Mucosal Immune Markers and Their Impact on Antimicrobial Resistance: A Systematic Review
Bibliographic record
Abstract
Objectives: This systematic review aimed to evaluate the role of secretory immunoglobulin A (sIgA) and related mucosal immune markers in modulating susceptibility to colonization or infection by antimicrobial-resistant (AMR) bacteria in human and animal models. Methods: Studies were included if they assessed mucosal sIgA levels about AMR outcomes, reported original data, and were published in English between 2015 and June 2025. Exclusion criteria included a lack of mucosal immune data or AMR-related endpoints. Databases searched included PubMed, ScienceDirect, and EBSCO (Knowledge and Library Hub), with the last search performed on June 10, 2025. The quality of the included studies was assessed using the checklist for quantitative studies, and data were extracted and synthesized narratively, stratified by immune markers, study design, and mucosal compartment. Results: After the systematic search of the databases considered for this review and review of available literature indexed in them, eleven studies that met the inclusion criteria of this study were included in this review. The mucosal sites investigated in most of these studies were gastrointestinal, respiratory, oral, and genitourinary mucosa. Of the eleven (11) studies, four carried out investigations in humans while the remaining seven utilized animal models. All the included studies reported secretory immunoglobulin A (sIgA) level, whereas five of the studies reported other indicators, such as cytokines and IgG, alongside sIgA levels. There was consistency in their results as increased sIgA levels were linked to decreased infection or colonization by AMR pathogens. Moreover, vaccination-based interventional studies showed an increase in sIgA post-vaccination or probiotic treatment. Conclusion: Although in this review we included studies with varying methodological approaches as well as studies featuring humans and animal models, the evidence from this review indicates that mucosal sIgA offers protection against antimicrobial-resistant (AMR) bacteria colonization. Therefore, findings from this study suggest that in the quest for addressing AMR that approaches targeting mucosal immunity preventative measures should be explored through standardized trials.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".