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Record W4412940509 · doi:10.3390/curroncol32080432

Immunologic Alteration After Total En-Bloc Spondylectomy with Anterior Spinal Column Reconstruction with Frozen Tumor-Containing Bone Autologous Grafts: A Case Report in a Prospective Study

2025· article· en· W4412940509 on OpenAlexvenueno aff
Hisaki Aiba, Hiroaki Kimura, Ryu Terauchi, Nobuyuki Suzuki, Kenji Kato, Kiyoshi Yagi, Makoto Yamaguchi, Shogo Suenaga, Toshiharu Shirai, Ayano Aso, Costantino Errani, Hideki Murakami

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmune systemT-cell receptorPathologyTransplantationSurgeryT cellImmunology

Abstract

fetched live from OpenAlex

Cryotherapy could stimulate immune responses and induce abscopal effects. A novel technique was developed for treating spinal bone tumors involving the use of frozen tumor-containing autologous bone grafts for anterior spinal reconstruction following total en-bloc spondylectomy, with the aim of activating cryoimmunity. This study focused on analyzing changes in the T-cell receptor (TCR) repertoire after surgery to evaluate T-cell diversity. Blood samples were collected pre- and post-operatively, with subsequent RNA extraction and immunosequencing. Compared to pre-surgery samples, the diversity and abundance of the Complementarity-Determining Region 3, regions of the TCR α and β chains decreased, suggesting that more selective clones may have emerged and influenced immune responses. Through TCR repertoire analysis, this study demonstrated that transplantation of frozen tumor-containing autologous bone impacted the immune system. This study is expected to provide a foundation for developing treatments that may enhance immune activation.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.344
Teacher spread0.326 · 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 designCase report
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
Published2025
Admission routes1
Has abstractyes

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