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.

To report two cases of psychiatric symptoms associated with zonisamide, an antiepileptic drug, and raise clinical awareness of this potential adverse effect. Two male patients with epilepsy treated with zonisamide were retrospectively analyzed. Case 1 (25 years old) developed acute emotional and behavioral abnormalities (e.g., insomnia, aggression, incoherent speech) after switching from sodium valproate to zonisamide (200 mg/day). Case 2 (48 years old) had long-term zonisamide use (≥5 years) with persistent treatment-resistant psychotic symptoms (e.g., delusions, command hallucinations). Clinical courses, medication adjustments, and symptom responses were documented. In Case 1, psychiatric symptoms resolved after discontinuing zonisamide and switching to sodium valproate, with improved mood stability and reduced impulsivity. In Case 2, despite escalating antipsychotic medications (risperidone, clozapine), psychotic symptoms persisted, likely due to ongoing zonisamide use. Both cases highlighted zonisamide’s potential to exacerbate or induce psychiatric manifestations, possibly via mechanisms involving sodium/calcium channel inhibition and neurotransmitter dysregulation (e.g., dopamine, serotonin). Zonisamide can cause or worsen psychiatric symptoms, particularly in vulnerable individuals. Clinicians should monitor for mental health changes during zonisamide treatment and consider drug discontinuation or substitution with alternative antiepileptics (e.g., sodium valproate) if psychiatric adverse effects emerge. Awareness of this association is crucial to avoid misdiagnosis and optimize epilepsy management.

ObjectiveTo investigate the mechanism by which Luoshi Neiyi prescription treats endometriosis （EMs） through the Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB） signaling pathway. MethodsAnimal experiments were conducted with 50 female SD rats， which were randomized into a sham operation group （10 rats） and a modeling group （40 rats）. An autologous endometrial transplantation method was used for the modeling of EMs. The 36 successfully modeled rats were randomly allocated into four groups （n=9 each）： The model group， the low-dose （7.87 g·kg^-1） Luoshi Neiyi prescription group， the high-dose （15.74 g·kg^-1） Luoshi Neiyi prescription group， and the dienogest （0.20 mg·kg^-1） group. The physiological status and ectopic lesion volumes of rats in each group were observed. Hematoxylin-eosin （HE） staining was used to observe the morphology of the eutopic endometrial tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the levels of inflammatory factors including interleukin-1β （IL-1β） and prostaglandin E₂ （PGE₂） in the serum of EMs rats. Immunohistochemistry was used to detect the expression of matrix metalloproteinase-9 （MMP-9） and vascular endothelial growth factor A （VEGFA） in the eutopic endometrial tissue. Western blot was adopted to determine the protein levels of TLR4， myeloid differentiation factor 88 （MyD88）， phosphorylated nuclear factor-κB （p-NF-κB）/NF-κB， MMP-9， and VEGFA in the eutopic endometrial tissue. In the cell experiments， the cell-counting kit-8 （CCK-8） assay was employed to screen the optimal concentration of Luoshi Neiyi prescription-containing serum， and a scratch assay was performed to assess the migration ability of iheESCs cells. Interventions with Luoshi Neiyi prescription-containing serum， resatorvid （TAK-242， a TLR4 inhibitor）， and lipopolysaccharides （LPS， a TLR4 agonist） were conducted， and Western blot was used to detect the expression of proteins related to the TLR4/NF-κB signaling pathway. ResultsThis experiment successfully replicated 36 EMs rat models. Compared with the sham operation group， the model group exhibited visible ectopic lesions on the abdominal wall and an increase in the writhing response score （P<0.01）. Furthermore， HE staining revealed the model group exhibited a thickened eutopic epithelium， stromal cell disarrangement， and evident infiltration of inflammatory cells. In addition， the model group showed elevated serum levels of IL-1β and PGE₂ （P<0.05， P<0.01）， increased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.01）， and up-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. Compared with the model group， all treatment groups exhibited a reduction in the ectopic lesion volume （P<0.01）. Furthermore， the writhing response scores were decreased in the high-dose Luoshi Neiyi prescription group and the dienogest group （P<0.05， P<0.01）. The pathological state of the ectopic endometrial tissue was alleviated to varying degrees in the treatment groups. Low-dose Luoshi Neiyi prescription reduced serum PGE₂ levels， and high-dose Luoshi Neiyi prescription and dienogest decreased serum IL-1β and PGE₂ levels in EMs rats （P<0.05， P<0.01）. The treatment groups showed decreased positive expression of MMP-9 and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01） and down-regulated protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA in the ectopic endometrial tissue （P<0.05， P<0.01）. The cell experiments showed that 5%， 10%， and 20% Luoshi Neiyi prescription-containing sera significantly reduced the viability and inhibited the migration of iheESCs. Compared with the control group， 5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups and the TAK-242 group showed reduced protein levels of TLR4， MyD88， p-NF-κB/NF-κB， MMP-9， and VEGFA （P<0.01）. Compared with the control group， the LPS group showed increased expression of the above proteins （P<0.01）. Compared with the LPS group， the LPS+5%， 10%， and 20% Luoshi Neiyi prescription-containing serum groups showed reduced expression of the above proteins （P<0.05， P<0.01）. ConclusionLuoshi Neiyi prescription may modulate the TLR4/NF-κB signaling pathway to reduce the inflammatory response and histopathological damage in the eutopic endometrium and suppress the adhesive， invasive， and angiogenic biological processes in the ectopic endometrial tissue， thereby exerting its therapeutic effect on EMs.

