Purpose: The aim of this study is to determine the impact of solute carrier family 3 member 2 (SLC3A2) on the malignant phenotype of gliomas and its role in regulating ferroptosis sensitivity. Materials and Methods: The malignant phenotype of glioma was assessed by cell proliferation assay, colony formation assay, EdU assay, wound healing, and Transwell experiments. We further validated the impact of reduced SLC3A2 expression on the sensitivity to ferroptosis in glioma cells through Cell Counting Kit-8 assays, flow cytometry, western blotting, and transmission electron microscopy. Western blot was used to explore how SLC3A2 affects glioma sensitivity to ferroptosis through the AKT/NF-E2-related factor 2 (NRF2)/glutathione peroxidase 4 (GPX4) axis. By establishing a subcutaneous xenograft tumor model in BALB/c-nude mice, we investigated the growth of tumors following the knockout of SLC3A2 in glioma cells. Results: Downregulation of SLC3A2 suppressed the malignant phenotype of glioma by blocking the cell cycle and epithelial-mesenchymal transition processes. On the other hand, loss of SLC3A2 not only downregulated SLC7A11 but also prevented the activation of the AKT/NRF2/GPX4 axis. These lead to increased accumulation of reactive oxygen species and lipid peroxides, ultimately enhancing the susceptibility of glioma to ferroptosis. Conclusion Our findings suggest that SLC3A2 is an oncogene in gliomas, promoting their occurrence and development. It plays a critical role in ferroptosis resistance through the AKT/NRF2/GPX4 axis.

A 44-year-old male presented with a 19-year history of urinary calculi and a 1-year history of polydipsia and weight loss. Laboratory tests revealed hyperparathyroidism and evidence of glucagonoma-associated diabetes. Imaging studies identified masses in the pancreatic head and body/tail, suggestive of glucagonoma and a parathyroid adenoma. Furthermore, the patient exhibited hypercalcitoninemia and elevated cortisol and adrenocorticotropic hormone levels. Genetic testing revealed a heterozygous MEN1 mutation [c.65T>G (p.Leu22Arg)], confirming the diagnosis of multiple endocrine neoplasia type 1 (MEN-1). The patient subsequently underwent near-total parathyroidectomy and total pancreatectomy. Postoperative immunohistochemical staining of the pancreatic tail tumor was positive for glucagon and calcitonin. The patient′s postoperative hormone levels (calcitonin, glucagon, adrenocorticotropic hormone, cortisol) normalized, suggesting a rare pancreatic neuroendocrine tumor (pNET) that was co-secreting multiple hormones. Postoperative management included pancreatic enzyme supplementation, calcium supplementation, vitamin D supplementation, and insulin for glycemic control. Follow-up evaluations at 10 months demonstrated a stable clinical condition, well-controlled blood glucose and biochemical parameters, and an acceptable quality of life. This case study highlights that the presence of pNETs should be considered in patients with MEN-1 and multiple abnormal hormone levels. Timely surgical management of the involved glands and postoperative complications can effectively improve prognosis.

Objective To investigate the prevalence of common chronic diseases and comorbidities among people aged ≥15 years in Urumqi, and to provide a theoretical basis for the prevention and treatment of chronic diseases in the region. Methods Using population health surveillance data from 2016 to 2021, a retrospective analysis was performed on the prevalence of 15 chronic diseases among permanent residents aged 15 years and above in Urumqi. Results From 2016 to 2021, the detection rate of common chronic diseases among people aged 15 years or above in Urumqi was higher among the female, urban, ≥65 years, and overweight and obese populations. Among the 15 prevalent chronic diseases, dyslipidemia (fatty liver) had the highest detection rate, while hypertension, diabetes mellitus, cholecystitis, and coronary heart disease (myocardial infarction) ranked second to fifth, in common chronic disease detection rates. The prevalence of comorbidities among patients with chronic diseases was consistent with that of chronic diseases, with the overall prevalence of women being higher than that of men (χ²=127.31, P<0.001), and the prevalence in urban areas being higher than that in rural areas (χ²=16503.75, P <0. 001). The prevalence of comorbidities increased gradually with age (χ²=9179.36, P <0.001). The proportion of people with comorbidities who had abnormal BMI was significantly higher than that of people with normal BMI (χ²=21699.61, P <0.001). The proportion of chronic disease patients with single diseases increased gradually from 2016 to 2020 and decreased significantly by 2021, while the proportion of patients with comorbidities increased significantly by 2021. The number of comorbidity disease types in the study population showed an increasing trend year by year with age. Conclusion The detection rate of chronic diseases among people over 15 years old is high in Urumqi, and there is an increasing prevalence of comorbidities in patients. Chronic diseases, mainly dyslipidemia (fatty liver), hypertension, diabetes, cholecystitis and coronary heart disease (myocardial infarction) are highly prevalent. These findings indicate the need for the development of comprehensive prevention and treatment intervention measures.

