The search for effective antiviral agents against dengue virus (DENV) remains a global priority due to the absence of specific therapeutics. The present study evaluates selected phytochemicals from Carica papaya, Moringa oleifera, and Tinospora cordifolia using an in-silico molecular docking approach to identify compounds with potential dengue-related inhibitory activity. Phytochemicals were screened based on drug-likeness and ADME properties, and their disease-associated gene targets were identified through database mining. Venn analysis identified plasminogen (PLG), a host protein implicated in dengue-associated thrombocytopenia and vascular complications, as a common target linked to both dengue pathophysiology and the selected phytochemicals. The human plasminogen protein (PDB ID: 8UQ6) was selected for molecular docking using PyRx, and interaction analyses were performed with BIOVIA Discovery Studio. Among the evaluated compounds, carpaine from Carica papaya exhibited the highest binding affinity (−9.2 kcal/mol), followed by hesperetin from Moringa oleifera (−8.3 kcal/mol), whereas phytochemicals from Tinospora cordifolia showed comparatively lower affinities. These findings suggest that Carica papaya and Moringa oleifera contain phytochemicals capable of interacting with dengue-associated host molecular pathways. However, as molecular docking provides predictive insights only, these results are limited to in-silico observations and require further in vitro, in vivo, and clinical validation.
Sev, a conventional Indian snack from chickpea (Cicer arietinum) flour, spices, and oil-frying, was innovated here as a baked, nutrient-dense variant with chickpea and jackfruit seed (Artocarpusheterophyllus) flours. Eight formulations were tested with varying proportions of these flours. Sensory evaluation using a nine-point hedonic scale by 25 untrained members showed formulation T2 (70:30; chickpea flour: jackfruit seed flour) received the highest score and was selected for analysis.Physical properties of the optimized baked nutrisev (T2) including weight, thickness, width, pH, texture, and colour were evaluated. Functional properties of both flours were determined for bulk density, water and oil absorption, foam capacity, and stability. The proximate composition showed moisture (4.2%), ash (2.8%), energy (217.8 kcal), carbohydrate (17.1 g), protein (25.1 g), fat (2.4 g), fibre (3.25 g), calcium (381.16 mg), phosphorus (202 mg), and iron (5.07 mg).Microbial analysis showed no yeast or mould growth, with a bacterial count of 5 × 10⁴ CFU/g. Antioxidant activity was approximately 71%, with total phenolic and flavonoid contents of 0.078 mg/g and 0.50 mg/g. Shelf-life evaluation was conducted over 45 days, comparing storage in LDPE and airtight containers. Cost analysis showed the baked nutrisev (₹17) was more economical than conventional deep-fried sev (₹20), providing a cost-effective, nutrient-dense, healthier alternative to traditional fried snacks.
Ten species of Cucurbitaceae Juss. were morphometrically analyzed based on their leaf characteristics using taxonomic analysis to provide information about the links between these types of plants. Numerical characteristics, including leaf length, petiole length, leaf breadth, and lamina length, are positively correlated with the resolved taxonomic relationships of different species within the same genus, according to taxonomic component analysis. A key factor in uniting the species within a genus is the principal component analysis results of five quantitative characters based on a similarity matrix, which demonstrate a significant correlation between leaf length and leaf breadth, leaf base nerve number, and the ratio of leaf lamina length to petiole length. However, they also significantly separate the species from one another. Morphometric features supported the Cucurbitaceae's current categorization. Following the extraction of the characters that significantly influenced the similarity between the chosen Cucurbitaceae species, it further displays the component matrix of Coccinia grandis (L.) Voigt, Ctenolepis garcinii (Burm.f.) C. B. Clarke, Cucumis melo L., Cucumis setosus Cogn., Diplocyclos palmatus (L.) C. Jeffrey, Momordica balsamina L., Momordica dioica roxb ex willd, Mukia maderaspatana (L.) Roem., Solena amplexicaulis (Lam.) Gandhi and Trichosanthes cucumerina L.
