The factors associated with increased risk of hospitalisation in people with diabetes following SARS-CoV- 2 infection: A national replication study

Aim: People with type 1 or type 2 diabetes have a higher hospital admission rate following Covid-19 infection. This study aims to determine the degree to which the results of a previous study in Greater Manchester (GM) could be replicated in national-level data for England. Method(s): We focussed on the univariable regression analysis, which shows the association between admission and Covid-19 infection in people with diabetes. Modelling was conducted using logistic regression on data from the Covid-IMPACT database. Odds ratios were compared descriptively with the previous study. Result(s): In people with type 2 diabetes, factors associated with an increased risk of hospitalisation similar to the previous study were: older age, male sex, higher social deprivation, higher body mass index (BMI), higher cholesterol, lower eGFR, taking an ACE-inhibitor/ ARB, not taking metformin, and having asthma or hypertension. Patients with COPD, and those taking aspirin or clopidogrel also had increased risk, but the national data showed a greater risk (GM COPD odds ratio 1.89 [1.63-2.19] vs national 2.34 [2.28-2.40] / aspirin 1.49 [1.34-1.66] vs 1.66 [1.63-1.70] / clopidogrel 1.71 [1.47-1.98] vs 1.99 [1.94-2.04]). Similar results were observed in patients with type 1 diabetes. However, due to the increase in sample size, many factors which were previously not statistically significant have become significant, such as in type 2 diabetes BMI, low HDL-cholesterol. Conclusion(s): We have successfully replicated the methods, results and conclusions of our previous study in relation to factors associated with increased risk of hospital admission in diabetes individuals. Regional databases are suitable for large cohort studies, and in this instance produced similar results to a national database, validating our previous findings.

Cardiometabolic and psychocognitive features of the post-COVID period in patients with atrial fibrillation.

The number of people with long-term consequences of COVID-19 is increasing worldwide. The long-term prognosis for patients remains poorly understood. Objective. To study cardiometabolic and psychocognitive features in comorbid elderly patients with atrial fibrillation (AF), de-pending on the presence of post-COVID syndrome (PCS). Material and methods. The observational analytical cohort study included 223 patients with AF and comorbidity (coronary artery disease, hypertension, obesity, type 2 diabetes mellitus) aged 60-74, who were divided into two groups: group 1 included 123 patients without COVID-19 and group 2 included 110 patients with a history of COVID-19 and the presence of PCS. The study evaluated laboratory and instrumental tests, and a general clinical study assessing psychocognitive disorders using the SPMSQ and HADS questionnaires was conducted. Results and discussion. In COVID-19 survivors, compared with patients of group 1, there were more pronounced atherogenic changes in total cholesterol (TC) (p=0.003), low-density lipoprotein cholesterol (p<0.001), and triglycerides (p=0.011). Lower dia-stolic blood pressure was found in COVID-19 survivors (p<0.001). In addition, patients in group 2 had higher median pulse pressure (p<0.001) and heart rate (p<0.001). In group 2 patients, a larger ascending aorta diameter was observed (p<0.001). The anx-iety-depressive syndrome was more common in COVID-19 survivors with comorbidities, and a statistically significant difference was found in clinical anxiety (24%, p=0.041) and subclinical depression (21%, p=0.015). When assessing cognitive function, mod-erate cognitive impairment was detected in 22% (p=0.005) of patients with PCS and severe cognitive impairment in 2% (p=0.007). Conclusion. In comorbid elderly patients with the post-COVID syndrome, a high prevalence of psychocognitive disorders and adverse cardiometabolic changes were observed, supporting the need for long-term monitoring of the general clinical condition and psychocognitive status of COVID-19 survivors.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Real-time vibration monitoring and analysis of agricultural tractor drivers using an IoT-based system

Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore-and-aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real-field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat-to-head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x-, y-, and z-axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID-19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x-, y-, and z-axes. The vibration energy was found to be dominant along the z-axis than the x- and y-axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x-, y-, and z-axes depicted the low-frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y- and z-axes when compared with that along the x-axis. The frequency range of 4-7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics

The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms. [ FROM AUTHOR] Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Leaving: Residential mobility and housing conditions in France during the Covid-19 epidemic

