A rapid decrease in viral gastroenteritis during winter 2019C20 and a return of norovirus and rotavirus activity during winter 2020C21 were observed while multiple nonpharmaceutical interventions for coronavirus disease were in effect in Hong Kong. The initial collateral benefit of coronavirus disease countermeasures that reduced the viral gastroenteritis burden is not sustainable.

BACKGROUND: Diabetic retinopathy, as a severe public health problem associated with vision loss, should be diagnosed early using an accurate screening tool. While many previous deep learning models have been proposed for this disease, they need sufficient professional annotation data to train the model, requiring more expensive and time-consuming screening skills. METHOD: This study aims to economize manual power and proposes a deep graph correlation network (DGCN) to develop automated diabetic retinopathy grading without any professional annotations. DGCN involves the novel deep learning algorithm of a graph convolutional network to exploit inherent correlations from independent retinal image features learned by a convolutional neural network. Three designed loss functions of graph-center, pseudo-contrastive, and transformation-invariant constrain the optimisation and application of the DGCN model in an automated diabetic retinopathy grading task. RESULTS: To evaluate the DGCN model, this study employed EyePACS-1 and Messidor-2 sets to perform grading results. It achieved an accuracy of 89.9% (91.8%), sensitivity of 88.2% (90.2%), and specificity of 91.3% (93.0%) on EyePACS-1 (Messidor-2) data set with a confidence index of 95% and commendable effectiveness on receiver operating characteristic (ROC) curve and t-SNE plots. CONCLUSION: The grading capability of this study is close to that of retina specialists, but superior to that of trained graders, which demonstrates that the proposed DGCN provides an innovative route for automated diabetic retinopathy grading and other computer-aided diagnostic systems.

BACKGROUND: Neurocritical care is a growing subspecialty. It concerns with the management of life-threatening neurological disorders. There is limited information regarding epidemiological data, disease characteristics, variability of clinical care, and in-hospital mortality of neurocritical patients worldwide. OBJECTIVES: To study the pattern of neurocritical disorders in intensive care units. METHODOLOGY: This prospective observational study was conducted on neurocritical patients who were admitted to four intensive care units of major hospitals in Khartoum state during the period from November 2020 to January 2021. RESULTS: Seventy-two neurocritical patients were included in this study, 40 (55.6%) were males and 32(44.4%) were females. Twenty-three (31.9%) patients were with stroke, 12 (16.7%) with encephalitis, 9 (12.5%) with status epilepticus, 6 (8.3%) with Guillain Barre syndrome, and 4(5.6%) with Myasthenia Gravis (MG). Twenty-three patients (39.9%) needed mechanical ventilation (MV), which was the major indication for intensive care unit admission. CONCLUSION: Stroke was the dominant diagnostic pattern requiring intensive care unit admission. Mechanical ventilation was the major indication for admission. Establishing specialized neurocritical intensive care units is highly recommended.

The current pandemic has changed the social life and learning of most children and adolescents around the world due to public health restrictions In response to this, they have spent more time on computer and television monitor screens, which should negatively impact their eye health Thus, here we discuss the possible association between the imposed home confinement and risk of developing 'quarantine myopia' in children and adolescents

Abstract Objectives The aim of the study was to investigate differences in the frequency and types of engagement in sport before, during, and after the COVID-19 stay-at-home order in Tyrol, Austria. Study design A representative population survey was conducted. Methods A sample of Tyroleans (N = 511) were questioned by a market research institute via online questionnaire or telephone survey. Results During the stay-at-home order survey participants did less sport than before and after the restrictions. However, within-group analyses revealed increasing sport participation in less active groups when comparing the pre- and post-COVID-19 period. Conclusions Despite the restrictions during the stay-at-home order, respondents did sport and promoted their health. Nevertheless, it is still necessary to investigate the long-term effects of the COVID-19 crisis on sport and exercise behaviour as well as the extent to which sport policy measures may be able increase sport participation.

