Integrating three-dimensional printing (3DP) in healthcare has modernized medical diagnostics and therapies by presenting various accurate, efficient, and patient-specific tailored solutions. This review critically examines the integration of 3DP in the development of miniaturized devices specifically tailored for point-of-care testing (PoCT) applications in healthcare. Focusing on progressive additive manufacturing techniques, such as material extrusion, vat photopolymerization, and powder bed fusion, the review classifies and evaluates their contributions toward designing compact, portable, and patient-specific diagnostic devices. Unlike previous reviews that treat 3DP or PoCT generically, this work uniquely bridges the technical innovations of 3DP with clinical applications by analyzing wearable sensors, biosensors, lab-on-chip systems, and microfluidic platforms. It highlights recent case studies, performance metrics, and the role of 3DP in enhancing diagnostic speed, accessibility, and personalization. The review also explores challenges such as material standardization and regulatory hurdles while outlining future directions involving artificial intelligence (AI), the Internet of Things (IoT), and multifunctional integration. This focused assessment establishes 3DP as a transformative force in decentralized and precision healthcare.

The "stress hormone," cortisol, is essential for controlling a variety of physiological functions. For determining health and identifying disorders caused by stress, accurate cortisol level monitoring is crucial. The ability to detect cortisol is necessary to determine stress levels, diagnose diseases such as Cushing’s and Addison’s, assess adrenal function, understand mood and sleep disorders, improve sports performance, advance research, direct hormone therapy, address pregnancy and fertility problems. The present article aims to give a summary of the best techniques available for cortisol monitoring along with an analysis of the issues that come with them; it also offers a viability report about the determination of cortisol concentrations in various bio-fluids. To improve the management of stress-related conditions, the review offers insight and suggestions for ongoing observation. This serves as a guide for improving cortisol detection and stress monitoring.

Combining virology with machine learning (ML) might completely transform the diagnosis of infectious diseases, particularly when it comes to precisely identifying viruses at the microscopic level. In an effort to greatly improve the speed and accuracy of microbiological diagnostics, this work presents an advanced ML framework that uses the YOLO (You Only Look Once) architecture for real-time viral identification. By comparing three top deep learning models such as convolutional neural network (CNN) and the latest YOLOv8. The study exhibits YOLOv8's superior performance, attaining a stunning validation accuracy of 75.4% over 100 epochs. This noteworthy advancement highlights YOLOv8's potential to deliver more rapid more precise diagnoses, which is essential for prompt action in infectious disease pandemics. The model's real-time capabilities underline the broader implications of machine learning in advancing global public health policies and redefining treatment paradigms, making it a crucial instrument for high-throughput viral screening. Our research represents an important advance in the field of microbiology and opens the door for the creation of scalable and effective diagnostic methods in the ongoing battle against infectious illnesses.

This research presents a comprehensive study on the development of a real-time Traffic Sign Recognition (TSR) and audio feedback system using deep learning techniques. Leveraging the German Traffic Sign Recognition Benchmark (GTSRB) dataset, the system employs Convolutional Neural Networks (CNNs) and YOLOv5 to classify 43 types of traffic signs under varying conditions. Data preprocessing techniques such as resizing, normalization, and augmentation were applied to enhance model robustness. The CNN model achieved a recognition accuracy of 95.3%, effectively addressing challenges like occlusion, lighting variations, and small object recognition. Furthermore, a real-time audio feedback mechanism using the pyttsx3 library was integrated to announce detected signs, improving driver awareness and safety. Comparative analysis revealed CNN's superior performance in classification tasks over YOLO for this application. The proposed system demonstrates potential for integration into Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, offering significant improvements in road safety and driving convenience.