A mathematical model of dispersed bioparticle-blood flow through the stenosed coronary artery under the pulsatile boundary conditions is proposed. Blood is assumed to be an incompressible non-Newtonian fluid and its flow is considered as turbulence described by the Reynolds-averaged Navier-Stokes equations. Bioparticles are assumed to be spherical shape with the same density as blood, and their translation and rotational motions are governed by Newtonian equations. Impact of particle movement on the blood velocity, the pressure distribution, and the wall shear stress distribution in three different severity degrees of stenosis including 25%, 50%, and 75% are investigated through the numerical simulation using ANSYS 18.2. Increasing degree of stenosis severity results in higher values of the pressure drop and wall shear stresses. The higher level of bioparticle motion directly varies with the pressure drop and wall shear stress. The area of coronary artery with higher density of bioparticles also presents the higher wall shear stress.

This study investigates the therapeutic potential of an herbal-based topical cream for treating psoriasis, a chronic inflammatory skin condition that detrimentally affects patients' quality of life. T...

The aim of this study is to investigate the potential therapeutic benefits of using cannabis-enriched topical cream containing of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), either alone (THC:CBD) or in combination with a polyherbal formulation for treating psoriasis. To evaluate the efficacy and safety of both formulations, a crossover, randomized, single-blinded study was conducted on 20 volunteers who were monitored for key indicators such as PASI score, PDI, DLQI, and blood profile. The present study involved two eight-week treatment periods with each formulation, separated by a two-week washout period. The results showed that the group using the cannabis cream comprising of THC:CBD alone experienced a significant reduction in PASI score after four weeks over the course of the 8-week study. Furthermore, the combination of THC:CBD and the polyherbal formulation was found to be more effective in reducing disease severity and improving patient quality of life. No significant adverse reactions were observed, and there was no difference in blood profile before and after treatment. These findings indicate that both topical formulations are safe and effective for treating psoriasis, with the combined formulation showing greater potential than using topical cannabis alone.

Road traffic accidents (RTAs) pose a significant global challenge, particularly in Thailand. This study investigates the impact of resampling techniques on machine learning (ML) models for classifying road accident severity in Thailand, utilizing data from 31,817 road traffic accidents collected between January 1, 2021, and December 31, 2022. The primary challenge addressed is class imbalance, where fatal accidents represent a small fraction of the dataset. Three popular ML models, including Random Forest (RF), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), were evaluated with four resampling techniques: Imbalanced (IB), Under-sampling (US), Over-sampling (OS), and Combined Sampling (CS). These resampling approaches generated 12 ML models, whose performance was evaluated under three different train/test split ratios: 70/30, 80/20, and 90/10. Compared to the IB approach, the results demonstrate that all US, OS and CS techniques significantly improved model performance, particularly in terms of F1 score, G-mean, and balanced accuracy. Among the models, RF-CS, KNN-OS, and XGB-CS exhibited the best classification performance. Although these evaluation metrics improved over the imbalanced scheme, KNN’s overall performance in detecting fatal accidents was weaker compared to RF and XGB. Specifically, KNN struggled more with the imbalanced dataset, even after applying resampling techniques. These findings suggest that choosing the appropriate resampling techniques is crucial for enhancing model performance in classifying accident severity.

Erythemato-squamous diseases (ESD) are dermatological diseases that significantly impact the quality of life of an increasing number of patients worldwide. This study used a publicly available clinical dataset of 366 patients from the Department of Computer Engineering and Information Science, Bilkent University with 34 predictors contributing to the classification of ESDs. The data was curated to ensure unbiasedness and accuracy before applying principal component analysis and machine learning models to identify crucial factors in classifying the six main types of ESDs. 31 different machine learning models, including Tree, Linear Discriminant, Quadratic Discriminant, Naïve Bayes, SVM, KNN, Ensemble, Neural Network, and Kernel were trained, validated, and the classification accuracy was compared. The model that is the most adequate is the Fine KNN, which has the highest cross-validation classification accuracy at 100%. This model requires only eight predictors: itching, the Koebner phenomenon, follicular papules, fibrosis of the papillary dermis, spongiosis, inflammatory mononuclear infiltrate, bandlike infiltrate, and age.