scholarly journals ELEVATED SERUM PENTRAXIN-3 LEVELS IS POSITIVELY CORRELATED TO DISEASE SEVERITY AND COAGULOPATHY IN COVID-19 PATIENTS

2020 ◽  
Vol 13 (1) ◽  
pp. e2021015
Author(s):  
Ming Tong ◽  
Ying Xiong ◽  
Chen Zhu ◽  
Hong Xu ◽  
Qing Zheng ◽  
...  

Abstract BACKGROUND Coronavirus disease 2019 (COVID-19) is highly contagious and deadly and is associated with coagulopathy. Pentraxin-3(PTX3) participates in innate resistance to infections and plays a role in thrombogenesis. PURPOSE The present study aimed to investigate the role of PTX3 in coagulopathy in patients with COVID-19. METHODS A retrospective study including thirty-nine COVID-19 patients enrolled in Hunan, China were performed. The patients were classified into the D-dimer_L (D-dimer?1mg/L) and D-dimer_H (D-dimer?1mg/L) groups basing on the plasma D-dimer levels on admission. Serum PTX3 levels were detected by enzyme-linked immunosorbent assays and compared between those two groups, and then linear regression models were applied to analyze the association between PTX3 and D-dimer. RESULTS Our results showed that serum PTX3 levels (median values, 10.21 vs 3.36, P < 0.001), chest computerized tomography scores (median values, 10.0 vs 9.0, P < 0.05), and length of stay (16.0±4.2 vs 10.7±3.6, P = 0.001) in the D-dimer_H group were significantly higher than that in D-dimer_L group. The coefficient of determination for PTX3 was 0.651 (P < 0.001) in the D-dimer_H group. CONCLUSION Serum level of PTX3 was positively correlated with disease severity and coagulopathy. Detection of serum PTX3 level could assist to identify severer patients on admission and may be a potential therapeutic target for coagulopathy in patients with COVID-19.

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.


2020 ◽  
Vol 9 (7) ◽  
pp. 2244 ◽  
Author(s):  
Matteo Nicola Dario Di Minno ◽  
Ilenia Calcaterra ◽  
Roberta Lupoli ◽  
Antonio Storino ◽  
Giorgio Alfredo Spedicato ◽  
...  

Background: Complications of coronavirus disease 2019 (COVID-19) include coagulopathy. We performed a meta-analysis on the association of COVID-19 severity with changes in hemostatic parameters. Methods: Data on prothrombin time (PT), activated partial thromboplastin time (aPTT), D-Dimer, platelets (PLT), or fibrinogen in severe versus mild COVID-19 patients, and/or in non-survivors to COVID-19 versus survivors were systematically searched. The standardized mean difference (SMD) was calculated. Results: Sixty studies comparing 5487 subjects with severe and 9670 subjects with mild COVID-19 documented higher PT (SMD: 0.41; 95%CI: 0.21, 0.60), D-Dimer (SMD: 0.67; 95%CI: 0.52, 0.82), and fibrinogen values (SMD: 1.84; 95%CI: 1.21, 2.47), with lower PLT count (SMD: −0.74; 95%CI: −1.01, −0.47) among severe patients. Twenty-five studies on 1511 COVID-19 non-survivors and 6287 survivors showed higher PT (SMD: 0.67; 95%CI: 0.39, 0.96) and D-Dimer values (SMD: 3.88; 95%CI: 2.70, 5.07), with lower PLT count (SMD: −0.60, 95%CI: −0.82, −0.38) among non-survivors. Regression models showed that C-reactive protein values were directly correlated with the difference in PT and fibrinogen. Conclusions: Significant hemostatic changes are associated with COVID-19 severity. Considering the risk of fatal complications with residual chronic disability and poor long-term outcomes, further studies should investigate the prognostic role of hemostatic parameters in COVID-19 patients.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2557-2557
Author(s):  
Nirmish Shah ◽  
Marilyn J. Telen ◽  
Thomas L. Ortel

