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Enregistrement W1979765117 · doi:10.2106/jbjs.n.01293

Knee Trauma and Posttraumatic Osteoarthritis—More Science Needed

2015· letter· en· W1979765117 sur OpenAlexaff
Edward J. Harvey

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueOsteoarthritis Treatment and Mechanisms
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésOsteoarthritisMedicinePhysical medicine and rehabilitationAlternative medicinePathology

Résumé

récupéré en direct d'OpenAlex

Commentary We do not understand posttraumatic arthritis. Preliminary research with preclinical models is under way1,2 to better understand the postinjury intra-articular cascade that leads to posttraumatic arthritis. Certainly clarification of the pathways would allow modulation of healing and perhaps identify targets in the chain for therapeutic approaches. We have had little success for many reasons: lack of a good preclinical model, paucity of research, lack of controls, failure to differentiate between systemic and local conditions, and no large clinical trials, among others. We absolutely need the information that Haller et al. sought in the clinical setting. Haller et al. evaluated inflammatory cytokine concentrations in human synovial fluid following acute tibial plateau fractures. Their hypothesis was that there would be an elevated inflammatory response following intra-articular fracture and that the inflammatory response would be greater after high-energy injuries than after low-energy injuries. Fractures heal through inflammation, and supposedly higher-energy trauma results in more inflammation. The difficulty is in specifying which marker shows up and when it appears. We are also not sure whether there are missing markers or temporal relationships between markers that may affect the cascade. The authors quantified the concentrations of interferon-gamma (IFN-γ), interleukin-1 beta (IL-1β), interleukin-1 receptor antagonist (IL-1RA), IL-2, IL-4, IL-6, IL-7, IL-8, IL-10, IL-12 (p70), IL-13, IL-17A, tumor necrosis factor-alpha (TNF-α), monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein-1 beta (MIP-1β) with multiplex assays (see glossary below). They were hoping that the large grab bag of inflammatory markers would provide insight into the pathways of healing or arthritis. My initial thought was that this would be difficult without real-time values but perhaps there would be some room for interpolation of the values that could provide insight into the inflammatory cascade and/or healing following cartilage injury. Why did the authors choose all of these markers? Probably because it is the best way to start searching for an answer: look at every marker and hope that there is a difference somewhere. If you find something pertinent, then look at candidate gene relationships. Definitely this is a fishing expedition utilizing a large number of inflammatory markers (and these are only the ones we know of). Genomics and proteomics are unfortunately not as easy as running a large assay and seeing what is the highest bar on a graph. Synovial fluid aspirates were obtained from injured and uninjured joints. Twenty patients requiring external fixation followed by delayed fixation underwent knee aspiration at both procedures. Forty-five patients with an average age of forty-two years were enrolled in the study. There were twenty-four low-energy and twenty-one high-energy tibial plateau injuries. Despite the authors’ claim that the study was adequately powered, it seems that they did not include enough patients. Some of the studied markers, including MCP-1, have a high interracial and interpatient variation at baseline, let alone during the disease course. There was a significant difference (p < 0.001) between the injured and uninjured knees in terms of concentrations of IL-1β, IL-6, IL-8, IL-10, IL-1RA, and MCP-1. What does this mean? First of all, the significance levels are not that high—adjustment for repeated measures for the number of analytes directly named brings the significant alpha value to p < 0.003. Some multiplex assays run millions of analytes so the adjusted p value might be even lower, making all p values in this study nonsignificant. Assuming that the mentioned interest points were the only ones examined, values of p = 0.001 are still not that far from being merely trends. Because of the lack of raw data to examine, there is a high probability of a few outliers throwing the results one way or another. Inflammation has to occur for bone to heal. Do these data mean that only cartilage is affected by the inflammatory markers, or is it just healing bone that is affected? Is there some marker that shows only cartilage healing or is there one that shows cartilage death—as opposed to bone apoptosis—posttrauma? There are a lot of questions that make the study hard to interpret. The authors found no detectable difference in synovial fluid cytokine concentrations between high and low-energy injuries. That is a surprising result and is probably due to the small population cohort in the study. Some Schatzker classifications may be high-energy