An analog circuit fault diagnosis method using improved sparrow search algorithm and support vector machine.

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy.

Gambogic acid exhibits promising anticancer activity by inhibiting the pentose phosphate pathway in lung cancer mouse model.

BACKGROUND: The pentose phosphate pathway (PPP) plays a crucial role in the material and energy metabolism in cancer cells. Targeting 6-phosphogluconate dehydrogenase (6PGD), the rate-limiting enzyme in the PPP metabolic process, to inhibit cellular metabolism is an effective anticancer strategy. In our previous study, we have preliminarily demonstrated that gambogic acid (GA) induced cancer cell death by inhibiting 6PGD and suppressing PPP at the cellular level. However, it is unclear whether GA could suppress cancer cell growth by inhibiting PPP pathway in mouse model. PURPOSE: This study aimed to confirm that GA as a covalent inhibitor of 6PGD protein and to validate that GA suppresses cancer cell growth by inhibiting the PPP pathway in a mouse model. METHODS: Cell viability was detected by CCK-8 assays as well as flow cytometry. The protein targets of GA were identified using a chemical probe and activity-based protein profiling (ABPP) technology. The target validation was performed by in-gel fluorescence assay, the Cellular Thermal Shift Assay (CETSA). A lung cancer mouse model was constructed to test the anticancer activity of GA. RNA sequencing was performed to analyze the global effect of GA on gene expression. RESULTS: The chemical probe of GA exhibited high biological activity in vitro. 6PGD was identified as one of the binding proteins of GA by ABPP. Our findings revealed a direct interaction between GA and 6PGD. We also found that the anti-cancer activity of GA depended on reactive oxygen species (ROS), as evidenced by experiments on cells with 6PGD knocked down. More importantly, GA could effectively reduce the production of the two major metabolites of the PPP in lung tissue and inhibit cancer cell growth in the mouse model. Finally, RNA sequencing data suggested that GA treatment significantly regulated apoptosis and hypoxia-related physiological processes. CONCLUSION: These results demonstrated that GA was a covalent inhibitor of 6PGD protein. GA effectively suppressed cancer cell growth by inhibiting the PPP pathway without causing significant side effects in the mouse model. Our study provides in vivo evidence that elucidates the anticancer mechanism of GA, which involves the inhibition of 6PGD and modulation of cellular metabolic processes.

Coronary Artery Fistula and Severe Coronary Artery Stenosis: A Case Report and an Insight for Potential Pathogenesis of Coronary Artery Atherosclerosis.

Coronary artery fistulae (CAF) are a rare anomaly characterized by abnormal connections between a coronary artery and a cardiac chamber or a great vessel, with most patients remaining asymptomatic. Despite being predisposed to severe complications like heart failure, patients with CAF infrequently experience severe stenosis in the coronary artery. This study delineates a case involving a 46-year-old male presenting with a fistula bridging the right coronary artery (RCA) and right atrium (RA), manifesting a pronounced 99% stenosis at the right extremity of the coronary artery proximal to the fistula. Concurrently, the individual exhibits six conventional risk factors: age over 40, male gender, hypertension, diabetes, smoking, and hypertriglyceridemia. Following pharmaceutical intervention, the patient was discharged and subjected to extended follow-up. This case highlights the dual processes of "accelerating damage" and "retarding renewal" in the progression of atherosclerosis. Factors such as shear stress, smoking, and hypertension are posited to expedite endothelial cell damage, while aging and diabetes may impede the renewal and repair of these cells. Together with the concept of secondary atherosclerotic plaque healing, this case prompts the introduction of a "Double Endothelial Healings" hypothesis, proposing a potential pathogenetic mechanism for coronary artery atherosclerosis.

High-altitude hypoxia exposure inhibits erythrophagocytosis by inducing macrophage ferroptosis in the spleen.

