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IntroductionThis study examines a set of oculomotor measurements, or “oculometric” biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes.MethodsWe measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM).ResultsSignificant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not.DiscussionOur data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.

Recently, pressure-sensing mats have been widely used to capture static and dynamic pressure over sleep for posture recognition. Both a full-size mat with a low-density sensing array for figuring out the structure of the whole body and a miniature scale mat with a high-density sensing array for identifying the local characteristics around the chest have been investigated. However, both of the mat systems may face the challenge in the trade-off between the computational complexity (involving the size, density, etc. of the mat) and the performance of sleep posture recognition, where high performance may requires overcomplex computation and result in time latency in real-time sleep posture monitoring. In this paper, a lightweight neural network named ConcatNet, is proposed to realize sleep postures (supine, left, right, and prone) recognition in real time while maintaining a favorable performance. In ConcatNet, the inception module is proposed to extract the image features under multiple receptive fields, while the multi-layer feature fusion module is utilized to fuse deep and shallow features to enhance the model performance. To further improve the efficiency of the model, the depthwise convolution is adopoted. ConcatNet models in 3 different scales (ConcatNet-S, ConcatNet-M, and ConcatNet-L) are built to explore the impact of the sensor density on sleep posture recognition performance. Experimental results exhibit that ConcatNet-M corresponding to medium sensor density ( ${16}\times {16}$ ) achieved the best comprehensive performance, with short-term data cross-validation accuracy at 95.56% and overnight data testing accuracy at 94.68%. The model size is 7.91KB, FLOPs is 56.47K, and the inference time is only 0.38ms, which shows an outstanding performance of real-time sleep posture recognition with minimum consumption, indicating the potential to be deployed in mobile devices.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating “avatars” (herein defined as an extension of “digital twins”) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Abstract Brain growth occurs during the first 2 weeks of postnatal development in rats. This developmental period is equivalent to the third trimester of human gestation. Dendritic arborization, axonal growth, and gliogenesis are observed along with a strong maturation of neurotransmission during this critical development period. Furthermore, nicotine exposure during early development causes deficiencies in sensory and cognitive processing in adults. In this study, we further investigated the gene expression of neuron groups and the influence of perinatal nicotine exposure on gene expressions of neurons within the sub-regions of the ventral tegmental area (VTA) in 1 week, 2 week and 3-week-old rat pups. We exposed pregnant rats to nicotine perinatally on gestational day 7 through postnatal day 14. Pups are exposed to nicotine during pregnancy and through breastfeeding to investigate its effect in rat pups during early neuronal development. Real time PCR was used to find the relative expressions of gamma-aminobutyric acid (GABA), dopamine, and glutamate neuron markers within the three sub-regions of the VTA including the parabrachial pigmented nucleus (PBP), parainterfascicular (PIF), and paranigral nucleus (PN). Our results indicated that during early maturation, the dopamine marker tyrosine hydroxylase (TH) showed a consistently increased significance in PN sub-region compared to PIF and PBP. These results suggest that following perinatal nicotine exposure, VTA dopamine neurons, especially within the PN sub-region, are significantly excited starting from birth.

Glioblastoma Multiforme (GBM) is the most malignant type of all brain tumors. Current GBM treatment options include surgery, followed by radiation and chemotherapy. However, GBM can become resistant to therapy, resulting in tumor recurrence. GBM cells develop resistance to treatments by either downregulating cell death pathways (CD95) or upregulating cell survival pathways (NF-κB (p65)). Healthy tissues can be affected by the increased therapeutic dose. Therefore, it is important to develop a method that can only target GBM tumor cells, thereby reducing the non-specific uptake which will reduce the side effects. Here we demonstrate an application of novel priori activation of apoptosis pathways of tumor technology (AAAPT), which has been used to demonstrate the effect of targeted tumor sensitizers to make chemotherapy work at lower doses in breast, lung and prostate cancers. Treatment of GBM spheroids with AAAPT in 3D PEGDA microwells, showed an increase in cell death, an upregulation of cell death pathways, and a downregulation of cell survival pathways, in comparison to Temozolomide (TMZ), an oral alkylating agent, which is a commonly used chemotherapy in the treatment of GBM. The dose of AAAPT sensitizers may provide a promising method to increase treatment efficacy and reduce off-target toxicity, as an alternative to existing methods which cause significant off-target damage.

The new era of space exploration is increasing the astronaut's number and diversity in low orbit and beyond. The influx of such a diverse crew population will also increase the need for medical technologies to ensure safe and productive missions. Such a need represents a unique opportunity to innovate and develop diagnostics and treatment tools to meet future needs. Historically, terrestrial regulatory oversight of biomedical design processes was considered separate from spaceflight regulatory processes because it did not address spaceflight constraints. These constraints challenge the creative development of unique solutions for use in space. Translation between healthcare innovation in spaceflight to healthcare on Earth and vice versa requires understanding the commonalities, unique needs and constraints. This manuscript provides a framework for comparing Earth-space design processes and a perspective on the best practices to improve healthcare equity and health outcomes.

