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

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.

Machine Learning for Sudden Cardiac Death Prediction in the Atherosclerosis Risk in Communities Study

Introduction Sudden cardiac death (SCD) is a devastating consequence often without antecedent expectation. Current risk stratification methods derived from baseline independently modeled risk factors are insufficient. Novel random forest machine learning (ML) approach incorporating time-dependent variables and complex interactions may improve SCD risk prediction. Methods Atherosclerosis Risk in Communities (ARIC) study participants were followed for adjudicated SCD. ML models were compared to standard Poisson regression models for interval data, an approximation to Cox regression, with stepwise variable selection. Eighty-two time-varying variables (demographics, lifestyle factors, clinical characteristics, biomarkers, etc.) collected at four visits over 12 years (1987-98) were used as candidate predictors. Predictive accuracy was assessed by area under the receiver operating characteristic curve (AUC) through out-of-bag prediction for ML models and 5-fold cross validation for the Poisson regression models. Results Over a median follow-up time of 23.5 years, 583 SCD events occurred among 15,661 ARIC participants (mean age 54 years and 55% women). Compared to different Poisson regression models (AUC at 6-year ranges from 0.77-0.83), the ML model improved prediction (AUC at 6-year 0.89). Top predictors identified by ML model included prior coronary heart disease (CHD), which explained 47.9% of the total phenotypic variance, diabetes mellitus, hypertension, and T wave abnormality in any of leads I, aVL, or V6. Using the top ML predictors to select variables, the Poisson regression model AUC at 6-year was 0.77 suggesting that the non-linear dependencies and interactions captured by ML, are the main reasons for its improved prediction performance. Conclusions Applying novel ML approach with time-varying predictors improves the prediction of SCD. Interactions of dynamic clinical characteristics are important for risk-stratifying SCD in the general population.

Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

Association of Retinal Age Gap with Arterial Stiffness and Incident Cardiovascular Disease

Background: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for ageing and mortality. Objective: To investigate associations of retinal age gap with arterial stiffness index (ASI) and incident cardiovascular disease (CVD). Methods: A deep learning (DL) model was trained based on 19,200 fundus images of 11,052 participants without any past medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35,917 participants. Regression models were used to assess the association between retinal age gap and ASI. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. Results: We found each one-year increase in retinal age gap was associated with increased ASI (β=0.002, 95% confidence interval [CI]: 0.001-0.003, P<0.001). After a median follow-up of 5.83 years (interquartile range [IQR]: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each one-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio [HR]=1.03, 95% CI: 1.01-1.06, P=0.012). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (HR=1.05; 95% CI, 1.00-1.10; P-overall <0.0001; P-nonlinear=0.0681). Conclusion: We found that retinal age gap was significantly associated with ASI and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators

Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.

Control and Prevent Land Subsidence Caused by Foundation Pit Dewatering in a Coastal Lowland Mega City: Indicator Definition, Numerical Simulation and Regression Analysis

Coastal mega cities are often commercial centers because of convenient traffic. Safe elevation above sea level is vital for their sustainable development. Global climate change and sea level rising increase flood risk especially in the lowland subsidence area. Shanghai of China was selected as research background. Although groundwater exploitation had been strictly restrained to control land subsidence and reserve safe elevation, lowering groundwater level during underground excavation cannot be avoided. Foundation pit dewatering (FPD) was intensively performed in underground exploitation during urbanization and city renewal. The FPD settlement accelerated land subsidence. Controlling FPD subsidence was urgent. Normally, the maximum horizontal influence radius of foundation pit excavation was less than three times excavation depth (H), and the 3H settlement was only caused by the FPD. The 3H maximum settlement was defined as the evaluating indicator of FPD land subsidence, and the corresponding 3H drawdown was defined as the control indicator of land subsidence. The FPD conceptual models were established on the basis of estimation and investigation of foundation pit information, including pit area, pit shape, pit depth, and curtain depth. Numerical models were established and a total of 5650 FPD numerical simulations were performed to investigate the land subsidence and FPD drawdown. Multi-factor regression analysis was conducted to obtain relations between land subsidence and FPD drawdown. Regression models were established between the 3H drawdown and the shape, area, depth, and curtain depth of foundation pit on the basis of the numerical simulations. A typical example introduced to verify the regression models. The regression models were used to manage the FPD land subsidence by controlling the 3H FPD drawdown. The results can provide reference for the land subsidence control in a coastal lowland city.