The spur growth of the pharmaceutical industry took a digital transformation face with help of artificial intelligence (AI) machine learning (ML). These technology is not only taken keen role in analytical tool but also help in developing drug formulation manufacturing aspects. A tremendous shift was observed between 2020 and 2025 AI and ML have taken rapid role in application enabling in developing predictive model right from the primary physical chemical properties optimization of pharmaceutical parameters and technology driver of automation of process control. This review article put emphasis on recent existing advances alongside highlighting the technology in making more neural connections in network and support the vectors system in machines. The generated models were helped in redefining the design and quality. These technologies help in innovation in predicting excipient compatibility developed a simulator resolution profile finally helping to develop robust formulation part improving overall reproducibility and efficacy. The other technology such as Quantum machine learning federal learning and twin technology have made their own place in creating a new era of collaborative and autonomous formulation development.
Diabetes mellitus (DM) is a metabolic disorder marked by chronic hyperglycemia and characterized by micro- and macrovascular complications. Oxidative stress, a state of redox imbalance, is an important etiological factor in the pathophysiology of insulin resistance, β-cell glucotoxicity, inflammation, and cardiovascular complications of diabetes, resulting from the accumulation of reactive oxygen and nitrogen species (ROS/RNS). Chronically elevated glucose levels lead to the generation of reactive oxygen species (ROS) through glucose auto-oxidation, advanced glycation end-product formation, and activation of the polyol pathway and protein kinase C (PKC) signaling. Insulin signaling is also compromised by oxidative stress through inhibition of insulin signaling pathways, suppression of GLUT4-dependent glucose uptake, and stimulation of stress kinases and inflammatory pathways. Pancreatic β-cells, owing to their low antioxidant defense, are particularly vulnerable to oxidative damage, ultimately leading to their functional exhaustion. The purpose of this review is to discuss the contribution of exogenous and endogenous antioxidants in maintaining redox homeostasis, protecting β-cells, improving insulin sensitivity, modulating inflammation, and preventing vascular complications. Emerging strategies such as Nrf2-mediated antioxidant signaling, nano-antioxidants, and targeted antioxidant delivery systems show promising potential for improving glycemic control and reducing diabetes-related complications through redox-sensitive therapeutic approaches.
The main dietary triglyceride hydrolyzing catalyst, and a long time pharmacological parameter of decreased dietary caloric intake, is pancreatic lipase (PL). Recent progress in structural biology, high resolution crystalography and computational models has given a new understanding of the catalytic triad of PL and interfacial activation, lid dynamics and stabilization of colipase dependent. These mechanistic underpinnings have facilitated more rational search of the varied classes of inhibitors including covalent β-lactones to reversible natural products (flavonoids, aurones, chalcones) and contemporary synthetic scaffolds like thiazolidinedione, triazole, and multi-target hybrid chemotypes. The mechanism by which the inhibitors interact with the hydrophobic acyl-binding tunnel, oxyanion hole, and aromatic platform around Ser152 is now understood using quantitative structure-activity correlations, molecular docking, molecular dynamics simulations, and pharmacophore models. The new approaches to medicinal chemistry, such as allosteric inhibition of lid movement, partial inhibition to enhance the safety, the investigation of non-2-lactone electrophiles, and AI-assisted scaffold discovery provide avenues to effective, yet safer inhibitors. The enzymatic mechanism, structural biology, SAR trends, and computational methods have been incorporated in this review to present a single framework in designing next-generation pancreatic lipase inhibitors.
Heavy metal contamination represents a persistent global environmental challenge due to its non-biodegradable nature, ecological toxicity, and severe risks to human health. Microbial bioremediation has emerged as a sustainable alternative to conventional physicochemical methods, with Lysinibacillus species gaining increasing attention for their exceptional metal tolerance and detoxification capabilities. The present study provides a comprehensive bibliometric and network-based evaluation of global research trends related to Lysinibacillus-mediated bioremediation of heavy metals. Bibliometric data were retrieved from the Dimensions AI database and analysed using VOSviewer to identify publication trends, leading countries, institutions, journals, and collaborative networks. To complement this analysis, a curated gene-species interaction network was constructed and analysed in Cytoscape to elucidate functional relationships among key microbial taxa and metal-resistance genes. The results reveal a sharp increase in research output since 2017, with India, China, and the United States emerging as major contributors. Network analysis identified Lysinibacillus sphaericus as a central and multifunctional node strongly associated with critical metal-resistance genes such as merA, arsB, and czcC, as well as pathways involved in organic pollutant degradation. The integrated findings highlight the ecological and biotechnological significance of Lysinibacillus, particularly L. sphaericus, as a keystone organism for designing effective synthetic microbial consortia for complex heavy metal remediation strategies.
