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Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

Wenjun Zhu1, Jinmi Li1, Yiming Yang1

  • 1Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, China.

Medical Principles and Practice : International Journal of the Kuwait University, Health Science Centre
|February 14, 2026
PubMed
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A machine learning model predicts sepsis-associated liver injury (SALI) prognosis using routine biomarkers. The Random Forest model shows promise for guiding clinical decisions and improving outcomes in SALI patients.

Keywords:
Liver injuryMachine learningPrognosisSepsis

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Area of Science:

  • * Critical care medicine
  • * Machine learning in healthcare
  • * Hepatology

Background:

  • * Sepsis-associated liver injury (SALI) affects ~40% of sepsis cases, contributing to high mortality.
  • * Current prognostic models for SALI are lacking, hindering timely clinical interventions.
  • * Precise prognostic tools are needed to guide treatment and reduce mortality in SALI patients.

Purpose of the Study:

  • * To develop and validate a machine learning (ML)-based prognostic model for SALI.
  • * To utilize conventional biomarkers for predicting SALI patient outcomes.
  • * To guide clinical decision-making and potentially reduce SALI-related mortality.

Main Methods:

  • * Retrospective analysis of 307 SALI patients, split into training (80%) and validation (20%) sets.
  • * Feature selection using LASSO regression on hematological, liver/renal function, and coagulation parameters.
  • * Nine ML algorithms were trained, with the Random Forest model selected for performance evaluation via AUC and SHAP for interpretability.

Main Results:

  • * Red blood cell distribution width-coefficient of variation (RDW-CV), anion gap (AG), and high-sensitivity cardiac troponin (hs-cTn) were key prognostic factors.
  • * The Random Forest model achieved an AUC of 0.816 in the validation set and 0.781 in external validation.
  • * SHAP analysis provided interpretability for the model's predictions.

Conclusions:

  • * The developed Random Forest model demonstrates potential for guiding clinical decisions in SALI management.
  • * Further external validation is necessary before widespread clinical implementation.
  • * The model offers a promising tool for improving prognostic accuracy in sepsis-associated liver injury.