BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

ObjectiveTo systematically analyze the epidemiological characteristics and cause-of-death distribution among death cases with hepatitis C in Hongkou District of Shanghai, and to provide a scientific basis for optimizing healthcare resources allocation and targeted hepatitis C prevention and control measures. MethodsA retrospective cross-sectional study was conducted by integrating historical surveillance data from China Information System of Disease Prevention and Control and Shanghai Hongkou District Death Medical Registration System. Data on demographic characteristics, hepatitis C-related clinical and management records, and underlying causes of death for cases with hepatitis C between 2012 and 2024 in Hongkou District of Shanghai, were collected. Descriptive analyses were performed to analyze the epidemiological characteristics and cause-of-death distribution of death cases, and comparative analyses were conducted across different subgroups. ResultsA total of 204 hepatitis C-related deaths were identified in Hongkou District, Shanghai, from 2012 to 2024. The average age at death was (69.49±12.75) years The majority decedents were males (71.57%) and retired (73.53%). The top three underlying causes of death were malignant tumors (45.10%), cerebrovascular diseases (15.20%) and cardiovascular diseases (12.25%), collectively accounting for 72.55% of all deaths. Deaths attributed to hepatitis C accounted for 9.80% (20/204), with a mean age at death of (63.41±11.81) years. No statistically significant differences were observed in the proportion of hepatitis C-attributed deaths across different subgroups (all P>0.05). The proportion of premature deaths was 55.88% (114/204), with a mean age at death of (60.02±6.89) years. The proportion of premature deaths was higher among males (60.27%), laboratory-diagnosed patients (62.69%), patients with other liver diseases (72.06%), and those non-compliant with follow-up (70.97%) compared to their respective counterparts (all P<0.05). Additionally, homemakers /unemployed patients (100.00%) and employed patients (88.89%) had a significantly higher proportion of premature deaths compared to retired patients (42.67%) (P<0.001). There was a statistically significant difference in the distribution of causes of death between the premature death group and the non-premature death group (χ²=14.93， P=0.048). The top three causes of premature deaths were malignant tumors (50.00%), hepatitis C (12.28%) and cerebrovascular diseases (10.53%). Regarding the proportion of deaths occuring prematurely, other viral hepatitis had the highest percentage (75.00%), followed by diabetes mellitus (71.43%) and hepatitis C (70.00%). ConclusionThe majority of death cases with hepatitis C were males and retirees in Hongkou District, Shanghai. The leading cause of death was malignant tumors, while hepatitis C ranked as the fourth underlying cause, as well as served as the second leading cause of premature death following malignant tumors. Premature death was closely associated with gender, occupation, diagnostic classification, presence of other liver diseases, and follow-up compliance, highlighting the importance of enhanced health management and targeted interventions among high-risk groups.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To design a performance testing platform to evaluate the working status and performance characteristics of the ventilator proportional solenoid valve.Methods The performance testing platform had its hardware including a high-pressure gas source,a pressure regulating valve,sensors and etc,and its software designed based on PyQt5 and composed of several modules for data acquisition,parameter setting,image display,indicator computation,result output and etc.Two kinds of proportional solenoid valves(Valve 1、Valve2)were selected for static and dynamic tests to verify the performance of the platform.Results The platform developed facilitated the proportional solenoid valve to carry out accurate computation of static and dynamic indicators at real time and time domain and waveform feature extraction of sensor data by precision control and data acquisition for the proportional solenoid valve.Static tests showed that Valve 1 gained advantages over Valve 2 in static flow characteristics involving in lowered repeatability,return error and offset while enhanced stability;dynamic tests indicated Valve 2 had rapid flow variations and significant flow fluctuation impacts,Valve 1 showed smooth dynamic response changes,and Valve 2 behaved better than Valve 1 in dynamic performance.Conclusion The testing platform developed comprehensively demonstrates the performance characteristics and working performance of the ventilator proportional solenoid valve,which is of great significance to enhance the reliability and safety of the ventilator.[Chinese Medical Equipment Journal,2025,46(1):13-19]

