The decision to adopt a renal-sparing regimen was predominantly made on a clinically reactive basis within the first 24 hours post-LT in 77%, and was preordained in 23%. Cost-effectiveness analysis did not find evidence of a significant cost saving when using a renal-sparing strategy.

This study provides real-world analysis of the use of a renal-sparing immunosuppression regimen in LT. Although improvements in incidence of AKI in the short term were demonstrated, this did not translate to cost savings or improved renal outcomes after 3 months.

This study provides real-world analysis of the use of a renal-sparing immunosuppression regimen in LT. Although improvements in incidence of AKI in the short term were demonstrated, this did not translate to cost savings or improved renal outcomes after 3 months.Parkinsons disease (PD) is the second most neurodegenerative disease, which results in gradual loss of movements. To diagnose PD in a clinical setting, clinicians generally use clinical manifestations like motor and non-motor symptoms and rate the severity based on unified Parkinsons disease rating scale (UPDRS). Such clinical assessment largely depends on the expertise and experience of the clinicians and it is subjective leading to variation in assessment between clinicians. As the gait of people with Parkinson’s generally differs from gait of healthy age-matched adults, the assessment of gait abnormalities can lead to not only the diagnosis of PD but also the rating of severity level based on motor symptoms. Hence, in this paper, a data-driven gait classification framework using the supervised machine learning algorithms is presented. Using the publicly available gait datasets acquired using vertical ground reaction force (VGRF) sensors, we present a correlation based feature extraction technique for improved stage classification of PD. Significant biomarkers from spatiotemporal gait features are obtained based on the correlation, and the normal distribution of the gait dataset is assessed using the Shapiro-Wilk test. Subsequently, four supervised machine learning algorithms, namely, K-nearest neighbours (KNN), Naive Bayes (NB), Ensemble classifier (EC) and Support vector machine (SVM) are used to rate the severity level of PD according to the Hoehn and Yahr (H&Y) scale. The performance of the classifiers, assessed using the confusion matrix and parallel coordinate plots, highlights that SVM can result in a classification accuracy of 98.4%. Moreover, with minimal gait feature set acquired based on the rank correlation, the proposed approach outperforms several other state-of-the-art methods that have used the same dataset for PD stage classification.A high anterior lip on a total knee prosthesis is an effective way of reducing anterior translation, but the effect on joint wear is unclear. Using finite element analysis (FEA), this study quantitatively compared wear rates and anterior contact stresses in three posterior stabilized knee prostheses with different heights for the anterior lip during six daily activities (walking, stair ascent, stair descent, sit-to-stand, pivot turn and crossover turn). The wear rate and location of maximum wear depth were similar for the three lip heights tested, but the knee with the highest anterior lip also showed slight anterior wear scaring due to articular contact stress during swing phase, which was highly dependent on the shape of the contact interface. This study illustrates that tibial inserts with a high anterior lip maintain a wear rate similar to moderate and low lip posterior stabilized designs.Pressure mapping technologies provide the opportunity to estimate trends in posture and mobility over extended periods in individuals at risk of developing pressure ulcers. The aim of the study was to combine pressure monitoring with an automated algorithm to detect posture and mobility in a vulnerable population of Spinal Cord Injured (SCI) patients. Pressure data from able-bodied cohort studies involving prescribed lying and sitting postures were used to train the algorithm. This was tested with data from two SCI patients. Variations in the trends of the centre of pressure (COP) and contact area were assessed for detection of small- and large-scale postural movements. Intelligent data processing involving a deep learning algorithm, namely a convolutional neural network (CNN), was utilised for posture classification. COP signals revealed perturbations indicative of postural movements, which were automatically detected using individual- and movement-specific thresholds. CNN provided classification of static postures, with an accuracy ranging between 70-84% in the training cohort of able-bodied subjects. A clinical evaluation highlighted the potential of the novel algorithm to detect postural movements and classify postures in SCI patients. Combination of continuous pressure monitoring and intelligent algorithms offers the potential to objectively detect posture and mobility in vulnerable patients and inform clinical-decision making to provide personalized care.Distraction Osteogenesis (DO) is an emerging limb lengthening method for the reconstruction of the hard tissue and the surrounding soft tissue, in different human body zones. DO plays an important role in treating bone defects in Maxillofacial Reconstruction Applications (MRA) due to reduced side effects and better formed bone tissue compared to conventional reconstruction methods i.e. autologous bone graft, and alloplast implantation. Recently, varying techniques have been evaluated to enhance the characteristics of the newly formed tissues and process parameters. Dorsomorphin mouse Promising results have been shown in assisting DO treatments while benefiting bone formation mechanisms by using physical stimulation techniques, including photonic, electromagnetic, electrical, and mechanical stimulation technique. Using assisted DO techniques has provided superior results in the outcome of the DO procedure compared to a standard DO procedure. However, DO methods, as well as assisting technologies applied during the DO procedure, are still emerging. Studies and experiments on developed solutions related to this field have been limited to animal and clinical trials. In this review paper, recent advances in physical stimulation techniques and their effects on the outcome of the DO treatment in MRA are surveyed. By studying the effects of using assisting techniques during the DO treatment, enabling an ideal assisted DO technique in MRA can be possible. Although mentioned techniques have shown constructive effects during the DO procedure, there is still a need for more research and investigation to be done to fully understand the effects of assisting techniques and advanced technologies for use in an ultimate DO procedure in MRA.