Proceedings of Women in Academia, Research and Management for Work-life Initiatives for Sustainable Health & Empowering Safety (WARM-WISHES 2026)

In traditional healthcare system, medicinal plants are widely used for curing various diseases by utilizing different parts of plants (leaves, steam, flower, roots and seeds). Ocimum sanctum (Holy basil) is recognized as queen of herbs that is bestowed with many therapeutical properties and used for treating several ailments such as cancer, diabetes, anemia, diarrhea and bacterial infections. The present study reviewed the chemical composition of essential oil and solvent-soluble extracts and pharmacological activities of O. sanctum. The bioactive components identified from oil includes camphene, sabinene, thymol, carvacrol, eugenol ocimene, with eugenol being the most active constituents responsible for biological actions. The different extracts of Holy basil contain fatty acids (oleanolic acid, stearic acid, sinapic acid), flavonoids (luteolin, orientin, quercetin), tannins, saponins that are embarked with several therapeutic potential as anti-cancer, antibacterial, antioxidant, anti-diabetic agents. The pharmacological properties reported in the present review confirm the curative measures of O. sanctum and the studies supported the use of this plant for human and animal pharmacotherapy. However, despite the widespread pharmacological activities of O. sanctum, experimental human clinical trials are needed for establishing the effective and safe doses for treating various diseases.

Currently, clinical bacteriology finds itself at the crossroads, as increasingly more advanced diagnostic techniques such as MALDI-TOF mass spectrometry or whole-genome sequencing (WGS) become available and popular. However, despite these technological innovations, this review highlights the significance and value of classic biochemical analyses, particularly those involving the DNase test, in the modern context. The classic DNase test continues to prove its reliability by demonstrating high sensitivity and specificity values ranging from 96% to 99% regarding the detection of Staphylococcus aureus. Although the use of contemporary technologies, e.g., MALDI-TOF, allows obtaining the diagnosis faster, these tools may fail to achieve the level of accuracy provided by traditional techniques, especially in case of rare species of bacteria or lack of data in the spectrum database. Additionally, modern automated devices such as VITEK 2 operate based on the same biochemical principles as the classic approaches; thus, their implementation represents further development of the latter.

As for developing nations, classic biochemical analysis still represents the most cost-effective and sustainable method for conducting the diagnostics without requiring sophisticated equipment such as a spectrophotometer. Overall, this review reveals that the "tiered" strategy combining the approaches discussed above should represent the cornerstone of the diagnostics of the field.

Green tea, from Camellia sinensis, is a source of bioactive polyphenols, mainly catechins with antibacterial properties. Various types of catechins include epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin (EGC) and epicatechin (EC), among which EGCG is the most abundant and bioactive. This review summarises the antibacterial and antibiofilm activities of these catechins against some clinically important Gram-positive and Gram-negative bacterial pathogens.

Catechins have been studied for their broad - spectrum antibacterial property through multiple pathways. For example, they can interfere with DNA and RNA synthesis, block key enzymes in bacterial metabolism and replication, disrupt the bacterial cell membrane, causing loss of intracellular components that are important to maintain bacterial viability.

Studies show that catechins disrupt quorum-sensing systems, which lowers the production of virulence factors, biofilm formation, and bacterial communication. Moreover, catechins cause oxidative stress in bacterial cells by producing reactive oxygen species (ROS), which damages bacterial cells, resulting in cell death.

Polyphenols found in green tea also demonstrate synergistic interactions with conventional antibiotics, enhancing their efficacy against multidrug-resistant (MDR) strains such as Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. When it comes to addressing antibiotic resistance mechanisms, this synergy is especially intriguing.

Polycystic ovary syndrome (PCOS) is a complex endocrine–metabolic disorder characterized by hyperandrogenism, ovulatory dysfunction, insulin resistance, and chronic low-grade inflammation. Although genetic predisposition contributes to disease susceptibility, accumulating evidence suggests that epigenetic modifications play a critical role in modulating gene expression in PCOS without altering the underlying DNA sequence. DNA methylation, histone modifications, and microRNA dysregulation have been implicated in abnormal steroidogenesis, impaired insulin signaling, and disrupted folliculogenesis. Concurrently, ferroptosis—an iron-dependent, lipid peroxidation-driven form of regulated cell death—has emerged as a potential contributor to ovarian dysfunction. Increased oxidative stress, altered iron metabolism, and reduced antioxidant defense mechanisms observed in PCOS create a cellular environment conducive to ferroptotic damage, particularly within granulosa cells essential for follicular maturation.

Recent studies suggest that epigenetic alterations may influence the expression of key ferroptosis-regulating genes, including GPX4, SLC7A11, and iron-handling proteins, thereby enhancing cellular susceptibility to oxidative injury. This integrated perspective proposes that epigenetic dysregulation may serve as an upstream mechanism that sensitizes ovarian tissue to ferroptosis, linking metabolic stress and reproductive dysfunction in PCOS. Understanding this epigenetic–ferroptotic interplay may provide novel insights into disease pathogenesis and identify potential biomarkers and therapeutic targets. This review synthesizes current evidence on epigenetic mechanisms and ferroptotic signaling in PCOS and highlights future research directions to clarify their mechanistic crosstalk.

