Search

Platelets have an active energy metabolism mediated by mitochondria. However, the role of mitochondria in platelet adhesion, activation, and thrombus formation under blood flow conditions remains to be elucidated. Blood specimens were obtained from healthy adult volunteers. The consumption of glucose molecules by platelets was measured after 24 hours. Platelet adhesion, activation, and thrombus formation on collagen fibrils and immobilized von Willebrand factor (VWF) at a wall shear rate of 1,500 s

We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to segment cells and a CNN is used to classify the segmented cells into eight types of cells during the reprogramming and hiPSC formation based on cellular morphology on the microscopy images. The numbers of respective types of cells in cell clusters before the hiPSC formation stage are used to predict if hiPSC regions can be well formed lately. Experimental results show good prediction by the criteria using the numbers of different cells in the clusters. Time-series images with respective types of classified cells can be used to visualize and quantitatively analyze the growth and transition among dispersed cells not in cell clusters, various types of cells in the clusters before the hiPSC formation stage and hiPSC cells.

Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires large amounts of labelled data; however, the sample size of such medical image datasets is generally small. This study proposes a novel training framework for building deep learning models of disease detection and classification with small datasets. Our approach is based on a hierarchical classification method where the healthy/disease information from the first model is effectively utilized to build subsequent models for classifying the disease into its sub-types via a transfer learning method. To improve accuracy, multiple input datasets were used, and a stacking ensembled method was employed for final classification. To demonstrate the method’s performance, a labelled dataset extracted from volumetric ophthalmic optical coherence tomography data for 156 healthy and 798 glaucoma eyes was used, in which glaucoma eyes were further labelled into four sub-types. The average weighted accuracy and Cohen’s kappa for three randomized test datasets were 0.839 and 0.809, respectively. Our approach outperformed the flat classification method by 9.7% using smaller training datasets. The results suggest that the framework can perform accurate classification with a small number of medical images.

Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such a problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.

Purpose: There is an unclear relationship between ocular blood flow (OBF) and the structural characteristics of the optic nerve head (ONH) in glaucoma, a multifactorial disease. This study used las...

Human induced pluripotent stem cells (hiPSCs) can differentiate into three germ layer cells, i.e. ectoderm, mesoderm and endoderm, on micropatterned chips in highly synchronous and reproducible manners. The cells are confined within the chip, expanding two-dimensionally as almost in the form of monolayer, thus to be ideal for serving quantitative analysis of their pluripotency. We present a new U-Net (MP-UNet) structure for cell segmentation of early spatial patterning of hiPSCs on micropattern chips using Hoechst fluorescence images. In this structure, the encoding/decoding layers can be dynamically adjusted to extract sufficient image features and be flexible to image sizes. Dice and weight loss functions are designed to identify slight difference in low signal-to-noise ratio, high boundary-to-area ratio and compacted cell images. Several sizes of Hoechst images were tested to show MP-UNet can achieve high accuracy in cell regions and number counting for various sizes of micropattern chips, thus to be excellent quantitative tool for early spatial patterning of hiPSCs.

Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs.We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation.In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069-3.250; p 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies.Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

BACKGROUND Artificial intelligence (AI) has made considerable progress in image recognition, especially in the analysis of endoscopic images. The availability of large-scale annotated datasets has contributed to the recent progress in this field. Datasets of high-quality annotated endoscopic images are widely available, particularly in Japan. A system for collecting annotated data reported daily could aid in accumulating a significant number of high-quality annotated datasets. AIM We assessed the validity of using daily annotated endoscopic images in a constructed reporting system for a prototype AI model for polyp detection. METHODS We constructed an automated collection system for daily annotated datasets from an endoscopy reporting system. The key images were selected and annotated for each case only during daily practice, not to be performed retrospectively. We automatically extracted annotated endoscopic images of diminutive colon polyps that had been diagnosed (study period March-September 2018) using the keywords of diagnostic information, and additionally collect the normal colon images. The collected dataset was devised into training and validation to build and evaluate the AI system. The detection model was developed using a deep learning algorithm, RetinaNet. RESULTS The automated system collected endoscopic images (47,391) from colonoscopies (745), and extracted key colon polyp images (1356) with localized annotations. The sensitivity, specificity, and accuracy of our AI model were 97.0%, 97.7%, and 97.3% (n = 300), respectively. CONCLUSION The automated system enabled the development of a high-performance colon polyp detector using images in endoscopy reporting system without the efforts of retrospective annotation works.

