A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

RAPID IDENTIFICATION OF ETIOLOGICAL AGENTS OF RESPIRATORY TRACT INFECTIONS DURING THE COVID-19 PANDEMIC USING THE MULTIPLEX-PCR METHOD.

Objective. Most respiratory infections have similar symptoms, so it is clinically difficult to determine their etiology. This study aimed to show the importance of molecular diagnostics in identifying the etiological agent of respiratory infections, especially during the coronavirus disease 2019 (COVID-19) pandemic. Methods. A total of 849 samples from patients hospitalized at the University Clinical Center Kragujevac (from January 1 to August 1, 2022) were examined using automated multiplex-polymerase chain reaction (PCR) tests. The BioFire-FilmArray-Respiratory Panel 2.1 test was used for 742 nasopharyngeal swabs [identification of 19 viruses (including SARS-CoV-2) and four bacteria], while the BioFire-FilmArray-Pneumonia Panel was used [identification of 18 bacteria and nine viruses] (BioMerieux, Marcy l'Etoile, France) for 107 tracheal aspirates. The tests were performed according to the manufacturer's instructions, and the results were available within an hour. Results. In 582 (78.4%) samples, the BioFire-FilmArray-Respiratory Panel 2.1 plus test identified at least one pathogen. The rhinovirus (20.6%), SARS-CoV-2 (17.7%), influenza A (17.5%), respiratory syncytial virus (12.4%), and parainfluenza 3 (10.1%) were the most common. Other viruses were found less frequently, and Bordetella parapertussis was detected in one sample. In 85 (79.4%) samples, the BioFire-FilmArray-Pneumonia Panel test identified at least one bacterium or virus. The most prevalent bacteria were Staphylococcus aureus (42.4%), Haemophilus influenzae (41.2%), Streptococcus pneumoniae (36.5%), Moraxella catarrhalis (22.3%), and Legionella pneumophila (2.4%). Among viruses, rhinovirus (36.5%), adenovirus (23.5%), influenza A (11.8%), and the genus Coronavirus (4.7%), were detected. Conclusion. Multiplex-PCR tests improved the implementation of therapeutic and epidemiological measures, preventing the spread of the COVID-19 infection and Legionnaires' disease.Copyright © 2022, Serbian Medical Society. All rights reserved.

