Link Between the G Protein-Coupled Receptor (GPCR), SARS-CoV-2 Spike Protein, ACE2, Vaccines, and Long COVID (preprint)

The knowledge of COVID-19 impact on the human body has increased rapidly. Although many people recover from COVID-19, some continue to experience persistent symptoms that have been identified as Long COVID. This condition can have a severe impact on quality of life, and it remains a significant concern for medical professionals and researchers. One of the key components of the SARS-CoV-2 virus that enables it to enter human cells is the spike (S) protein. Recent studies have revealed a complex network of interactions between G proteins, spike (S) protein, and the Renin-Angiotensin System (RAS) may be responsible, at least in part, for long COVID. SARS-CoV-2 can also affect the brain, leading to neurological symptoms such as confusion, memory loss, and fatigue. Increasing evidence suggests that COVID-19 is not just a respiratory illness since it is likely that the virus could influence signal transduction pathways such as G-protein-coupled receptor (GPCR), among others, in the brain, either directly or indirectly, affecting neural functions. These interactions with the spike (S) protein and RAS, alongside the brain, are complex and require deep research to understand their implications for Long COVID-19 manifestation fully. While recent research has shed light on the complex interactions between G proteins, spike (S) protein, the brain, and the angiotensin system, this article explores these interconnected pathways and their implications for long COVID-19 manifestations. The present review summarises current research on different molecular mechanisms in Long COVID pathophysiology and may help identify possible targets or new strategies for the diagnosis and treatment.

Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults (preprint)

Background Lower Respiratory Tract Infections (LRTI) pose a serious threat to older adults but may be underdiagnosed due to atypical presentations. Here we assess LRTI symptom profiles and syndromic (symptom-based) case ascertainment in older (≥65y) as compared to younger adults (<65y). Methods We included adults (≥18y) with confirmed LRTI admitted to two acute care Trusts in Bristol, UK from 1st August 2020- 31st July 2022.  Logistic regression was used to assess whether age ≥65y reduced the probability of meeting syndromic LRTI case definitions, using patients’ symptoms at admission. We also calculated relative symptom frequencies (log-odds ratios) and evaluated how symptoms were clustered across different age groups. Results Of 17,620 clinically confirmed LRTI cases, 8,487 (48.1%) had symptoms meeting the case definition. Compared to those not meeting the definition these cases were younger, had less severe illness and were less likely to have received a SARS-CoV-2 vaccination or to have active SARS-CoV-2 infection. Prevalence of dementia/cognitive impairment and levels of comorbidity were lower in this group. After controlling for sex, dementia and comorbidities, age ≥65y significantly reduced the probability of meeting the case definition (aOR=0.67, 95% CI:0.63-0.71). Cases aged ≥65y were less likely to present with fever and LRTI-specific symptoms (e.g., pleurisy, sputum) than younger cases, and those aged ≥85y were characterised by lack of cough but frequent confusion and falls. Conclusions LRTI symptom profiles changed considerably with age in this hospitalised cohort. Standard screening protocols may fail to detect older and frailer cases of LRTI based on their symptoms.

Extended Duration and Intense Manifestations of COVID-19-Linked Cytotoxic Lesions of the Corpus Callosum: Case Reports and Literature Review (preprint)

While clinical instances of cytotoxic lesions of the corpus callosum (CLOCCs) are well-documented, international reports specific to COVID-19-related cases remain limited. This paper presents the case of a 40-year-old female patient admitted due to "sudden dizziness and poor limb coordination for 7 weeks following fever." She tested positive for COVID-19 and experienced symptoms like dizziness, temporary confusion, nausea, vomiting, cerebellar speech issues, and ataxia after fever onset. Later, she developed pyramidal tract symptoms and behavioral abnormalities. Head MRI revealed abnormal high signal in the splenium of the corpus callosum and abnormal signals in the left cerebellar peduncle on DWI. With no significant medical history and exclusion of other causes during treatment (including steroid therapy and two doses of intravenous immunoglobulin), a follow-up MRI after one month showed the lesions had disappeared. However, clinical recovery was slow, with residual symptoms persisting for almost a year, including involuntary tremors in the upper limbs and head. Phenytoin, gabapentin, and pregabalin showed limited effectiveness in treatment, but Arotinolol and donepezil led to slight improvement in involuntary tremors. This case suggests that COVID-19-associated CLOCCs might have a protracted course and severe symptoms, demanding differentiation from ischemic cerebrovascular diseases, particularly in early stages.

