Telepsychiatry: Access in Rural Areas

Rural areas have experienced higher than average healthcare workforce problems, especially concerning limited access to mental health services. Telepsychiatry may provide at least a partial solution, as it has improved access and quality of care available in rural environments despite implementation problems. As technology continues to advance access, telepsychiatry will also need to strengthen making access more readily available. Additional research is required to identify modalities and diverse methods that can be used to increase access to mental health services further and improve outcomes in rural and underserved areas

The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

BackgroundA formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping. ObjectiveThis study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers. MethodsWe used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip. ResultsThe highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively. ConclusionsWe created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors

Ohio History Spring 2020

https://kent-islandora.s3.us-east-2.amazonaws.com/node/10118/OH-v127n1-thumb.jpgOHIO HISTORY Contents for Volume 127, Number 1, Spring 2020 Contributors ...... 6 Editor’s Note ...... 8 &nbsp; Cincinnati’s Base Hospital No. 25: A Community’s Contribution to World War I &nbsp;Richard M. Prior and Kimberly Mullins ......&nbsp;9 Supreme Court Appointments in Presidential Election Years: The Case of John Hessin Clarke Jonathan L. Entin ...... 30 “True” Conservatives in Fifties America: Robert A. Taft and the Politics of a Hoosier Soldier in Korea Douglas A. Dixon ...... 58 Everett Tilson: Pioneer in the Condemnation of White Privilege Paul Burnam ...... 87 Barack Obama Day and the Hazelwood Subdivison: Public Rituals of Empowerment in an African American Community Michael H. Washington ...... 104 &nbsp; Book Reviews ...... 121 On the cover: Surgical ward at Christmas dinner, Base Hospital No. 25 (National Library of Medicine, Image A08578)</p

Expression of the inhibitory receptor subunit GABAA α2 in neurons and glia of zebrafish embryos

The inhibitory GABAA receptors regulate most regions of the nervous system. Identification of the cell types and tissues that express different GABAA receptor subunits could greatly benefit research into a variety of mental illnesses including epilepsy, drug abuse, anxiety disorders, depression, and schizophrenia. As part of a biotechniques course at SUNY Alfred State College of Technology, students identified regions in the nervous system of zebrafish embryos that expressed the GABAA α2 subunit. Students synthesized fluorescent mRNA in situ probes directed against the GABAA α2 subunit using a variety of technologies. First, students cloned a fragment of the GABAA α2 subunit gene from whole RNA using reverse transcription PCR. They then cloned the resulting cDNA into a plasmid vector. Utilizing this plasmid, students generated fluorescent in situ probes. Combining these in situ probes with antibodies specific to neurons and glia allowed students to determine which cell types express the GABAA α2 subunit. This project allowed students to carry out a modern biotechnology project from design to completion. Students generated previously unpublished data and produced stunning images while gaining practical skills applicable to both academic and industry research projects

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

BackgroundAutomated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. ObjectiveWe designed a strategy to gamify the collection and labeling of child emotion–enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. MethodsWe leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion–centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. ResultsThe classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining “anger” and “disgust” into a single class. ConclusionsThis work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts

Dysplastic Stem Cell Plasticity Functions as a Driving Force for Neoplastic Transformation of Precancerous Gastric Mucosa

© 2022 The AuthorsBackground &amp; Aims: Dysplasia carries a high risk of cancer development; however, the cellular mechanisms for dysplasia evolution to cancer are obscure. We have previously identified 2 putative dysplastic stem cell (DSC) populations, CD44v6neg/CD133+/CD166+ (double positive [DP]) and CD44v6+/CD133+/CD166+ (triple positive [TP]), which may contribute to cellular heterogeneity of gastric dysplasia. Here, we investigated functional roles and cell plasticity of noncancerous Trop2+/CD133+/CD166+ DSCs initially developed in the transition from precancerous metaplasia to dysplasia in the stomach. Methods: Dysplastic organoids established from active Kras-induced mouse stomachs were used for transcriptome analysis, in vitro differentiation, and in vivo tumorigenicity assessments of DSCs. Cell heterogeneity and genetic alterations during clonal evolution of DSCs were examined by next-generation sequencing. Tissue microarrays were used to identify DSCs in human dysplasia. We additionally evaluated the effect of casein kinase 1 alpha (CK1α) regulation on the DSC activities using both mouse and human dysplastic organoids. Results: We identified a high similarity of molecular profiles between DP- and TP-DSCs, but more dynamic activities of DP-DSCs in differentiation and survival for maintaining dysplastic cell lineages through Wnt ligand-independent CK1α/β-catenin signaling. Xenograft studies demonstrated that the DP-DSCs clonally evolve toward multiple types of gastric adenocarcinomas and promote cancer cell heterogeneity by acquiring additional genetic mutations and recruiting the tumor microenvironment. Last, growth and survival of both mouse and human dysplastic organoids were controlled by targeting CK1α. Conclusions: These findings indicate that the DSCs are de novo gastric cancer-initiating cells responsible for neoplastic transformation and a promising target for intervention in early induction of gastric cancer.N

CarolFest 2012 December 5, 2012

Concert ProgramCarolFest 2012 December 5, 201

Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal Symptoms

Treating opioid use disorder (OUD) is a significant healthcare challenge in the United States. Remaining abstinent from opioids is challenging for individuals with OUD due to withdrawal symptoms that include restlessness. However, to our knowledge, studies of acute withdrawal have not quantified restlessness using involuntary movements. We hypothesized that wearable accelerometry placed mid-sternum could be used to detect withdrawal-related restlessness in patients with OUD. To study this, 23 patients with OUD undergoing active withdrawal participated in a protocol involving wearable accelerometry, opioid cues to elicit craving, and non-invasive Vagal Nerve Stimulation (nVNS) to dampen withdrawal symptoms. Using accelerometry signals, we analyzed how movements correlated with changes in acute withdrawal severity, measured by the Clinical Opioid Withdrawal Scale (COWS). Our results revealed that patients demonstrating sinusoidal&ndash;i.e., predominantly single-frequency oscillation patterns in their motion almost exclusively demonstrated an increase in the COWS, and a strong relationship between the maximum power spectral density and increased withdrawal over time, measured by the COWS (R = 0.92, p = 0.029). Accelerometry may be used in an ambulatory setting to indicate the increased intensity of a patient&rsquo;s withdrawal symptoms, providing an objective, readily-measurable marker that may be captured ubiquitously