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ObjectiveThe aim of this study was to characterize the phenotype and genotype of patients with Yao syndrome (YAOS), with focus on comparing to prior cohorts, identifying novel features, and describing treatment observations.MethodsA retrospective medical records review of patients with YAOS seen at Mayo Clinic was conducted to characterize clinical features, NOD2 genotypes, and therapeutic trials and responses.ResultsTwenty-two patients diagnosed with YAOS were included. Eighteen patients (81.8%) were female and twenty (90.9%) were White. Mean age at symptom onset was 24.0 ± 14.8 years. Common clinical manifestations included fever (81.8% of patients), rash (95.5%), chronic gastrointestinal symptoms (100%), arthralgia/arthritis (95.5%), and sicca symptoms (68.2%). NOD2 genotypes as single variants included IVS8 + 158 in 14 patients (63.6%), R702W in 8 patients (36.4%), 1007fs in 4 (18.2%), and one patient had only a previously unreported rare variant. Eight patients (36.4%) had compound (two or more) NOD2 variants. Potential comorbidities of YAOS observed in this cohort included gastrointestinal dysmotility, autonomic dysfunction, and mast cell activation-like symptoms. Glucocorticoid responsiveness was observed in 15 of 20 patients exposed (75%). Eleven patients (50.0%) received IL-1 inhibitor therapy, and one patient (4.5%) received IL-6 inhibitor therapy with adequate disease control.ConclusionOur findings substantiate the occurrence of fevers, arthralgia/arthritis, rash, chronic gastrointestinal symptoms, and sicca-like symptoms described previously in patients with YAOS. Novel clinical features and one NOD2 variant not previously described were identified. Glucocorticoids, biologic IL-1 inhibitors, and IL-6 receptor inhibitors appeared to be effective for treatment of patients with YAOS.

Active learning is a field of machine learning that seeks to find the most efficient labels to annotate with a given budget, particularly in cases where obtaining labeled data is expensive or infeasible. This is becoming increasingly important with the growing success of learning-based methods, which often require large amounts of labeled data. Computer vision is one area where active learning has shown promise in tasks such as image classification, semantic segmentation, and object detection. In this research, we propose a pool-based semi-supervised active learning method for image classification that takes advantage of both labeled and unlabeled data. Many active learning approaches do not utilize unlabeled data, but we believe that incorporating these data can improve performance. To address this issue, our method involves several steps. First, we cluster the latent space of a pre-trained convolutional autoencoder. Then, we use a proposed clustering contrastive loss to strengthen the latent space's clustering while using a small amount of labeled data. Finally, we query the samples with the highest uncertainty to annotate with an oracle. We repeat this process until the end of the given budget. Our method is effective when the number of annotated samples is small, and we have validated its effectiveness through experiments on benchmark datasets. Our empirical results demonstrate the power of our method for image classification tasks in accuracy terms.

Background: Identifying neurological biomarkers to aid with diagnoses and treatment effect evaluation of mood and cognitive symptoms in major depressive disorder (MDD) is an unmet need. This study explores the Brain Network Analytics (BNA) tool, which provides information on cortical patterns compared to a healthy normative group, for improving MDD management. Methods: BNA was used to extract event-related potentials (ERPs) from high-density EEG, and its clinical utility to assess the effects of vortioxetine on depression and cognition was evaluated. Twenty-five MDD subjects (20–65 years old) with cognitive dysfunction underwent an 8-week open-label treatment using vortioxetine. ERP data, generated during cognitive tasks, were collected at pre-treatment, two weeks and eight weeks into treatment. Correlations with MDD symptoms and cognitive functioning were investigated. Exploratory analyses assessed the differences in baseline and endpoint ERP characteristics between MDD subjects and matched controls (N = 41). Results: Compared to controls, MDD subjects exhibited increased baseline latencies of P200 and P3b ERP components, which normalized at treatment endpoint. P200 and P300 in the MDD group were correlated with clinical assessments, and indicated a pro-cognitive effect of vortioxetine independent of its antidepressant effect. Limitations: This study was limited by a small sample size and lack of a placebo condition. Conclusions: Our results suggest that the application of BNA can potentially support MDD diagnosis and monitor treatment efficacy across various symptom and functional domains. Further studies are warranted to explore the role of BNA in clinical research and practice.

