A cost-effective method for detecting mutations in the human FAM111B gene associated with POIKTMP syndrome
Bibliographic record
Abstract
Abstract Background Mutations of the human FAM111B gene are associated with hereditary fibrosing poikiloderma with tendon contracture, myopathy, and pulmonary fibrosis (POIKTMP), a rare and autosomal dominant multi-systemic fibrosing disease. To date, a total of 36 cases are documented, with eleven associated mutations identified and confirmed by Whole-Exome Sequencing and Sanger sequencing. However, these methods require a certain level of expertise. The FAM111B gene was annotated using the SNAPGENE tool to identify various restriction enzymes. The enzymes that cut at the positions where mutations of interest have been reported were selected. The method was implemented using the DNA samples extracted from the skin fibroblast collected from an affected South African family and unrelated control. Results The findings showed that of the eleven FAM111B mutational sites investigated with this method, ten mutations can be identified including the known mutation FAM111B NM_198947.4: c.1861T>G (pTyr621Asp) associated with the POIKTMP in South Africa. Conclusions Limited access to molecular diagnosis contributes to why POIKTMP is rarely diagnosed. Our study describes an inexpensive PCR–RFLP method to screen for POIKTMP FAM111B gene mutations. The PCR–RFLP can be used as a cost-effective method for diagnosing FAM111B mutations in POIKTMP, and it does not require having robust experience in molecular biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".