Analysis of obstetric complications and uterine connective tissue in tenascin-X-deficient humans and mice
Bibliographic record
Abstract
Tenascin-X (TNX) is a large, multi-domain, extracellular matrix glycoprotein. Complete deficiency of TNX in humans leads to a recessive form of Ehlers-Danlos syndrome (EDS), and TNX haploinsufficiency is a cause of hypermobility type EDS. EDS patients appear to have a higher risk of several complications during pregnancy, such as pelvic instability, premature rupture of membranes, and postpartum hemorrhage. Here, we present a study of genitourinary and obstetric complications in TNX-deficient women of reproductive age. We have found complications, such as uterus prolapses, that are in agreement with previous findings in other EDS types. In TNX knockout (KO) mice, we have observed mild pregnancy-related abnormalities. Morphological and immunohistological analysis of uterine tissues has not revealed obvious quantitative or spatial differences between TNX KO and wildtype mice with respect to collagen types I, III, V, and XII or elastic fibers. We conclude that TNX-deficient women are at risk of obstetric complications, but that TNX KO mice show only a mild phenotype. Furthermore, we show that TNX is involved in the stability of elastic fibers rather than in their initial deposition.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".