ANTIPHOSPHOLIPID ANTIBODIES AND VASCULAR THROMBOSIS IN PATIENTS WITH THE MOST SEVERE FORMS OF COVID-19

BackgroundThe presence of antiphospholipid antibodies (aPL) has been observed in patients with COVID-19 (1,2), suggesting that they may be associated with deep vein thrombosis, pulmonary embolism, or stroke in severe cases (3). Antiphospholipid syndrome (APS) is a systemic autoimmune disorder and the most common form of acquired thrombophilia globally. At least one clinical criterion, vascular thrombosis (arterial, venous or microthrombosis) or pregnancy morbidity and at least one laboratory criterion- positive aPL two times at least 12 weeks apart: lupus anticoagulant (LA), anticardiolipin (aCL), anti-β2-glycoprotein 1 (anti-β2GPI) antibody, have to be met for international APS classification criteria(4). Several reports also associate anti-phosphatidylserine/prothrombin antibodies (aPS/PT) with APS.ObjectivesTo combine clinical data on arterial/venous thrombosis and pregnancy complications before and during hospitalisation with aPL laboratory findings at 4 time points (hospital admission, worsening of COVID-19, hospital discharge, and follow-up) in patients with the most severe forms of COVID-19 infection.MethodsPatients with COVID-19 pneumonia were consequetively enrolled, as they were admitted to the General hospital Pancevo. Exclusion criteria were previous diagnosis of inflammatory rheumatic disease and diagnosis of APS. Clinical data were obtained from the medical records. Laboratory results, including LA, aCL, anti-β2GPI, and aPS/PT antibodies were taken at hospital admission, worsening (defined as cytokine storm, connection of the patient to the respirator, use of the anti-IL-6 drug- Tocilizumab), at hospital discharge and at 3-months follow-up and sent to University Medical Centre Ljubljana, Slovenia for analysis. Statistics was performed by using SPSS 21.Results111 patients with COVID-19 pneumonia were recruited;7 patients died during hospitalisation (none were aPL-positive on admission and at the time of worsening), 3 due to pulmonary artery embolism. All patients were treated according to a predefined protocol which included antibiotics, corticosteroids, anticoagulation therapy and specific comorbidity drugs;patients with hypoxia were supported with oxygen. During hospitalisation, pulmonary artery thrombosis occurred in 5 patients, one was aPL-positive at all time points (was diagnosed with APS), others were negative. In addition, 9/101 patients had a history of thrombosis (5 arterial thrombosis (coronary and cerebral arteries), none of whom was aPL-positive on admission and at follow-up, and 4 venous thrombosis, one of which was aPL-positive at all time points and received an APS diagnosis). Among 9/101 patients with a history of thrombosis, 55.6% were transiently positive at the time of discharge, compared to patients without prior thrombosis, in whom 26.1% were transiently positive at the hospital release (p=0.074). Two patients had a history of pregnancy complications (both had miscarriage after 10th week of gestation), but did not have aPL positivity at any time point.ConclusionAlthough aPL was expected to be associated with vascular disease in the most severe forms of COVID-19, all patients that have died in our cohort were aPL negative. At hospital discharge, 56% of patients with a history of arterial or venous thrombosis had positive aPL that became negative at the 3-months follow-up (were transienlty positive), which should be considered when prescribing therapy after hospitalisation.References[1]Trahtemberg U, Rottapel R, Dos Santos CC, et al. Anticardiolipin and other antiphospholipid antibodies in critically ill COVID-19 positive and negative patients. Annals of the Rheumatic Diseases 2021;80:1236-1240.[2]Stelzer M, Henes J, Saur S. The Role of Antiphospholipid Antibodies in COVID-19. Curr Rheumatol Rep. 2021;23(9):72-4.[3]Xie Y, Wang X, Yang P, Zhang S. COVID-19 complicated by acute pulmonary embolism. Radiology: Cardiothoracic Imaging 2020: 2: e200067.[4]Miyakis S, Lockshin MD, Atsumi T, Branch DW, Brey RL, et al. J.Thromb.Haemost. 2006;4: 295-306.Acknowledgements:NIL.Disclosure of nterestsNone Declared.

Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Prescription Opioid Use Among Cancer Patients before and after the Onset of COVID-19: Evidence from Real-World Data

Objectives: Opioids play a significant role in the effective management of cancer-related pain. The COVID-19 lock down may have reduced access to opioids and caused a decline in the use of prescription of opioids among cancer survivors. This study compared opioid prescription rates among cancer survivors before and after the onset of COVID-19 pandemic using real-world electronic health records (EHR). Method(s): Cohort analyses of cancer patients using data from EHR database from the TriNetX, a global federated health research network across 76 healthcare organizations. We analyzed changes in prescription opioid use before (March 1, 2018, through March 1, 2019) and after onset of COVID-19 (April 01, 2020, through March 2021) among cancer survivors. The key outcome variable was any opioid prescription within 1 year of cancer diagnosis. One-to-one propensity score matching was used to balance the characteristics (age, sex, race, diagnoses including diabetes, hypertensive diseases, overweight, mood disorders, and visual disturbances) of the two cohorts. Data were analyzed using the TriNetX platform. Result(s): There were 1,502,143 cancer survivors before COVID-19 and 1,412,599 cancer survivors after the onset of COVID-19. The one-to-one propensity-score match yielded 1,382,561 cancer patients, mean age 64 at cancer diagnosis, and 73% were white. Percentage of opioid use among cancer patients declined from 35.6% before the COVID-19 to 35.1% after the onset of the pandemic (OR=0.976, 95% CI 0.971-0.981). Average number of opioid prescriptions within 1 year of cancer diagnosis declined from 5.7 before to 5.3 after the COVID-19 onset (p<0.001). Conclusion(s): Among cancer survivors, a small decline in prescription opioid use was observed after the onset of COVID-19 pandemic. Future studies are needed to distinguish the impact of revised guidelines, opioid prescription policy changes, and COVID-19 lock down on lower rates of prescription opioid use among cancer survivors.Copyright © 2023

Features of liver function in pediatric patients infected with Delta variant versus Omicron variant of severe acute respiratory syndrome coronavirus 2.

