Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Design of Bionanomaterial of Chitosan Carbohydrate Polymer Composited with Broccoli Extract and Zinc Oxide Nanoparticles: Anticancer Activity in Human Osteosarcoma

In the current research, a chitosan/broccoli extract/ZnO nanoparticle (CH/BE/ZnO) bionanocomposite was created. The physicochemical properties of CH/BE/ZnO bionanocomposite were investigated using a variety of methods, including field emission scanning electron microscopy (FESEM), elemental analysis (CHN-O), X-ray diffraction (XRD), Fourier transform infrared spectrum (FTIR), Brunauer-Emmett-Teller (BET), and transmission electron microscopy (TEM). The CH/BE/ZnO bionanocomposite's biological activity was assessed by examining its cytotoxicity capabilities against a bone cancer cell line (MG63). The total pore volume and specific surface area of CH/BE/ZnO are 0.134 cm3/g and 16.99 m2/g, respectively. The IC50 results for CH/BE/ZnO bionanocomposite in bone cancer investigations using the MTT test against the MG63 cell line was 115 μg/mL. The results indicate that the CH/BE/ZnO bionanocomposite is an effective chemotherapeutic agent against human osteosarcoma. The CH/BE/ZnO bionanocomposite showed high performance and structure, which means innovating nanomaterial agents for biological applications in the future.

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

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.

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

BACKGROUND

Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.

OBJECTIVE

The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.

METHODS

A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.

RESULTS

We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.

CONCLUSIONS

The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.

TRIAL REGISTRATION

PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.

Nanotechnology in the Diagnosis and Treatment of Antibiotic-Resistant Infections

The development of antimicrobial resistance (AMR), along with the relative reduction in the production of new antimicrobials, significantly limits the therapeutic options in infectious diseases. Thus, novel treatments, especially in the current era, where AMR is increasing, are urgently needed. There are several ongoing studies on non-classical therapies for infectious diseases, such as bacteriophages, antimicrobial peptides, and nanotechnology, among others. Nanomaterials involve materials on the nanoscale that could be used in the diagnosis, treatment, and prevention of infectious diseases. This review provides an overview of the applications of nanotechnology in the diagnosis and treatment of infectious diseases from a clinician's perspective, with a focus on pathogens with AMR. Applications of nanomaterials in diagnosis, by taking advantage of their electrochemical, optic, magnetic, and fluorescent properties, are described. Moreover, the potential of metallic or organic nanoparticles (NPs) in the treatment of infections is also addressed. Finally, the potential use of NPs in the development of safe and efficient vaccines is also reviewed. Further studies are needed to prove the safety and efficacy of NPs that would facilitate their approval by regulatory authorities for clinical use.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.