LGL Leukemia and HTLV
Bibliographic record
Abstract
Samples were obtained from 53 large granular lymphocytic leukemia (LGLL) patients and 10,000 volunteer blood donors (VBD). Sera were screened in an HTLV-1 enzyme immunoassay (EIA) and further analyzed in peptide-specific Western blots (WB). DNAs were analyzed by HTLV-1, -2, -3, and -4-specific PCR. Forty four percent of LGLL patients vs. 0.12 % of VBD had anti-HTLV antibodies via EIA (p < 0.001). WB and PCR revealed that four LGLL patients (7.5%) vs. one VBD patient (0.01%) were infected with HTLV-2 (p < 0.001), suggesting an HTLV-2 etiology in a minority of cases. No LGLL patient was positive for HTLV-1, -3, or -4, whereas only one EIA-positive VBD was positive for HTLV-1 and none for HTLV-3 or -4. The HTLV EIA-positive, PCR-negative LGLL patients' sera reacted to epitopes within HTLV p24 gag and gp21 env. Other then the PTLV/BLV viruses, human endogenous retroviral element HERV K10 was the only sequence homologous to these two HTLV peptides, raising the possibility of cross-reactivity. Although three LGLL patients (5.7%) vs. none of 110 VBD patients tested positive for antibodies to the homologous HERV K10 peptide (p = 0.03), the significance of the anti-HTLV seroreactivity observed in many LGLL patients remains unclear. Interestingly, out of 36 HTLV-1-positive control subjects, 3 (8%) (p = 0.014) were positive for antibodies to HERV K10; all three had myelopathy. Out of 64 HTLV-2-positive control subjects 16 (25%) (p = <0.001) were positive for HERV K10 antibodies, and 4 (6%) of these had myelopathy. Out of 22 subjects with either HTLV-1 or -2 myelopathy, 7 (31.8%) were positive for HERV K10 antibodies, and out of 72 HTLV-infected subjects without myelopathy, 12 (16.7%) were positive for anti-HERV K10 antibodies (p = 0.11). The prevalence of anti-HERV K10 antibodies in these populations and the clinical implications thereof need to be pursued further.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".