Objective To summarize and analyze the features of liver function in pediatric patients infected with Delta variant versus Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS - CoV - 2). Methods In this study,an analysis was performed for the liver function test results of the locally transmitted or imported pediatric patients with SARS - CoV - 2 infection during isolation who were admitted to Guangzhou Eighth People's Hospital,Guangzhou Medical University,since May 21,2021,and the clinical data and the constituent ratio of liver injury were compared between the pediatric patients infected with Delta variant and those infected with Omicron variant. The independent samples t - test or the Mann - Whitney U test was used for comparison of continuous data between two groups,and the chi - square test or the Fisher's exact test was used for comparison of categorical data between two groups. Results A total of 85 pediatric patients infected with SARS - CoV - 2 were enrolled,among whom there were 32 (37. 6%)pediatric patients infected with Delta variant and 53 (62. 4%)pediatric patients infected with Omicron variant,and there were no significant differences between the two groups in age,sex, body height,body weight,and comorbidities (all P > 0. 05). There were no significant differences between the two groups in alanine aminotransferase (ALT),aspartate aminotransferase (AST),alkaline phosphatase (ALP),gamma - glutamyl transpeptidase,total bilirubin,albumin,and cholinesterase (all P > 0. 05),and the pediatric patients infected with Omicron variant had a significantly higher level of total bile acid (TBA)than those infected with Delta variant (Z = - 2. 336,P = 0. 020). However,the median values of TBA were within the normal range and the ratios of abnormal TBA were no significant difference between the two groups (P > 0. 05). Among the 85 pediatric patients,10 (11. 8%)had a mild increase in liver function parameters,among whom 7 had an increase in TBA,1 had an increase in ALT, 1 had increases in ALT and AST,and 1 had an increase in ALP. The analysis of liver injury in the pediatric patients infected with Delta variant or Omicron variant showed that there was no significant difference in the constituent ratio of liver injury caused by the two variants (6. 3% vs 15. 1%,chi2 = 0. 691,P = 0. 406). Conclusion Mild liver injury is observed in pediatric patients infected with Delta and Omicron variants of SARS - CoV - 2,but further studies are needed to evaluate the long - term influence of such infection on liver function.Copyright © 2022 Editorial Board of Jilin University

