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Shadman Nemati,1 Faeze Keihanian,2,3 Amin Saeidinia,4,5 Mahdi Bakhshaei6 1Rhino-sinus, Ear, and Skull Base Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran; 2Cardiology Department, Imam Reza & Ghaem Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; 3Pharmaceutical Research Division, Booali Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; 4Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran; 5Pharmaceutical Research Division, Booali Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; 6Faculty of Medicine, Ear, Nose and Throat Department, Mashhad University of Medical Sciences, Mashhad, IranCorrespondence: Amin SaeidiniaFaculty of Medicine, Mashhad University Complex, Azadi Square, Mashhad 9177948564, IranTel +98 511 9119451607Email saeidiniaa971@mums.ac.irIntroduction: Nasal polyposis (NP) is a frequent problem during adulthood. Treatment of NP is primarily based on drugs, such as oral or topical steroids and in some types, by surgery. Despite of available therapeutic options for NP, recurrence after polypectomy is found. Vascular endothelial growth factor (VEGF) is a known factor involved in NP. Bevacizumab is a monoclonal antibody, which acts against VEGF.Aim: Regarding the availability of bevacizumab and its use in ophthalmic off-label application, in this study, we hypothesized that it could be a choice of non-invasive treatment. The researchers aimed at evaluating the use of bevacizumab in vitro on the growth of NP.Materials and methods: In this experimental study, the researchers used eight non-allergic NP tissues from patients admitted for polypectomy clinic of Imam Reza Hospital, Mashhad. Tissues were cultured in DMEM medium based on standard protocols in the presence or absence of bevacizumab (10 to 250 μM) then incubated. The mean of the responses was reported. The level of VEGF and MTT test for NP epithelial cell viability were determined for each group. Data were analyzed using the SPSS software.Results: The researchers demonstrated that bevacizumab leads to a decrease in the level of VEGF (the most common cause of angiogenesis in NP) in media culture of NP, dose-dependently (P

Background and Objectives: Community question-answering (CQA) websites have become increasingly popular as platforms for individuals to seek and share knowledge. Identifying users with a special shape of expertise on CQA websites is a beneficial task for both companies and individuals. Specifically, finding those who have a general understanding of certain areas but lack expertise in other fields is crucial for companies who are planning internship programs. These users, called dash-shaped users, are willing to work for low wages and have the potential to quickly develop into skilled professionals, thus minimizing the risk of unsuccessful recruitment. Due to the vast number of users on CQA websites, they provide valuable resources for finding individuals with various levels of expertise. This study is the first of its kind to directly classify CQA users based solely on the textual content of their posts. Methods: To achieve this objective, we propose an ensemble of advanced deep learning algorithms and traditional machine learning methods for the binary classification of CQA users into two categories: those with dash-shaped expertise and those without. In the proposed method, we used the stack generalization to fuse the results of the dep and machine learning methods. To evaluate the effectiveness of our approach, we conducted an extensive experiment on three large datasets focused on Android, C#, and Java topics extracted from the Stack Overflow website. Results: The results on four datasets of the Stack Overflow, demonstrate that our ensemble method not only outperforms baseline methods including seven traditional machine learning and six deep models, but it achieves higher performance than state-of-the-art deep models by an average of 10% accuracy and F1-measure. Conclusion: The proposed model showed promising results in confirming that by using only their textual content of questions, we can classify the users in CQA websites. Specifically, the results showed that using the contextual content of the questions, the proposed model can be used for detecting the dash-shaped users precisely. Moreover, the proposed model is not limited to detecting dash-shaped users. It can also classify other shapes of expertise, such as T- and C-shaped users, which are valuable for forming agile software teams. Additionally, our model can be used as a filter method for downstream applications, like intern recommendations. [ABSTRACT FROM AUTHOR]