In the spring of 2020, a national lockdown was declared in France to control the Covid-19 epidemic - the longest and strictest to date. This led to spontaneous population movements, widely publicised in the media as the urban exodus of Parisians. But were departures from large cities to low-density areas the only types of mobility during this period? And what can we learn from housing changes during lockdown about the ordinary residential practices and dwelling conditions of households, as well as about the resources mobilised to cope with these exceptional situations? Based on the EpiCOV survey (Inserm-Drees), conducted in France among a sample of 135,000 people aged 15 and over at the end of the first lockdown, this article examines the different types of residential mobility that took place in the spring of 2020 on French territory. While this study does not predict whether or not these movements are to become permanent, or whether they would be repeated during further phases of lockdown, it highlights the heterogeneity of mobility practices, captured here in terms of distance travelled, changes within the urban gradient, types and conditions of housing, as well as the variety of resources mobilised according to sex, age and social position of individuals. While long-distance mobility more often concerns wealthy populations, local movements are almost as frequent and concern more heterogeneous fractions of the population. © 2023 Geographie-Cites. All rights reserved.

Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

Dynamic Properties for a Second-Order Stochastic SEIR Model with Infectivity in Incubation Period and Homestead-Isolation of the Susceptible Population

In this article, we analyze a second-order stochastic SEIR epidemic model with latent infectious and susceptible populations isolated at home. Firstly, by putting forward a novel inequality, we provide a criterion for the presence of an ergodic stationary distribution of the model. Secondly, we establish sufficient conditions for extinction. Thirdly, by solving the corresponding Fokker–Plank equation, we derive the probability density function around the quasi-endemic equilibrium of the stochastic model. Finally, by using the epidemic data of the corresponding deterministic model, two numerical tests are presented to illustrate the validity of the theoretical results. Our conclusions demonstrate that nations should persevere in their quarantine policies to curb viral transmission when the COVID-19 pandemic proceeds to spread internationally. © 2023 by the authors.

The Influence of Density and Mixed Land Use on The Spread of Covid-19 in Surakarta City

Covid-19 has a significant risk of spreading in urban areas because of the aglomeration of built-up areas and people. It frequently contains a mix of land uses and is accessible to urban amenities. Due to the area's extensive usage of mixed land uses, it is better able to provide internal urban services on its own. Greater use of area lockdown and social separation strategies could result from this situation. The most populous city in the province of Central Java, Surakarta, has a significant risk of contracting COVID-19. The purpose of this study is to evaluate the impact of density and levels of mixed land use on the Covid-19 distribution in Surakarta City.Population density is used to calculate density. The entropy index approach was used to measure the amount of mixed land use. It is a method for calculating the balance between each form of land use. The availability of current land use data being processed by the spatial analysis with the Arc GIS application provided help for the analysis. Additionally, it makes use of information on Covid-19 cases in relation to the general populace that is supplied by the Surakarta Municipality. The relationship between mixed land use and Covid-19 risk was analyzed using a linear regression approach. The study's findings indicated a minor influence between density and the spread of COVID-19. Meanwhile, the level of mixed land use does not influence the spread of the Covid-19 virus in Surakarta City.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

Critical Commentary: Beyond growth and density: Recentring the demographic drivers of urban health and risk in the global south

Debates within urban studies concerning the relationship between urbanisation and infectious disease focus on issues of urban population growth, density, migration and connectivity. However, an effective long-term risk and wellbeing agenda, without which the threat of future pandemics cannot be mitigated, must also take account of demographic forces and changes as critical drivers of transmission and mortality risk within and beyond cities. A better understanding of the dynamics of fertility, mortality and changing age structures – key determinants of urban decline/growth in addition to migration – provides the foundation upon which healthier cities and a healthy global urban system can be developed. The study of how basic demographic attributes and trends are distributed in space and how they interact with risks, including those of infectious disease, must be incorporated as a priority into a post-COVID-19 urban public health agenda. This perspective concurs with recent debates in urban studies emphasising the demographic drivers of urban change. Moreover, it raises critical questions about the microbial and environmental emphasis of much research on the interface of urban health and governance.

Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin's central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin's development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.

