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Interdisciplinary combination and integration of different cutting-edge techniques are also discussed with details. The review is closed with the conclusion and future trends.Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative features but do not explore relationship between image features or effectively represent users based on features. In these methods, each image is recognized as an equivalent, and each user is regarded as the distribution of all images shared by the corresponding user. However, the nuances of guilty users and innocent users are difficult to recognize with this flattened method. In this paper, the steganographer detection task is formulated as a multiple-instance learning problem in which each user is considered to be a bag, and the shared images are multiple instances in the bag. Specifically, we propose a similarity accumulation graph convolutional network to represent each user as a complete weighted graph, in which each node corresponds to features extracted from an image and the weight of an edge is the similarity between each pair of images. The constructed unit in the network can take advantage of the relationships between instances so that common patterns of positive instances can be enhanced via similarity accumulations. Instead of operating on a fixed original graph, we propose a novel strategy for reconstructing and pooling graphs based on node features to iteratively operate multiple convolutions. This strategy can effectively address oversmoothing problems that render nodes indistinguishable although they share different instance-level labels. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness and reliability ability across image domains, even in the context of large-scale social media scenarios. Moreover, the experimental results also indicate that the proposed network can be generalized to other multiple-instance learning problems.

To determine the sleep architecture and sleep respiratory abnormalities and to correlate with sleep symptoms in patients with Myotonic dystrophy type 1 (DM1).

We recruited a cohort of genetically confirmed patients with DM1, who attended the Neuromuscular clinic between July 2016 and December 2019. Clinical, sleep and whole night polysomnography data were collected. The analysis of sleep architecture, sleep respiratory parameters and comparison with healthy controls (HC) was performed in our sleep laboratory.

A total of 59 patients with DM1 underwent sleep evaluation. Hypersomnolence in 42 (77.8%), ESS>10 in 23 (39%), and PSQI>5 in 18 (30.5%) were found in patients with DM1. Thirty-one (68.89%) patients with DM1 and 22 (95.65%) HC had more than 4-h of total sleep time (TST). More than 4h of TST was taken to compare respiratory and sleep architecture parameters. Patients with DM1 had reduced sleep efficiency, reduced N2 sleep, and increase in N1 sleep, wake index, stage shift index, nocturnal sleep-onset REM periods compared to HC. selleck compound AHI>15 was found in 16 (51.61%) DM1 and in 3 HC (13.64%). AHI had positive correlation with BMI, but not with age, ESS or disease progression (MIRS). All DM1 with AHI>15; 8(80%) and 1(33.33%) in AHI5to15, and AHI<5 groups, respectively had hypersomnolence.

In this first study on Indian cohort, daytime hypersomnolence, poor nocturnal sleep quality, sleep architecture irregularities are identified to be common in patients with DM1. These abnormalities may be explained by sleep-related breathing disorders that are highly prevalent in these patients.

In this first study on Indian cohort, daytime hypersomnolence, poor nocturnal sleep quality, sleep architecture irregularities are identified to be common in patients with DM1. These abnormalities may be explained by sleep-related breathing disorders that are highly prevalent in these patients.

Solriamfetol is developed for the treatment of excessive sleepiness in adult patients with narcolepsy and obstructive sleep apnea (OSA). No systematic review of existing literature has been investigated before. Therefore, the meta-analysis is conducted to assess the efficacy and safety of solriamfetol for excessive sleepiness in narcolepsy and OSA.

PubMed, Embase and Cochrane Library databases were searched from earliest date to July 2020 for randomized controlled trials (RCTs) and the primary outcomes were change from baseline in mean sleep latency and Epworth Sleepiness Scale (ESS).

We pooled 1177 patients from five RCTs and found solriamfetol led to a significant increment in mean sleep latency (MD=9.52, 95% CI 7.60 to 11.44, P<0.00001) and a reduction in ESS score (MD=-3.74, 95% CI-4.38 to-3.09, P<0.00001) compared with placebo. The proportion of patients with at least one adverse event was significantly increased in solriamfetol group (RR=1.42, 95% CI 1.24 to 1.64, P<0.00001), while no statistical differences existed in the risk of at least one serious adverse event between solriamfetol and controlled group (RR=0.95, 95% CI 0.24 to 3.77, P=0.39).

A dose of 150mg solriamfetol is proved to be the appropriate and stable dose for excessive sleepiness. In addition, solriamfetol showed good efficacy for excessive sleepiness in narcolepsy and OSA but also significantly increases the risk of adverse events.

A dose of 150 mg solriamfetol is proved to be the appropriate and stable dose for excessive sleepiness. In addition, solriamfetol showed good efficacy for excessive sleepiness in narcolepsy and OSA but also significantly increases the risk of adverse events.

After coronavirus disease 2019 (COVID-19) outbreak, striking decreases in the number of hospital admissions for acute coronary syndromes (ACSs) and rises in rates of out-of-hospital cardiac arrest (OHCA) have been noted.

This is an analysis of prospectively collected data from a cardiology department in a single, large volume hospital of the National Health System of the Metropolitan area of Athens.

We investigated the numbers of OHCA and hospital admissions for ACS during a 1-year period and made comparisons between the pre-COVID-19 and the COVID-19 outbreak periods.

One hundred and eighty five patients were admitted during the total period of observation with the diagnosis of ACS. The mean monthly number of admissions for ACS for the pre-COVID-19 era was significantly higher than that for the post-COVID-19 era (20.1±7.8 vs 8.8±6.5 admissions, Ρ=0.024). The cases of OHCA which were transferred to our emergency room department by emergency medical services during the same period were nominally lower in the prepandemic compared with the postpandemic era (1.