The health care, environmental, and industrial infrastructures encounter challenging issues when dealing with biofilm forming opportunistic pathogens because of their persistence, antimicrobial resistance, and the capacity to coexist in a complex mixed microbial composition. Fast and non-invasion methods of detecting microbial content in biofilms are hence key towards successful monitoring and control. This paper suggests a hyperspectral morphotoxic profiling workflow that uses artificial intelligence and is applicable to analyzing mixed biofilm contaminations of opportunistic pathogens. The method combines hyperspectral imaging and artificial intelligence to identify and make sense of spectral-morphological features of microbial biofilms. The first stage entails the hyperspectral image data acquisition and preprocessing to eliminate noise and standardize spectral content, and the second stage is the dimensionality reduction to retain the most informative spectral data and reduce the complexity of the data. They then train a convolutional neural network to be able to learn discriminative spatial and spectral patterns related to the structures of microbial biofilms and presence of the pathogen in the processed images. The trained model is tested on the basis of automatic delivery of microbial signatures in intricate biofilm settings. To ensure ease of application and illustrate practicality, a simple web based interface will be built with flask framework where one will be able to upload images and get instant AI based predictions through an interactive solution. The current framework of hyperspectral imaging, artificial intelligence, and web-based implementation with a focus on microbial profiling in only a few seconds is discussed as a potential option, and hyperspectral imaging could be used in the context of environmental toxicology, monitoring infections, water quality assessment, and managing biofilm in industries.

The present study aimed to assess and compare the antimicrobial effectiveness of three natural plants - Neem (Azadirachta indica), Turmeric (Curcuma longa) and Guava (Psidium guajava) against one of the endodontic pathogens, that is, Enterococcus faecalis.  Broth-based assays were used to determine minimum inhibitory concentrations (MICs) by measuring optical density.

All the extracts used in the study showed antimicrobial potential, with Curcuma longa (turmeric) having the best inhibitory effect. Psidium guajava (guava), on the other hand, was relatively least active. The observed antimicrobial effects in the extracts are attributed to various bioactive compounds present in these plants. Neem leaves (Azadirachta indica) have azadirachtin, nimbin and nimbidin; turmeric rhizome has curcumin, while guava leaves are known to contain flavonoids such as quercetin and tannins.  The phytochemicals may have demonstrated antimicrobial effects against bacteria by disrupting cell wall integrity, inhibiting essential enzymes, and interfering with microbial metabolic pathways.

These findings suggest that turmeric extract has significant antibacterial potential and can serve as a natural alternative for managing endodontic infections. The article highlights the promise of plant extracts in developing new therapeutic protocols for dental procedures.

Cancer is a primary cause of morbidity and mortality worldwide and represents a substantial burden on health care systems. In 2020, there were more than 19.3 million new cases and over 10 million deaths from cancer annually. Despite the progress in traditional therapy, problems like as drug resistance, toxicity and recurrence are still present. Medicinal plant phytochemicals have attracted a lot of interest in the recent years as potential anticancer medications because of their structural diversity, biological activity and relatively low toxicity. The study was conducted to evaluate the anticancer efficacy of some phytochemicals extracted from five traditionally used medicinal plants i.e. Curcuma longa (turmeric), Azadirachta indica (neem), Allium sativum (garlic), Solanum lycopersicum (tomato) and Withania somnifera (ashwagandha) against important oncogenic protein targets by using a structure based molecular docking methodology. A preliminary screening of more than 60 phytochemicals was identified by a detailed literature study and validated by the IMPPAT 2.0 database. The proteins KRAS, EGFR, HER2, HRAS and CTNNB1 crystal structures were retrieved from the RCSB Protein Data Bank. The three-dimensional ligand structures were retrieved from the PubChem database. Molecular docking simulation was performed using CB-Dock2 platform. CB-Dock2 predicts the ligand-protein binding affinity by integrating cavity detection and scoring mechanism of AutoDock Vina. The maximum binding affinity was observed with azadiradione from Azadirachta indica with KRAS (Vina score = –15.3 kcal/mol) showing a somewhat persistent association in the active region of the protein. Other important chemicals included bisdemethoxycurcumin binding to HRAS (–9.4 kcal/mol), sitoindosidelx interacting with EGFR (–10.7 kcal/mol) and nicotiflorin targeting KRAS (–10.9 kcal/mol). Beta-carotene, isotretinoin, and zeaxanthin, carotenoid chemicals from Solanum lycopersicum, showed strong interactions with HER2. The results suggest that phytochemicals from medicinal plants like turmeric, ashwagandha and neem have great anticancer potential and might be lead molecules for developing novel medications.

