In Western societies, death is a social and educational taboo. Poor education about death and mourning processes and overprotective family and social attitudes move children away from death to avoid “unnecessary suffering.” The COVID-19 outbreak highlighted these shortcomings and the difficult management of grief's complexity under sudden and unexpected scenarios. The need for immediate and constant updates related to COVID-19 benefited from social media coverage's immediacy. The use of YouTube as a digital platform to disseminate/search for knowledge exploded, raising the need to conduct ethnographic studies to describe this community's people and culture and improve the booming social media's educational capacity and quality. The present virtual ethnography studied 255,862 YouTube views/users and their behavior related to “Vuela Mariposa, Vuela,” a children's story available online since 2009 (not monetized) about the cycle of life, death, and disenfranchised grief (not acknowledged by society) that went viral (+>999%) on May. To our knowledge, this case study is the first original research that explores the ethnography of (i) a viral video, (ii) on death and grief taboo topics, (iii) for prescholars, and (iv) before and during the COVID pandemic. The quantitative and qualitative analyses identified a change in the users' profiles, engagement, and feedback. During the previous 11 years, the users were 35–44 years old Mexican and Spanish women. Those in grief used narrative comments to explain their vital crisis and express their sorrow. In the pandemic, the analysis pointed to Ecuador as the virality geographical niche in a moment when the tragic scenarios in its streets were yet unknown. The timeline match with the official records confirmed the severity of their pandemic scenario. The viral video reached a broad population, with normal distribution for age, and including male gender. Engagement by non-subscribers, direct search (traffic sources), and mean visualization times suggested educational purposes as confirmed by the users' feedback with critical thinking referring to the cycle of life's meaning and societal mourning. For the youngest users, the video was part of academic assignments. The ethnography pointed at YouTube as a flexible education resource, immediately reaching diverse users, and being highly sensitive to critical events. In many Western societies, focused on youth and productivity, after a century of health and social advances increasing life expectancy by half a century, death is a taboo –3. Most https://www.gmc-uk.org)  ). In par-uk.org) . The inc-uk.org) .Thanatology and current practice in death education are an emerging field in postmodern Western societies, using interdisciplinary approaches  to countThe present work is presented as a case study aimed to determine the ethnographic profile of 255,862 YouTube users  and their behavior related to “Vuela Mariposa, Vuela,” a children's story available online since 2009 (not monetized) about the cycle of life, death, and disenfranchised grief (not acknowledged by society) that went viral (+>999%) on May 2020. To our knowledge, this brief report is the first original research that explores the ethnography describing the YouTube community's people and culture associated with (i) a viral video, (ii) dealing with death and grief taboo topics, (iii) created as a material for prescholars, and (iv) quantifying the change in the ethnography profile before and during the COVID-19 pandemic. The children's story was first published open access in Encuentos.com, a digital editorial before the illustrated book, which also includes a guide for parents and teachers, was published in an independent editorial. The story is also part of a scholarly book for American students. Its professional use is cited in a doctoral thesis and several professional websites and blogs of Spanish and Latino–American psychologists specialized in managing children's grief.The material consists of two videos hosted on a non-monetized YouTube channel. They present a Spanish children's story on the cycle of life, death, and disenfranchised grief entitled “¡Vuela, mariposa! ¡Vuela!”  and its This study's participants are constituted by an independent, anonymous sample of 255,862 YouTube users, an estimated number by YouTube Analytics based on the number of views from their publication dates  until August 31, 2020. In each ethnography analysis –6, the nAll the data from the video publication date until August 31, 2020, were considered for the comparative analysis. As the two versions of the story's publication dates differ in 3 days, the publication date of June 25, 2009, was taken as a standard reference. It was verified that this did not affect the total computations. The data were obtained for three time intervals: the total and the two periods that comprise the historic or pre-pandemic period  and the first 174 days of the COVID-19 pandemic . After that, the data of the viral version were analyzed on the day it went viral.The virtual ethnographic analysis was made through three instruments. First, YouTube Analytics provides the traffic sources used to watch the video, geographical areas, sex and age of the users, and other social engagement indicators. The concepts behind the different variables are obvious or intuitive, but some may need to be defined. These are Traffic sources , viewing time , average view duration (estimated average minutes viewed per replay), impressions (number of times video thumbnails have been shown to viewers), and impressions CTR .The data to elaborate the two videos' comparative temporal maps were obtained through YouTube's analytical engine. Total dates were defined from June 25, 2009, to August 31, 2020. The temporal map of the evolution of COVID-19 for Ecuador was taken from the official source of COVID-19 alerts in Google, as offered as of September 1 (the latest to collect data possible for this publication) cites Wikipedia as a source, Creative Commons, free to use.Analytics of the viral video's engagement indicators measured by the number of views, time of visualization, and the mean duration of visualization was obtained from YouTube Analytics for the day the video went viral. In addition, sharing was measured by the service used to share, for both periods, the historical and the pandemic.Finally, for the users' feedback, content analysis on the comments posted by users in the viral video was performed. Based on the user profile, three participations or user's typologies were considered: E, entities; W, women; and M, men. Content analysis was done on users' comments to the video for both periods (historic and COVID-19). The presence of grief was recorded and qualified in five typologies as follows: personal loss , loss of a friend, loss of a person in the school, societal loss (referred to COVID-19), and disenfranchised grief (loss of a pet). The content analysis searched for several items related to the video and the story, the emotions, and feelings raised; the cognitive aspects such as critical thoughts; a summary of the video and its interpretation, providing new opinions; consideration of its use; and the target population.t test, χ2 test, or Fisher exact test. p < 0.05 value was considered statistically significant.The data are presented and analyzed using quantitative and qualitative descriptive methods. Differences were analyzed with Student The video statistics provided by YouTube Studio for the video and its English version in the two periods of time, the pandemic and the history of the previous 11 years, are depicted in three tables according to three main analytical domains: (1) indicators and traffic sources ; (2) ageThe database of indicators and traffic sources for “¡Vuela Mariposa!¡Vuela!”  showed t2 = 30.570, 1 dg, p = 0.0001), as compared to historical, and leading the traffic, ahead of “external source” (34.1%) and “YouTube search” (12.2%), the main sources during the previous years .During the preceding 11 years, the video captured 624 subscribers, 200 before the pandemic and 64.1% (400) of them in 174 days. The percentage of clicks during the pandemic was twice those of the previous years (10.0 vs. 5.5%). Compared to videos with the same duration, YouTube Analytics scored the audience's relative retention as “high” until minute 2:50 and “over the mean” until minute 6:05. The video received 892 likes vs. 51 dislikes and recorded a 94.6% active response to satisfaction, similar to the mean 94.4% given to the channel. The main traffic source changed during the pandemic with “director unknown” (48.6%) showing a statistically significant increase  as compared to the historic.As a comparison, the indicators and traffic sources of the English version Fly, Butterfly! Fly! is indicated in 2s, age intervals from 18 to 54 years, p = 0.0001; 55–64 years old, p = 0.0015). The mean duration of visualizations per age group was similar, covering ~50% of the video duration, with the lowest time  in the youngest age group (13–17 years old) and the highest  in those 55–64 years old. Men also viewed the video, with a 30.9% participation, spending 4:21 min (54.2%) of visualization time than 69.1% of women, who spent 4:17 min (53.5%). No data were available for the English version.Since its publication, the age and sex of the audience have been very uniform, with 100% of visits being 35–44-year-old women, who spend a mean of 2:51 min in the visualizations, a 35.5% of the video duration. During the pandemic, the audience was broader, showing a normal distribution with the maximum in the 25–34 year (25.8%) and 35–44 year (24.8%) ranges, an increase that reached statistical significance except at the two tails of the curve  and Spaniards (8.9%), became viral during the pandemic in another location, Ecuador (84.9%). The few data registered for the English version also pointed at Ecuador (23.9%) as a geographical area, whereas no information was available for the other areas.Viral video's engagement indicators  show thaThe “service used to share” analytics is depicted in Before the pandemic, the feedback from users recorded as commentaries to the video was low but increased with time, reaching a total number of 33 comments . Accordit test, p = 0.0001). One-third of users (30.3%) found the video “helpful” for themselves or those who had lost a loved one, and they thanked for sharing (21.2%). Users qualified the video as touching or evoking their sadness (18.2%), whereas others referred to it as inviting to do reflections and/or expressed their opinion (18.2%). Thus, some users used the comments to summarize the story. Others extracted one or several messages regarding human qualities, existence, or values such as friendship, acceptance, and immortality of love. Other users used the commentary to provide their point of view and perspective. Finally, a few (9%) used or added an “I love it” to their commentary. This pattern was changed during the pandemic. The ratio of users referring to be in grief or evoking their grief during the semester of the pandemic was 5  over 57 participants (8.8%), whereas the previous averaged number per year was 1.18 ± 0.23 over 3.0 ± 0.47 participants per year (64.8%), 13 over 