Flow cytometry as an improved method for the titration of <i>Chlamydiaceae</i> and other intracellular bacteria
Bibliographic record
Abstract
Chlamydiaceae is a family of intracellular bacteria causing a range of diverse pathological outcomes. The most devastating human diseases are ocular infections with C. trachomatis leading to blindness and genital infections causing pelvic inflammatory disease with long-term sequelae including infertility and chronic pelvic pain. In order to enable the comparison of experiments between laboratories investigating host-chlamydia interactions, the infectious titer has to be determined. Titer determination of chlamydia is most commonly performed via microscopy of host cells infected with a serial dilution of chlamydia. However, other methods including fluorescent ELISpot (Fluorospot) and DNA Chip Scanning Technology have also been proposed to enumerate chlamydia-infected cells. For viruses, flow cytometry has been suggested as a superior alternative to standard titration methods. In this study we compared the use of flow cytometry with microscopy and Fluorospot for the titration of C. suis as a representative of other intracellular bacteria. Titer determination via Fluorospot was unreliable, while titration via microscopy led to a linear read-out range of 16 - 64 dilutions and moderate reproducibility with acceptable standard deviations within and between investigators. In contrast, flow cytometry had a vast linear read-out range of 1,024 dilutions and the lowest standard deviations given a basic training in these methods. In addition, flow cytometry was faster and material costs were lower compared to microscopy. Flow cytometry offers a fast, cheap, precise, and reproducible alternative for the titration of intracellular bacteria like C. suis. © 2016 International Society for Advancement of Cytometry.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".