Different CVDs (CVD) are the leading wreak of mortality and disability worldwide. The pathology of CVD is complex; multiple biological pathways have been involved. Biomarkers act as a measure of usual or pathogenic biological processes. They play a significant part in the definition, prognostication, and decision-making with respect to the treatment of cardiovascular events. Inthis article, we had summarized key biomarkers which are essential to predict CVDs. We had studied prevalence, pattern of expression of biomarkers (salivary, inflammatory, oxidative stress, chemokines, antioxidants, genetic, etc.), its measurable impact, benefits of early detection and its scope. A considerable number of deaths due to cardiovascular diseases (CVDs) can be attributed to tobacco smoking and it rises the precarious of deathfrom coronary heart disease and cerebrovascular diseases. Cytokines which is categorized into pro inflammatory and anti-inflammatory take part in as biomarkers in CHD, MI, HF. Troponin, growth differentiation factor-15(GDF-15), C-reactive protein, fibrinogen, uric acid diagnose MI and CAD. Matrix Metalloproteins, Cell Adhesion Molecules, Myeloperoxidase, Oxidative stress biomarkers, Incendiary biomarkers are useful to predict the risk of UA, MI, and HF. Increased Endothelin-1, Natriuretic peptides, copeptin, ST-2, Galectin-3, mid-regional-pro-adrenomedullin, catecholamines are used to prognosticate Heart failure. Modern technologies like Artificial Intelligence (AI), Biosensor and high-speed data communication made it possible to collect the high-resolution data in real time. The high-resolution data can be analyzed with advance Machine Learning (ML) algorithms, it will not only help to discover the disease patterns but also an real-time and objective monitoring of bio-signals can help to discover the unknown patterns linked with CVD.

In the present work, molecular docking analyses of few thiazolidinediones into the catalytic domain of protein PPAR-gamma is reported for the discovery of antidiabetic agents. Protein PPARgamma is involved in carbohydrate and fat metabolism hence it will be a useful target for treating type-2 diabetes. With this view, mapping of ligand binding domain of protein PPAR-gamma was carried out using online server such as uniport; pharmacophoric points using co-crystal rosiglitazone were studied. Molecular docking of thiazolidine-2,4-diones was carried out using Vlife MDS suite. Binding energy and interactions such as hydrogen bond, Vander Wall pi stacking and hydrophobic interactions, which happened between ligands and the protein, have been studied. Compounds exhibiting strong affinity and interactions in the pocket where rosiglitazone binds will be taken for wet laboratory synthesis.

Purpose To develop and validate a reverse-phase high-performance liquid chromatography (RP-HPLC) bioanalytical method for quantifying cefpodoxime proxetil (CFP) epimers in human plasma and to integrate metabolite synthesis, hydrolysis kinetics, antibacterial evaluation, in silico toxicity prediction, and analytical sustainability assessment for its active metabolite, cefpodoxime acid (CFA). Methods CFP was extracted from spiked human plasma by liquid–liquid extraction and quantified using an RP-HPLC–UV method validated conferring to US FDA (2018) bioanalytical guidance. CFA was synthesised by alkaline hydrolysis, structurally characterised, and chromatographically separated from CFP, although wide-ranging bioanalytical validation of CFA was not performed because of limited metabolite availability. Hydrolysis kinetics, antibacterial activity against Escherichia coli ATCC 25,922 and Bacillus subtilis ATCC 6633, TOPKAT toxicity prediction, and AGREE and White Analytical Chemistry (WECA) sustainability assessments were also performed. Results The method reached baseline separation within 15 min and was linear over 200–6400 ng/mL using weighted (1/X²) regression (r² > 0.999). Validation demonstrated satisfactory selectivity, accuracy, precision, recovery, dilution integrity, and stability. CFP underwent rapid first-order alkaline hydrolysis to CFA (k = 0.049 min⁻¹; t½ = 14.0 min). CFA retained antibacterial activity comparable to CFP against the tested reference strains. The method achieved an AGREE score of 0.65 and a WECA score of 61. Conclusion The validated RP-HPLC technique provides a simple, reliable, and cost-effective platform for CFP bioanalysis and chromatographic monitoring of CFA, supporting pharmacokinetic, bioavailability, and bioequivalence studies while incorporating complementary physicochemical, biological, and sustainability evaluations.

Protein EVI2A (Ecotropic viral integration site 2A) is a type 1 single pass membrane protein containing 236 amino acid residues. EVI2A is associated with several human diseases such as schizophrenia and numerous malignancies including breast and ovarian cancers. Protein 3D structure helps in understanding the molecular function of the proteins and their important role in the biological scenario if any. Till date no 3D structure of protein EVI2A has been reported in public or private databases. To fill that gap, we evaluated some computational models including comparative methods, de novo approach, ab initio and threading based methods. The multiple models, including 3D model from I-Tasser, afforded a good agreement of output and structural features. A complete model of protein EVI2A was validated by ProSa and Ramachandran analyses. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS package and active site prediction was carried out using CASTp. The predicted model could be a starting point for structural biologists, drug discovery groups, and scientific community to further enhance their studies. .