Indian Journal of Biochemistry and Biophysics (IJBB)

Hepatocyte lipoapoptosis as a mechanism of chronic liver injury in obese mice model: a close link with fetuin-A

Guria S — 2026-01-20

Obesity is a major risk factor for type 2 diabetes, cardiovascular diseases, hypertension, dyslipidemia, non-alcoholic fatty liver disease and several cancers. Liver, a vital metabolic organ and a primary insulin target, is vulnerable to high dietary fat. In conditions of obesity, the hepatic secretory profile of hepatokines is altered. One of the major hepatokines, fetuin-A (FetA) is over-secreted in obesity-induced diabetes and promotes insulin resistance and inflammation. The present study was undertaken to examine hepatocyte lipoapoptosis in obesity and the potential involvement of fetuin-A in such lipotoxicity. We used obese diabetic mice models by feeding high fat diet for different durations, short-term D1 (4-week), moderate-term D2 (16-week) and prolonged-term D3 (32-week). For understanding FetA effect, in vivo and in vitro experiments were carried out. Blood parameters, whole-body glucose tolerance, hepatocyte lipid and ROS accumulation, TUNEL assay, and immunoblotting analyses were performed. ROS induced apoptosis progressively in lipid-laden hepatocytes of obese diabetic mice. In FetA-treated mice, hepatic oxidative stress and histopathological alterations were observed. Hepatocyte apoptotic markers were elevated by FetA in vivo and in vitro. Taken together, the data implied pivotal role of FetA in mediating hepatocyte lipoapoptosis and thereby, exacerbating liver damage associated with obesity and type 2 diabetes.

Revolutionizing drug discovery in lung cancer: An artificial intelligence (AI)-assisted framework for identifying target antigens for antibody-drug conjugates

Christanto A — 2026-01-20

Identifying appropriate target antigens continues to be a hindrance in the development of antibody-drug conjugates (ADCs), particularly for lung adenocarcinoma (LUAD). This paper presents a rule-based, Artificial Intelligence (AI)-assisted system that automates the processes of data harmonization, filtering, and prioritization within extensive transcriptome datasets. The TCGA-LUAD (20,530 genes) and GTEx lung (57,233 genes) datasets were harmonized; protein-coding, surface-localized molecules were evaluated for differential expression (Δlog2), housekeeping or essentiality, projected internalization, solubility, and subcellular accessibility. We ranked the candidate molecules by using a composite score, combining normalized Δlog2 and internalization category. We then curated the ranked molecules for their function, relevance to cancer, tissue specificity, and translational feasibility. These processes results in 647 high confidence surface molecule candidates. Several recognized ADC targets (CEACAM5, MET, LRRC15, MUC16) were included in this list, supporting internal validity. Five antigens (PROM2, DSG2, SEZ6L2, CDH3, and CDCP1) met the quantitative thresholds and translational criteria, with commercial antibodies available for testing. Thus, this reproducible, scalable workflow may reduce subjective bias, clarify decision logic, and offer a general template for antigen discovery in the oncology settings. We believe that this combination of scalability, automation, standardization and validation represents a substantial step compared to conventional expert-curated, manually filtered workflows.

Optical properties of DNA-polydimethylsiloxane double layered structures

Gasparyan L — 2026-01-20

To create nanoscale UV sensors and devices based on biomaterials, a detailed study of their optical properties and current transport mechanisms is necessary. In this paper, we study the optical properties of a bilayer structure containing DNA. The transfer matrix method is used. The analysis is performed considering both the real and imaginary parts of the refractive indices of both layers. Reflectance, transmittance, and absorptance spectra are determined. At the average values of the real and imaginary parts of the refractive indices of DNA absorptance passes through a maximum at 215-220 nm and a minimum at 250-260 nm. When considering the spectral distribution of the real and imaginary parts of the DNA refractive index, the absorbance shifts along the wavelength toward longer waves, passes through a minimum at 220 nm, and then through a maximum at 257 nm. The extremes of the absorptance shift to the long-wave region with an increase in the angle of incidence of the radiation. An attempt is made to explain the spectral behaviour of DNA absorptance and its dependence on the value of the imaginary part of DNA refractive index and the angle of incidence of the irradiation. Using the approximation method based on the spectral dependence of acceptance, a formula for the absorption coefficient of DNA is obtained. The acceptability of using the transfer matrix method for studying the optical parameters of organic materials with a complex refractive index is shown.

