A 10-year retrospective analysis of radiographic bone-level changes of implants supporting single-unit crowns in periodontally compromised vs. periodontally healthy patients

AIM: To compare the 10-year peri-implant bone loss (BL) rate in periodontally compromised (PCP) and periodontally healthy patients (PHP) around two different implant systems supporting single-unit crowns. MATERIALS AND METHODS: In this retrospective, controlled study, the mean BL (mBL) rate around dental implants placed in four groups of 20 non-smokers was evaluated after a follow-up of 10 years. Two groups of patients treated for periodontitis (PCP) and two groups of PHP were created. For each category (PCP and PHP), two different types of implant had been selected. The mBL was calculated by subtracting the radiographic bone levels at the time of crown cementation from the bone levels at the 10-year follow-up. RESULTS: The mean age, mean full-mouth plaque and full-mouth bleeding scores and implant location were similar between the four groups. Implant survival rates ranged between 85% and 95%, without statistically significant differences (P>0.05) between groups. For both implant systems, PCP showed statistically significantly higher mBL rates and number of sites with BL> or =3 mm compared with PHP (P<0.0001). CONCLUSIONS: After 10 years, implants in PCP yielded lower survival rates and higher mean marginal BL rates compared with those of implants placed in PHP. These results were independent of the implant system used or the healing modality applied.

Prognostic factors in apical surgery with root-end filling: a meta-analysis

INTRODUCTION: Apical surgery has seen continuous development with regard to equipment and surgical technique. However, there is still a shortage of evidence-based information regarding healing determinants. The objective of this meta-analysis was to review clinical articles on apical surgery with root-end filling in order to assess potential prognostic factors. METHODS: An electronic search of PubMed and Cochrane databases was performed in 2008. Only studies with clearly defined healing criteria were included, and data for at least two categories per prognostic factor had to be reported. Prognostic factors were divided into patient-related, tooth-related, or treatment-related factors. The reported percentages of healed teeth ("the healed rate") were pooled per category. The statistical method of Mantel-Haenszel was applied to estimate the odds ratios and their 95% confidence intervals. RESULTS: With regard to tooth-related factors, the following categories were significantly associated with higher healed rates: cases without preoperative pain or signs, cases with good density of root canal filling, and cases with absence or size < or = 5 mm of periapical lesion. With regard to treatment-related factors, cases treated with the use of an endoscope tended to have higher healed rates than cases without the use of an endoscope. CONCLUSIONS: Although the clinician may be able to control treatment-related factors (by choosing a certain technique), patient- and tooth-related factors are usually beyond the surgeon's power. Nevertheless, patient- and tooth-related factors should be considered as important prognostic determinants when planning or weighing apical surgery against treatment alternatives.

Finite element analysis of aortic root dilation: a new procedure to reproduce pathology based on experimental data

Sinotubular junction dilation is one of the most frequent pathologies associated with aortic root incompetence. Hence, we create a finite element model considering the whole root geometry; then, starting from healthy valve models and referring to measures of pathological valves reported in the literature, we reproduce the pathology of the aortic root by imposing appropriate boundary conditions. After evaluating the virtual pathological process, we are able to correlate dimensions of non-functional valves with dimensions of competent valves. Such a relation could be helpful in recreating a competent aortic root and, in particular, it could provide useful information in advance in aortic valve sparing surgery.

Mass Transport Perspective on an Accelerated Exclusion Process: Analysis of Augmented Current and Unit-Velocity Phases

In an accelerated exclusion process (AEP), each particle can "hop" to its adjacent site if empty as well as "kick" the frontmost particle when joining a cluster of size ℓ⩽ℓ_{max}. With various choices of the interaction range, ℓ_{max}, we find that the steady state of AEP can be found in a homogeneous phase with augmented currents (AC) or a segregated phase with holes moving at unit velocity (UV). Here we present a detailed study on the emergence of the novel phases, from two perspectives: the AEP and a mass transport process (MTP). In the latter picture, the system in the UV phase is composed of a condensate in coexistence with a fluid, while the transition from AC to UV can be regarded as condensation. Using Monte Carlo simulations, exact results for special cases, and analytic methods in a mean field approach (within the MTP), we focus on steady state currents and cluster sizes. Excellent agreement between data and theory is found, providing an insightful picture for understanding this model system.

Quantitative motor unit action potential analysis in 2 paraspinal neck muscles in adult Royal Dutch Sport horses

BACKGROUND: Reference values for quantitative electromyography (QEMG) in neck muscles of Royal Dutch Sport horses are lacking. OBJECTIVE: Determine normative data on quantitative motor unit action potential (QMUP) analysis of serratus ventralis cervicis (SV) and brachiocephalicus (BC) muscle. ANIMALS: Seven adult normal horses (mean age 9.5 standard deviation [SD] +/- 2.3 years, mean height 1.64 SD +/- 4.5 cm, and mean rectal temperature 37.6 SD +/- 0.3 degrees C). METHODS: An observational study on QMUP analysis in 6 segments of each muscle was performed with commercial electromyography equipment. Measurements were made according to formerly published methods. Natural logarithm transformed data were tested with ANOVA and posthoc testing according to Bonferroni. RESULTS: Mean duration, amplitude, phases, turns, area, and size index (SI) did not differ significantly among the 6 segments in each muscle. Mean amplitude, number of phases, and SI were significantly (P < .002) higher in SV than BC, 520 versus 448 muV, 3.0 versus 2.8 muV, and 0.48 versus 0.30 muV, respectively. In SV 95% confidence intervals (CI) for amplitude, duration, number of phases, turns, polyphasia area, and SI were 488-551 muV, 4.3-4.6 ms, 2.9-3.0, 2.4-2.6, 7-12%, 382-448, and 0.26-0.70, respectively; in BC this was 412-483 muV, 4.3-4.7 ms, 2.7-2.8, 2.4-2.6, 4-7%, 393-469, and 0.27-0.34, respectively. Maximal voluntary activity expressed by turns/second did not differ significantly between SV and BC with a 95% CI of 132-173 and 137-198, respectively. CONCLUSIONS AND CLINICAL IMPORTANCE: The establishment of normative data makes objective QEMG of paraspinal muscles in horses suspected of cervical neurogenic disorders possible. Differences between muscles should be taken into account.

