Characterization of an engineered mucus microenvironment for in vitro modeling of host–microbe interactions
Bibliographic record
Abstract
Abstract The human mucus layer plays a vital role in maintaining health by providing a physical barrier to pathogens. This biological hydrogel also provides the microenvironment for commensal bacteria. Common models used to study host–microbe interactions include gnotobiotic animals or mammalian–microbial co-culture platforms. Many of the current in vitro models lack a sufficient mucus layer to host these interactions. In this study, we engineered a mucus-like hydrogel Consisting of a mixed alginate-mucin (ALG-MUC) hydrogel network by using low concentration calcium chloride (CaCl 2 ) as crosslinker. We demonstrated that the incorporation of ALG-MUC hydrogels into an aqueous two-phase system (ATPS) co-culture platform can support the growth of a mammalian monolayer and pathogenic bacteria. The ALG-MUC hydrogels displayed selective diffusivity against macromolecules and stability with ATPS microbial patterning. Additionally, we showed that the presence of mucin within hydrogels contributed to an increase in antimicrobial resistance in ATPS patterned microbial colonies. By using common laboratory chemicals to generate a mammalian–microbial co-culture system containing a representative mucus microenvironment, this model can be readily adopted by typical life science laboratories to study host–microbe interaction and drug discovery.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".