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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Function to simulate WBC activity over time
def simulate_wbc_activity(days):
# Creating a simple simulation for WBC activity
# This can be replaced with more complex logic based on real data
neutrophils = np.random.normal(20, 5, days).clip(0, 50)
lymphocytes = np.random.normal(15, 4, days).clip(0, 40)
monocytes = np.random.normal(10, 3, days).clip(0, 30)
eosinophils = np.random.normal(5, 2, days).clip(0, 20)
return pd.DataFrame({
"Day": range(1, days + 1),
"Neutrophils": neutrophils,
"Lymphocytes": lymphocytes,
"Monocytes": monocytes,
"Eosinophils": eosinophils
})
# Streamlit UI
st.title("WBC Simulation in Post-Surgery Hematoma and Infection")
st.markdown("""
## Understanding White Blood Cells (WBC)
In the event of a hematoma and infection post-surgery, different types of WBCs play crucial roles.
- **Neutrophils 🧦**: First responders to infection sites.
- **Lymphocytes 🧡**: Provide long-term immunity.
- **Monocytes 🧜**: Transform into macrophages to engulf pathogens.
- **Eosinophils 🧝**: Combat multicellular parasites and allergic reactions.
""")
# User input for simulation days
days = st.slider("Select number of days for the simulation", 1, 30, 7)
# Simulating WBC activity
wbc_data = simulate_wbc_activity(days)
# Plotting the WBC activity
st.line_chart(wbc_data.set_index("Day"))
st.markdown("""
## Simulation Insights
The graph above simulates the activity of different WBC types over time post-surgery.
- **Peak in Neutrophils**: Indicates the body's immediate response to infection.
- **Lymphocytes Trend**: Shows the development of immunity over time.
- **Monocytes and Eosinophils**: Fluctuate based on the severity of the infection and allergic reactions.
""")
st.markdown("""
### Note
This is a simplified simulation intended for educational purposes and does not represent actual medical data.
""")
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