Discover the most talked about and latest scientific content & concepts.

Journal: IEEE transactions on bio-medical engineering


Tissue engineering has been a promising field of research, offering hope for bridging the gap between organ shortage and transplantation needs. However, building three-dimensional (3D) vascularized organs remains the main technological barrier to be overcome. Organ printing, which is defined as computer-aided additive biofabrication of 3D cellular tissue constructs, has shed light on advancing this field into a new era. Organ printing takes advantage of rapid prototyping (RP) technology to print cells, biomaterials, and cell-laden biomaterials individually or in tandem, layer by layer, directly creating 3D tissue-like structures. Here, we overview RP-based bioprinting approaches and discuss the current challenges and trends towards fabricating living organs for transplant in the near future.

Concepts: Extracellular matrix, Organelle, Organ, Biomaterial, Biocompatibility, Tissue engineering, Skin, Organ transplant


We present a novel method for estimating respiratory rate in real-time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory induced variation is analyzed using Fast Fourier Transforms. The proposed Smart Fusion method then combines the results of the three respiratory induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2 and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.

Concepts: Discrete Fourier transform, Fast Fourier transform, Estimation, Root mean square, Pulse oximetry, Oxygen saturation, Capnography, Pulse oximeter


One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

Concepts: Scientific method, Statistics, Ontology, Interoperability


The usage of the systemic opioid remifentanil in relieving the labor pain has attracted much attention recently. An optimal dosing regimen for administration of remifentanil during labor relies on anticipating the timing of uterine contractions. These predictions should be made early enough to maximize analgesia efficacy during contractions and minimize the impact of the medication between contractions. We have designed a knowledge-assisted sequential pattern analysis framework to 1) predict the intrauterine pressure in real-time; 2) anticipate the next contraction; and, 3) develop a sequential association rule mining approach to identify the patterns of the contractions from historical patient tracings. The basis of this framework is a sequential association rule based collaborative filtering strategy that dynamically selects a better training dataset from historical patient tracings, which are similar to the current patients contraction pattern, and the current patients most recent training time series. A k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) approach is used to establish the long-term time series prediction. Further, a postprediction process is proposed to enhance the predictive value. The findings validate that the framework is effective, robust, and efficient for uterine contraction prediction.

Concepts: Childbirth, Statistics, Prediction, Futurology, Future, Prophecy, Statistical classification, Contraction


The Raven-II is a platform for collaborative research on advances in surgical robotics. Seven Universities have begun research using this platform. The Raven-II system has two three DOF spherical positioning mechanisms capable of attaching interchangeable four DOF instruments. The Raven-II software is based on open standards such as Linux and ROS to maximally facilitate software development. The mechnism is robust enough for repeated experiments and animal surgery experiments, but is not engineered to sufficient safety standards for human use. Mechanisms in place for interaction among the user community and dissemination of results include an electronic forum, an online software SVN repository, and meetings and workshops at major robotics conferences.

Concepts: Medicine, Surgery, Science, Open Standards, Software engineering, Robotic surgery, Standards organization, System software


Breathwalk is a science of combining specific patterns of footsteps synchronized with the breathing. In this study, we developed a multimedia-assisted Breathwalk-aware system which detects user’s walking and breathing conditions and provides appropriate multimedia guidance on the smartphone. Through the mobile device, the system enhances user’s awareness of walking and breathing behaviors. As an example application in slow technology, the system could help meditator beginners learn “walking meditation,” a type of meditation which aims to be as slow as possible in taking pace, to synchronize footstep with breathing, and to land every footstep with toes first. In the pilot study, we developed a walking-aware system and evaluated whether multimedia-assisted mechanism is capable of enhancing beginner’s walking awareness while walking meditation. Experimental results show that it could effectively assist beginners in slowing down the walking speed and decreasing incorrect footsteps. In the second experiment, we evaluated the Breathwalk-aware system to find a better feedback mechanism for learning the techniques of Breathwalk while walking meditation. The experimental results show that the visual-auditory mechanism is a better multimedia-assisted mechanism while walking meditation than visual mechanism and auditory mechanism.

Concepts: Science, Experiment, Learning, Religion, Zen, Zazen, Buddhist meditation


Whole brain MRI registration has many useful applications in group analysis and morphometry yet accurate registration across different neuropathological groups remains challenging. Structure-specific information, or anatomical guidance, can be used to initialize and constrain registration to improve accuracy and robustness. We describe here a multi-structure diffeomorphic registration approach that uses concurrent subcortical and cortical shape matching to guide the overall registration. Validation experiments carried out on openly-available datasets demonstrate comparable or improved alignment of subcortical and cortical brain structures over leading brain registration algorithms. We also demonstrate that a group-wise average atlas built with multi-structure registration accounts for greater inter-subject variability and provides more sensitive tensor-based morphometry measurements.

Concepts: Better, Brain, Improve, Measurement, Magnetic resonance imaging, Cerebral cortex, Accuracy and precision, Procrustes analysis


Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems-a laser scanner and the current standard electromagnetic digitization system (Polhemus)-using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.

Concepts: Brain, Medical imaging, Brain tumor, Magnetic resonance imaging, Radiology, Source, Albert Einstein, 3D scanner


The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e.g. the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density hidden Markov model, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the United States. The proposed system can provide real-time surveillance results which are weeks before the CDCs reports. This paper focused on monitoring the infectious disease in the United States, however, we believe similar approach may be used to monitor epidemics for the developing countries as well.

Concepts: Medicine, Epidemiology, Cancer, Disease, Infectious disease, Infection, Pandemic, Epidemic


To present the first real-time a posteriori error-driven adaptive finite element approach for realtime simulation and to demonstrate the method on a needle insertion problem.