The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4-2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.

The purpose of this study was to develop and evaluate a novel elbow arthroscopy system with superimposed bone and nerve visualization using preoperative computed tomography (CT) and magnetic resonance imaging (MRI) data. We obtained bone and nerve segmentation data by CT and MRI, respectively, of the elbow of a healthy human volunteer and cadaveric Japanese monkey. A life size 3-dimensional (3D) model of human organs and frame was constructed using a stereo-lithographic 3D printer. Elbow arthroscopy was performed using the elbow of a cadaveric Japanese monkey. The augmented reality (AR) range of error during rotation of arthroscopy was examined at 20 mm scope-object distances. We successfully performed AR arthroscopy using the life-size 3D elbow model and the elbow of the cadaveric Japanese monkey by making anteromedial and posterior portals. The target registration error was 1.63 ± 0.49 mm (range 1-2.7 mm) with respect to the rotation angle of the lens cylinder from 40° to - 40°. We attained reasonable accuracy and demonstrated the operation of the designed system. Given the multiple applications of AR-enhanced arthroscopic visualization, it has the potential to be a next-generation technology for arthroscopy. This technique will contribute to the reduction of serious complications associated with elbow arthroscopy.

The nasal airway is an extremely complex structure, therefore grid generation for numerical prediction of airflow in the nasal cavity is time-consuming. This paper describes the development of a voxel-based model with a Cartesian structured grid, which is characterized by robust and automatic grid generation, and the simulation of the airflow and air-conditioning in an individual human nasal airway. Computed tomography images of a healthy adult nose were used to reconstruct a virtual three-dimensional model of the nasal airway. Simulations of quiet restful inspiratory flow were then performed using a Neumann boundary condition for the energy equation to adequately resolve the flow and heat transfer. General agreements of airflow patterns, which were a high-speed jet posterior to the nasal valve and recirculating flow that occupied the anterior part of the upper cavity, and temperature distributions of the airflow and septum wall were confirmed by comparing in-vivo measurements with numerical simulation results.

We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memory dependent LSTM networks may not predict accurately. The P-LSTM network incorporates the images newly generated from cell imaging progressively into LSTM training to emphasize the LSTM short-term memory and thus improve the prediction accuracy. The new images are input into a buffer to be selected for batch training. For real-time processing, parallel computation is introduced to implement concurrent training and prediction on partitioned images.Two types of stem cell images were used to show effectiveness of the P-LSTM network. One is for tracking of ES cell colonies. The actual and predicted ES cell images possess similar colony areas and the same transitions of colony states (moving, merging or morphology changing), although the predicted colony mergers may delay in several time-steps. The other is for prediction of iPS cell reprogramming from the CD34+ human cord blood cells. The actual and predicted iPS cell images possess high similarity evaluated by the PSNR and SSIM similarity evaluation metrics, indicating the reprogramming iPS cell colony features and morphology can be accurately predicted.

PurposeAbnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians.MethodsThe running pattern of the choroidal vessels in en face images of Haller’s layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI’s answers were compared using confidence scores (CSs).ResultsThe choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group.ConclusionAI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.

We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.

This paper presents a retinal thickness analysis method from 3D images acquired by optical coherence tomography (OCT). Given OCT images with segmented boundaries of retinal layers, medial axes of the layers are computed by medial axis transforms (MAT), and thickness is evaluated based on Euclidean distance fields. Since the MAT computes the closest points on the boundary of the layer, it can compute more correct thickness than conventional methods that evaluate Y-axis-aligned thickness. Experimental results show that our method can detect thin-parts around distorted regions, or a clue of high myopia. This is useful for early diagnosis of high myopia and other eye diseases.

