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Individual Brain Metabolic Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation Predicts Seizure Outcomes of Temporal Lobe Epilepsy.

Objective: We aimed to use an individual metabolic connectome method, the Jensen-Shannon Divergence Similarity Estimation (JSSE), to charact … the information from connection and topological metrics, which was conducted by the multiple kernel support vector machine. The validation was performed using the nested leave-one-out cross-validation strategy to confirm the performance of the methods. Results: With a median follow-up of 33 months, 50% of patients achieved SZF. No relevant differences in clinical featur …

Effect of Comprehensive Rehabilitation Training Program in Orthopedic Nursing of Patients with Residual Limb Injury Caused by Crush.

This study was developed to explore the role and application value of a comprehensive rehabilitation training (CRT) program based on the remote monitoring system of limb rehabilitation training (LRT-RM system) in the rehabilitation nursing of patients with residual limb injuries caused by crush. The LRT-RM system was constructed based on the characteristics of limb movement and using the time-domain analysis method and support vector machine (SVM). The 84 crush injury patients were selected as the research objects and divided into a control group (Con group, received conventional rehabilitatio…

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy.

BACKGROUND CONTEXT: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitat … tions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature s …

Bio-fertilizer Affects Structural Dynamics, Function, and Network Patterns of the Sugarcane Rhizospheric Microbiota.

Fertilizers and microbial communities that determine fertilizer efficiency are key to sustainable agricultural development. Sugarcane is an … ect size analysis (LEfSe) searched for biomarkers under different fertilizer treatments. Meanwhile, support vector machine (SVM) assessed the importance of the microbial genera contributing to the variability between fertilizers, of interest were the bacteria Anaerolineace, Vulgatibacter, and Paenibacillus and the fungi Cochliobolus, Sordariales, and Dothideomycetes bet …

Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis.

BACKGROUND: The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI-RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. PURPOSE: To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa. STUDY TYPE: Retrospective. POPULATION: Ninety patients; 44 consecutive TZ PC…

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both h … ies for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on …

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury.

UNLABELLED: For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological … e regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/ …

Objective classification system for sagittal craniosynostosis based on suture segmentation.

PURPOSE: Spring-assisted surgery is an effective and minimally invasive treatment for sagittal craniosynostosis (CSO). The principal barrier … CSO patients were classified into four subtypes: anterior, central, posterior, and complex with the support vector machine approach. Fivefold cross validation (CV) was employed to evaluate the capability of selected features in discriminating the four subtypes in 33 sagittal CSO patients. Receiver operating characteristics (ROC) curves were used to assess the robustness …

Interpretable whole-brain prediction analysis with GraphNet.

Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led to broad application of "off-the-shelf" classification and regression methods. These generic approaches allow investigators to use ready-made algorithms to accurately decode perceptual, cognitive, or behavioral states from distributed patterns of neural activity. However, when applied to correlated whole-brain fMRI data these methods

Продольная цифровая Фенотиляция многоразового склероза, использующая пассивно оповещенные поводы и экологические оценки моментальных изменений: оценка реального мира.

ПОДСКАЗКА: Продольное отслеживание симптомов множественного склероза (MS) в окружающей среде человека среды может улучшить самоконтроль и клиническое управление людьми с MS. Традиционные методы отслеживания симптомов основаны на самоотчетах и клинических посещениях, которые могут быть редкими, субъективными и обременительными. Цифровые фенотификации с использованием пассивно собранных данных с датчиков смартфонов и фитнес-трекеров являются многообещающей альтернативой для непрерывного мониторинга симптомов в режиме реального времени с минимальным бременем для пациента. ЦЕЛЬ: Мы поставили перед

Совпадение в аннотации оригинала
BACKGROUND: Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's environment may improve self-monitoring and clinica … d (called action) and one from the preceding period (called context). Using an ML pipeline based on support vector machines and AdaBoost, we evaluated the predictive performance of sensor-based models, both with and without ecological momentary assessment inputs. RESULTS: Between November 16, 2019, and January 24, 2021, overall, 104 people with MS (women: n=88, 84.6%; …

Toward reliable computer-aided brain tumor diagnosis: a contrast-enhanced deep learning approach with hybrid KNN classification.

Timely and accurate diagnosis of brain tumors using magnetic resonance imaging (MRI) is a major concern in neuro-oncology because of the pot … layer for this representation, three supervised learning methods, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained on this set of features and compared with the softmax head of the CNN. The experiments were performed on a publicly available, three-source combined MRI dataset of 7,023 images across glioma, meningioma, p …

Exploring Vitamin D Signaling-Associated Biomarkers and Their Diagnostic Value in Diabetic Retinopathy: A Combined Transcriptomic and Single-Cell Analysis With Experimental Validation.

