Inheritance of β Hemoglobin Gene Mutation: Potential Method of Newborn Screening of Sickle Cell Anemia in Bangladesh
Bibliographic record
Abstract
Objectives: Sickle cell anemia is the most common genetic disorder that affects hemoglobin. People with this disorder have atypical hemoglobin molecules called hemoglobin S, which can distort red blood cells into a sickle, or crescent shape. Sickle cell disease is an increasing global health problem. Estimates suggest that every year approximately 300,000 infants are born with sickle cell anemia, which is defined as an autosomal recessive disorder. The objective of this study is to show that the newborn screening of sickle cell anemia is possible through the procedure by utilizing the cord blood. Materials and Methods: A total number of 30 samples were collected from individual mother and cord blood. DNA was extracted from 13 mothers and 13 fetal cord blood samples and used these DNA to detect sickle cell anemia using wild type and mutant type primer. Results: β hemoglobin gene was amplified by wild type and mutant type primer using PCR and revealed 517bp and 267bp length DNA fragments, respectively. In this study, it was observed that only one homozygous (Hb S/S) mother and newborn found. Most of the mothers and newborns were carrier of sickle cell anemia which means they were heterozygous (Hb A/S). Two pairs were found where mother was carrier, but newborns were healthy (Hb A/A). Conclusions: With this study, it can come to the point that the newborn screening of sickle cell anemia is possible through the procedure by utilizing the cord blood that is wasted every time during delivery. By maternal screening in this way, the probability of disease transmission can also be checked earlier. For Bangladesh, this approach can be an effective tool for screening sickle cell anemia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".