Background/Aims: Acetyl coenzyme A (acetyl-CoA) is one of the most essential metabolites in cell metabolism but its function and concentration in hepatocellular carcinoma (HCC) remain elusive and controversial. Methods: A comprehensive analysis of acetyl-CoA levels and acetyl-CoA synthetase 2 (ACSS2) expression across a range of samples, including patient specimens from both hepatitis B virus (HBV) positive and HBV negative HCC individuals, HBV-transgenic mouse HCC models, and multiple cell lines. Furthermore, to evaluate the functional significance of ACSS2 in HBV-related HCC, we implemented both genetic and pharmacological inhibition strategies targeting ACSS2. Molecular mechanism and mitophagy assessment were revealed by cleavage under target and tagmentation sequencing, RNA sequencing, bioinformatic analyses, transmission electron microscopy and JC-1 staining. Results: Our study revealed a distinct metabolic signature of HBV-related HCC, marked by elevated acetyl-CoA, which was driven by ACSS2. ACSS2 was upregulated by the carbohydrate response element-binding protein in HBV-related HCC. Furthermore, ACSS2 improved tumor cell proliferation, an effect that was dependent on its enzymatic activity. Mechanistically, ACSS2-induced acetyl-CoA accumulation activated voltage-dependent anion channels 1 transcription through increased H3K27ac occupancy, which subsequently promoted mitophagy and HBV-related HCC tumorigenesis. Notably, targeting ACSS2 by depletion or inhibition with a catalytic inhibitor significantly suppressed tumor growth. Conclusions These findings not only illustrate the interplay between metabolic reprogramming, epigenetic modification, and tumorigenesis in the context of HBV infection, but also highlight ACSS2 as a novel metabolic vulnerability in HBV-related HCC. Therefore, targeting ACSS2 could be a novel strategy against HBV-related HCC.

Objective To analyze the relationship between the visceral adiposity index(VAI)and nocturia in the US adult population.Methods A cross-sectional study was performed.Data from subjects aged≥20 years in the National Health and Nutrition Examination Survey(NHANES)database from 2007 to 2020 were collected,including waist circumference,triglyceride,body mass index(BMI),high-density lipoprotein,age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,cancer,and nocturnal urination frequency.Weighted analysis,multivariate logistic regression,generalized additive model(GAM),and curve fitting were employed to evaluate the association between VAI and nocturia,adjusting for age,gender,race,poverty income ratio,education level,marital status,smoking,alcohol consumption,sleep disorders,depression,occupation,hypertension,diabetes,congestive heart failure,and cancer.Subgroup analyses were conducted based on age,gender,race,hypertension and diabetes to further evaluate the relationship between VAI and the risk of nocturia.Results A total of 29,196 American adults were included.All subjects were divided into 4 groups based on VAI quartiles:Q1 group(0.32≤VAI<1.01),Q2 group(1.01≤VAI<1.70),Q3 group(1.70≤VAI<2.95),and Q4 group(2.95≤VAI<13.59),with nocturia prevalence rates of 28.5%,31.4%,33.3%,and 34.9%,respectively.In subgroup analyses,the risk of nocturia significantly increased with higher VAI in the 20-40 age group,females and other Hispanics(OR=1.04,95%CI 1.01-1.08,P=0.006;OR=1.02,95%CI 1.00-1.04,P=0.035;OR=1.05,95%CI 1.01-1.09,P=0.026).GAM analysis results showed a nonlinear relationship between VAI and nocturia.Conclusion VAI is positively associated with the risk of nocturia,and may be an effective indicator for predicting the risk of nocturia occurrence.

Honey, a natural substance, has long been valued for its dual role in both food and medicine in diverse cultural traditions, particularly in traditional Chinese medicine (TCM). It is rich in sugars, amino acids, enzymes, polyphenols, and flavonoids that contribute to its antimicrobial, antioxidant, and immunomodulatory properties. Additionally, honey is effective in managing some conditions, such as antibiotic-resistant infections, inflammation, and oxidative stress-related diseases. This review explores the extensive health benefits of honey, emphasizing the homology between food and medicine, as proposed by TCM philosophy. Further, this review explores the traditional applications of honey in respiratory health, wound healing, and gastrointestinal support, along with modern scientific validation of these uses. Moreover, the role of honey as a dietary supplement, functional food, and preservative in culinary practices is examined. Overall, this review highlights the synergy between ancient wisdom and contemporary science, advocating for the continued exploration of the role of honey in health, nutrition, and medicine.

Objective:To explore the expression regulation of lipid metabolism gene ABHD5 in testes.Methods:Differential gene analysis was performed by integrating databases of TCGA and GTEx to identify the target gene ABHD5.The expression trends of ABHD5 gene in testicular carcinoma tissue were analyzed.Human testis single-cell atlases were obtained from the Human Protein Atlas and Male Health Atlas databases to determine the expression distribution of ABHD5 across different testicular cell types.Additionally,the GTEx database was utilized to visualize the expression pattern of ABHD5 in the testis,thereby enhancing the understanding of its transcriptional profile.The relationship between ABHD5 expression and age was assessed through integrated database analysis.Western blotting and immunofluorescence were performed to detect differential expressions of ABHD5 in testicular tissues of young and aged mice respectively.Results:The TCGA database indicated that the expression of ABHD5 in human testicular carcinoma tissue was significantly lower than that in normal testicular tissue which showed a negative correlation with patient survival.ABHD5 was highly ex-pressed in germ cells of the testis reveaked from Human Protein Atlas and Male Health Atlas databases.The stability of ABHD5 protein was crucial for testicular tissue,and its expression decreased with age.Furthermore,Western blot and immunofluorescence staining demonstrated that ABHD5 expression in the testicular tissue of aged mice was significantly lower than that in young mice.Conclu-sion:ABHD5 plays an important role in testicular tissue,and may be inseparable from testicular tumors and reproductive aging.How-ever,its mechanism of action remains to be further studied.

