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.

Over the years,the American Diabetes Association(ADA)has been actively committed to the development and promotion of standards for the diagnosis,treatment,and daily care of diabetes.Since 1989,it has updated the diabetes diagnosis and treatment standards every year,which have become one of the most authoritative guidelines in diabetes and have been recognized and adopted by various countries.On December 10,2024,the 2025 Standards of Care in Diabetes were released,incorporating the latest evidence-based medicine content related to diabetes and its complications and comorbidities.It aims to provide guidance on the diagnosis,treat-ment,and management of the condition for clinicians,patients and their families,and researchers.This article interprets the major up-dates from the Standards.

OBJECTIVE To investigate the therapeutic mechanisms of electroacupuncture at"Tianshu"(ST25)and"Sanyinjiao"(SP6)acupoints combined with metformin in the treatment of type 2 diabetes mellitus(T2DM)using serum non-targeted metabolomics.METHODS Male SD rats were randomly divided into blank group,model group,metformin group,electroacupunc-ture group,and acupuncture-medicine combination(electroacupuncture combined with metformin)group.A type 2 diabetes model was established by high-fat diet combined with intraperitoneal injection of streptozotocin.The metformin group was treated with 250 mg·kg-1 metformin by gavage,the electroacupuncture group was treated with bilateral Tianshu and Sanyinjiao,the acupuncture-medicine combination group was treated with metformin by gavage combined with electroacupuncture,and the blank group and model group were treated with normal saline by gavage.All rats were treated 6 times a week for 7 weeks.After the intervention,the blood glucose level in the tail vein of the rats was measured using a blood glucose meter in the fasting state.The blood glucose levels of the rats were measured at 30,60,120,and 240 min after intraperitoneal injection of 50%glucose solution(4 mL·kg-1)to evaluate glu-cose tolerance.The serum insulin level of the rats was detected by ELISA and the insulin resistance index was calculated.The blood biochemical parameters were measured by an automatic blood biochemical analyzer.HE staining was used to evaluate the pathological conditions of the liver and pancreatic tissues of the rats.Ultra-performance liquid chromatography-mass spectrometry(UPLC-MS)technology was used for mass spectrometry detection to identify differential metabolites,and MetaboAnalyst 5.0 was used for pathway enrichment analysis.RESULTS Compared with the blank group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the model group rats were significantly increased(P<0.001),blood TP and GLB were significantly de-creased(P<0.01),AST,ALT,and ALP were significantly increased(P<0.05,P<0.01,P<0.001),and obvious inflammatory cell infiltration and pathological damage were observed in the liver and pancreas tissues;compared with the model group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the acupuncture-medicine combination group were sig-nificantly decreased(P<0.05,P<0.01,P<0.001),blood ALP was significantly decreased(P<0.01),TP and GLB were significant-ly increased(P<0.05),and the pathological damage of the liver and pancreas was significantly improved.Serum metabolomics showed that the metabolic profiles of the groups were well distinguished.Compared with the blank group,the differential metabolites in the model group were enriched in histidine metabolism,thiamine metabolism,taurine and hypotaurine metabolism,ascorbic acid and alde-hyde ester metabolism,valine,leucine and isoleucine biosynthesis pathways;compared with the model group,237 metabolites such as 3-aminoadipic acid,3-oxocyclobutanecarboxylic acid and phosphorylcholine in the acupuncture-medicine combination group were sig-nificantly reduced,and the pathways were enriched in histidine metabolism,linoleic acid metabolism,thiamine metabolism,taurine and hypotaurine metabolism,valine,leucine and isoleucine biosynthesis pathways.CONCLUSION Electroacupuncture combined with metformin can effectively improve the glucose and lipid metabolism of T2DM rats,and its potential mechanism may be related to the regulation of amino acid metabolism.

Objective:To investigate the independent risk factors of recurrent laryngeal nerve(RLN)injury after microwave ablation(MWA)for thyroid nodules of different malignant stratification.Methods:The medical records of 240 patients,who underwent microwave ablation for thyroid nodules in the department of ultrasound,Beijing Friendship Hospital Affiliated to Capital Medical University from September 2022 to August 2024,were retrospectively selected.All thyroid nodule cases were categorized based on the American College of Radiology Thyroid Imaging Reporting and Data System(TI-RADS)classification criteria and whether occurred RLN injury during the ablation procedure.A total of 54 patients with RLN injury and 65 patients without RLN injury,who were classified as TI-RADS 4a or higher than that,were divided into the high-risk group,and 35 patients with RLN injury and 86 patients without RLN injury,who were classified below TI-RADS 4a,were divided into the low-risk group.And then,a series of parameters included the benign and malignant nodules,the upper diameter of nodules,the left and right diameters of nodules,anteroposterior diameters of nodules,the aspect ratio(>1,≤1),overall echo,calcification,location,cystic solidity,and ablation parameters were analyzed.The risk factors of RLN injury of two groups were analyzed by using single factor and multi factor analysis.Results:There were not significant differences in the benign and malignant nodules,the upper diameters of nodules,the left and right diameters of nodules,anteroposterior diameters of nodules,the volume of nodules,overall echo,calcification,ldiametersocation,and cystic solidity between high and low-risk groups(P>0.05).In high-risk group,the distance between nodules and esophageal groove of trachea was less or equal to 2mm,and the increase of nodule volume were independent risk factors for RLN injury(OR=4.199,1.002,P<0.05),respectively.In the low-risk group,the nodule,which location was on the Zuckerkandl tubercle(Z-nodule),was risk factor that significantly increased RLN injury(OR=3.296,P<0.05).Conclusion:For nodules with differently malignant risk,the anatomical location,volume parameters and optimized ablation plan should be paid special attention before surgery,so as to reduce the risk of RLN injury.