NEUTROPHIL TO LYMPHOCYTE RATIO (NLR) PREDICTS FRACTURE IN PATIENTS ON LONG-TERM GLUCOCORTICOID

BackgroundLong-term glucocorticoid (GC) exposure leads to systemic bone loss and fracture. In addition, GC is known to increase white blood cell (WBC) amount and change the distribution of differential count (DC). Neutrophil-to-Lymphocyte ratio (NLR) has been studied as an optimal marker of subclinical inflammation, predicting the prognosis of cardiovascular diseases, cancers and even covid-19 infection. For patients under long-term GC exposure, the hemogram change might be a potential parameter to predict prognosis.ObjectivesThis pilot study aims to investigate if GC related WBC-DC change, including NLR, is associated with future fractures during 3 years follow-up.MethodsThis retrospective study is based on a registry, conducted in Kaohsiung Chang Gung Memorial Hospital, Taiwan, from September 2014 till April 2021, aimed to monitor bone mineral density (BMD) changes and fractures in patients with autoimmune diseases. All recruited patients were followed at least 3 years and took X-ray images annually to capture new fragility fracture, including morphometric vertebral fractures. We screened participants who used GC continuously at least 3 months before the index day. We recorded the complete blood count (CBC) and WBC-DC values at least twice during the period of 3 months before and after the index day, and excluded patients who were febrile, under infection status, diagnosed as cancers or cardiovascular diseases at the index day. The NLR was calculated by the absolute neutrophil count divided by absolute lymphocyte count individually.ResultsA total of 346 participants were enrolled in current study, and 101 (29.2%) suffered from new fragility fracture in 3 years. Among patients with fracture and non-fracture, conventional fracture risk factors, such as age, BMD, and previous fracture remained significantly different, while the WBC revealed no difference (Table 1). Nevertheless, the absolute neutrophil and lymphocyte count were significantly higher and lower in the fracture group, respectively, and no difference in the monocyte, eosinophil, and basophil count. We compared different WBC ratio, and NLR is significantly higher in the fracture group, providing the odds ratio of 1.24 (95% confidence interval 1.07-1.44, p=0.005). Figure 1 showed that the observed fracture risk raised as the NLR values increased.ConclusionIn patients under long-term GC, NLR might be a helpful marker to predict fracture, and higher NLR indicates higher fracture risks.Figure 1.Observed fracture rate is associated with baseline NLR[Figure omitted. See PDF]Table 1.Demographic characteristics of enrolled patients on long-term glucocorticoid.Fracture N=101No-Fracture N=245p-valueAge63.7 ± 9.056.5 ± 9.6<0.001*Sex(women)89(88.1)210(85.7)0.55BMI24.1 ± 3.923.4 ± 3.90.14Previous Fracture64(63.4)55(22.4)<0.001*Total hip BMD0.738 ± 0.1330.790 ± 0.1220.001*Femoral neck BMD0.575 ± 0.1130.626 ± 0.109<0.001*Lumbar BMD0.841 ± 0.2000.855 ± 0.1500.49WBC7.3 ± 2.16.9 ±1.70.14Hemoglobin12.8 ± 1.512.9 ± 1.40.33Platelet239.2 ± 64.7247.9 ± 71.40.30Neutrophil67.3 ± 9.764.3 ± 9.70.009*Lymphocyte24.3 ± 8.726.6 ± 9.50.04*Monocyte6.2 ± 1.86.3 ± 1.60.52Eosinophil1.8 ± 1.81.9 ± 1.30.77Basophil0.4 ± 0.20.4 ± 0.20.18NLR (Neutrophil to lymphocyte)3.3 ± 1.72.8 ± 1.40.004*NMR (Neutrophil to monocyte)11.9 ± 4.511.0 ± 3.60.04*LMR (Lymphocyte to monocyte)4.2 ± 1.74.5 ± 1.90.20AcknowledgementsThis work was supported by funding grant CMRPG8J0331 from the Chang Gung Memorial Hospital (https://www.cgmh.org.tw).Disclosure of InterestsNone Declared.

A narrative review on radiation risk from imaging for COVID-19: Breaking the myths and the mithya

Radiotherapy-induced secondary malignancy is a well-known occurrence. During the COVID-19 pandemic, many people have undergone serial computed tomography (CT) imaging, and concerns have been raised regarding radiation-induced malignancies due to frequent scanning. Accordingly, various low and ultra-low-dose CT (LDCT) thorax protocols have been developed to reduce the dose of radiation. Major governing bodies worldwide have established guidelines regarding the indications for CT scans and chest X-rays during the pandemic. We, therefore, aimed to provide facts about the effects of radiation (both diagnostic and therapeutic). Through this article, we intend to break the myths and 'mithya' (misbeliefs) regarding diagnostic radiation and its association with cancer in this COVID-19 era. For this review, we performed a search in Google using specific keywords pertaining to imaging during COVID-19 and radiation risk. We also included the names of various global governing bodies in the Google search. We included only full text articles and guidelines from authentic websites. From this review, we conclude that if we follow the recommendations of various global governing bodies and use CT scan only in cases of moderate to severe COVID-related symptoms, adhere to the principle of 'as low as reasonably achievable' for radiation protection, and use LDCT scan protocols, we can significantly reduce the mean effective radiation dose delivered and the estimated cancer risk.Copyright © 2023 Cancer Research, Statistics, and Treatment. All rights reserved.

Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Should population density be used to rank social vulnerability in disaster preparedness planning?

The CDC Social Vulnerability Index (SVI) was developed to help public health officials and policymakers to identify geospatial variations in social vulnerability for each community to better respond to hazardous events, including disease outbreaks. However, the SVI does not include information on population density, which is a significant omission when considering the usefulness of the index in allocating scarce resources such as medical supplies and personnel, bedding, food, and water to locations they are most needed. Using county-level data from the initial U.S. COVID-19 outbreak, we provide empirical evidence that the existing SVI underestimates (overestimates) county-level infection rates in densely (sparsely) populated counties if population density is not accounted for. Population density remains significant even after allowing for spatial spillover effects. Going forward, the inclusion of population density to construct SVI can improve its usefulness in aiding policymakers in allocating scarce resources for future disasters, especially those with spatial dependence. © 2022 Elsevier B.V.

Incidence of Heparin Induced Thrombocytopenia (HIT) in patients with severe COVID-19

Introduction To prevent and treat thrombotic complications in patients hospitalized with severe COVID-19 infection, anticoagulation treatments primarily with heparin and low molecular weight heparin have been recommended. Heparin-induced thrombocytopenia (HIT) is a rare but conceivably fatal reaction to heparin that is characterized by a sudden drop in platelet count accompanied by new onset of thrombosis 4-10 days after heparin exposure. The purpose of this retrospective study was to investigate the prevalence of thrombocytopenia and HIT in hospitalized COVID-19 patients, as well as their association with mortality. Methods 3,672 plasma samples were collected from patients admitted to the first wave of COVID-19 in our institution at New York City (March to May 2020). All patients admitted with a platelet count of less than 150 k/ul were assigned to the thrombocytopenic group. In addition, two groups with similar demographics and normal platelet counts were randomly selected based on discharge outcome: alive vs. deceased (n= 88 per group). PF4 IgG Elisa and heparin neutralization were carried out in accordance with the manufacturer's instructions. A positive HIT result required an optical density (OD) greater than 0.4 and heparin neutralization greater than 50%. Statistical analysis was done in R studio (V.1.4.1717) to analyze demographics (age, gender, ethnicity), initial laboratory data, anticoagulation on admission, and thrombosis. Results Only 86 of the 3,672 (2.3%) patients admitted had thrombocytopenia. Only 1 of the 86 patients tested positive for HIT (1.1%). 4 cases of the non-survivors (4.5%) tested positive for HIT compared to none of the survivors in the two groups with normal platelet counts. One of these 4 cases had a history of thrombosis (DVT). Interestingly, the PF4 Elisa ODs in non-survivors were significantly higher than in survivors (0.09 vs. 0.06, p-value< 0.001). Although the platelet count did not differ significantly between the two groups, the mean platelet volume (MPV) on admission and its maximum peak during hospitalization were significantly higher in non-survivors than in survivors. Conclusions We only found HIT positive cases among non-survivors, implying that HIT is associated with COVID severity. The incidence of HIT in severe COVID-19 patients appears to be higher than the pre-COVID-19 historical rates of HIT in hospitalized patients (<1%). Although thrombocytopenia is relatively uncommon in COVID-19 patients, the MPV was significantly higher in non-survivors, suggesting that platelet activation and destruction may explain the higher rate of HIT in COVID-19.

Long-term coagulation abnormalities after COVID-19.

Objective: Hypercoagulation and high incidence of thrombosis during COVID-19 is well established. However, there is a lack of data, how it changes over time. The main purpose of our study was to access different parts of hemostasis in few months after acute disease. Material and methods. Patients discharged from our hospital were invited for follow up examination in 2,3-3,8 (group 1 - 55 pts) or 4,6-5,7 months (group 2 - 45 pts) after admission. Control group (37 healthy adults) had been collected before pandemic started. Standard coagulation tests, aggregometry, thrombodynamics and fibrinolysis results were compared between groups. Result(s): D-dimer was significantly higher, and was APPT was significantly lower in group 2 compared to group 1, while fibrinogen, prothrombin levels didn't differ. Platelet aggregation induced by ASA, ADP, TRAP, spontaneous aggregation didn't differ significantly between groups. Thrombodynamics revealed hypocoagulation in both group 1 and group 2 compared to control: V, mum/min 27,3 (Interquartile range (IQR) 26,3;29,4) and 28,3 (IQR 26,5;30,1) vs. 32,6 (IQR 30,4;35,9) respectively;all p < 0,001. Clot size and density in both group 1 and group 2 were significantly lower than in control group. Fibrinolysis appeared to be enhanced in x2 compared to control and group 1. Lysis progression, %/min was higher: 3,5 (2,5;4,8) vs. 2,4 (1,6;3,5) and 2,6 (2,2;3,4) respectively, all p < 0,05. Lysis onset time in both group 1 and group 2 was significantly shorter compared to control. Conclusion(s): We revealed normalization of parameters of clot formation process in 2-6 months after COVID-19, while fibrinolysis remained still enhanced. Further study is required to investigate the clinical significance of these changes.Copyright © Creative Cardiology 2021.