The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.

This paper describes the simulation of airflow in human nasal airways using voxel-based modeling characterized by robust, automatic, and objective grid generation. Computed tomography scans of a healthy adult nose are used to reconstruct 3D virtual models of the nasal airways. Voxel-based simulations of restful inspiratory flow are then performed using various mesh sizes to determine the level of granularity required to adequately resolve the airflow. For meshes with close voxel spacings, the model successfully reconstructs the nasal structure and predicts the overall pressure drop through the nasal cavity.

Background: In this study, we proposed an averaged airway model design based on four healthy subjects and numerically evaluated its effectiveness for predicting the airflow and particle transport through an airway. Methods: Direct-averaged models of the conducting airways of four subjects were restored by averaging the three-dimensional (3D) skeletons of four healthy airways, which were calculated using an inverse 3D thinning algorithm. We simulated the airflow and particle transport in the individual and the averaged airway models using computational fluid dynamics. Results: The bifurcation geometry differs even among healthy subjects, but the averaged model retains the typical geometrical characteristics of the airways. The Reynolds number of the averaged model varied within the range found in the individual subject models, and the averaged model had similar inspiratory flow characteristics as the individual subject models. The deposition fractions at almost all individual lobes ranged within the variation observed in the subjects, however, the deposition fraction was higher in only one lobe. The deposition distribution at the main bifurcation point differed among the healthy subjects, but the characteristics of the averaged model fell within the variation observed in the individual subject models. On the contrary, the deposition fraction of the averaged model was higher than that of the average of the individual subject models and deviated from the range observed in the subject models. Conclusion: These results indicate that the direct-averaged model may be useful for predicting the individual airflow and particle transport on a macroscopic scale.

Introduction Thrombin inhibitor and anti-Xa are now widely used in clinical practice. However, the difference between thrombin inhibitor and anti-Xa in prevention of thrombosis is still to be elucidated. Materials and methods Computer simulator implementing the function of platelet, coagulation, fibrinolysis and blood flow was developed. The function of thrombin is defined as to activated platelet at the rate of 0.01 s−1 and to produce fibrin at the rate of 0.1 s−1 in control. The effect of thrombin inhibitor was settled to reduce the rate of platelet activation and fibrin generation changed from 10 to 100% as compared to the control. The local thrombin generation rate on activated platelet was settled as 1.0 s−1 as a control. The effect of anti-Xa was settled to reduce to thrombin generation rate on activated platelet from 10% to 100% as compared to the control. The sizes of thrombi formed at site of endothelial injury in the presence and absence of thrombin inhibitor and anti-Xa were compared. Results and conclusions The size of thrombi formed by 30-s perfusion of blood at site of endothelial injury reduced both in the presence of thrombin inhibitor and anti-Xa. There was significant positive relationship between thrombin inhibitor effect and the size of formed thrombi with R value of 0.96. (p

A multidisciplinary approach reveals key insights into the principles of collective cell migration, which is involved in fundamental biological processes. The conformational plasticity of a single molecule, JRAB/MICAL-L2, provides “law and order” in collective cell migration., In fundamental biological processes, cells often move in groups, a process termed collective cell migration. Collectively migrating cells are much better organized than a random assemblage of individual cells. Many molecules have been identified as factors involved in collective cell migration, and no one molecule is adequate to explain the whole picture. Here we show that JRAB/MICAL-L2, an effector protein of Rab13 GTPase, provides the “law and order” allowing myriad cells to behave as a single unit just by changing its conformation. First, we generated a structural model of JRAB/MICAL-L2 by a combination of bioinformatic and biochemical analyses and showed how JRAB/MICAL-L2 interacts with Rab13 and how its conformational change occurs. We combined cell biology, live imaging, computational biology, and biomechanics to show that impairment of conformational plasticity in JRAB/MICAL-L2 causes excessive rigidity and loss of directionality, leading to imbalance in cell group behavior. This multidisciplinary approach supports the concept that the conformational plasticity of a single molecule provides “law and order” in collective cell migration.

