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Single-cell technologies like flow cytometry (FCM) provide valuable biological data for knowledge discovery in complex cellular systems like tissues and organs. FCM data contains multi-dimensional information about the cellular heterogeneity of intricate cellular systems. It is possible to correlate single-cell markers with phenotypic properties of those systems. Cell population identification and clinical outcome prediction from single-cell measurements are challenging problems in the field of single cell analysis. In this paper, we propose a hybrid learning approach to predict clinical outcome using samples' single-cell FCM data. The proposed method is efficient in both i) identification of cellular clusters in each sample's FCM data and ii) predict clinical outcome (healthy versus unhealthy) for each subject. Our method is robust and the experimental results indicate promising performance.

Background: Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects., Results: Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.

Visualization and clustering of single-cell mass cytometry (CyTOF) data are analytic techniques to identify different cell types. Most of such techniques, such as Euclidean norm, lose their effectiveness when the data dimension increases due to the curse of dimensionality. In this paper, we propose a new cell distance (called CytoRFD) that works based on Random Forest (RF) concept. The experimental results show that the proposed distance can achieve a much higher quality and effectiveness in large data analysis than traditional metrics specially for CyTOF data.

3D visualization of breast tumors are shown to be effective by previous studies. In this paper, we introduce a new augmented reality application that can help doctors and surgeons to have a more accurate visualization of breast tumors; this system uses a marker-based image-processing technique to render a 3D model of the tumors on the body. The model can be created using a combination of breast 3D mammography by experts. We have tested the system using an Android smartphone and a head-mounted device. This proof of concept can be useful for oncologists to have a more effective screening, and surgeons to plan the surgery.

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Pressure ulcers are high prevalence complications among bed-bound patients which are not only extremely painful and difficult to treat, but also impose a great burden in our health-care system. We target automatic posture detection which is a key module in all pressure ulcer monitoring platforms. Using data collected from a commercially-available pressure mapping system, we applied deep neural networks to automatically classify in-bed posture using features extracted from the histogram of gradient technique. High accuracy of up to 98% was achieved in classifying five different in-bed postures for more than 60,000 pressure images.

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

In this work, EEG spectral features of different subjects are uniquely mapped into a 2D feature space. Such distinctive 2D features pave the way to identify subjects from their EEG spectral characteristics in an unsupervised manner without any prior knowledge. First, we extract power spectral density of EEG signals in different frequency bands. Next, we use t-distributed stochastic neighbor embedding to map data points from high dimensional space in a visible 2D space. Such non-linear data embedding method visualizes different subjects' data points as well-separated islands in two dimensions. We use a fuzzy c-means clustering technique to identify different subjects without any prior knowledge. The experimental results show that our proposed method efficiently (precision greater than 90%) discriminates 10 subjects using only the spectral information within their EEG signals.

Seizures affect each patient differently, so personalization is a vital part of developing a reliable nonEEG based seizure detection system. This personalization must be done while the patient is undergoing video EEG monitoring in an epilepsy monitoring unit (EMU) because seizure detection by EEG is considered to be the ground truth. We propose the use of confidence interval analysis for determining how many seizures must be captured from a patient before we can reliably personalize such a seizure detection system for him/her. Our analysis indicates that 6 to 8 seizures are required. In addition, we create seizure likelihood tables for future use by said system by comparing the number of times a prespecified biosignal activity level is induced by seizure to the total number of occurrences of that level of activity. We focus on complex partial seizures in this paper because they are more difficult to detect than are generalized seizures.

Cerebral ischemia is worldwide the third largest cause of mortality and disability in old people, and oxidative stress plays a considerable role in this process. In this study, for the fi rst time, we evaluated the effects of Trolox as an antioxidative agent in ischemia induced by reperfusion. Twenty-four Syrian male mice were randomly divided into the 3 groups. Both common carotid arteries of Syrian mice were ligated bilaterally for 20 min, blood fl ow was restored and Trolox (50 mg/kg) was immediately injected after induced ischemia. Shuttle box results showed an improvement in memory in the Trolox group compared to the ischemia group, however, these improvements were not signifi cant. Histopathological results showed a signifi cant increase in the number of healthy cells in the hippocampal CA1 region in the Trolox group compared to the ischemia group (p < 0.001). Also, caspase-3, as an apoptosis marker, was signifi cantly decreased in the Trolox group compared to the ischemia group (p < 0.01). Ultimately, as an anti-apoptotic factor, c-JUN was increased statistically in the Trolox group compared to the ischemia group (p < 0.01). Our study showed that after cerebral ischemia reperfusion, Trolox prescription increased anti-apoptotic proteins and decreased proapoptotic proteins thus protects neurons of the hippocampus and caused improvement of memory. Ultimately, these results would suggest some important treatment strategies after cerebral ischemia reperfusion.

