Stepwise Variable Selection Method Research Articles

Health and wellbeing of staff working at higher education institutions globally during the post-COVID-19 pandemic period: evidence from a cross-sectional study

BackgroundThe ongoing global crisis of Higher Education (HE) institutions during the post-COVID-19 pandemic period has increased the likelihood of enduring psychological stressors for staff. This study aimed to identify factors associated with job insecurity, burnout, psychological distress and coping amongst staff working at HE institutions globally.MethodsAn anonymous cross-sectional study was conducted in 2023 with staff at HE institutions across 16 countries. Job insecurity was measured using the Job Insecurity Scale (JIS), burnout using the Perceived Burnout measure question, psychological distress using the Kessler Psychological Distress Scale (K10), and coping using the Brief Resilient Coping Scale. Multivariable logistic regression with a stepwise variable selection method was used to identify associations.ResultsA total of 2,353 staff participated; the mean age (± SD) was 43(± 10) years and 61% were females. Most staff (85%) did not feel job insecurity, one-third (29%) perceived burnout in their jobs, more than two-thirds (73%) experienced moderate to very high levels of psychological distress, and more than half (58%) exhibited medium to high resilient coping. Perceived job insecurity was associated with staff working part-time [Adjusted Odds Ratio 1.53 (95% Confidence Intervals 1.15–2.02)], having an academic appointment [2.45 (1.78–3.27)], having multiple co-morbidities [1.86 (1.41–2.48)], perceived burnout [1.99 (1.54–2.56)] and moderate to very high level of psychological distress [1.68 (1.18–2.39)]. Perceived burnout was associated with being female [1.35 (1.12–1.63)], having multiple co-morbidities [1.53 (1.20–1.97)], perceived job insecurity [1.99 (1.55–2.57)], and moderate to very high levels of psychological distress [3.23 (2.42–4.30)]. Staff with multiple co-morbidities [1.46 (1.11–1.92)], mental health issues [2.73 (1.79–4.15)], perceived job insecurity [1.61 (1.13–2.30)], and perceived burnout [3.22 (2.41–4.31)] were associated with moderate to very high levels of psychological distress. Staff who perceived their mental health as good to excellent [3.36 (2.69–4.19)] were more likely to have medium to high resilient coping.ConclusionsFactors identified in this study should be considered in reviewing and updating current support strategies for staff at HE institutions across all countries to reduce stress and burnout and improve wellbeing.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.–9 a.m.) and dusk (4 p.m.–6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

LASSO and Elastic Net Tend to Over-Select Features

Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The LASSO approach selects features with large regression estimates, rather than based on statistical significance, that are associated with the outcome by imposing an L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select even more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model may lose prediction accuracy. In this paper, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation-based method selects a very small number of features while maintaining a high prediction power, whereas LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high-dimensional data, even without advanced bioinformatics knowledge.

The effect of attention control and academic motivation on perceived learning in hybrid occupational therapy education

Introduction: Many of the studies on learning have focused on face-to-face or online learning, and information on hybrid learning is limited. The aim of this study is to examine the predictors of perceived learning in occupational therapy students in terms of different variables in the hybrid education process. Method: This study, which was planned in descriptive cross-sectional design, was carried out online using Google Forms. Attentional Control Scale, Academic Motivation Scale, and Perceived Learning Scale were used in this study. Multiple linear regression analysis was performed with the stepwise variable selection method. Findings: The coefficient of the Academic Motivation Scale Amotivation variable, which made the greatest contribution to the variance rate explained by the regression model, was −0.407. A one-unit increase in the Academic Motivation Scale Intrinsic Motivation and Academic Motivation Scale Extrinsic Motivation variables caused an increase in the Perceived Learning Scale total score of 0.198 and 0.364 standard deviations (SDs), and a one-unit increase in the Academic Motivation Scale_Amotivation variable caused a decrease in the Perceived Learning Scale total score of 0.407 SD. Conclusion: The most important predictor of perceived learning is amotivation. We suggest that improving the intrinsic and extrinsic motivation and reducing amotivation in students studying at universities offering hybrid learning can be used as a strategy that increases attentional control and perceived learning.

