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Record W3013797797 · doi:10.26776/ijemm.05.01.2020.02

Toxicity of Metal Implants and Their Interactions with Stem Cells: A Review

2020· review· en· W3013797797 on OpenAlexaff
Azin Mirzajavadkhan, Saba Rafieian, Muhammad Hasibul Hasan

Bibliographic record

VenueInternational Journal of Engineering Materials and Manufacture · 2020
Typereview
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiomaterialStem cellBiocompatible materialCorrosionToxicityMaterials scienceNanotechnologyChemistryBiomedical engineeringMetallurgyMedicineCell biologyBiology

Abstract

fetched live from OpenAlex

The development of biomaterials has increased rapidly in order to alter the fate of stem cells and use them in therapeutic applications. Currently, many biomaterials are used in the biomedical industry. Biomaterials act as a “nitch” which is an environment regulating the development and self-renewal behaviour of stem cells. The stem cells receive signals from the “nitch” and proceeds accordingly. In order to control the behaviour of stem cells, the chemical and physical properties of the biomaterial should be taken into consideration. This review paper focuses on the different type of metals used in biomaterials, identifying their current issues and challenges including fatigue, corrosion resistance, and the toxicity caused by metal ions released in the body. It also provides detailed explanations about the impact of various metal implants such as stainless steel, cobalt-chromium, and titanium on stem cells and the toxicity caused by the interaction of biomaterials and various trace elements with the hostile body environment.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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 designOther design
Domainnot available
GenreReview

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

Citations13
Published2020
Admission routes1
Has abstractyes

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