Infrared Spectroscopic Identification of β-Thalassemia
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the potential of infrared (IR) spectroscopy as a fast and reagent-free adjunct tool in the diagnosis and screening of beta-thalassemia. METHODS: Blood was obtained from 56 patients with beta-thalassemia major, 1 patient with hemoglobin H disease, and 35 age-matched controls. Hemolysates of blood samples were centrifuged to remove stroma. IR absorption spectra were recorded for duplicate films dried from 5 microL of hemolysate. Differentiation between the two groups of hemoglobin spectra was by two statistical methods: an unsupervised cluster analysis and a supervised linear discriminant analysis (LDA). RESULTS: The IR spectra revealed changes in the secondary structure of hemoglobin from beta-thalassemia patients compared with that from controls, in particular, a decreased alpha-helix content, an increased content of parallel and antiparallel beta-sheets, and changes in the tyrosine ring absorption band. The hemoglobin from beta-thalassemia patients also showed an increase in the intensity of the IR bands from the cysteine -SH groups. The unsupervised cluster analysis, statistically separating spectra into different groups according to subtle IR spectral differences, allowed separation of control hemoglobin from beta-thalassemia hemoglobin spectra, based mainly on differences in protein secondary structure. The supervised LDA method provided 100% classification accuracy for the training set and 98% accuracy for the validation set in partitioning control and beta-thalassemia samples. CONCLUSION: IR spectroscopy holds promise in the clinical diagnosis and screening of beta-thalassemia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".