Relationship between plant species diversity and aboveground biomass in alpine grasslands on the Qinghai–Tibet Plateau: Spatial patterns and the factors driving them

Alpine grasslands are important ecosystems on the Qinghai–Tibet Plateau and are extremely sensitive to climate change. However, the spatial responses of plant species diversity and biomass in alpine grasslands to environmental factors under the background of global climate change have not been thoroughly characterized. In this study, a random forest model was constructed using grassland ground monitoring data with satellite remote sensing data and environmental variables to characterize the plant species diversity and aboveground biomass of grasslands in the Three-River Headwaters Region within the Qinghai–Tibet Plateau and analyze spatial variation in the relationship between the plant species diversity and aboveground biomass and their driving factors. The results show that (1) the selection of characteristic variables can effectively improve the accuracy of random forest models. The stepwise regression variable selection method was the most effective approach, with an R2 of 0.60 for the plant species diversity prediction model and 0.55 for the aboveground biomass prediction model, (2) The spatial distribution patterns of the plant species diversity and aboveground biomass in the study area were similar, they were both high in the southeast and low in the northwest and gradually decreased from east to west. The relationship between the plant species diversity and aboveground biomass varied spatially, they were mostly positively correlated (67.63%), but they were negatively correlated in areas with low and high values of plant species diversity and aboveground biomass, and (3) Analysis with geodetector revealed that longitude, average annual precipitation, and elevation were the main factors driving variation in the plant species diversity and aboveground biomass relationship. We characterized plant species diversity and aboveground biomass, as well as their spatial relationships, over a large spatial scale. Our data will aid biodiversity monitoring and grassland conservation management, as well as future studies aimed at clarifying the relationship between biodiversity and ecosystem functions.

Synthesis, Characterization and Evaluation of Some Novel 2-Mercaptobenzothiazole Derivatives: In Silico and Experimental Approach

Recently, QSAR technique and molecular docking have become widespread in designing new series of compounds, depending upon the different models developed, before actually synthesizing and testing them. Due to the diverse activity profile of substituted Benzothiazole, it has become a pharmacophore of interest. The main motive of the study was to design some novel 2- Mercaptobenzothiazole molecules with potential anti-inflammatory effect. Thus, in order to get a better insight into the structural features of 2- Mercaptobenzothiazole, a series of analogues were taken from the literature and 2D, 3D QSAR models were developed and molecular docking and ADME studies were also performed. For 2D QSAR study, stepwise forwarding variable selection method, coupled with Partial Least Square (SWF-PLS) regression-based method, was used to generate the best 2D model giving r2= 0.9425, q2=0.9018, F-value=90.0118, pred_r2=0.8976. In contrast, the forward stepwise variable selection k-nearest neighbour molecular field analysis approach was used to generate best 3D QSAR model (q2=0.8171 and pred_r2=0.7006). Docking studies were performed on 29 derivatives of 2-Mercaptobenzothiazoles. The ligands were prepared and docked against protein S. aureus TYrRS with a resolution of 3.2Å. The three derivatives K6, K18 and K20 were found to have the best docking scores (-48.44, -47.46, -48.52). Further, synthesis of these three derivatives was carried out. Spectral characterization was carried out by IR, 1H NMR and 13C NMR and all the structures were found to be in agreement with the data obtained. In vitro anti-inflammatory evaluation of these compounds was carried out by following the protein denaturation method and HRBC membrane stabilization method. Compound K20 displayed potential anti-inflammatory activity, which may be attributed to the electronegative group on the phenyl ring substituted on the pyrazole part of the pharmacophore.

