Treatment of tibial diaphyseal fractures with closed flexible intramedullary ender nails: 39 fractures followed for a period of two to seven years

Abstract OBJECTIVE: To assess the efficacy of flexible intramedullary Ender nails for the treatment of tibial diaphyseal. MATERIALS AND METHODS: This is a retrospective review of patients treated with the Ender Nail for both open and closed tibial shaft fractures. Between January 1989 and December 1994, 43 fractures were treated with these nails. The configuration of each fracture was determined using the Orthopedic Trauma Association classification and the extent of soft tissue damage was assessed using the Gustilo and Anderson\u27s classification. Four patients were excluded from the study due to inadequate follow-up. There were 27 closed and 12 open fractures. RESULTS: The average time to union in 34 out of 39 fractures was 17 weeks. Delayed union and malunion occurred in two patients each and superficial wound infection in 1 patient. Nonunion occurred in 5 fractures that were all located in the distal 1/3 of the tibial diaphysis. We attribute this high rate of non-union to a poor rotational control on the distal fragment by these nails. CONCLUSIONS: The Ender nails provide effective fixation for the OTA stable class of tibial fractures, where they produce good axial and rotational stability by virtue of their three-point fixation. Rotational and angular stability should be improved by a supplementary cast immobilization for fractures with unstable configuration and those located in the distal third of the diaphysis

Residual Weighted Learning for Estimating Individualized Treatment Rules

Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has some problems in performance. In this article, we propose a general framework, called Residual Weighted Learning (RWL), to improve finite sample performance. Unlike OWL which weights misclassification errors by clinical outcomes, RWL weights these errors by residuals of the outcome from a regression fit on clinical covariates excluding treatment assignment. We utilize the smoothed ramp loss function in RWL, and provide a difference of convex (d.c.) algorithm to solve the corresponding non-convex optimization problem. By estimating residuals with linear models or generalized linear models, RWL can effectively deal with different types of outcomes, such as continuous, binary and count outcomes. We also propose variable selection methods for linear and nonlinear rules, respectively, to further improve the performance. We show that the resulting estimator of the treatment rule is consistent. We further obtain a rate of convergence for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed RWL methods is illustrated in simulation studies and in an analysis of cystic fibrosis clinical trial data.Comment: 48 pages, 3 figure

Metagenomic sequencing unravels gene fragments with phylogenetic signatures of O2-tolerant NiFe membrane-bound hydrogenases in lacustrine sediment

Many promising hydrogen technologies utilising hydrogenase enzymes have been slowed by the fact that most hydrogenases are extremely sensitive to O2. Within the group 1 membrane-bound NiFe hydrogenase, naturally occurring tolerant enzymes do exist, and O2 tolerance has been largely attributed to changes in iron–sulphur clusters coordinated by different numbers of cysteine residues in the enzyme’s small subunit. Indeed, previous work has provided a robust phylogenetic signature of O2 tolerance [1], which when combined with new sequencing technologies makes bio prospecting in nature a far more viable endeavour. However, making sense of such a vast diversity is still challenging and could be simplified if known species with O2-tolerant enzymes were annotated with information on metabolism and natural environments. Here, we utilised a bioinformatics approach to compare O2-tolerant and sensitive membrane-bound NiFe hydrogenases from 177 bacterial species with fully sequenced genomes for differences in their taxonomy, O2 requirements, and natural environment. Following this, we interrogated a metagenome from lacustrine surface sediment for novel hydrogenases via high-throughput shotgun DNA sequencing using the Illumina™ MiSeq platform. We found 44 new NiFe group 1 membrane-bound hydrogenase sequence fragments, five of which segregated with the tolerant group on the phylogenetic tree of the enzyme’s small subunit, and four with the large subunit, indicating de novo O2-tolerant protein sequences that could help engineer more efficient hydrogenases

Deep generative adversarial residual convolutional networks for real-world super-resolution

Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as assumption that the LR image is a bicubic down-sampled version of the HR image. However, such degradation process is not available in real-world settings i.e. inherent sensor noise, stochastic noise, compression artifacts, possible mismatch between image degradation process and camera device. It reduces significantly the performance of current SISR methods due to real-world image corruptions. To address these problems, we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN1) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques. We demonstrate our proposed approach in quantitative and qualitative experiments that generalize robustly to real input and it is easy to deploy for other downscaling operators and mobile/embedded devices

