Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications.

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients' health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel.

A Preliminary Investigation of Thermally Stable Schiff Base Metal Complexes for Hyperthermia: Synthesis and Biological Evaluation.

A novel Schiff base ligand (L), bearing NO donor sites, was derived from the condensation of 5-chloromethylisophthaldehyde and phenylpropanolamine (PPA). Mononuclear Co(II), Cu(II), and Zn(II) complexes were synthesized and were characterized by FTIR, UV-Vis, H NMR, ESI-mass spectroscopy, molar conductance, and thermal and electrochemical studies. The thermal investigation revealed that the complexes were stable up to 150-250 °C and began to degrade in stages, resulting in the development of respective metal oxides.

Association Between Chronic Kidney Disease Risk Categories and Abdominal Aortic Calcification: Insights From the National Health and Nutrition Examination Survey.

Background: Abdominal aortic calcification (AAC) is a critical indicator of cardiovascular risk, particularly in patients with chronic kidney disease (CKD). Traditional classification systems may underestimate the risk in those with moderate CKD. This study aimed to evaluate the association between CKD risk categories - defined by both estimated glomerular filtration rate (eGFR) and albuminuria - and the prevalence of severe AAC.

Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network.

Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters-applied pressure, ink concentration, nozzle diameter, and printing velocity-to achieve uniform, high-quality prints with the desired filament diameter. Traditional trial-and-error methods often result in significant material waste and time consumption.