Abstract Abstract 2557 Poster Board II-534 Introduction: The pathophysiology of sickle cell disease (SCD) is complex, with increasing evidence of abnormalities in nearly every component of hemostasis and a pronounced prothrombotic state. Many factors contribute to this prothrombotic state, including: 1) an increase in thrombin production and 2) an increase in circulating procoagulant microparticles. Currently little is known about procoagulant microparticles and their role in thrombin generation in SCD. We have therefore attempted to define the role of thrombin and procoagulant microparticles in SCD utilizing a thrombin generation assay (TGA) and to correlate the results with D-dimer levels, which are known to be abnormal in SCD. The use of TGA allows examination of lag phase, acceleration phase, peak thrombin, and endogenous thrombin potential (ETP). Patients and Methods: The study included a total of 20 HbSS adult and pediatric patients (mean age=22 years, range 3–35) who were admitted for vaso-occlusive crisis. Patients were excluded if they had been admitted within the last 2 weeks, were on chronic transfusion, or were on antiplatelet or anticoagulant medications. Blood was drawn within 36 hours of admission (crisis) and again following discharge, at least 2 weeks later (steady state). Nine of the enrolled patients were followed from inpatient to an outpatient follow up visit. All plasma was promptly separated and frozen, stored at −80C, thawed once and subsequently tested within 4 hours. For each patient, D-dimer and TGA were performed on platelet poor plasma (PPP). In addition, following initial thaw of PPP, samples were spun for 30 minutes at 30,000g to remove microparticles and isolate particle free plasma (PFP). TGA was also performed on PFP from both inpatient and outpatient samples. Results: The mean D-dimer during inpatient crisis (5358 ± 2052 ng/ml) was significantly higher than during outpatient steady state (1256 ± 298, p=0.042). In comparing crisis and steady state by TGA, there was a significant increase in mean ETP (1267 ± 56 nM vs 923 ± 231, p=0.032) and mean acceleration phase (5.14 ± 0.34 min vs 14.21 ± 6.75, p=0.038). Both lag phase (p=0.066) and peak thrombin (p=0.057) were not statistically different between crisis and steady states. Analysis of PPP and PFP by TGA revealed that all phases including ETP (p=0.070) and peak thrombin (p=0.080) were not statistically different between microparticle rich and poor plasma. Linear regression models for inpatient D-dimer versus age were also performed and a significant decrease in D-dimer was seen with increasing age (r2=0.20, p=0.044). This decreasing trend with age was also seen with outpatient D-dimer, although it did not reach significance (r2=0.21, p=0.076). There was no significant trend seen in linear regression models performed for TGA phases versus age. Linear regression models were performed for D-dimer with each phase of TGA and no significant correlation or trend was seen. Conclusions: Our initial results using TGA to evaluate the increase in hypercoagulability seen in patients with SCD during an acute crisis indicate that the expected increase is also seen with certain phases of the TGA, when compared to outpatient non-crisis state. ETP and acceleration phase were significantly elevated during crisis, indicating that the rate of thrombin production and total thrombin are abnormally increased during this time. Peak thrombin and lag phase also approached significance. In evaluating the significance of microparticles, we were unable to show a significant decrease in thrombin production in microparticle free plasma by use of the TGA. With inclusion of both pediatric and adult patients, it was also found that D-dimer significantly decreased with age, which is not consistent with other chronic disease states which exhibit a gradual increase with age. It is possible that a broader age range of patients may be needed to better determine this trend. Finally, comparison of D-dimer with each phase of TGA did not reveal a significant correlation. This may be due to other influencing factors on D-dimer which do not affect TGA. Thus, TGA appears to be a promising technique with which to assess thrombin generation and the hypercoagulable state in SCD; further studies of increased numbers of patients are needed to validate the use of TGA, and define the contribution of microparticles, in SCD. Disclosures: Shah: Thrasher Foundation: Research Funding.


