Epidemiology of multi-drug resistant Tuberculosis in the western region of Kenya.

Multidrug-resistant tuberculosis (TB) (MDR-TB), or TB that is simultaneously resistant to both isoniazid (INH) and rifampicin (RIF), is a barrier to successful TB control and treatment. Stratified data on MDR-TB, particularly in the high-burden western Kenya region, remain unknown. This data is important to monitor the efficacy of TB control and treatment efforts.

Feasibility and utility of a combined nasogastric-tube-and-string-test device for bacteriologic confirmation of pulmonary tuberculosis in young children.

For microbiological confirmation of pediatric pulmonary tuberculosis (PTB), gastric aspirates (GA) are often operationally unfeasible without hospitalization, and the encapsulated orogastric string test is not easily swallowed in young children. The Combined-NasoGastric-Tube-and-String-Test (CNGTST) enables dual collection of GA and string specimens. In a prospective cohort study in Kenya, we examined its feasibility in children under five with presumptive PTB and compared the bacteriological yield of string to GA.

Catalytic Antibodies May Contribute to Demyelination in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Here we report preliminary data demonstrating that some patients with myalgic encephalomyelitis/chronic fatiguesyndrome (ME/CFS) may have catalytic autoantibodies that cause the breakdown of myelin basic protein (MBP). We propose that these MBP-degradative antibodies are important to the pathophysiology of ME/CFS, particularly in the occurrence of white matter disease/demyelination. This is supported by magnetic resonance imagining studies that show these findings in patients with ME/CFS and could explain symptoms of nerve pain and muscle weakness.

Comparison of logistic regression with regularized machine learning methods for the prediction of tuberculosis disease in people living with HIV: cross-sectional hospital-based study in Kisumu County, Kenya.

Background: Tuberculosis (TB) is a major public health concern, particularly among people living with the Human immunodeficiency Virus (PLWH). Accurate prediction of TB disease in this population is crucial for early diagnosis and effective treatment. Logistic regression and regularized machine learning methods have been used to predict TB, but their comparative performance in HIV patients remains unclear.