BACKGROUND:Pulmonary arterial hypertension is a destructive cardiopulmonary disease for which there is no cure.An association between plasma proteins and pulmonary arterial hypertension has been suggested,but the causal relationship has not been specifically elucidated.OBJECTIVE:To elucidate the causal relationship between plasma proteome and pulmonary arterial hypertension using a two-sample Mendelian randomization method,thereby searching for potential therapeutic targets for pulmonary arterial hypertension.METHODS:Plasma Protein Gene-Wide Association Analysis Statistics for 4 907 Aptamer Measurements in 35 559 Icelanders from the Icelandic Database;Genome-wide association analysis statistics for pulmonary arterial hypertension were obtained from the Finn Gen database,version R9,including 234 cases and 265 626 controls.Analyses were performed using Mendelian randomization and Bayesian co-localization analysis,the findings were examined using sensitivity analyses,and protein-protein interaction network maps were constructed to explore the causal relationship between plasma proteins and pulmonary arterial hypertension.RESULTS AND CONCLUSION:(1)The results of inverse variance weighting,maximum likelihood and Wald ratio methods showed 19 proteins causally associated with pulmonary arterial hypertension(P＜0.05).Among them,10 plasma proteins,including Beta-1,3-N-acetylglucosaminyltransferase manic fringe(odds ratio[OR]=0.12,95％confidence interval[CI]0.02-0.61,P=0.01)and interferon alpha/beta receptor 1(OR=0.45,95％CI 0.24-0.84,P=0.012),might be associated with a reduced risk of pulmonary arterial hypertension.In contrast,nine plasma proteins,such as glucoside xylosyltransferase 1(OR=3.48,95％CI 1.51-8.00,P=0.003)and plasminogen(OR=42.78,95％CI 2.49-734.31,P=0.01),might be associated with an increased risk of pulmonary arterial hypertension.After the false discovery rate was corrected,19 proteins remained significantly associated with pulmonary arterial hypertension.(2)Multiple sensitivity analyses such as the MR-Egger intercept test and leave-one-out method showed no horizontal multiplicity or heterogeneity in the results of the study,indicating the stability of the study's results.(3)Bayesian co-localization analysis showed that six plasma proteins,including plasminogen(PPH4=1.0)and glucoside xylosyltransferase 1(PPH4=0.94),had PPH4＞0.8,suggesting that plasma proteins and the genome-wide association study of pulmonary arterial hypertension had similar causal variance in terms of genetic association.(4)By constructing a protein-protein interaction network map,plasminogen,Annexin A1,fibrinogen gamma chain and matrix metalloproteinase 7 were found to be core proteins.(5)The article used Mendelian randomization analysis to reveal a potential causal association between 4 907 plasma proteins and pulmonary arterial hypertension,suggesting that plasma proteins may be potential therapeutic targets for pulmonary arterial hypertension.The core proteins identified in the study also provide a theoretical basis for further in-depth study of the pathophysiological mechanisms of pulmonary arterial hypertension.Secondly,analyses using the large-scale international databases of Iceland and FinnGen provide new research directions and treatment ideas for pulmonary arterial hypertension in specific populations and environments,as well as ideas and methods that can be used to prevent and treat pulmonary arterial hypertension in China.

ObjectiveTo analyze the epidemiological characteristics of a local dengue fever cluster outbreak in Qingpu District of Shanghai in 2024, and to provide a reference for subsequent dengue fever prevention and control. MethodsSeven confirmed local dengue fever cases reported through the National Notifiable Infectious Diseases Surveillance System in Qingpu District of Shanghai in 2024 were selected as the research subjects. Descriptive epidemiological methods were used to conduct investigation and analysis from the aspects of onset, medical treatment and reporting, clinical symptoms, travel and contact history within 15 days before onset, and activity trajectories. ResultsA total of 7 cases were identified in this outbreak. None of the cases had a travel history to dengue-endemic areas within 15 days prior to onset, while all had shared exposure environments and mosquito bite histories, indicating a local clustered transmission pattern. The main clinical manifestations included fever (100.00%) and myalgia (42.86%). All 7 cases were positive for dengue virus serotype 2 (DENV-2) by nucleic acid testing. Genetic sequencing showed that the virus strains belonged to the Cosmopolitan genotype and were most closely related to the epidemic DENV strains circulating in southern China in recent years. ConclusionThis outbreak might be a local secondary infection caused by the short-term stay of dengue fever-infected individuals, and the possible source of importation was dengue fever endemic areas in southern China.