Author(s):  
Daisuke Miyazawa ◽  
Gen Kaneko

AbstractIdentification of biomedical and socioeconomic predictors for the number of deaths by COVID-19 among countries will lead to the development of effective intervention. While previous multiple regression studies have identified several predictors, little is known for the effect of mask non-wearing rate on the number of COVID-19-related deaths possibly because the data is available for limited number of countries, which constricts the application of traditional multiple regression approach to screen a large number of potential predictors. In this study, we used the hypothesis-driven regression to test the effect of limited number of predictors based on the hypothesis that the mask non-wearing rate can predict the number of deaths to a large extent together with age and BMI, other relatively independent risk factors for hospitalized patients of COVID-19. The mask non-wearing rate, percentage of age ≥ 80 (male), and male BMI showed Spearman’s correlations up to about 0.8, 0.7, and 0.6 with the number of deaths per million from 22 countries from mid-March to mid-June, respectively. The observed number of deaths per million were significantly correlated with the numbers predicted by the lasso regression model including four predictors, age ≥ 80 (male), male BMI, and mask non-wearing rates from mid-March and late April to early May (Pearson’s coefficient = 0.918). The multiple linear regression models including the mask non-wearing rates, age, and obesity-related predictors explained up to 79% variation of the number of deaths per million. Furthermore, 56.8% of the variation of mask non-wearing rate in mid-March, the strongest predictor of the number of deaths per million, was predicted by age ≥ 80 (male) and male BMI, suggesting the confounding role of these predictors. Although further verification is needed to identify causes of the national differences in COVID-19 mortality rates, these results highlight the importance of the mask, age, and BMI in predicting the COVID-19-related deaths, providing a useful strategy for future regression analyses that attempt to contribute to the mechanistic understanding of COVID-19.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 11-12
Author(s):  
Mohsin Sheraz Mughal ◽  
Ikwinder Preet Kaur ◽  
Ali R. Jaffery ◽  
Chang Wang ◽  
Muhammad Asif ◽  
...  

Introduction:The underlying pathophysiology of severe COVID-19 involves cytokine storm syndrome that is associated with an elevation of immunoinflammatory cytokines [1]. This hyper-inflammatory state has been implicated with coagulopathy among severely sick patients with COVID-19. Inflammation and coagulopathy are interlinked processes [2]. Coagulopathy has been associated with high mortality in COVID-19 patients [3]. LMWH is traditionally used for its anticoagulant and antithrombotic properties, however, its anti-inflammatory effect has not been fully elucidated. A study done by Shastri et al. suggested that LMWH can inhibit the release of different cytokines (IL-4, IL-5, IL-13, and TNF-α) [4]. Recent retrospective studies on COVID-19 illustrated that the LMWH (40-60 mg, subcutaneously every day) was associated with better prognosis as measured by (28 days of survival) in severely sick patients meeting sepsis-induced coagulopathy (SIC≥4) criteria compared to nonusers [5]. The potential role of escalated/therapeutic LMWH (1mg/kg/subcutaneously every 12 hours) remains unclear. This study involves a retrospective analysis of the potential role of an escalated dose of LMWH to alter the hyper-inflammatory state in hospitalized patients with COVID-19 and compared outcomes to those patients who received a low dose (40-60 mg, subcutaneously every day) of LMWH. Methods:Adult patients with confirmed SARS-CoV-2 infection by nasopharyngeal (NP) polymerase chain reaction (PCR) who were hospitalized from March 1st to April 20, 2020, were included. They were divided into two cohorts based on the dose of LMWH; cohort 1 (40-60 mg, subcutaneously every day) and cohort 2 (1mg/kg/subcutaneously every 12 hours). Categorical variables were compared by conducting a chi-square test or Fisher's exact test while continuous ones were compared by conducting a median two-sample test. Results:The median values of PT, PTT, INR, CRPmax, LDHmax, ferritinmax, D-dimermax, are mentioned in table 1. Incidence of thrombotic events (deep venous thrombosis, ischemic stroke, pulmonary embolism) was higher in cohort 1 (n=3, 4.8%) compared to cohort 2 (n=1, 2.6%). Cohort 2 had a higher number of patients who received ICU level of care (n=24) compared to the 6 patients in cohort 1. Out of 24 patients in cohort 2, 18 patients received invasive mechanical ventilation. The median value of length of stay in the hospital (10.0 days) and all-cause mortality (31.6 %) were higher in cohort 2 as compared to cohort 1 (p&lt;0.05). Discussion:Infections have the ability to trigger systemic inflammation [6]. The interplay between the host system and its response to foreign pathogens can lead to the activation of coagulation pathways. SARS-CoV-2 entry via ACE-2 receptors on endothelial cells is likely associated with endothelial dysfunction. This endotheliopathy plays a significant role in COVID-19 related microcirculatory changes [7]. Severe COVID-19, a hyperinflammatory state, is marked by elevated inflammatory markers including D-dimer, ferritin, IL-6, LDH, and CRP levels. Elevated D-dimer levels have been correlated with disease severity and poor outcomes in hospitalized patients with COVID-19 [8]. The incidence of VTE and pulmonary embolism among COVID-19 ICU patients was higher in a study from France [9]. The patient population who received the escalated dose of LMWH in our study either had SIC score ≥ 4 or D-dimer ≥ 2.2 (FEU). This data indicated that the median value of peak inflammatory markers in cohort 1 was lower (p&lt;0.05) when compared to cohort 2. Patients in cohort 2 were sicker than cohort 1, as evidenced by a statistically significant longer length of hospital stay and a higher rate of ICU admission. However, the potential dose-dependent anti-inflammatory effect of LMWH was not observed. Additional studies evaluating comorbidities and disease severity in both cohorts may yield different results. Conclusion:Aside from the known anticoagulant benefit of LMWH, there was no additional anti-inflammatory role with higher doses (1mg/kg/subcutaneously every 12 hours) of LMWH. Disclosures No relevant conflicts of interest to declare.


