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Benson Rahbek posted an update 6 months, 3 weeks ago
baseline HbA1c or high BMI may benefit the most from patient-centered digital health coaching programs when compared to their lower risk counterparts. While all participants improved in physical and mental health categories, participants with high HbA1c experienced the greatest HbA1c reduction and individuals with the highest baseline BMI lost the most weight. These results may be used to inform referrals for patients who are more likely to benefit from digital health coaching.
To present an overview of the status of medical physics in radiotherapy in China, including facilities and devices, occupation, education, research, etc. MATERIALS AND METHODS The information about medical physics in clinics was obtained from the 9-th nationwide survey conducted by the China Society for Radiation Oncology in 2019. The data of medical physics in education and research was collected from the publications of the official and professional organizations.
By 2019, there were 1463 hospitals or institutes registered to practice radiotherapy and the number of accelerators per million population was 1.5. There were 4172 medical physicists working in clinics of radiation oncology. The ratio between the numbers of radiation oncologists and medical physicists is 3.51. Approximately, 95% of medical physicists have an undergraduate or graduate degrees in nuclear physics and biomedical engineering. 86% of medical physicists have certificates issued by the Chinese Society of Medical Physics. There has been a fast growth of publications by authors from mainland of China in the top international medical physics and radiotherapy journals since 2018.
Demand for medical physicists in radiotherapy increased quickly in the past decade. The distribution of radiotherapy facilities in China became more balanced. High quality continuing education and training programs for medical physicists are deficient in most areas. The role of medical physicists in the clinic has not been clearly defined and their contributions have not been fully recognized by the community.
Demand for medical physicists in radiotherapy increased quickly in the past decade. The distribution of radiotherapy facilities in China became more balanced. High quality continuing education and training programs for medical physicists are deficient in most areas. The role of medical physicists in the clinic has not been clearly defined and their contributions have not been fully recognized by the community.The long term municipal solid wastes (MSW) management plan of Khulna city has to be focused on the Bangladesh Delta Plan 2100. In most developing countries, conventional system of MSW management approach has been found inadequate due to complex nature of MSW. This study presents a system dynamics (SD) model to predict generation, collection, treatment and landfill capacity of MSW until the year of 2050 to analyze the necessity for MSW management for the coastal city of Khulna, Bangladesh. Simulation results show that MSW generation increases from 168 thousand tons in year 2020 to 1.2 million tons with a per capita generation from 0.117 tons to 0.561 tons by year 2050. The total fund required for collection and landfill capacity also increases, while treatment capacity decreases over time, resulting a piling up of massive amount of uncleared MSW of 10.3 million tons in year 2050 from 152 thousand tons in year 2020. The uncleared and untreated MSW, composite index and public concern increases with time in an exponential nature for the projection period of next thirty years. The population in this model is considered as the only linear growth factor which increases from 1.5 million in year 2020 to 2.24 million by year 2050. The developed SD model also shows that the policy of only to increase collection capacity with the increased allocation of budget is not adequate for improving environmental sustainability, rather an increase of budget is essential for developing MSW treatment facility. In this study, validation methods including behavior sensitivity, data sensitivity and dimensional consistency in extreme condition has been performed to validate the model. The outcome of this SD model can be used as a dynamic testing module for MSW management policy analysis and strategic measures that can be implemented effectively in the context of developing counties.
Diabetic retinopathy is a type of diabetes that causes vascular changes that can lead to blindness. TAE226 The ravages of this disease cannot be reversed, so early detection is essential. This work presents an automated method for early detection of this disease using fundus colored images.
A bio-inspired approach is proposed on synaptic metaplasticity in convolutional neural networks. This biological phenomenon is known to directly interfere in both learning and memory by reinforcing less common occurrences during the learning process. Synaptic metaplasticity has been included in the backpropagation stage of a convolution operation for every convolutional layer.
The proposed method has been evaluated by using a public small diabetic retinopathy dataset from Kaggle with four award-winning convolutional neural network architectures. Results show that convolutional neural network architectures including synaptic metaplasticity improve both learning rate and accuracy. Furthermore, obtained results outperform other methods in current literature, even using smaller datasets for training. Best results have been obtained for the InceptionV3 architecture with synaptic metaplasticity with a 95.56% accuracy, 94.24% F1-score, 98.9% precision and 90% recall, using 3662 images for training.
Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, training simplicity and high performance.
Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, training simplicity and high performance.Kidney cancer is a dangerous disease affecting many patients all over the world. Early-stage diagnosis and correct identification of kidney cancer subtypes play an essential role in the patient’s survival; therefore, its subtypes diagnosis and classification are the main challenges in kidney cancer treatment. Medical studies have proved that miRNA dysregulation can increase the risk of cancer. Thus, in this paper, we propose a new machine learning approach for significant miRNAs identification and kidney cancer subtype classification to design an automatic diagnostic tool. The proposed method contains two main steps feature selection and classification. First, we apply the feature selection algorithm to choose the candidate miRNAs for each subtype. The feature selection algorithm utilizes the AMGM measure to select significant miRNAs with high discriminant power. Next, the candidate miRNAs are fed to a classifier to evaluate the candidate features. In the classification step, the proposed self-organizing deep neuro-fuzzy system is employed to classify kidney cancer subgroups.
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