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

Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry (LC-MS) remains a formidable challenge. Herein, a metabolic reaction-based molecular networking (MRMN) strategy is introduced, which enables the "one-pot" discovery of prototype drugs and their metabolites. MRMN constructs networks by matching metabolic reactions and evaluating MS² spectral similarity, incorporating innovations and improvements in feature degradation of MS² spectra, exclusion of endogenous interference, and recognition of redundant nodes. A minimum 75% correlation between structural similarity and MS² similarity of neighboring metabolites was ensured, mitigating false negatives due to spectral feature degradation. At least 79% of nodes, 49% of edges, and 97% of subnetworks were reduced by an exclusion strategy of endogenous ions compared to the Global Natural Products Social Molecular Networking (GNPS) platform. Furthermore, an approach of redundant ions identification was refined, achieving a 10%-40% recognition rate across different samples. The effectiveness of MRMN was validated through a single compound, plant extract, and mixtures of multiple plant extracts. Notably, MRMN is freely accessible online at https://yaolab.network, broadening its applications.

ObjectiveTo explore the diagnostic-therapeutic course and epidemiological characteristics of the first imported kala-azar case reported in Changning District of Shanghai, so as to provide references for early detection, timely diagnosis, and effective management of the kala-azar cases in non-epidemic areas. MethodsIn 2025, a comprehensive epidemiological investigation and a complete collection of clinical records of one kala-azar patient imported from other provinces reported by a medical institution in this city were conducted. The source of infection, process of diagnosis and treatment, and clinical outcome of the case were systematically analyzed. ResultsThe case was a male migrant worker from Shanxi Province currently residing in Shanghai. In June 2023, the patient had a history of exposure to mosquito bites during his stay in a kala-azar endemic area. The initial onset occurred in December 2023, with clinical manifestations including unexplained fever accompanied by leukopenia, and subsequently gradually developed night sweats and fatigue during the following months. In June 2024, the patient self-perceived significant weight loss, and physical examination revealed hepatomegaly and splenomegaly, leading to a diagnosis of hemophagocytic syndrome (HPS). Throughout the whole course of the illness, the patient sought treatment at hematology departments of several hospitals multiple times, where he was managed as a case of HPS. In January 2025, a large number of Leishmania donovani were detected in the patient's bone marrow smear, confirming kala-azar. After 2 weeks of standardized treatment with amphotericin B liposomal, there was significant improvement. Combined with the epidemiological investigation, it was speculated that the incubation period of this case was about 6 months, whereas the interval from initial onset to definite diagnosis was approximately 14 months. ConclusionKala-azar has atypical early clinical symptoms, which are prone to confusion with HPS. For patients with recurrent unexplained fever accompanied by peripheral cytopenias, hepatomegaly and splenomegaly, medical institutions should consider the possibility of infectious diseases. Close collaboration between medical institutions and centers for disease control and prevention is essential to conduct a detailed epidemiological investigation and laboratory screening, ensuring confirmed cases receive standardized treatment.

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.

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.