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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.