The roles of nuclear orphan receptor NR2F6 in anti-viral innate immunity.

Proper transcription regulation by key transcription factors, such as IRF3, is critical for anti-viral defense. Dynamics of enhancer activity play important roles in many biological processes, and epigenomic analysis is used to determine the involved enhancers and transcription factors. To determine new transcription factors in anti-DNA-virus response, we have performed H3K27ac ChIP-Seq and identified three transcription factors, NR2F6, MEF2D and MAFF, in promoting HSV-1 replication. NR2F6 promotes HSV-1 replication and gene expression in vitro and in vivo, but not dependent on cGAS/STING pathway. NR2F6 binds to the promoter of MAP3K5 and activates AP-1/c-Jun pathway, which is critical for DNA virus replication. On the other hand, NR2F6 is transcriptionally repressed by c-Jun and forms a negative feedback loop. Meanwhile, cGAS/STING innate immunity signaling represses NR2F6 through STAT3. Taken together, we have identified new transcription factors and revealed the underlying mechanisms involved in the network between DNA viruses and host cells.

Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET.

OBJECTIVE: Multimodal imaging techniques have furthered our understanding of how different aspects of Alzheimer's disease (AD) pathology relate to one another. Diffusion tensor imaging (DTI) measures such as mean diffusivity (MD) may be a surrogate measure of the changes in gray matter structure associated with AD. Positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) has been used to quantify synaptic loss, which is the major pathological correlate of cognitive impairment in AD. In this study, we investigated the relationship between gray matter microstructure and synaptic density. METHODS: DTI was used to measure MD and [11C]UCB-J PET to measure synaptic density in 33 amyloid-positive participants with AD and 17 amyloid-negative cognitively normal (CN) participants aged 50-83. Univariate regression analyses were used to assess the association between synaptic density and MD in both the AD and CN groups. RESULTS: Hippocampal MD was inversely associated with hippocampal synaptic density in participants with AD (r = -0.55, p <0.001, df = 31) but not CN (r = 0.13, p = 0.62, df = 15). Exploratory analyses across other regions known to be affected in AD suggested widespread inverse associations between synaptic density and MD in the AD group. CONCLUSION: In the setting of AD, an increase in gray matter MD is inversely associated with synaptic density. These co-occurring changes may suggest a link between synaptic loss and gray matter microstructural changes in AD. Imaging studies of gray matter microstructure and synaptic density may allow important insights into AD-related neuropathology.

Prevention of postoperative nausea and vomiting after orthognathic surgery: a scoping review.

INTRODUCTION: Postoperative nausea and vomiting (PONV) is one of the most common adverse events following orthognathic surgery. It's a distressing feeling for patients and continues to be the cause of postoperative complications such as bleeding, delayed healing, and wound infection. This scoping review aims to identify effective PONV prophylaxis strategies during orthognathic surgery that have emerged in the past 15 years. METHODS: We searched Pubmed, Cochrane Controlled Register of Trials, and Embase from 2008 to May 2023. Studies meeting the following criteria were eligible for inclusion: (1) recruited patients undergo any orthognathic surgery; (2) evaluated any pharmacologic or non-pharmacologic method to prevent PONV. Studies meeting the following criteria were excluded: (1) case series, review papers, or retrospective studies; (2) did not report our prespecified outcomes. RESULTS: Twenty-one studies were included in this review. Pharmacological methods for PONV prevention include ondansetron and dexamethasone (3 studies), peripheral nerve block technique (4 studies), dexmedetomidine (1 study), pregabalin (2 studies), nefopam (2 studies), remifentanil (1 study), propofol (2 studies), and penehyclidine (1 study). Non-pharmacologic methods include capsicum plaster (1 study), throat packs (2 studies) and gastric aspiration (2 studies). CONCLUSIONS: Based on current evidence, we conclude that prophylactic antiemetics like dexamethasone, ondansetron, and penehyclidine are the first defense against PONV. Multimodal analgesia with nerve block techniques and non-opioid analgesics should be considered due to their notable opioid-sparing and PONV preventive effect. For the non-pharmacological methods, throat packs are not recommended for routine use because of their poor effect and serious complications. More prospective RCTs are required to confirm whether gastric aspiration can prevent PONV effectively for patients undergoing orthognathic surgery.

The Role of Interferon Regulatory Factors in Liver Diseases.

