Eastern New Mexico University

1,3-Dipole-based nitrogen containing carbanion, particularly azomethine ylide, is widely used in cycloaddition reactions. It has important and widespread application not only limited in the synthesis of core structural motifs like pyrrolidine and spiropyrrolidines but also in stereocontrolled dipolarophile for building important classes of heterocyclic compounds. Herein, we will explore the current development of azomethine ylide and all the possible applications of this small but elegant fragment using a green approach. This review article will also covers the application of this super energetic, pharmaceutically important moiety in the field of asymmetric, organocatalytic, and transition metal-catalyzed synthesis processes coupled with a theoretical approach. The development of an environmentally safe multicomponent, tandem synthesis method mediated by azomethine ylide will be discussed in detail in this current review article, which will be important for researchers in the field of organic synthesis, heterocyclic chemistry, and medicinal chemistry.

Childhood anemia remains a major public health challenge in Nepal and is associated with impaired growth, cognition, and increased morbidity. Using World Health Organization hemoglobin thresholds, we defined anemia status for children aged 6-59 months and formulated a binary classification task by grouping all anemia severities as anemic versus not anemic. We analyzed Nepal Demographic and Health Survey (NDHS 2022) microdata comprising 1,855 children and initially considered 48 candidate features spanning demographic, socioeconomic, maternal, and child health characteristics. To obtain a stable and substantiated feature set, we applied four features selection techniques (Chi-square, mutual information, point-biserial correlation, and Boruta) and prioritized features supported by multi-method consensus. Five features: child age, recent fever, household size, maternal anemia, and parasite deworming were consistently selected by all methods, while amenorrhea, ethnicity indicators, and provinces were frequently retained. We then compared eight traditional machine learning classifiers (LR, KNN, DT, RF, XGBoost, SVM, NB, LDA) with two deep learning models (DNN and TabNet) using standard evaluation metrics, emphasizing F1-score and recall due to class imbalance. Among all models, logistic regression attained the best recall (0.701) and the highest F1-score (0.649), while DNN achieved the highest accuracy (0.709), and SVM yielded the strongest discrimination with the highest AUC (0.736). Overall, the results indicate that both machine learning and deep learning models can provide competitive anemia prediction and the interpretable features such as child age, infection proxy, maternal anemia, and deworming history are central for risk stratification and public health screening in Nepal.

Background: Heavy metal contamination in Nigerian water bodies poses persistent environmental and public health risks. Traditional laboratory-based detection methods, while accurate, are limited by cost, accessibility, and real-time applicability, creating a need for advanced monitoring approaches. Objectives: This review systematically synthesises recent advances in smart electrochemical sensors integrated with Internet of Things (IoT) technologies for heavy metal detection, highlighting technological innovations, field applications, and unresolved deployment challenges in Nigerian aquatic environments. Methods: Peer-reviewed literature was systematically surveyed to synthesise advances in heavy metal contamination assessment and smart electrochemical sensing technologies relevant to Nigerian aquatic systems. Publications spanning the early 2000s to 2023 were retrieved from major scientific databases, focusing on (i) environmental and toxicological studies of heavy metal exposure, (ii) electrochemical sensor development, including nanomaterial-enhanced and screen-printed platforms, and (iii) IoT-enabled water quality monitoring architectures. Additional studies addressing energy infrastructure, connectivity, cost considerations, and capacity constraints in Nigeria were included to contextualise deployment feasibility. The collected literature was qualitatively analysed to identify technological progress, practical applications, and persistent research gaps. Results: Nanomaterial-enhanced electrodes, such as graphene, carbon nanotubes, and metal nanoparticles, improve sensitivity, selectivity, and detection limits for Pb, Cd, Hg, As, and Cr. Miniaturised platforms, including screen-printed and microfluidic sensors, facilitate portable and multiplexed detection. IoT integration enables real-time data acquisition, cloud-based analytics, and remote monitoring, demonstrated across the Niger Delta, mining-affected rivers, and urban-industrial water bodies. Field studies reveal high correlation with laboratory measurements but also highlight technical, infrastructural, and economic challenges, including biofouling, sensor drift, power and connectivity limitations, high costs, and limited local expertise. Regulatory gaps and lack of standardised protocols further constrain national-scale deployment. Conclusion: Smart electrochemical sensors with IoT integration offer transformative potential for continuous and distributed monitoring of heavy metal contamination in Nigerian water systems. Despite demonstrable field viability, unresolved gaps remain in autonomous operation, multiplexed detection, long-term stability, and data integration, indicating critical areas for further investigation to fully leverage these technologies for water quality management.

Lipids are a diverse group of hydrophobic molecules including fats, oils, phospholipids, and steroids that are vital for numerous biological functions including energy storage, cellular structure, and signaling whose composition and metabolism undergo profound transformations with age. These age-related shifts due to increased lipid peroxidation, disrupted cholesterol homeostasis, and altered membrane phospholipid content, actively contribute to progressive loss in cellular homeostasis and pathogenesis of major age-related diseases. This review explores the critical role of lipids: as master regulators of cellular signaling pathways, and as key drivers of chronic inflammation and metabolic dysfunction. Dysregulated lipid metabolism is central to cardiovascular disease which is driven by altered myocardial energy substrate utilization and lipoprotein dynamics. In neurodegenerative disorders like Alzheimer’s and Parkinson’s disease, disruptions in ceramide, cholesterol, and specialized pro-resolving lipid mediators fuel neuroinflammation and protein aggregation. Furthermore, we explore the dual role of dietary lipids, which can either exacerbate or mitigate age-related decline, highlighting the potential of personalized nutritional approaches and lipid-targeting therapeutics. By integrating the mechanisms of lipid signaling, inflammation, and metabolic regulation, this analysis highlights that lipids are not merely passive structural components but active drivers of the aging process, positioning lipid metabolism as a promising frontier for interventions aimed at promoting health span and combating age-related disease.

Sports non-fungible tokens (NFTs) have rapidly emerged as tradable digital goods within platform-mediated marketplaces, reshaping how sports organizations, athletes, and brands design fan experiences and monetize digital assets. To consolidate fragmented scholarship and clarify the concept space, this study conducts a systematic quantitative literature review combined with thematic analysis, following PRISMA 2020 and a SPIDER-guided review logic. Searches across six major databases (Web of Science, Scopus, ScienceDirect, PubMed, IEEE Xplore, ProQuest) plus Google Scholar (2017–March 2025) yielded 40 peer-reviewed studies that met predefined inclusion criteria and passed quality appraisal. Results show a sharp growth of sports-NFT research from 2021 to 2024, with strong inter-disciplinary convergence spanning sports marketing, information systems, computer science, and law. Integrating findings through a consumer-value lens, we inductively propose a five-type taxonomy—collectible, empowerment, identity/authentication, physical-asset linked, and virtual-interaction NFTs—each associated with distinct value mechanisms and e-commerce functionalities. The thematic synthesis further identifies four dominant research streams (industry digitalization, consumer psychology/behavior, legal–regulatory issues, and digital marketing), while revealing gaps in theory operationalization, method diversity (e.g., limited experiments/longitudinal designs), cross-context generalizability, and governance/sustainability. The study contributes to marketing and management scholarship by positioning sports NFTs as emerging technologies that reorganize customer engagement, brand-community building, and governance in platform-mediated sport markets, and it offers a research agenda for measuring consumer, brand, and organizational effects.