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Identification of metabolism regulators as diagnostic markers for ulcerative colitis and their correlation with immune infiltration.

This study determined novel metabolism-related diagnostic biomarkers for ulcer-ative colitis (UC) and assessed their correlation with immune cell infiltration levels. Transcriptome data of UC was downloaded from the Gene Expression Omnibus (GEO) database, metabolism-related genes were summarised from the Gene Set Enrichment Analysis (GSEA) database. A total of 537 metabolism-related differen-tially expressed genes (DEGs) in UC were applied to functional enrichment analy-sis. We processed least absolute shrinkage and selection operator (LASSO) regres-sion analysis and support vector machine-rec…

Optimization of Doses of Antibiotics and Cuminaldehyde to Combat Methicillin-Resistant Staphylococcus aureus (MRSA): A Study With Machine Learning.

Methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant organism, can cause a spectrum of infections in the human host involving biofilm. Therefore, novel therapeutic approaches need to be explored to mitigate this persistent infection. This study investigated a combinatorial approach that incorporates cuminaldehyde (a phytochemical) alongside aminoglycoside antibiotics (gentamicin and tobramycin) to improve antibiofilm efficacy by addressing multiple targets. In this regard, to achieve precise dosing of the chosen compounds for effective biofilm management, different machine lear

Children identified for classroom-based math support show altered resting-state connectivity in parietal brain regions.

Approximately 25% of school-age children experience mathematics difficulties (MD). Mathematics skills have been correlated with resting-state functional connectivity (rsFC) between parietal brain regions, including the intraparietal sulcus (IPS) and the angular gyrus (AG), and a fronto-parietal network. However, the patterns of rsFC in children with MD remain less clear. This study conducted a seed-to-voxel analysis contrasting the rsFC profiles of 46 1st graders with MD (mean age = 6.95, sd = 0.34) and 60 1st graders with typical achievement (mean age = 7.08, sd = 0.38) in subdivisions of the

Wave storm dynamics and clustering, and their impacts on beach erosion.

We analyse a 36-year hydrodynamic and morphological dataset from the Hasaki coast, Japan, comprising 501 wave storm events (405 individual and 96 clustered events) to investigate the impact of storm dynamics and clustering on beach erosion. Focusing on the wave component of storms, events are identified using wave height thresholds. Daily and weekly beach profile measurements from the Hasaki Oceanographic Research Station are used to quantify erosion. The study examines the seasonal influences on Hasaki beach, the characteristics and temporal evolution of storms, and their associated erosional

Development of Predictive Models for Long-Term Endoscopic Response to Ustekinumab in Crohn's Disease Based on Plasma Proteomics.

PURPOSE: Ustekinumab (UST) is effective for Crohn's disease (CD), yet reliable biomarkers for predicting long-term response remain scarce. T … n=20) using ELISA. Predictive models were constructed using logistic regression, random forest, and support vector machine (SVM) algorithms. RESULTS: Non-responders had elevated baseline interleukin-8 (IL8) and CD6 levels and reduced thymic stromal lymphopoietin (TSLP) compared to responders. ELISA confirmed differential expression of IL8, CD6, and TSLP, with CD6 showi …

Mechanism of Pipeline Leakage Sound Generation and Leak Detection Technology Under Multiple Operational Conditions.

Acoustic emission detection technology is widely employed for leakage detection in water supply systems. However, this approach heavily reli … , and frequency entropy are extracted as key feature parameters. A leakage detection model based on Support Vector Machine (SVM) is subsequently developed, achieving an accuracy of 98.6% across a wide range of operating conditions. This research enhances the capability for high-accuracy leakage detection with limited field data, offering valuable technical insights for …

Investigation of Angiotensin I-Converting Enzyme Inhibitory Peptides Derived from Germinated Lamtoro Gung Using In Vitro and In Silico Approaches.

Angiotensin I-converting enzyme inhibitory (ACE-I) peptides derived from food sources have been extensively researched. These peptides can b … characterized using liquid chromatography-mass spectrometry. The peptide sequence with the highest support vector machine score was chosen for molecular docking simulation to examine the interaction between peptides and ACE. The sample germinated for 48 h had the highest ACE-I activity (70.62%) as confirmed by the increased amount of LMW peptides (<1 and 1－3.5 kDa). Fo …

A Fuzzy Cognitive Map-based Framework for Alzheimer's Disease Diagnosis Using Multimodal Magnetic Resonance Imaging-Positron Emission Tomography Registration.

