Objective:This study aimed to evaluate the effects of hypertensive disorders of pregnancy (HDP), including their clinical subtypes, on neonatal heel blood methionine levels and explore potential dose-effect relationships.Methods:A retrospective cohort study was conducted among 11 007 singleton pregnancies and their neonates delivered at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, from July 2021 to October 2022. Participants were stratified into an HDP group [ n=992; 480 with gestational hypertension, 512 with preeclampsia (including 229 severe cases)] and a non-HDP control group ( n=10 015). Methionine concentrations were measured using tandem mass spectrometry from heel blood dried filter paper samples collected within 72 hours post-delivery. Statistical analyses included non-parametric tests to compare intergroup differences, multiple linear regression to evaluate the effects of HDP on methionine levels, and multivariate logistic regression to identify risk factors for hypermethioninemia (>50 μmol/L). Results:(1) Baseline data: Maternal age was higher in the HDP group compared to controls [33 (30-36) vs. 33 (30-35) years, Z=－2.29, P=0.022], with elevated pre-pregnancy body mass index (BMI) [23 (21-26) vs. 21 (20-23) kg/m2, Z=－17.15, P<0.001] and increased gestational hyperglycemia prevalence [26.5% (263/992) vs. 19.8% (1 986/10 015), χ2=27.95, P<0.001]. (2) Methionine level: Neonates in the HDP group exhibited higher methionine levels [25.96 (21.58-30.89) vs. 24.77 (20.45-29.53) μmol/L, Z=－5.26, P<0.001], with a severity-dependent gradient: gestational hypertension [25.83 (21.77-30.61)], preeclampsia [26.05 (21.23-31.11)], and severe preeclampsia [26.15 (21.25-32.13)] ( Z=2.97, 3.92, 2.26; all P<0.05). Trend analysis confirmed a dose-effect relationship between HDP and neonatal methionine ( χ2=7.82, P=0.005). (3) Multivariate analysis: After adjusting for confounding factors such as maternal age and BMI, HDP remained independently associated with elevated methionine levels ( β=0.93, 95% CI: 0.47-1.40, t=3.92, P<0.001) and increased hypermethioninemia risk ( OR=2.75, 95% CI: 1.13-6.68). Subgroup analysis revealed ORs of 3.20 (95% CI: 1.07-9.57) for gestational hypertension, 3.25 (95% CI: 1.09-9.72) for preeclampsia, and 5.23 (95% CI: 1.54-17.82) for severe preeclampsia (all P<0.05). (4) Neonatal outcomes: Neonates in the HDP group had lower birth weights [3 230 (2 910-3 560) vs. 3 335 (3 070-3 600) g, Z=－7.43, P<0.001] and higher fetal growth restriction rates [10.3% (102/992) vs. 3.1% (306/10 015), χ2=136.47, P<0.001]. Conclusions:HDP demonstrates an elevation of neonatal methionine levels, correlating with disease severity, particularly in severe preeclampsia. These findings underscore the necessity for enhanced metabolic monitoring and long-term follow-up in offspring of mothers with HDP, especially those with severe preeclampsia.

Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Objective:This study aimed to evaluate the effects of hypertensive disorders of pregnancy (HDP), including their clinical subtypes, on neonatal heel blood methionine levels and explore potential dose-effect relationships.Methods:A retrospective cohort study was conducted among 11 007 singleton pregnancies and their neonates delivered at Beijing Obstetrics and Gynecology Hospital, Capital Medical University, from July 2021 to October 2022. Participants were stratified into an HDP group [ n=992; 480 with gestational hypertension, 512 with preeclampsia (including 229 severe cases)] and a non-HDP control group ( n=10 015). Methionine concentrations were measured using tandem mass spectrometry from heel blood dried filter paper samples collected within 72 hours post-delivery. Statistical analyses included non-parametric tests to compare intergroup differences, multiple linear regression to evaluate the effects of HDP on methionine levels, and multivariate logistic regression to identify risk factors for hypermethioninemia (>50 μmol/L). Results:(1) Baseline data: Maternal age was higher in the HDP group compared to controls [33 (30-36) vs. 33 (30-35) years, Z=－2.29, P=0.022], with elevated pre-pregnancy body mass index (BMI) [23 (21-26) vs. 21 (20-23) kg/m2, Z=－17.15, P<0.001] and increased gestational hyperglycemia prevalence [26.5% (263/992) vs. 19.8% (1 986/10 015), χ2=27.95, P<0.001]. (2) Methionine level: Neonates in the HDP group exhibited higher methionine levels [25.96 (21.58-30.89) vs. 24.77 (20.45-29.53) μmol/L, Z=－5.26, P<0.001], with a severity-dependent gradient: gestational hypertension [25.83 (21.77-30.61)], preeclampsia [26.05 (21.23-31.11)], and severe preeclampsia [26.15 (21.25-32.13)] ( Z=2.97, 3.92, 2.26; all P<0.05). Trend analysis confirmed a dose-effect relationship between HDP and neonatal methionine ( χ2=7.82, P=0.005). (3) Multivariate analysis: After adjusting for confounding factors such as maternal age and BMI, HDP remained independently associated with elevated methionine levels ( β=0.93, 95% CI: 0.47-1.40, t=3.92, P<0.001) and increased hypermethioninemia risk ( OR=2.75, 95% CI: 1.13-6.68). Subgroup analysis revealed ORs of 3.20 (95% CI: 1.07-9.57) for gestational hypertension, 3.25 (95% CI: 1.09-9.72) for preeclampsia, and 5.23 (95% CI: 1.54-17.82) for severe preeclampsia (all P<0.05). (4) Neonatal outcomes: Neonates in the HDP group had lower birth weights [3 230 (2 910-3 560) vs. 3 335 (3 070-3 600) g, Z=－7.43, P<0.001] and higher fetal growth restriction rates [10.3% (102/992) vs. 3.1% (306/10 015), χ2=136.47, P<0.001]. Conclusions:HDP demonstrates an elevation of neonatal methionine levels, correlating with disease severity, particularly in severe preeclampsia. These findings underscore the necessity for enhanced metabolic monitoring and long-term follow-up in offspring of mothers with HDP, especially those with severe preeclampsia.

Objective To investigate the expression of inositol polyphosphate 4-phosphatase type Ⅱ B(INPP4B)in colorectal cancer(CRC)and the relevant clinical significance,to determine the relationship between INPP4B and matrix metallopeptidase 7(MMP7)in CRC cells,and to make preliminary exploration of the effects of INPP4B on the proliferation and migration of CRC cells and mechanisms involved.Methods The TIMER2.0 and GEPIA2 databases were used to analyze the differences in INPP4B expression between cancer and para-cancerous tissues and the effects of such differences on the prognosis of CRC.The expression of INPP4B in 102 surgically resected CRC tumors was determined by immunohistochemistry(IHC),and the correlation between INPP4B and clinical pathological indicators was analyzed.In CRC cells with overexpressed/knocked-down INPP4B,the expression of INPP4B and MMP7 were examined by real time fluorogenic quantitative PCR,the protein expression of INPP4B was assessed by Western blot,cell proliferation was determined using the CellTiter 96? AQueous One assay,and cell migration and invasion were assessed using wound healing assay and real-time label-free dynamic cell analysis(RTCA).The LinkedOmics database was used to analyze signaling pathways related to INPP4B function,and the role of potential key molecules was validated at the cellular level.Results Analysis with the TIMER2.0 database and GEPIA2 database showed elevated INPP4B expression(colon adenocarcinoma[COAD]:2.30,rectal adenocarcinoma[READ]:2.33)in CRC compared to normal tissue(COAD:1.91,READ:1.89).IHC testing confirmed that INPP4B was upregulated