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Модель " Коопман " , ориентированная на прогнозируемый контроль функциональной электрической стимуляции лодыжки дорсифлексиона и помощи в планировании.

Функциональная электрическая стимуляция (ФЭУ) может быть эффективным средством усиления паретической функции мышц и восстановления нормальной функции лодыжки. Наш подход предусматривает использование в реальном масштабе времени модели предсказательного контроля (МПК), основанной на концепции Koopman Operation (KOT). Эти рамки точно отражают сложную нелинейную динамику движения лодыжки в линейной форме, что позволяет применять линейные подходы к контролю для крайне нелинейной динамики FES. Наш метод точно предсказывает движения лодыжки, вызванные FES, с учетом нелинейной динамики приведения в д

From virtual to reality: innovative practices of digital twins in tumor therapy.

BackgroundAs global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.ObjectiveThis study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.MethodsRelevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distri

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… Additionally, four studies used support vector machines (SVMs) for disease classification tasks [ 54 , 58 , 89 , 90 ]. Hybrid models integrating cross-domain algorithms provided key support for tumor dynamic simulation and personalized treatment optimization [ 44 , 83 , 91 ]. … | [ 56 ] Natural language processing | Radiology reports | Multi-report model's Accuracy, Recall, F1 Score higher than single report analysis | [ 57 ] KNN-weighted dual support vector machine | Breast cancer imaging | Outperformed comparison models in Accuracy, Recall, and F1 Score across different …

Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.

BackgroundBone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM.MethodsTwo reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China

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… Our search strategy utilized a combination of keywords and subject terms such as “Artificial Intelligence,” “Random Forest,” “Deep Learning,” “Radiomics,” “Support Vector Machine,” “Decision Tree,” “Machine Learning,” “Bone and Bones,” “Neoplasm Metastasis,” “Sensitivity,” and “Specificity.” … Vector Machine (SVM). …

Искусственные интеллектуальные технологии зондирования для борьбы с раком, диабетом и COVID-19.

Интеграция искусственного интеллекта (АИ) с износимыми сенсорными технологиями может привести к революции в мониторинге и лечении различных хронических заболеваний и острых заболеваний. Комбинированные износы АИ классифицируются по таким основополагающим методам зондирования, как электрохимическая, колориметрическая, химическая, оптическая, а также давление/стабильность. Алгоритмы АИ повышают эффективность изношенных датчиков, обеспечивая индивидуализированный, постоянный надзор и прогнозный анализ, помогая распознавать время и оптимизируя терапевтические методы. В этой рукописи рассматриваютс

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… A combination of three image color spaces, RGB, HSV, and LAB, and four machine learning (ML) models: logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN) for their precision in predicting analyte concentrations. … After pre-processing the data, a support vector machine model trained on the Pima Indian Diabetes Database is used to forecast the risk of diabetes. …

Landscape of small nucleic acid therapeutics: moving from the bench to the clinic as next-generation medicines.

The ability of small nucleic acids to modulate gene expression via a range of processes has been widely explored. Compared with conventional treatments, small nucleic acid therapeutics have the potential to achieve long-lasting or even curative effects via gene editing. As a result of recent technological advances, efficient small nucleic acid delivery for therapeutic and biomedical applications has been achieved, accelerating their clinical translation. Here, we review the increasing number of small nucleic acid therapeutic classes and the most common chemical modifications and delivery platf

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as w

Adeno-associated virus as a delivery vector for gene therapy of human diseases.

Adeno-associated virus (AAV) has emerged as a pivotal delivery tool in clinical gene therapy owing to its minimal pathogenicity and ability to establish long-term gene expression in different tissues. Recombinant AAV (rAAV) has been engineered for enhanced specificity and developed as a tool for treating various diseases. However, as rAAV is being more widely used as a therapy, the increased demand has created challenges for the existing manufacturing methods. Seven rAAV-based gene therapy products have received regulatory approval, but there continue to be concerns about safely using high-dos

Machine learning triumphs in differentiating fungal and bacterial infections.

