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Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination ( R 2 ) of this paper's method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects. [ABSTRACT FROM AUTHOR]

The chronic metabolic disorder known as diabetes mellitus, which is defined by hyperglycemia, poses a significant threat to the health of people all over the world. The categorization is broken down into two primary categories: Type 1 and Type 2, with each category having its own unique causes and approaches to treatment. It is very necessary for the effective management of illnesses to have both the prompt detection and the exact prediction of outcomes. The applications of machine learning and data mining are becoming increasingly important as tools in this setting. The current research study analyses the usage of machine learning models, specifically Voting Ensembles, for the goal of predicting diabetes. Specifically, the researchers were interested in how accurate these models were. Using GridSearchCV, the Voting Ensemble, which consists of LightGBM, XGBoost, and AdaBoost, is fine-tuned to manage outliers. This may be done with or without the Interquartile Range (IQR) pre-processing. The results of a comparative analysis of performance, which is carried out, illustrate the benefits that are linked with outlier management. According to the findings, the Voting Ensemble model, when paired with IQR pre-processing, possesses greater accuracy, precision, and AUC score, which makes it more acceptable for predicting diabetes. Despite this, the strategy that does not use the IQR continues to be a workable and reasonable alternative. The current study emphasizes both the significance of outlier management within the area of healthcare analytics and the effect of data preparation procedures on the accuracy of prediction models. Both of these topics are brought up because of the relevance of the current work. [ABSTRACT FROM AUTHOR]

The ongoing exploration of outlier detection involves the development and refinement of diverse statistical analysis procedures. In this paper, we examine the performance of the standardized range and relative range in detecting outliers in a skewed data. The proposed approach considers Weibull distribution as a placeholder for skewed data. The empirical construction of the PDFs of the standardized range and relative range from the Weibull distribution along with the simulation work for outlier detection reveal that while the relative range possesses comparable probability behavior to the standardized range, it outperforms the standardized range in detecting outliers from skewed data. Furthermore, the simulated outlier detection framework has been applied to real datasets. The results showed that relative range continue to outperform standardised range in identifying outliers. [ABSTRACT FROM AUTHOR]

IntroductionGlobal Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.MethodsThis paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.ResultsOn the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.DiscussionThese findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model’s use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

PurposeAccurate neural rim measurement based on optic disc imaging is important to glaucoma severity grading and often performed by trained glaucoma specialists. We aim to improve upon existing automated tools by building a fully automated system (RimNet) for direct rim identification in glaucomatous eyes and measurement of the minimum rim-to-disc ratio (mRDR) in intact rims, the angle of absent rim width (ARW) in incomplete rims, and the rim-to-disc-area ratio (RDAR) with the goal of optic disc damage grading.DesignRetrospective cross sectional study.ParticipantsOne thousand and twenty-eight optic disc photographs with evidence of glaucomatous optic nerve damage from 1021 eyes of 903 patients with any form of primary glaucoma were included. The mean age was 63.7 (± 14.9) yrs. The average mean deviation of visual fields was -8.03 (± 8.59).MethodsThe images were required to be of adequate quality, have signs of glaucomatous damage, and be free of significant concurrent pathology as independently determined by glaucoma specialists. Rim and optic cup masks for each image were manually delineated by glaucoma specialists. The database was randomly split into 80/10/10 for training, validation, and testing, respectively. RimNet consists of a deep learning rim and cup segmentation model, a computer vision mRDR measurement tool for intact rims, and an ARW measurement tool for incomplete rims. The mRDR is calculated at the thinnest rim section while ARW is calculated in regions of total rim loss. The RDAR was also calculated. Evaluation on the Drishti-GS dataset provided external validation (Sivaswamy 2015).Main outcome measuresMedian Absolute Error (MAE) between glaucoma specialists and RimNet for mRDR and ARW.ResultsOn the test set, RimNet achieved a mRDR MAE of 0.03 (0.05), ARW MAE of 31 (89)°, and an RDAR MAE of 0.09 (0.10). On the Drishti-GS dataset, an mRDR MAE of 0.03 (0.04) and an mRDAR MAE of 0.09 (0.10) was observed.ConclusionsRimNet demonstrated acceptably accurate rim segmentation and mRDR and ARW measurements. The fully automated algorithm presented here would be a valuable component in an automated mRDR-based glaucoma grading system. Further improvements could be made by improving identification and segmentation performance on incomplete rims and expanding the number and variety of glaucomatous training images.