in clinical CRC tissues and paracancerous tissues(P＜0.001).Cox regression model analysis showed that INPP4B(hazards ratio[HR]=1.457,95％confidence interval[CI]:1.003-2.115)affected the prognosis of CRC,and the Kaplan-Meier curve showed that patients with high INPP4B expression had shorter overall survival(P＜0.05).x2 test was performed to analyze the relationship between INPP4B expression and clinicopathological indexes,and it was found that high expression of INPP4B was correlated with lymph node metastasis(x2=3.997,P=0.046)and neural invasion(x2=8.511,P=0.004).In in vitro experiments,CRC cells overexpressing INPP4B showed a significantly increased cell proliferation and migration compared to the cells in the control group(P＜0.05).Analysis using the LinkedOmics database showed that INPP4B was correlated with extracellular matrix remodeling and cell migration.Pearson's correlation analysis showed that MMP7 was positively correlated with INPP4B(r=0.3782,P＜0.001).INPP4B overexpression or knockdown in vitro also led to the upregulation or the downregulation of MMP7 expression in CRC cells.Conclusion INPP4B is highly expressed in CRC tissues and significantly correlated with lymph node metastasis,neural invasion,and patient prognosis.MMP7 may mediate the role of INPP4B in promoting CRC cell migration and invasion.

Vitamin C is an essential micronutrient for human and its deficiency will lead to scurvy.In recent years,as a new therapeutic strategy,high dose intravenous vitamin C(HDIVC)has been widely studied in critical cared diseases.In this review,we summarized the progress of HDIVC in sepsis,including its research history,rationality of usage,controversy and prospect,problems and outlook.The application of HDIVC in critical cared diseases underwent three periods:early,preheating and outbreaking period.Given the reduction of vitamin C in critical diseases,it requires rapid intravenous supplementation.We confirmed the therapeutic efficacy of HDIVC in COVID-19 and severe acute pancreatitis respectively.It is well known that HDIVC has effect of suppressing inflammatory responses,stabilizing the circulation and improving the immunity.But the application of HDIVC in critical cared diseases is still controversial,for the opposing findings in multiple large-scale randomized controlled trials.Future studies should be better to pay further attention on the dose and time of vitamin C and vitamin C plasma concentration monitoring.

Objective:To optimize the stimulation and activation system of mouse CD3 + T cells in vitro and explore the optimal infection time of CD3 + T cells to establish mouse CD19 chimeric antigen receptor T cells (mCD19 CAR-T) , and to also verify its killing effect in vivo and in vitro. Method:Splenic CD3 +T cells were isolated and purified using magnetic beads, and the cells were cultured in Soluble anti-CD3/CD28, PMA+Ionomycin, and Plated anti-CD3/CD28. Cell activation and apoptosis were assessed by flow cytometry after 8, 24, 48, and 72 hours. ScFv plasmid of mouse CD19 antibody was transfected to plat-E cells to package retrovirus. Activated CD3 + T cells were infected to construct mouse-specific CD19 chimeric antigen receptor T cells (mCD19 CAR-T) , and mCD19 CAR-T cells were co-cultured with B-cell lymphoma cell line A20 in vitro. The specific toxicity of A20 was detected by flow cytometry, and mCD19 CAR-T cells were infused into the lymphoma mouse model to detect its killing effect and distribution. Results:The activation effect of Plated anti-CD3/CD28 on CD3 + T cells was superior, with the cells exhibiting good viability 24–48 hours after stimulation. Established mCD19 CAR-T cells with stable efficiency[ (32.27±7.56) % ] were specifically able to kill A20 tumor cells (The apoptosis rate was 24.3% at 48 h) . In vivo detection showed a non-significant decrease in the percentage[ (1.83±0.58) % ] of splenic CD19 + cells 6 days after mCD19 CAR-T cell infusion. A marked clearance in bone marrow and spleen appeared on day 12 compared with the A20 group, and this difference was statistically significant[spleen: (0.36±0.04) % vs (47.00±13.46) % , P<0.001; bone marrow: (1.82±0.29) % vs (37.30±1.44) % , P<0.0001]. Moreover, mCD19 CAR-T cells were distributed in high proportions in the peripheral blood, spleen, and bone marrow[ (2.90±1.12) % , (4.96±0.80) % , (13.55±1.56) % ]. Conclusion:This study demonstrated an optimized activation system and the optimal infection time of CD3 + T cells. Furthermore, stable constructed mCD19 CAR-T cells showed a remarkable killing ability in vitro and in vivo.