Objective To explore the effect of transjugular intrahepatic portosystemic shunt(TIPS)in treating patients with hepatic sinusoidal obstruction syndrome(HSOS)caused by gynura segetum,and to analyze the mid-to-long-term survival.Methods The clinical data of 44 patients with HSOS caused by gynura segetum,who were admitted to the First Affiliated Hospital of University of Science and Technology of China from April 2016 to September 2022 to receive TIPS treatment,were retrospectively analyzed.The preoperative and postoperative liver and kidney functions,ascites,and portal vein pressure were compared.Ultrasonography examination was performed to check the patency of stent and the thickness of ascites,the postoperative complications were recorded.Kaplan-Meier curve was used to calculate the cumulative survival so as to evaluate the survival of patients.Results Based on the severity of the illness,the 44 patients were divided into mild group(n=5),moderate group(n=13),severe group(n=19)and very severe group(n=7).Successful TIPS operation was achieved in all the patients,with a surgical success rate of 100％.No serious complications occurred.After TIPS,the incidence of hepatic encephalopathy(HE)was 20.5％(9/44).The portal pressure decreased from preoperative(42.00±0.91)cm H2 O to postoperative(18.27±0.67)cm H2O,the difference was statistically significant(P＜0.001).The postoperative blood creatinine,aminotransferase,total bilirubin and Child-Pugh score were decreased when compared with their preoperative values,while the albumin level was increased when compared with its preoperative value,the differences were statistically significant(all P＜0.05).The postoperative one-year and 5-year cumulative survival rates were 90.9％and 88.6％respectively.The 1-,3-,6-and 12-month survival rates in mild group were all 100％,in moderate group were 100.0％,100.0％,92.3％and 92.3％respectively,in severe group were 94.7％,94.7％,94.7％and 89.5％respectively,and in very severe group were 85.7％,71.4％,71.4％and 71.4％respectively.Conclusion For the treatment of HSOS caused by gynura segetum,TIPS can significantly improve the patient's ascites with low overall complication rate and high mid-to-long-term survival rate.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

A 44-year-old male presented with a 19-year history of urinary calculi and a 1-year history of polydipsia and weight loss. Laboratory tests revealed hyperparathyroidism and evidence of glucagonoma-associated diabetes. Imaging studies identified masses in the pancreatic head and body/tail, suggestive of glucagonoma and a parathyroid adenoma. Furthermore, the patient exhibited hypercalcitoninemia and elevated cortisol and adrenocorticotropic hormone levels. Genetic testing revealed a heterozygous MEN1 mutation [c.65T>G (p.Leu22Arg)], confirming the diagnosis of multiple endocrine neoplasia type 1 (MEN-1). The patient subsequently underwent near-total parathyroidectomy and total pancreatectomy. Postoperative immunohistochemical staining of the pancreatic tail tumor was positive for glucagon and calcitonin. The patient′s postoperative hormone levels (calcitonin, glucagon, adrenocorticotropic hormone, cortisol) normalized, suggesting a rare pancreatic neuroendocrine tumor (pNET) that was co-secreting multiple hormones. Postoperative management included pancreatic enzyme supplementation, calcium supplementation, vitamin D supplementation, and insulin for glycemic control. Follow-up evaluations at 10 months demonstrated a stable clinical condition, well-controlled blood glucose and biochemical parameters, and an acceptable quality of life. This case study highlights that the presence of pNETs should be considered in patients with MEN-1 and multiple abnormal hormone levels. Timely surgical management of the involved glands and postoperative complications can effectively improve prognosis.