Abstract Background In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. Methods Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. Results Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1152 patients have been evaluated with an overall solved or likely solved rate of 17.5% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. Conclusion Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

Arabic students of IAIN Kendari are unique in terms of their living environment which still had internet network issues lead to different experiences in using Zoom. Research on the sustainability of Zoom-based Arabic learning during the Covid-19 pandemic aimed to analyze creative and innovative learning models and observe through the perception of Arabic students of IAIN Kendari towards the use of Zoom as a learning medium during COVID-19. A qualitative and phenomenological approaches were used. The data were collected by interviews and documentation. The respondents in this study were 20 Arabic course students from the 2019 batch who attended lectures using Zoom. Data analysis, data reduction, data display and conclusion were discussed. The results showed varied responses from students to Arabic learning based on Zoom. Most of the students stated that Zoom was well applied in learning compared to other media. It was also proved efficacious with a few palpable advantages, such as students’ visibility during classes and room capacity to accommodate larger numbers of students. The results illustrated that Arabic learning on Zoom, both during and post Covid-19, may continue by considering the condition of the student learning environment. After participating in distance learning through its application, the changes occurring at the level of students pertain to increased levels of involvement in the use of ICT, leading to the learners who initially did not use the platforms and switching to Zoom as a learning medium and benefiting from discussions and other existing features.

BackgroundWhile surgical simulation is regularly used in surgical training in high-income country settings, it is uncommon in low- and middle-income countries, particularly for surgical training that primarily occurs in rural areas. We designed and evaluated a novel surgical simulator for improving trachomatous trichiasis (TT) surgery training, given that trichiasis is mostly found among the poorest individuals in rural areas.Methodology/principal findingsTT surgery programs were invited to incorporate surgical simulation with a new, high fidelity, low-cost simulator into their training. Trainees completed standard TT-surgery training following World Health Organization guidelines. A subset of trainees received three hours of supplemental training with the simulator between classroom and live-surgery training. We recorded the time required to complete each surgery and the number of times the trainer intervened to correct surgical steps. Participants completed questionnaires regarding their perceptions. We also assessed trainer and trainee perceptions of surgical simulation training as part of trichiasis surgery training. 22 surgeons completed standard training and 26 completed standard training plus simulation. We observed 1,394 live-training surgeries. Average time to first live-training surgery completion was nearly 20% shorter the simulation versus the standard group (28.3 vs 34.4 minutes; p = 0.02). Trainers intervened significantly fewer times during initial live-training surgeries in the simulation group (2.7 vs. 4.8; p = 0.005). All trainers indicated the simulator significantly improved training by allowing trainees to practice safely and to identify problem areas before performing live-training surgeries. Trainees reported that simulation practice improved their confidence and skills prior to performing live-training surgeries.ConclusionsA single high-fidelity surgical simulation session can significantly improve critical aspects of initial TT surgeries.

Abstract Background and Aims Hyperbaric oxygen therapy (HBOT), utilizes 100% oxygen at pressures greater than sea‐level atmospheric pressure, for the treatment of conditions in which the tissues starve for oxygen. The Undersea and Hyperbaric Medical Society (UHMS) has granted HBOT approval for the treatment of various conditions. On the other hand, applying informatics registry systems can improve care delivery, ameliorate outcomes, and reduce the costs and medical errors for the patients receiving HBOT treatment. Therefore, we aimed to design, develop, and evaluate a registry system for patients undergoing HBOT. Methods In the first phase, the conceptual and logical models were designed after conducting symposiums with experts and having other experts review the models. In the second phase, the system was developed on the web using ASP.NET and C# programming languages frameworks. The last phase involved Nielsen's heuristic evaluation method for the system's usability. Five experts evaluated the system, including three health information management specialists and two medical informatics specialists. Results The hyperbaric patient information registry system (HPIRS) interacts with three types of users—a specialist physician, a nurse, and a system administrator. A scenario for each predefined activity was designed, and all the information was stored in the SQL servers. The five experts independently found 152 issues, of which 84 were duplicates. The 68 distinct issues of the system were then resolved. Conclusions The design and development of such registry systems can make data available and stored carefully to improve clinical care and medical research and decrease costs and errors. These registries can provide the healthcare systems with E‐health applications, improved data management, more secure data transfer, and support for statistical reporting. The implemented heuristic evaluation method can also provide a low‐cost and readily available system to fix the issues of the designed systems.

Saffron authenticity is important for the saffron industry, consumers, food industry, and regulatory agencies. Herein we describe a combo of two novel methods to distinguish genuine saffron from fake in a user-friendly manner and without sophisticated instruments. A smartphone coupled with Foldscope was used to visualize characteristic features and distinguish “genuine” saffron from “fake.” Furthermore, destaining and staining agents were used to study the staining patterns. Toluidine blue staining pattern was distinct and easier to use as it stained the papillae and the margins deep purple, while its stain is lighter yellowish green toward the central axis. Further to automate the process, we tested and compared different machine learning-based classification approaches for performing the automated saffron classification into genuine or fake. We demonstrated that the deep learning-based models are efficient in learning the morphological features and classifying samples as either fake or genuine, making it much easier for end-users. This approach performed much better than conventional machine learning approaches (random forest and SVM), and the model achieved an accuracy of 99.5% and a precision of 99.3% on the test dataset. The process has increased the robustness and reliability of authenticating saffron samples. This is the first study that describes a customer-centric frugal science-based approach to creating an automated app to detect adulteration. Furthermore, a survey was conducted to assess saffron adulteration and quality. It revealed that only 40% of samples belonged to ISO Category I, while the average adulteration percentage in the remaining samples was 36.25%. After discarding the adulterants from crude samples, their quality parameters improved significantly, elevating these from ISO category III to Category II. Conversely, it also means that Categories II and III saffron are more prone to and favored for adulteration by fraudsters.

