Present status of nanoparticle research for treatment of Tuberculosis
Bibliographic record
Abstract
Nanotechnology has offered enormous improvement in field of therapeutics by means of designing of drug delivery systems and opened the possibility of controlling infections at the molecular level. Nanocarriers can cross biological barriers and are able to target cellular reservoirs of Mycobacterium tuberculosis (M. tuberculosis). Nanoparticle-based systems have significant potential for treatment and prevention of tuberculosis (TB). A variety of nanocarriers have been widely evaluated as potential drug delivery systems for various administration routes. Targeting the drugs to certain physiological sites such as the lymph nodes has emerged as a promising strategy in treating TB with improved drug bioavailability and reduction of the dosing frequency. Nanotechnology based rational targeting may improve therapeutic success by limiting adverse drug effects and requiring less frequent administration regimes, ultimately resulting in more patients compliance and thus attain higher adherence levels. The development of nanoparticle based aerosol vaccine is undergoing which could serve as new platform for immunization. Present article compiles the general physiological aspects of the infection along with the relevance nanocarriers used in prevention of tuberculosis.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".