Plasma microRNA profiling predicts HIV-associated neurocognitive disorder
Bibliographic record
Abstract
OBJECTIVE: HIV-associated neurocognitive disorder (HAND) is a common neurological disorder among HIV-infected patients despite the availability of combination antiretroviral therapy. Host-encoded microRNAs (miRNA) regulate both host and viral gene expression contributing to HAND pathogenesis and can also serve as disease biomarkers. Herein, plasma miRNA profiles were investigated in HIV/AIDS patients with HAND. METHODS: Discovery and Validation Cohorts comprising HIV/AIDS patients were studied that included patients with and without HAND (non-HAND). Plasma miRNA levels were measured by array hybridization and verified by quantitative real-time reverse transcriptase PCR (qRT-PCR). Multiple bioinformatic and biostatistical analyses were applied to the data from each cohort. RESULTS: Expression analyses identified nine miRNAs in the Discovery Cohort (HAND, n = 22; non-HAND, n = 25) with increased levels (≥two-fold) in the HAND group compared with the non-HAND group (P < 0.05). In the Validation Cohort (HAND, n = 12; non-HAND, n = 12) upregulation (≥two-fold) of three miRNAs (miR-3665, miR-4516 and miR-4707-5p) was observed in the HAND group that were also increased in the Discovery Cohort's HAND patients, which were verified subsequently by qRT-PCR. Receiver-operating characteristic curve analyses for the three miRNAs also pointed to the diagnosis of HAND (area under curve, 0.87, P < 0.005). Bioinformatics tools predicted that all three miRNAs targeted sequences of genes implicated in neural development, cell death, inflammation, cell signalling and cytokine functions. CONCLUSION: Differentially expressed plasma-derived miRNAs were detected in HIV/AIDS patients with HAND that were conserved across different patient cohorts and laboratory methods. Plasma-derived miRNAs might represent biomarkers for HAND and also provide insights into disease mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".