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.

Objective: To analyze the prevalence trends of different types of elevated blood pressure and their association with nutritional status among primary and secondary school students in Inner Mongolia from 2019 to 2024, providing references for targeted prevention strategies. Methods: From September 2019 to 2024, a stratified random cluster sampling method was used to select 12 primary and secondary schools from each league city in Inner Mongolia Autonomous Region. A total of 177 108, 137 758, 190 182, 180 084 , 188 056, 180 351 primary and secondary school students (excluding grades one to three of primary school) were included for physical examination. The correlation between their nutritional status and high blood pressure was analyzed based on the basic situation of 129 821 primary and secondary school students who completed a questionnaire survey at the same time in 2024. Statistical analysis was conducted using a Chi-square test and multiple Logistic regression model. Results: From 2019 to 2024, the detection rates of elevated blood pressure were 13.60%, 13.68%, 17.60%, 17.24%, 14.77% and 15.96%, respectively. The rates for isolated systolic hypertension were 4.24%, 5.83%, 7.26%, 7.19%, 6.24% and 6.93%; isolated diastolic hypertension rates were 6.38%, 4.99%, 6.23 %, 6.41%, 5.39% and 5.66%; and combined systolic and diastolic hypertension rates were 2.97%, 2.86%, 4.11%, 3.65%, 3.14 % and 3.36%. Multivariate Logistic regression analysis showed that girls, junior high school, senior high school, overweight, and obesity were positively associated with elevated blood pressure risk ( OR =1.27, 1.25, 1.32, 1.66, 3.07, all P <0.05); conversely, county residence, Mongolian ethnicity, and other ethnicities showed negative associations ( OR =0.90, 0.93, 0.90, all P <0.05). Conclusions Overweight and obesity among children and adolescents are closely related to various types of elevated blood pressure. Prevention strategies should prioritize effectively controlling weight issues among children and adolescents, thereby effectively reducing the incidence of elevated blood pressure.

Objective To observe the effect of intra-arterial nimodipine perfusion after interventional embolization in the treatment of symptomatic cerebral vasospasm(SCVS)after aneurysmal subarachnoid hemorrhage(aSAH)and explore its influence on vascular endothelial function.Methods Patients with SCVS after aSAH were divided into treatment group and control group according to cohort methods.Both groups received interventional embolization based on symptomatic supportive treatment,and were given conventional 3H treatments such as blood dilution,dilatation and pressure enhancement after surgery,and the treatment group was additionally given intra-arterial perfusion of nimodipine(20％nimodipine was injected by electronic pump through the femoral artery sheath at a rate of 0.2 mg·min-1 for 2 mg once a day,vascular sheath was removed and nimodipine tablet after 7 days of medication was taken orally for 60-120 mg twice a day after meals),and both groups were treated for 14 days.The cerebrovascular blood flow velocity,laboratory indicators,postoperative complications and prognosis were compared between the two groups.Results There were 41 cases in treatment group,and 38 cases in control group.After 2 weeks of treatment,the average blood flow velocities of anterior cerebral artery(ACA)in treatment group and control group were(84.32±5.27)and(93.46±5.61)cm·s-1,the average blood flow velocities of middle cerebral artery(MCA)were(86.05±5.94)and(95.23±6.37)cm·s-1,the average blood flow velocities of posterior cerebral artery(PCA)were(59.41±4.82)and(71.56±5.39)cm·s-1 respectively(all P＜0.05).The levels of serum endothelin-1(ET-1)in treatment group and control group after 2 weeks of treatment were(76.32±10.58)and(94.16±10.98)pg·mL-1;the levels of vascular endothelial growth factor(VEGF)were(127.45±14.83)and(164.85±15.62)ng·mL-1;the levels of soluble fms-like tyrosine kinase-1(sFlt-1)were(103.67±15.34)and(114.98±16.43)ng·L-1;the levels of plasma soluble intercellular adhesion molecule-1(sCAM-1)were(234.81±62.79)and(285.36±90.24)ng·mL-1;the levels of hypoxia-inducible factor-2α(HIF-2α)were(98.74±7.56)and(102.49±8.35)pg·mL-1;the levels of serum nitric oxide(NO)were(43.16±4.91)and(39.72±5.37)mmol·L-1,all with significant difference(all P＜0.05).The incidence rates of delayed cerebral vasospasm(DCVS)and hydrocephalus were 4.88％and 9.76％in treatment group after surgery,lower than 21.05％and 28.95％in control group(all P＜0.05).The proportion of Glasgow outcome scale(GOS)score of 5 points in treatment group at 3 months after surgery was higher than that in control group(78.05％vs 55.26％,P＜0.05).Conclusion After interventional embolization,intra-arterial perfusion of nimodipine for SCVS after aSAH can help to relieve the inflammatory response,improve the vascular endothelial function and reduce the cerebral blood flow velocity,and it plays a positive role on reducing the cerebral tissue injury and improving the prognosis of patients.

