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Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in communication, social interaction, and repetitive behavior. The declaration of the COVID-19 pandemic in March 2020 resulted in significant changes in daily life due to restrictive measures. This period posed particular challenges for families with children living with autism, given the limitations in medical care and social services., Objective: This study aimed to understand how families with autistic children perceive stress during the pandemic and the coping strategies employed in unexpected situations., Method: A total of 44 families with children with ASD and 300 control families, including 44 control families in a matched subsample, were included in the study. To assess stress and parental coping with COVID-19-related stress, the Responses to Stress Questionnaire (Adult Self-Report RSQ-COVID-19) was utilized., Results: Caregivers of autistic children experienced significantly higher stress levels (p = .027, d = 0.479) during the pandemic, with notable stressors such as limited access to medical care and challenges associated with remote work. Despite expectations, coping strategy differences were not statistically significant., Conclusion: Families and supporters of children with autism naturally encounter various experiences and challenges stemming from their additional needs. Our study's results highlight an accentuation of stress during challenging situations. As these situations may recur in the future, there is a need to design and implement support plans for these families, appropriate intervention programs, and preparations for the utilization of telemedicine tools., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Knedlíková et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: Exophytic Sinonasal Papilloma (ESP) is a benign tumor of the sinonasal tract. Complete surgical excision by endoscopic surgery is the treatment of choice. However, a high recurrence rate (36% at 5-year follow-up) is associated with this method, which may indicate the presence of microorganisms such as Human Papillomavirus (HPV). It is important to note that the standard treatment for ESP does not include antiviral drugs. In our study, we are testing the effectiveness of an interferon-containing drug in reducing recurrence and postoperative reactions in patients with ESP., Methods: We included 78 patients aged 23-83 years with a confirmed diagnosis of ESP by rhinoscopy and nasal endoscopy and a positive PCR test for HPV in nasal scrapings. To compare the results, we divided the patients into main and control groups. The main group received recombinant human interferon after surgery, while the control group did not receive the drug. We performed a statistical analysis to compare the proportion of patients without reactive manifestations at different stages of the postoperative period, as well as to compare the proportion of patients with recurrent ESP at certain stages of observation., Results: The introduction of recombinant human interferon accelerated the resolution of postoperative reactions and promoted the healing of the nasal mucosa after surgical removal of the ESP. We also found a statistically significant association between treatment with recombinant interferon and a reduction in the recurrence rate of ESP., Conclusion: According to the results of the study, it was found that in the main group of patients who received rhIFN-α2b (recombinant human Interferon alpha 2b) in the postoperative period, the frequency of relapses of ESP and the time of postoperative recovery were significantly lower than in patients in the control group who did not take the drug., Level of Evidence: Cohort Study., (Copyright © 2024 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier España S.L.U. All rights reserved.)

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO 2 ) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO 2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO 2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Pavel Tsvetkov reports financial support was provided by Russian Science Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace., Competing Interests: The authors declare there are no competing interests., (©2022 St et al.)