Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Advanced Science, Год журнала: 2024, Номер unknown

Опубликована: Июль 3, 2024

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting outcomes, to also include reducing the risk comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery new markers for various health conditions. Integration wearables with intelligent frameworks represents ground-breaking innovations automation operations, conducting advanced large-scale data analysis, generating predictive models, facilitating remote guided clinical decision-making. These substantially alleviate socioeconomic burdens, creating a paradigm shift diagnostics, revolutionizing medical assessments technology development. This review explores critical topics recent progress development 1) systems solutions physiological monitoring, as well 2) discussing current trends adoption smart technologies within settings developing biological assays, ultimately 3) exploring utilities platforms discovery. Additionally, translation from research labs broader applications. It addresses associated risks, biases, challenges widespread Artificial Intelligence (AI) integration diagnostics systems, while systematically outlining potential prospects, challenges, opportunities.

Язык: Английский

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Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach

International Immunopharmacology, Год журнала: 2025, Номер 148, С. 114090 - 114090

Опубликована: Янв. 22, 2025

Язык: Английский

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Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 20, 2024

Abstract Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 patients collected 2020 2023 four cohorts in Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From discovery cohort combining three European 804 patients, age RNA LEF1-AS1 identified as predictive features, yielding an AUC 0.83 (95% CI 0.82–0.84) balanced accuracy 0.78 0.77–0.79) feedforward neural network classifier. Validation independent Canadian 482 showed consistent performance. Cox regression analysis indicated that higher levels correlated reduced (age-adjusted hazard ratio 0.54, 95% 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability be measured hospital settings. Here, we demonstrate promising enhancing patient management.

Язык: Английский

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Progress in toxicogenomics to protect human health

Nature Reviews Genetics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 2, 2024

Язык: Английский

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Harnessing Epigenetics: Innovative Approaches in Diagnosing and Combating Viral Acute Respiratory Infections

Pathogens, Год журнала: 2025, Номер 14(2), С. 129 - 129

Опубликована: Фев. 1, 2025

Acute respiratory infections (ARIs) caused by viruses such as SARS-CoV-2, influenza viruses, and syncytial virus (RSV), pose significant global health challenges, particularly for the elderly immunocompromised individuals. Substantial evidence indicates that acute viral can manipulate host's epigenome through mechanisms like DNA methylation histone modifications part of immune response. These epigenetic alterations persist beyond phase, influencing long-term immunity susceptibility to subsequent infections. Post-infection modulation host may help distinguish infected from uninfected individuals predict disease severity. Understanding these interactions is crucial developing effective treatments preventive strategies ARIs. This review highlights critical role following ARIs in regulating innate defense mechanisms. We discuss implications diagnosing, preventing, treating infections, contributing advancement precision medicine. Recent studies have identified specific changes, hypermethylation interferon-stimulated genes severe COVID-19 cases, which could serve biomarkers early detection progression. Additionally, therapies, including inhibitors methyltransferases deacetylases, show promise modulating response improving patient outcomes. Overall, this provides valuable insights into landscape ARIs, extending traditional genetic perspectives. are essential advancing diagnostic techniques innovative address growing threat emerging causing globally.

Язык: Английский

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Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy

Regional Anesthesia & Pain Medicine, Год журнала: 2025, Номер 50(2), С. 110 - 120

Опубликована: Фев. 1, 2025

Pain affects millions worldwide, posing significant challenges in diagnosis and treatment. Despite advances understanding pain mechanisms, there remains a critical need for validated biomarkers to enhance diagnosis, prognostication, personalized therapy. This review synthesizes recent advancements identifying validating acute chronic biomarkers, including imaging, molecular, sensory, neurophysiological approaches. We emphasize the emergence of composite, multimodal strategies that integrate psychosocial factors improve precision applicability management. Neuroimaging techniques like MRI positron emission tomography provide insights into structural functional abnormalities related pain, while electrophysiological methods electroencepholography magnetoencepholography assess dysfunctional processing neuroaxis. Molecular cytokines, proteomics, metabolites, offer diagnostic prognostic potential, though extensive validation is needed. Integrating these with clinical practice can revolutionize management by enabling treatment strategies, improving patient outcomes, potentially reducing healthcare costs. Future directions include development composite biomarker signatures, artificial intelligence, signature integration decision support systems. Rigorous standardization efforts are also necessary ensure clinically useful. Large-scale collaborative research will be vital driving progress this field implementing practice. comprehensive highlights potential transform management, offering hope improved personalization, outcomes.

Язык: Английский

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Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Current Oncology, Год журнала: 2024, Номер 31(9), С. 5255 - 5290

Опубликована: Сен. 6, 2024

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning natural language processing, their applications HNC. The integration of with imaging techniques, genomics, electronic health records explored, emphasizing its role early detection, biomarker discovery, planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, real-time monitoring systems are poised to further advance field. Addressing these fostering among experts, clinicians, researchers crucial developing equitable effective applications. future HNC holds significant promise, offering potential breakthroughs diagnostics, personalized therapies, improved patient outcomes.

Язык: Английский

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Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 2, 2024

Abstract Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The utilized machine learning algorithms incorporating stacking. GSE37642 dataset was used as the training data, and GSE12417 TCGA-LAML cohorts were validation data. Stacking merge three prediction models, subsequently using random forests algorithm refit final stacking linear predictor clinical factors. model, featuring factors, achieved AUC values of 0.840, 0.876 0.892 1, 2 3 years within dataset. In external dataset, corresponding AUCs 0.741, 0.754 0.783. predictive performance in surpasses that simply incorporates all predictors. Additionally, exhibited good calibration accuracy. conclusion, our findings indicate novel refines for AML patients, while strategy displays potential integration.

Язык: Английский

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Wearable MOF Biosensors: A New Frontier in Real-Time Health Monitoring

TrAC Trends in Analytical Chemistry, Год журнала: 2025, Номер unknown, С. 118156 - 118156

Опубликована: Янв. 1, 2025

Язык: Английский

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Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances

Mathematics, Год журнала: 2025, Номер 13(3), С. 420 - 420

Опубликована: Янв. 27, 2025

Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling robust analysis complex, high-dimensional, heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, differential expression analysis, network inference, somatic mutation calling, can be reframed as binary classification thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due differences distributions, noise levels, underlying biological contexts. Ensemble particularly unsupervised ensemble approaches, emerges compelling solution by integrating predictions from multiple algorithms leverage strengths mitigate weaknesses. review focuses on principles recent advancements with particular emphasis methods. These demonstrate ability address critical challenges such lack labeled integration operating different scales. Overall, this highlights transformative potential learning advancing predictive accuracy, robustness, interpretability applications.

Язык: Английский

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