Jinnah University for Women

BACKGROUND:Cardiotoxic effects of doxorubicin (DOX) have been reported in cancer patients, which mainly stem from the production of reactive oxygen species (ROS). This oxidative damage contributes to myocardial injury, inflammation, and altered cardiac function. Lauric acid (LA) is a medium-chain saturated fatty acid present in coconut oil and is known for its antioxidant properties, making it a potential cardioprotective candidate against DOX-induced toxicity. OBJECTIVES:The present study was conducted to evaluate the cardioprotective effects of LA in a DOX-induced cardiotoxicity model by assessing biochemical, hematological, oxidative stress, histological, and molecular parameters in Wistar rats. METHODS:Wistar rats were divided into four groups (n = 6): control (Tween 20), LA (500 mg/kg), DOX (15 mg/kg) and LA + DOX groups. The rats were sacrificed and evaluated for hematological indices, lipid profile and cardiac biomarkers, including CK-MB, cardiac troponin I (cTnI) and LDH. Cardiac tissue was analyzed for MDA, CAT, SOD, histopathological assessment, gene expression (NF-κB p65, IL-6 and TNF-α) and cytokine levels (IL-6 and TNF-α) after the 14-day study period. Additional comparative evaluation between DOX and LA + DOX groups was performed to determine the extent of cardioprotection. RESULTS:The DOX reduced the body weight of rats, dysregulated hematological and lipid profiles and upregulated cardiac markers in serum. It also increased MDA levels, depleted CAT and SOD levels, altered cardiac cell histoarchitecture and elevated the gene and protein expressions of the cytokines. LA mitigated DOX-mediated cardiotoxicity by reducing oxidative stress and inflammation. LA also improved biochemical values, preserved myocardial structure and restored the antioxidant status compared to the DOX group. CONCLUSION:In conclusion, LA treatment protects the cardiac tissue against DOX-induced cardiotoxicity, as evidenced by its antioxidant and anti-inflammatory potential, supporting its possible therapeutic role.

BACKGROUND AND STUDY AIMS:Constipation is the most common gastrointestinal disorder, significantly imposing a substantial economic burden and impacting quality of life. The prevalence of constipation in Pakistan is 16.1%, indicating increased need of management within the Pakistani population. Therefore, this study aimed to develop a herbal candy and to evaluate the awareness, practices, usage patterns, and sensory perception of laxative for constipation management. PATIENTS AND METHODS:An observational cross-sectional study was conducted from 2024 to 2026. A total of 386 participants were selected for the study to determine the efficacy of laxative candy in relieving constipation. Approval for the study was granted by the Local Research Ethics Committee of the University. However, appropriate statistical tests were used to analyze the data collected. RESULTS:The study results revealed that 57.4 % of participants took treatment measures for constipation. For one month, 34.6 % used sennative and 59.4 % experienced constipation relief for 3-4 days. For its taste and effectiveness the product was positively rated. A total of 70 % participants found laxative candies more effective than previous treatments. However, 19.9 % participants reported side effects having abdominal pain and cramps. Whereas, 79.8 % reported the product safe. Further, the correlation analysis revealed strong associations laxatives (r = 1.000, p < 0.01), and 74.9 % were willing to recommend the product. CONCLUSION:For relieving constipation, sennative observed to be an effective and well-tolerated option. Further, research is needed to evaluate efficacy in broader populations and long-term safety.