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.

Glioblastoma Multiforme (GBM) cells interact with a complex, heterogeneous tumor microenvironment (TME). This TME consists of astrocytes, endothelial cells, microglia, and pericytes, which together play a role in GBM progression and resistance. However, there are not enough in vitro three-dimensional (3D) models to study the effect of the TME on GBM resistance to chemotherapeutics. In this study, we created a GBM TME by culturing GBM cells with media that had been conditioned by human astrocytes (HA) in 3D microwells. In order to investigate the effect of the TME on GBM resistance to chemotherapeutic agents, cells were treated with Temozolomide (TMZ) in combination with nuclear factor- $\kappa \text{B}$ (NF- $\kappa \text{B}$ ) inhibitor “Bay 11-7082”. We examined the influence of HA conditioned media (CM) on the expression of various genes and the response to TMZ and Bay 11–7082 in our 3D cultures. Our data suggested that proteins and metabolic factors produced by HA in CM can significantly alter GBM response to chemotherapeutics. Our results indicated lower levels of apoptosis- and drug resistance-related genes were detected in LN229 and U87 cultures in their respective cell culture media compared to HA CM. Our results confirmed HA affect GBM response to therapy.

Abstract The ventral tegmental area (VTA) is the origin of dopaminergic neurons and the dopamine (DA) reward pathway. This pathway has been widely studied in addiction and drug reinforcement studies and is believed to be the central processing component of the reward circuit. In this study, we used a well-established rat model to expose mother dams to alcohol, nicotine-alcohol, and saline perinatally. DA and non-DA neurons collected from the VTA of the rat pups were used to study expression profiles of miRNAs and mRNAs. miRNA pathway interactions, putative miRNA-mRNA target pairs, and downstream modulated biological pathways were analyzed. In the DA neurons, 4607 genes were differentially upregulated and 4682 were differentially downregulated following nicotine-alcohol exposure. However, in the non-DA neurons, only 543 genes were differentially upregulated and 506 were differentially downregulated. Cell proliferation, differentiation, and survival pathways were enriched after the treatments. Specifically, in the PI3K/AKT signaling pathway, there were 41 miRNAs and 136 mRNAs differentially expressed in the DA neurons while only 16 miRNAs and 20 mRNAs were differentially expressed in the non-DA neurons after the nicotine-alcohol exposure. These results depicted that chronic nicotine and alcohol exposures during pregnancy differentially affect both miRNA and gene expression profiles more in DA than the non-DA neurons in the VTA. Understanding how the expression signatures representing specific neuronal subpopulations become enriched in the VTA after addictive substance administration helps us to identify how neuronal functions may be altered in the brain.

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called “MobileNetV2,” which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

To monitor dopamine neural activities in the ventral tegmental area (VTA) in response to nicotine, we designed a dopamine platform based on a microimaging device and a microdialysis system. We measured dopamine (DA) release in the prefrontal cortex (PFC) and the nucleus accumbens (NAc) by a micro dialysis system and monitored DA neurons activities by fluorescence imaging in the VTA using a microimaging device simultaneously. GCaMP6 transgenic mice were used in this study, and the change in the fluorescence ratio intensity associated with nicotine administration was estimated. Our results suggested that nicotine administration increased the DA neurons activity in the VTA as well as the DA release in the PFC and the NAc. The proposed platform has the potential to monitor neural activities in the deep brain regions of rodents.

Recent studies have demonstrated microarray expression data can be used to identify gene regulatory pathways. However, one of the major challenges is to utilize the large microarray data (genes and micro-RNAs) to have an efficient computational model. Therefore, there is an urgent need to reduce the dimensionality of these large sets using machine learning methods without compromising the accuracy. This requires an appropriate machine learning algorithm to select the significant features from these large datasets. Therefore, in this study, we use a supervised method based on a Random Forest to identify significant features from three microarray datasets from prenatal nicotine, alcohol, and nicotine and alcohol exposure groups in two different cell types (dopamine and non-dopamine neurons). Our approach was computationally efficient to reduce the dimensionality of extremely large microarray datasets. Furthermore, our results indicated that using only the top 20% of features was sufficient to confirm the genetic pathways previously identified when using all of the features in the model.

Optogenetics is a powerful tool for controlling biological functions using light. Optical fibers have been extensively utilized in optical stimulation devices for optogenetics. However, the use of optical fibers results in a small photo-stimulation region. In this study, micro-LED array devices were developed to achieve large-area photo-stimulation in the brain of a large animal, such as macaque monkeys. Planar and linear micro-LED array devices were designed and fabricated to photo-stimulate the prefrontal cortex (PFC) and ventral tegmental area (VTA) of the brain and induce a neurochemical response. Device operation, optical intensity, and safety were first characterized using rats. Subsequently, the devices were used to photo-stimulate the brain of macaque monkeys. In addition, microdialysis in the PFC was performed. The devices detected modulated levels of dopamine in the brains. Thus, the photo-stimulation of both the PFC and VTA were successfully achieved, and the effectiveness of the developed micro-LED array devices was demonstrated. The study will help facilitate further studies on micro-LED array stimulation for system-wide optogenetic manipulation in large animals.

Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor, accounting for 50% of all cases. GBM patients have a five-year survival rate of merely 5.6% and a median overall survival of 14.6 months with the “Stupp” regimen, 20.9 months with tumor treatment fields (TTF, OptuneR) in patients who participated in clinical trials, and 11 months for all GBM patients prior to TTF use. Objective: Our group recently developed a brain cancer chip which generates tumor spheroids, and provides large-scale assessments on the response of tumor cells to various concentrations and combinations of drugs. This platform could optimize the use of tumor samples derived from GBM patients to provide valuable insight on the tumor growth and responses to drug therapies. To minimize any sample loss in vitro, we improved our brain cancer chip system by adding an additional laminar flow distribution layer, which reduces sample loss during cell seeding and prevents spheroids from escaping from the microwells. Methods: In this study, we cultured 3D spheroids from GBM cell lines and patient-derived GBM cells in vitro, and investigated the effect of the combination of Temozolomide and nuclear factor-κB inhibitor on tumor growth. Results: Our study revealed that these drugs have synergistic effects in inhibiting spheroid formation when used in combination. Conclusions: These results suggest that the brain cancer chip enables large-scale, inexpensive and sample-effective drug screening to 3D cancer tumors in vitro, and could be applied to related tissue engineering drug screening studies.

Measurements of the deep brain area of rodents has been one of the most important methods to study deep brain activities, in part due to limited access of this region. In order to address this problem, we have designed and fabricated an implantable microimaging device for fluorescence imaging in the deep brain of rodents. In this study, we established a new fabrication method of a flat and uniformed fluorescence filter for an implantable microimaging device which obtained higher quality images, and aimed to show the performance of the device in the ventral tegmental area (VTA) of rats. We introduced GCaMP6s in the VTA of rats by adeno-associated viral (AAV) injection. The change in the fluorescence intensity was associated with nicotine administration and was observed by using our custom implantable microimaging device. We showed that nicotine administration significantly affected the excitation of DA neurons in different sub-regions of the VTA, reaching a maximum activation between 6-10 minutes following nicotine exposure, followed by a decrease after 31-35 minutes. Therefore, we believe our implantable microimaging device can potentially be used to monitor the neural activation within the sub-regions of the VTA in response to acute nicotine exposure by using GCaMP.

Studies involving turbulent flow have been carried out in many parts of the cardiovascular system, and it has been widely reported that turbulence related to stenosis (narrowing) of arteries creates audible sounds, which may be analyzed to yield information about the nature and severity of the blockage. Results so far indicate that the high frequency content of the sounds generally increases with the degree of stenosis. In this paper, we designed and built an MEMs microphone array and a signal acquisition board to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds from patients with single coronary occlusions, before and after stent placement procedures. The nonlinear dynamic analysis (approximate entropy) measures of the diastolic heart sounds recorded from eight patients with single coronary occlusions and two normal subjects were estimated. In addition, a spectral analysis based on the fast Fourier transform was used to estimate the energy content of the recorded signals. Results suggest the presence of high nonlinear (approximate entropy) values of diastolic heart sounds associated with coronary artery disease (p

This paper summarizes the panel discussion at the IEEE Engineering in Medicine and Biology Point-of-Care Healthcare Technology Conference (POCHT 2013) held in Bangalore India from Jan 16-18, 2013. Modern medicine has witnessed interdisciplinary technology innovations in healthcare with a continuous growth in life expectancy across the globe. However, there is also a growing global concern on the affordability of rapidly rising healthcare costs. To provide quality healthcare at reasonable costs, there has to be a convergence of preventive, personalized, and precision medicine with the help of technology innovations across the entire spectrum of point-of-care (POC) to critical care at hospitals. The first IEEE EMBS Special Topic POCHT conference held in Bangalore, India provided an international forum with clinicians, healthcare providers, industry experts, innovators, researchers, and students to define clinical needs and technology solutions toward commercialization and translation to clinical applications across different environments and infrastructures. This paper presents a summary of discussions that took place during the keynote presentations, panel discussions, and breakout sessions on needs, challenges, and technology innovations in POC technologies toward improving global healthcare. Also presented is an overview of challenges and trends in developing and developed economies with respect to priority clinical needs, technology innovations in medical devices, translational engineering, information and communication technologies, infrastructure support, and patient and clinician acceptance of POC healthcare technologies.

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults because of its highly invasive behavior. The existing treatment for GBM, which involves a combination of resection, chemotherapy, and radiotherapy, has a very limited success rate with a median survival rate of