Phytochemical investigation and integrating network pharmacology of Trigonella foenum graecum Linn. for antidiabetic potential

Kumar P — 2026-01-20

In this present work, we have investigated phytochemicals and therapeutic action of Trigonella foenum graecum Linn., in diabetic mellitus condition by network pharmacology. In this study, we employed identified active compounds from Trigonella foenum graecum Linn., leaves extract were identify by HPLC and SwissADME, STRING, Cytoscape, KEGG and GO software used for purpose of protein-protein interaction, protein compound interaction and pathways enrichment analysis. These approach to elucidate the therapeutic mechanisms of Trigonella foenum graecum Linn., specifically targeting the advanced glycation end products PI3K-Akt signaling pathway implicated in DM pathogenesis. In the present study, we identified key diosgenin, trigonelline and quercetin bioactive compounds in Trigonella foenum graecum Linn. A total of 259 compound related targets and 17738 diabetic mellitus related targets with 224 interacting targets between them. PI3K, AKT, GSK3, mTOR, CDK, MDM2, CREB, IKK and Mcl-1 highly expressed targets. Out of the 224 targets that intersected, 40 targets were directly linked to PI3K-Akt signaling pathway. This work underscores importance of network pharmacology in unravelling complex mechanisms underlying the therapeutic effects of natural products, offering insights for the development of innovative treatments for DM and related complications. Trigonella foenum graecum Linn. might exert an antidiabetic effect by regulating PI3K-Akt signaling pathway.

In vitro anti-mitotic activity of Hibiscus rosa-sinensis extract: Network pharmacology, Molecular docking, MM/GBSA dynamic simulation, and DFT calculation

Thapa S — 2026-01-20

The limitations and adverse effects of current chemotherapeutic drugs make the search for novel, plant-derived anticancer agents a global priority. Despite its widespread application in traditional medicine, Hibiscus rosa-sinensis has not been extensively studied for anti-mitotic activity as a key cancer inhibitor. This integrated study included in vitro tests and computational modeling to evaluate Hibiscus rosa-sinensis extract's anti-mitotic activity. in vitro results showed a dose-dependent reduction of root development (up to 76.3%) and a significant drop in mitotic index (16.2% to 2.6%) at 400 µg/mL. Seven bioactive compounds identified by phytochemical screening have been examined using network pharmacology to determine their interaction with mitosis-regulating targets, including cancer cell proliferation-associated EGFR and AKT1 proteins. Quercetin-3,7-diglucoside bound strongly to EGFR (-9.6 kcal/mol) and AKT1 (-11.0 kcal/mol) in molecular docking, which was supported by free energy calculations and molecular dynamics simulations as stable interactions. DFT study confirmed the compound's electronic stability and reactivity. These findings suggest Hibiscus rosa-sinensis may be a promising anti-mitotic agent and worth further study for cancer therapy.

Enhancing diagnostic precision and accuracy in invasive lobular carcinoma through machine learning approaches

Priya LS — 2026-01-20

Invasive Lobular Carcinoma (ILC) is a type of breast cancer that forms in the lobules of the breast and is characterized by small, non-cohesive cells that invade surrounding tissues in a unique pattern. Invasive Lobular carcinoma mostly affects women compared to men. Various techniques are available to detect the presence of ILC, like mammography, Ultrasound, and MRI. Invasive lobular carcinoma is not present in a mass, making it difficult to detect ILC in some imaging techniques. Machine learning (ML) techniques are being used to improve the prediction and diagnosis of ILC. It involves data collection from electronic health records, imaging studies, and genomic data from Kaggle, and using different models, such as supervised learning and unsupervised learning, to predict ILC. In this current study, various algorithms have been used to predict and improve the accuracy and precision level of ILC diagnosis. Results found that Elastic Net and Logistic Regression have shown higher accuracy. ML is very useful for radiologists, oncologists, and patients in early-stage prediction of ILC, which is helpful in personalizing treatment plans.

Computational analysis of non-competitive enzyme inhibition in microbial and biomedical applications

Patel K — 2026-01-20

Understanding physiological responses within the human body requires a detailed investigation of enzyme kinetics, a fundamental aspect of biochemical processes. This study employs mathematical modeling to analyze enzyme behavior in living organisms, focusing on the impact of non-competitive inhibitors on catalytic activity. Enzyme inhibitors are known to modify enzyme-substrate interactions, either slowing down or completely halting catalytic reactions. Unlike competitive inhibitors, non-competitive inhibitors bind to enzymes in a way that prevents direct competition with the substrate, altering the reaction dynamics. This work formulates a system of nonlinear differential equations to predict product formation in enzyme-substrate-inhibitor interactions. By computing threshold values, the study examines enzyme efficiency, substrate conversion rates, and complexity in biochemical reactions. Stability analysis is conducted to determine asymptotically stable conditions for optimal enzyme-substrate reactions. Numerical simulations provide insights into the impact of inhibition on biochemical pathways, offering applications in drug design, enzyme regulation, and metabolic engineering. The findings contribute to medical biotechnology and bio-informatics by enhancing our understanding of enzymatic control mechanisms and their relevance in therapeutic interventions.

