Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/TNB.2023.3302773 | DOI Listing |
Lipids Health Dis
January 2025
Department of Urology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road Jinan, Shandong, 250012, People's Republic of China.
Background: An association exists between obesity and reduced testosterone levels in males. The propose of this research is to reveal the correlation between 15 indices linked to obesity and lipid levels with the concentration of serum testosterone, and incidence of testosterone deficiency (TD) among adult American men.
Methods: The study utilized information gathered from the National Health and Nutrition Examination Survey (NHANES) carried out from 2011 to 2016.
BMC Health Serv Res
January 2025
School of Humanities and Social Sciences, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, China.
Background: To address the health inequity caused by decentralized management, China has introduced a provincial pooling system for urban employees' basic medical insurance. This paper proposes a research framework to evaluate similar policies in different contexts. This paper adopts a mixed-methods approach to more comprehensively and precisely capture the causal effects of the policy.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Human Movement Science, Hunan Normal University, 36 Lushan Road, Changsha, Hunan, China.
Loneliness and low self-esteem are among the more prominent mental health problems among left-behind children, but most of the current research stays in cross-sectional surveys, with fewer studies proposing specific solutions. In addition, although the effective impact of dance interventions on loneliness and self-esteem has been demonstrated, the impact in the group of left-behind children remains under-explored. Therefore, this study validated the effectiveness of a dance intervention on loneliness and self-esteem in left-behind children through a 16-week randomised controlled trial.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFSci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!