Sulfur mustard (SM) or mustard gas is a chemical alkylating agent that causes blisters in the skin (blister gas), burns the eyes and causes lung injury. Some major cellular pathways are involved in the damage caused by mustard gas such as NF-κb signaling, TGF-β signaling, WNT pathway, inflammation, DNA repair and apoptosis. MicroRNAs are non-coding small RNAs (19-25 nucleotides) that are involved in the regulation of gene expression and are found in two forms, extracellular and intracellular. Changes in the levels of extracellular microRNAs are directly associated with many diseases, it is thus common to study the level of extracellular microRNAs as a biomarker to determine the pathophysiologic status. In this study, 32 mustard gas injured patients and 32healthy subjects participated. Comparative evaluation of miR-9 and miR-143 expression in urine samples was performed by Real Time PCR and Graph Pad software. The Mann Whitney t-test analysis of data showed that the expression level of miR-143 and miR-9 had a significant decrease in sulfur mustard individuals with the respective p-value of 0.0480 and 0.0272 compared to normal samples, with an imbalance of several above mentioned pathways. It seems that reducing the expression level of these genes has a very important role in the pathogenicity of mustard gas injured patients.

Patients with Hydrocephalus are usually treated by diverting the excess Cerebrospinal Fluid (CSF) to other parts of the body using shunts. More than 40 percentage of shunts implanted fail within the first two years. Obstruction in the shunts is one of the major causes of failure (45 percent) and the detection of obstruction reduces the complexity of the revision surgery. This paper describes a proposed wireless monitoring system for clog detection and flow measurement in shunts. A prototype was built using multiple pressure sensors along the shunt catheters for sensing the location of clog and flow rate. Regular monitoring of flow rates can be used to adjust the valve in the shunt to prevent over drainage or under drainage of CSF. The accuracy of the flow measurement is more than 90 percent.

Sleep state detection is valuable in assessing patient's sleep quality and in-bed general behavior. In this paper, a novel classification approach of sleep states (sleep, pre-wake, wake) is proposed that uses only surface pressure sensors. In our method, a mobility metric is defined based on successive pressure body maps. Then, suitable statistical features are computed based on the mobility metric. Finally, a customized random forest classifier is employed to identify various classes including a new class for pre-wake state. Our algorithm achieves 96.1% and 88% accuracies for two (sleep, wake) and three (sleep, pre-wake, wake) class identification, respectively.

We propose an algorithm for separating arterial and venous-related signals using second-order statistics of red and infrared signals in a blind source separation technique. The separated arterial signal is used to compute accurate arterial oxygen saturation. We have also introduced an algorithm for extracting the respiratory pattern from the extracted venous-related signal. In addition to real-time monitoring, respiratory rate is also extracted. Our experimental results from multiple subjects show that the proposed separation technique is extremely useful for extracting accurate arterial oxygen saturation and respiratory rate. Specifically, the breathing rate is extracted with average root mean square deviation of 1.89 and average mean difference of -0.69.

Single signal seizure detection algorithms suffer from high false positive rates. We have found a set of signals which can be easily monitored by a wristworn device and which produce a distinctive pattern during seizure for patients in an epilepsy monitoring unit (EMU). This pattern is much less likely to be reproduced by nonseizure events in the patient's daily life than are changes in heart rate alone. We collected 108 hours of data from three EMU patients who suffered a combined total of seven seizures, then developed a time series analysis/pattern recognition based algorithm which distinguishes the seizures from nonseizure events with 100% accuracy.

