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.

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.

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.

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.

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.

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.

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.

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.

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.

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.