A case of subtrochanteric femur fracture nonunion with failed implant in situ treated with exchange nailing using interlock nail and autologous bone grafting: a case report

Subtrochanteric femur fracture accounts for 25% of all hip fracture and may land up in non-union due to the inadequate reduction and fixation tech, local muscle pull over fragments, biomechanical stress in subtrochanteric region and soft tissue interposition etc., non-union are managed with various choices of implants like exchange nailing , angle blade plate , dynamic condylar screw, augmentation of previous hardware with plate and by providing biological environments at fracture site using  bone graft. Strict adherence to principles of providing stability to fracture and providing environment for bony growth gives good clinical outcome. A 52 years old male with subtrochanteric femur fracture was operated with long PFN, later presented to us after 18 months with failure of the hardware and atrophic non-union manifesting as pain during walking and limping. Patient was operated with removal of implant and exchange nailing using femur interlock nail and autologous bone grafting from iliac crest graft. 1 year follow up showed complete bony union and abundant of callus formation. Patient is currently doing all the daily activities and have no complaints at present. At 1 year follow up there is complete union at non-union site and good clinical outcome is achieved. Exchange nailing with interlock nail and autologous bone grafting for treatment of atrophic non-union of subtrochanteric femur fractures gives good clinical outcome

Role of imaging in the management of thyroglossal duct cyst carcinomas (TGC-TIRADS): a single centre retrospective study over 16 years

IntroductionThyroglossal duct cyst (TGDC) is the most frequently encountered developmental anomaly in thyroid genesis with a reported incidence of 7% in the adult population. The cyst is known to develop anywhere along the pathway of thyroid descent but is more frequently seen in the infrahyoid neck in the midline. The incidence of malignancy in a TGDC is approximately 1%; a majority of these are papillary carcinomas. This study was conducted at a single tertiary care centre which spanned over a decade which adds practice changing evidence-based knowledge to existing literature on this rare entity. A comprehensive study which conclusively establishes the imaging features predictive of malignancy in TGDC carcinomas (TGDCa), the protocol for optimal management, clinical outcome and long-term survival of these patients is not available. Although TGDC carcinoma is thought to have an excellent prognosis, there is not enough data available on the long-term survival of these patients. The aim of this study was to identify whether neck ultrasound (US) can serve as an accurate imaging tool for the preoperative diagnosis of TGDC carcinomas.MethodsWe accessed the electronic medical records of 86 patients with TGDC between January 2005 to December 2021. Of these, 22 patients were detected with TGDC papillary carcinoma on histopathologic examination. Relevant imaging, treatment and follow up information for all cases of TGDC carcinoma were retrospectively reviewed. We compared US characteristics predictive of malignancy across outcomes groups; malignant vs benign using the Chi-square test. Based on the results, a TGC-TIRADS classification was proposed with calculation of the percentage likelihood of malignancy for each category.ResultsCompared to benign TGDCs, malignant TGDCs were more likely to present with following US characteristics: irregular or lobulated margins (90.40 vs. 38.10%), solid-cystic composition (61.90 vs. 17.07%), internal vascularity (47.62 vs. 4.88 %), internal calcification (76.19 vs. 7.32 %) (each p value < 0.005). Calcifications and internal vascularity were the most specific while irregular/lobulated margins were the most sensitive feature for malignancy. AUC under the ROC curve was 0.88. Allpatients were operated and were disease free at the end of 5 years or till the recent follow up.DiscussionUS is the imaging modality of choice for pre-operative diagnosis of TGDC carcinoma. Thepre-operative diagnosis and risk stratification of thyroglossal lesions will be aided by the application of the proposed TGC-TIRADS classification, for which the percentage likelihood of malignancy correlated well with the results in our study. Sistrunk procedure is adequate for isolated TGDC carcinoma; suspicious neck nodes on imaging also necessitates selective nodal dissection. Papillary carcinomas have an excellent prognosis with low incidence of disease recurrence

Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%

Cloning, expression and purification of recombinant dermatopontin in Escherichia coli.

