Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning DOI Creative Commons

Yaqi Zhong,

Xingte Chen,

Shiji Wu

и другие.

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Сен. 3, 2024

Background Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in outcomes partly due to the resistance conferred by stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates role EIF5A radiotherapy its impact on CSCs TiME. Methods Predictive models preoperative (preRT) were developed using machine learning, identifying as key gene associated with radioresistance. expression was analyzed via bulk RNA-seq single-cell (scRNA-seq). Functional assays vivo experiments validated EIF5A’s radioresistance TiME modulation. Results significantly upregulated radioresistant colorectal (CRC) tissues. knockdown CRC cell lines reduced viability, migration, invasion after radiation, increased radiation-induced apoptosis. Mechanistically, promoted (CSC) characteristics through Hedgehog signaling pathway. Analysis revealed that radiation-resistant group had an immune-desert phenotype, characterized low infiltration. In showed led infiltration CD8+ T M1 macrophages, decreased M2 macrophages Tregs following radiation therapy, thereby enhancing response. Conclusion contributes promoting CSC traits pathway modulating immune-suppressive state. Targeting could enhance sensitivity improve responses, offering potential therapeutic strategy optimize patients.

Язык: Английский

Using AI to detect and treat delirium DOI Creative Commons
Marcus Young, Katarzyna Kotfis, Rinaldo Bellomo

и другие.

Intensive Care Medicine, Год журнала: 2025, Номер unknown

Опубликована: Янв. 20, 2025

Язык: Английский

Процитировано

3
Machine learning and flavoromics-based research strategies for determining the characteristic flavor of food: A review DOI

Donglin Cai,

Xueqing Li,

Huifang Liu

и другие.

Trends in Food Science & Technology, Год журнала: 2024, Номер 154, С. 104794 - 104794

Опубликована: Ноя. 17, 2024

Язык: Английский

Процитировано

11
A comprehensive review for chronic disease prediction using machine learning algorithms DOI Creative Commons
Rakibul Islam, Azrin Sultana, Mohammad Rashedul Islam

и другие.

Journal of Electrical Systems and Information Technology, Год журнала: 2024, Номер 11(1)

Опубликована: Июль 16, 2024

Abstract The past few years have seen an emergence of interest in examining the significance machine learning (ML) medical field. Diseases, health emergencies, and disorders may now be identified with greater accuracy because technological advancements advances ML. It is essential especially to diagnose individuals chronic diseases (CD) as early possible. Our study has focused on analyzing ML’s applicability predict CD, including cardiovascular disease, diabetes, cancer, liver, neurological disorders. This offered a high-level summary previous research ML-based approaches for predicting CD some instances their applications. To wrap things up, we compared results obtained by various studies methodologies well tools employed researchers. factors or parameters that are responsible improving model different works also identified. For identifying significant features, most authors variety strategies, where least absolute shrinkage selection (LASSO), minimal-redundancy-maximum-relevance (mRMR), RELIEF extensively used methods. wide range ML approaches, support vector (SVM), random forest (RF), decision tree (DT), naïve Bayes (NB), etc., been widely used. Also, several deep techniques hybrid models create prediction models, resulting efficient reliable clinical decision-making models. benefit whole healthcare system, our suggestions enhancing CD.

Язык: Английский

Процитировано

10
Green Hydrogen Revolution: Advancing Electrolysis, Market Integration, and Sustainable Energy Transitions Towards a Net-Zero Future DOI Creative Commons

V. Sakthi Murugan,

G Lakshmikanth,

N. Balaji

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104849 - 104849

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

2
Integrated Approach for Biomarker Discovery and Mechanistic Insights into the Co-Pathogenesis of Type 2 Diabetes Mellitus and Non-Hodgkin Lymphoma DOI Creative Commons
Yidong Zhu, Jun Liu, Bo Wang

и другие.

Diabetes Metabolic Syndrome and Obesity, Год журнала: 2025, Номер Volume 18, С. 267 - 282

Опубликована: Янв. 1, 2025

Type 2 diabetes mellitus (T2DM) is associated with an increased risk of non-Hodgkin lymphoma (NHL), but the underlying mechanisms remain unclear. This study aimed to identify potential biomarkers and elucidate molecular co-pathogenesis T2DM NHL. Microarray datasets NHL were downloaded from Gene Expression Omnibus database. Subsequently, a protein-protein interaction network was constructed based on common differentially expressed genes (DEGs) between explore regulatory interactions. Functional analyses performed mechanisms. Topological analysis machine learning algorithms applied refine hub gene selection. Finally, quantitative real-time polymerase chain reaction validate in clinical samples. Intersection DEGs identified 81 shared genes. suggested that immune-related pathways played significant role three genes: GZMM, HSPG2, SERPING1. Correlation revealed correlations these immune cells, underscoring importance dysregulation pathogenesis. The expression successfully validated pivotal as key contributors. These findings provide insight into complex interplay

Язык: Английский

Процитировано

1
Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project DOI Creative Commons
Hanna Kwiendacz,

Bi Huang,

Yang Chen

и другие.