OBJECTIVES: Patients with multiple myeloma (MM) are immunocompromised and highly susceptible to severe infections. Invasive fungal disease (IFD) carries a particularly high mortality rate, yet its presentation often mimics bacterial infection, leading to misdiagnosis, inappropriate antibiotic use, and delayed antifungal therapy. This case-control study aimed to develop and validate a machine learning-based model to accurately distinguish IFD from bacterial infection in MM patients. METHODS: In this retrospective case-control study, we analyzed epidemiological, clinical, and laboratory data fro

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… This study employed and compared nine machine learning algorithms: LR, Support Vector Machine (SVM), KNN, Random Forest (RF), Gradient Boosting, LightGBM, XGBoost, AdaBoost, and Decision Tree. … Vector Machine (SVM), K-Nearest Neighbors (KNN), AdaBoost, and Decision Tree. ( B ) Precision-recall trade-off plot of the models Stability verification of the optimal model To assess the stability of the optimal LR model’s performance, a stratified bootstrap sampling technique was utilized, involving …

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the develo

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… Computational algorithms with support vector machines (SVM) have been applied using data sets of approximately 100–200 samples for ACP prediction by Tyagi et al. (AntiCP) [ 47 ] and Hajisharifi et al. [ 48 ] in early development. … -C. mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides Int. J. Mol. Sci. 2019 20 1964 10.3390/ijms20081964 31013619 PMC6514805 55. Schaduangrat N. Nantasenamat C. Prachayasittikul V. Shoombuatong W. …

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

ObjectivesTo develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU.DesignA retrospective cohort study.SettingFive university hospitals in the Netherlands between 2002 and 2015.PatientsA total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.InterventionsNone.Measurements and main resultsWe created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventila

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… Another retrospective study compared prediction models for in-hospital mortality in patients with hematologic malignancies admitted to the ICU, acting as a proof-of-concept study for support vector machine modeling ( 15 ). … Verplancke T, Van Looy S, Benoit D, et al. : Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med Inform Decis Mak 2008; 8:56. 16. …

Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.

BackgroundConcurrent chemoradiotherapy (CCRT) was highly effective in treating cervical cancer (CC) but raised the risk of bone marrow suppression. However, models to predict the risk of myelosuppression in CC patients after CCRT based on computed tomography (CT) imaging histology are immature.MethodsThe Region of interest (ROI) of the CT images was segmented, and radiomic features were extracted. Next, important features were further selected. The training and test sets were split 7:3, and 8 machine learning algorithms were applied to classify the features in the training set. The model's per

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… Subsequently, a cervical cancer bone marrow suppression response classification imaging model was constructed using eight machine learning algorithms available caret package (1.0.2) [ 23 ], including regularized logistic regression (regLogistic), support vector machine (SVM), random forest (RF), k-nearest … vector machine K-nearest neighbors Receiver operating characteristic Decision curve analysis Bone marrow Para aortic lymph nodes Author contributions Conceptualization, Chen Sun and Alan Chu; Data curation, Jinghui Yang; Formal analysis, Shijia Liu; Funding acquisition, Mengxi Li,Shijia Liu,Xin …

Nanomaterials in gene therapy and genome editing: challenges and emerging directions.

Nanomaterials are redefining the landscape of gene and genome editing, yet their translation to clinical reality remains constrained by multiple unresolved challenges. While they provide structural and functional advantages for delivering nucleic acids and CRISPR/Cas systems across biological barriers, their behavior within living systems is often unpredictable, leading to issues such as off-target editing, immune activation, and inconsistent biodistribution. The design of nanocarriers, whether lipid-based, polymeric, inorganic, must therefore balance efficiency with safety, integrating physic

Identification and validation of CKAP2 as a novel biomarker in the development and progression of rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a common chronic joint disease. However, many patients exhibit inadequate responses to treatment due to disease heterogeneity. Identifying novel biomarkers for RA is crucial for advancing molecular diagnosis and identifying potential therapeutic targets. METHODS: Synovial tissue transcriptome data from RA patients and healthy controls were obtained from the GEO database. Differentially expressed gene (DEG) analysis, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA) identified key gene modules in RA. Machine learni

Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers.

