Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

In recent years, the study of single-cell transcriptome sequencing technology in the heterogeneity of astrocytes (astrocytes) after spinal cord injury (SCI) has provided new perspectives on post-traumatic nerve regeneration and repair. To provide a review on the research progress of single-cell sequencing technology in astrocytes after spinal cord injury (SCI), and to more comprehensively and deeply elaborate the application of single-cell sequencing technology in the field of astrocytes after SCI. Single-cell sequencing technology can analyse the transcriptomes of individual cells in a high-throughput manner, thus revealing fine differences in cell types and states. By using single-cell sequencing technology, the heterogeneity of astrocytes after SCI and their association with nerve regeneration and repair were revealed. In conclusion, the application of single-cell sequencing technology provides an important tool to reveal the heterogeneity of astrocytes after SCI, to further explore the mechanisms of astrocytes in SCI, and to develop intervention strategies targeting their regulatory mechanisms in order to improve the therapeutic efficacy of SCI. The discovery of changes in astrocyte transcriptome dynamics has improved researchers' understanding of spinal cord injury lesion progression and provided new insights into the treatment of spinal cord injury at different time points. To date, all of these findings need to be validated by more basic research and sufficient clinical trials. In the future, single-cell sequencing technology, through interdisciplinary collaboration with bioinformatics, computer science, tissue engineering, and clinical medicine, is expected to open a new window for the treatment of spinal cord injury.
Spinal Cord Injuries/metabolism* ; Astrocytes/cytology* ; Single-Cell Analysis/methods* ; Humans ; Animals ; Transcriptome ; Nerve Regeneration

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

Objective: To develop a modified calculation formula for renal tumor volume and tumor contact surface area (CSA) based on the modeling results of 3D Slicer software, and to create a webpage of the calculation formula for use. Methods: The general information and tumor anatomical data of 98 patients who underwent partial nephrectomy during Jan.2021 and Jul.2023 in the Second Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed.The imaging data were input into 3D Slicer software in the form of Dicom files for tumor and ipsilateral kidney modeling to obtain tumor anatomical data.The relationship between tumor anatomical parameters and tumor volume and CSA was analyzed using multifactorial linear regression.The initial modified formulas (V2, C2) and the optimized modified formulas (V3, C3) for tumor volume over CSA were established, respectively, after insignificant variables were eliminated.The mean square error (MSE) and Akaike information criterion (AIC) of the modified and traditional formulas (V1, C1) were compared, and the formula with the smallest MSE and AIC was selected as the optimal tumor volume and CSA calculation formula.The median tumor volume and CSA obtained from 3D modeling were used as the cutoff values.The optimal formula and conventional formula were applied to calculate tumor volume and CSA for all patients, and risk stratification was performed for all patients based on these cutoff values, and the perioperative indicators of patients in the upper and lower groups were compared.Finally, an online calculation tool was developed based on HTML. Results: Based on multifactorial linear regression analysis, we obtained the modified tumor volume calculation formula: V=0.382abc+2.488a+2.372b－4.146c+1.948（V2）, V=0.469abc－4.586c+13.816（V3）; the modified tumor CSA calculation formula CSA=2.469a －2.262L －19.23a+6.206b+1.212c+18.017L+1.616h－3.97h －2.185h/h －0.388（C2）, CSA=2.376a －2.144L －20.157a+5.024b+1.128c+17.578L+2.525h－2.634（C3）.Both of the modified volume formula (MSE=151.298 vs. 127.807 vs. 104.106) and modified CSA formula (MSE=309.878 vs.23.556 vs.30.388) had smaller errors compared to the conventional formula.The modified volume calculation formula showed that bleeding was more and thermal ischemia time was longer in patients with larger tumor volumes than in patients with smaller tumor volumes (P<0.05); and the modified CSA calculation formula showed that bleeding was more, surgery and thermal ischemia time were longer in patients with high CSA than in patients with low CSA (P＜0.05).Finally, V3 and C3 are selected as the best calculation formula, and a web page (https://lizihao-bot.github.io/RCC-Calculate/) was established for easy use. Conclusion: This study combined data from a medical information technology platform with numerical modeling methods to provide a faster and more accurate method to calculate the renal tumor volume and CSA.Meanwhile, a webpage version of the tool was developed to enhance its practicability.

