ObjectiveTo establish a mouse model of hypoxic pulmonary hypertension (HPH) through intermittent hypoxia induction, and to develop a comprehensive and reliable evaluation system for the HPH model, thereby providing experimental evidence for mechanistic studies and translational applications related to this disease. MethodsTwenty-four male specific pathogen-free (SPF) BALB/c mice were randomly divided into a control group and a model group, with 12 mice per group. Mice in the model group were placed in a hypobaric oxygen chamber control system to establish the HPH model, whereas mice in the control group received no intervention. After 28 days of modeling, a comprehensive evaluation of the pathophysiological characteristics of both groups was performed using a general condition scoring scale, echocardiography, hemodynamic measurements, blood gas analysis, hematological tests, organ coefficient determination, histopathological examination, and enzyme-linked immunosorbent assay (ELISA). In addition, correlation analyses were conducted among echocardiographic parameters, the contents of endothelin‑1 (ET‑1) and N‑terminal pro‑B‑type natriuretic peptide (NT‑proBNP) in lung tissue, and other measured indices. ResultsWith prolonged modeling duration time, body weight, water intake, and activity level of mice in the model group were significantly decreased compared with the control group (P<0.05). Echocardiography showed that, compared with the control group, the model group exhibited increased right ventricular dimensions (P<0.01), right ventricular anterior wall thickening (P<0.01), and widened main pulmonary artery diameter (P<0.01), whereas the peak systolic velocity across the pulmonary valve, tricuspid annular plane systolic excursion, and peak systolic velocity of the tricuspid annulus were significantly decreased (P<0.001); tricuspid regurgitation was observed in some model animals. Hemodynamic results revealed that right ventricular systolic pressure was elevated in the model group compared with the control group (P<0.001). Blood gas analysis showed that pH, partial pressure of oxygen, oxygen saturation, actual bicarbonate, and total carbon dioxide were all decreased in the model group compared with the control group (P<0.01). Hematological indices demonstrated that lymphocyte counts (P<0.05) and reticulocyte counts (P<0.001) were decreased in the model group compared with the control group. Compared with the control group, the organ coefficients of heart and lung in the model group were significantly increased (P<0.05 and P<0.001). Pathological examination revealed that the right ventricular hypertrophy index was significantly increased in the model group (P<0.001), with varying degrees of damage to the right ventricle, pulmonary artery, and pulmonary vessels; both the pulmonary artery wall thickness percentage and the pulmonary wall area percentage were significantly elevated (P<0.001). ELISA results showed that the levels of ET‑1 and NT‑proBNP in lung tissues were significantly increased in the model group compared with the control group (P<0.05 and P<0.001, respectively). Correlation analysis indicated that some echocardiographic parameters were highly correlated with multiple indices in the development of HPH (P<0.05). ConclusionEchocardiography can accurately assess a series of hemodynamic changes in HPH, including right ventricular structural, functional impairment, and pulmonary hypertension. Laboratory tests not only help verify whether the model has been successfully established, but also provide deeper insights into the pathogenesis of HPH, evaluate the effects of interventions, and offer scientific evidence for clinical outcomes. Pathological examination can further confirm the alterations in pulmonary vascular remodeling and increased right heart load. This multimodal analysis provides a reliable animal model and evaluation paradigm for both basic and translational research on HPH, and is of great significance for exploring disease mechanisms and developing precision therapeutic strategies.

Objective To analyze the current status of clinical trial registration in the TCM treatment of diabetic nephropathy(DN);To provide references for the registration and implementation of relevant clinical trials.Methods Clinical trials about TCM treatment for DN registered in the Chinese Clinical Trial Registry(ChiCTR)and the U.S.Clinical Trial Registry(ClinicalTrials.gov)from their inception to August 18,2024 were retrieved.The following information was analyzed:registration year,geographical distribution,funding sources,TCM syndrome patterns of participants,number of research centers,sample size,study type,study design,randomization method,blinding method,intervention measures,outcome indicators and safety indicators.Results A total of 88 clinical trials were included,comprising 79 interventional studies and 9 observational studies.The number of registrations has increased annually.The domestic registered trials were distributed across 17 provincial-level administrative regions in China,with Beijing and Shanghai having the highest number of registrations.The primary sources of funding were local and national government funds.The most common TCM syndrome pattern among participants was qi-yin deficiency with blood stasis.Most trials were single-center studies,with the majority having a sample size between 31 and 60.The predominant study type was interventional,mostly randomized controlled trials(RCTs),with simple randomization being the most frequently used method.31 trials reported blinding methods,with double-blinding being the most common.The intervention measures were mostly oral Chinese patent medicines or TCM compounds,and the outcome indicators were mainly efficacy indicators,with less safety indicators.Conclusion The number of registered clinical trials on TCM treatment for DN has increased annually;however,the overall number remains limited.There is uneven regional distribution and incomplete registration information for various factors such as randomization methods,blinding methods,number of research centers,intervention measures,and outcome indicators.