Living therapeutics" is an innovative category of living organisms that can be engineered to prevent, detect, and treat human diseases. With the help of synthetic biology and genetic engineering, microbial cells with the ability to recognize disease-associated cues, synthesize drugs within the host body, and respond to complex biological environments have now been created. This review explores the fundamental approaches used for constructing engineered bacteria, such as the creation of synthetic gene circuits, selecting suitable hosts or chassis, and incorporating biosensing and biocontainment strategies. Finally, we discuss the primary applications of engineered bacteria with a focus on gastrointestinal disease treatment, cancer therapy, metabolic disease management, and infectious disease treatment. Despite all of this, however, there are numerous hurdles yet to be conquered, including issues related to safety, genetic stability, targeted delivery, and regulatory clearance. For bacterial systems to move from the lab bench to the bedside, certain parameters need to be taken care of. On the whole, genetically modified bacteria represent an exciting frontier within the field of precision medicine, with the capacity to revolutionize therapeutic strategies with highly selective interventions.

Polysaccharides sourced from marine environments (termed “marine sugars”)—notably fucoidan, chitosan, alginate, carrageenan, laminarin, and ulvan—exhibit natural bioactivity and beneficial material properties that make them attractive candidates for oncology drug delivery systems (DDS). Recent developments (2023-2025) indicate that receptor-targeted strategies, like P-selectin–fucoidan interactions, notably enhance transcytosis and promote deeper tumor infiltration. Simultaneously, stimuli-sensitive hydrogels and designed nanoparticles facilitate controlled, localized drug delivery, whereas innovative combined therapies effectively integrate these polysaccharide carriers with leading chemotherapy, radiotherapy, and immunotherapies. This review thoroughly integrates structural–functional relationships, key design strategies, significant preclinical results, production scaling, and vital safety considerations. Additionally, a strategic translational route for progressing marine-sugar DDS to clinical evaluation is outlined. Key translational challenges—including consistent material characterization, strict management of endotoxins and impurities, and clear structure–activity profiles for sulfation and molecular weight—are highlighted, along with suggested steps to connect laboratory findings to clinical applications.

Widespread declines in fertility and the rising burden of reproductive disorders have increased the search for environmental determinants of reproductive health. Ambient air pollution is now recognized as a critical contributor to reproductive deterioration. Beyond its established respiratory effects, pollutants such as PM2.5, polycyclic aromatic hydrocarbons, nitrogen oxides, and heavy metals exert profound biological effects on reproductive systems.

This systematic review synthesizes mechanistic evidence published between 2015 and 2025 to elucidate how air pollution influences reproductive health through oxidative stress, endocrine disruption, and epigenetic reprogramming. Studies were systematically retrieved from PubMed and Scopus, focusing on experimental and epidemiological data.

Previous evidence has demonstrated that exposure to pollutants causes an overproduction of reactive oxygen species, leading to mitochondrial dysfunction, lipid peroxidation and genomic instability in germ cells. Air pollutants also induce endocrine disruption by disturbing the hypothalamic–pituitary–gonadal axis, leading to hormonal imbalances that impact gametogenesis and reproductive function. Importantly, emerging data suggest that epigenetic mechanisms, such as aberrant DNA methylation and changes in microRNA expression, are responsible for persistent and possibly transgenerational effects of exposure.

These pathways act not in isolation but as an integrated network driving reproductive toxicity. Despite significant advances, important gaps remain in long-term human studies, dose-response relationships, and sex-specific mechanisms. Addressing these challenges will be vital for designing targeted interventions and informing policies to reduce the reproductive health burden associated with air pollution.

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is one of the leading causes of cancer-related mortality (death) worldwide. Its incidence is particularly high in areas where hepatitis B and C virus infections are endemic, as well as in patients with chronic liver disease and cirrhosis. HCC is often diagnosed at an advanced stage because of the lack of specific early clinical symptoms, resulting in limited treatment options and poor prognosis. Recent advances in genomics, transcriptomics and bioinformatics have provided insight into the molecular mechanisms of HCC initiation and progression. Analysis of differential gene expression and functional enrichment and pathways analysis have helped identify key regulatory genes, hub genes and dysregulated signalling pathways associated with tumour development. In this report, we discuss the gene expression profiles of HCC by means of high-throughput data analysis, particularly on the identification of possible biomarkers for early diagnosis and novel therapeutic targets. We also discuss integration of bioinformatics tools like STRING, Cytoscape and DAVID for construction of interaction networks, functional annotation of genes and visualization of enriched pathways. The findings highlight the necessity of molecular profiling for the understanding of the complexity of HCC and the progress of personalized medicine. Further studies are needed to translate these molecular insights into effective diagnostic, prognostic and therapeutic strategies with improved clinical outcomes in HCC patients.

