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In fermentation processes, changes in yeast cell count and substrate concentration are indicators of yeast performance. Therefore, monitoring the composition of the biological suspension, particularly the dispersed solid phase (i.e., yeast cells) and the continuous liquid phase (i.e., medium), is a prerequisite to ensure favorable process conditions. However, the available monitoring methods are often invasive or restricted by detection limits, sampling requirements, or susceptibility to masking effects from interfering signals. In contrast, ultrasound measurements are non-invasive and provide real-time data. In this study, the suitability to characterize the dispersed and the liquid phase of yeast suspensions with ultrasound was investigated. The ultrasound signals collected from three commercially available Saccharomyces yeast were evaluated and compared. For all three yeasts, the attenuation coefficient and speed of sound increased linearly with increasing yeast concentrations (0.0–1.0 wt%) and cell counts (R2 > 0.95). Further characterization of the dispersed phase revealed that cell diameter and volume density influence the attenuation of the ultrasound signal, whereas changes in the speed of sound were partially attributed to compositional variations in the liquid phase. This demonstrates the ability of ultrasound to monitor industrial fermentations and the feasibility of developing targeted control strategies. [ABSTRACT FROM AUTHOR]

Mapping the skin with various biophysical properties can demonstrate not only skin condition but also organize a convenient approach to managing diseases. The aim of this study, mapping of the regional alteration for 6 biophysical parameters with noninvasive method. Totally 39 dogs [healthy (n=16) and canine atopic dermatitis (n=23)] were enrolled. Skin pH, hydration, melanin, sebum content and temperature for pinna, perilabial region, hind-fore feet (dorsal metacarpus-dorsal metatarsus), axillae, lateral thorax/flanks, elbows, abdomen/inguinal and ventral tail were measured. Atopic dogs exhibited more acidic skin (P

Mapping the skin with various biophysical properties can demonstrate not only skin condition but also organize a convenient approach to managing diseases. The aim of this study, mapping of the regional alteration for 6 biophysical parameters with noninvasive method. Totally 39 dogs [healthy (n=16) and canine atopic dermatitis (n=23)] were enrolled. Skin pH, hydration, melanin, sebum content and temperature for pinna, perilabial region, hind-fore feet (dorsal metacarpus-dorsal metatarsus), axillae, lateral thorax/flanks, elbows, abdomen/inguinal and ventral tail were measured. Atopic dogs exhibited more acidic skin (P<0.001) compared to healthy dogs, with the thorax/ flanks region showing significant regional differences (P=0.037). Hydration levels were consistently lower in atopic dogs (P<0.001), with significant regional differences observed in the abdominal/inguinal region (P=0.037). Skin temperatures were generally higher in atopic dogs, with a significant increase noted in the tail (P=0.037), while other areas showed no significant differences. Melanin and sebum content did not exhibit significant regional variations. In conclusion, this study demonstrated regional variations in biophysical parameters in atopic dogs using non-invasive measurements. These findings provide valuable insights for understanding canine atopic dermatitis and its management. [ABSTRACT FROM AUTHOR]

The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes. [ABSTRACT FROM AUTHOR]

This study addresses the limitations of current tonometry techniques by exploring vibroacoustic properties for estimating intraocular pressure (IOP), a key diagnostic parameter for monitoring glaucoma—a significant risk factor for vision loss. Utilizing vivo porcine eyeballs, we investigated the relationship between IOP and the nonlinear vibration transfer function ratio (NVTFR). Through applying varying vibration levels and analyzing responses with transfer function analysis and univariate regression, we identified a strong negative correlation between NVTFR and IOP, evidenced by a Pearson correlation coefficient of −0.8111 and significant results from generalized linear model (GLM) regression (p-value < 0.001). These findings indicate the potential of NVTFR as a vital indicator of IOP changes. Our study highlights the feasibility of using vibroacoustic properties, specifically NVTFR, to measure IOP. While further refinement is necessary for in vivo application, this approach opens new possibilities for non-invasive and patient-friendly IOP monitoring, potentially enhancing ophthalmology diagnostic techniques and providing a foundation for future research and development in this critical area. [ABSTRACT FROM AUTHOR]

