Artificial intelligence

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, synaptic dysfunction, amyloid-β deposition, tau hyper phosphorylation, oxidative stress, neuroinflammation, and mitochondrial dysfunction. Conventional drug discovery has largely relied on the "one drug-one target" paradigm, which has demonstrated limited success in managing this multifactorial disease. Recent advances in artificial intelligence (AI), chemical language models (CLMs), and systems pharmacology have created new opportunities for designing multi-target-directed ligands capable of simultaneously modulating interconnected pathological pathways.Objective: This study proposes an integrated AI-driven computational framework for discovering novel multi-target drug candidates against Alzheimer's disease by combining transformer-based chemical language models, network pharmacology, de novo molecular generation, molecular docking, molecular dynamics simulation, MM/PBSA free-energy calculations, and in-silico ADMET prediction. Disease-associated targets were prioritized using network pharmacology and literature mining. Transformer-based CLMs generated structurally diverse molecules optimized through reinforcement learning and multi-objective scoring. Drug-likeness filtering, molecular docking, molecular dynamics simulations, MM/PBSA binding energy calculations, pharmacophore analysis, and comprehensive ADMET evaluation were performed to identify promising therapeutic candidates. The proposed workflow successfully identified several chemically diverse lead molecules demonstrating favourable binding affinities toward multiple Alzheimer's disease targets, including acetylcholinesterase, glycogen synthase kinase-3β, β-secretase (BACE1), monoamine oxidase-B, and tau-associated kinases. Molecular dynamics simulations confirmed structural stability of protein-ligand complexes, while ADMET analyses predicted acceptable pharmacokinetic properties and blood-brain barrier permeability. Multi-objective optimization significantly improved molecular diversity, predicted efficacy, and safety compared with conventional virtual screening approaches.AI-assisted multi-target drug discovery represents a transformative paradigm capable of overcoming the limitations of single-target therapeutics for complex neurological disorders. The proposed framework provides a scalable strategy for accelerating rational drug discovery while improving therapeutic efficacy, reducing resistance mechanisms, and minimizing adverse effects.

Lassa fever and septic fever are important infectious diseases which cause high morbidity, mortality and healthcare burden, especially in resource-limited areas. The clinical features of both diseases are so similar, and include fever, hypotension, respiratory failure, and multi-organ failure, that early diagnosis and treatment are challenging. The clinical features, epidemiological features, risk factors, early clinical predictors and biomarkers of disease severity and mortality are analyzed. Prognosis assessment and therapeutic monitoring is an important role of a number of biomarkers, such as lactate, procalcitonin, cystatin-C, and inflammatory cytokines. The review also notes that artificial intelligence, machine learning, 3D imaging technologies and epidemiological surveillance systems are increasingly playing a key role in the prediction, monitoring and clinical decision-making of diseases. The combination of biomarker-based diagnostics, AI-driven predictive modelling, and digital healthcare technologies could greatly enhance the management of infectious diseases and the preparedness of healthcare systems. The multidisciplinary research and international co-operation continue to be critical to decrease the global burden of severe infectious diseases.

The fusion of graphene neural interfaces with adaptive artificial intelligence (AI) systems has become a critical breakthrough in human-centred human-centred neurotechnologies and personalised healthcare. Graphene has an outstanding electrical conductivity, flexibility, transparency, light architecture and biocompatibility, making it an ideal material for wearable and implantable neural devices. At the same time, artificial intelligence systems that adapt their performance benefit the interpretation of the neural signals, learning in real time, signal recognition, and performance of rehabilitation. This review covers the structural and functional characteristics of graphene neural interfaces, adaptive AI in neural signal processing, and the synergy and application of both to brain–computer interfaces (BCIs), neuroprosthetics, assistive communication systems, and personalized neurotherapy. Humans studies show that graphene-AI systems have boosted the stability of neural signals, motor control, speech decoding and rehabilitation efficiency, as well as neural monitoring and remote healthcare. The review also covers critical issues like long-term biocompatibility, privacy of neural data, algorithmic transparency, cybersecurity, and regulatory approval. While small-scale clinical trials and the absence of standardized frameworks pose challenges, the potential applications of graphene-AI combination in neurological rehabilitation and intelligent healthcare systems are promising.