Microfluidic Technology for Antibacterial Resistance Study and Antibiotic Susceptibility Testing: Review and Perspective
Bibliographic record
Abstract
A review on microfluidic technology for antibacterial resistance study and antibiotic susceptibility testing (AST) is presented here. Antibiotic resistance has become a global health crisis in recent decades, severely threatening public health, patient care, economic growth, and even national security. It is extremely urgent that antibiotic resistance be well looked into and aggressively combated in order for us to survive this crisis. AST has been routinely utilized in determining bacterial susceptibility to antibiotics and identifying potential resistance. Yet conventional methods for AST are increasingly incompetent due to unsatisfactory test speed, high cost, and deficient reliability. Microfluidics has emerged as a powerful and very promising platform technology that has proven capable of addressing the limitation of conventional methods and advancing AST to a new level. Besides, potential technical challenges that are likely to hinder the development of microfluidic technology aimed at AST are observed and discussed. To conclude, it is noted that (1) the translation of microfluidic innovations from laboratories to be ready AST platforms remains a lengthy journey and (2) ensuring all relevant parties engaged in a collaborative and unified mode is foundational to the successful incubation of commercial microfluidic platforms for AST.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".