From cells to organisms, every living system is three-dimensional (3D), but the performance of fluorescence microscopy has been largely limited when attempting to obtain an overview of systems’ dynamic processes in three dimensions. Recently, advanced light-sheet illumination technologies, allowing drastic improvement in spatial discrimination, volumetric imaging times, and phototoxicity/photobleaching, have been making live imaging to collect precise and reliable 3D information increasingly feasible. In particular, lattice light-sheet microscopy (LLSM), using an ultrathin light-sheet, enables whole-cell 3D live imaging of cellular processes, including mitosis, at unprecedented spatiotemporal resolution for extended periods of time. This technology produces immense and complex data, including a significant amount of information, raising new challenges for big image data analysis and new possibilities for data utilization. Once the data are digitally archived in a computer, the data can be reused for various purposes by anyone at any time. Such an information science approach has the potential to revolutionize the use of bioimage data, and provides an alternative method for cell biology research in a data-driven manner. In this article, we introduce examples of analyzing digital mitotic spindles and discuss future perspectives in cell biology.

Intracellular and intercellular Ca

In joint surgery, evaluation of the relative positions and angles among joint structures (bones, ligaments, muscle, and cartilages, etc.) in range of motion, lifting and weight bearing of the joint is required. However, current volume visualization techniques provide only static 3D images of anatomic structures in volume data. We propose a method to manipulate (reposition, resize and bend) the joint structures in a volume, by which surgeons can visualize and evaluate the critical positions or angles of the joint structures, and thus plan surgery to correct the morphologic pathology of the joint structures. We also propose a system with a real-time cutting simulation function together with the proposed structure manipulation functions by which surgeons can rehearse and verify joint surgery.

Nuclear pore complexes (NPCs) maintain cellular homeostasis by mediating nucleocytoplasmic transport. Although cyclin-dependent kinases (CDKs) regulate NPC assembly in interphase, the location of NPC assembly on the nuclear envelope is not clear. CDKs also regulate the disappearance of pore-free islands, which are nuclear envelope subdomains; this subdomain gradually disappears with increase in homogeneity of the NPC in response to CDK activity. However, a causal relationship between pore-free islands and NPC assembly remains unclear. Here, we elucidated mechanisms underlying NPC assembly from a new perspective by focusing on pore-free islands. We proposed a novel framework for image-based analysis to automatically determine the detailed ‘landscape’ of pore-free islands from a large quantity of images, leading to the identification of NPC intermediates that appear in pore-free islands with increased frequency in response to CDK activity. Comparison of the spatial distribution between simulated and the observed NPC intermediates within pore-free islands showed that their distribution was spatially biased. These results suggested that the disappearance of pore-free islands is highly related to de novo NPC assembly and indicated the existence of specific regulatory mechanisms for the spatial arrangement of NPC assembly on nuclear envelopes.

We present a deep learning architecture Convolutional Neural Networks (CNNs) for automatic classification and recognition of reprogramming and reprogrammed human Induced Pluripotent Stem (iPS) cell regions in microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. The learning results demonstrate that our CNNs can achieve the Top-1 and Top-2 error rates of 9.2% and 0.84%, respectively, to produce probability maps for the automatic analysis. The implementation results show that this automatic method can successfully detect and localize the human iPS cell formation, thereby yield a potential tool for helping iPS cell culture.

The acinus consists of complex, branched alveolar ducts and numerous surrounding alveoli, and so in this study, we hypothesized that the particle deposition can be much influenced by the complex acinar geometry, and simulated the airflow and particle deposition (density = 1.0 g/cm(3), diameter = 1 and 3 μm) numerically in a pulmonary acinar model based on synchrotron micro-CT of the mammalian lung. We assumed that the fluid-structure interaction was neglected and that alveolar flow was induced by the expansion and contraction of the acinar model with the volume changing sinusoidally with time as the moving boundary conditions. The alveolar flow was dominated by radial flows, and a weak recirculating flow was observed at the proximal side of alveoli during the entire respiratory cycle, despite the maximum Reynolds number at the inlet being 0.029. Under zero gravity, the particle deposition rate after single breathing was less than 0.01, although the particles were transported deeply into the acinus after inspiration. Under a gravitational field, the deposition rate and map were influenced strongly by gravity orientation. In the case of a particle diameter of 1 μm, the rate increased dramatically and mostly non-deposited particles remained in the model, indicating that the rate would increase further after repeated breathing. At a particle diameter of 3 μm, the rate was 1.0 and all particles were deposited during single breathing. Our results show that the particle deposition rate in realistic pulmonary acinar model is higher than in an idealized model.