PSYCHOLOGICAL DISTRESS OVER 12 MONTHS POST-DIAGNOSIS IN A NATIONAL EARLY INFLAMMATORY ARTHRITIS COHORT

BackgroundIn inflammatory arthritis patients, the concomitant decline of their mental wellbeing is an increasing concern[1,2]. It is important to not only describe the trajectory of psychological distress in early disease stages, but also understand which clinical outcome measures are most associated with these changes.ObjectivesUsing data from the National Early Inflammatory Arthritis Audit (NEIAA), we assessed trends in psychological wellbeing over 12 months after initial diagnosis and mapped these against clinical outcomes to identify significant associations.MethodsNEIAA collects data from patients referred with suspected early inflammatory arthritis in rheumatology services in England and Wales. We used data provided by 20,472 patients eligible for follow-up (diagnosis of inflammatory arthritis) between May 1st, 2018, and April 1st, 2022. Data items included baseline demographics e.g., age and gender, and clinical variables e.g., rheumatic disease comorbidity index (RDCI), DAS28, and patient reported outcomes.Psychological distress was measured by the sum score of Patient Health Questionnaire Anxiety and Depression Screener (PHQ4ADS). Using mixed effects regression models, we analysed the co-variability of PHQ4ADS with demographic factors and clinical outcomes over 12 months. Time was included as a dummy-coded covariant.ResultsThe analysis included 36% of patients (7,378 out of 20,472) who completed the baseline patient outcome survey. In this cohort, PHQ4ADS scores decreased from a baseline average of 4.7 (CI: [4.6, 4.8]) to 2.62 (CI: [2.5, 2.8]) at 12 months post-diagnosis. The proportion of patients screening positive decreased from 50.0% (CI: [48.9, 51.1]) at baseline to 23.8% (CI: [21.8, 25.9]) at 12 months.At baseline, psychological distress correlated significantly with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28 (Figure 1). No significant correlations were found between psychological distress and working diagnosis, seropositivity, or the assessment being recorded after the start of the COVID-19 pandemic. Younger ages were nonlinearly associated with higher distress levels (coefficient per decade: -0.006;p<0.001;CI: [-0.009, -0.003]) (Figure 1a). Distress levels in females were higher than that of males (coefficient: 0.5;p<0.001;CI: [0.4, 0.7]) (Figure 1b). White patients reported lower PHQ4ADS scores compared to non-white patients (coefficient: -0.7;p<0.001;CI: [-1.0, -0.4]) (Figure 1c). Higher distress levels were also associated with higher RDCI (coefficient: 0.2;p<0.001;CI: [0.1, 0.3]) and prior diagnosis of depression (coefficient: 1.8;p<0.001;CI: [1.5, 2.2]) (Figure 1d, 1e). Furthermore, higher baseline DAS28 scores correlated with more severe psychological distress (coefficient: 0.8;p<0.001;CI: [0.7, 0.8]) (Figure 1f).By 12-months, psychological distress decreased significantly overall, which correlated significantly with ethnicity (coefficient: 0.8;p=0.005;CI: [0.3, 1.4]) and baseline DAS28 (coefficient: -0.5;p<0.001;CI: [-0.6, -0.4]). Compared to white patients, the reduction was significantly greater for non-white patients, but the level of distress was no longer different at 12 months (Figure 1c). While those with higher baseline DAS28 showed a greater reduction in psychological distress, the distress levels remained higher at 12 months (Figure 1f).Figure 1.Changes in psychological distress correlated with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28.[Figure omitted. See PDF]ConclusionIn this early inflammatory arthritis cohort, mental health burden was high. Age, gender, ethnicity, RDCI, prior depression diagnosis and baseline DAS28 significantly correlated with psychological distress at baseline. Supporting mental health should be a focus of clinical care for this population and it may be beneficial to use an approach that is culturally valid for non-white patients and accounts for multimorbidity.References[1]Euesden, J, et al. Psychosomatic medicine 79.6 (2017): 638.[2]Lwin, MN, et al. Rheumatology and therapy 7.3 (2020): 457-471.AcknowledgementsThe authors would like to thank the Healthcare Quality Improvement Partnership (HQIP) as the commisioner of NEIAA, British Society for Rheumatology as the audit providers, Net Solving as the audit platform developers, and the Wellcome Trust (ST12406) for funding to support L.Z..Disclosure of InterestsLucy Zhao: None declared, James Galloway Speakers bureau: Has received honoraria from AbbVie Celgene, Chugai, Gillead, Janssen, Eli Lilly, Pfizer, Roche, and UCB, Jo Ledingham: None declared, Sarah Gallagher: None declared, Neena Garnavos: None declared, Paul Amlani-Hatcher: None declared, Nicky Wilson: None declared, Lewis Carpenter Consultant of: Statistical consultancy for Pfizer, Kirsty Bannister: None declared, Sam Norton Speakers bureau: Has received honoraria from Janssen and Pfizer.

