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Record W3156931985 · doi:10.24908/iqurcp.9468

27. Low Frequency Ultrasound Imaging of Apoptosis in Tumor Response: Non-Invasive Monitoring of Chemotherapy Effects

2018· article· en· W3156931985 on OpenAlexvenueno aff
Stephanie Zhou

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsnot available
Fundersnot available
KeywordsChemotherapyUltrasoundMedicineApoptosisIn vivoRadiologyProgrammed cell deathCellPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Due to the growing costs of chemotherapy, previous imaging techniques such as MRI or CT scans have become too time-consuming in the assessment of chemotherapy’s effects. With results generated about 2 weeks later, the patient is exposed to the negative side effects of these medications with the possibility that chemotherapy may not be improving their prognosis. Thus, ultrasound has become increasingly popular as a method to determine chemotherapy’s effect on tumors within 24 hours. Both low and high-frequency ultrasound are novel, noninvasive methods for detecting cell death based on changes in cell morphology. Condensation, fragmentation and alterations in the cell nucleus during apoptosis are linked to changes in the cell’s acoustic properties, as indicated by experimental evidence. In this study, quantitative ultrasound was used to follow responses of tumor models to chemotherapy in vivo. As studies have shown that structural changes can occur as early as 24 hours after treatment, ultrasound imaging was administered before and 24 hours after treatment. Changes in ultrasound parameters such as spectral slope, Y-intercept, and midband fit were analyzed relative to pretreatment control data and when compared to changes in the tumors seen through cell staining, changes consistent with cell death were observed.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.366
Teacher spread0.342 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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