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.

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.

OBJECTIVE: To evaluate the anti-hepatocellular carcinoma (HCC) activity of total alkaloids from Gelsemium elegans Benth. (TAG) in vivo and in vitro and to elucidate their potential mechanisms of action through transcriptomic analysis. METHODS: TAG extraction was conducted, and the primary components were quantified using high-performance liquid chromatography (HPLC). The effects of TAG (100, 150, and 200 µg/mL) on various tumor cells, including SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116, were assessed. Effects of TAG on HCC proliferation and apoptosis were detected by colony formation assays and cell stainings. Caspase-3, Bcl-2, and Bax protein levels were detected by Western blotting. In vivo, a tumor xenograft model was developed using H22 cells. Totally 40 Kunming mice were randomly assigned to model, cyclophosphamide (20 mg/kg), TAG low-dose (TAG-L, 0.5 mg/kg), and TAG high-dose (TAG-H, 1 mg/kg) groups, with 10 mice in each group. Tumor volume, body weight, and tumor weight were recorded and compared during 14-day treatment. Immune organ index were calculated. Tissue changes were oberseved by hematoxylin and eosin staining and immunohistochemistry. Additionally, transcriptomic and metabolomic analyses, as well as quatitative real-time polymerase chain reaction (RT-qPCR), were performed to detect mRNA and metabolite expressions. RESULTS: HPLC successfully identified the components of TAG extraction. Live cell imaging and analysis, along with cell viability assays, demonstrated that TAG inhibited the proliferation of SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116 cells. Colony formation assays, Hoechst 33258 staining, Rhodamine 123 staining, and Western blotting revealed that TAG not only inhibited HCC proliferation but also promoted apoptosis (P<0.05). In vivo experiments showed that TAG inhibited the growth of solid tumors in HCC in mice (P<0.05). Transcriptomic analysis and RT-qPCR indicated that the inhibition of HCC by TAG was associated with the regulation of the key gene CXCL13. CONCLUSION TAG inhibits HCC both in vivo and in vitro, with its inhibitory effect linked to the regulation of the key gene CXCL13.
Animals ; Apoptosis/drug effects* ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/genetics* ; Humans ; Alkaloids/therapeutic use* ; Gelsemium/chemistry* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Mice ; Xenograft Model Antitumor Assays

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 systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

Objective To summarize the clinical and genetic characteristics of end-stage renal disease caused by PLCE1 gene mutations.Methods A retrospective analysis of the clinical and genetic features of three children from a family with PLCE1 gene mutations was conducted,along with a literature review of hereditary kidney disease cases caused by PLCE1 gene mutations.Results The proband was an 8-year-old male presenting with nephrotic syndrome stage 4 chronic kidney disease.Renal biopsy showed focal segmental glomerulosclerosis.Two years and five months after kidney transplantation,the patient had persistent negative proteinuria and normal renal function.Whole-exome sequencing identified two pathogenic heterozygous variants:c.961C>T and c.3255_3256delinsT,with c.3255_3256delinsT being a novel mutation.Family screening revealed no renal involvement in the parents,but among five siblings,one brother died at age of 4 years from end-stage renal disease.A 7-year-old sister presented with proteinuria and bilateral medullary sponge kidney,with proteinuria resolving after one year of follow-up.A 3-year-old brother died after kidney transplantation due to severe pneumonia.The literature review included 45 patients with hereditary kidney disease caused by PLCE1 gene mutations.The main clinical phenotype was nephrotic syndrome(87%,39/45),and renal pathology predominantly showed focal segmental glomerulosclerosis(57%,16/28).No mutation hotspots were identified.Conclusions Compound heterozygous mutations in the PLCE1 gene can lead to rapid progression of the disease to end-stage renal disease,with favorable outcomes following kidney transplantation.Family screening is crucial for early diagnosis,and medullary sponge kidney may be a novel phenotype associated with these gene mutations.Citaion:[Chinese Journal of Contemporary Pediatrics,2025,27(5):580-587]

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.

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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.