A design of Convolutional Neural Network model for the Diagnosis of the COVID-19 (preprint)

With the spread of COVID-19 around the globe over the past year, the usage of artificial intelligence (AI) algorithms and image processing methods to analyze the X-ray images of patients' chest with COVID-19 has become essential. The COVID-19 virus recognition in the lung area of a patient is one of the basic and essential needs of clicical centers and hospitals. Most research in this field has been devoted to papers on the basis of deep learning methods utilizing CNNs (Convolutional Neural Network), which mainly deal with the screening of sick and healthy people.In this study, a new structure of a 19-layer CNN has been recommended for accurately recognition of the COVID-19 from the X-ray pictures of chest. The offered CNN is developed to serve as a precise diagnosis system for a three class (viral pneumonia, Normal, COVID) and a four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A comparison is conducted among the outcomes of the offered procedure and some popular pretrained networks, including Inception, Alexnet, ResNet50, Squeezenet, and VGG19 and based on Specificity, Accuracy, Precision, Sensitivity, Confusion Matrix, and F1-score. The experimental results of the offered CNN method specify its dominance over the existing published procedures. This method can be a useful tool for clinicians in deciding properly about COVID-19.

Brain infection by wild-type SARS-CoV-2 and the B.1.617.2 and B.1.1.529 variants of concern is a severe outcome in K18-hACE2 transgenic mice (preprint)

Background: SARS-CoV-2 is a respiratory virus with neurological complications including loss of smell and taste, headache, and confusion that can persist for months or longer. Severe neuronal cell damage has also been reported in some cases. The objective of this study was to compare the infectivity of Wild-type, Delta, and Omicron variants in transgenic mice that express the human angiotensin-converting enzyme 2 (hACE2) receptor under the control of the keratin 18 promoter (K18) and characterize the progression of infection and inflammatory response in the lung and brain of these animals. Methods: K18-hACE2 female mice were intranasally infected with Wild-type, Delta, or Omicron variants and euthanized either at 3 days post-infection (dpi) or at the humane endpoint. None of the animals infected with the Omicron variant reached the humane endpoint and were euthanized at day 8 dpi. Virological and immunological analyses were performed in the lungs, olfactory bulbs, medulla oblongata, and brains. Results: We established that Wild-type, Delta, and Omicron infect the lung and brain of K18-hACE2 mice. At 3 dpi, mice infected with the Omicron variant show lower levels of viral RNA than those infected with Wild-type or Delta in the lung and brain. However, they still demonstrate upregulation of cytokines and chemokines, indicating that the Omicron variant can induce pulmonary and neuronal inflammation despite reduced viral proliferation after infection. At the humane endpoint/8dpi, there is a significant increase in viral RNA in mice infected with the Wild-type or Delta variant brains. However, viral RNA levels in Omicron-infected mice did not increase significantly as compared to 3dpi, and the expression of cytokines and chemokines in the brain, olfactory bulb, and medulla oblongata was downregulated, suggesting that infection by the Omicron variant results in attenuated neuroinflammation as compared with Wild-type and Delta.

Cognitive Profile of SARS-cov-2 Recovered Patients: A Comprehensive Analysis of Persistent Dysfunctions in Long COVID (preprint)

Long Covid is a term used for patients who have recovered from COVID-19 but exhibit persistent cognitive dysfunction, including mental confusion, difficulties in attention, impairment in executive functions and slow movements, among other common symptoms. A study was conducted with 65 patients who had a positive RT-PCR diagnosis and reported symptoms of cognitive impairment, such as memory loss and attention difficulties after recovery. The patients underwent neuropsychological evaluation and completed questionnaires on cognition, mood, and quality of life. During the cognitive screening, 71% of the patients showed alterations, with deficits in visual memory (69%), language (54%), visuospatial construction (49%), verbal episodic memory (37%), executive functions (36%), attentional abilities (34%), and premorbid intelligence (12%). It is important to highlight the need for treatments and further studies to understand the long-term side effects of this disease.

