In recent years,the problem of population aging in China has been continuously intensifying.The mechanism of aging and its prevention and treatment have gradually become research hotspots.The successful establishment of aging animal models is an important part of aging-related research,and behavioral experiments can effectively evaluate these models.This article reviews the methods of constructing aging animal models from different aspects,analyzes the advantages and disadvantages of various behavioral evaluation experiments and their comparisons,with the aim of providing theoretical references for aging-related research.

Objective To investigate the effects and underlying mechanisms of down-regulating m6A demethylase fat mass and obesity-associated protein(FTO)on proplatelet formation in the MEG-01 megakaryocytic cells.Methods ①MEG-01 cells were treated with 1 nmol/L phorbol myristate acetate(PMA)(treatment group)or DMSO(control group)for 72 h.FTO expression was measured by Western blotting and RT-qPCR.② MEG-01 cells were infected with targeted FTO shRNA(knockdown group,sh-FTO)or negative control shRNA(negative control group,sh-NC)viruses.FTO knockdown group and negative control group MEG-1 cells were treated with 1 nmol/L PMA for 72 h,and the protein and mRNA expression levels of FTO were detected by Western blotting and RT-qPCR.Cell cycle,viability and apoptosis were assessed by propidium iodide(PI)DNA staining,CCK-8 assay and Annexin V-FITC/PI double staining and TUNEL staining.The expression of cleaved Caspase-3 protein was determine by Western blotting.Megakaryocyte maturation was assessed by CD41/CD61 staining.Proplatelet formation was observed under bright field and detected by CD61 immunofluorescence assay.The expression of apoptosis-related molecules(Caspase3,BAD,BAK1,BCL2 and MCL1)was detected by RT-qPCR,and the protein change of BCL2 was further verified by Western blotting.The dataset was screened out from the gene expression omnibus(GEO)database,and then analyzed with University of California,Santa Cruz(UCSC)genome browser to compare the methylation sequencing peaks on BCL2 mRNA,and m6A methylated RNA immunoprecipitation(m6A-RIP)was used to assess the m6A methylation levels of BCL2 target gene mRNAs in MEG-01 megakaryocytes.Then,the changes in the m6A methylation enrichment level of BCL2 mRNA were observed between the sh-NC group and the sh-FTO group.mRNA stability and ribosome profiling assays were performed to assess translational efficiency of target genes.Results ①PMA treatment upregulated the expression of FTO at protein(P＜0.05)and mRNA(P＜0.01)levels.② FTO shRNA resulted in reduced FTO expression at both mRNA and protein levels(P＜0.01).Compared to the negative control group,the FTO knockdown group showed more cells arrested at the G1/S phase[(60.80±1.29)%vs(72.13±1.18)%,P＜0.01],significantly reduced cell viability[(1.17±0.03)%vs(0.69±0.05)%,P＜0.01],increased Annexin V-FITC/PI positive cells[(12.87±0.83)%vs(17.45±1.58)%,P＜0.01],more TUNEL positive cells[(1.03±0.27)%vs(17.49±9.91)%,P＜0.01],enhanced protein level of cleaved Caspase-3(P＜0.01),decreased proportion of CD41/CD61 positive cells[(51.63±1.13)%vs(34.08±0.53)%,P＜0.01],and less proplatelet formation in MEG-01 megakaryocytes[(26.49±6.73)%vs(13.31±5.97)%,P＜0.01].③Compared to sh-NC group,the FTO knockdown group had significantly decreased protein and mRNA expression of anti-apoptotic molecule BCL2(P＜0.01).UCSC GEO sequencing data revealed there were m6A methylation modification sites on BCL2 mRNA,which was verified through m6A-RIP experiment in MEG-01 megakaryocytes.Compared with GAPDH mRNA,BCL2 mRNA exhibited a significantly enriched m6A signal(P＜0.01).Compared to sh-NC group,a significant increase in m6A methylation modification was observed on BCL2 mRNA.BCL2 mRNA stability was significantly decreased,and its translation efficiency significantly was decreased(P＜0.01).Conclusion m6 A demethylase FTO rgulates BCL2 mRNA stability and translation efficiency,thereby promoting proplatelet formation in MEG-01 megakaryocytes.

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 To assess the current status of occupational radiation hazards in animal diagnosis and treatment institutions in Foshan City. Methods A total of 214 animal diagnosis and treatment institutions in Foshan City in 2024 were selected as the study subjects using the judgment sampling method. The radiation protection management status was investigated. Results A total of 178 out of the 214 animal diagnosis and treatment institutions were equipped with radiation diagnostic equipment in Foshan City. Among these 178 institutions, 98 (accounting for 55.1%) obtained permits from the ecology and environmental department, 21 (accounting for 11.8%) completed occupational hazard project declarations, 53 (accounting for 29.8%) conducted workplace radiation level monitoring, 132 (accounting for 74.2%) were equipped with radiation protection equipment, 40 (accounting for 22.5%) conducted occupational health examinations for the radiation staff, 39 (accounting for 21.9%) provided radiation protection knowledge training for the radiation staff, and 52 (accounting for 29.2%) performed personal radiation dose monitoring. However, none of the institutions implemented the “Three Simultaneities (design, construct, put into operation and use simultaneously with the main body of the construction project)” system for occupational disease prevention facilities in construction projects. Conclusion sAnimal diagnostic and treatment institutions in Foshan City have low levels of radiation protection management and inadequate occupational health monitoring. The radiation staff has low awareness of radiation protection, Relevant department should strengthen supervision and management, organize radiation protection knowledge training, and standardize occupational health management to effectively safeguard workers' health rights.

