Detection of malignant <scp>B</scp> lymphocytes by <scp>PCR</scp> clonality assay using direct lysis of cerebrospinal fluid and low volume specimens
Bibliographic record
Abstract
INTRODUCTION: The diagnosis of lymphoid malignancies is often challenging in paucicellular specimens. PCR may also be limited by insufficient cells for DNA isolation and incomplete coverage of gene rearrangements. This study aims to evaluate a PCR method for IgH clonality using direct cell lysates. METHODS: PCR amplification used cell lysate from detergent-based lysis and BIOMED-2 primers. CSF specimens were tested for 20 patients with primary CNS lymphoma or systemic lymphoma suspected for CNS involvement. Cytology and flow cytometry analysis was performed in parallel with PCR. RESULTS: Direct lysis produced a better yield than the column-based method for DNA isolation. PCR using lysate showed an efficiency of clonality detection from a minimum of 20 tumor cells. PCR clonality was found in nine of the 20 CSFs, and positive PCR was concordant with both cytology and flow cytometry in seven cases. There were two cases positive for PCR, but indeterminate for flow cytometry because of insufficient cell events. Of the eleven PCR-negative cases, two were considered as false negative, as flow cytometry showed positive for malignant cells. The PCR was also performed successfully with a specimen from the anterior chamber of the eye. CONCLUSION: PCR clonality with direct cell lysis of CSF is feasible, and it may overcome the limitation of DNA isolation. This PCR method may be particularly useful for small volume and low cell CSF when flow cytometry is inconclusive.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".