BACKGROUND:Pulmonary arterial hypertension is a destructive cardiopulmonary disease for which there is no cure.An association between plasma proteins and pulmonary arterial hypertension has been suggested,but the causal relationship has not been specifically elucidated.OBJECTIVE:To elucidate the causal relationship between plasma proteome and pulmonary arterial hypertension using a two-sample Mendelian randomization method,thereby searching for potential therapeutic targets for pulmonary arterial hypertension.METHODS:Plasma Protein Gene-Wide Association Analysis Statistics for 4 907 Aptamer Measurements in 35 559 Icelanders from the Icelandic Database;Genome-wide association analysis statistics for pulmonary arterial hypertension were obtained from the Finn Gen database,version R9,including 234 cases and 265 626 controls.Analyses were performed using Mendelian randomization and Bayesian co-localization analysis,the findings were examined using sensitivity analyses,and protein-protein interaction network maps were constructed to explore the causal relationship between plasma proteins and pulmonary arterial hypertension.RESULTS AND CONCLUSION:(1)The results of inverse variance weighting,maximum likelihood and Wald ratio methods showed 19 proteins causally associated with pulmonary arterial hypertension(P＜0.05).Among them,10 plasma proteins,including Beta-1,3-N-acetylglucosaminyltransferase manic fringe(odds ratio[OR]=0.12,95％confidence interval[CI]0.02-0.61,P=0.01)and interferon alpha/beta receptor 1(OR=0.45,95％CI 0.24-0.84,P=0.012),might be associated with a reduced risk of pulmonary arterial hypertension.In contrast,nine plasma proteins,such as glucoside xylosyltransferase 1(OR=3.48,95％CI 1.51-8.00,P=0.003)and plasminogen(OR=42.78,95％CI 2.49-734.31,P=0.01),might be associated with an increased risk of pulmonary arterial hypertension.After the false discovery rate was corrected,19 proteins remained significantly associated with pulmonary arterial hypertension.(2)Multiple sensitivity analyses such as the MR-Egger intercept test and leave-one-out method showed no horizontal multiplicity or heterogeneity in the results of the study,indicating the stability of the study's results.(3)Bayesian co-localization analysis showed that six plasma proteins,including plasminogen(PPH4=1.0)and glucoside xylosyltransferase 1(PPH4=0.94),had PPH4＞0.8,suggesting that plasma proteins and the genome-wide association study of pulmonary arterial hypertension had similar causal variance in terms of genetic association.(4)By constructing a protein-protein interaction network map,plasminogen,Annexin A1,fibrinogen gamma chain and matrix metalloproteinase 7 were found to be core proteins.(5)The article used Mendelian randomization analysis to reveal a potential causal association between 4 907 plasma proteins and pulmonary arterial hypertension,suggesting that plasma proteins may be potential therapeutic targets for pulmonary arterial hypertension.The core proteins identified in the study also provide a theoretical basis for further in-depth study of the pathophysiological mechanisms of pulmonary arterial hypertension.Secondly,analyses using the large-scale international databases of Iceland and FinnGen provide new research directions and treatment ideas for pulmonary arterial hypertension in specific populations and environments,as well as ideas and methods that can be used to prevent and treat pulmonary arterial hypertension in China.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

