Yenepoya University

Across four experiments, this research investigates how ego-driven motivations, social comparison, and scarcity cues interact to influence consumers' willingness to purchase unaffordable products and their subsequent post-purchase regret. Experiment 1 demonstrates that ego boosts significantly increase financial risk tolerance and willingness to purchase products exceeding budget constraints. Experiment 2 reveals that exposure to high-status peer endorsements amplifies aspirational purchasing intentions for unaffordable products. Experiment 3 shows that scarcity cues heighten urgency and override budgetary constraints, particularly when combined with psychological arousal. Experiment 4 establishes that elevated ego amplifies post-purchase regret for unaffordable purchases, as inflated expectations clash with actual outcomes. Grounded in self-enhancement theory, social comparison theory, the scarcity heuristic, and cognitive dissonance theory, these findings illuminate the interplay between intrinsic psychological motivations and extrinsic situational triggers in driving indulgent consumption. The research raises important ethical concerns about marketing tactics that exploit psychological vulnerabilities, particularly for financially constrained consumers. Implications include recommendations for transparent advertising, financial literacy interventions, and regulatory safeguards to mitigate manipulative marketing practices and promote responsible consumer behavior.

Gaucher disease (GD) is a common lysosomal storage disorder caused by mutations in the glucocerebrosidase-1 (GBA1) gene, resulting in the deficiency of the lysosomal enzyme β-glucocerebrosidase (GCase) and the subsequent accumulation of glucosylceramide (GluCer) and glucosylsphingosine (GlcSph; Lyso-Gb1). However, major gaps remain regarding the management of neurological symptoms and the validation of effective biomarkers. This review summarizes current insights from preclinical models and omics-based approaches to understand GD pathophysiology, biomarker discovery, and emerging therapeutic strategies. A comprehensive literature review was performed focusing on preclinical models (mouse, zebrafish, Drosophila, medaka, canine, and ovine) and omics technologies, including proteomics, genomics, and metabolomics. Relevant studies on biomarkers and therapies were critically analysed. The preclinical model has shed light on several important pathogenic mechanisms, including lipid accumulation, immune dysfunction, and neuroinflammation. There have been advances in the treatment of GD using enzyme replacement therapy (ERT) and substrate reduction therapy (SRT), but these are limited by their high cost, lack of accessibility, and lack of effectiveness against the neuronopathic variants. Different biomarkers such as chitotriosidase (ChT), GlcSph, and CCL18 have been identified for the diagnosis and monitoring of GD. At the same time, multi-omics-based technologies have improved our understanding of the pathophysiology of GD and helped in the identification of potential biomarkers and therapeutic targets. Emerging technologies like gene therapy, pharmacological chaperones, and nanovesicle-based systems show promise but need to be validated. Although considerable progress has been made in GD research, major gaps still exist in terms of biomarker validation, managing neuronopathic disease, and epidemiology. The combination of multi-omics approaches and translational models could facilitate precise diagnosis and therapy.

The Developmental Origins of Health and Disease (DOHaD) hypothesis asserts that detrimental prenatal conditions, such as dietary deficiencies, may lead to enduring health consequences. Perinatal undernutrition, an important concern during fetal development, may affect growth and metabolic programming, resulting in lasting health implications. Maternal nutrition is crucial in modulating fetal endocrine systems and metabolic functions, influencing the development, blood circulation, and nutrient absorption. The present study examines the impact of perinatal undernutrition on the composition of gut microbiota and metabolite levels in offspring of undernourished dams, using an Albino Wistar rat model. Furthermore, we investigated the combined impact of astaxanthin (AsX) and docosahexaenoic acid (DHA) supplementation on cardiometabolic outcomes in these progenies. Astaxanthin, a powerful antioxidant, and DHA, an omega-3 fatty acid, have shown the ability to favorably alter the gut flora and metabolic pathways. The direct influence of AsX on gut microbiota remains unexplored, whereas DHA’s role in fostering beneficial microbes and regulating metabolite production is well documented. The current study used metabolomics and metagenomics to investigate the intricate relationship between metabolites and gut microbiota in health and disease, offering insights into fetal programming and possible strategies to improve offspring health. The results highlight the need to address perinatal undernutrition and enhance gut health through targeted dietary interventions to improve long-term health outcomes.

PRDM2 is a histone methyltransferase that regulates gene expression through histone H3 lysine 9 methylation. It is involved in the DNA damage response by controlling chromatin remodeling and maintaining genomic integrity. However, the functional relevance of its phosphorylation remains poorly understood. To address this gap, we systematically characterized PRDM2 phosphosites and their associated phospho-signaling networks using large-scale cellular phosphoproteomics data curated from PubMed-indexed articles. Frequency-based ranking revealed Ser643 and Ser421 as predominant phosphosites, detected across 334 and 141 qualitative datasets and 70 and 47 differential cellular phosphoproteomics datasets, respectively. To explore PRDM2-associated phospho-signaling, expression co-regulation analysis was performed to identify phosphosites in other proteins exhibiting consistently similar or opposing expression patterns relative to the predominant PRDM2 phosphosites. This analysis identified 1,251 phosphosites in other proteins showing high-confidence expression co-regulation with PRDM2 Ser421 and 715 phosphosites with PRDM2 Ser643. Functional enrichment revealed significant associations with cell cycle regulation, chromatin organization, RNA processing, and DNA damage response (DDR) pathways. Notably, phosphosites in 30 DDR-related proteins positively co-regulated with Ser643 and 28 with Ser421. Additionally, ATR was identified as a potential kinase predicted to phosphorylate the predominant PRDM2 sites, and its phosphosites exhibited consistent expression co-regulation with PRDM2 sites. Collectively, this study establishes a comprehensive phospho-signaling framework for PRDM2, uncovering its strong association with DNA damage response pathways and providing mechanistic insights into its regulatory network. Clinical trial registration: This study is not part of any clinical trial.

Cancer remains a major health challenge globally, with significant regional variations in incidence and mortality. In India, the incidence of cancer is projected to increase over the next decade, with marked regional disparities. Despite the availability of data from cancer registries, there is a gap in the use of real-time data and advanced forecasting techniques to understand spatial and temporal trends in cancer incidence. This study aims to fill this gap by employing real-time data and advanced forecasting methodologies to analyze the spatio-temporal patterns of cancer incidence in India. Secondary data on cancer incidence from 36 regions (states and union territories) in India, obtained from the Open Government Data (OGD) Platform India, was used. The study focused on calculating the Cancer Crude Incidence Rate (CCIR) for the years 2018 to 2024. A Bayesian Spatio-Temporal Conditional Autoregressive model was used to model the spatio-temporal dynamics of CCIRs. Additionally, an Artificial Neural Network (ANN) was employed to project cancer incidence rates for 2025 and 2026. Spatial clustering techniques, including Local Moran’s I statistic, were used to identify cancer hotspots and cold spots across regions. From 2018 to 2024, temporal trends revealed an increasing CCIR across multiple regions, with notable spatial heterogeneity. The Bayesian model highlighted distinct spatio-temporal dependencies, while the ANN model demonstrated robust predictive accuracy for CCIR projections in 2025 and 2026. Emerging hotspots and cold spots were identified, providing actionable insights for targeted cancer control interventions. The study highlights the increasing cancer burden in India and the significant regional variations in incidence. The findings emphasize the need for region-specific strategies and highlight the utility of real-time data and advanced models for forecasting and spatial analysis in public health.