*4.7. Identification of Problems*

Based on the previous stages, the reengineering team defined that the main problem was that the decorating area has two lines, where the same activities are carried out during the shoe manufacturing process. One of the two lines has a U type flow line (Line 2) and, the other line lacks of design (Line 1), including the latter being the one that has many conflicts during the process due to reduced spaces between one activity. The has a bad sequence in the flow of the material and inadequate order of operations and equipment. For this reason, it is one of the areas with the greatest conflicts within the company and in which the proposal detailed in the next section will focus.

### *4.8. Analysis of the Process and Proposal*

In this phase, the proposal was generated, considering the results of the previous phases. Of the ideas that were generated in the training phase of the team, it was selected to merge both lines of scenery due to their economic viability. The others were discarded because they would generate problems among the workers or because of the economic cost to the company, which, at that time, it could not assume.

In the proposal, a change is made in the distribution of machinery and equipment. The old plant layout is shown in Figure 6. The area and its activities: flaming, cleaning, painting, gloss, fitting, packing and stowage is presented. This area is divided in two line 1 and line 2, according to what was observed in the company. In these lines, there is no logical sequence in the workflow and in the route of the materials, which causes delays in production. The Desma 1 and Desma 2 machines are distributed in different areas with one at 25 meters and the other at 15 meters, respectively, which is due to their technical conditions that cannot be moved. In the third activity, painting on the soles of the shoe, line 1, also has flow problems. It is necessary to pass the shoes under a table to get to the next activity where paint is applied to the sole of the shoe. Later, another operator performs the application of polish gloss, where both lines share the machinery, which causes accumulation of shoes.

After analyzing the current process, an improvement to the production process is proposed in which a change is made in the distribution of machinery and equipment. The proposed plant layout is shown in Figure 7, where the work area and a new U-type material flow path can be seen, which occupies the same dimensions, allowing for greater mobility and eliminating unnecessary work such as passing the shoes under a table to get to the next activity. Additionally, a merger of both lines

of work is proposed, becoming a single line of work within the decorating area with a new distribution and with more space between activities. The flow will be as follows. At the beginning, two operators go to the injection department and take 100 pairs of shoes from the warehouse, which are transported in a box to the decorating area. The shoe is taken to the first activity in the area, flaming, in this activity. Unlike the previous process, you can have 100 instead of 50 pairs of shoes, allowing the shoe to be closer to the operation and the three operators to take it and process it faster, which sends it to the next activity: cleaning. In this activity, eleven people clean the shoe and, unlike the previous configuration, the shoe is stored in an orderly manner by preventing the worker from being surrounded by shoes.

**Figure 6.** Flow of the current process elaborated by the author based on what was observed in the company.

As shown in Figure 6, where we observe the poor design of the work area, a bad flow of material and a total disorder of the area. Once the shoe is cleaned, it is taken to the painting activity in which three operators participate in painting the sole, so that they can be processed in the application of the gloss by two operators. Instantly, two operators place a paper inside the shoe for interior protection, and the shoelace is placed in it, while obtaining the finished shoe. Subsequently, it is packaged and labeled, according to the characteristics of the shoe. Lastly, three operators stow the boxes, until obtaining five pairs of shoes and placing a safety clasp, to transport them to the "Finished Product Warehouse" area.

The proposal was validated with a Simulation in the Software Arena™ and is described in the next section. Once the proposal was validated through simulation, a meeting was held. There, the leader spoke of the need to redesign the decorating area. It is necessary to mention that the designation of the activities, necessary resources, and the time to achieve it were decided by the company and did not allow that the activities and results achieved were published. Therefore, only the proposal validation is detailed in the following section. Although, the proposal was implemented and brought good results, even superior to those generated through simulation.