Hemorrhage Risk after Stereotactic Radiosurgery for Patients with Unruptured Brain Arteriovenous Malformation: Significance of Vascular Compactness

<h3>Purpose/Objective(s)</h3> Our purpose was to investigate whether the morphology (i.e., compact/ diffuse) of brain arteriovenous malformations (bAVMs) correlates with the incidence of hemorrhagic events in patients with unruptured bAVMs after receiving non-invasive stereotactic radiosurgery (SRS). <h3>Materials/Methods</h3> This study retrospectively included 264 adult patients with unruptured bAVMs undergoing upfront SRS from multi-institutions. Two senior neurosurgeons confirmed the unruptured presentation. The follow-ups were performed in a 6-month interval. Hemorrhagic events were defined as bleeding signs on computed tomography (CT) or magnetic resonance images (MRI). The morphology of bAVMs was evaluated using fully automated segmentation, which calculated the proportion of vessel, brain tissue, and cerebrospinal fluid in bAVMs on T2-weighted MRI. Compactness index, defined as a ratio of vessel to brain tissue, was proposed and further dichotomized based on Youden indices to classify the bAVMs into compact or diffuse type. Cox proportional hazard model was used to identify the independent factors for the occurrence of post-SRS hemorrhage. <h3>Results</h3> The median follow-up duration was 62.1 months. Post-SRS hemorrhage occurred in 14 (5.3%) patients with 2 bAVMs bleeding twice, incurring an annual bleeding risk of 0.9%. Multivariable analysis with stepwise backward variable selection method revealed that bAVM morphology (compact versus diffuse), bAVM volume and prescribed margin dose were significant predictors for post-GKRS hemorrhage. The incidence density of post-GKRS hemorrhage in the compact and diffuse bAVMs were 4.9 and 11.8 (hemorrhagic events/ 1000 person-years (P-Ys)). Among the diffuse bAVMs (n=153), the post-GKRS hemorrhage rate was lower for smaller bAVM volume, <15ml versus 15-30ml versus >30ml (2.2 versus 13.5 versus 26.3 hemorrhagic events/ 1000 P-Ys; p=0.044) and for bAVMs receiving margin dose >16.6 Gy versus <16.6 Gy (5.2 versus 25.8 hemorrhagic events/ 1000 P-Ys; p=0.004). For the compact bAVMs (n=111), the post-GKRS hemorrhage rate was not significantly different between the dose and volume groups. <h3>Conclusion</h3> Compact and smaller bAVMs, with higher prescribed margin dose harbor lower risks of post-SRS hemorrhage. Among the diffuse bAVMs, post-GKRS hemorrhage risk varied with bAVM volume and margin dose. The finding could help guide GKRS treatment decision-making for the unruptured bAVMs.

Endurance Versus Non-endurance Athletes: A Descriptive Discriminant Analysis Based On Echocardiography

PURPOSE: To discriminate between endurance and non-endurance athletes according to echocardiographic measures. METHODS: Echocardiographic exams carried out in 201 male and female athletes of diverse sports were studied (Age = 21.7 ± 5.4 years; mean ± SD). A set of fifteen measures was considered: Left ventricular diastolic diameter (LVDD), Left ventricular diastolic diameter index (LVDDI), Interventricular septum thickness (IST), Left ventricular posterior wall thickness (LVPWT), Relative wall thickness (RWT), Left ventricular mass (LVM), Left ventricular mass index (LVMI), Left auricular diameter (LAD), Aortic root diameter (ARD), Inferior vena cava diameter (IVCD), E-wave velocity (EV), A-wave velocity (AV), E/A ratio (EAR), E/e′ ratio (Ee′R) and Right ventricular systolic velocity (RVS). The athletes were categorized in two groups based on the characteristics of their sport discipline: “Endurance” (cardiorespiratory endurance) and “Other” (Non-Endurance). Gender-stratified analyses were performed. MANOVA was firstly implemented to evaluate for a significance difference between endurance and non-endurance athletes. Post-hoc analysis was done by means of Descriptive discriminant analysis; in order to obtain a more reduced set of variables, Stepwise variable selection method was previously performed using the Wilk’s Lambda criterion. Statistical significance was fixed at the 0.05 level. RESULTS: MANOVA was statistically significant in men (Wilk's λ = 0.78, F = 1.81, p = 0.04) and in women (Wilk's λ = 0.63, F = 2.82, p = 0.002). The Stepwise procedure selected two variables in the male (IST and LVDDI) and female (LVMI and LVDDI) strata. The discriminant function was highly significant in both cases (males: Wilk's λ = 0.86, F = 8.59, p < 0.001; females: Wilk's λ = 0.77, F = 13.21, p < 0.001). In men, the standardized discriminant coefficient and the structure coefficient were higher for IST (0.96 and 0.81) than for LVDDI (0.60 and 0.36). And in women, the coefficients corresponding to LVMI (0.62 and 0.84) were similar to the ones of LVDDI (0.59 and 0.81). CONCLUSIONS: In the male athletes, IST showed a more important contribution for distinguishing between the endurance and non-endurance groups than LVDDI. And in the female athletes, LVMI and LVDDI had a comparable contribution.

