Contribution of ABO-Rhesus/Electrophoresis of hemoglobin methods and Short Tandem Repeats analysis in the determination of paternity in Burkina Faso, West Africa
Bibliographic record
Abstract
Abstract Background: the establishment of filiation by the current ABO, HLA, MNS, Kells and serum tests, pose a real reliability problem. It is then necessary to combine these methods with or to use high-performance methods such as microsatellite genetic analysis or short tandem repeats. This study aimed to compare the short tandem repeat technique with ABO/Rhesus system in combination with electrophoresis of hemoglobin. Methods: Fourteen (14) contested paternity trios were investigated. Blood samples were collected to determine blood groups using the Beth-Vincent method and the type of hemoglobin by electrophoresis. Blood spots on FTA paper were used for the analysis of 16 STR loci (D8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818, FGA, Amel) by capillary electrophoresis on the ABI 31310 Genetic Analyzer. The generated sequences were analyzed with GeneMapper® software version 3.2.1. The data were analyzed to determine the paternity index and the probability of paternity. Results: Of the fourteen (14) trios studied, ten (10) cases were probable inclusion, three (03) cases were exclusion and one (01) case was an undetermined paternity outcome with the ABO-Rhesus/ electrophoresis of hemoglobin system. While the analysis of genetic polymorphisms in DNA gave five (05) inclusions versus nine (09) exclusions of paternity. Of the 10 probable inclusion cases given by the ABO-Rhesus/Electrophoresis of hemoglobin system, only 05 cases (50%) were confirmed for inclusion by Short tandem repeat analysis. Conclusion: The analysis of short tandem repeat with sixteen genetic markers is more reliable in determining paternity than ABO-Rhesus/hemoglobin electrophoresis techniques.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".