The interferon regulatory factors (IRFs) family comprises 11 members that are involved in various biological processes such as antiviral defense, cell proliferation regulation, differentiation, and apoptosis. Recent studies have highlighted the roles of IRF1-9 in a range of liver diseases, including hepatic ischemia-reperfusion injury (IRI), alcohol-induced liver injury, Con A-induced liver injury, nonalcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC). IRF1 is involved in the progression of hepatic IRI through signaling pathways such as PIAS1/NFATc1/HDAC1/IRF1/p38 MAPK and IRF1/JNK. The regulation of downstream IL-12, IL-15, p21, p38, HMGB1, JNK, Beclin1, ß-catenin, caspase 3, caspase 8, IFN-γ, IFN-ß and other genes are involved in the progression of hepatic IRI, and in the development of HCC through the regulation of PD-L1, IL-6, IL-8, CXCL1, CXCL10, and CXCR3. In addition, IRF3-PPP2R1B and IRF4-FSTL1-DIP2A/CD14 pathways are involved in the development of NAFLD. Other members of the IRF family also play moderately important functions in different liver diseases. Therefore, given the significance of IRFs in liver diseases and the lack of a comprehensive compilation of their molecular mechanisms in different liver diseases, this review is dedicated to exploring the molecular mechanisms of IRFs in various liver diseases.

Research progress of SIRTs activator resveratrol and its derivatives in autoimmune diseases.

Autoimmune diseases (AID) have emerged as prominent contributors to disability and mortality worldwide, characterized by intricate pathogenic mechanisms involving genetic, environmental, and autoimmune factors. In response to this challenge, a growing body of research in recent years has delved into genetic modifications, yielding valuable insights into AID prevention and treatment. Sirtuins (SIRTs) constitute a class of NAD-dependent histone deacetylases that orchestrate deacetylation processes, wielding significant regulatory influence over cellular metabolism, oxidative stress, immune response, apoptosis, and aging through epigenetic modifications. Resveratrol, the pioneering activator of the SIRTs family, and its derivatives have captured global scholarly interest. In the context of AID, these compounds hold promise for therapeutic intervention by modulating the SIRTs pathway, impacting immune cell functionality, suppressing the release of inflammatory mediators, and mitigating tissue damage. This review endeavors to explore the potential of resveratrol and its derivatives in AID treatment, elucidating their mechanisms of action and providing a comprehensive analysis of current research advancements and obstacles. Through a thorough examination of existing literature, our objective is to advocate for the utilization of resveratrol and its derivatives in AID treatment while offering crucial insights for the formulation of innovative therapeutic approaches.

Research progress on Sirtuins (SIRTs) family modulators.

Sirtuins (SIRTs) represent a class of nicotinamide adenine dinucleotide (NAD+)-dependent protein deacetylases that exert a crucial role in cellular signal transduction and various biological processes. The mammalian sirtuins family encompasses SIRT1 to SIRT7, exhibiting therapeutic potential in counteracting cellular aging, modulating metabolism, responding to oxidative stress, inhibiting tumors, and improving cellular microenvironment. These enzymes are intricately linked to the occurrence and treatment of diverse pathological conditions, including cancer, autoimmune diseases, and cardiovascular disorders. Given the significance of histone modification in gene expression and chromatin structure, maintaining the equilibrium of the sirtuins family is imperative for disease prevention and health restoration. Mounting evidence suggests that modulators of SIRTs play a crucial role in treating various diseases and maintaining physiological balance. This review delves into the molecular structure and regulatory functions of the sirtuins family, reviews the classification and historical evolution of SIRTs modulators, offers a systematic overview of existing SIRTs modulation strategies, and elucidates the regulatory mechanisms of SIRTs modulators (agonists and inhibitors) and their clinical applications. The article concludes by summarizing the challenges encountered in SIRTs modulator research and offering insights into future research directions.

Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G.

Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

Transcription Factor Pdr3p Promotes Carotenoid Biosynthesis by Activating GAL Promoters in Saccharomyces cerevisiae.

