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Background: The confirmatory diagnosis of Osteogenesis Imperfecta (OI) requires invasive, commonly bone biopsy, time consuming and destructive methods. This paper proposes an alternative method using a combination of two-photon excitation fluorescence (TPEF) and second-harmonic generation (SHG) microscopies from easily obtained human skin biopsies. We show that this method can distinguish subtypes of human OI. Methodology/Principal Findings: Different aspects of collagen microstructure of skin fresh biopsies and standard H&E-stained sections of normal and OI patients (mild and severe forms) were distinguished by TPEF and SHG images. Moreover, important differences between subtypes of OI were identified using different methods of quantification such as collagen density, ratio between collagen and elastic tissue, and gray-level co-occurrence matrix (GLCM) image-pattern analysis. Collagen density was lower in OI dermis, while the SHG/autofluorescence index of the dermis was significantly higher in OI as compared to that of the normal skin. We also showed that the energy value of GLCM texture analysis is useful to discriminate mild from severe OI and from normal skin. Conclusions/Significance: This work demonstrated that nonlinear microscopy techniques in combination with image-analysis approaches represent a powerful tool to investigate the collagen organization in skin dermis in patients with OI and has the potential to distinguish the different types of OI. The procedure outlined in this paper requires a skin biopsy, which is almost painless as compared to the bone biopsy commonly used in conventional methods. The data presented here complement existing clinical diagnostic techniques and can be used as a diagnostic procedure to confirm the disease, evaluate its severity and treatment efficacy. [ABSTRACT FROM AUTHOR]

Early stage detection of lymphoma cells is invaluable for providing reliable prognosis to patients. However, the purity of lymphoma cells in extracted samples from human patients' marrow is typically low. To address this issue, we report here our work on using optically-induced dielectrophoresis (ODEP) force to rapidly purify Raji cells' (a type of Burkitt's lymphoma cell) sample from red blood cells (RBCs) with a label-free process. This method utilizes dynamically moving virtual electrodes to induce negative ODEP force of varying magnitudes on the Raji cells and RBCs in an optically-induced electrokinetics (OEK) chip. Polarization models for the two types of cells that reflect their discriminate electrical properties were established. Then, the cells' differential velocities caused by a specific ODEP force field were obtained by a finite element simulation model, thereby established the theoretical basis that the two types of cells could be separated using an ODEP force field. To ensure that the ODEP force dominated the separation process, a comparison of the ODEP force with other significant electrokinetics forces was conducted using numerical results. Furthermore, the performance of the ODEP-based approach for separating Raji cells from RBCs was experimentally investigated. The results showed that these two types of cells, with different concentration ratios, could be separated rapidly using externally-applied electrical field at a driven frequency of 50 kHz at 20 Vpp. In addition, we have found that in order to facilitate ODEP-based cell separation, Raji cells' adhesion to the OEK chip's substrate should be minimized. This paper also presents our experimental results of finding the appropriate bovine serum albumin concentration in an isotonic solution to reduce cell adhesion, while maintaining suitable medium conductivity for electrokinetics-based cell separation. In short, we have demonstrated that OEK technology could be a promising tool for efficient and effective purification of Raji cells from RBCs. [ABSTRACT FROM AUTHOR]

During the last decade markerless motion capture techniques have gained an increasing interest in the biomechanics community. In the clinical field, however, the application of markerless techniques is still debated. This is mainly due to a limited number of papers dedicated to the comparison with the state of the art of marker based motion capture, in term of repeatability of the three dimensional joints' kinematics. In the present work the application of markerless technique to data acquired with a marker-based system was investigated. All videos and external data were recorded with the same motion capture system and included the possibility to use markerless and marker-based methods simultaneously. Three dimensional markerless joint kinematics was estimated and compared with the one determined with traditional marker based systems, through the evaluation of root mean square distance between joint rotations. In order to compare the performance of markerless and marker-based systems in terms of clinically relevant joint angles estimation, the same anatomical frames of reference were defined for both systems. Differences in calibration and synchronization of the cameras were excluded by applying the same wand calibration and lens distortion correction to both techniques. Best results were achieved for knee flexion-extension angle, with an average root mean square distance of 11.75 deg, corresponding to 18.35% of the range of motion. Sagittal plane kinematics was estimated better than on the other planes also for hip and ankle (root mean square distance of 17.62 deg e.g. 44.66%, and 7.17 deg e.g. 33.12%), meanwhile estimates for hip joint were the most incorrect. This technique enables users of markerless technology to compare differences with marker-based in order to define the degree of applicability of markerless technique. [ABSTRACT FROM AUTHOR]

