Selective drug deposition in lungs through pulmonary drug delivery system for effective management of drug-resistant TB
Bibliographic record
Abstract
INTRODUCTION: The emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) is a major health issue and continues to be a global health concern. Despite significant advancements in treatment modalities, ~1.6 million deaths worldwide occur due to TB infection. This is because of tuberculosis reservoirs in the alveoli making it a challenge for the formulation scientist to target this. AREAS COVERED: This review recent investigations on the forefront of pulmonary drug delivery for managing MDR-TB and XDR-TB. Novel delivery systems like liposomes, niosomes, employing carbohydrate, and -coated molecules via conjugation to selectively deliver the drugs to the lung TB reservoir via pulmonary administration are discussed. EXPERT OPINION: Poor patient adherence to treatment due to side effects and extended therapeutic regimen leads to drug-resistant TB. Thus, it is essential to design novel strategies this issue by developing new chemical entities and/or new delivery systems for delivery to the lungs, consequently reducing the side effects, the frequency and the duration of treatment. Delivery of drugs to enhance the efficacy of new/existing anti-TB drugs to overcome the resistance and enhance patient compliance is underway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".