Vibrational Spectroscopic Based Approach for Diagnosing <i>Babesia bovis</i> Infection
Bibliographic record
Abstract
Babesia bovis parasites present a serious and significant health concern for the beef and dairy industries in many parts of the world. Difficulties associated with the current diagnostic techniques include the following: they are prone to human error (microscopy) or expensive and time-consuming (polymerase chain reaction) to perform. Little is known about the biochemical changes in blood that are associated with Babesia infections. The discovery of new biomarkers will lead to improved diagnostic outcomes for the cattle industry. Vibrational spectroscopic technologies can record a chemical snapshot of the entire organism and the surrounding cell thereby providing a phenotype of the organism and the host infected cell. Here, we demonstrate the applicability of vibrational spectroscopic imaging techniques including Atomic Force Microscopy Infrared (AFM-IR) and confocal Raman microscopy to discover new biomarkers for B. bovis infections. Furthermore, we applied Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) to detect B. bovis in red blood cells (RBCs). Based on changes in the IR spectral bands, with ATR-FTIR in combination with Partial Least Squares-Discriminant Analysis we were able to discriminate infected samples from controls with a sensitivity and specificity of 92.0% and 91.7%, respectively, in less than 2 min, excluding sample extraction and preparation. The proposed method utilized a lysis approach to remove hemoglobin from the suspension of infected and uninfected cells, which significantly increased the sensitivity and specificity compared to measurements performed on intact infected red blood cells (intact infected RBC, 77.3% and 79.2%). This work represents a holistic spectroscopic study from the level of the single infected RBC using AFM-IR and confocal Raman to the detection of the parasite in a cell population using ATR-FTIR for a babesiosis diagnostic.
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 | 0.001 | 0.001 |
| 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".