Ards Prediction
Ards Prediction - Related Videos
07:20
Lavage-induced Surfactant Depletion in Pigs As a Model of the Acute Respiratory Distress Syndrome (ARDS)
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Cited by 21 •
2016
Repeated pulmonary lavages in anesthetized pigs induce lung injury resembling major aspects of human acute respiratory distress syndrome (ARDS). For this purpose the lungs are repeatedly lavaged with 0.9% saline at 37 °C. The goal of the protocol is a reproducible mitigation of gas exchange and hemodynamics for research in ARDS.
08:22
The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit
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2025
Here, we present a protocol for bedside POCUS application in ARDS management within the ICU. It details standardized techniques for lung, cardiac, diaphragm, venous, and gastric assessments to guide ventilation, predict weaning, and prevent complications.
02:27
Predicting Molecular Geometry
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2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
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Cited by 79 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
13:42
RNA Secondary Structure Prediction Using High-throughput SHAPE
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Cited by 16 •
2013
High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.
FAQs
These models synthesize clinical risk factors with physiologic measurements, laboratory results, imaging findings, and, when available, biomarkers. Statistical or machine-learning methods then evaluate combinations of these inputs to identify patterns associated with later respiratory deterioration. The value comes from integrating multiple signals rather than relying on one observation alone.
Physiologic measurements can show how a patient's respiratory status is changing before advanced respiratory failure develops. Within a prediction assessment, they complement risk factors, laboratory data, and imaging findings, helping models or clinicians interpret whether the overall pattern suggests increasing vulnerability. This makes repeated or combined assessment relevant to early recognition and monitoring.
Statistical and machine-learning approaches serve the same broad predictive purpose but may analyze clinical data through different modeling strategies. The available framework supports using both to detect patterns linked with future deterioration, rather than treating either as a standalone diagnosis. Their output is best understood as a risk assessment that informs attention and evaluation.
Biomarkers can provide an additional signal when a prediction approach includes them. They are considered alongside clinical risk factors, physiologic measurements, laboratory results, and imaging findings, rather than replacing those sources. Their inclusion broadens the information available for recognizing patterns associated with future respiratory deterioration during a patient's overall assessment.
A practical assessment begins by assembling available clinical risk factors, physiologic measurements, laboratory results, imaging findings, and, in some approaches, biomarkers. These inputs are considered together through a statistical or machine-learning model, or as part of clinical evaluation. The resulting pattern supports estimation of future risk rather than confirmation of established injury.
A higher estimated risk can support closer monitoring, timely evaluation of contributing conditions, and consideration of critical-care resource allocation. The estimate helps organize clinical attention before advanced respiratory failure develops. Its practical relevance lies in recognizing patients whose overall pattern suggests possible deterioration, allowing care teams to assess changing risk more deliberately.
In research settings, ARDS prediction can help identify patients at risk before advanced respiratory failure develops. That timing may improve clinical trial design while enabling investigators to study mechanisms and outcomes earlier in the disease course. Prediction therefore has value beyond bedside monitoring, linking risk assessment with broader investigation of how ARDS develops and progresses.