Understanding Cholesterol–Mycolic Acid–Phosphatidylcholine Interactions: Advancing Electrochemical Detection of Tuberculosis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Immunodetection of active tuberculosis (TB), including in human immnodeficiency virus (HIV)-positive patients, is crucial for effective treatment and disease elimination. Mycolic acid (MA) is a key antigen for detecting TB antibodies, although antimycolic acid antibodies (AMAAs) have not yet been isolated. However, AMAA levels are elevated in TB-infected patients and can interact with mycolic acid antigen (MAA). A significant challenge in TB detection arises from the cross-reactivity of cholesterol (Ch) and anticholesterol antibodies (AChAs) due to the cholesteroid nature of MAA. For the first time, the cholesteroidal nature of MAA has been established through electrochemical experiments and supported by theoretical density functional theory (DFT) calculations. An electrochemical TB immunosensor was developed by using a glassy carbon electrode modified with MAA-confined activated carbon (GCE–AC–MAA). Electrochemical analysis of TB-positive serum revealed activity similar to that of AChA in the presence of phosphatidylcholine (PC)/MAA, demonstrating cross-reactivity. The optimal detection protocol involved preincubating TB serum in liposomes to free AMAA, followed by electrochemical immunosensor detection. DFT calculations showed that cholesterol interacts with MAA (p-band center, ε p = −11.3922 eV) but more strongly in the presence of PC/MAA (ε p = −11.2695 eV). As the p-band center approaches the Fermi level, the bond length between cholesterol and the adsorbent shortens, increasing the interaction strength. The results indicate that the shorter the bond length between the adsorbate (Ch) and the adsorbent (PC, MA, or PC/MA), the stronger the p-band center (i.e., strong binding to the atomic nucleus). These findings provide valuable insights for improving TB immunodetection strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".