Chhatrapati Shahu Ji Maharaj University (CSJMU), formerly Kanpur University, is a public state university located in Kanpur, Uttar Pradesh, India. It is administered under the state legislature of the government of Uttar Pradesh.
Abstract Introduction Brain disorders, including Parkinson’s disease, epilepsy, traumatic brain injury, and psychiatric illnesses, continue to pose substantial clinical and societal burdens. Traditional treatment approaches are often limited by poor targeting efficiency and the complexity of neural networks. Neuroengineering combines principles of engineering, materials science, and neuroscience to develop innovative tools capable of monitoring and modulating brain activity. Advances in neural interfaces, neurostimulation technologies, and bioengineered therapeutic systems are enabling more precise and minimally invasive treatment strategies, offering new possibilities for restoring neurological function and improving patient outcomes. Short summary Brain disorders represent one of the greatest challenges in modern medicine, demanding innovative approaches that integrate neuroscience, engineering, and technology. Neuroengineering has emerged as a transformative discipline that bridges these domains, offering new strategies for diagnosis, monitoring, and therapeutic intervention. This review discusses recent advances in neuroengineering technologies, including neural interfaces, neurostimulation systems, nanotechnology-based therapeutics, and tissue engineering approaches supported by artificial intelligence for precision brain disorder management. Conclusion Neuroengineering represents a rapidly evolving field with the potential to transform brain healthcare through innovative diagnostic and therapeutic technologies. Continued interdisciplinary collaboration and technological refinement are essential to overcome translational challenges and achieve widespread clinical implementation.
NiO/CuO/Co3O4 ternary nanocomposites (TNCs) were synergistically designed via hydrothermal method to enhance interfacial charge transfer and redox activity for high-efficiency supercapacitor applications. The structural analysis using XRD confirmed the coexistence of cubic NiO, monoclinic CuO and spinal Co3O4 phases with an average crystallite size of 21 nm, indicating the formation of a well-defined ternary system. XPS analysis verified the presence of Ni2+/ Ni3+, Cu2+ and Co2+/Co3+ oxidation states along with oxygen vacancies. FESEM and HRTEM studies revealed a hexagonal plate-like morphology with well-defined lattice fringes and polycrystalline nature of the prepared material. BET analysis showed a high specific surface area of 131 m2g− 1 with mesoporous structure ( 1.93 nm pore size), facilitating efficient electrolyte access and charge transport. Electrochemical measurements demonstrated excellent pseudocapacitive behaviour with a high specific capacitance of 1523 F g− 1 at 10 mV s− 1 scan rate and 1160 F g− 1 at 2 A g− 1 current density from CV and GCD curves, respectively. The electrode exhibited superior cyclic stability with 97.8
Sericulture is the practice of rearing silkworms on specific host plants for the production of natural silk. It has been carried out since ancient times. Among the various silkworms, the fully domesticated mulberry (Bombyx mori), the semi-domesticated Eri (Samia ricini) and the wild species or vanya silkworms- Muga (Antheraea assamensis) and Tasar (Antheraea mylitta) represent the major sources of commercial silk in India. Each species has distinct physiological and anatomical features that determine its feeding behaviour, silk production and adaptability to environmental conditions. Although their ecology, host plants, silk characteristics and rearing practices differ among these species, the internal anatomy of these silkworms follows a conserved lepidopteran pattern. In the present study, comparative analysis of internal organs and other external structures of all four different silkworm species were examined using a stereo zoom microscope. The major internal organ includes a digestive tract, tracheal system, circulatory system, nervous system, excretory system, silk gland and reproduction system. Observations revealed species-specific differences in tracheal coloration, silk gland development, fat-body texture and reproductive morphology, influenced by their ecological adaptation and host-plant specialization. External features such as true legs, pseudo-legs, spiracles, eyes and antennal structures of adult moths are also documented to provide integrated anatomical profiles. Such comparative analyses have helped to elucidate functional adaptations concerning feeding habits, cocoon formation, spinning behaviour and environmental tolerance.
Over the last two decades, triazoles have been a fascinating heterocyclic scaffold for the synthesis and design of versatile medicinal compounds. 1,2,4- triazoles are extensively employed for their numerous clinical roles, i.e., antifungal, antiviral, antibacterial, anti-inflammatory, and anti-tubercular owing to its enzyme-inhibitory activities that targets CYP51, aromatase, and MmpL3. Hence, five-membered aromatic 1,2,4- triazoles have gained potent attention owing to their flexible synthesis route that allows the development of biological compounds in an easy manner. This comprehensive study systematically portrays different conventional and green chemistry approaches for synthesis, different biological properties, and therapeutic potential of 1,2,4-triazole derivatives. Structural properties elucidate the influence of substitution patterns on molecular stability, electronic distribution, and reactivity, which underpin their functional applications. The present study emphasized emerging therapeutic potential of 1,2,4-triazole-MOFs, including designing of controlled drug delivery systems, theronostics, biosensors, and corrosion inhibitors. The structural flexibility, surface functionality, biocompatibility, and biodegradability not only enable MOFs for encapsulating myriad therapeutics (small molecules-macromolecules) but also protect compounds from enzymatic degradation. Moreover, the intrinsic magnetic and luminescent features of certain 1,2,4-linked MOFs pose a versatile arena for the development of theranostics, real-time imaging, and combined drug delivery.
Artificial intelligence (AI) is now a key player in modern microbiology, as it enables high-resolution analyses of genomic, metagenomic, and clinical data for the monitoring of infectious disease and antimicrobial resistance (AMR). Considerable advancements in deep learning, transformer-based sequence models, graph neural networks, and multimodal architectures have greatly improved microbial classification accuracy, antibiotic resistance gene (ARG) detection, and resistance prediction. Taking metagenomic sequencing into consideration, these advancements have contributed to the development of sensitive, scalable, and non-invasive methods to profile microbiomes, determine novel resistance, and monitor AMR trends at the population level. This review summarizes recent advances in AI-aided microbiology, with a particular emphasis on AMR surveillance. Specific topics include deep learning frameworks for ARG annotation, emerging approaches to identifying new resistance genes, and multimodal applications (genomic and clinical metadata) aimed at improving phenotype prediction. The role of metagenome-assembled genomes (MAGs) to enhance AMR surveillance efforts is noted, along with their noted limitations relative to isolate genomes. The discussion includes the examination of explainable AI (XAI) techniques including SHAP, attention mechanism approaches, and gradient-based attribution approaches, with the aim of increasing transparency and clinical explainability. We also cover potential applications including AI-enabled non-invasive fecal microbiome diagnostics, laboratory automation, and environmental surveillance. While there has been significant progress, unresolved issues exist relating to dataset variations, liability of models to datasets, interpretability, and regulatory approval. Overcoming these barriers, however, will require standardized frameworks for these workflows, privacy-preserving federated learning methods, and interpretable AI frameworks for clinical and public health tools. AI could fundamentally change AMR surveillance by allowing for earlier resistance detection, advanced risk assessment recommendation, and improved monitoring strategies globally.
