Should we treat soft tissue injuries with Actovegin?

Actovegin is a biological drug produced from deproteinised hemodialysate of calf serum with over 50 years of history for its clinical use. There have been many in vitro studies to speculate its potential role and mechanism of action in cells; due to the nature of this drug and serum based culture techniques for most in vitro experiments, presumptuous conclusions and claims from these studies on performance enhancement should be cautiously interpreted. There have been well-designed human in vivo studies suggesting it does not enhance human performance, and has potentially good clinical applications to treat injuries, strokes and diabetes. Recently, evidence has emerged suggesting Actovegin has anti-inflammatory and anti-apoptotic effects on injured tissues; further clinical research is needed to define these effects. This article also provides a narrative review of Actovegin summarizing outcomes from recent publications

SHORT-TERM BIOMECHANICAL ADAPTATION IN A MAXIMUM VELOCITY FIELD SPORT SPRINTING PROTOCOL: PILOT INVESTIGATION

The aim of this study was to investigate short-term biomechanical adaptation of maximum velocity running in response to two sprint protocols; anticipated, where the athlete knew and unanticipated, where he didn't know the required sprint distance prior to entering a test zone. An automatic motion analysis system was used to track sagittal plane marker locations during anticipated and unanticipated maximum velocity sprints performed by an experienced male university football player (age: 23 years, body mass: 85 kg, stature: 1.86 m). Significant increases for the anticipated condition (

Actovegin equal to performance enhancing drug doping: fact or fiction?

Actovegin is a biological drug that has been used for the treatment of sports muscle injuries. Several in vitro studies have shed light on potential mechanisms of action and the drug has consistently demonstrated its potential to reduce return from injury time for muscle tears in elite athletes. Yet it was banned for a time under the International Olympic Committee (IOC) as a blood doping agent, this ban was based on presumptuous conclusions and subsequently lifted after no indisputable evidence could be provided. This editorial aims to provide readers with some of the key, objective facts relating to Actovegin and then based on this, will offer an informed opinion on its role in sports medicine. We also hope to highlight the importance of evidence-based medicine, particularly in the volatile field of Sports Medicine, and the need for facts, not fiction

Can we use biomarkers of coagulation to predict which patients with foot and ankle injury will develop vein thrombosis?

Background Our aim was to determine whether plasma levels of Tissue Factor (TF), Vascular Cell Adhesion Molecule 1 (VCAM-1), Interleukin 6 (IL-6) or D-dimer after foot and ankle injury could predict which patients would develop deep vein thrombosis (DVT). Methods Patients aged 18–60 years with acute foot and ankle injury had venous blood sample to measure TF, VCAM-1, IL-6 and D-dimer within 3 days of injury. Patients had bilateral lower limb venous ultrasound to assess for DVT on discharge from clinic. Results 21 of 77 patients were found to have DVT (27%). There was no statistically significant association between levels of TF, VCAM-1, IL-6 or D-dimer and subsequent development of DVT. Conclusion Tissue Factor (TF), Vascular Cell Adhesion Molecule-1 (VCAM-1), Interleukin-6 (IL-6) and D-dimer levels were not associated with development deep vein thrombosis in patients with acute foot and ankle injury

Player-surface interactions: Perception in elite soccer and rugby players on artificial and natural turf

Artificial turf (AT) is common at all levels of soccer and rugby. Employing an interdisciplinary design, this study aimed to examine the extent to which the negative attitude commonly expressed by players concerning AT is based on the difference in technique between AT and natural turf (NT), or due to pre-existing biases. Thirty professional soccer and rugby players performed a defined set of movements with masked and normal perception conditions on NT and AT. Two-dimensional kinematic analysis (100 Hz) of characteristics in parallel to a psychological assessment of the impact of cognitive bias for a playing surface was assessed. No significant interaction effects between the level of perception and surface type were found. For AT, contact time (CT) was shorter across conditions, while for NT rugby players had longer CT during acceleration/deceleration phases and shorter flight times. Pre-existing negative bias against AT was found during the normal perception trials in the technology acceptance model (Usefulness and Ease of Use) and the general preference questions on how much the athlete would like to play a game on it. The results suggest that opinion was not driven by surface characteristics, but by a cognitive bias, players brought with them to the pitch

