Follow-up study of abnormal biological indicators and gene expression in the peripheral blood of three accidentally exposed persons
Bibliographic record
Abstract
In order to identify biomarkers for early diagnosis and/or for therapeutic targets in the delayed health effects of ionizing radiation, we analyzed the subgroups of lymphocytes, serum protein levels and gene expression profiles in the peripheral blood of three ⁶⁰Co γ-ray accidentally exposed persons during the three years after irradiation. Flow cytometry analyses and agarose gel electrophoresis were applied to investigate the subgroups of lymphocytes and the composition of serum proteins, respectively. Gene expression profiling was obtained using a whole genome gene expression chip assay. Both the percentage of CD4+ T lymphocytes and the ratio of Th to Ts were reduced compared with the normal control values. The percentage of albumin decreased whereas beta globulin increased. There were 285 up-regulated and 446 down-regulated genes in irradiated samples relative to the control samples. The expression of KDR, CEACAM8 and OSM was validated by RT-PCR. The majority of the differentially expressed genes encode proteins associated with the immune response, inflammation, oncogenesis, cell structure, oxidative stress, neuro-hormone regulation, reproduction, susceptibility to psychiatric disorders, or transcriptional regulation. We have identified a number of promising novel candidates that have potential for serving as biomarkers for delayed damage. Furthermore, the changes in the immunological indicator CD4+ T cells, and the ratio of CD4+ T to CD8+ T cells may be biomarkers for the prediction of delayed damage by ionizing radiation. The findings of our study are useful for forming a comprehensive understanding of the mechanisms underlying the delayed effects of ionizing radiation.
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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 | 0.001 | 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 | 0.000 |
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".