Objective:To investigate the risk factors for patients with hypertriglyceridemia-related acute pancreatitis (HTG-AP) developing into severe acute pancreatitis or experiencing organ failure.Methods:This retrospective cohort study collected clinical data from 2 429 patients diagnosed with acute pancreatitis from five hospitals in China between January 2019 and December 2023 using a pre-designed data collection form. The cohort included 1 516 males and 913 females,with an age of (50.2±16.5)years(range: 11 to 99 years). Among them,353 patients (16.1%) had HTG-AP,while 1 846 (83.9%) had non-HTG-AP. HTG-AP was defined as serum triglyceride levels>500 mg/dl with other etiologies excluded. Intergroup comparisons were performed using t-tests,Mann-Whitney U test or χ2 tests,respectively. Univariate and multivariate logistic regression analyses were conducted to assess risk factors for severe acute pancreatitis after adjusting for potential confounders,and a predictive model was developed and validated. Results:Compared with other etiologies,HTG-AP patients had a higher risk of progressing to SAP ( OR=1.415,95% CI: 0.866 to 2.312, P=0.017) and organ failure ( OR=1.256,95% CI: 1.015 to 1.554, P=0.036). Among HTG-AP patients,risk factors for SAP included body mass index ( OR=1.856,95% CI: 1.742 to 1.987, P=0.033),fasting blood glucose ( OR=1.128,95% CI: 1.036 to 1.229, P=0.006),white blood cell count( OR=1.162,95% CI: 1.055 to 1.281, P=0.002),and the presence of pleural effusion ( OR=13.151,95% CI: 4.330 to 19.946, P<0.01). A nomogram prediction model for SAP in HTG-AP was constructed based on these risk factors,demonstrating good discriminative ability with area under the curve values of 0.877 in the training set and 0.894 in the validation set,along with satisfactory calibration. Conclusions:HTG-AP patients are at higher risk of developing SAP and organ failure. The risk prediction model incorporating body mass index,fasting blood glucose,white blood cell count,and pleural effusion shows good predictive value for SAP.

Objective To explore the predictive value of serum high-mobility group box protein B1(HMGB1)and soluble receptor for advanced glycation end-products(sRAGE)in the occurrence and short-term prognosis of sepsis-associated encephalopathy(SAE).Methods Clinical data of 228 patients with sepsis were retrospectively analyzed.According to the presence of SAE,patients were divided into the SAE group(96 cases)and the non-SAE group(132 cases).General clinical data,laboratory test results,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHEⅡ)scores,Sequential Organ Failure Assessment(SOFA)scores and serum HMGB1 and sRAGE levels were compared between the two groups.Multivariate Logistic regression analysis was performed to determine factors influencing SAE occurrence.Receiver operating characteristic(ROC)curves were plotted to evaluate the predictive ability of HMGB1,sRAGE and the HMGB1/sRAGE ratio to predict the occurrence and short-term prognosis of SAE.Kaplan-Meier survival curves were used to compare the 28-day mortality rates of SAE patients with different HMGB1 and sRAGE expression levels.Results Compared to the non-SAE group,patients in the SAE group exhibited elevated serum HMGB1 levels,decreased sRAGE levels and an increased HMGB1/sRAGE ratio(P＜0.05).The areas under the curve(AUC)for predicting SAE using HMGB1,sRAGE and the HMGB1/sRAGE ratio were 0.826(95%CI:0.770-0.872),0.682(95%CI:0.617-0.742)and 0.895(95%CI:0.848-0.932),respectively,indicating predictive value.Among the 96 SAE patients,52(54.2%)died within 28 days.There were no statistically significant differences in HMGB1,sRAGE and the HMGB1/sRAGE ratio between surviving and deceased patients(P＞0.05).Similarly,there were no significant differences in 28-day mortality rates between SAE patients with different HMGB1 or sRAGE expression levels.Conclusion Elevated serum HMGB1 and reduced sRAGE are of significant value in the auxiliary diagnosis of SAE,but have limited clinical predictive value for short-term prognosis.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

Objective To investigate the correlation of serum human epididymis protein 4(HE4)and lung injury Prediction(LIPS)score with sepsis-associated acute respiratory distress syndrome(ARDS)and 28 d mortality.Methods A total of 207 patients with sepsis admitted to the hospital from January 2021 to January 2024 were selected.According to the presence or absence of ARDS,the patients were divided into ARDS group(73 cases)and non-ARDS group(134 cases).According to the 28 d survival,the ARDS group was further di-vided into death group(26 cases)and survival group(47 cases).HE4 level was detected by electrochemilumi-nescence,and LIPS score was calculated.Multivariate Logistic regression model was used to analyze the influ-encing factors of death in ARDS patients.Point biserial correlation was used to analyze the correlation between serum HE4,LIPS score and 28 d death in ARDS patients.The receiver operating characteristic(ROC)curve was used to evaluate the value of serum HE4 and LIPS score in the diagnosis of sepsis-associated ARDS and predicting 28 d mortality.Results Compared with non-ARDS group,the serum HE4 level and LIPS score were significantly increased in ARDS group(P<0.05).The independent risk factors for sepsis-associated ARDS were increased SOFA score,increased blood lactic acid,increased HE4 and increased LIPS score(P<0.05).The 28 d mortality of 73 patients with sepsis-associated ARDS was 35.62%(26/73).Compared with the survival group,the serum HE4 and LIPS scores were increased in the death group(P<0.05).Serum HE4 and LIPS scores were positively correlated with 28 d mortality in patients with sepsis-associated ARDS(r=0.605,0.579,P<0.05).The area under the curve(AUC)of serum HE4 combined with LIPS score in the di-agnosis of sepsis-associated ARDS was 0.909,which was larger than 0.828 and 0.806 of serum HE4 and LIPS score alone(Z=3.360,3.387,all P<0.05).The AUC of serum HE4 combined with LIPS score in predicting 28 d mortality of patients with sepsis-associated ARDS was 0.891,which was larger than 0.808 and 0.779 of by serum HE4 and LIPS score alone(Z=2.028,2.017,all P<0.05).Conclusion The elevated serum HE4 level and LIPS score are independent risk factors for sepsis-associated ARDS,and are closely related to 28 d mortality.The combination of serum HE4 and LIPS score has a high value in the diagnosis of sepsis-associated ARDS and the prediction of 28 d mortality.

Objective To screen out the indexes of medical insurance fund under China Healthcare Security Diagno-sis Related Groups(CHS-DRG)payment based on Delphi method and improve the efficiency of medical insurance fund supervision.Methods Using the Delphi method,32 experts in the medical insurance field were selected and 2 rounds of expert questionnaires were conducted to design an evaluation scale for the monitoring indicators of medi-cal insurance funds under CHS-DRG payment.The consultation results were evaluated from three aspects of motiva-tion,authority,and coordination.Results A medical insurance fund supervision index system based on three dimen-sions of medical insurance fund use quality,efficiency and safety supervision is constructed,including 3 first-level indicators,7 second-level indicators and 44 third-level indicators.Results The construction ofmedical insurance fund supervision index system under CHS-DRG payment should respect the actual clinical diagnosis and treatment,strengthen the intelligent supervision method,and timely intervene in medical insurance fund management risks.

Objective:To explore the feasibility of using a simplified multiple sleep latency test (MSLT) for the diagnosis of narcolepsy type 1.Methods:Data from 158 patients with narcolepsy type 1 and 58 patients with non-type 1 narcolepsy who underwent overnight video-polysomnography (V-PSG) and MSLT in the Sleep Center, Department of Neurology, Xijing Hospital, Air Force Military Medical University from March 2019 to April 2024 were retrospectively collected. By reducing the number of naps in the MSLT, the diagnostic consistency between the simplified MSLT and the standard 5-nap MSLT was evaluated using the receiver operating characteristic (ROC) curve. The DeLong test was used to compare whether there was a statistically significant difference between the simplified MSLT and the standard 5-nap MSLT. Cohen′s Kappa statistical analysis was performed to compare the diagnostic consistency between the simplified MSLT and the standard 5-nap MSLT.Results:The age of the 216 patients who were ultimately enrolled was 17 (13, 30) years, including 152 male patients (70.4%). The Cohen′s Kappa between the simplified 3-nap MSLT and the standard 5-nap MSLT was 0.875, which was 0.903 between the simplified 4-nap MSLT and the standard 5-nap MSLT (Bonferroni-corrected, both P0.001), indicating high and statistically significant agreement for both simplified protocols with the standard test. However, the DeLong test revealed that the area under the curve of the standard 5-nap MSLT (0.900, 95% CI 0.863-0.938) differed significantly from that of the simplified 3-nap MSLT (0.860, 95% CI 0.817-0.904; P0.05), whereas no significant difference was observed between the standard 5-nap MSLT and the simplified 4-nap MSLT (0.876, 95% CI 0.834-0.918; P0.05). Consequently, performing only the first 4 naps was sufficient for diagnosing narcolepsy type 1. Conclusion:The simplified 4-nap MSLT, specifically the first to fourth naps, may be used for the diagnosis of narcolepsy type 1.

Objective:To analyze the factors associated with non-radiographic bone erosion in gout pa-tients,to improve the understanding of bone erosion in gout,and to promote the early detection of bone erosion.Methods:A retrospective analysis was conducted on the medical records of gout patients treated at Beijing Jishuitan Hospital from January 2018 to January 2022.Bone erosion was detectable by ultra-sound but not detected by X-ray as non-radiographic bone erosion;no bone erosion was detected by both ultrasound and joint X-ray as undetected bone erosion.A case-control study was used,and the two groups were matched 1∶2 according to age and sex.The differences between the two groups were com-pared in terms of general information,joint involvement characteristics,laboratory indicators and compli-cations.In the univariate analysis,P＜0.1 was included in the multivariate analysis,and the conditional Logistic regression was used for the multivariate analysis.P＜0.05 was considered to have statistically significant differences.Results:Among the 41 patients with non-radiographic bone erosion,the top three joints with bone erosion before its occurrence were metatarsophalangeal joint(12 cases),ankle(10 ca-ses),and knee(7 cases).There were 82 patients undetected with bone erosion.There were no signifi-cant differences in general information between the two groups(P＞0.05),including age,gender,body mass index,and alcohol consumption history.The characteristics of affected joints in the non-radio-graphic bone erosion group were compared with those in the no bone erosion detected,and the former had more affected joints(P=0.02),and a higher proportion of patients with at least 3 attacks of gout per year(P＜0.001).There were no significant differences in serum uric acid,fasting blood glucose,cholesterol,triglycerides,low-density lipoprotein,high-density lipoprotein,creatinine,homocysteine,white blood cell count,and urine pH between the two groups(P＞0.05).The results of multivariate analysis showed that at least 3 flares of gout per year was an independent risk factor for radiologically negative bone erosion in patients with gout,with an OR(95％CI)of 5.139(1.529-17.271).Conclusion:At least 3 flares of gout per year predicts the occurrence of radiologically negative bone erosion,and these patients should be given more attention to achieving treatment targets.