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Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model. Specifically, we show that ginger-derived exosome-like nanoparticle (GELN) preferentially taken up by Lachnospiraceae and Lactobacillaceae mediated by digalactosyldiacylglycerol (DGDG) and glycine, respectively. We further demonstrate that GELN aly-miR159a-3p enhances anti-PD-L1 therapy in melanoma by inhibiting the expression of recipient bacterial phospholipase C (PLC) and increases the accumulation of docosahexaenoic acid (DHA). An increased level of circulating DHA inhibits PD-L1 expression in tumor cells by binding the PD-L1 promoter and subsequently prevents c-myc-initiated transcription of PD-L1. Colonization of germ-free male mice with gut bacteria from anti-PD-L1 non-responding patients supplemented with DHA enhances the efficacy of anti-PD-L1 therapy compared to controls. Our findings reveal a previously unknown mechanistic impact of PNP on human tumor immunotherapy by modulating gut bacterial metabolic pathways. The impact of diet on gut microbiota and metabolites in context of cancer immunotherapy remains elusive. Here the authors reveal ginger-derived exosome-like nanoparticle (GELN) aly-miR159a-3p enhances anti-PD-L1 therapy by increasing microbiota-dependent docosahexaenoic acid (DHA) level thus limiting tumor cell PD-L1 expression. [ABSTRACT FROM AUTHOR]

Linezolid-resistant enterococci have increased in recent years due to the worldwide spread of acquired resistance genes (cfr , optrA , and poxtA) in clinical, animal, and environmental settings. This study investigated the carriage of optrA -positive enterococci among patients in the anorectal surgery ward in Hangzhou, China, and characterized the genetic context of optrA. A total of 173 wound secretion samples were obtained to screen optrA -positive enterococci. Of the 173 samples, 15 (8.67%) were positive for optrA , including 12 Enterococcus faecalis , two E. faecium , and one E. hirae. Multilocus sequence type analysis revealed that 12 optrA -positive E. faecalis isolates belonged to eight different sequence types (STs), of which ST16 was the main type. Eight optrA variants were identified, whose optrA flanking regions with a fexA gene downstream were bounded by different mobile genetic elements. Furthermore, the optrA gene in 8 out of 15 optrA -positive enterococci could be successfully transferred through conjugation. The findings revealed a high carriage rate of optrA in enterococci from one anorectal surgery ward in China. The dissemination of optrA -positive enterococci isolates in clinical settings should be continually monitored. [ABSTRACT FROM AUTHOR]

Essential amino acids (EAA) and microRNAs (miRs) control biological activity of a cell. Whether EAA regulates the activity of miR has never been demonstrated. Here, as proof‐of‐concept, a tryptophan (Trp, an EAA) complex containing Argonaute 2 (Ago2) and miRs including miR‐193a (Trp/Ago2/miR‐193a) is identified. Trp binds miR‐193a‐3p and interacts with Ago2. Trp/Ago2/miR‐193a increases miR‐193a‐3p activity via enhancing Argonaute 2 (Ago2) RNase activity. Other miRs including miR‐103 and miR‐107 in the Trp complex enhance miR‐193a activity by targeting the same genes. Mechanistically, the Trp/Ago2/miR‐193a complex interacts with Trp‐binding pockets of the PIWI domain of Ago2 to enhance Ago2 mediated miR activity. This newly formed Ago2/Trp/miR‐193a‐3p complex is more efficient than miR‐193a‐3p alone in inhibiting the expression of targeted genes and inhibiting colon cancer liver metastasis. The findings show that Trp regulates miR activity through communication with the RNA‐induced silencing complexes (RISC), which provides the basis for tryptophan based miR therapy. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD). Materials and methods: A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed. Radiomics features of whole lung were extracted from the non-contrast chest CT images. A radiomics signature was constructed with algorithms. Combined with the radiomics score and independent clinical factors, multivariate logistic regression analysis was used to establish a radiomics nomogram. ROC curve was used to analyze the prediction performance of the model. Results: Age, weight, and GOLD were the independent clinical factors. A total of 1218 features were extracted and reduced to 15 features to build the radiomics signature. In the training cohort, the combined model (area under the curve [AUC], 0.731) showed better discrimination capability (p < 0.001) than the clinical factors model (AUC, 0.605). In the internal validation cohort, the combined model (AUC, 0.727) performed better (p = 0.032) than the clinical factors model (AUC, 0.629). In the external validation cohort, the combined model (AUC, 0.725) performed better (p < 0.001) than the clinical factors model (AUC, 0.690). Decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. Conclusion: The CT-based whole lung radiomics nomogram has the potential to identify the risk of CVD in patients with COPD. Clinical relevance statement: This study helps to identify cardiovascular disease risk in patients with chronic obstructive pulmonary disease on chest CT scans. Key Points: • To investigate the value of CT-based whole lung radiomics features in identifying the risk of cardiovascular disease in chronic obstructive pulmonary disease patients. • The radiomics nomogram showed better performance than the clinical factors model to identify the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. • The radiomics nomogram demonstrated excellent performance in the training, internal validation, and external validation cohort (AUC, 0.731; AUC, 0.727; AUC, 0.725). [ABSTRACT FROM AUTHOR]