Estimation of euphotic zone depth in shallow inland water using inherent optical properties and multispectral remote sensing imagery

Euphotic zone depth (Zeu) is a key indicator of underwater light environment that regulates different biogeochemical and physical processes in aquatic ecosystem. Remote sensing approaches offer a unique opportunity to frequently monitor Zeu, although its estimation from diffuse attenuation coefficient (Kd) requires depth profiles of the down-welling irradiance (Ed). Measuring Ed values at different water depths (i.e., Ed profiling) in shallow waterbodies is a challenging task with the available profiling equipment. In this study, Kd values were estimated from measured absorption coefficient (a) data at multiple locations in the Chilika lagoon, which is a shallow waterbody located in the eastern coast of India and is a prestigious Ramsar site. Specifically, remote sensing reflectance (Rrs), a values (wavelength: 400–715 nm at 4 nm interval), Secchi disc depth (ZSD), suspended particulate matter (SPM) and chlorophyll-a (Chl-a) contents were measured during Oct. 07–08, 2018 and Feb. 02–08, 2019 in the Chilika lagoon. Atmospherically corrected Sentinel 2A/2B (S2A/S2B) MSI- Rrs data (level 2A) were then used to develop algorithms for mapping Zeu for the entire lagoon. Modeling results showed that Rrs values at band 4 (665 nm) or band 5 (704 nm) extracted from the S2A/S2B MSI data may be used to estimate Kd of photosynthetically active radiation (PAR) (observed range: 1.29 m−1 to 10.74 m−1) with the root-mean-squared error (RMSE) as low as 0.81 m−1. However, uncertainties associated with atmospheric correction of S2A/S2B MSI-single band Rrs values are high when compared with in situ Rrs. Thus, a multivariate modeling approach was adopted with Rrs- band ratio and band difference variables selected by stepwise, forward, and backward variable selection method with p < 0.05. Modeling results showed that the band differences between band 2 and band 4 and band 3 and band 5 may be used to estimate Kd(PAR) with improved mean absolute percentage difference (MAPD) while ensuring low uncertainty associated with atmospheric correction. Spatio-temporal dynamics from S2A/S2B MSI for 85 different dates during Jan. 2017 to June 2021 provided seasonal patterns for Zeu in the Chilika lagoon.

Factors Affecting Readmission to a Drug Rehabilitation Center in Brunei Darussalam

The objective of this study is to examine the sociodemographic factors contributing to the readmission to a drug rehabilitation center among drug addicts in Brunei Darussalam. This retrospective study used de-identified data obtained from the Al-Islah Rehabilitation Center for the period 1 January 2010 to 31 December 2017. Univariate and multiple logistic regression analyses with a stepwise variable selection method were used to estimate and determine significant factors associated with readmission to the center. A total of 92 out of 705 subjects (13%) were readmitted to the rehabilitation center within the study period, with ages ranging from 20 to 59 years (M = 36.1, SD = 8.6 years). Multiple logistic regression found that subjects who were in the age group of 40 to 49 (OR = 2.24, 95% CI [1.16, 4.37]) resided in the Belait district (OR = 1.94, 95% CI [1.00, 3.63]), divorced or widowed (OR = 2.14, 95% CI [1.19, 3.82]), had committed other criminal offenses (OR = 1.91, 95% CI [1.16, 3.11]), and voluntarily admitted (OR = 2.33, 95% CI [1.34, 4.01]), were found to be significantly more likely to get readmitted to the rehabilitation center. This study provides evidence of sociodemographic factors in relation to readmission to the rehabilitation center in Brunei Darussalam.

Outcomes of Patients With Hematologic Malignancies Who Received Inpatient Palliative Care Consultation.

