m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

BACKGROUND

1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

OBJECTIVE

Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

METHODOLOGY

The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.

RESULTS

The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.

CONCLUSION

For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

Developing a length of stay prediction model for newborns, achieving better accuracy with greater usability

BACKGROUND

One in ten newborn children is born prematurely. The elongated length of stay (LOS) of these children in the Neonatal Intensive Care Unit (NICU) has important implications on hospital occupancy figures, healthcare and management costs, as well as the psychology of parents. In order to allow accurate planning and resource allocation, this study aims to create a generalizable and robust model to predict the NICU LOS of preterm newborns.

METHODS

Data were collected from a large tertiary center NICU between 2011 and 2018 and relates to 5,362 newborns. The selected model was externally validated using a data set of 8,768 newborns from another tertiary center NICU. This report compares several models, such as Random Forest (RF), quantile RF, and other feature selection methods, including LASSO and AIC step-forward selection. In addition, a novel step-forward selection based on False Discovery Rate (FDR) for quantile regression is presented and evaluated.

RESULTS

A high-orderquantile regression model for predicting preterm newborns' LOS that uses only four features available at birth had more attractive properties than other richer ones. The model achieved a Mean Absolute Error (MAE) of 6.26 days on the internal validation set (average LOS 27.04) and an MAE of 6.04 days on the external validation set (average LOS 29.32). The suggested model surpassed the accuracy obtained by models in the literature. It is shown empirically that the FDR-based selection has better properties than the AIC-based step-forward selection approach.

CONCLUSION

This paper demonstrates a process to create a predictive model for NICU LOS in preterm newborns, where each step is reasoned. We obtain a simple and robust model for NICU LOS prediction, which achieves far better results than the current model used for financing NICUs. Utilizing this model, we have created an easy-to-use online web application to ease parents' worries and to assist NICU management: https://tzviel.shinyapps.io/calcuLOS.

Mediators for Effect of Mindfulness in Promoting Marital Satisfaction: Modeling Structural Equations in an Experimental Study

Objective: The present study evaluated the model of mediating variables concerning effectiveness of mindfulness in improving women's marital satisfaction. Method: This study was quasi-experimental with pre-test, post-test and control groups. The statistical population included every married woman in Tehran in the year 1396 (2017). The research sample was composed of 60 married women with moderate marital satisfaction. They were chosen using the convenient sampling technique and were randomly assigned to control and experimental groups. The experimental group attended 8 sessions of Mindfulness-Based Stress Reduction (MBSR) and the control group was on a waiting list. Both groups answered the Five Facet Mindfulness Questionnaire (FFMQ), the Depression, Anxiety, and Stress Scale (DASS), Difficulties in Emotion Regulation Scale (DERS), Interpersonal Reactivity Inventory (IRI), Needs for Intimacy Questionnaire of Bagarozi, Positive and Negative Affect Scale (PANAS) and the Couple Satisfaction Index (CSI) before and at end of intervention. The data were analyzed by Structural Equation Modeling (SEM) using PLS-22 and SPSS-23. Results: In the initial hypothetical model, which included the direct effect of mindfulness on marital satisfaction and its indirect effects through psychological distress, emotion regulation, empathy, marital intimacy and positive emotion on marital satisfaction, coefficients of all model paths were significant at 95% level except direct path of mindfulness to marital satisfaction (t = 1.14), marital intimacy to marital satisfaction (t = 0.48) and positive affection to marital satisfaction (t = 1.75). After removing the non-significant paths, the model was tested again and the model fit index (GOF = 0.47) was a very good fit for the modified overall model. Conclusion: The results identified mindfulness-based stress reduction intervention was effective in enhancing marital satisfaction through reducing psychological distress and improving emotion regulation and empathy.

Factors related to baseline CD4 cell counts in HIV/AIDS patients: comparison of poisson, generalized poisson and negative binomial regression models

Objective

CD4 Lymphocyte Count (CD4) is a major predictor of HIV progression to AIDS. Exploring the factors affecting CD4 levels may assist healthcare staff and patients in management and monitoring of health cares. This retrospective cohort study aimed to explore factors associated with CD4 cell counts at the time of diagnosis in HIV patients using Poisson, Generalized Poisson, and Negative Binomial regression models.

