AIMC Topic: Mice

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EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.

International journal of biological macromolecules
Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fra...

Involvement of disulfidptosis in the pathophysiology of autism spectrum disorder.

Life sciences
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with oxidative stress recognized as a key pathogenic mechanisms. Oxidative stress disrupts intracellular dynamic- thiol/disulfide homeostasis (DTDH), potentially leading to disu...

Deep Learning Enhanced Near Infrared-II Imaging and Image-Guided Small Interfering Ribonucleic Acid Therapy of Ischemic Stroke.

ACS nano
Small interfering RNA (siRNA) targeting the NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome has emerged as a promising therapeutic strategy to mitigate infarct volume and brain injury following ischemic stroke. However, the cl...

Predicting inflammatory response of biomimetic nanofibre scaffolds for tissue regeneration using machine learning and graph theory.

Journal of materials chemistry. B
Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling phases, respectively. The inflammatory phase is key for successful tissue reconstruction and triggers ...

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.

Proceedings of the National Academy of Sciences of the United States of America
Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, a logic-bas...

SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.

Neuroinformatics
Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source software tools in the scientific community for automating seizure detection based on electrographic...

A deep learning strategy to identify cell types across species from high-density extracellular recordings.

Cell
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals an...

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks.

Methods (San Diego, Calif.)
Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quant...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

Advanced healthcare materials
The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offers a superior solution to the lack of specificity in traditional antitumor strategies. However, current therapeutic platforms for this strategy are pri...

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...