2018 ◽  
Vol 34 (3) ◽  
pp. 323-334
Author(s):  
Nadya Mincheva ◽  
Mitko Lalev ◽  
Magdalena Oblakova ◽  
Pavlina Hristakieva

The prediction of chicks? weight before hatching is an important element of selection, aimed at improving the uniformity rate and productivity of birds. With this regards, our goal was to develop and evaluate optimum models for similar prediction in two White Plymouth Rock chickens lines - line L and line K on the basis of the incubation egg weight and egg geometry characteristics - egg maximum breadth (B), egg length (L), geometric mean diameter (Dg), egg volume (V), egg surface area (S). A total of 280 eggs (140 from each line) laid by 40-weekold hens were randomly selected. Mean arithmetic values, standard deviations and coefficients of variation of studied parameters were determined for each line. Correlation coefficients between the weight of hatchlings and predictors were the highest for egg weight, geometric mean diameter, volume and surface area of eggs (r=0.731-0.779 for line L; r=0.802-0.819 for line ?). Nine linear regression models were developed and their accuracy evaluated. The regression equations of hatchlings? weight vs egg length had the lowest coefficient of determination (0.175 for line K and 0.291 for line L), but when egg length and breadth entered the model together, its value increased significantly up to 0.541 and 0.665 for lines L and K, respectively. The weight of day-old chicks from line L could be predicted with higher accuracy with a model involving egg surface area apart egg weight (ChW=0.513EW+0.282S - 10.345; R2=0.620). In line ? a more accurate prognosis was attained by adding egg breadth as an additional predictor to the weight in the model (ChW=0.587EW+0.566? - 19.853; R2=0.692). The study demonstrated that multiple linear regression models were more precise that single linear models.