Biogenic synthesis and characterization of gold nanoparticles using Vitis vinifera seed extract: Exploring antioxidant, anti-inflammatory and anti-cancer activities

Kowsalya B — 2026-01-20

The synthesis of gold nanoparticles (AuNPs) using a newly developed green approach shows great potential because of its non-toxic and eco-friendly properties. In this study, biosynthesis of AuNPs was prepared using aqueous seed extract of Vitis vinifera. The presence of AuNPs was confirmed by UV-Vis spectroscopy, which revealed a peak at 550 nm, A Fourier-transform infrared (FTIR) study shows spectra that are identical to functional groups and X-ray diffraction (XRD) analysis demonstrated that AuNPs were of excellent purity with crystalline cubic structure phases in nature. Scanning electron microscopy (SEM) demonstrated the objects usual spherical shape and smooth surface, while Transmission electron microscopy (TEM) investigation demonstrated a spherical morphology with particle sizes ranging from 5 nm to more. Energy dispersive x-ray spectroscopy (EDX) confirmed the presence of Au elements at 2.2 keV. Also, evaluated antioxidant activity observed more effective and suggesting that they may possibly reduce damage produced by oxidative stress. The anti-inflammatory activity had a stronger effect, indicating their potential use in the treatment of inflammatory diseases. Further, the results of in vitro cytotoxicity activity of the samples against (HT-29) cell lines demonstrated the highest percentage of cell cytotoxicity. Therefore, all of these findings show that AuNPs produced through the plant-mediated method of synthesis may be cost-effective and environmentally safe, and that they may have interesting biological activity that could lead to biomedical application.

In silico screening identifies Daphnodorin-C as a potential inhibitor of the PMK1 pathway in the management of rice blast disease

Sambangi RR — 2026-01-20

Rice blast disease, caused by the fungus Magnaporthe oryzae, is one of the most devastating threats to global food security, especially in nations where rice is staple food. Despite the availability of chemical fungicides, the emergence of resistant fungal strains necessitate the search for alternative, natural and environment friendly solutions. In this study, we utilized in silico screening approaches to identify potential inhibitors targeting the PMK1 pathway, a critical regulator in the appressorium formation and fungal infection. Natural compounds from economically important plants of south Asia available in NPASS database were screened against the target PMK1 protein. In the screening Daphnodorin-C from Daphne odora was identified as a promising candidate, with a docking score of -10.9 kcal/mol. Molecular dynamics simulations (MDS) confirmed the stability of the Daphnodorin-C–PMK1 complex over 100 ns simulation period, with root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) values within permissible limits. Daphnodorin-C exhibited moderate toxicity against T. pyriformis and minnows, suggesting moderate effect on aquatic life if contaminated. It’s in silico safety profile shows no hepatotoxicity or skin sensitization, limited skin permeability, though with mild immune and respiratory effects. These findings suggest that Daphnodorin-C could be a promising lead compound for rice blast disease management.

Evaluating the efficacy of Tribulus terrestris on pH dependent uric acid crystallization under experimental condition

Krishnan AAS — 2026-01-20

The increasing incidence of kidney stone disease, particularly due to dietary and metabolic risk factors, highlights the urgent need for effective and natural therapeutic strategies. While most studies focus on calcium oxalate stones, uric acid stones have received comparatively less attention. Tribulus terrestris has been extensively documented in Ayurvedic literature for its effectiveness in preventing kidney stones, especially those formed from calcium oxalate. This study investigates the activity of Tribulus terrestris as a potential inhibitor of uric acid crystallization. The inhibition was evaluated by measuring the delay in induction time and reduction in mean crystal size. Crystallization of uric acid in the presence of aqueous Tribulus terrestris extract showed a notable delay in induction time and reduction in crystal size. At pH 4.5, increasing the extract concentration from 37.3 μg/mL to 149.2 μg/mL led to a 38-fold increase in induction time. A ~3-fold decrease in mean crystal size was observed when extract concentration increased from 0 to 37.3 μg/mL. Phytochemical analysis revealed the presence of phenols, flavonoids, saponins, and sterols. SEM and PXRD confirmed reduced crystallinity, while EDS analysis showed altered C, N, and O composition, suggesting phytochemical incorporation into the uric acid lattice, resulting in inhibition of crystallization.

CSIR NIScPR — 2026-01-20

IJBB