Rapid detection of circulating fibrocytes by flowcytometry in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
BACKGROUND: Current protocols for detection of circulating fibrocytes (CFs) in peripheral blood described in various pulmonary and nonpulmonary disorders involve complex and time consuming, non standardized techniques. OBJECTIVE: Testing a method to rapidly detect and quantify CFs using whole blood lysis flow cytometry-based assay in patients with idiopathic pulmonary fibrosis (IPF) and healthy controls. METHODS: One milliliter of venous blood sample in ethylenediaminetetraacetic acid (EDTA) from 33 IPF patients and 35 healthy control subjects was collected. Using whole blood lysis method peripheral blood leukocytes were labeled with monoclonal antibodies for cell surface (CD34 and CD45) and intracellular markers (collagen-1) for flow cytometric analysis. CFs were defined as CD45(+) cells coexpressing collagen-I and CD34 molecules. RESULTS: In 29 (87.8%) IPF patients and 10 (28.5%) control subjects, a well-defined highly granular CD45(+) cell population was detected in dot plots generated by side scatter properties of CD45(+) cells. These CD45(+) cells were identified as CFs on the basis of coexpression of collagen-I and CD34; none of the other cell types in the peripheral blood were labeled with these monoclonal antibodies. In IPF patients the percentage of CFs was significantly higher compared to healthy controls (median (range): 1.37% (0.52-5.65) and 1.04% (0.1-1.84), respectively; P = 0.03). CONCLUSIONS: Whole blood lysis method combined with fluorescence-activated cell sorting (FACS) allows detecting a well-defined homogeneous population of CFs. This method is simple, reproducible, and provides an accurate and rapid estimation of CFs.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".