Nuclear volume and the number of nuclear pore complexes (NPCs) on the nucleus almost double during interphase in dividing cells. How these events are coordinated with the cell cycle is poorly understood, particularly in mammalian cells. We report here, based on newly developed techniques for visualizing NPC formation, that cyclin-dependent kinases (Cdks), especially Cdk1 and Cdk2, promote interphase NPC formation in human dividing cells. Cdks seem to drive an early step of NPC formation because Cdk inhibition suppressed generation of 'nascent pores', which we argue are immature NPCs under the formation process. Consistent with this, Cdk inhibition disturbed proper expression and localization of some nucleoporins, including Elys/Mel-28, which triggers postmitotic NPC assembly. Strikingly, Cdk suppression did not notably affect nuclear growth, suggesting that interphase NPC formation and nuclear growth have distinct regulation mechanisms.

The human finger is said to be the extension of the brain and can convey the information on mechanical, thermal, and tissue damaging. The quantitative prediction of blood flow rate and heat generation are of great importance for diagnosing blood circulation illness and for the noninvasive measurement of blood glucose. In this study, we developed a coupled thermofluid model to simulate blood flow in large vessels and living tissue. The finite element (FE) model to analyze the blood perfusion and heat transport in the human finger was developed based on the transport theory in porous media. With regard to the blood flow in the large arteries and veins, the systemic blood circulation in the upper limb was modeled based on the one-dimensional flow in an elastic tube. The blood pressure and velocity in each vessel were first computed and the corresponding values for the large vessels in the finger were subsequently transferred to the FE model as the boundary conditions. The realistic geometric model for the human finger was constructed based on the MRI image data. After computing the capillary pressure and blood velocity in the tissue, the temperatures in the large vessels and the tissue of the finger were computed simultaneously by numerically solving the energy equation in porous media. The computed blood flow in tissues is in agreement with the anatomical structure and the measurement. It is believed that this analysis model will have extensive applications in the prediction of peripheral blood flow, temperature variation, and mass transport.

Pre-eclampsia affects approximately 5% of all pregnant women and remains a major cause of maternal and fetal morbidity and mortality. The hypertension associated with pre-eclampsia develops during pregnancy and remits after delivery, suggesting that the placenta is the most likely origin of this disease. The pathophysiology involves insufficient trophoblast invasion, resulting in incomplete narrow placental spiral artery remodeling. Placental insufficiency, which limits the maternal-fetal exchange of gas and nutrients, leads to fetal intrauterine growth restriction. In this study, in our attempt to develop a new therapy for pre-eclampsia, we directly rescued placental and fetal hypoxia with nano-scale size artificial oxygen carriers (hemoglobin vesicles). The present study is the first to demonstrate that artificial oxygen carriers successfully treat placental hypoxia, decrease maternal plasma levels of anti-angiogenic proteins and ameliorate fetal growth restriction in the pre-eclampsia rat model.

Purpose This study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments. Methods This study enrolled 163 eyes of 105 OAG patients (age: 62.3 ± 12.6, mean deviation of Humphrey field analyzer: -8.9 ± 7.5 dB). The eyes were classified into Nicolela’s 4 optic disc types by 3 glaucoma specialists. Randomly, 114 eyes were selected for training data and 49 for test data. A neural network (NN) was trained with the training data and evaluated with the test data. We used 91 types of quantitative data, including 7 patient background characteristics, 48 quantified OCT (swept-source OCT; DRI OCT Atlantis, Topcon) values, including optic disc topography and circumpapillary retinal nerve fiber layer thickness (cpRNFLT), and 36 blood flow parameters from laser speckle flowgraphy, to build the machine learning classification model. To extract the important features among 91 parameters, minimum redundancy maximum relevance and a genetic feature selection were used. Results The validated accuracy against test data for the NN was 87.8% (Cohen’s Kappa = 0.83). The important features in the NN were horizontal disc angle, spherical equivalent, cup area, age, 6-sector superotemporal cpRNFLT, average cup depth, average nasal rim disc ratio, maximum cup depth, and superior-quadrant cpRNFLT. Conclusion The proposed machine learning system has proved to be good identifiers for different disc types with high accuracy. Additionally, the calculated confidence levels reported here should be very helpful for OAG care.