Background: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. Materials and Methods: Equipped with a moisture sensor and a multi‐light‐source camera, Skinly can assess age‐related skin parameters and specific skin properties. Utilizing state‐of‐the‐art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. Results: Our findings revealed that the Skinly device can accurately measure age‐associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. Conclusion: Thanks to its capability for multi‐parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time‐consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis. [ABSTRACT FROM AUTHOR]

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A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors. [ABSTRACT FROM AUTHOR]

Abstract Background Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. Methods Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. Results Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. Conclusions A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

In fermentation processes, changes in yeast cell count and substrate concentration are indicators of yeast performance. Therefore, monitoring the composition of the biological suspension, particularly the dispersed solid phase (i.e., yeast cells) and the continuous liquid phase (i.e., medium), is a prerequisite to ensure favorable process conditions. However, the available monitoring methods are often invasive or restricted by detection limits, sampling requirements, or susceptibility to masking effects from interfering signals. In contrast, ultrasound measurements are non-invasive and provide real-time data. In this study, the suitability to characterize the dispersed and the liquid phase of yeast suspensions with ultrasound was investigated. The ultrasound signals collected from three commercially available Saccharomyces yeast were evaluated and compared. For all three yeasts, the attenuation coefficient and speed of sound increased linearly with increasing yeast concentrations (0.0–1.0 wt%) and cell counts (R2 > 0.95). Further characterization of the dispersed phase revealed that cell diameter and volume density influence the attenuation of the ultrasound signal, whereas changes in the speed of sound were partially attributed to compositional variations in the liquid phase. This demonstrates the ability of ultrasound to monitor industrial fermentations and the feasibility of developing targeted control strategies.

To compare noninvasive external jugular vein oxygen saturations (SjvO 2) and central venous oxygen saturation (ScvO 2) from a blood sample in patients admitted to the intensive care unit. A prospective, comparative, monocentric clinical trial design was used. The study was performed in the Department of Anaesthesiology, Pharmacology, Intensive Care and Emergency Medicine, University Hospitals of Geneva (Switzerland). A total of 79 patients were enrolled; patients with confirmed COVID-19 infection requiring invasive mechanical ventilation (patients with COVID-19, n = 36) and patients after liver transplantation (posttransplant patients, n = 43). Simultaneous measurement of SjvO 2 by near-infrared spectroscopy and ScvO 2 from central venous blood samples using a blood gas analyzer in stable hemodynamic conditions. A strong linear correlation was evidenced in both the COVID-19 and posttransplant patient groups between the 2 modalities. The Bland–Altman analysis showed low bias in accordance with low percentage error in both groups (0.57% and 8.09% for patients with COVID-19; 0.00% and 13.72% for posttransplant patients). Central venous oxygen saturation can be estimated reasonably by the continuous noninvasive measurement of SjvO 2 using near-infrared spectroscopy. [ABSTRACT FROM AUTHOR]

The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes.

This study addresses the limitations of current tonometry techniques by exploring vibroacoustic properties for estimating intraocular pressure (IOP), a key diagnostic parameter for monitoring glaucoma—a significant risk factor for vision loss. Utilizing vivo porcine eyeballs, we investigated the relationship between IOP and the nonlinear vibration transfer function ratio (NVTFR). Through applying varying vibration levels and analyzing responses with transfer function analysis and univariate regression, we identified a strong negative correlation between NVTFR and IOP, evidenced by a Pearson correlation coefficient of −0.8111 and significant results from generalized linear model (GLM) regression (p-value < 0.001). These findings indicate the potential of NVTFR as a vital indicator of IOP changes. Our study highlights the feasibility of using vibroacoustic properties, specifically NVTFR, to measure IOP. While further refinement is necessary for in vivo application, this approach opens new possibilities for non-invasive and patient-friendly IOP monitoring, potentially enhancing ophthalmology diagnostic techniques and providing a foundation for future research and development in this critical area.

