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Interferon gamma (IFN-γ), can serve as an active diagnostic biomarker of a broad spectrum of diseases such as auto inflammatory disease, viral and bacterial, parasites infections, and tumor control. The low physiological concentration of IFN-γ at pg‧mL -1 level for most diseases such as tuberculosis and lung cancer demand highly sensitive and selective detection methods. To achieve the goal, a novel paper-based SERS aptasensor towards rapid, dual-modal (visual and ultrasensitive) detection of IFN-γ is presented for the first time. A lateral flow platform with low-cost and user-friendly format in this study is adopted. The detection relies on the competition of the specific aptamer sequence of IFN-γ between its complementary DNA in the test line and IFN-γ in the sample solution. The presence of IFN-γ can be easily observed in the test line by naked eye and detected at pg‧mL -1 level by a portable Raman spectrometer. Linear detection range of 10-2000 pg‧mL -1 could be obtained with detection limit of 8.7 pg‧mL -1 . In addition, as low as 10 pg/mL of IFN-γ in human serum could be detected, which is comparable with the results from ELISA.Clinical Relevance- This study establishes a simple, rapid, and low-cost assay for dual-modal detection of IFN-γ, which is in urgent demand in clinics especially vitally important in resource-limited areas.

Neurofilament light chain (NF-L) is a protein found in neurons of the nervous system and is widely used as a biomarker for neurological disorders. However, the current methods for detecting NF-L levels are complicated, expensive, and require specialized equipment, making it challenging to implement in a point-of-care (POC) setting. In this study, we developed a gold nanoshell (AuNS)-assisted lateral flow assay (LFA) based test strip for the POC detection of NF-L at a low ng/mL level (8 ng/mL = 117.65 pM). The test strip is a simple, rapid, and cost-effective method for detecting NF-L, making it suitable for use in a POC setting for the diagnosis and treatment of various neurological disorders. With its ease of use and reliability, the paper-based LFA is a valuable tool for the diagnosis and management of neurological conditions.Clinical Relevance- The AuNS-assisted LFA test strip developed in this study offers a rapid, cost-effective, and simple method for detecting NF-L levels, making it of great interest to practicing clinicians for the diagnosis of various neurological diseases such as HIV-associated dementia (HID), Amyotrophic Lateral Sclerosis (ALS), and Creutzfeldt-Jakob disease (CJD).

Recent research activities in the area of low-cost sensing and diagnostics that are realized on cellulosic paper substrate are presented. First a three-dimensional origami paper-based analytical device (omPAD) with multiple electrochemical sensors, an integrated sample reservoir and tight integration with a custom CMOS potentiostat is presented. Second, an optical sensor array with built-in microfluidic channel for sample delivery is presented. The sensors are fabricated using a combination of wax printing and screen-printing using a solution based approach in ambient conditions without the need for expensive fabrication equipment or a cleanroom. Readout is based on using existing consumer grade electronic devices like flatbed scanner (for optical sensor) or custom designed CMOS potentiostat (for electrochemical sensors). Together the 3D paper-based analytical device with integrated sensor, microfluidics and portable readout instrumentation demonstrates a low-cost, self-contained system suitable for sensing and point-of-care diagnostics.

In a pilot randomized controlled trial involving overweight or obese participants with type 2 diabetes, we find that smartphone users have sharply higher adherence to self-monitoring of diet, physical activity, blood glucose, and body weight, as compared to paper diary users. By characterizing the pattern of adherence with the probability of continuation of active and inactive sequences of self-monitoring, we find that smartphone users have longer active sequences of self-monitoring of all four behaviors that were being monitored. Smartphone users are also quicker to resume self-monitoring of diet and physical activity after a lapse in self-monitoring, whereas paper diary users have shorter inactive sequences for monitoring blood glucose and body weight. The findings are informative for data collection methodology in this burgeoning area of research.

A low-cost and enzyme-free glucose paper sensor is presented as a promising alternative to glucose test strips. This paper-based glucose sensor is prepared with molecularly imprinted (MIP) polyaniline (PANI) electrode. The determination of glucose concentrations was studied by the impedance change of the paper sensor before and after the blood samples dispensing at a low frequency. A comparison of the linear and polynomial regression was applied to analyze the impedance ratio as a function of glucose concentrations. The proposed glucose paper sensor showed a limit of detection (LoD) of 1.135 mM. This novel and non-enzymatic paper sensor suggests a low-cost glucose test assay and can improve the quality of routine testing for diabetic patients.

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Smart devices are quickly becoming ubiquitous with the rise of portable biosensors and the internet of things. There exists particular interest in enhancing common objects to have smart capabilities and finding inexpensive solutions for diagnostic tools. One such example is transforming paper items into interactive devices and point-of-care analytic products. Due to the lightweight, flexible, and cost-efficient qualities of paper, unobtrusively powering these devices remains an outstanding problem. In this paper, we demonstrate an electrostatic human-touch powered energy harvesting system, integrated with flexible painted conductive electrodes on paper. This system harvests 8.5 nJ of energy and reaches a voltage of 1.3 V on a 10 nF energy storage capacitor. This technology not only provides a method of powering paper-based products with routine human gestures but can also detect human touch for input communication to sensors.

