Statistical Inferences of HIVRNA and Fracture Based on the PAK1 Expression via Neural Network Model

Area of Science:

• Immunology
• Orthopedics
• Biostatistics

Background:

• Acquired immune deficiency syndrome (AIDS) and fractures pose significant global health risks.
• Identifying reliable biomarkers for HIV RNA and fracture is crucial for early detection and management.

Purpose of the Study:

• To investigate the correlation between specific blood markers and HIV RNA with fracture.
• To develop a predictive model for forecasting HIV RNA levels in patients with fractures.

Main Methods:

• Recruited 48 patients with HIV and fracture and 112 controls.
• Measured blood neutrophil count (NEU), white blood cell count (WBC), PAK1, and HIV RNA.
• Utilized Pearson's chi-squared test and a BP neural network model for analysis.

Main Results:

• Strong correlations were found between PAK1, NEU, WBC, and HIV RNA with fracture.
• A neural network model demonstrated good fitting effects with R² ≈ 0.82.
• The model successfully predicted HIV RNA with fracture based on the combined biomarkers.

Conclusions:

• A neural network model utilizing NEU, WBC, and PAK1 can effectively predict HIV RNA with fracture.
• This innovative model offers a novel approach for forecasting HIV RNA levels in fracture patients.