12,312 research outputs found
Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies
© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio
Personalized modeling for real-time pressure ulcer prevention in sitting posture
, Ischial pressure ulcer is an important risk for every paraplegic person and
a major public health issue. Pressure ulcers appear following excessive
compression of buttock's soft tissues by bony structures, and particularly in
ischial and sacral bones. Current prevention techniques are mainly based on
daily skin inspection to spot red patches or injuries. Nevertheless, most
pressure ulcers occur internally and are difficult to detect early. Estimating
internal strains within soft tissues could help to evaluate the risk of
pressure ulcer. A subject-specific biomechanical model could be used to assess
internal strains from measured skin surface pressures. However, a realistic 3D
non-linear Finite Element buttock model, with different layers of tissue
materials for skin, fat and muscles, requires somewhere between minutes and
hours to compute, therefore forbidding its use in a real-time daily prevention
context. In this article, we propose to optimize these computations by using a
reduced order modeling technique (ROM) based on proper orthogonal
decompositions of the pressure and strain fields coupled with a machine
learning method. ROM allows strains to be evaluated inside the model
interactively (i.e. in less than a second) for any pressure field measured
below the buttocks. In our case, with only 19 modes of variation of pressure
patterns, an error divergence of one percent is observed compared to the full
scale simulation for evaluating the strain field. This reduced model could
therefore be the first step towards interactive pressure ulcer prevention in a
daily setup. Highlights-Buttocks biomechanical modelling,-Reduced order
model,-Daily pressure ulcer prevention
Detection of internal quality in kiwi with time-domain diffuse reflectance spectroscopy
Time-domain diffuse reflectance spectroscopy (TRS), a medical sensing technique, was used to evaluate internal kiwi fruit quality. The application of this pulsed laser spectroscopic technique was studied as a new, possible non-destructive, method to detect optically different quality parameters: firmness, sugar content, and acidity. The main difference with other spectroscopic techniques is that TRS estimates separately and at the same time absorbed light and scattering inside the sample, at each wavelength, allowing simultaneous estimations of firmness and chemical contents. Standard tests (flesh puncture, compression with ball, .Brix, total acidity, skin color) have been used as references to build estimative models, using a multivariate statistical approach. Classification functions of the fruits into three groups achieved a performance of 75% correctly classified fruits for firmness, 60% for sugar content, and 97% for acidity. Results demonstrate good potential for this technique to be used in the development of new sensors for non-destructive quality assessment
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table
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