Knee instability caused by altered graft mechanical properties after anterior cruciate ligament reconstruction:the early onset of osteoarthritis?

Anterior cruciate ligament (ACL) rupture is a very common knee joint injury. Torn ACLs are currently reconstructed using tendon autografts. However, half of the patients develop osteoarthritis (OA) within 10 to 14 years postoperatively. Proposedly, this is caused by altered knee kine(ma)tics originating from changes in graft mechanical properties during the in vivo remodeling response. Therefore, the main aim was to use subject-specific finite element knee models and investigate the influence of decreasing graft stiffness and/or increasing graft laxity on knee kine(ma)tics and cartilage loading. In this research, 4 subject-specific knee geometries were used, and the material properties of the ACL were altered to either match currently used grafts or mimic in vivo graft remodeling, i.e., decreasing graft stiffness and/or increasing graft laxity. The results confirm that the in vivo graft remodeling process increases the knee range of motion, up to &gt;300 percent, and relocates the cartilage contact pressures, up to 4.3 mm. The effect of remodeling-induced graft mechanical properties on knee stability exceeded that of graft mechanical properties at the time of surgery. This indicates that altered mechanical properties of ACL grafts, caused by in vivo remodeling, can initiate the early onset of osteoarthritis, as observed in many patients clinically.</p

Truncating the i-leader open reading frame enhances release of human adenovirus type 5 in glioma cells

Background: The survival of glioma patients with the current treatments is poor. Early clinical trails with replicating adenoviruses demonstrated the feasibility and safety of the use of adenoviruses as oncolytic agents. Antitumor efficacy has been moderate due to inefficient virus replication and spread. Previous studies have shown that truncation of the adenovirus i-leader open reading frame enhanced cytopathic activity of HAdV-5 in several tumor cell lines. Here we report the effect of an i-leader mutation on the cytopathic activity in glioma cell lines and in primary high-grade glioma

Directed adenovirus evolution using engineered mutator viral polymerases

Adenoviruses (Ads) are the most frequently used viruses for oncolytic and gene therapy purposes. Most Ad-based vectors have been generated through rational design. Although this led to significant vector improvements, it is often hampered by an insufficient understanding of Ad’s intricate functions and interactions. Here, to evade this issue, we adopted a novel, mutator Ad polymerase-based, ‘accelerated-evolution’ approach that can serve as general method to generate or optimize adenoviral vectors. First, we site specifically substituted Ad polymerase residues located in either the nucleotide binding pocket or the exonuclease domain. This yielded several polymerase mutants that, while fully supportive of viral replication, increased Ad’s intrinsic mutation rate. Mutator activities of these mutants were revealed by performing deep sequencing on pools of replicated viruses. The strongest identified mutators carried replacements of residues implicated in ssDNA binding at the exonuclease active site. Next, we exploited these mutators to generate the genetic diversity required for directed Ad evolution. Using this new forward genetics approach, we isolated viral mutants with improved cytolytic activity. These mutants revealed a common mutation in a splice acceptor site preceding the gene for the adenovirus death protein (ADP). Accordingly, the isolated viruses showed high and untimely expression of ADP, correlating with a severe deregulation of E3 transcript splicing

The influence of between-farm distance and farm size on the spread of classical swine fever during the 1997-1998 epidemic in The Netherlands.

As the size of livestock farms in The Netherlands is on the increase for economic reasons, an important question is how disease introduction risks and risks of onward transmission scale with farm size (i.e. with the number of animals on the farm). Here we use the epidemic data of the 1997-1998 epidemic of Classical Swine Fever (CSF) Virus in The Netherlands to address this question for CSF risks. This dataset is one of the most powerful ones statistically as in this epidemic a total of 428 pig farms where infected, with the majority of farm sizes ranging between 27 and 1750 pigs, including piglets. We have extended the earlier models for the transmission risk as a function of between-farm distance, by adding two factors. These factors describe the effect of farm size on the susceptibility of a 'receiving' farm and on the infectivity of a 'sending' farm (or 'source' farm), respectively. Using the best-fitting model, we show that the size of a farm has a significant influence on both farm-level susceptibility and infectivity for CSF. Although larger farms are both more susceptible to CSF and, when infected, more infectious to other farms than smaller farms, the increase is less than linear. The higher the farm size, the smaller the effect of increments of farm size on the susceptibility and infectivity of a farm. Because of changes in the Dutch pig farming characteristics, a straightforward extrapolation of the observed farm size dependencies from 1997/1998 to present times would not be justified. However, based on our results one may expect that also for the current pig farming characteristics in The Netherlands, farm susceptibility and infectivity depend non-linearly on farm size, with some saturation effect for relatively large farm sizes

Estimated transmission kernels λ^c and their confidence bounds.

<p>The basic kernel parameterization is given by <i>c = </i>0 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095278#pone.0095278.e004" target="_blank">Equation (1</a>) and (2) without farm-size dependence (dashed blue line) and the best-fit kernel <i>c = </i>5 (solid red line), where N_S is set equal to the average size of the farms in the OA (1038.3) and N_I to the average size of the infected farms in the OA (1515.7), with their confidence bounds (thinner lines).</p

Comparison of the best-fit model prediction to the observed epidemic in 1997/1998.

<p>Number of newly infected farms per 28-day period: as derived directly from the 1997/1998 CSF epidemic data (bars) and as predicted by the fitted <i>c = </i>5 model for the between-farm transmission risk (line with symbols). Here time t = 0 corresponds to 24 December 1996.</p

The relative susceptibility or infectivity of a farm as a function of its size, according to the best-fit parameterization <i>c = </i>5 (maximum susceptibility or infectivity equals 1).

<p>Full line: point estimate (d = 1.76), short-dashed line: lower bound (d = 1.21), long-dashed line: upper bound (d = 2.66).</p

Map with the Outbreak Area (OA, black circle) in The Netherlands.

<p>This includes the infected farms (red dots) and the high-risk areas (blue). The high-risk areas for transmission of CSF (blue) were calculated using the basic kernel (without farm-size dependence), using the method of Boender et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095278#pone.0095278-Boender1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095278#pone.0095278-Boender3" target="_blank">[20]</a>.</p

AIC values (Akaike's Information Criterion, see [13]) and parameter estimations for candidate kernel parameterizations for the <i>full</i> dataset.

<p>AIC values (Akaike's Information Criterion, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095278#pone.0095278-Akaike1" target="_blank">[13]</a>) and parameter estimations for candidate kernel parameterizations for the <i>full</i> dataset.</p