Generation of a medicine food homology formula and its likely mechanism in treatment of microvascular angina.

Microvascular angina (MVA) is the most common cause of cardiac ischemic chest pain in patients without obstructive coronary artery disease (CAD) and lacks of effective treatment means. Medicine food homology (MFH) involves substances with both nutritional and medicinal qualities that have the potential to improve MVA symptoms as medicines, dietary supplements. However, research on MFH formula (MFHF) for MVA is not available. The study aims to generate a core MFHF for MVA through data mining and offer scientific backing for the utilization of edible medications in the prevention and alleviation of MVA. 11 databases were utilized to construct a database of MFH drugs, and the MFHF was generated through frequency analysis, association rule analysis, and clustering analysis. The composition of the formula is Codonopsis Radix, Astragali Radix, Platycodonis Radix, Persicae Semen, Glycyrrhizae Radix Et Rhizoma, Angelicae Sinensis Radix, and Allii Macrostemonis Bulbus. Through network pharmacology and molecular docking, we identified five major active components of MFHF: Adenosine, Nonanoic Acid, Lauric Acid, Caprylic Acid, and Enanthic Acid, along with nine core targets (NFKB1, ALB, AKT1, ACTB, TNF, IL6, ESR1, CASP3, and PTGS) for the improvement of MVA. These 5 active components have various biological activities, such as reducing oxidative stress, anti-inflammation, analgesia effect, inhibiting platelet aggregation, vasodilatation, vascular endothelial protection, and cardio-protection. GO and KEGG enrichment analyses revealed that MFHF mainly acted on the response to xenobiotic stimulus, integrative component of the plasma membrane, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, pathways in cancer, lipid and atherosclerosis, human cytomegalovirus infection, and the PI3K-Akt signaling pathway, which are the main pathogenesis of MVA.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge. RESULTS: In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution. CONCLUSIONS: To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

Intercalating Organic Hybrid Cadmium Antimony Sulfide Nanoparticles into Graphene Oxide Nanosheets for Electrochemical Lithium Storage.

Inorganic metal sulfides have received extensive investigation as anode materials in lithium-ion batteries (LIBs). However, applications of crystalline organic hybrid metal sulfides as anode materials in LIBs are quite rare. In addition, combining the nanoparticles of crystalline organic hybrid metal sulfides with conductive materials is expected to enhance the electrochemical lithium storage performance. Nevertheless, due to the difficulty of harvesting the nanoparticles of crystalline organic hybrid metal sulfides, this approach has never been tried to date. Herein, nanoparticles of a crystalline organic hybrid cadmium antimony sulfide (1,4-DABH2)Cd2Sb2S6 (DCAS) were prepared by a top-down method, including the procedures of solvothermal synthesis, ball milling, and ultrasonic pulverization. Thereafter, the nanoparticles of DCAS with sizes of ∼500 nm were intercalated into graphene oxide nanosheets through a freeze-drying treatment and a DCAS@GO composite was obtained. Compared with the reported Sb2S3- and CdS-based composites, the DCAS@GO composite exhibited superior electrochemical Li+ ion storage performance, including a high capacity of 1075.6 mAh g-1 at 100 mA g-1 and exceptional rate tolerances (646.8 mAh g-1 at 5000 mA g-1). In addition, DCAS@GO can provide a high capacity of 705.6 mAh g-1 after 500 cycles at 1000 mA g-1. Our research offers a viable approach for preparing the nanoparticles of crystalline organic hybrid metal sulfides and proves that intercalating organic hybrid metal sulfide nanoparticles into GO nanosheets can efficiently boost the electrochemical Li+ ion storage performance.

Stepwise analysis of thyroid diagnostic modalities with genomic imprinting detection.