Carbon nanotube (CNT) has been utilized for the biological detection due to its extremely sensitive to biological molecules. A paper-based CNT sensing microfluidic device has been developed for the detection of protein, i.e., biotin-avidin, binding. We have developed a fabrication method that allows controlled deposition of bundled CNTs with well-defined dimensions to form sensors on paper. Then, polydimethyl siloxane (PDMS) was used to pattern the hydrophobic boundary on paper to form the reaction sites. The proposed fabrication method is based on vacuum filtration process with a metal mask covering on a filter paper for the definition of the dimension of sensor. The length, width, and thickness of the CNT-based sensors are readily controlled by the metal mask and the weight of the CNT powder used during the filtration process, respectively. Homogeneous deposition of CNTs with well-defined dimensions can be achieved. The CNT-based sensor on paper has been demonstrated on the detection of the protein binding. Biotin was first immobilized on the CNT's sidewall and avidin suspended solution was applied to the site. The result of the biotin-avidin binding was measured by the resistance change of the sensor, which is a label-free detection method. It showed the CNT is sensitive to the biological molecules and the proposed paper-based CNT sensing device is a possible candidate for point-of-care biosensors. Thus, electrical bio-assays on paper-based microfluidics can be realized to develop low cost, sensitive, and specific diagnostic devices.

This paper presents a hybrid wearable bio-sensing system, which combines traditional small-area low-power and high-performance System-on-Chip (SoC), flexible paper substrate and cost-effective Printed Electronics. Differential bio-signals are measured, digitized, stored and transmitted by the SoC. The total area of the chip is 1.5 × 3.0 mm(2). This enables the miniaturization of the wearable system. The electrodes and interconnects are inkjet printed on paper substrate and the performance is verified in in-vivo tests. The quality of electrocardiogram signal sensed by printed electrodes is comparable with commercial electrodes, with noise level slightly increased. The paper-based inkjet printed system is flexible, light and thin, which makes the final system comfortable for end-users. The hybrid bio-sensing system offers a potential solution to the next generation wearable healthcare technology.

The relevance of pH assessment in clinical analysis, environmental and industrial control, has raised the demand for the development of portable, low cost and easy-to-use monitoring systems. This paper proposes a pH sensor printed on a paper support passivated with a solid-ink coating. The sensor exploits the pH sensitivity of a reduced graphene oxide functionalized with 3-(4-aminophenil)propionic acid. The sensor responded in the pH range [4], [10] and had a sensitivity of 46 mV/pH. Tests on human plasma and seawater proved this pH sensor to have similar performances than those of a commercial pH-meter with an uncertainty of 0.1 and 0.2 pH unit in plasma and seawater, respectively.

In the presented work, we combine a commonplace commodity, tissue paper, with biosensing capabilities to provide on-the-go detection of biomarkers. We developed a simple light-weight sensor using single walled carbon nanotubes (SWCNT) on commercially available tissue paper. As a proof of concept, we show that these sensors can be used to quantify Interleukin-6 protein concentration. Detection is carried out by examining the modulation in electrical properties of the substrate induced by addition of protein on these sensors. Our results show that these sensors can provide the foundation for development of tissue paper based portable sensors for biomarkers relevant to on-the-go testing.

Follicle-stimulating hormone (FSH) is an important indicator of ovarian reserve function in women in clinical testing. In this work, a label-free paper-based immunosensor was developed for electrochemical rapid detection of FSH. A hydrophilic channel surrounded with hydrophobic barriers was firstly fabricated on the chromatography paper by wax printing technology. Then three electrodes were screen-printed on the circle zones of the channel, in which one carbon electrode further modified by reduced graphene-oxide /thionine /gold nanoparticles nanocomposites and FSH monoclonal antibody was used as the working electrode to provide sensitivity of the immunosensor. The detection of FSH is based on the decreased electrochemical current of Thi produced from the specific binding of the FSH and anti-FSH, and the decrease of the current is proportional to the concentration of the FSH. The experimental results exhibited that the immunosensor could be used to detect the standard FSH in range of 1-100 mIU/mL with the detection limit of 1 mIU/mL. And the proposed immunosensor had been successfully applied to detect FSH in serum samples.

In this paper, we propose an end-to-end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs). Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster-RCNN, then the R-peak can be located and measured through a CNN. Validated by clinical ECG data in the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database and real ECG paper from Peking University People's Hospital, the proposed method can achieve the recall of 98.32%, the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.

Urinalysis allows obtaining information about many of the body's functions. Normally, urine is a very easily collected specimen, but in some cases, it can be an uncomfortable process. Moreover, standard methods such as microscopic examination or urine culturing represent time-consuming procedures. In this regard, this work explores the design of a device that combines disposable diapers, as a platform for sample collection, with part of the dipsticks method. Even though this is a desirable combination for a faster and comfortable approach, it is also convenient to extend the prescribed time, provided by the dipstick manufacturer, to read the results. This allows the stability and validity of the results for longer, preferably until the moment of the diaper changing, and it was also considered in the design. The desired conditions were reproduced for the reagent pads of glucose and nitrite. The results show that the RGB profile, obtained in glucose detection, varied after approximately 60 min. However, when the developed colors are visually compared with the reference colors, the results suggest that there were not significant variations. In the nitrite assay, by analyzing the RGB profile or by visual comparison with the reference colors, the color does not appear to change over time, but the results were not the expected. It seems that the proposed design is promising regarding results stability, but more experiments need to be done to check if the variations are constant for more biomarkers in the conditions provided by the device.

Among all infections, urinary tract infections (UTI) are one of the most common. Nowadays the procedures to analyze urine and consequently detect UTI are often painful and time-consuming. Recent studies about microfluidic paper-based devices have developed the interest of researchers due their outstanding characteristics. In this paper is presented a novel design for a microfluidic paper-based device for screening and analysis of multiple biomarkers from urine samples on diapers. The device consists on a set of eight layers. It was designed based on the previous attempts to improve and overcome some problems detected as the continuous entrance of fluids, the possibility of contamination and the invalidity of results due to communication between different reagent pads. One approach was create a "self-locking" mechanism that closes the sample inlet in approximately four minutes solving the first two problems. Furthermore, is important that comfort is guaranteed, hence a device with a total thickness of 5,3 mm is presented. This device can keep the results for eight hours and can be used as a low-cost and more effective alternative than conventional methods being a strategy with potential for the diagnostic and analysis of biological samples in the future improving healthcare.