Objective To investigate the factors influencing the severity of cerebral white matter hyperintensity in hypertensive patients with acute cerebral infarction,and focus on the independ-ent roles of treatment adherence,advanced age,and multidimensional risk factors.Methods A single-center cross-sectional study was conducted on 262 hypertensive patients with acute cerebral infarction admitted to our department from January 2020 to June 2023.According to their score of Morisky Medication Adherence Scale(MMAS-8),they were divided into an adherence group(116 cases)and a non-adherence group(146 cases).Their demographic data,medical history,and blood pressure data were collected through electronic medical records.Fazekas score was assessed blind-ly to quantify the severity of white matter hyperintensity.MMAS-8 and Montreal Cognitive Assessment Scale(MoCA)were used to assess the behavioral indicators,and modified Rankin Scale(mRS)was employed to evaluate the neurological function.Univariate and multivariate logistic regression analyses were performed to screen the risk factors.Results There were statis-tical differences in marital status,mRS score and Fazekas score between the adherence group and the non-adherence group(P＜0.05,P＜0.01).The non-adherence group had significantly higher diastolic blood pressure than the adherence group[(87.29±7.51)mm Hg(1 mm Hg=0.133 kPa)vs(83.98±8.11)mm Hg,P=0.001].Univariate logistic regression analysis showed age of≥80 years,duration of hypertension≥5 years,systolic blood pressure,and MoCA score＜10 were no-tably correlated with severity of white matter hyperintensity(P＜0.01).Multivariate logistic re-gression analysis indicted that age of≥80 years(OR=3.305,95％CI:1.560-7.001,P=0.002),systolic blood pressure(OR=1.026,95％CI:1.008-1.044,P=0.003),MoCA score＜10(OR=5.210,95％CI:2.060-13.176,P=0.000),and treatment non-adherence(OR=2.380,95％CI:1.346-4.209,P=0.003)were independent risk factors for white matter hyperintensity severity,and duration of hypertension showed no significance in the multivariate model(P＞0.05).Conclusion Poor treatment adherence and advanced age are core factors for white matter hyper-intensity progression in hypertensive patients with acute cerebral infarction.

Objective This study focuses on a tertiary public hospital in Ningxia to explore the practical application of discipline-specific research performance evaluation.To establish a performance evaluation index system tailored to the characteris-tics of tertiary public hospitals in underdeveloped regions and propose strategies for improving research performance evaluation through empirical research,thereby promoting high-quality hospital development.Methods Guided by the performance evalua-tion indicators for tertiary public hospitals and the accreditation standards for tertiary hospitals,and aligned with the hospital's o-verall work plan,a multi-dimensional and multi-level evaluation method was adopted.Following the SMART principles(Specific,Measurable,Achievable,Relevant,Time-bound)and differentiated scoring principles,research performance evaluation indica-tors were summarized,screened,and weighted.An empirical analysis of the research status of 20 disciplines from 2021 to 2024 was conducted to establish and continuously optimize a research performance evaluation index system suited to the hospital's needs.Results A research performance evaluation index system for public hospital disciplines was finalized,comprising 5 first-level indicators,14 second-level indicators,and 40 third-level indicators(22 quantitative and 18 qualitative).This system standardized the management of discipline-specific research and effectively promoted steady growth,structural adjustment,and development in hospital research.Conclusion Constructing a scientific,standardized,and operable research performance eval-uation index system is of significant importance for enhancing the research level of disciplines in public hospitals and strengthening discipline construction.

This article reported a case of generalized arterial calcification in infancy (GACI), which was suspected prenatally and confirmed postnatally. At 24 weeks of gestation, a systematic ultrasound revealed aortic wall thickening with hyperechogenicity, a thickened mitral valve accompanied by hyperechoic changes, thickened and calcified tricuspid valves, and multiple hyperechoic foci in both ventricles. A follow-up echocardiography at 28 weeks showed no significant progression. Emergency cesarean section was performed at 31 weeks due to fetal distress. Postnatal echocardiography confirmed generalized arterial calcification. Whole-exome trio sequencing identified two variants in the ENPP1 gene: c.1274-2A>G and c.1437+3(IVS14)_c.1437+6(IVS14)delGAGT. Based on imaging, laboratory results, and genetic testing, the infant was diagnosed with GACI type 1, concomitant with autosomal recessive hypophosphatemic rickets type 2. Given the poor prognosis, the family withdrew from further medical intervention.

Hepatic fibrosis(HF)is an inevitable process for many chronic liver diseases to develop into liver cirrhosis and even liver cancer.More than 80%of hepatocellular carcinomas are formed after the process of chronic hepatitis,HF or liver cirrhosis.The immune microenvironment produced by inflammation and fi-brosis plays a significant role in promoting the occurrence and development of hepatocellular carcinoma.He-patic stellate cells(HSC),extracellular matrix(ECM),and fibroblasts are involved in the progression of HF by influencing innate immunity and adaptive immunity.This article,by analyzing the role of immune cells in HF,is expected to provide new intervention approaches and therapeutic targets for the clinical treatment of HF,thereby further enhancing the effectiveness of immunotherapy and offering safer and more effective treat-ment options for HF patients.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.