Objective:To investigate the predictive ability of Glypican-3 (GPC3) positive hepatocellular carcinoma based on the hepatobiliary specific contrast agent gadoxetate disodium enhancement of the liver imaging reporting and data system version 2018 (LI-RADS v2018) imaging features, and to assess the relevant clinical imaging features for postoperative recurrence in GPC3 positive HCC patients.Methods:This study was a cohort study. A total of 122 hepatocellular carcinoma patients who underwent gadoxetate disodium enhanced MRI examination with hepatic tumor resection in Henan Provincial People′s Hospital from January 2017 to December 2021 were retrospectively collected, including 96 GPC3 positive and 26 GPC3 negative patients. The imaging features defined by LI-RADS v2018 of HCC lesions were analyzed. Patients were followed up for 40 months to determine recurrence free survival (RFS). The logistic regression was used to analyze the risk factors of GPC3 positivity. An imaging model, and a clinical-imaging model which combined the patient′s alpha-fetoprotein levels were constructed. The efficacy of the model for predicting GPC3 positivity was assessed using receiver operating characteristic curves. Kaplan-Meier method was used to draw the survival curve, and the log-rank test was used to compare the RFS between GPC3 positive and negative patients. Risk factors affecting the recurrence of GPC3 positive HCC were assessed by Cox regression.Results:The results of logistic multivariate regression analysis confirmed that rim enhancement ( OR=5.685, 95% CI 1.229-26.287, P=0.026) and irregular tumor margin at hepatobiliary phase ( OR=4.431, 95% CI 1.684-11.663, P=0.003) were independent risk factors for GPC3 positive HCC. The area under the curve for predicting GPC3 positivity was 0.745 (95% CI 0.636-0.854) for the imaging model and 0.776 (95% CI 0.677-0.876) for the clinical-imaging model. The mean RFS in the GPC3 positive group was 22 months, and it was 32 months in the negative group. There was a statistically significant difference in RFS between the two groups ( χ2=5.15, P=0.023). The multivariate Cox regression analysis showed that the arterial rim enhancement ( HR=5.460, 95% CI 1.966-15.162, P=0.001), microvascular invasion ( HR=2.402, 95% CI 1.210-4.769, P=0.012), portal vein tumor thrombus ( HR=3.226, 95% CI 1.114-9.344, P=0.031) were independent risk factors for recurrence after hepatic tumor resection for GPC3-positive HCC. Conclusions:A model based on the LI-RADS v2018 imaging features of hepatobiliary specific contrast agent gadoxetate disodium enhancement can effectively predict GPC3 positive HCC. The arterial rim enhancement, microvascular invasion and portal vein tumor thrombus are independent risk factors for postoperative recurrence of GPC3 positive HCC.

Objective To explore the application of seasonal autoregressive integrated moving average (ARIMA) model in the prediction of brucellosis in Urumqi, and to use this model to predict the incidence trend of brucellosis in Urumqi. Methods The monthly incidence data of brucellosis in Urumqi from January 2010 to December 2021 were selected to construct the ARIMA prediction model. The prediction effect of the model was evaluated by mean standard deviation (RMSE) and mean absolute error (MAE). The monthly incidence of brucellosis in Urumqi in 2022 was predicted by the constructed model. Results The incidence of brucellosis in Urumqi had obvious seasonal distribution, and the cases were concentrated from May to July. ARIMA(1,1,1)(1,0,1)12 was the optimal prediction model, with RMSE=0.883 and MAE=5.24. The monthly incidence of brucellosis in Urumqi in 2022 was predicted to be 7, 4, 4, 6, 9, 9, 10, 7, 7, 5, 5, and 5 cases, respectively. Conclusion ARIMA model can well fit and predict the monthly incidence of brucellosis in Urumqi and provide a basis for the monitoring and prevention of brucellosis.