Background Systemic inflammation indices, including neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), derived neutrophil/lymphocyte ratio (dNLR), neutrophil/lymphocyte*platelet ratio (NLPR), aggregate index of systemic inflammation (AISI), systemic inflammation response index (SIR-I), and systemic inflammation index (SII) are well-expressed inflammatory indices that have been used to predict the severity and mortality of various inflammatory diseases. This study aimed to investigate the role of systemic inflammatory markers in predicting mortality in non-elderly and elderly COVID-19 patients. Methods In a retrospective study, laboratory parameters were examined for 1,792 COVID-19 patients (elderly = 710 and non-elderly = 1,082). The ability of inflammatory markers to distinguish the severity of COVID-19 was determined by receiver operating characteristic (ROC) analysis, and survival probability was determined by the mean of Kaplan–Meier curves, with the endpoint being death. Results In the non-survivor non-elderly and elderly patients, the parameters PLR, MLR, dNLR, NLPR, AISI, SIR-I, and SII were significantly higher than in the surviving patients. WBC count (HR = 4.668, 95% CI = 1.624 to 13.413, P < 0.01), neutrophil count (HR = 6.395, 95% CI = 2.070 to 19.760, P < 0.01), dNLR (HR = 0.390, 95% CI = 0.182 to 0.835, P < 0.05), and SII (HR = 10.725, 95% CI = 1.076 to 106.826, P < 0.05) were significantly associated with survival. On the other hand, in elderly patients, it was found that WBC count (HR = 4.076, 95% CI = 2.176 to 7.637, P < 0.001) and neutrophil count (HR = 2.412, 95% CI = 1.252 to 4.647, P < 0.01) were significantly associated with survival. Conclusion WBC count and neutrophil count in non-elderly and elderly patients, were reliable predictors of mortality.

Role of leukocytes and systemic inflammation indexes (NLR, PLR, MLP, dNLR, NLPR, AISI, SIR-I, and SII) on admission predicts in-hospital mortality in non-elderly and elderly COVID-19 patients

Background The characteristic symptom of coronavirus disease 2019 (COVID-19) is respiratory distress, but neurological symptoms are the most frequent extra-pulmonary symptoms. This study aims to explore the current status and hot topics of neurology-related research on COVID-19 using bibliometric analysis. Methods Publications regarding neurology and COVID-19 were retrieved from the Web of Science Core Collection (WoSCC) on March 28 2022. The Advanced search was conducted using “TS = (‘COVID 19’ or ‘Novel Coronavirus 2019’ or ‘Coronavirus disease 2019’ or ‘2019-nCOV’ or ‘SARS-CoV-2’ or ‘coronavirus-2’) and TS = (‘neurology’or ‘neurological’ or ‘nervous system’ or ‘neurodegenerative disease’ or ‘brain’ or ‘cerebra’ or ‘nerve’)”. Microsoft Excel 2010 and VOSviewer were used to characterize the largest contributors, including the authors, journals, institutions, and countries. The hot topics and knowledge network were analyzed by CiteSpace and VOSviewer. Results A total of 5,329 publications between 2020 and 2022 were retrieved. The United States, Italy, and the United Kingdom were three key contributors to this field. Harvard Medical School, the Tehran University of Medical Sciences, and the UCL Queen Square Institute of Neurology were the major institutions with the largest publications. Josef Finsterer from the University of São Paulo (Austria) was the most prolific author. Tom Solomon from the University of Liverpool (UK) was the most cited author. Neurological Sciences and Frontiers in Neurology were the first two most productive journals, while Journal of Neurology held the first in terms of total citations and citations per publication. Cerebrovascular diseases, neurodegenerative diseases, encephalitis and encephalopathy, neuroimmune complications, neurological presentation in children, long COVID and mental health, and telemedicine were the central topics regarding the neurology-related research on COVID-19. Conclusion Neurology-related research on COVID-19 has attracted considerable attention worldwide. Research topics shifted from “morality, autopsy, and telemedicine” in 2020 to various COVID-19-related neurological symptoms in 2021, such as “stroke,” “Alzheimer's disease,” “Parkinson's disease,” “Guillain–Barre syndrome,” “multiple sclerosis,” “seizures in children,” and “long COVID.” “Applications of telemedicine in neurology during COVID-19 pandemic,” “COVID-19-related neurological complications and mechanism,” and “long COVID” require further study.