OBJECTIVES: This paper looks at how trends in contraceptive prescribing by General Practices in England were affected by the Covid pandemic and lockdown. It compares English prescribing data from May 2014 to May 2021, including the period of 'lockdown', from April 20-June 20. DESIGN & SETTING: A retrospective analysis of the English Prescribing Dataset which reports monthly on prescribed items from English General Practices was carried out. Data on all forms of prescribed contraceptive methods were extracted using British National Formulary (BNF) codes, and total quantities tabulated by method, then transformed into 'months of contraception provided' by each method. RESULTS: Prescription of the combined oral contraceptive pill reduced by 22% during the period of lockdown compared to the same three months in 2019. Prescriptions of Progestogen-Only pills remained stable. This continued a trend in oral contraceptive prescribing evident from May14. Prescription of long-acting methods reduced during the period of lockdown, with the greatest reductions in implants (76% reduction from pre-lockdown levels), intra-uterine systems (79% reduction from pre-lockdown levels) and intrauterine devices (76% reduction from pre-lockdown levels). These rates of contraceptive provision recovered quickly after the period of lockdown ended. CONCLUSIONS: The disruption of face-to-face contraceptive consultations in General Practice during a Covid-19 'lockdown' has resulted in a reduction in oestrogen -containing methods compared to progestogen-only methods, which require less face-to-face monitoring. Implant and intrauterine contraceptive device prescription reduced by three quarters over the first three months of lockdown, but rebounded in the next year.

BACKGROUND: The aim of study was to evaluate the retinal layers and macular capillary network with OCTA in acromegaly patients, to compare with healthy population. METHODS: In this prospective, observational, and comparative study, 40 acromegaly patients and 40 healthy control participants were included. Serum IGF-1 levels and disease duration of all patients were noted. Macular layers and angiography scanning was performed with a Zeiss Cirrus 5000 OCTA system. Macular thickness, RNFL, and GC-IPL values were obtained. For central vessel and perfusion density, central 6 mm was obtained and was evaluated by dividing into three groups (inner, outer, full). FAZ parameters were evaluated dividing into three groups (area, perimeter, circularity index). Analysis of the data was performed with the SPSS for Windows. RESULTS: There was no significant difference between the patient group and the control group in terms of age, gender, best corrected visual acuity (BCVA), spherical equivalent (SE), intraocular pressure (IOP), and axial length (AL). The mean follow-up period after diagnosis was 11.0 5.5 years. Central and mean macular thicknesses were also significantly higher in the acromegaly group (p < 0.05). Superior, inferior, and average RNFL thicknesses were also significantly thinner in the acromegaly group (p < 0.05). When OCTA parameters were compared between groups, there was a significant decrease in central vessel density (CVD) and central perfusion density (CPD) values in all regions in acromegaly group compared with controls (p < 0.05). CONCLUSION: Our findings with OCTA show that acromegaly causes a significant capillary network decrease according to the healthy subjects.

The aim of the study was to evaluate the longitudinal changes in mental well-being during the switch of the intervention from a personal contact to a remotely delivered Mental Health Prevention and Promotion Programme in elderly women at high risk of developing depression. The study included 70 women aged over 60 with a mean age of 72.28 years. In order to determine mental well-being, the Geriatric Depression Scale (GDS) was used at four time points: January, April, September and December 2020. A self-developed questionnaire was used to determine predictors of mental well-being. Two-way ANOVA demonstrated a lack of significant differences between the means in the GDS scores at the four data collection time points (p = 0.21). Frequent use of green areas reduced the GDS score on average by 1.52 points (p = 0.01), while owning a garden by 1.51 points (p = 0.04). The illness of a family member increased the GDS score by an average of 1.7 points (p = 0.02). No significant mood deterioration was found between January 2020 and December 2020 in the studied group of elderly women at a high risk of developing depression, which suggests that the remote support provided in the mental health promotion programme was effective.

COVID-19 reported cases in Malaysia is increasing every day. The lab facilities for testing COVID-19 has almost reached their capacity. Traditionally, to detect the COVID-19, a swab test is used. This method will take some time and be costly. The swab test kits are very scarce, and the human resources to do this test are limited. Through modern technology, there is a probability of detecting positive COVID-19 using X-ray images with deep learning. In this paper, Convolutional Neural Network (CNN) approach is used to detect Covid-19 through digital X-ray images. The 2D convolution kernel layer consists of three layers. The first layer has a 3 3 kernel, the second part has a 5 5 kernel, and the third part has a 7 7 kernel. Then, the output will be combined into one layer. Afterwards, the concatenated layer continued with another sequential process consisting of two convolution processes, ReLU and max pooling. Next, the model is then flattened, dropout and dense. A total of 2100 positive Covid-19 and negative Covid-19 images from Github and Kaggle databases have been used in this research. Based on the experiment done, the accuracy was almost 96%. ? 2021 IEEE.

