of superoxide into oxygen and H2O2 [99,131]. The fluid in the anterior chamber has very low SOD activity, so there are no apparent differences between diabetic patients and controls. However, it should be noted that the humor of the anterior chamber has a high content of ascorbate, which contributes significantly to protection against superoxide [132]. Significantly lower SOD levels in the vitreous have been found in diabetic patients with PDR than in non-diabetic controls examined post mortem [132]. There is a flow of fluid from the vitreous to the choriocapillary that holds the retina together, this is where SOD can contribute to protection against superoxide radicals in the extracellular space of the retina [132].

Determination of total antioxidant capacity (TAC) is a method for rapid quantification of antioxidant efficacy in disease prevention [133,134], defined as the "cumulative action of all antioxidants present in plasma and body fluids, providing an integrated parameter rather than the simple sum of measurable antioxidant values" [135]. Reduced capacity has been observed in the AH of patients with PDR [104] and there is a close correlation between OS and morphological changes in the TM [136], in patients with PDR suggests that the involvement of the anterior chamber may be caused in part by redox state imbalances [104]. Actually, the antioxidants of each system can exert their activity with different mechanisms and different efficiency; therefore, the assessment of TAC could be much more important than the concentration of antioxidants individually [104]. Izuta et al. reported that the TAC scan of the vitreous actually increases in patients with PDR [137].

Oxidation of DNA components by ROS and reactive nitrogen species (RNS) is the main source of DNA damage leading to various types of DNA modification, including chain breakage, nucleotide oxidation, base loss, and adduct formation [138]. The radicals can react with all the purine and pyrimidine bases, as well as with the deoxyribose skeleton, generating several products, one of them 8-hidroxi-2-deoxiguanosine (8-OHdG) [139]. It has been estimated that several thousand 8-OHdG lesions can form daily in a mammalian cell, this represents 5% of all oxidative lesions, which is why it is one of the most widely used DNA oxidation biomarkers to measure OS [140]. Their levels in aqueous humor increases and the total antioxidant status decreases in the serum and aqueous humor of glaucoma patients [141]. High myopia is a degenerative disease [142]. Therefore, 8-OHdG was found at lower levels in it compared to the control group; their levels are positively correlated with central corneal thickness (CCT) and negatively correlated with long axial length (AXL). Myopic patients presented 212.5 ± 103.2 μg/mL versus 311.6 ± 127.7 μg/mL in the control group with cataract patients [109] (see Table 1).

In diabetic patients with PDR, lipid peroxidation is a very pronounced process in humoral parts of the body, which is why it is responsible for the OS induced in diabetic patients [143]. Polyunsaturated fatty acid (PUFA) molecules are present in cell membranes and are prone to oxidation due to the presence of susceptible carbon double bonds [144,145]. The eye, in general, but particularly the macula, is susceptible to OS due to its high metabolic activity and the large amount of PUFA in the photoreceptor membranes [145]. The determination of the final product of lipid oxidation is widely used as a marker of OS. MDA together with 4-hydroxy-2-nonenal (4-HNE) represent the most investigated final product of lipid oxidation. 4-HNE is a stable and biologically very active molecule whose presence is not restricted to the site of its origin, since it can diffuse through the membrane barrier [146]. The elevated blood levels of MDA in patients with diabetes reflect similar increases in the level of AH, which points to the involvement of OS and lipid peroxidation in the progression of DR to the proliferative form [95]. Cell-free vitreous is the target of 4-HNE, although aging has been reported to increase specific conjugates in retinal proteins [147]. After laser treatment, this biomarker accumulates in this relatively separate compartment giving rise to conjugated humor proteins and deterioration of the antioxidant activity by the lipoperoxidation product will make the vitreous more vulnerable to pro-oxidative effect [148]. Besides, it could interfere with the expression of different collagen subtypes effecting a change in the expression profile of the collagen subclasses, having consequences for the functional integrity of the vitreous as seen in the aged eye [149]. On the other hand, the results on the changes of MDA in the vitreous of diabetic patients with PDR obtained by Brzovi´c-Šari´c et al. agree with what has been reported, which showed an increase in vitreous MDA in patients with PDR. So in general, direct lipid peroxidation (LPO) method is more sensitive and provides a better picture of the

