ObjectiveNeuroinflammation plays a crucial role in both the onset and progression of ischemic stroke, exerting a significant impact on the recovery of the central nervous system. Excessive neuroinflammation can lead to secondary neuronal damage, further exacerbating brain injury and impairing functional recovery. As a result, effectively modulating and reducing neuroinflammation in the brain has become a key therapeutic strategy for improving outcomes in ischemic stroke patients. Among various approaches, targeting immune regulation to control inflammation has gained increasing attention. This study aims to investigate the role of in vitro induced regulatory T cells (Treg cells) in suppressing neuroinflammation after ischemic stroke, as well as their potential therapeutic effects. By exploring the mechanisms through which Tregs exert their immunomodulatory functions, this research is expected to provide new insights into stroke treatment strategies. MethodsNaive CD4⁺ T cells were isolated from mouse spleens using a negative selection method to ensure high purity, and then they were induced in vitro to differentiate into Treg cells by adding specific cytokines. The anti-inflammatory effects and therapeutic potential of Treg cells transplantation in a mouse model of ischemic stroke was evaluated. In the middle cerebral artery occlusion (MCAO) model, after Treg cells transplantation, their ability to successfully migrate to the infarcted brain region and their impact on neuroinflammation levels were examined. To further investigate the role of Treg cells in stroke recovery, the changes in cytokine expression and their effects on immune cell interactions was analyzed. Additionally, infarct size and behavioral scores were measured to assess the neuroprotective effects of Treg cells. By integrating multiple indicators, the comprehensive evaluation of potential benefits of Treg cells in the treatment of ischemic stroke was performed. ResultsTreg cells significantly regulated the expression levels of both pro-inflammatory and anti-inflammatory cytokines in vitro and in vivo, effectively balancing the immune response and suppressing excessive inflammation. Additionally, Treg cells inhibited the activation and activity of inflammatory cells, thereby reducing neuroinflammation. In the MCAO mouse model, Treg cells were observed to accumulate in the infarcted brain region, where they significantly reduced the infarct size, demonstrating their neuroprotective effects. Furthermore, Treg cell therapy notably improved behavioral scores, suggesting its role in promoting functional recovery, and increased the survival rate of ischemic stroke mice, highlighting its potential as a promising therapeutic strategy for stroke treatment. ConclusionIn vitro induced Treg cells can effectively suppress neuroinflammation caused by ischemic stroke, demonstrating promising clinical application potential. By regulating the balance between pro-inflammatory and anti-inflammatory cytokines, Treg cells can inhibit immune responses in the nervous system, thereby reducing neuronal damage. Additionally, they can modulate the immune microenvironment, suppress the activation of inflammatory cells, and promote tissue repair. The therapeutic effects of Treg cells also include enhancing post-stroke recovery, improving behavioral outcomes, and increasing the survival rate of ischemic stroke mice. With their ability to suppress neuroinflammation, Treg cell therapy provides a novel and effective strategy for the treatment of ischemic stroke, offering broad application prospects in clinical immunotherapy and regenerative medicine.

Objective To evaluate the role of distortion product otoacoustic emissions (DPOAE) testing in evaluating early hearing loss among noise-exposed workers. Methods A total of 174 noise-exposed workers in a metal shipbuilding enterprise were selected as the research subjects by the convenience sampling method. Pure tone audiometry (PTA), DPOAE and the level of noise exposure were conducted on the workers. The rank correlation analysis was used to analyze the correlation between DPOAE amplitude and PTA threshold. The multilevel model was used to analyze the effects of gender, age, noise exposure intensity, cumulative noise exposure (CNE), hearing loss classification and PTA threshold on DPOAE results. Results At the frequencies of 0.50, 1.00, 2.00, 3.00, 4.00, 6.00 and 8.00 kHz, the DPOAE amplitude was negatively correlated with the PTA threshold (rank correlation coefficients were -0.12, -0.48, -0.47, -0.18, -0.23, -0.44, -0.19, respectively, all P<0.01). At the most frequencies, DPOAE amplitude was negatively correlated with age and CNE (all P<0.05). The results of multilevel model analysis showed that there were significant differences in DPOAE amplitudes at certain frequencies across gender, age, noise intensity, CNE, and hearing loss classification (all P<0.05). Significant differences in DPOAE responses were found among different CNE and hearing loss groups (all P<0.01). Conclusion DPOAE testing can objectively reflect the hearing status of noise-exposed workers and could be considered for inclusion in routine hearing monitoring to facilitate early detection of noise-induced hearing loss.