Urinary tract infections (UTI) are common infection diseases in elderly patients. The conventional method of detecting UTI involves the collection of significant urine samples from the elderly patients. However, this is a very difficult and time-consuming procedure. This paper addresses the development of a microfluidic paper-based analytical device (μPAD) to detect UTI from urine collected from adult diapers. The design and fabrication for the μPAD is shown. The fabrication process involves melting solid wax on top of filter paper using a hot plate, followed by pattern transfer using a mold with rubbed wax. To demonstrate the feasibility of the proposed method, the μPAD with deposited nitrite reagent had detected different concentrations of nitrite solutions from 0.5 ppm to 100 ppm spiked in urine samples. A calibration curve was obtained by plotting the gray scale intensity values against the various nitrite concentrations. The results showed that the proposed paper-based device holds great potential as low-cost, disposable solution to sensitively detect UTI markers in urine sampled from diapers.

Metal hydride (MH) actuators have a simple structure and a number of features that make them attractive for use in rehabilitation engineering and assistive technology. The MH actuator provides a high power-to-weight ratio, high-strain actuation, human-compatible softness, and noiseless operation, while being environmentally benign. On the other hand, there remain technical challenges to be overcome to improve the MH actuator regarding its speed of operation and energy efficiency, given the low heat conductivity of the MH powder that is used as the pressure generator for soft actuation. To overcome the issues of low heat conductivity and the handling of MH powder, we developed an MH paper, which is a special paper incorporating MH powder and carbon fiber, for use as a new pressure-generating element for a soft MH actuator system. In addition, the basic properties and structure of the proposed MH paper were investigated through scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and several thermodynamic experiments. The results of these experiments showed that the hydrogen absorption and desorption rates of the MH paper were significantly higher than those of the MH powder around room temperature.

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Rotary blood pumps which have contact-less suspension are small, durable and widely used for left ventricular assist devices (LVADs). In order to design a total artificial heart (TAH) with rotary blood pumps, two pumps one for each ventricle, are controlled independently. Some of the challenges for the development of a TAH includes the requirement of a small size and the anatomical fitting of inlets and outlets which should be arranged closely on the circumference in the same direction. And they should be combined into a unit. In this paper, a helical flow total artificial heart (HFTAH) combing two centrifugal pumps with helical inlet in face is proposed in order to achieve a smaller TAH. To examine the pump performance, a preliminary test model for left ventricle was built, the size of the pump was 69.0mm in diameter and 45.0mm height. The size of the impeller was 44.0mm in diameter and 23.0mm height including a 15.0mm-height hydrodynamic bearing. The pump was externally driven by a direct current motor. 5.0L/min flow rate against 100mmHg pressure difference was obtained, where the total power consumption was 5.0W, the system efficiency was 23% with a rotational speed of 2070rpm. In this system, maximum pressure head, flow rate and efficiency were 420mmHg, 15.0L/min and 26%, respectively. In acute animal experiments with three healthy adult goats, the total biventricular bypass assist system using the pumps was able to maintain the maximum aortic flow at approximately 5.0L/min, and the pulmonary arterial flow at approximately 4.6L/min, the mean aorta pressure was 105mmHg, and the mean pulmonary artery pressure was 51mmHg. The development of the control method is undergoing, and a driving system and the pump aiming at the chronic animal experiments will be developed.

This paper presents a precise sampling microstructure formed as a paper strip. We have been developing dialysis system that can be implanted into a human body as an artificial kidney. For the safe use of our artificial kidney, the patients' urine needs to be constantly monitored to detect the abnormal value of ion concentration essential for human life. We are conceiving the monitoring system based on sampling by a paper strip. In this study, we fabricated the strip consisting of slanted and interlocked micropillars for the sampling, known as synthetic microfluidic paper. The paper-like substrate can be fabricated with a well-controlled geometry and subsequently enables precise sampling. Through the conducted experiments, it was shown that synthetic microfluidic paper had better mechanical properties, showed more precision in sampling than paper filter as well as corresponding liquid holding capability to the paper filter. Our proposed paper-based sampling system is expected to lead to the development of minimally invasive ion monitoring system with quantitative sampling strip.

This paper proposes a new disposable soft 3D force sensor that can be used to calculate either force or displacement and vibrations. It uses three Hall Effect sensors orthogonally placed around a cylindrical beam made of silicon rubber. A niobium permanent magnet is inside the silicon. When a force is applied to the end of the cylinder, it is compressed and bent to the opposite side of the force displacing the magnet. This displacement causes change in the magnetic flux around the ratiomatric linear sensors (Hall Effect sensors). By analysing these changes, we calculate the force or displacement in three directions using a lookup table. This sensor can be used in minimal invasive surgery and haptic feedback applications. The cheap construction, bio-compatibility and ease of miniaturization are few advantages of this sensor. The sensor design, and its characterization are presented in this work.

