The aim of this study was to evaluate the relevance of the in vitro permeation method used at our laboratory in predicting in vivo dermal and transdermal absorption. Two different emulsions, a submicron oil-in-water (o/w) emulsion and a semisolid water-in-oil (w/o) emulsion, containing a model compound were investigated. The in vitro permeation rate of the compound from these emulsions was measured using static diffusion cells with human skin as membrane. The emulsions were allowed to remain in contact with the skin in the donor chamber for 15, 60, and 240 min. The study was monitored for 240 min and the steady state flux was calculated. The systemic concentration of the compound was measured in vivo as a function of time after dermal application to healthy volunteers with 15 and 60 min of application. A short-lasting i.v. infusion study in healthy volunteers was used to simulate the i.v. bolus dose. Numerical convolution was used to predict the in vivo plasma concentration of the compound while the in vivo absorption rate of the compound was estimated using numerical deconvolution. To establish correlation, the predicted in vivo flux was compared with the corresponding observed in vitro parameter after adjusting for the lag time. No major differences were seen in the systemic plasma levels between the two emulsions, which is in close agreement with the steady state flux measured in vitro. A linear correlation representing a point-to-point relationship was established for each of the investigated formulations and application times. The longer application time was predicted more accurately for both emulsions.
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http://dx.doi.org/10.1002/jps.10203 | DOI Listing |
Background: A decline in Instrumental Activities of Daily Living (IADLs) indicates cognitive impairment, a marker of early detection of Alzheimer's disease (AD). Obtaining hand information within the assessment of IADLs may be an innovative approach to predicting cognitive decline. Hands play a vital role while performing IADL and can be used in assessing human visuomotor skills.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA.
Background: Brain aging (BA) involves the gradual deterioration of brain systems and is associated with chronological age (CA). Measures of BA have been validated and adopted in aging and neurological disease research (Biondo, 2022; Eickhoff, 2021) and could be a useful clinical tool. BA predicts CA in healthy adults (Cole, 2017) and accelerated BA precede Alzheimer's disease (AD) symptoms (Elliott, 2021).
View Article and Find Full Text PDFBiol Imaging
November 2024
Institut de Recherche en Informatique de Toulouse (IRIT), CNRS & Université de Toulouse, Toulouse, France.
We propose a neural network architecture and a training procedure to estimate blurring operators and deblur images from a single degraded image. Our key assumption is that the forward operators can be parameterized by a low-dimensional vector. The models we consider include a description of the point spread function with Zernike polynomials in the pupil plane or product-convolution expansions, which incorporate space-varying operators.
View Article and Find Full Text PDFPLoS One
January 2025
Geosciences Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, KSA.
Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar.
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