BACKGROUND: The current preoperative malignancy risk evaluation for thyroid nodules involves stepwise diagnostic modalities including ultrasonography, thyroid function serology and fine-needle aspiration (FNA) cytopathology, respectively. We aimed to substantiate the stepwise contributions of each diagnostic step and additionally investigate the diagnostic significance of quantitative chromogenic imprinted gene in-situ hybridization (QCIGISH)-an adjunctive molecular test based on epigenetic imprinting alterations. METHODS: A total of 114 cytopathologically-diagnosed and histopathologically-confirmed thyroid nodules with complete ultrasonographic and serological examination records were evaluated using QCIGISH in the study. Logistic regression models for thyroid malignancy prediction were developed with the stepwise addition of each diagnostic modality and the contribution of each step evaluated in terms of discrimination performance and goodness-of-fit. RESULTS: From the baseline model using ultrasonography [area under the receiver operating characteristics curve (AUROC): 0.79; 95% confidence interval (CI): 0.71-0.86], significant improvements in thyroid malignancy discrimination were observed with the stepwise addition of thyroid function serology (AUROC: 0.82; 95% CI: 0.74-0.90; P=0.23) and FNA cytopathology (AUROC: 0.88; 95% CI: 0.81-0.94; P=0.02), respectively. The inclusion of QCIGISH as an adjunctive molecular test further advanced the preceding model's diagnostic performance (AUROC: 0.95; 95% CI: 0.91-1.00, P=0.007). CONCLUSIONS: Our study demonstrated the significant stepwise diagnostic contributions of standard clinical assessments in the malignancy risk stratification of thyroid nodules. However, the addition of molecular imprinting detection further enabled a more accurate and definitive preoperative evaluation especially for morphologically indeterminate thyroid nodules and cases with potentially discordant results among standard modalities.

PD-1: A critical player and target for immune normalization.

Immune system imbalances contribute to the pathogenesis of several different diseases, and immunotherapy shows great therapeutic efficacy against tumours and infectious diseases with immune-mediated derivations. In recent years, molecules targeting the programmed cell death protein 1 (PD-1) immune checkpoint have attracted much attention, and related signalling pathways have been studied clearly. At present, several inhibitors and antibodies targeting PD-1 have been utilized as anti-tumour therapies. However, increasing evidence indicates that PD-1 blockade also has different degrees of adverse side effects, and these new explorations into the therapeutic safety of PD-1 inhibitors contribute to the emerging concept that immune normalization, rather than immune enhancement, is the ultimate goal of disease treatment. In this review, we summarize recent advancements in PD-1 research with regard to immune normalization and targeted therapy.

Elucidating the impact of biochar with different carbon/nitrogen ratios on soil biochemical properties and rhizosphere bacterial communities of flue-cured tobacco plants.

Background and aims: In agriculture, biochar (BC) and nitrogen (N) fertilizers are commonly used for improving soil fertility and crop productivity. However, it remains unclear how different levels of BC and N fertilizer affect soil fertility and crop productivity. Methods: This study elucidates the impact of different application rates of BC (0, 600, and 1200 kg/ha) and N fertilizer (105 and 126 kg/ha) on biomass accumulation, soil microbial biomass of carbon (SMC) and nitrogen (SMN), and soil biochemical properties, including soil organic carbon (SOC), total nitrogen (TN), soil nitrate nitrogen (NO3--N), ammonium nitrogen (NH4+-N), urease (UE), acid phosphatase (ACP), catalase (CAT), and sucrase (SC) of tobacco plants. In addition, a high throughput amplicon sequencing technique was adopted to investigate the effect of different application rates of BC/N on rhizosphere bacterial communities of tobacco plants. Results: The results confirm that high dosages of BC and N fertilizer (B1200N126) significantly enhance dry matter accumulation by 31.56% and 23.97% compared with control B0N105 and B0N126 under field conditions and 23.94% and 24.52% under pot experiment, respectively. The soil biochemical properties, SMC, and SMN significantly improved under the high application rate of BC and N fertilizer (B1200N126), while it negatively influenced the soil carbon/nitrogen ratio. Analysis of rhizosphere bacteriome through amplicon sequencing of 16S rRNA revealed that the structure, diversity, and composition of rhizosphere bacterial communities dramatically changed under different BC/N ratios. Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, and Acidobacteria were highly abundant bacterial phyla in the rhizosphere of tobacco plants under different treatments. Co-occurrence network analysis displayed fewer negative correlations among rhizosphere bacterial communities under high dosages of biochar and nitrogen (B1200N126) than other treatments, which showed less competition for resources among microbes. In addition, a redundancy analysis further proved a significant positive correlation among SMC, SMN, soil biochemical properties, and high dosage of biochar and nitrogen (B1200N126). Conclusions: Thus, we conclude that a high dosage of BC (1200 kg/ha) under a high application rate of N fertilizer (126 kg/ha) enhances the biomass accumulation of tobacco plants by improving the soil biochemical properties and activities of rhizosphere bacterial communities.

