Mutations of the TGFBR2 gene in Chinese patients with Marfan-related syndrome
Bibliographic record
Abstract
PURPOSE: Transforming growth factor beta receptors II gene (TGFBR2) mutations associated with Marfan syndrome and Marfan-associated disorders have been investigated. However, such studies are limited in China. To obtain more information about TGFBR2 mutations, we analyzed 6 unrelated Chinese patients with Marfan-associated disorders and without ocular manifestation. METHODS: The genomic DNA from blood leukocytes of these 6 patients and their relatives was isolated, and the entire coding region of TGFBR2 was amplified using PCR. We determined the sequence of TGFBR2 with the ABI 3100 Genetic Analyzer. RESULTS: Three mutations were identified in TGFBR2. Two mutations were associated with Loeys-Dietz syndrome (LDS), which were distributed as following: one missense mutation R528C (caused by a 1582C > T substitution) and one polymorphism T315M (a rare SNP). The third mutation was a novel silent mutation associated with MFS2, which was K291K caused by an 873 C > T substitution. CONCLUSIONS: The TGFBR2 gene missense mutations are possibly causative mutations of Loeys-Dietz syndrome. This result suggests an increase in the mutation spectrum of Marfan-related disorders in China and possibly world-wide.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".