Spectral flow cytometry analysis revealed peripheral blood immune cell population changes in Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Spectral flow cytometry is an emerging tool for characterizing cell populations based on protein markers. We utilized technology to profile peripheral blood mononuclear cells (PBMCs) from patients with chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF) and healthy controls, aiming to identify distinct immune signatures. We recruited five IPF patients (median age of 69 years;100% male), five COPD patients (68 years; 80% male), and five healthy controls (70 years; 80% male). PBMCs were isolated from whole blood and cryopreserved and subsequently stained with a 35-antibody panel. Data acquisition was performed using the Cytek® Aurora 5-laser cytometer, and quality control was performed using FlowJo gating software. Python packages Flowkit and scanpy were used for further downstream analysis. We found that monocytes, both classical and non-classical, were higher in IPF but compared to COPD and controls (p =0.008). However, CD8+ T-cells expressing high levels of CCR7 were downregulated in both IPF and COPD (p =0.008), and this cell population was negatively correlated with COPD-assessment score in COPD participants (R=-0.75, p < 0.01). Furthermore, CD4+ T-cells with high expression levels of KLRG1 were also downregulated in COPD but not in IPF (p = 0.032). These findings highlight distinct immune dysregulation profiles in COPD and IPF, with IPF characterized by an increase in monocytes while COPD patients had reduced T-cell subsets associated with worsening symptoms.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".