A transmission model clarifies the effects of influenza on pneumococcal pneumonia and bridges the gap between individual animal experiments and human epidemiological data (Shrestha et al., this issue).
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Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data.
Healthcare (Basel)
December 2024
Union Square Medical Associates, 595 Buckingham Way, Suite 350, San Francisco, CA 94132, USA.
Background/objectives: Although eligibility criteria for clinical trials significantly impact study outcomes, these criteria are often established without scientific justification, leading to delayed recruitment, small sample sizes, and limited study generalizability. Persistent Lyme disease (PLD) presents unique challenges due to symptom variability, inconsistent treatment responses, and the lack of reliable biomarkers, underscoring the need for scientifically justified eligibility criteria.
Objective: This study examines the effects of commonly used enrollment criteria on sample yield in PLD clinical trials using real-world data (RWD) from the MyLymeData patient registry.
The prognosis for delayed immune recovery in HIV-infected children might be associated with pre-cART CD4 T cell count irrespective of co-infection with tuberculosis.
BMC Res Notes
January 2025
Ragon Institute of MGH, MIT, and Harvard, 600 Main Street, Cambridge, MA, 02139, USA.
Background: Immune reconstitution following the initiation of combination antiretroviral therapy (cART) significantly impacts the prognosis of individuals infected with human immunodeficiency virus (HIV). Our previous studies have indicated that the baseline CD4 T cells count and percentage before cART initiation are predictors of immune recovery in TB-negative children infected with HIV, with TB co-infection potentially causing a delay in immune recovery. However, it remains unclear whether these predictors consistently impact immune reconstitution during long-term intensive cART treatment in TB-negative/positive children infected with HIV.
View Article and Find Full Text PDFCo-infection mathematical model for HIV/AIDS and tuberculosis with optimal control in Ethiopia.
PLoS One
December 2024
Department of Mathematical Sciences, College of Science, Engineering and Technology, University of South Africa, South Africa.
The co-epidemics of HIV/AIDS and Tuberculosis (TB) outbreak is one of a serious disease in Ethiopia that demands integrative approaches to combat its transmission. In contrast, epidemiological co-infection models often considered a single latent case and recovered individuals with TB. To bridge this gap, we presented a new optimal HIV-TB co-infection model that considers both high risk and low risk latent TB cases with taking into account preventive efforts of both HIV and TB diseases, case finding for TB and HIV/AIDS treatment.
View Article and Find Full Text PDFA dynamical optimal control theory and cost-effectiveness analyses of the HBV and HIV/AIDS co-infection model.
Front Public Health
November 2024
Department of Mathematics, Natural and Computational Sciences, Debre Berhan University, Debre Berhan, Ethiopia.
Studies have shown that the co-infection of Human Immunodeficiency Virus (HIV) and Hepatitis B Virus (HBV) poses a major threat to the public health due to their combined negative impacts on health and increased risk of complications. Even though, some scholars formulated and analyzed the HBV and HIV co-infection model they did not consider the compartment that contains protected individuals against both HBV and HIV infections. They incorporated the optimal control theory and cost-effectiveness analysis simultaneously.
View Article and Find Full Text PDFSpatio-temporal modeling of co-dynamics of smallpox, measles, and pertussis in pre-healthcare Finland.
PeerJ
September 2024
Department of Forestry, University of Helsinki, Helsinki, Finland.
Article Synopsis
- * Research often overlooks rural areas when studying infections, leading to a lack of historical data and presenting significant challenges for analysis.
- * A Bayesian spatio-temporal model was applied to 19th-century Finnish parish data, revealing that diseases like measles, pertussis, and smallpox had interconnected dynamics possibly due to weakened immunity from prior infections or nutritional issues.
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