Lactic acid bacteria (LAB) derived from indigenous dairy sources represent a valuable and largely untapped reservoir for the development of safe, effective, and cost-efficient probiotic formulations. The present study aimed to isolate, characterize, and functionally evaluate LAB from fresh and fermented milk samples obtained from local dairy producers in Lucknow, Uttar Pradesh, India. Using selective media — de Man, Rogosa and Sharpe (MRS) agar, milk agar, and M17 agar — five phenotypically distinct isolates designated M1 through M5 were recovered and subjected to a comprehensive battery of morphological, physiological, biochemical, and functional tests. All five isolates were confirmed as Gram-positive, catalase-negative, non-spore-forming organisms consistent with the defining characteristics of LAB. Bacilli-shaped isolates (M1–M3) were provisionally identified as belonging to the Lactobacillus group, while cocci-shaped isolates (M4–M5) were consistent with Lactococcus or Leuconostoc genera. Biochemical profiling revealed differential patterns of carbohydrate fermentation, citrate utilization, and exopolysaccharide (EPS) production ranging from 95.3 mg/L (M5) to 117.3 mg/L (M2). Probiotic functional assessment demonstrated that M1, M2, and M3 exhibited robust tolerance to simulated gastric acid stress (pH 3.0, 2 h) and bile salt concentrations of 0.3%, both critical determinants of gastrointestinal survivability. Haemolytic activity testing confirmed the absence of beta-haemolysis in all isolates, indicating a favourable safety profile. Antibiotic susceptibility profiling showed general sensitivity to erythromycin, clindamycin, cephalothin, and oxytetracycline, with intrinsic resistance to ofloxacin and co-trimoxazole — a resistance pattern characteristic of and acceptable in probiotic LAB. Collectively, isolates M1, M2, and M3, with M3 being most promising overall, meet the key criteria for probiotic candidacy. These findings advocate for systematic exploitation of indigenous dairy microbiota as sustainable platforms for next-generation probiotic innovations.

Amenorrhea is a condition where a woman does not get her period. This condition is often related to problems with the pituitary-ovarian axis. The pituitary-ovarian axis is a system that helps control when a woman gets her period. Amenorrhea is also associated with changes in the way that hormonal receptors work. These hormonal receptors, such as Estrogen Receptor alpha and Progesterone Receptor are important for a woman to have a menstrual cycle.

Hormone replacement therapy is one way that doctors treat Amenorrhea. However using hormone replacement therapy for a time can cause problems with a womans metabolism. Because of this doctors need to find ways to treat Amenorrhea.

This study looked at how certain phytoestrogens, such as Quercetin, Genistein, Diosgenin, Apigenin and Vitexin might be used to treat Amenorrhea. The study used computers to analyze the information, which is called a bioinformatics-based approach. The researchers wanted to see if these phytoestrogens, such as Quercetin, Genistein, Diosgenin, Apigenin and Vitexin could be used as an alternative, to hormone replacement therapy. The molecular docking analysis was performed using AutoDock Vina for determining their binding interactions with ESR1 (PDB ID: 1A52) and PGR (PDB ID: 1A28). Docking studies revealed that Quercetin exhibited the highest binding affinity for PGR at -9.74 kcal / mol, and Diosgenin exhibited the strongest binding with ESR1 at -8.88 kcal / mol.

The team did research on how these compounds work in the body and if they are safe. They used tools like SwissADME and pkCSM to see how well the body absorbs these compounds and if they are toxic. The results showed that these compounds are absorbed well in the stomach and are generally safe.

In the end the team thinks that these natural compounds could work like steroids to affect hormone levels and might be options to study for new treatments, for amenorrhea.

Chronic inflammation, oxidative stress, energy-defective intermediates homeostasis has been gradually recognized in their relevant diseases: metabolic disorders and autoimmune muscle disease including myositis. Forkhead box O3a (FOXO3a), an important transcription factor plays important role in regulating essential cellular processes such as stress response, apoptosis, metabolism homeostasis, and muscle protein turnover. Recent advances in computational biology have allowed for high-throughput screening of FOXO3a-targeting molecules in silico. Flavonoids and polyphenols are different subcategories of phytochemicals that gained attraction due to their remarkable antioxidant, anti-inflammatory, and multi-target abilities. At the same time, drug repurposing strategy is a cost-effective approach because it identifies new therapeutic uses for known approved drugs which have an established safety profile. Here, we review FOXO3a in metabolic and autoimmune muscle disease. It also emphasizes the use of in silico methods to screen phytochemicals and repurposed drugs that may target FOXO pathways as novel therapies for future pharmacologic development.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, neuronal degeneration, and memory impairment. Despite extensive research, effective disease-modifying therapies remain limited. Drug repurposing combined with computational approaches has emerged as a promising strategy for identifying potential therapeutic candidates with reduced development time and cost. The present study employed molecular docking to evaluate the interaction of ten bioactive compounds, Apigenin, Berberine, Curcumin, Huperzine A, Kaempferol, L-Theanine, Quercetin, Resveratrol, Withaferin A, and Sorafenib, with three proteins implicated in AD pathogenesis: Nicastrin, TREM2, and Cystatin C. Protein structures were generated using homology modelling through SWISS-MODEL, and docking simulations were conducted using AutoDock Vina. Binding affinities were evaluated using docking scores (kcal/mol). Among the evaluated compounds, Sorafenib and Withaferin A exhibited the strongest interactions across all targets, particularly with Cystatin C, showing binding energies of -7.7 kcal/mol and -7.6 kcal/mol respectively. Other phytochemicals such as Curcumin, Berberine, and Huperzine A demonstrated moderate binding affinities. These findings indicate that Sorafenib and Withaferin A may act as promising multi-target therapeutic candidates for Alzheimer’s disease. Further experimental validation through in vitro and in vivo studies is necessary to confirm their therapeutic potential.

