Molecular characterization of hemoglobinopathies and thalassemias in Northern Guangdong Province, China
Bibliographic record
Abstract
ABSTRACT: To detect the molecular characterization of hemoglobinopathies and thalassemias in Northern Guangdong Province of China.We recruited 10,285 subjects who were screened for hemoglobin (Hb) variants and thalassaemia genotypes in the outpatient department of Yuebei People's Hospital from January 2018 to December 2020. The subjects collected venous blood samples for blood cell parameter analysis and Hb electrophoresis analysis. When the average red blood cell volume is <82 fL, or the average red blood cell Hb is <27 pg, or HbA2 > 3.5%, or HbA2 < 2.5%, or HbF > 2.0%, the screening is positive if one of them is satisfied. All subjects who were screened positive were tested for the thalassaemia gene by gap-polymerase chain reaction, PCR-based reverse dot blot, and DNA sequencing.Among all subjects screened, the overall prevalence of hemoglobinopathies and thalassemias were 0.46% (47/10,285) and 21.02% (2162/10,285) in Northern Guangdong Province. We found that Hb Q-Thailand is the most common, and other types of hemoglobinopathies are followed by Hb E, Hb New York, Hb G-Chinese, Hb G-Coushatta, Hb J-Bangkok, Hb J-Broussais, Hb Ottawa, and Hb G-Taipei. We identified 1340 cases (13.03%) of α-thalassemia, mainly includes --SEA deletion (71.64%), -α3.7 deletion (12.01%), -α4.2 deletion (4.78%). And identified 652 cases (6.34%) of β-thalassemia, the most prevalent being CD 41/42(-TTCT) (35.89%), IVS-II-654 (C > T) (33.44%), CD 17 (A > T) (10.28%) and -28(A > G) (9.66%). Furthermore, there are 170 cases (1.65%) of α combined β thalassaemia. In addition, we found a rare case with -80 (T > A) of β-thalassemia. The results of this study found a high prevalence of hemoglobinopathies and thalassemias in Northern Guangdong Province, China. There were some differences molecular characterizations of thalassemia in different areas of China.Our results enriched the related information of hemoglobinopathies and thalassemias in the region, which provided valuable references for the prevention and control of thalassemia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".