BackgroundWe evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort.MethodsThis prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan-Meier curves and Cox proportional-hazard models were performed.Findings1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9-32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8-29.2)] died during hospitalization (median time 14 (IQR,9-24) days). Older age [vs

ObjectiveTo explore the population health impact of treating all US adults eligible for the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) with icosapent ethyl (IPE), we estimated (1) the number of ASCVD events and healthcare costs that could be prevented; and (2) medication costs.MethodsWe derived REDUCE-IT eligible cohorts in (1) the National Health and Nutrition Examination Surveys (NHANES) 2009-2014 and (2) the Optum Research Database (ORD). Population sizes were obtained from NHANES and observed first event rates (composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, unstable angina requiring hospitalization, or coronary revascularization) were estimated from the ORD. Hazard ratios from REDUCE-IT USA estimated events prevented with IPE therapy. The National Inpatient Sample estimated event costs (facility and professional) and daily IPE treatment cost was approximated at $4.59.ResultsWe estimate 3.6 million US adults to be REDUCE-IT eligible, and the observed five-year first event rate without IPE of 19.0% (95% confidence interval [CI] 16.6%-19.5%) could be lowered to 13.1% (95% CI 12.8%-13.5%) with five years of IPE treatment, preventing 212,000 (uncertainty range 163,000-262,000) events. We projected the annual IPE treatment cost for all eligible persons to be $6.0 billion (95% CI $4.7-$7.5 billion), but saving $1.8 billion annually due to first events prevented (net annual cost $4.3 billion). The total five-year event rate (first and recurrent) could be reduced from 42.5% (95% CI 39.6%-45.4%) to 28.9% (95% CI 26.9-30.9%) with five years of IPE therapy, preventing 490,000 (uncertainty range 370,000-609,000) events (net annual cost $2.6 billion).ConclusionsTreating all REDUCE-IT eligible US adults has substantial medication costs but could prevent a substantial number of ASCVD events and associated direct costs. Indirect cost savings by preventing events could outweigh much of the incurred direct costs.

We propose a method to estimate a sample skewness from the given summary statistics and give explicit formulas for the most common scenarios. We show that our method provides a nearly unbiased estimator for the non-parametric skewness measure. We empirically evaluate the performance on real-life data sets of COVID-19 vaccination status. We also demonstrate how the method can be applied to detect the skewness of the underlying distribution. [ABSTRACT FROM AUTHOR]

Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of this paper’s method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects.

ObjectivesTo compare echocardiographic and invasive mean gradients obtained concomitantly in degenerated bioprosthetic surgical aortic valves (SAVRs).MethodsIn a multicenter study, we compared concomitant echocardiographic and invasive mean gradients of SAVR, obtained before valve-in-valve transcatheter aortic valve replacement in all patients, patients with primary stenosis (AS), primary aortic regurgitation (AR), and mixed aortic valve disease (MAVD), and in small versus large valves (≤ or >23 mm). Dimensionless index (DI) was calculated in all groups.ResultsIn total, 74 patients were included and data presented as median (interquartile range). Echocardiography-catheterization mean gradient discordance was observed in all patients (invasive = 22 mm Hg [11-34] vs echocardiographic = 32 mm Hg [21-42], P = .013), small valves (invasive = 15 mm Hg [8-34] vs echocardiographic = 28 mm Hg [21-41], P = .013), and large valves (invasive = 20 mm Hg [8.5-27.13] vs echocardiographic = 32 mm Hg [25.5 - 41.5], P

Background: The application of classsification methods through multivariate and machine learning techniques has enormous significance in agricultural sector. It is vital to classify various types of seeds as well as identify the quality of seeds which has a great impact on the production of crops. There is a wide range of genetic variations in dry beans all over the world. Many studies have been conducted previously on various dataset to indentify the sorts of dry beans, however most of them focused on machine learning techniques with binary classification. Objective: The aim of this study is to investigate a reliable classifier which has the lowest noise implications and establish an algorithm for dry bean classification effectively. This paper focuses on outlier removals, oversampling with Adaptive Synthetic (ADASYN) algorithm and finding the best classifier to guarantee the highest possible accuracy. Methods: The raw dataset for this study was accessed from UCI Machine Learning Repository. The dataset contained grains having 16 features, 12 dimensions, and 4 distinct shapes. For the purpose of eliminating missing values from the dataset, interquartile range (IQR) with python programming was utilized. Eight most popular classifiers were used in this study which are Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perception (MLP) with balanced and imbalanced classes. The authors utilized frequency tables, bar diagrams, boxplots, analysis of variance for descriptive analysis as well as data preprocessing. Results: The XGB classifier preferably outperformed than other classifiers with balanced and imbalanced distribution of dry beans within each class. It has acquired accuracy (ACC) 93.0% and 95.4% in imbalanced and balanced classes respectively. In case of balanced dataset, after application of ADASYN algorithm both KNN and RF techniques also performed well regarding the Classification Accuracy (ACC), Sensitivity (SE), Specificity (SP) and Cohen's kappa coefficient (Kappa) etc. The most important attributes for classifying the dry beans were found ShapeFactor2, Minor Axis Length, and ShapeFactor1 along with EquivDiameter, Roundness and ConvexArea. Conclusions: For classification of dry seeds, the XGB classifier had performed well when the dataset contained both balanced and imbalanced distribution in classes. Also, it is the primary approach of identifying the classes of seeds/beans with balanced or not. If the classes of the target variable are balanced well, then the KNN and RF algorithms may be applied along with XGB technique for more accurate classification.

Background: Coronavirus disease 2019 (COVID-19) continues to spread quickly throughout the world, mainly due to the lack of effective drug therapies and vaccines. The effectiveness of the antiviral drug umifenovir needs to be further clarified. Methods: This retrospective cohort study included 1254 patients who were diagnosed with COVID-19 between February 19 and April 5, 2020 in Hubei Maternity and Child Health Hospital. They were divided into umifenovir group (n = 760, 60.60%) and control group (n = 496) without using umifenovir. The primary endpoint was a composite of intubation or death in a time-to-event analysis. The clinical outcomes were compared between the two groups using multivariable Cox analysis with inverse probability weighting according to the propensity score. Results: A total of 760 (60.60%) patients received umifenovir, and 496 patients did not do so. Of the enrolled patients, 1049 (83.65%) had mild or moderate COVID-19, and the remaining 205 had severe or critical COVID-19. The mortality rate in the umifenovir group was 2.76% (21/760) versus 2.02% (10/494) in the control group. In terms of treatment outcomes, the discharge status of the patients in the umifenovir group was no better than that in the control group after propensity score matching (n = 485 in each group). In addition, the respiratory rate, a severe condition, or critical condition of the disease were the three main risk factors affecting the endpoint of death (p = 0.0028, p = 0.0009 and p < 0.0001, respectively). Conclusion: This retrospective cohort study showed that oral administration of umifenovir alone did not improve outcomes for patients with COVID-19. [ABSTRACT FROM AUTHOR]

