genpark-personal-agent-pulmonary-dynamic-airway-closing-pressure-pel-skill

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SUMMARY

Deterministic personal agent skill for calculating Dynamic Airway Closing Pressure (P_cl), static recoil P_st(L), and transmural gradient.

README.md

Pulmonary Dynamic Airway Closing Pressure Skill

License
Python
OpenClaw
GenPark
MCP

Dynamic Airway Closing Pressure ($P_{\text{cl}}$) and Static Recoil Pressure ($P_{\text{st}(L)}$) Analyzer based on Mead's Equal Pressure Point (EPP) physiological formulation.

flowchart TD
    A[Pleural Pressure P_pl & Static Recoil P_st] --> B[Alveolar Pressure P_alv = P_pl + P_st]
    C[Expiratory Airflow & Upstream Resistance] --> D[Frictional Drop: Delta P = Flow * R_us]
    B & D --> E[Intrabronchial Pressure P_bronchial]
    E & A --> F[Transmural Gradient: P_tm = P_bronchial - P_pl]
    F --> G[Equal Pressure Point & Dynamic Closure Risk Assessment]

Features

  • Zero PIP Dependencies: Implemented strictly with Python Standard Library (math, json, sys, typing).
  • Mead's EPP Physics: Models the transition from effort-dependent to effort-independent flow limitation.
  • Dynamic Collapse Prevention: Identifies premature peripheral airway closure and gas trapping.
  • Dual Architecture: Native Python import and Model Context Protocol (MCP) JSON-RPC 2.0 stdio server.

Quickstart

python example_usage.py

License

Apache-2.0

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