Subjects: Medicine, Pharmacy >> Clinical Medicine submitted time 2023-12-06 Cooperative journals: 《中国全科医学》
Abstract： Background Acute pancreatitis（AP） is one of the common gastrointestinal emergencies，and the disease progression of moderately severe and severe AP is rapid. Early and accurate identification is crucial for effective intervention and prognosis assessment. there is still a lack of effective and simple predictive indicators. Objective To investigate the early dynamic changes and predictive value of the systemic inflammation response index（SIRI） in patients with AP. Methods A total of 221 AP patients who met the inclusion and exclusion criteria at the Department of Gastroenterology，Beijing Tiantan Hospital，Capital Medical University，were included as study subjects from August 2020 to March 2023. According to the revised 2012 Atlanta criteria，patients were categorized into mild group（MAP group，mild acute pancreatitis） and non#2;mild group（non-MAP group，including moderate severe and severe acute pancreatitis）. The SIRI values（SIRI 0 h，SIRI 48 h） and C-reactive protein（CRP） levels（CRP 0 h，CRP 48 h） during admission and within 48 hours of admission for the patients were collected by reviewing cases. The receiver operating characteristic（ROC） curve was plotted，and the area under the curve （AUC） was calculated to analyze the predictive value of SIRI for non-MAP and compare it with CRP as a common clinical indicator of inflammation. Results A total of 221 AP patients were finally included，102 with MAP and 119 with non-MAP. SIRI 0 h and SIRI 48 h were higher in patients in the non-MAP group than in the MAP group（P<0.001）. The ROC curve showed that the AUC for SIRI 0 h and SIRI 48 h in predicting non-MAP were 0.685（95%CI=0.615-0.756） and 0.753（95%CI=0.689-0.816）， respectively，with no significant difference with CRP［0.607（95%CI=0.533-0.681） and 0.752（95%CI=0.687-0.817）］ during the corresponding time intervals（Z=1.67，P=0.095；Z=0.02，P=0.981）. The optimal cut-off value for SIRI 48h to predict non-MAP was 2.49，with sensitivity，specificity，positive predictive value，and negative predictive value of 81.51%， 58.82%，69.78%，and 73.17%，respectively. Conclusion SIRI is an affordable and readily available test that can be used as an indicator for assessing the severity of early-stage AP.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2018-06-15 Cooperative journals: 《南方医科大学学报》
Abstract： Objective To propose arectal toxicity prediction method based on deformable surface dose accumulation. Methods The clinical data were collected retrospectively from 42patients receiving radiotherapy for cervical cancer. With the first fraction as the reference, the other fractions of rectum surface were registered to the reference fraction to obtain the deformation vector fields (DVFs), which were used to deform and sum the fractional rectal doses to yield the cumulative rectal dose. The cumulative rectal dose was flattened via 3D-2D mapping to generate a 2D rectum surface dose map. Two dosimetric features, namely DVPs and DGPs were extracted. Logistic regression embedded with sequential forward feature selection was used as the prediction model. The predictive performance was evaluated in terms of the accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). Results Significant improvements for rectum surface DIR were achieved. The best predictive results were achieved by using both DVPs and DGPs as the features with a sensitivity of 79.5%, a specificity of 81.3% and an AUC of 0.88. Conclusion The proposed method is feasible for predicting clinical rectal toxicity in patients undergoing radiotherapy for cervical cancer.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Because of existing compressive sensing reconstruction algorithms had lower reconstruction quality and longer reconstruction time, this paper proposed a block compressive sensing reconstruction algorithm based on matrix manifold separable dictionary construction. The proposed algorithm first solved matrix manifold model to get separable sparse representation matrices and made it orthogonalized. Then, it constructed a random measurement matrix and combined it with the sparse representation matrices by matrix operation to get separable dictionary. Finally, the algorithm applied this separable dictionary in signal compressive sensing, and realized fast reconstruction of the signal by linear operation. Experimental results indicate that the proposed algorithm has a great advantage in reconstruction accuracy and running time compared with current popular compressive sensing reconstruction algorithms, and it has a good application value in the field with high real-time requirement.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract： Objective To evaluate the association between SLC22A1 expression and the outcomes of hepatocellular carcinoma (HCC) patients. Methods A tissue microarray of 303 HCC and matched adjacent noncancerous liver tissues (ANLTs) were constructed. The expression of SLC22A1 was tested by immunohistochemistry (IHC) and scored by two pathologists according to a 12-score scale (a score>6 was defined as high expression, and a score≤6 as low expression). The correlation of SLC22A1 expression with the clinicopathological features and the patients' outcome was analyzed. Results All the ANLTs had a IHC score of 12, as compared to only 29 (9.6%) of the HCC tissues. The patients were divided into 2 groups based on the IHC scores: 59% (180/303) in low expression group and 41% (123/303) in high expression group. The disease-free survival (DFS) rates and overall survival (OS) rates were significantly lower in low SLC22A1 expression group than in the high expression group. The 1-, 3-, and 5-year DFS rates were 43%, 31% and 27% in the low expression group, and were 58%, 47% and 43% in the high expression group, respectively. The 1-, 3-, and 5-year OS rates were 66%, 38% and 32% in low expression group, and were 80%, 57% and 50% in the high expression group, respectively. A low expression of SLC22A1 was positively correlated with the tumor diameter, BCLC stage, tumor differentiation, and AFP levels (P<0.05), and was an independent predictor of poor overall survival (HR=1.454; 95% CI, 1.050-2.013). Conclusion Down-regulation of SLC22A1 is a malignant feature and a potential prognostic marker of HCC.
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