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The search for therapeutic strategies to promote neuronal regeneration following injuries toward functional recovery is of great importance. Brief low-frequency electrical stimulation (ES) has been reported as a useful method to improve neuronal regeneration in different animal models; however, the effect of ES on single neuron behavior has not been shown. Here, we study the effect of brief ES on neuronal regeneration of the CNS of adult medicinal leeches. selleck chemicals Studying the regeneration of selected sets of identified neurons allow us to quantify the ES effect per cell type at the single-cell level. Chains of the CNS that were subjected to cut injury were observed for 3 d, and the spontaneous regeneration was compared with the electrically stimulated injured chains. We show that the ES improves the efficiency of regeneration of Retzius cells, as larger masses of the total branching tree traverse the injury site with better directed growth with no effect on the average branching tree length. No antero-posterior polarity was found along regeneration within the leech CNS. Moreover, the microglial cell distribution was examined revealing more microglial cells in proximity to the stimulation site compared with non-stimulated. Our results lay a foundation for future ES-based neuroregenerative therapies.The brains of male and female mice are shaped by genetics and hormones during development. The enzyme aromatase helps establish sex differences in social behaviors and in the neural circuits that produce these behaviors. The medial amygdala of mice contains a large population of aromatase neurons and is a critical hub in the social behavior network. Moreover, the neural representation of social stimuli in the medial amygdala displays clear sex differences that track developmental changes in social behaviors. Here, we identify a potential anatomic basis for those sex differences. We found that sensory input from the accessory olfactory bulb (AOB) to aromatase neurons is derived nearly exclusively from the anterior AOB, which selectively responds to chemosensory cues from conspecific animals. Through the coordinated use of mouse transgenics and viral-based circuit-tracing strategies, we demonstrate a clear sex difference in the volume of synapses connecting the accessory olfactory bulb to aromatase-expressing neurons in the medial amygdala of male versus female mice. This difference in anatomy likely mediates, at least in part, sex differences in medial amygdala-mediated social behaviors.Parenting in the NICU is an intense journey. Parents struggle to build intimacy with their child amid complex emotions and medical uncertainties. They need to rapidly adapt their vision of parenthood to the realities of intensive care. The psychological impact of this journey can have important effects on their psychological health. For parents of sick older children, “good parent” beliefs have been shown to foster positive growth. This concept is also essential for parents of infants in the NICU, although their path is complex.We write as clinicians who were also families in the NICU. We suggest parents need to hear and internalize 3 important messages that overlap but are each important you are a parent, you are not a bad parent, and you are a good parent. We offer practical suggestions to NICU clinicians that we believe will help NICU parents cope while their infant is in the NICU and afterward.Down syndrome disintegrative disorder (DSDD), a developmental regression in children with Down syndrome (DS), is a clinical entity that is characterized by a loss of previously acquired adaptive, cognitive, and social functioning in persons with DS usually in adolescence to early adulthood. Initially reported in 1946 as “catatonic psychosis,” there has been an increasing interest among the DS community, primary care, and subspecialty providers in this clinical area over the past decade. This condition has a subacute onset and can include symptoms of mood lability, decreased participation in activities of daily living, new-onset insomnia, social withdrawal, autistic-like regression, mutism, and catatonia. The acute phase is followed by a chronic phase in which baseline functioning may not return. No strict criteria or definitive testing is currently available to diagnose DSDD, although a comprehensive psychosocial and medical evaluation is warranted for individuals presenting with such symptoms. The etiology of DSDD is unknown, but in several hypotheses for regression in this population, psychological stress, primary psychiatric disease, and autoimmunity are proposed as potential causes of DSDD. Both psychiatric therapy and immunotherapies have been described as DSDD treatments, with both revealing potential benefit in limited cohorts. In this article, we review the current data regarding clinical phenotypes, differential diagnosis, neurodiagnostic workup, and potential therapeutic options for this unique, most disturbing, and infrequently reported disorder.Objectives To investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists. Methods We retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme. Results The Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa 0.423, p less then 0.001). Conclusion Our experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.