genpark-personal-agent-pulmonary-dynamic-airway-closing-pressure-pel-skill
mcp
Uyari
Health Uyari
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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
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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