关键词： Micronutrient   COVID-19   umbrella review   systematic reviews   meta-analyses   CORONAVIRUS DISEASE 2019   VITAMIN-D DEFICIENCY   CLINICAL CHARACTERISTICS   IMMUNE-SYSTEM   MODULATION   ZINC   RECEPTOR   RISK  

摘要： Introduction: Micronutrients are clinically important in managing COVID-19, and numerous studies have been conducted, but inconsistent findings ***: To explore the association between micronutrients and COVID-19. Methods: PubMed, Web of Science, Embase, Cochrane Library and Scopus for study search on July 30, 2022 and October 15, 2022. Literature selection, data extraction and quality assessment were performed in a double-blinded, group discussion format. Meta-analysis with overlapping associations were reconsolidated using random effects models, and narrative evidence was performed in tabular presentations. Results: 57 reviews and 57 latest original studies were included. 21 reviews and 53 original studies were of moderate to high quality. Vitamin D, vitamin B, zinc, selenium, and ferritin levels differed between patients and healthy people. Vitamin D and zinc deficiencies increased COVID-19 infection by 0.97-fold/0.39-fold and 1.53-fold. Vitamin D deficiency increased severity 0.86-fold, while low vitamin B and selenium levels reduced severity. Vitamin D and calcium deficiencies increased ICU admission by 1.09 and 4.09-fold. Vitamin D deficiency increased mechanical ventilation by 0.4-fold. Vitamin D, zinc, and calcium deficiencies increased COVID-19 mortality by 0.53-fold, 0.46-fold, and 5.99-fold, respectively. Conclusion: The associations between vitamin D, zinc, and calcium deficiencies and adverse evolution of COVID-19 were positive, while the association between vitamin C and COVID-19 was insignificant.

关键词： COVID-19   Vaccine effectiveness   Multicentre study   Test-negative design   Europe   SARS-CoV-2  

摘要： In autumn 2023, European vaccination campaigns predominantly administered XBB.1.5 vaccine. In a European multicentre study, we estimated 2023 COVID-19 vaccine effectiveness (VE) against laboratory-confirmed symptomatic infection at primary care level between September 2023 and January 2024. Using a test- negative case-control design, we estimated VE in the target group for COVID-19 vaccination overall and by time since vaccination. We included 1057 cases and 4397 controls. Vaccine effectiveness was 40 % (95 % CI: 26-53 %) overall, 48 % (95 % CI: 31-61 %) among those vaccinated < 6 weeks of onset and 29 % (95 % CI: 3-49 %) at 6-14 weeks. Our results suggest that COVID-19 vaccines administered to target groups during the autumn 2023 campaigns showed clinically significant effectiveness against laboratory-confirmed, medically attended symptomatic SARS-CoV-2 infection in the 3 months following vaccination. A longer study period will allow for further variant-specific COVID-19 VE estimates, better understanding decline in VE and informing booster administration policies.

关键词： COVID-19   Care deficits   Structural determinants of health   Asylum Seekers   Ireland  

摘要： Responding to the need for qualitative research that reveals the lived reality of how forced migrants endured the COVID-19 pandemic this paper presents findings from eleven interviews with asylum seekers residing in Ireland's Direct Provision (DP) accommodation system that detail care deficits before, during and after COVID-19 along with analysis of how care is discussed within Irish policy documents concerned with the health and wellbeing of asylum seekers. The research contributes personal testimony and documentary evidence of the inability of DP to properly adapt to the pandemic and its failure to protect the health and wellbeing of asylum seekers given pre-existing care deficits. The paper argues that an ethic of care practiced for and with asylum seekers must ensure they are not re-traumatised, and their health disparities are not exacerbated during public health crises and beyond. The findings are relevant to efforts to reform how international protection responsibilities are enacted in Ireland and other destinations of forced migrants, including EU member states.

关键词： COVID-19   SARS-CoV-2   cardiovascular diseases   machine learning   prediction of survival   FEATURE-SELECTION METHOD   T-TEST   COHORT  

摘要： Introduction Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a highly contagious viral disease. Cardiovascular diseases and heart failure elevate the risk of mechanical ventilation and fatal outcomes among COVID-19 patients, while COVID-19 itself increases the likelihood of adverse cardiovascular *** We collected blood samples and clinical data from hospitalized cardiovascular patients with and without proven COVID-19 infection in the time period before the vaccine became available. Statistical correlation analysis and machine learning were used to evaluate and identify individual parameters that could predict the risk of needing mechanical ventilation and patient *** Our results confirmed that COVID-19 is associated with a severe outcome and identified increased levels of ferritin, fibrinogen, and platelets, as well as decreased levels of albumin, as having a negative impact on patient survival. Additionally, patients on ACE/ARB had a lower chance of dying or needing mechanical ventilation. The machine learning models revealed that ferritin, PCO2, and CRP were the most efficient combination of parameters for predicting survival, while the combination of albumin, fibrinogen, platelets, ALP, AB titer, and D-dimer was the most efficient for predicting the likelihood of requiring mechanical *** We believe that creating an AI-based model that uses these patient parameters to predict the cardiovascular patient's risk of mortality, severe complications, and the need for mechanical ventilation would help healthcare providers with rapid triage and redistribution of medical services, with the goal of improving overall survival. The use of the most effective combination of parameters in our models could advance risk assessment and treatment planning among the general population of cardiovascular patients.

