8-Year-Old Child with Cerebral Palsy Treated with Pelvic Osteotomies Using 3.5 mm Blade Plate Having Subsequent Bilateral Implant Aseptic Loosening: A Case Report

Background: Cerebral palsy (CP) is a central problem of the brain due to neurological insult that affects muscle posture, tone, and movement, resulting in poor motor control and dysfunctional muscle balance affecting hip joints in the growing child. Surgical treatment of hip and, if present, acetabular dysplasia addresses the femoral neck-shaft angle, appropriate muscle lengthening, and deficiency of acetabular coverage, as necessary. The surgeons perform proximal femoral osteotomies (PFOs) mostly with fixed angled blade plates (ABP) with proven success. The technique using an ABP is common and requires detailed attention to perform and to teach.  The case: In this case, an eight-year-old ambulatory patient with CP underwent bilateral proximal varus femoral derotational and pelvic osteotomies for the neuromuscular hip condition with a 3.5 mm Locking Cannulated Blade System (OP-LCP) by OrthoPediatrics Corp instead of the use of the conventional 4.5 mm ABP procedure, resulting in aseptic loosening. Conclusion: Due to the child’s underdeveloped posture, the surgeon utilized the 3.5 mm instrumentation for a child-size implant, which worked sufficiently for the surgery but may not have loosened if a similar child-size blade plate system of 4.5 mm screws was implanted. While the ABP and OP-LCP systems are effective and safe for internal corrections of PFOs, the OP-LCP system may aid the residents in learning the procedure with higher confidence, fewer technical inaccuracies, and refined outcomes. Both systems are safer and viable for the treatment of neuromuscular hip conditions

Addressing Adversities in Caring for Mental Health in Rural Settings: A Conversation with Rural Mental Healthcare Practice Co-founder Dr. Taryn S. Van Gilder-Pierce

Due to lack of mental healthcare facilities in rural areas, the population often resorts to private practice practitioners to address their need for mental health services. Dr. Taryn S. Van Gilder-Pierce and her husband, Dr. William D. Pierce founded their private practice in Yankton, SD in 2001. She has more than 25 years of training and experience treating individuals, married couples, families, and groups in rural South Dakota. The interview delves into the challenges faced by early career professionals in building a practice in remote areas and extends into the room for expansion within the field of rural mental health provision of services

Model Selection of Discrete Classifiers under Class and Cost Distribution Change: An Empirical Study

A variety of important machine learning applications require predictions on test data with different characteristics than the data on which a model was trained and validated. In particular, test data may have a different relative frequency of positives and negatives (i.e., class distribution) and/or different mislabeling costs of false positive and false negative errors (i.e., cost distribution) than the training data. Selecting models that have been built in conditions that are substantially different from the conditions under which they will be applied is more challenging than selecting models for identical conditions. Several approaches to this problem exist, but they have mostly been studied in theoretical contexts. This paper presents an empirical evaluation of approaches for model selection under class and cost distribution change, based on Receiver Operating Characteristic (ROC) analysis. The analysis compares the ROC Convex Hull (ROCCH) method with other candidate approaches for selecting discrete classifiers for several UCI Machine Learning Repository and simulated datasets. Surprisingly, the ROCCH method did not perform well in the experiments, despite being developed for this task. Instead, the results indicate that a reliable approach for selecting discrete classifiers on the ROC convex hull is to select the model that optimizes the cost metric of interest on the validation data (which has the same characteristics as the training data) but weighted by the class and/or cost distributions anticipated at test time

Medical Student Research Journals: The International Journal of Medical Students (IJMS) Legacy

The International Journal of Medical Students (IJMS) has emerged over the past decade as a critical platform for showcasing medical student innovation and experiences. Though the work of trainees has historically been undervalued and over scrutinized, the IJMS is committed to highlighting the immense capacity for novel and robust research in this cohort. Thus, supporting an upcoming generation of leaders in medicine and academia to gain confidence in their work and contribute positively to the scientific community. In this issue of the IJMS, we are proud to present 16 articles from the Americas, Europe, Asia, and Africa. Original research articles cover a breadth of topics, including medical training, impacts of the COVID-19 pandemic on teaching and communication, pediatric respiratory illness, gender equity in medicine, understudied illnesses, and cardiovascular disease. The IJMS is proud to feature first-hand experiences of medical trainees in each issue. Accordingly, in the present issue perspectives of six medical students are outlined following unique and career-altering experiences. From working in palliative care to international outreach program, local vaccination initiatives, and the creation of a student-oriented research and innovation council in India. The IJMs extends our gratitude to our contributors, team, and readers for another remarkable issue

Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

Label noise tolerance of deep semantic segmentation networks for extracting buildings in ultra-high-resolution aerial images of semi-built environments

Freely available building maps of rapidly changing built and semi-built environments may contain label noise. When temporal correspondence between images and labels does not hold, the labels may be subject to incorrectly observed building instances. For example, in most growing semi-built environments, such as the Kutupalong mega-camp in Bangladesh, labels corresponding to a past date may not be updated or might not have been properly labelled, resulting in label noise. Tagging/labelling can be done either manually (by humans) or automatically (by a machine/model). We manually label images for our stricter evaluation regime, but a trained model can automatically label images without human supervision. Our best performing model generates labels which improve F1-score by 17.2% and improve Intersection-over-Union score by 23.2%, when compared to the fidelity of commonly used noisy labels. Our stricter evaluation regime reveals interesting insights about the paradoxical behaviour of deep neural networks in conjunction to label noise

Effects of CBD (Cannabidiol) on the physiology of Nile tilapia (Oreochromisn niloticus) as a chronic stress mitigating agent In-vivo.

This study evaluates the effects of Cannabidiol (CBD) on the physiology of stressed and non-stressed Nile tilapia, reared in a recirculating aquaculture system. Tilapia were fed with and without CBD (0.001% of feed weight) and with and without hydrocortisone stress hormone (0.01% of body weight) every day for four weeks. This experiment compared the plasma cortisol, blood glucose and protein levels, liver and spleen somatic indices (HSI and SSI, respectively), and lysozyme activity of the fish. Stress group (S) had a significantly higher value than the control group (C) in two of the parameters, glucose and lysozyme activity, this is an indication of stress. CBD had a stress reducing effect under stressed conditions in lysozyme activity. Although not significant, the stress reducing effect of CBD on stress biomarkers such as glucose and HSI also seemed promising. Further investigation into the matter may not just be useful in stress mediation in aquatic organisms but may also have implications in human medicine as well

Osteopathic manipulative treatment: Facial muscle energy, direct MFR, and BLT procedure – for TMJ dysfunction

The temporomandibular joint is pivotal towards mastication and vocalization. Dysfunction in this joint can occur with macrotrauma, microtrauma, or maladaptive behaviors. A myriad of treatment options exist, both invasive and noninvasive. This activity reviews the evaluation and treatment of temporomandibular dysfunction and highlights the osteopathic provider's role in evaluating and treating this condition utilizing muscle energy, myofascial release, and balanced ligamentous technique