33 participants (39.4%) during the whole period . Societal loss, related to the current situation, was referred to in two of five comments. Evocation of the grief for the loss of a pet (considered disenfranchised grief) was also mentioned here for the first time. The other two explicit comments were for a personal loss and the loss of a friend. The length of comments with explicit reference to a grief experience was as long as those that did not (17.0 ± 5.44 vs. 16.1 ± 2.03) . Also, the use of emoticons was observed in the comments . The content analysis confirmed the categorization of topics but changed their incidence. Thus, while the video's beauty was one of the two most common observations (40.4%), there was an increase in the number of users referring to critical thinking elicitation; users summarized the messages and some added opinions (43.9%). Some comments explicitly referred to watching the video as an assignment from their teacher or as docents, and these comments received positive reinforcement from others (up to 35 likes). The comments also qualified the video as helpful for their new role as bereavement counselors to help others.The content analysis allowed us to identify eight topics . During The present study prompted by a children's story about the cycle of life, death, and grief hold in a YouTube channel for more than 11 years, suddenly becoming viral (+>999% increase of views) in May 2020, 3 months after the pandemic was declared . Therefoper se recognized a need for users to find and visualize material related to death and mourning processes that, as confirmed through the temporal overlapping with the tragic events in Ecuador and the user's feedback, mainly was aimed to help manage the sudden and dramatic number of deaths and consequent difficulties of the mourning process. The time when the video went viral was also determinant because time is considered a critical factor to help disrupt grief's vulnerability and support other protective factors  the English version during the same period of the pandemic and (2) the historical records obtained during the previous 11 years.The indicators showed that virality allowed to achieve, in a brief time, approximately half of the amount of activity accumulated during the precedent years, as measured by the number of visualizations and impressions. Viewing times of virtual social media are usually very short and, in most cases, rarely exceed 3 min. Therefore, the average viewing duration of the video was greater than the standard and may support its educational use/purpose. Besides, the fact that the time of visualization was equal to pre-pandemic and the mean duration of the visualizations was longer talks in favor of the video's potential impact at the educational level. It also helps to discard that the virality resulted in a sudden but short exposure to the video content, which would reduce this potential. The 2-fold increase in the number of subscribers and the percentage of clicks, which are relevant to measure the impact and quality of the materials, suggests that the viral video was translated into the channel's adherence. Here it is important to note that, as a comparison, these variables were not modified in the English version. It would have been interesting to compare the profiles with those of another contemporary children's story addressing other physical changes or loss in the natural life cycle, such as the first tooth loss. Other interesting comparisons could be made with virtual material about fear of death when experiencing natural disasters. Recent reports on higher acute grief after death due to COVID-19 compared to natural loss allow the researchers to predict that pandemic-related increases in pathological grief are foreseen as a worldwide public health concern .Traffic sources were also informative about the way the video went viral. During the pandemic, the access to the video was direct, in contrast to external sources or YouTube search as main sources used during the previous years. Incoming traffic channeled through websites or direct recommendations via links means that other people participate in the election. These indicators suggest a goal-directed behavior of users compatible with educational purposes. The posterior analysis of sharing was also illustrative and confirmed that WhatsApp's direct links were the most used service to spread the word.Apart from the geographical area, age, and sex were two relevant socio-demographic factors to identify the audience's profile, which expanded from all cases being “35–44 year-old women” user profile to a broader normal distribution of ages, also including the presence of men. Age and sex factors are critical to determine the users' profile and elucidate the potential needs of users covered by the material. Thus, during the historic, the adult female profile would respond to mothers confronting the grief process or women in parenting or professional roles toward pre-scholars, as confirmed in the content analysis of the user's feedback. Similarly, the broader coverage of ages and sexes during the pandemic was informative of new use of the pre-scholar material among teenagers as academic assignments or men as fathers/docents. This information helped define subsequent actions for the audience, as a target population, in agreement with other efforts to elaborate and share action lines for death in preschool education . Thus, aThe temporal maps showed that the video became viral immediately after the most severe days of the pandemic outbreak in Ecuador. Among the engagement indicators on the day the video went viral, it is noteworthy that non-subscription as the majoritarian status, the direct traffic source indicating direct connection, and the use of a computer as a device instead of mobile phones, despite WhatsApp was identified as the service used for sharing using the Share on YouTube button. The overrepresentation of females in the two studied periods is common and agrees with bereavement research using voluntary-response sampling and is considered that it may reflect a more vital need for women to share their feelings . In thisThe user's feedback to the video can be considered an important exercise to deal with the “word taboo”  –3. DespiIn the present work, the comparative content analysis highlighted a change in the user's profile concerning the mourning process. This aspect is important as listening and understanding the user's opinions must improve the cultural and pedagogic quality of death education . In the The content analysis of the comments written during the pandemic indicated societal mourning and included disenfranchised grief. The broader range of age of users, including the youth, would also explain the use of emoji and emoticons to express emotions among those not in grief. However, the most relevant difference was that, in agreement with the current situation, most of the comments reflected critical thinking and provided a space for users to summarize the meaning of the children's story. They were also used to provide their own opinion on the scale of values and other important issues related to the cycle of life, death, and mourning. In this sense, this change of expressivity patterns suggests that the material covered the personal needs of bereaved mothers/professionals during the historic, whereas the educational purpose and use of the video were more evident in the new context of COVID-19. As mentioned in some comments, the video was promoted from educative scenarios as an academic assignment for teenagers. Similar to grief support being part of health education , the eduUnderstanding the nature and pattern of misinformation infodemic during large-scale disease outbreaks deserves special attention . When reThe most important limitation to this work is intrinsic to the virality nature of the material and that the ethnographic analysis focused on a case study. Besides, the singularity video's virality associated with a specific scenario could also be considered a limitation. Other limitations include those related to the sources provided by the analytics of this social media, the spontaneous and voluntary-response sampling, and self-reports in the user's feedback.In summary, this ethnographic analysis on a case study provided evidence that, under singular circumstances, a YouTube video dealing with the idea of death, a taboo topic even in its most censored forms such as disenfranchised grief presented in the children's story, was able to go viral. The quantitative and qualitative analyses identified a change in the users' profiles, engagement, and feedback. The analysis pointed to Ecuador as the geographical niche of the viral virus before the severity of the nation's pandemic scenarios was known. Engagement by non-subscribers, direct traffic sources, and mean visualization times suggested educational purposes as confirmed by the users' feedback enriched by critical thinking, referring to the cycle of life's meaning and societal mourning. The broad coverage of all age ranges and the inclusion of male gender talk favoring this virtual resource's potential and flexibility allowed an immediate switch of users' profiles responding to their vital crisis needs. Thus, the ethnography pointed at YouTube as a flexible education resource, immediately reaching diverse users and being highly sensitive to critical events. Good practices on using YouTube as a source and resource of health education can make it a promising tool for native-digital users and precedent generations. More importantly, it talks in favor of good practices in this popular social media as eligible as “palliative social media,” helping to mitigate the death taboo in the current Western societies in a world devastated by the COVID-19 pandemic.The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.The present research is the result of the work of LG-L.