OBJECTIVE: To evaluate the clinical efficacy and safety of Yangxue Qingnao Pills (YXQNP) combined with amlodipine in treating patients with grade 1 hypertension. METHODS: This is a multicenter, randomized, double-blind, and placebo-controlled study. Adult patients with grade 1 hypertension of blood deficiency and Gan (Liver)-yang hyperactivity syndrome were randomly divided into the treatment or the control groups at a 1:1 ratio. The treatment group received YXQNP and amlodipine besylate, while the control group received YXQNP's placebo and amlodipine besylate. The treatment duration lasted for 180 days. Outcomes assessed included changes in blood pressure, Chinese medicine (CM) syndrome scores, symptoms and target organ functions before and after treatment in both groups. Additionally, adverse events, such as nausea, vomiting, rash, itching, and diarrhea, were recorded in both groups. RESULTS: A total of 662 subjects were enrolled, of whom 608 (91.8%) completed the trial (306 in the treatment and 302 in the control groups). After 180 days of treatment, the standard deviations and coefficients of variation of systolic and diastolic blood pressure levels were lower in the treatment group compared with the control group. The improvement rates of dizziness, headache, insomnia, and waist soreness were significantly higher in the treatment group compared with the control group (P<0.05). After 30 days of treatment, the overall therapeutic effects on CM clinical syndromes were significantly increased in the treatment group as compared with the control group (P<0.05). After 180 days of treatment, brachial-ankle pulse wave velocity, ankle brachial index and albumin-to-creatinine ratio were improved in both groups, with no statistically significant differences (P>0.05). No serious treatment-related adverse events occurred during the study period. CONCLUSIONS Combination therapy of YXQNP with amlodipine significantly improved symptoms such as dizziness and headache, reduced blood pressure variability, and showed a trend toward lowering urinary microalbumin in hypertensive patients. These findings suggest that this regimen has good clinical efficacy and safety. (Registration No. ChiCTR1900022470).
Humans ; Amlodipine/adverse effects* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Hypertension/complications* ; Middle Aged ; Treatment Outcome ; Drug Therapy, Combination ; Adult ; Blood Pressure/drug effects* ; Double-Blind Method ; Aged ; Antihypertensive Agents/adverse effects*

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 Analyze the epidemiological characteristics of respiratory virus infections in Kunming area during 2023-2024,and explore the detection rates of different virus types and their distribution patterns across different time periods and age groups.Methods A total of 10354 nasopharyngeal swab or sputum samples were retrospectively collected from patients with acute respiratory infections who visited the Third People's Hospital of Kunming City between March 2023 and June 2024.Multiple pathogens were detected using real-time fluorescent quantitative polymerase chain reaction(RT-qPCR)technology.A retrospective analysis was then conducted on the clinical laboratory detection results,statistically analyzing the overall detection rates of various respiratory viruses,multiple infection phenomena,gender differences,age distribution,seasonal variations,infection site differences,and monthly infection situations.Results Among the 10354 patient respiratory samples tested,3368 pathogen infections were detected,with a detection rate of 32.53%(3368/10354).204 patients presented with mixed infections of≥2 pathogens,with a mixed detection rate of 6.06%(204/3368).The single detection rate for females was significantly higher than males(P<0.001),and the multiple infection detection rate for males is significantly higher than females(P<0.05),indicating that males may have a higher risk of concurrent infections.Among different age groups,the virus detection rate was highest in the 5-18 years age group at 55.87%.Significant differences were observed in the detection rates of FluA,FluB,and SIV-H3 across different disease types(P<0.05).In March 2023,the detection rate was highest at 54.27%(5619/10354),with Influenza A virus(FluA)and seasonal influenza H3 subtype(SIV-H3)being the most detected pathogens.Conclusion In acute respiratory infection(ARI)cases in the Kunming area,FluA,FluB,and SIV-H3 were the primary viral pathogens,with the region's viral epidemic characteristics closely related to patient age stages,seasonal changes,and infection site factors.

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.