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer.

Objective: The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data. Methods: A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively. Results: The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly. Conclusion: The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

FOXA1/MND1/TKT axis regulates gastric cancer progression and oxaliplatin sensitivity via PI3K/AKT signaling pathway.

BACKGROUND: Drug resistance is a main factor affecting the chemotherapy efficacy of gastric cancer (GC), in which meiosis plays an important role. Therefore, it is urgent to explore the effect of meiosis related genes on chemotherapy resistance. METHODS: The expression of meiotic nuclear divisions 1 (MND1) in GC was detected by using TCGA and clinical specimens. In vitro and in vivo assays were used to investigate the effects of MND1. The molecular mechanism was determined using luciferase reporter assay, CO-IP and mass spectrometry (MS). RESULTS: Through bioinformatics, we found that MND1 was highly expressed in platinum-resistant samples. In vitro experiments showed that interference of MND1 significantly inhibited the progression of GC and increased the sensitivity to oxaliplatin. MND1 was significantly higher in 159 GC tissues in comparison with the matched adjacent normal tissues. In addition, overexpression of MND1 was associated with worse survival, advanced TNM stage, and lower pathological grade in patients with GC. Further investigation revealed that forkhead box protein A1 (FOXA1) directly binds to the promoter of MND1 to inhibit its transcription. CO-IP and MS assays showed that MND1 was coexpressed with transketolase (TKT). In addition,TKT activated the PI3K/AKT signaling axis and enhanced the glucose uptake and lactate production in GC cells. CONCLUSIONS: Our results confirm that FOXA1 inhibits the expression of MND1, which can directly bind to TKT to promote GC progression and reduce oxaliplatin sensitivity through the PI3K/AKT signaling pathway.

Mycobacterium tuberculosis suppresses APLP2 expression to enhance its survival in macrophage.

Mycobacterium tuberculosis (M.tb), the most successful pathogen responsible for approximately 1.6 million deaths in 2021, employs various strategies to evade host antibacterial defenses, including mechanisms to counteract nitric oxide (NO) and certain cytokines. While Amyloid ß (A4) precursor-like protein 2 (Aplp2) has been implicated in various physiological and pathological processes, its role in tuberculosis (TB) pathogenesis remains largely uncharted. This study unveils a significant reduction in Aplp2 levels in TB patients, M.tb-infected macrophages, and mice. Intriguingly, Aplp2 mutation or knockdown results in diminished macrophage-mediated killing of M.tb, accompanied by decreased inducible nitric oxide synthase (iNOS) expression and reduced cytokine production, notably interleukin-1ß (Il-1ß). Notably, Aplp2 mutant mice exhibit heightened susceptibility to mycobacterial infection, evident through aggravated histopathological damage and increased lung bacterial loads, in contrast to Mycobacterium bovis BCG-infected wild-type (WT) mice. Mechanistically, the cleaved product of APLP2, AICD2, generated by γ-secretase, translocates to the nucleus, where it interacts with p65, culminating in enhanced the nuclear factor κB (NF-κB) transcriptional activity. This interaction triggers the upregulation of Il-1ß and iNOS expression. Collectively, our findings illuminate Aplp2's pivotal role in safeguarding against mycobacterial infections by promoting M.tb clearance through NO- or IL-1ß-mediated bactericidal effects. Therefore, we unveil a novel immune evasion strategy employed by M.tb, which could potentially serve as a target for innovative TB interventions.

Social networks and the mental health among Chinese older adults: the mediating role of loneliness and moderating role of Internet use.

