Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

AIM To explore the clinical effects of oral administration and enema of Tongfu Paiqi Mixture on patients with acute exacerbation of chronic obstructive pulmonary disease complicated with gastrointestinal dysfunction.METHODS One hundred and eight patients were randomly assigned into control group(54 cases)for 5-day intervention of both oral administration and enema of Tongfu Paiqi Mixture simulant and conventional treatment,and observation group(54 cases)for 5-day intervention of both oral administration and enema of Tongfu Paiqi Mixture and conventional treatment.The changes in clinical effects,GIDS score,Phlegm and Blood Stasis Obstructing Lung Syndrome score,gastrointestinal mucosal barrier serum markers(DAO,D-LA,IFABP),intra-abdominal pressure,gastrointestinal hormones(MTL,GAS,VIP),inflammatory factors(IL-1β,IL-6,TNF-α,PCT),pulmonary function indices(FEV1、FEV1％)and PaO2/FiO2 were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased GIDS score,Phlegm and Blood Stasis Obstructing Lung Syndrome score,intra-abdominal pressure,VIP,gastrointestinal mucosal barrier serum markers,inflammatory factors(P＜0.05),and increased MTL,GAS,pulmonary function indices,PaO2/FiO2(P＜0.05),especially for the observation group(P＜0.05).CONCLUSION For the patients with acute exacerbation of chronic obstructive pulmonary disease complicated with gastrointestinal dysfunction,the oral administration and enema of Tongfu Paiqi Mixture can regulate gastrointestinal hormones,reduce inflammatory degree,protect intestinal barrier functions,alleviate clinical symptoms,and improve pulmonary functions.

Objective To compare the value of ultrashort echo time(UTE)and T1W sequences for detecting bone erosions of gouty arthritis.Methods Forty-four gouty patients were prospectively enrolled,including 32 cases with affected feet and 12 cases with affected knee.MR UTE and T1W sequence scanning of the affected area were performed,and subjectively scoring of imaging quality of 2 kinds of MRI were evaluated,respectively.Then total number and total score of bone erosions of each case were calculated according to all affected bones.Taken DECT as reference standard,the efficacy of UTE and T1WI for detecting bone erosions was assessed through comparing with DECT using Kappa coefficient.Results The imaging quality score of T1WI was lower than that of DECT(all P＜0.05),while no significant difference was found between UTE and DECT(all P＞0.05).There was high agreement between UTE and DECT for detecting bone erosions(κ=0.949),while the agreement between T1WI and DECT ranged from good to high(κ=0.718 to 0.805).The total number and total score of bone erosions based on T1WI were significantly lower than those based on DECT(all P＜0.05),while no significant difference was found between UTE and DECT(all P＞0.05).Conclusion UTE was better than T1WI for detecting bone erosions of gouty arthritis.

Objective To design a whole-process intelligent service platform to enhance services for inpatients.Methods A whole-process intelligent service platform for inpatients was designed with an intelligentized rehabilitation assessment model based on the support vector machine algorithm,which was constructed with the hyper-converged infrastructure,designed with the B/S architecture and developed using the front-end and back-end separation mode.The front end used Vue.js and Ant Design of Vue for user interface construction and JavaScript for data interaction,and the back end was developed with Java.The platform developed was composed of three functional modules for medical service management,intelligent rehabilitation management and system management.Results The platform developed provided whole-process inpatient service before,during and after hospitalization to enhance inpatient rehabilitation.Conclusion The platform developed is able to meet the health management needs of inpatients in terms of treatment-rehabilitation-follow-up,which improve the service quality of hospitals.[Chinese Medical Equipment Journal,2025,46(6):34-41]

Objective To design a whole-process intelligent service platform to enhance services for inpatients.Methods A whole-process intelligent service platform for inpatients was designed with an intelligentized rehabilitation assessment model based on the support vector machine algorithm,which was constructed with the hyper-converged infrastructure,designed with the B/S architecture and developed using the front-end and back-end separation mode.The front end used Vue.js and Ant Design of Vue for user interface construction and JavaScript for data interaction,and the back end was developed with Java.The platform developed was composed of three functional modules for medical service management,intelligent rehabilitation management and system management.Results The platform developed provided whole-process inpatient service before,during and after hospitalization to enhance inpatient rehabilitation.Conclusion The platform developed is able to meet the health management needs of inpatients in terms of treatment-rehabilitation-follow-up,which improve the service quality of hospitals.[Chinese Medical Equipment Journal,2025,46(6):34-41]

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

AIM To explore the clinical effects of oral administration and enema of Tongfu Paiqi Mixture on patients with acute exacerbation of chronic obstructive pulmonary disease complicated with gastrointestinal dysfunction.METHODS One hundred and eight patients were randomly assigned into control group(54 cases)for 5-day intervention of both oral administration and enema of Tongfu Paiqi Mixture simulant and conventional treatment,and observation group(54 cases)for 5-day intervention of both oral administration and enema of Tongfu Paiqi Mixture and conventional treatment.The changes in clinical effects,GIDS score,Phlegm and Blood Stasis Obstructing Lung Syndrome score,gastrointestinal mucosal barrier serum markers(DAO,D-LA,IFABP),intra-abdominal pressure,gastrointestinal hormones(MTL,GAS,VIP),inflammatory factors(IL-1β,IL-6,TNF-α,PCT),pulmonary function indices(FEV1、FEV1％)and PaO2/FiO2 were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased GIDS score,Phlegm and Blood Stasis Obstructing Lung Syndrome score,intra-abdominal pressure,VIP,gastrointestinal mucosal barrier serum markers,inflammatory factors(P＜0.05),and increased MTL,GAS,pulmonary function indices,PaO2/FiO2(P＜0.05),especially for the observation group(P＜0.05).CONCLUSION For the patients with acute exacerbation of chronic obstructive pulmonary disease complicated with gastrointestinal dysfunction,the oral administration and enema of Tongfu Paiqi Mixture can regulate gastrointestinal hormones,reduce inflammatory degree,protect intestinal barrier functions,alleviate clinical symptoms,and improve pulmonary functions.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

ObjectiveTo analyze the expression level of triggering receptor expressed on myeloid cells-1 （TREM-1） in serum and ascites of patients with cirrhotic ascites， and to investigate its correlation with clinical features and inflammatory markers and its role in the diagnosis of infection and prognostic evaluation. MethodsA total of 110 patients with cirrhotic ascites who were hospitalized in The Fifth Hospital of Shijiazhuang from January 2019 to December 2020 were enrolled， and according to the presence or absence of intra-abdominal infection， they were divided into infection group with 72 patients and non-infection group with 38 patients. The patients with infection were further divided into improvement group with 38 patients and non-improvement group with 34 patients. Clinical data and laboratory markers were collected from all patients. Serum and ascites samples were collected， and ELISA was used to measure the level of TREM-1. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the Kruskal-Wallis H test was used for comparison between multiple groups； the chi-square test was used for comparison of categorical data between two groups. A Spearman correlation analysis was used to investigate the correlation between indicators. A multivariate Logistic regression analysis was used to identify the influencing factors for the prognosis of patients with cirrhotic ascites and infection. The receiver operating characteristic （ROC） curve was used to evaluate the diagnostic and prognostic efficacy of each indicator， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsThe level of TREM-1 in ascites was significantly positively correlated with that in serum （r=0.50， P<0.001）. Compared with the improvement group， the non-improvement group had a significantly higher level of TREM-1 in ascites （Z=-2.391， P=0.017） and serum （Z=-2.544， P=0.011）， and compared with the non-infection group， the infection group had a significantly higher level of TREM-1 in ascites （Z=-3.420， P<0.001）， while there was no significant difference in the level of TREM-1 in serum between the two groups （P>0.05）. The level of TREM-1 in serum and ascites were significantly positively correlated with C-reactive protein （CRP）， procalcitonin （PCT）， white blood cell count， and neutrophil-lymphocyte ratio （r=0.288， 0.344， 0.530， 0.510， 0.534， 0.454， 0.330， and 0.404， all P<0.05）. The ROC curve analysis showed that when PCT， CRP， and serum or ascitic TREM-1 were used in combination for the diagnosis of cirrhotic ascites with infection， the AUCs were 0.715 and 0.740， respectively. The multivariate Logistic regression analysis showed that CRP （odds ratio ［OR］=1.019， 95% confidence interval ［CI］： 1.001‍ ‍—‍ ‍1.038， P=0.043） and serum TREM-1 （OR=1.002， 95%CI： 1.000‍ ‍—‍ ‍1.003， P=0.016） were independent risk factors for the prognosis of patients with cirrhotic ascites and infection， and the combination of these two indicators had an AUC of 0.728 in predicting poor prognosis. ConclusionThe level of TREM-1 is closely associated with the severity of infection and prognosis in patients with cirrhotic ascites， and combined measurement of TREM-1 and CRP/PCT can improve the diagnostic accuracy of infection and provide support for prognostic evaluation.