https://www.frontiersin.org/articles/10.3389/fpubh.2022.937008/pdf

A bibliometric analysis of COVID-19 publications in neurology by using the visual mapping method

Cancer patients constitute one of the highest-risk patient groups during the COVID-19 pandemic. In this study, it was aimed to perform a systematic review and meta-analysis to determine both the incidence and ICU (Intensive Care Unit) admission rates and mortality in SARS-CoV-2 infected cancer patients. The PRISMA guidelines were closely followed during the design, analysis, and reporting of this systematic review and meta-analysis. A comprehensive literature search was performed for the published papers in PubMed/Medline, Scopus, medRxiv, Embase, and Web of Science (WoS) databases. SARS-CoV-2 infection pooled incidence in the cancer populations and the risk ratio (RR) of ICU admission rates/mortality in cancer and non-cancer groups, with 95% confidence intervals (CIs), were calculated using the random-effects model. A total of 58 studies, involving 709,908 participants and 31,732 cancer patients, were included in this study. The incidence in cancer patients was calculated as 8% (95% CI: 8–9%). Analysis results showed that mortality and ICU admission rate was significantly higher in patients with cancer (RR = 2.26, 95% CI: 1.94–2.62, P < 0.001; RR = 1.45, 95% CI: 1.28–1.64, p < 0.001, respectively). As a result, cancer was an important comorbidity and risk factor for all SARS-CoV-2 infected patients. This infection could result in severe and even fatal events in cancer patients. Cancer is associated with a poor prognosis in the COVID-19 pandemic. Cancer patients should be assessed more sensitively in the COVID-19 outbreak. 

/pdf/effects-of-sars-cov-2-infections-in-patients-with-cancer-on-md5eh8dr.pdf

Effects of SARS-CoV-2 infections in patients with cancer on mortality, ICU admission and incidence: a systematic review with meta-analysis involving 709,908 participants and 31,732 cancer patients

The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs’ communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09899444.pdf

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

The human gut microbiome interacts with many diseases, with recent small studies suggesting a link with COVID-19 severity. Exploring this association at the population-level may provide novel insights and help to explain differences in COVID-19 severity between countries. We explore whether there is an association between the gut microbiome of people within different countries and the severity of COVID-19, measured as hospitalisation rate. We use data from the large (n = 3,055) open-access gut microbiome repository curatedMetagenomicData, as well as demographic and country-level metadata. Twelve countries were placed into two groups (high/low) according to COVID-19 hospitalisation rate before December 2020 (ourworldindata.com). We use an unsupervised machine learning method, Topological Data Analysis (TDA). This method analyses both the local geometry and global topology of a high-dimensional dataset, making it particularly suitable for population-level microbiome data. We report an association of distinct baseline population-level gut microbiome signatures with COVID-19 severity. This was found both with a PERMANOVA, as well as with TDA. Specifically, it suggests an association of anti-inflammatory bacteria, including Bifidobacteria species and Eubacterium rectale, with lower severity, and pro-inflammatory bacteria such as Prevotella copri with higher severity. This study also reports a significant impact of country-level confounders, specifically of the proportion of over 70-year-olds in the population, GDP, and human development index. Further interventional studies should examine whether these relationships are causal, as well as considering the contribution of other variables such as genetics, lifestyle, policy, and healthcare system. The results of this study support the value of a population-level association design in microbiome research in general and for the microbiome-COVID-19 relationship, in particular. Finally, this research underscores the potential of TDA for microbiome studies, and in identifying novel associations.

COVID-19 severity is associated with population-level gut microbiome variations

Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19). Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications. Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057–1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582–1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355–1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055–1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067–1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118–1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507–2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427–2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323–2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40–80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged <40 years, only cancer (OR = 17.112 [95% CI 6.264–39.73], p < 0.001) and ICH (OR = 31.538 [95% CI 5.213–158.787], p < 0.001) were significantly associated with higher odds of death. For patients aged >80 years, only age (OR = 1.033 [95% CI 1.008–1.059], p = 0.01) and male gender (OR = 1.585 [95% CI 1.301–1.933], p < 0.001) were associated with higher odds of death. The incidence of most complications increases with age, but arrhythmias, gastrointestinal bleeding, and sepsis were more common in younger deceased patients with COVID-19, with only arrhythmia reaching statistical difference (p = 0.039). We found a relatively poor correlation between preexisting risk factors and complications. Conclusions: Coronavirus disease 2019 are disproportionally affected by age for its clinical manifestations, risk factors, complications, and outcomes. Prior complications have little effect on the incidence of extrapulmonary complications.