**Figure 7.** Flow of the proposal process, merger of both lines, elaborated by the author.


**3. Results**

#### *3.1. Spatial Patterns of the LST and SOS in Hangzhou*

The average LST in spring of 2018 was utilized to explore the difference of LST between that in the urban and the rural Figure 4. The results showed that there was a significant spatial heterogeneity in the LST of Hangzhou, showing a gradient of high in the urban and low in the rural. In terms of spatial distribution, the area with a LST greater than 23.5 ◦C accounted for 10.9%, mainly distributed in the urban area; 16.6%, 32.9% and 26.6% of the area with a LST of 21.5–23.5, 19.5–21.5 and 17.5–19.5 ◦C distributed in the middle area of Hangzhou, respectively. The area with a LST less than 17.5 ◦C (accounting for 13%) was mainly distributed in the northern and southern edges of Hangzhou. Moreover, as shown in the inset chart in Figure 4, the LST followed a generally decreasing urban–rural gradient, showing a significant urban heat island effect that the LST was highest (24 ◦C) in the urban and lowest (18.6 ◦C) in the rural. In the range of 0–6 km from the urban, the LST decreased fast (0.72 ◦C/km), and the decrease tended to slow down (0.08 ◦C/km) after 6 km.

**Figure 4.** Spatial distribution of land surface temperature in spring of 2018 in Hangzhou. The histogram denotes the frequency distribution of difference temperature. The inset graph denotes the variation trend of temperature in the buffer zone at different distances from urban. The solid lines denote the urban boundaries, and the wide and short dotted lines denote the 10 km and 20 km buffers of the urban boundaries, respectively.

As shown in Figure 5, the annual average SOS of Hangzhou from 2006 to 2018 showed a significant spatial heterogeneity. The SOS was earlier in the urban and later in the rural. In terms of spatial distribution, the area with the SOS less than 76 day of year (termed DOY) accounted for 8.9%, mainly distributed in the urban and the area within 2 km from the urban, located in the east and south of Hangzhou. About 23.1%, 31.1% and 26.6% of the area with SOS of 76–83, 83–88 and 88–94 DOY were distributed in the middle area of Hangzhou, respectively. The area with SOS more than 94 DOY (accounting for 10.3%) mainly distributed in the northern edges of Hangzhou, in the mountainous areas with

higher elevations. Besides, as shown in the inset chart in Figure 5, the SOS followed a generally increasing urban–rural gradient, that is, from urban (79 DOY) to rural (87 DOY), the SOS was continuously delayed by 8 days. In the range of 0–6 km from the urban, the SOS increased fast (1.02 days/km), and the increase tended to slow down (0.14 days/km) after 6 km.

**Figure 5.** Spatial distribution of annual average of the start of the growing season (SOS) from 2006 to 2018 in Hangzhou. The histogram denotes the frequency distribution of difference SOS. The inset graph denotes the variation trend of SOS in the buffer zone at different distances from urban. The DOY denotes the day of year. The solid lines denote the urban boundaries, and the wide and short dotted lines denote the 10 km and 20 km buffers of the urban boundaries, respectively.

We further explored the spatial difference of SOS between that in the urban and the rural of Hangzhou from 2006 to 2018, and we found that the results from each year had little significant fluctuations. Therefore, in order to avoid information redundancy and excessively long images, we displayed the results every 4 years (2006, 2010, 2014 and 2018) Figure 6. The results showed that although the absolute value of SOS varied from year to year, the spatial differentiation of SOS yearly was consistent with the annual average SOS from 2006 to 2018 in Figure 5. They both showed a significant spatial heterogeneity that the SOS was smaller in the eastern and southern area of Hangzhou and larger in the northern marginal area. As shown in the inset chart in Figure 6, the SOS of the urban was 9, 9, 6 and 6 days earlier than the rural in 2006, 2010, 2014 and 2018, respectively. In addition, the SOS followed a generally increasing urban–rural gradient. In 2006, 2010, 2014 and 2018, the SOS increased fast (1.25, 1.05, 0.83 and 0.93 days/km, respectively) within the range of 0–6 km from the urban, while it tended to be stable (0.07, 0.21, 0.05 and 0.04 days/km, respectively) after 6 km.

Combined with the analysis of the average LST in spring of 2018 and SOS across Hangzhou above, the difference in LST and SOS presented an opposite state and change trend. That is, the LST tended to be higher in the urban and lower in the rural, while SOS tended to be earlier in the urban and later in the rural. The LST followed a generally decreasing urban–rural gradient, while the opposite occurred for SOS, but both the LST and SOS varied greatly within the range of 0–6 km and then tended to be stable. In summary, the LST and SOS showed a negative correlation, and the coupling relationship between them would be further explored below.