A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

Malnutrition Prevalence and Economic Burden Among US Hospitalized Older Adult Patients with and without COVID-19

Objectives: Malnutrition is a prevalent condition affecting 30-50% of hospitalized patients. Malnutrition is linked to impairments in health outcomes and increased economic burden on healthcare systems. We assessed the prevalence and burden of malnutrition by examining demographic characteristics, Disease Related Group (DRG) payments and associated claims among Medicare inpatients (65+ years) with and without COVID-19. Method(s): Hospital inpatient COVID-19 claims from the Centers for Medicare & Medicaid Services (CMS) Inpatient Prospective Payment System (IPPS) between October 2020 - September 2021 were analyzed. The International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM) were used for malnutrition diagnoses. Demographic variables were compared based on the COVID-19 status;economic burden was analyzed by DRG payment of malnutrition cases with and without COVID-19. Result(s): Among 7,394,657 Medicare inpatient claims, only 12% had a documented malnutrition diagnosis. Of these patients, 1.2% had COVID-19. Regardless of COVID-19 status, malnourished patients averaged 75 years of age, and were predominantly female (54%) and White (78%) followed by Black (14%), and Hispanic (2%). Sepsis, kidney failure, and urinary tract infection (UTI) were the most common primary diagnoses in malnourished patients, regardless of COVID-19 status. Malnourished patients with COVID-19 had significantly higher DRG payments ($27,407 vs. $18,327) and increased cost of outlier payment ($3,208 vs. $2,049) compared to those without COVID-19, regardless of other diagnoses. Conclusion(s): Malnutrition diagnosis was confirmed in only 12% of the Medicare inpatients, thus suggesting that malnutrition continues to be underdiagnosed and undertreated - evidenced by high rates of hospitalizations/claims and payments in both COVID-19 and non-COVID-19 cases. It is imperative for hospitals to implement nutrition-focused protocols to identify, diagnose and address malnutrition among all Medicare inpatients regardless of COVID-19 status (and especially among patients with sepsis, kidney failure, and UTI). Nutrition-focused protocols can effectively improve patient health outcomes and reduce healthcare costs.Copyright © 2023

Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Mixup-Inf-Net: A data augmentation algorithm for segmentation of new coronary pneumonia infections

In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.

IMPACT OF THE COVID-19 PANDEMIC ON SEPTIC ARTHRITIS MANAGEMENT

BackgroundSince 2020, the SARS-Cov-2 pandemic has disrupted the organization of healthcare systems worldwide.ObjectivesThis study aimed to assess the impact of this pandemic on septic arthritis management in a tertiary rheumatology department.MethodsIt was a single-center descriptive case-control study, which included patients hospitalized for septic arthritis between January 2018 and December 2021, whose diagnosis was retained after positive bacterial growthor on culture on according to presumptive criteria. Our patients were divided into two groups: G1: patients hospitalized during the COVID-19 pandemic (2020-2021), and G2: patients hospitalized during a similar period before the COVID-19 pandemic (2018-2019). In both groups, septic arthritis prevalence was calculated, socio-demographic characteristics, risk factors, clinical, paraclinical, and therapeutic data were collected. COVID-19 status was reported in the G1.ResultsTwenty-two patients were enrolled: G1 (n = 15), G2 (n = 7). The prevalence of septic arthritis was 0.77% and 0.36% respectively. The median age was 54.6±12.25 and 54.29±21.81 years old respectively. Diabetes was found in 26, 7% in G1 and 28.6% in G2. During the pandemic, arthropathy and oral corticosteroids use were noted in 53.3% and 28.6% of patients versus 26.7% and 14.3% in G2. The diagnosis delay and the prior use of antibiotic therapy were more significant in G1: 14.08[7-30] d versus 6.5[3.25-19.25] d, and 46.7% versus 14.3%. The knee was the most common localization in both groups. Other joints were affected in G1: shoulder (n = 2), hip (n = 1), and sacroiliac (n = 1). The most common germ was staphylococcus aureus. The duration of hospitalization and duration of antibiotic therapy in G1 and G2 were 26.07±9.12d versus 27.43±10.87d and 50±10d versus 48±25.79d, respectively. Concerning COVID-19 status, 33.3% of patients in G1 have received their vaccination and no recent SARS-Cov2 infection was noted before hospitalization. During the pandemic, synovectomy was required in three patients, one of whom was also transferred to intensive care for septic shock (two of these three patients are being followed for rheumatoid arthritis, and only one has never been vaccinated against COVID-19).ConclusionDuring the COVID-19 pandemic, the prevalence of septic arthritis in our department was higher and the diagnosis was delayed. Duration of hospitalization was not impacted, however, atypical localisations, prior use of antibiotics, recourse to synovectomy, and transfer to intensive care were reported. These results suggest an inadequate and difficult access to healthcare services during the lockdown, as well as an impact of social distancing on the immune system [1, 2]. More studies are needed to confirm these findings.References[1]Robinson E. Pires et al, What Do We Need to Know about Musculoskeletal Manifestations of COVID-19? A Systematic Review, JBJS Rev. 2022 Jun 3;10(6)[2]Pantea Kiani et al, Immune Fitness and the Psychosocial and Health Consequences of the COVID-19 Pandemic Lockdown in The Netherlands: Methodology and Design of the CLOFIT Study, Eur J Investig Health Psychol Educ. 2021 Feb 20;11(1):199-218Acknowledgements:NIL.Disclosure of InterestsNone Declared.

