Identification of AgRP cells in the murine hindbrain that drive feeding.

OBJECTIVE: The central melanocortin system is essential for the regulation of food intake and body weight. Agouti-related protein (AgRP) is the sole orexigenic component of the central melanocortin system and is conserved across mammalian species. AgRP is currently known to be expressed exclusively in the mediobasal hypothalamus, and hypothalamic AgRP-expressing neurons are essential for feeding. Here we characterized a previously unknown population of AgRP cells in the mouse hindbrain. METHODS: Expression of AgRP in the hindbrain was investigated using gene expression analysis, single-cell RNA sequencing, immunofluorescent analysis and multiple transgenic mice with reporter expressions. Activation of AgRP neurons was achieved by Designer Receptors Exclusively Activated by Designer Drugs (DREADD) and by transcranial focal photo-stimulation using a step-function opsin with ultra-high light sensitivity (SOUL). RESULTS: AgRP expressing cells were present in the area postrema (AP) and the adjacent subpostrema area (SubP) and commissural nucleus of the solitary tract (cNTS) of the mouse hindbrain (termed AgRPHind herein). AgRPHind cells consisted of locally projecting neurons as well as tanycyte-like cells. Food deprivation stimulated hindbrain Agrp expression as well as neuronal activity of subsets of AgRPHind cells. In adult mice that lacked hypothalamic AgRP neurons, chemogenetic activation of AgRP neurons resulted in hyperphagia and weight gain. In addition, transcranial focal photo-stimulation of hindbrain AgRP cells increased food intake in adult mice with or without hypothalamic AgRP neurons. CONCLUSIONS: Our study indicates that the central melanocortin system in the hindbrain possesses an orexigenic component, and that AgRPHind neurons stimulate feeding independently of hypothalamic AgRP neurons.

A dataset for medical instructional video classification and question answering.

This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions. We believe medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Toward this, we created the MedVidCL and MedVidQA datasets and introduce the tasks of Medical Video Classification (MVC) and Medical Visual Answer Localization (MVAL), two tasks that focus on cross-modal (medical language and medical video) understanding. The proposed tasks and datasets have the potential to support the development of sophisticated downstream applications that can benefit the public and medical practitioners. Our datasets consist of 6,117 fine-grained annotated videos for the MVC task and 3,010 questions and answers timestamps from 899 videos for the MVAL task. These datasets have been verified and corrected by medical informatics experts. We have also benchmarked each task with the created MedVidCL and MedVidQA datasets and propose the multimodal learning methods that set competitive baselines for future research.

A dataset for plain language adaptation of biomedical abstracts.

Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research.

A survey of automated methods for biomedical text simplification.

OBJECTIVE: Plain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges. MATERIALS AND METHODS: We search English language literature using lists of synonyms for both the task (eg, "text simplification") and the domain (eg, "biomedical"), and searching for all pairs of these synonyms using Google Scholar, Semantic Scholar, PubMed, ACL Anthology, and DBLP. We expand search terms based on results and further include any pertinent papers not in the search results but cited by those that are. RESULTS: We find 45 papers that we deem relevant to the automatic simplification of biomedical text, with data spanning 7 natural languages. Of these (nonexclusively), 32 describe tools or methods, 13 present data sets or resources, and 9 describe impacts on human comprehension. Of the tools or methods, 22 are chiefly procedural and 10 are chiefly neural. CONCLUSIONS: Though neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.

ASB4 modulates central melanocortinergic neurons and calcitonin signaling to control satiety and glucose homeostasis.

Variants in the gene encoding ankyrin repeat and SOCS box-containing 4 (ASB4) are linked to human obesity. Here, we characterized the pathways underlying the metabolic functions of ASB4. Hypothalamic Asb4 expression was suppressed by fasting in wild-type mice but not in mice deficient in AgRP, which encodes Agouti-related protein (AgRP), an appetite-stimulating hormone, suggesting that ASB4 is a negative target of AgRP. Many ASB4 neurons in the brain were adjacent to AgRP terminals, and feeding induced by AgRP neuronal activation was disrupted in Asb4-deficient mice. Acute knockdown of Asb4 in the brain caused marked hyperphagia due to increased meal size, and Asb4 deficiency led to increased meal size and food intake at the onset of refeeding, when very large meals were consumed. Asb4-deficient mice were resistant to the meal-terminating effects of exogenously administered calcitonin and showed decreased neuronal expression of Calcr, which encodes the calcitonin receptor. Pro-opiomelanocortin (POMC) neurons in the arcuate nucleus in mice are involved in glucose homeostasis, and Asb4 deficiency specifically in POMC neurons resulted in glucose intolerance that was independent of obesity. Furthermore, individuals with type 2 diabetes showed reduced ASB4 abundance in the infundibular nuclei, the human equivalent of the arcuate nucleus. Together, our results indicate that ASB4 acts in the brain to improve glucose homeostasis and to induce satiety after substantial meals, particularly those after food deprivation.