#ColonCancer: Social Media Discussions About Colorectal Cancer During the COVID-19 Pandemic.

PURPOSE: Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Social media platforms such as Twitter are extensively used to communicate about cancer care, yet little is known about the role of these online platforms in promoting early detection or sharing the lived experiences of patients with CRC. This study tracked Twitter discussions about CRC and characterized participating users to better understand public communication and perceptions of CRC during the COVID-19 pandemic. METHODS: Tweets containing references to CRC were collected from January 2020 to April 2021 using Twitter's Application Programming Interface. Account metadata was used to predict user demographic information and classify users as either organizations, individuals, clinicians, or influencers. We compared the number of impressions across users and analyzed the content of tweets using natural language processing models to identify prominent topics of discussion. RESULTS: There were 72,229 unique CRC-related tweets by 31,170 users. Most users were male (66%) and older than 40 years (57%). Individuals accounted for most users (44%); organizations (35%); clinicians (19%); and influencers (2%). Influencers made the most median impressions (35,853). Organizations made the most overall impressions (1,067,189,613). Tweets contained the following topics: bereavement (20%), appeals for early detection (20%), research (17%), National Colorectal Cancer Awareness Month (15%), screening access (14%), and risk factors (14%). CONCLUSION: Discussions about CRC largely focused on bereavement and early detection. Online coverage of National Colorectal Cancer Awareness Month and personal experiences with CRC effectively stimulated goal-oriented tweets about early detection. Our findings suggest that although Twitter is commonly used for communicating about CRC, partnering with influencers may be an effective strategy for improving communication of future public health recommendations related to CRC.

Association of Children's Mode of School Instruction with Child and Parent Experiences and Well-Being During the COVID-19 Pandemic - COVID Experiences Survey, United States, October 8-November 13, 2020.

In March 2020, efforts to slow transmission of SARS-CoV-2, the virus that causes COVID-19, resulted in widespread closures of school buildings, shifts to virtual educational models, modifications to school-based services, and disruptions in the educational experiences of school-aged children. Changes in modes of instruction have presented psychosocial stressors to children and parents that can increase risks to mental health and well-being and might exacerbate educational and health disparities (1,2). CDC examined differences in child and parent experiences and indicators of well-being according to children's mode of school instruction (i.e., in-person only [in-person], virtual-only [virtual], or combined virtual and in-person [combined]) using data from the COVID Experiences nationwide survey. During October 8-November 13, 2020, parents or legal guardians (parents) of children aged 5-12 years were surveyed using the NORC at the University of Chicago AmeriSpeak panel,* a probability-based panel designed to be representative of the U.S. household population. Among 1,290 respondents with a child enrolled in public or private school, 45.7% reported that their child received virtual instruction, 30.9% in-person instruction, and 23.4% combined instruction. For 11 of 17 stress and well-being indicators concerning child mental health and physical activity and parental emotional distress, findings were worse for parents of children receiving virtual or combined instruction than were those for parents of children receiving in-person instruction. Children not receiving in-person instruction and their parents might experience increased risk for negative mental, emotional, or physical health outcomes and might need additional support to mitigate pandemic effects. Community-wide actions to reduce COVID-19 incidence and support mitigation strategies in schools are critically important to support students' return to in-person learning.

Preliminary detection of covid-19 using deep learning and machine learning techniques on radiological data

Covid-19 is a dangerous pandemic in the year 2020." Covid-19 positive" is the most negative word heard this year, which caused terror worldwide. As it is a contagious pandemic, early detection of this pandemic will minimize its threat. The primary issue is its detection. To detect Covid-19 through a blood test, a person must wait for an extended period to get the results. Using our model, one can primarily detect Covid-19 immediately using Deep Learning algorithm CNN and Machine Learning algorithm Logistic Regression. Input to these techniques is radiological data like CT-Scan and X-ray images. Covid-19 positive cases will be easily detected faster with the help of this model. © 2021, Engg Journals Publications. All rights reserved.