A Case of X-Linked Moesin-Associated Immune Deficiency in a Pediatric Patient

Background: X-Linked Moesin-Associated Immune Deficiency (X-MAID) is a rare severe combined immunodeficiency (SCID) subtype that can present at any age due to its variability. Depending on severity, patients demonstrate failure to thrive, recurrent bacterial and viral infections, and increased susceptibility to varicella zoster. It has been characterized by marked lymphopenia with hypogammaglobulinemia and impaired T-cell migration and proliferation. Case Presentation: This is a report of a Cuban 7-year-old male with poor weight gain and facial dysmorphia. He had a history of recurrent bacterial gastrointestinal infections and pneumonia beginning at 4 months of age. He additionally had 4-6 upper respiratory tract and ear infections annually. While still living in Cuba, he was admitted for a profound EBV infection in the setting of significant leukopenia. A bone marrow biopsy confirmed no malignancy. After he moved to the United States, his laboratory work-up revealed marked leukopenia with low absolute neutrophil and lymphocyte count with low T and B cells, very low immunoglobulin levels IgG, IgA, and IgM, and poor vaccination responses to streptococcus pneumonia, varicella zoster, and SARS-CoV-2. Genetic testing revealed a missense pathogenic variant c.511C>T (p.Arg171Trp) in the moesin (MSN) gene associated with X-MAID. He was managed with Bactrim and acyclovir prophylaxis, and immunoglobulin replacement therapy, and considered for hematopoietic stem cell transplantation. Discussion(s): Diagnosis of X-MAID should be considered in patients with recurrent infections and profound lymphopenia. As with SCID, early diagnosis and intervention is of utmost importance to prevent morbidity and mortality. This case demonstrates the importance of genetic testing in identifying this disease as it may prompt an immunologist to consider HSCT if conservative management is suboptimal. In the current literature, HSCT appears promising, but the long-term outcomes have yet to be described.Copyright © 2023 Elsevier Inc.

A Comprehensive Review of Sound-Based Modalities for Automatic COVID-19 Detection using Deep Learning-Based Techniques

The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.

Could platelet mass index (PMI) be a new prognostic biomarker for COVID-19?

Aim: Although most patients with COVID-19 experience respiratory tract infections, severe reactions to the virus may cause coagulation abnormalities that mimic other systemic coagulopathies associated with severe infections, such as disseminated intravascular coagulation and thrombotic microangiopathy. Fluctuations in platelet markers, which are an indicator of the acute phase response for COVID-19, are of clinical importance. The aim of this study is to evaluate the relationship between disease severity and Platelet Mass Index (MPI) parameters in COVID-19 patients. Material(s) and Method(s): This retrospective observational study was conducted with patients who were diagnosed with COVID-19 in a tertiary hospital. The study was continued with the remaining 280 patients. All laboratory data were scanned retrospectively from patient files and hospital information system. Result(s): A very high positive correlation was found between PMI and PLT. The PMI value in women was significantly higher than in men. It was observed that PMI did not differ significantly in terms of mortality, intubation, CPAP and comorbidity. PMI vs. Pneumonia Ct Severity Score, biochemistry parameters (AST, CRP), hemogram parameters (WBC, HGB, HCT, MCV, LYM, MPV EO) and coagulation factors (aPTT and FIB) at various levels of positive/negative, weak and strong, and significant relationship was found. There was no significant relationship between hormone and D-dimer when compared with PMI. Discussion(s): Although platelet count alone does not provide information about the prognosis of the disease, PMI may guide the clinician as an indicator of lung damage in seriously ill patients.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Assessment of biventricular myocardial systolic function in COVID-19 patients: A Tissue Doppler Imaging echocardiography study