Objective To investigate the genetic causes of auditory neuropathy with optic atrophy in a family.Methods The proband's medical history and family history were inquired in detail,and relevant clinical examina-tions were performed to confirm the diagnosis of auditory neuropathy with optic atrophy,and the genetic pedigree of the family was drawn.Peripheral blood of proband(Ⅲ-7)was collected for whole exome sequencing,and the patho-genicity of the detected mutations were interpreted.Blood samples of proband's wife(Ⅲ-8),eldest daughter(Ⅳ-7),second daughter(Ⅳ-9)and son(Ⅳ-10)were tested for mutation sites by Sanger sequencing.Combined with clinical manifestations and examination results,the family was studied.Results The genetic pattern of this family was autosomal dominant.The proband showed decreased visual acuity at the age of 19,bilateral sensorineural deaf-ness at the age of 30,and decreased speech recognition rate.Among 20 members of the family of 5 generations,10(2 deceased)showed similar symptoms of hearing and visual impairment.Proband(Ⅲ-7),eldest daughter(Ⅳ-7)and son(Ⅳ-10)underwent relevant examination.Pure tone audiometry showed bilateral sensorineural deafness.ABR showed no response bilaterally.The 40 Hz AERP showed no response in both ears.OAE showed responses in some or all of the frequencies.No stapedial reflex was detected.The eye movement of Ⅲ-7 and Ⅳ-10 were reasona-ble in all directions,and color vision was normal.Ocular papilla atrophy was observed in different degrees in fundus examination.OCT showed thinning of optic disc nerve fibers in both eyes,and visual evoked potential showed pro-longed P100 wave peak.They were diagnosed as hereditary auditory neuropathy with optic atrophy.A mutation of the OPA1 gene c.1334G＞A(p.Arg445His,NM_015560.2)at a pathogenic locus on chromosome 3 was detected by whole exon detection in Ⅲ-7.The results of generation sequencing analysis showed that the OPA1 gene c.1334G＞A(p.Arg445His,NM_015560.2)mutation of chromosome 3 was also found in Ⅳ-7 and Ⅳ-10.Meanwhile,the gen-otypes of Ⅲ-8 and Ⅳ-9 were wild homozygous,that is,no mutation occurred.Conclusion The OPA1 c.1334G＞A(p.Arg445His,NM_015560.2)mutation site might be the pathogenic mutation in this family.

ObjectiveTo investigate the correlation among the botanical characteristics， biological characteristics， chemical composition， and medicinal properties and efficacy of traditional Chinese medicines （TCM） from latex-containing plants， so as to strengthen the theory of "identifying symptoms for qualities" and provide a reference for the development and utilization of the latex-containing plant resources. MethodStatistics on the meridians for properties and tastes， efficacy， medicinal parts， family and genus， and chemical components of TCM from latex-containing plants were carried out. A total of 53 TCM from latex-containing plants included in the 2020 edition of the Chinese Pharmacopoeia were screened by mining the Chinese Botanical Journal， Chinese Materia Medica， Dictionary of Traditional Chinese Medicines， and related literature. In addition， their meridians for properties and tastes， medicinal parts， chemical components， and TCM classifications were summarized and statistically analyzed by using Excel 2013 and ChiPlot 2023.3.31 software. ResultIt was found that latex-containing plants were mainly distributed in one kingdom， one phylum， two classes， and 20 families， and most of the TCM from latex-containing plants belonged to Dicotyledonaceae under Angiosperms. In terms of properties and tastes， plain>cold>warm>cool>hot and bitter>pungent>sweet>sour>salty. In terms of meridians， liver>lung>kidney>spleen=large intestine=stomach>heart>bladder=gallbladder=small intestines. In terms of medicinal parts， roots （root， rhizomes， tuberous root， and root bark）>resin>seed>whole herb （whole herb and above-ground part）>stem （stem and branch）>fruit>leaf>flower=skin. In terms of research on chemical components， they were mostly glycosides. In terms of TCM classification， they were mostly medicines for activating blood circulation and removing blood stasis. ConclusionThe TCM from latex-containing plants is mainly plain， with a uniform warm and cold distribution. The tastes are mainly bitter and pungent， and the major meridians are the liver and lung. The roots and resins are mainly used as medicines. The components mostly contain glycosides， alkaloids， and volatile oils， and most of them are medicines for activating blood circulation and removing blood stasis， as well as for removing heat and toxins. There is a certain degree of correlation among the growth habits， medicinal parts， chemical components， and the properties， tastes， and efficacy of the TCM from latex-containing plants. It may provide a reference for resource development and utilization of TCM from latex-containing plants.

ObjectiveThe relevant laws among the biological characteristics， medicinal parts， growth environment， and medicinal properties and efficacy of traditional Chinese medicines （TCM） from fungi were excavated， so as to strengthen the theory of distinguishing symptoms for quality and provide a reference for the development and utilization of TCM from fungi. MethodThe medicinal parts， meridians for properties and tastes， heterotrophic mode， and efficacy of commonly used TCM from fungi were summarized. By consulting the Compendium of Materia Medica， Shennong Materia Medica， Flora of China， and literature， the TCM from fungi indexed in the 2020 edition of the Chinese Pharmacopoeia and some local pharmacopeias were checked. ResultA total of 28 common TCM from fungi were selected. Different TCMs from fungi have different meridians for properties and tastes， medicinal parts， habits， and growth environments. The relevant information was counted. Among the four properties， plain>cold>warm. Among the five tastes， sweet>bitter>light>pungent=salty. In terms of medicinal parts， fruiting body>sclerotia>complex>spermia=outer skin=other. In terms of meridians， lung>liver=heart>spleen=kidney>stomach. In terms of habits， parasitism>saprophysis>symbiosis=facultative parasitism=facultative saprophysis. ConclusionTCM from fungi are mainly parasitic and saprophytic， and the plain property and sweet taste the most. The meridians are mostly lung， heart， and liver. Nourishment and diuresis are the main efficacy. There is a certain correlation between the color， habit， medicinal parts， and growth environment of TCM from fungi and their properties， tastes， and efficacy， providing comprehensive literature reference and theoretical basis for their in-depth research， clinical use， and resource development.