The present study was conducted to evaluate the impact of different seed pretreatment agents on germination and seedling growth in Lablab purpureus (L.) Sweet (variety Kokan Bhushan), under in vitro saline conditions. Five different pretreatments were provided to the seeds: distilled water, NaCl (100 mM), NaCl (500 mM), KCl (100 mM) and KCl (500 mM). Untreated seeds served as control. The pretreated and untreated seeds were germinated in vitro on half strength Murashige and Skoog’s basal medium supplemented with graded series of salt stress (0, 60, 80 and 100 mM). The germinated seedlings were maintained in saline stress for six weeks. The results demonstrated that pretreatments with distilled water, NaCl (100 mM) and KCl (100 mM) significantly enhanced the germination percentage, shoot and root length, fresh and dry weight of the seedlings both in presence and absence of salinity stress. Pretreated seeds also exhibited improved seedling vigour index and salt tolerance indices. Seed pretreatment with KCl (100 mM) proved most effective followed by NaCl (100 mM) and distilled water. This study substantiates that KCl and NaCl seed pretreatments optimized under in vitro conditions, represent a simple, cost-effective and practical approach in Lablab purpureus (L.), for upscaling to salinity affected fields.
India's rich biodiversity is complemented by its vast knowledge of medicinal plants and traditional practices, with plant-based compounds having been harnessed as therapeutic agents for centuries. Cancer, a leading cause of global mortality, is fundamentally driven by DNA abnormalities that disrupt the function of key metabolic proteins. Ras proteins show a vital role in cell signalling, regulating cell proliferation and apoptosis, but mutations in these proteins may results in uncontrolled cell growth. This study investigated the anticancer potential of phytochemicals against mutated H-RAS, N-RAS, and K-RAS (RAS proteins) using molecular docking analysis. Aegle marmelos, rich in over a hundred phytochemicals with reported anticancer properties, was the source of 89 molecules screened as ligands. Seventy-eight molecules exhibited strong binding affinity (∆Gbind ≤ -5 kcal/mol) for H-Ras, 74 for K-Ras, and 77 for N-Ras, with α-amyrin and lupeol emerging as top leads for H-Ras, betulinic acid, β-amyrin, and lupeol for K-Ras, and βand α-amyrins for N-Ras. α-Amyrin emerged as a promising lead molecule against mutated Ras proteins, showing strong potential against H-Ras and N-Ras, and comparable efficacy to top leads against K-Ras. To overcome the challenges observed in drug likeness prediction connected with alpha amyrin, nano emulsion formulations strategies can improve its bioavailability and efficacy. Further validation of the lead molecules' anticancer efficacy requires comprehensive preclinical and clinical studies to confirm their biological activity.
Magnesium (Mg) alloys have gained considerable attention as potential materials for biodegradable orthopaedic implants because of their mechanical compatibility with natural bone and excellent biocompatibility. Despite these advantages, their rapid corrosion in physiological environments restricts their long-term structural integrity. In the present study, Mg–4Zn–0.2Ca–xY (x = 3, 6, 9, and 12 wt.%) alloys were developed to evaluate the influence of Y (yttrium) content on microstructural characteristics, mechanical performance, and degradation behaviour. The alloys were synthesized using the casting technique and systematically examined through microstructural analysis, mechanical testing, and corrosion evaluation in simulated body fluid. The addition of Y promoted noticeable grain refinement and enhanced phase distribution, leading to improved mechanical properties and corrosion resistance. Among the investigated compositions, the Mg–4Zn–0.2Ca–6Y alloy demonstrated a more homogeneous microstructure along with superior strength and hardness compared to other variants. Furthermore, this composition exhibited a relatively slower and more uniform degradation rate, indicating improved stability under simulated physiological conditions. The outcomes of this study suggest that controlled incorporation of Y in Mg–Zn–Ca alloys can effectively tailor their structural and degradation properties. Therefore, the Mg–4Zn–0.2Ca–6Y alloy shows strong potential as a biodegradable material for orthopaedic implant applications requiring balanced mechanical reliability and controlled corrosion behaviour.