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development. [ABSTRACT FROM AUTHOR]

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.

The representation of temperature measurement methods is worldwide, as is the number of industries that use them. Their utilization allows one to assess the object’s temperature relatively accurately and further according to the defined dependencies to evaluate its condition. In the medical sector, non-invasive temperature measurement methods are increasingly gaining ground. In our work, we focus on the design of a system for measuring skin temperature using digital DS18B20 thermometers. The technical design consists of the mechanical design of the sensor housing based on the requirements of healthcare professionals, the design of a 1-wire bus active pull-up driver with improved features, and the user software, allowing for the future implementation in various, not necessarily medical environments. The main contribution lies in optimizing the hardware design concerning the components’ parameters and surge protection. In the medical field, the aim was to compare changes in skin temperature in the shoulder region depending on the type of muscle contraction during static and dynamic components of movement and to record the thermal work of the muscle during activity in real-time. We experimented by applying temperature sensors to the deltoid muscle of the dominant limb in a temperature environment of 25.76 °C. In the experimental measurements, we demonstrated that during the three selected muscle work movements, there was a temperature change in the respective regions of the deltoid muscle for each of the three, which the temperature sensors could capture. Our results were statistically significant when comparing the baseline values, and the highest temperature reached. The level of significance $\alpha \leqslant 0.05$ and a low percentage of errors were valid.

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction. [ABSTRACT FROM AUTHOR]

Although “film thickness” and “viscosity” represent two of the most important physical parameters in lubricated tribological contacts, their in situ measurement in tribosystems is still difficult. Sensors based on ultrasonic reflectance are a class of sensing devices that allow the direct measurement of these properties. For tribologists especially, the condition changes of tribosystems are of interest, as they determine the smooth operation, proper lubrication condition or the tribosystems’ change to impending failures. Here, ultrasonic reflectance sensors can provide valuable transient information, as they are capable of measuring the evolution of film thickness and viscosity directly, non-invasive, and in situ, thereby bringing a large benefit over classic commercial condition monitoring sensors such as vibration sensors. This paper reviews the methods of operation of ultrasonic reflectance devices, their historical evolution, limitations, potentials, and outline trends of application for the challenges faced by the tribological community and society. [ABSTRACT FROM AUTHOR]

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system’s volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

Aim: To assess the usefulness and efficacy of pulse oximetry (disappearance/reappearance of plethsmographic waves) as a method of non-invasive blood pressure monitoring in neonates. Objective: To investigate the reliability of the plethysmographic wave form of the pulse oximeter to measure the systolic blood pressure. Study Setting: A prospective observational study was done to assess usefulness and efficacy of pulse oximetry (disappearance/reappearance of plethysmographic waves) as a method of non-invasive blood pressure monitoring in neonates. Material and Methods: The study was conducted among 500 neonates to investigate the reliability of the plethysmographic waveform of the pulse oximeter to measure the systolic blood pressure as it is a easy way to perform and non invasive. Statistical Analysis Used: The results will compare and analyse statistically by Pearson correlation co-efficient. Regression modeling will carried out to explain the relationship of non-invasive blood pressure with mean DP and RP and attempted to predict the non-invasive blood pressure from mean DP/mean RP. Results: The study results revealed that NIBP systolic and diastolic both correlated with DP and RP of pulse oximetry plethysmograph. Conclusion: Study concluded that pulse oximetry is a reliable tool in measuring blood pressure in neonates (appearance and disappearance of plethysmogram).

A measurement setup for localising people in indoor environment based on a system characterized by acquisition of audio files and images implemented on a sensorised social robot is proposed. The audio signal processing for human voice identification applies the segmentation methodology for the direction of arrival (DOA) estimation. The audio signal analysis evaluates the performance of beamforming algorithms and of Covariance Matrix Fitting (CMF) when optimization in beamforming algorithm and alternative microphones’ configurations have been evaluated by simulations. Tests shows an accuracy in people detection of the optimized beamforming algorithm comparable to CMF method (96.5% and 96.6% respectively) with a lower computational cost. An image acquisition procedure has been then activated on the robot and the localisation of the people is performed using YOLO-v3 algorithm. Monte Carlo method applied to evaluate the propagation of uncertainty of the whole processing system presents a global accuracy of 98.2 ± 0.8%.