The highly organized collagen network of human lumbar annulus fibrosus (AF) is fundamental to preserve the mechanical integrity of the intervertebral discs. In the healthy AF, fibers are embedded in a hydrated matrix and arranged in a crosswise fashion, giving an anisotropic structure capable to undergo large strains. For finite element analysis (FEA) of spine, modelling a realistic intervertebral disc geometry has always been a challenge. This paper proposes a simple yet efficient workflow details for generating structured mesh of the ground substance of the AF and the method for generating collagen fibers with controllable angles that are embedded in AF.Clinical Relevance- The biomechanical response of spine is usually studied by finite element analysis (FEA) of the assembly of vertebra and IVD and other components. The FEA results are always dependent on the correct generation of the geometry and the material of the components. For IVD, creating structured mesh with crosswise collagen fibers with adjustable angles will provide a better control over the anisotropic property definitions of the IVD and approaching a more realistic simulation.

Falls are among the most devastating events that can happen to an older person. Automatic fall detection systems aim to solve this problem by alerting carers and family the moment a fall occurs. This paper presents the development of an unobtrusive fall detection system using ultra-wideband (UWB) radar. The proposed system employed a ceiling-mounted UWB radar to avoid object occlusion and allow for flexible implementation. An innovative pre-processing method was developed to effectively enhance motion and reduce noise from raw UWB data. We designed a trial protocol composed of common types of falls in older population and activities of daily living (ADL). A fall detection algorithm based on convolutional neural networks was developed with simulated falls and ADLs obtained from ten participants following the trial protocol in a clear and cluttered living environment. The fall detection system achieved an accuracy of 93.97%, with a sensitivity of 95.58% and specificity of 92.68%.

Remote camera-based estimation of blood oxygen saturation (SpO 2 ) using visible lights has been studied recently, typically for red (660 nm) and green (550 nm) wavelengths. This paper investigates the impact of different skin penetration depths of red and green wavelengths on the SpO 2 estimation based on mathematical modeling and experiments, where the SpO 2 -calibritability between two illumination setups, narrow-band red/green and narrow-band red/infrared, are statistically compared using the "ratio-of-ratios" method. The results show that the performance of the setup using red/green is less consistent among 17 volunteers than the setup using red/infrared, and larger SpO 2 disparity between different skin regions (by SpO 2 imaging) have been found for individuals in the red/green wavelengths setup. The use of visible light (red and green) may impose a risk of SpO 2 calibration due to the different skin penetration depths of these two wavelengths.

Mental illnesses, such as depression or post-traumatic stress disorder (PTSD), often impact an individual's physiological reaction to stress via their cortisol response. We examine the regulation of cortisol within the hypothalamic-pituitary-adrenal (HPA) axis as a dynamic biosystem. We investigate the difference between the cortisol regulation of people with PTSD and healthy controls. Additionally, we investigate models for potential therapies based on proportional-derivative (PD) control to normalize and improve the stress response of those with PTSD. Such a design may be relevant to improving the treatment of PTSD, as current medical interventions are frequently imprecise and may result in unwanted side effects. The use of a PD controller in administration of hormone therapy, in contrast, can be highly measurable and patient specific.Clinical Relevance- This paper offers an approach to treating post-traumatic stress disorder (PTSD) via stabilizing an individual's endocrinal regulation as a potential future alternative to current pharmaceutical practices.

Selecting the single best blastocyst based on morphological appearance for implantation is a crucial part of in vitro fertilization (IVF). Various deep learning and computer vision-based methods have recently been applied for assessing blastocyst quality. However, to the best of our knowledge, most previous works utilize classification networks to give a qualitative evaluation. It would be challenging to rank blastocyst quality with the same qualitative result. Thus, this paper proposes a regression network combined with a soft attention mechanism for quantitatively evaluating blastocyst quality. The network outputs a continuous score to represent blastocyst quality precisely rather than some categories. As to the soft attention mechanism, the attention module in the network outputs an activation map (attention map) localizing the regions of interest (ROI, i.e., inner cell mass (ICM)) of microscopic blastocyst images. The generated activation map guides the entire network to predict ICM quality more accurately. The experimental results demonstrate that the proposed method is superior to traditional classification-based networks. Moreover, the visualized activation map makes the proposed network decision more reliable.

This paper proposes a locally modified phantom model to numerically assess the worst-case configuration of orthopedic implants under magnetic resonance imaging (MRI). The proposed model is developed based on the standard American Society for Testing and Materials (ASTM) phantom and bone models with cancellous or cortical materials. Three orthopedic implant families, metallic rods, a nail and screw system, and a plate and screw system, are studied. The worst-case configurations of orthopedic implants are identified inside the proposed model and ASTM phantom. These worst-case heating configurations are then implanted in a human body model to evaluate the RF-induced heating in terms of peak SAR1g. For the orthopedic implants fully inside the bone, like the rod and the nail and screw systems, the peak SAR1g values of worst-case configurations obtained from the proposed phantom model are higher than those obtained inside the ASTM phantom. For the orthopedic implants that are mainly outside the bone, such as the plate and screw system, similar worst-case configurations lead to a negligible variation of peak SAR1g inside the human body model.Clinical Relevance- The new phantom model leads to more accurate predictions of the worst-case configuration of orthopedic implants for MR conditional labeling.

This paper presents a practical application of health technology assessment for 21 mechanical ventilators, located at a secondary care Hospital in the State of Mexico, Mexico. A global indicator is obtained, by using a mathematical tool, which involves 14 variables grouped into three partial indicators: technical, clinical and economic. The final aim of this approach is to provide evidence for decision-making, in order to prioritize the replacement of medical equipment, taking into consideration its performance features, the needs of demand for care, and the economic implications for the Hospital.Clinical Relevance-Invasive Mechanical Ventilation (IMV), is a clinical procedure which implies a total or partial replacement of lung function. Given the life support implications, it is important to assure security and reliability in medical equipment intended for this therapy. Therefore, in order to keep updated the IMV devices, in addition to requiring preventive maintenance programs, medical technology replacement programs are necessary.