Palliative care (PC) plays an established role in improving outcomes in patients with solid tumors, yet these services are underutilized in hematologic malignancies (HMs). We reviewed records of hospitalized patients with active HM to determine associations between PC consultation and length of stay, intensive care unit stay, 30-day readmission, and 6-month mortality compared with those who were not seen by PC. We reviewed all oncology admissions at our institution between 2013 and 2019 and included patients with HM actively on treatment, stratified by those seen by PC to controls not seen by PC. Groups were compared using Wilcoxon rank-sum, chi-square, and Fisher's exact tests on the basis of the type and distribution of data. Multiple logistic regression models with stepwise variable selection methods were used to find predictors of outcomes. Three thousand six hundred fifty-four admissions were reviewed, among which 370 unique patients with HM were included. Among these, 102 (28%) patients saw PC, whereas the remaining 268 were controls with similar comorbidities. When compared with controls, PC consultation was associated with a statistically significant reduction in 30-day readmissions (16% v 27%; P = .024), increased length of stay (11.5 v 6 days; P < .001), increased intensive care unit admission (28% v 9%; P < .001), and increased 6-month mortality (67% v 15%; P < .001). These data were confirmed in multivariable models. In this retrospective study, more than two thirds of patients with HM did not receive PC consultation despite having similar comorbidities, suggesting that inpatient PC consultation is underutilized in patients with HM, despite the potential for decreased readmission rates.

A Combined Approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and In silico ADME/Tox Prediction of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one Analogs to Identify Potential Reverse Transcriptase Inhibitor: Anti-HIV Agents.

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, costeffective, and high throughput screening method to identify potential reverse transcriptase inhibitors. A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square is associated with the Sphere Exclusion method (PLS-SE), having r2 and q2 values 0.9509 and 0.8038, respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant, which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-crossvalidated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed, which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties. The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

Allostatic Load, Single, and Dual Chronic Conditions: Evidence from the National Health and Nutrition Examination Survey

Background:Allostatic load (AL) is defined as a cumulative burden of chronic stress and life events, which involves the interaction of different physiological systems at varying degrees of activity. AL is suspected of contributing to health disparities among different populations. Suppressed or overactive physiological systems can interrupt AL affecting proper tissue and organ function leading to disease. The objective of our study was to determine the association of AL with dual chronic conditions.Methods:We used data from the National Health and Nutrition Examination Survey (NHANES). For the current analysis, we used the data cycles of 2007–2010, which is the most recent data that collected comprehensive measures of the composite AL outcome variable. Descriptive, bivariate, and multivariable logistic regression, with stepwise forward variable selection method (P < 0.05), were conducted using STATA/IC 15.0.Results:AL levels were high among 20% of the respondents (n = 2179). Having a lower income to poverty ratio, being married, physical inactivity, experiencing sleep problems, and a history of smoking were significantly associated with high AL (P < 0.05). Non-Hispanic blacks [odds ratio (OR): 1.8; 95% confidence interval (CI): 1.6–2.4] and Mexicans and other Hispanics (OR: 1.4; 95% CI: 1.1–1.7) had higher AL compared to Caucasians. Having cardiovascular disease (CVD) (OR: 1.7; 95% CI: 1.4–2.2) and diabetes (OR: 4.7; 95% CI: 3.8–5.7) independently, as well as both CVD and diabetes (OR: 3.1; 95% CI 2.7–3.6), were associated with higher odds of AL. We conducted an age-adjusted regression model that indicated higher odds of elevated AL among females with diabetes independently (OR: 1.4; 95% CI: 1.2–1.9) and with both CVD and diabetes (OR: 1.6; 95% CI: 1.2–2.1) compared to men.Conclusions:Despite the significant impact and association of AL with overall health, there is minimal evidence of its risk factors and linkage to disease burden. Modifiable lifestyle factors were associated with a higher AL. There is a critical need to support ethnic and gender contextual interventions to reduce the burden of AL on chronic conditions.

Evaluation of the Relationships between Simple Anthropometric Measures and Bioelectrical Impedance Assessment Variables with Multivariate Linear Regression Models to Estimate Body Composition and Fat Distribution in Adults: Preliminary Results.