BackgroundDetection of cancer at the early stage currently offers the only viable strategy for reducing disease-related morbidity and mortality. Various approaches for multi-cancer early detection are being explored, which largely rely on capturing signals from circulating analytes shed by tumors into the blood. The fact that biomarker concentrations are limiting in the early stages of cancer, however, compromises the accuracy of these tests. We, therefore, adopted an alternate approach that involved interrogation of the serum metabolome with machine learning-based data analytics. Here, we mon

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… Owing to non‐linearity in the data between classification variables and feature variation, and the limitation of samples, we used nonlinear Support vector machine (SVM) for the purpose. …

Exploring The Role of TOP2A in the Intersection of Pathogenic Mechanisms Between Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis Based on Bioinformatics.

BackgroundRheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF) share a common pathogenic mechanism, but the underlying mechanisms remain ambiguous. Our study aims at exploring the genetic-level pathogenic mechanism of these two diseases.MethodsWe carried out bioinformatics analysis on the GSE55235 and GSE213001 datasets. Machine learning was employed to identify candidate genes, which were further verified using the GSE92592 and GSE89408 datasets, as well as quantitative real-time PCR (qRT-PCR). The expression levels of TOP2A in RA and IPF in vitro models were confirmed using West

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… Machine Learning We utilized least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) learning to identify potential candidate genes associated with the diagnosis of IPF and RA. … What is a support vector machine? Nat Biotechnol . 2006;24:1565–1567. doi: 10.1038/nbt1206-1565 24. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods . 2015;12:453–457. doi: 10.1038/nmeth.3337 25. …

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning.

Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The modu

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… The e1071 package was used to build the support vector machine (SVM) model, a typical supervised machine learning method. … vector machine; XGBoost, Xtreme Gradient Boosting. …

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia, leukemia, and lymphoma. This review explores how AI enhances diagnostic accuracy, efficiency, and patient outcomes by processing complex datasets and identifying patterns beyond human capability. AI-driven advancements in hematology include innovations in image analysis, genomic data int

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… Algorithms such as support vector machines, random forests, and neural networks are commonly used to develop predictive models for diagnosing blood disorders. … AI utilizes supervised learning algorithms, such as support vector machines (SVMs) and decision trees, to detect patterns in labeled hematological data [ 23 ] . These algorithms are trained on historical data to recognize specific disease characteristics and classify new patient samples. …

Deep Learning-Based 30-Day Mortality Prediction in Critically Ill Bone and Bone Marrow Metastasis Patients: A Multicenter Retrospective Cohort Study.

Bone and bone marrow Metastasis (BBM) are life-threatening complications of advanced malignancies, frequently requiring intensive care and associated with high short-term mortality. However, prognostic tools specifically tailored to critically ill BBM patients are limited. This multicenter cohort study aimed to develop and validate deep learning models for predicting 30-day mortality using ICU data from MIMIC-IV, eICU-CRD, and the First Affiliated Hospital of Xinjiang Medical University. After univariate screening, XGBoost-Boruta and Lasso regression identified 11 key clinical features within

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… A set of traditional machine learning models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), AdaBoost, XGBoost, LightGBM, and CatBoost, was also developed to serve as comparators to the deep learning models. … Comparative Performance of DL Models Traditional non-deep learning models, including logistic regression, support vector machines, k-nearest neighbors, random forests, and gradient boosting variants (e.g., XGBoost, CatBoost, LightGBM), were unable to achieve accurate prediction of 30-day mortality in …

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine lear

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… For instance, Burns et al. [ 66 ] used a combination of watershed segmentation and a support vector machine classifier, achieving sensitivity of 79.0–90.0% in detecting osteoblastic spinal metastases. …

Цифровой дистанционный мониторинг людей с множественным склерозом.

ВВЕДЕНИЕ: Множественный склероз (MS) является хроническим нейродегенеративным заболеванием, которое затрагивает более 2,8 млн. человек во всем мире и приводит к значительным симптомам моторного и немоторного происхождения. Эффективный мониторинг заболеваний имеет решающее значение для улучшения результатов лечения пациентов, но часто затрудняется ограниченностью редких клинических оценок. Цифровые средства дистанционного мониторинга с использованием больших данных, и МА открывает новые возможности для отслеживания симптомов в режиме реального времени и выявления развития заболеваний. МЕТОДЫ: В