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.

To assess the current status of early surgical site infections following prosthetic implantation in plastic surgery across four provincial-level administrative regions in China, and to explore the effectiveness of standardized training for infection prevention and control.

Objective: To identify the latent classes of acceptance and striving and their associated factors among nursing students, and to examine the mediating role of positive acceptance and striving in the association between self transcendence and subjective well being, so as to provide evidence for targeted interventions to improve subjective well being among nursing students. Methods: From July to October 2025, a convenience sample of 861 nursing students was recruited from five schools in Sichuan and Chongqing, China. Participants were surveyed using the Self transcendence Scale, the Positive Acceptance and Striving Scale, and the General Well being Scale. Latent profile analysis was conducted to identify profiles of positive acceptance and striving. Logistic regression analysis was used to examine associated factors, and relative mediation analysis was performed to assess differences in the mediating effect across latent profiles. Results: Three latent classes of acceptance and striving were identified: the low acceptance-low striving profile (46.34%, n =399), the moderate acceptance-moderate striving profile (40.88%, n =352), and the high acceptance-high striving profile (12.78%, n =110). Multivariable Logistic regression analysis showed that, with the low acceptance-low striving profile as the reference, nursing students with higher self transcendence scores ( OR =151.85) and those who studied nursing because of personal preference ( OR =14.72) or parental expectations ( OR =11.16) were more likely to be classified into the high acceptance-high striving profile (all P <0.05). Male students ( OR =0.19), those perceiving nurses social status as average ( OR =0.35), and those receiving moderate teacher support ( OR =0.23) were less likely to be classified into the high acceptance-high striving profile (all P <0.05). Positive acceptance and striving partially mediated the association between self transcendence and subjective well being, with a total effect of β =0.97 and a mediating effect of β =0.48; the mediating effect accounted for 49.34% of the total effect. Relative mediation analysis showed that the mediating effect was statistically significant only in the low acceptance-low striving profile ( β =0.21), whereas the direct effects were stronger in the moderate acceptance-moderate striving and high acceptance-high striving profiles ( β =0.39, 1.06)(all P <0.05). Conclusions Positive acceptance and striving among nursing students exhibit substantial heterogeneity. Its predictive effect on subjective well being and the underlying mediating mechanism vary across latent profiles. Profile specific interventions should therefore be implemented to improve subjective well being among nursing students.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

ObjectiveTo assess the therapeutic effectiveness and safety of the traditional Chinese medicine compound Shenlong decoction in addressing the symptoms of pulmonary deficiency and stasis in patients with idiopathic pulmonary fibrosis （IPF）. MethodsSixty eligible patients with lung deficiency and collateral stasis syndrome of IPF were randomly assigned to the observation （30 patients） and control groups （30 patients）. All patients underwent standard Western medical therapy. Additionally，the observation group received Shenlong decoction granules，while the control group received a placebo. Both treatments were packaged in four doses of 10.5 g each，taken twice daily for three months. The indexes of the patients during the treatment cycle were observed，and the main indexes include traditional Chinese medicine （TCM） syndrome scores and 6 min walk test （6MWT）. The secondary indexes include pulmonary function test ［actual value/expected value of total lung volume （TLC%），actual value/expected value of vital capacity（FVC%），actual/predicted diffusing capacity of the lung for carbon monoxide（DLCO%），actual/predicted forced expiratory volume in one second （FEV₁%），and FEV₁/ forced vital capacity （FVC）］，blood gas analysis ［arterial blood diathesis partial pressure of oxygen （PaO₂），partial pressure of carbon dioxide （PaCO₂），and arterial oxygen saturation （SaO₂）］，serum inflammatory factors ［transforming growth factor-β₁ （TGF-β₁），interleukin-4 （IL-4），interleukin-13 （IL-13），interleukin-12 （IL-12），and gamma-interferon （IFN-γ）］，and quality of survival evaluation ［St George's Respiratory Questionnaire （SGRQ） score］. The patients' clinical manifestations were determined at the end of the treatment， and the occurrence of adverse events was recorded. ResultsA total of 53 patients completed the study，comprising 27 in the control group and 26 in the observation group. Upon completion of the treatment period，the control group achieved a total effective rate of 33.33% （9/27），whereas the observation group demonstrated a total effective rate of 53.85% （14/26），which was statistically superior to the control group （χ²=4.034，P<0.05）. After the treatment，the TCM syndrome scores，6MWT，DLCO%，FEV₁%，PaO₂，PaCO₂，TGF-β₁，IL-4，IL-13，IL-12，and IFN-γ in the two groups were all significantly improved （P<0.01）. Compared with those in the control group after treatment at the same period，the TCM syndrome scores，6MWT，PaO₂，and PaCO₂ were significantly improved in the observation group after 60 days and 90 days of medication （P<0.01）. Three months after the end of medication，the SGRQ score in the observation group showed significant improvement when compared to that in the control group （P<0.05），and no severe adverse events were reported during the follow-up period. ConclusionCompound Shenlong decoction can alleviate clinical symptoms such as shortness of breath and wheezing in patients with lung deficiency and collateral stasis syndrome of IPF，enhance exercise tolerance，improve the quality of life，and have certain potential advantages in improving pulmonary function.