Age-Related Risk Factors and Complications of Patients With COVID-19: A Population-Based Retrospective Study

Background and Purpose To investigate the effect of prior ischemic stroke on the outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19), and to describe the incidence, clinical features, and risk factors of acute ischemic stroke (AIS) following COVID-19. Methods In this population-based retrospective study, we included all the hospitalized positive patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020. Clinical data were extracted from administrative datasets coordinated by the Wuhan Health Commission. The propensity score matching and multivariate logistic regression analyses were used to adjust the confounding factors. Results There are 36,358 patients in the final cohort, in which 1,160 (3.2%) had a prior stroke. After adjusting for available baseline characteristics, patients with prior stroke had a higher proportion of severe and critical illness and mortality. We found for the first time that the premorbid modified Rankin Scale (MRS) grouping (odds ratio [OR] = 1.796 [95% CI 1.334–2.435], p < 0.001) and older age (OR = 1.905 [95% CI 1.211–3.046], p = 0.006) imparted increased risk of death. AIS following COVID-19 occurred in 124 (0.34%) cases, and patients with prior stroke had a much higher incidence of AIS (3.4%). Logistic regression analyses confirmed an association between the severity of COVID-19 with the incidence of AIS. COVID-19 patients with AIS had a significantly higher mortality compared with COVID-19 patients without stroke and AIS patients without COVID-19. Conclusions Coronavirus disease 2019 patients with prior stroke, especially those with the higher premorbid MRS or aged, have worse clinical outcomes. Furthermore, COVID-19 increases the incidence of AIS, and the incidence is positively associated with the severity of COVID-19.

Association Between Ischemic Stroke and COVID-19 in China: A Population-Based Retrospective Study

The combination of nonorthogonal multiple access (NOMA) technique and unmanned aerial vehicle (UAV) provides an effective solution for achieving massive connections and improving spectrum efficiency. However, the related security risk becomes serious due to the line-of-sight (LoS) channels involved and high transmit power for weaker users in NOMA-UAV networks. In this article, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming, where a UAV flies straightly to serve multiple ground users in the presence of a passive eavesdropper. During the flight, only the closest NOMA users are chosen to connect with the UAV in each time slot to achieve high LoS probability. To balance the security and transmission performance, the tradeoff between the jamming power and the sum rate is investigated by jointly optimizing the power allocation, the user scheduling and the UAV trajectory. The formulated problem is mixed-integer and nonconvex due to the coupled variables. To address this, we first decompose the problem into two subproblems of power allocation and trajectory optimization. Then, they are transformed into convex ones via the first-order Taylor expansion. After that, an iterative algorithm is proposed to solve the convex problem. Finally, numerical results show that the security of the network is well enhanced and verify the effectiveness of the proposed scheme.

Joint Trajectory and Power Optimization for Jamming-Aided NOMA-UAV Secure Networks

Key Points Question Can natural language processing (NLP) be used to generate chest radiograph (CXR) captions? Findings In this diagnostic study including 74 082 CXR cases labeled with NLP for 23 abnormal signs to train convolutional neural networks, an independent prospective test data set of 5091 participants was examined. The reporting time using NLP-generated captions as prior information was 283 seconds, significantly shorter than the normal template (347 seconds) and rule-based model (296 seconds), while maintaining good consistency with radiologists. Meaning The findings of this study suggest that NLP can be used to generate CXR captions, which provides a priori information for writing reports and may make CXR interpretation more efficient.

/pdf/comparison-of-chest-radiograph-captions-based-on-natural-3oixp86t.pdf

Mingqian Liu

Papers

Age-Related Risk Factors and Complications of Patients With COVID-19: A Population-Based Retrospective Study

Association Between Ischemic Stroke and COVID-19 in China: A Population-Based Retrospective Study

Joint Trajectory and Power Optimization for Jamming-Aided NOMA-UAV Secure Networks

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists