Search Results (41)

Background/Objective: Abdominal examination by medical doctors is undertaken to observe abdominal shape and tenderness, but it is not typically quantified. Our goal was to explore the potential of physical metrics for identifying significant differences between individuals with fullness and discomfort in the chest and hypochondrium (FDCH) and those without FDCH. We utilized a 3D camera and a digital algometer to obtain these metrics. Methods: We screened sixty participants with functional dyspepsia and complaints of epigastric discomfort or pain and sixty healthy participants without any digestive problems as a case-control study. We assessed the degree of agreement with FDCH of the abdominal signs diagnosed by traditional East Asian medicine doctors by performing clinical studies that involved assessing abdomens with the aforementioned devices. Results: Algometric features such as pressure, depth, and stiffness (defined as the pressure-to-depth ratio) were significantly lower in the FDCH group than in the non-FDCH group, with mean differences across locations ranging from −1.47 to −0.86, −8.75 to −4.46, and −0.31 to −0.12, respectively. Therefore, the physical algometric features decreased, the skin stiffness decreased, and the sensitivity increased. The point estimates for the mean differences in the geometric factor of depth between FDCH and non-FDCH across the locations ranged from −2.09 to −1.66, with generally smaller depth values in the FDCH group, indicating a flat or drooping abdominal shape. Conclusions: The algometric and geometric metrics showed differences between the FDCH and non-FDCH groups, and various physical metrics will be expanded to identify other diseases through the collection of more clinical data in future. Trial registration/Protocol registration: CRIS and KCT0003369. Full article

Tactile sensation across the whole hand, including the fingers and palm, is essential for manipulation and, therefore, is expected to be similarly useful for enabling dexterous robot manipulation. Tactile sensation would ideally be distributed (over large surface areas), have a high precision, and provide measurements in multiple axes, allowing for effective manipulation and interaction with objects of varying shapes, textures, friction, and compliance. Given the complex geometries and articulation of state-of-the-art robotic grippers and hands, they would benefit greatly from their surface being instrumented with a thin, curved, and/or flexible tactile sensor technology. However, the majority of current sensor technologies measure tactile information across a planar sensing surface or instrument-curved skin using relatively bulky camera-based approaches; proportionally in the literature, thin and flexible tactile sensor arrays are an under-explored topic. This paper, presents a thin, flexible, non-camera-based optical tactile sensor design as an investigation into the feasibility of adapting our novel LiVec sensing principle to curved and flexible surfaces. To implement the flexible sensor, flexible PCB technology is utilized in combination with other soft components. This proof-of-concept design eliminates rigid circuit boards, creating a sensor capable of providing localized 3D force and 3D displacement measurements across an array of sensing units in a small-thickness, non-camera-based optical tactile sensor skin covering a curved surface. The sensor consists of 16 sensing units arranged in a uniform 4 × 4 grid with an overall size of 30 mm × 30 mm × 7.2 mm in length, width, and depth, respectively. The sensor successfully estimated local XYZ forces and displacements in a curved configuration across all sixteen sensing units, the average force bias values ($\overline{\mu }$) were −1.04 mN, −0.32 mN, and −1.31 mN, and the average precision ($\overline{SD}$) was 54.49 mN, 55.16 mN and 97.15 mN, for the X, Y, Z axes, respectively, the average displacement bias values ($\overline{\mu }$) were 1.58 μm, 0.29 μm, and −1.99 μm, and the average precision values ($\overline{SD}$) were 221.61 μm, 247.74 μm, and 44.93 μm for the X, Y, and Z axes, respectively. This work provides crucial insights into the design and calibration of future curved LiVec sensors for robotic fingers and palms, making it highly suitable for enhancing dexterous robotic manipulation in complex, real-world environments. Full article

Emerging radar sensing technology is revolutionizing cardiovascular monitoring by eliminating direct skin contact. This approach captures vital signs through electromagnetic wave reflections, enabling contactless blood pressure (BP) tracking while maintaining user comfort and privacy. We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. Drawing on the intrinsic link between systolic (SBP) and diastolic (DBP) blood pressures, early estimates from Stage 2 are used to expand the feature set for the second-stage network, boosting its predictive power. Validation achieved clinically acceptable errors (SBP: −1.09 ± 5.15 mmHg, DBP: −0.26 ± 4.35 mmHg). Notably, this high degree of accuracy, combined with the ability to estimate BP at 2 s intervals, closely approximates real-time, beat-to-beat monitoring, representing a pivotal breakthrough in non-contact BP monitoring. Full article

