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The coronavirus disease 2019 (COVID-19) pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is causing considerable morbidity and mortality worldwide. Multiple reports have suggested that patients with heart failure (HF) are at a higher risk of severe disease and mortality with COVID-19. Moreover, evaluating and treating HF patients with comorbid COVID-19 represents a formidable clinical challenge as symptoms of both conditions may overlap and they may potentiate each other. Limited data exist regarding comprehensive management of HF patients with concomitant COVID-19. Since these issues pose serious new challenges for clinicians worldwide, HF specialists must develop a structured approach to the care of patients with COVID-19 and be included early in the care of these patients. Therefore, the Heart Failure Association of the European Society of Cardiology and the Chinese Heart Failure Association & National Heart Failure Committee conducted web-based meetings to discuss these unique clinical challenges and reach a consensus opinion to help providers worldwide deliver better patient care. The main objective of this position paper is to outline the management of HF patients with concomitant COVID-19 based on the available data and personal experiences of physicians from Asia, Europe and the United States. [ABSTRACT FROM AUTHOR]

Since the first case of COVID‐19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID‐19 and COVID‐19 findings from computed tomography images using pre‐trained models using two different datasets. COVID‐19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy‐paving pattern, ground‐glass opacity, ground‐glass opacity and consolidation, ground‐glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre‐trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient‐weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID‐19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID‐19 in the lungs was an innovative and successful approach. [ABSTRACT FROM AUTHOR]

A diagnosis of paroxysmal nocturnal hemoglobinuria (PNH) was elicited during acute COVID-19 infection. COVID-19 spike proteins trigger the alternative pathway of complement. Acute SARS-CoV-2 infection possibly expanded an existing PIG-A mutation. [ABSTRACT FROM AUTHOR]

By means of the CNN, the article 'Medical image analysis of multiple myeloma based on convolutional neural network', provides the application of neural network algorithm in multiple myeloma (He & Zhang, 2022). Corona virus disease 19 (COVID-19) is an infectious disease which is having a significant health and economic impact across the world. In the future development of intelligent medical imaging technology, artificial neural networks will be trained to perform medical image recognition and diagnosis tasks, which can reduce diagnosis time and improve diagnosis efficiency. [Extracted from the article]

Background: Sjögren's ('SHOW‐grins') is a chronic debilitating autoimmune disease characterised by dry eyes and dry mouth, secondary to reduced exocrine function of both the lacrimal and salivary glands. The persistent, severe and serious systemic complications of Sjögren's are poorly understood and often unappreciated, resulting in significant morbidity and treatment burden. This study aimed to explore the experiences of those living with Sjögren's, specifically access to healthcare and attitude towards telemedicine. Additionally, we sought to collect information regarding the impact of the pandemic on their quality of life (QoL). Methods: One hundred and ninety‐four individuals attended an Irish Sjögren's Webinar. Attendees were invited to participate in two online surveys after the webinar. The first survey gathered information related to demographics, disease and experiences during the COVID‐19 pandemic. A combination of bespoke items and validated questionnaires (EULAR Sjögren's Syndrome Patient Reported Index [ESSPRI], COVID‐19 Impact on Quality of Life [COV19‐QoL]) was used. The second survey consisted of a shortened Telehealth Usability Questionnaire. Both were prepared in collaboration with a patient advocate. Results: Survey 1: n = 76; response rate = 39.2%. Thirty‐one respondents (41.4%) to survey 1 reported a delay of ≥5 years between the onset of symptoms and diagnosis. Dry mouth was the most common symptom experienced (76.8%, n = 63), followed by dry eye (74.4%, n = 61), fatigue (57.3%, n = 47) and joint pain (53.7%, n = 44), but a range of other symptoms were also reported. COV19‐QoL results indicated that the pandemic had a detrimental effect on participants' overall QoL (4.0 ± 1.0) and physical health (4.0 ± 0.8) in particular. COV19‐QoL and ESSPRI scores were moderately correlated (0.36, p =.002). Over 70% of respondents had a medical appointment cancelled, delayed or rescheduled (n = 60). Survey 2: n = 57; response rate = 29.4%. Those that had interacted with telemedicine reported largely positive experiences with the virtual model. Conclusion: Clinicians should be aware of the range of symptoms experienced by patients with Sjögren's beyond those of sicca (dry eye and dry mouth) and fatigue. COVID‐19 has negatively influenced the self‐reported health and well‐being of those with Sjögren's, particularly those with higher symptom scores. It is vital that optimised telemedicine models are implemented to ensure continuity in the provision of healthcare for those with chronic illness such as Sjögren's and in preparation for possible future pandemics. Patient or Public Contribution: A group of people living with Sjögren's co‐designed the structure and content of the webinar where the survey was shared. A public and patient involvement (PPI) contributor also collaborated in the selection of questionnaires used in the study, ensuring that the questions asked would best reflect the priorities of patients. They contributed to the writing of this manuscript as co‐authors. Additionally, the research team and Sjögren's patients who contributed to this work have gone on to establish Sjögren's Research Ireland, a collaboration between patient advocates, researchers and PPI facilitators. [ABSTRACT FROM AUTHOR]

