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Record W3214381411 · doi:10.1182/blood-2021-151731

Multispectral Imaging for Microchip Electrophoresis Enables Point-of-Care Newborn Hemoglobin Variant Screening

2021· article· en· W3214381411 on OpenAlexaff
Ran An, Yuning Huang, Anne D. Rocheleau, Alireza Avanaki, Priyaleela Thota, Yuncheng Man, Zoe Sekyonda, Catherine Segbefia, Yvonne Adomakoh, Enoch Mensah, Isaac Odame, Umut A. Gürkan

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsNewborn screeningMedicineContext (archaeology)PopulationPediatricsHemoglobin variantsHemoglobinBiologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction: Hemoglobin (Hb) disorders are among the world's most common monogenic diseases. Nearly 7% of the world's population carry Hb gene variants. Sickle cell disease (SCD) arises when hemoglobin variant mutations are inherited homozygously (HbSS) or paired with another β-globin gene mutation. Epidemiological modeling shows that universal screening could save the lives of up to 9.8 million newborns with SCD by 2050 with 85% born in sub-Saharan Africa (SSA). The World Health Organization (WHO) estimates that early diagnosis of SCD coupled with intervention programs would prevent 70% of existing SCD mortality. SCD newborn screening performed in centralized laboratories has dramatically reduced SCD mortality in resource-rich countries. SCD newborn screening requires sensitive detection of low levels of certain Hb variants (i.e., sickle Hb, HbS) in the context of high levels of expression of other Hb variants (i.e., fetal Hb, HbF). The current centralized tests used for newborn screening for SCD are high performance liquid chromatography (HPLC) and isoelectric focusing. However, in SSA and central India, where >90% of annual SCD births occur, newborn screening programs have not been implemented universally due to the cost and logistical burden of laboratory diagnostic tests. As a result, there is a need for affordable, portable, easy-to-use, accurate, point-of-care (POC) tests to facilitate decentralized hemoglobin testing in low-resource settings to enable nationwide newborn screening. Methods: We have leveraged the WHO listed Hb electrophoresis test and developed a POC electrophoresis microchip, Gazelle-Multispectral (Fig. 1A) that enables sensitive detection and identification of Hb types under both white and ultraviolet (UV-410nm) light illumination. We hypothesized that the high absorbance of Hb at UV wavelength would enhances the limit of detection and allow the detection of Hb types at low concentrations thus enabling SCD screening in newborns. To test this hypothesis, we have conducted clinical testing of over 321 subjects under the age of 6 months many with Hb variants including HbA (normal hemoglobin), HbF, HbS, and HbC (hemoglobin C variant) at Korle Bu Teaching Hospital, the largest public hospital in Ghana, under IRB-approved protocol. Results and Discussion: Separated Hb variants are imaged under both white light illumination (Fig. 1B) and UV light illumination (Fig. 1C). The acquired data under white light illumination demonstrates the natural red color of hemoglobin but does not have good enough sensitivity to detect low concentration Hb types (Fig. 1B). The acquired data under UV light illumination is used for sensitive and accurate identification and quantification of Hb variants (Fig. 1C&D), and to automatically generate report for result interpretation (Fig. 1E). Following the Standards for Reporting of Diagnostic Accuracy Studies guideline, 294 out of 321 (91.6%) tests were recognized as 'Valid', while 27 out of 321 (8.4%) tests were recognized as 'Inconclusive'. Gazelle-Multispectral determined Hb variant levels demonstrated high association with Person Correlation Coefficients of 0.97, 0.97, 0.89, and 0.94 for Hb A, Hb F, HbS, and HbC compared to HPLC. Bland-Altman analysis demonstrated high reproducibility with mean bias±1.96×standard deviation of 2.3%±11.4%, -2.7%±-13.3%, 0.8%±6.2%, and -0.3%±2.7%, for HbA, HbF, HbS, and HbC, respectively. Subjects with disease were identified at 100% sensitivity and specificity from normal subjects and subjects with trait. Additionally, subjects with trait were identified at 98.1% sensitivity and 97.0% specificity from normal subjects (Fig 1. Table). Conclusion: In summary, Gazelle-Multispectral imaging enables affordable and rapid identification of common Hb variants in newborns at the point-of-need. The Gazelle-Multispectral reader provide animated on-screen instructions with step-by-step guidance for test procedures to minimize user errors. The internally integrated data analysis algorithm automatically reports Hb type identification and quantification results in an objective and easily understandable manner. Gazelle-Multispectral is a versatile, mass-producible, multispectral detection-based electrophoresis platform for affordable, rapid, and accurate diagnostic and newborn screening programs for SCD at the POC in low resource settings where the prevalence of SCD is high. Figure 1 Figure 1. Disclosures An: Hemex Health: Consultancy, Patents & Royalties. Rocheleau: Hemex Health: Current Employment. Avanaki: Hemex Health: Current Employment. Thota: Hemex Health: Current Employment. Odame: Novartis: Other: Steering Committee; Global Blood Therapeutics: Other: DSMB; Novo Nordisk: Membership on an entity's Board of Directors or advisory committees. Gurkan: Hemex Health, Inc.: Current Employment, Patents & Royalties; Biochip Labs: Patents & Royalties; Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.006
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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