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Browse 8,366 clinical trials for leukemia. Find studies that match your criteria and connect with research centers.
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NCT05310357
Chromosomal instability (CIN) refers to the ongoing genomic change, which involves the amplification or deletion of chromosome copy number or structure. The changes rang from point mutation to small-scale genomic change and even the change of whole chromosome number. It has been reported that the characteristics of genomic rearrangement can be used as a marker of clinical outcome of high-grade serous ovarian cancer, and specific genomic rearrangement are related to the poor prognosis. In noninvasive gene detection with low coverage, patients diagnosed with ovarian cancer have deteriorating progression-free and overall survivals regardless of the tumor stage when somatic copy number distortion (sCNA) exceeds the threshold in plasma. The detection rate of sCNA increased along with the tumor stage. We enrolled those as our target patients, who are diagnosed with high-grade serous ovarian cancer and willing to take part in. The CIN in peripheral cell-free DNA was observed before initial treatment, after primary debulking or staging surgeries, before recurrence and during the process of recurrence treatment. Our aim is to explore the application of CIN in peripheral tumor DNA in the detection of minimal residual lesions (MRD) after primary treatment and recurrence monitoring.
NCT05308563
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests and the tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls n the past one year and verified in a 6-month follow up. The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools.