Exploring cuproptosis-related molecular clusters and immunological characterization in ischemic stroke through machine learning.

Objective: Ischemic stroke (IS) is a significant health concern with high disability and fatality rates despite available treatments. Immune cells and cuproptosis are associated with the onset and progression of IS. Investigating the interaction between cuproptosis-related genes (CURGs) and immune cells in IS can provide a theoretical basis for IS treatment. Methods: We obtained IS datasets from the Gene Expression Omnibus (GEO) and employed machine learning to identify CURGs. The diagnostic efficiency of the CURGs was evaluated using receiver operating characteristic (ROC) curves. KEGG and gene set enrichment analysis (GSEA) were also conducted to identify biologically relevant pathways associated with CURGs in IS patients. Single-cell analysis was used to confirm the expression of 19 CURGs, and pathway activity calculations were performed using the AUCell package. Additionally, a risk prediction model for IS patients was developed, and core modules and hub genes related to IS were identified using weighted gene coexpression network analysis (WGCNA). We classified IS patients using a method of consensus clustering. Results: We established a precise diagnostic model for IS. Enrichment analysis revealed major pathways, including oxidative phosphorylation, the NF-kappa B signaling pathway, the apoptosis pathway, and the Wnt signaling pathway. At the single-cell level, compared to those in non-IS samples, 19 CURGs were primarily overexpressed in the immune cells of IS samples and exhibited high activity in natural killer cell-mediated cytotoxicity, steroid hormone biosynthesis, and oxidative phosphorylation. Two clusters were obtained through consensus clustering. Notably, immune cell types including B cells, plasma cells, and resting NK cells, varied between the two clusters. Furthermore, the red module and hub genes associated with IS were uncovered. The expression patterns of CURGs varied over time. Conclusion: This study developed a precise diagnostic model for IS by identifying CURGs and evaluating their interaction with immune cells. Enrichment analyses revealed key pathways involved in IS, and single-cell analysis confirmed CURG overexpression in immune cells. A risk prediction model and core modules associated with IS were also identified.

Decoding single-cell molecular mechanisms in astrocyte-to-iN reprogramming via Ngn2- and Pax6-mediated direct lineage switching.

BACKGROUND: The limited regenerative capacity of damaged neurons in adult mammals severely restricts neural repair. Although stem cell transplantation is promising, its clinical application remains challenging. Direct reprogramming, which utilizes cell plasticity to regenerate neurons, is an emerging alternative approach. METHODS: We utilized primary postnatal cortical astrocytes for reprogramming induced neurons (iNs) through the viral-mediated overexpression of the transcription factors Ngn2 and Pax6 (NP). Fluorescence-activated cell sorting (FACS) was used to enrich successfully transfected cells, followed by single-cell RNA sequencing (scRNA-seq) using the 10 × Genomics platform for comprehensive transcriptomic analysis. RESULTS: The scRNA-seq revealed that NP overexpression led to the differentiation of astrocytes into iNs, with percentages of 36% and 39.3% on days 4 and 7 posttransduction, respectively. CytoTRACE predicted the developmental sequence, identifying astrocytes as the reprogramming starting point. Trajectory analysis depicted the dynamic changes in gene expression during the astrocyte-to-iN transition. CONCLUSIONS: This study elucidates the molecular dynamics underlying astrocyte reprogramming into iNs, revealing key genes and pathways involved in this process. Our research contributes novel insights into the molecular mechanisms of NP-mediated reprogramming, suggesting avenues for optimizing the efficiency of the reprogramming process.

Unveiling the role of HIST2H2AC in stroke through single-cell and transcriptome analysis.

Stroke is a leading cause of death and disability, and genetic risk factors play a significant role in its development. Unfortunately, effective therapies for stroke are currently limited. Early detection and diagnosis are critical for improving outcomes and developing new treatment strategies. In this study, we aimed to identify potential biomarkers and effective prevention and treatment strategies for stroke by conducting transcriptome and single-cell analyses. Our analysis included screening for biomarkers, functional enrichment analysis, immune infiltration, cell-cell communication, and single-cell metabolism. Through differential expression analysis, enrichment analysis, and protein-protein interaction (PPI) network construction, we identified HIST2H2AC as a potential biomarker for stroke. Our study also highlighted the diagnostic role of HIST2H2AC in stroke, its relationship with immune cells in the stroke environment, and our improved understanding of metabolic pathways after stroke. Overall, our research provided important insights into the pathogenesis of stroke, including potential biomarkers and treatment strategies that can be explored further to improve outcomes for stroke patients.

A promise for neuronal repair: reprogramming astrocytes into neurons in vivo.

Massive loss of neurons following brain injury or disease is the primary cause of central nervous system dysfunction. Recently, much research has been conducted on how to compensate for neuronal loss in damaged parts of the nervous system and thus restore functional connectivity among neurons. Direct somatic cell differentiation into neurons using pro-neural transcription factors, small molecules, or microRNAs, individually or in association, is the most promising form of neural cell replacement therapy available. This method provides a potential remedy for cell loss in a variety of neurodegenerative illnesses, and the development of reprogramming technology has made this method feasible. This article provides a comprehensive review of reprogramming, including the selection and methods of reprogramming starting cell populations as well as the signaling methods involved in this process. Additionally, we thoroughly examine how reprogramming astrocytes into neurons can be applied to treat stroke and other neurodegenerative diseases. Finally, we discuss the challenges of neuronal reprogramming and offer insights about the field.

Identification of disulfidptosis-related genes and analysis of immune infiltration characteristics in ischemic strokes.

Immune infiltration plays a pivotal role in the pathogenesis of ischemic stroke. A novel form of cell death known as disulfidptosis has emerged in recent studies. However, there is currently a lack of research investigating the regulatory mechanism of disulfidptosis-related genes in immune infiltration during ischemic stroke. Using machine learning methods, we identified candidate key disulfidptosis-related genes (DRGs). Subsequently, we performed an analysis of immune cell infiltration to investigate the dysregulation of immune cells in the context of ischemic stroke. We assessed their diagnostic value by employing receiver operating characteristic (ROC) curves. To gain further insights, we conducted functional enrichment analyses to elucidate the signaling pathways associated with these seven DRGs. We identified two distinct subclusters based on the expression patterns of these seven DRGs. The unique roles of these subclusters were further evaluated through KEGG analysis and immune infiltration studies. Furthermore, we validated the expression profiles of these seven DRGs using both single-cell datasets and external datasets. Lastly, molecular docking was performed to explore potential drugs for the treatment of ischemic stroke. We identified seven DRGs. The seven DRGs are related to immune cells. Additionally, these seven DRGs also demonstrate potential diagnostic value in ischemic stroke. Functional enrichment analysis highlighted pathways such as platelet aggregation and platelet activation. Two subclusters related to disulfidptosis were defined, and functional enrichment analysis of their differentially expressed genes (DEGs) primarily involved pathways like cytokine-cytokine receptor interaction. Single-cell analysis indicated that these seven DRGs were primarily distributed among immune cell types. Molecular docking results suggested that genistein might be a potential therapeutic drug. This study has opened up new avenues for exploring the causes of ischemic stroke and developing potential therapeutic targets.

Transcriptomic analysis reveals the potential biological mechanism of AIS and lung adenocarcinoma.

Introduction: Acute ischemic stroke (AIS) and lung adenocarcinoma (LUAD) are associated with some of the highest morbidity and mortality rates worldwide. Despite reports on their strong correlation, the causal relationship is not fully understood. The study aimed to identify and annotate the biological functions of hub genes with clinical diagnostic efficacy in AIS and LUAD. Methods: Transcriptome and single-cell datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified the differentially expressed genes (DEGs) upregulated in AIS and LUAD and found 372 genes intersecting both datasets. Hub genes were identified using protein-protein interaction (PPI) networks, and the diagnostic and prognostic utility of these hub genes was then investigated using receiver operating characteristic (ROC) curves, survival analysis, and univariable Cox proportional hazard regression. Single-cell analysis was used to detect whether the hub genes were expressed in tumor epithelial cells. The immune microenvironment of AIS and LUAD was assessed using the CIBERSORT algorithm. The protein expression of these hub genes was tracked using the Human Protein Atlas (HPA). We calculated the number of positive cells using the digital pathology software QuPath. Finally, we performed molecular docking after using the Enrichr database to predict possible medicines. Results: We identified the molecular mechanisms underlying hub genes in AIS and LUAD and found that CCNA2, CCNB1, CDKN2A, and CDK1 were highly expressed in AIS and LUAD tissue samples compared to controls. The hub genes were mainly involved in the following pathways: the cell cycle, cellular senescence, and the HIF-1 signaling pathway. Using immunohistochemical slices from the HPA database, we confirmed that these hub genes have a high diagnostic capability for AIS and LUAD. Further, their high expression is associated with poor prognosis. Finally, curcumin was tested as a potential medication using molecular docking modeling. Discussion: Our findings suggest that the hub genes we found in this study contribute to the development and progression of AIS and LUAD by altering the cellular senescence pathway. Thus, they may be promising markers for diagnosis and prognosis.