Lessons learned from the experiences and perspectives of frontline healthcare workers on the COVID-19 response (preprint)

Background During the COVID-19 pandemic, healthcare systems and healthcare workers (HCWs) faced significant demands and unique challenges. In this qualitative study, we explore the effects of the COVID-19 public health policies on British Columbia’s frontline HCWs, describe what worked in the management of the pandemic, and elucidate the lessons learned that could be applied to future pandemic preparedness, recovery and response.Methods This qualitative descriptive study is part of a larger, national multi-case study on pandemic policy communication and uptake. Semi-structured interviews were conducted from November 2020- June 2021 with fourteen HCWs working in long-term care (LTC), acute care and public health settings. Data were inductively coded, and analyzed following a resilience framework for public health emergency preparedness, which emphasizes the essential elements of a public health system, vital to all phases of health emergency management, readiness, response and recovery.Results HCWs experienced confusion, frustration, uncertainty, anxiety, fatigue and stress, during the pandemic and detailed challenges that affected policy implementation. This included communication and coordination inconsistencies between the province and regional health authorities; lack of involvement of frontline staff in pandemic planning; inadequate training and support; inadequate personal protective equipment resource capacity and mobilization; and staffing shortages. HCWs recommended increased collaboration between frontline staff and policy makers, investment in preparing and practicing pandemic plans, and the need for training in emergency management and infection prevention and control.Conclusions Pandemic planning, response and recovery should include inputs from actors/key stakeholders at the provincial, regional and local levels, to facilitate better coordination, communication and outcomes. Also, given the critical roles of frontline HCWs in policy implementation, they should be adequately supported and consideration must be given to how they interpret and act on policies. Bi-directional communication channels should be incorporated between policymakers and frontline HCWs to verify the appropriate adoption of policies, reflective learning, and to ensure policy limitations are being communicated and acted upon by policy makers.

Children's care: family centred but child focused.

Family-centred care is a widely used but loosely defined model of care often used in children's nursing. Although this allows for flexibility in its application, it also means that nurses can have very different ideas about its meaning. Recent decisions about the implementation of the COVID-19 vaccination programme for children under the age of 16 years in the UK and other countries have further confused this, as it has brought into question the relative position of children and their families in the decision-making process. Over time, the legislative and social positions of children have changed. Children are increasingly seen as being separate but related to their family, with an emphasis on their own human, legal, and ethical rights, including allowing children to choose the type of support they require for their care to reduce any undue stress. This article puts these into a current and contextual framework to better help nurses understand the historical as well as the contemporary reasons for the status of family-centred care today.

Analysing the psychosocial and health impacts of Long COVID in Pakistan: A cross sectional study (preprint)

Long COVID corresponds to the occurrence of symptoms beyond twelve weeks after the onset of acute COVID-19 illness. The study aimed to analyze impacts of long COVID on the general health and psychosocial well-being of the Pakistani population. This cross-sectional study aimed to analyse the impacts of long COVID on general health and psychosocial well-being. For this study, the participants were interviewed, and their responses were recorded on a questionnaire capturing information on demographics, COVID-19 status, duration of symptoms and long COVID symptoms. The psychological impacts of the pandemic were assessed using scales like Short Mood and feeling questionnaire (sMFQ), Warwick-Edinburgh Mental Well-being Scale (WEMWBS), Generalized Anxiety Disorder Assessment (GAD-7) and Perceived Stress Scale (PSS). Regression analysis was conducted to analyse the predictors of long COVID. A total of 300 participants were interviewed, of which 155 (52%) had COVID-19 illness. Of these 54 (35%) had persistent symptoms for a period of more than 12 weeks classified as long COVID. Muscle problems and fatigue were the most frequent (14.7%) symptoms encountered, followed by breathing problems (12.6%) and cognitive issues (12.6%). Symptoms such as decrease in appetite and confusion or disorientation during the initial phase of the infection were associated with long COVID. Majority of the participants (83.3%) had moderate level of perceived stress while moderate to severe levels of stress was observed in 17.3% of the individuals. Moreover, a high level positive mental wellbeing was also observed.

COVID-19 and COVID-19 Vaccine in Japan—A Review from a General Physician's Perspective (preprint)

More than 3 years have passed since the emergence of COVID-19. On May 8, 2023, COVID-19 in Japan was downgraded to Category 5 by the Infectious Disease Control Law. In Japan, at the beginning of the COVID-19 pandemic in 2020, cases of infection and deaths from severe disease were few compared with those of Western countries. However, in the medical field, screening for COVID-19 was given top priority, resulting in confusion and proving disadvantageous for many patients, also the overreaction to COVID-19 as the most important issue in society can be attributed largely to statements by infectious disease experts. In addition, the mRNA vaccine emerged in 2021, and most of the population was vaccinated up to two times within a short period of less than 1 year because infectious disease experts strongly promoted vaccination. After 2022, when vaccination progressed, and the Omicron strain, which is an attenuated strain, became the mainstay of the SARS-CoV-2, the number of severe cases of COVID-19 decreased significantly; however, the number of infected people increased dramatically instead. A significant portion of the population is thought to have hybrid immunity due to vaccination plus natural infection and maintains high antibody titers. Henceforth, additional vaccination should be given preferentially to those who will benefit most from it. Conversely, measures against COVID-19 caused serious damage to the economy and society. Policies that not only address countermeasures against infection, but also those that encompass the economy and society as a whole are necessary.