Objective: Today, there are various non-invasive techniques available for the determination of blood glucose levels. In this study, the level of blood glucose was determined by developing a new device using near-infrared (NIR) wavelength, glass optical waveguide, and the phenomenon of evanescent waves. Materials and Methods: The body's interstitial fluid has made possible the development of new technology to measure the blood glucose. As a result of contacting the fingertip with the body of the borehole rod, where electromagnetic waves are reflected inside, evanescent waves penetrate from the borehole into the skin and are absorbed by the interstitial fluid. The electromagnetic wave rate absorption at the end of the borehole rod is investigated using a detection photodetector, and its relationship to the people's actual blood glucose level. Following precise optimization and design of the glucose monitoring device, a statistical population of 100 participants with a maximum blood glucose concentration of 200 mg/dL was chosen. Before measurements, participants put their index finger for 30 seconds on the device. Results: According to this experimental study, the values measured by the innovative device with Clark grid analysis were clinically acceptable in scales A and B. The Adjusted Coefficient of Determination of the data was estimated to be 0.9064. Conclusion: For future investigations, researchers are recommended to work with a larger statistical population and use error reduction trends to improve the accuracy and expand the range of measurements.

Infrared thermography is a technique that has been utilized to assess the thermal status of animals. This study proposes a convolutional neural network (CNN)-based method for the individual classification of the thermal condition of dairy cows using thermal images of specific body surface regions. The experiment was carried out with 26 lactating cows (Holstein) during summer and winter (40 days). Meteorological data were collected every 30 min and rectal temperature (t rectal), respiratory rate (RR), and surface temperature (t sf) were measured three times a day (5 a.m., 1 p.m., and 7 p.m.). t sf was correlated with RR and t rectal and selected as input data of the models. Thus, thermal images were labelled according to the RR and t rectal categories and employed in the investigation of four CNN-based models constructed by supervised learning and cross-validation protocols. Performance was assessed by confusion matrix metrics (accuracy, precision, recall, and F1-score) comparing the predicted labels and true labels. The best results were obtained with the models that used forehead images. The model using images labelled according to three RR thermal levels has an accuracy of 76%, and the model labelled according to three t rectal thermal levels has an accuracy of 71%. The method based on deep learning allowed us to generate a computational classifier that considers not only the temperature intensities from thermographic images but also their distribution profile to identify patterns referring to thermal conditions in dairy cows. • Deep learning-based method looks at the temperature distribution of thermographic. • CNN algorithm complements average, maximum and minimum data from thermal images. • CNN-based model classifies cow's thermal images according to thermal levels. [ABSTRACT FROM AUTHOR]

This work introduces a microwave-based system able to detect tumours in breast phantoms in a non-invasive way. The data acquisition system is composed of a hardware system which involves high-frequency components (antennas, switches and cables), a microcontroller, a vector network analyser used as measurement instrument and a computer devoted to the control and automation of the operation of the system. Concerning the software system, the computer runs a Python script which is in charge of mastering and automatising all the required stages for the data acquisition, from initialisation of the hardware system to performing and saving the measurements. We also report on the design of the high-performance broadband antenna used to carry out the measurements, as well as on the algorithm employed to build the final medical images, based on an adapted version of the so-called Improved Delay-and-Sum (IDAS) algorithm improved by a Hamming window filter and averaging preprocessing. The calibration and start-up of the system are also described. The experimental validation includes the use of different tumour models with different dielectric properties inside the breast phantom. The results show promising tumour detection capabilities, even when there is low dielectric contrast between the tumoural and healthy tissues, as is the usual case for dense breasts in young women. [ABSTRACT FROM AUTHOR]