Overweight and obesity are conditions associated with sedentary lifestyle and accumulation of abdominal fat, determining increased mortality, favoring chronic diseases, and increasing cardiovascular risk. Although the evaluation of body composition and fat distribution are highly relevant, the high cost of the gold standard techniques limits their wide utilization. Therefore, the aim of this work was to explore the relationships between simple anthropometric measures and BIA variables using multivariate linear regression models to estimate body composition and fat distribution in adults. In this cross-sectional study, sixty-eight adult individuals (20 males and 48 females) were subjected to bioelectrical impedance analysis (BIA), anthropometric measurements (waist circumference (WC), neck circumference (NC), mid-arm circumference (MAC)), allowing the calculation of conicity index (C-index), fat mass/fat-free mass (FM/FFM) ratios, body mass index (BMI) and body shape index (ABSI). Statistical analyzes were performed with the R program. Nonparametric Statistical tests were applied to compare the characteristics of participants of the groups (normal weight, overweight and obese). For qualitative variables, the Fisher's exact test was applied, and for quantitative variables, the paired Wilcoxon signed-rank test. To evaluate the linear association between each pair of variables, the Pearson correlation coefficient was calculated, and Multivariate linear regression models were adjusted using the stepwise variable selection method, with Akaike Information Criterion (p ≤ 0.05). BIA variables with the highest correlations with anthropometric measures were total body water (TBW), body fat percentage (BFP), FM, FFM and FM/FFM. The multiple linear regression analysis showed, in general, that the same variables can be estimated through simple anthropometric measures. The assessment of fat distribution in the body is desirable for the diagnosis and definition of obesity severity. However, the high cost of the instruments (dual energy X-ray absorptiometry, hydrostatic weighing, air displacement plethysmography, computed tomography, magnetic resonance) to assess it, favors the use of BMI in the clinical practice. Nevertheless, BMI does not represent a real fat distribution and body fat percentage. This highlights the relevance of the findings of the current study, since simple anthropometric variables can be used to estimate important BIA variables that are related to fat distribution and body composition.

High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China

BackgroundLittle is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. ObjectiveThis study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. MethodA 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. ResultThe final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm−3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. ConclusionWe concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.

Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer.

To develop a prediction model with computed tomography (CT) images and to build a nomogram incorporating known clinicopathologic variables for individualized estimation of epithelial-to-mesenchymal transition (EMT) subtype gastric cancer. Patients who underwent primary resection of gastric cancer (GC) and molecular subgroup analysis (n = 451) were reviewed. Multivariable analysis using a stepwise variable selection method was performed to build a predictive model for EMT subtype GC. A nomogram using the results of the multivariable analysis was constructed. An optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype was determined. The predictive model for the EMT subtype was internally validated by bootstrap resampling method. There were 88 patients with EMT subtype and 363 patients with non-EMT subtype based on transcriptome analysis. The patient's age, Lauren classification, and mural stratification on CT were variables selected for the predictive model. The area under the curve (AUC) of the model was 0.865, and the validated AUC of the bootstrap sample was 0.860. The optimal cutoff value of total prognostic points for the prediction of EMT subtype was 94.622, with 90.9% sensitivity, 67.2% specificity, and 71.8% accuracy. A predictive model using patient's age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. A nomogram was built which would serve as a useful screening tool for an individualized estimate of EMT subtype. • A predictive model for epithelial-to-mesenchymal transition (EMT) subtype incorporating patient's age, Lauren classification, and mural stratification on CT was built. • The predictive model had high diagnostic accuracy (area under the curve (AUC) = 0.865) and was validated (bootstrap AUC = 0.860). • Adding CT findings to clinicopathologic variables increases the accuracy of the predictive model than using only.

