Background: Hearing loss (HL) is one of the most common sensory disorders, affecting over 5-8% of the world's population. Approximately half of HL cases are attributed to genetic factors. In hereditary deafness, about 75-80% is inherited through autosomal recessive inheritance, and common pathogenic genes include GJB2 and SLC26A4. Pathogenic variants in the SLC26A4gene are the leading cause of hereditary hearing loss in humans, second only to the GJB2 gene. Variants in the SLC26A4gene cause hearing loss, which can be non-syndromic autosomal recessive deafness (DFNB4, OMIM #600791) associated with enlarged vestibular aqueduct (EVA) or Pendred syndrome (Pendred, OMIM #605646). DFNB4 is characterized by sensorineural hearing loss combined with EVA or less common cochlear malformation defect. Pendred syndrome is characterized by bilateral sensorineural hearing loss with EVA and an iodine defect that can lead to thyroid goiter. Currently, it is known that EVA is associated with variants in the SLC26A4 gene and is a penetrant feature of SLC26A4-related HL. Predominant mutations in these genes differ significantly across populations. For instance, predominant SLC26A4 mutations differ among populations, including p.T416P and c.1001G>A in Caucasians, p.H723R in Japanese and Koreans, and c.919-2A>G in Han Taiwanese and Han Chinese. On the other hand, there has been no study of hearing loss related to SLC26A4 gene variants among Mongolians, which is the basis of our research. Aim: We aimed to identify the characteristics of the SLC26A4 gene variants in Mongolian people with Enlarged vestibular aqueduct and Mondini malformation. Materials and Methods: In 2022-2024, We included 13 people with hearing loss and enlarged vestibular aqueduct, incomplete cochlea (1.5 turns of the cochlea with cystic apex- incomplete partition type II- Mondini malformation) were examined by CT scan of the temporal bone in our study. WES (Whole exome sequencing) analysis was performed in the Genetics genetic-laboratory of the National Taiwan University Hospital. Results: Genetic analysis revealed 26 confirmed pathogenic variants of bi-allelic SLC26A4 gene of 8 different types in 13 cases, and c.919-2A>G variant was dominant with 46% (12/26) in allele frequency, and c.2027T>A (p.L676Q) variant 19% (5/26), c.1318A>T(p.K440X) variant 11% (3/26), c.1229C>T (p.T410M) variant 8% (2/26) ) , c.716T>A (p.V239D), c.281C>T (p.T94I), c.1546dupC, and c.1975G>C (p.V659L) variants were each 4% (1/26)- revealed. Two male children, 11 years old (SLC26A4: c.919-2A>G) and 7 years old (SLC26A4: c.919-2A>G:, SLC26A4: c.2027T>A (p.L676Q))had history of born normal hearing and progressive hearing loss. Conclusions 1. 26 variants of bi-allelic SLC26A4 gene mutation were detected in Mongolian people with EVA and Mondini malformation, and c.919-2A>G was the most dominant allele variant, and rare variants such as c.1546dupC, c.716T>A (p.V239D) were detected. 2. Our study shows that whole-exome sequencing (WES) can identify gene mutations that are not detected by polymerase chain reaction (PCR) or NGS analysis.

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.

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.

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

OBJECTIVE: To investigate the clinical efficacy and safety of venetoclax (VEN) in combination with hypomethylating agent (HMA) in the treatment of patients with high-risk myelodysplastic syndromes (MDS). METHODS: A total of 30 patients with high-risk MDS who received the combination of VEN and HMA from March 2019 to November 2022 were included. The overall response rate (ORR), modified overall response rate (mORR), overall survival (OS), progression-free survival (PFS), and adverse events of all included patients were evaluated. RESULTS: Among the 30 high-risk MDS patients treated with VEN combined with HMA regimen, 24 cases achieved complete response (CR)/ marrow complete response (mCR), 2 cases achieved partial response (PR), the ORR was 24/30, the median OS was 28.1 months, and the median PFS was 28.1 months. In addition, patients who achieved complete remission / marrow complete remission after treatment had a significantly longer OS than those who did not. Moreover, 12 patients were treated with allogeneic hematopoietic stem cell transplantation (allo-HSCT). There were grade 3 or higher hematologic adverse events including thrombocytopenia (14 cases), neutropenia (14 cases), febrile neutropenia (10 cases) and anemia (7 cases) as well as gastrointestinal adverse events of any grade, such as vomiting (7 cases), diarrhea (5 cases), and constipation (4 cases). CONCLUSION VEN in combination with HMA is an effective and safe treatment option in patients with high-risk MDS. This regimen combined with allo-HSCT can improve the prognosis of these patients. Continuous attention to the monitoring and management of adverse events is essential for the patients' safety in this combination therapy.
Humans ; Myelodysplastic Syndromes/drug therapy* ; Sulfonamides/therapeutic use* ; Bridged Bicyclo Compounds, Heterocyclic/therapeutic use* ; Female ; Male ; Treatment Outcome ; Middle Aged ; Aged ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Adult

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.

Several types of arthritis share the common feature that the generation of inflammatory mediators leads to joint cartilage degradation. However, the shared mechanism is largely unknown. H2BK120ub1 was reportedly involved in various inflammatory diseases but its role in the shared mechanism in inflammatory joint conditions remains elusive. The present study demonstrated that levels of cartilage degradation, H2BK120ub1, and its regulator WW domain-containing adapter protein with coiled-coil (WAC) were increased in cartilage in human rheumatoid arthritis (RA) and osteoarthritis (OA) patients as well as in experimental RA and OA mice. By regulating H2BK120ub1 and H3K27me3, WAC regulated the secretion of inflammatory and cartilage-degrading factors. WAC influenced the level of H3K27me3 by regulating nuclear entry of the H3K27 demethylase KDM6B, and acted as a key factor of the crosstalk between H2BK120ub1 and H3K27me3. The cartilage-specific knockout of WAC demonstrated the ability to alleviate cartilage degradation in collagen-induced arthritis (CIA) and collagenase-induced osteoarthritis (CIOA) mice. Through molecular docking and dynamic simulation, doxercalciferol was found to inhibit WAC and the development of cartilage degradation in the CIA and CIOA models. Our study demonstrated that WAC is a key factor of cartilage degradation in arthritis, and targeting WAC by doxercalciferol could be a viable therapeutic strategy for treating cartilage destruction in several types of arthritis.