Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

Background: Type 2 diabetes mellitus (T2DM) is a complex, multifactorial disease with a significant global burden. Although genome-wide association studies (GWAS) have identified many T2DM-associated variants, most lie in non-coding regions, making it difficult to interpret their functional roles. Methods: We aimed to identify genetically regulated Cytosine–phosphate–Guanine (CpG) sites associated with T2DM by conducting a methylome-wide association study (MWAS), followed by Mendelian randomization (MR) and functional validation using human pancreatic cells and mouse models. MWAS was performed using summary statistics from large-scale GWAS and a DNA methylation (DNAm) prediction model to test associations between genetically predicted DNAm and T2DM. Results: We identified 111 CpG sites significantly associated with T2DM in Europeans, including 8 novel sites near genes not previously linked to T2DM. These findings were replicated in independent datasets. Many CpGs also showed associations with cardiometabolic traits, highlighting shared epigenetic mechanisms. Trans-ethnic MR analysis confirmed consistent effects for six CpGs in East Asians. Functional analysis revealed that several CpGs regulate gene expression in human pancreatic α- and β-cells. Among them, 2´-5´-oligoadenylate synthetase like (OASL) expression, regulated by a significant CpG, was differentially expressed in α-cells of T2DM cases compared to controls. Supporting evidence from mouse models suggests a role for OASL in glucose regulation. Conclusion Our study identifies novel genetically regulated CpG sites associated with T2DM risk and highlights OASL as a potential epigenetic regulator of glucose metabolism in α-cells. These findings provide mechanistic insights into the epigenetic architecture of T2DM and suggest potential targets for cross-ethnic biomarker development and therapeutic intervention.

Objective To investigate the effects of varying durations of overwork on cardiomyocyte pyroptosis in mice.Methods A total of 24 SPF KM mice were randomly divided into four groups(n=6)using a random number table:control group,2-week overwork(W2)group,4-week overwork(W4)group,and 6-week overwork(W6)group.Mice in control group were normally raised,while those in W2,W4,and W6 groups were forced to stand in water for 8 h and then restrained for 3 h daily for 2,4,6 weeks,respectively.The general condition and weekly weight changes of the mice were observed.After modeling,blood samples were collected,and hearts were excised.Myocardial histopathological changes were assessed using hematoxylin and eosin(HE)staining.The localization of gasdermin D(GSDMD)protein in myocardial tissue was detected through immunohistochemical staining,and the expression levels of pyroptosis-related proteins[NOD-like protein receptor 3(NLRP3),Caspase-1,GSDMD]in myocardial tissue were analyzed using Western blotting.The contents of interleukin-1β(IL-1β)and interleukin-18(IL-18)in serum and myocardial tissues were measured using ELISA.Results(1)The weight of control group mice increased steadily within 2 weeks.In W2 group,there was no significant weight change within 2 weeks,while in W4 and W6 groups,the body weights were higher than their initial values from the 2nd to 6th week.Compared with control group,the body weights of W2,W4,and W6 groups were lower than those of control group in the 1st and 2nd week,with statistically significant differences(P<0.05).The activity levels of the mice in W2,W4,and W6 groups initially increased and then decreased,with their fur becoming dull and falling out,and their mental state deteriorating.(2)In control group,cardiomyocytes were neatly arranged,and the nuclear morphology was normal.Compared with control group,in W2 group,cardiomyocyte arrangement was less regular,and capillary congestion was increased.In W4 group,the vascular congestion in the myocardium was significantly increased,the interstitial tissue was hyperplastic,and vacuolization appeared around the nuclei.In W6 group,the myocardial interstitium was loose,fat infiltration was increased,vacuolization around the nuclei was increased,and myocardial fibers were swollen,and the arrangement was disordered.(3)GSDMD was mainly located in the cytoplasm of cardiomyocytes.Compared with control group,the expression levels of NLRP3,Caspase-1,and GSDMD proteins in W2,W4,and W6 groups were significantly increased,and the expression levels were in the order of W6 group>W4 group>W2 group,with significant differences(P<0.05).(4)Compared to control group,the levels of IL-1β in serum and myocardial tissues of W2,W4,and W6 groups were significantly increased.In serum,the level of IL-1β in W6 group was higher than those in W2 and W4 groups,and in myocardial tissue,the levels in W4 and W6 groups were higher than those in the W2 group,with significant differences(P<0.05).There were no significant differences in IL-1β levels in serum among W2 and W4 groups,nor were there significant differences in myocardial tissue between W4 and W6 groups(P>0.05).Compared with control group,the levels of IL-18 in serum and myocardial tissue of W4 and W6 groups were significantly increased(P<0.05).In serum,the levels of IL-18 in W4 and W6 groups were higher than that in W2 group,and in myocardial tissue,the level in W6 group was higher than those in W2 and W4 groups,with the differences being statistically significant(P<0.05).Conclusions Overwork can cause structural damage to mouse myocardial tissue,increase the expression of pyroptosis proteins NLRP3,Caspase-1,GSDMD,and aggravate myocardial inflammatory responses in overworked mice.Cardiomyocyte pyroptosis may be one of the factors contributing to sudden cardiac death induced by overwork.

