Foodborne pathogens such as Escherichia coli (E. Coli), Salmonella, and Listeria monocytogenes continue to pose a major potential threat to global public health and therefore rapid, accurate, and field-deployable detection methods are still extremely desirable. This review describes cutting-edge examples of advanced biosensing platforms for the strategy of detecting these priority pathogens, focusing on clinical detection and highlighting electrochemical, optical, and microfluidic sensing modalities. This has been enabled by recent advances in functional nanomaterials, molecular recognition elements (including aptamers and nanozymes), and surface engineering strategies rendering sensors much ‘smarter’/improved in terms of sensitivity, specificity, and behaviour towards complex food matrices. However blending these biosensors with artificial intelligence (AI) and Machine Learning (ML) enabled intelligent pattern recognition, real-time analytics, and multiplexing at high-speed, turning traditional detection systems into smart diagnostic devices. We critically review recent case studies in light of biosensor design, signal transduction mechanisms, models of AI, performance validation, and applicability in different food environments. The principal challenges are identified which include matrix interference, instability of biorecognition elements, limitations in scalability, and the need for regulatory standardization. We discuss these with associated mitigation strategies that are technically sound, including ratiometric sensing, microfluidic pre-treatment techniques, explainable AI, and printable electronics. Forward-looking, we discuss biosensors enabled by being self-powered, biosensor hubs with modular pathogen panels, blockchain incorporation, and standardized validation pipelines. This review offers a prospective view toward enabling intelligent, robust, and regulation-ready biosensing platforms for next-generation food safety monitoring through the bridging of technological innovations with practical implementation.

Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare.

In recent times, smart phones are playing a vital role to recognize the human activities and became a well-known field of research. Detail overview of various research papers on human activity recognition are discussed in this paper. Artificial Intelligence(AI) models are developed to recognize the activity of the human from the provided UCI online storehouse. The data chosen is multivariate and we have applied various machine classification techniques Random Forest, kNN, Neural Network, Logistic Regression, Stochastic Gradient Descent and Naïve Bayes to analyse the human activity. Besides building AI models, the dimension of the dataset is reduced through feature selection process.Precision and Recall values were calculated and a Confusion Matrix for each model was made. Experiment results proved that the Neural Network and logistic regression provides better accuracy for human activity recognition compared to other classifiers such as k-nearest neighbor (KNN), SGD , Random Forest and Naïve Bayes though they take higher computational time and memory resources.

The convergence of natural language processing (NLP) and genomics has given rise to a new class of transformer-based models—genome large language models (Gene-LLMs)—capable of interpreting the language of life at an unprecedented scale and resolution. These models represent a revolution in the field of bioinformatics since they use only raw nucleotide sequences, gene expression data, and multi-omic annotations, leveraging self-supervised pretraining to decipher complex regulatory grammars hidden within the genome. This survey presents a comprehensive overview of the Gene-LLM lifecycle, including stages such as raw data ingestion, k-mer or gene-level tokenization, and pretext learning tasks like masked nucleotide prediction and sequence alignment. We specify their wide range of applications, spanning crucial downstream activities such as finding the enhancer or promoter, modeling the chromatin state, predicting the RNA–protein interaction, and creating synthetic sequences. We further explore how Gene-LLMs have created an impact on functional genomics, clinical diagnostics, and evolutionary inference by analyzing recent benchmarks, including CAGI5, GenBench, NT-Bench, and BEACON. We also highlight recent advances encoder–decoder modifications and the incorporation of positional embeddings, a feature specific to living organisms, which may enhance both interpretability and translational potential. Finally, this study outlines a pathway toward federated genomic learning, multimodal sequence modeling, and low-resource adaptation for rare variant discovery, establishing Gene-LLMs as a cornerstone technology for the responsible and proactive future of biomedicine.

Myocardial Infarction (MI) is a heart disease due to lack of blood and oxygen flow to some part of the heart muscle. MI is a leading cause of death globally. To decrease the likelihood of MI complications, it is essential to diagnose and treat MI promptly. A Light Weight Multi-branch Network (LW-MN) based feature extraction and classification of MI from Electrocardiogram image is proposed. Extracting the information from Electrocardiogram images without segmenting the leads separately may result in loss of information. Segmenting the 12 leads by detecting the text above each lead will extract the information accurately without any information loss. Light Weight model used to extract features and fusing all twelve leads features using the depth fusion method. The outcome from the fusion method is fed into DenseNet-161 classification algorithm. LW-MN model acquired a classification accuracy, specificity, sensitivity, and F1-score of 96.09%, 97.33%, 97.78%, and 95.18%.