关键词： COVID-19   protein C   protein S   antithrombin 3   D-Dimer   ACUTE RESPIRATORY SYNDROME   CORONAVIRUS DISEASE 2019   ANTICOAGULANT   MORTALITY   ACTIVATION  

摘要： Objectives COVID-19 in it is more severe form is characterized by a hyperinflammatory condition, hypercoagulation state and the appearance of pulmonary microembolism. In this study we wanted to correlate levels of D-Dimer, protein C, protein S and antithrombin 3 with severity of disease and clinical outcome. Methods We included 134 of patients who were divided in 3 groups regarding oxygen support (high flow oxygen therapy, mechanical ventilation and oxygen supplementation with nasal cannula or mask). Results Concentration of D-Dimer, and activity of protein C and antithrombin 3 are presented as mean +/- SD and differed significantly between patients on mechanical ventilation (3.26 +/- 1.15 mg/L, 86 +/- 22.55 %, 81.21 +/- 17.61 %)/HFNO (2.35 +/- 1.68 mg/L, 109.6 +/- 26.96 %, 94.67 +/- 17.49 %)/BNC (1.37 +/- 1.17 mg/L, 116.92 +/- 28.16 %, 103.29 +/- 15.63 %) with p<0.001 for all parameters. Mortality in oxygen group was 10.9 %, in HFNC group 40.7 % and in mechanical ventilated group 80 %. Conclusions determination of anticoagulant factors in COVID-19 patients may indicate which of them are at increased risk of developing severe disease, venous thromboembolism and fatal clinical outcome.

关键词： Transfer learning   Machine learning   COVID-19 misinformation   Urdu language   Twitter datasets   BERT-based feature extraction   LANGUAGE  

摘要： Social media has become a prominent news source for many individuals due to its convenience and accessibility. Unfortunately, this accessibility has also facilitated the rapid dissemination of misinformation, irrespective of its credibility, particularly over the past decade. Misinformation spreaders take advantage of serious situations, like the COVID-19 pandemic, to spread false information. While numerous studies have focused on detecting misinformation in the English language, research in Urdu language misinformation detection remains limited. Our contribution to this field is twofold: firstly, we have compiled a substantial and diverse dataset of Urdu tweets. Secondly, we have introduced a novel approach incorporating feature extraction and ensemble learning techniques, complemented by high-performance filtering and voting classifiers designed explicitly for the COVID-19 Urdu dataset. The research paper employs a BERT-based transfer learning approach for feature extraction, followed by machine learning and ensemble methods for adequate classification. To evaluate the effectiveness of the proposed model, this paper conducted a comparative analysis against state-of-the-art methods. Experimental results demonstrate that the proposed model for detecting misinformation surpassed existing approaches when applied to the COVID-19 Twitter Urdu Tweets dataset, achieving an impressive F1 score of 0.93 and an accuracy of 0.92. This highlights the superior performance and efficacy of our method compared to previous methodologies.

关键词： Enzymes  

摘要： The unprecedented and widespread use of disposable face masks during the COVID-19 pandemic, along with their improper disposal, poses a significant risk of escalating the already alarming problem of plastic pollution. This study examines the role of mask leachates as potential sources of microplastics and evaluates their negative impact on the algae Chlorella sp. and the crustacean Artemia salina. Experimental results indicated that disposable surgical masks have the potential to leach out polypropylene microplastics. When these leachates came into contact with algae, it results in reduced cell viability, elevated levels of oxidative stress, and increased activity of antioxidant enzymes. A. salina exhibited increased mortality rates upon exposure to the leachates from the mask. The effects were associated with an elevation in reactive oxygen species (ROS) and lipid peroxidation (LPO), as well as enhanced activities of antioxidant enzymes such as catalase (CAT). Moreover, our study also observed the entanglement of mask microplastic fibers onto A. salina, resulting in an increased mortality rate for the organism. Furthermore, confocal microscopic images revealed the accumulation of mask fibers in the digestive tract of A. salina, causing significant damage to its gut. © 2024, The Authors. All rights reserved.

关键词： RSV   severe acute lower respiratory tract infection   children   nomogram   post-COVID-19 period   machine learning   SYNCYTIAL VIRUS   RISK-FACTORS   BRONCHIOLITIS   IMMUNITY  

摘要： Introduction: Off-season upsurge of respiratory syncytial virus (RSV) infection with changed characteristics and heightened clinical severity during the post-COVID-19 era are raising serious concerns. This study aimed to develop and validate a nomogram for predicting the risk of severe acute lower respiratory tract infection (SALRTI) in children hospitalized for RSV infection during the post-COVID-19 era using machine learning techniques. Methods: A multicenter retrospective study was performed in nine tertiary hospitals in Yunnan, China, enrolling children hospitalized for RSV infection at seven of the nine participating hospitals during January-December 2023 into the development dataset. Thirty-nine variables covering demographic, clinical, and laboratory characteristics were collected. Primary screening and dimension reduction of data were performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by identification of independent risk factors for RSV-associated SALRTI using Logistic regression, thus finally establishing a predictive nomogram model. Performance of the nomogram was internally evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) based on the development dataset. External validation of our model was conducted using same methods based on two independent RSV cohorts comprising pediatric RSV inpatients from another two participating hospitals between January-March 2024. Results: The development dataset included 1102 patients, 239 (21.7%) of whom developed SALRTI; while the external validation dataset included 249 patients (142 in Lincang subset and 107 in Dali subset), 58 (23.3%) of whom were diagnosed as SALRTI. Nine variables, including age, preterm birth, underlying condition, seizures, neutrophil-lymphocyte ratio (NLR), interleukin-6 (IL-6), lactate dehydrogenase (LDH), D-dimer, and co-infection, were eventually confirmed as the independent risk factors of