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Record W4403545554 · doi:10.7150/thno.99931

Supramolecularly engineered bacteria mediated calcium overload and immunotherapy of tumors

2024· article· en· W4403545554 on OpenAlexaff
Beibei Xie, Lin Dong, Leo D. Wang, Ruibing Wang, Chunlai Li

Bibliographic record

VenueTheranostics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsEarl Haig Secondary School
FundersFundo para o Desenvolvimento das Ciências e da TecnologiaNational Natural Science Foundation of China
KeywordsCalciumBacteriaImmunotherapyCancer researchChemistryImmune systemMedicineBiologyImmunologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Intracellular Ca 2+ nanogenerators, such as calcium carbonate, calcium peroxide, and calcium phosphate nanoparticles, have shown promise in calcium overload-mediated tumor therapy.However, their effectiveness is often hampered by poor targeting, low accumulation, and limited penetration into tumor cells, leading to suboptimal therapeutic outcomes.This strategy aims to achieve synergistic Ca 2+ overload and immunotherapy of tumors.Methods: A supramolecular conjugate of engineered living bacteria (facultative anaerobic Salmonella typhimurium VNP20009, VNP) with CaCO3 nanoparticles was developed for targeted delivery of curcumin-loaded CaCO3 into tumors.Results: Both CaCO3 nanoparticles and the loaded Ca 2+ efflux inhibiting agent, curcumin (CUR), demonstrated significant enhancement of intracellular Ca 2+ overload, resulting in apoptosis of tumor cells via mitochondrial dysfunction.Moreover, VNP exhibited excellent tumor-targeting ability, colonization in tumor tissues, and anticancer activity with minimal side effects. Conclusion:The conjugate of VNP and CaCO3 not only enhances the efficiency of common cancer treatments but also synergizes Ca 2+ overload with cancer immunotherapy, thereby offering a promising approach for improving therapeutic outcomes in cancer treatment.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.383

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 head0.270
Teacher spread0.261 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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