High-altitude polycythemia (HAPC) affects individuals living at high altitudes, characterized by increased red blood cells (RBCs) production in response to hypoxic conditions. The exact mechanisms behind HAPC are not fully understood. We utilized a mouse model exposed to hypobaric hypoxia (HH), replicating the environmental conditions experienced at 6000 m above sea level, coupled with in vitro analysis of primary splenic macrophages under 1% O2 to investigate these mechanisms. Our findings indicate that HH significantly boosts erythropoiesis, leading to erythrocytosis and splenic changes, including initial contraction to splenomegaly over 14 days. A notable decrease in red pulp macrophages (RPMs) in the spleen, essential for RBCs processing, was observed, correlating with increased iron release and signs of ferroptosis. Prolonged exposure to hypoxia further exacerbated these effects, mirrored in human peripheral blood mononuclear cells. Single-cell sequencing showed a marked reduction in macrophage populations, affecting the spleen's ability to clear RBCs and contributing to splenomegaly. Our findings suggest splenic ferroptosis contributes to decreased RPMs, affecting erythrophagocytosis and potentially fostering continuous RBCs production in HAPC. These insights could guide the development of targeted therapies for HAPC, emphasizing the importance of splenic macrophages in disease pathology.

Research hotspots of polycystic ovary syndrome and hyperandrogenism from 2008 to 2022: bibliometric analysis.

BACKGROUND: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrine disorder in female adults, and hyperandrogenism (HA) is the typical endocrine feature of PCOS. This study aims to investigate the trends and hotspots in the study of PCOS and HA. METHODS: Literature on Web of Science Core Collection (WoSCC) from 2008 to 2022 was retrieved, and bibliometric analysis was conducted using VOSviewer and CiteSpace software. RESULTS: A total of 2,404 papers were published in 575 journals by 10,121 authors from 2,434 institutions in 86 countries. The number of publications in this field is generally on the rise yearly. The US, China and Italy contributed almost half of the publications. Monash University had the highest number of publications, while the University of Adelaide had the highest average citations and the Karolinska Institute had the strongest cooperation with other institutions. Lergo RS contributed the most to the field of PCOS and HA. The research on PCOS and HA mainly focused on complications, adipose tissue, inflammation, granulosa cells, gene and receptor expression. CONCLUSION: Different countries, institutions, and authors should facilitate cooperation and exchanges. This study will be helpful for better understanding the frontiers and hotspots in the areas of PCOS and HA.

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model.

N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-terminal acetylation modifications is important to gain insight into cellular processes and other possible functional mechanisms. Although some algorithmic models have been proposed, most have been developed based on traditional machine learning algorithms and small training datasets. Their practical applications are limited. Nevertheless, deep learning algorithmic models are better at handling high-throughput and complex data. In this study, DeepCBA, a model based on the hybrid framework of convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism deep learning, was constructed to detect the N-terminal acetylation sites. The DeepCBA was built as follows: First, a benchmark dataset was generated by selecting low-redundant protein sequences from the Uniport database and further reducing the redundancy of the protein sequences using the CD-HIT tool. Subsequently, based on the skip-gram model in the word2vec algorithm, tripeptide word vector features were generated on the benchmark dataset. Finally, the CNN, BiLSTM, and attention mechanism were combined, and the tripeptide word vector features were fed into the stacked model for multiple rounds of training. The model performed excellently on independent dataset test, with accuracy and area under the curve of 80.51% and 87.36%, respectively. Altogether, DeepCBA achieved superior performance compared with the baseline model, and significantly outperformed most existing predictors. Additionally, our model can be used to identify disease loci and drug targets.

A novel long non-coding RNA LINC00524 facilitates invasion and metastasis through interaction with TDP43 in breast cancer.

Breast cancer (BC) remains a significant health concern worldwide, with metastasis being a primary contributor to patient mortality. While advances in understanding the disease's progression continue, the underlying mechanisms, particularly the roles of long non-coding RNAs (lncRNAs), are not fully deciphered. In this study, we examined the influence of the lncRNA LINC00524 on BC invasion and metastasis. Through meticulous analyses of TCGA and GEO data sets, we observed a conspicuous elevation of LINC00524 expression in BC tissues. This increased expression correlated strongly with a poorer prognosis for BC patients. A detailed Gene Ontology analysis suggested that LINC00524 likely exerts its effects through RNA-binding proteins (RBPs) mechanisms. Experimentally, LINC00524 was demonstrated to amplify BC cell migration, invasion and proliferation in vitro. Additionally, in vivo tests showed its potent role in promoting BC cell growth and metastasis. A pivotal discovery was LINC00524's interaction with TDP43, which leads to the stabilization of TDP43 protein expression, an element associated with unfavourable BC outcomes. In essence, our comprehensive study illuminates how LINC00524 accelerates BC invasion and metastasis by binding to TDP43, presenting potential avenues for therapeutic interventions.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues.