status than MDA [105]. A significant correlation has been shown between the increase in LPO in the vitreous and an increase in the expression of VEGF; therefore, it seems that the determination of LPO in serum could be a good predictor of the onset of OS in the vitreous [105,150]. Because the retina is rich in PUFAs and there is an increase in glucose oxidation and oxygen absorption, it is susceptible to an increase in OS. The structural and functional changes observed in it may be due to lipid peroxidation of the vascular endothelium [151]. Isoprostanes (F2-IsoPs) are stable products, whose formation increases with exposure to OS, they have gained acceptance as a reliable marker of oxidative damage in both in vivo and in vitro animal models and their use is becoming more frequent [32], as a chemically stable prostaglandin-like isomers generated by the reaction of PUFAs in membrane phospholipids. They are formed in lipid membranes and then released freely by the action of phospholipase. An important fact is that they are not a ffected by lipid content in the diet and, therefore, its measurement in biological fluids can provide an estimate of endogenous production [152].

NO is one of the most abundant free radicals in the human body, which can also react with other ROS, this causes cellular dysfunction and apoptosis [153]. It is synthesized by endothelial cells and is an important vasoactive agent that a ffects blood flow along with other vascular functions [154]. In the eye, neuronal nitric oxide synthase is believed to be responsible for producing NO in photoreceptors and bipolar cells, and as far as endothelial nitric oxide synthase is concerned, it is present in vascular endothelial cells [155]. However, inducible nitric oxide synthase found in Muller's cells and in the retinal pigment epithelium could be involved in phagocytosis of the external segment of the photoreceptor, in addition to infectious, inflammatory, and ischemic processes, and in the pathogenesis of DR [156]. In a Kulaksızoglu's study, the NO levels found by the measurement of nitrite and nitrate in the aqueous humor were significantly higher in patients with PDR than in controls without diabetes [95]. Another study noted that improved NO production in the eyes of people with diabetes is consistent with reports of it as a mediator of physiological and pathological processes in the retina [157]. NO is a highly important intercellular signaling molecule that plays a role in vasodilatory responses. In addition, it is involved in basal conditions and after retinal ischemia in the control of retinal blood flow [158]. The use of aqueous humor as a biological sample is proposed to assess the course of di fferent eye disorders showing an increase in pro-oxidative molecules and reduction in antioxidants [159,160]. In the study by Yilmaz et al. nitrite levels in vitreous humor of patients with PDR were higher than those of the control group (patients undergoing vitrectomy for idiopathic macular hole). However, among patients with PDR, there were no significant di fferences between the levels corresponding to type I and type II diabetes ( *P* = 0.56). On the other hand, patients with type I diabetes had a mean concentration of 0.524 ± 0.27 μM in vitreous, while those with type II diabetes had a mean of 0.383 ± 0.17 μM [112] (see Table 1). Other possibility is that overproduction of NO can cause damage to the retina by disrupting the rod outer segment membrane phagocytosis by retinal pigment epithelium (RPE) cells. Then, an accumulation of ROS occurs between the photoreceptors and the RPE cells, resulting in the degeneration of the photoreceptors [161].

#### *5.2. Inflammation: Cytokines and Relevance in DR*

DM patients with advanced diabetic retinopathy have more dry eye issues associated with neurotrophic keratopathy which could cause severe vision loss due to cornea ulcer or neurotrophic ulcer, this could lead an increased expression of proinflammatory cytokines, like inducible protein-10 (IP-10) and monocyte chemoattractant protein-1 (MCP-1), and decreased levels of antiangiogenic cytokines which demonstrate that an inflammatory reaction occurs in the ocular surface of diabetic patients [162]. The alterations in the ocular surface have been associated to inflammatory processes and microvascular damage that involves mediators like Th1 and Th17 and IL-1ß, IL-17A, TNFα, and mainly Epidermal Growth Factor (EGF) found elevated in patients with diabetes mellitus [126].