PurposeAcanthamoeba keratitis often is refractory to medical and surgical therapy, primarily because of the remarkable resilience of Acanthamoeba cysts. In this study, we directly compared the cysticidal activity and potency of several candidate medical therapies in vitro.DesignExperimental study.ParticipantsIn vitro Acanthamoeba specimens obtained from 9 patients with keratitis seen at the Francis I. Proctor Foundation from 2008 through 2012.MethodsThe minimum cysticidal concentration (MCC) of povidone iodine, natamycin, and chlorhexidine was investigated using an established assay technique. The relative potency of each agent was estimated starting with concentrations commonly used in clinical practice and determining the number of two-fold dilutions required to reach the MCC. Statistical comparisons of relative potency were performed using bootstrap simulations and permutation tests.Main outcome measuresMinimum cysticidal concentration and the number of two-fold dilutions required to reach the MCC.ResultsThe MCC for chlorhexidine ranged from 3.1 to 25 μg/ml (median, 12.5 μg/ml; interquartile range [IQR], 6.25-12.5 μg/ml), the MCC for natamycin ranged from 390.6 to 3125 μg/ml (median, 390.6 μg/ml; IQR, 390.6-781.2 μg/ml), and the MCC for povidone iodine ranged from 0.3 to 78.1 μg/ml (median, 2.4 μg/ml; IQR, 0.6-9.8 μg/ml). Doses commonly used in clinical practice (povidone iodine 1%, natamycin 5%, and chlorhexidine 0.04%) were approximately 12, 7, and 5 two-fold dilutions higher than the drug's corresponding median MCC, respectively (P < 0.001, comparing 3 drugs). Povidone iodine 1% had the highest potency of the 3 medications tested, requiring more dilutions than natamycin 5% (P < 0.001) and chlorhexidine 0.04% (P < 0.001) to reach the MCC.ConclusionsAll 3 medications demonstrated in vitro cysticidal activity in each of the 9 isolates. The potency of 1% povidone iodine was greater than standard formulations of natamycin or chlorhexidine. Although its clinical efficacy is yet to be determined, povidone iodine may be considered as a potential adjuvant treatment in cases of recalcitrant Acanthamoeba keratitis.

Background: The application of classsification methods through multivariate and machine learning techniques has enormous significance in agricultural sector. It is vital to classify various types of seeds as well as identify the quality of seeds which has a great impact on the production of crops. There is a wide range of genetic variations in dry beans all over the world. Many studies have been conducted previously on various dataset to indentify the sorts of dry beans, however most of them focused on machine learning techniques with binary classification. Objective: The aim of this study is to investigate a reliable classifier which has the lowest noise implications and establish an algorithm for dry bean classification effectively. This paper focuses on outlier removals, oversampling with Adaptive Synthetic (ADASYN) algorithm and finding the best classifier to guarantee the highest possible accuracy. Methods: The raw dataset for this study was accessed from UCI Machine Learning Repository. The dataset contained grains having 16 features, 12 dimensions, and 4 distinct shapes. For the purpose of eliminating missing values from the dataset, interquartile range (IQR) with python programming was utilized. Eight most popular classifiers were used in this study which are Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perception (MLP) with balanced and imbalanced classes. The authors utilized frequency tables, bar diagrams, boxplots, analysis of variance for descriptive analysis as well as data preprocessing. Results: The XGB classifier preferably outperformed than other classifiers with balanced and imbalanced distribution of dry beans within each class. It has acquired accuracy (ACC) 93.0% and 95.4% in imbalanced and balanced classes respectively. In case of balanced dataset, after application of ADASYN algorithm both KNN and RF techniques also performed well regarding the Classification Accuracy (ACC), Sensitivity (SE), Specificity (SP) and Cohen’s kappa coefficient (Kappa) etc. The most important attributes for classifying the dry beans were found ShapeFactor2, Minor Axis Length, and ShapeFactor1 along with EquivDiameter, Roundness and ConvexArea. Conclusions: For classification of dry seeds, the XGB classifier had performed well when the dataset contained both balanced and imbalanced distribution in classes. Also, it is the primary approach of identifying the classes of seeds/beans with balanced or not. If the classes of the target variable are balanced well, then the KNN and RF algorithms may be applied along with XGB technique for more accurate classification. [ABSTRACT FROM AUTHOR]

Recently, various methods have been developed to estimate the sample mean and standard deviation when only the sample size, and other selected sample summaries are reported. In this paper, we provide a unified approach to optimal estimation that can be easily adopted when only some summary statistics are reported. We show that the proposed estimators have the lowest variance among linear unbiased estimators. We also show that in the most commonly reported cases, that is, when only a three-number or five-number summary is reported, the newly proposed estimators match the previously developed estimators. Finally, we demonstrate the performance of the estimators numerically. [ABSTRACT FROM AUTHOR]

Aims: This study aims to determine the prevalence of chronic pelvic pain syndrome (CPPS) symptoms among male-medical students in Riyadh, Saudi Arabia. Settings and Design: It is a cross-sectional study at King Saudi Bin Abdulaziz University for Health Sciences, College of Medicine at Riyadh, Saudi Arabia. Subjects and Methods: A total of 300 male medical students were given a hardcopy questionnaire to be filled anonymously that includes demographic characteristics, several questions regarding the health status, habits of the participants, and the National Institutes of Health Chronic Prostatitis Symptom Index for evaluating CPPS symptoms. Statistical Analysis Used: SPSS (v21) was used for the analysis. Qualitative data presented by frequencies and percentages and quantitative data presented by median and interquartile range (Q1 to Q3). The Chi-square test and binary logistic regression were used to assess the relationship between the outcome variables with demographical data. Results: In total 300 male medical students were surveyed, 245 medical students returned the questionnaire giving a response rate of 81.6%. The prevalence of CPPS symptoms among all participants was 57.9%. Majority of the participants does not have any associated medical/psychological conditions 152 (62%). Students with irritable bowel syndrome (IBS) or/and anxiety have statistically significant association with CPPS symptoms (P < 0.05). A total of 23% of students with CPPS reported being impaired with their quality of life (QOL), while 77% are satisfied. Conclusions: The prevalence of CPPS symptoms among male medical students is high. Anxiety, IBS, and lack of activity might be associated with CPPS symptoms, which have a significant negative impact on the QOL. For the future direction, a large-scale global study should be conducted to link the effects of CPPS symptoms on male medical students well-being.

Aims: This study aims to determine the prevalence of chronic pelvic pain syndrome (CPPS) symptoms among male-medical students in Riyadh, Saudi Arabia. Settings and Design: It is a cross-sectional study at King Saudi Bin Abdulaziz University for Health Sciences, College of Medicine at Riyadh, Saudi Arabia. Subjects and Methods: A total of 300 male medical students were given a hardcopy questionnaire to be filled anonymously that includes demographic characteristics, several questions regarding the health status, habits of the participants, and the National Institutes of Health Chronic Prostatitis Symptom Index for evaluating CPPS symptoms. Statistical Analysis Used: SPSS (v21) was used for the analysis. Qualitative data presented by frequencies and percentages and quantitative data presented by median and interquartile range (Q1 to Q3). The Chi-square test and binary logistic regression were used to assess the relationship between the outcome variables with demographical data. Results: In total 300 male medical students were surveyed, 245 medical students returned the questionnaire giving a response rate of 81.6%. The prevalence of CPPS symptoms among all participants was 57.9%. Majority of the participants does not have any associated medical/psychological conditions 152 (62%). Students with irritable bowel syndrome (IBS) or/and anxiety have statistically significant association with CPPS symptoms (P < 0.05). A total of 23% of students with CPPS reported being impaired with their quality of life (QOL), while 77% are satisfied. Conclusions: The prevalence of CPPS symptoms among male medical students is high. Anxiety, IBS, and lack of activity might be associated with CPPS symptoms, which have a significant negative impact on the QOL. For the future direction, a large-scale global study should be conducted to link the effects of CPPS symptoms on male medical students well-being. [ABSTRACT FROM AUTHOR]

Introduction: Global Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis., Methods: This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model., Results: On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease., Discussion: These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Praveen, Hasan, Abdullah, Sirisha, Tirumanadham, Islam, Ahmed, Ahmed, Noboni, Sampedro, Yeun and Ghazal.)

Optimization is very useful in the problems like transportation problems which is a special case of linear programming problems. It is an extremely handy tool for demand and supply chains to minimize time or cost and maximize profit. In this paper, we presented a new ranking method and a new algorithm to solve pentagonal FTP. We compared the solution with the proposed algorithm with the solution of existing methods. Here we transform FTP into a crisp problem and by using the proposed ranking technique we solved numerical examples to demonstrate the method. [ABSTRACT FROM AUTHOR]

A feature of rabbit gut-associated lymphoid tissue is that its structure is more developed than in other animal species. In rabbits it is composed of sacculus rotundus, vermiform appendix and Peyer’s patches. These immune formations contain an organized component of lymphoid tissue – lymphoid nodules (B-cell zone) and interfollicular region (T-cell). Secondary lymphoid nodules with germinal centers presented in them are formed due to antigen stimulation. The caecum of Hyplus rabbits at the age of 30 -, 60 - and 90-days was investigated. Each age group consisted of 5 rabbits. Experimental rabbits are clinically healthy, unvaccinated and untreated against ecto- and endoparasites. Peyer’s patches of the caecum were selected for the study and fixed in 10% of formalin. Subsequently, the specimens stained with hematoxylin-eosin were prepared from the obtained samples. On the 30th day of life, Peyer’s patches in the cecum were detected by gross examination. On the histological level, they had formed interfollicular region and lymphoid nodules. In turn, lymphoid nodules were divided into primary and secondary ones. A well-defined mantle zone and germinal centers were observed in the secondary lymphoid nodules. The regularities of their area indicators increase (mean value, median and interquartile range (IQR)) and their correlation were studied. The most intensive growth of the mantle area and the germinal center was observed from the 30th to the 60th day. The relative area of the mantle zone and the germinal center as part of the secondary lymphoid nodule was determined. Its value did not change during the experimental period.

IntroductionThe Glasgow Coma Scale (GCS) score is the primary method of assessing consciousness after traumatic brain injury (TBI), and the clinical standard for classifying TBI severity. There is scant literature discerning the influence of circadian rhythms or emergency department (ED) arrival hour on this important clinical tool.MethodsRetrospective cohort analysis of adult patients suffering blunt TBI using the National Sample Program of the National Trauma Data Bank, years 2003-2006. ED arrival GCS score was characterized by midday (10 a.m.-4 p.m.) and midnight (12 a.m.-6 a.m.) cohorts (N=24548). Proportions and standard errors are reported for descriptive data. Multivariable regressions using odds ratios (OR), mean differences (B), and their associated 95% confidence intervals [CI] were performed to assess associations between ED arrival hour and GCS score. Statistical significance was assessed at p

Aim: To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D). Methods: This is an exploratory, cross‐sectional analysis of baseline data collected from a prospective, multicentre, 5‐year follow‐up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values. Results: In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non‐achievement groups. Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L. Conclusion: Our results showed a negative correlation between TIR and the range of blood glucose variations visually represented in AGP. The results also showed that the range of blood glucose variations in AGP is associated with indices of intraday and interday blood glucose variations and also with hypoglycaemia. Our results may provide new perspectives in the assessment and application of AGP in the clinical setting. [ABSTRACT FROM AUTHOR]

With the recent increase in diabetes incidence worldwide, research has been conducted to predict diabetes through various machine learning and deep learning technologies. In this work, we present a model for predicting diabetes using machine learning techniques with German Frankfurt Hospital data. We apply outlier handling using Interquartile Range (IQR) techniques and Pearson correlation and compare model-specific diabetes prediction performance with Decision Tree, Random Forest, Knn (k-nearest neighbor), SVM (support vector machine), Bayesian Network, ensemble techniques XGBoost, Voting, and Stacking. As a result of the study, the XGBoost technique showed the best performance with 97% accuracy on top of the various scenarios. Therefore, this study is meaningful in that the model can be used to accurately predict and prevent diabetes prevalent in modern society [ABSTRACT FROM AUTHOR]