In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields immediate and precise estimations of entry hole diameter, thereby facilitating the attainment of these objectives. The principal aim of this research is to develop sophisticated machine learning-based models proficient in predicting entry hole diameter under full downhole conditions. Ten machine learning and deep learning models have been developed utilizing readily available parameters routinely gathered during perforation operations, including perforation depth, rock density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength, casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables. These models are trained by utilizing actual casing entry hole diameter data acquired from deployed downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from 53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms. These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The results of the most effective machine learning models, specifically Gradient Boosting, Random Forest, AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE) in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%, 4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089, and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and MSE values verify the remarkably high accuracy of the generated models. This paper offers novel insights by demonstrating the improvements achieved in ongoing perforation operations through the application of a machine learning model for predicting entry hole diameter. The utilization of machine learning models presents a more accurate, expedient, real-time, and economically viable alternative to empirical models and deployed downhole cameras. Additionally, these machine learning models excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a challenge that a singular empirical model struggled to address. Full article

Ultrasound is a versatile and well-established technique using sound waves with frequencies higher than the upper limit of human hearing. Typically, therapeutic and diagnosis ultrasound operate in the frequency range of 500 kHz to 15 MHz with greater depth of penetration into the body. However, to achieve improved spatial resolution, high-frequency ultrasound (>15 MHz) was recently introduced and has shown promise in various fields such as high-resolution imaging for the morphological features of the eye and skin as well as small animal imaging for drug and gene therapy. In addition, high-frequency ultrasound microbeam stimulation has been demonstrated to manipulate single cells or microparticles for the elucidation of physical and functional characteristics of cells with minimal effect on normal cell physiology and activity. Furthermore, integrating machine learning with high-frequency ultrasound enhances diagnostics, including cell classification, cell deformability estimation, and the diagnosis of diabetes and dysnatremia using convolutional neural networks (CNNs). In this paper, current efforts in the use of high-frequency ultrasound from imaging to stimulation as well as the integration of deep learning are reviewed, and potential biomedical and cellular applications are discussed. Full article

Little research has been carried out in terms of modeling and control of analgesia. However, emerging new technology and recent prototypes paved the way for several ideas on pain modeling for control. Recently, such an idea has been proposed for measuring the Depth of Analgesia (DoA). In this paper, that solution is further exploited towards obtaining a novel fractional-order model and dedicated controller for DoA. First, clinical data from patients undergoing general anesthesia are used to determine a commensurate fractional-order model of the skin impedance at each sampling period. Second, we provide a proof of concept indicating that fractional order changes due to variations in the infused opioid drug (Remifentanil). Third, a fractional-order model for DoA is developed correlating the changes in the pain index (as the output signal) and the Remifentanil infusion rate (as the input signal). Standard optimization routines are used to estimate the parameters. A database of 19 real patients is used. Lastly, a preliminary fractional-order controller is designed and tested in simulation for the 19 patients. The closed-loop simulation results correspond to the expected clinical outcomes. Full article

External exposure to skin from beta-emitter radionuclides following severe reactor accidents or nuclear testing can result in beta burning and other health complications. The skin absorbed dose coefficient (SADC) measures the energy deposition into the skin during such accidents. The U.S. Environmental Protection Agency has published several reports to measure the possible energy deposition into the skin in such accidents. However, the most recent SADC published by Federal Guidance Report (FGR) 12 was computed only at one meter above the contaminated surface. Therefore, it was necessary to develop a model to estimate the absorbed dose coefficients for skin at different heights. In this manuscript, Geant4, a Monte Carlo simulator toolkit, was used to estimate the absorbed dose coefficients from electron sources located on the soil surface with energies ranging from 0.1 to 4 MeV. The energy deposited from primary electrons, secondary electrons, and photons in a 50 µm thick layer of epidermis tissue (Basal Cells Layer) located at a depth of 50 µm from the skin surface was estimated at several discrete heights of human leg phantom. More than 40% of the total energy deposited comes from secondary electrons and photons in energy sources of 0.1 and 0.2 MeV on average, but for higher energies, this percentage is less than 1%, which indicates primary electrons are the main source of the deposited energy in the skin. Furthermore, the results showed the energy deposited into skin closer to the ground was 50–100% higher than the previously estimated doses for 1 m above the ground. The results from Geant4 showed a great correlation (R² = 0.972) with the FGR 12 data at one meter height, and they were aligned with the published values from FGR 12, which validated the simulation results. Therefore, the calculated dose coefficients for different energy sources and different heights could be used in radiation protection measurements. Full article

Breast milk contains numerous factors that are involved in the maturation of the immune system and development of the gut microbiota in infants. These factors include transforming growth factor-β1 and 2, immunoglobin A, and lactoferrin. Breast milk factors may also affect epidermal differentiation and the stratum corneum (SC) barrier in infants, but no studies examining these associations over time during infancy have been reported. In this single-center exploratory study, we measured the molecular components of the SC using confocal Raman spectroscopy at 0, 1, 2, 6, and 12 months of age in 39 infants born at our hospital. Breast milk factor concentrations from their mothers’ breast milk were determined. Correlation coefficients for the two datasets were estimated for each molecular component of the SC and breast milk factor at each age and SC depth. The results showed that breast milk factors and molecular components of the SC during infancy were partly correlated with infant age in months and SC depth, suggesting that breast milk factors influence the maturation of the SC components. These findings may improve understanding of the pathogenesis of skin diseases associated with skin barrier abnormalities. Full article