Ferroptosis is defined as a non-apoptotic mechanism of regulated cell death, characterized by iron dependent accumulation of lipid hydroperoxides. Since its discovery, ferroptosis has grown as a fundamental biological process implicated in several pathological conditions including neurodegeneration, ischemia-reperfusion injury, inflammatory disorders and cancer. There are two major pathways for ferroptosis-the canonical and the non- canonical pathway. The canonical pathway involves the iron dependent accumulation of lipid hydroperoxides and is regulated by three major systems (1) the glutathione peroxidase 4 (GPx4)–glutathione system which serves as the major antioxidant defense against ferroptosis, (2) the Xc−  transporter system that provides raw material for fueling the GPx4 antioxidant system and (3) iron metabolism pathways that provide the catalytic substrate for lipid peroxidation via Haber -Wieiss and Fenton chemistry. The non- canonical pathways or the GPx4 independent pathways include the ferroptosis suppressor protein 1 (FSP1)–ubiquinol system, the GTP cyclohydrolase 1 (GCH1)–tetrahydrobiopterin system, and the DHODH mitochondrial defense. Thus, ferroptosis can be regarded as a multi-layered network affecting biology and progression of diseases. Ferroptosis is also regulated by transcriptional factors like the p53 tumor suppressor and Nrf2, heat shock proteins, and epigenetic modulators in a cell-type and context-dependent manner. The present review comprehensively delineates the molecular pathways of ferroptosis regulation, with emphasis on both traditional and emerging pathways, and discusses how this mechanistic understanding is being translated into therapeutic strategies for cancer sensitization and neuroprotection.

While tobacco and alcohol have been associated with Oral Squamous Cell Carcinoma (OSCC), there is increasing and worrying evidence suggesting a growing incidence in young, non-tobacco users; and, these tumours have been characterized as not having the 'classic' carcinogen-driven mutational signatures. This suggests that other molecular mechanisms may be responsible; DNA methylation, the canonical epigenetic modification controlling gene expression, has been identified as an early, stable, and possibly reversible event in oral carcinogenesis, however its specific role in young non-tobacco-related OSCC has not been fully synthesized. Here, we critically review the current evidence for DNA methylation changes in young non-tobacco-related OSCC. Increasing evidence suggests recurring promoter hypermethylation of tumour suppressor genes involved in cell cycle control, apoptosis, DNA repair and cell adhesion, alongside global epigenome-wide methylation reprogramming that does not seem dependent on tobacco status. Crucially, these methylation changes have also been identified in histologically normal adjacent mucosa and salivary DNA, thereby suggesting field cancerisation through an epigenetic mechanism as an early event in OSCC pathogenesis. The presented evidence indicates that, in young non-tobacco users, epigenetics may be the initiating factor in OSCC pathogenesis rather than a subsequent response to exogenous carcinogens. Underscoring the fact that age- and exposure-specific epigenomic syntheses have not been made to date, this review concludes that well-powered, age-stratified, epigenome-wide and longitudinal analyses are urgently required; better understanding of the specific DNA methylation signature for this patient group may lead to the identification of non-invasive diagnostic biomarkers, refined risk stratification and a new pathway for epigenetically mediated therapies in young-onset OSCC.

Every application, server, and network device generate logs whenever it performs an action, making these logs a valuable source of information for determining whether a system is in a normal state or has any fault that can cause failure. In large distributed systems, the volume of generated logs is extremely high, often reaching millions of log entries on a daily basis, which makes the manual analysis of these logs almost impossible. Traditional rule-based log management methods require engineers to anticipate all possible faults and their corresponding alerts, making them ineffective for detecting new types of anomalies according to the changing times. This paper presents a machine learning based log intelligence system that processes raw, unstructured log data and performs anomaly detection through a complete pipeline of log parsing, preprocessing, and finally model evaluation. The dataset used is collected from the LogHub repository and is called the HDFS dataset. The HDFS dataset contains about 11 million logs. In this project we have used 3 approaches for log analysis: Isolation Forest (unsupervised algorithm), Random Forest (supervised algorithm), and LSTM (deep learning). The model is trained on each of the methods listed above and then is evaluated using the metrics of precision, recall, accuracy and F1 score. The result after evaluation shows that random forest achieves the highest F1 score of 0.97, then comes LSTM with the score of 0.89 and Isolation Forest a score of 0.79. The results achieved clearly demonstrate that machine learning and deep learning can be used to detect log anomalies in real world data for IT operations.

Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as one of the major challenges in modern healthcare settings, ranked by the World Health Organization (WHO) as a critical-priority pathogen requiring urgent development of new antimicrobials. The organism's ability to form robust biofilms on both biotic and abiotic surfaces represents a central pillar of its pathogenicity in hospital-acquired infections (HAIs), including ventilator-associated pneumonia (VAP), catheter-associated bloodstream infections (CABSI), wound infections, and urinary tract infections. This review summarizes recent findings on the molecular mechanisms underpinning biofilm formation in CRAB, encompassing the roles of surface adhesins, quorum sensing, extracellular polymeric substances, and horizontal gene transfer in carbapenem resistance. We further examine the clinical epidemiology of CRAB-associated HAIs, including attributable mortality, morbidity, and economic burden across global healthcare systems. The article critically evaluates current and emerging therapeutic strategies, including polymyxins, tigecycline, cefiderocol, sulbactam-durlobactam, novel bacteriophage-based therapies, anti-biofilm compounds, and photodynamic inactivation approaches. Significant therapeutic gaps persist, underscoring the need for multidisciplinary strategies that integrate robust infection prevention measures, antimicrobial stewardship, and translational research into next-generation anti-biofilm agents. Future directions in genomic surveillance and combination therapy optimization are discussed.

Cheese are complex, diverse matrices of fermented milk with dynamic rheology. Microbial succession coupled with biochemistry impacts the flavor, texture, and quality attributes such as safety, nutrition, and authenticity. Routine evaluation of cheese relies on conventional methods are insufficient for monitoring the multitude of changes that occur due to ripening. The intersection of advanced omic technologies, molecular cell biology, and instrumental analytical chemistry has revolutionized research in dairy science and quality assurance system in modern dairy industry. The objective of this paper is to provide a comprehensive overview of innovative analytical methods of cheese evaluation and the chemical and microbial characterization of cheese. This method includes HPLC, GC-MS, HRMS, FTIR, NMR, PCR, biosensors, metagenomics, and NGS. With these technologies, the detection of dairy pathogens, as well as metabolomic profiling of peptides, detection of volatile dairy flavor compounds, and the authentication of dairy products is achieved with great sensitivity and reproducibility. The development of flavor and ripening is further analysed by the combined approaches of chemical characterization and microbial genomics. Other technologies that will likely serve to create the future boundaries of quality assurance in the dairy industry are the use of AI and analytics, blockchain for end-to-end traceability, and precision fermentation. The integration methods of analysis developed in the dairy industry stand to greatly improve food safety, proactive compliance with regulatory standards and the competitiveness of the dairy industry on a global scale.

We are witnessing a paradigm shift in the world energy sector as the unsustainability of the fossil fuel era is becoming more evident. As the world's crude oil reserves are likely to become depleted between 10-20 years from now and the dire environmental impacts of greenhouse gas (GHG) emissions continue to grow, the need for sustainable and renewable energy sources is more critical than ever for environmental and economic sustainability. Biofuels, produced from a range of biological feedstocks, hold the key to a carbon-neutral bio-economy. Our review examines the development of biofuel technologies from first-generation feedstocks (food crops) to fourth-generation genetically engineered and carbon-negative biofuels. We find that although first-generation biofuels (i.e., ethanol from corn, biodiesel from rapeseed) are currently leading the market, they are limited by the "food versus fuel" conundrum and land-use footprint. Second-generation lignocellulosic biofuels and third-generation algal biofuels offer better sustainability but are limited by cost and technical challenges. Fourth-generation fuels, incorporating carbon-capture and metabolic engineering, are the pinnacle of sustainable fuel production but are still in the early stages of development. This report identifies that "drop-in" fuels compatible with existing technologies, waste-to-energy systems (e.g., waste cooking oil), and integrated biorefineries are critical. Notwithstanding progress in technology, global-scale biofuel integration is hampered by resource constraints, regulatory complexities and market dynamics. This report concludes that a sustainable energy future will be achieved through a complex approach including advanced conversion technologies (e.g., consolidated bioprocessing), enabling policy settings (e.g., carbon taxes), and localised production to promote local economic development and environmental sustainability.

Parkinson's disease is a progressive neurodegenerative disorder that causes motor symptoms such as tremor, stiffness, bradykinesia, and postural instability, as well as a variety of non-motor consequences. Despite being the second most common neurodegenerative illness worldwide, effective disease-modifying medicines are still elusive. For decades, conventional therapy techniques such as levodopa, dopamine agonists, monoamine oxidase-B inhibitors, and deep brain stimulation (DBS) have served as the cornerstone of Parkinson's disease management. While these treatments relieve symptoms and enhance quality of life, their long-term efficacy is restricted by consequences like motor fluctuations, dyskinesias, and decreased responsiveness.

Recent advances in neuroscience have fueled the development of emerging therapies that aim to go beyond symptomatic relief. These include gene therapies targeting dopamine synthesis and α-synuclein aggregation, stem cell–based neuronal replacement strategies, novel neuroprotective agents, and precision medicine approaches. In addition, innovative drug delivery systems and non-invasive neuromodulation techniques are being explored to enhance treatment outcomes.