**Figure 6.** Spatial distribution of the start of the growing season (SOS) in Hangzhou (left charts), and variation trend of SOS in the buffer zone at different distances from urban (right charts) in 2006, 2010, 2014, and 2018. The black line denotes the urban boundary, dark gray denotes the buffer of 10 km, and light grey denotes the buffer of 20 km. The DOY denotes the day of year. The solid lines denote the urban boundaries, and the wide and short dotted lines denote the 10 km and 20 km buffers of the urban boundaries, respectively.

#### *3.2. Relationship between LST and SOS in Hangzhou*

The results above showed that the spatial distribution and urban–rural gradient of the average LST in spring and SOS showed an opposite trend. Therefore, it could be inferred that there was negative correlation between LST and SOS. Figure 7 shows the coupling relationship between LST and SOS, and the 94.6% of the area conformed to the inference above. The LST and SOS showed a significant negative correlation accounted for 53.9%, more than a half of the total area. Among them, the low LST-high SOS (accounting for 6.2%) mainly distributed in the northern edge of Hangzhou, the outer rural farthest from urban. The medium LST–medium SOS (45.3%) was mainly distributed in the middle part of Hangzhou, with a moderate distance from urban. The high LST–low SOS (2.4%) was mainly distributed in urban and within 2 km from urban. At the same time, there was 40.7% of the area that LST and SOS showed a weaker negative correlation, including low LST–medium SOS (10.9%), medium LST–low SOS (12.2%), medium LST–high SOS (10.1%), and high LST–medium SOS (7.5%), mainly distributed in the area between urban and outer rural. Besides, there was 5.4% of the area that exhibited a contrary relationship to the inference. That is, the LST and SOS showed a significantly positive correlations, which were low LST–low SOS (3.3%) and high LST–high SOS (2.1%). It might be related to the threshold of LST and SOS for the classification. In addition, in areas with low, moderate and high LST, 83%, 67% and 82% of the SOS has a medium-high, moderate and medium-low distribution, respectively. Overall, the results above confirmed the inference that spring LST was negatively correlated with SOS.

**Figure 7.** Coupling relationship between land surface temperature in spring and the start of the growing season (SOS) of 2018 in Hangzhou. The 3D pie chart denotes the percentage of each coupling relationship, and the flat pie charts denote the percentage of SOS by level at different temperatures. The solid lines denote the urban boundaries, and the wide and short dotted lines denote the 10 km and 20 km buffers of the urban boundaries, respectively.

In order to further verify the inference above, the annual average SOS of 2006–2018 and its change trend at different levels of LST was calculated to explore the relationship between LST and SOS. As shown in Figure 8a, the annual average SOS at low, medium, and high LST were 88.9, 85.8, and 85.0 DOY, respectively. The SOS continently decreased with the increase of the LST. That is, the spring phenology continued to advance. As shown in Figure 8b, the change trend of SOS at low, medium, and high LST were −5.1, −3.9, and

−2 days/10 years, respectively. The downward trend of SOS decreased with the increase of LST. That is, the rate of advancement of phenology continuously slowed down. In general, the inference established that there was a negative correlation between SOS and LST, and it developed in a consistent direction, showing a trend of convergence.

**Figure 8.** Distribution of (**a**) the annual average of the start of growing season (SOS) of 2006–2018 and (**b**) its interannual variation trend under different temperatures. Slope denotes the slope of the linear regression line between SOS and year. Low, Medium and High denote different temperatures according to the natural breakpoint classification method. The DOY denotes the day of year. In the box charts, the box denote the values of median, lower quartile (Q1) and upper quartile (Q3), respectively; the error bars denote the values of Q1 − 1.5(Q3 − Q1) and Q3 + 1.5(Q3 − Q1), respectively.

