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.

This research was aimed at finding the cytotoxic potential of the mixed ligand copper(II) complex [Cu(tdp)(phen)](ClO₄)—where H(tdp) is the tetradentate ligand 2-[(2-(2-hydroxyethylamino)-ethylimino)methyl]phenol, and phen is 1,10-phenanthroline—to two genotypically different breast cancer cells, MCF-7 (p53⁺ and ER⁺) and MDA-MB-231 (p53^- and ER^-). The complex has been already shown to be cytotoxic to ME180 cervical carcinoma cells. The special focus in this study was the induction of cell death by apoptosis and necrosis, and its link with ROS. The treatment brought about nuclear fragmentation, phosphatidylserine externalization, disruption of mitochondrial trans-membrane potential, DNA damage, cell cycle arrest at sub-G1 phase, and increase of ROS generation, followed by apoptotic death of cells during early hours and a late onset of necrosis in the cells surviving the apoptosis. The efficacy of the complex against genotypically different breast cancer cells is attributed to a strong association through p53-mitochondrial redox—cell cycle junction. The ADMET properties and docking of the complex at the active site of Top1 are desirable attributes of a lead molecule for development into a therapeutic. Thus, it is shown that the copper(II)–phenolate complex[Cu(tdp)(phen)]⁺ offers potential to be developed into a therapeutic for breast cancers in general and ER-negative ones in particular.

Obesity is a global health challenge characterized by significant heterogeneity in causes and treatment responses, complicating sustainable management. This narrative review explores the genomic architecture of obesity and its implications for personalized interventions, focusing on how genetic variations influence key biological pathways and treatment outcomes. A comprehensive literature search, guided by the authors’ expertise, was conducted to identify key publications on the genomics of obesity and personalized approaches. The selection of articles prioritized those that provided direct insights into the genomic basis of obesity and its potential for informing tailored strategies. Genomic studies reveal both monogenic and polygenic influences on obesity, identifying numerous susceptibility loci. Genome-wide association studies (GWASs) have linked common variants in genes like FTO and MC4R to increased BMI and appetite dysregulation, respectively. Epigenetic research highlights the role of DNA methylation and other modifications in gene–environment interactions. Genetic and polygenic risk scores (GRSs and PRSs) show potential for refining risk stratification and predicting treatment response. The gut microbiome and metabolome also contribute to obesity pathogenesis, offering novel targets for intervention. Personalized medicine offers significant potential for improving obesity management through tailored interventions based on an individual’s genetic and ‘omics’ profile. Future research should focus on elucidating the functional consequences of identified variants, exploring gene–environment interactions, and developing strategies to overcome current limitations in clinical translation. With continued advancements, precision medicine can enhance treatment efficacy, increase sustainability, and help reduce the global burden of obesity-related diseases.

Fragrances are generally synthetic, natural or a combination of both. Ittar or Attar is a natural fragrance material synthesized from plants which were majorly used by people worldwide as a matter of pride, deodorization and attractant. The effect and role of using Ittar on mammalian system is still not known at the behavioral and neurological level. This study was aimed to understand the constituents and the effect of Ittar on mice by measuring their behavioral and neurological changes upon exposure to Ittar. Batches of animals were exposed to ten different types of Ittar for 1, 2 and 3 weeks respectively. Behavior of the animals exposed to the Ittar perfumes were analyzed against the control animals behavior for physiological activities. Also, the expression level of neurotransmitter such as Dopamine, 5-HIAA, HVA and Serotonin were evaluated based on HPLC analysis. Based on this study it was found that all the ten Ittars were different for its different constituents with some basic overlapping molecules among them. The neurotransmitter such as DOPA, 5-HT, Homovanillic Acid and Serotonin were found non-significantly increased in the total brain after a three week long continuous exposures of Ittar to the animal. Histological analysis of the ittar exposed mice skin showed mild to massive changes in both epidermal and dermal layers characterized with intense infiltration of inflammatory cells, hyperplasia, hyperchromasia and edema formation against control animals in few of the Ittars. From this study it is evident that Ittar increases mood of the animal through the up-regulation of various neurotransmitter but subtle effect on the skin because of the presence of various chemical constituents of the Ittar. Based on this study it is evident that use of Ittar as fragrance might be beneficial at the molecular level and it will be appropriate to apply without direct exposure to skin.