Bedridden patients have a high risk of developing pressure ulcers. Risk assessment for pressure ulceration is critical for preventive care. For a reliable assessment, we need to identify and track the limbs continuously and accurately. In this paper, we propose a method to identify body limbs using a pressure mat. Three prevalent sleep postures (supine, left and right postures) are considered. Then, predefined number of limbs (body parts) are identified by applying Fuzzy C-Means (FCM) clustering on key attributes. We collected data from 10 adult subjects and achieved average accuracy of 93.2% for 10 limbs in supine and 7 limbs in left/right postures.

It is known that prolonged pressure on the plantar area is one of the main factors in developing foot ulcers. With current technology, electronic pressure monitoring systems can be placed as an insole into regular shoes to continuously monitor the plantar area and provide evidence on ulcer formation process as well as insight for proper orthotic footwear design. The reliability of these systems heavily depends on the spatial resolution of their sensor platforms. However, due to the cost and energy constraints, practical wireless in-shoe pressure monitoring systems have a limited number of sensors, i.e., typically K < 10. In this paper, we present a knowledge-based regression model (SCPM) to reconstruct a spatially continuous plantar pressure image from a small number of pressure sensors. This model makes use of high-resolution pressure data collected clinically to train a per-subject regression function. SCPM is shown to outperform all other tested interpolation methods for K < 60 sensors, with less than one-third of the error for K = 10 sensors. SCPM bridges the gap between the technological capability and medical need and can play an important role in the adoption of sensing insole for a wide range of medical applications.

Purpose: Exosomes (EXOs) are acellular vehicles used for cancer immunotherapy due to their immune-inducing properties. We synthesized a novel structure based on EXOs and staphylococcal enterotoxin B (SEB) and surveyed its cytostatic effect on a pancreatic cell line., Methods: EXOs were purified from tumor cells and SEB was anchored on it by protein transfer method. To determine the cytotoxic and apoptosis-inducing effect of this structure, treated cells with different concentrations of EXO/SEB were examined by MTT assay and Hoechst staining method. In addition, the expression rate of bcl-2, bax, bak, fas, bcl-xl and the activity of caspase-3 and caspase-9 were assessed., Results: We observed that 0.5 and 2.5 μg/100μl of EXO/ SEB significantly (p<0.001) stimulated apoptosis after 24 hrs. The concentrations of 0.5 and 2.5μg/100μl of EXO/SEB raised the expression rate of bax, bak, fas (p<0.001) but had no impact on bcl-2 and bcl-xl after 48 hrs. Furthermore, it was shown that 0.5, 2.5 and 5 μg/100μl of EXO/SEB only increased the activity of caspase-3 after 48 hrs (p<0.001)., Conclusion: Our designed structure, the EXO/SEB, is a novel model being able to induce apoptosis.

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation ( SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

Pressure ulcer is one of the most prevalent problems for bed-bound patients in hospitals and nursing homes. Pressure ulcers are painful for patients and costly for healthcare systems. Accurate in-bed posture analysis can significantly help in preventing pressure ulcers. Specifically, bed inclination (back angle) is a factor contributing to pressure ulcer development. In this paper, an efficient methodology is proposed to classify bed inclination. Our approach uses pressure values collected from a commercial pressure mat system. Then, by applying a number of image processing and machine learning techniques, the approximate degree of bed is estimated and classified. The proposed algorithm was tested on 15 subjects with various sizes and weights. The experimental results indicate that our method predicts bed inclination in three classes with 80.3% average accuracy.

EEG based monitoring for the purpose of assessing a patient's neurological status is conspicuous and uncomfortable at best. We are analyzing a set of physiological signals that may be monitored comfortably by a wrist worn device. We have found that these signals and machine based classification allows us to accurately discriminate among four stress states of individuals. Further, we have found a clear change in these signals during the 70 minutes preceding a single convulsive epileptic seizure. Our classification accuracy on all data has been greater than 90% to date.

Malignancy-associated hypercalcemia is a common finding among adult malignancies. However, the incidence of malignancy-induced hypercalcemia associated with germ cell tumor among pediatric patients is very rare. We describe a 9-year-old girl with an ovarian dysgerminoma presenting with chronic constipation and hypercalcemia. We review some of the causes of malignancy-associated hypercalcemia described in literature and treatment strategies. We also recommend considering oncological processes in the presence of hypercalcemia.