#### *3.3. Relationship between LST and SOS at Sample Points*

The relationship between LST and SOS at the sample point scale was further employed Figure 9. The LST in the urban of Hangzhou was significantly higher than that in the rural, exhibiting a significant urban heat island effect. In terms of the LST during the daytime in spring, the difference between that in the urban (23.0 ± 1.2 ◦C) and the rural (19.7 ± 1.0 ◦C) was significant, reaching 3.3 ± 1.0 ◦C. From 2006 to 2018, the difference continued to increase, with an average annual increase of 0.2 ◦C (*p* < 0.01). For the LST during the nighttime in spring, the difference between that in the urban (8.2 ± 1.0 ◦C) and the rural (7.0 ± 0.7 ◦C) reached 1.2 ± 0.3 ◦C. Compared with the daytime, the urban heat island effect was weaker at night, reduced by 2.1 ◦C. Integrating the temperature during the daytime and nighttime Figure 9c, the difference of the daily average temperature between that in the urban (15.6 ± 0.8 ◦C) and the rural (13.3 ± 0.8 ◦C) reached 2.3 ± 0.5 ◦C, and it continued to increase from 2006 to 2018 (Slope = 0.09 ◦C/years, *p* < 0.01). To further compare the difference of SOS between that in the urban and the rural Figure 9d, the SOS of urban (79.3 ± 5.6 DOY) was 7.4 ± 2.7 days earlier than that in the rural (86.7 ± 5.0 DOY). It indicated that the plant phenology changed significantly under different environmental backgrounds in the urban and the rural. Besides, it was worth noting that, except 2012 (1.5 days) and 2015 (12.9 days), the difference of SOS between that in the urban and the rural was relatively stable from 2006 to 2018, with a difference of 7.5 ± 1.6 days, and it was consistent with the difference of 6–9 days in Hangzhou Figure 6.

As shown in Figure 10, the LST during the daytime rather than nighttime showed a statistically significantly negative correlation with SOS both in the urban and the rural. In terms of the LST during the daytime Figure 10a, the sample points of LST–SOS in the urban and the rural distributed significantly separately, with LST of 19–28 ◦C and 16–23 ◦C, and SOS of 40–100 DOY and 50–110 DOY in the urban and the rural, respectively. Besides, the SOS in the rural was statistically significantly negatively correlated with the LST (Slope = −1.64 days/◦C; *p* < 0.01). The correlation in urban had a degree of significance (Slope = −1.01 days/◦C, *p* < 0.05), and the change trend of the SOS with LST increasing in the urban was smaller than that in the rural. For the LST during the nighttime Figure 10b, the sample points of LST-SOS in the rural and the urban had poor separation and high similarity. There was less statistically significant correlation between SOS and LST (*p* > 0.05), indicating that the LST during the nighttime had little effect on SOS. However, the fitted trends all showed that the SOS decreased with the increasing LST, which was consistent with the response of SOS to LST in Figure 8.

**Figure 9.** The interannual changes and differences of (**a**) the daytime land surface temperature in spring, (**b**) the nighttime land surface temperature in spring, (**c**) the daily average temperature, and (**d**) the start of growing season (SOS) in the urban and the rural from 2006 to 2018. The red/blue dotted lines denote the interannual changes, and the gray bars denote the differences. The DOY denotes the day of year.

**Figure 10.** The response of SOS in the urban and the rural to (**a**) daytime temperature and (**b**) nighttime temperature. The SOS means the start of the growing season. The red/blue solid points denote urban/rural sample data, the red/blue solid lines denote linear regression lines, and the regression equation and significance are shown in the illustration. The DOY denotes the day of year.

#### *3.4. Relative Contributions of* Δ*LST to* Δ*SOS at Sample Points*

The temperature contribution separation model was utilized to explore the contributions of the ΔLST to the ΔSOS under urbanization. The difference of SOS under urbanization between predicted by the model (7.3 ± 1.3 days) and observed through data (7.4 ± 2.7 days) was −0.16 ± 1.4 days Figure 11a. For the results predicted by the model Figure 11b, the ΔLST played a significant role in the advance of SOS under urbanization and the proportion of its contributions was relatively stable, despite the interannual fluctuations. Besides, we found that the ΔSOS dominated by the ΔLST contributed 72 ± 13.3% (5.3 ± 1.7 of 7.3 ± 1.3 days) to the difference of SOS between that in the urban and the rural Figure 11a. The advance of 28 ± 13.3% (1.9 ± 0.7 days) of SOS under urbanization was dominated by other factors such as photoperiod and air pollution. Overall, the local warming effect induced by the urban heat island effect produced substantial impacts on plant phenology under urbanization, but the impact of other factors also cannot be ignored.