Background: The 2019 novel coronavirus disease (COVID-19) has been reported as pandemy and the number of patients continues to rise. Based on recent data, cardiac injury is a prominent feature of the disease, leading to increased morbidity and mortality. In the present study we aimed to evaluate myocardial dysfunction using transthoracic echocardiography (TTE) and tissue Doppler imaging (TDI) in hospitalized COVID-19 patients. Methods and Results: We recruited 30 patients (56.7% male, 55.80 +/- 14.949 years) who were hospitalized with the diagnosis COVID-19 infection. We analyzed left ventricular (LV) and right ventricular (RV) conventional and TDI parameters at the time of hospitalization and during the course of the disease. Patients without any cardiac disease and with preserved LV ejection fraction (EF) were included. TTE examination was performed and all the variables were recorded and analyzed retrospectively. We observed that both LV and RV conventional echocardiographic parameters were similar when the day of admission to the hospital was compared to the 5th day of the disease. Regarding TDI analysis, we demonstrated significant impairment in LV septal and lateral deformation (P < 0.001). In the correlation analysis no marked correlation was observed between impairment in LV deformation and inflammation biomarkers. Conclusion(s): Cardiac involvement is an important feature of the COVID-19 infection but the exact mechanism is still undefined. Echocardiography is an essential technique to describe myocardial injury and provide new concepts for the possible definitions of cardiac dysfunction.Copyright © 2023 The Author(s).

Depression Severity Among Patients with Psychiatric Disorders during the COVID-19 Pandemic

Objectives: The impact of the COVID-19 pandemic on mental health is not yet well-studied. This study's objective is to describe demographic characteristics of the population diagnosed with depression or anxiety, and to compare PHQ9 scores before and after the pandemic. Method(s): A retrospective cohort study was performed using Komodo Health's healthcare claims and EMR data, which included Patient Health Questionnaire-9 (PHQ9) survey responses. The study's baseline and follow-up periods were set as one year before and after 03/01/2020. Patients selected were >=18 years of age, had a MDD, GAD, or other psychiatric diagnosis in both periods, and had taken at least one PHQ9 survey in both periods, resulting in 10,433 patients. Demographic characteristics were described across age, gender, and race/ethnicity, and a subgroup analysis was performed on PHQ9 scores and depression categories using averages (mean, SD) and odds ratios. Result(s): Demographic analysis showed depression severity correlated with patients who were younger, female, and Black or Hispanic. Younger patients (<30) were more likely than older (>=30) to be in the moderately severe category or worse (PHQ9 score >=15) in both time periods (ORs 1.72 and 1.62, p<0.001). This was also true for female as compared to male (ORs 1.45 and 1.49, p<0.001), and Black or Hispanic as compared to White (ORs 1.87 and 1.47, p<0.001). However, mean PHQ9 scores tended to decrease in the follow-up period. The overall mean decreased slightly from 6.28 (SD 6.05) to 5.68 (SD 5.82), which was consistent in nearly all age, gender, and race/ethnicity subcategories. Conclusion(s): While the improvements in average PHQ9 scores were counterintuitive, given the harmful impacts of the pandemic, existing correlations between demographics and depression severity remained. One possible explanation is that this cohort definition selected for patients who received more consistent mental healthcare. Further study will investigate this and other possible factors.Copyright © 2023

Influencing factors of negative conversion time of nucleic acid detection in 228 patients infected with Omicron variant of SARS-CoV-2 in Shanghai

