Sublingual Immunotherapy as an Alternative to Induce Protection Against Acute Respiratory Infections
Bibliographic record
Abstract
Sublingual route has been widely used to deliver small molecules into the bloodstream and to modulate the immune response at different sites. It has been shown to effectively induce humoral and cellular responses at systemic and mucosal sites, namely the lungs and urogenital tract. Sublingual vaccination can promote protection against infections at the lower and upper respiratory tract; it can also promote tolerance to allergens and ameliorate asthma symptoms. Modulation of lung's immune response by sublingual immunotherapy (SLIT) is safer than direct administration of formulations by intranasal route because it does not require delivery of potentially harmful molecules directly into the airways. In contrast to intranasal delivery, side effects involving brain toxicity or facial paralysis are not promoted by SLIT. The immune mechanisms underlying SLIT remain elusive and its use for the treatment of acute lung infections has not yet been explored. Thus, development of appropriate animal models of SLIT is needed to further explore its potential advantages. This work shows how to perform sublingual administration of therapeutic agents in mice to evaluate their ability to protect against acute pneumococcal pneumonia. Technical aspects of mouse handling during sublingual inoculation, precise identification of sublingual mucosa, draining lymph nodes and isolation of tissues, bronchoalveolar lavage and lungs are illustrated. Protocols for single cell suspension preparation for FACS analysis are described in detail. Other downstream applications for the analysis of the immune response are discussed. Technical aspects of the preparation of Streptococcus pneumoniae inoculum and intranasal challenge of mice are also explained. SLIT is a simple technique that allows screening of candidate molecules to modulate lungs' immune response. Parameters affecting the success of SLIT are related to molecular size, susceptibility to degradation and stability of highly concentrated formulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".