**Figure 11.** (**a**) The contributions of the difference of land surface temperature between urban and rural (ΔLST) and other factors to the difference of plant phenology under urbanization and (**b**) their interannual variations from 2006 to 2018. The SOS means the start of the growing season. Quantile chart denotes the distribution of SOS differences from 2006 to 2018. Solid red points represent abnormal values. The DOY denotes the day of year.


*4.4. Excretion*

Around 99% of PTS is excreted through non-renal pathways, while 0.219% goes through the hepatic pathway [8]. A very small fraction of it was found to be excreted in urine [8]. When comparing intravenous doses of resveratrol and PTS in male rats, the clearance of PTS was much less than that of resveratrol, indicating a longer therapeutic availability [3]. Interestingly, the analysis of the urine samples in cannulated rats identified the parent PTS and its glucuronidated metabolite, which had been previously identified only in the systemic circulation [8]. Increasing the dose of PTS from 2.5 mg/kg to 25 mg/kg decreased the clearance rate by almost half, which is attributed to the saturation or partial saturation of PTS metabolism [24].

#### *4.5. Toxicity*

The administration of PTS, even in high doses, was observed to be nontoxic in mouse models. Four groups of mice were fed PTS doses ranging from 0 to 3000 mg/kg body weight/day for 4 weeks, and there were no significant alterations in the consumption of food or water or in weight gain [23]. Additionally, the in vivo administration of PTS attenuated tumorigenesis and metastasis with negligible toxicity [29]. Pharmacologically, PTS, when intravenously administered, has been noted to be safe, as the compound did not exhibit toxicity specific to any organ [30]. In humans, PTS has been observed to exhibit safety at doses up to 250 mg/day [31].

#### **5. Major Pathways Associated with PTS**

#### *5.1. Antioxidative Pathway: Activation of Nrf2 Signaling*

The antioxidant activity of PTS has been extensively studied and implicated in anticarcinogenesis, the modulation of neurological disorders, the attenuation of vascular diseases, and diabetes management [7]. Extensive evidence has indicated that PTS reduces oxidative stress by attenuating the production of the superoxide anion and hydrogen peroxide, which are implicated in the initiation and progression of various pathogenic processes [7]. Nrf2, a nuclear transcription factor, is one of the major players in regulating cytoprotective and antioxidant genes, which also includes phase II metabolic and antioxidant enzymes [32]. The stimulation of Nrf2 signaling has been identified to produce anti-cancer, anti-diabetic, cardioprotective, and neuroprotective effects [33]. Kelch-like ECH-associated protein 1 (Keap-1) is a negative regulator of Nrf2 and targets the transcription factor for ubiquitylation and degradation. Our lab investigated the protective properties of PTS in pancreatic β-cell apoptosis through an Nrf2-mediated mechanism [32] (Figure 1). We found that PTS activates the Nrf2 pathway, thereby triggering the expression of Nrf2 downstream target genes to facilitate cellular protection in INS-1E cells. In particular, we demonstrated that PTS binds to the arginine residues of Keap-1 and facilitates its disassociation from Nrf2 [34]. Interestingly, PTS has also been reported to mediate the recruitment and interaction of the P62 autophagic cargo with Keap-1, thereby inducing the autophagic degradation of Keap-1 [35]. The major downstream targets activated following PTS administration were antioxidative enzymes, including HO 1, SOD, catalase, and GPX. Moreover, we observed the upregulation of anti-apoptotic gene expression, with the associated downregulation of the expression of the pro-apoptotic mediators Bax (Bcl-2 associated X protein) and caspase-3. Our evidence was strongly suggestive of the protective effect of PTS administration against hyperglycemia-induced oxidative damage in pancreatic β-cells [32] (Figure 1).

Our lab inferred that PTS protects β-cells in Streptozotocin (STZ)-induced diabetic mice, an effect accompanied by the induction of Nrf2 and the consequent upregulation of its target genes. We also investigated the anti-peroxidative role of PTS in the STZ-induced diabetic model. In addition, PTS normalized the circulatory concentration of VLDL and LDL while reducing lipid peroxidation in STZ-induced diabetic mice. Notably, the livers of diabetic mice indicated collapsed hepatic microvesicles on H&E staining due to altered lipid metabolism [36]. PTS administration reduced structural and functional alterations in the hepatic tissue, indicating its protective function in diabetic dyslipidemia mediated via Nrf2 activation [37] (Figure 1).