Background: Although a large body of research suggests that social networks from family and friends are important factors in protecting the mental health of older adults, we know little about the mediating and moderating mechanisms behind this relationship. Using China as an example, this study aims to investigate a comprehensive model that includes social networks, loneliness, Internet use, and mental health outcomes in the older population. Methods: We analyzed data from 7,648 Chinese older people over 60 using the 2018 CLASS survey. We studied how various social networks affect their mental health. Using SPSS's PROCESS macro, we first employed descriptive statistics to examine the characteristics of the participants and calculate the correlations of core variables. Then, we assessed whether loneliness mediated this relationship and tested the moderated mediation effect of Internet use. Our findings shed light on these complex dynamics. Results: The statistics indicate a positive correlation between social networks and mental health. Furthermore, mediation models revealed that loneliness moderates the relationship between social networks and mental health. In addition, moderated mediation models revealed that Internet use played a distinct function in the family networks model compared to the friend networks model. Internet use moderates explicitly the effects of family networks on loneliness and friend networks on mental health. Conclusion: The findings emphasize the importance of differentiating the types of social networks to understand their impact on older adults well-being, encouraging policymakers, medical professionals, and families to adopt more targeted approaches when devising policy interventions and medical strategies, especially for older individuals with insufficient social support. Additionally, we urge governments to recognize the varying types of social networks among older populations and harness the protective effects of Internet technology on their well-being within a digital society.

Prediction of overall survival after radical gastrectomy using nomograms created by tumor markers.

Prediction of prognosis after radical resection of gastric cancer has not been well established. Therefore, we aimed to establish a prognostic model based on a new score system of patients with gastric cancer. A total of 1235 patients who underwent curative gastrectomy at our hospital from October 2015 to April 2017 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. Construction of the nomogram was based on Cox proportional hazard regression models. The construction of the new score models was analyzed by the receiver operating characteristic curve (ROC curve), calibration curve, and decision curve. Multivariate analysis showed that tumor size, T, N, carcinoembryonic antigen, CA125, and CA19-9 were independent prognostic factors. The new score model had a greater AUC (The area under the ROC curve) than other systems, and the C-index of the nomogram was highly reliable for evaluating the survival of patients with gastric cancer. Based on the tumor markers and other clinical indicators, we developed a precise model to predict the prognosis of patients with gastric cancer after radical surgery. This score system can be helpful to both surgeons and patients.

Synthesis of bifunctional thermal response promoters for improved high-solids enzymatic hydrolysis of corncob residues.

The enzymatic hydrolysis cost of lignocellulose can be reduced by improving enzymatic hydrolysis and recycling cellulase by adding additives. A series of copolymers P(SSS-co-SPE) (PSSPs) were synthesized using sodium p-styrene sulfonate (SSS) and sulfobetaine (SPE) as monomers. PSSP exhibited upper critical solution temperature response. PSSP with high molar ratio of SSS displayed more significant improved hydrolysis performance. When 10.0 g/L PSSP5 was added to the hydrolysis system of corncob residues, and substrate enzymatic digestibility at 72 h (SED@72 h) increased by 1.4 times. PSSP with high molecular weight and moderate molar ratio of SSS, had significant temperature response, enhanced hydrolysis, and recovering cellulase properties. For high-solids hydrolysis of corncob residues, SED@48 h increased by 1.2 times with adding 4.0 g/L of PSSP3. Meanwhile, 50% of cellulase amount was saved at the room temperature. This work provides a new idea for reducing the hydrolysis cost of lignocellulose-based sugar platform technology.

A Type I/Type III PKS Hybrid Generates Cinnamomycin A-D.

3,5-Dihydroxybenzoic acid (3,5-DHBA), biosynthesized by type III PKS and tailoring enzymes, is an unconventional starter unit for bacterial type I PKS. Genome mining of 3,5-DHBA-specific biosynthetic gene clusters could lead to discovering new type I/type III PKS hybrids. Herein, we report the discovery and characterization of atypical compounds, namely cinnamomycin A-D, exhibiting selective antiproliferative activity. The biosynthetic pathway of cinnamomycins was proposed based on genetic manipulation, enzymatic reaction, and precursor feeding.