With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization. This study employed both large-scale computational screening and machine learning to investigate the adsorption and diffusion properties of thousands of metal–organic frameworks (MOFs) in six gas binary mixtures of CH4 (H2/CH4, N2/CH4, O2/CH4, CO2/CH4, H2S/CH4, He/CH4) for methane purification. Firstly, a univariate analysis was conducted to discuss the relationships between the performance indicators of adsorbents and their characteristic descriptors. Subsequently, four machine learning methods were utilized to predict the diffusivity/selectivity of gas, with the light gradient boosting machine (LGBM) algorithm emerging as the optimal one, yielding R2 values of 0.954 for the diffusivity and 0.931 for the selectivity. Furthermore, the LGBM algorithm was combined with the SHapley Additive exPlanation (SHAP) technique to quantitatively analyze the relative importance of each MOF descriptor, revealing that the pore limiting diameter (PLD) was the most critical structural descriptor affecting molecular diffusivity. Finally, for each system of CH4 mixture, three high-performance MOFs were identified, and the commonalities among high-performance MOFs were analyzed, leading to the proposals of three design principles involving changes only to the metal centers, organic linkers, or topological structures. Thus, this work reveals microscopic insights into the separation mechanisms of CH4 from different binary mixtures in MOFs. [ABSTRACT FROM AUTHOR]

Endophthalmitis is an acute inflammatory intraocular condition that can cause permanent vision loss. The treatment strategy and visual outcome partly depend on the identification of the agents of pathogens. In this study, metagenomic sequencing was conducted to investigate the microbial and antibiotic resistance genes (ARGs) composition in the vitreous (intraocular body fluid) of an endophthalmitis patient, who progressed rapidly and accompanied by severe pain. Metagenomic sequencing data revealed that the vitreous sample was predominated by Streptococcus, with a low-diversity microbiome in the vitreous. This strain harbor’s the ARGs mainly against beta-lactam, macrolide-lincosamide–streptogramin, and multidrug. Additionally, metagenome-assembled genome sequence of Streptococcus sp. v1. nov. was identified. The Tetra Correlation Search (TCS) analysis uncovered that the closest relative of the Streptococcus sp. v1. nov. was Streptococcus mitis SK321. Pan/core genome analysis for Streptococcus sp. v1. nov. and TCS top 25 hits strains revealed that most unique genes of Streptococcus sp. v1. nov. were linked to ATP-binding cassette transport system, which could indicate unique virulence and pathogenic potentials of Streptococcus sp. v1. nov. In addition, a total of 7 virulence factors were identified, and the overwhelming of them were classified into “offensive virulence factors”. The high pathogenicity of Streptococcus sp. v1. nov. could be a reason for the patient’s rapid disease progression. Our study was first isolated an ocular pathogen with highly virulent based on metagenomic sequencing and bioinformatics analysis, which has important reference value for revealing the composition and genome characteristics of pathogens in endophthalmitis patient in the future. [ABSTRACT FROM AUTHOR]

Wireless local area networks (WLANs) bring great convenience for people, however, they also consume a huge amount of energy, for most access points (APs) typically operate at the maximum transmit (TX) power all day. In this connection, this paper aims to minimize the overall TX power of all APs by jointly optimizing the location, state and TX power of each AP on the premise of full effective coverage. This paper considers not only three-dimensional scenarios with various obstacles, but also the fluctuation of user density in different time periods and different coverage intensity requirements. To solve the above problem, this paper proposes an improved elite golden jackal optimization (GJO) algorithm, named IEGJO, by introducing the global search strategy and elite evolution strategy into GJO. The performance of IEGJO was extensively evaluated and compared with eight state-of-the-art heuristic algorithms on 20 popular benchmark functions. The experimental results indicate that the IEGJO algorithm outperforms other algorithms in terms of comprehensive performance and ranks first. Then, this paper develops optimal AP configurations method based on IEGJO and applies it to optimize a WLAN in a campus building. The simulation results show that the total TX power of the system is reduced by 81.33%, while still guaranteeing the full effective coverage requirements. The source code is available on https://github.com/iNet-WZU/IEGJO. [ABSTRACT FROM AUTHOR]

