Advances in AI-assisted biochip technology for biomedicine

Machine learning and clinical EEG data for multiple sclerosis: A systematic review

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques - including Deep Learning (DL) models - offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and DL models to EEG data for MS. It explores the methodologies used, with a focus on DL architectures such as Convolutional Neural Networks (CNNs) and hybrid models, and highlights recent advancements in ML techniques and EEG technologies that have significantly improved MS diagnosis and monitoring. The review addresses the challenges and potential biases in using ML-based EEG analysis for MS. Strategies to mitigate these challenges, including advanced preprocessing techniques, diverse training datasets, cross-validation methods, and explainable Artificial Intelligence (AI), are discussed. Finally, the paper outlines potential future applications and trends in ML for MS management. This review underscores the transformative potential of ML-enhanced EEG analysis in improving MS management, providing insights into future research directions to overcome existing limitations and further improve clinical practice.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

Unraveling the future: hot topics shaping molecular diagnostics today

INTRODUCTION

This special report highlights the transformative potential of advanced diagnostic technologies in modern healthcare, emphasizing their role in enhancing disease detection, treatment personalization, and patient outcomes.

AREAS COVERED

Innovations such as Next-Generation Sequencing (NGS), liquid biopsy, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) -based diagnostics, Point-of-Care (PoC) testing, microbiome analysis, and Artificial Intelligence are reshaping the diagnostic landscape. These methods facilitate early identification of diseases, enable tailored therapies based on individual genetic profiles, and provide noninvasive monitoring options. Furthermore, telemedicine enhances access to care while reducing costs associated with traditional healthcare delivery. Despite these advancements, challenges remain regarding regulatory compliance, data privacy concerns, and disparities in access to diagnostic services. The report underscores the need for ongoing collaboration among stakeholders to address these limitations effectively.

EXPERT OPINION

By prioritizing equitable access and continuously evaluating emerging technologies' impact on patient safety and health outcomes, the healthcare system can harness the full potential of modern diagnostics to improve global health.

Editorial Perspective: Advancements in Microfluidics and Biochip Technologies

Microfluidics and biochip technologies continue to play a key role in driving innovation across biomedical, environmental and engineering disciplines [...].

Wearable Aptasensors

This Perspective explores the revolutionary advances in wearable aptasensor (WA) technology, which combines wearable devices and aptamer-based detection systems for personalized, real-time health monitoring. The devices leverage the specificity and sensitivity of aptamers to target specific molecules, offering broad applications from continuous glucose tracking to early diagnosis of diseases. The integration of data analytics and artificial intelligence (AI) allows early risk prediction and guides preventive health measures. While challenges in miniaturization, power efficiency, and data security persist, these devices hold significant potential to democratize healthcare and reshape patient-doctor interactions.

Impact of Metabolites from Foodborne Pathogens on Cancer

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins and secondary metabolites, and their implications for human health, particularly concerning cancer risk. We examine various pathogens such as Salmonella sp., Campylobacter sp., Escherichia coli, and Listeria monocytogenes, detailing the specific metabolites of concern and their carcinogenic mechanisms. This study discusses analytical techniques for detecting these metabolites, such as chromatography, spectrometry, and immunoassays, along with the challenges associated with their detection. This study covers effective control strategies, including food processing techniques, sanitation practices, regulatory measures, and emerging technologies in pathogen control. This manuscript considers the broader public health implications of pathogen metabolites, highlighting the importance of robust health policies, public awareness, and education. This review identifies research gaps and innovative approaches, recommending advancements in detection methods, preventive strategies, and policy improvements to better manage the risks associated with foodborne pathogens and their metabolites.