Somatic Cell Reprogramming for Nervous System Diseases: Techniques, Mechanisms, Potential Applications, and Challenges.

Nervous system diseases present significant challenges to the neuroscience community due to ethical and practical constraints that limit access to appropriate research materials. Somatic cell reprogramming has been proposed as a novel way to obtain neurons. Various emerging techniques have been used to reprogram mature and differentiated cells into neurons. This review provides an overview of somatic cell reprogramming for neurological research and therapy, focusing on neural reprogramming and generating different neural cell types. We examine the mechanisms involved in reprogramming and the challenges that arise. We herein summarize cell reprogramming strategies to generate neurons, including transcription factors, small molecules, and microRNAs, with a focus on different types of cells.. While reprogramming somatic cells into neurons holds the potential for understanding neurological diseases and developing therapeutic applications, its limitations and risks must be carefully considered. Here, we highlight the potential benefits of somatic cell reprogramming for neurological disease research and therapy. This review contributes to the field by providing a comprehensive overview of the various techniques used to generate neurons by cellular reprogramming and discussing their potential applications.

LF2MV: Learning An Editable Meta-View Towards Light Field Representation.

Light fields are 4D scene representations that are typically structured as arrays of views or several directional samples per pixel in a single view. However, this highly correlated structure is not very efficient to transmit and manipulate, especially for editing. To tackle this issue, we propose a novel representation learning framework that can encode the light field into a single meta-view that is both compact and editable. Specifically, the meta-view composes of three visual channels and a complementary meta channel that is embedded with geometric and residual appearance information. The visual channels can be edited using existing 2D image editing tools, before reconstructing the whole edited light field. To facilitate edit propagation against occlusion, we design a special editing-aware decoding network that consistently propagates the visual edits to the whole light field upon reconstruction. Extensive experiments show that our proposed method achieves competitive representation accuracy and meanwhile enables consistent edit propagation.

Perceptual-Aware Sketch Simplification Based on Integrated VGG Layers.

Deep learning has been recently demonstrated as an effective tool for raster-based sketch simplification. Nevertheless, it remains challenging to simplify extremely rough sketches. We found that a simplification network trained with a simple loss, such as pixel loss or discriminator loss, may fail to retain the semantically meaningful details when simplifying a very sketchy and complicated drawing. In this paper, we show that, with a well-designed multi-layer perceptual loss, we are able to obtain aesthetic and neat simplification results preserving semantically important global structures as well as fine details without blurriness and excessive emphasis on local structures. To do so, we design a multi-layer discriminator by fusing all VGG feature layers to differentiate sketches and clean lines. The weights used in layer fusing are automatically learned via an intelligent adjustment mechanism. Furthermore, to evaluate our method, we compare our method to state-of-the-art methods through multiple experiments, including visual comparison and intensive user study.

ASCII Art Synthesis from Natural Photographs.

While ASCII art is a worldwide popular art form, automatic generating structure-based ASCII art from natural photographs remains challenging. The major challenge lies on extracting the perception-sensitive structure from the natural photographs so that a more concise ASCII art reproduction can be produced based on the structure. However, due to excessive amount of texture in natural photos, extracting perception-sensitive structure is not easy, especially when the structure may be weak and within the texture region. Besides, to fit different target text resolutions, the amount of the extracted structure should also be controllable. To tackle these challenges, we introduce a visual perception mechanism of non-classical receptive field modulation (non-CRF modulation) from physiological findings to this ASCII art application, and propose a new model of non-CRF modulation which can better separate the weak structure from the crowded texture, and also better control the scale of texture suppression. Thanks to our non-CRF model, more sensible ASCII art reproduction can be obtained. In addition, to produce more visually appealing ASCII arts, we propose a novel optimization scheme to obtain the optimal placement of proportional-font characters. We apply our method on a rich variety of images, and visually appealing ASCII art can be obtained in all cases.