Systematic Review of Cryptococcus neoformans Seroprevalence, Antifungal Susceptibility, and Pathogenesis in Patients With HIV/AIDS on Combination Antiretroviral Therapy in Abuja, Nigeria
Bibliographic record
Abstract
For individuals living with HIV/AIDS in low-resource settings such as Sub-Saharan African countries, including Nigeria, cryptococcal meningitis (CM) is a significant contributor to both mortality and morbidity. Despite advancements in antiretroviral therapy (ART), which have markedly transformed the treatment and management of HIV, CM remains a considerable challenge. It primarily arises from delays in diagnosis, limited access to antifungal treatments, and a lack of healthcare resources and infrastructure. With the historical correlation between the rising prevalence of HIV/AIDS and the increased incidence of Cryptococcus neoformans infections, understanding the genetic diversity and virulence factors of these pathogens is essential. Sub-Saharan Africa continues to face elevated rates of CM-related deaths. This underscores the urgent need for effective intervention strategies. The objective of this study is to determine the seroprevalence of the Cryptococcus species within the Abuja population, evaluate the susceptibility of circulating C. neoformans clinical isolates to the antifungal medications widely used for treating cryptococcosis, and assess the correlation between the levels of micronutrients and the progression of Cryptococcus infection in individuals with HIV/AIDS. To this end, we conducted an extensive literature search across various online databases, including Embase, PubMed, Scopus, Web of Science, and Google Scholar. The studies analyzed included those with a crossover design, randomized controlled trials (RCTs), systematic reviews, meta-analyses, and prospective cohort studies focused on palliative care in heart failure patients. Fifteen studies were included in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study findings support public health initiatives by informing screening protocols, enhancing treatment regimens, and improving overall patient care outcomes. By advancing the understanding of the pathogenesis and transmission of C. neoformans, this study contributes to global efforts aimed at reducing CM-associated morbidity and mortality rates among patients with HIV. In this context, it establishes a seroprevalence baseline for cryptococcosis in Abuja, Nigeria, identifies virulence-associated genetic markers, and recommends integrating cryptococcal screening into HIV care and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".