Background The purpose of this study is presenting a method to predict the presence of an open urinary tract and the position of the opening in laparoscopic partial nephrectomy from three dimensional (3D) computed tomography (CT) images by using novel image segmentation and visualization techniques. Methods From CT images of patients who underwent laparoscopic partial nephrectomy, 3D regions of the kidney, urinary tract, and tumor were segmented. For each patient, multiple virtual resection planes of the kidney with different surgical margins (1 mm to 5 mm, every 1 mm) were generated and the presence of an open urinary tract and the position of the opening were predicted from the images. Results We compared the predictions with actual operations in 5 cases by using recorded video of the operations and operative notes. In terms of the presence of an open urinary tract, agreement of the predictions and the intraoperative results was obtained in all patients. The expected positions of the openings were close to those in the actual operations. Conclusions We have developed a method to virtually visualize the resection plane of laparoscopic partial nephrectomy. Image segmentation methods used in this study were precise and effective. The comparison indicated that our method accurately predicted the presence of an open urinary tract and the position of the opening and provided useful preoperative information.

[Background]: Activation of caspases is crucial for the execution of apoptosis. Although the caspase cascade associated with activation of the initiator caspase-8 (CASP8) has been investigated in molecular and biochemical detail, the dynamics of CASP8 activation are not fully understood. [Methodology/Principal Findings]: We have established a biosensor based on fluorescence resonance energy transfer (FRET) for visualizing apoptotic signals associated with CASP8 activation at the single-cell level. Our dual FRET (dual-FRET) system, comprising a triple fusion fluorescent protein, enabled us to simultaneously monitor the activation of CASP8 and its downstream effector, caspase-3 (CASP3) in single live cells. With the dual-FRET-based biosensor, we detected distinct activation patterns of CASP8 and CASP3 in response to various apoptotic stimuli in mammalian cells, resulting in the positive feedback amplification of CASP8 activation. We reproduced these observations by in vitro reconstitution of the cascade, with a recombinant protein mixture that included procaspases. Furthermore, using a plasma membrane-bound FRET-based biosensor, we captured the spatiotemporal dynamics of CASP8 activation by the diffusion process, suggesting the focal activation of CASP8 is sufficient to propagate apoptotic signals through death receptors. [Conclusions]: Our new FRET-based system visualized the activation process of both initiator and effector caspases in a single apoptotic cell and also elucidated the necessity of an amplification loop for full activation of CASP8.

Caspase-8 (CASP8) is a cysteine protease that plays a pivotal role in the extrinsic apoptotic signaling pathway via death receptors. The kinetics, dynamics, and selectivity with which the pathway transmits apoptotic signals to downstream molecules upon CASP8 activation are not fully understood. We have developed a system for using high-sensitivity FRET-based biosensors to monitor the protease activity of CASP8 and its downstream effector, caspase-3, in living single cells. Using this system, we systematically investigated the caspase cascade by regulating the magnitude of extrinsic signals received by the cell. Furthermore, we determined the molar concentration of five caspases and Bid required for hierarchical transmission of apoptotic signals in a HeLa cell. Based on these quantitative experimental data, we validated a mathematical model suitable for estimation of the kinetics and dynamics of caspases, which predicts the minimal concentration of CASP8 required to act as an initiator. Consequently, we found that less than 1% of the total CASP8 proteins are sufficient to set the apoptotic program in motion if activated. Taken together, our findings demonstrate the precise cascade of CASP8-mediated apoptotic signals through the extrinsic pathway.