This review highlights the strengths and limitations of conventional therapies while critically evaluating the promise of emerging approaches. By comparing established practices with novel strategies, the paper underscores the evolving landscape of Parkinson’s disease management and the potential for future interventions to achieve true disease modification.

Identification and validation of drug targets remains a challenge in drug discovery. It is one of the primary causes of high failure rates of therapeutics and increasing drug development costs. Computational biology has emerged as an approach to address these challenges by developing in silico models to understand the underlying disease pathophysiology. Herein, we provide a comprehensive review of contemporary computational approaches that include integrative bioinformatics, network pharmacology, systems biology, and machine learning to aid target identification. We analyse and report the successes of these approaches for several complex diseases such as neurological disorders (Alzheimer's disease, Parkinson's disease, Huntington's disease), autoimmune disorders (Rheumatoid arthritis, Type 1 diabetes, Systemic lupus erythematosus), and metabolic disorders (Type 2 Diabetes, COPD, Coronary artery disease). For each disease, we provide representative examples highlighting the use of computational strategies to interrogate high-throughput omics datasets to identify key molecular drivers, regulatory networks, and druggable targets. This review highlights the computational methods accelerating the translation of large-scale multi-omics data into validated drug targets. Ultimately, these methods improve the precision and success of drug development for complex human diseases.

The extensive use of synthetic polymers has caused some of the most critical as well as complicated environmental issues of the 21st century. One of the major issues among them is rapid accumulation of plastic waste. Traditional methods for waste disposal like landfilling, incineration and ocean dumping are held responsible for causing several severe ecological and public health risks. The traditional methods also failed as a long-term and sustainable solution for plastic waste. In recent years, certain modern approaches like microbial and enzymatic biodegradation have shown greater efficiency in curbing issues linked to plastic waste management. Microbial biodegradation emphasizes on breaking of complex polymer chains into simpler monomers through the action of enzymes. These plastic-based compounds are utilized as carbon and energy sources by microbes. Advances in biotechnology, environmental microbiology, synthetic biology, genetic engineering and enzyme technology have enhanced the efficiency of microbes and enzymes involved in plastic degradation. PETase and MHETase are the enzymes that have previously shown capability to degrade one of the most used plastics i.e., polyethylene terephthalate (PET). The technical advancements such as molecular engineering, microbial consortia designing and CRISPR-based genome editing strategies such as PlastiCRISPR are becoming vital for developing engineered and modified microbial strains and systems that show optimized and enhanced capabilities for plastic degradation and formation of useful biochemical residues. This review aims to emphasise on greater environmental and economic causes through the application of scientific temperament. It provides an overview on plastic pollution, limitations of traditional waste management approaches and advancing biodegradation technologies. Detailed emphasis is placed on microbial biodegradation mechanisms, enzymatic degradation pathways, and advanced biotechnological methods that can help tackle plastic waste problems and convert them into other useful by-products, cycling into the circular economy.

Zinc oxide (ZnO) nanoparticles were synthesized in aqueous extract of leaf of Azadirachta indica (neem) which acts as a reducing and stabilizing agent in this study. Precursor used were zinc acetate dihydrate under optimized conditions (pH 8, 70 °C) and the precipitate was oven dried. The high crystallinity of the nanoparticles was further confirmed by using the UV– Visible spectroscopy with its distinct absorbance peak at 370–380 nm. The antifungal, plant growth promoting and synergistic antibacterial activities of ZnO nanoparticles were evaluated at 2, 5 and 10 mg/mL. Antifungal testing was performed on fungal strains from rotten jackfruit. Nigella sativa and Trigonella foenum-graecum were used for plant assays and antibacterial synergy was evaluated in combination with chloramphenicol with E. coli R28 and ATCC 10536 bacteria. The findings reveal that the bio-engineered nanoparticles are highly effective in inhibiting the growth of fungus and also increase seedling vigor in both the plant models (Chikkanna et al., 2018; Golipalle et al., 2025). The 5 mg/mL concentration was the most effective with the best biological response, including maximum fungal inhibition, seed germination, root elongation and enhanced antibacterial activity. There was minimal effect from the lower concentration, but it was less effective at 10 mg/mL, perhaps because of aggregation of the nanoparticles or phytotoxicity. These findings indicate that the neem-mediated ZnO nanoparticles are potential candidates in plant biotechnology and antimicrobial application as sustainable means.

