Assessment of cell free mitochondrial DNA as a biomarker of disease severity in different viral infections
Bibliographic record
Abstract
Objective: Cell Free mitochondrial DNA (CF mt-DNA) has emerged as a novel biomarker to investigate disease pathophysiology of different infections. The present study was designed to elucidate the association between CF mt-DNA, IL-6 and viral load in HIV, HBV and HCV infections and predict its role as a potential biomarker to assess the disease severity in viral infections. Methods: Total 120 blood samples were collected from January 2018 to December 2018 of HIV, HBV and HCV patients and healthy controls (30 samples in each group). DNA and RNA were extracted from the serum to determine the levels of CF mt-DNA and viral load, respectively. IL-6 from the serum of infected individuals was quantified with ELISA. Results: HCV patients showed the highest levels of CF mt-DNA, IL-6 and viral load, followed by HBV and HIV. Significant correlation was found between CF mt-DNA and IL-6 among the HBV patients (p=0.017). However, no significant correlation of CF mt-DNA was observed with IL-6 in HIV and HCV or with the viral load in any of the three infections. Conclusion: Elevated CF mt-DNA indicates its role in severity of viral infections. Independence of CF mt-DNA expression from viral load and IL-6 in case of HIV and HCV suggests involvement of other inflammatory pathways regulating CF mt-DNA elevation. doi: https://doi.org/10.12669/pjms.36.5.2476 How to cite this:Ali Z, Waseem S, Anis RA, Anees M. Assessment of cell free mitochondrial DNA as a biomarker of disease severity in different viral infections. Pak J Med Sci. 2020;36(5):860-866. doi: https://doi.org/10.12669/pjms.36.5.2476 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".