782 consecutive construction work accidents: who is at risk? A 10-year analysis from a Swiss university hospital trauma unit

BACKGROUND: Mortality and morbidity are particularly high in the building industry. The annual rate of non-fatal occupational accidents in Switzerland is 1,133 per 100,000 inhabitants. METHODS: Retrospective analysis of the electronic database of a university emergency centre. Between 2001 and 2011, 782 occupational accidents to construction workers were recorded and analysed using specific demographic and medical keywords. RESULTS: Most patients were aged 30-39 (30.4%). 66.4% of the injured workers were foreigners. This is almost twice as high as the overall proportion of foreigners in Switzerland or in the Swiss labour market. 16% of the Swiss construction workers and 8% of the foreign construction workers suffered a severe injury with ISS >15. There was a trend for workers aged 60 and above to suffer an accident with a high ISS (p = 0.089). CONCLUSIONS: As in other European countries, most patients were in their thirties. Older construction workers suffered fewer injuries, although these tended to be more severe. The injuries were evenly distributed through the working days of the week. A special effort should be made that current health and safety measures are understood and applied by foreign and older construction workers.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Impacted maxillary canines and root resorptions of neighbouring teeth: a radiographic analysis using cone-beam computed tomography

The study analyses the location of impacted maxillary canines and factors influencing root resorptions of adjacent teeth using cone-beam computed tomography (CBCT). In addition, the interrater reliability between observers of two different dental specialties for radiographic parameters will be evaluated. CBCT images of patients who were referred for radiographic localization of impacted maxillary canines and/or suspicion of root resorptions of adjacent teeth were included. The study analysed the exact three-dimensional location of the impacted canines in the anterior maxilla, frequency and extent of root resorptions, and potential influencing factors. To assess interrater agreement, Cohen's correlation parameters were calculated. This study comprises 113 patients with CBCT scans, and 134 impacted canines were analysed retrospectively. In the patients evaluated, 69 impacted canines were located palatally (51.49 per cent), 41 labially (30.60 per cent), and 24 (17.91 per cent) in the middle of the alveolar process. Root resorptions were found in 34 lateral incisors (25.37 per cent), 7 central incisors (5.22 per cent), 6 first premolars (4.48 per cent), and 1 second premolar (0.75 per cent). There was a significant correlation between root resorptions on adjacent teeth and localization of the impacted canine in relation to the bone, as well as vertical localization of the canine. Interrater agreement showed values of 0.546-0.877. CBCT provides accurate information about location of the impacted canine and prevalence and degree of root resorption of neighbouring teeth with high interrater correlation. This information is of great importance for surgeons and orthodontists for accurate diagnostics and interdisciplinary treatment planning.

Changes of Root Length and Root-to-Crown Ratio after Apical Surgery: An Analysis by Using Cone-beam Computed Tomography

INTRODUCTION Apical surgery is an important treatment option for teeth with post-treatment periodontitis. Although apical surgery involves root-end resection, no morphometric data are yet available about root-end resection and its impact on the root-to-crown ratio (RCR). The present study assessed the length of apicectomy and calculated the loss of root length and changes of RCR after apical surgery. METHODS In a prospective clinical study, cone-beam computed tomography scans were taken preoperatively and postoperatively. From these images, the crown and root lengths of 61 roots (54 teeth in 47 patients) were measured before and after apical surgery. Data were collected relative to the cementoenamel junction (CEJ) as well as to the crestal bone level (CBL). One observer took all measurements twice (to calculate the intraobserver variability), and the means were used for further analysis. The following parameters were assessed for all treated teeth as well as for specific tooth groups: length of root-end resection and percentage change of root length, preoperative and postoperative RCRs, and percentage change of RCR after apical surgery. RESULTS The mean length of root-end resection was 3.58 ± 1.43 mm (relative to the CBL). This amounted to a loss of 33.2% of clinical and 26% of anatomic root length. There was an overall significant difference between the tooth groups (P < .05). There was also a statistically significant difference comparing mandibular and maxillary teeth (P < .05), but not for incisors/canines versus premolars/molars (P = .125). The mean preoperative and postoperative RCRs (relative to CEJ) were 1.83 and 1.35, respectively (P < .001). With regard to the CBL reference, the mean preoperative and postoperative RCRs were 1.08 and 0.71 (CBL), respectively (P < .001). The calculated changes of RCR after apical surgery were 24.8% relative to CEJ and 33.3% relative to CBL (P < .001). Across the different tooth groups, the mean RCR was not significantly different (P = .244 for CEJ and 0.114 for CBL). CONCLUSIONS This CBCT-based study demonstrated that the RCR is significantly changed after root-end resection in apical surgery irrespective of the clinical (CBL) or anatomic (CEJ) reference levels. The lowest, and thus clinically most critical, postoperative RCR was observed in maxillary incisors. Future clinical studies need to show the impact of resection length and RCR changes on the outcome of apical surgery.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.