Objective To investigate gender differences and age-related changes in body composition(BC)among Miao adults in Rongshui,Guangxi Province,and to provide the basis for assessing nutritional status and health.Methods With informed consent,630 Miao adults(218 males,412 females)were randomly selected for this study.Body composition was assessed using bioelectrical impedance analysis(BIA).Results Weight,fat-free mass,muscle mass,trunk muscle mass,limb muscle mass,waist-to-hip ratio(WHR),body water,presumtion of bone mass and protein were significantly higher in males than in females.And the fat mass,trunk fat mass,limb fat mass,visceral fat content,subcutaneous fat content and percentage of body fat were significantly higher in females than in males.According to the evaluation of body mass index(BMI)and WHR,the proportion of overweight and obesity of Miao adults was higher than the average level of Miao residents,and their obesity was characterized by central obesity.With age,weight,fat mass,muscle mass,fat-free mass,limb muscle mass,limb fat mass,subcutaneous fat content,percentage of body fat,body water,presumtion of bone mass,and protein of Rongshui Miao adults showed a gradual decreasing trend,while visceral fat content and WHR increased progressively.BMI in male Miao adults,along with BMI,fat mass,trunk fat mass,subcutaneous fat content,percentage of body fat,and body water in female Miao adults,showed a trend of increasing followed by decreasing,peaking at the age of 40-49 years.Conclusion The body composition of Miao adults in Rongshui,Guangxi,exhibits significant gender differences and age-related variation change patterns,which may increase the risk of sarcopenia and metabolic diseases with aging.

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.

Aim To explore the effects of D-limonene on the steatosis of primary mouse hepatocytes and its potential mechanism of action.Methods Oleic acid-induced steatosis in primary mouse hepatocytes was used as a model to observe the effects of D-limonene on cell viability,cellular lipid content,and intracellular expression of proteins such as AMP-activated protein kinase(AMPK),acetyl-coenzyme A carboxylase 1(ACC1),and carnitine palmitoyl transferase 1A(CPT1A).Results It was found that a low dose of D-limonene could effectively enhance the viability of primary mouse hepatocytes.When oleic acid at a con-centration of 300 μmol·L-1 successfully induced steatosis in primary mouse hepatocytes,D-limonene re-duced the lipid content of the cells,and D-limonene up-regulated the cellular AMPK expression level,down-regulated the cellular ACC1 and fatty acid synthetase(FAS)expression levels,which in turn promoted the overexpression of CPT1A.Conclusions D-limonene has the effect of reducing lipid deposition in primary mouse hepatocytes,and the mechanisms may be related to the activation of AMPK,the inhibitions of ACC1 and FAS,and the up-regulation of CPT1A protein expres-sion level.