TNFα a cytokine strongly correlated with insulin resistance by changing phosphorylation of insulin receptor substrate-1 and interfering with insulin signaling cascade and leading chronic inflammation due to leukostasis induce by VEGF, IL-1 α and platelet-activating factor in the retinal

vasculature, but also it is a mediator of apoptosis in retinal neurons and endothelial cells. TNFα in tears was significantly higher in patients with PDR (13.5 pg/mL) compared with NPDR (2.8 pg/mL) [122]. In a recent cross-sectional study in Asian patients with DR, there was evaluated the di fferences in Total Protein Concentrations (TPC) and TNFα in tears correlated with the three stages of NPDR (mild, moderate and severe), they found a decreased in TPC in moderate and severe NPDR compared with higher concentrations of TNFα in the same stages, but in mild NPDR and patients without retinopathy the concentrations were similar. In this study, there was also higher levels of TNFα correlated with higher levels of HbA1c which are explained by AGEs that activate proinflammatory pathways and promotes angiogenesis and microvascular changes in the retina [163]. TNFα is one of the cytokines that induces disruption of the BRB by loosening the tight junctions between individual retinal endothelial cells and also between cells of the RPE, causing BRB breakdown. It promotes the irreversible adhesion of leukocytes to the endothelium and increases the production of ROS [164]. It also plays an important role in neovascularization and vasomotor reactions [165]. Actually, increased levels of TNFα have been found in the vitreous body of patients with T2DM and PDR compared with a control group [166]. There is a study where patients with PDR were divided into subgroups based on disease progression and regression, the vitreous levels of VEGF and IL-6 were significantly higher in the eyes of patients in the progression group with 1789.2 pg/mL (198.5–3436.8) and 347.2 pg/mL (26.2–758.6), respectively (see Table 2), than they were in eyes with PDR regression [116].

**Table 2.** miRNA's found in human eye fluids of patients. A comparison of some of the most commonly found miRNAs in vitreous or aqueous humor from patients with and without proliferative diabetic retinopathy. PDR: proliferative diabetic retinopathy, NDM: non-diabetic, PVD: proliferative vitreoretinal disease, MH: macular hole without any other condition, DME: diabetic macular edema.

+Biomarker found but not compared to another matrix or group.

In the inflammatory pathway, IL-6 has a role with induction of multiple process like synthesis and release of acute phase reactants and matrix metalloproteinase 9 (MMP-9), decreased tear production and apoptosis, induced di fferentiation of Th17, and the release of other proinflammatory cytokines like IL-1ß, the increased levels in this cytokine are particularly associated with increased metaplasia and keratinization of the ocular surface. Additionally, the secretion of interferon gamma (IFN-y) plays a multiple role with adaptive and innate response on the ocular surfaces inducing cell loss in the conjunctival goblet cells that reduces mucin production and apoptosis on lacrimal acini, associating high levels of this cytokine with severity of the tear film dysfunction [73,113,185]. Furthermore, decrease in the concentrations of proteins lysozyme, lactoferrin and albumin is more frequent in patients with PDR than NPDR [186], that could be associated with changes on ocular surface like tear film dysfunction, progressive loss of corneal epithelia and degeneration of nerve fibers [187], increasing the risk of corneal ulcerations and deteriorate their visual quality. In other studies have been analyze tear samples in di fferent stages of diabetic retinopathy and found a decreased in the content of tears proteins in the onset of DR as well, tear film dysfunction due to malfunctioning in the tear formation or more diluted tears. Lactotransferrin (LTF) and Immunoglobulin λ are increased in PDR together with another tear proteins expressed with frequency in PDR like lipocalin 1 (LCN1), lysozyme C (LYZ), lipophilin A, lacritin (LACRT) lipidic carriers for retinoids required for tear production, their increase are specifically associated as predictors for DR progression, some are linked to inflammation secondary to neovascularization, bleeding or macular edema [188]. Interestingly, IL-6 levels were positively correlated with the DR stage (PDR: 47.68 vs. NPDR: 29.68 pg/mL; *p* < 0.001) [115]. Elevated levels of IL-6 have been reported to participate as a proinflammatory and angiogenic factor in PDR and DM, is also involved in crossing both the blood–brain barrier and the blood–retinal barrier [189,190], and their levels in DR patients increased significantly compared to those in a non-diabetic control group. IL-6 levels were significantly correlated with PDR and it can act as one of the main drivers to generate a change in the cytokine profile in the aqueous humor of DR patients [115,191].