Globalization and Regionalization: At a Glance on Debate in Pursuit of Guiding Principles Leading Policy Implications

The debate of Globalization and Regionalization is becoming extensive and significant and in response to these observations, various standpoints, in favor or against, are also emerging and have fueled the debate. Huge complexities are emerging to comprehend what is intended by both. The proponents of both are presenting contrasting, conflicting and debatable explanations and interpretations; however, a clear demarcation between these is still blurry. Due to broadness of both concepts, in this study, instead of making another attempt to provide the summary of different forms, definitions, contradictory or conflicting debate about globalization and regionalization, attention is given on those aspects which are contested by the proponents of regionalization. After rigorous examination and comparisons of various viewpoints of proponents of both, few guidelines are proposed for policy makers to develop some policies to ease the challenge currently micro and macro players are facing about decision making about these two. The policy makers should understand that survival of countries resides in internally connected dynamic and pluralistic business blocs in which the member states not only trade freely but fairly and fearlessly

Editorial: Manufacturing, Characterisation and Properties of Advanced Nanocomposites

No Abstract availabl

Biodiesel production from Cannabis sativa oil from Pakistan

The present study was appraised using response surface methodology for process optimization owing to strong interaction of reaction variables: NaOCH3 catalyst concentration (0.25–1.50%), methanol/oil molar ratio (3:1–9:1), reaction time (30–90 min), and reaction temperature (45–65°C). The quadratic polynomial equation was determined using response surface methodology for predicting optimum methyl esters yield from Cannabis sativa oil. The analysis of variance results indicated that molar ratio and reaction temperature were the key factors that appreciably influence the yield of Cannabis sativa oil methyl esters. The significant (p < 0.0001) variable interaction between molar ratio × catalyst concentration and reaction time × molar ratio was observed, which mostly affect the Cannabis sativa oil methyl esters yield. The optimum Cannabis sativa oil methyl esters yield, i.e., 86.01% was gained at 53°C reaction temperature, 7.5:1 methanol/oil molar ratio, 65 min reaction time, and 0.80% catalyst concentration. The results depicted a linear relationship between observed and predicted values. The residual analysis predicted the appropriateness of the central composite design. The Cannabis sativa oil methyl esters, analyzed by gas chromatography, elucidated six fatty acid methyl esters (linoleic, α-linolenic, oleic, palmitic, stearic, and γ-linolenic acids). In addition, the fuel properties, such as kinematic viscosity at 40°C; cetane number; acid value; flash point; cloud, pour, and cold filter plugging points; ash content; density; and sulphur content, of Cannabis sativa oil methyl esters were evaluated and discussed with reference to ASTM D 6751 and EU 14214 biodiesel specifications

Association of Physical Activity with Co-morbid Conditions in Geriatric Population

To find out association of physical activity with co-morbid conditions in geriatric population, a cross-sectional study was conducted in different cties of Pakistan in 2015. A total of 114 participants were inducted by non-probability convenience sampling technique. Data was collected after informed verbal consent by a validated questionnaire that is Rapid Assessment of Physical Activity (RAPA). Participants were categorized into two groups i.e. physically active and physically inactive. Data was entered and analyzed in SPSS version 20. There were 66 (57.9%) males and 48 (42.1%) females with mean age of 57.04±7.348 years. Among hypertensive individuals (n=43, 37.7%) there were 39 (90.7%) physically inactive, among individuals having angina (n=17, 14.9%) there were 15 (88.2%) physically inactive. Out of 37 (32.5%) diabetics, 35 (94.6%) were physically inactive. Among individuals suffering from arthritis (n=40, 35.1%), there were 38 (95%) physically inactive. A significant association was found between physical activity and diabetes and arthritis with p-value of 0.048 and 0.029 respectively. Physical activity is significantly associated with diabetes and arthritis in geriatric population. Adequate physical activity should be performed to reduce the risk of co-morbid conditions and improve the quality of life in geriatric population