Role of portable and wearable sensors in era of electronic healthcare and medical internet of things

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Staphylococcus aureus - Review on potential targets for sensors development

Staphylococcus aureus (S. aureus) is accountable for a wide variety of clinical disease with a high rate of morbidity and mortality around the globe. It has a leading place into the ESKAPE group that includes six pathogens and exhibit multidrug resistance and are the major cause of healthcare associated infections: Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. A critical overview regarding the development of sensors for both S. aureus and his, more dangerous alter ego, Methicillin-resistant S. aureus (MRSA) was presented focusing on the bacteria targets starting with the detection of the whole cell, up to specific wall components, toxins or other virulence factors. The literature data was systematically assessed having in sight the design of the sensing platforms, the analytical performances, and possible courses of action to be implemented in real practice as point-of-care (POC) devices. Moreover, a distinct section was dedicated to commercially available devices and out of the box approaches, namely the use of bacteriophages as an alternative to antimicrobial therapy and as sensors modifiers. The reviewed sensors and devices were discussed in terms of their suitability for different biosensing applications, in early screening of contamination regarding food analysis, environmental monitoring and in clinical diagnosis.