Elevated serum gamma globulins in apparently healthy Nigerians living in Ogbomoso: a possible manifestation of phagocytic dysfunction
Bibliographic record
Abstract
Background: Serum protein abnormalities, particularly elevated gamma globulins (hypergammaglobulinemia, HGG), have been reported in apparently healthy Nigerians living in Ogbomoso and elsewhere. Since the mechanisms for this phenomenon have not been fully substantiated, we hypothesized that impaired neutrophil phagocytosis could contribute to this condition. Methods: Healthy humans exhibiting HGG were identified using serum protein electrophoresis performed on cellulose acetate gel in barbital buffer (pH 8.6). GelQuant image analysis and quantitation software were further employed to quantify the gamma globulin fraction. Neutrophils were isolated from K3EDTA anticoagulated peripheral blood using Histopaque neutrophil isolation reagent. Neutrophil phagocytic activity was analyzed using a non-subjective commercial colorimetric phagocytosis assay kit. Results: The purity and viability of isolated neutrophils were approximately 94% and 92%, respectively. Ex-vivo phagocytic activity of neutrophils isolated from apparently healthy subjects exhibiting HGG, expressed as a percentage of the average absorbance of the control group, was 48.1 ± 8.6% which was significantly lower (p < 0.05) compared to the controls (98.9 ± 14.3%). Conclusion: Since neutrophils play crucial roles in innate immune responses, impairment of neutrophil phagocytic activity may lead to persistent antigenic stimulations of the adaptive immune system. This could in turn orchestrate gamma globulins expression leading to HGG. Statement of novelty: We demonstrated reduced neutrophil phagocytic activity as a possible basis for hypergammaglobulinemia in healthy Nigerians, perhaps for the first time.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".