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Unraveling the complex pathophysiology of white matter hemorrhage in intracerebral stroke: A single-cell RNA sequencing approach.

AIM: This study aims to elucidate the cellular dynamics and pathophysiology of white matter hemorrhage (WMH) in intracerebral hemorrhage (ICH). METHODS: Using varying doses of collagenase IV, a consistent rat ICH model characterized by pronounced WMH was established. Verification was achieved through behavioral assays, hematoma volume, and histological evaluations. Single-cell suspensions from the hemorrhaged region of the ipsilateral striatum on day three post-ICH were profiled using single-cell RNA sequencing (scRNA-seq). Gene Ontology (GO) and gene set variation analysis (GSVA) further interpreted the differentially expressed genes (DEGs). RESULTS: Following WMH induction, there was a notable increase in the percentage of myeloid cells and oligodendrocyte precursor cells (OPCs), alongside a reduction in the percentage of neurons, microglia, and oligodendrocytes (OLGs). Post-ICH WMH showed homeostatic microglia transitioning into pro-, anti-inflammatory, and proliferative states, influencing lipid metabolic pathways. Myeloid cells amplified chemokine expression, linked with ferroptosis pathways. Macrophages exhibited M1 and M2 phenotypes, and post-WMH, macrophages displayed a predominance of M2 phenotypes, characterized by their anti-inflammatory properties. A surge in OPC proliferation aligned with enhanced ribosomal signaling, suggesting potential reparative responses post-WMH. CONCLUSION: The study offers valuable insights into WMH's complex pathophysiology following ICH, highlighting the significance and utility of scRNA-seq in understanding the cellular dynamics and contributing to future cerebrovascular research.

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We investigated the changes of soil nutrients and plant communities in the artificial sand fixation forests of Caragana korshinskii with different ages. The results showed that soil organic carbon and soil total nitrogen contents increased with the stand ages, and were significantly higher in 40 and 50 year-old than other ages. Soil organic carbon and total nitrogen contents recovered much faster in the surface layer (0-10 cm) than in others. Soil nutrient stoichiometric ratios (C:P, N:P) in the 0-10 cm soil layer differed significantly among different stand ages. With the increases of stand age, C and N contents in C. korshinskii leaves increased significantly, and reached the maximum at 50 year-old. Leaf P content increased first and then decreased, being maximum at 18 year-old. Leaf C:N first increased and then decreased, being maximum at 12 year-old. The contents of photosynthetic pigments and leaf C:P and N:P decreased first and then increased, being minimum at 18 year-old. C. korshinskii was mainly influenced by N availability before 40 year-old, but mainly limited by P after. The species number, density, and vegetation cover of annual and perennial herbaceous plants increased with stand ages, and the community shifted from a simple shrub plant community to a complex shrub-herb community. The biomass of C. korshinskii and herbaceous plants increased significantly with stand age, and had a significant positive correlation with the contents of soil organic carbon, total nitrogen and N:P.

Vacancies tailoring lattice anharmonicity of Zintl-type thermoelectrics.

While phonon anharmonicity affects lattice thermal conductivity intrinsically and is difficult to be modified, controllable lattice defects routinely function only by scattering phonons extrinsically. Here, through a comprehensive study of crystal structure and lattice dynamics of Zintl-type Sr(Cu,Ag,Zn)Sb thermoelectric compounds using neutron scattering techniques and theoretical simulations, we show that the role of vacancies in suppressing lattice thermal conductivity could extend beyond defect scattering. The vacancies in Sr2ZnSb2 significantly enhance lattice anharmonicity, causing a giant softening and broadening of the entire phonon spectrum and, together with defect scattering, leading to a ~ 86% decrease in the maximum lattice thermal conductivity compared to SrCuSb. We show that this huge lattice change arises from charge density reconstruction, which undermines both interlayer and intralayer atomic bonding strength in the hierarchical structure. These microscopic insights demonstrate a promise of artificially tailoring phonon anharmonicity through lattice defect engineering to manipulate lattice thermal conductivity in the design of energy conversion materials.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation.

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.

MOTIVATION: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. RESULTS: We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

OGG1 prevents atherosclerosis-induced vascular endothelial cell injury through mediating DNA damage repair.

