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>

Objectives Eukaryotic translation initiation factor 4E (eIF4E) is activated in cancers in response to stress. This is regulated by MAP kinase interacting serine/threonine kinase (MNK) in cancerous but not normal cells. Chemoresistance causes treatment failure in advanced cervical cancer. In this study, we addressed chemotherapy effects on eIF4E for cervical cancer and reversal effects by MNK inhibitor cercosporamide for chemo-resistance mitigation. Methods Cell assays and mouse tumour models were used to determine the efficacy of cercosporamide. Western blotting was applied to understand the affected cell signaling after cercosporamide treatment. Key findings Cercosporamide spared normal cervical epithelial cells. On cervical cancer cell lines, it showed inhibition of cell growth and migration, and induced apoptosis. Cercosporamide was effective on chemoresistant cancer cells and augmented the efficiency of doxorubicin and cisplatin both in vitro and in vivo. Cercosporamide suppressed eIF4E signaling. Of note, chemotherapy increased p-eIF4E. Cercosporamide abolished chemotherapy-induced eIF4E activation. The higher level of p-eIF4E in cancer cells compared with normal cervical epithelial cells explains the preferential toxicity of cercosporamide. Conclusions This work demonstrates the ability of cercosporamide to overcome chemoresistance and highlight preferential inhibition of eIF4E via MNK inhibition in cervical cancer.

Objective: To analyze the correlation between the vascular endothelial function (characterized by endothelin-1 and nitric oxide) and the renal hemodynamics in patients with hypertensive disorders in pregnancy (HDP) by color Doppler ultrasound. Method: Depending on the severity of the disease, 76 HDP patients were divided into three groups, namely, pregnancy-induced hypertension (PIH) group ([Formula: see text]), mild preeclampsia (PE) group ([Formula: see text]), and severe PE group ([Formula: see text]). In the meantime, 28 healthy pregnant women were selected as controls. Color Doppler ultrasound was performed to determine the following parameters in the interlobar arteries of the kidney: Resistance index (RI), peak end-diastolic velocity (EDV), pulsatility index (PI), peak systolic velocity (PSV), and S/D ratio. The correlations of these parameters with the serum levels of ET-1 and NO were then analyzed. Result: (1) In the interlobar arteries of the kidney, RI, S/D, PI were positively significantly correlated to the serum level of ET-1 in HDP patients (All [Formula: see text]) and negatively to the serum level of NO (All [Formula: see text]). (2) RI, S/D, PI of the mild and severe PE groups were significantly higher than those of the control group (All [Formula: see text]). However, EDV of the mild and severe PE groups was significantly lower than that of the control group (All [Formula: see text]). (3) The serum level of ET-1 was significantly higher in the HDP patients than in the control group ([Formula: see text]). However, the serum level of NO was significantly lower in the former than in the latter ([Formula: see text]). As HDP became more severe, there was an elevation in the serum level of ET-1 and a decrease in NO. Conclusion: Indicators of renal hemodynamics measured by color Doppler ultrasound were correlated to the serum levels of ET-1 and NO characterizing the vascular endothelial function. They were sensitive indicators reflecting hemodynamic changes and renal impairment in HDP patients.

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