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A pilot study on the effects of olfactory stimulation with white musk aromatic oil on psychophysiological activity: a crossover study
AbstractStudies on the compounds of aromatic oils and their effects on psychophysiological changes in humans are often conducted separately. To obtain better validation, a suitable protocol is needed that can be extrapolated to large-scale olfactory stimulation experiments. Unfortunately, this type of study is still rarely performed. In this situation, we propose a randomized crossover pilot study on olfactory stimulation with aromatic oils in relation to changes in psychophysiological activity by focusing on white musk aromatic oil due to its popularity in the community. Chemical profiling by TDU-GC-MS (thermal desorption gas chromatography/mass spectrometry) was performed to understand the compounds of the aromatic oils presented. To understand the changes in the participants’ impressions and mood states, POMS 2 (Profile of Mood States 2nd Edition) and VAS (Visual analogue scale) were performed in addition to physiological evaluation by using EEG (electroencephalogram), ECG (electrocardiogram) and salivary amylase measurements. The proposed pilot study showed “gorgeous”, “sweet”, and “like” impression toward white musk aromatic oil under VAS evaluation. Mood evaluation under POMS 2 variables such as Fatigue-Inertia (FI), Tension-anxiety (TA) and TMD (total mood disturbance) were significantly decreased under white musk aromatic oil inhalation. Under current protocol, we can also see the changes in autonomic activity and brain activity during olfactory stimulation. This pilot study could be the first step towards a larger sample size experiment on olfactory stimulation. This experiment has been registered to UMIN Clinical Trials Registry with register ID : UMIN000051972 on 24/08/2023.
Association analysis of dry heat or wet cold weather and the risk of urolithiasis hospitalization in a southern Chinese city
Statistical and data visualization techniques to study the role of one-electron in the energy of neutral and charged clusters of Na39
Quantitative analysis on the optical kerr impact and third harmonic generation in beltrami-shaped curved graphene
Chinese herbal medicine recognition network based on knowledge distillation and cross-attention
A multilevel social network approach to studying multiple disease-prevention behaviors
AbstractThe effective prevention of many infectious and non-infectious diseases relies on people concurrently adopting multiple prevention behaviors. Individual characteristics, opinion leaders, and social networks have been found to explain why people take up specific prevention behaviors. However, it remains challenging to understand how these factors shape multiple interdependent behaviors. We propose a multilevel social network framework that allows us to study the effects of individual and social factors on multiple disease prevention behaviors simultaneously. We apply this approach to examine the factors explaining eight malaria prevention behaviors, using unique interview data collected from 1529 individuals in 10 hard-to-reach, malaria-endemic villages in Meghalaya, India in 2020–2022. Statistical network modelling reveals exposure to similar behaviors in one’s social network as the most important factor explaining prevention behaviors. Further, we find that households indirectly shape behaviors as key contexts for social ties. Together, these two factors are crucial for explaining the observed patterns of behaviors and social networks in the data, outweighing individual characteristics, opinion leaders, and social network size. The results highlight that social network processes may facilitate or hamper disease prevention efforts that rely on a combination of behaviors. Our approach is well suited to study these processes in the context of various diseases.
Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image
Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types
Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence
AbstractSkin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.