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The relationship between moderate alcohol drinking or other alcohol drinking patterns such as frequency, beverage type, and situation of drinking and cognitive function is not sufficiently clear in older people. The purpose of this study was to investigate the association between alcohol drinking patterns and cognitive function in community-dwelling Japanese people aged 75 and over.This study was a cross-sectional design based on a prospective cohort study called the SONIC study. Subjects were older people aged 75-77 or 85-87 who voluntarily participated in 2016-2017. Drinking information was collected for daily drinking frequency, daily drinking intake, beverage type, and non-daily drinking opportunity. Cognitive function was measured using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Other potential confounding factors evaluated were age, sex, medical factors, and psychosocial factors. An analysis of covariance was performed to evaluate the MoCA-J score relative to drinking frequency or alcohol intake. Multiple regression analysis was performed to investigate the association between beverage type or non-daily drinking opportunity and the MoCA-J score.The final number of participants analyzed was 1,226. The MoCA-J score for participants who reported drinking alcohol 1–6 days/week was significantly higher than that for those who reported drinking none or every day. No significant difference in the MoCA-J score was observed relative to daily alcohol intake. In terms of beverage type, wine was associated positively with the MoCA-J score. Non-daily drinking opportunity was also associated positively with the MoCA-J score.Moderate-frequency drinking, wine consumption, and non-daily drinking opportunities were associated with higher cognitive function in community-dwelling Japanese aged 75 and over. Further longitudinal studies are needed to clarify the causal relationships.The online version contains supplementary material available at 10.1186/s12877-022-02852-8. Dementia, which is especially common among older people, affects about 50 million people worldwide and is a major public health problem , 2. BecaExcessive drinking is commonly cited as a risk factor for dementia and cognitive decline , 5. HoweTherefore, the purpose of this study was to investigate the association between drinking patterns and cognitive function in community-dwelling Japanese people aged 75 and over to provide evidence to contribute to the optimal drinking recommendations in this super-aged society.This study was a cross-sectional design based on a prospective cohort study called the SONIC  study, an ongoing study since 2010 with follow-ups every 3 years [3)”. The alcohol concentration of each beverage was set at 5% for beer, 25% for Japanese spirits, 15% for sake, 12% for wine, and 42% for whiskey. Daily alcohol intake was classified into the following categories: “None (0 g)”, “Moderate (>0 g and <40 g)”, “Moderate to Excessive (≥40 g and <60 g)”, and “Excessive (≥60 g)” for men. For women, the threshold values used were half as high as those used for men [The drinking patterns, such as daily drinking frequency, daily alcohol intake, beverage type, and the situation of drinking: non-daily drinking opportunity, were collected by doctors, nurses, public health nurses, and dietitians. The daily drinking frequency was collected as follows: “None”, “<1 day in 2–3 months”, “1 day in 2–3 months”, “1 day/month”, “2–3 days/month”, “1 day/week”, “2 days/week”, “3 days/week”, “4 days/week”, “5 days/week”, “6 days/week”, and “Every day”. The data were then classified into four categories: “None”, “<1 day/week”, “1–6 days/week”, and “Every day”. Daily alcohol intake was calculated using a measuring cup after considering the beverage type. The formula for calculating alcohol intake was: “alcohol concentration (%) × amount (mL) × density , which was administered by trained gerontologists and psychologists. The MoCA-J, which is based on the MoCA, is a tool for sensitively detecting mild cognitive impairment in older Japanese people, and the score was evaluated from 0 to 30 and adjusted for years of education (1 point added for 12 years or less) , 23. A hMedical factors were collected from interviews and blood samples, which were obtained by doctors, nurses, public health nurses, and dietitians. Smoking was evaluated as currently smoking or not. Stroke was assessed as positive if there was previous medical documentation. Hypertension was defined as positive if an individual had a systolic blood pressure ≥140 mmHg, had a diastolic blood pressure ≥90 mmHg, or was receiving antihypertensive treatment . DiabetePsychological and social factors were collected by gerontologists and psychologists. Mental health was measured using the Japanese version of the WHO Five Well-Being Index (WHO-5-J), and a score of 13 or higher was evaluated as good .Social factors were evaluated, including living alone (or living with someone else), frequency of going out , education , and economic status .t-test were performed for continuous variables. To evaluate the average MoCA-J score relative to daily drinking frequency or daily alcohol intake, analysis of covariance (ANCOVA) adjusted for potential confounders was performed. Multiple comparison tests for the categorical variables treated as dummy variables were used with the Tukey-Kramer tests for post hoc analysis. Univariate regression analysis was performed to examine the relationship between beverage type or non-daily drinking opportunity and the MoCA-J scores. Multiple regression analysis adjusted for potential confounders was performed using the drinking pattern variables. In each multivariable analysis, the interaction with sex was examined concerning the drinking pattern variable and the MoCA-J score. p-values <0.05 were considered statistically significant. All analyses were performed using SPSS Statistics version 27 .Descriptive data are shown as a percentage (%) or mean ± standard deviation (SD). For comparisons between characteristics, chi-square test or Fisher’s exact test were used for categorical variables, and one-way analysis of variance (ANOVA) or The number of participants analyzed in this study was 1,226. Table Figure Figure p<0.01) and non-daily drinking opportunity  significantly positively correlated with the MoCA-J scores  in the associations between all drinking patterns and cognitive function  For drinking frequency, the cognitive function of moderate-frequency drinkers, such as those who drank 1–6 days/week, was higher than those for individuals who reported their consumption as none or every day; (2) For beverage type, the cognitive function of wine drinkers was higher than that of non-drinkers; and (3) For drinking opportunity, the cognitive function of those who engaged in non-daily drinking opportunities was higher than that of those who did not. Daily alcohol intake was not associated with increased cognitive function.Few studies have reported an association between cognitive function and drinking frequency among the alcohol drinking patterns of the older population . This isExcessive drinking has a negative effect on dementia and cognitive function , 5. HoweFor the type of alcohol consumed, we found that only wine was positively associated with cognitive function Table . This reIn addition, non-daily drinking opportunity was associated with higher cognitive function. Similar to the relationship between drinking frequency and cognitive function, social factors such as communication with others may have positively influenced cognitive function , 31. TodThis study has several limitations. First, a survival bias effect may play a role in the association between alcohol drinking patterns and cognitive function. The subjects in this study were older people who voluntarily participated, and they tended to be healthier than the average older Japanese population because they had enough physical function to participate in a site survey. Second, drinking information was evaluated by interviews. Self-reporting methods of typical weekly alcohol intake are useful for epidemiological studies, but not perfect , 40. ForIn conclusion, we observed that moderate-frequency drinking, wine consumption, and non-daily drinking opportunities were associated with higher cognitive function in community-dwelling Japanese aged 75 and over. Although the association between drinking frequency, situation of drinking, beverage type and cognitive function is new findings, the reverse association between drinking and cognitive function is often concerned. Further longitudinal studies are needed to clarify the causal relationships.Additional file 1:  Figure S1. Details for daily drinking frequency.Additional file 2: Table S1. Comparison of characteristics relative to daily alcohol intake.Additional file 3: Table S2. Comparison of characteristics relative to wine consumption.Additional file 4: Table S3. Comparison of characteristics relative to non-daily drinking opportunity.Additional file 5: Table S4. Comparison of characteristics relative to sex.


Background: The coronavirus 2019 (COVID-19) pandemic has led to global effects on human interaction and mental health. The most drastic changes are seen in ways people continue to stay connected with each other. Video-conferencing applications like Zoom gained popularity and have become the primary means of communication for social or work events and meetings. These applications have also in many places replaced face-to-face healthcare visits and have penetrated school-based learning. The long-term implications of this digital technology on self-esteem and body image require further study.Main Body: Video-conferencing applications have led to people being more conscious of their appearance and this has resulted in increased cases of body dysmorphic disorder (BDD). There is increased focus on body appearance and cosmetic procedures to fix minor defects. Although the treatment for BDD is like depression, it requires the personalization of therapy specific to the needs of the patient.Conclusion: In this review, we aim to highlight the impact of the pandemic on body image and the long-term implications of virtual conferencing. The review also highlights available pharmacological and non-pharmacological treatment approaches in the management of body dysmorphic disorder related to virtual video conferencing. Body dysmorphic disorder (BDD), also referred to sometimes as dysmorphophobia, is categorized as an obsessive-compulsive disorder (OCD) according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). It is defined as an obsession over minor shortcomings which appear insignificant to others. It is associated with repetitive behaviors which involve either constant grooming or checking one's appearance more frequently than an average person. This compulsion leads to significant deterioration in the levels of functioning .The coronavirus 2019 (COVID-19) pandemic has brought premature changes to the way people interact. Work from home (WFM) has skyrocketed during the time of the pandemic . The majVideo-conferencing applications are web-based applications that provide quality sharing of audio, video, media, screen share, etc. Due to the pandemic, people were required to use these applications for online lectures, webinars, conferences, court hearings, follow-up appointments with clinicians among other reasons ,6. These“Zoom dysmorphia” is used as a collective term for all the internet-based video conferencing applications and their effect on people’s perception of the way they look on a screen. The COVID-19 pandemic may have shone a brighter light on Zoom dysmorphia, which has been prevalent since the pre-COVID-19 era .Interestingly, the prevalence of BDD is much higher in non-psychiatric settings than in psychiatric ones. The premise behind this observation is that many patients with undiagnosed BDD are frequent follow-ups in dermatological clinics where they are seeking procedures done to enhance and fix their appearance. According to a systematic review, an estimated 1.9% of adults were diagnosed as having BDD in the local community, while this percentage increased to 5.8% to 7.4% in psychiatric settings and further increased to a range of 9.2% to 20.1% in various cosmetic and dermatological settings . Even psThe rise of telemedicine during the pandemic has also resulted in increased use of video conferencing for physician and non-physician visits. Taking into consideration the continued future use of technology to supplement traditional “face-to-face” visits, it seems imperative to review the pros and cons and psychiatric implications of virtual connectivity in the current and post-pandemic era.MethodsFor the purposes of this narrative review, the authors decided to conduct a thorough database search to study the impact of video conferencing on body image. Data were explored on Google Scholar and PubMed using keywords “Dysmorphia”, “Zoom”, “Videoconferencing”, “COVID-19”. The inclusion criteria included all publications since December 2020, written in the English language, about the human species. We excluded commentaries, book chapters, letters to the editors and publications which were not reviewed in the systematic and traditional manner, experimental studies, as well as studies that did not meet the outcome of interest.Our initial search led us to 66 articles which after a thorough abstract review led us to finalize 35 articles. We did not perform any systematic review of our article or quality assessment of the selected publications used as reference. A thorough abstract review was conducted to highlight articles of interest.DiscussionPatients with BDD are persistently concerned about different features of their looks. Virtually any portion of the human body may be involved in BDD, but the more common parts are the ones that are most exposed on video such as the head and face, particularly the nose, skin, and hair . Other pThe rise of video applications that allow one to alter one’s image while on video has also resulted in heightened anxiety regarding one’s appearance with often frantic efforts to “fix” any assumed blemishes. Patients may exhibit compensatory behaviors such as wearing excessive make-up, dressing in expensive clothing, applying background filters, or wearing extravagant jewelry to mask perceived deficiencies on video. Changing hairstyles or colors on a regular basis for different meetings may also indicate underlying cognitive errors regarding one’s appearance. Being hyper-focused on camera angles or camera positions may also suggest psychopathology.Over an entire lifetime, a patient with BDD may be preoccupied with an average of five to seven different parts of his body . PatientThe person’s awareness regarding his preoccupations is often lacking. These beliefs may be of a delusional nature which is around 32% to 38% in BDD patients . DelusioAs a result of these uncomfortable thoughts, patients with BDD are bound to have impairments in one or more circles of their lives like academic, occupational, and social life . They arEvidence-based treatmentsPatients with symptoms suggestive of BDD often present to either primary care offices or specialty clinics such as dermatology or plastic surgery. It is important to recognize patients presenting with perceived defects utilizing a thorough history, physical examination, and screening tools. The treatment for BDD has a similar framework to depression as it involves the use of selective serotonin reuptake inhibitor (SSRI) and cognitive behavioral therapy (CBT). CBT involves several components or aspects of BDD that need to be worked on . It invoThe efficacy of CBT has been analyzed by several studies, but most of them did not conduct head-to-head trials comparing CBT with another treatment to provide a control -29. In oIn another study, 36 patients with BDD were randomized to receive CBT or a 12-week waitlist. Each patient in the CBT arm received individual CBT for a total of 22 sessions over the span of 24 weeks. While there was no difference in the two groups at the end of 12 weeks, a statistically significant difference was reported at the end of the 24 weeks. This difference was also present at further follow-up visits. A study that compared CBT to anxiety management (AM) in BDD patients provided interesting results . It was Although not much has been said about the dosing of selective serotonin reuptake inhibitors (SSRI) in BDD patients, experts have suggested that higher doses of SSRI than those used in depression may be required to effectively treat BDD. Some individuals may even require supratherapeutic doses for maximal efficacy . This maMany studies have been conducted to test the effectiveness of antidepressants on BDD. A randomized controlled trial (RCT) demonstrated that the SSRI fluoxetine was significantly more useful than placebo in BDD patients . AnotherA lot is yet to be uncovered about the treatment of BDD. There are no studies comparing CBT and SSRI to evaluate which modality provided greater symptom reduction for BDD patients. Another area of research would be to look for any additive effects CBT may provide to SSRI’s efficacy of treatment and vice versa. Data on neuromodulation techniques and efficacy for symptom reduction in patients with BDD is currently limited.Video-conferencing popularity has dramatically increased during the COVID-19 pandemic. Social distancing rules and a ban on travel have led people from all age groups to use video-conferencing apps for their day-to-day social activities like meetings, birthdays, exercises, and so on. These video-conferencing applications have aggravated body dysmorphic disorder with serious long-term concerns. Patients with pre-existing BDD are spending an excessive amount of time and money getting multiple treatments to change perceived defects. It is imperative to screen patients in both primary care offices as well as specialty care offices for symptoms suggestive of body dysmorphia. It is also imperative to have a continued discussion regarding the utility of teleconferencing and its impact on mental health in the post-pandemic era.The mainstay of treatment is a combination of cognitive-behavioral therapy and antidepressant medications (SSRI). On an individual basis, it remains to be seen which one of these two modalities is more effective in treating BDD and further research needs to be conducted on neuromodulation and alternative treatment approaches that can hopefully provide a more robust solution.


Expert review of seizure semiology looking at video recordings independent of EEG has been found to be useful for diagnosing functional seizures. Videos recorded outside the hospital containing “spells” have similar sensitivity to EEG when quality recordings are evaluated. Recently, smartphone videos were shown to serve as an adjunct to standard history and physical examination with similar diagnostic yields when compared to diagnostic video-EEG monitoring and reviewed by experts. However, caution must be exercised when interpreting videos of paroxysmal neurological events recorded by caregivers to ensure proper video quality is maintained and recorded event is representative. In this report, we present a case of initial identification of and event falsely suggesting functional seizures in a patient with epilepsy. The smartphone video of a “seizure” was recorded by his wife using her smartphone. Despite a quality recording and a history consistent with epilepsy, the smartphone video reviewed during evaluation in the clinic suggested a functional behavior in contrast to the history that suggested epilepsy manifest as convulsions. Instead of bilateral tonic-clonic motor movements, bizarre, intermittent non-clonic wild flinging movements and vocalization were identified on the smartphone video. The discordance between the clnical history and ideo prompted inpatient video-EEG monitoring. The same nonepileptic semiology was subsequently clarified to represent a physiological nonepileptic event. The event on the smartphone was typical of his agitated post-ictal state following an electroclinical tonic-clonic seizure. With treatment the seizures became controlled with antiseizure medication in long-term follow-up. We highlight the pitfalls using patient-recorded smartphone videos in patients diagnosed with epilepsy. Understanding the utility of smartphones as an adjunct to the clinical history will help in differentiating epileptic from functional seizures. He was born at term without any developmental delays and had no risk factors for epilepsy including traumatic brain injury, brain surgery, febrile seizures, central nervous system infections, or family history of seizures and no significant past medical or psychiatric comorbidities. Three years prior to his presentation he had his first seizure. He did not remember the event, but while attending basic training in the Army, he was reportedly found in the shower confused by his fellow soldiers. There was no tongue bite or urinary incontinence, but he was disoriented afterward for much of that day. He had another episode within the same month while he was performing physical training exercises, whereby he collapsed and remained confused for hours, but no report of witnessed convulsions. An evaluation at that time was unrevealing. He had 12 episodes in the next 3 years. They were all similar, some associated with lateral tongue laceration suffered during the event. He was seizure-free for 6 months and then began to have spells at least monthly. He denied an aura or premonition preceding his seizures. His wife reported at night that he would “cry” at the onset and then appears to have clonic jerking bilaterally and symmetrically, up to 3 minutes in duration. He was reported to be distressed for a few minutes after the episodes. Brain MRI was reportedly normal and EEG abnormal, but the reports were unavailable. He had been taking levetiracetam 3000 mg daily with topiramate 50 mg daily. He had also tried valproic acid but reportedly had abnormal labatory studies so this was discontinued. At his appointment, it was determined that he would continue his current regimen of levetiracetam, and topiramate was increased to 100 mg total daily. A presumptive diagnosis of epilepsy was made upon clinical grounds though the classification included focal epilepsy localized to the frontal head region or genetic generalized epilepsy manifest as recurrent nocturnal generalized tonic-clonic seizures. At his follow up appointment, a high-resolution 3-T brain MRI was performed and was normal without intracranial abnormalities. EEG demonstrated 3–4 Hz generalized polyspike-and-wave discharges supporting a clinical diagnosis of genetic generalized epilepsy. The patient and his wife had recorded a video of his habitual seizures, which was reviewed an epileptologists (WOT). As noted in the video, he appears agitated and combative and is thrashing his extremities in a non-rhythmic and discontinuous manner with side to side head movements with eyes closed . He and 3This case highlights the pitfalls of using patient-recorded smartphone videos alone as a diagnostic tool without considering history, physical examination, and ancillary testing. Despite the patient correctly diagnosed with epilepsy on clinical grounds, classification remained unclear and the smartphone video served in this case to be a pitfall in deference to the history based upon a post-ictal semiology that was recorded supporting a nonepileptic event. This subsequently led to an otherwise potentially unavoidable LTVEM session where discordant information could be resolved by other means and provide a definitive diagnosis or establish a dual diagnosis.Recent