The purpose of the study was to investigate the role of the signal transducer and activator of transcription 3 (STAT3), signal transduction protein in regulating the biological characteristics of stem cells in cervical carcinoma. Overexpressed plasmid of STAT3 was constructed and used to transfect SiHa into cervical carcinoma cells. STAT3-targeted specific siRNA was designed and produced. The effects of STAT3 upregulation (or inhibition) on the expression of NANOG, OCT4 and SOX2 markers of stem cells were measured, using western blot analysis and RT-qPCR. In addition, the tumor sphere experiment was also conducted to detect the formation of tumor spheres after the intervention of expression of STAT3 and the expression of STAT3, NANOG, OCT4 and SOX2 was detected in 35 cases of cervical carcinoma tissues and 31 cases of normal cervical tissues using immunohistochemistry. We determined whether the STAT3 overexpression plasmid was successfully constructed using enzyme digestion, PCR for bacterium solution, western blot analysis and RT-qPCR and found that the plasmid met the requirements of subsequent procedures. Compared with the empty plasmid group and STAT3 low expression group, the mRNA and protein expression of markers of stem cells, OCT4, SOX2 and NANOG were significantly elevated in the STAT3 overexpression group with statistically significant differences (P<0.05), the formation ratio of tumor spheres in the STAT3 overexpression group was also significantly higher than those in the other two groups (P<0.05). The positive expression of STAT3, OCT4, NANOG and SOX2 in the cervical squamous carcinoma group was also markedly higher than that in the chronic cervicitis group (P<0.05). This study led us to a conclusion that STAT3 can regulate the characteristics of stem cells in cervical carcinoma, and STAT3, NANOG, OCT4 and SOX2 are highly expressed in cervical squamous carcinoma, thus able to promote the progression of cervical carcinoma.

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Purpose. Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). Methods. In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People’s Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group ( n = 108 ) and the recurrent group ( n = 117 ), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data ( P < 0.05 ) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. Results. Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05 ). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. Conclusion. The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.

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<abstract> <sec><title>Objective</title><p>To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients.</p> </sec> <sec><title>Method</title><p>We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set.</p> </sec> <sec><title>Result</title><p>For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P &lt; 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P &lt; 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P &lt; 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P &lt; 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set.</p> </sec> <sec><title>Conclusion</title><p>Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.</p> </sec> </abstract>