Myositis refers to a cluster of rare chronic autoimmune disorders defined by muscle inflammation and progressive weakness. Standard therapy such as corticosteroids and immunosuppressants has considerable toxicity associated with them and is frequently ineffective in controlling the disease. Peroxisome proliferator-activated receptor gamma (PPARγ), a ligand activated nuclear receptor, has been implicated in myositis as well for its function on the regulation of inflammation and metabolism. PPARγ activation inhibits inflammatory pathways including, in particular, NF-κB signaling and guides immune resolution. In this review, the mechanistic role of PPARγ in myositis is discussed and drug repurposing, a strategy to screen for well-known FDA-approved compounds with putative PPARγ-modulating activity, is considered. Moreover, the similarities between phytochemicals (in particular flavonoids) and small molecules render the former as natural ligands

Triple-negative breast cancer (TNBC) is one of the most aggressive forms of breast cancer and it is very difficult to treat because it lacks the common hormone receptors which is used in targeted therapies. The main aim of this study was identifying an important target gene involved in TNBC and to evaluate the potential of selected natural compounds using computational approaches. Genes related to TNBC they were collected from the Gene Cards database, and the most relevant genes were selected for further analysis. A protein-protein interaction (PPI) network was then constructed and analysed using Cytoscape software. Based on network parameters such as degree and betweenness centrality, CHEK2 (Checkpoint kinase 2) was identified as a main target gene because of its strong interactions with other associated genes. Five phytochemicals-curcumin, piperine, cinnamaldehyde, 6-gingerol, and allicin were selected from previously published studies for further investigation. The three-dimensional structure of the CHEK2 protein was obtained using PDB database and evaluated before docking analysis. Molecular docking was then performed using the Dock Thor with a blind docking approach to study the interaction of these compounds with the target protein. The docking results showed that all phytochemicals exhibited stable binding with the protein. Among them, curcumin demonstrated the highest binding affinity (−9.386 kcal/mol), followed by piperine (−8.847 kcal/mol), cinnamaldehyde (−8.12 kcal/mol), 6-gingerol (−7.933 kcal/mol), and allicin (−7.855 kcal/mol). These results indicates that curcumin may have stronger interaction potential with the CHEK2 protein. In conclusion, the study highlights the importance of network-based target identification and it supports the potential role of natural phytochemicals, particularly curcumin, in TNBC treatment. Further experimental validation is required to confirm these findings.

The persistence of bacterial infections despite antibiotic susceptibility represents a major challenge in modern medicine, largely driven by phenotypic antibiotic tolerance mediated by persister cells within biofilm-forming populations. Basically, persister cells represent a temporary, non-inheritable subpopulation of bacteria whose dormancy or low metabolism makes them capable to evade the killing effect of antibiotics that normally eliminate the bacteria. By forming biofilms, bacteria not only protect persister cells through physical barriers to antibiotics but also further strengthen this tolerance, since biofilms create a nutrient-limited microenvironment with oxygen gradients and make it difficult for antibiotics to penetrate. In fact, the three factors together lead to a lifestyle of persister cells, which explains why the treatment is ineffective and the infection comes back. The pathways that lead to the production of persister cells are quite complex, as they involve several different cellular mechanisms and processes, such as toxin-antitoxin modules, generation of the stringent response through (p)ppGpp signaling, drop in the levels of ATP, and different stress-response routes. What is more, the system of quorum sensing leading to cell-cell communication and signalling inside biofilms is of utmost importance in the regulation of persistence and the survival of the community at the same time. In contrast to antibiotic resistance, which is the result of genetic mutations, the state of tolerance mediated by persisters is transient and capable of reversal, so when the antibiotic pressure is not there, cells can grow again. From a clinical perspective, persister cells cause infections that continue over a long time or keep coming back, like urinary tract infections, tuberculosis, and infections related to medical devices. They make it very difficult to treat with medicines. Unfortunately, the treatment methods we have today still fall short of eliminating these cells. However, new techniques are being researched that may improve clinical outcomes, including bacteriophage therapy, anti-persister medications, biofilm destruction, stimulation of persister cell metabolism, and nanotechnology-based treatments. Therefore, developing novel treatments for persistent bacterial infections requires a deep understanding of the biology of persister cells.

Down syndrome (DS), also known as trisomy 21, is the most common chromosomal aberration, characterized by global intellectual disability and developmental defects. The syndrome results from an extra copy of chromosome 21, leading to gene dosage changes and normal development. Cytogenetically, DS has been classified into complete trisomy 21, Robertsonian translocation trisomy 21 and mosaic trisomy 21, with different mechanisms of occurrence and recurrence risks. Diagnosis is important for confirming disease, prognosis, prenatal diagnosis, recurrence risk and genetic counselling. Traditional karyotyping has been the gold standard of cytogenetic diagnosis for a long time, as it allows observation of the chromosomes and detection of both numeric and structural abnormalities. Traditional cytogenetics still has clinical value, despite modern diagnostic methods (FISH, chromosomal microarray analysis, quantitative fluorescent PCR, next-generation sequencing, and non-invasive prenatal testing) providing better efficiency, speed and genomic data. It is particularly required for the diagnosis of balanced translocations, chromosomal rearrangements and low-level mosaicism that may escape detection by molecular methods. Moreover, recent advances in artificial intelligence, digital karyotyping and cytogenomic platforms have significantly improved the efficiency of cytogenetic analysis. This review attempts to impart the cytogenetics of DS, the role of conventional karyotyping, the new molecular diagnostic methods, a comparison between the conventional and molecular approaches, technological advances and future perspectives on the integration of genomics in diagnostics while emphasizing the continued importance of classical karyotyping despite ongoing advances in molecular technology.