Lipopolysaccharide Detection with Glycan-Specific Lectins─a Nonspecific Binding Approach Applied to Surface Plasmon Resonance
Bibliographic record
Abstract
The detection and classification of lipopolysaccharides (LPS), pivotal constituents of Gram-negative bacteria, are fundamental to the progression of biosensing technologies in fields such as healthcare, environmental monitoring, and food safety. This study presents an innovative approach utilizing a panel of glycan-selective lectins in conjunction with surface plasmon resonance (SPR) providing a novel perspective on the evolution of biosensors within the context of the ongoing tension between the highly selective, one-probe-one-target methodology and the broader, resource-intensive approach that integrates complex and costly technological tools into the biosensing discipline. Guided by the principles of lean development, we employed a panel of lectins to construct multiprobe detection profiles, thereby facilitating the precise classification of LPS variants while minimizing both variability and resource expenditure. Advanced machine learning techniques were applied to optimize feature selection and enhance classification accuracy, demonstrating that a minimal set of four lectins sustains exceptional predictive performance. This synergy between traditional affinity techniques and data science enhances assay engineering efficiency, scalability, and integration into routine workflows, supporting frontline pathogen monitoring. This innovative approach holds promise for addressing global health challenges, providing more profound insights into biosensing methodologies, and expanding pathogen screening networks closer to the public and health safety management bodies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".