Background There are currently no data available that compares the volume and the walk-out rate of patients who presented to the emergency department (ED) before and after the coronavirus disease 2019 (COVID-19) peak pandemic period during the USA national lockdown. Objective EDs measure metrics such as walk-out rate (WOR), which is reflected in leaving against medical advice (AMA), elopement, left after triage (LAFT), and left without being seen (LWBS). In this study, we sought to determine the effects of the COVID-19 pandemic on the ED WOR. Methods A retrospective analysis was used to assess the overall ED census and the walk-out rate for three community EDs during the March to June 2020 national lockdown. This period was compared to the same period for 2019. Results The walk-out rate during the COVID-19 pandemic was inversely proportional to the overall ED census. While overall ED visits decreased by 26% to 42% during the period of March to June 2020 as compared to March to June 2019, the patient walk-out rates increased from 25% to 65%. Conclusion The national lockdown during the COVID-19 pandemic created a paradoxical finding with a decrease in ED census but an increase in ED walk-out rates. This decline in census and increase in WOR raises concern that patients avoid going to the hospitals to seek medical attention because the concern of contracting the virus prevails, resulting in increased walkouts. With this retrospective study, we can anticipate that future newly discovered emerging diseases causing national lockdowns will result in a high probability of avoidance of emergency care.

In 2015, an outbreak of Middle East respiratory syndrome coronavirus occurred in Saudi Arabia and necessitated special measures to be implemented to control the spread of the virus. In this article, we will discuss how the outbreak was managed in the vascular and interventional radiology (VIR) suite in a large tertiary care hospital in Saudi Arabia. Various measures were taken to reduce the risk of transmission of infection. Unit-level education played an important role in the care of patients. A hospital-wide educational program was implemented to ensure zero transmission of infection. Special attention was made to monitor staff who acquired the virus. VIR suite was able to handle the situation and control the outbreak.

The presence of CD4+ T cells in the healthy brain parenchyma remains controversial due to the barrier function of the glia limitans. Pasciuto, Burton, Roca et al. in Cell describe the dynamic recruitment of CD4+ T cells within the brain parenchyma, their unexpected contribution to microglial maturation, and, ultimately, their influence on behavior.

Background: Covid-19 has started to spread within China since the end of December 2019. Despite governments immediate actions and strict control, more and more people were infected every day. As such a contagious virus can spread easily and rapidly between people, the whole country was put into lockdown and people were forced into isolation. In order to understand the impact of Covid-19 on mental health well-being, Chinese researchers have conducted several studies. However, no consistent results were obtained. Therefore, a meta-analysis was conducted. Methods: We searched Embase, PubMed, and Web of Science databases to find literature from December 2019 to April 2020 related to Covid-19 and mental health, among which results such as comments, letters, reviews and case reports were excluded. The incidence of anxiety and depression in the population was synthesized and discussed. Results: A total of 27,475 subjects were included in 12 studies. Random effect model is used to account for the data. The results showed that the incidence of anxiety was 25% (95% CI: 0.19C0.32), and the incidence of depression was 28% (95% CI: 0.17C0.38). Significant heterogeneity was detected across studies regarding these incidence estimates. Subgroup analysis included the study population and assessment tools, and sensitivity analysis was done to explore the sources of heterogeneity. Conclusions: Owing to the significant heterogeneity detected in studies regarding this pooled prevalence of anxiety and depression, we must interpret the results with caution. As the epidemic is ongoing, it is vital to set up a comprehensive crisis prevention system, which integrating epidemiological monitoring, screening and psychological crisis prevention and interventions.

