AIMC Topic: Calcium
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Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.
Journal of endourology
May 30, 2024
Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological...
A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging.
Cell calcium
Apr 24, 2024
The release of Ca ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classi...
Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.
Biological chemistry
Apr 24, 2024
Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish...
Machine learning reveals the control mechanics of an insect wing hinge.
Nature
Apr 17, 2024
Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs, but are novel structures that are attached to th...
System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.
Cell research
Apr 11, 2024
The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the ...
Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images.
Biomedical physics & engineering express
Mar 13, 2024
Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for clas...
End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.
PLoS computational biology
Mar 13, 2024
Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential feat...
AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.
Abdominal radiology (New York)
Jan 27, 2024
PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition m...
Study on the Effect of Low Calcium Dialysate on Biochemical Profile of Adynamic Bone Disease in Patients on Maintenance Hemodialysis.
Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Jan 17, 2024
In recent years, adynamic bone disease (ABD) has become a common skeletal lesion in adult patients with chronic kidney disease. We aimed to compare the effects of low calcium dialysate (LCD) and standard calcium dialysate of our facility [high calciu...
Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.
Abdominal radiology (New York)
Dec 29, 2023
PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.
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