Development of Impedimetric Immunosensors for the Diagnosis of DOCK8 and STAT3 Related Hyper‐Immunoglobulin E Syndrome
Bibliographic record
Abstract
Abstract Hyper Immunoglobulin E syndrome (HIES) is a rare inherited inborn error of primary immunodeficiency. HIES is characterized by high levels of serum immunoglobulin E (IgE), severe eczema, which overlap with atopic dermatitis, as well as lung infections and even death. HIESs are genetically inherited by heterozygous or homozygous mutations in signal transducer and activator of transcription 3 (STAT3) or Dedicator of cytokinesis 8 (DOCK8) genes, in autosomal dominant and recessive forms, respectively. Therefore, the early detection of DOCK8 and STAT3 protein levels in humans would facilitate the early diagnosis of these disorders and thus, help in the disease management. Here, we present the development of immunosensors for the detection of DOCK8 and STAT3 using electrochemical impedance spectroscopy in a label‐free format. The immunosensors were fabricated by the covalent attachment of specific antibodies for DOCK8 and STAT3 on gold electrodes via cysteamine/phenylene diisothiocyanate linkers. The detection was achieved by monitoring the change in the charge transfer resistance (Rct) of ferro/ferricyanide redox couple upon binding of the proteins to the immunosensor surface. These biosensors enabled the detection of DOCK8 and STAT3 levels with low detection limits of 1.2 and 9.0 pg/ml, respectively. The immunosensor was also applied for the detection of DOCK8 in human serum samples showing high recovery percentages which indicates great promise of this method for early diagnosis of HIES in newborn infants.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".