Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer’s disease

Cell Reports, Journal Year: 2024, Volume and Issue: 43(5), P. 114128 - 114128

Published: April 21, 2024

Shifts in the magnitude and nature of gut microbial metabolites have been implicated Alzheimer's disease (AD), but host receptors that sense respond to these are largely unknown. Here, we develop a systems biology framework integrates machine learning multi-omics identify molecular relationships with non-olfactory G-protein-coupled (termed "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs 335 metabolites. Using genetics-derived Mendelian randomization integrative analyses brain transcriptomic proteomic profiles, orphan (i.e., GPR84) as potential drug targets AD triacanthine experimentally activates GPR84. demonstrate phenethylamine agmatine significantly reduce tau hyperphosphorylation (p-tau181 p-tau205) patient induced pluripotent stem cell-derived neurons. This study demonstrates uncover GPCR microbiota other complex diseases if broadly applied.

Language: Английский

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Advancing Alzheimer's Therapy: Computational Strategies and Treatment Innovations

IBRO Neuroscience Reports, Journal Year: 2025, Volume and Issue: 18, P. 270 - 282

Published: Feb. 4, 2025

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting need for innovative therapeutic approaches. This review explores challenges AD integration computational methodologies to advance interventions. A comprehensive analysis recent literature was conducted elucidate broad scope etiology limitations drug discovery Our findings underscore critical role models in elucidating mechanisms, identifying targets, expediting discovery. Through simulations, researchers can predict efficacy, optimize lead compounds, facilitate personalized medicine Moreover, machine learning algorithms enhance early diagnosis enable precision analyzing multi-modal datasets. Case studies highlight application techniques therapeutics, including suppression crucial proteins implicated progression repurposing existing drugs management. Computational interplay between oxidative stress neurodegeneration, offering insights into potential Collaborative efforts biologists, pharmacologists, clinicians are essential translate clinically actionable interventions, ultimately improving patient outcomes addressing unmet medical needs individuals affected AD. Overall, integrating represents promising paradigm shift solutions overcome transform landscape treatment.

Language: Английский

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Clinical Importance of Amyloid Beta Implication in the Detection and Treatment of Alzheimer’s Disease

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(5), P. 1935 - 1935

Published: Feb. 24, 2025

The role of amyloid beta peptide (Aβ) in memory regulation has been a subject substantial interest and debate neuroscience, because both physiological clinical issues. Understanding the dual nature Aβ is crucial for developing effective treatments Alzheimer's disease (AD). Moreover, accurate detection quantification methods isoforms have tested diagnostic purposes therapeutic interventions. This review provides insight into current knowledge about vivo vitro by fluid tests brain imaging (PET), which allow preclinical recognition disease. Currently, priority development new therapies given to potential changes progression In light increasing amounts data, this was focused on employment

Language: Английский

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Artificial Intelligence for Predicting Progression and Personalizing Healthcare to Alzheimer's Disease Patients

Advances in healthcare information systems and administration book series, Journal Year: 2025, Volume and Issue: unknown, P. 155 - 190

Published: Jan. 10, 2025

This chapter explains the use of Deep Learning Models from Artificial Intelligence (AI) that take Structural and Functional Magnetic Resonance Imaging (S/FMRI) data to classify Alzheimer's disease (AD) progression stages. Early accurate diagnosis AD is necessary for timely intervention, treatment planning, providing personalized healthcare. Current limitations in diagnostic methods necessitate using AI such as Convolutional Neural Networks (CNN) Recurrent (RNN) extract features MRI develop models predicting Mild Cognitive Impairment (MCI), AD, Dementia. Initial results a case study applied methodology demonstrated improved classification accuracy over traditional accurately classifying stages developing patient care. With more refinement technologies progress, these computational approaches can drastically positively change Healthcare professionals benefit this by understanding how be implemented deal with neurodegenerative diseases.

Language: Английский

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Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2798 - 2798

Published: March 5, 2025

Drug discovery and development remains a complex time-consuming process, often hindered by high costs low success rates. In the big data era, artificial intelligence (AI) has emerged as promising tool to accelerate optimize these processes, particularly in field of oncology. This review explores application AI-based methods for drug repurposing natural product-inspired design cancer, focusing on their potential address challenges limitations traditional approaches. We delve into various approaches (machine learning, deep others) that are currently being employed purposes, role experimental techniques By systematically reviewing literature, we aim provide comprehensive overview current state AI-assisted cancer workflows, highlighting AI’s contributions accelerating development, reducing costs, improving therapeutic outcomes. also discusses opportunities associated with integration AI pipeline, such quality, interpretability, ethical considerations.

