The Modulation of Septic Shock: A Proteomic Approach
Bibliographic record
Abstract
Sepsis poses a significant challenge due its lethality, involving multiple organ dysfunction and impaired immune responses. Among several factors affecting sepsis, monocytes play a crucial role; however, their phenotype, proteomic profile, and function in septic shock remain unclear. Our aim was to fully characterize the subpopulations and proteomic profiles of monocytes seen in septic shock cases and discuss their possible impact on the disease. Peripheral blood monocyte subpopulations were phenotype based on CD14/CD16 expression by flow cytometry, and proteins were extracted from the monocytes of individuals with septic shock and healthy controls to identify changes in the global protein expression in these cells. Analysis using 2D-nanoUPLC-UDMSE identified 67 differentially expressed proteins in shock patients compared to controls, in which 44 were upregulated and 23 downregulated. These proteins are involved in monocyte reprogramming, immune dysfunction, severe hypotension, hypo-responsiveness to vasoconstrictors, vasodilation, endothelial dysfunction, vascular injury, and blood clotting, elucidating the disease severity and therapeutic challenges of septic shock. This study identified critical biological targets in monocytes that could serve as potential biomarkers for the diagnosis, prognosis, and treatment of septic shock, providing new insights into the pathophysiology of the disease.
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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 | 0.001 | 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".