Private rate of returns to investment in education for teachers with bachelor’s degree in public secondary schools in Kenya

The theory of human capital postulates an increase in earnings at different levels of educational qualifications. However, there are mixed findings and interpretations on return to investment in education around the world. This is attributed to differences in methodology and methods of data analysis. By employing the Mincer regression equation this paper presents findings on private rate of return to investment in higher education for teachers with bachelor’s degree using a sample of 484 teachers. Primary data was collected using a questionnaire. The multivariate regression results showed years of schooling negatively affected private rate of return to schooling for secondary school teachers having bachelor’s degree, while experience and experience squared had positive effect on private rate of return to schooling. Based on Mincer regression equation generated, the private rate of return to schooling for secondary school teachers having bachelor’s degree in Kenya was 58.18%. Owing to increasing direct private costs to education, it is profitable for individuals intending to invest in higher education do so at a younger age so as to reap maximally from investment in higher education

The development of a component to improve the loading safety of bone-anchored prostheses

Use of socket prostheses Currently, for individuals with limb loss, the conventional method of attaching a prosthetic limb relies on a socket that fits over the residual limb. However, there are a number of issues concerning the use of a socket (e.g., blisters, irritation, and discomfort) that result in dissatisfaction with socket prostheses, and these lead ultimately a significant decrease in quality of life. Bone-anchored prosthesis Alternatively, the concept of attaching artificial limbs directly to the skeletal system has been developed (bone anchored prostheses), as it alleviates many of the issues surrounding the conventional socket interface.Bone anchored prostheses rely on two critical components: the implant, and the percutaneous abutment or adapter, which forms the connection for the external prosthetic system (Figure 1). To date, an implant that screws into the long bone of the residual limb has been the most common intervention. However, more recently, press-fit implants have been introduced and their use is increasing. Several other devices are currently at various stages of development, particularly in Europe and the United States. Benefits of bone-anchored prostheses Several key studies have demonstrated that bone-anchored prostheses have major clinical benefits when compared to socket prostheses (e.g., quality of life, prosthetic use, body image, hip range of motion, sitting comfort, ease of donning and doffing, osseoperception (proprioception), walking ability) and acceptable safety, in terms of implant stability and infection. Additionally, this method of attachment allows amputees to participate in a wide range of daily activities for a substantially longer duration. Overall, the system has demonstrated a significant enhancement to quality of life. Challenges of direct skeletal attachment However, due to the direct skeletal attachment, serious injury and damage can occur through excessive loading events such as during a fall (e.g., component damage, peri-prosthetic fracture, hip dislocation, and femoral head fracture). These incidents are costly (e.g., replacement of components) and could require further surgical interventions. Currently, these risks are limiting the acceptance of bone-anchored technology and the substantial improvement to quality of life that this treatment offers. An in-depth investigation into these risks highlighted a clear need to re-design and improve the componentry in the system (Figure 2), to improve the overall safety during excessive loading events. Aim and purposes The ultimate aim of this doctoral research is to improve the loading safety of bone-anchored prostheses, to reduce the incidence of injury and damage through the design of load restricting components, enabling individuals fitted with the system to partake in everyday activities, with increased security and self-assurance. The safety component will be designed to release or ‘fail’ external to the limb, in a way that protects the internal bone-implant interface, thus removing the need for restorative surgery and potential damage to the bone. This requires detailed knowledge of the loads typically experienced by the limb and an understanding of potential overload situations that might occur. Hence, a comprehensive review of the loading literature surrounding bone anchored prostheses will be conducted as part of this project, with the potential for additional experimental studies of the loads during normal activities to fill in gaps in the literature. This information will be pivotal in determining the specifications for the properties of the safety component, and the bone-implant system. The project will follow the Stanford Biodesign process for the development of the safety component

Dopamine induces functional extracellular traps in microglia

Dopamine (DA) plays many roles in the brain, especially in movement, motivation, and reinforcement of behavior; however, its role in regulating innate immunity is not clear. Here, we show that DA can induce DNA-based extracellular traps in primary, adult, human microglia and BV2 microglia cell line. These DNA-based extracellular traps are formed independent of reactive oxygen species, actin polymerization, and cell death. These traps are functional and capture fluorescein (FITC)-tagged Escherichia coli even when reactive oxygen species production or actin polymerization is inhibited. We show that microglial extracellular traps are present in Glioblastoma multiforme. This is crucial because Glioblastoma multiforme cells are known to secrete DA. Our findings demonstrate that DA plays a significant role in sterile neuro-inflammation by inducing microglia extracellular traps

