Background: Visceral white adipose tissue (vWAT) inflammation is a critical pathology of obesity-caused heart damage and is closely associated with adipocyte endoplasmic reticulum (ER) dysfunction. Serine (or cysteine) peptidase inhibitor, clade A, member 3C (Serpina3c) has been identified as an adipokine with anti-vWAT inflammatory effects. However, it remains unclear whether Serpina3c deficiency promotion of vWAT inflammation involves adipocyte ER dysfunction and whether it further contributes to heart damage in obesity. Methods: Wild type and Serpina3c knockout (Serpina3c–/–) mice were fed a high-fat diet (HFD) for 12 weeks. An adeno-associated virus (AAV) was injected locally into epididymal white adipose tissue (eWAT) of Serpina3c–/– mice to induce eWAT-adipocyte- specific overexpression of Serpina3c (AAV-Serpina3c) or knockdown of hypoxia-inducible factor 1α (AAV-shHIF1α). In vitro experiments were performed in 3T3-L1 adipocytes. Results: Serpina3c–/– mice exhibited more severe eWAT, serum and heart inflammation after HFD feeding. Consistently, these adverse phenotypes were mitigated in AAV-Serpina3c and AAV-shHIF1α mice. Mechanistically, ER oxidoreductase 1α (Ero1α) and protein disulfide isomerase (PDI) family members PDIA3 and PDIA4 were found to be target genes of HIF1α. In the obese mice, Serpina3c deficiency caused adipocyte more hypertrophy, and activated HIF1α-Ero1α/PDI mediated ER overoxidation and ER stress in eWAT. Subsequently, this led to increased adipocyte apoptosis and chemokine production and decreased adiponectin expression, which promoted macrophage infiltration and M1 polarization in eWAT, thus exacerbating eWAT inflammation and ultimately facilitating serum and distal heart inflammation. Conclusion These findings indicate that Serpina3c is a significant regulator of adipocyte ER redox homeostasis, thus highlighting Serpina3c as a potential therapeutic target for obesity-related eWAT inflammation and heart damage.

Objective: To evaluate the incremental vaccine effectiveness (VE) of two dose of the mumps containing vaccine (MuCV) in chidren, so as to provide a basis for optimizing mumps immunization strategies. Methods: A 1∶2 frequency matched case-control study was conducted by using reported mumps cases in childcare centers or schools from Lu an, Hefei, Ma anshan and Huainan cities of Anhui Province from September 1, 2023 to June 30, 2024, as a case group(383 cases). And healthy children in the same classroom were selected as a control group(766 cases). The MuCV immunization histories of participants were collected to estimate the incremental VE of the second dose of MuCV against mumps. Group comparisons were performed using the Chi square test or t-test. For matched case-control pairs, the Cox regression model was employed to calculate the odds ratio (OR) with 95% confidence interval (CI) for two dose MuCV vaccination and to estimate the incremental vaccine effectiveness (VE). Results: There were no statistically significant differences between the case and control groups regarding gender, age, dosage of MuCV vaccination and the time interval since the last dose vaccination( χ 2/t=0.05, 0.20, 0.94, -0.02, P >0.05). The proportions of the case and control groups vaccinated with two doses of MuCV were 26.63% and 29.37%, respectively, and the overall incremental VE of the second dose of MuCV was 40.73% (95% CI=3.03%-63.77%, P <0.05). Subgroup analyses revealed that the incremental VE for children with a period of ≥1 year between the two doses of MuCV was 54.13% (95% CI=1.90%-78.56%, P <0.05), while for children with a period of <1 year, it was 30.63% (95% CI=-28.59%-62.58%, P >0.05). The incremental VE of the second dose of MuCV was 30.36% (95% CI=-25.95%-61.50%, P >0.05) in kindergarten children and 66.73% (95% CI=14.92%-86.99%, P <0.05) in elementary and secondary school students. The incremental VE was 28.78% (95% CI=-27.46%-60.21%, P >0.05) within five years of the last dose of MuCV vaccination and 66.07% (95% CI=-41.56%-91.87%, P >0.05) for vaccinations administered beyond five years. Conclusions The second dose of MuCV may offer additional protection for children; however, extending the interval between two dose of MuCV (<1 year) has shown limited incremental protective effects. Therefore, it is crucial to consider optimizing current immunization strategies for mumps.

