Enzymes are highly selective and efficient biological catalysts that play a critical role in modern industrial biocatalysis. Their ability to operate under mild conditions and reduce environmental impact makes them ideal alternatives to conventional chemical catalysts. This review provides a comprehensive overview of advances in enzyme-based catalysis, focusing on enzyme classification, engineering strategies, and industrial applications. The six major enzyme classes—hydrolases, oxidoreductases, transferases, lyases, isomerases, and ligases—are discussed in the context of their catalytic roles across sectors such as pharmaceuticals, food processing, textiles, biofuels, and environmental remediation. Recent developments in protein engineering, including directed evolution, rational design, and computational modeling, have significantly enhanced enzyme performance, stability, and substrate specificity. Emerging tools such as machine learning and synthetic biology are accelerating the discovery and optimization of novel enzymes. Progress in enzyme immobilization techniques and reactor design has further improved process scalability, reusability, and operational robustness. Enzyme sourcing has expanded from traditional microbial and plant origins to extremophiles, metagenomic libraries, and recombinant systems. These advances support the integration of enzymes into green chemistry and circular economy frameworks. Despite challenges such as enzyme deactivation and cost barriers, innovative solutions continue to emerge. Enzymes are increasingly enabling cleaner, safer, and more efficient production pathways across industries, supporting the global shift toward sustainable and circular manufacturing.

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine.

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation and treatment. In the context of healthcare, artificial intelligence (AI) uses sophisticated computational methods to evaluate, decipher and draw conclusions from patient data. AI has the potential to revolutionize the healthcare industry in several ways, including better managerial effectiveness, individualized treatment regimens and diagnostic improvements. In this research, the ECG signals are preprocessed for noise elimination and heartbeat segmentation. Multi-feature extraction is employed to extract features from preprocessed data, and an optimization technique is used to choose the most feasible features. The i-AlexNet classifier, which is an improved version of the AlexNet model, is used to classify between normal and anomalous signals. For experimental evaluation, the proposed approach is applied to PTB and MIT_BIH databases, and it is observed that the suggested method achieves a higher accuracy of 98.8% compared to other works in the literature.

Hypertension is a major contributor to heart disease, renal failure, and stroke. High salt is one of the significant risk factors associated with the onset and persistence of hypertension. Experimental and observational studies have confirmed cardiovascular and non-cardiovascular detrimental effects associated with chronic intake of high salt. Because of convenience and present urban lifestyles, consumption of fast food has led to daily salt intake above the recommended level by the World Health Organization. This study provides an understanding of the body regulatory mechanisms that maintain sodium homeostasis under conditions of high salt intake, without health consequences, and how these mechanisms adapt to chronic high salt load, leading to adverse cardiovascular, renal, and non-cardiovascular outcomes. Recent research has identified several mechanisms through which high sodium intake contributes to hypertension. Of them, heightened renin–angiotensin–aldosterone and sympathetic activity associated with impaired pressure diuresis and natriuresis and decreased renal excretory response are reported. Additionally, there is the possibility of endothelial and nitric oxide dysfunction leading to vascular remodeling. These changes raise cardiac output and peripheral vascular resistance. Knowing how these collective mechanisms adapt to chronic intakes of high salt helps develop effective therapeutic policies to fight salt-induced hypertension.

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients’ chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model’s input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model’s performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50’s precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing—detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics.

Background/Objectives: Although deep learning methods have achieved promising performance in recent years, comparatively less attention has been given to explicitly modeling periodic and multi-scale temporal dynamics for ECG-specific representation learning within TimesNet-based frameworks. In this work, we propose an ECG-specific TimesNet-based framework for multi-label classification of multi-lead ECG recordings that incorporates periodicity-aware temporal modeling. Methods: The proposed framework utilizes Fast Fourier Transform (FFT)-guided temporal decomposition to identify dominant frequency components and reshapes ECG sequences into period-aligned representations to better capture intra-period morphological patterns and inter-period rhythm dependencies. Multi-scale convolutional TimesBlocks are further employed to learn rhythm-aware and morphology-aware temporal representations. Results: The proposed framework was evaluated on the PTB-XL dataset using two experimental settings: Three-Class classification (NORM, AFIB, PVC) and Five-Class classification (NORM, AFIB, MI, PVC, STTC). Experiments were conducted using a one-vs-rest multi-label learning strategy with independent class probability estimation. The framework achieved mean one-vs-rest test AUC values of 0.956 and 0.913 for the Three-Class and Five-Class settings, respectively. Experimental results indicated that the reduced classification complexity in the Three-Class setting was associated with improved feature separability, more stable decision boundaries, and enhanced discriminative representation learning. Latent-space visualization using UMAP and PCA demonstrated clearer clustering in the Three-Class configuration, while gradient-based interpretability analysis highlighted physiologically relevant ECG waveform regions contributing to model predictions. In addition, computational profiling demonstrated practical feasibility with approximately 1.957 million trainable parameters, 13.14 GFLOPs computational complexity, 5.230 ms average inference latency per ECG recording, and a throughput of approximately 191 ECG recordings per second on GPU hardware. Conclusions: These findings suggest that periodicity-aware temporal modeling can improve ECGF representation learning while demonstrating practical potential for computationally efficient and interpretable automated ECG analysis applications.