Migrants and Migration in Modern North America: Cross-Border Lives, Labor Markets, and Politics by Dirk Hoerder and Nora Faires, eds. (review)
Bibliographic record
Abstract
Paraneoplastic neurological syndromes (PNS) are devastating neurological disorders secondary to cancer, associated with onconeural autoantibodies. Such antibodies are directed against neuronal antigens aberrantly expressed by the tumor. The detection of onconeural antibodies in a patient is extremely important in diagnosing a neurological syndrome as paraneoplastic (70% is not yet known to have cancer) and in directing the search for the underlying neoplasm. At present six onconeural antibodies are considered 'well characterized' and recognize the antigens HuD, CDR62 (Yo), amphiphysin, CRMP-5 (CV2), NOVA-1 (Ri), and Ma2. The gold standard of detection is the characteristic immunohistochemical staining pattern on brain tissue sections combined with confirmation by immunoblotting using recombinant purified proteins. Since all six onconeural antibodies are usually analyzed simultaneously and objective cut-off values for these analyses are warranted, we developed a multiplex assay based on Luminex technology. Reaction of serial dilutions of six onconeural standard sera with microsphere-bound antigens showed lower limits of detection than with Western blotting. Using the six standard sera at a dilution of 1:200, the average within-run coefficient of variation (CV) was 4% (range 1.9-7.3%). The average between-run within-day CV was 5.1% (range 2.9-6.7%) while the average between-day CV was 8.1% (range 2.8-11.6%). The shelf-life of the antigen coupled microspheres was at least two months. The sensitivity of the multiplex assay ranged from 83% (Ri) to 100% (Yo, amphiphysin, CV2) and the specificity from 96% (CV2) to 100% (Ri). In conclusion, Luminex-based multiplex serology is highly reproducible with high sensitivity and specificity for the detection of onconeural antibodies. Conventional immunoblotting for diagnosis of onconeural antibodies in the setting of a routine laboratory may be replaced by this novel, robust technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".