Fake News Detection and Behavioral Analysis: Case of COVID-19 (preprint)

While the world has been combating COVID-19 for over three years, an ongoing "Infodemic" due to the spread of fake news regarding the pandemic has also been a global issue. The existence of the fake news impact different aspect of our daily lives, including politics, public health, economic activities, etc. Readers could mistake fake news for real news, and consequently have less access to authentic information. This phenomenon will likely cause confusion of citizens and conflicts in society. Currently, there are major challenges in fake news research. It is challenging to accurately identify fake news data in social media posts. In-time human identification is infeasible as the amount of the fake news data is overwhelming. Besides, topics discussed in fake news are hard to identify due to their similarity to real news. The goal of this paper is to identify fake news on social media to help stop the spread. We present Deep Learning approaches and an ensemble approach for fake news detection. Our detection models achieved higher accuracy than previous studies. The ensemble approach further improved the detection performance. We discovered feature differences between fake news and real news items. When we added them into the sentence embeddings, we found that they affected the model performance. We applied a hybrid method and built models for recognizing topics from posts. We found half of the identified topics were overlapping in fake news and real news, which could increase confusion in the population.

Dual-role nurse interpreter perceptions of language barriers and Spanish-speaking patients: A qualitative study.

AIM: As the United States population is ever changing and is growing in diverse population patterns, the health care system is called to initiate responsive health care practices that are based on the public's changing and diverse cultural patterns. This study sought to explore the perceptions of certified medical interpreter dual-role nurses and their experiences with Spanish-speaking patients from admission to discharge in hospital stays. DESIGN: A qualitative descriptive case study was applied in this study. METHODS: Data was collected from nurses working at a United States Southwest Borderland hospital using purposive sampling and semi-structured in-depth interviews. A total of four dual-role nurses participated, and thematic narrative analysis was applied. RESULTS: Four major themes emerged. The main themes were "being a dual-role nurse interpreter," "patient experience," "culture and competence, "and "nursing and caring," With each major theme multiple sub themes emerged. Two sub themes emerged with "being a dual-role nurse interpreter," and two sub themes emerged with "patient experiences." The major themes that emerged from the interviews indicated that the language barrier greatly affects Spanish-speaking patients in their hospital stay. Participants reported having at least one encounter with a Spanish-speaking patient that was not afforded interpretation services or had interpretation from someone other than a qualified interpreter. Patients experienced confusion, apprehension and anger associated with not being able to communicate their needs to the healthcare system. CONCLUSIONS: According to the experiences of the certified dual-role nurse interpreters, having a language barrier makes a tremendous impact on the care of Spanish-speaking patients. Nurse participants describe how patients and their family members experience dissatisfaction, anger and confusion when there is a language barrier and most importantly how language barriers have detrimental effects on patients with wrong medication prescriptions and wrong diagnosis. CLINICAL RELEVANCE: When hospital administration recognize and support nurses as certified medical interpreters as a key component for patient care when assisting persons with limited English proficiency, patients are empowered to become active members of their healthcare regimen. The role of dual-role nurses enables brokering between the healthcare system and serves as a tool to bridge health disparities based on linguistic inequities existing in healthcare. Recruitment and retention of certified medical interpreter trained Spanish-speaking nurses deter errors in healthcare and makes a positive impact on the healthcare regimen of Spanish-speaking patients enabling patient empowerment through education and advocacy.

