Rapid improvement in post-infectious gastroparesis symptoms with mirtazapine

We report the case of a 34-year-old woman with severe post-infectious gastroparesis who was transferred from an outside medical facility for a second opinion regarding management.This patient had no prior history of gastrointestinal symptoms.However,in the aftermath of a viral illness,she developed two months of intractable nausea,vomiting,and oral intake intolerance that resulted in numerous hospitalizations for dehydration and electrolyte disturbances.A solid-phase gastric emptying scan had confirmed delayed emptying,confirming gastroparesis.Unfortunately,conventional pro-kinetic agents and numerous anti-emetic drugs provided little or no relief of the patient’s symptoms.At our institution,the patient experienced a cessation of vomiting,reported a significant reduction in nausea,and toler-ated oral intake shortly after taking mirtazapine.Based on mirtazapine’s primary action as a serotonin(5-HT)1a receptor agonist,we infer that this receptor system mediated the clinical improvement through a combination of peripheral and central neural mechanisms.This report highlights the potential utility of 5-HT1a agonists in the management of nausea and vomiting.We conclude that mirtazapine may be effective in treating symptoms associated with non-diabetic gastroparesis that are refractory to conventional therapies.     

A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach

This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community. In Stage 1, regression models were employed to determine unique sensor-based behavioral predictors of pain. These sensor-based parameters were used to create a classification model to predict the weekly recalled pain intensity and interference level using a deep neural network model, a machine learning approach, in Stage 2. Daily step count was a unique predictor for both pain intensity (75% Accuracy, F1 = 0.58) and pain interference (82% Accuracy, F1 = 0.59). The developed classification model performed well in this dataset with acceptable accuracy scores. This study demonstrated that machine learning technique can be used to identify the relationship between patients' pain and the risk factors.     

Relate better and judge less: Poverty simulation promoting culturally competent care in community health nursing

The study aim was to evaluate the effectiveness of a poverty simulation in increasing understanding of and attitudes toward poverty and resulting in changes in clinical practice among nursing seniors. A poverty simulation was conducted using a diverse group of nursing professors and staff from local community agencies assuming the role of community resource providers. Students were assigned roles as members of low-income families and were required to complete tasks during a simulated month. A debriefing was held after the simulation to explore students' experiences in a simulated poverty environment. Students' understanding of and attitude toward poverty pre- and post-simulation were examined. Changes in the students' clinical experiences following the simulation were summarized into identified categories and themes. The poverty simulation led to a greater empathy for the possible experiences of low income individuals and families, understanding of barriers to health care, change in attitudes towards poverty and to those living in poverty, and changes in the students' nursing practice. Use of poverty simulation is an effective means to teach nursing students about the experience of living in poverty. The simulation experience changed nursing students' clinical practice, with students providing community referrals and initiating inter-professional collaborations.     

Self-organizing maps based on limit cycle attractors

Recent efforts to develop large-scale brain and neurocognitive architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason for this is that most conventional SOMs use a static encoding representation: each input pattern or sequence is effectively represented as a fixed point activation pattern in the map layer, something that is inconsistent with the rhythmic oscillatory activity observed in the brain. Here we develop and study an alternative encoding scheme that instead uses sparsely-coded limit cycles to represent external input patterns/sequences. We establish conditions under which learned limit cycle representations arise reliably and dominate the dynamics in a SOM. These limit cycles tend to be relatively unique for different inputs, robust to perturbations, and fairly insensitive to timing. In spite of the continually changing activity in the map layer when a limit cycle representation is used, map formation continues to occur reliably. In a two-SOM architecture where each SOM represents a different sensory modality, we also show that after learning, limit cycles in one SOM can correctly evoke corresponding limit cycles in the other, and thus there is the potential for multi-SOM systems using limit cycles to work effectively as hetero-associative memories. While the results presented here are only first steps, they establish the viability of SOM models based on limit cycle activity patterns, and suggest that such models merit further study.