LncRNA-miRNA-mRNA regulatory axes as potential biomarkers in cervical cancer: a comprehensive overview.

Despite the recent advances in vaccination and treatment strategies, cervical cancer continues to claim numerous lives every year. Owing to the fact that non-coding RNAs (ncRNAs) such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) interact with coding transcripts, and effectuate key roles in the tumorigenesis and metastasis of cervical cancer, there has been extensive research in recent years to explore their potential as biomarkers for early detection, or as therapeutic targets. Through this review, we aim to provide a comprehensive overview of the recent advancements in discoveries about cervical cancer-associated lncRNA-miRNA-mRNA axes, their dysregulation, and their roles in various signaling pathways associated with the growth, survival, invasion, and metastasis of cervical cancer cells.

Navigating the challenges in radiology training expansion: costs and benefits.

Clinical and interventional radiology services play a vital role in the provision of modern healthcare, but there is a widening gap between the capacity of the imaging workforce and increasing demand. In recent years there has been a programme of training expansion in England supported by tariff level funding from NHS England (Workforce Training and Education Directorate), enhancing long-term radiology workforce sustainability and bringing quality benefits for patients, departments, and trusts. Expansion is a multifaceted and challenging process in the current NHS climate, involving coordination of funding, capacity, and sustained educational quality.

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions.

Exploring the role of grafting in abiotic stress management: Contemporary insights and automation trends.

Grafting is a technique that involves attaching a rootstock to the aerial part of another genotype or species (scion), leading to improved crop performance and sustainable growth. The ability to tolerate abiotic stresses depends on cell membrane stability, a reduction in electrolyte leakage, and the species of scion and rootstock chosen. This external mechanism, grafting, serves as a beneficial tool in influencing crop performance by combining nutrient uptake and translocation to shoots, promoting sustainable plant growth, and enhancing the potential yield of both fruit and vegetable crops.

st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.

Objective: Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.