Lymphocytotoxic Activity of Monoclonal Immunoglobulins in Plasmalymphocytic Diseases
Bibliographic record
Abstract
95 of 1,019 (9.3%) sera with monoclonal immunoglobulins (MIg) were found to have cold-reacting lymphocytotoxins (LCT). There was no difference in the prevalence of LCT in multiple myeloma, macroglobulinemia, cancer, lymphoma or benign monoclonal gammopathy. Prevalence of LCT was similar in various classes and types of MIg with the exception of the IgG/lambda group in which LCT were more common than in IgG/K (p = 0.013). IgMs had the most potent whereas IgAs had the weakest LCT activity. MIg were purified from 61 LCT-positive sera. 25 pure MIg (41%) had LCT activity. In the rest, LCT activity resided in other fractions. 64 sera with LCT were tested against B and T cells; 56% were equally cytotoxic to B and T cells, 39% killed more B cells and 5% killed more T cells. 18 purified lymphocytotoxic MIg killed both B and T cells. When serial dilutions of sera, and of purified MIg were tested, in all but one instance the reactivity with the T cells weakened more than that with the B cells. Lymphocytotoxins absorbed to and eluted from the peripheral blood lymphocytes or separately from B or from T cells retained LCT activity against B and T lymphocytes. It may be concluded that about one tenth of sera with M components have lymphocytotoxic activity and that in about 40% of these positive sera, this activity is related to the monoclonal immunoglobulins. LCT react with both B and T cells. Antilymphocyte activity of MIgs may play a role in immunoregulatory abnormalities in plasmalymphocytic diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".