Mitotic apparatus, which comprises hundreds of microtubules, plays an essential role in cell division, ensuring the correct segregation of chromosomes into each daughter cell. To gain insight into its regulatory mechanisms, it is essential to detect and analyze the behavior of individual microtubule filaments. However, the discrimination of discrete microtubule filaments within the mitotic apparatus is beyond the capabilities of conventional light microscopic technologies. Recently, we detected three-dimensional (3-D) microtubule growth dynamics within the cellular cytoplasmic space using lattice light-sheet microscopy in conjunction with microtubule growth marker protein end-binding 1, a microtubule plus-end-tracking protein, which was fused to green fluorescent protein (EB1-GFP). This technique enables high-resolution 3-D imaging at subsecond intervals. We adapted mathematical computing and geometric representation techniques to analyze spatial variations in microtubule growth dynamics within the mitotic spindle apparatus. Our analytical approach enabled the different dynamic properties of individual microtubules to be determined, including the direction and speed of their growth, and their growth duration within a 3-D spatial map. Our analysis framework provides an important step toward a more comprehensive understanding of the mechanisms driving cellular machinery at the whole-cell level.

Computer simulation techniques for cardiac beating motions potentially have many applications and a broad audience. However, most existing methods require enormous computational costs and often show unstable behavior for extreme parameter sets, which interrupts smooth simulation study and make it difficult to apply them to interactive applications. To address this issue, we present an efficient and robust framework for simulating the cardiac beating motion. The global cardiac motion is generated by the accumulation of local myocardial fiber contractions. We compute such local-to-global deformations using a kinematic approach; we divide a heart mesh model into overlapping local regions, contract them independently according to fiber orientation, and compute a global shape that satisfies contracted shapes of all local regions as much as possible. A comparison between our method and a physics-based method showed that our method can generate motion very close to that of a physics-based simulation. Our kinematic method has high controllability; the simulated ventricle-wall-contraction speed can be easily adjusted to that of a real heart by controlling local contraction timing. We demonstrate that our method achieves a highly realistic beating motion of a whole heart in real time on a consumer-level computer. Our method provides an important step to bridge a gap between cardiac simulations and interactive applications.

This is report regarding a 28-year-old woman who conceived and delivered a healthy child following treatment for brain metastasis of choriocarcinoma in 1980 and a prolonged postoperative disease-free period. The patient had delivered a hydatidiform mole. Eight months afterwards she was admitted to our hospital with occipital pain, vomiting and stupor, and upon CT examination was found to have a brain tumor. The surgically removed tumor was pathologically diagnosed as choriocarcinoma. Postoperative methotrexate chemotherapy rapidly lowered the preoperative urinary human chorionic gonadotrophin (19 IU/ml), and allowed restoration of the preoperative LH level, consciousness, ambulation, and manifest ovulation. Occasional mild cramps were received by continuous use of anticonvulsants which did not affect her daily life. Four and one-half years postoperatively she conceived, and had a healthy boy weighing 2,294 g at the 39th week of gestation in June 1985. Both mother and baby have been doing well for 7 postpartum years.

SummaryGenome information, which is three-dimensionally organized within cells as chromatin, is searched and read by various proteins for diverse cell functions. Although how the protein factors find their targets remains unclear, the dynamic and flexible nature of chromatin is likely crucial. Using a combined approach of fluorescence correlation spectroscopy, single-nucleosome imaging, and Monte Carlo computer simulations, we demonstrate local chromatin dynamics in living mammalian cells. We show that similar to interphase chromatin, dense mitotic chromosomes also have considerable chromatin accessibility. For both interphase and mitotic chromatin, we observed local fluctuation of individual nucleosomes (∼50 nm movement/30 ms), which is caused by confined Brownian motion. Inhibition of these local dynamics by crosslinking impaired accessibility in the dense chromatin regions. Our findings show that local nucleosome dynamics drive chromatin accessibility. We propose that this local nucleosome fluctuation is the basis for scanning genome information.