ContextDuring puberty, reactivation of the reproductive axis occurs during sleep, with LH pulses specifically tied to deep sleep. This association suggests that deep sleep may stimulate LH secretion, but there have been no interventional studies to determine the characteristics of deep sleep required for LH pulse initiation.ObjectiveThe objective of this study was to determine the effect of deep sleep fragmentation on LH secretion in pubertal children.Design and settingStudies were performed in a clinical research center.SubjectsFourteen healthy pubertal children (11.3-14.1 y) participated in the study.InterventionsSubjects were randomized to two overnight studies with polysomnography and frequent blood sampling, with or without deep sleep disruption via auditory stimuli.ResultsAn average of 68.1 ±10.7 (± SE) auditory stimuli were delivered to interrupt deep sleep during the disruption night, limiting deep sleep to only brief episodes (average length disrupted 1.3 ± 0.2 min vs normal 7.1 ± 0.8 min, P < .001), and increasing the number of transitions between non-rapid eye movement (NREM), REM, and wake (disrupted 274.5 ± 33.4 vs normal 131.2 ± 8.1, P = .001). There were no differences in mean LH (normal: 3.2 ± 0.4 vs disrupted: 3.2 ± 0.5 IU/L), LH pulse frequency (0.6 ± 0.06 vs 0.6 ± 0.07 pulses/h), or LH pulse amplitude (2.8 ± 0.4 vs 2.8 ± 0.4 IU/L) between the two nights. Poisson process modeling demonstrated that the accumulation of deep sleep in the 20 minutes before an LH pulse, whether consolidated or fragmented, was a significant predictor of LH pulse onset (P < .001).ConclusionIn pubertal children, nocturnal LH augmentation and pulse patterning are resistant to deep sleep fragmentation. These data suggest that, even when fragmented, deep sleep is strongly related to activation of the GnRH pulse generator.

Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies. Methods: To address this problem, we proposed a graph convolutional neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a graph convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the graph convolutional network for learning essential features from the final affective picture of tweets. Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art graph convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the therapeutic effects of 1 per cent and 0.01 per cent peracetic acid as an antifungal agent in animal otomycosis. Method: After creating a superficial scratch in the external auditory canal of guinea pigs, a suspension of Aspergillus niger , Aspergillus fumigatus and candida were inoculated into the ears of the animals. After otomycosis, the effect of 1 per cent or 0.01 per cent peracetic acid on otomycosis was evaluated by otomicroscopy and culture at 10 days post-treatment and compared with 2 per cent acetic acid as the control. Results: A 10-day treatment with 1 per cent peracetic acid and 2 per cent acetic acid (control) showed normal otomicroscopy and negative cultures compared with 0.01 per cent peracetic acid. Drug sedimentation or other side effects in the external auditory canal or tympanic membrane were not observed during treatment with peracetic acid. Conclusion: The findings of this study confirm that the treatment of otomycosis with 1 per cent peracetic acid in an animal model is beneficial and may be a novel therapeutic treatment for otomycosis. [ABSTRACT FROM AUTHOR]

Plants’ resistance to drought stress largely depends on their polyamine contents. The present study aims to investigate the enhancing effects of foliar application of Putrescine (Put) on short-term tolerance against drought stress in ornamental sunflowers (Helianthus annus L. cv. Sunbright Kids). Plants were irrigated at 40 and 80% field capacity (FC). Foliar spray of Put (0, 75 and 150 mgl-1 ) was applied twice during the growing period. Water stress at 40% FC, compared to 80% FC, significantly affected plant physiological characteristics. While the exogenous application of Put, in comparison with the control, significantly increased chlorophyll b, total chlorophyll, carotenoids, soluble carbohydrate, leaf potassium (K) and nitrogen and root K contents, photosynthetic rate, membrane stability index and photosystem II quantum florescent efficiency (Fv/Fm), it significantly decreased stomatal conductance of CO2 (gs) and transpiration rate (Tr). The results confirmed that the foliar application of Put protected plants against drought stress by promoting plants RWC, MSI, Chl and Pn. Accordingly, the reduction in stomatal gs and K+ content may lead to stomatal closure and reduction in Tr. Stomatal images are in agreement with gs and Tr reduction. It was concluded that in order to improve the sunflower plants tolerance to drought stress, the foliar application of commercial Put (75 mgl-1 ) is recommended for this cultivar. [ABSTRACT FROM AUTHOR]