Wrinkles, crucial for age estimation and skin quality assessment, present challenges due to their uneven distribution, varying scale, and sensitivity to factors like lighting. To overcome these challenges, this study presents facial wrinkle detection with multiscale spatial feature fusion based on image enhancement and an adaptively spatial feature fusion squeeze-and-excitation Unet network (ASFF-SEUnet) model. Firstly, in order to improve wrinkle features and address the issue of uneven illumination in wrinkle images, an innovative image enhancement algorithm named Coiflet wavelet transform Donoho threshold and improved Retinex (CT-DIR) is proposed. Secondly, the ASFF-SEUnet model is designed to enhance the accuracy of full-face wrinkle detection across all age groups under the influence of lighting factors. It replaces the encoder part of the Unet network with EfficientNet, enabling the simultaneous adjustment of depth, width, and resolution for improved wrinkle feature extraction. The squeeze-and-excitation (SE) attention mechanism is introduced to grasp the correlation and importance among features, thereby enhancing the extraction of local wrinkle details. Finally, the adaptively spatial feature fusion (ASFF) module is incorporated to adaptively fuse multiscale features, capturing facial wrinkle information comprehensively. Experimentally, the method excels in detecting facial wrinkles amid complex backgrounds, robustly supporting facial skin quality diagnosis and age assessment. Full article

Optical techniques are often inadequate in estimating bruise age since they are not sensitive to the depth of chromophores at the location of the bruise. To address this shortcoming, we used pulsed photothermal radiometry (PPTR) for depth profiling of bruises with two wavelengths, 532 nm (KTP laser) and 1064 nm (Nd:YAG laser). Six volunteers with eight bruises of exactly known and documented times of injury were enrolled in the study. A homogeneous part of the bruise was irradiated first with a 5 ms pulse at 532 nm and then with a 5 ms pulse at 1064 nm. The resulting transient surface temperature change was collected with a fast IR camera. The initial temperature–depth profiles were reconstructed by solving the ill-posed inverse problem using a custom reconstruction algorithm. The PPTR signals and reconstructed initial temperature profiles showed that the 532 nm wavelength probed the shallow skin layers revealing moderate changes during bruise development, while the 1064 nm wavelength provided additional information for severe bruises, in which swelling was present. Our two-wavelength approach has the potential for an improved estimation of the bruise age, especially if combined with modeling of bruise dynamics. Full article

Previously, we reported the use of confocal Raman spectroscopy (CRS) as a novel non-invasive approach to determine drug disposition in the skin in vivo. Results obtained by CRS were found to correlate with data from the well-established in vitro permeation test (IVPT) model using human epidermis. However, these studies used simple vehicles comprising single solvents and binary or ternary solvent mixtures; to date, the utility of CRS for monitoring dermal absorption following application of complex marketed formulations has not been examined. In the present work, skin delivery of diclofenac sodium (DFNa) from two topical dermatological drug products, namely Diclac^® Lipogel 10 mg/g and Primofenac^® Emulsion gel 1%, was determined by IVPT and in vivo by both CRS and tape stripping (TS) methodologies under similar experimental conditions. The in vivo data were evaluated against the in vitro findings, and a direct comparison between CRS and TS was performed. Results from all methodologies showed that Diclac promoted significantly greater DFNa delivery to the skin (p < 0.05). The cumulative amounts of DFNa which permeated at 24 h in vitro for Diclac (86.5 ± 9.4 µg/cm²) were 3.6-fold greater than the corresponding amounts found for Primofenac (24.4 ± 2.7 µg/cm²). Additionally, total skin uptake of DFNa in vivo, estimated by the area under the depth profiles curves (AUC), or the signal intensity of the drug detected in the upper stratum corneum (SC) (4 µm) ranged from 3.5 to 3.6-fold greater for Diclac than for Primofenac. The shape of the distribution profiles and the depth of DFNa penetration to the SC estimated by CRS and TS were similar for the two methods. However, TS data indicated a 4.7-fold greater efficacy of Diclac relative to Primofenac, with corresponding total amounts of drug penetrated, 94.1 ± 22.6 µg and 20.2 ± 7.0 µg. The findings demonstrate that CRS is a methodology that is capable of distinguishing skin delivery of DFNa from different formulations. The results support the use of this approach for non-invasive evaluation of topical products in vivo. Future studies will examine additional formulations with more complex compositions and will use a wider range of drugs with different physicochemical properties. The non-invasive nature of CRS coupled with the ability to monitor drug permeation in real time offer significant advantages for testing and development of topical dermatological products. Full article

