The Influence of 3D Porous Chitosan-Alginate Biomaterial Scaffold Properties on the Behavior of Breast Cancer Cells

The tumor microenvironment plays an important role in regulating cancer cell behavior. The tumor microenvironment describes the cancer cells, and the surrounding endothelial cells, fibroblasts, and mesenchymal stem cells, along with the extracellular matrix (ECM). The tumor microenvironment stiffens as cancer undergoes malignant progression, providing biophysical cues that promote invasive, metastatic cellular behaviors. This project investigated the influence of three dimensional (3D) chitosan-alginate (CA) scaffold stiffness on the morphology, growth, and migration of green fluorescent protein (GFP) – transfected MDA-MB-231 (231-GFP) breast cancer (BCa) cells. The CA scaffolds were produced by the freeze casting method at three concentrations, 2 wt%, 4 wt%, and 6 wt% to provide different stiffness culture substrates. The CA scaffold material properties were characterized using scanning electron microscopy imaging for pore structure and compression testing for Young\u27s Modulus. The BCa cell cultures were characterized at day 1, 3, and 7 timepoints using Alamar Blue assay for cell number, fluorescence imaging for cell morphology, and single-cell tracking for cell migration. Pore size calculations using SEM imaging yielded pore sizes of 253.29 ± 52.45 µm, 209.55 ± 21.46 µm, and 216.83 ± 32.63 µm for 2 wt%, 4 wt%, and 6 wt%, respectively. Compression testing of the CA scaffolds yielded Young\u27s Modulus values of 0.064 ± 0.008 kPa, 2.365 ± 0.32 kPa and 3.30 ± 0.415 kPa for 2 wt%, 4 wt%, and 6 wt% CA scaffolds, respectively. The results showed no significant difference in cell number among the 3D CA scaffold groups. However, the 231-GFP cells cultured in 2 wt% CA scaffolds possessed greater cellular size, area, perimeter, and lower cellular circularity compared to those in 4 wt% and 6 wt% CA scaffolds, suggesting a more prominent presence of cell clusters in softer substrates compared to stiffer substrates. The results also showed cells in 6 wt% CA having a higher average cell migration speed compared to those in 2 wt% and 4 wt% CA scaffolds, indicating a positive relationship between substrate stiffness and cell migration velocity. Findings from this experiment may contribute to the development of enhanced in vitro 3D breast tumor models for basic cancer research using 3D porous biomaterial scaffolds

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

THE EFFECT OF SUPPORT FROM ORGANIZATIONS AND TRANSFORMATIONAL LEADERSHIP ON AFFECTIVE COMMITMENT OF EMPLOYEES OF INDUSTRIAL ENTERPRISES OF VIETNAM

He result of a survey of 547 workers at Vietnamese industrial enterprises shows that the organizational support and the transformation leadership have a positive and statistically significant correlation with emotional commitment. The results of the regression analysis confirm that the organizational support has more influence than the transformational leadership on emotional commitment. In addition, the social package and charisma have the strongest effect on emotional commitment. This study also showed that transformational leadership has a moderating effect on the interaction between organizationalal support and emotional commitment. The study suggests some recommendations to improve the workers’s emotional commitment to the organization

Dynamic priority based reliable real-time communications for infrastructure-less networks

This paper proposes a dynamic priority system at medium access control (MAC) layer to schedule time sensitive and critical communications in infrastructure-less wireless networks. Two schemes, priority enabled MAC (PE-MAC) and optimized PE-MAC are proposed to ensure real-time and reliable data delivery in emergency and feedback systems. These schemes use a dynamic priority mechanism to offer improved network reliability and timely communication for critical nodes. Both schemes offer a notable improvement in comparison to the IEEE 802.15.4e low-latency deterministic networks. To ensure more predictable communication reliability, two reliability centric schemes, quality-ensured scheme (QES) and priority integrated QES, are also proposed. These schemes maintain a pre-specified successful packet delivery rate, hence improving the overall network reliability and guaranteed channel access

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning

Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-modal data, thus limiting its potential on exploiting valuable multimodal data for future personalized applications. Furthermore, the majority of FL approaches still rely on the labeled data at the client side, which is limited in real-world applications due to the inability of self-annotation from users. In light of these limitations, we propose a novel multimodal FL framework that employs a semi-supervised learning approach to leverage the representations from different modalities. Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset. Our FedMEKT iteratively updates the generalized global encoders with the joint embedding knowledge from the participating clients. Thereby, to address the modality discrepancy and labeled data constraint in existing FL systems, our proposed FedMEKT comprises local multimodal autoencoder learning, generalized multimodal autoencoder construction, and generalized classifier learning. Through extensive experiments on three multimodal human activity recognition datasets, we demonstrate that FedMEKT achieves superior global encoder performance on linear evaluation and guarantees user privacy for personal data and model parameters while demanding less communication cost than other baselines