Single-cell proteomic analysis of peripheral blood mononuclear cells in patients with pulmonary long COVID
Bibliographic record
Abstract
Abstract An estimated 15% of patients are expected to continue to exhibit respiratory symptoms after their SARS-CoV-2 infection. The mechanisms behind these symptoms remain unknown. We recruited twenty-three patients (65% female) who were on average 406 ± 251 days post-COVID-19. The cohort consists of primarily mild (non-hospitalized) patients (87%), with an average age of 43 ± 15 years. Nine of these patients had self-reported dyspnea, which we defined as pulmonary long COVID (PLC). The remaining 14 patients recovered without persistent dyspnea. We performed a 38-panel cytometry by time-of-flight on the peripheral blood mononuclear cells of this cohort. Quality control was performed using FloJo. Further analysis, was done using the CyCombine, CATALYST, and FlowSom packages in R. Our final annotation uncovered 17 different cell clusters. In PLC patients, double-positive T-cells (DPTs) were significantly increased (Wilcoxon p<0.05), indicating that T-cell development or differentiation may be altered in these patients. Though DPTs are known to be increased in females, there was no significant difference in the proportion of DPTs between males and females in our cohort. PLC stem cells had increased expression of CD304 and CD200R (Wilcoxon p<0.05). CD304 is as a co-receptor for SARS-CoV-2, while CD200R is a limiter of pro-inflammatory activity. These findings indicate that the immune landscape in PLC patients is altered, compared to those who recover without residual pulmonary symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".