Multi-wavelength pulse transmit time (MV-PTT) is a potential tool for remote blood pressure (BP) monitoring. It uses two wavelengths, typically green (G) and near-infrared (NIR), that have different skin penetration depths to measure the PTT between artery and arterioles of a single site of the skin for BP estimation. However, the impact of wavelength selection for MV-PTT based BP calibration is unknown. In this paper, we explore the combination of different wavelengths of camera photoplethysmography for BP measurement using a modified narrow-band camera centered at G-550/R-660/NIR-850 nm, especially focused on the comparison between G-R (full visible) and G-NIR (hybrid). The experiment was conducted on 17 adult participants in a dark chamber with their BP significantly changed by the protocol of ice water stimulation. The experimental results show that the MV-PTT obtained by G-NIR has a higher correlation with BP, and the fitted model has lower MAE in both the systolic pressure (5.78 mmHg) and diastolic pressure (6.67 mmHg) than others. It is confirmed that a hybrid wavelength of visible (G) and NIR is still essential for accurate BP calibration due to their difference in skin penetration depth that allows proper sensing of different skin layers for this measurement.

The finite element method (FEM) has become an increasingly popular tool for the computational modeling of multiscale biological systems, including the electrode-tissue interface and the behavior of individual neural cells. However, a significant challenge in these studies is integrating multiple levels of complexity, each with its biophysical properties. This paper presents a single platform solution for modeling these multiscale systems using the finite element method. The proposed method combines different finite element formulations tailored to the specific biophysical properties of each scale into a single unified simulation platform. The results of this method are compared to experimental data to demonstrate the accuracy and efficacy of the proposed approach. With the goal of eliciting the most significant possible response from the retinal ganglion cell's (RGC) multiple components, we devised an electrical stimulation strategy and electrode placement setup that took into account both the RGC's horizontal and vertical location. We found that the activity in a single RGC model could be elicited by a cathodic pulse of amplitude 34 µA. We observed that the optimum electrode placement for a neural response is around the initial axon segment, 30 μm from the soma, and 10 μm above the RGC. Our results show that the proposed method can accurately capture the complex behavior of these multiscale systems and provide a valuable tool for further research in retinal prostheses.Clinical Relevance- To develop efficient electrical stimulation schemes for retinal prosthesis applications, this research can shed light on the behavior of the electrode-tissue interface and individual neural cells. Electrical stimulation of RGCs has shown promise in the application of retinal prostheses. Still, a thorough understanding of the electrode-induced electric field is essential for the design of effective and safe stimulation protocols. Electrical stimulation's side effects may require knowledge of multiple physics disciplines (such as thermal or structural deformation owing to implant placement inside the eye). Finding a solution to diseases that cause vision impairment could be aided by a finite element method (FEM) framework that simulates the neuronal response to extracellular electrical stimulation for realistic 3D cell and electrode geometries.

This paper presents pose tracking experiments using a supermicrosurgical robot designed to consider teleoperation with multiple surgeons. Currently, existing supermicrosurgical robots assist only the primary surgeon. However, both primary and assistant surgeons need a high-precision motion for critical tasks that can easily damage microtissue. To assist multiple surgeons in supermicrosurgery with a surgical robot, dynamic collision avoidance becomes a critical issue due to the operation in a narrow surgical site. As a milestone to overcome this issue, we first developed a pose tracking algorithm by analyzing the inverse kinematics based on null-space control and a weighting matrix. Moreover, we also developed a control framework based on fully open-source software to run the pose tracking algorithm. Finally, we validated the proposed pose tracking algorithm by performing line tracing and rubber ring transferring experiments.

Point-of-care testing (POCT) can be performed near the site of the patient to achieve results in a few minutes. Different POCT devices are available in the market, such as microfluidic chips and paper-based lateral flow assays (LFAs). The paper-based LFAs have certain advantages, such as being cheap and disposable, able to detect a wide range of biomolecules, and the fluid flows through them via capillary action eliminating the need for external forces. The LFAs can be optimized for the sensitive and rapid detection of biomolecules. In this study, paper-based fluorescent LFAs platforms using aptamers as the biorecognition molecules were developed for the POCT of insulin. Various parameters were optimized such as concentrations of aptamers, the type of reporter molecules, the volume of sample, and the assay time to quantify insulin levels using a standard LFA reader. The fluorescent LFAs exhibited a linear detection range of 0.1-4 ng.mL -1 with a limit of detection (LOD) 0.1 ng.mL -1 . The developed LFAs will help to achieve insulin measurement in a few minutes and will be easy to perform by end-users without the requirement of sophisticated instruments, laboratory set-up, and trained personnel. The developed device will be useful for the measurement of insulin levels in biological samples without the need for pretreatment, reducing the overall cost and time of testing. Moreover, the POCT device were fabricated using paper which is a low-cost (approximately AUD 2 per strip) option and is disposable.Clinical Relevance- POCT monitoring of insulin can facilitate both disease diagnosis and management. The developed LFAs have the capability of rapidly testing insulin concentration within several minutes. It will benefit both patients for at-home daily insulin monitoring and clinicians for hospital rapid insulin testing.

Left Ventricular Assist Devices have been successfully used for the treatment of Congestive Heart failure in patients who are not eligible for heart transplantation. This paper describes the implementation and comparison of the performance of a pressure sensor-based feedback controller. The strategies were tested on a mock loop of the systemic circulation. The results show that the use of pressure sensors generated a more accurate response of the controller compared to the use of estimators.Clinical Relevance- The study describes the integration of a LVAD physiological controller to a dynamic wireless monitoring system.