Toxicant testing is a major component of the environmental solution testing program. This test system aims to evaluate the sensitivity of the test species, evaluate comparability between laboratory test results, and classify possible variability sources such as healthy organisms, organism batches, laboratory water, and food quality changes. For the continuous assessment of sensitivity and accuracy of laboratory toxicity tests, control charts are often used. The cumulative sum (CUSUM) charting structure is a popular monitoring tool that is often used due to its outstanding ability in recognizing small‐to‐moderate unusual changes in process parameters. The CUSUM control charts have now been widely accepted/applied for monitoring the process dispersion under the assumption that the process does not have outliers. In practice, most of the processes have outliers. Therefore, the use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. Hence, in this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in the variable LC50 by using quantitative structure–activity relationship (QSAR) aquatic toxicity data set. The proposed IQR‐CUSUM charts are compared to its counterparts by using a variety of main output run‐length functions, such as average run length (ARL), standard deviation of run length (SDRL), and extra quadratic loss (EQL), derived from the distribution of run‐length. Further, results revealed that the proposed CUSUM charts have better detection ability as compared to existing counterpart charts, both in theoretical and practical considerations. Control charts are often used for process monitoring. Cumulative sum (CUSUM) charts are popular monitoring tool. The use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. In this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in LC50 by using quantitative structure‐activity relationship (QSAR) aquatic toxicity data set. The results revealed that the proposed charts outer performed as compared to existing counterpart charts. [ABSTRACT FROM AUTHOR]

Animal evidence suggests an association between exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and adverse pregnancy outcomes. Epidemiologic studies report inconsistent results, but are limited by narrow range of exposure, small sample size, and lack of a biologic measure of highest lifetime exposure. On July 10, 1976, a chemical explosion in Seveso, Italy resulted in the highest known residential exposure to TCDD. In 1996, we initiated the Seveso Women's Health Study (SWHS), a retrospective cohort of TCDD exposure and reproductive health. Individual-level TCDD was measured in serum collected soon after the explosion. After 20years of follow-up, we found no association between maternal TCDD in 1976 serum or estimated at pregnancy and spontaneous abortion (SAB), fetal growth, or gestational length. Here, we present an updated analysis of TCDD exposure and adverse pregnancy outcomes from a subsequent follow-up of the SWHS cohort in 2008-2009. SWHS women had 1211 post-explosion pregnancies through the 2008-2009 follow-up. We found no association between TCDD estimated at pregnancy and SAB, fetal growth, or gestational length. However, we found a non-significant inverse association between maternal 1976 serum TCDD and birthweight (adjusted β=-22.8, 95% CI: -80.1, 34.6). The association was stronger among first post-explosion births, but remained non-significant (adjusted β=-47.7, 95% CI: -107.3, 11.9). SWHS is the first study to be able to consider two potentially relevant measures of TCDD exposure: highest lifetime dose and in utero. Our results, although non-significant, suggest that highest dose may be more relevant in epidemiologic studies of TCDD and pregnancy outcomes.

Perfluoroalkyl acids (PFAAs) are persistent, synthetic compounds that are used in a number of consumer products. Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have been associated with cardiovascular risk factors, and changes in gene expression and DNA methylation in animals and cellular systems. However, whether PFAA exposure is associated with LINE-1 DNA methylation, a potential marker of cardiovascular risk, in humans remains unknown. We sought to evaluate the cross-sectional associations between serum PFAAs and LINE-1 DNA methylation in a population highly exposed to PFOA. We measured serum PFAAs twice four to five years apart in 685 adult participants (47% male, mean age±SD=42±11years). We measured percent LINE-1 DNA methylation in peripheral blood leukocytes at the second time point (follow-up), and estimated absolute differences in LINE-1 methylation associated with an interquartile (IQR) shift in mean PFAA serum levels. IQR increases in mean serum PFOA, PFOS, perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS) were associated with differences of -0.04 (p=0.16), 0.20 (p=0.001), 0.06 (p=0.19), and 0.02 (p=0.57), respectively, in % LINE-1 methylation at follow-up after adjustment for potential confounders. We observed a monotonic increase in LINE-1 DNA methylation across tertiles of PFOS and PFNA (ptrend=0.02 for both associations), but not across tertiles of PFOA or PFHxS (ptrend=0.71 and 0.44, respectively). In summary, serum PFOS was associated with LINE-1 methylation, while serum PFOA, PFHxS, and PFNA were not. Additional research is needed to more precisely determine whether these compounds are epigenetically active.

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