There are about 36818 clinical studies being (or have been) conducted in China. The country of the clinical trial is determined by the location of where the clinical research is being studied. Most studies are often held in multiple locations & countries.
Based on the high expression of specific receptors on the surface of diseased tissues and neovascularization, noninvasive targeted molecular imaging can be used to visualize lesions in vitro by combining specific ligands labeled with short half-life isotopes. Lung cancer tissues express fibroblast activating protein FAP, and also have high expression of integrin αVβ3 receptor on the surface of blood vessels. In this study, a novel dual-target imaging agent 68Ga-FAPI-RGD was used for PET/CT imaging of lung cancer.
It is planned to enroll 495 acute exacerbation of chronic obstructive pulmonary disease patients, and they will be randomly assigned to the high-dose test group, normal dose test group or control group at a ratio of 1:1:1, with 165 patients in each group. The course of treatment is 90 days, and the total follow-up time is one year. The purpose of the study is to evaluate the effectiveness and safety of different doses of bacterial lysates (Staphylococcus and Neisseria Tablets) in the treatment of acute exacerbation of chronic obstructive pulmonary disease.
This is a phase 1/phase 2, multicenter, open-label study to evaluate the safety, tolerability, PK, PD, immunogenicity and preliminary efficacy of M701 in patients with treatment of malignant pleural effusions caused by NSCLC.
As a new dual receptor (integrin αvβ3 and FAP) targeting PET radiotracer, 68Ga-FAPI-RGD is promising as an excellent imaging agent applicable to various cancers. In this research, we investigate the safety, biodistribution and radiation dosimetry of 68Ga-FAPI-RGD in healthy volunteers. Moreover, we evaluate the potential usefulness of 68Ga-FAPI-RGD positron emission tomography/computed tomography (PET/CT) for the diagnosis of primary and metastatic lesions in various types of cancer, and compared with 18F-FDG PET/CT.
As the most common type of primary liver cancer, hepatocellular carcinoma (HCC) has become a big challenge all over the world. Most patients are not available to curative resection when first diagnosed. There are a variety of treatment options for advanced HCC. However, due to the heterogeneity of HCC, the overall response rate (ORR) is not high for systemic therapies. Therefore, appropriate selection of patients who are suitable for individual systemic therapies is important for clinical decision-making.
Chronic kidney disease (CKD) is a progressive disease with hidden epidemics and one of the most significant contributing factors to end-stage renal disease (ESRD), cardiovascular comorbidities, cachexia and anemia, which accounts for a nearly 1.2 million populations died per year.
This multi-center, randomized, double-blind, sham-controlled trial aims to investigate the effect and safety of TaVNS in treating radiotherapy-related neuropathic pain.
The current study is a phase II multi-center single arm trial to evaluate the efficacy and safety of low-dose radiotherapy (3 Gy*4f) in indolent lymphoma.
Radiotherapy is one of the main treatments for locally advanced esophageal carcinoma (EC). The accuracy of the existing imaging methods in diagnosing and predicting therapeutic efficacy is disappointing, which increases the difficulty in clinical decision-making. In this study, based on a continuous cohort of EC treated with radiotherapy, the clinical and pathological factors of the patients are used to classify them into the appropriate therapeutic group. By multiple liquid biopsy technologies, combining with radiomics, we intend to construct prediction models of prognosis, therapeutic effect and toxicity. The aim of this RWS is to provide appropriate individualized regimen, further optimize the treatment mode based on precision radiotherapy and improve the outcome and quality of life of EC patients.
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.