Study of Cell Antigens and Intracellular DNA by Identification of Element-Containing Labels and Metallointercalators Using Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
The enumeration of absolute cell numbers and cell proliferation in clinical samples is important for diagnostic and research purposes. Detection of cellular DNA with fluorescent dyes is the most commonly used approach for cell enumeration in cytometry. Inductively coupled plasma mass spectrometry (ICPMS) has been recently introduced to the field of protein and cell surface antigen identification via ICPMS-linked immunoassays using element-labeled affinity reagents such as gold and lanthanide-conjugated antibodies. In the present work, we describe novel methods for using metallointercalators that irreversibly bind DNA and low concentrations of rare earth metals added to cell growth media for rapid and sensitive measurement of cell numbers by mass spectrometry. We show that Ir- and Rh-containing metallointercalators are useful reagents for labeling cells and normalizing signals when studying antigen expression on different types and numbers of cells. Results are presented for solution analysis performed by conventional ICPMS and compared to measurements obtained on the novel flow cytometer mass spectrometer (FC-MS) instrument, designed to analyze multiple antigens and DNA simultaneously in single cells.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".