Dura mater (DM) is a connective tissue with dense collagen, which is a protective membrane surrounding the human brain. The optical clearing (OC) method was used to make DM more transparent, thereby allowing to increase in-depth investigation by confocal Raman micro-spectroscopy and estimate the diffusivity of 50% glycerol and water migration. Glycerol concentration was obtained, and the diffusion coefficient was calculated, which ranged from 9.6 × 10⁻⁶ to 3.0 × 10⁻⁵ cm²/s. Collagen-related Raman band intensities were significantly increased for all depths from 50 to 200 µm after treatment. In addition, the changes in water content during OC showed that 50% glycerol induces tissue dehydration. Weakly and strongly bound water types were found to be most concentrated, playing a major role in the glycerol-induced water flux and OC. Results show that OC is an efficient method for controlling the DM optical properties, thereby enhancing the in-depth probing for laser therapy and diagnostics of the brain. DM is a comparable to various collagen-containing tissues and organs, such as sclera of eyes and skin dermis. Full article

Background: Optical coherence tomography (OCT) is a non-invasive imaging technique based on the interferometry of backscattered light. However, strong light scattering hinders its applicability in clinical dermatology. The strength of scattering is exemplified by the attenuation coefficient which is the rate of OCT signal decay in depth. Attenuation can be reduced by topical application of hyperosmotic liquids with a high refractive index, namely optical clearing agents (OCAs). In this study, we assessed the impact of different OCAs to enhance skin optical permeability in OCT images. In vivo tests were carried out to determine the OCT attenuation coefficient (μ_OCT) and epidermal thickness in the treated and untreated epidermis. Methods: Four OCAs were studied: Propylenglycol, propylenglycol combined with oleic acid in equal proportions (1:1 v/v), Vaseline, and liquid Vaseline. Percentage change of μ_OCT and epidermal thickness were estimated by OCT imaging of a healthy forearm skin, prior to the application of each OCA and after the application, at two time points, t₁ = 5 min, and t₂ = 90 min. μ_OCT was quantitatively obtained by fitting the OCT signal to a single scattering model. Results: The application of OCAs induced significant changes in both μ_OCT (decreased) and epidermal thickness (increased). The synergistic effect of the combined propylenglycol with oleic acid reduced the μ_OCT by 43% while propylenglycol induced the highest increase (33%) in epidermal thickness, both at t₂. Conclusions: Topical administration of propylenglycol combined with oleic acid can reduce light attenuation in OCT imaging within the clinically relevant timeframe of 90 min. Full article

The accurate estimation of skin and skull optical properties over a wide wavelength range of laser radiation has great importance in optogenetics and other related applications. In the present work, using the Kubelka–Munk model, finite-element solution of the diffusion equation, inverse adding-doubling (IAD), and Monte-Carlo simulation, we estimated the refractive index, absorption and scattering coefficients, penetration depth, and the optical fluence distribution in rabbit head tissues ex vivo, after dividing the heads into three types of tissues with an average thickness of skin of 1.1 mm, skull of 1 mm, and brain of 3 mm. The total diffuse reflectance and transmittance were measured using a single integrating sphere optical setup for laser radiation of 532, 660, 785, and 980 nm. The calculated optical properties were then applied to the diffusion equation to compute the optical fluence rate distribution at the boundary of the samples using the finite element method. Monte-Carlo simulation was implemented for estimating the optical fluence distribution through a model containing the three tissue layers. The scattering coefficient decreased at longer wavelengths, leading to an increase in optical fluence inside the tissue samples, indicating a higher penetration depth, especially at 980 nm. In general, the obtained results show good agreement with relevant literature. Full article

Phenolic compounds with miscellaneous biological activities are an interesting component in dermatology and cosmetology practices. The aim of our study was to determine the phenolic compounds released from emulsion, emulgel, gel, ointment, and oleogel formulations penetration into human skin layers, both the epidermis and dermis, and estimate their antioxidant activity. The ex vivo penetration study was performed using Bronaugh type flow-through diffusion cells. Penetration studies revealed that, within 24 h, the chlorogenic acid released from the oleogel penetrated into skin layers to a depth of 2.0 ± 0.1 µg/mL in the epidermis and 1.5 ± 0.07 µg/mL in the dermis. The oleogel-released complex of phenolic compounds penetrating into epidermis showed the strongest DPPH free radical scavenging activity (281.8 ± 14.1 µM TE/L). The study estimated a strong positive correlation (r = 0.729) between the amount of quercetin penetrated into epidermis and the antioxidant activity detected in the epidermis extract. Plant based phenolic compounds demonstrated antioxidant activity and showed great permeability properties through the skin. Full article