scholarly journals Different Effects of Aging on Intraocular Pressures Measured by Three Different Tonometers

2021 ◽  
Vol 10 (18) ◽  
pp. 4202
Author(s):  
Kazunobu Sugihara ◽  
Masaki Tanito
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Corneal Thickness ◽  
Regression Analyses ◽  
Mixed Effect ◽  
Effect Regression ◽  
Older Subjects ◽  
Young Subjects ◽  
Highly Correlated ◽  
Effects Of Aging

This study aimed to compare intraocular pressures (IOP) using different tonometers, Goldmann applanation (IOPGAT), non-contact (IOPNCT), and rebound (IOPRBT), and to assess the effects of aging and central corneal thickness (CCT) on the measurements. The IOPGAT, IOPNCT, IOPRBT, mean patient age (65.1 ± 16.2 years), and CCT (521.7 ± 39.2 µm) were collected retrospectively from 1054 eyes. The differences among IOPs were compared by the paired t-test. Possible correlations between devices, age, and CCT were assessed by linear regression analyses. The effects of age and CCT on the IOP reading were assessed by mixed-effects regression models. The IOPGAT values were 2.4 and 1.4 mmHg higher than IOPNCT and IOPRBT, respectively; the IOPNCT was 1.0 mmHg lower than IOPRBT (p < 0.0001 for all comparisons). The IOPs measured by each tonometer were highly correlated with each other (r = 0.81–0.90, t = 45.2–65.5). The linear regression analyses showed that age was negatively correlated with IOPNCT (r = −0.12, t = −4.0) and IOPRBT (r = −0.14, t = −4.5) but not IOPGAT (r = 0.00, t = −0.2); the CCT was positively correlated with IOPGAT (r = 0.13, t = 4.3), IOPNCT (r = 0.29, t = 9.8), and IOPRBT (r = 0.22, t = 7.2). The mixed-effect regression models showed significant negative correlations between age and IOPNCT (t = −2.6) and IOPRBT (t = −3.4), no correlation between age and IOPGAT (t = 0.2), and a significant positive correlation between CCT and the tonometers (t = 3.4–7.3). No differences between IOPGAT and IOPRBT were seen at the age of 38.8 years. CCT affects IOPs from all tonometers; age affects IOPNCT and IOPRBT in different degrees. IOPRBT tended to be higher than IOPGAT in young subjects, but this stabilized in middle age and became higher in older subjects.

Application of Mixed-Effect Regression Models for Compiling a Land Value Map

2018 ◽  
Author(s):  
Radoslaw Cellmer ◽  
Aneta Cichulska ◽  
Malgorzata Renigier-Bilozor ◽  
Andrzej Bilozor
Keyword(s):  
Regression Models ◽  
Land Value ◽  
Mixed Effect ◽  
Effect Regression

Emergency department and ‘Google flu trends’ data as syndromic surveillance indicators for seasonal influenza

2014 ◽  
Vol 142 (11) ◽  
pp. 2397-2405 ◽  
Author(s):  
L. H. THOMPSON ◽  
M. T. MALIK ◽  
A. GUMEL ◽  
T. STROME ◽  
S. M. MAHMUD
Keyword(s):  
Emergency Department ◽  
Linear Regression ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Linear Regression Models ◽  
Physician Visits ◽  
Influenza Activity ◽  
Ed Visits ◽  
Highly Correlated

SUMMARYWe evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data – total ED visits attributed to influenza-like illness (ILI) (‘ED ILI volume’) and percentage of visits attributed to ILI (‘ED ILI percent’) – and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

scholarly journals In VivoCorneal Biomechanical Properties with Corneal Visualization Scheimpflug Technology in Chinese Population

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Wu ◽  
Lei Tian ◽  
Yi-fei Huang
Keyword(s):  
Linear Regression ◽  
Healthy Subjects ◽  
Chinese Population ◽  
Regression Models ◽  
Corneal Thickness ◽  
Intraclass Correlation ◽  
Biomechanical Properties ◽  
Linear Regression Models ◽  
Corneal Volume ◽  
Biomechanical Parameters