Objective To survey conflict occurrence in surrogate decision makers for respiratory intensive care unit(RICU)patients with mechanical ventilation and explore its influencing factors.Methods A total of 101 surrogate decision-makers of mechanical ventilation patients in RICU were subjected from the Second Affiliated Hospital of Army Medical University from June 2024 to December 2024 by convenience sampling.General data questionnaire,Preparation for Decision Making Scale and Decisional Conflict Scale were used to conduct questionnaire survey.One-way ANOVA,Pearson correlation analysis and multiple linear regression analysis were employed to identify the influencing factors.Results The total score of decision readiness was 64.14±22.35,the total score of decision conflict was 35.18±26.47,and the incidence of decision conflict was 69.31%in the participants.Univariate analysis showed that there were statistically differences in whether knowing about prognosis,relationship with patients,awareness of patients'ideas about treatment,and understanding of information related to mechanical ventilation among the participants(P<0.05).Pearson correlation analysis indicated that decision readiness was negatively correlated with decision conflict and its 3 dimensions(P<0.001).The results of multiple linear regression analysis revealed that understanding of information related to mechanical ventilation and decision readiness were influencing factors for the decision conflict of the participants(P<0.001).Conclusion The decision conflict level is quite higher in the surrogate decision makers for RICU patients with mechanical ventilation,and the level is negatively correlated with the understanding of mechanical ventilation related information and decision readiness.

Receptor-interacting protein kinase 1 (RIPK1) plays an essential role in regulating the necroptosis and apoptosis in cerebral ischemia-reperfusion (I/R) injury. However, the regulation of RIPK1 kinase activity after cerebral I/R injury remains largely unknown. In this study, we found the downregulation of protein arginine methyltransferase 1 (PRMT1) was induced by cerebral I/R injury, which negatively correlated with the activation of RIPK1. Mechanistically, we proved that PRMT1 directly interacted with RIPK1 and catalyzed its asymmetric dimethylarginine, which then blocked RIPK1 homodimerization and suppressed its kinase activity. Moreover, pharmacological inhibition or genetic ablation of PRMT1 aggravated I/R injury by promoting RIPK1-mediated necroptosis and apoptosis, while PRMT1 overexpression protected against I/R injury by suppressing RIPK1 activation. Our findings revealed the molecular regulation of RIPK1 activation and demonstrated PRMT1 would be a potential therapeutic target for the treatment of ischemic stroke.

Objective To investigate the clinical efficacy and safety of intravenous thrombolysis with tenecteplase in the treatment of CWS.Methods A prospective study was conducted on 136 CWS patients consecutively admitted in Department of Neurology of Zhengzhou People's Hospital from March 2019 to March 2024.They were randomly divided into a tenecteplase group(67 cases)and a control group(69 cases).NIHSS was used to evaluate the recovery of neurological function after treatment.mRS was employed to assess long-term prognosis.Results Significantly larger proportion of white matter lesions and higher baseline SBP level were observed in the tenecteplase group than the control group(P＜0.05).The tenecteplase group obtained obviously greater ratio of overall recovery than the control group,with notably lower NIHSS score,incidence of new CWS attacks and proportion of new acute cerebral infarction at 24 and 72 h and 7 d after treat-ment(P＜0.05,P＜0.01).Moreover,the proportion of mRS score of 0-2 was notably greater,while that of the score of 3-6 was lower in the tenecteplase group than the Control group(P＜0.05).Intravenous thrombolysis with tenecteplase was an influencing factors for 90-day mRS score of 0-2 and of 3-6 in the CWS patients(OR=0.264,95％CI:0.089-0.813;OR=4.144,95％CI:1.184-14.506,P＜0.05).Conclusion Intravenous thrombolysis with tenecteplase for CWS significantly improves the proportion of patients with good prognosis.