Assessment of Serum Lipocalin-2 Level as Early Predictor Marker to Severe COVID-19 Disease

The coronavirus disease of 2019 (COVID-19) is an emerging coronavirus that affects people's respiratory systems (Severe acute respiratory syndrome coronavirus) and can the rapidly growing COVID-19 pandemic represents a significant global challenge, It can be considered that lipocalin-2 was highly associated with the severity of COVID-19. Therefore, it may be a useful biomarker for diagnosing disease severity in COVID-19 patients. lipocalin-2 was initially identified as a secreted protein from human neutrophils. Alveolar type II cells that have been damaged primarily express this substance. To verify lipocalin-2's potential as a diagnosing biomarker for COVID-19 patients, Lipocalin-2 levels in the blood were examined in this pilot investigation. To examine the relationship between serum lipocalin-2 levels and the severity of COVID-19 infection to see if this protein may be utilized as a disease indicator. This study was done in a case-control study with One hundred and twenty patients (79 males, 41 females) with COVID-19 who participated in the research. The COVID-19 patients were divided into three groups based on the severity of the illness: critical disease (n = 30), severe disease (n = 30), and mild/moderate disease (n = 60), with (n = 60) healthy volunteers serving as the control group (35 males, 25 females). Between January 2022 and May 2022, the patients were obtained from Al-Amal hospitals and the AL-Shefaa centre in ALNajaf City, Iraq., All of the patients' fundamental clinical and demographic data were collected along with blood samples. Enzyme-linked immunosorbent tests were used to measure the blood's level of LCN2 (ELISA). The levels of total cholesterol, triglycerides, and High-Density lipoprotein were assessed using colorimetric methods. Ichroma was tested for serum ferritin, D-dimer, and CBC by Swelab. ran a statistical study to see if they were related to the severity of the disease. Higher lipocalin-2 levels were observed in the patient group, particularly in cases of mild/moderate (1.32±0.30) (P. 0.001), severe(2.16±0.42) (P. 0.001), and critical(4.71±1.01) (P. 0.001) comparing cases to healthy controls (0.86±0.51) respectively, groupings. (SPO2 %, Hb, TC, HDL, LDL, and lymphocyte) levels were found to significant negative correlation with one another in the COVID-19 patient group, with p-values=0.001 for each of these relationships. Moreover, a significant positive correlation between (TG, VLDL-C, WBCs, neutrophil, platelet, N/L ratio, D-dimer, Ferritin, and CRP, p.value=0.001 for each one of them) levels with lipocalin-2 in the COVID-19 patients group. a cut-off value of 1.215 (ng/mL) for lipocalin-2 predicted severe COVID-19 with a sensitivity of 81.7 % and a specificity of 80.2 % (AUC: 0.9, 95%CI 0.852-0.949;p<0.0001). [ FROM AUTHOR] Copyright of Egyptian Academic Journal of Biological Sciences, C Physiology & Molecular Biology is the property of Egyptian Academic Journal of Biological Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Type 1 diabetes and environment . . . and other stories

A linked ecological analysis of environmental and demographic variables identified several factors, including poor air quality, outdoor light at night, and higher population density that were negatively associated with the incidence of diabetes (Diabetologia doi:10.1007/s00125-020-05087-7). A case-control study using a database of people known to have autoimmune disease raises anxiety about central nervous system inflammatory events (JAMA Neurol doi:10.1001/jamaneurol.2020.1162). A history of exposure to TNF inhibitors carried a threefold increase in risk both of demyelinating diseases, such as multiple sclerosis and optic neuritis, and of non-demyelinating conditions, such as encephalitis, neurosarcoidosis, and vasculitis.