Since December 2019, a pandemic caused by a new coronavirus has spread to more than 170 countries around the world. Worsening infected patients requiring intensive care unit (ICU) admission, associated with 30% of mortality. A part of worsening is mediated by haemostasis deregulation. The primary aim of this study was to determine if haematological biomarkers, in addition to clinical risk factors on hospital admission, predict worsening (defined by ICU admission and/or death) in Covid-19 infected patients, and secondary, if they could predict the occurrence of thrombotic events. Thirty-five of the 99 patients got clinically worse and 8 developed pulmonary embolism (PE). Final model of the logistic regression analysis revealed that oxygenodependence (RR=7.27[1.50-19.31]), fibrinogen levels (RR=1.45[1.17-1.82]), thrombin peak (RR=1.28[1.03-1.59]), monocytes counts below 0.2 G/L (RR=2.88[1.67-3.19]) and prothrombin fragment 1+2 (F1+2) higher than 290 pM (RR=2.39[1.20-3.30]) were associated with clinical worsening. Fibrinogen level threshold of 5.5 g/L, thrombin peak measurement threshold of 99 pM and oxygenodepence allowed prediction of clinical outcome in near than 80% of our cohort. Moreover, using ROC curves, thrombin peak and F1+2 on admission with a threshold of 204 nM and 393 pM were sensitive and specific to predict PE (AUC: 85.7% Se: 70.8% Sp: 100% and AUC: 81.5% Se: 75.0% Sp: 86.8% respectively). In conclusion, we described a rapid decision tree to predict outcome of SARS-CoV-2 infected patients based on fibrinogen, TGA peak and oxygen dependence. Furthermore, thrombin peak and F1+2 seem to be specific haematological marker to predict PE, even in patients with thromboprophylaxis.

This chapter describes the musculoskeletal (MSK) context in children and young people as an important contributor to the global non-communicable disease burden. Through selected MSK conditions, we describe the impact on patients, families and communities and highlight the challenges that need to be addressed. We focus on opportunities for better working together and describe exemplar initiatives to raise awareness, workforce capacity building, models of care and research agendas to have a greater global context.

In this paper we reflect on a number of IT related challenges during the COVID19 pandemic, primarily from a CIO and IT professionals perspective. We consider three time periods, namely the period before the pandemic, the response to the pandemic and the period after it. For each period we discuss the key challenges that practitioners faced and outline important areas to consider for the future. Hopefully, the lessons learnt and the experiences gained will positively inform future academic research and practice.

Background: The coronavirus disease 2019 (COVID 19) is a zoonotic viral infection that originated in Wuhan, China, in December 2019. It was declared a pandemic by the World Health Organization shortly thereafter. This pandemic is going to have a lasting impact on the functioning of pathology laboratories due to the frequent handling of potentially infectious samples by the laboratory personnel. To deal with this unprecedented situation, various national and international guidelines have been put forward outlining the precautions to be taken during sample processing from a potentially infectious patient. Purpose: Most of these guidelines are centered around laboratories that are a part of designated COVID 19 hospitals. However, proper protocols need to be in place in all laboratories, irrespective of whether they are a part of COVID 19 hospital or not as this would greatly reduce the risk of exposure of laboratory/hospital personnel. As part of a laboratory associated with a rural cancer hospital which is not a dedicated COVID 19 hospital, we aim to present our institute's experience in handling pathology specimens during the COVID 19 era. Conclusion: We hope this will address the concerns of small to medium sized laboratories and help them build an effective strategy required for protecting the laboratory personnel from risk of exposure and also ensure smooth and optimum functioning of the laboratory services.

Data accumulation in public databases has resulted in extensive use of meta-analysis, a statistical analysis that combines the results of multiple studies. Oxidative stress occurs when there is an imbalance between free radical activity and antioxidant activity, which can be studied in insects by transcriptome analysis. This study aimed to apply a meta-analysis approach to evaluate insect oxidative transcriptomes using publicly available data. We collected oxidative stress response-related RNA sequencing (RNA-seq) data for a wide variety of insect species, mainly from public gene expression databases, by manual curation. Only RNA-seq data of Drosophila melanogaster were found and were systematically analyzed using a newly developed RNA-seq analysis workflow for species without a reference genome sequence. The results were evaluated by two metric methods to construct a reference dataset for oxidative stress response studies. Many genes were found to be downregulated under oxidative stress and related to organ system process (GO:0003008) and adherens junction organization (GO:0034332) by gene enrichment analysis. A cross-species analysis was also performed. RNA-seq data of Caenorhabditis elegans were curated, since no RNA-seq data of insect species are currently available in public databases. This method, including the workflow developed, represents a powerful tool for deciphering conserved networks in oxidative stress response.

We analyse prior risk factors for severe, critical or fatal courses of Covid-19 based on a retrospective cohort using claims data of the AOK Bayern. As our main methodological contribution, we avoid prior grouping and pre-selection of candidate risk factors. Instead, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. Our approach has better predictive ability than well-specified morbidity groups but does not need prior subject-matter knowledge. The methodology and estimated coefficients are made available to decision makers to prioritize protective measures towards vulnerable subpopulations and to researchers who like to adjust for a large set of confounders in studies of individual risk factors.

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the users locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.