AIMC Topic: Mice

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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...

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

Communications biology
In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous hum...

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

eLife
The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a m...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor g...

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.

Scientific reports
Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its ...

Inhibition of CDC27 O-GlcNAcylation coordinates the antitumor efficacy in multiple myeloma through the autophagy-lysosome pathway.

Acta pharmacologica Sinica
Multiple myeloma (MM) is a prevalent hematologic malignancy characterized by abnormal proliferation of cloned plasma cells. Given the aggressive nature and drug resistance of MM cells, identification of novel genes could provide valuable insights for...

Machine learning-based characterization of PANoptosis-related biomarkers and immune infiltration in ulcerative colitis: A comprehensive bioinformatics analysis and experimental validation.

International immunopharmacology
Ulcerative colitis (UC) is a heterogeneous autoimmune condition. PANoptosis, a new form of programmed cell death, plays a role in inflammatory diseases. This study aimed to identify differentially expressed PANoptosis-related genes (PRGs) involved in...