Novel therapeutic approaches for targeting TB and HIV reservoirs prevailing in lungs
Bibliographic record
Abstract
INTRODUCTION: is the leading cause of death in HIV positive patients. In 2017, about 0.3 million HIV positive people died of tuberculosis. There is high load of mycobacteria and HIV in the lungs and eradication of the same is vital for patient survival. AREAS COVERED: This review focuses on the pathogenesis of HIV-TB coinfection and the current management approaches of this coinfection. It presents a detailed discussion of current investigations in novel drug delivery systems for effective targeting of HIV-TB lung reservoirs, especially via pulmonary drug delivery. Additionally, emphasis is given to the need of HIV-TB cotargeting, an unmet need in management of HIV-TB coinfection. EXPERT OPINION: To achieve the goal of complete eradication of HIV-TB reservoirs in lungs requires focused research strategies to be undertaken in the area of pulmonary delivery systems. These endeavors could eventually lead to better patient compliance and improved treatment outcomes. The treatment regimen of HIV-TB coinfection is associated with a major drawback of low therapeutic concentration of drugs in lungs. Nanotechnology provides an excellent platform for delivery of anti-TB and anti-HIV drugs via the pulmonary route thereby serving as a viable and effective means of managing the mycobacterial and HIV reservoirs in the lungs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".