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.

Epimedium sagittatum is a perennial herb of Berberidaceae. Its leaves have a long history of medicinal use in China. This plant is widely used as a Chinese traditional medicine，with the main functions of tonifying kidney Yang，strengthening bones and muscles，and dispelling wind and dampness. It can be used for treating kidney Yang deficiency，impotence，spermatorrhea，flaccidity of bones and muscles，rheumatic arthralgia，numbness，and spasms. The chemical constituents of this plant include flavonoids，polysaccharides，lignans，and alkaloids. Flavonoids are the main active ingredients. These compounds show a wide range of biological activities，including cartilage repair，anti-aging，anti-fatigue，cough-relieving，blood glucose-lowering，and anti-tumor effects. Modern pharmacological research has shown that E. sagittatum has definite pharmacological effects on the reproductive system，respiratory system，nervous system，cardiovascular system，skeletal system，etc. It has remarkable effects of helping pregnancy，resisting osteoporosis，controlling diabetes，improving immunity，and inhibiting tumor. Under the background of advocating one health and Chinese medicine，E. sagittatum is widely used in health care products，serving as the main raw material of various products. It has great market potential and is a Chinese medicinal herb with great clinical application and research value. This paper reviews the main chemical constituents and pharmacological effects of E. sagittatum based on domestic and foreign reports， providing a theoretical basis for further study on E. sagittatum and its safe clinical application.

Epimedium sagittatum is a perennial herb of Berberidaceae. Its leaves have a long history of medicinal use in China. This plant is widely used as a Chinese traditional medicine，with the main functions of tonifying kidney Yang，strengthening bones and muscles，and dispelling wind and dampness. It can be used for treating kidney Yang deficiency，impotence，spermatorrhea，flaccidity of bones and muscles，rheumatic arthralgia，numbness，and spasms. The chemical constituents of this plant include flavonoids，polysaccharides，lignans，and alkaloids. Flavonoids are the main active ingredients. These compounds show a wide range of biological activities，including cartilage repair，anti-aging，anti-fatigue，cough-relieving，blood glucose-lowering，and anti-tumor effects. Modern pharmacological research has shown that E. sagittatum has definite pharmacological effects on the reproductive system，respiratory system，nervous system，cardiovascular system，skeletal system，etc. It has remarkable effects of helping pregnancy，resisting osteoporosis，controlling diabetes，improving immunity，and inhibiting tumor. Under the background of advocating one health and Chinese medicine，E. sagittatum is widely used in health care products，serving as the main raw material of various products. It has great market potential and is a Chinese medicinal herb with great clinical application and research value. This paper reviews the main chemical constituents and pharmacological effects of E. sagittatum based on domestic and foreign reports， providing a theoretical basis for further study on E. sagittatum and its safe clinical application.

Pyroptosis is the programmed death of cells accompanied by an inflammatory response and is widely involved in the development of a variety of diseases, such as infectious diseases, cardiovascular diseases, and neurodegeneration. It has been shown that cellular scorching is involved in the pathogenesis of pulmonary arterial hypertension ( PAH) in cardiovascular diseases. Patients with PAH have perivascular inflammatory infiltrates in lungs, pulmonary vasculopathy exists in an extremely inflam-matory microenvironment, and pro-inflammatory factors in cellular scorching drive pulmonary vascular remodelling in PAH patients. This article reviews the role of cellular scorch in the pathogenesis of PAH and the related research on drugs for the treatment of PAH, with the aim of providing new ideas for clinical treatment of PAH.

ObjectiveThe correlation of Pueraria lobata producing areas， climate factors， total flavonoids of P. lobata， polysaccharide content of P. lobata， and antioxidant activity of P.lobata for medicinal application was analyzed， and the relationship between climate factors and the formation of P. lobata quality was evaluated. MethodThe scavenging rates of 1,1-diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6-trinitrophenyl)hydrazyl（DPPH） and 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)（ABTS） radicals by total flavonoids and polysaccharides of P. lobata were detected， and the correlation between the contents of each component and the information of producing areas and climate factors was analyzed. ResultThe ABTS⁺ scavenging rate by total flavonoids of P.lobata was negatively correlated with altitude （P<0.05） and positively correlated with annual sunshine hours （P<0.05）. The altitude was positively correlated with the total flavonoid content， while the annual sunshine hours were negatively correlated with the total flavonoid content. There was a negative correlation between total flavonoid content and ABTS⁺ scavenging rate by total flavonoids. In other words， lower altitude and longer annual sunshine hours indicated lower total flavonoid content and higher ABTS⁺ scavenging rate by total flavonoids. The ABTS⁺ scavenging rate by polysaccharides of P. lobata was negatively correlated with the frost-free period （P<0.05） and the mean temperature in July （P<0.01）. There was a positive correlation between the polysaccharide content of P. lobata and the frost-free period. The mean temperature in July was positively correlated with the polysaccharide content of P. lobata （P<0.05）. The polysaccharide content of P. lobata was negatively correlated with the ABTS⁺ scavenging rate by polysaccharides of P. lobata. In other words， a shorter frost-free period in the producing area and lower mean temperature in July indicated lower polysaccharide content of P. lobata and higher ABTS⁺ scavenging rate by polysaccharides of P. lobata. The mean temperature in July was significantly correlated with the contents of total flavonoids and polysaccharides in P. lobata samples （P<0.05）. The lower mean temperature in July was often accompanied by lower total flavonoid content of P. lobata， lower polysaccharide content of Pueraria lobata， and stronger antioxidant activity of P. lobata samples. ConclusionThe ability of P. lobata to remove ABTS⁺ is stronger than that of DPPH⁺. There is a significant correlation between climate factors， content， and antioxidant capacity in each producing area. Further research on the internal law of the formation of medicinal active components of P. lobata induced by core climate factors will provide a scientific basis for revealing the formation mechanism of genuine P. lobata and the subsequent control of P. lobata quality according to the environment of producing areas.