OENO One ◽  
2021 ◽  
Vol 55 (4) ◽  
pp. 209-226
Author(s):  
Carlos Lopes ◽  
Jorge Cadima

Recent advances in machine vision technologies have provided a multitude of automatic tools for recognition and quantitative estimation of grapevine bunch features in 2D images. However, converting them into bunch weight (BuW) is still a big challenge. This paper aims to compare the explanatory power of the number of visible berries (#vBe) and the bunch area (BuA) in 2D images, in order to predict BuW. A set of 300 bunches from four grapevine cultivars were picked at harvest and imaged using a digital RGB camera. Then each bunch was manually assessed for several morphological attributes and, from each image, the #vBe was visually assessed while BuA was segmented using manual labelling combined with an image processing software. Single and multiple regression analysis between BuW and the image-based variables were performed and the obtained regression models were subsequently validated with two independent datasets.The high goodness of fit obtained for all the linear regression models indicates that either one of the image-based variables can be used as an accurate proxy of actual bunch weight and that a general model is also suitable. The comparison of the explanatory power of the two image-based attributes for predicting bunch weight showed that the models based on the predictor #vBe had a slightly lower coefficient of determination (R2) than the models based on BuA. The combination of the two image-based explanatory variables in a multiple regression model produced predictor models with similar or noticeably higher R2 than those obtained for single-predictor models. However, adding a second variable produced a higher and more generalised gain in accuracy for the simple regression models based on the predictor #vBe than for the models based on BuA. Our results recommend the use of the models based on the two image-based variables, as they were generally more accurate and robust than the single variable models. When the gains in accuracy produced by adding a second image-based feature are small, the option of using only a single predictor can be chosen; in such a case, our results indicate that BuA would be a more accurate and less cultivar-dependent option than the #vBe.


Author(s):  
Simone J.J.M. Verswijveren ◽  
Cormac Powell ◽  
Stephanie E. Chappel ◽  
Nicola D. Ridgers ◽  
Brian P. Carson ◽  
...  

Aside from total time spent in physical activity behaviors, how time is accumulated is important for health. This study examined associations between sitting, standing, and stepping bouts, with cardiometabolic health markers in older adults. Participants from the Mitchelstown Cohort Rescreen Study (N = 221) provided cross-sectional data on activity behaviors (assessed via an activPAL3 Micro) and cardiometabolic health. Bouts of ≥10-, ≥30-, and ≥60-min sitting, standing, and stepping were calculated. Linear regression models were fitted to examine the associations between bouts and cardiometabolic health markers. Sitting (≥10, ≥30, and ≥60 min) and standing (≥10 and ≥30 min) bouts were detrimentally associated with body composition measures, lipid markers, and fasting glucose. The effect for time spent in ≥60-min sitting and ≥30-min standing bouts was larger than shorter bouts. Fragmenting sitting with bouts of stepping may be targeted to benefit cardiometabolic health. Further insights for the role of standing need to be elicited.


2021 ◽  
pp. 026010602098486
Author(s):  
Zon-Shuan Chang ◽  
Ali Boolani ◽  
Deirdre A. Conroy ◽  
Tom Dunietz ◽  
Erica C. Jansen

Background: Breakfast skipping has been related to poor mood, but the role of sleep in this relationship remains unclear. Aim: To evaluate whether breakfast skipping associated with mood independently of sleep, and whether sleep interacted with breakfast skipping. Methods: During an in-person research visit, a sample of 329 adults completed questionnaires regarding last night’s sleep, current morning breakfast intake, and mid-morning mood states. Sex-stratified linear regression models examined associations between breakfast skipping and mood and interactions with sleep. Results: Among males, those who did not consume breakfast had less vigor independent of sleep (β=−2.72 with 95% CI −4.91, −0.53). Among females, those who did not consume breakfast had higher feelings of anxiety (β=1.21 with 95% CI −0.04, 2.47). Interaction analyses revealed that males with longer time to fall asleep and longer night-time awake time had higher depression scores in the presence of breakfast skipping, and females with more night-time awake time and shorter duration had higher fatigue and less vigor if they were also breakfast skippers. Conclusion: Breakfast skipping and poor sleep may jointly affect mood.


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