46 research outputs found
Ventricular assist device implantation in the elderly
BACKGROUND:
Dramatic advances in ventricular assist device (VAD) design and patient management have made mechanical circulatory support an attractive therapeutic option for the growing pool of elderly heart failure patients.
METHODS:
A literature review of all relevant studies was performed. No time or language restrictions were imposed, and references of the selected studies were checked for additional relevant citations.
RESULTS:
In concordance with the universal trend in mechanical circulatory support, continuous flow devices appear to have particular benefits in the elderly. In addition, the literature suggests that early intervention before the development of cardiogenic shock, important in all patients, is particularly paramount in older patients.
CONCLUSIONS:
The ongoing refinement of patient selection, surgical technique, and post-operative care will continue to improve surgical outcomes, and absolute age may become a less pivotal criterion for mechanical circulatory support. However, clear guidelines for the use of mechanical circulatory support in the elderly remain undefined
Almanac: Retrieval-Augmented Language Models for Clinical Medicine
Large-language models have recently demonstrated impressive zero-shot
capabilities in a variety of natural language tasks such as summarization,
dialogue generation, and question-answering. Despite many promising
applications in clinical medicine, adoption of these models in real-world
settings has been largely limited by their tendency to generate incorrect and
sometimes even toxic statements. In this study, we develop Almanac, a large
language model framework augmented with retrieval capabilities for medical
guideline and treatment recommendations. Performance on a novel dataset of
clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and
resident physicians demonstrates significant increases in factuality (mean of
18% at p-value < 0.05) across all specialties, with improvements in
completeness and safety. Our results demonstrate the potential for large
language models to be effective tools in the clinical decision-making process,
while also emphasizing the importance of careful testing and deployment to
mitigate their shortcomings
Regulation of branching dynamics by axon-intrinsic asymmetries in Tyrosine Kinase Receptor signaling
Axonal branching allows a neuron to connect to several targets, increasing neuronal circuit complexity. While axonal branching is well described, the mechanisms that control it remain largely unknown. We find that in the Drosophila CNS branches develop through a process of excessive growth followed by pruning. In vivo high-resolution live imaging of developing brains as well as loss and gain of function experiments show that activation of Epidermal Growth Factor Receptor (EGFR) is necessary for branch dynamics and the final branching pattern. Live imaging also reveals that intrinsic asymmetry in EGFR localization regulates the balance between dynamic and static filopodia. Elimination of signaling asymmetry by either loss or gain of EGFR function results in reduced dynamics leading to excessive branch formation. In summary, we propose that the dynamic process of axon branch development is mediated by differential local distribution of signaling receptors
A Generalizable Deep Learning System for Cardiac MRI
Cardiac MRI allows for a comprehensive assessment of myocardial structure,
function, and tissue characteristics. Here we describe a foundational vision
system for cardiac MRI, capable of representing the breadth of human
cardiovascular disease and health. Our deep learning model is trained via
self-supervised contrastive learning, by which visual concepts in cine-sequence
cardiac MRI scans are learned from the raw text of the accompanying radiology
reports. We train and evaluate our model on data from four large academic
clinical institutions in the United States. We additionally showcase the
performance of our models on the UK BioBank, and two additional publicly
available external datasets. We explore emergent zero-shot capabilities of our
system, and demonstrate remarkable performance across a range of tasks;
including the problem of left ventricular ejection fraction regression, and the
diagnosis of 35 different conditions such as cardiac amyloidosis and
hypertrophic cardiomyopathy. We show that our deep learning system is capable
of not only understanding the staggering complexity of human cardiovascular
disease, but can be directed towards clinical problems of interest yielding
impressive, clinical grade diagnostic accuracy with a fraction of the training
data typically required for such tasks.Comment: 21 page main manuscript, 4 figures. Supplementary Appendix and code
will be made available on publicatio
Computational Protein Design to Re-Engineer Stromal Cell-Derived Factor-1α (SDF) Generates an Effective and Translatable Angiogenic Polypeptide Analog
BACKGROUND: Experimentally, exogenous administration of recombinant stromal cell-derived factor-1α (SDF) enhances neovasculogenesis and cardiac function after myocardial infarction. Smaller analogs of SDF may provide translational advantages including enhanced stability and function, ease of synthesis, lower cost, and potential modulated delivery via engineered biomaterials. In this study, computational protein design was used to create a more efficient evolution of the native SDF protein.
METHODS AND RESULTS: Protein structure modeling was used to engineer an SDF polypeptide analog (engineered SDF analog [ESA]) that splices the N-terminus (activation and binding) and C-terminus (extracellular stabilization) with a diproline segment designed to limit the conformational flexibility of the peptide backbone and retain the relative orientation of these segments observed in the native structure of SDF. Endothelial progenitor cells (EPCs) in ESA gradient, assayed by Boyden chamber, showed significantly increased migration compared with both SDF and control gradients. EPC receptor activation was evaluated by quantification of phosphorylated AKT, and cells treated with ESA yielded significantly greater phosphorylated AKT levels than SDF and control cells. Angiogenic growth factor assays revealed a distinct increase in angiopoietin-1 expression in the ESA- and SDF-treated hearts. In addition, CD-1 mice (n=30) underwent ligation of the left anterior descending coronary artery and peri-infarct intramyocardial injection of ESA, SDF-1α, or saline. At 2 weeks, echocardiography demonstrated a significant gain in ejection fraction, cardiac output, stroke volume, and fractional area change in mice treated with ESA compared with controls.
CONCLUSIONS: Compared with native SDF, a novel engineered SDF polypeptide analog (ESA) more efficiently induces EPC migration and improves post-myocardial infarction cardiac function and thus offers a more clinically translatable neovasculogenic therapy
Regulation of branching dynamics by axon-intrinsic asymmetries in Tyrosine Kinase Receptor signaling
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data