Point of care sepsis diagnosis: Exploring microfluidic techniques for sample preparation, biomarker isolation, and detection
Bibliographic record
Abstract
According to the third international consensus definition (sepsis-3), sepsis is defined as life-threatening organ dysfunction resulting from an uncontrolled host response to infection. Sepsis remains a leading cause of global mortality, largely due to the difficulty of achieving a timely diagnosis. The conventional diagnostic approaches for sepsis often face limitations in speed, portability, sensitivity, and specificity, which can lead to delayed or missed diagnoses. In response, microfluidic devices have emerged as powerful tools for point-of-care precise sample handling and preparation, enabling efficient isolation and detection of sepsis-causing bacteria and biomarkers. Fabrication techniques of these microfluidic devices, ranging from photolithography to xurography, have significantly advanced and paved the way for complex designs and improved functionality. Microfluidic platforms offer various benefits in sepsis diagnosis and prognosis. They facilitate rapid and automated sample processing, enhancing turnaround times and reducing the risk of contamination. Moreover, the integration of microfluidic systems with advanced detection methods enables the simultaneous analysis of multiple biomarkers, thereby enhancing diagnostic accuracy and prognostic capabilities. This review explores the evolution of sepsis diagnosis from traditional lab based methods to the use of microfluidic technology that can facilitate point of care diagnostics and discusses emerging trends in this field.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".