Bibliometric and visual analysis of RAN methylation in cardiovascular disease.

Background: RNA methylation is associated with cardiovascular disease (CVD) occurrence and development. The purpose of this study is to visually analyze the results and research trends of global RNA methylation in CVD. Methods: Articles and reviews on RNA methylation in CVD published before 6 November 2022 were searched in the Web of Science Core Collection. Visual and statistical analysis was performed using CiteSpace 1.6.R4 advanced and VOSviewer 1.6.18. Results: There were 847 papers from 1,188 institutions and 63 countries/regions. Over approximately 30 years, there was a gradual increase in publications and citations on RNA methylation in CVD. America and China had the highest output (284 and 259 papers, respectively). Nine of the top 20 institutions that published articles were from China, among which Fudan University represented the most. The International Journal of Molecular Sciences was the journal with the most studies. Nature was the most co-cited journal. The most influential writers were Zhang and Wang from China and Mathiyalagan from the United States. After 2015, the primary keywords were cardiac development, heart, promoter methylation, RNA methylation, and N6-methyladenosine. Nuclear RNA, m6A methylation, inhibition, and myocardial infarction were the most common burst keywords from 2020 to the present. Conclusions: A bibliometric analysis reveals research hotspots and trends of RNA methylation in CVD. The regulatory mechanisms of RNA methylation related to CVD and the clinical application of their results, especially m6A methylation, are likely to be the focus of future research.

Three-dimensional chromatin architecture datasets for aging and Alzheimer's disease.

Recently, increasing studies are indicating a close association between dysregulated enhancers and neurodegenerative diseases, such as Alzheimer's disease (AD). However, their contributions were poorly defined for lacking direct links to disease genes. To bridge this gap, we presented the Hi-C datasets of 4 AD patients, 4 dementia-free aged and 3 young subjects, including 30 billion reads. As applications, we utilized them to link the AD risk SNPs and dysregulated epigenetic marks to the target genes. Combining with epigenetic data, we observed more detailed interactions among regulatory regions and found that many known AD risk genes were under long-distance promoter-enhancer interactions. For future AD and aging studies, our datasets provide a reference landscape to better interpret findings of association and epigenetic studies for AD and aging process.

Targeting T-cell metabolism to boost immune checkpoint inhibitor therapy.

Immune checkpoint inhibitors (ICIs) have shown promising therapeutic effects in the treatment of advanced solid cancers, but their overall response rate is still very low for certain tumor subtypes, limiting their clinical scope. Moreover, the high incidence of drug resistance (including primary and acquired) and adverse effects pose significant challenges to the utilization of these therapies in the clinic. ICIs enhance T cell activation and reverse T cell exhaustion, which is a complex and multifactorial process suggesting that the regulatory mechanisms of ICI therapy are highly heterogeneous. Recently, metabolic reprogramming has emerged as a novel means of reversing T-cell exhaustion in the tumor microenvironment; there is increasing evidence that T cell metabolic disruption limits the therapeutic effect of ICIs. This review focuses on the crosstalk between T-cell metabolic reprogramming and ICI therapeutic efficacy, and summarizes recent strategies to improve drug tolerance and enhance anti-tumor effects by targeting T-cell metabolism alongside ICI therapy. The identification of potential targets for altering T-cell metabolism can significantly contribute to the development of methods to predict therapeutic responsiveness in patients receiving ICI therapy, which are currently unknown but would be of great clinical significance.

Social isolation and loneliness among Chinese older adults: Examining aging attitudes as mediators and moderators.

Due to labor migration and social changes, the Chinese elderly are facing significant social isolation, along with changes in aging attitudes. However, whether social isolation affects loneliness among the Chinese elderly and whether this relationship is moderated and mediated by aging attitudes is unclear. This empirical study aimed to respond to the above questions in the Chinese context, Based on the data from the 2014 China Longitudinal Aging Social Survey (N = 6,645), the results showed that social isolation is a positive predictor of loneliness; aging attitudes mediate the relationship between social isolation and loneliness. Social isolation affects the loneliness of the elderly partially by weakening positive aging attitudes and strengthening negative aging attitudes; aging attitudes moderate the effect of social isolation on loneliness. For those older adults with higher positive aging attitudes, social isolation has a much smaller effect on loneliness. While for those older adults with higher negative aging attitudes, social isolation has a more substantial effect on their feelings of loneliness. Our results indicate that less social isolation is an effective way to relieve loneliness, and maintaining higher positive aging attitudes and lower negative aging attitudes, is important for the Chinese elderly to prevent loneliness when facing social isolation.

