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Record W4411001385 · doi:10.3390/bios15060352

Autofluorescence Imaging of Parathyroid and Thyroid Under Visible and Near-IR Light Excitation

2025· article· en· W4411001385 on OpenAlexafffund
Zhenguo Wu, Sam M. Wiseman, Haishan Zeng

Bibliographic record

VenueBiosensors · 2025
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsAutofluorescenceThyroidVisible spectrumChemistryNuclear magnetic resonanceMedicineMaterials sciencePathologyFluorescenceOpticsInternal medicineOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Identifying parathyroid glands during surgery is challenging and time-consuming due to their small size (3-5 mm) and camouflaged appearance in the background of the thyroid, lymph nodes, fat, and other neck structures. For the gland itself, it is also important to differentiate abnormal ones from normal ones. Accidental damage or removal of the normal glands can result in complications like hypocalcemia, which may necessitate lifelong medication dependence, and, in extreme cases, lead to death. The study of autofluorescence optical properties of normal and abnormal parathyroid glands and the surrounding tissue will be helpful for developing non-invasive detection devices. The near-infrared (NIR) autofluorescence characteristics of parathyroid and thyroid tissues have been studied extensively and are now used for parathyroid gland detection during surgery. Additionally, there have been a few reports on the UV-visible light-excited autofluorescence characteristics of these tissues with a focus on spectroscopy. However, there is a lack of high-resolution, side-by-side autofluorescence imaging comparisons of both tissue types under various excitation wavelengths, ranging from visible to NIR. We developed a standalone tabletop autofluorescence imaging system to acquire images of ex vivo specimens in the operating room under different excitation wavelengths: visible 405 nm, 454 nm, 520 nm, 628 nm, and NIR 780 nm. Autofluorescence imaging features of parathyroid adenomas for each excitation wavelength were described and compared. It was found that visible light excites much stronger autofluorescence from parathyroid adenoma tissue compared to NIR light. However, NIR excitation provides the best intensity difference/contrast between parathyroid adenoma and thyroid tissue, making it optimal for differentiating these two tissue types, and detecting parathyroid adenoma during surgery. The high fluorescent site under the NIR 780 nm excitation also generates high fluorescence under visible excitation wavelengths. Heterogeneous fluorescence patterns were observed in most of the parathyroid adenoma cases across all the excitation wavelengths.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 teacher head, 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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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