<i>Hydra</i> and the hair follicle – An unconventional comparative biology approach to exploring the human holobiont
Bibliographic record
Abstract
The microbiome of human hair follicles (HFs) has emerged as an important player in different HF and skin pathologies, yet awaits in-depth exploration. This raises questions regarding the tightly linked interactions between host environment, nutrient dependency of host-associated microbes, microbial metabolism, microbe-microbe interactions and host immunity. The use of simple model systems facilitates addressing generally important questions and testing overarching, therapeutically relevant principles that likely transcend obvious interspecies differences. Here, we evaluate the potential of the freshwater polyp Hydra, to dissect fundamental principles of microbiome regulation by the host, that is the human HF. In particular, we focus on therapeutically targetable host-microbiome interactions, such as nutrient dependency, microbial interactions and host defence. Offering a new lens into the study of HF - microbiota interactions, we argue that general principles of how Hydra manages its microbiota can inform the development of novel, microbiome-targeting therapeutic interventions in human skin disease.
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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 | 0.001 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".