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In photocatalytic schemes, it is common practice to employ a sacrificial agent for scavenging photogenerated holes and enhancing the yield and efficiency of electron harvesting. However, little att...

Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the whole X-ray images, which include other objects that may introduce distractions. Instead, our framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand. The proposed framework is composed of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment. The Mask R-CNN subnet segments the hands from X-ray images to avoid the distractions of other objects (e.g., X-ray tags). The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians. We evaluate the performance of the proposed pipeline on the RSNA pediatric bone age dataset 1 and the results demonstrate its superiority over the previous methods.1http://rsnachallenges.cloudapp.net/competitions/4

Bridge to recovery with left ventricular assist device (LVAD) support has been more prominent with volume displacement pumps (VDPs) than with rotary blood pumps (RBPs), which may be due to VDPs providing greater ventricular unloading and coronary artery flow. To compare ventricular unloading and coronary flow of VDPs and RBPs in a repeatable environment, a physiologic coronary circulation was added to a pre-existing mock circulatory loop. In this study, a physiologic coronary circulation, mimicking a healthy or diseased auto-regulatory response was implemented in a mock circulatory loop. Using the mock circulation loop, a VDP with original (Björk-Shiley) and then replacement (jellyfish) valves was operated in clinically recommended modes and compared to full and partial assist RBP operating at constant speed and rapid speed modulated modes. The Björk-Shiley VDP resulted in increased pressure-volume area, which resulted in greater coronary artery flow when compared to the improved jellyfish valves. Full assist RBP support reduced left ventricular stroke work, pressure-volume area and coronary flow compared to partial assist, whilst the effect of speed modulation modes was not as significant. Of all LVAD operating modes, the counter-pulsed VDP with jellyfish valves demonstrated the greatest reduction in pressure-volume area and improved coronary flow. This study provides a basis for further investigation into RBP speed modulation profiles to match the improved haemodynamic performance of VDPs.

Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly available mammography datasets, in large part due to privacy concerns and the high cost of generating expert annotations. Limited dataset size is further exacerbated by substantial class imbalance since “normal” images dramatically outnumber those with findings. Given the rapid progress of generative models in synthesizing realistic images, and the known effectiveness of simple data augmentation techniques (e.g. horizontal flipping), we ask if it is possible to synthetically augment mammogram datasets using generative adversarial networks (GANs). We train a class-conditional GAN to perform contextual in-filling, which we then use to synthesize lesions onto healthy screening mammograms. First, we show that GANs are capable of generating high-resolution synthetic mammogram patches. Next, we experimentally evaluate using the augmented dataset to improve breast cancer classification performance. We observe that a ResNet-50 classifier trained with GAN-augmented training data produces a higher AUROC compared to the same model trained only on traditionally augmented data, demonstrating the potential of our approach.

Drug monitoring plays crucial roles in doping control and precision medicine. It helps physicians tailor drug dosage for optimal benefits, track patients' compliance to prescriptions, and understand the complex pharmacokinetics of drugs. Conventional drug tests rely on invasive blood draws. While urine and sweat are attractive alternative biofluids, the state-of-the-art methods require separate sample collection and processing steps and fail to provide real-time information. Here, a wearable platform equipped with an electrochemical differential pulse voltammetry sensing module for drug monitoring is presented. A methylxanthine drug, caffeine, is selected to demonstrate the platform's functionalities. Sweat caffeine levels are monitored under various conditions, such as drug doses and measurement time after drug intake. Elevated sweat caffeine levels upon increasing dosage and confirmable caffeine physiological trends are observed. This work leverages a wearable sweat sensing platform toward noninvasive and continuous point-of-care drug monitoring and management.

The fluctuating nature of ocean waves requires the use of energy storage in wave energy systems to smooth output power. This is particularly true when a wave energy system connects to a remote, weak grid. The energy storage requirement will vary depending on the number of wave energy generators and storage placement within the collector system. This paper investigates the variation of storage size with location and numbers of WECs in a weak grid. The impact of temporal phase shift between waves is also studied.