BACKGROUND: Diabetic retinopathy (DR) can significantly impair vision and lead to blindness. Vitamin D (VD) has been shown to enhance the pr … enes. Candidate genes were selected based on the intersection of DEGs and model genes. "Boruta" and support vector machine-recursive feature elimination (SVM-RFE), along with expression validation, were used to screen for biomarkers. Further analyses included immune infiltration, gene set enrichment analysis (GSEA), regulatory network construction, and drug prediction. …

Biochar-vermicompost-inorganic N/P₂O₅ integration improves maize yield and soil chemical properties in acidic soils.

Improving soil chemical quality while enhancing maize yield is critical for sustainable crop production in acidic soils. We conducted a two- … ongly positively correlated with improved soil chemical properties. Among predictive models, Linear Support Vector Machine (LSVM) provided the highest accuracy (R2 = 0.923) for estimating maize yield under integrated nutrient management. These results indicate that integrating BC and VC with reduced inorganic N/P₂O₅, in combination with lime, can enhance soil chemical q …

Diagnostic classification of children and adolescents with high-functioning autism spectrum disorder based on brain functional network characteristics of the dense individualized and common connectivity-based cortical landmark model.

OBJECTIVE: To investigate whether structure-informed functional connectivity patterns derived from the Dense Individualized and Common Conne … al connectivity was calculated among DICCCOL landmarks. Classification was performed using a linear support vector machine within a fully nested leave-one-out cross-validation framework. All supervised procedures, including FDR-corrected group comparisons, correlation-based feature selection, feature standardization, and hyperparameter optimization, were conducted exclu …

Integrated single-cell and network analysis identifies SLAMF7 as a secretory mediator of the joint-lung axis in rheumatoid arthritis-associated interstitial lung disease targetable by Tanshinone IIA.

BACKGROUND: Interstitial lung disease (ILD) is one of the most serious extra-articular complications of rheumatoid arthritis (RA). However, … n network analysis (WGCNA). Shared candidate genes were further prioritized using LASSO regression, support vector machine-recursive feature elimination (SVM-RFE), and external validation in an independent RA-ILD dataset. Protein-protein interaction (PPI) network analysis and functional enrichment were used to define a shared inflammatory program and guide compound pred …

The Role of Endoplasmic Reticulum Stress in Polycystic Ovary Syndrome and Exploration of Potential Therapeutic Targets.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women. In recent years, endoplasmic reticulum (ER) stress has gained increasing attention in the pathogenesis of PCOS. This study aims to explore the potential role of ER stress in PCOS by constructing a predictive model based on ER stress-related genes, and further evaluate the characteristics of immune infiltration and screen potential drugs. Five algorithms, including Lasso, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Algorithm (XGB), and Generalized Linear Model (GLM), were used to screen key genes a…

Development and validation of a radiomics-visual evoked potential nomogram for preoperative prediction of visual outcome after endoscopic craniopharyngioma resection.

OBJECTIVE: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endona … tion operator algorithm was used to select features and construct the radiomic score (Rad-score). A support vector machine (SVM) classifier was adopted to construct a radiomic model. Moreover, a clinical-radiomic nomogram was built by multivariable logistic regression. The performance of the nomogram was assessed by its discrimination, calibration, and clinical utility. …

Comprehensive and advanced T cell cluster analysis for discriminating seropositive and seronegative rheumatoid arthritis.

OBJECTIVE: Rheumatoid arthritis (RA) is classified into seropositive (SP-RA) and seronegative (SN-RA) types, reflecting distinct immunologic … pes: CD161+ for TCL31 and HLA-DR+CD38+TIM-3+ for TCL35, suggesting distinct pro-inflammatory roles. Support vector machine analysis (bootstrap n = 1000) validated the D-TCLs' discriminative power, achieving an accuracy of 86.2%, sensitivity of 85.7%, and specificity of 80.9%. CONCLUSIONS: This study advances our understanding of immunological distinctions between SP-RA …

CD163-Mediated Small-Vessel Injury in Alzheimer's Disease: An Exploration from Neuroimaging to Transcriptomics.

Patients with Alzheimer's disease (AD) often present with imaging features indicative of small-vessel injury, among which, white-matter hype … enes related to the mechanism of small-vessel injury in MCI. Subsequently, the Boruta algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm were performed to identify feature-selection genes. Finally, the mechanism of small-vessel injury was analyzed in MCI from the immunological perspectives; the relationship of feature-selection genes …

Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning.

BACKGROUND: Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retractio … omes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% a …