Cardiovascular Diabetology, Год журнала: 2025, Номер 24(1)

Опубликована: Фев. 15, 2025

Язык: Английский

Процитировано

1
XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer’s Disease DOI Creative Commons
Lorenza Brusini, Federica Cruciani, G Dallaglio

и другие.

IEEE Journal of Translational Engineering in Health and Medicine, Год журнала: 2024, Номер 12, С. 569 - 579

Опубликована: Янв. 1, 2024

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced diffusion magnetic resonance imaging (dMRI) literature. This study investigates potential novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A $\beta $ -/tau-) A +/tau+ or +/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used highlight most influential features SVMs classifications validate results by seeing explanations' recurrence across methods.TBSS analysis revealed significant differences -/tau- other groups line with The best SVM classification performance reached an accuracy 0.73 using advanced compared more standard ones. explainability suggested results' stability central role cingulum show early sign AD.By relying XAI interpretation outcomes, AMURA indices can be considered viable amyloid tau pathology. Clinical impact: pre-clinical research timely AD diagnosis acquiring clinically feasible dMR images, advantages invasive methods employed nowadays.

Язык: Английский

Процитировано

5
Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study DOI Creative Commons
Guangyu Lu,

Zhixia Su,

Xiaoping Yu

и другие.

Cancer Medicine, Год журнала: 2025, Номер 14(1)

Опубликована: Янв. 1, 2025

ABSTRACT Background Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness assessing nodules remains underexplored. Methods Employing a prospective study design from June 2023 to January 2024 at Affiliated Hospital Yangzhou University, we assessed using Mayo Clinic model and collected breath samples alongside lifestyle health examination data. We applied five machine learning (ML) algorithms develop predictive models which were evaluated area under curve (AUC), sensitivity, specificity, other relevant metrics. Results A total 267 participants enrolled, including 210 with low‐risk 57 moderate‐risk Univariate analysis identified 11 VOCs associated nodule malignancy, two factors (smoke index sites tobacco smoke inhalation) one clinical metric (nodule diameter) as independent predictors The logistic regression integrating data achieved an AUC 0.91 (95% CI: 0.8611–0.9658), while random forest incorporating 0.99 0.974–1.00). Calibration curves indicated strong concordance between predicted observed risks. Decision confirmed net benefit these over traditional methods. nomogram was developed aid clinicians based on VOCs, lifestyle, Conclusions integration ML biomarkers provides robust framework assessment These offer safer alternative methods may enhance early management Further validation through larger, multicenter studies is necessary establish their generalizability. Trial Registration: Number ChiCTR2400081283

Язык: Английский

Процитировано

0
Diagnosis of Malignant Endometrial Lesions from Ultrasound Radiomics Features and Clinical Variables Using Machine Learning Methods DOI Creative Commons
Shanshan Li, Jiali Wang, Li Zhou

и другие.

Clinical and Experimental Obstetrics & Gynecology, Год журнала: 2025, Номер 52(1)

Опубликована: Янв. 13, 2025

Background: The prognosis of patients with early diagnosis malignant endometrial lesions is good. We aimed to identify benign and in tissue, explore effective methods for assisting diagnosis, improve the accuracy precision identifying lesions. Methods: 1142 ultrasound radiomics 18 clinical features from 1254 were analyzed, which 36 selected machine learning. sketched region interest (ROI) abnormalities on images. Then, extracted. Six common learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, Gradient Boosting k-Nearest Neighbors, employed changes tissue. Cross-validation grid search techniques hyperparameter tuning utilized obtain best model performance. Accuracy, precision, sensitivity, F1-scores, area under curve (AUC), cross-validation average score bootstrap also used evaluate algorithm performance, classification accuracy, generalization capability. Results: combined 21 characteristics 15 develop validate six algorithms. After internal validation, models Forest models, 89%, 93%, sensitivity 97%, F1-score 95%, AUC as well a 10-fold 95% 94%, implying flawless test set. Conclusions: identified or And algorithms have demonstrated excellent performance This significant enhancing diagnostic improving treatment outcomes long-term management.

Язык: Английский

Процитировано

0
Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3 DOI
Yazhi Wang,

Peng Wang

HORMONES, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0