At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study (preprint)

By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Understanding Health System Resilience in responding to a pandemic: experience and lessons from an evolving context of federalization in Nepal (preprint)

Introduction The COVID-19 pandemic has tested the resilience capacities of health systems worldwide and has highlighted the need to understand the concept, pathways, and elements to resilience in different country contexts. In this study, we assessed the health system response to COVID-19 and examined the processes of policy formulation, communication and implementation at the three tiers of government in Nepal, including the dynamic interactions between tiers. Nepal was experiencing the early stages of federalization reform when COVID-19 pandemic hit the country and clarity in roles and capacity to implement functions were the prevailing challenges especially among the subnational governments.Methods We adopted a cross-sectional exploratory design, using mixed methods. We carried out a document review of all policy documents introduced in response to COVID-19 from January-December 2020, and collected qualitative data through 22 key informant interviews at three tiers of government, during January-March 2021. Two municipalities were purposively selected for data collection in Lumbini province. Our analysis is based on a resilience framework that has been developed by our research project, ReBUILD for Resilience, which helps to understand pathways to health system resilience through absorption, adaptation and transformation.Results In the newly established federal structure, the existing emergency response structure and plans were utilized, which were yet to be tested in the decentralized structure. Federal government effectively led the policy formulation process, with minimal engagement of sub-national governments. The local governments did not demonstrate resilience capacities, due to the novelty of the federal system and their consequent lack of experience, confusion on roles, insufficient management capacity and governance structures at local level and limited availability of human, technical and financial resources.Conclusions The study findings emphasize the importance of strong and flexible governance structures and strengthened capacity of subnational governments to effectively manage pandemics. The study elaborates on the key areas and pathways that contribute to resilience capacities of health systems from the experience of Nepal. We draw out lessons for other fragile and shock-prone settings.

Neurological Manifestations in COVID-19 Infection: A Systematic Review and Meta-Analysis.

BACKGROUND: Growing evidence showed that coronavirus disease 2019 (COVID-19) infection may present with neurological manifestations. This review aimed to determine the neurological manifestations and complications in COVID-19. METHODS: We conducted a systematic review and meta-analysis that included cohort and case series/reports involving a population of patients confirmed with COVID-19 infection and their neurologic manifestations. We searched the following electronic databases until April 18, 2020: PubMed, Embase, Scopus, and World Health Organization database (PROSPERO registration number: CRD42020180658). RESULTS: From 403 articles identified, 49 studies involving a total of 6,335 confirmed COVID-19 cases were included. The random-effects modeling analysis for each neurological symptom showed the following proportional point estimates with 95% confidence intervals: "headache" (0.12; 0.10-0.14; I2 = 77%), "dizziness" (0.08; 0.05-0.12; I2 = 82%), "headache and dizziness" (0.09; 0.06-0.13; I2 = 0%), "nausea" (0.07; 0.04-0.11; I2 = 79%), "vomiting" (0.05; 0.03-0.08; I2 = 74%), "nausea and vomiting" (0.06; 0.03-0.11; I2 = 83%), "confusion" (0.05; 0.02-0.14; I2 = 86%), and "myalgia" (0.21; 0.18-0.25; I2 = 85%). The most common neurological complication associated with COVID-19 infection was vascular disorders (n = 23); other associated conditions were encephalopathy (n = 3), encephalitis (n = 1), oculomotor nerve palsy (n = 1), isolated sudden-onset anosmia (n = 1), Guillain-Barré syndrome (n = 1), and Miller-Fisher syndrome (n = 2). Most patients with neurological complications survived (n = 14); a considerable number of patients died (n = 7); and the rest had unclear outcomes (n = 12). CONCLUSION: This review revealed that neurologic involvement may manifest in COVID-19 infection. What has initially been thought of as a primarily respiratory illness has evolved into a wide-ranging multi-organ disease.

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany (preprint)

The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.

Deprivation effect on COVID-19 cases incidence and severity: a geo-epidemiological study in PACA region, France (preprint)

