Identification and prioritization of risks for new entrants in automobile sector using Monte Carlo based approach.

The automotive industry serves as a crucial support system for the economies of industrialized nations in their pursuit of international market competitiveness. Despite this industry's importance, most developing countries face the challenge of acquiring a reasonable economic position at the global level in the automotive sector for various reasons. The most salient reasons include inconsistent government policies, multiple taxes, investor insecurity, political instability, and currency devaluation. Identifying risks is crucial for a new entrant in the already-established automotive industry. The researchers have used multiple (qualitative and quantitative) techniques to identify and prioritize risks in setting up manufacturing plants. The efforts to tackle these identified risks are undertaken at the domestic and government levels to smoothen the establishment of industry. The risks are first identified, in the current study, by reviewing the previous literature and conducting interviews of the various stakeholders (automotive dealers, managers, and customers). Then this study uses Monte Carlo simulation (MCS) approach and develops a risk exposure (high, medium, or low) matrix for the automotive industry of Pakistan. The findings reveal that the depreciation of local currency against the foreign exchange, oligopoly nature of competition, and low market acceptability of new entrants due to their products' image are the most critical risks the automobile industry faces. These findings will help automotive research institutes in developing national policies that specifically aim to support new players in the automotive industry, particularly in addressing high-priority hazards. The results may also provide valuable insights for new participants seeking to identify and address the key challenges in the Pakistani automotive industry before entering it.

In-depth in-vitro and in-vivo anti-diabetic evaluations of Fagonia cretica mediated biosynthesized selenium nanoparticles.

Therapeutic moieties derived from medicinal plants as well as plants-based ecofriendly processes for producing selenium nanoparticles have shown great promise in the management of type 2 diabetes mellitus (T2DM). The current study was aimed to assess the anti-diabetic potentials of Fagonia cretica mediated biogenic selenium nanoparticles (FcSeNPs) using in-vitro and in-vivo approaches. The bio-synthesized FcSeNPs were characterized using various techniques including UV-VIS spectrophotometry and FTIR analysis. The in-vitro efficacy of FcSeNPs were assessed against α-glucosidase, α-amylase enzymes as well as the anti-radical studies were performed using DPPH and ABTS free radicals scavenging assays. For in-vivo studies, 20 Male Balb/C albino-mice were randomly divided into 4 groups (n = 5) including normal group, disease group (Diabetic group with no treatment), control group and treatment group (Diabetic group treated with FcSeNPs). Further, biochemistry markers including pancreas, liver, kidney and lipid profile were assessed for all treatment groups. The FcSeNPs exhibited a dose-dependent inhibition against α-amylase and α-glucosidase at 62-1000 µg mL-1 concentration with IC50 values of 92 and 100 µg mL-1 respectively. In antioxidant experiments, the FcSeNPs demonstrated significant radicals scavenging effect against DPPH and ABTS radicals. In STZ-induced diabetic mice, a considerable decline in blood glucose level was observed after treatment with FcSeNPs. Anti-hyperglycemic effect of FcSeNPs treated animals were high (105 ± 3.22**) as compared to standard drug (128.6 ± 2.73** mg dL-1). Biochemical investigations revealed that all biochemical parameters for pancreas, liver function, renal function panel and lipid profile were significantly lowered in FcSeNPs treated animals. Our findings indicate a preliminary multi-target efficacy for FcSeNPs against type-2 diabetes and thus warrant further detailed studies.

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

In Silico Strategies for Designing of Peptide Inhibitors of Oncogenic K-Ras G12V Mutant: Inhibiting Cancer Growth and Proliferation.

Ras plays a pivotal function in cell proliferation and is an important protein in signal transduction pathways. Mutations in genes encoding the Ras protein drive the signaling cascades essential for malignant transformation, tumour angiogenesis, and metastasis and are responsible for above 30% of all human cancers. There is evidence that N-Ras, K-Ras, and H-Ras play significant roles in human cancer. The mutated K-Ras protein is typically observed in malignant growths. Mutant K-Ras is the most common in lung, colon, and pancreatic cancers. The purpose of this research was to create peptides that inhibit K-Ras G12V. The crystal structure of the mutant K-Ras G12V-H-REV107 complex was obtained from a protein data bank. Further, we used a residue scan approach to create unique peptides from the reference peptide (H-REV107). AMBER molecular dynamics simulations were used to test the stability of the top four proposed peptides (based on binding free energies). Our findings showed that the top four selected peptides had stronger interactions with K-Ras than the reference peptide and have the ability to block the activation function of K-Ras. Our extensive analyses of binding affinities showed that our designed peptide possesses the potential to inhibit K-Ras and to reduce the progression of cancer.

Incorporating management opinion in green supplier selection model using quality function deployment and interactive fuzzy programming.

The need for environmental protection and involvement of ecological aspects in the business operations is forcing the organizations to re-examine their action plans and rebuild their supply chain activities. Many organizations are incorporating environmental rules and regulations in their everyday matters by focusing on green supplier selection. The proposed research paper develops a multi-objective interactive fuzzy programming model for the selection of suppliers. This model works on a business quartet of green appraisal score, cost, quality, and time. The model uses an environmental scale for different green parameters and all the suppliers are scored based on this scale. In this research model, Quality Function Deployment (QFD) methodology is integrated with the multi-objective interactive fuzzy programming. QFD technique is utilized to compute the weights of several green factors used for the selection of suppliers. The model uses a Fuzzy linguistic scale and a triangular membership function to link expert opinions along with their experience to solve the problem. Finally, the model is validated on a numerical case study of the textile industry for green supplier selection which achieves a 100% satisfaction for cost and time, 75% satisfaction for green appraisal score, and 93.95% for the quality. The proposed model assists the decision-makers in selecting green suppliers to improve the overall sustainability of their organizations.