Objective:To quickly identify the causes of morphological abnormalities of microcytic hypochromic erythrocytes that are detected during health checkups for recruitment of flying cadets, and to explore its role in medical selection.Methods:Students with hemoglobin (Hb)≥110 g/L and morphological abnormalities of microcytic hypochromic erythrocytes detected during the 2023 medical selection of flying cadets by Guangzhou Selection Center were selected. Their medical history was collected, and iron metabolism, Hb electrophoresis and hemoglobin H (HbH) inclusion bodies were examined to screen for thalassemia and iron deficiency. The diagnosis of thalassemia was confirmed by thalassemia gene testing. Those with iron deficiency received iron supplementation therapy and the recovery of Hb was observed.Results:Ninety-one students were diagnosed with Hb≥110 g/L and morphological abnormalities of microcytic hypochromic erythrocytes, accounting for 4.35% of the total. Among these cases, 85 with abnormal Hb electrophoresis and/or positive HbH inclusion body detection were confirmed as thalassemia minor via thalassemia genetic testing, and 3 cases with normal iron metabolism, Hb electrophoresis, and negative HbH inclusion body detection. A total of 88 cases of thalassemia minor were diagnosed, accounting for 96.70% of the total. Among them, 2 cases were complicated with iron deficiency while 3 were diagnosed with iron deficiency erythropoiesis. Out of the 91 students with Hb≥110 g/L and morphological abnormalities of microcytic hypochromic erythrocytes, 9 were recruited, including 7 cases with thalassemia minor (Hb≥130 g/L), 1 case with thalassemia minor combined with iron deficiency erythropoiesis (Hb≥130 g/L after iron supplementation), and 1 case with iron deficiency erythropoiesis (Hb≥130 g/L after iron supplementation). Among the 9 recruits, 8 were followed up for over one year and the results of their military physical fitness tests all reached or exceeded the standards, but the remaining one dropped out and lost contact.Conclusions:Among physical examinees during medical selection of flying cadets in South China, thalassemia is the leading cause of morphological abnormalities of microcytic hypochromic erythrocytes. Results of iron metabolism, Hb electrophoresis, and HbH inclusion body detection can help identify thalassemia and iron deficiency quickly. Cases of morphological abnormalities of microcytic hypochromic erythrocytes caused by iron deficiency can be considered eligible for selection after Hb levels return to normal following iron supplementation therapy. Students who are diagnosed with thalassemia with Hb<130 g/L can be determined as ineligible. Such rapid identification can facilitate the medical selection of the above 2 types of students.

Objective To compare the serological and pathological characteristics of 3 nonalcoholic steatohepatitis(NASH)models:high-fat diet(HFD)with carbon tetrachloride(CCl4)injection,methionine and choline deficient diet(MCD),and Aymlin liver NASH(AMLN)diet-induced NASH models.Methods 3 NASH models were established by feeding mice an HFD with CCl4 injection for 10 weeks,MCD for 8 weeks and NASH for 26 weeks.After feeding,serum alanine aminotransferase(ALT),aspartate aminotransferase(AST),glucose(GLU),liver triglyceride(TG),total cholesterol(TC),and malondialdehyde(MDA)levels and superoxide dismutase(SOD)activity were measured.Insulin levels were measured by enzyme-linked immunosorbent assay(ELISA)and the homeostasis model assessment of insulin resistant(HOMA-IR)index was calculated.Hematoxylin-eosin(HE),Sirius red,and oil red staining were used to indicate pathological changes to the liver.The NAS score was used to grade the pathology.Results Compared to each normal control(NC)group mice,all mice in the 3 model groups had an obvious increase in serum transaminase and liver TG,TC,MDA levels and SOD activity.The levels of serum FINS,GLU and the HOMA-IR index were significantly increased in the AMLN and CCl4+HFD model groups but decreased in the MCD model group.According to the HE,oil red staining and NAS score,mice in all 3 groups had NASH phenotypic changes.Liver collagen deposition was most obvious in mice in theCCl4+HFD model group.Liver lipid droplets were most abundant in the AMLN model group.Conclusions All the above 3 animal models can stably simulate the serological and pathological changes of NASH in human.The AMLN model can simulate the progress and mechanism of the disease,as well as systemic metabolic disorders such as insulin resistance and oxidative stress.However,it is time-consuming and the fibrosis progression rate is slow.The MCD diet can simulate the serological and pathological features of NASH in 8 weeks,but no obesity or insulin resistance occurred.The CCl4 combined with HFD model can induce NASH model in 10 weeks,which can simulate its serological and pathological changes,and the liver has obvious fibrous deposition and oxidative stress damage.

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.