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BACKGROUND: Adverse childhood experiences (ACEs), in combination with adverse community environments, can result in traumatic stress reactions, increasing a person's risk for chronic physical and mental health conditions. Family resilience refers to the ability of families to withstand and rebound from adversity; it involves coping with disruptions as well as positive growth in the face of sudden or challenging life events, trauma, or adversities. This study aimed to identify factors contributing to family and community resilience from the perspective of families who self-identified as having a history of adversity and being resilient during the COVID-19 pandemic. METHODS: This study used Photovoice, a visual participatory research method which asks participants to take photographs to illustrate their responses to a research question. Participants consisted of a maximum variation sample of families who demonstrated family level resilience in the context of the pair of ACEs during the COVID-19 pandemic. Family members were asked to collect approximately five images or videos that illustrated the facilitators and barriers to well-being for their family in their community. Semi-structured in-depth interviews were conducted using the SHOWeD framework to allow participants to share and elucidate the meaning of their photos. Using thematic analysis, two researchers then independently completed line-by-line coding of interview transcripts before collaborating to develop consensus regarding key themes and interpretations. RESULTS: Nine families were enrolled in the study. We identified five main themes that enhanced family resilience: (1) social support networks; (2) factors fostering children's development; (3) access and connection to nature; (4) having a space of one's own; and (5) access to social services and community resources. CONCLUSIONS: In the context of additional stresses related to the COVID-19 pandemic, resilient behaviours and strategies for families were identified. The creation or development of networks of intra- and inter-community bonds; the promotion of accessible parenting, housing, and other social services; and the conservation and expansion of natural environments may support resilience and health.

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High resolution ultrasound (US) and magnetic resonance (MR) neurography are both imaging modalities that are commonly used for assessing peripheral nerves including the sural nerve (SN). The SN is a cutaneous sensory nerve which innervates the lateral ankle and foot to the base of the fifth metatarsal. It is formed by contributing nerves from the tibial and common peroneal nerves with six patterns and multiple subtypes described in literature. In addition to the SN being a cutaneous sensory nerve, the superficial location enables the nerve to be easily biopsied and harvested for a nerve graft, as well as increasing the susceptibility to traumatic injury. As with any peripheral nerves, pathologies such as peripheral nerve sheath tumors and neuropathies can also affect the SN. By utilizing a high frequency probe in US and high-resolution MR neurography, the SN can be easily identified even with the multiple variations given the standard distal course. US and MRI are also useful in determining pathology of the SN given the specific image findings that are seen with peripheral nerves. In this review, we evaluate the normal imaging anatomy of the SN and discuss common pathologies identified on imaging.

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Acquired thrombotic thrombocytopenic purpura (TTP) is an uncommon microangiopathic disorder that can have variable presentations and can be precipitated by a multitude of stressors to the body, most commonly sepsis. TTP is caused by a deficiency of ADAMTS13 leading to intravascular clotting causing thrombocytopenia and microangiopathic hemolytic anemia. TTP can be associated with various other pathologic conditions. One such rare association has been reported with diabetic ketoacidosis (DKA). Here, we present an even less appreciated presentation in association with DKA. A 62-year-old African American male with previously diagnosed prediabetes presented with DKA and developed hemodynamically significant bleeding. He was confirmed to have TTP that responded to plasmapheresis. TTP is a life-threatening illness if not treated urgently with plasmapheresis with or without rituximab. As acquired TTP most commonly occurs during stress on the body, it is important to treat the underlying stressor. Early identification and initiation of appropriate interventions are crucial to reducing mortality associated with TTP. Furthermore, we need to appreciate less commonly associated conditions such as DKA among patients.

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PURPOSE: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. MATERIALS AND METHODS: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. RESULTS: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. CONCLUSION: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

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