1,25(OH)2D3 Promotes Macrophage Efferocytosis Partly by Upregulating ASAP2 Transcription via the VDR-Bound Enhancer Region and ASAP2 May Affect Antiviral Immunity.

The active form of vitamin D3, i.e., 1,25(OH)2D3, exerts an anti-inflammatory effect on the immune system, especially macrophage-mediated innate immunity. In a previous study, we identified 1,25(OH)2D3-responsive and vitamin D receptor (VDR)-bound super-enhancer regions in THP-1 cells. Herein, we examined the transcriptional regulation of ArfGAP with SH3 Domain, Ankyrin Repeat and PH Domain 2 (ASAP2) (encoding a GTPase-activating protein) by 1,25(OH)2D3 through the top-ranked VDR-bound super-enhancer region in the first intron of ASAP2 and potential functions of ASAP2 in macrophages. First, we validated the upregulation of ASAP2 by 1,25(OH)2D3 in both THP-1 cells and macrophages. Subsequently, we identified three regulatory regions (i.e., the core, 1,25(OH)2D3-responsive, and inhibitory regions) in the VDR bound-enhancer of ASAP2. ASAP2 promoted RAC1-activity and macrophage efferocytosis in vitro. Next, we assessed the functions of ASAP2 by mass spectrometry and RNA sequencing analyses. ASAP2 upregulated the expressions of antiviral-associated genes and interacted with SAM and HD domain-containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1). In vivo, vitamin D reduced the number of apoptotic cells in experimental autoimmune encephalomyelitis (EAE) and promoted macrophage efferocytosis in peritonitis without changing the mRNA level of ASAP2. Thus, we could better understand the regulatory mechanism underlying ASAP2 transcription and the function of ASAP2, which may serve as a potential treatment target against inflammatory diseases and virus infections.

Dynamic changes in brain structure in patients with post-traumatic stress disorder after motor vehicle accident: A voxel-based morphometry-based follow-up study.

Objectives: To investigate the dynamic changes of emotional and memory-related brain regions in post-traumatic stress disorder (PTSD) patients and trauma-exposed subjects, who experienced motor vehicle accident (MVA). Materials and methods: Functional Magnetic Resonance imaging (fMRI) and general data were collected from trauma victims who had experienced MVA within 2 days, and their social support and coping style were evaluated. The PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (PCL-5) is used for screening and diagnosis. Subsequently, 17 PTSD patients and 23 car accident trauma-exposed individuals completed a second fMRI scan at 2 months. Data were analyzed by using voxel-based morphometry (VBM) to examine the volume changes of relevant brain regions. Correlation analysis was used to assess the correlation between the regions of interest (ROIs) and the total scores on the clinical scales. Subsequently, the relationship between the total PCL-5 scores and the individual dimensions of the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rate Scale (SSRS) was studied. Results: In comparison with the control group, the results showed a reduction in right SFG volume in the PTSD group at 2 months. Similarly, a comparison within the PTSD group revealed a reduction in the left STG volume at 2 months. Compared with the control group, PTSD patients showed a more negative coping style and worse performance in objective and subjective support. In addition, the total PCL-5 scores were negatively associated with positive coping, objective support, and subjective support. Conclusion: The occurrence of PTSD may be related to reduced volume of the right SFG and left STG, and that patients with PTSD receive less social support and tend to cope in a negative manner in the face of stressful events. These results suggest that within 2 months of the MVA, changes in gray matter volume have occurred in some brain regions of those suffering from PTSD. We believe the results of our study will provide useful insights into the neuropsychological mechanisms underlying PTSD.