The global economy has been dramatically impacted by COVID‐19, which has spread to be a pandemic. COVID‐19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription‐polymerase chain reaction (RT‐PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID‐19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID‐19 having an accuracy, sensitivity, specificity, and F1‐score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase‐chain reaction (RT‐PCR) test has been used for COVID‐19 testing its reliability is very low. Therefore, computed tomography and X‐ray images have been widely used. Artificial intelligence coupled with X‐ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine‐tuned deep learning architectures has been made to speed up the detection and classification of COVID‐19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine‐tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset‐1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset‐2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X‐ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%. [ABSTRACT FROM AUTHOR]

We aim to contribute to improving the management of pandemic crises. Our focus is on federal systems, as these are particularly powerful in dealing with environmental complexity. Through four waves of the pandemic, spanning a year and a half, we studied crisis management based on the case of the Swiss Federation. This research concentrates on three research questions: (1) How is the management of the Covid‐19‐Crisis organized in Switzerland? (2) How effective is this organization in coping with the dynamics of emergency? (3) What lessons can be learned for the design of a crisis management in the future? We apply the Viable System Model (VSM) as a framework for our study, elaborating a diagnosis and a design for the management of epidemic or pandemic crises. The VSM is a conceptual tool that is particularly strong for analysing federal systems. Hence, substantial insights have surfaced to orientate a future crisis management. [ABSTRACT FROM AUTHOR]

Major changes have occurred in these last years in heart failure (HF) management. Landmark trials and the 2021 European Society of Cardiology guidelines for the diagnosis and treatment of HF have established four classes of drugs for treatment of HF with reduced ejection fraction: angiotensin‐converting enzyme inhibitors or an angiotensin receptor‐neprilysin inhibitor, beta‐blockers, mineralocorticoid receptor antagonists, and sodium‐glucose co‐transporter 2 inhibitors, namely, dapagliflozin or empagliflozin. These drugs consistently showed benefits on mortality, HF hospitalizations, and quality of life. Correction of iron deficiency is indicated to improve symptoms and reduce HF hospitalizations. AFFIRM‐AHF showed 26% reduction in total HF hospitalizations with ferric carboxymaltose vs. placebo in patients hospitalized for acute HF (P = 0.013). The guanylate cyclase activator vericiguat and the myosin activator omecamtiv mecarbil improved outcomes in randomized placebo‐controlled trials, and vericiguat is now approved for clinical practice. Treatment of HF with preserved ejection fraction (HFpEF) was a major unmet clinical need until this year when the results of EMPEROR‐Preserved (EMPagliflozin outcomE tRial in Patients With chrOnic HFpEF) were issued. Compared with placebo, empagliflozin reduced by 21% (hazard ratio, 0.79; 95% confidence interval, 0.69 to 0.90; P < 0.001), the primary outcome of cardiovascular death or HF hospitalization. Advances in the treatment of specific phenotypes of HF, including atrial fibrillation, valvular heart disease, cardiomyopathies, cardiac amyloidosis, and cancer‐related HF, also occurred. Coronavirus disease 2019 (COVID‐19) pandemic still plays a major role in HF epidemiology and management. All these aspects are highlighted in this review. [ABSTRACT FROM AUTHOR]

Objectives: Accurate estimation of the risk of SARS‐CoV‐2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13‐item CORC (COVID [or coronavirus] Rule‐out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter‐rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt. Methods: This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS‐CoV‐2 test result and confidence (0%–100%). The criterion standard for SARS‐CoV‐2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs). Results: For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579–0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591–0.697). The agreement for clinician gestalt binary determination of SARs‐CoV‐2 status was κ = 0.534 (95% CI = 0.437–0.632) with median confidence of 76% (first–third quartile = 66–88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR−) of 0.24 (95% CI = 0.10–0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS‐CoV‐2 test was 58% (95% CI = 28%–85%) and positive LR was 13.1 (95% CI = 4.5–37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR− of 0.57 (95% CI = 0.39–0.74). Conclusion: In this prospective study, the CORC score and rule demonstrated good inter‐rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs‐CoV‐2 infection. [ABSTRACT FROM AUTHOR]