metaphyseal injuries without a lot of articular injury or low-energy metaphyseal injuries with substantial articular injury. More exposed fracture surface area may increase the concentrations of the inflammatory markers. It would seem that any fracture near the knee will change the inflammatory marker levels in the aspirate. In the end, the authors thought that their findings showed that the articular surface is exposed to acute and sustained concentrations of multiple inflammatory cytokines following intra-articular fracture. The concentrations of IL-10, IL-1RA, IL-6, IL-8, and MCP-1 were significantly greater at the second aspiration than at the first. The authors stated that, because inflammatory cytokines have been associated with the development of primary and inflammatory arthritis, their research may indicate that these factors could play a role in the development of posttraumatic osteoarthritis. The next question is: do these sustained inflammatory markers cause posttraumatic osteoarthritic changes or just show that the fracture is healing? If we start by eliminating some or one of these markers at a time, will we arrive at a circumstance where posttraumatic osteoarthritis does not develop? It is this type of information that may help us develop approaches to limiting posttraumatic arthritis in the future. Glossary of Terms IFN-γ, or type-II interferon, is an important activator of macrophages and inducer of Class-II major histocompatibility complex (MHC) molecule expression. Aberrant IFN-γ expression is associated with a number of autoinflammatory and autoimmune diseases. IL-1β is produced by activated macrophages as a proprotein, which is proteolytically processed to its active form by caspase 1/interleukin-1 converting enzyme (CASP1/ICE). This cytokine is an important mediator of the inflammatory response and is involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis. IL-1RA is a member of the interleukin-1 cytokine family and inhibits IL-1α and IL-1β as well as modulates a variety of IL-1 immune and inflammatory responses. IL-2 has key roles in key functions of the immune system primarily via its direct effects on T cells. IL-4 decreases the production of T helper 1 (Th1) cells, macrophages, and IFN-γ. IL-6 is a mediator of the acute phase response. It acts on pattern recognition receptors (PRRs), including Toll-like receptors (TLRs). These are present on the cell surface and intracellular compartments and induce intracellular signaling cascades that give rise to inflammatory cytokine production. IL-7 stimulates the differentiation of multipotent (pluripotent) hematopoietic stem cells and is important for proliferation during certain stages of B-cell, T-cell, and NK (natural killer)-cell life. IL-8, also known as neutrophil chemotactic factor, is a potent promoter of angiogenesis. IL-8 can be secreted by any cells with Toll-like receptors that are involved in the innate immune response. IL-10 is a cytokine with multiple effects on immunoregulation and inflammation. It also enhances B-cell survival and proliferation and antibody production. IL-10 inhibits production of proinflammatory cytokines such as IFN-γ, IL-2, IL-3, TNF-α, and GM-CSF (granulocyte-macrophage colony-stimulating factor). IL-12 stimulates the production of IFN-γ and TNF-α and reduces IL-4-mediated suppression of IFN-γ. IL-12 also has anti-angiogenic activity, by increasing production of IFN-γ, which in turn increases the production of a chemokine called inducible protein-10 (IP-10) or chemokine (C-X-C motif) ligand 10 (CXCL10). IL-13 has anti-inflammatory properties but mainly in relation to airway disease. IL-17 acts in delayed-type reactions by increasing chemokine production to recruit inflammatory cells to the site of inflammation. It induces the production of many cytokines (IL-6, IL-1β, TGF-β [transforming growth factor-beta], and TNF-α) and chemokines (IL-8 as well as MCP-1) as well as PGE2 (prostaglandin E2). TNF-α is involved in systemic inflammation by stimulating the acute phase reaction. MCP-1, or chemokine (C-C motif) ligand 2 (CCL2), is anchored in the plasma membrane of endothelial cells and probably is involved in osteoclast function as well. It is implicated in the pathogenesis of diseases characterized by monocyte infiltrates, such as psoriasis and rheumatoid arthritis. MIP-1β, or CCL4, indirectly induces the synthesis and release of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-α.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,040
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,066

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,040
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0030,002
Études des sciences et des technologies0,0020,005
Communication savante0,0040,011
Science ouverte0,0050,003
Intégrité de la recherche0,0230,030
Charge utile insuffisante (le modèle a refusé de juger)0,0200,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,041
Tête enseignante GPT0,261
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2015
Routes d'admission1
Résumé présentoui

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