By countering oxidative damage, PTS treatment inhibited human retinal endothelial cell proliferation and delayed the progression of diabetic retinopathy [38]. In the livers of IUGR piglets, PTS attenuated liver injury caused by Nrf2 activation and the consequent induction of the antioxidant response [39]. Notably, adrenocorticotropic hormone (ACTH) was observed to interfere with Nrf2 signaling in metastatic cells. PTS reduced ACTH activity and was effective against various melanoma cell lines, including MelJuso, A2058, and MeWo [30]. PTS blocked cellular inflammation and oxidative stress in azoxymethaneinduced colon carcinogenesis, thereby reducing tumorigenesis. Through the activation of Nrf2, PTS countered the induction of NF-κB (nuclear factor-kappaB) and diminished the levels of oxidative stress mediators, including inducible nitric oxide synthase (iNOS), Cyclo-oxygenase-2 (COX-2), and aldolase reductase in an AOM-induced colon cancer rodent model [40]. Furthermore, by maintaining glutathione, catalase, SOD, and GSH peroxidase activity through the Nrf2–antioxidant response, PTS showed anti-cancer activity in a UVB-stimulated skin cancer model [41]. In the innate immune system, neutrophils produce reactive oxygen species (ROS) to destroy pathogens with the help of NADPH oxidase, which produces a superoxide anion. The overproduction of ROS can cause tissue damage that is observed in diseases such as rheumatoid arthritis and ischemic injury. PTS lowered the neutrophil count in arthritic animals and facilitated a mild decrease in ROS production, with a limited effect on neutrophil activity [42].

In a dose-dependent manner, PTS has been identified to exhibit a potent antioxidant effect against several free radicals, including 2,2-Diphenyl-1-picryl-hydrazyl (DPPH), 2,2 -Azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS), hydroxyl, superoxide, and hydrogen peroxide. Furthermore, PTS treatment is associated with increased antioxidant enzymes, such as SOD and GPX, via Nrf2 activation in neuronal cells [43]. In Alzheimer's disease models, PTS increased PPAR-α, a modulator of neural antioxidant activities [43].

**Figure 1. Nrf-2-mediated antioxidant pathway of pterostilbene:** Activation and phosphorylation of Nrf-2 signaling is the major mechanism through which the antioxidative response is induced by PTS. Ubiquitination mediated by Cullin-3 (CUL-3) leads to the proteasomal degradation of Nrf-2. PTS inhibits the ubiquitin–proteasome system, thereby increasing the accumulation of Nrf-2. PTS also enables the phosphorylation of Nrf-2, which is critical in the nuclear translocation of the transcription factor. Moreover, PTS phosphorylates and activates the ERK signaling pathway, which mediates the dissociation of Keap-1, resulting in Nrf-2 activation. Furthermore, PTS stimulates the binding of Keap-1 and p62, which enhances the activation of Nrf2. Following its activation and nuclear translocation, Nrf-2 binds to ARE and induces the expression of antioxidant enzymes, which in turn critically attenuate oxidative damage in host cells.

#### *5.2. Pro- and Anti-Apoptotic Pathways*

PTS inhibited cell proliferation and acted as an active inducer of apoptosis in certain cancerous cell lines [8]. Moreover, PTS treatment induced caspase release and O2 − production, which depolarizes the mitochondrial membrane, triggering the intrinsic mitochondrialderived apoptosis of cancerous cells [44,45]. Chakraborty et al. identified that PTS modified markers associated with mitochondrial apoptosis and improved the expression of the antioxidant enzymes GPx, GR, and GSH in an in vitro prostate cancer model [46]. Moreover, PTS induced apoptosis in gastric adenocarcinoma cells through the increased upregulation of cytochrome C, Bad, Bax, and caspases [47]. Genomic analysis revealed that PTS treatment in pancreatic cancer upregulated pro-apoptotic genes and anti-proliferative markers [48].