Purpose. To determine the repeatability of recalculated corneal visualization Scheimpflug technology (CorVis ST) parameters and to study the variation of biomechanical properties and their association with demographic and ocular characteristics.Methods. A total of 783 healthy subjects were included in this study. Comprehensive ophthalmological examinations were conducted. The repeatability of the recalculated biomechanical parameters with 90 subjects was assessed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Univariate and multivariate linear regression models were used to identify demographic and ocular factors.Results. The repeatability of the central corneal thickness (CCT), deformation amplitude (DA), and first/second applanation time (A1/A2-time) exhibited excellent repeatability (CV%≤3.312% and ICC≥0.929 for all measurements). The velocity in/out(Vin/out), highest concavity- (HC-) radius, peak distance (PD), and DA showed a normal distribution. Univariate linear regression showed a statistically significant correlation betweenVin,Vout, DA, PD, and HC-radius and IOP, CCT, and corneal volume, respectively. Multivariate analysis showed that IOP and CCT were negatively correlated withVin, DA, and PD, while there was a positive correlation betweenVoutand HC-radius.Conclusion. The ICCs of the recalculated parameters, CCT, DA, A1-time, and A2-time, exhibited excellent repeatability. IOP, CCT, and corneal volume significantly influenced the biomechanical properties of the eye.

scholarly journals Human Sex Ratio at Birth: Mythical and Observable Trends

2020 ◽  
Author(s):  
Yanan Long ◽  
Qi Chen ◽  
Henrik Larsson ◽  
Andrey Rzhetsky
Keyword(s):  
Sex Ratio ◽  
The United States ◽  
Time Series Models ◽  
Agricultural Activity ◽  
Health Record ◽  
Sex Ratio At Birth ◽  
Regression Analyses ◽  
Mixed Effect ◽  
Effect Regression ◽  
Over Time

AbstractThe human sex ratio at birth (SRB) is defined as the ratio between the number of newborn boys to the total number of newborns per time unit. It is, typically, slightly greater than (more boys than girls) and fluctuates over time. In this study, we sought to “myth-check” previously reported associations (and test new hypotheses) using variants of mixed-effect regression analyses and time-series models on two very large electronic health record datasets, representing the populations in the United States and Sweden, respectively. Our results revealed that neither dataset supported models in which the SRB changed seasonally or in response to variations in ambient temperature, and that an increased level of a diverse array of pollutants were associated with lower SRBs. Moreover, we found that increased levels of industrial and agricultural activity, which served as proxies for water pollution, were also associated with lower SRBs.

Rho kinase-mediated local cold-induced cutaneous vasoconstriction is augmented in aged human skin

2007 ◽  
Vol 293 (1) ◽  
pp. H30-H36 ◽  
Author(s):  
Caitlin S. Thompson-Torgerson ◽  
Lacy A. Holowatz ◽  
Nicholas A. Flavahan ◽  
W. Larry Kenney
Keyword(s):  
Rho Kinase ◽  
Age Difference ◽  
Local Cooling ◽  
Kinase Inhibition ◽  
Doppler Flowmetry ◽  
Forearm Skin ◽  
Older Subjects ◽  
Young Subjects ◽  
Effects Of Aging ◽  
Cutaneous Vasoconstriction

Cutaneous vasoconstriction (VC), a critical thermoregulatory response to cold, is generally impaired with aging. However, the effects of aging on local cooling-induced VC and its underlying mechanisms are poorly understood. We tested whether aged skin exhibits attenuated localized cold-induced VC and whether Rho kinase-mediated cold-induced VC is augmented with age. Skin blood flow was monitored with laser Doppler flowmetry (LDF) on seven young and seven older subjects. Cutaneous vascular conductance (CVC; LDF/mean arterial pressure) was expressed as percentage change from baseline (%ΔCVCbase). In protocol 1, two forearm skin sites were cooled to six temperatures (31.5–19°C) for 10 min each or two temperatures (29°C, 24°C) for 30 min each, with no age differences in the magnitude of VC. In protocol 2, three forearm skin sites were instrumented for intradermal microdialysis and cooled to 24°C for 40 min. During minutes 1–5, there was no age difference in CVC responses at control sites (young: −45 ± 6% vs. older: −46 ± 3%, P > 0.9). Adrenoceptor antagonism (yohimbine + propranolol) abolished VC in young (to +15 ± 13%, P < 0.05) but only partially inhibited VC in older subjects (to −23 ± 6%, P < 0.05). Rho kinase inhibition plus adrenoceptor antagonism (yohimbine + propranolol + fasudil) abolished VC in both groups. During minutes 35–40, there was no age difference in control (young: −77 ± 4% vs. older: −70 ± 2%, P > 0.3) or adrenoceptor-antagonized responses (young: −61 ± 3% vs. older: −55 ± 2%, P > 0.3); however, Rho kinase inhibition plus adrenoceptor antagonism blocked more VC in older compared with young subjects (−19 ± 11% vs. −35 ± 3%, P < 0.05). Although its magnitude remains unaffected, cold-induced VC becomes less dependent on adrenergic and more dependent on Rho kinase signaling with advancing age.