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: This study investigates the efficacy and mechanisms of Qingjie Fuzheng Granules (QFG) in inhibiting colitis-associated colorectal cancer (CAC) development via RNA sequencing (RNA-seq) and 16S ribosomal RNA (rRNA) correlation analysis. METHODS: CAC was induced in BALB/c mice using azoxymethane (AOM) and dextran sulfate sodium (DSS), and QFG was administered orally to the treatment group. The effects of QFG on CAC were evaluated using disease index, histology, and serum T-cell ratios. RNA-seq and 16S rRNA analysis assessed the transcriptome and microbiome change. Key pharmacodynamic pathways were identified by integrating these data and confirmed via Western blotting and immunofluorescence. The link between microbiota and CAC-related markers was explored using linear discriminant analysis effect size and Spearman correlation analysis. RESULTS: Long-term treatment with QFG prevented AOM/DSS-induced CAC formation, reduced levels of interleukin (IL)-1β, tumor necrosis factor-alpha (TNF-α), IL-6, and interferon γ (IFN-γ), and increased CD3⁺ and CD4⁺/CD8⁺ T cells ratio, without causing hepatic or renal toxicity. A 16S rRNA analysis revealed that QFG rebalanced the Firmicutes/Bacteroidetes ratio and mitigated AOM/DSS-induced microbiota disturbances. Transcriptomics and Western blotting analysis identified the nucleotide-binding oligomerization domain-containing protein 2 (NOD2)/nuclear factor kappa-B (NF-κB) pathway as key for QFG's treatment against CAC. Furthermore, QFG decreased the abundance of Bacilli, Bacillales, Staphylococcaceae, Staphylococcus, Lactobacillales, Aerococcus, Alloprevotella, and Akkermansia, while increasing Clostridiales, Lachnospiraceae, Lachnospiraceae_NK4A136_group, Ruminococcaceae, and Muribaculaceae, which were highly correlated with CAC-related markers or NOD2/NF-κB pathway. CONCLUSION By mapping the relationships between CAC, immune responses, microbiota, and key pathways, this study clarifies the mechanism of QFG in inhibiting CAC, highlighting its potential for clinical use as preventive therapy.

This study examines the effects of zearalenone(ZEA)on the survival and function of lu-teinized granulosa cells,and studies the role of activating transcription factor 6(ATF6)in regula-ting apoptosis and functional abnormalities of luteinized granulosa cells induced by ZEA.An in vitro model of luteinized granulosa cells was utilized to examine the effects of ZEA treatment on apoptosis,hormone secretion,and the expression of relevant proteins.Furthermore,the expression of ATF6 was manipulated using siRNA to elucidate its regulatory function in the ZEA-induced damage of luteinized granulosa cells in mice.Our findings revealed that ZEA inhibited the activity of luteinized granulosa cells and reduced the secretion of estradiol(E2)and progesterone(P4)in a dose-dependent manner.The expression levels of p-IRE1,ATF6 and StAR in both low(20 pmol/L)and high(40 μmol/L)ZEA groups were significantly increased after 24 h(P＜0.05).GRP78 had no significant change at low concentration treatment(P＞0.05),but significantly increased at high concentration treatment(P＜0.05).Similarly,ATF4 and p-EIF2α had no significant change at low concentration treatment(P＞0.05),but significantly decreased at high concentration treat-ment(P＜0.05).HSD3B2 and CYP19A1 were significantly decreased in both low and high concentration treatments(P＜0.05).After 48 h of treatment,ATF6 and GRP78 were significantly increased in both low and high concentration treatments(P＜0.05).p-IRE1 was significantly de-creased at low concentration treatment(P＜0.05),but remained unchanged at high concentration treatment(P＞0.05).ATF4,p-EIF2α,HSD3B2 and CYP19A1 were significantly decreased in both low and high concentration treatments(P＜0.05).St AR was significantly increased in both low and high concentration treatments(P＜0.05).Interference with the expression of ATF6 could sig-nificantly reduce the apoptosis induced by low concentration group(P＜0.05),and enhanced the hormone secretion in both high and low concentration groups(P＜0.05).In conclusion,ZEA can cause damage to luteinized granulosa cells and activate ATF6 signaling pathway.Interference with ATF6 can alleviate apoptosis and hormone secretion disturbance induced by low concentration ZEA,but has limited effect on damage caused by high concentration ZEA.

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.