Image classification-based brain tumour tissue segmentation

Brain tumour tissue segmentation is essential for clinical decision making. While manual segmentation is time consuming, tedious, and subjective, it is very challenging to develop automatic segmentation methods. Deep learning with convolutional neural network (CNN) architecture has consistently outperformed previous methods on such challenging tasks. However, the local dependencies of pixel classes cannot be fully reflected in the CNN models. In contrast, hand-crafted features such as histogram-based texture features provide robust feature descriptors of local pixel dependencies. In this paper, a classification-based method for automatic brain tumour tissue segmentation is proposed using combined CNN-based and hand-crafted features. The CIFAR network is modified to extract CNN-based features, and histogram-based texture features are fused to compensate the limitation in the CIFAR network. These features together with the pixel intensities of the original MRI images are sent to a decision tree for classifying the MRI image voxels into different types of tumour tissues. The method is evaluated on the BraTS 2017 dataset. Experiments show that the proposed method produces promising segmentation results

Player-surface interactions: perception in elite soccer and rugby players on artificial and natural turf

Artificial turf (AT) is common at all levels of soccer and rugby. Employing an interdisciplinary design, this study aimed to examine the extent to which the negative attitude commonly expressed by players concerning AT is based on the difference in technique between AT and natural turf (NT), or due to pre-existing biases. Thirty professional soccer and rugby players performed a defined set of movements with masked and normal perception conditions on NT and AT. Two-dimensional kinematic analysis (100 Hz) of characteristics in parallel to a psychological assessment of the impact of cognitive bias for a playing surface was assessed. No significant interaction effects between the level of perception and surface type were found. For AT, contact time (CT) was shorter across conditions, while for NT rugby players had longer CT during acceleration/deceleration phases and shorter flight times. Pre-existing negative bias against AT was found during the normal perception trials in the technology acceptance model (Usefulness and Ease of Use) and the general preference questions on how much the athlete would like to play a game on it. The results suggest that opinion was not driven by surface characteristics, but by a cognitive bias, players brought with them to the pitch

Combined features in region of interest for brain tumor segmentation

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively

Update on the Role of Actovegin in Musculoskeletal Medicine: A Review of the Past 10 Years.

Background: Actovegin is a biological drug with a controversial history of use in the treatment of sports injuries during the past 60 years. Particular concerns have been raised about its ergogenic potential to enhance performance, but some of these have been based on little more than anecdote. Objectives: In this article, we review the most recent scientific evidence to determine the clinical efficacy, safety profile, and legal status of Actovegin. Methods: We considered all studies directly commenting on experience with Actovegin use as the primary intervention within the past 10 years. Outcomes included mechanisms of action, clinical efficacy in enhancing muscle repair, any report of safety issues, and any evidence for ergogenic effect. Results: Our database search returned 212 articles, abstracts were screened, and after inclusion/exclusion criteria were applied, 25 articles were considered: Publications included 11 primary research articles (7 in vitro studies and 4 clinical trials), 8 review articles, 5 editorials, and a single case report. Conclusions: Current literature is still yet to define the active compound(s) of Actovegin, but suggests that it shows antioxidant and antiapoptotic properties, and may also upregulate macrophage responses central to muscle repair. Clinical efficacy was supported by one new original research article, and the use of Actovegin to treat muscle injuries remains safe and supported. Two articles argued the ergogenic effect of Actovegin, but in vitro findings did not to translate to the outcomes of a clinical trial. An adequate and meaningful scientific approach remains difficult in a field where there is immense pressure to deliver cutting-edge therapies