Introduction: Severe hypertriglyceridemia during pregnancy is a rare yet potentially life-threatening disorder. It is frequently linked to acute pancreatitis and poses considerable risks for both maternal and fetal morbidity. Case: We report a 35-year-old G4P3 female at 33 + 6 weeks gestation with underlying type 2 diabetes mellitus who presented with acute abdominal pain, nausea, vomiting, and poor oral intake, initially mistaken for preterm labor and sepsis. Further evaluation revealed profound hypertriglyceridemia (triglycerides 79.1 mmol/L) with markedly elevated total cholesterol (>32 mmol/L) and raised serum amylase, consistent with hypertriglyceridemia-induced acute pancreatitis. She was managed in a multidisciplinary setting with intravenous insulin infusion, dextrose supplementation, aggressive fluid resuscitation, and empiric antibiotics. Due to clinical deterioration and ongoing risk of maternal complications, she underwent emergency lower segment caesarean section and exploratory laparotomy at 34 weeks of gestation. Intraoperative findings included inflammatory intra-abdominal fluid and a bulky pancreas, supporting the diagnosis. Rapid biochemical improvement was observed following treatment, with triglyceride levels decreasing by approximately 70% within 24 hours (79.1– 24.0 mmol/L) and achieving >90% reduction over 10 days. Postpartum management included initiation of omega-3 fatty acids, fibrate, and statin therapy, resulting in sustained lipid control. Ophthalmologic examination demonstrated lipemia retinalis, further confirming severe dyslipidemia. Conclusion This case highlights the importance of early recognition of severe hypertriglyceridemia in pregnancy, particularly in patients with metabolic risk factors such as diabetes. Prompt initiation of insulin therapy can achieve rapid triglyceride reduction and may obviate the need for plasmapheresis in selected cases. Delivery should be considered in the setting of maternal instability. Long-term lipid management postpartum is essential for preventing recurrence and reducing future metabolic risk. A multidisciplinary approach remains critical in optimizing both maternal and fetal outcomes in this rare but serious condition.
Female ; Pregnancy ; Acute Disease ; Pancreatitis

Objective:This study aims to investigate the expression of uridine diphosphate-glucose[]pyrophos-phorylase 2(UGP2)in breast cancer(BC)tissues and its oncogenic mechanism,assessing its potential value as a diag-nostic and prognostic biomarker for breast cancer.Methods:(1)Online database analysis was conducted to assess UGP2 mRNA and protein expression levels in breast cancer and explore their correlation with clinical characteristics.Im-munohistochemistry(IHC)was used to verify UGP2 expression in human breast cancer tumor tissues and evaluate its relationship with clinicopathological features.(2)Kaplan-Meier survival analysis and COX regression models were used to analyze the impact of UGP2 expression on breast cancer patient prognosis.(3)Bioinformatics methods were em-ployed to investigate the correlation between UGP2 and tumor immune cell infiltration,and to predict the biological func-tions and associated signaling pathways of UGP2 in breast cancer.Results:(1)The mRNA and protein expression levels of UGP2 were upregulated in breast cancer tissues(both P<0.05),and were negatively correlated with ER-positive and PR-positive status(OR<1,P<0.05),while positively correlated with Ki-67 levels and the triple-negative breast cancer(TNBC)subtype(OR>1,P<0.05).(2)Elevated expression levels of UGP2 were associated with poorer survival rates in breast cancer patients(both P<0.05)and were identified as an independent adverse prognostic factor for breast cancer(HR=1.40,P<0.05).(3)Functional analysis results suggested that UGP2 may promote tumor progression by regulating metabolism,hormone signaling,and the immune microenvironment.Additionally,UGP2 expression was negatively cor-related with NK cell activation status and positively correlated with the inhibitory state.Conclusion:UGP2 expression is elevated in breast cancer tissues and is closely associated with poor patient prognosis.It may promote cancer pro-gression through mechanisms such as metabolic reprogramming and immune suppression.UGP2 shows promise as a potential biomarker and therapeutic target in breast cancer,providing a basis for personalized treatment.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective:This paper measures and analyzes the supply levels across districts,and explore the equity status and spatial-temporal evolution characteristics of medical resource supply during the 13th Five-Year Plan to the14th Five-Year Plan periods,aiming to provide references for future planning.Methods:Based on the public service population radiation model,the radiation population numbers of each district in Beijing in each year are calculated.Literature-based relevant indicators are selected.The improved CRITIC-entropy weight method is used to measure the supply level of each medical and health institutions.District-level supply levels per 100,000 radiated population were calculated using population density weighting.And the Gini coefficient and Moran's index are used to analyze its spatial and temporal distribution equilibrium.Results:During the 13th Five-Year Plan to the 14th Five-Year Plan periods,the overall medical resource supply level in Beijing showed an upward trend,with a slight decline in core areas and significant growth in urban development new towns.Regional disparities narrowed,and the global Moran's index values indicated positive spatial correlations in supply levels.Conclusion:The decentralization of non-capital functions in Beijing has achieved notable results,though improvements in outer suburbs remain gradual.Future efforts should strengthen cross-regional coordination,integrate innovations like internet-based medical consultations,and promote a more balanced healthcare resource supply structure aligned with public health needs.