AH analysis provides useful information to understand the pathogenesis and responses to treatment of various eye conditions. The analysis of AH of DR patients has allow to identify some of the mediators (cytokines, chemokines, among other factors) involved in the pathogenesis of DR. Like IL-8, the concentration of VEGF is found to be increased in AH of diabetic patients [192]. Most studies have worked on aqueous humor cytokines in patients with PDR. However, the dynamic changes between the levels of cytokines in the di fferent states of severity of DR must be analyzed in greater detail. Chen et al. found that IL-6 had positive correlations with IL-8, IP-10, leukemia inhibitory factor (LIF) and hepatocyte growth factor (HGF) in the DR group, while it presented negative correlations with IL-9, IL-21, IL-23, IL-27, and IL-31.

IL-8 is an important chemoattractant that regulates chronic leukocytic inflammation in the vascular walls and ultimately leads to capillary occlusion and retinal ischemia [193], its levels have been reported in the AH of DR patients with 42.20 ± 33.03 pg/mL (mean ± SD) [115]. IL-8 expression can be hypoxia-induced and mediated by nuclear factor kappa B (NF-kB). IL-8 can induce angiogenesis in vitro and in vivo, and its elevated levels have been found in aqueous and vitreous of patients with ocular vascular disease, including DR [194]. Other studies have reported that elevated levels of vitreous IL-8 are associated with increasing levels of retinal ischemia and an increased degree of gliotic obliteration of large vessels in patients with PDR (see Table 2) [119,193]. Sun et al. found that the level of IL-8 in aqueous humor was significantly higher than that in the vitreous in PDR patients [119].

IL-10 is an anti-inflammatory cytokine and antiangiogenic mediator produced by monocytes and macrophages its antitumor e ffects have been associated with its ability to prevent angiogenesis associated with tumor growth [195]. In Mao C study, the median IL-10 concentration was higher in the vitreous of the PDR patients (224.789 pg/mL) than in the control group (160.143 pg/mL) and it was statistically significantly, which was di fferent from Hernández C study [121,196]. A predominance of macrophages (50%) has been determined in the vitreous samples of patients with PDR by cytological examination, this could explain the greater production of IL-10 resistant to other proinflammatory cytokines due to the increase in macrophages [125]. It suppresses the expression of the receptor for proinflammatory cytokines such as IL-1 and tumor necrosis factor α (TNFα) and inhibits the activation of its receptors by reducing the synthesis of these cytokines it limits inflammation. Its antiangiogenic effect has recently been shown to be associated with the downregulation of VEGF expression [197]. Dong et al. found that IL-10 levels in the AH decreased with increasing severity of DR.

TNFα is one of the main inflammatory response cytokines with chemotactic action on monocytes and neutrophils that activates them as macrophages, improving their cytotoxicity, while being one of the mediators of this cytotoxicity [198], with levels in aqueous humor of 4.04 ± 1.83 pg/mL (mean ± SD) in DR patients [115]. VEGF, also known as vascular permeability factor (VPF or vasculotropin), considered today the main factor controlling angiogenesis and vascular permeability and causes much of the pathogenesis of PDR and diabetic macular edema [199]. It is produced by endothelial cells, macrophages, CD4 lymphocytes, plasma cells, among others [200,201]. One study showed that the VEGF level was significantly elevated in diabetic subjects compared to the non-diabetic control group. However, there was no correlation between VEGF and DR severity (*p* = 0.357) [115].

