Identify. Quantify. Predict. Why Immunologists Should Widely Use Molecular Imaging for Coronavirus Disease 2019
Bibliographic record
Abstract
Molecular imaging using PET/CT or PET/MRI has evolved from an experimental imaging modality at its inception in 1972 to an integral component of diagnostic procedures in oncology, and, to lesser extent, in cardiology and neurology, by successfully offering in-vivo imaging and quantitation of key pathophysiological targets or molecular signatures, such as glucose metabolism in cancerous disease. Apart from metabolism probes, novel radiolabeled peptide and antibody PET tracers, including radiolabeled monoclonal antibodies (mAbs) have entered the clinical arena, providing the in-vivo capability to collect target-specific quantitative in-vivo data on cellular and molecular pathomechanisms on a whole-body scale, and eventually, extract imaging biomarkers possibly serving as prognostic indicators. The success of molecular imaging in mapping disease severity on a whole-body scale, and directing targeted therapies in oncology possibly could translate to the management of Coronavirus Disease 2019 (COVID-19), by identifying, localizing, and quantifying involvement of different immune mediated responses to the infection with SARS-COV2 during the course of acute infection and possible, chronic courses with long-term effects on specific organs. The authors summarize current knowledge for medical imaging in COVID-19 in general with a focus on molecular imaging technology and provide a perspective for immunologists interested in molecular imaging research using validated and immediately available molecular probes, as well as possible future targets, highlighting key targets for tailored treatment approaches as brought up by key opinion leaders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.023 |
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".