Objective To investigate the clinical efficacy and safety of intravenous thrombolysis with tenecteplase in the treatment of CWS.Methods A prospective study was conducted on 136 CWS patients consecutively admitted in Department of Neurology of Zhengzhou People's Hospital from March 2019 to March 2024.They were randomly divided into a tenecteplase group(67 cases)and a control group(69 cases).NIHSS was used to evaluate the recovery of neurological function after treatment.mRS was employed to assess long-term prognosis.Results Significantly larger proportion of white matter lesions and higher baseline SBP level were observed in the tenecteplase group than the control group(P＜0.05).The tenecteplase group obtained obviously greater ratio of overall recovery than the control group,with notably lower NIHSS score,incidence of new CWS attacks and proportion of new acute cerebral infarction at 24 and 72 h and 7 d after treat-ment(P＜0.05,P＜0.01).Moreover,the proportion of mRS score of 0-2 was notably greater,while that of the score of 3-6 was lower in the tenecteplase group than the Control group(P＜0.05).Intravenous thrombolysis with tenecteplase was an influencing factors for 90-day mRS score of 0-2 and of 3-6 in the CWS patients(OR=0.264,95％CI:0.089-0.813;OR=4.144,95％CI:1.184-14.506,P＜0.05).Conclusion Intravenous thrombolysis with tenecteplase for CWS significantly improves the proportion of patients with good prognosis.

Objective To investigate the changes of inflammatory factors in bronchoalveolar lavage fluid(BALF),lung tissue,and serum of a rat pneumonia model induced by inhalation of lipopolysaccharides(LPS).Methods Three days after modeling by LPS 4 mg/mL inhalation,15 min/d,was conducted while monitoring the particle size distribution and aerosol concentration of LPS,the degree of inflammation in lung tissues of rats in each group was observed via HE staining,and neutrophils in BALF were counted by microscope.The contents of interferon gamma(IFN-γ),interleukin-1 beta(IL-1 β),IL-4,IL-5,IL-6,IL-10,IL-13,tumor necrosis factor alpha(TNF-α),and KC/GRO in lung tissue,serum,and BALF were detected by Meso Scale Discovery.Results The lung histopathology of model rats displayed focal and diffuse alveolar epithelial necrosis with shedding and the aggregation and infiltration of inflammatory cells.The particle size distribution of atomized LPS was as follows,Dv(10)=0.6974μm,Dv(50)=3.387 μm,Dv(90)=8.836 μm.The aerosol concentration of LPS was 4.08 g/m3,and the calculated inhalation dose for rats was 47.10 mg/kg.The neutrophil count(P＜0.01)and contents of IL-1β,IL-6,and TNF-α(P＜0.05,P＜0.001,P＜0.001)in the BALF,and the contents of IL-1β,IL-6,and KC/GRO in lung tissue(P＜0.01,P＜0.05,P＜0.01),of model rats were significantly increased.No biologically significant changes were observed in inflammatory factor levels in the serum.Conclusions In the acute pneumonia model induced by inhalation of LPS,significant changes in inflammatory factors such as IL-1β,IL-6,KC/GRO,and TNF-α were observed in both lung tissue and bronchoalveolar lavage fluid(BALF),while no notable changes in these inflammatory factors were detected in serum.This indicates that the inflammation responses are primarily localized in the lungs.

Objective:To explore the value of nomogram model based on multimodal ultrasound features for predicting the malignant risk of micro lesions in breast areola region.Methods:The case data of Beijing Tiantan Hospital affiliated to Capital Medical University from May 2020 to July 2024 were retrospectively analyzed. A total of 50 patients with benign intraductal papilloma（bIDP group）and 54 patients with malignant risk breast tumor（mrBT group）were found to have micro lesions in breast areola region and confirmed by puncture or surgical pathology. Clinical data，conventional ultrasound and contrast-enhanced ultrasound features were compared between the two groups. Multivariate Logistic regression analysis and Lasso regression analysis were performed on statistically significant factors to screen out influencing factors. ROC curves were plotted to evaluate diagnostic efficacy，nomogram model and clinical decision curves were constructed to evaluate clinical benefits.Results:The differences of age，nipple discharge presentation，conventional ultrasound features（including boundary，morphology，aspect ratio，internal echo，internal microcalcification，far-field echo，peripheral irregular hyperechoic ring，dilate of peripheral ducts），and contrast-enhanced ultrasound features（including wash-in time，enhancement intensity，enhancement mode，enhancement scope，blood perfusion defect，crab foot sign，penetrating vessels）were statistically significant between the bIDP group and mrBTgroup（all P<0.05）. Regression analysis showed that age，uniformity of internal echo within the lesion，dilation of surrounding ducts，and enhanced crab foot sign were the affect factors for the diagnosis of mrBT（all P<0.05）. Based on these factors，a nomogram model was constructed with an area under ROC curve（AUC）of 0.907（95% CI=0.851-0.963），a sensitivity of 0.907，and a specificity of 0.780. The decision curve analysis showed that the collective model had good predictive performance. Conclusions:The nomogram model based on multimodal ultrasound features has good value in predicting malignant risk micro breast tumor of areola region.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.