Bacteria of the family Rickettsiaceae have always been largely studied not only for their importance in the medical field, but also as model systems in evolutionary biology. In fact, they share a recent common ancestor with mitochondria. The most studied species, belonging to genera Rickettsia and Orientia, are hosted by terrestrial arthropods and include many human pathogens. Nevertheless, recent findings show that a large part of Rickettsiaceae biodiversity actually resides outside the group of well-known pathogenic bacteria. Collecting data on these recently described non-conventional members of the family is crucial in order to gain information on ancestral features of the whole group. Although bacteria of the family Rickettsiaceae, and of the whole order Rickettsiales, are formally described as non-flagellated prokaryotes, some recent findings renewed the debate about this feature. In this paper we report the first finding of members of the family displaying numerous flagella and active movement inside their host cells. These two new taxa are hosted in aquatic environments by protist ciliates and are described here by means of ultrastructural and molecular characterization. Data here reported suggest that the ancestor of Rickettsiales displayed flagellar movement and re-evaluate the hypothesis that motility played a key-role in the origin of mitochondria. Moreover, our study highlights that the aquatic environment represents a well exploited habitat for bacteria of the family Rickettsiaceae. Our results encourage a deep re-consideration of ecological and morphological traits of the family and of the whole order. [ABSTRACT FROM AUTHOR]

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies. [ABSTRACT FROM AUTHOR]

The regulation of chromosome separation during mitosis is not fully understood yet. Microtubules forming mitotic spindles are targets of treatment strategies which are aimed at (i) the triggering of the apoptosis or (ii) the interruption of uncontrolled cell division. Despite these facts, only few physical models relating to the dynamics of mitotic spindles exist up to now. In this paper, we present the first electromechanical model which enables calculation of the electromagnetic field coupled to acoustic vibrations of the mitotic spindle. This electromagnetic field originates from the electrical polarity of microtubules which form the mitotic spindle. The model is based on the approximation of resonantly vibrating microtubules by a network of oscillating electric dipoles. Our computational results predict the existence of a rapidly changing electric field which is generated by either driven or endogenous vibrations of the mitotic spindle. For certain values of parameters, the intensity of the electric field and its gradient reach values which may exert a not-inconsiderable force on chromosomes which are aligned in the spindle midzone. Our model may describe possible mechanisms of the effects of ultra-short electrical and mechanical pulses on dividing cells—a strategy used in novel methods for cancer treatment. [ABSTRACT FROM AUTHOR]

Cortical spreading depression (CSD) is a pathophysiological phenomenon, which underlies some neurological disorders, such as migraine and stroke, but its mechanisms are still not completely understood. One of the striking facts is that the spatiotemporal evolution of CSD wave is varying. Observations in experiments reveal that a CSD wave may propagate through the entire cortex, or just bypass some areas of the cortex. In this paper, we have applied a 2D reaction-diffusion equation with recovery term to study the spatiotemporal evolution of CSD. By modulating the recovery rate from CSD in the modeled cortex, CSD waves with different spatiotemporal evolutions, either bypassing some areas or propagating slowly in these areas, were present. Moreover, spiral CSD waves could also be induced in case of the transiently altered recovery rate, i.e. block release from the absolute refractory period. These results suggest that the refractory period contributes to the different propagation patterns of CSD, which may help to interpret the mechanisms of CSD propagation. [ABSTRACT FROM AUTHOR]