Objective To study the effects of Hedysarum polysaccharides polysaccharide(HPS)on the farnesoid X receptor(FXR)-fibroblast growth factor-19(FGF19)signaling pathway of diabetes rats.Methods Twelve Wistar male rats were randomly selected as the normal group,and the other rats were fed with a single intraperitoneal injection of streptozotocin(50 mg·kg-1 STZ)and a high sugar and high-fat diet to replicate the diabetes rat model.Model rats were randomly divided into model group,positive control group(given 400 mg·kg-1·d-1 suspension of Bifidobacterium quadruplex live bacterial tablets by gavage),experimental-H,-M,-L groups(given 200,100,and 50 mg·kg-1·d-1 doses of HPS suspension by gavage);normal group,and model group were given equal volume of purified water by gavage once a day for 8 consecutive weeks.Glucose(Glu)was detected by a blood glucose meter;and serum total glyceride(TG)and total cholesterol(TC)were detected by enzyme-linked immunosorbent assay reagent kit;the expressions of FXR、fibroblast growth factor receptors 4(FGFR4)relative mRNA expression level and protein were detected by real-time fluorescence quantitative polymerase chain reaction method and Western blot.Results The Glu concentrations in the normal group,model group,positive control group,and experimental-H groups were(7.66±0.61),(29.25±1.64),(23.31±3.02)and(19.31±5.13)mmol·L-1,respectively;the TG content were(957.00±113.73),(1 345.00±246.44),(958.00±96.53)and(964.00±130.22)μmol·L-1,respectively;the TC content were(161.65±4.53),(302.19±5.35),(236.09±5.14)and(165.58±2.58)μmol·L-1,respectively;the expression of FXR relative mRNA expression level were 1.00±0.06,0.48±0.02,0.67±0.04 and 0.92±0.04,respectively;the expression of FGFR4 relative mRNA expression level were 1.00±0.04,0.17±0.01,0.48±0.04 and 0.41±0.03;respectively.The above indexes of the model group were compared with the control group,and the above indexes of the control group and the experimental-H group were compared with the model group,and the differences were statistically significant(all P＜0.01).Conclusion HPS improves blood sugar,lowers blood lipids,and protects liver and intestinal tissues,possibly by regulating the FXR-FGF19 signaling pathway in intestinal tissue,and regulating bile acid synthesis.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

The detection rate of thyroid nodules has gradually increased in recent years.Comprehensive and accurate preoperative evaluation and early identification of risk factors help doctors to choose treatment options and improve prognosis.Radiomics extracts quantitative features from medical images for evaluation of thyroid nodules through high-throughput mining of invisible image features,which has been widely used and has excellent performance in the identification of benign and malignant thyroid nodules,lymph node metastasis,extrathyroidal extension,molecular biological changes,recurrence and prognosis of thyroid cancer.However,there are also shortcomings such as large differences in performance among models from different institutions.This article reviews the application value,limitations and future development prospects of radiomics in the thyroid nodules,so as to provide new ideas for clinical practice and research.

This paper uses literature and network data to systematically sort out the theoretical and practical foundations of global public health cooperation,combines expert interviews to conduct empirical analyses,and further explores China's strategies for participating in global public health cooperation through quantitative statistics and text mining of interview data,and proposes a plan for China's participation in global public health cooperation under the current international situation.Under the countercurrents to globalization,China should take its own public health capacity building as the foundation,put global security and health equity at the core,with a philosophy of open cooperation and sustainable development,actively promote bilateral and multilateral cooperation,focus on cultivating global health talents,and enhance the effectiveness of disease prevention and control by making use of existing platforms,international mechanisms and digital health technologies,so as to help build a Global Community of Health for All.

Autism spectrum disorder(ASD),characterized by unknown etiology and high heterogeneity,ne-cessitates precise diagnostic and intervention strategies.Neuroimaging techniques have shown great promise in un-covering the neural mechanisms of ASD,providing a foundation for aided diagnosis and transcranial magnetic stim-ulation(TMS)interventions.This review highlights that integrating multimodal neuroimaging and developing indi-vidualized indices with developmental specificity can significantly improve the accuracy of ASD diagnosis and clas-sification.Furthermore,TMS interventions guided by functional connectivity derived from functional magnetic reso-nance imaging(fMRI)offer a personalized approach to ASD treatment.

Acute mitochondrial damage and the energy crisis following axonal injury highlight mitochondrial transport as an important target for axonal regeneration. Syntaphilin (Snph), known for its potent mitochondrial anchoring action, has emerged as a significant inhibitor of both mitochondrial transport and axonal regeneration. Therefore, investigating the molecular mechanisms that influence the expression levels of the snph gene can provide a viable strategy to regulate mitochondrial trafficking and enhance axonal regeneration. Here, we reveal the inhibitory effect of microRNA-146b (miR-146b) on the expression of the homologous zebrafish gene syntaphilin b (snphb). Through CRISPR/Cas9 and single-cell electroporation, we elucidated the positive regulatory effect of the miR-146b-snphb axis on Mauthner cell (M-cell) axon regeneration at the global and single-cell levels. Through escape response tests, we show that miR-146b-snphb signaling positively regulates functional recovery after M-cell axon injury. In addition, continuous dynamic imaging in vivo showed that reprogramming miR-146b significantly promotes axonal mitochondrial trafficking in the pre-injury and early stages of regeneration. Our study reveals an intrinsic axonal regeneration regulatory axis that promotes axonal regeneration by reprogramming mitochondrial transport and anchoring. This regulation involves noncoding RNA, and mitochondria-associated genes may provide a potential opportunity for the repair of central nervous system injury.
Animals ; Zebrafish ; MicroRNAs/genetics* ; Nerve Regeneration/physiology* ; Mitochondria/metabolism* ; Zebrafish Proteins/genetics* ; Axons/metabolism* ; Signal Transduction/physiology* ; Axonal Transport/physiology* ; Nerve Tissue Proteins/genetics*