The Golgi apparatus(GA),an essential membrane organelle in eukaryotes positioned between the endoplasmic reticulum and the plasma membrane,is responsible for transporting and modifying proteins and lipids generated by the endoplasmic reticulum.The homeostasis of the endoplasmic reticulum and GA plays a crucial role in regulating cellular life.When GA is unable to bear the load of protein or lipid processing and transportation,it enters a state of stress.Cells sense this and activate Golgi-related quality control mechanisms.By regulating the structure and function of GA,it provides important protection for the cell or induces programmed cell death,known as the GA stress response.Understanding the mechanisms and outcomes of cellular GA stress is of great significance for exploring Golgi dynamics and its impact on human diseases.This review summarizes the structure and function of GA under physiological conditions,the phenomenon of GA stress under pathological conditions,and the role of GA in infectious diseases,aiming to provide a basis for the clinical study of new strategies for the prevention and treatment of infectious diseases.

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.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

AIM To study the chemical constituents from Euphorbia humifusa Willd.and their in vitro anti-hepatoma activity.METHODS Silica gel,D101 macroporous adsorption resin and semi-preparative RP-HPLC were used for isolated and purified,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-hepatocellular carcinoma activity was determined by MTT mothod.RESULTS Eighteen compounds were isolated and identified as 22-O-angeloyl-R1-barrigenol(1),dimethyl 3,3'-[oxybis(4,1-phenylene)](2E,2'E)-diacrylate(2),N-(3-methoxy-1,3-dioxopropyl)-D-tryptophan methyl ester(3),N-acetyltryptophan methyl ester(4),N-(methoxycarbonyl)-tryptophan methyl ester(5),(3β,5α,17β)-4,4,8,14-tetramethyl-18-norandrostane-3,17-diol(6),3β,18,19β-trihydroxylupane(7),pregnenolone(8),3-hydroxy-5,6-epoxy-7-megastigmen-9-one(9),dehydrovomifoliol(10),loliolide(11),2,2'-oxybis(1,4-di-tert-butylbenzene)(12),dibutyl phthalate(13),4-methoxycinnamic acid(14),3,4-dimethoxycinnamic acid(15),methyl 4-hydroxybenzoate(16),kaempferol(17),quercetin(18).The IC50 values of compounds 1,7 and 8 on HepG2 cells were(17.27±0.92),(19.11±2.14)and(7.53±1.09)μmol/L,respectively.CONCLUSION Compounds 1-16 are first isolated from this plant.Compounds 1,7 and 8 have anti-hepatoma activity.

Phosphodiesterase type 5(PDE5)participates in the physiological functions of multiple organs by regulating the nitric oxide(NO)-cyclic guanosine monophosphate(cGMP)pathway.As a long-acting PDE5 inhibitor,tadalafil has been used clinically for over 20 years and is approved for the treatment of male erectile dysfunction and lower urinary tract symptoms associated with benign prostatic hyperplasia.Furthermore,tadalafil maintains cGMP levels by inhibiting PDE5,thereby activa-ting the downstream NO-cGMP signaling pathway to exert multiple effects,including relaxing smooth muscle,exerting antioxidant actions,regulating apoptosis,and modulating metabolism.Clinical evidence indicates that tadalafil shows promising therapeutic potential in multi-organ system diseases such as pulmonary arterial hypertension,heart failure,type 2 diabetes,cognitive impair-ment,female infertility,and fetal growth restriction.This article systematically reviews the action mechanisms and research pro-gress of tadalafil in treating diseases beyond erectile dysfunction,aiming to summarize its potential for cross-disease system clini-cal applications and provide references for optimizing treatment strategies for patients with chronic diseases.

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.