Sudden cardiac death is often caused by cardiac arrhythmias. Recently, special attention has been given to a certain arrhythmogenic condition, the long-QT syndrome, which occurs as a result of genetic mutations or drug toxicity. The underlying mechanisms of arrhythmias, caused by the long-QT syndrome, are not fully understood. However, arrhythmias are often connected to special excitations of cardiac cells, called early afterdepolarizations (EADs), which are depolarizations during the repolarizing phase of the action potential. So far, EADs have been studied mainly in isolated cardiac cells. However, the question on how EADs at the single-cell level can result in fibrillation at the tissue level, especially in human cell models, has not been widely studied yet. In this paper, we study wave patterns that result from single-cell EAD dynamics in a mathematical model for human ventricular cardiac tissue. We induce EADs by modeling experimental conditions which have been shown to evoke EADs at a single-cell level: by an increase of L-type Ca currents and a decrease of the delayed rectifier potassium currents. We show that, at the tissue level and depending on these parameters, three types of abnormal wave patterns emerge. We classify them into two types of spiral fibrillation and one type of oscillatory dynamics. Moreover, we find that the emergent wave patterns can be driven by calcium or sodium currents and we find phase waves in the oscillatory excitation regime. From our simulations we predict that arrhythmias caused by EADs can occur during normal wave propagation and do not require tissue heterogeneities. Experimental verification of our results is possible for experiments at the cell-culture level, where EADs can be induced by an increase of the L-type calcium conductance and by the application of I blockers, and the properties of the emergent patterns can be studied by optical mapping of the voltage and calcium. [ABSTRACT FROM AUTHOR]

Knowledge of how a population of cancerous cells progress through the cell cycle is vital if the population is to be treated effectively, as treatment outcome is dependent on the phase distributions of the population. Estimates on the phase distribution may be obtained experimentally however the errors present in these estimates may effect treatment efficacy and planning. If mathematical models are to be used to make accurate, quantitative predictions concerning treatments, whose efficacy is phase dependent, knowledge of the phase distribution is crucial. In this paper it is shown that two different transition rates at the - checkpoint provide a good fit to a growth curve obtained experimentally. However, the different transition functions predict a different phase distribution for the population, but both lying within the bounds of experimental error. Since treatment outcome is effected by the phase distribution of the population this difference may be critical in treatment planning. Using an age-structured population balance approach the cell cycle is modelled with particular emphasis on the - checkpoint. By considering the probability of cells transitioning at the - checkpoint, different transition functions are obtained. A suitable finite difference scheme for the numerical simulation of the model is derived and shown to be stable. The model is then fitted using the different probability transition functions to experimental data and the effects of the different probability transition functions on the model's results are discussed. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new method to expressly use live/corporeal data in quantifying differences of time series data with an underlying limit cycle attractor; and apply it using an example of gait data. Our intention is to identify gait pattern differences between diverse situations and classify them on group and individual subject levels. First we approximated the limit cycle attractors, from which three measures were calculated: δM amounts to the difference between two attractors (a measure for the differences of two movements), δD computes the difference between the two associated deviations of the state vector away from the attractor (a measure for the change in movement variation), and δF, a combination of the previous two, is an index of the change. As an application we quantified these measures for walking on a treadmill under three different conditions: normal walking, dual task walking, and walking with additional weights at the ankle. The new method was able to successfully differentiate between the three walking conditions. Day to day repeatability, studied with repeated trials approximately one week apart, indicated excellent reliability for δM (ICCave > 0.73 with no differences across days; p > 0.05) and good reliability for δD (ICCave  =  0.414 to 0.610 with no differences across days; p > 0.05). Based on the ability to detect differences in varying gait conditions and the good repeatability of the measures across days, the new method is recommended as an alternative to expensive and time consuming techniques of gait classification assessment. In particular, the new method is an easy to use diagnostic tool to quantify clinical changes in neurological patients. [ABSTRACT FROM AUTHOR]

