AIMC Topic: Animals

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Etiology-Agnostic Diagnosis of Early Myocardial Ischemia via AI-Driven Label-Free Spectral Histopathology.

Analytical chemistry
Myocardial ischemia is a core pathological mechanism in diverse fatal diseases and can be triggered by multiple factors. Diagnosing early myocardial ischemia (EMI) caused by nontraditional factors (e.g., drugs or stress) remains challenging due to su...

Whole-genome sequencing reveals individual and cohort level insights into chromosome 9p syndromes.

Genome medicine
BACKGROUND: Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low-resolution strategies (i.e., karyotypes, chromosome microarrays). These studies have provided important initial insights into these syndromes. ...

EasyGeSe - a resource for benchmarking genomic prediction methods.

BMC genomics
BACKGROUND: Genomic prediction is a widely used method to predict phenotypes from genotypic data. Advances in both biological and computer science have enabled the generation of vast amounts of data and the development of new algorithms, specifically...

Artificial intelligence strategies based on random forests for detecting ischemia-reperfusion injury changes in kidney tissue during intravital imaging.

Scientific reports
This study presents a supervised machine learning approach using a Random Forest classifier to detect ischemia-reperfusion injury (IRI) in kidney tissue based on intravital two-photon microscopy data. A rodent model of unilateral renal IRI was used, ...

The impact of PANoptosis-related genes on immune profiles and subtype classification in ischemic stroke.

Scientific reports
Ischemic stroke (IS) is an acute neurological disorder causing brain dysfunction, with high mortality and disability. PANoptosis is a synchronized sort of regulated cell demise that combines the characteristics of pyroptosis, apoptosis, and necroptos...

Real-time self-supervised denoising for high-speed fluorescence neural imaging.

Nature communications
Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strateg...

Hyperparameter optimization ResNet by improved Beluga Whale Optimization.

PloS one
The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved belug...

Integrated experimental, computational and machine learning approaches for the development of Apremilast-Aceclofenac coamorphous systems.

International journal of pharmaceutics
Understanding the molecular mechanisms of drug coamorphization remains a key challenge in solid-state pharmaceutics. This study presents a molecular level strategy for designing drug-drug coamorphous systems (CAMs) of apremilast (APR) and aceclofenac...

Molecular Glues in Immunotherapy: Fine-Tuning Immune Responses for Precision Medicine.

International immunopharmacology
Molecular glues are a new class of tiny compounds that can rewire protein-protein interactions, providing a very selective mechanism to modify immunological signalling pathways. These substances allow immune regulators to be selectively degraded or s...

Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables.

Scientific reports
Effective prediction of Aedes mosquito abundance and dengue risk indicators such as the Aedes Index (AI) and Dengue Positive Trap Index (DPTI) is essential for early intervention and targeted vector control. However, current models often rely on coar...