studies have demonstrated the utility of smartphone videos in evaluating patients with FS when a high-quality ictal recording is obtained, especially when convulsive activity is present As opposed to inpatient LTVEM This case further highlights the importance of separating and defining ictal and postictal periods. The recorded post-ictal state felt to reflect an “ictal” event was prolonged and involved violent and disorganized movements of the extremities, both features that suggested FS Therefore, defining guidelines for the use of standardized outpatient smartphone video recordings to be used for clinical diagnosis is key when educating the patient and family to ensure optimal information is available for the clinician. In this case, the history that was obtained was consistent with a diagnosis of epilepsy blurred between focal and generalized epilepsies, however, the video suggested nonepileptic event potenetially reflecting a FS. Proper instruction and education of patients and their families regarding acquiring smartphone videos can help streamline resource utilization and potentially minimize healthcare expenditures by avoiding unnecessary testing. Clinicians who interpret the semiology observed in smartphone video should specifically inquire about the subjective symptoms and semiology before and after the time documented by the video to establish the full duration and context of the event that is viewed.Smartphone videos are a time-efficient adjunctive tool that provides complementary information to the standard history and physical examination 4Smartphone video recordings of seizures and seizure mimics are an increasing source of supplemental information provided by patients, family, friends, and caregivers to clinicians. These smartphone videos are useful as a clinical adjunct in concert with standard history and physical examination to arrive at a diagnosis in patients with paroxysmal neurological events. Despite the high sensitivity and specificity of high quality videos when viewed by experts, pitfalls may arise when over-emphasis is placed on videos alone as illustrated by this case. Patients and caregivers should be educated on when, what, and how to record a smartphone video at home during the typical patient event. Clarifying the importance of capturing the “ictal” state as close to the onset as possible while video recoding the entire event is crucial for ensuring accuracy. When discordant information from a smartphone video is submitted or when in doubt of the diagnosis, inpatient LTVEM should be pursued to obtain a definitive diagnosis. Smartphone videos will continue to increase in use as mobile health advances in many areas of medicine where functional neurological disorders exist.A signed informed consent was obtained from the patient to present their video information to be used in this publication as a separate consent from that obtained for video-EEG monitoring.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform. Cities, industries, shops, and homes all around the world are experiencing a sharp increase in the number of installed video cameras for surveillance, traffic/process monitoring, and other applications that require real-time video content analysis. Traditionally, a human operator needs to manually watch camera feeds and press a button in case of an event. Since security personnel can hardly remain alert for monitoring tasks after 20 min , in receWith the success of deep learning (DL), video analytics using DL is increasingly being employed to provide accurate and reliable detection, tracking, and classification of objects, people, and faces in video data. However, performing real-time and information-rich analytics using deep learning is a complex endeavor that requires not only high computational power but also a timely orchestration of several different algorithms and software modules. Most of the existing solutions for deploying DL models for video analytics rely on centralized architectures and/or cloud computing platforms hosted by large-scale IT-companies . They provide flexible and scalable solutions with developer-friendly environments and standard interfaces but are not able to meet the Quality of Experience (QoE) demands often associated with network latency and Round Trip Time (RTT) constraints, especially for multi-camera applications.Given the large scale of the data generated by cameras and the real-time requirements often involved, an edge solution is the most practical approach as it conducts the video analytics close to (or on) the cameras . By deplDistributed architecture for deploying the real-time analysis of video on a cluster of small cost-effective machines as well as on a single powerful machine;Resource allocation mechanism that automatically allocates every algorithm according to its deep learning framework, optimizing the use of GPU and CPU resources in the cluster;Modular organization of components that allows efficient scaling to a large number of video cameras with multiple processing pipelines per video stream;Frame-skipping policy, adopted for each component of the processing pipelines, that ensures a real-time behavior of the system;Python interfaces that allow researchers and developers to easily include and deploy new deep learning models using the most mature deep learning frameworks;Standard HTTP and WebRTC APIs for providing web-based video analytics services and allowing the integration of web-based client applications.In this work, we propose a distributed and scalable software framework for implementing real-time video analytics applications named Deep-Framework (DF). Deep-Framework exploits state-of-the-art distributed technologies and allows for the implementation of DL models developed with the most popular open-source DL libraries. The framework provides a simple interface for defining and configuring the computation cluster that abstracts the complex distribution and management of resources, a high-level API for consuming the extracted video data and monitoring the system performance in an open and easy to digest format, and a web-friendly streaming interface for consuming both the resulting data and the source video on client applications running on browsers or other web-based platforms. The contribution of the proposed framework to the state of the art resides in the novel combination of the following features:The rest of this paper is organized as follows. Related work is discussed in Existing solutions for performing real-time video analytics at the edge can be divided between application (and model) specific systems ,4,5,6, fNazare and Schwartz  proposedDeep-Framework is designed as a software framework to enable DL researchers and developers rapid prototyping of distributed, scalable, multi-algorithm, and latency-focused solutions in edge computing contexts. The methods and strategies adopted for developing the framework are chosen with two types of users in mind: application developers and DL DevOps engineers. The idea is that application developers can implement, configure, and use the framework codebase and integrated models to quickly deploy a complete real-time video analytics backend focusing on the end-user application and data consumption without requiring to handle the orchestration of services and model integration. On the other hand, the framework allows DL DevOps engineers to focus on the integration of new DL models simplifying the creation of processing pipelines and the deployment of a test application.Being a distributed edge framework, Deep-Framework is designed to be executed in a cluster of computational nodes. Each component of the framework works as an independent service that is deployed in one of the nodes as a Docker service in swarm mode . Each seInitializes the components, builds the pipelines, and creates the related services;Creates the cluster of nodes in which the services will be executed;Allocates the services with DL models that require GPU to nodes with GPU capability, taking into account the number of nodes, the available memory, and the installed DL frameworks; the other services are scheduled on a different node whenever possible. In Algorithm 1, this procedure is explained in detail;Defines the connection with video sources.The creation and deployment of processing pipelines and the orchestration of all services is managed by a component called DF Initializer ;It creates a list of objects, each composed of an identifier, the coordinates of the bounding box, and and/or a list of keypoints, if available, and sends them to the Collector component;It sends the list of objects and their ROIs to the connected descriptors, using a publish–subscribe messaging pattern.This component is responsible for extracting the coordinates of an object in an image and for tracking it along all received images. The object could be an entity that is depicted in a particular region of the image or in the entire image. Once the coordinates of the object have been estimated, they are sent, together with the corresponding Region of Interest (ROI), to the components dedicated to the information extraction. The framework provides a Python abstract class that defines a common interface for allowing the integration of custom detection and tracking models. In this way, developers only have to deal with the analysis of images and inference-related operations. Each Detector defines a different pipeline. The framework, therefore, wraps the custom code and performs functions as follows:Due to its fundamental function, the Detector gets priority in memory allocation procedure performed by the DF Initializer and described in Algorithm 1.It instantiates the desired Descriptor;It receives images and enforces the frame skipping mechanism;It extracts feature from ROIs and performs an average of the results obtained on N successive images in which the object is tracked.This is the component that carries out the analysis of the ROIs and/or coordinates, retrieved by the Detector, with the aim of extracting information about objects and/or their trajectories. In order to scale up this process, it is possible to create several instances (workers) for each Descriptor. Similar to the Detector, the DF provides a Python abstract class that defines a common interface for the integration of custom models. The framework, therefore, wraps the custom code and performs functions as follows:This component receives data from the detector and distributes them across all the instances of the Descriptor.It aggregates the results obtained by the workers instantiated for each Descriptor; only the most recent results are sent to the main Collector.Algorithm 2: Collector data aggregation and output message creation algorithm.For every result coming from Detector , it produces an output message aggregating the latest available results obtained from Sub Collectors (features) and the timestamps of the corresponding frames. The algorithm used by Collector to aggregate data and construct the output message is shown in Algorithm 2. Note that retrieving results from Descriptor Chains is a not blocking operation and that, if no new results are available from a specific Descriptor, the latest ones are aggregated. Therefore, though