iGEM Calgary 2014: B.s. Detector, A Multiplexed Rapid Diagnostic Device
Bibliographic record
Abstract
Febrile diseases such as meningitis, dengue fever, and typhoid fever are symptomatically similar to malaria and are thus often misdiagnosed in developing countries which lack the resources to maintain suitable healthcare infrastructures. Failure to accurately diagnosis such diseases within a patient is of particular concern as misdiagnosis prevents the appropriate treatments from being administered in a timely manner or often at all, which results in unnecessary human suffering and a significant financial burden on the patient’s respective health care system (Mabey, Peeling, Ustianowski, & Perkins, 2004). To address this global healthcare issue, the 2014 Calgary iGEM team (International Genetically Engineered Machine) has engaged with various stakeholders including researchers and end-users in developing countries with the hopes of mitigating the frequency of misdiagnosis. Using synthetic biology, we aim to develop a novel, nucleic acid-based, point-of-care device capable of diagnosing multiple infectious diseases simultaneously. Specifically, we are engineering Bacillus subtilis to generate a chromophoric reporter protein in response to pathogenic genetic markers indicative of said diseases. This engineered strain of B. subtilis will lie dormant as a collection robust bacterial spores in a portable, handheld device until ready to be activated and used. In theory, by sporulating the B. subtilis, we can effectively increase the shelf-life and durability of the device as it is transported to the end-user in the developing world. Our device will use a transcriptionally-regulated genetic circuit, in conjunction with the innate mechanism of homologous recombination found within B. subtilis, to detect and report the presence of multiple species of pathogens within the patient. Using only a minute sample of blood, the final device will enable users to differentiate between diseases based on a colorimetric output. Specialized training or outside resources will not be necessary to use the device and interpret its results. The strength of this diagnostic method lies in its modularity and high level of customization. Ultimately, our system is a platform technology which can be adapted to detect a wide variety pathogens by modifying the genetic markers to which our engineered B. subtilis binds to.
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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 | 0.002 | 0.003 |
| 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".