Real-time detection of basic physiological parameters such as blood pressure and heart rate is an important target in wearable smart devices for healthcare. Among these, the core body temperature is one of the most important basic medical indicators of fever, insomnia, fatigue, metabolic functionality, and depression. However, traditional wearable temperature sensors are based upon the measurement of skin temperature, which can vary dramatically from the true core body temperature. Here, we demonstrate a three-dimensional (3D) printed wearable “earable” smart device that is designed to be worn on the ear to track core body temperature from the tympanic membrane (i.e., ear drum) based on an infrared sensor. The device is fully integrated with data processing circuits and a wireless module for standalone functionality. Using this smart earable device, we demonstrate that the core body temperature can be accurately monitored regardless of the environment and activity of the user. In addition, a microphone and actuator are also integrated so that the device can also function as a bone conduction hearing aid. Using 3D printing as the fabrication method enables the device to be customized for the wearer for more personalized healthcare. This smart device provides an important advance in realizing personalized health care by enabling real-time monitoring of one of the most important medical parameters, core body temperature, employed in preliminary medical screening tests.

Perspiration-based wearable biosensors facilitate continuous monitoring of individuals' health states with real-time and molecular-level insight. The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. A wearable and miniaturized iontophoresis interface is an excellent solution to overcome this barrier. The iontophoresis process involves delivery of stimulating agonists to the sweat glands with the aid of an electrical current. The challenge remains in devising an iontophoresis interface that can extract sufficient amount of sweat for robust sensing, without electrode corrosion and burning/causing discomfort in subjects. Here, we overcame this challenge through realizing an electrochemically enhanced iontophoresis interface, integrated in a wearable sweat analysis platform. This interface can be programmed to induce sweat with various secretion profiles for real-time analysis, a capability which can be exploited to advance our knowledge of the sweat gland physiology and the secretion process. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. With our platform, we detected the elevated sweat electrolyte content of cystic fibrosis patients compared with that of healthy control subjects. Furthermore, our results indicate that oral glucose consumption in the fasting state is followed by increased glucose levels in both sweat and blood. Our solution opens the possibility for a broad range of noninvasive diagnostic and general population health monitoring applications.

Wearable perspiration biosensors enable real-time analysis of the sweat composition and can provide insightful information about health conditions. In this review, we discuss the recent developments in wearable sweat sensing platforms and detection techniques. Specifically, on-body monitoring of a wide spectrum of sweat biomarkers are illustrated. Opportunities and challenges in the field are discussed. Although still in an early research stage, wearable sweat biosensors may enable a wide range of personalized diagnostic and physiological monitoring applications.

A flexible and wearable microsensor array is described for simultaneous multiplexed monitoring of heavy metals in human body fluids. Zn, Cd, Pb, Cu, and Hg ions are chosen as target analytes for detection via electrochemical square wave anodic stripping voltammetry (SWASV) on Au and Bi microelectrodes. The oxidation peaks of these metals are calibrated and compensated by incorporating a skin temperature sensor. High selectivity, repeatability, and flexibility of the sensor arrays are presented. Human sweat and urine samples are collected for heavy metal analysis, and measured results from the microsensors are validated through inductively coupled plasma mass spectrometry (ICP-MS). Real-time on-body evaluation of heavy metal (e.g., zinc and copper) levels in sweat of human subjects by cycling is performed to examine the change in concentrations with time. This platform is anticipated to provide insightful information about an individual's health state such as heavy metal exposure and aid the related clinical investigations.

Applications of a 3D printing process are presented. This process integrates liquid-state printed components and interconnects with IC chips in all three dimensions, various orientations, and multiple printing layers to deliver personalized system-level functionalities. As an example application, a form-fitting glove is demonstrated with embedded programmable heater, temperature sensor, and the associated control electronics for thermotherapeutic treatment.