Pressure ulcer is a critical problem for bed-ridden and wheelchair-bound patients, diabetics, and the elderly. Patients need to be regularly repositioned to prevent excessive pressure on a single area of body, which can lead to ulcers. Pressure ulcers are extremely costly to treat and may lead to several other health problems, including death. The current standard for prevention is to reposition at-risk patients every two hours. Even if it is done properly, a fixed schedule is not sufficient to prevent all ulcers. Moreover, it may result in nurses being overworked by turning some patients too frequently. In this paper, we present an algorithm for finding a nurse-effort optimal repositioning schedule that prevents pressure ulcer formation for a finite planning horizon. Our proposed algorithm uses data from a commercial pressure mat assembled on the beds surface and provides a sequence of next positions and the time of repositioning for each patient.

The diabetic foot complications constitute a tremendous challenge for patients, caregivers, and the healthcare system. Studies show up to 25% of diabetic individuals will develop a foot ulcer during their lifetime and many of these patients eventually must undergo amputation as a result of infection due to untreated foot ulcers. With current technology, in-shoe monitoring systems can be implemented to continuously monitor at-risk ulceration sites based on known indicators such as peak pressure. The important parameters in designing a pressure-sensing insole include the number, location and size of sensors. In this paper, we aim at showing the criticality of sensor architectural tradeoff in developing the in-shoe plantar pressure monitoring systems. We evaluate this tradeoff by using our custom-made platform for data collection during normal walking.

The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patient's posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing algorithm is developed to keep an unobtrusive and informative record of patient's bed posture over time. The experimental results show that proposed algorithm can predict patient's bed posture with up to 97.7% average accuracy. This algorithm could ultimately be used with current support surface technologies to reduce the risk of ulcer development.

Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.

Purpose: Staphylococcal enterotoxin B (SEB) is a potent inducer of cytotoxic T-cell activity, cytokine production and necrosis induction in vivo. Monophosphoryl lipid A (MPL) is an adjuvant derived from the lipopolysaccharide of E. coli, Salmonella Minnesota Re595 and other gram negative bacteria. We investigated the possibility of the therapeutic application of SEB+ MPL in mice with fibrosarcoma., Methods: The antitumor effect of SEB+MPL, SEB and MPL in mice with inoculated fibrosarcoma tumor (WEHI-164) was examined by intravenous (i.v.) and intratumoral (i.t.) injection and the sizes of the inoculated tumors, IFN-gamma production, and CD4(+)/CD8(+) T cell infiltration were determined. The inoculated tumors were also examined histologically., Results: In the i.v.-injected group of mice with SEB+ MPL, reduction of tumor size showed a significant difference compared with mice in the i.t., the i.v. (MPL)-injected groups and the negative control group (p < 0.02). Moreover, the mice in the i.v. (SEB+ MPL)-injected group showed significantly higher levels of IFN-gamma (p < 0.009) and CD4(+)/CD8(+) T cell infiltration when compared with the other groups (p < 0.02). A significantly higher frequency of necrosis in tumor tissues was also observed in mice in the i.v. (SEB+ MPL)-injected group in comparison with other groups (p < 0.009)., Conclusion: Our findings suggest that tumor cell death is caused by increased cytotoxic T-cell activity, cytokine levels, in response to IV injection of SEB+MPL. They also suggest that tumor cell death by synergistic effect of one of the strongest bacterial superantigens (SEB) with monophosphoryl lipid A and SEB+MPL may be a good option for use as a novel therapy in patients with fibrosarcoma.

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks.

Sleep efficiency measures provide an objective assessment to gauge the quality of individual's sleep. In this study we present a home-based, automated and non-intrusive system that is based on Electrocardiogram (ECG) measurements and uses a multi-stage Support Vector Machines (SVM) classifier to measure three indices for sleep quality assessment per 30 s epoch segment: Sleep Efficiency Index, Delta-Sleep Efficiency Index and Sleep Onset Latency. This method provides an alternative to the intrusive and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method.