each output message contains results from all running algorithms, each result may be related to a different frame and thus be associated to a different timestamp. The resulting data stream is made available to the components that implement the streaming APIs.Parallelization of video analysis tasks is therefore achieved at 3 different levels: (1) routing the video stream to different pipelines, each one dedicated to the analysis of a specific type of objects or ROI, (2) extracting in parallel different features through dedicated Descriptor Chains, and (3) distributing across several workers the feature extracting tasks of each Descriptor.Regardless of the amount of computational resources allocated, real-time behavior is ensured by a frame-skipping algorithm, implemented in the Detector and the Descriptor, which basically consists in skipping the frames received during the previous computation and analysing only the most recent one.The DF is intended as a feature extractor engine, so none of the above components store anything permanently. In fact, in DF, the images received by the components remain in memory for the time needed by the computation operations. The video and the data resulting from the analysis can be possibly stored by an external application.The Stream Manager establishes the connection with the video source in order to grab the individual frames and send them, together with a timestamp, to the processing components. Sources can be RTSP/HTTP streams (IP cameras), WebRTC streams (web-based video providers), or video files . The DF can process multiple sources at once. For every source a Stream Manager instance is created. It also receives the resulting data from the Collector component and is able to stream it together with the input video stream through a WebRTC connection mediated by the Server. Stream Manager and all pipeline components are implemented in Python.The Server provides an HTTP API for distributing resulting data streams, sending statistics about the performance of individual components, controlling the execution of pipelines, and listing the available resources. Data streams, received from Collectors, are distributed through endpoints based on Server-Sent Events (SSE). Statistics are also distributed through SSE but are collected from a dedicated Monitor component that will be described in the following section. The control of pipeline execution is exerted through connections with Stream Managers and allows one to start or stop the grabbing of frames from a video source. The Server also acts as a WebRTC signaling server based on Websockets and allows client applications to establish peer-to-peer connections with single Stream Managers for receiving both video and data streams. Since WebRTC connections are bidirectional, this kind of communication allows external applications to also act as a video source, thus opening the possibility for clients or remote agents to use a single connection for both sending the video stream and receiving the resulting data. The Server is implemented in Node.js.The Monitor is connected with all pipeline components and receives and aggregates from them their operating metrics, which include the throughput (results per second) and the number of frames received, processed, and skipped. These metrics, once collected and aggregated, are made available through the Server and can be used to evaluate the performance of a specific component and of the whole system.Cluster data ;Frequency for generating statistics;Video sources and their types ;Detectors to use (among those available) for each video source along with their computation mode (CPU/GPU).Descriptors to use (among those available) for each detector (pipeline) along with their computation mode (CPU/GPU) and number of instances (workers).Deep-Framework, as described above, can be used from application developers and DL DevOps engineers. Application developers can use the framework by interacting with DF Initializer in order to configure and deploy the application services using already defined Detectors and Descriptors  Xeon(R) CPU E5-2630 v4 @ 2.20 GHz with 20 cores, 64 GB RAM, NVIDIA GeForce GTX 1080 Ti;PC2: OS Ubuntu 20.04 LTS, processor Intel(R) Core(TM) i9-9900K CPU @ 3.60 GHz with 16 cores, 32 GB RAM, NVIDIA GeForce RTX 2080 Ti.In order to evaluate the performance and scalability of the framework, we conducted two experiments aimed at evaluating the behavior of the system when scaling up a single Descriptor chain (increasing the number of Descriptor workers) and the computational cluster (increasing the number of nodes) for a constant load task. Two computers with the following characteristics are used:In the first experiment, a video with 24 fps, generated from a static image of size 1280 × 720 and containing four equal crops 151 × 176 of a face, was analyzed. The framework was deployed on a single node (PC1) with a single pipeline composed of a Detector based on MTCNN  and optiFor the second experiment, the same video stream was analyzed with the same Detector, but this time using six different Descriptors with equal computational complexity . We measured the average Descriptor throughput and the system throughput in four system configurations: one node in CPU mode, one node in GPU mode, two nodes in CPU mode, and two nodes in GPU mode. PC1 was used for one node measurements and both PC1 and PC2 for two node measurements. All Descriptors were deployed with one worker. The results are shown in In this section, the design and implementation of two real-world applications using Deep-Framework is described. The development of these applications intends to demonstrate the usefulness and flexibility of Deep-Framework as a tool for designing real-time video analytics solutions using state-of-the-art DL models. Since the goal is to show how these applications can be developed with Deep-Framework, details of the adopted DL models are not presented. In order to monitor the performance and visualize the results of both applications, a demo web application, based on AngularJS, was developed to show the extracted data overlaid on video in real-time. The performance results are summarized for the two applications and evaluated in terms of the respective real-time requirements.The goal of this application is to use a camera feed to characterize the customers of a clothing shop. The idea is to extract customer information at both face and body levels. From face analysis the application recognizes registered customers, estimates age and gender, estimates gaze direction and face emotive expression, and detects whether customers wear glasses or not. From body analysis the application recognizes the clothing type. The resulting data can be used for statistical analysis and triggering messages to shop assistants whenever some specific conditions are met. As shown in In the Detector of the face pipeline, a face detection and tracking approach is implemented based on the combination of MTCNN  and optiIn order to demonstrate the performance of the DF in this example application, the proposed solution was deployed on a cluster of two nodes , which characterize the two stages of the processing pipeline. The Detector identifies  objects and regions of interests that are subsequently passed to Descriptors to extract additional specific features. As shown in Different to other works that report the development of model-specific solutions ,4,5,6, DDF is designed for processing live video streams by implementing a frame-skipping policy which ensures a real-time behavior by always processing the latest available frame. The data stream resulting from a processing pipeline provides an output message for each frame analyzed by the Detector which determines the latency of the individuated objects or ROIs. On the other hand, the latency of each extracted feature strictly depends on the specificity of each Descriptor, which is in turn determined by the complexity of the inference model and the number of workers involved. For those reasons, not only the output stream will not contain an output message for each frame of the input stream, but not all features in an output message will be updated. This implies that a given output message may contain information about different frames. However, the associated timestamps, which are assigned by the Stream Manager at the moment of grabbing see , always In this paper, we present a novel edge-oriented framework to develop real-time multi-stream video analytics applications based on deep learning models. Deep-Framework allows the distribution of the computing effort among one or several machines with almost zero user configuration effort, automatizing the creation of the computation cluster and allocation of GPU resources. The framework has a scalable multi-stream architecture based on Docker services where each video feed can be independently processed by multiple processing pipelines, each of them able to execute several algorithms with multiple instances each. For deep learning DevOps engineers the framework provides a Python interface that allows the integration and deployment of models using the most deep learning popular frameworks. By providing standard HTTP and WebRTC APIs, Deep-Framework allows application developers to easily consume the resulting data and video streams with desktop, mobile, or browser clients.The experiments show how the scalability of the framework allows applications to fully exploit the computational resources available both at single-machine level and at cluster level in order to deliver timely results. Two different example applications that demonstrate the capability, scalability, and flexibility of the framework on real-world scenarios are also presented.https://github.com/crs4/deep_framework (accessed on 10 June 2021). Finally, Deep-Framework is under active development; feel free to suggest features at https://github.com/crs4/deep_framework/issues (accessed on 10 June 2021) so that we can continue to improve this tool for the deep learning video analytics community.Deep-Framework is an ongoing open-source project; those interested in learning more or using it in their own work can find more information and the source code at