BACKGROUND: Alzheimer's disease (AD) is a progressive and irreversible brain disorder, characterized by a gradual decline in cognitive and m … ion is achieved through an Autoencoder model. The reduced feature set is then classified using FCM, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron. RESULTS: The FCM-based approach demonstrates superior performance, achieving the highest accuracy of 93.71%, surpassing other classifiers tested. CONCLUSIONS: This study underscores the effectivenes …

Detection of ossicular chain pathologies using sweep frequency impedance with short-time stimulation and adaptive noise reduction.

Conductive hearing loss typically results from ossicular chain abnormalities, commonly ossicular fixation or separation. While a precise dia … to improve SFI performance. The method was applied to both healthy individuals and patients, and a support vector machine was employed to evaluate its accuracy in distinguishing fixation and separation in clinical practice. The proposed SFI yielded results consistent with the original SFI meter but significantly shortened the evaluation time to within 200 ms. Classific …

Изучение причинно-следственной связи и патогенеза множественных склерозов рака груди с помощью биоинформатического анализа.

Рак груди (БК) является одним из наиболее распространенных видов рака среди женщин во всем мире. Множественный склероз (МС) играет важную роль не только в патогенезе, но и в реакции на антиопухоль. Поэтому мы изучили причинно-следственную связь и патогенез между СУ и РС с помощью комплексного анализа менделийской случайности (MR) и биоинформатики. МЕТОДЫ: Мы провели анализ MR для изучения потенциальной причинно-следственной связи между BC и MS с помощью базы данных IEU. Для построения и подтверждения вероятной модели была принята комплексная стратегия регрессии с наименьшим абсолютным сокращен

Совпадение в аннотации оригинала
BACKGROUND: Breast cancer (BC) is one of the most prevalent cancers among women in the worldwide. Multiple sclerosis (MS) plays an important … atabase. A comprehensive strategy among least absolute shrinkage and selection operator regression, support vector machine, univariate and multivariate Cox regression analysis, time ROC regression, and survival analysis was adopted to construct and validate the prognostic model. Immune cell infiltration and chemotherapy sensitivity were used to evaluate the prognostic m …

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI.

OBJECTIVE: To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with a … s analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. RESULTS: The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal a …

Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid g … ubsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly …

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathologic … sent. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarker …

Continuous glucose data construction and risk assessment application of diabetic retinopathy complications for patients with type 2 diabetes mellitus.

Managing diabetes mellitus (DM) includes achieving acceptable blood glucose levels and minimizing the risk of complications from DM. The app … ssion (GPR) and long short-term memory (LSTM). Compared with the three commonly used LSTM, GPR, and support vector machine, the proposed model can construct accurate results. By using the constructed CGM data, the conventional metrics, such as the mean amplitude of glycemic excursion (MAGE), mean blood glucose (MBG), standard deviation (SD), and time in range (TIR), are …

A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study.

BACKGROUND: Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially h … acted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. RESULTS: A total of 158 patients were …

Unveiling convergent and divergent intrinsic brain network alternations in depressed adolescents engaged in non-suicidal self-injurious behaviors with and without suicide attempts.

AIMS: Limited understanding exists regarding the neurobiological mechanisms underlying non-suicidal self-injury (NSSI) and suicide attempts … m history. The alternations of resting-state functional connectivity (RSFC) were identified through support vector machine-based classification. RESULTS: Convergent alterations in NSSI and NSSI+SA predominantly centered on the inter-network RSFC between the Limbic network and the three core neurocognitive networks (SalVAttn, Control, and Default networks). Divergent al …

Proteomic Changes Induced by the Immunosuppressant Everolimus in Human Podocytes.

mTOR inhibitors (mTOR-Is) may induce proteinuria in kidney transplant recipients through podocyte damage. However, the mechanism has only been partially defined. Total cell lysates and supernatants of immortalized human podocytes treated with different doses of everolimus (EVE) (10, 100, 200, and 500 nM) for 24 h were subjected to mass spectrometry-based proteomics. Support vector machine and partial least squares discriminant analysis were used for data analysis. The results were validated in urine samples from 28 kidney transplant recipients receiving EVE as part of their immunosuppressive t…

Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics.

Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the … in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the …

Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment.

The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological im … cellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver o …

Accurate determination of alcohol-based diesels using optimal chemical factors.

Sustainable environmental policies and energy crises have led to a trend of blending different alcohols into diesel to partly replace the decreasing fossil fuels. To improve the rapidity and accuracy of determining alcohols exist in methanol and ethanol diesel, optimal chemical factors (OCF) feature selection schemes were presented based on different near infrared (NIR) characteristic absorption bands generated by different chemical structure information utilizing support vector machine (SVM). Through comparative analysis with SVM based on entire spectra, Monte Carlo uninformative variable eli…