5 results match your criteria: "The First People's Hospital of Kashi (Kashgar) Prefecture[Affiliation]"
The potential value of fatty acid binding protein 1 in Chronic HBV-related liver disease progression assessment.
BMC Infect Dis
October 2024
Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, 510630, People's Republic of China.
Article Synopsis
- The study examines the potential of fatty acid binding protein 1 (FABP1) as a serum biomarker for liver injury in chronic hepatitis B virus (HBV) patients.
- Research was conducted on 293 patients, showing that FABP1 levels in serum were significantly higher in those with chronic HBV-related diseases compared to healthy controls, especially as the disease progressed.
- A notable negative correlation was found between FABP1 expression and liver inflammation grades, indicating its potential role in monitoring disease progression, though no correlation with fibrosis was observed.
Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.
BMC Infect Dis
August 2024
Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnosis, Kashgar, 844000, China.
Background: Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better.
Methods: This research analyzed data (2020-2022) from a Kashgar pulmonary hospital's information system, involving 9570 eligible PTB patients.
Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.
Acad Radiol
August 2024
Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000. Electronic address:
Rationale And Objectives: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.
View Article and Find Full Text PDFDifferentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.
Eur J Med Res
December 2023
Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China.
Background: Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.
View Article and Find Full Text PDFDeep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes.
Eur J Radiol
December 2023
Clinical Medical Research Center, The First People's Hospital of Kashi (Kashgar) Prefecture, China. Electronic address:
Background: To predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission.
Methods: An internal cohort for model development consists of 204 bacteriologically-confirmed TB patients who completed anti-tuberculosis treatment, with one pretreatment and two follow-up CT images (612 scans). Three radiomics feature-based models (RM) with multiple classifiers of Bagging, Random forest and Gradient boosting and two deep-learning-based models (i.