Possibility of non-invasive hemoglobin concentration measurements with wearable devices have been evaluated. The proposed solution is based on the assumption that PPG waveform shape measured at various wavelengths in the reflectance mode carries information about in-depth distribution of optical pathlength in the tissue. Decomposition of temporal and spectral features of PPG signal have been applied to correct estimation of hemoglobin concentration. The dataset including 840 PPG waveforms from 170 volunteers have been collected for the purpose of neural network training and validation. The achieved performance (MAE~13.6 g/l, R~0.62) is confirmed with the invasive blood test.Clinical Relevance - This paper establishes possibility of non-invasive real time hemoglobin concentration measurements by means of low-cost wearable sensor with accuracy comparable to non-invasive clinical instruments.

Cervical cell detection is crucial to cervical cytology screening at early stage. Currently most cervical cell detection methods use anchor-based pipeline to achieve the localization and classification of cells, e.g. faster R-CNN and YOLOv3. However, the anchors generally need to be pre-defined before training and the detection performance is inevitably sensitive to these pre-defined hyperparameters (e.g. number of anchors, anchor size and aspect ratios). More importantly, these preset anchors fail to conform to the cells with different morphology at inference phase. In this paper, we present a key-points based anchor-free cervical cell detector based on YOLOv3. Compared with the conventional YOLOv3, the proposed method applies a key-points based anchor-free strategy to represent the cells in the initial prediction phase instead of the preset anchors. Therefore, it can generate more desirable cell localization effect through refinement. Furthermore, PAFPN is applied to enhance the feature hierarchy. GIoU loss is also introduced to optimize the small cell localization in addition to focal loss and smooth L1 loss. Experimental results on cervical cytology ROI datasets demonstrate the effectiveness of our method for cervical cell detection and the robustness to different liquid-based preparation styles (i.e. drop-slide, membrane-based and sedimentation).

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neuromotor deficits. The control strategy is based on impedance and does not require external force sensors at the end-effector for implementation. The experimental setup involved using the control algorithm to move the robot to a desired position, follow a desired trajectory while being moved out of the trajectory by the user, and reproduce three different rehabilitation exercises (passive, isometric, and ADL). The results suggest that the parameters of the control strategy can be adjusted to set the robot's compliance and support force according to the patient's needs. Ultimately, the study concluded that the proposed control strategy can serve as a foundation for rehabilitation robots, which could potentially improve access to continued upper-limb rehabilitation for stroke patients and reduce the number of healthcare professionals required per patient.Clinical Relevance- This paper proposes a human-robot interaction control scheme to be used in a robotic rehabilitation tool, with the goal of assisting both clinicians and patients during the rehabilitation exercises and ultimately reducing the load on healthcare system. This platform can be potentially used in telemedicine solutions for upper-limb rehabilitation of a wide range of patients with neuromotor deficits such as stroke.

People with Parkinson's Disease experience gait impairments that significantly impact their quality of life. Visual, auditory, and tactile cues can alleviate gait impairments, but they can become less effective due to the progressive nature of the disease and changes in people's motor capability. In this study, we develop a human-in-the-loop (HIL) framework that monitors two key gait parameters, stride length and cadence, and continuously learns a person-specific model of how the parameters change in response to the feedback. The model is then used in an optimization algorithm to improve the gait parameters. This feasibility study examines whether auditory cues can be used to influence stride length in people without gait impairments. The results demonstrate the benefits of the HIL framework in maintaining people's stride length in the presence of a secondary task.Clinical relevance- This paper proposes a gait rehabilitation framework that provides a personalized cueing strategy based on the person's real-time response to cues. The proposed approach has potential application to people with Parkinson's Disease.

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

The global community is still grappling with the SARS-CoV-2 pandemic, declared by the World Health Organization in March 2020. Radiology is an important screening method for the early detection of SARS-CoV-2. Doctors typically recommend that patients undergo one of the radiology procedures during the early stages of diagnosis. Recent research has focused on developing deep learning-based architectures that use either X-Rays or CT-Scans, but not both. This paper presents a multi-modal, multi-task learning framework that uses either the X-Rays or CT-Scans to identify SARS-CoV-2 patients. The framework employs a shared feature embedding that utilizes common information from both X-Rays and CT-Scans, as well as task-specific feature embeddings that are independent of the type of chest screening. The shared and task-specific embeddings are combined to obtain the final classification results, which have been shown to have an accuracy of 98.23% and 98.83% in detecting SARS-CoV-2 using X-Rays and CT-Scans, respectively.

Providing imaging during interventional treatments of cardiovascular diseases is challenging. Magnetic Resonance Imaging (MRI) has gained popularity as it is radiation-free and returns high resolution of soft tissue. However, the clinician has limited access to the patient, e.g., to their femoral artery, within the MRI scanner to accurately guide and manipulate an MR-compatible catheter. At the same time, communication will need to be maintained with a clinician, located in a separate control room, to provide the most appropriate image to the screen inside the MRI room. Hence, there is scope to explore the feasibility of how autonomous catheterization robots could support the steering of catheters along trajectories inside complex vessel anatomies.In this paper, we present a Learning from Demonstration based Gaussian Mixture Model for a robot trajectory optimisation during pulmonary artery catheterization. The optimisation algorithm is integrated into a 2 Degree-of-Freedom MR-compatible interventional robot allowing for continuous and simultaneous translation and rotation. Our methodology achieves autonomous navigation of the catheter tip from the inferior vena cava, through the right atrium and the right ventricle into the pulmonary artery where an interventions is performed. Our results show that our MR-compatible robot can follow an advancement trajectory generated by our Learning from Demonstration algorithm. Looking at the overall duration of the intervention, it can be concluded that procedures performed by the robot (teleoperated or autonomously) required significantly less time compared to manual hand-held procedures.

