scholarly journals The Relationship Between Plasma Tetrahydrocannabinol Levels and Intraocular Pressure in Healthy Adult Subjects

2022 ◽  
Vol 8 ◽  
Author(s):  
Sameh Mosaed ◽  
Andrew K. Smith ◽  
John H. K. Liu ◽  
Donald S. Minckler ◽  
Robert L. Fitzgerald ◽  
...  

BackgroundΔ9-tetrahydrocannabinol (THC) has been shown to decreased intraocular pressure (IOP). This project aims to define the relationship between plasma THC levels and IOP in healthy adult subjects.MethodsEleven healthy subjects received a single dose of inhaled cannabis that was self-administered in negative pressure rooms. Measurements of IOP and plasma THC levels were taken at baseline and every 30 min for 1 h and afterwards every hour for 4 h. IOP reduction and percent change in IOP over time were calculated. Linear regression models were used to measure the relationship between IOP and plasma THC levels. Two line linear regression models with F-tests were used to detect change points in the regression. Then, Pearson correlations were computed based on the change point.ResultsTwenty-two eyes met inclusion criteria. The average peak percentage decrease in IOP was 16% at 60 min. Percent IOP reduction as well as total IOP reduction demonstrated a negative correlation with THC plasma levels showing r-values of −0.81 and −0.70, respectively. F-tests revealed a change point in the regression for plasma levels >20 ng/ml. For levels >20 ng/ml, the correlation coefficients changed significantly with r-values of 0.21 and 0.29 (p < 0.01).ConclusionPlasma THC levels are significantly correlated with IOP reduction up to plasma levels of 20 ng/ml. Plasma levels >20 ng/ml were not correlated with further decrease in IOP. More research is needed to determine the efficacy of THC in reducing IOP for eyes with ocular hypertension and glaucoma.

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.


Author(s):  
Muhammad Bayu Nirwana ◽  
Dewi Wulandari

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021


2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.


2016 ◽  
Vol 25 (2) ◽  
pp. 225-230
Author(s):  
Cristina Fernandes do Amarante ◽  
Wagner de Souza Tassinari ◽  
Jose Luis Luque ◽  
Maria Julia Salim Pereira

Abstract The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.


2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Wester ◽  
J Pec ◽  
C Fisser ◽  
K Debl ◽  
O Hamer ◽  
...  

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): ReForM-B-Program Background Abnormal P-wave terminal force in lead V1 (PTFV1) is associated with atrial remodeling. The relationship between PTFV1 and atrial function after acute myocardial injury is insufficiently understood and may be elucidated by detailed feature tracking (FT) strain analysis of cardiac magnetic resonance images (CMR). Purpose We investigated the relationship between PTFV1 and left atrial (LA) strain (measured by CMR) in a patient cohort presenting with acute myocardial infarction (MI). Methods 56 patients with acute MI underwent CMR within 3-5 days after MI. PTFV1 was measured as the product of negative P-wave amplitude and duration in lead V1 (Fig. A). A PTFV1 >4000 ms*µV was defined as abnormal. CMR cine data were retrospectively analyzed using a dedicated FT software. LA strain (ε) and strain rate (SR) for atrial reservoir ([εs]; [SRs]), conduit ([εe]; [SRe]) and booster function ([εa]; [SRa]) were measured in two long-axis views (Fig. A). Results Patients with abnormal PTFV1 had significantly reduced LA conduit function εe and SRe (Fig. B + D). There was a significant negative correlation between the extent of PTFV1 and both εe and SRe (Fig. C + E). In univariate and multivariate regression models, both PTFV1 and age predicted atrial conduit function. In contrast, multiple clinical co-factors had no significant influence on εe (Table). Interestingly, linear regression models revealed only mild dependency of PTFV1 on conventional parameters of cardiac function such as left ventricular ejection fraction (p = 0.059; R²(adj.)=0.047), and no dependency on structural parameters such as LA area (p = 0.639; R²(adj.)=0.016), or LA fractional area change (p = 0.825; R²(adj.)=0.020). Conclusion Abnormal PTFV1 was associated with reduced LA function independent from numerous clinical co-factors in patients presenting with acute myocardial infarction. Table N = 56 Linear Regression Analysis Multiple Linear Regression Analysis (R2 (adj.)=0.376, p = 0.016) Variable B 95% CI P value R2 (adj.) B 95% CI P value PTFV1 [µV*ms] -1.628 17085.298 to 27210.854 0.013 0.092 -1.315 -2.614 to -0.016 0.047 Age [y] -425.775 24985.168 to 54634.995 0.002 0.145 -610.815 -982.78 to -238.849 0.001 Body mass indes [kg/m2] -185.653 -3259.187 to 47020.775 0.671 -0.015 -506.096 -1327.357 to 315.165 0.219 Creatinine kinase [U/l] -1.571 14806.991 to 24842.272 0.121 0.027 -1.791 -3.72 to 0.138 0.067 Male sex -893.28 10701.206 to 23504.066 0.802 -0.017 4275.631 -3842.517 to 12393.78 0.292 Estimated glomerular filtration rate [ml/min/1.73m2] 88.617 -4564.177 to 21395.361 0.202 0.012 -163.981 -331.343 to 3.381 0.054 Systolic blood pressure [mmHg] -2.001 14045.786 to 22037.253 0.095 0.038 29.331 -108.243 to 166.906 0.668 nt-pro brain natriuretic peptide [pg/ml] 24.629 -4060.804 to 30920.828 0.716 -0.016 1.015 -1.778 to 3.809 0.466 Univariate and multivariate linear regression models for left atrial conduit strain Abstract Figure


2017 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Tony Xu ◽  
Shayan Khalili ◽  
Cynthia Deng

This paper analyzes the relationship between the number of Twitter and Mendeley readers with the article’s subject, publisher, journal, and title length. It also looks at which country has the greatest number of readers to see if researchers can garner more visibility by publishing an article relevant to issues in those countries. The purpose of this report is to help researchers improve the visibility and impact value of their research. The data was gathered from 550,000 scientific research papers published between January 1st and July 1st of 2016. Python’s built-in JSON library was used to extract the number of Twitter and Mendeley readers, as well as the article count for each factor. The correlation between readers per article and each factor was then visualized using bubble graphs, linear regression models, and scatter plots. This paper concludes that the length of the title is the strongest factor affecting readership. In particular, titles with lengths between 51 and 90 characters have the greatest number of readers. Moreover, articles relevant to issues in countries with a higher GDP have the highest overall readership. On the other hand, the publisher and the journal did not have a significant effect on readership, while the subject of the article had a moderate effect on readership.


2018 ◽  
Vol 47 (7) ◽  
pp. 1056-1078 ◽  
Author(s):  
Riccardo Ladini ◽  
Moreno Mancosu ◽  
Cristiano Vezzoni

General consensus concerning the nature of the relationship between political disagreement and turnout has not yet been reached: while several studies have demonstrated the demobilizing effect of disagreement, others have found no significant evidence for this. Recently, scholars have argued that diversity — a situation in which some people are in agreement with ego and some are not — can boost electoral participation. The present article argues that the insights of previous studies are the result of two differentiated effects that depend on the level of intimacy of the discussants to which one is exposed. By employing a set of linear regression models on Italian National Election Study 2013 pre-electoral survey ( N > 8,000), we show that mixed political views among friends boost electoral participation. For what concerns relatives, the likelihood to vote decreases linearly with increasing disagreement.


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