The radial artery, one of the terminal branches of the forearm, is utilized for vascular access and in various non-invasive measurement method, providing crucial medical insights. Various sensor technologies have been developed, each suited to specific characterization requirements. The work presented in this paper is based on a systematic literature review of the main publications relating to this topic. Analysis of the forearm vascular system complex array of anatomical structures shows that the radial artery can be characterized by its size, position, elasticity, tissue evaluation, blood flow and blood composition. The survey of medical procedures for patient monitoring, diagnosis and pre-operative validation shows the use of measures for pulse wave, blood pressure, heart rate, skin temperature, tissue response,…By exploring sensor technologies used for artery characterization, we produce a synthesis of measurement principles, measured phenomena and measurement accuracy for capacitive, piezoresistive, bioimpedance, thermography, fiber optic based, piezoelectric and photoacoustic sensors. A comparative study is conducted for sensor technologies by considering the metrics of the information to be collected and the associated accuracy as well as the portability, the complexity of the processing, the cost and the mode of contact with the arm. Finally, a comprehensive framework is proposed to facilitate informed decisions in the development of medical devices tailored to specific characterization needs., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Aim: To assess the usefulness and efficacy of pulse oximetry (disappearance/reappearance of plethsmographic waves) as a method of non-invasive blood pressure monitoring in neonates. Objective: To investigate the reliability of the plethysmographic wave form of the pulse oximeter to measure the systolic blood pressure. Study Setting: A prospective observational study was done to assess usefulness and efficacy of pulse oximetry (disappearance/reappearance of plethysmographic waves) as a method of non-invasive blood pressure monitoring in neonates. Material and Methods: The study was conducted among 500 neonates to investigate the reliability of the plethysmographic waveform of the pulse oximeter to measure the systolic blood pressure as it is a easy way to perform and non invasive. Statistical Analysis Used: The results will compare and analyse statistically by Pearson correlation co-efficient. Regression modeling will carried out to explain the relationship of non-invasive blood pressure with mean DP and RP and attempted to predict the non-invasive blood pressure from mean DP/mean RP. Results: The study results revealed that NIBP systolic and diastolic both correlated with DP and RP of pulse oximetry plethysmograph. Conclusion: Study concluded that pulse oximetry is a reliable tool in measuring blood pressure in neonates (appearance and disappearance of plethysmogram). [ABSTRACT FROM AUTHOR]

Abstract Background Trends in non-invasive measurements of blood hemoglobin (Hb) may be useful for identifying the need for transfusion in the perioperative period. Methods Crystalloid fluid (5–20 mL/kg) was administered intravenously or by mouth to 30 volunteers and 33 surgical patients in five non-randomized clinical studies where Hb was measured on 915 occasions by non-invasive (Radical-7™) and invasive methodology. The hemodilution curves were compared by volume kinetic analysis and linear regression, with the slope and scattering of the data as key outcome measures. Results The slope was 1.0, indicating unity between the two modes of measuring Hb when crystalloid fluid was infused in volunteers; however, only 40–45% of the variability in the non-invasive Hb could be explained by the invasive Hb. Patients undergoing major surgery, who showed the most pronounced hemodilution (median 24 g/L); non-invasive Hb explained 72% of the variability but indicated only half the magnitude of the invasive Hb changes (slope 0.48, P

Abstract Background Pancreatic exocrine insufficiency (PEI) is associated with the outcome of pancreatic disease. However, there is no method for assessing PEI that can be used noninvasively and easily for outpatient. It has been reported that changes in intestinal bacteria caused by PEI may increase breath hydrogen concentration (BHC) levels during glucose or lactose loading. We have evaluated the usefulness of fasting breath hydrogen concentration (FBHC) measurement without glucose loading for the evaluation of PEI. Methods Sixty patients underwent FBHC measurement, BT-PABA testing, and microbiome analysis. They were classified into PEI group (PABA excretion rate