ObjectiveTo develop and evaluate an O-RADSbased multiparametric model integratingcontrast-enhanced US （CEUS） and the z-score standardized risk of ovarian malignancy algorithm （ROMA z-score） to improve the diagnostic accuracy for differentiating benign， borderline， and malignant ovarian epithelial tumors （OETs）. MethodsThis retrospective study included 129 patients with pathologically confirmed OETs between May 2018 and December 2024. Clinical variables （age， menopausal status， BMI， HE4， CA125） and sonographic features （conventional US and CEUS） were collected. Tumors were classified as benign， borderline， or malignant according to pathological findings. A random forest algorithm was used to contruct the predictive model. Receiver operating characteristic （ROC） curves were generated tocompare the diagnostic performance of O-RADS， O-RADS+CEUS， O-RADS+ROMA z-score， and O-RADS+ CEUS+ROMA z-score. ResultsAmong the 129 patients， 63 had benign tumors， 25 had borderline tumors， and 41 had malignant tumors. Compared with O-RADS alone， the combined O-RADS+CEUS+ROMA z-score model demonstrated superior diagnostic performance， with an AUC of 0.87 （95% CI： 0.79-0.95） vs. 0.64 （95% CI： 0.51-0.76）， P＜0.001， for differentiating benign from borderline tumors， and 0.90 （95% CI： 0.83-0.97） vs. 0.64 （95% CI： 0.50-0.78）， P＜0.001， for differentiating borderline from malignant tumors. The random forest analysis identified HE4， CA125 and CEUS features as the most important predictors，contributing 11.02， 8.82， 18.1 points， respectively， to the total importance score of 51.94. In the test cohort， the model achieved an overall diagnostic accuracy of 72.97%， with an AUC of 0.89（95%CI：0.77-1.00）， sensitivity of 67.06%， and specificity of 85.01%. ConclusionsO-RADS+CEUS+ROMA z-score multiparametric model demonstrated the highest diagnostic performance for in differentiating benign from borderline and borderline from malignant OETs. HE4， CA125 and CEUS features were identified as key predictive risk factors， supporting the role of this model in improving preoperative risk stratification of OETs.

Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.

Objective To understand the molecular epidemiological characteristics and hypervirulence evolution trend of ST11 carbapenem-resistant Klebsiella pneumoniae(CRKP)isolated from medical institutions in Shanghai,and provide scientific basis for formulating the prevention and control measures of drug-resistant organisms.Methods A total of 201 strains of CRKP isolated from 12 medical institutions in Shanghai from 2021 to 2022 were collected.Antimicrobial susceptibility testing and whole genome sequencing were performed.The concatenated data was used for multilocus sequencing typing(MLST),serum typing(wzi typing),as well as analysis of resistance and viru-lence genes.Results All 201 CRKP strains were multidrug-resistant organisms(MDROs).These strains were al-most completely resistant to carbapenems,cephalosporins,and quinolones.Drug resistance gene analysis showed that 93.03％of CRKP strains carried KPC gene.201 CRKP strains were divided into 6 ST types and 10 capsule wzi types,with ST11-KL64(n=104)being the dominant type,followed by ST15-KL19(n=54).52.24％(n=105)of CRKP carried rpmA/rpmA2+iucA+iutA+iroN genes.Conclusion CRKP isolated from medical institutions in Shanghai is mainly ST11-KL64 type with severe multidrug resistance,and more than half of the strains are hyper-virulent carbapenem-resistant Klebsiella pneumoniae(hv-CRKP).It is necessary to continuously strengthen the monitoring of the molecular characteristics of CRKP,so as to prevent outbreaks of healthcare-associated infection.

Objective:The prediction model of postoperative recurrence of ovarian endometriosis cyst(OEM)was constructed to provide a reference for evaluating postoperative recurrence of OEM patients.Methods:The clinical,pathological and follow-up data of 342 patients who underwent the initial laparoscopic ovarian cystectomy for OEM at The Second Hospital of Hebei Medical University from January 1,2017 to October 31,2021 were retro-spectively analyzed.Based on univariate and multivariate Cox regression analysis,the relevant factors affecting OEM recurrence were identified.According to the results of multivariate analysis,a nomogram was drawn.The C index and receiver operating characteristic(ROC)curve were used to test the efficiency of the prediction model.Results:The 2-year recurrence rate of OEM patients was 20.4%and the 5-year recurrence rate was 35.2%.Uni-variate and multivariate Cox regression analysis showed that menstrual cycle(HR 0.916,95%CI 0.860-0.976,P=0.006),delivery≥1(HR 0.376,95%CI 0.171-0.827,P=0.015),CA125≥100 U/ml(HR 1.790,95%CI 1.167-2.746,P=0.008),total cyst diameter≥10 cm(HR 2.254,95%CI 1.318-3.854,P=0.003),and postoperative medications(HR 0.434,95%CI 0.292-0.644,P=0.000)were associated with OEM recurrence and were included in the no-mogram prediction model.The C-index of the nomogram prediction model was 0.710(95%CI 0.665-0.754),and the area under the curve(AUC)of the ROC for OEM patients with recurrence at 2 years after surgery was 0.786 and 0.708 at 5 years.Conclusions:In this study,we developed a nomogram to predict the probability of recur-rence at 2 years and 5 years for OEM patients under 45 years who underwent conservative surgery,which has i-deal predictive performance and helps clinicians to screen high-risk patients,and thus give high-risk OEM patients additional attention and intervention.