Objective To construct a uricase-deficient mouse model with stable inheritance using the CRISPR/Cas9 system,and evaluate its ability to simulate the disease characteristics of patients with hyperuricemia.Methods Double single guide RNAs(sgRNAs)were designed on both sides of exon 2～4 of the Uox gene.sgRNA and Cas9 mRNA for gene knockout were microinjected into the fertilized eggs of mice.After culture for 2～4 h,the embryos were transferred to surrogate mother mice to produce an F0 generation.Uox-knockout mice were identified by polymerase chain reaction and sequencing analysis.Positive mice were then mated with wild-type(WT)mice to produce an F1 generation,and heterozygous female and male F1 mice were then selected to obtain homozygous F2 mice.Serum and urine levels of uric acid,creatinine,and urea,and serum alanine aminotransferase(ALT)and aspartate aminotransferase(AST)levels were detected and compared between homozygous and wild-type mice.Pathological changes in kidney and liver tissues were observed by hematoxylin and eosin and Masson staining.Results Urine levels of serum uric acid(male:(4116.8±1928.1)μmol/L,P＜0.001;female:(2998.0±547.7)μmol/L,P＜0.01)and serum levels of uric acid(male:(478.4±114.6)μmol/L,P＜0.001;female:(507.7±129.6)μmol/L,P＜0.001),creatinine((91.8±55.6)μmol/L,P＜0.001),urea((28.6±13.9)mmol/L,P＜0.05),ALT((53.3±23.3)U/L,P＜0.01),and AST((203.3±70.3)U/L,P＜0.001)were significantly increased in Uox-/-mice compared with WT mice.Histopathological examination showed moderate hepatocyte degeneration in the liver,moderate-to-severe tubular cystic dilation,degeneration,and fibrosis in the kidney,glomerular hypertrophy and hyperplasia,small-vessel dilation and congestion,and infiltration of stromal monocytes and lymphocytes in Uox-/-mice.Conclusions We successfully established a homozygous uricase-deficient mouse strain using CRISPR/Cas9 technology,as a suitable animal model for research in the field of hyperuricemia.

ObjectiveThe nucleolar protein PES1 (Pescadillo homolog 1) plays critical roles in ribosome biogenesis and cell cycle regulation, yet its involvement in cellular senescence remains poorly understood. This study aimed to comprehensively investigate the functional consequences of PES1 suppression in cellular senescence and elucidate the molecular mechanisms underlying its regulatory role. MethodsInitially, we assessed PES1 expression patterns in two distinct senescence models: replicative senescent mouse embryonic fibroblasts (MEFs) and doxorubicin-induced senescent human hepatocellular carcinoma HepG2 cells. Subsequently, PES1 expression was specifically downregulated using siRNA-mediated knockdown in these cell lines as well as additional relevant cell types. Cellular proliferation and senescence were assessed by EdU incorporation and SA-β-gal staining assays, respectively. The expression of senescence-associated proteins (p53, p21, and Rb) and SASP factors (IL-6, IL-1β, and IL-8) were analyzed by Western blot or qPCR. Furthermore, Northern blot and immunofluorescence were employed to evaluate pre-rRNA processing and nucleolar morphology. ResultsPES1 expression was significantly downregulated in senescent MEFs and HepG2 cells. PES1 knockdown resulted in decreased EdU-positive cells and increased SA‑β‑gal-positive cells, indicating proliferation inhibition and senescence induction. Mechanistically, PES1 suppression activated the p53-p21 pathway without affecting Rb expression, while upregulating IL-6, IL-1β, and IL-8 production. Notably, PES1 depletion impaired pre-rRNA maturation and induced nucleolar stress, as evidenced by aberrant nucleolar morphology. ConclusionOur findings demonstrate that PES1 deficiency triggers nucleolar stress and promotes p53-dependent (but Rb-independent) cellular senescence, highlighting its crucial role in maintaining nucleolar homeostasis and regulating senescence-associated pathways.

Objective To investigate the density,species composition,and seasonal prevalence of domestic rodents in different habitats within Lanzhou garrisons,providing basic information for rodent prevention and control.Methods A total of 12 monitoring sites were sampled across urban,suburban,and rural residential areas from 2014 to 2022.Rodent density was monitored using the night-trapping method in the middle of odd-numbered months.Results From 2014 to 2022,346 domestic rodents were captured using 122 035 effective traps,with an average domestic rodent density of 0.28％.The highest domestic rodent density was 0.63％in 2016,and the lowest was 0.07％in 2020,showing significant differences across years,with an overall trend of initially decreasing and then increasing(χ2=136.555,P<0.001).The dominant species was Rattus norvegicus,accounting for 83.24％of the total rodents captured.Rattus norvegicus accounted for a relatively high proportion across different years,with a statistically significant difference in species composition(χ2=20.931,P<0.05).Rodent densities and species composition also varied significantly among the monitored habitats(P<0.001),with the highest densities observed in rural residential areas and the lowest in urban areas.Seasonal variation in rodent densities showed a bimodal pattern,with smaller peaks in January or March and a larger peak in July.Conclusions Domestic rodent density in Lanzhou garrisons has shown an upward trend in the past few years.Rodent control measures should focus on barracks in rural residential areas,with targeted interventions to reduce the risk of rodent-borne diseases.