Prevelence of α-Globin Deletions in β-Thalassemia Minor Patients and Differences in Erythrocyte Indices.
Bibliographic record
Abstract
Abstract Introduction: β-thalassemia (thal) minor is caused by multiple β-globin (gb) gene mutations, most being located in the promotor and IVS-1. Large variations in erythrocyte indices between β-thal minor patients have been reported and are thought to be caused by the type of β-gb gene mutation. α-thal minor is caused by partial or total α-gb gene deletions. α-gb gene deletions have been reported in all racial origins. It is therefore likely that α-gb gene deletions occur in β-thal minor patients.This combination of gb genes mutations could also have an impact on erythrocyte indices. Objectives: The trial objectives were to determine the prevalence of α-gb gene deletions in β-thal minor patients and to compare erythrocyte indices between 3 groups. Group 1: no α-gb gene deletion, Group 2: deletion of 1 α-gb gene, Group 3: deletion of 2 α-gb genes. Methods: Diagnosis of β-thal minor was established using hemoglobin HPLC analysis (Variant II, Bio-Rad) with increased HbA2 +/− increased HbF without mutant Hb. The DNA of consecutive cases with newly diagnosed β-thal minor was extracted from leucocytes. A multiplex PCR assay was used to detect the presence of 7 α-globin gene deletions: −α3.7,− α4.2, −− SEA, −− FIL, −− MED, −− THAI, − α20.5. Data on age, sex and erythrocyte indices (Hb, MCV, RBC and RDW) was recorded. An ANOVA was used to compare groups. P significance was established at 0.01 to adjust for multiple analysis. Results: 300 specimen were collected in 9 months. 34 were excluded because of poor DNA quality or presence of a mutant Hb. 25 patients (9.4%) had at least 1 α-globin gene deletion. Group1 included 241 patients, Group 2: 20 patients and Group 3: 5 patients. No differences for age or sex were present between the groups. Differences in erythrocyte indices are reported in Table 1. LogMCV was used to adjust for variance heterogeneity. Conclusions: 9.4% of β-thal minor have α-gb gene deletions. The only parameter that is significantly different between groups is MCV (Hb not reaching the pre-specified level of 0.01). It is therefore possible that differences in MCV between patients with β-thal minor can be explained by α-gb gene deletions. The concommittant presence of β-gb and α-gb mutations might improve the β-gb/α-gb imbalance implicated in ineffective erythropoiesis and a significant increase in MCV (and Hb but not reaching statistical significance in this study due to the sample size). However, this parameter does not have sufficient power to differenciate or identify β-thal minor patients with α-globin gene deletions. Other factors certainly influence MVC variability in β-thal minor patients and the independant influence of this variable (α-gb gene deletions) will require further investigation. Erythrocyte indices between groups Parameter Group Mean Std Deviation 95% CI P MCV 1 65.8657 3.9845 65.3580–66.3734 <.0001 2 70.0600 7.5579 66.5228–73.5972 3 73.2600 3.6011 68.7886–77.7314 Log MCV 1 1.8176 .02637 1.8142–1.8209 <.0001 2 1.8432 .04504 1.8221–1.8643 3 1.8644 .02152 1.8377–1.8912 RDW 1 16.0004 1.2800 15.8380–16.1628 .068 2 15.4500 1.8958 14.5627–16.3373 3 15.0200 2.0909 12.4238–17.6162 RBC 1 5.7081 .7369 5.6144–5.8018 .885 2 5.6835 .6729 5.3686–5.9984 3 5.5500 .6181 4.7826–6.3174 Hemoglobin 1 119.7759 14.9010 117.8851–121.6668 .038 2 127.3000 13.6501 120.9115–133.6885 3 129.4000 12.3410 114.0766–144.7234
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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 | 0.002 |
| 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 | 0.000 | 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".