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 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.

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.

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 status and influencing factors of pressuring feeding style a-mong caregivers in remote rural areas of Sichuan province.Methods Multistage sampling was conducted to select infants of 6-11 months old who had received complementary food and their caregivers in remote rural areas of Si-chuan province.A questionnaire was used to collect sociodemographic characteristics of infants and their caregivers,pressuring feeding behaviors,feeding environment,and caregivers'negative emotions.Quantile regression was em-ployed to analyze the factors influencing pressuring feeding among caregivers of infants.Results A total of 1358 pairs of infants and their caregivers were included,with the pressuring feeding behavior score of 11(8,14).Parity was the protective factor for caregivers'pressuring feeding(β25=-1.17,P＜0.001;β50=-1.40,P=0.002;β75=-2.18,P＜0.001).Whether infants played with toys while eating(β25=1.00,P＜0.001;β50=1.20,P=0.003;β75=1.42,P＜0.001)and whether infants watched TV/mobile phones(β25=0.50,P=0.048;β50=1.07,P=0.004)were the risk factors.At the 75th percentile,caregivers'negative emotions were the risk factor for pressuring feeding(β75=0.94,P=0.015).Caregivers'education background(β25=0.83,P=0.034;β50=0.87,P=0.021)and family income(β75=1.09,P=0.012)were also significantly associated with pressuring feeding scores at different quartile points.Conclusions Pressuring feeding behaviors of caregivers in remote rural areas of Sichuan province need to be improved.Based on the characteristics of infants and their families,guidance should be carried out to improve the feeding environment and the mental health of caregivers,thereby promoting reasonable feeding behaviors among caregivers of infants in rural areas.

Purpose/Significance By investigating and analyzing the characteristics of scientific data citation of scientific data sharing institutions in the field of medical and population health,the paper provides references and guidance for open sharing and standardized ci-tation of scientific data.Method/Process Taking the National Population Health Data Center as an example,the literature analysis meth-od is used to analyze the scientific data citation behavior and its traceability in this field.Result/Conclusion It is found that there are non-standard citation behaviors such as inconsistent labeling positions and missing citation elements in the scientific data in the field of medical and population health,and a large number of open and shared scientific data cannot be effectively traced.It is urgent to improve the standards of scientific data citation,strengthen the management of scientific data platform,enhance the awareness of scientific re-searchers,and promote scientific data sharing and citation.