A One-Minute Blood Test to Monitor Immune Responses in COVID-19 Patients and Predict Clinical Risks of Developing Moderate to Severe Symptoms
Bibliographic record
Abstract
Abstract Coronavirus disease 2019 (COVID-19) has brought enormous loss and interruption to human life and the global economy since the first outbreak reported in China between late 2019 to early 2020, and will likely remain a public health threat in the months and years to come. Upon infection with SARS-CoV-2, the virus that causes COVID-19, most people will develop no or mild symptoms, however, a small percentage of the population will become severely ill, require hospitalization, intensive care, and some succumb to death. The current knowledge of COVID-19 disease progression with worsening symptom complex implicates the critical importance of identifying patients with high clinical risk compared to those who would be at lower risk for disease control and patient management with better therapeutic output. Currently no clinical test is available that can predict risk factors and immune status change at different severity scales. The immune system plays a critical role in the defense against infectious diseases. Extensive research has found that COVID-19 patients with poor clinical outcomes differ significantly in their immune responses to the virus from those who exhibit milder symptoms. We previously developed a nanoparticle-enabled blood test that can detect the humoral immune status change in animals. In this study, we applied this new test to analyze the immune response in relation to disease severity in COVID-19 patients. From the testing of 153 COVID-19 patient samples and 142 negative controls, we detected statistically significant differences between COVID-19 patients with no or mild symptoms from those who developed moderate to severe symptoms. Mechanistic study suggests that these differences are associated with type 1 versus type 2 immune responses. We conclude that this new rapid test could potentially become a valuable clinical tool for COVID-19 patient risk stratification and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".