The study covers the Analytical Quality by Design driven method for the determining Fimasartan, an antihypertensive drug. Critical Quality Attributes, namely, retention time, peak area and theoretical plates, were identified and assessed for method performance. An Ishikawa diagram along with Failure Mode Effects Analysis were employed to analyze risk and identify variables having a significant impact on method performance as well as reliability. Critical Method Variables, namely, mobile phase composition, flow rate, and detection wavelength, were systematically optimized. The chromatographic conditions after optimization were methanol proportion in mobile phase: 79.59%v/v, flow rate of 0.8 mL min-1 and detection wavelength 264 nm. These conditions resulted in a response time of 5.5 min., with adequate system suitability, comprising adequate theoretical plates and peak symmetry. Validation of optimized method was accomplished using the ICH Q2 (R1) guidelines with consideration of recent revisions in Q2(R2)., displaying high linearity, accuracy, precision, and robustness. Precision data revealed that %RSD values that were well within acceptable ranges, indicating reproducibility of the method. Integration of Analytical Quality by Design allowed for a comprehensive understanding of method variability and control techniques. Such techniques can be utilized for chromatographic estimation of other drugs.
Silver nanorods are widely employed in a variety of commercial, industrial, and biological applications. Although silver nanorods have several uses, nothing is known about their toxicity. This research aimed to evaluate the laboratory-based harmful effects of silver nanorods measuring 10 nm and 25 nm following exposure to human lung A549 cell lines. The cell viability was tested using tetrazolium assay and lactate dehydrogenase leakage assays . The oxidative stress induction markers such as, lipid peroxidation, glutathione levels and caspase-3 levels were estimated. And the inflammatory mediator interleukin-8 was estimated using biochemical assay kit. Cells exposed to silver nanorods displayed reduced cell viability and increased lactate dehydrogenase leakage, indicating cytotoxicity. Additionally, exposure to silver nanorods causes elevated levels of the inflammatory mediator interleukin-8, decreased levels of glutathione, and increased levels of lipid peroxidation and caspase-3, which indicate oxidative stress. When compared to 25 nm silver nanorods and quartz, a recognized toxicant, the 10 nm silver nanorods showed higher toxicity towards all metabolic parameters. Finally, our results suggesting that the toxicity of silver nanorods was dependent on their concentration and size.
Eye infection is a major health issue in the world, and it is considered a major cause of preventable visual impairment. Various eye infection therapies are available, but the delivery of these drugs is a challenging task due to anatomical and physiological limitations, leading to poor drug bioavailability and therapeutic response. The traditional eye infection drug delivery system has limitations, such as precorneal loss of the drug, frequent instillation, and poor patient compliance. Recent developments in ophthalmology have introduced various novel eye infection drug delivery systems, such as nanoparticles, niosomes, in situ gelling systems, contact lens-based drug delivery systems, micro-needle technology, and eye infection inserts and implants, including stimuli-sensitive systems. Along with these developments, artificial intelligence (AI) has been integrated into ophthalmology, enabling data science approaches for eye infection therapy, such as formulation optimization, eye infection pharmacokinetics, and selection of anti-microbial agents. Significant potential lies in AI-assisted modelling and machine learning techniques in the development, efficiency, and application of advanced drug delivery systems for the eyes. This review aims to critically discuss the latest developments in advanced drug delivery systems for the eyes in the management of eye infections and the role of AI in improving the accuracy of diagnosis, therapeutic targeting, and therapeutic outcomes. Overall, the application of advanced drug delivery systems and AI has immense potential in improving therapeutic outcomes in eye infections.