Results

Out of 4402 HIV patients diagnosis in Iran from 1987 to 2016, 3030 (68.8%) were males, and the mean age was 34.8 ± 10.4 years. The results indicate that the Negative Binomial model outperformed the other models in terms of AIC, log-likelihood and RMSE criteria. In this model, factors include sex, age, clinical stage and Tuberculosis (TB) co-infection were significantly associated with CD4 count (P < 0.05).

Conclusion

Given the effect of age, sex, clinical stage and stage of HIV on CD4 count of the patients, adopting policies and strategies to increase awareness and encourage people to seek early HIV testing and care is advantageous.

The application of a gamma distribution model to diffusion-weighted images of the orofacial region

OBJECTIVES

This study evaluated the correlation among the diffusion-derived parameters obtained by monoexponential (ME), intravoxel incoherent motion (IVIM) and γ distribution (GD) models and compared these parameters among representative orofacial tumours.

METHODS

Ninety-two patients who underwent 1.5 T MRI including diffusion-weighted imaging were included. The shape parameter (κ), scale parameter (θ), ratio of the intracellular diffusion (ƒ₁), extracellular diffusion (ƒ₂) and perfusion (ƒ₃) were obtained by the GD model; the true diffusion coefficient (D) and perfusion fraction (f) were obtained by the IVIM model; and the apparent diffusion coefficient (ADC) was obtained by the ME model.

RESULTS

ƒ₁ had a strongly negative correlation with the ADC (ρ = -0.993) and D (ρ = -0.926). A strong positive correlation between f and ƒ₃ (ρ = 0.709) was found. Malignant lymphoma (ML) had the highest ƒ₁, followed by squamous cell carcinoma (SCC), malignant salivary gland tumours, pleomorphic adenoma (Pleo) and angioma. Both the IVIM and GD models suggested the highest perfusion in angioma and the lowest perfusion in ML. The GD model demonstrated a high extracellular component in Pleo and revealed that the T_4a+T_4b SCC group had a lower ƒ₂ than the T₂+T₃ SCC group, and poor to moderately differentiated SCC had a higher ƒ₁ than highly differentiated SCC.

CONCLUSIONS

Given the correlation among the diffusion-derived parameters, the GD model might be a good alternative to the IVIM model. Furthermore, the GD model's parameters were useful for characterizing the pathological structure.

A Model Driven Approach to the Design of a Gamified e-Learning System for Clinical Guidelines

Clinical practice guidelines (CPGs) are indispensable in the practice of evidence-based medicine. However, the cost of effective CPG dissemination strategies is prohibitive and not cost-effective. Therefore, scalable strategies using available technology are needed. We describe a formal model-driven approach to design a gamified e-learning system for clinical guidelines. We employ gamification to increase user motivation and engagement in the training of guideline content. Our approach involves the use of models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game model to manage the training sessions. A game engine instantiates a training session by coupling the workflow and entity models to automatically generate questions based on the data in the model instances. Our approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.

Identification of A Gene Set Associated with Colorectal Cancer in Microarray Data Using The Entropy Method

Objective

We sought to apply Shannon’s entropy to determine colorectal cancer genes in a microarray dataset.

Materials and Methods

In the retrospective study, 36 samples were analysed, 18 colorectal carcinoma and 18 paired normal tissue samples. After identification of the gene fold-changes, we used the entropy theory to identify an effective gene set. These genes were subsequently categorised into homogenous clusters.

Results

We assessed 36 tissue samples. The entropy theory was used to select a set of 29 genes from 3128 genes that had fold-changes greater than one, which provided the most information on colorectal cancer. This study shows that all genes fall into a cluster, except for the R08183 gene.

Conclusion

This study has identified several genes associated with colon cancer using the entropy method, which were not detected by custom methods. Therefore, we suggest that the entropy theory should be used to identify genes associated with cancers in a microarray dataset.

Evaluating The Impact of Risk Factors on Birth Weight and Gestational Age: A Multilevel Joint Modeling Approach

Background

Abnormalities in birth weight and gestational age cause several adverse maternal and infant out- comes. Our study aims to determine the potential factors that affect birth weight and gestational age, and their association.

Materials and Methods

We conducted this cross-sectional study of 4415 pregnant women in Tehran, Iran, from July 6-21, 2015. Joint multilevel multiple logistic regression was used in the analysis with demographic and obstetrical variables at the first level, and the hospitals at the second level.

