Showing 221 - 240 results of 398 for search '"Chongqing Medical University"', query time: 0.07s Refine Results
  1. 221
  2. 222
  3. 223
  4. 224
  5. 225
  6. 226
  7. 227

    Efficacy and safety of anlotinib as maintenance treatment in extensive-stage small cell lung cancer: a single-armed single center retrospective study by Jin Xiong, Lei Xia, Lei Xia, Lei Xia

    Published 2025-01-01
    “…Anlotinib as a third-line or beyond therapy for ES-SCLC was proved to be effective.MethodsWe retrospectively screened of patients with ES-SCLC who started receiving anlotinib as first-line or second-line therapy at the Second Affiliated Hospital of Chongqing Medical University from November 2018 to December 2022. 30 patients treated with anlotinib based combination therapy and subsequent maintenance therapy were included. …”
    Get full text
    Article
  8. 228
  9. 229
  10. 230
  11. 231
  12. 232
  13. 233
  14. 234

    Misdiagnosis and analysis of clinical characteristics in patients with giant cystic pheochromocytoma/paraganglioma by Yue Zhang, Bo Zhou

    Published 2024-12-01
    “…Methods A total of 170 cases of pheochromocytoma and paraganglioma (PPGL) diagnosed at the First Affiliated Hospital of Chongqing Medical University from April 2011 to April 2020 were confirmed through clinical evaluation, measurement of catecholamine metabolites, imaging studies, or surgical pathology. …”
    Get full text
    Article
  15. 235
  16. 236
  17. 237

    A Microflow Chip Technique for Monitoring Platelets in Late Pregnancy: A Possible Risk Factor for Thrombosis by He C, Ma H, Zhang T, Liu Y, Zhang C, Deng S

    Published 2025-01-01
    “…Cui He,1 Haidong Ma,2 Tingting Zhang,1 Yu Liu,1 Cuiying Zhang,3 Surong Deng1 1Department of Blood Transfusion of Yong-chuan Hospital, Chongqing Medical University, Chongqing, 402160, People’s Republic of China; 2Department of Pharmacy of Yong-chuan Hospital, Chongqing Medical University, Chongqing, 402160, People’s Republic of China; 3Department of Obstetrics and Gynaecology of Yong-chuan Hospital, Chongqing Medical University, Chongqing, 402160, People’s Republic of ChinaCorrespondence: Surong Deng, Email 172346252@qq.comPurpose: To study the platelet adhesion and aggregation behaviour of late pregnancy women under arterial shear rate using microfluidic chip technology and evaluate the risk of thrombosis in late pregnancy.Methods: We included pregnant women who were registered in the obstetrics department of our hospital between January 2021 and October 2022 and underwent regular prenatal examinations. …”
    Get full text
    Article
  18. 238
  19. 239

    Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT by Liu W, Wang J, Lei Y, Liu P, Han Z, Wang S, Liu B

    Published 2025-01-01
    “…Wenjun Liu,1 Jin Wang,2 Yiting Lei,1 Peng Liu,3 Zhenghan Han,1 Shichu Wang,1 Bo Liu1 1Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China; 2College of Medical Informatics, Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Orthopedics, Daping Hospital, Army Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Bo Liu, Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China, Tel +8613996065698, Email boliu@hospital.cqmu.edu.cnBackground: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set. …”
    Get full text
    Article
  20. 240