An increased number of neutrophils (NEUs) has long been associated with infections in the knee joints; their contribution to knee osteoarthritis (KOA) pathophysiology remains largely unexplored. This study aimed to compare the phenotypic and functional characteristics of synovial fluid (SF)-derived NEUs in KOA and knee infection (INF).

Flow cytometric analysis, protein level measurements (ELISA), NEU oxidative burst assays, detection of NEU phagocytosis (pHrodo^TM Green Zymosan Biparticles^TM Conjugate for Phagocytosis), morphological analysis of the SF-derived/synovial tissue NEUs, and cultivation of human umbilical vein endothelial cells (HUVECs) using SF supernatant were used to characterise NEUs functionally/morphologically.

Results: Compared with INF NEUs, KOA NEUs were characterised by a lower expression of CD11b, CD54 and CD64, a higher expression of CD62L, TLR2 and TLR4, and lower production of inflammatory mediators and proteases, except CCL2.

Functionally, KOA NEUs displayed an increased production of radical oxygen species and phagocytic activity compared with INF NEUs. Morphologically, KOA and INF cells displayed different cell sizes and morphology, histological characteristics of the surrounding synovial tissues and influence on endothelial cells. KOA NEUs were further subdivided into two groups: SF containing <10% and SF with 10%–60% of NEUs. Analyses of two KOA NEU subgroups revealed that NEUs with SF <10% were characterised by 1) higher CD54, CD64, TLR2 and TLR4 expression on their surface; 2) higher concentrations of TNF-α, sTREM-1, VILIP-1, IL-1RA and MMP-9 in SFs.

Our findings reveal a key role for NEUs in the pathophysiology of KOA, indicating that these cells are morphologically and functionally different from INF NEUs. Further studies should explore the mechanisms that contribute to the increased number of NEUs and their crosstalk with other immune cells in KOA.

This study was supported by the Ministry of Health of the Czech Republic (NU20-06-00269; NU21-06-00370).

Precision medicine tailoring the patient pathway based on the risk, prognosis, and treatment response may bring benefits to the patients. To identify risk factors contributing to the early failure of treatment (development of events of interest) and when possible to change the prognosis via modifying these factors may improve the outcome and/or lower the risk of complications. There is an emerging goal to identify such parameters in total knee arthroplasty (TKA) thus lower the risk of revision surgery. The goal of this study was to identify factors explaining the risk for early revision of TKA using an artificial intelligence method appropriate for this task.

We applied a patient similarity network (PSN) for the identification of risk factors associated with early reoperations (n=109, 5.8%) in patients with TKA (n=1885). Next, an algorithm based on formal concept analysis was developed to support the patient decision on how to change modifying personal characteristics with respect to the estimated probability of reoperations.

The early reoperations were less frequent in women (4.4%, median time to reoperation 4.5 mo) than in men (8.2%, 10 mo), reaching the highest incidence in younger men (10.9%).

The most common reasons for total joint arthroplasty (TJA) failure are aseptic loosening (AL) and prosthetic joint infection (PJI). There is a big clinical challenge to identify the patients with high risk of AL/PJI before the TJA surgery. Although there is evidence that genetic factors contribute to the individual susceptibility to AL/PJI, a predictive model for identification of patients with a high genetic risk of TJA failure has not been developed yet.

We aimed to develop a risk evaluation tool utilising the AL/PJI-associated polymorphisms for identification of patients with high genetic risk of TJA failure based on inflammation-gene polymorphism panel.

Based on allele and genotype frequencies of twenty-five single nucleotide polymorphisms (SNPs) in TNF, IL2, IL6, IL10, IL1b, IL-1Ra, MBL2, MMP1, FTO genes and those influencing the serum levels of biomarkers of TJA outcomes (IL6, CCL2/MCP-1, CRP, ESR) in peripheral blood obtained from patients with TJA (AL, n=110; PJI, n=93; no complications, n=123), we calculated a hazard ratio and a relative entropy of alleles and genotypes associated with AL and PJI and their combinations in patient subgroups.

We conducted a risk evaluation tool based on the presence of risk alleles and genotypes in TNF (rs361525, rs1800629), DARC (rs12075), MBL2 (rs11003125) and FTO (rs9939609, rs9930506) genes associated with implant failure (AL/PJI). Of these, FTO gene variations (rs9939609, rs9930506) were associated mainly with PJI (P=0.001, OR=2.04, 95%CI=1.132–2.603; P=0.011, OR=1.72, 95%CI=1.338–3.096) and DARC (rs12075) with AL (P=0.005, OR=1.79, 95%CI=1.193–2.696). This tool calculates a hazard ratio of a combination of SNPs associated with AL and PJI for identification of patients with high and low risk of AL/PJI TJA failure.

We proposed a risk evaluation tool for stratification of patients before the TJA surgery based on the genetic risk of AL/PJI development. The effect size for each genotype combination described in the study is small. Further multiparametric data analysis and studies on an extended patient cohort and other non-genetic and genetic parameters are ongoing. Grant support: AZV MZ CR VES16-131852A, VES15-27726A, IGA LF UP_2016_011.

Aim

The diagnosis of periprosthetic joint infection (PJI) in total joint arthroplasty (TJA) remains a serious clinical challenge. Nowadays, limited biomarkers associated with PJI are available. We investigated therefore the utility of gene expression pattern of Toll-like receptors (TLR) and members of interleukin (IL)1/IL1R family, molecules critically involved in the innate immune response to invading pathogens, for detecting PJI in periprosthetic tissues around TJA.