MétaCan
Menu
Back to cohort
Record W1928049887 · doi:10.1002/jrs.4684

Study of both fingerprint and high wavenumber Raman spectroscopy of pathological nasopharyngeal tissues

2015· article· en· W1928049887 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Raman Spectroscopy · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsBC Cancer Agency
FundersNational Natural Science Foundation of China
KeywordsRaman spectroscopyFingerprint (computing)Nasopharyngeal carcinomaLinear discriminant analysisPrincipal component analysisNasopharyngeal cancerAnalytical Chemistry (journal)ChemistryReceiver operating characteristicNuclear magnetic resonanceSpectroscopyOpticsMedicineInternal medicineChromatographyPhysicsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

High wavenumber (HW) Raman spectroscopy has weaker fluorescence background compared with fingerprint (FP) region. This study aims to evaluate the discrimination feasibility of nasopharyngeal non‐cancerous and nasopharyngeal cancer (NPC) tissue with both FP and HW Raman spectroscopy. HW Raman spectra of nasopharyngeal tissue were obtained for the first time. Raman spectra were collected to differentiate nasopharyngeal non‐cancerous ( n = 37) from NPC ( n = 41) tissues in FP (800–1800cm −1 ), HW (2700–3100cm −1 ), and integrated FP/HW region. First, to assess the utility of this method, the averaged Raman spectral intensities and intensity ratios of corresponding Raman bands were analyzed in HW and FP regions, respectively. The results show that intensities as well as the ratios of specific Raman peaks might be helpful in distinguishing nasopharyngeal non‐cancerous from NPC tissue with the HW Raman spectroscopy, as with FP Raman reported before. The multivariate statistical method based on the combination of principal component analysis–liner discriminant analysis (PCA‐LDA), together with leave‐one‐patient‐out, cross‐validation diagnostic algorithm, was used for discriminating nasopharyngeal non‐cancerous from NPC tissue, generating sensitivities of 87.8%, 85.4%, and 95.1% and specificities of 86.5%, 91.9%, and 89.2%, respectively, with Raman spectroscopy in the FP, HW, and integrated FP/HW regions. The posterior probability of classification results and receiver operating characteristic curves were utilized to evaluate the discrimination of PCA‐LDA algorithm, verifying that HW Raman spectroscopy has a positive effect on the differentiation for the diagnosis of NPC tissue by integrated FP/HW Raman spectroscopy. What's more, the potential of Raman spectroscopy used for differentiating different pathology NPC tissues was also discussed. The results demonstrate that both FP and HW Raman spectroscopy have the potential for diagnosis and detection in early nasopharyngeal carcinoma, and HW Raman spectroscopy may improve the discrimination of NPC tissue compared with FP region alone, providing a promising diagnostic tool for the diagnosis of NPC tissue. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.342
Teacher spread0.319 · 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