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acumen hypotension prediction index software hpi software monitoring  (Edwards Lifesciences Inc)

 
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    Structured Review

    Edwards Lifesciences Inc acumen hypotension prediction index software hpi software monitoring
    Acumen Hypotension Prediction Index Software Hpi Software Monitoring, supplied by Edwards Lifesciences Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/predict+software/hypotension+index+prediction/pmc12963111-69-12-19
    Average 86 stars, based on 1 article reviews
    acumen hypotension prediction index software hpi software monitoring - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    other:

    Article Title: Comparison of a mean arterial pressure alarm to the hypotension prediction index in preventing intraoperative hypotension in elective moderate- to high-risk non-cardiac surgical patients: a study protocol for a blinded, parallel, randomized controlled trial with a non-inferiority framework.
    Article Snippet: The Hypotension Prediction Index (HPI) is a validated machine learning algorithm developed to predict hypotension up to fifteen minutes in advance (Edwards Lifesciences, Irvine, California) (7).

    Article Title: Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.
    Article Snippet: The Hypotension Prediction Index (HPI; Edwards Lifesciences, Irvine, California, USA) is an algorithm based on a complex analysis of features in high-fidelity arterial pressure waveform recordings developed to observe subtle signs that could predict the onset of hypotension in surgical and intensive care unit patients [75].

    Article Title: Comparison of a mean arterial pressure alarm to the hypotension prediction index in preventing intraoperative hypotension in elective moderate- to high-risk non-cardiac surgical patients: a study protocol for a blinded, parallel, randomized controlled trial with a non-inferiority framework
    Article Snippet: The Hypotension Prediction Index (HPI) is a validated machine learning algorithm developed to predict hypotension up to 15 min in advance (Edwards Lifesciences, Irvine, CA) [ ].

    Article Title: Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review
    Article Snippet: The Hypotension Prediction Index (HPI; Edwards Lifesciences, Irvine, CA, USA) is an algorithm based on a complex analysis of features in high-fidelity arterial pressure waveform recordings developed to observe subtle signs that could predict the onset of hypotension in surgical and intensive care unit patients [ ].

    Software:

    Article Title: Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study.
    Article Snippet: .. Among these, the Hypotension Prediction Index (HPI) is one of the most widely adopted models in clinical practice (commercialized as the Acumen HPI software by Edwards Lifesciences, integrated into the HemoSphere monitoring platform). ..

    Article Title: Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study
    Article Snippet: .. Among these, the Hypotension Prediction Index (HPI) is one of the most widely adopted models in clinical practice (commercialized as the Acumen HPI software by Edwards Lifesciences, integrated into the HemoSphere monitoring platform). ..

    Derivative Assay:

    Article Title: Artificial intelligence-based predictive hemodynamic monitoring in conjunction with goal-directed therapy reduces duration, frequency, and severity of intraoperative hypotension in major maxillofacial and otolaryngological surgery-a prospective randomized controlled pilot trial.
    Article Snippet: .. The Hypotension Prediction Index (HPI; Edwards Lifesciences, Irvine, CA, USA) uses features derived from the arterial pressure waveform to estimate the likelihood of IOH (MAP < 65 mmHg) before it occurs [12,13]. ..



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    A , schematic of the P. aeruginosa PA3211-PA3214 operon overexpression construct used for structural studies. B , consensus map (Map 4) of protein complex after affinity purification and size exclusion. Inset shows a slice through the side view and top view of the consensus map, in which density likely corresponding to the PA3213 MCE protein is observed inside the PA3214 lipoprotein pore. C , AlphaFold 3 prediction of the PA3211-PA3212-PA3213-PA3214 complex, with PA3214 fit into the consensus map (Map 4). D , Surface representation of the PA3214 structure after focused refinement (Map 2, Fig. S6), with C-terminal peptide of PA3213 shown as sticks. Boxed region shows the interaction between the C-terminal peptide of PA3213 and the hydrophobic groove of PA3214. E , top views of the PA3214 model in the closed and open conformations fit into the respective maps (Map 2, closed; Map 3, open). Each protomer is shown in a different color. F , examples of 2D classes representing likely compositional and conformational heterogeneity in PA3214 bound to PA3213.