• The float-sink motions of objects in a gas-solid fluidized bed were investigated. • A wireless sensor measures 3D position of object immersed in the fluidized bed. • The float–sink motions of objects in the fluidized bed differ markedly from those in liquids. • There are two barriers for the objects in the vertical direction in the bed. We developed a Lagrangian sensor system capable of wirelessly measuring the 3D motions of objects and applied it to examine the floating and sinking behaviors in a gas–solid fluidized bed. The system consisted of a sensor particle, comprising a 9-axis sensor and a wireless module integrated into a spherical outer shell, and three pairs of magnetic coils. By utilizing this system, we can non-invasively measure the 3D motions of objects, which cannot be observed from the outside. In this study, we examined the motions of objects with various densities in the gas–solid fluidized bed. We found the similarity and dissimilarity between the float–sink motions of objects in fluidized beds and those in liquids. The experimental results revealed that the vertical motion of an object in the fluidized bed depends on its density and can be categorized into two distinct states: floating near the surface or settling down at the bottom. However, within a specific range of object densities close to the apparent density of the fluidized bed, the object exhibited complicated motions in the bed. In such cases, slight density variations induced unpredictable changes between the floating and sinking states. The results also suggested that the settling velocity of the objects in the fluidized bed varies with their vertical positions in the bed. [Display omitted] [ABSTRACT FROM AUTHOR]

Short-term fluid loading is used as part of post-spaceflight medical procedures and clinical treatment in hospitals. Hypervolemia with hemodilution induced by rapid fluid infusion reportedly impaired dynamic cerebral autoregulation. However, the effects on intracranial pressure (ICP) remain unknown. Therefore, we estimated ICP noninvasively (nICP) to examine whether rapid fluid infusion would raise ICP. METHODS: Twelve healthy male volunteers underwent two discrete normal saline (NS) infusions (15 and 30 ml · kg−1 stages, NS-15 and NS-30, respectively) at a rate of 100 ml · min−1. The cerebral blood flow (CBF) velocity (CBFv) waveform from the middle cerebral artery obtained by transcranial Doppler ultrasonography was recorded, as was the arterial blood pressure (ABP) waveform at the radial artery obtained by tonometry. We then used these waveforms to calculate nICP, cerebral artery compliance, and the pulsatility index (PI) in an intracranial hydraulic model. RESULTS: nICP increased significantly in both infusion stages from preinfusion (preinfusion: 7.6 ± 3.4 mmHg; NS-15: 10.9 ± 3.3 mmHg; NS-30: 11.7 ± 4.2 mmHg). No significant changes were observed in cerebral artery compliance or PI. Although ABP did not change in any stage, CBFv increased significantly (preinfusion: 67 ± 10 cm · s−1; NS-15: 72 ± 12 cm · s−1; NS-30: 73 ± 12 cm · s−1). DISCUSSION: Hypervolemia with hemodilution induced by rapid fluid infusion caused increases in nICP and CBFv. No changes were observed in cerebral artery compliance or PI related to cerebrovascular impedance. These findings suggest that rapid fluid infusion may raise ICP with increased CBF. [ABSTRACT FROM AUTHOR]

Objective: Today, there are various non-invasive techniques available for the determination of blood glucose levels. In this study, the level of blood glucose was determined by developing a new device using near-infrared (NIR) wavelength, glass optical waveguide, and the phenomenon of evanescent waves. Materials and Methods: The body's interstitial fluid has made possible the development of new technology to measure the blood glucose. As a result of contacting the fingertip with the body of the borehole rod, where electromagnetic waves are reflected inside, evanescent waves penetrate from the borehole into the skin and are absorbed by the interstitial fluid. The electromagnetic wave rate absorption at the end of the borehole rod is investigated using a detection photodetector, and its relationship to the people's actual blood glucose level. Following precise optimization and design of the glucose monitoring device, a statistical population of 100 participants with a maximum blood glucose concentration of 200 mg/dL was chosen. Before measurements, participants put their index finger for 30 seconds on the device. Results: According to this experimental study, the values measured by the innovative device with Clark grid analysis were clinically acceptable in scales A and B. The Adjusted Coefficient of Determination of the data was estimated to be 0.9064. Conclusion: For future investigations, researchers are recommended to work with a larger statistical population and use error reduction trends to improve the accuracy and expand the range of measurements. [ABSTRACT FROM AUTHOR]