A synthesis of the range of loads applied on the residuum of individuals with transfemoral amputation fitted with bone-anchored prostheses

BACKGROUND Bone anchored prostheses have recently been implemented in the field of limb replacement, as it alleviates many of the issues surrounding the conventional socket interfaces [1]. However, due to the direct skeletal attachment, serious injury and damage can occur through excessive loading events such as a fall. For this reason, it is essential to understand the range of loads experienced within bone anchored prostheses to: optimize the design of componentry; provide safety solutions; and tailor rehabilitation programs accordingly. AIM The aim of this study was to review the current literature targeting direct measurement of the forces and moments within bone anchored prostheses, to provide a synthesis of the range of loads observed. METHOD A literature search was conducted to identify all articles related to the loading of bone anchored prostheses during: rehabilitation exercises; a variety of everyday activities; and adverse events (e.g., a fall). Studies were screened by examining whether direct measurement techniques (e.g., load transducers) were used to assess the three-dimensional forces and moments occurring within the bone anchored fixation of individuals with a transfemoral amputation. The three axes were defined as: Anterior Posterior (AP), Medial Lateral (ML), and Axial or Long (LG). The loading data were presented in raw units (Newtons) and a percentage of bodyweight (% BW) where possible. The data was mapped graphically to display the forces and moments for each activity analyzed across all studies. RESULTS This study included 11 articles published between 1990 and 2016. Frossard et al. (2010) presented data from a subject falling, reporting the largest recorded loading values, where a maximum force of 1145 N, and moment of 153 Nm, occurred along the long axis and medial-lateral axis of the prostheses respectively, which corresponds to 126 % BW and 16.8 % BWm [2]. For everyday activities, the combined average of the maximum values and corresponding standard deviations for each axes are shown in Table 1, which displays a small portion of the results. Table 1. Combined average value and standard deviation (in brackets) for the forces and moments applied on each axes of the bone anchored prostheses during everyday activities. DISCUSSION & CONCLUSION The range of loads presented in this study has implications for a variety of areas in the utilisation of bone anchored prostheses. For example, the mean and maximum loading values for everyday activities can be used in the design and optimisation of system components, and limits can be established for safety devices. Additionally, rehabilitation programs can be tailored to accommodate these verified loads which regularly occur through daily living. This study highlighted the limited loading information available, and the requirement for further research into the loads experienced by bone anchored prostheses. Overall, this study has demonstrated the large range of loads that occur within bone anchored prostheses, and provides a starting point for the optimisation of this technology

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Is an Impacted Morselized Graft in a Cage an Alternative for Reconstructing Segmental Diaphyseal Defects?

Large diaphyseal bone defects often are reconstructed with large structural allografts but these are prone to major complications. We therefore asked whether impacted morselized bone graft could be an alternative for a massive structural graft in reconstructing large diaphyseal bone defects. Defects in the femora of goats were reconstructed using a cage filled with firmly impacted morselized allograft or with a structural cortical autograft (n = 6 in both groups). All reconstructions were stabilized with an intramedullary nail. The goats were allowed full weightbearing. In all reconstructions, the grafts united radiographically. Mechanical torsion strength of the femur with the cage and structural cortical graft reconstructions were 66.6% and 60.3%, respectively, as compared with the contralateral femurs after 6 months. Histologically, the impacted morselized graft was replaced completely by new viable bone. In the structural graft group, a mixture of new and necrotic bone was present. Incorporation of the impacted graft into new viable bone suggests this type of reconstruction may be safer than reconstruction with a structural graft in which creeping substitution results in a mixture of viable and necrotic bone that can fracture. The data suggest that a cage filled with a loaded morselized graft could be an alternative for the massive cortical graft in reconstruction of large diaphyseal defects in an animal model

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction

Aim: Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods: This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist’s findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results: Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist’s findings. Substantial agreement observed between the classification by model and the radiologist’s given classification [K-0.695 (K is Cohen’s kappa score for interrater reliability)]. Conclusions: The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology