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Genomic Insights into Host Susceptibility to Periprosthetic Joint Infections: A Comprehensive Literature Review

2024· review· en· W4404971136 on OpenAlexaff
Juan D Lizcano, Anabelle Visperas, Nicolás S. Piuzzi, Hesham Abdelbary, Carlos A. Higuera

Bibliographic record

VenueMicroorganisms · 2024
Typereview
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsHuman leukocyte antigenBiologyGenetic associationGeneDiseaseGenome-wide association studyGenetic predispositionBioinformaticsComputational biologyMedicineGeneticsImmunologyGenotypeSingle-nucleotide polymorphismInternal medicineAntigen

Abstract

fetched live from OpenAlex

Periprosthetic joint infection (PJI) is a multifactorial disease, and the risk of contracting infection is determined by the complex interplays between environmental and host-related factors. While research has shown that certain individuals may have a genetic predisposition for PJI, the existing literature is scarce, and the heterogeneity in the assessed genes limits its clinical applicability. Our review on genetic susceptibility for PJI has the following two objectives: (1) Explore the potential risk of developing PJI based on specific genetic polymorphisms or allelic variations; and (2) Characterize the regulatory cascades involved in the risk of developing PJI. This review focused on clinical studies investigating the association between genetic mutations or variations with the development of PJI. The genes investigated in these studies included toll-like receptors and humoral pattern recognition molecules, cytokines, chemokines, mannose-binding lectin (MBL), bone metabolism molecules, and human leukocyte antigen. Among these genes, polymorphisms in IL-1, MBL, vitamin D receptors, HLA-C, and HLA-DQ might have a relevant impact on the development of PJI. The literature surrounding this topic is limited, but emerging transcriptomic and genome-wide association studies hold promise for identifying at-risk genes. This advancement could pave the way for incorporating genetic testing into preoperative risk stratification, enhancing personalized patient care.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.006

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 head0.026
GPT teacher head0.325
Teacher spread0.299 · 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 designNot applicable
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

Citations3
Published2024
Admission routes1
Has abstractyes

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