376-P: REWIND Data on Obesity and Cardiovascular (CV) Health: Waist-to-Hip Ratio (WHR) Independently Predicted CV Outcomes

Obesity is a major risk factor (RF) for health outcomes such as major adverse cardiovascular events (MACE) and cardiovascular disease (CVD)-related mortality. WHR is a validated measure of central adiposity and has been shown to predict CVD events. The aim of this post hoc analysis was to evaluate if WHR, adjusted for age and sex, was an independent RF for developing MACE-3 (non-fatal myocardial infarction (MI), non-fatal stroke, or CV death) and CVD-related mortality. Data from the REWIND trial placebo group (N=4952) were analyzed. All participants had type 2 diabetes, were aged 50 or older, and had either a previous CV event or CV RFs. During the median follow-up of 5.4 years, there were 663 patients with MACE-3 and 346 CVD-related deaths. A Cox proportional hazard model was used to investigate if WHR, adjusted for age and sex, was a significant RF for MACE-3 and CVD-related mortality, followed by testing additional baseline RFs (demographic, CVD history, and biochemical characteristics) using a stepwise variable selection method where WHR, age, and sex were forced into the model. BMI was tested similarly to WHR. WHR, but not BMI, was an independent risk factor for MACE-3 and CVD-related mortality. As expected, age and sex were significant RFs for both endpoints and were therefore adjusted in the model. Urinary albumin:creatinine ratio (UACR), non-HDL-C, and race (white) were significant RFs for MACE-3. UACR, LDL-C, history of MI, and race (American Indian/Alaska Native) were significant RFs for CVD-related mortality. In the resulting adjusted model, for every unit increase in WHR, there was an estimated 3.50-fold increase in MACE-3 risk (p=0.013) and 5.51-fold increase in CVD-related mortality risk (p=0.005). WHR was an independent predictor of MACE-3 and CVD-related mortality, indicating that central adiposity should be assessed and addressed in patients with or who have a high risk of CVD. Disclosure E. Franek: Advisory Panel; Self; Boehringer Ingelheim, Novo Nordisk, Speaker’s Bureau; Self; Boehringer Ingelheim. P. Pais: None. J. N. Basile: Other Relationship; Self; Medtronic, Research Support; Self; Eli Lilly and Company, ReCor. S. Raha: Employee; Self; Eli Lilly and Company. N. Ahmad: Employee; Self; Eli Lilly and Company. H. Kan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Bristol-Myers Squibb Company, GlaxoSmithKline plc. M. Konig: Employee; Self; Eli Lilly and Company.

Outcomes of Patients with Hematologic Malignancies Who Received Inpatient Palliative Care Consultation

Introduction: Supportive oncology provided by palliative care consultation has proven to prolong survival in randomized studies of patients with advanced cancers, particularly in those with solid tumors. However, the impact of palliative care consultation is less well characterized in patients with hematologic malignancies. We hypothesize that patients with hematologic malignancies are less likely to receive inpatient palliative care consultation. In this study, outcomes of patients with hematologic malignancies who received inpatient palliative care consultation were compared to a control group of disease-matched controls who were not seen by palliative care. Methods: A retrospective chart review of patients with hematologic malignancies admitted to our inpatient oncology service between 2013-2019 was performed. Data were summarized in SAS v9.4 (SAS, Cary, NC) using descriptive statistics for patients who received palliative care consultation and controls who did not, and these groups were also compared using Wilcoxon rank-sum, chi-square, and Fisher's exact tests as appropriate based on the type and distribution of the data. Multiple logistic regression models with stepwise variable selection methods were used to find predictors of outcomes. Results: Over the 7-year study period, there were 3,654 admissions to the oncology service, among which 370 hematologic malignancy patients who were actively on treatment were included. Of these, 102 (27%) received palliative care consultation, and the 268 who were not seen by palliative care served as a comparator group. Demographics, disease subgroups, and outcomes are summarized in the table. Groups were similar in terms of comorbidities, as shown in the table. Palliative care consults were greater for patients with acute leukemia and myelodysplasia (38% in each group), and least for those with chronic leukemias (15%) and myeloproliferative diseases (18%). Median length of stay was longer for patients seen by palliative care (11.5 vs. 6 days, P=.001), and these patients were more likely to be admitted to the intensive care (27.5 vs. 8.6%, P&amp;lt;.0001). Readmissions within 30 days of discharge were significantly lower among patients receiving palliative care consultation (15.7 vs 26.9%, P=.024), and 6-month morality was higher (66.7 vs 14.6%, P&amp;lt;.0001). Among all the patients who died within 6-months of admission, 36% did not receive palliative care consultation. Discussion: Patients with hematologic malignancies who received palliative care consultation had a lower 30-day readmission rate and higher 6-month mortality, despite lack of differences in the time from their cancer diagnosis compared to the control group not seen by palliative care. More than two thirds of all patients did not receive a palliative care consultation, nor did over a third of patients who expired within 6 months of admission. Further research is required to investigate other factors that might warranted palliative care consultation, such as the severity of illness at the time of hospitalization. Results of this study suggest that inpatient palliative care consultation is associated with decreased readmission rate, yet these services are underutilized in patients with hematologic malignancies who are projected to have shorter overall survival. Disclosures Braunstein: Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Verastem: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees.