Smooth pursuit performance in patients with affective disorders or schizophrenia and normal controls: analysis with specific oculomotor measures, RMS error and qualitative ratings

1995 ◽  
Vol 25 (2) ◽  
pp. 387-403 ◽  
Author(s):  
L. Friedman ◽  
J. A. Jesberger ◽  
L. J. Siever ◽  
P. Thompson ◽  
R. Mohs ◽  
...  
Keyword(s):  
Linear Regression ◽  
Smooth Pursuit ◽  
Affective Disorders ◽  
Regression Analyses ◽  
Performance Deficits ◽  
Catch Up ◽  
Rms Error ◽  
The Difference ◽  
Highly Correlated ◽  
Normal Controls

SynopsisSmooth pursuit performance in schizophrenia and affective disorders has generally been found to be abnormal using a variety of measures. The purpose of this study was to assess patients with these disorders and normal controls in order to compare the different measures across diagnoses. Smooth pursuit was assessed using quantitative specific measures (gain, catch-up saccade rate and amplitude, square-wave jerk rate, number of anticipatory saccades and total time scored), as well as two global measures: root mean-square error (RMS) and qualitative rating. As previously reported, patients with schizophrenia had low gain, increased catch-up saccade rate and spent less time engaged in scoreable smooth pursuit than normal controls. Patients with affective disorders were not statistically different from controls on any of these measures, and had significantly higher gain than patients with schizophrenia. RMS error and qualitative rating measures were highly correlated (r = 0·87). In linear regression analyses, the quantitative specific measures were highly significant predictors of both RMS error and qualitative ratings (P < 0·0001). Linear regression analyses and a modelling study indicated that one quantitative specific measure, the percent of time engaged in scoreable smooth pursuit (total time scored), was most related to global ratings. However, RMS error and qualitative ratings were less sensitive than total time scored to the difference between controls and patients with schizophrenia. These data indicate two smooth pursuit performance deficits in schizophrenia: patients spend less time engaged in scoreable smooth pursuit and have low gain (accompanied by increased compensatory saccades) when the smooth pursuit is engaged.

scholarly journals A Novel Method for Computationally Efficacious Linear and Polynomial Regression Analytics of Big Data in Medicine

2020 ◽  
Vol 14 (5) ◽  
pp. 1
Author(s):  
Ahmed Al-Imam
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Regression Models ◽  
Rank Test ◽  
Regression Analyses ◽  
Linear Regression Models ◽  
Signed Rank ◽  
Wilcoxon Signed Rank Test ◽  
Scale Down ◽  
Signed Rank Test

Background: Machine learning relies on a hybrid of analytics, including regression analyses. There have been no attempts to deploy a scale-down transformation of data to enhance linear regression models. Objectives: We aim to optimize linear regression models by implementing data transformation function to scale down all variables in an attempt to minimize the sum of squared error. Materials and Methods: We implemented non-Bayesian statistics using SPSS and MatLab. We used Excel to generate 40 trials of linear regression models, and each has 1,000 observations. We utilized SPSS to conduct regression analyses, Wilcoxon signed-rank test, and Cronbach&rsquo;s alpha statistics to evaluate the performance of the optimization model. Results: The scale-down transformation succeeded by significantly reducing the sum of squared errors [absolute Z-score=5.511, effect size=0.779, p-value&lt;0.001, Wilcoxon signed-rank test]. Inter-item reliability testing confirmed the robust internal consistency of the model [Cronbach&rsquo;s alpha=0.993]. Conclusions: The optimization model is valuable for high-impact research based on regression. It can reduce the computational processing demands for powerful real-time and predictive analytics of big data.

Sign in / Sign up

Export Citation Format

Share Document