Comparative study between dynamic hip screw and plate with proximal femoral nailing in trochanteric fractures of femur

Background: Incidence of intertrochanteric fractures has increased significantly during recent years and it will probably continue to rise in near future due to increased longevity of life. Closed methods of treatment for intertrochanteric fractures have increased mortality rates & have largely been abandoned. Rigid internal fixation and early mobilization has been the standard protocol of treatment nowadays. This study was intended to compare the results of Intertrochanteric fractures treated by dynamic hip screw (DHS) over proximal femoral nailing (PFN).Methods: This is a randomized prospective study of 102 cases of intertrochanteric fractures, admitted to Mysore Medical College & Hospital, Mysore and ESIC medical college and hospital Kalaburagi and Kamareddy Ortho & Trauma care Hospital Kalaburagi; treated with proximal femoral nailing and DHS. The patients were divided randomly into two groups, each of 51 patients, 51 were treated by Dynamic Hip Screw & 51 were treated with proximal femoral nail (PFN).All patients were followed up for one year.Results: In our series of 102 patients of Intertrochanteric fractures, 51 were treated with sliding hip screw with plate and 51 were treated by an intra-medullary hip screw. Most of the patients were between 51 to 80 years. Slip and trivial fall accounted for 70% cases. Right side was more common accounted for 58.34% of cases. Conclusions: From our study, we consider PFN as better alternative to DHS in more unstable fractures with technically difficulty and require more experience. Sliding hip screw remains the implant of choice for stable type fractures. We noticed less operative time, less blood loss, fewer intra-op complications in PFN group. The quality of the reduction achieved & proper positioning of the implant are important to achieve the best post-operative outcome

An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions

Track E Implementation Science, Health Systems and Economics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd

Frequent Itemsets Used in Mining of Train Delays

The Indian railway network has a high traffic density with Vijayawada as its gravity center. The star-shape of the network implies heavily loaded bifurcations in which knock-on delays are likely to occur. Knock-on delays should be minimized to improve the total punctuality in the network. Based on experience, the most critical junctions in the traffic flow are known, but others might be hidden. To reveal the hidden patterns of trains passing delays to each other, we study, adapt and apply the state-of-the-art techniques for mining frequent episodes to this specific problem