Antimicrobial peptides and proinflammatory cytokines in periprosthetic joint infection

Background: Differentiation between septic and aseptic loosening of joint replacements is essential for successful revision surgery, but reliable markers for the diagnosis of low-grade infection are lacking. The present study was performed to assess intra-articular and systemic levels of antimicrobial peptides and proinflammatory cytokines as diagnostic markers for periprosthetic joint infection. Methods: Fifteen consecutive patients with staphylococcal periprosthetic joint infections and twenty control patients with aseptic loosening of total hip and knee replacements were included in this prospective, single-center, controlled clinical trial. Expression of the antimicrobial peptides human β-defensin-2 (HBD-2), human β-defensin-3 (HBD-3), and cathelicidin LL-37 (LL-37) was determined by ELISA (enzyme-linked immunosorbent assay) in serum and joint aspirates. Proinflammatory cytokines were assessed in serum and joint aspirates with use of cytometric bead arrays. C-reactive protein in serum, microbiology, and histopathology of periprosthetic tissue served as the “gold standard” for the diagnosis of infection. Results: The antimicrobial peptides HBD-3 and LL-37 were significantly elevated in joint aspirates from patients with periprosthetic joint infection compared with patients with aseptic loosening, and the area under the curve (AUC) in a receiver operating characteristic curve analysis was equal to 0.745 and 0.875, respectively. Additionally, significant local increases in the proinflammatory cytokines interleukin (IL)-1β, IL-4, IL-6, IL-17A, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α were observed to be associated with infection. Logistic regression analysis indicated that the combination of an antimicrobial peptide with another synovial fluid biomarker improved diagnostic accuracy; the AUC value was 0.916 for LL-37 and IL-4, 0.895 for LL-37 and IL-6, 0.972 for HBD-3 and IL-4, and 0.849 for HBD-3 and IL-6. In contrast, the only antimicrobial peptides and cytokines in serum that showed a significant systemic increase in association with infection were HBD-2, IL-4, and IL-6 (all of which had an AUC value of <0.75). Conclusions: The present study showed promising results for the use of antimicrobial peptides and other biomarkers in synovial fluid for the diagnosis of periprosthetic joint infection, and analysis of the levels in synovial fluid was more accurate than analysis of serum.

Proactive Relay Selection with Joint Impact of Hardware Impairment and Co-channel Interference

In this paper, we investigate the end-to-end performance of dual-hop proactive decode-and-forward relaying networks with Nth best relay selection in the presence of two practical deleterious effects: i) hardware impairment and ii) cochannel interference. In particular, we derive new exact and asymptotic closed-form expressions for the outage probability and average channel capacity of Nth best partial and opportunistic relay selection schemes over Rayleigh fading channels. Insightful discussions are provided. It is shown that, when the system cannot select the best relay for cooperation, the partial relay selection scheme outperforms the opportunistic method under the impact of the same co-channel interference (CCI). In addition, without CCI but under the effect of hardware impairment, it is shown that both selection strategies have the same asymptotic channel capacity. Monte Carlo simulations are presented to corroborate our analysis.

Interpretable semantic vectors from a joint model of brain-and text-based meaning

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Shape Memory Alloy Features for Seismic Retrofitting of External RC Beam-Column Joint

Shape memory alloys (SMAs) have the ability to undergo large deformations with minimum residual strain and also the extraordinary ability to undergo reversible hysteretic shape change known as the shape memory effect. The shape memory effect of these alloys can be utilised to develop a convenient way of actively confining concrete sections to improve their shear strength, flexural ductility and ultimate strain capacity. Most of the previous work on active confinement of concrete using SMA has been carried out on circular sections. In this study retrofitting strategies for active confinement of non-circular sections have been proposed. The proposed schemes presented in this paper are conceived with an aim to seismically retrofit a beam-column joint in non-seismically designed reinforced concrete buildings.

The complex material behaviour of SMAs depends on number of parameters. Depending upon the alloying elements, SMAs exhibit different behaviour in different conditions and are highly sensitive to variation in temperature, phase in which it is used, loading pattern, strain rate and pre-strain conditions. Therefore, a detailed discussion on the behaviour of SMAs under different thermo-mechanical conditions is presented first in this paper.

The influence of area level social deprivation on preoperative disease severity and postoperative outcomes following unicompartmental knee joint replacement

BACKGROUND: This study investigated the effect of socioeconomic deprivation on preoperative disease and outcome following unicompartmental knee replacement (UKR).

METHODS: 307 Oxford UKRs implanted between 2008 and 2013 under the care of one surgeon using the same surgical technique were analysed. Deprivation was quantified using the Northern Ireland Multiple Deprivation Measure. Preoperative disease severity and postoperative outcome were measured using the Oxford Knee Score (OKS).

RESULTS: There was no difference in preoperative OKS between deprivation groups. Preoperative knee range of motion (ROM) was significantly reduced in more deprived patients with 10° less ROM than least deprived patients. Postoperatively there was no difference in OKS improvement between deprivation groups (p=0.46), with improvements of 19.5 and 21.0 units in the most and least deprived groups respectively. There was no significant association between deprivation and OKS improvement on unadjusted or adjusted analysis. Preoperative OKS, Short Form 12 mental component score and length of stay were significant independent predictors of OKS improvement. A significantly lower proportion of the most deprived group (15%) reported being able to walk an unlimited distance compared to the least deprived group (41%) one year postoperatively.

CONCLUSION: More deprived patients can achieve similar improvements in OKS to less deprived patients following UKR.

LEVEL OF EVIDENCE: 2b - retrospective cohort study of prognosis.

Joint Optimization of Area Spectral Efficiency and Delay over PPP Interfered Ad-Hoc Networks

To evaluate the performance of the co-channel transmission based communication, we propose a new metric for area spectral efficiency (ASE) of interference limited ad-hoc network by assuming that the nodes are randomly distributed according to a Poisson point processes (PPP). We introduce a utility function, U = ASE/delay and derive the optimal ALOHA transmission probability p and the SIR threshold τ that jointly maximize the ASE and minimize the local delay. Finally, numerical results have been conducted to confirm that the joint optimization based on the U metric achieves a significant performance gain compared to conventional systems.