Bibliographic record
Abstract
Human peripheral leukocytes are the key operatives in the human immune network, mediating immune responses through both innate and acquired immunity. While much progress has been made to elucidate the mechanisms of leukocyte functions, our understanding of the genetic blueprint of leukocytes and the molecular basis of their functioning is far from complete. This project was started in 1997 and aimed to profile gene expression of human peripheral leukocytes and to identify novel leukocyte-specific genes. More than 15,000 ESTs were generated from an adult leukocyte cDNA library, representing over 7,000 unique genes—or approximately one-third to one-half of the total number of genes expressed in leukocytes. During this project, we identified a novel EST showing sequence similarity to members of the C-type lectin superfamily. Further characterization revealed that this gene, CLEC-6, was a leukocyte-specific gene located in the C-type lectin superfamily cluster at chromosome 12q13, and generated a saccharide-modified polymer that showed no mannose-affinity. In order to identify genes differentially expressed in leukocytes during the immune system maturation, we generated over 5,000 umbilical cord blood ESTs. Northern in silico revealed that genes involved in the synthesis and functioning of MHC I molecules, including four MHC I molecules and beta-microglobulin, were down-regulated in the umbilical cord blood. To expand the utility of our leukocyte-derived ESTs, we developed a “LeukoChip” containing nearly 6,000 unique genes. With cDNA microarray, we explored the differential expression profile of coronary artery disease leukocytes, identified the up-regulated expression of three leukocyte-secreted proteins, and suggested the potential role of peripheral leukocyte in systemic inflammation during atherogenesis. In summary, the present leukocyte gene-profiling project combines genomic, computational and conventional molecular biology approaches to provide a basic molecular foundation that will be important for understanding gene expression and regulation in leukocyte function, development and pathogenesis.
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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 | 0.000 | 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) | 0.001 | 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".