The recent pandemic has made it more challenging to assess patients with suspected obstructive sleep apnea (OSA) with in laboratory polysomnography (PSG) due to concerns of patient and staff safety. The purpose of this study was to assess how Level II sleep studies  might be utilized in diagnostic algorithms of suspected OSA using a theoretical decision model.We examined four diagnostic algorithms for suspected OSA: an initial PSG approach, an initial LII approach, an initial Level III approach  followed by PSG if needed, and an initial LIII approach followed by LII if needed. Costs per patient assessed was calculated as a function of pretest OSA probability and a variety of other variables . The situation in British Columbia was used as a case study.< 0.80) while initial LIII followed by LII as needed was least costly at very high pretest probability (> 0.8). In patients with a pretest OSA probability of 0.5, costs per patient for initial PSG, initial LII, initial LIII followed by PSG, and initial LIII followed by LII were: $588, $417, $607, and $481 respectively.The variation in cost per test was calculated for each algorithm as a function of the above variables. For British Columbia, initial LII was the least costly across a broad range of pretest OSA probabilities (Using a theoretical decision model, we developed a preliminary cost framework to assess the potential role of LII studies in OSA assessment. Across a broad range of patient pretest probabilities, initial LII studies may provide substantial cost advantages. LII studies might be especially useful during pandemics as they combine the extensive physiologic information characteristic of PSG with the ability to avoid in-laboratory stays. More empiric studies need to be done to test these different algorithms.The online version contains supplementary material available at 10.1186/s41606-021-00063-5. Obstructive sleep apnea (OSA) is a common respiratory disease characterized by recurrent upper airway collapse during sleep leading to nocturnal hypoxemia and sleep fragmentation ) involve collection of multiple physiologic signals  and is the gold standard for OSA diagnosis. However, costs and access to PSG remain barriers; in some areas, wait times for PSG can exceed months to years  have become more popular. These involve collection of fewer physiologic signals at home . Sensitivity and specificity of Level III studies are high, ranging from 80 to 100% .PSG had a technical failure rate of 0.Patients diagnosed with OSA were started on CPAP.CPAP pressure was identified by a trial of auto-PAP.A certain proportion (A) of OSA patients did not improve on CPAP.In patients who did not improve with CPAP, a proportion (B) required another PSG (i.e. CPAP titration).The cost of PSG was $C.The cost of a CPAP trial was $D.Using PSG as the initial diagnostic test was the first clinical algorithm used. For this algorithm we made the following assumptions:i)There was a failure rate of proportion (J) with a LII study j)After failure of an LII study, patients had PSGk)LII was as sensitive and specific for OSA as PSGl)The cost of the LII study was $K.For this clinical algorithm, a Level II study was the initial test. We also made the following additional assumptions:m)There was a technical failure rate of proportion (E) with a portable study.n)Ambulatory studies had a sensitivity of F and specificity of G to detect OSA.o)Patients with a positive ambulatory study were started on CPAP.p)Patients without OSA did not respond to CPAP .q)Patients with a positive ambulatory test but who did not respond to CPAP (either because they didn’t have OSA or because they had OSA and did not respond) obtained a PSG.r)In patients with OSA who did not tolerate or improve with CPAP, a proportion (H) required only one PSG (i.e. diagnostic) and (1-H) required two PSG (i.e. diagnostic and CPAP titration).s)Patients with a negative ambulatory study but a positive PSG were considered to have OSA and started on CPAP .t)The cost of the LIII study was $I.In this algorithm, LIII was the initial test followed by PSG as required. We made the following additional assumptions:Patients had LIII study initially. If the study was negative OR if the study was positive and the patient did not respond to CPAP, an LII study was done. If there was failure of the LII, then PSG was done.To assess how the model might be applied to a specific scenario we considered the situation in British Columbia, Canada. We estimated values for the variables used in our model Table . For somFor costs of LI and LIII studies, we used Medical Service Plan 2020 reimbursement rates from British Columbia   to 1 (100% chance of OSA) to determine how this would affect costs of the various algorithms. Calculation of pretest probability involves consideration of the baseline prevalence in the referred population, clinical judgement after history/physical examination, and use of standardized instruments  .The results for LII tests were particularly illuminating. Specifically, Fig. If we consider patients with a pretest probability of 50%, costs per patient for initial PSG, initial LIII followed by PSG, initial LIII followed by LII, and initial LII are as follows: $588, $607, $481, and $417. Therefore, the use of the LII initial algorithm Fig.  could thIn our theoretical framework, we found that LII studies could be useful in the diagnostic assessment of patients with suspected OSA. The potential utility would vary substantially by a number of factors including: costs of studies , pretest probability of OSA, CPAP trial costs, and CPAP adherence. An excel spreadsheet allowing calculation of per patient costs according to the algorithm and incorporating these variables can be found in the We acknowledge that there are many limitations to our theoretical model. First, we have described a theoretical model rather than the results of an empiric experiment. Second, we have made a number of assumptions. Although we believe that the assumptions were reasonable, we recognize that some could be challenged . Second, certain populations would likely not be appropriate candidates for particular algorithms. For example, patients with substantial underlying heart failure or lung disease should probably not have LIII studies as an initial diagnostic test given the potential presence of non-OSA causes of nocturnal desaturation. Third, our models focused on symptomatic patients in whom tests are done predominately to rule in disease. The role of LIII or LII studies in ruling out disease in low probability patients, assessing patients with mild OSA, and those with substantial cardiopulmonary disease is unclear. Fourth, there is little data about use of LII studies in clinical care. For example, technical failure rates of LII across a broad range of patients is currently unknown. Anecdotally, LII studies are more difficult to set up at home compared to LIII studies. This may be particularly relevant in frail patients, patients with significant arthritic, neurologic or neuromuscular diseases, cognitively impaired patients, or patients who are not particularly technologically inclined. Therefore, these types of patients may not be appropriate candidates for an initial LII study. Furthermore, LII studies generally do not have monitoring of carbon dioxide or video. For certain patients such as suspected seizures or neuromuscular disease with hypoventilation, these data may be required, and LI study would be most appropriate. More empiric data concerning use of LII in clinical situations is required. Fifth, we have not considered costs for physician follow up appointments, costs not associated with medical care , or preferences . Sixth, we have not considered added benefits of LII and PSG over LIII in terms of potential diagnosis of other respiratory  or non-respiratory disorders . Furthermore, LII and PSG could allow assessment of more advanced physiologic metrics that cannot be readily obtained from LIII studies (Younes et al. Using a theoretical decision model, we have developed a preliminary framework to assess the potential role of LII studies in the assessment of patients with suspected OSA. Across a broad range of patient pretest probabilities, initial LII studies appear to provide substantial cost advantages. LII studies might be especially useful during pandemics as these tests combine the ability to have the extensive physiologic information characteristic of PSG with the ability to avoid overnight in-laboratory stays.Additional file 1.


Plasmodium malariae and Plasmodium ovale spp., is increasing, with some complications in infected individuals. The aim of this review is to provide a better understanding of the malaria prevalence and disease burden due to P. malariae and P. ovale spp.Recent studies indicate that the prevalence of non-falciparum malaria, includingP. malariae and P. ovale spp. were analysed using a random-effects model and the possible sources of heterogeneity were evaluated through subgroup analysis and meta-regression.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Joanna Briggs Institute prevalence study assessment tool were used to select and evaluate the studies, respectively. Six databases: PubMed, WHOLIS, Wiley Library, ScienceDirect, Web of Science and Google Scholar were used to screen articles published during the period January 2000–December 2020. The pooled prevalence estimates for P. malariae and P. ovale spp. pooled prevalence were 2.01% (95% CI 1.31–2.85%) and 0.77% (95% CI 0.50–1.10%) respectively, with the highest prevalence in the African Region. P. malariae was equally distributed among adults (2.13%), children (2.90%) and pregnant women (2.77%) (p = 0.862), whereas P. ovale spp. was more prevalent in pregnant women (2.90%) than in children ≤ 15 years (0.97%) and in patients > 15 years old (0.39%) (p = 0.021). In this review, data analysis revealed that P. malariae and P. ovale spp. have decreased in the last 20 years, but not significantly, and these species were more commonly present with other Plasmodium species as co-infections. No difference in prevalence between symptomatic and asymptomatic patients was observed for either P. malariae or P. ovale spp.Out of the 3297 studies screened, only 113 studies were included; among which 51.33% were from the African Region. The P. malariae and P. ovale spp. is very important for malaria elimination programmes and a particular focus towards improved tools for monitoring transmission for these non-falciparum species should be stressed upon to deal with increased infections in the future.Our analysis suggests that knowledge of the worldwide burden of The online version contains supplementary material available at 10.1186/s13071-021-04797-0. Plasmodium and transmitted by infected Anopheles female mosquitoes in tropical and subtropical countries. This disease is a major public health problem globally, with 229 million estimated cases in 2019, mostly in African and South-East Asia regions   guidelines were used to select studies for this review , and sixP. malariae and P. ovale spp. using molecular techniques as a species identification method were considered in this review. Only research articles published in peer-reviewed journals were included, and studies with no clear epidemiological data on Plasmodium spp. were excluded from the review. All articles were imported into Zotero bibliographic reference management software, and duplicate files were deleted. A data extraction form with the name of the first author, year of publication, period of data collection, geographical location (continent and country), target group (subject and subject status), type of study, sample size, sampling technique, diagnostic method, target gene and a positive result for each human Plasmodium species was prepared in a Microsoft Excel spreadsheet. The data files extracted independently by the first two authors were systematically evaluated for any inconsistencies, and any further discrepancies were resolved after discussion with the team supervisor.All epidemiological studies that reported the prevalence of The quality and risk of bias in included studies were assessed using an adapted version of the Joanna Briggs Institute (JBI) prevalence