Thromboembolic disease is a leading cause of morbidity and mortality worldwide. In the last several years there have been a number of studies attempting to identify mechanisms that stop thrombus growth. This paper identifies a novel mechanism related to formation of a fibrin cap. In particular, protein transport through a fibrin network, an important component of a thrombus, was studied by integrating experiments with model simulations. The network permeability and the protein diffusivity were shown to be important factors determining the transport of proteins through the fibrin network. Our previous in vivo studies in mice have shown that stabilized non-occluding thrombi are covered by a fibrin network (‘fibrin cap’). Model simulations, calibrated using experiments in microfluidic devices and accounting for the permeable structure of the fibrin cap, demonstrated that thrombin generated inside the thrombus was washed downstream through the fibrin network, thus limiting exposure of platelets on the thrombus surface to thrombin. Moreover, by restricting the approach of resting platelets in the flowing blood to the thrombus core, the fibrin cap impaired platelets from reaching regions of high thrombin concentration necessary for platelet activation and limited thrombus growth. The formation of a fibrin cap prevents small thrombi that frequently develop in the absence of major injury in the 60000 km of vessels in the body from developing into life threatening events. [ABSTRACT FROM AUTHOR]

Biological fluids fulfill key functionalities such as hydrating, protecting, and nourishing cells and tissues in various organ systems. They are capable of these versatile tasks owing to their distinct structural and viscoelastic properties. Characterizing the viscoelastic properties of bio-fluids is of pivotal importance for monitoring the development of certain pathologies as well as engineering synthetic replacements. Laser Speckle Rheology (LSR) is a novel optical technology that enables mechanical evaluation of tissue. In LSR, a coherent laser beam illuminates the tissue and temporal speckle intensity fluctuations are analyzed to evaluate mechanical properties. The rate of temporal speckle fluctuations is, however, influenced by both optical and mechanical properties of tissue. Therefore, in this paper, we develop and validate an approach to estimate and compensate for the contributions of light scattering to speckle dynamics and demonstrate the capability of LSR for the accurate extraction of viscoelastic moduli in phantom samples and biological fluids of varying optical and mechanical properties. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus 16 (HPV16) is a high-risk DNA tumour virus, which is a major causative agent of cervical cancer. Cellular transformation is associated with deregulated expression of the E6 and E7 oncogenes. E7 has been shown to bind a number of cellular proteins, including the cell cycle control protein pRb. In this study, RNA aptamers (small, single-stranded oligonucleotides selected for high-affinity binding) to HPV16 E7 were employed as molecular tools to further investigate these protein-protein interactions. Methodology/Principal Findings: This study is focused on one aptamer (termed A2). Transfection of this molecule into HPV16-transformed cells resulted in inhibition of cell proliferation (shown using real-time cell electronic sensing and MTT assays) due to the induction of apoptosis (as demonstrated by Annexin V/propidium iodide staining). GST-pull down and bead binding assays were used to demonstrate that the binding of A2 required N-terminal residues of E7 known to be involved in interaction with the cell cycle control protein, pRb. Using a similar approach, A2 was shown to disrupt the interaction between E7 and pRb in vitro. Furthermore, transfection of HPV16-transformed cells with A2 appeared to result in the loss of E7 and rise in pRb levels, as observed by immunoblotting. Conclusions/Significance: This paper includes the first characterisation of the effects of an E7 RNA aptamer in a cell line derived from a cervical carcinoma. Transfection of cells with A2 was correlated with the loss of E7 and the induction of apoptosis. Aptamers specific for a number of cellular and viral proteins have been documented previously; one aptamer (Macugen) is approved for clinical use and several others are in clinical trials. In addition to its role as a molecular tool, A2 could have further applications in the future. [ABSTRACT FROM AUTHOR]

Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug–target interactions enables us to suggest many new potential drug–target interactions for further studies. [ABSTRACT FROM AUTHOR]