ObjectiveTo assess the therapeutic effectiveness and safety of the traditional Chinese medicine compound Shenlong decoction in addressing the symptoms of pulmonary deficiency and stasis in patients with idiopathic pulmonary fibrosis （IPF）. MethodsSixty eligible patients with lung deficiency and collateral stasis syndrome of IPF were randomly assigned to the observation （30 patients） and control groups （30 patients）. All patients underwent standard Western medical therapy. Additionally，the observation group received Shenlong decoction granules，while the control group received a placebo. Both treatments were packaged in four doses of 10.5 g each，taken twice daily for three months. The indexes of the patients during the treatment cycle were observed，and the main indexes include traditional Chinese medicine （TCM） syndrome scores and 6 min walk test （6MWT）. The secondary indexes include pulmonary function test ［actual value/expected value of total lung volume （TLC%），actual value/expected value of vital capacity（FVC%），actual/predicted diffusing capacity of the lung for carbon monoxide（DLCO%），actual/predicted forced expiratory volume in one second （FEV₁%），and FEV₁/ forced vital capacity （FVC）］，blood gas analysis ［arterial blood diathesis partial pressure of oxygen （PaO₂），partial pressure of carbon dioxide （PaCO₂），and arterial oxygen saturation （SaO₂）］，serum inflammatory factors ［transforming growth factor-β₁ （TGF-β₁），interleukin-4 （IL-4），interleukin-13 （IL-13），interleukin-12 （IL-12），and gamma-interferon （IFN-γ）］，and quality of survival evaluation ［St George's Respiratory Questionnaire （SGRQ） score］. The patients' clinical manifestations were determined at the end of the treatment， and the occurrence of adverse events was recorded. ResultsA total of 53 patients completed the study，comprising 27 in the control group and 26 in the observation group. Upon completion of the treatment period，the control group achieved a total effective rate of 33.33% （9/27），whereas the observation group demonstrated a total effective rate of 53.85% （14/26），which was statistically superior to the control group （χ²=4.034，P<0.05）. After the treatment，the TCM syndrome scores，6MWT，DLCO%，FEV₁%，PaO₂，PaCO₂，TGF-β₁，IL-4，IL-13，IL-12，and IFN-γ in the two groups were all significantly improved （P<0.01）. Compared with those in the control group after treatment at the same period，the TCM syndrome scores，6MWT，PaO₂，and PaCO₂ were significantly improved in the observation group after 60 days and 90 days of medication （P<0.01）. Three months after the end of medication，the SGRQ score in the observation group showed significant improvement when compared to that in the control group （P<0.05），and no severe adverse events were reported during the follow-up period. ConclusionCompound Shenlong decoction can alleviate clinical symptoms such as shortness of breath and wheezing in patients with lung deficiency and collateral stasis syndrome of IPF，enhance exercise tolerance，improve the quality of life，and have certain potential advantages in improving pulmonary function.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Objective:To investigate the mechanism of the synergistic anti-leukemia effect of the combination of Bcl-2 inhibitor venetoclax(VEN)and histone deacetylase(HDAC)inhibitor chidamide(CDM)in T-cell acute lymphoblastic leukemia(T-ALL).Methods:The effect of VEN combined with CDM on the proliferation of T-ALL CEM and MOLT-4 cell lines was detected by CCK-8 assay.And the effects on the cell cycle and apoptosis were detected by flow cytometry.Cell cycle protein and apoptosis-related protein expression were detected by Western blot.The key pathways of VEN combined with CDM in T-ALL were screened through network pharmacology analysis,and verifying them in T-ALL cell lines,T-ALL patient cells and public databases.Results:VEN combined with CDM displayed a synergistic effect on cell proliferation of CEM and MOLT-4 cells.In cell cycle experiment,VEN combined with CDM induced G0/G1 phase arrest in CEM and MOLT-4 cells.Western blot experiment showed that VEN combined with CDM could significantly downregulate the expression of cyclin E2 and CDK2 and upregulate the expression of p21Waf1/Cip1.In the apoptosis experiment,VEN combined with CDM could significantly induce the apoptosis of CEM and MOLT-4 cells.Western blot experiment demonstrated that VEN combined with CDM promoted endogenous apoptosis by downregulating Mcl-1 and upregulating Bax and cleaved caspase-3 protein levels.Network pharmacology analysis identified 10 hub genes.KEGG enrichment analysis revealed the cell cycle,PI3K-AKT signaling pathway,and its downstream FoxO signaling pathway were significantly enriched.GO enrichment analysis revealed the G1/S transition of mitotic cell cycle,cyclin-dependent protein kinase holoenzyme complex,and kinase activity were significantly enriched.Western blot experiment showed that VEN combined with CDM could significantly downregulate the protein level of PI3K,AKT,and p-AKT,and upregulate FoxO1 in CEM and MOLT-4 cells.In T-ALL patients,FoxO1 showed significantly lower expression compared to the normal donors,and the same result was verified in the GSE13159 and GSE26713 datasets.Conclusion:The combination of VEN and CDM exerts synergistic anti-leukemia effects by inhibiting cellular proliferation,inducing G0/G1,phase arrest and promoting apoptosis through PI3K/AKT/FoxO1 axis in T-ALL.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.