a. h. karami

This study focuses on the precise production of droplets in a droplet-based microfluidic device, where monodisperse oil-in-water emulsions with controlled droplet sizes are generated. The primary objective is to achieve uniform emulsions by examining key parameters such as additives in the continuous aqueous phase, internal phase flow rates, and the microfluidic device's geometric characteristics. Initially, the geometric parameters of the microchip for emulsion formation were selected using numerical simulations, and the results were validated through experimental tests of emulsion production with the microchip. The precision of the outcomes is enhanced using an innovative 3D printing method for microchip manufacturing, enabling the creation of identical microdevice copies. In the experimental phase, the optimal conditions for producing uniform droplets were identified by examining the effects of various additives in the external aqueous phase, different internal phase flow rates, and the geometric parameters of the microfluidic device. The results demonstrate that the distance between the two capillaries can control droplet size and frequency, the internal phase flow rate, and the type of additive in the external phase, allowing for emulsions with droplet sizes ranging from 500 to 1000 microns. Specifically, the distance between the capillary tubes significantly affects droplet size, contributing to 30% of the variation when this distance is increased sixfold. Additionally, the study reveals that the increase in droplet diameter due to a higher internal phase flow rate varies with different additives in the external phase. For instance, sodium dodecyl sulfate (SDS) results in a 6.65-fold increase in droplet production frequency with a sixfold increase in the internal phase flow rate. Furthermore, the type of additive in the external phase can independently control droplet size. For example, with a specific internal-to-external phase ratio, oil droplets measure 600.8 μm in an external phase containing SDS, 582.2 μm with polyvinyl alcohol (PVA), and 615.4 μm with Triton X-100. This method can precisely control droplet size and frequency, making it suitable for generating precursor emulsions for engineered micro- and millimeter-sized polymer particles aimed at drug delivery or cell culture applications. The study successfully ensures consistent and uniform emulsions by manipulating these critical parameters through this combined approach of numerical simulations and experimental validation.

Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging.

To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.

Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired. The patients lived more than 2 years were classified as the ‘Alive’ class and otherwise as the ‘Dead’ class. In our proposed quantitative radiomic framework, we first extracted the associated regions of each lung lesion from pre-treatment CT images for each patient via grow cut segmentation algorithm. Then, 40 radiomic features were extracted from the segmented lung lesions. In order to enhance the generalizability of the classification models, the mutual information-based feature selection method was applied to each feature vector. We investigated the performance of six logistic regression-based classification models.

It was observed that the mutual information feature selection method can help the classifier to achieve better predictive results. In our study, the Logistic regression (LR) and Dual Coordinate Descent method for Logistic Regression (DCD-LR) models achieved the best results indicating that these classification models have strong potential for classifying the more important class (i.e., the ‘Alive’ class).

The proposed quantitative radiomic framework yielded promising results, which can guide physicians to make better and more precise decisions and increase the chance of treatment success.