Dermatopontin (DPT) is an extracellular matrix (ECM) protein with diversified pharmaceutical applications. It plays important role in cell adhesion/migration, angiogenesis and ECM maintenance. The recombinant production of this protein will enable further exploration of its multifaceted functions. In this study, DPT protein has been expressed in Escherichia coli (E.coli) aiming at cost effective recombinant production. The E.coli GJ1158 expression system was transformed with constructed recombinant vector (pRSETA-DPT) and protein was expressed as inclusion bodies on induction with NaCl. The inclusion bodies were solubilised in urea and renaturation of protein was done by on-column refolding procedure in Nickel activated Sepharose column. The refolded Histidine-tagged DPT protein was purified and eluted from column using imidazole and its purity was confirmed by analytical techniques. The biological activity of the protein was confirmed by collagen fibril assay, wound healing assay and Chorioallantoic Membrane (CAM) angiogenesis assay on comparison with standard DPT. The purified DPT was found to enhance the collagen fibrillogenesis process and improved the migration of human endothelial cells. About 73% enhanced wound closure was observed in purified DPT treated endothelial cells as compared to control. The purified DPT also could induce neovascularisation in the CAM model. At this stage, scaling up the production process for DPT with appropriate purity and reproducibility will have a promising commercial edge

Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks

Abstract Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects

Map Making in Social Indoor Environment Through Robot Navigation Using Active SLAM

Robotics has come a long way from industrial robotic arms and is all set to enter our homes. The capability of a robot to navigate in an unknown human populated environment with obstacles and making map simultaneously is one of the significant characteristics in the domain of autonomous robotics. Further, the problem of robot navigating in a social environment while ensuring human safety and comfort through social norms needs to be addressed. This article presents a solution for mapping of unknown terrains with dynamic obstacles using simultaneous localization in social environments through Adaptive Squashing Function based artificial neural network training, which is able to track the target orientation angles more efficiently as compared to conventional fixed slope squashing function based backpropagation training algorithm. The performance of different state of the art techniques have been compared with proposed work through simulation models. Simulation results demonstrated the effectiveness of the proposed algorithm in complex environment where the proposed algorithm converged in less than 50% of the iterations taken by the exhaustive search algorithms and approximately 33% of the iterations taken by random search algorithm. Further, the proposed approach was tested in the real-world settings, wherein the robot was deployed to create map for the Kalpana Chawla Center for Research in Space Science and Technology, Chandigarh University with mobile humans

Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems

The exploration and mapping of unknown environments, where the reliable exchange of data between the robots and the base station (BS) also plays a pivotal role, are some of the fundamental problems of mobile robotics. The maximum energy of a robot is utilized for navigation and communication. The communication between the robots and the BS is limited by the transmission range and the battery capacity. This situation inflicts constraints while designing an effective communication strategy for a multi-robot system (MRS). The biggest challenge lies in designing a unified framework for navigation and communication of the robots. The underlying notion is to utilize the minimum energy for communication (without limiting the range/efficiency of communication) to ensure that the maximum energy can be used for navigation (for larger area coverage). In this work, we present a communication strategy by using adaptive flower pollination optimization algorithm for MRS in conjunction with simultaneous localization and mapping (SLAM) technique for navigation and map making. The proposed strategy has been compared with multiple routing algorithms in terms of network life time and energy efficiency. The proposed strategy performs 4% better compared with harmony search algorithm (HSA) and approximately 10% better compared with distance aware residual energy-efficient stable election protocol (DARE-SEP) in terms of the total network lifetime when 50% of robots are alive. The performance drastically improves by 20% till the last robot is alive compared with HSA and approximately 26% compared with DARE-SEP. Hence, the energy saved during communication with the utilization of proposed strategy helps the robots explore more areas, which ultimately elevates the efficacy of the whole system

Sperm Cell Driven Microrobots-Emerging Opportunities and Challenges for Biologically Inspired Robotic Design

With the advent of small-scale robotics, several exciting new applications like Targeted Drug Delivery, single cell manipulation and so forth, are being discussed. However, some challenges remain to be overcome before any such technology becomes medically usable; among which propulsion and biocompatibility are the main challenges. Propulsion at micro-scale where the Reynolds number is very low is difficult. To overcome this, nature has developed flagella which have evolved over millions of years to work as a micromotor. Among the microscopic cells that exhibit this mode of propulsion, sperm cells are considered to be fast paced. Here, we give a brief review of the state-of-the-art of Spermbots - a new class of microrobots created by coupling sperm cells to mechanical loads. Spermbots utilize the flagellar movement of the sperm cells for propulsion and as such do not require any toxic fuel in their environment. They are also naturally biocompatible and show considerable speed of motion thereby giving us an option to overcome the two challenges of propulsion and biocompatibility. The coupling mechanisms of physical load to the sperm cells are discussed along with the advantages and challenges associated with the spermbot. A few most promising applications of spermbots are also discussed in detail. A brief discussion of the future outlook of this extremely promising category of microrobots is given at the end