Machine learning workflow for edge computed arrhythmia detection in exploration class missions.

Deep-space missions require preventative care methods based on predictive models for identifying in-space pathologies. Deploying such models requires flexible edge computing, which Open Neural Network Exchange (ONNX) formats enable by optimizing inference directly on wearable edge devices. This work demonstrates an innovative approach to point-of-care machine learning model pipelines by combining this capacity with an advanced self-optimizing training scheme to classify periods of Normal Sinus Rhythm (NSR), Atrial Fibrillation (AFIB), and Atrial Flutter (AFL). 742 h of electrocardiogram (ECG) recordings were pre-processed into 30-second normalized samples where variable mode decomposition purged muscle artifacts and instrumentation noise. Seventeen heart rate variability and morphological ECG features were extracted by convoluting peak detection with Gaussian distributions and delineating QRS complexes using discrete wavelet transforms. The decision tree classifier's features, parameters, and hyperparameters were self-optimized through stratified triple nested cross-validation ranked on F1-scoring against cardiologist labeling. The selected model achieved a macro F1-score of 0.899 with 0.993 for NSR, 0.938 for AFIB, and 0.767 for AFL. The most important features included median P-wave amplitudes, PRR20, and mean heart rates. The ONNX-translated pipeline took 9.2 s/sample. This combination of our self-optimizing scheme and deployment use case of ONNX demonstrated overall accurate operational tachycardia detection.

A Clinical Overview of Anorexia Nervosa and Overcoming Treatment Resistance.

Anorexia nervosa (AN) is a type of eating disorder that has been increasing in incidence and has been encountered more commonly by physicians in their daily practice. Both environmental and genetic risk factors paired along with a more susceptible neurobiology are at play in the emerging resistance to treatment in AN. Preoccupations with intense fear of weight gain, dietary restrictions, excessive exercise, and how the individual is perceived by society mixed with underlying psychopathology all further add to the issue. Many patients who fall into this cycle of obsessive and restrictive patterns refuse to get treatment. As clinicians, it is essential we recognize the early signs of both eating disorders during the initial primary care appointments. To review the literature on the etiology of AN, possible misdiagnosis leading to inappropriate management of this condition, and understand the treatment-resistant AN and its management. Additionally, it will explore possible reasons that contribute to the resistance to treatment, the underlying psychopathology of anorexics, its genetic predisposition, psychiatric comorbidities, identification of the early signs and symptoms, and timely prevention. Early recognition by a physician includes a thorough history and physical examination, pertinent laboratory, and electrolyte studies, and identifying comorbid psychiatric conditions. The treatment of AN is intricate and requires a holistic approach. Treatment includes multiple modalities such as nutritional rehabilitation and psychosocial and pharmacological therapies. An interdisciplinary team of medical professionals for managing chronic AN is recommended.

A Case Report on Neuroleptic Malignant Syndrome (NMS): How to Approach an Early Diagnosis.

Neuroleptic malignant syndrome (NMS) is a life-threatening neurological emergency that has been observed to occur in some patients following the administration of anti-dopaminergic agents or the rapid withdrawal of dopaminergic medications. In this report, the authors present a case of a 51-year-old male patient with a known history of cocaine abuse, who was given quetiapine during his hospitalization. This precipitated an episode of NMS that eventually concluded uneventfully due to quick diagnosis and management. Prompt recognition of the condition is required to reduce significant morbidity and mortality. Ultimately, maintaining vigilance for the clinical features of NMS is crucial for timely diagnosis and intervention.

A Misdiagnosed Case of Schizoaffective Disorder With Bipolar Manifestations.

Bipolar and schizoaffective disorders are both psychiatric illnesses that share common traits, but also significant differences. Due to the close overlap in symptoms, obtaining the correct diagnosis can be difficult. The management of these patients often poses a challenge to clinicians. Five years ago, our patient was misdiagnosed with bipolar disorder with psychotic features. It was later discovered that she was suffering from schizoaffective disorder, bipolar type. The schizoaffective disorder involves symptoms of both schizophrenia and mood disorder.

Lithium-Induced Sinoatrial Node Dysfunction.

Lithium is a common mood-stabilizing drug for manic patients. We describe a case of sinoatrial node dysfunction in a patient with serum lithium levels within the therapeutic range. Given the symptomology and severity of the patient's illness, after placing a permanent pacemaker, the patient was discharged on the preadmission dose of lithium.

Prevalence of posttraumatic stress symptoms after childbirth: does ethnicity have an impact?

The purpose of this descriptive, correlational study was to examine the prevalence of posttraumatic stress symptoms in mothers during the first 6 months after giving birth, as well as to investigate whether ethnicity has an impact on the occurrence of such symptoms. Twenty-two women completed the Perinatal Posttraumatic Stress Disorder (PTSD) Questionnaire at a community health center. Data analysis included descriptive statistics, correlations, and independent sample t-tests. Higher total Perinatal PTSD Questionnaire scores were related to higher numbers of both perinatal and postpartum complications. In addition, Hispanic women were found to be less likely to experience avoidance than Caucasian women. Although more research is needed, findings from this study demonstrate a preliminary relationship between the two variables, ethnicity and avoidance.