Immune Checkpoints Receptors Expression of Macrophage/Monocytes in Response to Acute Viral Respiratory Infection
Bibliographic record
Abstract
Background: We aimed to monitor the phenotypic changes in macrophages and their polarization in patients with acute viral respiratory diseases, including coronavirus disease diagnosis, focusing on the variations in the percentages of macrophages and monocytes and their sub-populations in those patients compared to healthy control. Moreover, we defined the correlation between macrophage subtypes and some inflammatory indices. Methods: Twenty-seven patients with clinical and radiologic diagnosis of acute viral respiratory infection admitted in Al-Azhar and Assiut University hospitals were recruited. Fresh peripheral blood samples were collected from all patients and healthy controls for flow cytometric analysis using BD FACSCanto II analyzer equipped with three lasers. Results: Compared to healthy controls, accumulation of cluster of differentiation (CD)11B + CD68 + macrophages (M) (P = 0.018), CD274 + M1 (P = 0.01), CD274 + M2 (P < 0.001), and CD80 - CD206 + M2 (P = 0.001) was more evident in patients. Moreover, CD273 + M2 (P = 0.03), CD80 + CD206 - M1 (P = 0.002), and CD80 + CD86 + M1 (P = 0.002) were highly expressed in controls compared with patients. Conclusion: The examination of clinical specimens obtained from patients with signs of acute respiratory viral infection showed the role of the macrophage in the immune response. Dysfunction in macrophages results in heightened immune activity and inflammation, which plays a role in the progression of viral diseases and the emergence of accompanying health issues. This malfunction in macrophages is a common characteristic seen in various viruses, making it a promising focus for antiviral therapies with broad applicability. The immune checkpoint could be a target for immune modulation in patients with severe symptoms. J Clin Med Res. 2024;16(5):232-242 doi: https://doi.org/10.14740/jocmr5098
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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 | 0.000 |
| 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".