Interestingly, PTS was reported to inhibit the effects of apoptosis in vascular endothelial cells [49]. Apoptosis induces plaque instability in atherosclerosis, where oxLDL (oxidized low-density lipoprotein) triggers the apoptosis of VEC by activating lectin-like oxLDL receptor-1. PTS inhibits the apoptosis induced by oxLDL and stimulates cytoprotective autophagic cell death in VECs, thereby dampening the atherosclerotic effect of oxLDL [50]. Notably, this effect was achieved by increasing the accumulation of intracellular calcium, followed by the subsequent activation of the AMPK α1 subunit (AMPKα1) [50]. PTS also suppressed the oxidative damage induced by oxLDL by reducing the mitochondrial membrane potential and lowering the levels of pro-apoptotic proteins such as Bax and p53 [49]. PTS administration to cochlear cells obtained from STZ-induced diabetic rats demonstrated the protection of the cochlea from ototoxicity through the inhibition of apoptosis [51]. Interesting work from our lab deduced that cytoprotection by PTS against cytokine-induced cellular damage in MIN6 mouse pancreatic cells involves the activation of Nrf2 signaling, associated with the inhibition of pro-apoptotic signaling through the attenuation of the BAX/Bcl-2 ratio and the reduced activity of caspase-3 [36]. Our evidence indicates that the PTS-mediated anti-apoptotic effect is also a consequence of Nrf-2 activation (Figure 2).

**Figure 2. Anti-apoptotic pathway of pterostilbene:** PTS mediates its cytoprotective effect mainly through the activation of Nrf-2, which in turn protects the mitochondrial functioning, thereby causing a reduction in the induction of pro-apoptotic factors such as cytochrome C, BAX, and caspase-3. In vascular endothelial cells, PTS protects against the initiation of apoptotic signaling by countering the effect of oxLDL in activating its receptor lectin-like oxidized low-density lipoprotein (LOX-1), thereby preventing the accumulation of P53 as well as the activation of NFκB. Moreover, PTS increases the intracellular calcium levels and promotes the cytoprotective autophagy of the cell, consequently preventing the deleterious effect of apoptosis.


**7. Cell culture models**

Traditionally animal models were commonly employed for carrying out study of different types of cancer for the past three decades [74]. These animal studies have many drawbacks including lack of high-throughput drug screening, longer time consumption to conduct tests and ethical controversies concerning animal testing. Cell culture is the most widely used alternative to animal studies and cell culture techniques can broadly be classified into 2D and 3D methods [75]. The potentialities of primary cancer cell models' cultures in preclinical studies for cancer research and drug discovery has amplified over the past few years. Primary cell cultures provide a good model system to understand normal and malignant biological activities. Carcinogenesis-related behavior such as apoptosis, proliferation, adhesion, differentiation, migration, senescence, invasion, angiogenesis, and other metabolic pathways have been studied in recent years. One of the major advantages is that the heterogeneity of cell populations composing a primary culture mimics the tumor microenvironment, crosstalk, and interactions between malignant and healthy cells, neither of which is possible with cell lines [76].

Most studies have shown that the cellular responses to drug treatments in 3D cell culture are significant and more similar to that of *in vivo* architecture when compared to 2Dcell culture. One of the most improved successful assays using 3D culture for cell-based screening in the early phase of drug discovery is cancer cell viability assessment. This assay is particularly useful to test the cytotoxic effects of compounds that may lead to cell death. It plays an essential role in checking how many cells are viable at the end of each experiment. Cell viability assay is closely followed by cell proliferation, cell migration and then cell signaling assays [77–79].

Currently a number of anticancer drugs belonging to different classes chemically are available. To be used as a potential anticancer agent, the testing compounds need to inhibit the growth and proliferation of cancer cell lines. This will further inhibit the signaling pathways by knocking in or knocking out a candidate gene thereby stopping the progression of tumor to fatal stages. For instance, antiproliferative investigations were performed on prostate cancer cell line DU-145 *in vitro* and *in vivo* using salvia miltiorrhizabunge [80]. Another example to justify this concept will be a study performed on PC-3 cell line. Generally, cancer cells express higher amount of Transferrin Receptors (TfR) for an increased uptake of iron in relative to normal cells. This higher amount of intracellular free iron is required for the growth and proliferation of cancer cells. Anhydrodihydroartemisinin (ADHA) was used to inhibit PC-3 cell lines through caspase-dependent pathway [81].