Cytoskeletal Tropomyosin as a Biomarker in <i>Clostridium difficile</i> Infection
Bibliographic record
Abstract
Background: Current diagnostics of Clostridium difficile infection (CDI) heavily relies on detection of the disease-causing organism. The objective of this study was to investigate a cytoskeletal protein, tropomyosin (Tpm), as a CDI biomarker. Methods: Fecal Tpm was tested by monoclonal antibodies (mAbs) in a 12-month prospective study. Remnant diarrheal clinical specimens and relevant clinical data were collected. The CDI positive (CDI + , n = 230) and CDI negative (CDI - , n = 228) groups were composed of samples testing positive or negative by polymerase chain reaction (PCR) (Xpert ® C. difficile /Epi, Cepheid), respectively. The other enteric pathogen (OEP) group (n = 52) was composed of specimens tested for the presence of other enteric pathogens or parasites by routine testing methods. Extracted fecal Tpm was detected by Western blot and the results were correlated with CDI based on clinical and microbiology laboratory data. Results: A total of 510 stool specimens were tested. Tpm is not stable in stool, suggesting the utility of fresh specimens. In the CDI + group, specificity and sensitivity of Tpm detection in correlation with a CDI were 93.2% and 53.7%, respectively, when only “true CDI” and “not CDI” were analyzed (110 samples). For CDI + samples, 23% did not satisfy CDI clinical signs. Tpm positives in the CDI - group (8.3%) had inflammatory bowel diseases. Conclusion: Tpm has a potential role as a CDI biomarker in combination with C. difficile PCR and an appropriate clinical evaluation. However, non-muscle Tpm, as a biomarker for CDI, suffers from a low sensitivity in our study. Therefore further investigation using larger cohorts is needed. J Clin Med Res. 2019;11(2):98-105 doi: https://doi.org/10.14740/jocmr3696
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".