Use of niosomes for the treatment of intracellular pathogens infecting the lungs
Bibliographic record
Abstract
The delivery of drugs in an encapsulated environment is designed to precisely target specific tissues, avoiding a systemic circulation of the drug. Lungs are organs exposed to the environment with multiple defense barriers. However, many pathogens can still colonize and infect the airways bypassing the hostile environment of the lungs. In more complicated situations, some pathogens have developed strategies to multiply and survive within macrophages, one of the first immune cell responses to clearing infections in mammals. Niosomes are artificial vesicles that can be loaded with drugs, offering an alternative strategy to treat intracellular pathogens as nanocarriers. Members of the mycobacteria genus are intracellular pathogens that have evolved to escape the immunological response, specifically in macrophages, the white cells responsible for the clearance of pathogens. This review analyzed the state-of-the-art niosome synthesis aimed at tackling the problem of intracellular pathogen therapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".