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other state-of-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multi-objective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA. [ABSTRACT FROM AUTHOR]

This study proposes a low-temperature transient liquid phase bonding (TLPB) method using Sn58Bi/porous Cu/Sn58Bi to enable efficient power-device packaging at high temperatures. The bonding mechanism is attributed to the rapid reaction between porous Cu and Sn58Bi solder, leading to the formation of intermetallic compounds with high melting point at low temperatures. The present paper investigates the effects of bonding atmosphere, bonding time, and external pressure on the shear strength of metal joints. Under formic acid (FA) atmosphere, Cu6Sn5 forms at the porous Cu foil/Sn58Bi interface, and some of it transforms into Cu3Sn. External pressure significantly reduces the micropores and thickness of the joint interconnection layer, resulting in a ductile fracture failure mode. The metal joint obtained under a pressure of 10 MPa at 250 °C for 5 min exhibits outstanding bonding mechanical performance with a shear strength of 62.2 MPa. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) contain more than 100 proteins. Whether there are EVs proteins that act as an 'organiser' of protein networks to generate a new or different biological effect from that identified in EV‐producing cells has never been demonstrated. Here, as a proof‐of‐concept, we demonstrate that EV‐G12D‐mutant KRAS serves as a leader that forms a protein complex and promotes lung inflammation and tumour growth via the Fn1/IL‐17A/FGF21 axis. Mechanistically, in contrast to cytosol derived G12D‐mutant KRAS complex from EVs‐producing cells, EV‐G12D‐mutant KRAS interacts with a group of extracellular vesicular factors via fibronectin‐1 (Fn1), which drives the activation of the IL‐17A/FGF21 inflammation pathway in EV recipient cells. We show that: (i), depletion of EV‐Fn1 leads to a reduction of a number of inflammatory cytokines including IL‐17A; (ii) induction of IL‐17A promotes lung inflammation, which in turn leads to IL‐17A mediated induction of FGF21 in the lung; and (iii) EV‐G12D‐mutant KRAS complex mediated lung inflammation is abrogated in IL‐17 receptor KO mice. These findings establish a new concept in EV function with potential implications for novel therapeutic interventions in EV‐mediated disease processes. [ABSTRACT FROM AUTHOR]

To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve (AUC) was used for performance evaluation in this study. The radiomics model got AUCs of 0.755 (95% CI 0.650–0.859), 0.71 (95% CI 0.543–0.875) and 0.712 (95% CI 0.500–0.923) for histological prediction in the training, the internal and external verification cohorts. The radiomics nomogram based on radiomics features and Carbohydrate antigen 125 (CA125) showed good discriminant performance in the training cohort (AUC: 0.777; 95% CI 0.679–0.875), the internal (AUC: 0.726; 95% CI 0.5591–0.8933) and external verification cohort (AUC: 0.720; 95% CI 0.5036–0.9358). The calibration curve of the radiomics nomogram also showed good results. The decision curve analysis (DCA) shows that the radiomics nomogram is clinically practical. The radiomics nomogram established and verified in this study showed good performance for the preoperative histological prediction of gastric cancer, which might contribute to the formulation of a better clinical treatment plan. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID‐19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID‐19 discrimination. Methods: A three dimensional algorithm that combined multi‐instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID‐19 from community acquired pneumonia (CAP) while logistic regression (LR), k‐nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID‐19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID‐19 and 183 CAP cases) and relatively small datasets (17 COVID‐19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID‐19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G‐mean were utilized for performance evaluation. Results: In the external test cohort, the relatively large data‐based 3DMTM‐LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM‐SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM‐SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN‐MMSD, LR‐MMSD, SVM‐MMSD, and 3DCM‐MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID‐19 discrimination. 3DMTM, trained with either CT or multi‐modal data, presented comparably excellent performance in COVID‐19 discrimination. Conclusions: The 3DMTM algorithm presented excellent robustness for COVID‐19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID‐19 discrimination with that trained with multi‐modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID‐19 discrimination, especially in the scenario with limited data for training. [ABSTRACT FROM AUTHOR]