One of the most challenging problems associated with the development of accurate and reliable application of computer vision and artificial intelligence in agriculture is that, not only are massive amounts of training data usually required, but also, in most cases, the images have to be properly labeled before models can be trained. Such a labeling process tends to be time consuming, tiresome, and expensive, often making the creation of large labeled datasets impractical. This problem is largely associated with the many steps involved in the labeling process, requiring the human expert rater to perform different cognitive and motor tasks in order to correctly label each image, thus diverting brain resources that should be focused on pattern recognition itself. One possible way to tackle this challenge is by exploring the phenomena in which highly trained experts can almost reflexively recognize and accurately classify objects of interest in a fraction of a second. As techniques for recording and decoding brain activity have evolved, it has become possible to directly tap into this ability and to accurately assess the expert’s level of confidence and attention during the process. As a result, the labeling time can be reduced dramatically while effectively incorporating the expert’s knowledge into artificial intelligence models. This study investigates how the use of electroencephalograms from plant pathology experts can improve the accuracy and robustness of image-based artificial intelligence models dedicated to plant disease recognition. Experiments have demonstrated the viability of the approach, with accuracies improving from 96% with the baseline model to 99% using brain generated labels and active learning approach.

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization.Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features.Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR).Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.

This paper is based on an ethnographic study of how domesticity is enacted and adapted at homeless shelters for determining which clients are “service worthy .” The study draws on nineteen placement meetings with homeless men and focuses on institutional mechanisms for encouraging homemaking skills or domesticity among clients. Adapting Robert K. Merton's typology of adaptations to social norms, as well as Jaber Gubrium's “task‐designated identity,” we showcase male clients' self‐presentation strategies for adapting to the institutional mandates of domesticity. Specifically, our qualitative analysis reveals four modes of task adaption: (1) task conformity by professing the desired norms in their service encounters, (2) task evasion to avoid conversations and related tasks, (3) task transformation by linking the task at hand with something other than originally intended, particularly by reframing biographies to meet the local goals of domesticity, and (4) task protestation, which involves questioning the rationale and necessity of assigned tasks. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of Covid-19 vaccination in Florida, focusing on demographic, economic, and political preferences. Using data from vaccine intake for Floridians from January to June 2021, we explore the spatial variation in Covid-19 vaccination patterns and regional characteristics to analyze the main features associated with vaccine intake. We use spatial econometric techniques to verify the spatial autocorrelation between vaccination rates and estimate models that can account for spillover effects between counties. Our data allow us to explore differential effects for vaccination patterns across population subgroups according to gender, race and ethnicity, and age. Our results show that political preference has the largest effect on vaccination patterns and that this effect was persistent over time. [ABSTRACT FROM AUTHOR]

Introduction: Exogenous insulin antibody syndrome (EIAS) rarely occurs in type 1 diabetes and should be considered in those with blood glucose levels outside the target range requiring greater than 2 units/kg/day of insulin without obesity. We describe the novel treatment of this condition using mycophenolate mofetil monotherapy in a pediatric patient in the outpatient setting. Case Presentation: A 17-year-old Caucasian male with type 1 diabetes experienced an abrupt increase in insulin requirements from 1.7 to 3.3 units/kg/day. Total insulin level was 7 µIU/mL with free insulin of 4.8 µIU/mL (68% of the total insulin), suggesting the presence of insulin antibodies. Switching from insulin aspart to glulisine was unsuccessful as insulin requirements increased to 4.4 units/kg/day. Treatment with oral mycophenolate mofetil decreased insulin requirements to 1.4 units/kg/day after 7 months. Total and free insulin levels improved to 5.2 and 4.6 µIU/mL, respectively (free insulin was 88% of total insulin). No adverse effects were encountered. Conclusion: Mycophenolate mofetil monotherapy is successful in safely treating EIAS in a pediatric patient. Established Facts: Exogenous insulin antibody syndrome (EIAS) is a rare condition that results when antibodies reversibly bind to exogenous insulin. Patients typically present with postprandial hyperglycemia and insulin resistance and/or nocturnal hypoglycemia. Management strategies are not clearly defined and include switching to an oral hypoglycemic agent, substituting insulin formulations, immunosuppressive therapies, or plasmapheresis. [ABSTRACT FROM AUTHOR]

The article looks forward to opportunities for enhanced collaborative landscapes of research in idiopathic inflammatory myopathies (IIM) and calcinosis. It comments on a study which characterized the quantitative and qualitative utility of whole-body computed tomography calcium scoring technique for assessing calcinosis distribution, pattern and burden in dermatomyositis (DM) and proposed IIM-calcinosis subtypes in juvenile and adult DM.