study assessment tool . The evaMetaprop command  software. Publication bias was also assessed using a funnel diagram by visually inspecting the plots and exact Egger’s test for small-study effects. Possible sources of heterogeneity were assessed using subgroup analysis and meta-regression. We computed a subgroup analysis including only studies conducted in defined groups: (i) pregnant women, (ii) children ≤ 15 years old and (iii) participants > 15 years old. In the pregnant women group, when several samples were taken , only the results of the peripheral blood were used in the meta-analysis. A meta-regression with the sample size, date of publication and period of data collection as a covariate was performed to evaluate whether they were confounders of the P. malaria and/or P. ovale spp. pooled prevalence. All statistical analyses were considered statistically significant for values of p < 0.05.Data analysis was performed with  command  in Statan = 32), (ii) article in a language other than English or French (n = 4), (iii) epidemiological data on imported malaria cases (n = 46), (iv) comparative studies or diagnosis made by a new method (n = 19) and (v) not related to our study framework (n = 350). Of the remaining 145 articles, 32 were excluded because of their methodology or a lack of epidemiological information  no access to the full text (n = 58) were published within the last 5 years, but no study was published in the year 2003 , the Eastern Mediterranean (EMR)  and WPR  reported the largest sample size ; pregnant women were investigated in 7.08% in the African Region (n = 7) [n = 1) [Of the 113 studies included in our review, more than half  –24 and R [n = 1) , and one [n = 1)   were seen in the majority of studies (n = 108), and a few also used P. knowlesi-specific primers for species identification and confirmation [P. ovale curtisi and P. ovale wallikeri [. The target genes for the PCR assay were mainly 18S rRNA (83.19%), cytochrome b (9.73%) and plasmepsin 4 (1.17%) genes [All included studies used molecular diagnostic methods including nested polymerase chain reaction (PCR) 71.43%), real-time polymerase chain reaction (RT-PCR) 10.71%), quantitative polymerase chain reaction (qPCR) (4.46%) and loop-mediated isothermal amplification (LAMP) (3.54%) for irmation –31. Only.71%, quaallikeri , 32–36. %) genes , 37. One%) genes  , with high and significant heterogeneity between the studies . In comparison to P. malariae, the prevalence of P. ovale spp. using the same model was found to be 0.77% , with significant heterogeneity   for P. ovale spp. The EMR showed the lowest prevalence of P. malariae of 0.06% (95% CI 0–0.52%) and no cases of P. ovale spp. The AMR had the second highest P. malariae prevalence of 2.94% (95% CI 0–12.56%). The differences among regions were found to be statistically significant   for P. malariae was found to be lower in asymptomatic than in symptomatic subjects, 1.31% (95% CI 0.60–1.71%) and 1.09% (95% CI 0.04–3.92%), respectively, with no significant difference (p = 0.792)  for P. ovale spp. infection between symptomatic 0.60% (95% CI 0.16–1.24%) and asymptomatic subjects 0.22% (95% CI 0.05–0.48%) , children (2.90% with 95% CI 0.94–5.84%) and pregnant women (2.77% with 95% CI 0.72–6.01%) (p = 0.862). In contrast, the P. ovale spp. were seen to be more prevalent in pregnant women 2.90% (95% CI 0.94–5.79%) than in children ≤ 15 years 0.97% (95% CI 0.08–2.66%) and in patients > 15 years old 0.39% (95% CI 0.10–0.85%) (p = 0.021)  appeared to be more prevalent than P. ovale wallikeri (1.55% with 95% CI 0.38–3.47%). However, there was no significant difference between them (p = 0.698)  versus 0.85% (95% CI 0.59–1.16%) in mono-infections, whereas P. ovale spp. showed a prevalence of 1.84% (95% CI 1.16–2.69%) in co-infections and 0.66% (95% CI 0.43–0.94%) in mono-infections. Analysis of the RD showed that both species were more likely to be present in co-infections than mono-infections in the studied subjects with RD value 4.16%  for P. malariae and 5.05%  for P. ovale spp.  or P. ovale spp.   , where the small study size may be the reason for bias towards P. ovale spp. prevalence  , 40. TheP. malariae infections (2.01%) was higher as compared to P. ovale spp. (0.77%), with AFR being the most frequently affected for both species. Epidemiological studies on malaria conducted in the AMR, SEAR and EMR did not very often pay attention to P. malariae and/or P. ovale spp. due to their low prevalence/severity [P. malariae prevalence of more than 40% in these regions [P. malariae and P. ovale spp. prevalence rates in these regions, as observed in this review. Negative correlations obtained in the meta-regression of prevalence as a function of data collection period indicate that the prevalence of both P. malariae and P. ovale spp. species have declined over the last 20 years, but not significantly. WHO has several strategic programmes to eliminate malaria, with a particular focus on P. falciparum and P. vivax malaria but none specific to P. malariae or P. ovale spp., which may have resulted in a low regression observed for these two species with a significant decline of P. falciparum and P. vivax prevalence recorded globally [The overall prevalence of severity , 41–46,  regions , 47. Thiglobally , 48.. [P. malariae increased 15-fold and that of P. ovale spp. increased fourfold. Moreover, in the last 5 years, fairly high prevalence has been reported for P. ovale spp. [P. malariae [P. ovale spp. was reported in 12 out of 26 provinces of the DRC during 2007. Six years later, in 2013, the demographic and health survey revealed P. ovale spp. in 24/26 provinces of DRC, with an increased prevalence in several provinces [P. ovale spp. that urgently needs to be addressed, as DRC is the second most affected country by malaria in the world [P. ovale subspecies in AFR [P. malariae [P. ovale spp. [P. malaria and P. ovale spp. are often misidentified as P. falciparum and P. vivax, respectively, which complicates the routine diagnosis [Gneme et al. , in a 4-ale spp. , 13 and malariae , 10, 12 malariae . The P. rovinces . This res in AFR , 35, 51.malariae , 52–54 aale spp.  infectioale spp. . P. malaiagnosis , 57 and iagnosis  which coP. malariae builds up with age similarly to P. falciparum, but incompletely due to lower parasitaemia, which induces limited immune stimulation. In this study, subgroup analysis showed that P. malariae appears to have the same distribution in children, adults and pregnant women, whereas the highest P. ovale spp. prevalence among the subgroups was observed in pregnant women. Knowing that P. malariae has the same therapeutic protocol as P. falciparum [P. malariae burden in this group, unlike P. ovale spp., which can remain dormant in the liver (hypnozoites stage) for long periods. A cross-sectional study including 4570 women of childbearing age conducted by Taylor et al. [P. malariae was more prevalent in non-pregnant than pregnant women in the Wocba region (DRC). Unfortunately, this observation could not be linked with any factor, as the authors had no data on ITPp or antenatal care. On the other hand, Mitchell and colleagues reported P. ovale spp. prevalence twice as high in men (mainly miners) as in women, suggesting that male susceptibility was possibly due to the high exposure in their work environment. Therefore, the conclusions as to the risk groups for these two species remain controversial.Children and pregnant women are well known as vulnerable groups to malaria infection. Woldearegai et al.  reportedlciparum , 58, ther et al.  found thPlasmodium species reveals that both P. malariae and P. ovale spp. occurred more in co-infections, as reported earlier [Plasmodium species can be explained by the density-dependent regulation model proposed by Bruce and Day [Plasmodium species is reinfected by a second species, the parasitaemia due to the first species is down-regulated. However, when the species with the highest parasitaemia exceeds a certain threshold, the host immune response is triggered to control the infection. Once the infection is under control, the system is deactivated, promoting the growth of the second infecting species. This cycle is believed to be responsible for the persistence of several species in the host. In addition, it is also known that P. malariae and P. ovale spp. preferentially develop in mature and younger red blood cells respectively [P. malariae and P. ovale spp. should not be neglected because of their low prevalence and disease severity.In the reported meta-analysis, the difference in risk of co-infections with another  earlier , 59–62.  and Day , 64. Thiectively , 66. Thifalciparum species based essentially on molecular techniques known to be the most sensitive diagnostic tool, and the global coverage of the WHO health regions in the last two decades, thus offering a global overview of P. malariae and P. ovale spp. malaria burden. However, the lack of whole information in some studies made it impossible to thoroughly analyse the data for identifying the environmental and biological factors that regulate the dynamics of these two species and which will be the subject of our future investigations.The strength of this study lies in the estimation of the prevalence rates of these two non-However, this study had certain limitations. First, the pooled prevalence data was estimated from studies conducted in different geographic areas and at different times, as reflected in the high heterogeneity observed. Second, the AFR had ten times as many papers as some of the regions included, which could be a source of imbalance in pooled prevalence data. Third, not all studies mentioned the characteristics of the study areas to permit us to explore other sources of heterogeneity. Fourth, only papers published in English or French were included in this review. Finally, field or laboratory working conditions that were not always clearly detailed and/or sometimes differed could have generated several other confounding variables that affected our results.P. malariae and P. ovale spp. prevalence in the last two decades. Based on our results, the prevalence of both species has declined slightly over this period, although high prevalence has been recorded in AFR and the AMR recently. Both species are equally prevalent in asymptomatic and symptomatic individuals of all age groups, which supports the interest of their inclusion in the new strategies for prevention, treatment and malaria elimination programmes.This study summarizes the distribution and the global trend of Additional file 1. PRISMA Checklist.Additional file 2. Search strategy.Additional file 3. Table S1. Score of included study. Table S2. Studies excluded from meta-analysis. Table S3. Characteristics of included studies. Table S4.Plasmodium malariae pooled prevalence. Table S5. Plasmodium ovale in future. pooled prevalence.Additional file 4. Comparison of P. ovale curtisi and P. ovale wallikeri prevalence.Additional file 5. Risk difference (RD) mono-infection vs mix infection.Additional file 6. Meta-regression of prevalence over the period of data collection by region.Additional file 7. Funnel plot for publication bias and Egger’s test for small study effects.