Language: Английский

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Challenges in translating laboratory findings to drug development

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 319 - 354

Published: Jan. 1, 2025

Language: Английский

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A neuromuscular clinician’s primer on machine learning

Journal of Neuromuscular Diseases, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Artificial intelligence is the future of clinical practice and increasingly utilized in medical management research. The release ChatGPT3 2022 brought generative AI to headlines rekindled public interest software agents that would complete repetitive tasks save time. intelligence/machine learning underlies applications devices which are assisting clinicians diagnosis, monitoring, formulation prognosis, treatment patients with a spectrum neuromuscular diseases. However, these have remained research sphere, neurologists as specialty running risk falling behind other specialties quicker embrace new technologies. While there many comprehensive reviews on use artificial medicine, our aim provide simple practical primer educate basics machine learning. This will help specializing electrodiagnostic medicine understand nerve muscle ultrasound, MRI imaging, electrical impendence myography, conductions electromyography cohort studies, limitations, pitfalls, regulatory ethical concerns, directions. question not whether change practice, but when how. How look back upon this period transition be determined by how much changed or fast embraced patient outcomes were improved.

Language: Английский

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Instruction multi-constraint molecular generation using a teacher-student large language model

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 23, 2025

While various models and computational tools have been proposed for structure property analysis of molecules, generating molecules that conform to all desired structures properties remains a challenge. We introduce multi-constraint molecular generation large language model, TSMMG, which, akin student, incorporates knowledge from small tools, namely, the "teachers." To train we construct set text-molecule pairs by extracting these "teachers," enabling it generate novel descriptions through text prompts. experimentally show TSMMG remarkably performs in meet complex requirements described natural across two-, three-, four-constraint tasks, with an average validity over 99% success ratio 82.58%, 68.03%, 67.48%, respectively. The model also exhibits adaptability zero-shot testing, creating satisfy combinations not encountered. It can comprehend inputs styles, extending beyond confines outlined presents effective using language. This framework is only applicable drug discovery but serves as reference other related fields.

Language: Английский

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Mechanistic Approach to Immunity and Immunotherapy of Alzheimer’s Disease: A Review

ACS Chemical Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 22, 2024

Alzheimer's disease (AD) is a debilitating neurodegenerative condition characterized by progressive cognitive decline and memory loss, affecting millions of people worldwide. Traditional treatments, such as cholinesterase inhibitors NMDA receptor antagonists, offer limited symptomatic relief without addressing the underlying mechanisms. These limitations have driven development more potent effective therapies. Recent advances in immunotherapy present promising avenues for AD treatment. Immunotherapy strategies, including both active passive approaches, harness immune system to target mitigate AD-related pathology. Active stimulates patient's response produce antibodies against AD-specific antigens, while involves administering preformed or cells that specifically amyloid-β (Aβ) tau proteins. Monoclonal antibodies, aducanumab lecanemab, shown potential reducing Aβ plaques slowing clinical trials, despite challenges related adverse responses need precise targeting. This comprehensive review explores role AD, evaluates current successes immunotherapeutic discusses future directions enhancing treatment efficacy.

Language: Английский

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Clinical metabolomics: useful insights, perspectives and challenges

Metabolism Open, Journal Year: 2024, Volume and Issue: 22, P. 100290 - 100290

Published: May 31, 2024

Metabolomics, a cutting-edge omics technique, is rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and discovery novel metabolite signatures predictive disease risk, aiding earlier detection, prognosis prediction treatment response. The capacity this approach to simultaneously quantify thousands metabolites, i.e. small molecules less than 1500 Da samples, positions it as promising tool for research clinical applications personalized medicine. Clinical metabolomics studies have proven valuable understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers risk. Liquid chromatography-mass spectrometry predominant analytical method used metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, chemistry imaging, health records, other pertinent health-related data may yield significant advances beneficial both public initiatives, precision medicine, rare disorders multimorbidity. This special issue has gathered original articles topics related well articles, reviews, perspectives highlights broader translational metabolic research. Additional necessary identify which metabolites consistently enhance risk across various populations are causally linked progression.

Language: Английский

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