Ovarian cancer is among the deadliest gynecological cancer, primarily largely attributed to delayed clinical detection, significant metastatic abilities, coupled with reduced responsiveness to chemotherapy. These challenges have sparked interest in bioactive compounds derived from plants as potentially safer and more effective treatments. Diosgenin and dioscin, both steroidal saponins sourced mainly from Dioscorea species, have shown promise as anticancer agents with diverse mechanisms of action. Existing experimental studies assessing the anticancer potential of diosgenin and dioscin in ovarian cancer are outlined, with focus on their biological effects and underlying mechanisms. Studies conducted in vitro and in vivo indicate that diosgenin can inhibit the growth, migratory activity, and invasive capacity of ovarian cancer cells while promoting programmed cell death and cell cycle arrest by modulating essential molecular pathways, including PI3K/Akt/mTOR, PTEN, NF-κB, and Bcl-2 family proteins. Likewise, dioscin demonstrates notable anticancer effects mediated through the induction of apoptosis and autophagy, reducing metastasis, reversing multidrug resistance, and improving chemosensitivity. Advances in nanocarrier delivery systems have also enhanced the bioavailability and effectiveness of these compounds. Despite the promising experimental results, clinical validation is still lacking. Overall, diosgenin and dioscin are promising natural options for ovarian cancer treatment, highlighting the need for additional studies on their mechanisms and clinical trials to support their development as viable anticancer therapies.
The present work aimed to develop a bioanalytical RP-HPLC technique for the simultaneous quantification of brexpiprazole (BREX) and fluoxetine hydrochloride (FLX) in human plasma. The chromatographic separation was performed on a Phenomenex C18 column (250 mm × 4.6 mm, 5 µm). The chromatogram was recorded at 224 nm. As an internal standard (IS), fimasartan was used. The mobile phase was composed of acetonitrile and 0.05% orthophosphoric acid in water (35:65), and with a flow rate of 1.0 mL/min. Separation was carried out in an isocratic mode. The protein precipitation method was used to isolate the analytes from the spiked plasma matrix. The retention times (RT) of BREX, IS, and FLX were 3.69, 4.91, and 6.82 min, respectively. With regression coefficients r² = 0.9996 and 0.9993 for BREX and FLX, respectively. The developed method demonstrated an acceptable linearity in the concentration range of 1.40–56 µg/mL. The extraction recovery of BREX was in the range of 82.57% to 85.26%, and for FLX, 91.74% to 94.05%. The stability studies showed no evidence of analyte degradation. Hence, the developed method can be used for the simultaneous quantification of BREX and FLX in biological samples.
Diabetes mellitus is a multifactorial metabolic disease that involves multiple complex molecular interactions ranging from genetic predisposition, proteomic alteration, and metabolic disturbance. Traditional single-omics approaches have been unable to capture the systemic landscape of diseases, including the understanding of disease onset, progression, heterogeneity, and response to treatment. Recent progress in multi-omics integration with the aid of artificial intelligence (AI) and machine learning (ML) has made biomarker discovery a revolution by mapping interconnected biological networks. This review provides a synthesis of the current state of progress in genomics, transcriptomics, proteomics, and metabolomics integration using AI-driven computational frameworks for the discovery of predictive, diagnostic, and prognostic biomarkers in diabetes. We discuss analytical pipelines, tools of network biology, deep learning architectures, the issues of clinical translation, ethical concerns, and future aspects of precision diabetology.
Colorectal cancer (CRC) is among the most common and lethal cancers approximately 40–50% of colorectal cancers, >90% of pancreatic ductal adenocarcinomas, and ~30% of lung adenocarcinomas harbor activating KRAS mutat approximately 40–50% of colorectal cancers, >90% of pancreatic ductal adenocarcinomas, and ~30% of lung adenocarcinomas harbor activating KRAS mutations ions worldwide. Mutations in KRAS play a central role in driving tumor progression and resistance to targeted therapies. Natural compounds, particularly berberine derived from Tinospora cordifolia, have been reported to exert anticancer effects; however, their direct interaction with KRAS has not been well established. This study explored the therapeutic potential of berberine against KRAS using molecular docking and In-silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analyses. Docking simulations performed with CB-Dock revealed a strong binding affinity, supported by a Pro SA-web Z-score of –6.9 and favourable binding interactions. Pharmacokinetic evaluation through Swiss ADME and pkCSM indicated high intestinal absorption (97.15%), moderate blood–brain barrier penetration, good oral bioavailability, and an acceptable toxicity profile. Structural validation using the Ramachandran plot confirmed the reliability of the protein–ligand complex, with 96% of residues located in the most favored regions. Collectively, these findings highlight berberine as a promising lead candidate for KRAS-targeted therapy in colorectal cancer, warranting further preclinical validation.
