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Your clinical importance of inhalation approach within persistent obstructive lung condition patients.
It has been argued that similar to addictive behaviors, problematic Social Network sites use (PSNSU) is characterized by sensitized reward processing and cue-reactivity. However, no study to our knowledge has yet investigated cue-reactivity in PSNSU. The present study aims at investigating cue-reactivity to Social Network sites (i.e., Facebook)-related visual cues in individuals identified as problematic vs. non-problematic Facebook users by the Problematic Facebook Use Scale.

The Event-Related Potentials (ERPs) were recorded during the passive viewing of Facebook-related, pleasant, unpleasant, and neutral pictures in 27 problematic and 26 non-problematic users. check details Moreover, craving for Facebook usage was collected using a Likert scale.

Despite problematic users were more likely to endorse higher craving than non-problematic ones, Facebook-related cues elicited larger ERP positivity (400-600 ms) than neutral, and comparable to unpleasant stimuli, in all Facebook users. Only in problematic users we found laeduced abilities to experience emotions would be the result of defective emotion regulation processes that allow craving states to capture more motivational/attentional resources at the expense of other emotional states.The COVID-19 pandemic created numerous barriers to the implementation of participant-facing research. For most, the pandemic required rapid transitioning to all virtual platforms. During this pandemic, the most vulnerable populations are at highest risk of falling through the cracks of engagement in clinical care and research. Nonetheless, we argue that we should reframe the discussion to consider how this transition may create opportunities to engage extensively to reach populations. Here, we present our experience in Atlanta (Georgia, United States) in transitioning a group visit model for South Asian immigrants to a virtual platform and the pivotal role community members in the form of community health workers can play in building capacity among participants. We provide details on how this model helped address common barriers to group visit models in clinical practice and how our community health worker team innovatively addressed the digital challenges of working with an elderly population with limited English proficiency.
The COVID-19 pandemic is still undergoing complicated developments in Vietnam and around the world. There is a lot of information about the COVID-19 pandemic, especially on the internet where people can create and share information quickly. This can lead to an infodemic, which is a challenge every government might face in the fight against pandemics.

This study aims to understand public attention toward the pandemic (from December 2019 to November 2020) through 7 types of sources Facebook, Instagram, YouTube, blogs, news sites, forums, and e-commerce sites.

We collected and analyzed nearly 38 million pieces of text data from the aforementioned sources via SocialHeat, a social listening (infoveillance) platform developed by YouNet Group. We described not only public attention volume trends, discussion sentiments, top sources, top posts that gained the most public attention, and hot keyword frequency but also hot keywords' co-occurrence as visualized by the VOSviewer software tool.

In this study, we reat practical information to make more effective policy reactions to help prevent the spread of the pandemic.
Our study shows that online resources can help the government quickly identify public attention to public health messages during times of crisis. We also determined the hot spots that most interested the public and public attention communication patterns, which can help the government get practical information to make more effective policy reactions to help prevent the spread of the pandemic.[This corrects the article DOI 10.2196/27348.].Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, which is tedious and prone to variability. In this paper, a fully automatic framework for PE severity quantification from CT images is proposed, comprising three steps (1) identification of the sternue's greatest depression point; (2) detection of 8 anatomical keypoints relevant for severity assessment; and (3) measurements' geometric regularization and extraction. The first two steps rely on heatmap regression networks based on the Unet++ architecture, including a novel variant adapted to predict 1D confidence maps. The framework was evaluated on a database with 269 CTs. For comparative purposes, intra-observer, inter-observer and intra-patient variability of the estimated indices were analyzed in a subset of patients. The developed system showed a good agreement with the manual approach (a mean relative absolute error of 4.41%, 5.22% and 1.86% for the Haller, correction, and asymmetry indices, respectively), with limits of agreement comparable to the inter-observer variability. In the intrapatient analysis, the proposed framework outperformed the expert, showing a higher reproducibility between indices extracted from distinct CTs of the same patient. Overall, these results support the feasibility of the developed framework for the automatic, accurate and reproducible quantification of PE severity in a clinical context.Scene graph generation (SGGen) is a challenging task due to a complex visual context of an image. Intuitively, the human visual system can volitionally focus on attended regions by salient stimuli associated with visual cues. For example, to infer the relationship between man and horse, the interaction between human leg and horseback can provide strong visual evidence to predict the predicate ride. Besides, the attended region face can also help to determine the object man. Till now, most of the existing works studied the SGGen by extracting coarse-grained bounding box features while understanding fine-grained visual regions received limited attention. To mitigate the drawback, this article proposes a region-aware attention learning method. The key idea is to explicitly construct the attention space to explore salient regions with the object and predicate inferences. First, we extract a set of regions in an image with the standard detection pipeline. Each region regresses to an object. Second, we propose the object-wise attention graph neural network (GNN), which incorporates attention modules into the graph structure to discover attended regions for object inference.
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