Results

We observed the following prevalence rates: preterm (5.5%), term (94%), and postterm (0.5%). Low birth weight (LBW) had a prevalence rate of 4.8%, whereas the prevalence rate for normal weight was 92.4, and 2.8% for macrosomia. Compared to term, older mother’s age [odds ratio (OR)=1.04, 95% confidence interval (CI): 1.02-1.07], preeclampsia (OR=4.14, 95% CI: 2.71-6.31), multiple pregnancy (OR=18.04, 95% CI: 9.75- 33.38), and use of assisted reproductive technology (ART) (OR=2.47, 95% CI: 1.64-33.73) were associated with preterm birth. Better socioeconomic status (SES) was responsible for decreased odds for postterm birth com- pared to term birth (OR=0.53, 95% CI: 0.37-0.74). Cases with higher maternal body mass index (BMI) were 1.02 times more likely for macrosomia (95% CI: 1.01-1.04), and male infant sex (OR=1.78, 95% CI: 1.21-2.60). LBW was related to multiparity (OR=0.59, 95% CI: 0.42-0.82), multiple pregnancy (OR=17.35, 95% CI: 9.73-30.94), and preeclampsia (OR=3.36, 95% CI: 2.15-5.24).

Conclusion

Maternal age, SES, preeclampsia, multiple pregnancy, ART, higher maternal BMI, parity, and male infant sex were determined to be predictive variables for birth weight and gestational age after taking into consideration their association by using a joint multilevel multiple logistic regression model

Public Knowledge of Oral Cancer and Modelling of Demographic Background Factors Affecting this Knowledge in Khartoum State, Sudan

OBJECTIVES

Knowledge of oral cancer affects early detection and diagnosis of this disease. This study aimed to assess the current level of public knowledge of oral cancer in Khartoum State, Sudan, and examine how demographic background factors affect this knowledge.

METHODS

This cross-sectional study involved 501 participants recruited by systematic random sampling from the outpatient records of three major hospitals in Khartoum State between November 2012 and February 2013. A pretested structured questionnaire was designed to measure knowledge levels. A logistic regression model was utilised with demographic background variables as independent variables and knowledge of oral cancer as the dependent variable. A path analysis was conducted to build a structural model.

RESULTS

Of the 501 participants, 42.5% had no knowledge of oral cancer, while 5.4%, 39.9% and 12.2% had low, moderate and high knowledge levels, respectively. Logistic regression modelling showed that age, place of residence and education levels were significantly associated with knowledge levels (P = 0.009, 0.017 and <0.001, respectively). According to the structural model, age and place of residence had a prominent direct effect on knowledge, while age and residence also had a prominent indirect effect mediated through education levels.

CONCLUSION

Education levels had the most prominent positive effect on knowledge of oral cancer among outpatients at major hospitals in Khartoum State. Moreover, education levels were found to mediate the effect of other background variables.

A Statistical Modeling Framework for Projecting Future Ambient Ozone and its Health Impact due to Climate Change

The adverse health effects of ambient ozone are well established. Given the high sensitivity of ambient ozone concentrations to meteorological conditions, the impacts of future climate change on ozone concentrations and its associated health effects are of concern. We describe a statistical modeling framework for projecting future ozone levels and its health impacts under a changing climate. This is motivated by the continual effort to evaluate projection uncertainties to inform public health risk assessment. The proposed approach was applied to the 20-county Atlanta metropolitan area using regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program. Future ozone levels and ozone-related excesses in asthma emergency department (ED) visits were examined for the period 2041-2070. The computationally efficient approach allowed us to consider 8 sets of climate model outputs based on different combinations of 4 RCMs and 4 general circulation models. Compared to the historical period of 1999-2004, we found consistent projections across climate models of an average 11.5% higher ozone levels (range: 4.8%, 16.2%), and an average 8.3% (range: -7% to 24%) higher number of ozone exceedance days. Assuming no change in the at-risk population, this corresponds to excess ozone-related ED visits ranging from 267 to 466 visits per year. Health impact projection uncertainty was driven predominantly by uncertainty in the health effect association and climate model variability. Calibrating climate simulations with historical observations reduced differences in projections across climate models.

Predicting complications of percutaneous coronary intervention using a novel support vector method

OBJECTIVE

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).

MATERIALS AND METHODS

Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered.

RESULTS

The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases).

CONCLUSIONS

The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.