The vitreous cavity contains different types of unique cells, such as retinal cells, RPE, choroid, and retinal vessels. Although the AH and the vitreous fluid do not flow with each other, some proteins can be exchanged between these two compartments. IL-6, IL-8, VEGF, and TNF-α have been observed to be elevated in serum and vitreous fluid from patients with DR [202]. Murugeswari et al. found that the levels of IL-6, IL-8, MCP-1, and VEGF present in the vitreous humor were significantly higher in PDR patients than in patients with macular hole [203]. In another study, a comprehensive analysis of mediators in the vitreous fluids of patients with PDR and in patients with other eye diseases was performed and elevated levels of VEGF, MCP-1, IL-8 and IL-6 were found compared to the control group [204]. IL-6 normally participates in acute phase reactions such as hematopoiesis, IL-2 induction, and differentiation of keratinocytes and B and T lymphocytes. In vitro studies suggest that NO may be acting as the intermediate molecule in IL-6-induced VEGF synthesis. Precisely, the synthesis of IL-6 can be induced in vitro by hypoxia and tissue hyperglycemia [205].

Interleukin 17 (IL-17) plays an important role in a wide variety of immunological diseases, but on the ocular surface it promotes neutrophil infiltration in tissues that induce synthesis and secretion of matrix metalloproteinases and ROS that generate disruption in the corneal epithelium, loss of epithelial functionality and induction of apoptosis. This contributes to the induction of neovascularization with stimulation of proangiogenic factor and modulation of cytoskeleton, which was examined in the vitreous and plasma of patients with DR, with levels significantly increased in PDR [114,206,207].


*2.5. Preparation of RNA-Lipoplexes (RNA-LPX)*

A lipid film was formed by the evaporation of an ethanolic solution containing DOTMA and DOPE at a molar ratio of 1:1 (10 mM, 400 µL). PBS(-) (800 µL) was then added to the lipid film, followed by incubation for 7 min at room temperature to hydrate the lipids. To form empty liposomes, the lipid film was then sonicated for approximately 30 s in a bath-type sonicator. After dilution with PBS(-) 5.92 times (final lipid concentration of 0.845 mM), equal volumes of the liposome suspension and the mRNA solution (0.2 mg/mL in PBS(-)) were mixed to form RNA-LPX with a nitrogen per phosphate ratio of 1.3/2. The resulting sample was used in in vivo experiments without further purification.


**5. Conclusions**

This section concludes the main findings of the present research work. The investigation of mass and thermal energy transfer of the 3D Carreau fluid moving through a permeable and stretching (shrinking) sheet is undertaken by considering the effects of thermal radiations, cross diffusion, and Hall current. Suitable similarity relations are employed in order to transform the set of coupled PDEs to a system of coupled ODEs. The set of coupled ODEs is then solved through the well-known standard analytical technique of HAM. The influence of the relevant physical variables on the hydromagnetic behavior of the Carreau fluid 3D flow is examined through various plots. The variations of the coefficients of skin friction, local Nusselt, and Sherwood numbers with the changing parameter values are shown through various tables. The important findings of this work are outlined as below:

**Author Contributions:** Conceptualization, A.U., I., and M.M.S.; software, S.K., A.U., and M.A.; writing—original draft preparation, I., M.M.S., and T.A.; writing—review and editing, A.K., T.A., S.K., and W.K.M.; formal analysis, W.K.M., I., and M.A.; validation, I., S.K., and A.U.; methodology, A.U., T.A., M.A., and W.K.M.; investigation, W.K.M., A.K., and S.K.; resources, M.M.S., A.K., and S.K.; project administration, M.M.S., M.A., and W.K.M.; funding acquisition, A.K. and T.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Not applicable.

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

**Acknowledgments:** Aziz Khan and Thabet Abdeljawad acknowledge the support provided by Prince Sultan University for funding this work through research group Nonlinear Analysis Methods in Applied Mathematics (NAMAM)group number RG-DES-2017–01-17.

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

#### **Abbreviations**

The below mentioned parameters and abbreviations with their possible dimensions are used in this article: