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.

OBJECTIVES: To evaluate the safety and efficacy of thiotepa (TT)-containing conditioning regimens for allogeneic hematopoietic stem cell transplantation (HSCT) in children with inborn errors of immunity (IEI). METHODS: Clinical data of 22 children with IEI who underwent HSCT were retrospectively reviewed. Survival after HSCT was estimated using the Kaplan-Meier method. RESULTS: Nine patients received a traditional conditioning regimen (fludarabine + busulfan + cyclophosphamide/etoposide) and underwent peripheral blood stem cell transplantation (PBSCT). Thirteen patients received a TT-containing modified conditioning regimen (TT + fludarabine + busulfan + cyclophosphamide), including seven PBSCT and six umbilical cord blood transplantation (UCBT) cases. Successful engraftment with complete donor chimerism was achieved in all patients. Acute graft-versus-host disease occurred in 12 patients (one with grade III and the remaining with grade I-II). Chronic graft-versus-host disease occurred in one patient. The incidence of EB viremia in UCBT patients was lower than that in PBSCT patients (P<0.05). Over a median follow-up of 36.0 months, one death occurred. The 3-year overall survival (OS) rate was 100% for the modified regimen and 88.9% ± 10.5% for the traditional regimen (P=0.229). When comparing transplantation types, the 3-year OS rates were 100% for UCBT and 93.8% ± 6.1% for PBSCT (P>0.05), and the 3-year event-free survival rates were 100% and 87.1% ± 8.6%, respectively (P>0.05). CONCLUSIONS TT-containing conditioning for allogeneic HSCT in children with IEI is safe and effective. Both UCBT and PBSCT may achieve high success rates.
Humans ; Retrospective Studies ; Transplantation Conditioning/methods* ; Thiotepa/therapeutic use* ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Male ; Female ; Child, Preschool ; Infant ; Child ; Transplantation, Homologous ; Graft vs Host Disease ; Adolescent

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective To 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.Methods A 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).Results The 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.Conclusion The 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.

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.

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.

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.