Tafenoquine (TF), a quinoline-derived antimalarial compound, had been utilized both as a chemoprophylactic agent and in combination therapies such as with artesunate. Despite its potent efficacy against Plasmodium falciparum, its clinical application had been restricted due to reports of neurotoxicity and adverse neuropsychiatric reactions. Previous computational investigations indicated that TF functioned as a dual cholinesterase inhibitor with a high affinity for protein targets, findings that were subsequently corroborated through in vitro enzymatic inhibition studies. Malaria continued to represent a significant global health challenge, particularly within tropical and subtropical regions, owing to the emergence of Plasmodium falciparum and Plasmodium vivax strains resistant to conventional antimalarial drugs. Targeting essential parasitic enzymes, including aspartic proteases, had been recognized as a promising strategy for discovering novel chemotherapeutic candidates. In the present study, an in silico molecular docking approach was employed to examine a series of Tafenoquine analogues as potential inhibitors of critical Plasmodium proteins. Among the fourteen designed derivatives, TF4A, TF8A, TF3A, and TF1A exhibited stronger binding affinities than the parent compound Tafenoquine, with docking energies of −8.1, −8.5, −8.0, and −8.2 kcal/mol, respectively. Additionally, ADMET evaluation and drug-likeness analyses demonstrated that these analogues possessed acceptable pharmacokinetic characteristics and conformed to Lipinski’s rule of five, suggesting good oral bioavailability and favorable physicochemical behavior. Collectively, the computational findings indicated that halogen-substituted Tafenoquine analogues, particularly TF8A and TF1A, established stable interactions within the catalytic pockets of target proteins and exhibited enhanced binding energies. Therefore, these derivatives could be considered as promising lead scaffolds for future antimalarial drug development. Nevertheless, further in vitro and in vivo investigations would be necessary to validate their efficacy, metabolic stability, and safety profiles.
Kalanchoe pinnata (Lam.) Pers.: a medicinal plant traditionally used for various ailments, has gained attention for its curing various infectious diseases. This plant being a succulent herb with various medicinal applications for several diseases especially cancer have shown its mechanism by several approaches. This herb contains gallic acid, caffeic acid, coumaric acid, stigmasterol, campesterol, and other elements; it is these phytochemicals that participate in the regulation of cell proliferation, regulation, oxidative stress, and apoptosis. They also have the potentiality to act as a drug agents for tumours resistance therapy. Cancer is a global health problem that is associated with the life style changes. Out of which pancreatic cancer represents the most lethal among all; despite being all the advances made in these years. This present work assessed the effect of Kalanchoe pinnata (Lam.) Pers.: extract against the human pancreatic cancer cell line PANC-1 using flow cytometry based analyses. PANC-1 cells were treated with the increasing concentrations of Kalanchoe pinnata (Lam.) Pers. :( 150-450µg/ml). Cell viability were examined using the MTT assay while the cell cycle and apoptosis were determined using the flow cytometry. The results indicated the significance in the increase of cytotoxicity; decrease in the cell viability with the change of concentration. Flow cytometric analysis were carried out for the induction of the apoptosis which demonstrated that the extracts were capable for the induction of cell cycle arrest. The Study outcomes suggests that Kalanchoe pinnata (Lam.) Pers.: exhibits potent in vitro anticancer activity against PANC-1 cells through dose-dependent induction of apoptosis.
By permitting the simultaneous separation, identification, and quantification of complex mixtures, hyphenated analytical techniques—combinations of chromatographic separation with spectroscopic or spectrometric detection—have revolutionized contemporary pharmaceutical analysis. Drug discovery, impurity profiling, bioanalysis, and quality control have all seen a rise in the use of techniques including LC-MS, GC-MS, LC-NMR, CE-MS, and SFC-MS within the past 20 years. This review critically assesses their analytical performance, limitations, and recent advances, such as miniaturized systems, green chemistry techniques, and AI-driven data processing, whereas the literature currently in publication emphasizes their instrumental configurations and routine applications. In addition to improving existing pharmaceutical workflows, these developments are opening the door for future integration with biologics, nanomedicine, and precision medicine. Views on cost-effective instrumentation, regulatory harmonization, and the changing role of hyphenated technologies in shaping. The article concludes with perspectives on regulatory harmonization, cost-effective instrumentation, and the evolving role of hyphenated technologies in shaping sustainable and personalized healthcare solutions.
