Fyn, Blk, and Lyn kinase inhibitors: A mini-review on medicinal attributes, research progress, and future insights.

Fyn, Blk, and Lyn are part of a group of proteins called Src family kinases. They are crucial in controlling cell communication and their response to the growth, changes, and immune system. Blocking these proteins with inhibitors can be a way to treat diseases where these proteins are too active. The primary mode of action of these inhibitors is to inhibit the phosphorylation of Fyn, Blk, and Lyn receptors, which in turn affects how signals pass within the cells. This review shows the structural and functional aspects of Fyn, Blk, and Lyn kinases, highlighting the significance of their dysregulation in diseases such as cancer and autoimmune disorders. The discussion encompasses the design strategies, SAR analysis, and chemical characteristics of effective inhibitors, shedding light on their specificity and potency. Furthermore, it explores the progress of clinical trials of these inhibitors, emphasizing their potential therapeutic applications.

Druggable targets of protein tyrosine phosphatase Family, viz. PTP1B, SHP2, Cdc25, and LMW-PTP: Current scenario on medicinal Attributes, and SAR insights.

Protein tyrosine phosphatases (PTPs) are the class of dephosphorylation enzymes that catalyze the removal of phosphate groups from tyrosine residues on proteins responsible for various cellular processes. Any disbalance in signal pathways mediated by PTPs leads to various disease conditions like diabetes, obesity, cancers, and autoimmune disorders. Amongst the PTP superfamily, PTP1B, SHP2, Cdc25, and LMW-PTP have been prioritized as druggable targets for developing medicinal agents. PTP1B is an intracellular PTP enzyme that downregulates insulin and leptin signaling pathways and is involved in insulin resistance and glucose homeostasis. SHP2 is involved in the RAS-MAPK pathway and T cell immunity. Cdk-cyclin complex activation occurs by Cdc25-PTPs involved in cell cycle regulation. LMW-PTPs are involved in PDGF/PDGFR, Eph/ephrin, and insulin signaling pathways, resulting in certain diseases like diabetes mellitus, obesity, and cancer. The signaling cascades of PTP1B, SHP2, Cdc25, and LMW-PTPs have been described to rationalize their medicinal importance in the pathophysiology of diabetes, obesity, and cancer. Their binding sites have been explored to overcome the hurdles in discovering target selective molecules with optimum potency. Recent developments in the synthetic molecules bearing heterocyclic moieties against these targets have been explored to gain insight into structural features. The elaborated SAR investigation revealed the effect of substituents on the potency and target selectivity, which can be implicated in the further discovery of newer medicinal agents targeting the druggable members of the PTP superfamily.

Dynamical Analysis of COVID-19 Model Incorporating Environmental Factors.

The continuing coronavirus pandemic has come up with considerable questions in front of the world. Presently, India is among concerned countries in Asia. Even though the recovery rate is more than the death rate, it is affecting human lives and experiencing losses to the market. Several methods were employed to study the spread of novel coronavirus. Mathematical modeling is one of the prominent techniques to evaluate the dynamics of novel coronavirus. In this work, we extend the mathematical model SEIAQRDT by incorporating environmental transmission to analyze the transmission of coronavirus in India. The notable aspect of the model incorporates asymptomatic population, quarantine individuals, and environmental transmission factors. These factors have enormous significance in the ongoing COVID-19 outbreak. The basic reproduction number R 0 is calculated theoretically. Bifurcation analysis of R 0 is also done analytically. The existence and stability analysis of disease-free equilibrium (DFE) and endemic equilibrium (EE) points are established. The impact of environmental factors in spreading COVID-19 pandemic is deliberated. The case study for India and Italy is presented and compared with real data, and the results are in accordance with the real situation.

SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India.

COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strategies, incorporating quarantine, self-isolation, and tracing of asymptomatic cases. Mathematical modeling is one of the prominent techniques for predicting and controlling the spread of COVID-19. The predictions of earlier proposed epidemiological models (e.g. SIR, SEIR, SIRD, SEIRD, etc.) are not much accurate due to lack of consideration for transmission of the epidemic during the latent period. Moreover, it is important to classify infected individuals to control this pandemic. Therefore, a new mathematical model is proposed to incorporate infected individuals based on whether they have symptoms or not. This model forecasts the number of cases more accurately, which may help in better planning of control strategies. The model consists of eight compartments: susceptible (S), exposed (E), infected (I), asymptomatic (A), quarantined (Q), recovered (R), deaths (D), and insusceptible (T), accumulatively named as SEIAQRDT. This model is employed to predict the pandemic results for India and its majorly affected states. The estimated number of cases using the SEIAQRDT model is compared with SIRD, SEIR, and LSTM models. The relative error square analysis is used to verify the accuracy of the proposed model. The simulation is done on real datasets and results show the effectiveness of the proposed approach. These results may help the government and individuals to make the planning in this pandemic situation.

Determination of safe limit for arsenic contaminated irrigation water using solubility free ion activity model (FIAM) and Tobit Regression Model.

Irrigation water contaminated with arsenic acts as a potent source of contamination to humans through water-soil-crop-food transfer so quantification of safe limit for irrigation water is also critical. A pot experiment was conducted to determine the safe limit for As contaminated irrigation water with two soil types (alluvial and red) using ten levels of contaminated irrigation water (0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.50, 1.75, 2.0, 2.25 mg L-1), applied 5 times in rice (Variety: Sushak Samrat),used as a test crop. The results reveal that the different fractions of arsenic in terms of its profusion followed the order F4 > F2 > F5 > F3 > F1 and F4 > F3 > F2 > F5 > F1 across all the doses of As for alluvial soil and red soil respectively. The safe limit of irrigation water in terms of risk assessment expressed as Hazard Quotient (HQ) was at 0.75 mg L-1 and the solubility FIAM can effectively predict the As content in rice grain in both the soils. The Tobit Regression Model in alluvial soil quantified the safe limit for As in irrigation water from 1.20 to 0.10 mg L-1 for available soil As 0.25-3.0 mg kg-1 and in red soil, the range was from 0.10 to 0.40 mg L-1 for soil As 1.0 to 0.25 mg kg-1 provided that the As content in rice grain is < 0.4 mg kg-1. This proved to be an effective protocol for estimation of safe limits after proper validation and calibration.