Major depressive disorder is one of the major contributors to disability worldwide with an estimated prevalence of 4%. Depression is a heterogeneous disease often characterized by an undefined pathogenesis and multifactorial phenotype that complicate diagnosis and follow-up. Translational research and identification of objective biomarkers including inflammation can assist clinicians in diagnosing depression and disease progression. Investigating inflammation markers using machine learning methods combines recent understanding of the pathogenesis of depression associated with inflammatory changes as part of chronic disease progression that aims to highlight complex interactions. In this paper, 721 patients attending a diabetes health screening clinic (DiabHealth) were classified into no depression (none) to minimal depression (none-minimal), mild depression, and moderate to severe depression (moderate-severe) based on the Patient Health Questionnaire (PHQ-9). Logistic Regression, K-nearest Neighbors, Support Vector Machine, Random Forest, Multi-layer Perceptron, and Extreme Gradient Boosting were applied and compared to predict depression level from inflammatory marker data that included C-reactive protein (CRP), Interleukin (IL)-6, IL-1β, IL-10, Complement Component 5a (C5a), D-Dimer, Monocyte Chemoattractant Protein (MCP)-1, and Insulin-like Growth Factor (IGF)-1. MCP-1 and IL-1β were the most significant inflammatory markers for the classification performance of depression level. Extreme Gradient Boosting outperformed the models achieving the highest accuracy and Area Under the Receiver Operator Curve (AUC) of 0.89 and 0.95, respectively.Clinical Relevance- The findings of this study show the potential of machine learning models to aid in clinical practice, leading to a more objective assessment of depression level based on the involvement of MCP-1 and IL-1β inflammatory markers with disease progression.

Within this paper, we demonstrate the feasibility of the FPGA implementation as well as the 180nm CMOS circuit design of a particular biologically plausible supervised learning algorithm (ReSuMe). Based on the Spike-Timing-Dependent Plasticity (STDP) learning phenomenon, this design proposes a fully configurable implementation of STDP learning window function to adjust the learning process for different applications, optimizing results for each use case. The CMOS implementation in 180nm technology node supplied with 1.8V shows a core area of 0.78mm 2 and verifies the suitability of an on-chip ReSuMe learning algorithm implementation and its capability of integration with a multitude of external and already designed structures of Spiking Neural Networks (SNNs).

Drifted by the hype of new and efficient machine learning and artificial intelligence models aiming to unlock the information wealth hidden inside heterogeneous datasets across different markets and disciplines, healthcare data are in the center of novel technological advancements in predictive health diagnostics, remote healthcare, assistive leaving and wellbeing. Nevertheless, this emerging market has underlined the necessity of developing new methods and updating existing ones for preserving the privacy of the data and their owners, as well as, ensuring confidentiality and trust throughout the health care data processing pipelines. This paper presents one of the key innovations of a Horizon Europe funded project named "TRUSTEE", which focuses on building a trust and privacy framework for cross-European data exchange by employing a secure and private federated framework to empower companies, organizations, and individuals to securely access data across different disciplines, use and re-use data and metadata to extract knowledge with trust. In particular we present our work on implementing strong authentication and continuous authorization schemes based on the duality of eIDAS trust framework and Self Sovereign Identity (SSI) management to ensure security and trust over authentication, authorization and accounting processes for healthcare.

Machine learning in medical ultrasound faces a major challenge: the prohibitive costs of producing and annotating clinical data. Optimizing the data collection and annotation will improve model training efficiency, reducing project cost and times. This paper prescribes a 2-phase method for cost optimization based on iterative accuracy/sample size predictions, and active learning for annotation optimization., Methods: Using public breast, fetal, and lung ultrasound datasets we can: Optimize data collection by statistically predicting accuracy for a desired dataset size; and optimize labeling efficiency using Active Learning, where predictions with lowest certainty were labelled manually using feedback. A practical case study on BUSI data was used to demonstrate the method prescribed in this work., Results: With small data subsets, ~10%, dataset size vs. final accuracy relations can be predicted with diminishing results after 50% usage. Manual annotation was reduced by ~10% using active learning to focus the annotation., Conclusion: This led to cost reductions of 50%-66%, depending on requirements and initial cost model, on BUSI dataset with a negligible accuracy drop of 3.75% from theoretical maximums.Clinical Relevance- This work provides methodology to optimize dataset size and manual data labelling, this allows generation of cost-effective datasets, of interest to all, but particularly for financially limited trials and feasibility studies, Reducing the time burden on annotating clinicians.

IoT in healthcare brought a technological revolution in rehabilitation, enabling the patients to get rehabilitation in the comfort of their home, while being supervised remotely by the professionals. IoT enabled rehabilitation ensures a best possible way towards recovery, without inflicting further injuries. It also allows customization of rehabilitation plan based on the patient profile and progress, while helping in defining their personalised milestones. The main contribution of this paper is a novel wheelchair based IoT enabled 'XoRehab", exoskeleton for rehabilitation; a device for lower limb, with a backrest mechanism. This involves development of a well-trained robot based on electromechanical actuation with micro-controller and feedback system, which can replace the physical trainer or physiotherapist. This autonomous device will control speed of flexion and extension on patient's limbs to improve efficiency of the treatment. It also provides repetitive motion of flexion and extension. The edge system design to upload local rehabilitation data and logs to the cloud server is presented. We propose an opensource Elasticsearch Logstash Kibana (ELK) stack for providing the cloud services, which is a scalable solution. The design of the developed prototype has been discussed in detail along with the functional test results.Clinical Relevance- The introduction of well-trained exoskeleton in the rehabilitation process, gives a high level of performance in patient recovery, and it contributes to expeditious restoration of their joints and improvement in muscle and nerve system.