A variety of cardiac arrhythmias are initiated by a focal excitation that disrupts the regular beating of the heart. In some cases it is known that these excitations are due to calcium (Ca) release from the sarcoplasmic reticulum (SR) via propagating subcellular Ca waves. However, it is not understood what are the physiological factors that determine the timing of these excitations at both the subcellular and tissue level. In this paper we apply analytic and numerical approaches to determine the timing statistics of spontaneous Ca release (SCR) in a simplified model of a cardiac myocyte. In particular, we compute the mean first passage time (MFPT) to SCR, in the case where SCR is initiated by spontaneous Ca sparks, and demonstrate that this quantity exhibits either an algebraic or exponential dependence on system parameters. Based on this analysis we identify the necessary requirements so that SCR occurs on a time scale comparable to the cardiac cycle. Finally, we study how SCR is synchronized across many cells in cardiac tissue, and identify a quantitative measure that determines the relative timing of SCR in an ensemble of cells. Using this approach we identify the physiological conditions so that cell-to-cell variations in the timing of SCR is small compared to the typical duration of an SCR event. We argue further that under these conditions inward currents due to SCR can summate and generate arrhythmogenic triggered excitations in cardiac tissue. [ABSTRACT FROM AUTHOR]

Calpain is an intracellular Ca2+ -activated protease that is involved in numerous Ca2+ dependent regulation of protein function in many cell types. This paper tests a hypothesis that calpains are involved in Ca2+ -dependent increase of the late sodium current (INaL) in failing heart. Chronic heart failure (HF) was induced in 2 dogs by multiple coronary artery embolization. Using a conventional patch-clamp technique, the whole-cell INaL was recorded in enzymatically isolated ventricular cardiomyocytes (VCMs) in which INaL was activated by the presence of a higher (1μM) intracellular [Ca2+] in the patch pipette. Cell suspensions were exposed to a cell- permeant calpain inhibitor MDL-28170 for 1–2 h before INaL recordings. The numerical excitation-contraction coupling (ECC) model was used to evaluate electrophysiological effects of calpain inhibition in silico. MDL caused acceleration of INaL decay evaluated by the two-exponential fit (τ1 = 42±3.0 ms τ2 = 435±27 ms, n = 6, in MDL vs. τ1 = 52±2.1 ms τ2 = 605±26 control no vehicle, n = 11, and vs. τ1 = 52±2.8 ms τ2 = 583±37 ms n = 7, control with vehicle, P<0.05 ANOVA). MDL significantly reduced INaL density recorded at –30 mV (0.488±0.03, n = 12, in control no vehicle, 0.4502±0.0210, n = 9 in vehicle vs. 0.166±0.05pA/pF, n = 5, in MDL). Our measurements of current-voltage relationships demonstrated that the INaL density was decreased by MDL in a wide range of potentials, including that for the action potential plateau. At the same time the membrane potential dependency of the steady-state activation and inactivation remained unchanged in the MDL-treated VCMs. Our ECC model predicted that calpain inhibition greatly improves myocyte function by reducing the action potential duration and intracellular diastolic Ca2+ accumulation in the pulse train. Conclusions: Calpain inhibition reverses INaL changes in failing dog ventricular cardiomyocytes in the presence of high intracellular Ca2+. Specifically it decreases INaL density and accelerates INaL kinetics resulting in improvement of myocyte electrical response and Ca2+ handling as predicted by our in silico simulations. [ABSTRACT FROM AUTHOR]

Associative classification mining (ACM) can be used to provide predictive models with high accuracy as well as interpretability. However, traditional ACM ignores the difference of significances among the features used for mining. Although weighted associative classification mining (WACM) addresses this issue by assigning different weights to features, most implementations can only be utilized when pre-assigned weights are available. In this paper, we propose a link-based approach to automatically derive weight information from a dataset using link-based models which treat the dataset as a bipartite model. By combining this link-based feature weighting method with a traditional ACM method-classification based on associations (CBA), a Link-based Associative Classifier (LAC) is developed. We then demonstrate the application of LAC to biomedical datasets for association discovery between chemical compounds and bioactivities or diseases. The results indicate that the novel link-based weighting method is comparable to support vector machine (SVM) and RELIEF method, and is capable of capturing significant features. Additionally, LAC is shown to produce models with high accuracies and discover interesting associations which may otherwise remain unrevealed by traditional ACM. [ABSTRACT FROM AUTHOR]