A high prevalence of liver stiffness, as determined by elevated transient elastography liver stiffness measurement, was previously found in a cohort of HIV-infected Ugandans in the absence of chronic viral hepatitis. Given the role of immune activation and microbial translocation in models of liver disease, a shared immune mechanism was hypothesized in the same cohort without other overt causes of liver disease. This study examined whether HIV-related liver stiffness was associated with markers of immune activation or microbial translocation (MT). A retrospective case-control study of subjects with evidence of liver stiffness as defined by a transient elastography stiffness measurement ‡ 9.3 kPa (cases = 133) and normal controls (n = 133) from Rakai, Uganda was performed. Cases were matched to controls by age, gender, HIV, hepatitis B virus (HBV), and highly active antiretroviral therapy (HAART) status. Lipopolysaccharide (LPS), endotoxin IgM antibody, soluble CD14 (sCD14), C-reactive protein (CRP), and D-dimer levels were measured. Conditional logistic regression was used to estimate adjusted matched odds ratios (adjMOR) and 95% confidence intervals. Higher sCD14 levels were associated with a 19% increased odds of liver stiffness (adjMOR = 1.19, p = 0.002). In HIV-infected individuals, higher sCD14 levels were associated with a 54% increased odds of having liver stiffness (adjMOR = 1.54, p < 0.001); however, the opposite was observed in HIV-negative individuals (adjMOR = 0.57, p = 0.001). No other biomarker was significantly associated with liver stiffness, and only one subject was found to have detectable LPS. Liver stiffness in HIV-infected Ugandans is associated with increased sCD14 indicative of monocyte activation in the absence of viral hepatitis or microbial translocation, and suggests that HIV may be directly involved in liver disease.

In this study, we identified predictors of malaria, developed data mining, statistically enhanced rule-based classification to diagnose malaria and developed an automated system to incorporate the rules and statistical models. The aim of the study was to develop a statistical prototype to perform clinical diagnosis of malaria given its adverse effects on the overall healthcare, yet its treatment remains very expensive for the majority of the patients to afford. Model validation was performed using records from two hospitals (training and predictive datasets) to evaluate system sensitivity, specificity and accuracy. The overall sensitivity of the rule-based classification obtained from the predictive dataset was 70 % [68–74; 95 % CI] with a specificity of 58 % [54–66; 95 % CI]. The values for both sensitivity and specificity varied by age, generally showing better performance for the data mining classification rules for the adult patients. In summary, the proposed system of data mining classification rules provides better performance for persons aged at least 18 years. However, with further modelling, this system of classification rules can provide better sensitivity, specificity and accuracy levels. In conclusion, using the system provides a preliminary test before confirmatory diagnosis is conducted in laboratories.

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