Bark protects the tree against environmental insults. Here, we analyzed whether this defensive strategy could be utilized to broadly enhance protection against colitis. As a proof of concept, we show that exosome‐like nanoparticles (MBELNs) derived from edible mulberry bark confer protection against colitis in a mouse model by promoting heat shock protein family A (Hsp70) member 8 (HSPA8)‐mediated activation of the AhR signaling pathway. Activation of this pathway in intestinal epithelial cells leads to the induction of COP9 Constitutive Photomorphogenic Homolog Subunit 8 (COPS8). Utilizing a gut epithelium‐specific knockout of COPS8, we demonstrate that COPS8 acts downstream of the AhR pathway and is required for the protective effect of MBELNs by inducing an array of anti‐microbial peptides. Our results indicate that MBELNs represent an undescribed mode of inter‐kingdom communication in the mammalian intestine through an AhR‐COPS8‐mediated anti‐inflammatory pathway. These data suggest that inflammatory pathways in a microbiota‐enriched intestinal environment are regulated by COPS8 and that edible plant‐derived ELNs may hold the potential as new agents for the prevention and treatment of gut‐related inflammatory disease. Synopsis: Mulberry bark derived exosome‐like nanoparticles (MBELNs) prevent gut inflammation via plant heat shock protein HSPA8‐mediated activation of AhR/COPS8 pathways. Treatment with MBELNs promotes the restoration of gut microbiome homeostasis, ameliorating intestinal inflammatory pathologies. Mulberry bark derived exosome‐like nanoparticles (MBELNs) prevent mouse colitis via the AhR/COPS8 pathway.Binding of MBELN‐derived heat shock protein HSPA8 to AhR leads to the activation of AhR signaling.Activation of AhR leads to the induction of an array of anti‐microbial peptides (AMPs) via COP9/COPS8.AhR/COPS8‐dependent induction of AMPs inhibits intestinal inflammation and alters fecal gut microbiota composition. [ABSTRACT FROM AUTHOR]

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. [ABSTRACT FROM AUTHOR]

High-fat diet (HFD) decreases insulin sensitivity. How high-fat diet causes insulin resistance is largely unknown. Here, we show that lean mice become insulin resistant after being administered exosomes isolated from the feces of obese mice fed a HFD or from patients with type II diabetes. HFD altered the lipid composition of exosomes from predominantly phosphatidylethanolamine (PE) in exosomes from lean animals (L-Exo) to phosphatidylcholine (PC) in exosomes from obese animals (H-Exo). Mechanistically, we show that intestinal H-Exo is taken up by macrophages and hepatocytes, leading to inhibition of the insulin signaling pathway. Moreover, exosome-derived PC binds to and activates AhR, leading to inhibition of the expression of genes essential for activation of the insulin signaling pathway, including IRS-2, and its downstream genes PI3K and Akt. Together, our results reveal HFD-induced exosomes as potential contributors to the development of insulin resistance. Intestinal exosomes thus have potential as broad therapeutic targets. High-fat diet plays a role in development of insulin resistance. Here, the authors report a mechanism that underlies the development of diet induced insulin resistance through the activation of an aryl hydrocarbon receptor mediated signalling pathway in the liver by faecal exosomes derived from intestinal cells. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA), a typical environmental endocrine-disrupting chemical, induces epigenetic inheritance. Whether histone acetylation plays a role in these effects of BPA is largely unknown. Here, we investigated histone acetylation in male rats after long-term exposure to a 'safe' dose of BPA. Twenty adult male rats received either BPA (50 μg/kg·bw/day) or a vehicle diet for 35 weeks. Decreased protein lysine-acetylation levels at approximately ~17 kDa and ~25 kDa, as well as decreased histone acetylation of H3K9, H3K27 and H4K12, were detected by Western blot analysis of testes from the treated rats compared with controls. Additionally, increased protein expression of deacetylase Sirt1 and reduced binding of Sirt1, together with increased binding of estrogen receptor β (ERβ) to caveolin-1 (Cav-1), a structural protein component of caveolar membranes, were detected in treated rats compared with controls. Moreover, decreased acetylation of Cav-1 was observed in the treated rats for the first time. Our study showed that long-term exposure to a 'safe' dose of BPA reduces histone acetylation in the male reproductive system, which may be related to the phenotypic paternal-to-offspring transmission observed in our previous study. The evidence also suggested that these epigenetic effects may be meditated by Sirt1 via competition with ERβ for binding to Cav-1. [ABSTRACT FROM AUTHOR]