OBJECTIVE: This study was designed to investigate the role of 8-oxoguanine DNA glycosylase 1 (OGG1) in preventing atherosclerosis-induced vascular EC injury, thereby providing a theoretical basis for the exploration of drug targets and treatment methods for atherosclerosis. METHODS: Human umbilical vein cell line (EA.hy926) was treated with ox-LDL to construct an in vitro atherosclerotic cell model. pcDNA3.1-OGG1 was transfected into EA.hy926 cells to overexpress OGG1. qRT-PCR, CCK-8 assay, flow cytometry, oil red O staining, ELISA, comet assay and western blot were used to evaluate the OGG1 expression, viability, apoptosis level, lipid droplet content, 8-OHdG level and DNA damage of cells in each group. RESULTS: Compared with the Control group, ox-LDL stimulation of endothelial cells significantly decreased cell viability, promoted apoptosis and DNA damage, and increased intracellular levels of 8-OHdG and γH2AX, while decreasing protein levels of PPARγ, FASN, FABP4, RAD51 and POLB. However, overexpression of OGG1 can significantly inhibit ox-LDL damage to endothelial cells, promote lipid metabolism, decrease lipid droplet content, and improve DNA repair function. CONCLUSION: Over-expression of OGG1 improves DNA repair. Briefly, OGG1 over-expression enhances the DNA damage repair of ECs by regulating the expression levels of γH2AX, RAD51 and POLB, thereby enhancing cell viability and reducing apoptosis.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes.

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

Deep medullary vein damage correlates with small vessel disease in small vessel occlusion acute ischemic stroke.

OBJECTIVES: We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients. METHODS: The DMV was divided into six segments according to the regional anatomy. The total DMV score (0-18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed. RESULTS: We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2-6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3-9)) was significantly higher than other groups (p < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV (R = 0.67, p < 0.01), rather than centrum semivole PVS (CS-PVS) (R = 0.17, p = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87-345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively. CONCLUSION: The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients. CLINICAL RELEVANCE STATEMENT: The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications. KEY POINTS: • The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

CD4+ T cell immunity is dependent on an intrinsic stem-like program.

CD4+ T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+ T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+ T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+ T cells develop into TCF1hi effector precursor (TEP) cells and TCF1-CXCR6+ effectors in transplant recipients. The TCF1-CXCR6+CD4+ effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+ TEP cells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1-CXCR6+ effectors from TCF1hiCD4+ TEP cells. Mechanistically, TCF1 sustains the CD4+ TEP cell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+ TEP cells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+ TEP cells and have implications for T cell-related immunotherapies.

Emerging Neuroprotective Strategies: Unraveling the Potential of HDAC Inhibitors in Traumatic Brain Injury Management.

Traumatic brain injury (TBI) is a significant global health problem, leading to high rates of mortality and disability. It occurs when an external force damages the brain, causing immediate harm and triggering further pathological processes that exacerbate the condition. Despite its widespread impact, the underlying mechanisms of TBI remain poorly understood, and there are no specific pharmacological treatments available. This creates an urgent need for new, effective neuroprotective drugs and strategies tailored to the diverse needs of TBI patients. In the realm of gene expression regulation, chromatin acetylation plays a pivotal role. This process is controlled by two classes of enzymes: histone acetyltransferase (HAT) and histone deacetylase (HDAC). These enzymes modify lysine residues on histone proteins, thereby determining the acetylation status of chromatin. HDACs, in particular, are involved in the epigenetic regulation of gene expression in TBI. Recent research has highlighted the potential of HDAC inhibitors (HDACIs) as promising neuroprotective agents. These compounds have shown encouraging results in animal models of various neurodegenerative diseases. HDACIs offer multiple avenues for TBI management: they mitigate the neuroinflammatory response, alleviate oxidative stress, inhibit neuronal apoptosis, and promote neurogenesis and axonal regeneration. Additionally, they reduce glial activation, which is associated with TBI-induced neuroinflammation. This review aims to provide a comprehensive overview of the roles and mechanisms of HDACs in TBI and to evaluate the therapeutic potential of HDACIs. By summarizing current knowledge and emphasizing the neuroregenerative capabilities of HDACIs, this review seeks to advance TBI management and contribute to the development of targeted treatments.