Pleiotropic drug resistance (PDR) family proteins have been extensively studied for their roles in transporting hydrophobic substances, including carotenoids. Overexpression of the PDR family regulator Pdr3p was recently found to boost the biosynthesis of carotenoids, which could not be explained by enhanced product secretion due to the meager extracellular proportions. To provide insights into the possible mechanism, comparative transcriptomics, reverse metabolic engineering, and electrophoretic mobility shift assay (EMSA) were conducted. Transcriptomic data suggested an unexpected correlation between Pdr3p overexpression and the transcriptional levels of GAL promoter-driven genes. This assumption was verified using mCherry and the lycopene synthetic pathway as the reporters. qRT-PCR and EMSA provided further evidence for the activation of GAL promoters by Pdr3p binding to their upstream activation sequences (UASs). This work gives insight into the mechanism of Pdr3p-promoted carotenoid production and highlights the complicated metabolic networking between transcriptional factors and promoters in yeast.

Chirality-driven strong thioredoxin reductase inhibition.

Overexpression of thioredoxin reductase (TXNRD) plays crucial role in tumorigenesis. Therefore, designing TXNRD inhibitors is a promising strategy for targeted anticancer drug development. However, poor selectivity has always been a challenge, resulting in unavoidable toxicity in clinic. Herein we demonstrate a strategy to develop highly selective chiral metal complexes-based TXNRD inhibitors. By manipulating the conformation of two distinct weakly interacting groups, we optimize the compatibility between the drug and the electrophilic group within the active site of TXNRD to enhance their non-covalent interaction, thus effectively avoids the poor selectivity deriving from covalent drug interaction, on the basis of ensuring the strong inhibition. Detailed experimental and computational results demonstrate that the chiral isomeric drugs bind to the active site of TXNRD, and the interaction strength is well modulated by chirality. Especially, the meso-configuration, in which the two large sterically hindered active groups are positioned on opposite sides of the drug, exhibits the highest number of non-covalent interactions and most effective inhibition on TXNRD. Taken together, this work not only provides a novel approach for developing highly selective proteinase inhibitors, but also sheds light on possible underlying mechanisms for future application.

Synaptic density patterns in early Alzheimer's disease assessed by independent component analysis.

Synaptic loss is a primary pathology in Alzheimer's disease and correlates best with cognitive impairment as found in post-mortem studies. Previously, we observed in vivo reductions of synaptic density with [11C]UCB-J PET (radiotracer for synaptic vesicle protein 2A) throughout the neocortex and medial temporal brain regions in early Alzheimer's disease. In this study, we applied independent component analysis to synaptic vesicle protein 2A-PET data to identify brain networks associated with cognitive deficits in Alzheimer's disease in a blinded data-driven manner. [11C]UCB-J binding to synaptic vesicle protein 2A was measured in 38 Alzheimer's disease (24 mild Alzheimer's disease dementia and 14 mild cognitive impairment) and 19 cognitively normal participants. [11C]UCB-J distribution volume ratio values were calculated with a whole cerebellum reference region. Principal components analysis was first used to extract 18 independent components to which independent component analysis was then applied. Subject loading weights per pattern were compared between groups using Kruskal-Wallis tests. Spearman's rank correlations were used to assess relationships between loading weights and measures of cognitive and functional performance: Logical Memory II, Rey Auditory Verbal Learning Test-long delay, Clinical Dementia Rating sum of boxes and Mini-Mental State Examination. We observed significant differences in loading weights among cognitively normal, mild cognitive impairment and mild Alzheimer's disease dementia groups in 5 of the 18 independent components, as determined by Kruskal-Wallis tests. Only Patterns 1 and 2 demonstrated significant differences in group loading weights after correction for multiple comparisons. Excluding the cognitively normal group, we observed significant correlations between the loading weights for Pattern 1 (left temporal cortex and the cingulate gyrus) and Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019), Mini-Mental State Examination (r = 0.48, P = 0.0055) and Logical Memory II score (r = 0.44, P = 0.013). For Pattern 2 (temporal cortices), significant associations were demonstrated between its loading weights and Logical Memory II score (r = 0.34, P = 0.0384). Following false discovery rate correction, only the relationship between the Pattern 1 loading weights with Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019) and Mini-Mental State Examination (r = 0.48, P = 0.0055) remained statistically significant. We demonstrated that independent component analysis could define coherent spatial patterns of synaptic density. Furthermore, commonly used measures of cognitive performance correlated significantly with loading weights for two patterns within only the mild cognitive impairment/mild Alzheimer's disease dementia group. This study leverages data-centric approaches to augment the conventional region-of-interest-based methods, revealing distinct patterns that differentiate between mild cognitive impairment and mild Alzheimer's disease dementia, marking a significant advancement in the field.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.