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.

BACKGROUND: The purpose of this study was to evaluate the safety and efficacy of subsequent radiotherapy (RT) following first-line treatment with durvalumab plus chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC). METHODS: A total of 122 patients with ES-SCLC from three hospitals during July 2019 to December 2021 were retrospectively analyzed. Inverse probability of treatment weighting (IPTW) analysis was performed to address potential confounding factors. The primary focus of our evaluation was to assess the impact of RT on progression-free survival (PFS) and overall survival (OS). RESULTS: After IPTW analysis, 49 patients received durvalumab plus platinum-etoposide (EP) chemotherapy followed by RT (Durva + EP + RT) and 72 patients received immunochemotherapy (Durva + EP). The median OS was 17.2 months vs . 12.3 months (hazard ratio [HR]: 0.38, 95% confidence interval [CI]: 0.17-0.85, P = 0.020), and the median PFS was 8.9 months vs . 5.9 months (HR: 0.56, 95% CI: 0.32-0.97, P = 0.030) in Durva + EP + RT and Durva + EP groups, respectively. Thoracic radiation therapy (TRT) resulted in longer OS (17.2 months vs . 14.7 months) and PFS (9.1 months vs . 7.2 months) compared to RT directed to other metastatic sites. Among patients with oligo-metastasis, RT also showed significant benefits, with a median OS of 17.4 months vs . 13.7 months and median PFS of 9.8 months vs . 5.9 months compared to no RT. Continuous durvalumab treatment beyond progression (TBP) prolonged OS compared to patients without TBP, in both the Durva + EP + RT (NA vs . 15.8 months, HR: 0.48, 95% CI: 0.14-1.63, P = 0.238) and Durva + EP groups (12.3 months vs . 4.3 months, HR: 0.29, 95% CI: 0.10-0.81, P = 0.018). Grade 3 or 4 adverse events occurred in 13 (26.5%) and 13 (18.1%) patients, respectively, in the two groups; pneumonitis was mostly low-grade. CONCLUSION Addition of RT after first-line immunochemotherapy significantly improved survival outcomes with manageable toxicity in ES-SCLC.
Humans ; Small Cell Lung Carcinoma/therapy* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Lung Neoplasms/therapy* ; Aged ; Antibodies, Monoclonal/therapeutic use* ; Adult ; Immunotherapy/methods* ; Aged, 80 and over

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 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.

OBJECTIVE: To assess the value of combined detection strategies using multiple technologies for the genetic testing and prenatal diagnosis for pedigrees affected with Duchenne muscular dystrophy (DMD) for optimizing genetic counseling and reproductive guidance. METHODS: This study has involved 142 subjects from 65 suspected DMD families who had visited the First Affiliated Hospital of Chongqing Medical University from January 2018 to December 2023. A combination of multiple ligation-dependent probe amplification (MLPA), quantitative fluorescence PCR, and next-generation sequencing (NGS) was used. After confirming the genetic diagnosis of the probands, prenatal diagnosis was provided for carrier mothers. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2021-264). RESULTS: Among the 142 subjects tested, 73 cases of large deletions/duplications and 15 cases of small variants of the DMD gene were detected. The hotspot regions for the variants were exons 45 to 55. A total of 41 variant types were identified, of which 3 were previously unreported. In 19 families with suspected patients, 7 exonic deletions, 2 exonic duplications, and 3 small variants were identified. Prenatal diagnosis was performed on 48 fetuses from 46 families, revealing 16 affected male fetuses (including 12 with deletion variants, 2 with duplication variants, and 2 with small variants). Seven carrier females were identified among the 16 female fetuses (including 6 with deletions and 1 with duplication). Among the couples with an affected fetus, 16 had opted to terminate the pregnancy, while the parents of 32 fetuses had chosen to continue with the pregnancy. In families undergoing prenatal diagnosis, 53 (79.1%) pregnant women and their family members were found to carry mutations of the DMD gene. CONCLUSION The combined detection strategy of MLPA, qPCR, and NGS can encompass large deletions/duplications and small variants of the DMD gene, providing timely and accurate prenatal diagnosis for families affected by DMD. In conjunction with genetic counseling, this can effectively reduce the risk of producing affected offspring, which is crucial for the prevention and control of this disease.
Humans ; Muscular Dystrophy, Duchenne/diagnosis* ; Prenatal Diagnosis/methods* ; Female ; Male ; Pregnancy ; Pedigree ; Genetic Testing/methods* ; Dystrophin/genetics* ; Adult ; Genetic Counseling ; High-Throughput Nucleotide Sequencing/methods* ; Exons

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.