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: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

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: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

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: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

BACKGROUND:Muscle aging is closely related to various epigenetic changes,and exercise has a certain regulatory effect on these epigenetic changes.However,the specific mechanism is not fully understood. OBJECTIVE:To review the epigenetic mechanisms of skeletal muscle and how exercise can improve skeletal muscle aging and promote adaptive changes in muscle through these epigenetic mechanisms,aiming to provide a more comprehensive understanding of skeletal muscle aging and disease mechanisms. METHODS:During the period from June 1st to August 1st,2023,literature searches were conducted for relevant literature published from database inception to August 2023 in databases including Web of Science,PubMed,CNKI,WanFang,and VIP.The search terms used included"skeletal muscle,""muscle,""aging,""older adult,""aging,""exercise,""physical exercise,""epigenetic,"and"epigenetics"in Chinese as well as"skeletal muscle,muscle,aging,older adult,senescence,age,exercise,sports,physical activity,epigenetic,epigenetics"in English.Boolean logic operators were used to connect the search terms for retrieval,and corresponding strategies were developed.According to the predetermined inclusion and exclusion criteria,70 eligible articles were selected. RESULTS AND CONCLUSION:Epigenetics refers to the phenomenon where gene expression and function are regulated without changes in gene sequence,and epigenetic changes in skeletal muscle are an important field.The epigenetic mechanisms of skeletal muscle play an important role in muscle aging,mainly involving DNA methylation,histone modification,regulation of non-coding RNAs,chromatin remodeling,changes in mitochondrial function and expression changes of aging-related genes.Exercise significantly regulates the epigenetics of skeletal muscle,including promoting DNA methylation,muscle histone modification,regulating miRNA expression,and regulating lncRNA expression,regulating muscle factors(such as interleukin-6),regulating mitochondrial function(such as peroxisome proliferators-activated receptors γ co-activator 1α).Future studies are recommended for long-term,cross-diverse population-based exercise interventions;the application of multi-omics techniques such as proteomics and metabolomics;strengthening the understanding of epigenetic changes at the single-cell level;cross-species comparative studies as well as human clinical trials for the translation of animal model findings to humans;strategies for combining exercise and pharmacological interventions to assess their synergistic effects;and epigenetic studies of crosstalk interactions between skeletal muscle and different organs.

BACKGROUND:How to effectively promote bone regeneration and bone reconstruction after bone injury has always been a key issue in clinical bone repair research.The use of biological and degradable materials loaded with bioactive factors to treat bone defects has excellent application prospects in bone repair. OBJECTIVE:To investigate the effect of polylactic acid-glycolic acid copolymer(PLGA)composite scaffold modified by lysine-grafted graphene oxide nanoparticles(LGA-g-GO)on osteogenic differentiation and new bone formation. METHODS:PLGA was dissolved in dichloromethane and PLGA scaffold was prepared by solvent evaporation method.PLGA/GO composite scaffolds were prepared by dispersing graphene oxide uniformly in PLGA solution.LGA-g-GO nanoparticles were prepared by chemical grafting method,and the PLGA/LGA-g-GO composite scaffolds were constructed by blending LGA-g-GO nanoparticles at different mass ratios(1%,2%,and 3%)with PLGA.The micromorphology,hydrophilicity,and protein adsorption capacity of scaffolds of five groups were characterized.MC3T3 cells were inoculated on the surface of scaffolds of five groups to detect cell proliferation and osteogenic differentiation. RESULTS AND CONCLUSION:(1)The surface of PLGA scaffolds was smooth and flat under scanning electron microscope,while the surface of the other four scaffolds was rough.The surface roughness of the composite scaffolds increased with the increase of the addition of LGA-g-GO nanoparticles.The water contact angle of PLGA/LGA-g-GO(3%)composite scaffolds was lower than that of the other four groups(P<0.05).The protein adsorption capacity of PLGA/LGA-g-GO(1%,2%,and 3%)composite scaffolds was stronger than PLGA and PLGA/GO scaffolds(P<0.05).(2)CCK-8 assay showed that PLGA/LGA-g-GO(2%,3%)composite scaffold could promote the proliferation of MC3T3 cells.Alkaline phosphatase staining and alizarin red staining showed that the cell alkaline phosphatase activity in PLGA/LGA-g-GO(2%,3%)group was higher than that in the other three groups(P<0.05).The calcium deposition in the PLGA/GO and PLGA/LGA-g-GO(1%,2%,and 3%)groups was higher than that in the PLGA group(P<0.05).(3)In summary,PLGA/LGA-g-GO composite scaffold can promote the proliferation and osteogenic differentiation of osteoblasts,and is conducive to bone regeneration and bone reconstruction after bone injury.