IntroductionThe spread of the COVID-19 pandemic, and its severity, is spatially heterogenous. At the individual level, the socioeconomic status (SES) profile is known to be associated with COVID-19 incidence and severity. The aim of this geo epidemiological study was to investigate the link between SES profile and potential confounders, and COVID-19 incidence and hospitalization rates, at a fine geographical scale. MethodsWe analyzed COVID-19 incidence and severity during two epidemic waves between September 2020 and June 2021, in Provence Alpes Cotes dAzur, a 5 million inhabitants French region. The region is divided into sub-municipal areas that we have classified according to their SES profile. We then conducted a spatial analysis of COVID-19 indicators depending on SES profile, age structure, and health services provision. This analysis considered spatial autocorrelation between areas. ResultsCOVID-19 incidence rates in more deprived areas were similar to those in wealthiest ones. Hospitalization rates of COVID-19 cases in conventional care units were greater in more deprived vs wealthiest areas: Standardized Incidence Ratio (SIR) were respectively 1.34 [95% confidence interval 1.18 - 1.52] and 1.25 [1.13 - 1.38] depending on the epidemic wave. This gap was even greater regarding hospitalization rates of cases in critical care units: SIR = 1.64 [1.30 - 2.07] then 1.33 [1.14 - 1.55] depending on the epidemic wave. Hospitalization rates of COVID-19 cases in conventional care units were also greater in areas with high proportion of elderly people vs young people: SIR respectively 1.24 [1.11 - 1.38] and 1.22 [1.13 - 1.32] depending on the wave. ConclusionConsidering age structure and health services provision, a deprived SES profile is associated to a greater COVID-19 severity in terms of hospitals admissions, in conventional care units and in critical care units. This result implies targeting risk prevention efforts on these areas in pandemic situations, and highlights the need to develop access to healthcare to deprived populations in anticipation of periods of crisis. Key messagesWhat is already known on this topic - Socioeconomic status is associated to COVID-19 incidence and severity, at an individual scale or at a large spatial scale. What this study adds - We showed the positive relationship between deprivation and COVID-19 incidence and hospitalization rates at a fine sub-municipal geographical scale. We considered confusion factors like demographic structure and health services provision. How this study might affect research, practice or policy - These findings may help predict at a fine scale where the impact will be most severe in pandemic situations and make it possible to target risk prevention efforts on these areas.

Misdiagnosis of multisystem inflammatory syndrome in children: A diagnostic challenge.

AIMS: As the COVID-19 pandemic continues, multisystem inflammatory syndrome in children (MIS-C) maintains its importance in the differential diagnosis of common febrile diseases. MIS-C should be promptly diagnosed because corticosteroid and/or intravenous immunoglobulin treatment can prevent severe clinical outcomes. In this study, we aimed to evaluate clinical presentation, diagnostic parameters and management of MIS-C and compare its clinical features to those of common febrile disease. METHODS: This study was conducted at a tertiary-level university hospital between December 2020 and October 2022. One hundred and six children who were initially considered to have MIS-C disease were included in the study. During the follow-up period in the hospital, when the clinical and laboratory findings were re-evaluated, 38 out of 106 children were diagnosed differently. The clinical and laboratory findings of 68 children followed up with the diagnosis of MIS-C and 38 children who were initially misdiagnosed as MIS-C but with different final diagnoses were retrospectively compared. RESULTS: We identified 68 patients with MIS-C and 38 patients misdiagnosed as MIS-C during the study period. Infectious causes (71%), predominantly bacterial origin, were the most frequently confused conditions with MIS-C. Patients with MIS-C were older and had a more severe clinical course with high rates of respiratory distress, shock, and paediatric intensive care unit admission. While rash and conjunctivitis were more common among patients with MIS-C, cough, abdominal pain and diarrhoea were observed more frequently in patients misdiagnosed as MIS-C. Lower absolute lymphocyte counts, platelet counts and higher C-reactive protein and fibrinogen levels, pathological findings on echocardiography were the distinctive laboratory parameters for MIS-C. Multivariate analysis showed that older age, presence of conjunctivitis, high level of serum CRP and lower platelets were the most discriminative predictors for the diagnosis of MIS-C. CONCLUSION: There are still no specific findings to diagnose MIS-C, which therefore can be confused with different clinical conditions. Further data are needed to assist the clinician in the differential diagnosis of MIS-C and the diagnostic criteria should be updated over time.

Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective (preprint)

The COVID-19 pandemic has undoubtedly changed the standards and affected all aspects of our lives, especially social communication. It has forced people to extensively wear medical face masks, in order to prevent transmission. This face occlusion can strongly irritate emotional reading from the face and urges us to incorporate the whole body as an emotional cue. In this paper, we conduct insightful studies about the effect of face occlusion on emotion recognition performance, and showcase the superiority of full body input over the plain masked face. We utilize a deep learning model based on the Temporal Segment Network framework, and aspire to fully overcome the face mask consequences. Although facial and bodily features can be learned from a single input, this may lead to irrelevant information confusion. By processing those features separately and fusing their prediction scores, we are more effectively taking advantage of both modalities. This framework also naturally supports temporal modeling, by mingling information among neighboring frames. In combination, these techniques form an effective system capable of tackling emotion recognition difficulties, caused by safety protocols applied in crucial areas.