Automatic segmentation of sublingual images and color quantification of sublingual vein are of great significance to disease diagnosis in traditional Chinese medicine. With the development of computer vision, automatic sublingual image processing provides a noninvasive way to observe patients' tongue and is convenient for both doctors and patients. However, current sublingual image segmentation methods are not accurate enough. Besides, the differences in subjective judgments by different doctors bring more difficulties in color analysis of sublingual veins. In this paper, we propose a method of sublingual image segmentation based on a modified UNet++ network to improve the segmentation accuracy, a color classification approach based on triplet network, and a color quantization method of sublingual vein based on linear discriminant analysis to provide intuitive one-dimensional results. Our methods achieve 88.2% mean intersection over union (mIoU) and 94.1% pixel accuracy on tongue dorsum segmentation, and achieves 69.8% mIoU and 82.7% pixel accuracy on sublingual vein segmentation. Compared with the state-of-art methods, the segmentation mIoUs are improved by 5.8% and 5.3% respectively. Our sublingual vein color classification method has the highest overall accuracy of 81.2% and the highest recall for the minority class of 77.5%, and the accuracy of color quantization is 90.5%.Clinical Relevance- The methods provide accurate and quantified information of the sublingual image, which can assist doctors in diagnosis.

Depression is one of the most occurring civilizational diseases. In this paper, we propose a new approach for detecting depression through the analysis of social media content using face analysis, emotion recognition neural networks, and speech processing. We utilized audio-visual analysis and acquired more than 605 features in the time domain. Those are fed to machine learning and deep learning models for depression classification. Our approach outperforms the other state-of-the-art models, achieving the F1-score 0.77. The results have the potential to provide valuable insights for mental health professionals, offer early detection and intervention, and serve as a resource for individuals seeking help with their mental health. This study enables real-time analysis and represents a significant advancement in mental health and technology and has the potential to impact society.Clinical relevance-The system aims to provide a fast and accurate way to detect depression in individuals through online recordings. The use of multimodal information (e.g. audio, image) enhances the performance of the non-verbal behavioral analysis. The end-to-end system reduces the need for manual analysis by mental health professionals and increases the efficiency of depression screening. The system can potentially help identify individuals who are at risk for depression, enabling early intervention and treatment. The results from the system can complement traditional assessments and support mental health professionals in making a diagnosis. The system can be used in real-time processing, f.e. during online calls, and provide objective measurements summarizing the overall behavior based on computer vision and audio analysis.

Visualization of endovascular tools like guidewire and catheter is essential for procedural success of endovascular interventions. This requires tracking the tool pixels and motion during catheterization; however, detecting the endpoints of the endovascular tools is challenging due to their small size, thin appearance, and flexibility. As this still limit the performances of existing methods used for endovascular tool segmentation, predicting correct object location could provide ways forward. In this paper, we proposed a neighborhood-based method for detecting guidewire endpoints in X-ray angiograms. Typically, it consists of pixel-level segmentation and a post-segmentation step that is based on adjacency relationships of pixels in a given neighborhood. The latter includes skeletonization to predict endpoint pixels of guidewire. The method is evaluated with proprietary guidewire dataset obtained during in-vivo study in six rabbits, and it shows a high segmentation performance characterized with precision of 87.87% and recall of 90.53%, and low detection error with a mean pixel error of 2.26±0.14 pixels. We compared our method with four state-of-the-art detection methods and found it to exhibit the best detection performance. This neighborhood-based detection method can be generalized for other surgical tool detection and in related computer vision tasks.Clinical Relevance- The proposed method can be provided with better tool tracking and visualization systems during robot-assisted intravascular interventional surgery.

Block-design is a popular experimental paradigm for functional near-infrared spectroscopy (fNIRS). Traditional block-design analysis techniques such as generalized linear modeling (GLM) and waveform averaging (WA) assume that the brain is a time-invariant system. This is a flawed assumption. In this paper, we propose a parametric Gaussian model to quantify the time-variant behavior found across consecutive trials of block-design fNIRS experiments. Using simulated data at different signal-to-noise ratios (SNRs), we demonstrate that our proposed technique is capable of characterizing Gaussian-like fNIRS signal features with ≥3dB SNR. When used to fit recorded data from an auditory block-design experiment, model parameter values quantitatively revealed statistically significant changes in fNIRS responses across trials, consistent with visual inspection of data from individual trials. Our results suggest that our model effectively captures trial-to-trial differences in response, which enables researchers to study time-variant brain responses using block-design fNIRS experiments.

Liver cancer has been one of the top causes of cancer-related death. For developing an accurate treatment strategy and raising the survival rate, the differentiation of liver cancers is essential. Multiphase CT recently acts as the primary examination method for clinical diagnosis. Deep learning techniques based on multiphase CT have been proposed to distinguish hepatic cancers. However, due to the recurrent mechanism, RNN-based approaches require expensive calculations whereas CNN-based models fail to explicitly establish temporal correlations among phases. In this paper, we proposed a phase difference network, termed as Phase Difference Network (PDN), to identify two liver cancer, hepatocellular carcinoma and intrahepatic cholangiocarcinoma, from four-phase CT. Specifically, the phase difference was used as interphase temporal information in a differential attention module, which enhanced the feature representation. Additionally, utilizing a multihead self-attention module, a transformer-based classification module was employed to explore the long-term context and capture the temporal relation between phases. Clinical datasets are used in experiments to compare the performance of the proposed strategy versus conventional approaches. The results indicate that the proposed method outperforms the traditional deep learning based methods.