Mucus production, host-microbiome interactions, hormone sensitivity, and innate immune responses modeled in human cervix chips
Bibliographic record
Abstract
ABSTRACT Modulation of mucus production by the human endo– and ecto-cervical epithelium by steroid hormones and associated interactions with commensal microbiome play a central role in the physiology and pathophysiology of the female reproductive tract. However, most of our knowledge about these interactions is based on results from animal studies or in vitro models that fail to faithfully mimic the mucosal environment of the human cervix. Here we describe microfluidic organ-on-a-chip (Organ Chip) models of the human cervical mucosa that recreate the cervical epithelial-stromal interface with a functional epithelial barrier and produce abundant mucus that has compositional, biophysical, and hormone-responsive properties similar to the living cervix. Application of continuous fluid flow to chips lined with primary human cervical epithelial cells from a commercial source that contained a mixture of primary human ecto– and endo-cervical epithelial cells promoted preferential expression of the ecto-cervical phenotype, whereas use of periodic flow including periods of stasis induced endo-cervical specialization. When the periodic flow Cervix Chips were co-cultured with living Lactobacillus crispatus and Gardnerella vaginalis bacterial communities to respectively mimic the effects of human host interactions with optimal (healthy) or non-optimal (dysbiotic) microbiome associated with an ascending infection in the female reproductive tract, significant differences in tissue innate immune responses, barrier function, cell viability and protein profile, and mucus composition were detected reminiscent of those observed in vivo . Thus, these Organ Chip models of human cervix provide a physiologically relevant experimental in vitro model to study cervical mucus physiology and its role in human host-microbiome interactions as well as a potential preclinical testbed for development of therapeutic interventions to enhance women’s health.
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) | 0.000 | 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) | 0.001 | 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".