Wei Liu,1 Xingquan Huang,1,2 Xiuying Zhang,1 Xiaoling Cai,1 Xueyao Han,1 Xianghai Zhou,1 Ling Chen,1 Rui Zhang,1 Siqian Gong,1 Yanai Wang,1 Linong Ji1 1Department of Endocrinology and Metabolism, Peking University People’s Hospital, Beijing, People’s Republic of China; 2Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, People’s Republic of ChinaCorrespondence: Linong JiDepartment of Endocrinology and Metabolism, Peking University People’s Hospital, No. 11, Xi Zhi Men Nan Street, Beijing 100044, People’s Republic of ChinaTel +86-10-88324108Fax +86-10-88324371Email jiln@bjmu.edu.cnPurpose: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of immune-mediated diabetes that combines phenotypical features of both type 2 diabetes mellitus (T2DM) and type 1 diabetes mellitus (T1DM), meaning that accurate and early diagnosis of this subtype of diabetes is critical for optimal long-term management. Urinary C-peptide creatinine ratio (UCPCR) represents a non-invasive and practical method for assessing endogenous insulin production to facilitate diabetes classification. However, no study to date has reported the use of UCPCR in identifying LADA.Patients and methods: A total of 574 subjects were included in our study (42 LADA, 61 T1DM, 471 T2DM). All participants were evaluated for UCPCR and underwent clinical and laboratory evaluations. UCPCR was compared among different subtypes of diabetes using multinomial regression analysis, and a receiver operating characteristic (ROC) curve was used to identify its performance in diagnosing LADA.Results: UCPCR was lower in LADA (0.4±0.6 nmol/mmol) compared with T2DM (1.2±0.9 nmol/mmol), but higher than in T1DM (0.2±0.3 nmol/mmol) (p

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

Backgrounds: Acute myocardial infarction (AMI) affects the autonomic nervous system (ANS) function. The aim of our study is to detect the particular patterns of ANS regulation in AMI. We hypothesize that altered ANS regulation in AMI patients causes synchronized neural discharge (clustering phenomenon) detected by non-invasive skin sympathetic nerve activity (SKNA). Methods: Forty subjects, including 20 AMI patients and 20 non-AMI controls, participated in the study. The wide-band bioelectrical signals (neuECG) were continuously recorded on the body surface for 5 min. SKNA was signal processed to depict the envelope of SKNA (eSKNA). By labeling the clusters, the AMI subjects were separated into non-AMI, non-cluster appearing (AMINCA), and cluster appearing (AMICA) groups. Results: The average eSKNA was significantly correlated with HRV low-frequency (LF) power (rho = −0.336) and high-frequency power (rho = −0.372). The cross-comparison results demonstrated that eSKNA is a valid surrogate marker to assess ANS in AMI patients. The frequency of cluster occurrence was 0.01–0.03 Hz and the amplitude was about 3 μV. The LF/HF ratio of AMICA (median: 1.877; Q1–Q3: 1.483–2.413) revealed significantly lower than AMINCA (median: 3.959; Q1–Q3: 1.840–6.562). The results suggest that the SKNA clustering is a unique temporal pattern of ANS synchronized discharge, which could indicate the lower sympathetic status (by HRV) in AMI patients. Conclusion: This is the first study to identify SKNA clustering phenomenon in AMI patients. Such a synchronized nerve discharge pattern could be detected with non-invasive SKNA signals. SKNA temporal clustering could be a novel biomarker to classify ANS regulation ability in AMI patients. Clinical and Translational Significance: SKNA is higher in AMI patients than in control and negatively correlates with parasympathetic parameters. SKNA clustering is associated with a lower LF/HF ratio that has been shown to correlate with sudden cardiac death in AMI. The lack of SKNA temporal clustering could indicate poor ANS regulation in AMI patients. [ABSTRACT FROM AUTHOR]