Pharmacophore modelling, QSAR study, molecular docking and insilico ADME prediction of 1,2,3-triazole and pyrazolopyridones as DprE1 inhibitor antitubercular agents

Tuberculosis (TB) is a chronic lung infected airborne disease caused by Mycobacterium tuberculosis (MTB). The development of resistance towards available antitubercular agents leads to the discovery of new drugs for treatment against these resistant bacteria. Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) is a vulnerable target for the design of antitubercular agents which are more acting against multidrug resistant bacterial pathogens. DprE1 is an oxidase involved in the synthesis of arabinogalactan. Inhibition of DprE1 leads to blocking off cell wall synthesis, causing the death of the bacteria. A series of 50 DprE1 inhibitors having activity were subjected to 2D, 3D QSAR, Pharmacophore Modeling, Molecular Docking and in silico ADME studies. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. 2D-QSAR and 3D- QSAR models developed by Partial Least Square associated with the Sphere Exclusion method (PLS-SE) and StepWise variable selection method (SW-kNN MFA) based on k- Nearest Neighbor technique are more significant which have cross-validated squared correlation coefficient (q2), coefficient of determination (r2), Fisher ratio (F) values as 0.7499, 0.8917 and 85.04 and the internal (q2 = 0.8198), external (pred_r2 = 0.6109) model validation correctly predicts activity ~ 81% and ~ 61% for the training and test set, respectively. Pharmacophore model was developed with two aromatic regions (Aro), one aliphatic (Ala) and one hydrogen donor (HDr). Docking studies of the selected inhibitors with the active site of DprE1 enzyme showed hydrogen bond interaction with Gly-116, His-131, Arg-118, Thr-117 and Gln-299 residues present at the active site. The results of the present work provide more useful information and important structural insights for designing DprE1 inhibitors with much more enhanced potency.

Quantifying the Relationship between Adaptive Traits and Agro-climatic Conditions

Background &amp; Aim: Durum wheat is an economically important and regularly eaten food for billions of people in the world. In the International Center for Agriculture Research in the Dry Areas (ICARDA), genbanks are using Focused Identification of the Germplasm Strategy (FIGS) to find out and quantify relationships between agro-climatic conditions and the presence of specific traits. Hence, the study is aimed to investigate the predictive value of various types of long-term agro-climatic variables on the future values of different traits.&#x0D; Method: Ordinary multiple linear regression with stepwise variable selection method on the complete data set, and multiple linear regression models with predictors selected by penalized methods with mean square error cross-validation as a model selection criterion, are used to analyze 238 durum wheat landraces. Each of the models are fitted on Days to Heading and Days to Maturity response variables with 57 predictor variables, independently. Ordinary least square and weighted least square estimation methods were used.&#x0D; Result: Findings implied that there is high multicollinearity among the predictor variables. It is found that there are some predictors which affect positively and some others affect negatively for both Days to Heading and Days to Maturity using both ordinary and shrinkage based models. It is revealed that the prediction from the lasso based model is not that much reasonable. Furthermore, for the Days to Heading showed that there seems better prediction as their predicted value increase continuously as a function of the actual values though there is considerable variability.&#x0D; Conclusion: In conclusion, inferences and predictions by the ordinary MLR models are not trusted due to the presence of multicollinearity, and violation of some model assumptions. However, predictions using the models with predictors selected by the shrinkage methods may be better as the effects of the variability on these methods are minimal. Moreover, the WLS methods might give more sensible predictions than the OLS estimation methods. Better predictions were found on the Days to Heading.