Objectives: Physicians' gestalt is central in the diagnostic pipeline of suspected COVID‐19, due to the absence of a single tool allowing conclusive rule in or rule out. The aim of this study was to estimate the diagnostic test characteristics of physician's gestalt for COVID‐19 in the emergency department (ED), based on clinical findings or on a combination of clinical findings and bedside imaging results. Methods: From April 1 to April 30, 2020, patients with suspected COVID‐19 were prospectively enrolled in two EDs. Physicians prospectively dichotomized patients in COVID‐19 likely or unlikely twice: after medical evaluation of clinical features (clinical gestalt [CG]) and after evaluation of clinical features and results of lung ultrasound or chest x‐ray (clinical and bedside imaging–integrated gestalt [CBIIG]). The final diagnosis was adjudicated after independent review of 30‐day follow‐up data. Results: Among 838 ED enrolled patients, 193 (23%) were finally diagnosed with COVID‐19. The area under the curve (AUC), sensitivity, and specificity of CG and CBIIG for COVID‐19 were 80.8% and 91.6% (p < 0.01), 82.9% and 91.4% (p = 0.01), and 78.6% and 91.8% (p < 0.01), respectively. CBIIG had similar AUC and sensitivity to reverse transcription–polymerase chain reaction (RT‐PCR) for SARS‐CoV‐2 on the first nasopharyngeal swab per se (93.5%, p = 0.24; and 87%, p = 0.17, respectively). CBIIG plus RT‐PCR had a sensitivity of 98.4% for COVID‐19 (p < 0.01 vs. RT‐PCR alone) compared to 95.9% for CG plus RT‐PCR (p = 0.05). Conclusions: In suspected COVID‐19, CG and CBIIG have fair diagnostic accuracy, in line with physicians' gestalt for other acute conditions. Negative RT‐PCR plus low probability based on CBIIG can rule out COVID‐19 with a relatively low number of false‐negative cases. [ABSTRACT FROM AUTHOR]

Although presentation of COVID‐19 in patients with immunodeficiency could be mild, it should not be missed, while early diagnosis and appropriate treatment can survive infected patients. Because even severe infections in PID patients may be presented with few symptoms and signs, this diagnosis should be considered in those immunocompromised patients who have exacerbating preexisting symptoms. [ABSTRACT FROM AUTHOR]

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time-consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X-ray images of normal, pneumonia and coronavirus disease-2019 (COVID-19) patients and process them to boost the COVID-specific features (opacities etc.), which enable to perform sensitive identification of COVID-19 patients. The sets of original and processed images are used with a stack of pre-trained deep models for ensemble learning. They used VGG-16 as base-learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base-learner predictions. The proposed fusion-based model is trained and tested for three types of classification, TYPE-I: binary (NORMAL/ABNORMAL), TYPE-II: binary (PNEUMONIA/COVID-19) and TYPE-III: multi-class (NORMAL/PNEUMONIA/COVID-19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID-19 patients and thereby mitigating the effect of the virus. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) has widely spread all over the world and the numbers of patients and deaths are increasing. According to the epidemiology, virology, and clinical practice, there are varying degrees of changes in patients, involving the human body structure and function and the activity and participation. Based on the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) and its biopsychosocial model of functioning, we use the WHO Family of International Classifications (WHO‐FICs) framework to form an expert consensus on the COVID‐19 rehabilitation program, focusing on the diagnosis and evaluation of disease and functioning, and service delivery of rehabilitation, and to establish a standard rehabilitation framework, terminology system, and evaluation and intervention systems based the WHO‐FICs. [ABSTRACT FROM AUTHOR]

Background: Almost the entire World is experiencing the Coronavirus‐Disease‐2019 (COVID‐19) pandemic, responsible, at the end of May 2020, of more than five million people infected worldwide and about 350,000 deaths. In this context, a deep reorganization of allergy clinics, in order to ensure proper diagnosis and care despite of social distancing measures expose, is needed. Main text: The reorganization of allergy clinics should include programmed checks for severe and poorly controlled patients, application of digital medicine service for mild‐to‐moderate disease in well‐controlled ones, postponement of non urgent diagnostic work‐ups and domiciliation of therapies, whenever possible. As far as therapies, allergen immunotherapy (AIT) should not be stopped and sublingual immunotherapy (SLIT) fits perfectly for this purpose, since a drug home‐delivery service can be activated for the entire pandemic duration. Moreover, biologic agents for severe asthma, chronic spontaneous urticaria and atopic dermatitis should be particularly encouraged to achieve best control possible of severe disease in times of COVID‐19 and, whenever possible, home‐delivery and self‐administration should be the preferred choice. Conclusion: During COVID‐19 pandemic, allergists have the responsibility of balancing individual patients' needs with public health issues, and innovative tools, such as telemedicine and digital medicine services, can be helpful to reduce the risk of viral spreading while delivering up‐to‐date personalized care. [ABSTRACT FROM AUTHOR]