Tumor microenvironment plays a key role in oncogenesis, development and therapeutic response. In recent years, scholars at home and abroad have paid more and more attention to the regulation of tumor microenvironment. Radiation therapy, as an important means of tumor treatment, not only kills tumor cells directly, but also affects the efficacy of radiation therapy by influencing natural killer (NK) cells to regulate the tumor microenvironment. In this article, the research progress in the regulation of tumor microenvironment by radiation therapy through NK cells was reviewed, including the mechanism of NK cell regulation by radiation therapy, the functional changes of NK cells in the tumor microenvironment, as well as the therapeutic strategies based on the above mechanism and the potential application prospects, aiming to provide novel ideas for future basic research and clinical applications.

Objective:To evaluate the potential value of 18×10 3 translocator protein (TSPO) radioligand ( N, N-diethy1-2-(2-(4-(2- 18F-fluoroethoxy) phenyl)-5, 7-dimethylpyrazolo[1, 5-A]pyrimidin-3-yl)acetamide, 18F-DPA-714) PET compared with conventional MR in the detection of autoimmune encephalitis (AE), the correlation with clinical symptoms, and the monitoring of immunotherapy efficacy in patients with AE. Methods:From December 2021 to June 2024, 45 AE patients (17 males, 28 females, age (38.3±17.0) years) diagnosed at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine and 10 healthy volunteers (7 males, 3 females, age (28.7±5.1) years) were enrolled in this prospective study. All participants underwent baseline 18F-DPA-714 PET/MR scans, and 23 of these AE patients underwent further follow-up 18F-DPA-714 PET/MR scans. 18F-DPA-714 PET positivity was defined as having an uptake intensity threshold higher than the mean SUV ratio (SUVR)+ 2 s of the corresponding brain region in healthy controls. MR positivity was defined as abnormal hyperintensity in a specific brain region or multiple brain regions on the T 2 fluid attenuated inversion recovery (FLAIR). The positive detection rates of 18F-DPA-714 PET and MR was analyzed using McNemar χ2 test, and the differences in the uptake intensity (SUVR) of 18F-DPA-714 between symptomatic and non-symptomatic groups, and between remission and non-remission groups after immunotherapy were compared using independent-sample t test or Wilcoxon rank sum test. Spearman rank correlation analysis was used to analyze the correlation between the changing rate of SUVR and the changing of the modified Rankin Scale (mRS) score before and after treatment. Results:The positive detecting rate of 18F-DPA-714 PET for AE was significantly higher than that of MR (73.3%(33/45) vs 35.6%(16/45); χ2=11.56, P=0.001). The cerebellar SUVR of ataxia patients was significantly higher than that of asymptomatic patients (1.22(1.06, 1.33) vs 1.08(0.99, 1.20); Z=-2.14, P=0.034). Follow-up imaging showed that the SUVR of patients in the remission group after immunotherapy was significantly lower than that in the non-remission group ((-15.19±10.17)% vs (14.26±13.36)%; t=5.81, P<0.001). There was a significant correlation between the changing rate of SUVR and the changing of the mRS score before and after treatment ( rs=0.65, P<0.001). Conclusion:Compared with conventional MR, 18F-DPA-714 PET has a higher positive detecting rate for AE, and has the potential to reflect the clinical symptoms of AE and monitor the efficacy of immunotherapy.