INTRODUCTION:Clathrin protein (CP) plays a vital role in essential cellular processes, including endocytosis and signal transduction. Dysfunctions in CP have been associated with various diseases, including neurodegenerative disorders and cancer, making it a significant focus of biomedical research. METHODS:This study introduces Pred-CLGRUs, a novel computational framework for predicting Clathrin protein using Gated Recurrent Units (GRUs). Pred-CLGRUs utilize a Position-Specific Scoring Matrix (PSSM) in conjunction with the Discrete Cosine Transform (DCT) to enhance feature extraction while reducing noise and redundancy. A potential explanation for this mismatch could be that the Average Block-PSSM feature is derived from the Position-Specific Scoring Matrix (PSSM). The proposed model is trained using four deep learning architectures: GRUs, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs). RESULTS:The PSSM-DCT input enabled GRUs to achieve 93.33% accuracy, producing a Matthews Correlation Coefficient value of 0.86. Pred-CLGRUs enabled GRUs to achieve an accuracy of 93.33%, sensitivity of 94.44%, and specificity of 92.22%, while maintaining a Matthews correlation coefficient of 0.86. DISCUSSION:We began by calculating PSSM to detect evolutionary changes. The compression method named DCT was applied within each PSSM during the second implementation step. CONCLUSION:The predictive performance can be significantly enhanced if the model can incorporate heterogeneous types of biological information, such as structure, evolution, and function. We plan to design a novel prediction model that combines Capsule Neural Networks (CapsNet) with DeepWalk to obtain structural features.

Backgorund: Flavor retention in food is governed by complex macromolecular interactions among proteins, lipids, and carbohydrates. Although traditional thermal processing often degrades volatile profiles, nonthermal technologies are emerging as sustainable alternatives that better preserve flavor integrity. Scope and approach: This review integrates mechanistic insights into flavor matrix interactions with advances in nonthermal processing (high-pressure processing, pulsed electric fields, cold plasma, ultrasound) and high-resolution analytics (GC-MS, GC & times; GC-TOF-MS, NMR, SERS, and machine learning assisted flavoromics). Special emphasis is placed on molecular binding behaviors, structural modifications induced by processing, and predictive modeling of flavor release, and the role of these innovations in advancing sustainable food preservation. Key findings and conclusions: Nonthermal technologies significantly reduce the thermal degradation of volatiles while modifying protein conformation and lipid oxidation pathways, thereby enhancing flavor stability and clean label appeal. Recent studies demonstrate that controlled denaturation of proteins (e.g., beta-lactoglobulin, soy isolates) increases hydrophobic binding sites, improving retention of key aroma compounds. Encapsulation within protein-polysaccharide complexes further enables targeted release in plant-based and dairy matrices. Furthermore, the adoption of these nonthermal methods aligns with sustainability goals by reducing energy consumption and minimizing waste compared to conventional thermal operations. Machine learning models now allow accurate prediction of sensory outcomes from chemical data, advancing toward precision flavor design. Further efforts must bridge molecular insights with scalable green processing to meet consumer demand for natural, flavorful, and sustainable foods.

There is a global rise in antimicrobial resistance (AMR), hence the need for new drugs that surpass conventional antibiotic treatments. There are no doubts about the tremendous potentials of AuNPs as effective antimicrobial materials because of their physicochemical properties and compatibility with biomolecules. Nevertheless, designing AuNP-ligand based antimicrobials necessitates knowledge on the molecular basis of AuNPs. In this study, we have proposed a combined computational approach involving molecular docking, molecular dynamic (MD) simulation, artificial intelligence, and nano-bioinformatics that will guide the rational design of AuNP-based antimicrobials. This review focuses on the methods of docking for nanoparticles, particularly in parameterization of metal surfaces, ligand flexibility, and solvent considerations. The MD simulation of AuNPs is also covered in this work. The focus is placed on cutting-edge techniques such as nanotherapeutic design using artificial intelligence, personalized medicine using the concept of the digital twin, quantum mechanics and nano-immunoinformatics. Combining these advances in computer science, we offer a perspective on implementing in-silico design strategies for nanomedical AuNP-based anti-microbial agents. We highlight potential shortcomings concerning the lack of standardization, experimental verification, force fields' accuracy, long-term molecular dynamics simulation, database creation, and regulation. The future of anti-microbial therapy is within computational nanomedicine, while the use of docking, MD, and AI combined with experimental validation will provide rationally designed AuNPs to fight AMR.