Dual trajectories of serum brain-derived neurotrophic factor and cognitive function in people living with HIV
Bibliographic record
Abstract
This study aimed to identify the interrelationships between mature BDNF (mBDNF), precursor BDNF (proBDNF) trajectories, and cognitive performance in individuals with HIV from sub-Saharan Africa over 96 weeks following antiretroviral therapy (ART) initiation. Using data from 154 participants in the ACTG 5199 study (ClinicalTrials.gov NCT00096824, 2005-06-23) in Johannesburg and Harare (2006-2009), we measured serum mBDNF and proBDNF levels via ELISA and assessed cognitive performance with neuropsychological tests. Group-based trajectory modelling indicated two mBDNF trajectories-"Stable Ascent" (83.9%) and "Peak with Gradual Decline" (16.1%)-and two proBDNF trajectories-"Gradual Increase" (85.7%) and "Gradual Decline" (14.3%). These were linked to three cognitive trajectories: "Low Baseline-Slow Improvement," "Gradual Improvement," and "Late Surge." The "Stable Ascent" mBDNF group showed a significant probability of "Gradual Improvement" (68%) in cognitive performance and a "Late Surge" (9.5%). In contrast, the "Peak with Gradual Decline" mBDNF trajectory saw no "Late Surge." A "Gradual Increase" in proBDNF corresponded to a 67.7% chance of "Gradual Improvement" in cognition. Findings suggest BDNF isoforms as potential biomarkers for cognitive interventions in HIV, emphasizing that stable or increasing BDNF levels post-ART are linked to favourable cognitive outcomes. Further research is needed to develop BDNF-based cognitive health strategies to improve outcomes for people with HIV.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".