In vitro studies have established the prevalent theory that the mitochondrial kinase PINK1 protects neurodegeneration by removing damaged mitochondria in Parkinson's disease (PD). However, difficulty in detecting endogenous PINK1 protein in rodent brains and cell lines has prevented the rigorous investigation of the in vivo role of PINK1. Here we report that PINK1 kinase form is selectively expressed in the human and monkey brains. CRISPR/Cas9-mediated deficiency of PINK1 causes similar neurodegeneration in the brains of fetal and adult monkeys as well as cultured monkey neurons without affecting mitochondrial protein expression and morphology. Importantly, PINK1 mutations in the primate brain and human cells reduce protein phosphorylation that is important for neuronal function and survival. Our findings suggest that PINK1 kinase activity rather than its mitochondrial function is essential for the neuronal survival in the primate brains and that its kinase dysfunction could be involved in the pathogenesis of PD.

Objective:To analyze the clinical characteristics and pathogen distributions of the patients with bone and joint infection.Methods:The clinical data and etiological results of 225 patients with bone and joint infection from January 2008 to October 2020 in Huashan Hospital, Fudan University were retrospectively analyzed.Statistical analysis was conducted by chi-square test.Results:Of the 225 cases with bone and joint infection, 75.6%(170/225) were extremities and other osteomyelitis, 16.0%(36/225) were suppurative arthritis, 8.4%(19/225) were spinal osteomyelitis. Non-implants related infection accounted for 80.4%(181/225) of the cases, while 19.6%(44/225) of the cases were implants related infection. The main clinical manifestations were localized pain (48.4%(109/225)), dyskinesia (47.6%(107/225)), localized swelling (28.9%(65/225)), fever (28.0%(63/225)), and increased purulent exudation (24.9%(56/225)). The proportions of localized pain (55.8%(101/181)) and fever (31.5%(57/181)) of non-implants infection were higher than those of implants infection (18.2%(8/44) and 13.6%(6/44), respectively), while the proportion of increased purulent exudation in implants infection (50.0%(22/44)) was higher than that in non-implants infection (18.8%(34/181)). There were all significant differences between the two groups ( χ2=15.49, 5.60 and 18.45, respectively, all P<0.050). Of the 225 cases, 63 cases(28.0%) had complications with other site infection, especially soft tissue infection and bloodstream infection. A total of 106 strains of pathogens were isolated from 225 specimens, 58.5%(62/106) of them were Gram positive bacterium.Among them, 34.0%(36/106) were Staphylococcus aureus, with the rate of methicillin resistant Staphylococcus aureus (MRSA) isolation accounting for 11.3%(12/106). Laboratory tests showed that 40.4%(91/225) of the patients had elevated erythrocyte sedimentation rate (ESR), 32.9%(74/225) patients had elevated C-reactive protein (CRP). Proportions of patients with elevated ESR (43.6%(79/181)) and CRP (37.6%(68/181)) in non-implants infection were significantly higher than those in implants infection (27.3%(12/44) and 13.6%(6/44), respectively). There were significant differences between the two groups ( χ2=3.94 and 9.19, respectively, P=0.047 and 0.002, respectively). Conclusions:The main clinical manifestations of bone and joint infection are localized pain, dyskinesia, localized swelling, fever and increased purulent exudation. Patients with bone and joint infection are easy to be complicated with soft tissue infection and bloodstream infection, and often accompanied by increased ESR and CRP levels. Gram positive bacterium are the main pathogens.