Objective: To analyze and compare the effects of different clinical characteristics on the negative conversion time of nucleic acid detection after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infection, and to provide a scientific basis for the isolation and treatment of coronavirus disease 2019 (COVID-19). Methods: The epidemiological and clinical data of 228 mild SARS-CoV-2 Omicron variant infected patients diagnosed in Shanghai were retrospectively collected from April 27, 2022 to June 8, 2022 in Wujiaochang designated Hospital, Yangpu District, Shanghai. The negative conversion time of nucleic acid detection was used as the outcome variable, and the patients were divided into A (18 days) and B (>18 days). Univariate and multivariate logistic regression analysis were used to analyze the influencing factors of the negative conversion time of nucleic acid detection. Results: The mean nucleic acid conversion time of 228 patients was (18.7+or-12.1) d, with the median time of 18 (2-46) d. Among them, 120 patients in group A had an average nucleic acid conversion time of (13.2+or-2.0) d, and 108 cases in group B had an average nucleic acid conversion time of (20.8+or-1.3) d. Univariate analysis showed that there were no statistically significant differences in the effects of hypertension, coronary heart disease, diabetes, hypokalemia, malignant tumors, neuropsychiatric diseases, chronic digestive diseases on the negative nucleic acid conversion time (P > 0.05);however, there were significant differences in the effects of combined cerebrovascular disease, leukopenia, chronic respiratory system diseases and vaccination on the negative nucleic acid conversion time (P < 0.05). Further multivariate logistic regression analysis revealed that the combination of chronic respiratory diseases and non-vaccination were significant risk factors for prolongation of negative nucleic acid conversion time (P < 0.05). Conclusions: The results of this study show that gender, age and whether hypertension, coronary heart disease, diabetes mellitus, hypokalemia, malignant tumor, neuropsychiatric disease and chronic digestive disease have no significant effect on the nucleic acid conversion time, whereas chronic respiratory disease and no vaccination are significantly correlated with the prolongation of nucleic acid conversion time in SARS-CoV-2 Omicron-infected patients.

Bibliometric analysis of treating COVID-19 with combined traditional Chinese and western medicine based on citespace

Objective: This study focused on the systematic literature measurement and visual analysis of treating COVID-19 with combined Chinese and Western medicine to explore the development status, research hot spots and trends, and cutting-edge dynamics. It provides data, information support, and development references for treating COVID-19with combined Chinese and Western medicine. Methods: Three major databases of China National Knowledge Infrastructure(CNKI), Wanfang Data, and China Science and Technology Journal Database were used as data sources to retrieve the relevant literature on the combined Chinese and Western medicine for the treatment of COVID-19 from March2019 to March 2022. Statistical analysis was performed using bibliometric methods and CiteSpace software. Visual analysis was performed on publishing trends, research institution cooperation, author cooperation, keyword co-occurrence and clustering, and research hotspots. Results: A total of 476 articles were included in this study, with the largest number published in 2020. Statistical analysis of published units showed that the cooperative relationship between institutions was discrete, indicating that the research on treating novel coronavirus with combined Chinese and western medicine was extensive. Hot spots in the research were mainly reflected in four aspects:coronavirus, diagnosis and treatment plan, combined Chinese and Western medicine, and epidemic prevention and control. Conclusion: The research theme of treating COVID-19 with combined Chinese and Western medicine dynamically changes with the epidemic development, which is guided by clinical efficacy. It mainly focuses on the formulation of diagnosis and treatment plans. The cooperation between authors and institutions is insufficient, and communication needs to be further strengthened.

Psychological intervention for three COVID patients who suffered online violence.

Three patients diagnosed with COVID-19 were all young women in their thirties who have suffered from Internet violence in their personal life after hospitalization. They showed significant emotional distress such as, depression state, acute stress disorder, and dissociative disorder. The current study adopts short-term, individualized and comprehensive psychological interventions, including psychological support, encouragement, listening, safety confirmation, catharsis, psychological suggestion, and stimulation of internal potential to treat patients. The third case was provided with psychological interventions combined with antipsychotic treatment. After timely psychological interventions all three patients achieved sound results.Copyright © 2021 Chinese Medical Journals Publishing House Co.Ltd.