Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare.

Chikungunya virus (CHIKV) is an infectious agent spread by mosquitos, that has engendered endemic or epidemic outbreaks of Chikungunya fever (CHIKF) in Africa, South-East Asia, America, and a few European countries. Like most tropical infections, CHIKV is frequently misdiagnosed, underreported, and underestimated; it primarily affects areas with limited resources, like developing nations. Due to its high transmission rate and lack of a preventive vaccine or effective treatments, this virus poses a serious threat to humanity. After a 32-year hiatus, CHIKV reemerged as the most significant epidemic ever reported, in India in 2006. Since then, CHIKV-related research was begun in India, and up to now, more than 800 peer-reviewed research papers have been published by Indian researchers and medical practitioners. This review gives an overview of the outbreak history and CHIKV-related research in India, to favor novel high-quality research works intending to promote effective treatment and preventive strategies, including vaccine development, against CHIKV infection.

The novel pathogenic virus was discovered in Wuhan, China (December 2019), and quickly spread throughout the world. Further analysis revealed that the pathogenic strain of virus was corona but it was distinct from other coronavirus strains, and thus it was renamed 2019-nCoV or SARS-CoV-2. This coronavirus shares many characteristics with other coronaviruses, including SARS-CoV and MERS-CoV. The clinical manifestations raised in the form of a cytokine storm trigger a complicated spectrum of pathophysiological changes that include cardiovascular, kidney, and liver problems. The lack of an effective treatment strategy has imposed a health and socio-economic burden. Even though the mortality rate of patients with this disease is lower, since it is judged to be the most contagious, it is considered more lethal. Globally, the researchers are continuously engaged to develop and identify possible preventive and therapeutic regimens for the management of disease. Notably, to combat SARS-CoV-2, various vaccine types have been developed and are currently being tested in clinical trials; these have also been used as a health emergency during a pandemic. Despite this, many old antiviral and other drugs (such as chloroquine/hydroxychloroquine, corticosteroids, and so on) are still used in various countries as emergency medicine. Plant-based products have been reported to be safe as alternative options for several infectious and non-infectious diseases, as many of them showed chemopreventive and chemotherapeutic effects in the case of tuberculosis, cancer, malaria, diabetes, cardiac problems, and others. Therefore, plant-derived products may play crucial roles in improving health for a variety of ailments by providing a variety of effective cures. Due to current therapeutic repurposing efforts against this newly discovered virus, we attempted to outline many plant-based compounds in this review to aid in the fight against SARS-CoV-2.

Background: Antimicrobial peptides (AMPs) are unique natural antibiotics that are crucial effectors of innate immune systems in almost all living organisms. Several different plant antimicrobial peptides have been identified and isolated, demonstrating a high level of protection against various types of bacteria, insects, nematodes and other microbes. Along with antimicrobial function, these peptides play a wide range of crucial function in plants, such as regulation of stomata, ion channel, heavy metals and membrane fluidity. Main body: Antimicrobial peptides show a continuum of toxicity for a variety of plants and animals pathogenic microbes and even show cytotoxicity against cancer cells. Numerous studies have shown that transgenic plants have increased the expression of AMP-encoding genes in response to biotic and abiotic stresses, and plants that express transgenic AMP genes are more responsive to biotic, abiotic and other functions. In addition to being a molecule with protective properties, various allergic reactions are associated with some phytopeptides and proteins, in particular non-specific lipid transfer protein (nsLTP) and peptide-like hevein. Pru p3 from peach is the most clinically important allergen within the nsLTP family that cause real food allergies and also triggers extreme clinical reactions. Similarly, latex-fruit syndrome was primarily associated with well-studied latex allergen Hevein (Hev b8, Hev b6) and class I chitinases. Short conclusions: Several findings have shown that, in the near future, transgenic plants based on AMPs against the verity of pathogenic fungi, bacteria and other abiotic stresses will be released without any adverse effects. Recent study reason that association of lipid with nsLTP enhances allergic sensitization and hevein-like domain of chitinase I essentially plays a role in cross-sensitivity of latex with different fruits and nuts. This review discusses the structures and various functions of lipid transfer protein and hevein-like peptide.

Plants usually produce a large number of secondary metabolite molecules, including antimicrobial peptides that typically interact with membranes of pathogens and kill these. Cysteine is more abundant in plant antimicrobial peptides than other sources. These phytopeptides can be classified into 5–8 distinct groups, based on the length, secondary structure type, number and model of disulphide bridges, and others. Among the different groups of phytopeptides, cyclotides are end-to-end cyclic with a disulphide bridge in the knot fashion. Cyclotide peptides are primarily known for its anti-nematode, anti-mollusk, and anti-trematode action. This review aims to provide detailed insights on cyclotides, from their origin, and structural and functional characterization, to their therapeutic potential, biotechnological application, and future prospective.

Obesity is a multifactorial condition projected to affect over half of the global population by 2035, posing significant clinical and socioeconomic challenges. Traditional metrics such as body mass index lack precision in predicting individual risk, disease progression, and therapeutic response due to the heterogeneous nature of obesity. Advances in omics technologies such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics have enabled the identification of molecular subtypes and candidate biomarkers that offer deeper insights into obesity pathophysiology. Genomic studies have revealed hundreds of loci associated with obesity related traits, while polygenic risk scores offer modest improvements in early risk prediction. Epigenomic profiling, particularly deoxy ribose nucleic acid (DNA) methylation signatures such as those at carnitine palmitoyl transferase 1A (CPT1A) and hypoxia inducible factor 3 subunit alpha (HIF3A), has uncovered modifiable pathways linked to adiposity and metabolic dysfunction. These findings are increasingly being integrated with other omics layers to improve stratification and therapeutic targeting. Metabolomic subtypes, including ceramide driven insulin resistance and branched chain amino acid (BCAA) dominant dysregulation, have shown potential in guiding treatment selection, such as sodium glucose cotransporter 2 (SGLT2) inhibitors or glucagon like peptide-1 (GLP-1) agonists. Proteomic markers like proprotein convertase subtilisin/kexin type 9 (PCSK9) and retinol binding protein 4 (RBP4) are being evaluated for cardiovascular risk stratification independent of body mass index (BMI). Integrative multiomics frameworks and AI driven models are beginning to bridge molecular data with clinical phenotypes, enabling patient stratification and risk modeling. However, most findings remain in research grade environments, and clinical translation is limited by cohort diversity, data harmonization challenges, and the lack of standardized validation protocols. This review synthesizes evidence from single and multiomics studies, highlights emerging biomarkers and molecular subtypes, and discusses the potential of omics guided frameworks to inform precision obesity care.