    Journal: bioRxiv

    Article Title: Interactions of outer membrane lipoproteins P. aeruginosa PA3214 and E. coli PqiC with their MCE protein binding partners, PA3213 and PqiB

    doi: 10.64898/2026.05.09.724024

    Figure Lengend Snippet: A , schematic of the P. aeruginosa PA3211-PA3214 operon overexpression construct used for structural studies. B , consensus map (Map 4) of protein complex after affinity purification and size exclusion. Inset shows a slice through the side view and top view of the consensus map, in which density likely corresponding to the PA3213 MCE protein is observed inside the PA3214 lipoprotein pore. C , AlphaFold 3 prediction of the PA3211-PA3212-PA3213-PA3214 complex, with PA3214 fit into the consensus map (Map 4). D , Surface representation of the PA3214 structure after focused refinement (Map 2, Fig. S6), with C-terminal peptide of PA3213 shown as sticks. Boxed region shows the interaction between the C-terminal peptide of PA3213 and the hydrophobic groove of PA3214. E , top views of the PA3214 model in the closed and open conformations fit into the respective maps (Map 2, closed; Map 3, open). Each protomer is shown in a different color. F , examples of 2D classes representing likely compositional and conformational heterogeneity in PA3214 bound to PA3213.

    Article Snippet: We utilized the AlphaFold 3 prediction software available on Google DeepMind’s AlphaFold Server ( ) ( https://alphafoldserver.com/ ) to make predictions for the PA3211-PA3214 complex and the PqiBC complex.

    Techniques: Over Expression, Construct, Affinity Purification

    A , cartoon representation PqiC octamer and surface representation of a monomer, where hydrophobic groove residues identified as important by DMS are depicted in green on both the octamer and the monomer. B , DMS data corresponding to the residues shown in A , as presented in . C , Alphafold 3 prediction of PqiC in complex with the C-terminal region of PqiB. Surface representation of the PqiC predicted model with C-terminal peptide of PqiB shown as sticks. Inset focuses on the predicted interaction between PqiB and PqiC, showing the C-terminal peptide of PqiB and surrounding regions in the hydrophobic groove of PqiC. D , representative Western blot from a pull-down assay to assess the interaction between PA3213 and PA3214. All four subunits of the PA3211-PA3214 complex were over-expressed, with a His tag on the PA3214 bait, and interaction with untagged PA3213 was assessed using an ⍺-PA3213 antibody. Blots showing the solubilized membrane fraction of each strain (input, expression control), and the results of the pull-down are shown. Three independent purifications were performed starting with three different inoculations, with similar results. E , representative Western blot from a pull-down assay to assess the interaction between PqiB and PqiC. All three subunits of the PqiABC complex were over-expressed, with a His tag on the PqiB bait, and interaction with untagged PqiC was assessed using an ⍺-PqiC antibody. Blots showing the solubilized membrane fraction of each strain (input, expression control), and the results of the pull-down are shown. Three independent purifications were performed starting with three different inoculations, with similar results.

    Journal: bioRxiv

    Article Title: Interactions of outer membrane lipoproteins P. aeruginosa PA3214 and E. coli PqiC with their MCE protein binding partners, PA3213 and PqiB

    doi: 10.64898/2026.05.09.724024

    Figure Lengend Snippet: A , cartoon representation PqiC octamer and surface representation of a monomer, where hydrophobic groove residues identified as important by DMS are depicted in green on both the octamer and the monomer. B , DMS data corresponding to the residues shown in A , as presented in . C , Alphafold 3 prediction of PqiC in complex with the C-terminal region of PqiB. Surface representation of the PqiC predicted model with C-terminal peptide of PqiB shown as sticks. Inset focuses on the predicted interaction between PqiB and PqiC, showing the C-terminal peptide of PqiB and surrounding regions in the hydrophobic groove of PqiC. D , representative Western blot from a pull-down assay to assess the interaction between PA3213 and PA3214. All four subunits of the PA3211-PA3214 complex were over-expressed, with a His tag on the PA3214 bait, and interaction with untagged PA3213 was assessed using an ⍺-PA3213 antibody. Blots showing the solubilized membrane fraction of each strain (input, expression control), and the results of the pull-down are shown. Three independent purifications were performed starting with three different inoculations, with similar results. E , representative Western blot from a pull-down assay to assess the interaction between PqiB and PqiC. All three subunits of the PqiABC complex were over-expressed, with a His tag on the PqiB bait, and interaction with untagged PqiC was assessed using an ⍺-PqiC antibody. Blots showing the solubilized membrane fraction of each strain (input, expression control), and the results of the pull-down are shown. Three independent purifications were performed starting with three different inoculations, with similar results.

    Article Snippet: We utilized the AlphaFold 3 prediction software available on Google DeepMind’s AlphaFold Server ( ) ( https://alphafoldserver.com/ ) to make predictions for the PA3211-PA3214 complex and the PqiBC complex.

    Techniques: Western Blot, Pull Down Assay, Membrane, Expressing, Control