Temporomandibular joint dysfunction and orthognathic surgery: a retrospective study

<p>Abstract</p> <p>Background</p> <p>Relations between maxillo-mandibular deformities and TMJ disorders have been the object of different studies in medical literature and there are various opinions concerning the alteration of TMJ dysfunction after orthognathic surgery. The purpose of the present study was to evaluate TMJ disorders changes before and after orthognathic surgery, and to assess the risk of creating new TMJ symptoms on asymptomatic patients.</p> <p>Methods</p> <p>A questionnaire was sent to 176 patients operated at the Maxillo-Facial Service of the Lille's 2 Universitary Hospital Center (Chairman Pr Joël Ferri) from 01.01.2006 to 01.01.2008. 57 patients (35 females and 22 males), age range from 16 to 65 years old, filled the questionnaire. The prevalence and the results on pain, sounds, clicking, joint locking, limited mouth opening, and tenseness were evaluated comparing different subgroups of patients.</p> <p>Results</p> <p>TMJ symptoms were significantly reduced after treatment for patients with pre-operative symptoms. The overall subjective treatment outcome was: improvement for 80.0% of patients, no change for 16.4% of patients, and an increase of symptoms for 3.6% of them. Thus, most patients were very satisfied with the results. However the appearance of new onset of TMJ symptoms is common. There was no statistical difference in the prevalence of preoperative TMJ symptoms and on postoperative results in class II compared to class III patients.</p> <p>Conclusions</p> <p>These observations demonstrate that: there is a high prevalence of TMJ disorders in dysgnathic patients; most of patients with preoperative TMJ signs and symptoms can improve TMJ dysfunction and pain levels can be reduced by orthognathic treatment; a percentage of dysgnathic patients who were preoperatively asymptomatic can develop TMJ disorders after surgery but this risk is low.</p

Comparative genomics of European Avian Pathogenic E. coli (APEC)

Background Avian pathogenic Escherichia coli (APEC) causes colibacillosis, which results in significant economic losses to the poultry industry worldwide. However, the diversity between isolates remains poorly understood. Here, a total of 272 APEC isolates collected from the United Kingdom (UK), Italy and Germany were characterised using multiplex polymerase chain reactions (PCRs) targeting 22 equally weighted factors covering virulence genes, R-type and phylogroup. Following these analysis, 95 of the selected strains were further analysed using Whole Genome Sequencing (WGS). Results The most prevalent phylogroups were B2 (47%) and A1 (22%), although there were national differences with Germany presenting group B2 (35.3%), Italy presenting group A1 (53.3%) and UK presenting group B2 (56.1%) as the most prevalent. R-type R1 was the most frequent type (55%) among APEC, but multiple R-types were also frequent (26.8%). Following compilation of all the PCR data which covered a total of 15 virulence genes, it was possible to build a similarity tree using each PCR result unweighted to produce 9 distinct groups. The average number of virulence genes was 6-8 per isolate, but no positive association was found between phylogroup and number or type of virulence genes. A total of 95 isolates representing each of these 9 groupings were genome sequenced and analysed for in silico serotype, Multilocus Sequence Typing (MLST), and antimicrobial resistance (AMR). The UK isolates showed the greatest variability in terms of serotype and MLST compared with German and Italian isolates, whereas the lowest prevalence of AMR was found for German isolates. Similarity trees were compiled using sequencing data and notably single nucleotide polymorphism data generated ten distinct geno-groups. The frequency of geno-groups across Europe comprised 26.3% belonging to Group 8 representing serogroups O2, O4, O18 and MLST types ST95, ST140, ST141, ST428, ST1618 and others, 18.9% belonging to Group 1 (serogroups O78 and MLST types ST23, ST2230), 15.8% belonging to Group 10 (serogroups O8, O45, O91, O125ab and variable MLST types), 14.7% belonging to Group 7 (serogroups O4, O24, O35, O53, O161 and MLST type ST117) and 13.7% belonging to Group 9 (serogroups O1, O16, O181 and others and MLST types ST10, ST48 and others). The other groups (2, 3, 4, 5 and 6) each contained relatively few strains. However, for some of the genogroups (e.g. groups 6 and 7) partial overlap with SNPs grouping and PCR grouping (matching PCR groups 8 (13 isolates on 22) and 1 (14 isolates on 16) were observable). However, it was not possible to obtain a clear correlation between genogroups and unweighted PCR groupings. This may be due to the genome plasticity of E. coli that enables strains to carry the same virulence factors even if the overall genotype is substantially different. Conclusions The conclusion to be drawn from the lack of correlations is that firstly, APEC are very diverse and secondly, it is not possible to rely on any one or more basic molecular or phenotypic tests to define APEC with clarity, reaffirming the need for whole genome analysis approaches which we describe here. This study highlights the presence of previously unreported serotypes and MLSTs for APEC in Europe. Moreover, it is a first step on a cautious reconsideration of the merits of classical identification criteria such as R typing, phylogrouping and serotyping

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

A Simple High-Content Cell Cycle Assay Reveals Frequent Discrepancies between Cell Number and ATP and MTS Proliferation Assays

<div><p>In order to efficiently characterize both antiproliferative potency and mechanism of action of small molecules targeting the cell cycle, we developed a high-throughput image-based assay to determine cell number and cell cycle phase distribution. Using this we profiled the effects of experimental and approved anti-cancer agents with a range mechanisms of action on a set of cell lines, comparing direct cell counting <i>versus</i> two metabolism-based cell viability/proliferation assay formats, ATP-dependent bioluminescence, MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) reduction, and a whole-well DNA-binding dye fluorescence assay. We show that, depending on compound mechanisms of action, the metabolism-based proxy assays are frequently prone to 1) significant underestimation of compound potency and efficacy, and 2) non-monotonic dose-response curves due to concentration-dependent phenotypic ‘switching’. In particular, potency and efficacy of DNA synthesis-targeting agents such as gemcitabine and etoposide could be profoundly underestimated by ATP and MTS-reduction assays. In the same image-based assay we showed that drug-induced increases in ATP content were associated with increased cell size and proportionate increases in mitochondrial content and respiratory flux concomitant with cell cycle arrest. Therefore, differences in compound mechanism of action and cell line-specific responses can yield significantly misleading results when using ATP or tetrazolium-reduction assays as a proxy for cell number when screening compounds for antiproliferative activity or profiling panels of cell lines for drug sensitivity.</p></div

Comparison of assay results for HT29 cells.

<p>Values in bold indicate ATP or MTS assay log EC₅₀ differing by >1 log unit from cell count EC₅₀, or ATP or MTS assay E_max differing by >25% from the cell count E_max. ND, valid curve fits could not be obtained according to the criteria described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063583#s2" target="_blank">Materials and Methods</a>.</p

Effects of drug treatment on mitochondrial function.

<p>Basal oxygen consumption rate (OCR) determined for cells treated with the indicated compounds (etoposide, 10 µM; gemcitabine 0.1 µM; paclitaxel 0.01 µM; PD901 1 µM, VX-680 0.2 µM) were normalized for cell number. Per-cell OCR is compared with normalized ATP-generated RLU (<b>A</b>) and mitochondrial mass (<b>B</b>). Cells analyzed for mitochondrial mass by MitoTracker Deep Red staining were also stained with the mitochondrial membrane potential-sensitive dye TMRE, and the mean integrated intensities compared (<b>C</b>). All data were normalized as a ratio of the mean DMSO-treated values and are the mean of four replicate wells, error bars show standard deviation.</p

Cell cycle profiles derived from high-content assay.

<p>Cells in the same images used for direct cell counting were classified into five cell cycle bins by integrated DNA intensity. <b>A.</b> Stacked bars show the relative frequencies of the sub-populations at the indicated concentrations. Each bar is the average of two wells. Black circles indicate relative cell number. <b>B.</b> Histograms of integrated DNA intensity derived from the images from individual wells showing the change in cell cycle profile from EC₉₀ (upper panels) to higher doses of etoposide, gemcitabine and VX-680.</p

High-content cell cycle analysis.

<p>HT29 cells treated for 48 hours with the indicated treatments in a 384-well plate were fixed and stained with Hoechst 33452, imaged, and integrated staining intensity of individual nuclei was estimated as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063583#s2" target="_blank">Materials and Methods</a>. <b>A</b>. DNA content histograms for log-2 transformed DNA intensity values normalized to the modal value of the 2N DNA. Automatic classification into sub-G1, 2N, S-phase, 4N and 8N populations is indicated. <b>B</b>. DNA content histograms acquired by flow cytometry, raw FL2-A data was normalized and binned in the same way as high-content data. <b>C</b>. Quantitation of the sub-population fractions of the histograms.</p

Comparison of ATP and MTS assays with direct cell counting.

<p>Replicate plates of HT29 cells were treated as indicated for 48 hours then analyzed by ATP or MTS assay or high-content cell counting. <b>A.</b> Normalized values for direct cell number (red circles), ATP assay (RLU) (blue triangles) and MTS assay (E₄₉₀) (green squares), lines indicate fits to 4-parameter logistic model. If no line is shown then regression did not result in a curve that met acceptance criteria. <b>B.</b> Normalized values for direct cell number (red circles), and DNA assay (CyQuant) (blue triangles) <b>C.</b> Fold change in normalized ratios of ATP RLU signal to cell number (green squares) and MTS assay signal to cell number (blue triangles)<b>D.</b> Fold change in normalized ratio of DNA assay signal to cell number.</p