Bibliographic record
Abstract
PURPOSE OF THE REVIEW: During this era of unprecedented antiretroviral therapeutic efficacy, there is hope for successfully treated individuals to achieve a longevity approaching that of the general population. However, the recent identification of a higher incidence of cardiovascular, bone, metabolic, neurocognitive and other aging comorbidities is of major concern and may compromise that ability. The purpose of this review is to focus on the dynamic process of immune remodelling, known as immune senescence, which occurs during HIV infection, and how it impacts on long-term comorbidities. RECENT FINDINGS: Early aging in those with HIV appears to stem from persistent chronic inflammation and residual immune activation despite successful antiretroviral therapy. Multiple similarities exist between the T-cell-senescent phenotypes found in many chronic autoimmune and inflammatory conditions, including HIV disease, and the elderly. The immune risk phenotype is linked to poor clinical outcomes in the elderly and may also have clinical consequences in those with HIV. SUMMARY: Immune senescence results in functional impairments of immunity and a reduced ability to adapt to metabolic stress. Understanding the factors driving the development of immune senescence is critical for the development of strategies to prevent early aging.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".