The study highlights the development of hydrogel beads for the detection of Nickel (Ni(II)) and Chromium (Cr(VI)) in industrial wastewater, providing an innovative solution to heavy metal pollution in a cost-effective and environmentally friendly manner. The beads change color when exposed to metals, making detection fast and simple. This technique makes a significant contribution to occupational health and safety by providing real-time detection of harmful heavy metals, thereby reducing the risk of exposure to workers. Additionally, the hydrogel beads aim to protect both worker health and surrounding ecosystems by helping industries meet environmental standards. Key findings include detection limits of 2.5 mg/mL for nickel and 1 mg/mL for chromium, and color changes stabilizing within 10 min. The hydrogels exhibited excellent swelling behavior with equilibrium swelling ratios of 72.65% for nickel and 64.18% for chromium, providing high efficiency in moisture absorption and retention. These features, combined with their ability to function without pretreatment or pH adjustment, offer an accessible and effective solution for managing metal pollution in industrial environments. Overall, the hydrogel beads demonstrated a success rate of 90.56% for nickel and 91.60% for chromium in detecting and measuring metal ions, providing an accessible and effective method for managing metal contamination in industrial environments while protecting both worker health and environmental integrity.

Hydrogels are frequently utilized in industries such as healthcare and food due to their biodegradable nature and cost-effectiveness. Their high selectivity in liquid retention makes them ideal for applications in the removal and detection of heavy metals in liquids. In our study, we adapted hydrogel beads specifically for the detection of Ni(II) and Cr(VI) metals in industrial wastewater. Upon immersion in liquid, the hydrogels exhibit a color change within 2 minutes, reaching equilibrium at 10 minutes. The color change is observed as transparent to pink for nickel and transparent to magenta for chromium. The detection limits are 2.5 mg/mL for nickel and 1 mg/mL for chromium. These hydrogel beads can be used in wastewater without the need for pre-treatment or pH adjustment. This study plays a crucial role in occupational health and safety, particularly in industrial wastewater management. The accumulation of toxic and carcinogenic heavy metals such as Ni(II) and Cr(VI) in workplaces and the surrounding environment poses serious health risks. Effective removal of these metals from water is critical for protecting water resources and reducing occupational health risks. The use of hydrogel beads offers a rapid, cost-effective, and environmentally friendly solution for the detection of these metals. In industrial facilities, the immediate detection and removal of toxic substances to which workers may be exposed enhances occupational health and safety measures. Additionally, this technology aids industrial operations in meeting their environmental responsibilities, making it a valuable tool in the field of occupational health and safety.

Cortisol, known as the body's stress hormone, regulates metabolism, inflammation, and the immune system. It affects various bodily systems such as the nervous, immune, cardiovascular, respiratory, reproductive, and musculoskeletal systems. Normal sweat cortisol levels range from 8 to 142 ng/ml, with elevated levels indicating stress (200 ng/ml in women, 500 ng/ml in men). This study focuses on colorimetric cortisol analysis using a microfluidic layer to collect sweat samples. Samples are mixed with a specific color reagent. Cortisol levels are quantitatively analyzed using Z-Score values and RGB channel density via photographs taken with the ESP 32 module. The aim is to determine cortisol levels, aiding in diagnosing conditions like Cushing's syndrome and Addison's Disease, leveraging wearable technology. The developed system integrates a microcontroller into the wearable microfluidic system for cortisol measurements throughout the day. Data is displayed and recorded on a computer for streamlined cortisol monitoring. The blue tetrazolium method used offers stable colors and rapid reactions (10 minutes). The Limit of Detection (LOD) is 0.3 ug/mL, with a detection limit of 1.1 ug/mL. Combined with the ESP 32 Cam module, the system detects cortisol concentrations from 0.8 – 60 ug/mL.

Background/Objectives: Cell-free DNA (cfDNA) is a valuable biomarker for cancer diagnosis and therapy monitoring; however, its low abundance and fragmented nature present major challenges for reliable isolation, particularly from limited plasma volumes. Here, we report the development and evaluation of a novel magnetically assisted microfluidic chip with a three-inlet design for efficient cfDNA extraction from small-volume plasma samples. Methods: The platform enables controlled infusion of plasma, lysis buffer, and magnetic nanoparticle suspensions at defined flow rates. An external magnetic field selectively captures cfDNA-bound nanoparticles while efficiently removing background impurities. Results: Direct comparison with two in vitro diagnostic (IVD)-certified commercial cfDNA extraction kits showed that the microfluidic system achieved comparable cfDNA yields at standard plasma volumes and superior performance at reduced input volumes. High DNA purity and integrity were confirmed by quantitative PCR amplification of a housekeeping gene and clinically relevant targets. The complete workflow required approximately 9 min, used minimal equipment, reduced contamination risk, and enabled rapid processing with future potential for parallel multi-chip configurations. Conclusions: These findings establish the proposed microfluidic platform as a rapid, reproducible, and scalable alternative to conventional cfDNA extraction methods. By significantly improving recovery efficiency from small plasma volumes, the system enhances the clinical feasibility of liquid biopsy applications in cancer diagnostics and precision medicine.

This study presents a new method for determining cortisol hormone levels, a key biomarker of stress, using microfluidic pads to collect sweat samples. The pads facilitate the colorimetric detection of cortisol levels via the blue tetrazolium method. The resulting color change is analytically assessed using Convolutional Neural Networks (CNN), Decision Trees, and Vector Regression, alongside advanced image processing techniques. The developed algorithm is robust, providing reliable results despite hardware variations and color distortion, enhancing the system's applicability and generalizability across different environments. Validation studies conducted with ELISA and a colorimeter revealed that the system achieved an accuracy of 84.2% in determining users' cortisol levels. Additionally, psychosocial stress levels were assessed using the Copenhagen Psychosocial Risk Assessment and the Perceived Stress Scale tests during the collection of sweat samples from 20 participants. The results demonstrated a significant correlation between cortisol levels and stress, confirming the method's reliability and effectiveness in various applications.