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The blood–brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models. [ABSTRACT FROM AUTHOR]

To retain the advantageous properties of Al2O3 ceramics, their electrical conductivity must be controlled using a small amount of the conducting phase by pressure-less sintering in an air atmosphere. In this study, the electrical conductivity of Al2O3 ceramics was successfully increased from 10−16 to over 10° S/cm by precipitating a small amount of indium tin oxide (ITO) grain boundary phase. An ITO phase of only 3-mol% (4.45 vol. %) was propagated three-dimensionally in pressure-less sintered Al2O3. Electric discharge machining (EDM) showed the machinability of electrically conductive Al2O3/3-mole% ITO grain boundary composites. Furthermore, the temperature dependency of the electrical conductivity was identified as metallic, exhibiting an electrical conductivity that decreases slightly with increases in measurement temperature, as observed in metals and ITO. The temperature dependence of the electrical conductivity of ITO was maintained in the Al2O3/ITO grain boundary composites. [ABSTRACT FROM AUTHOR]

In this study, we demonstrated the pervasiveness of HIV-associated neurocognitive disorders (HAND) among a selection of Japanese patients as well as evaluated and compared the Mini Mental State Examination (MMSE) and the International HIV Dementia Scale (IHDS) for use as a screening tool among combination anti-retroviral therapy (cART)-naïve and cART experienced patients. The MMSE and the IHDS have both been used as HAND screening tests around the world with variable success. It has been reported the increased usage of cART the utility of these screening tests may have been diminished due to the decreased severity of impairment and the altered pattern of neurocognitive impairments in cART era HAND patients. It is therefore possible the MMSE and the IHDS may still be useful among cART-naïve patients even in the cART era. However, only one study has investigated and compared the screening results of the IHDS among cART-naïve and cART experienced patients. All HIV positive patients who visited, or were admitted, to the Ryukyu University Hospital between January 2009 and March 2014 were evaluated for inclusion. Selected patients (n = 49) had data without omission for all tests. The overall prevalence of HAND in our cohort was 44%. The area under the curve (AUC), for all subjects using the MMSE and the IHDS, were 0.60 and 0.69, respectively. However, the AUC among cART-naïve patients were 0.58 and 0.76 for the MMSE and the IHDS, respectively. Whereas, cART experienced patients had an AUC of 0.60 and 0.61, respectively. Overall, the MMSE demonstrated a poor screening ability for HAND, regardless of cART usage (the cut-off value of 27 had a Youden's J-Index of 0.1, in all groups). Alternatively, the IHDS was moderately useful for HAND screening among cART-naïve patients (the cut-off value of 11 had a Youden's J-Index of 0.4), but performed poorly as a screening test among cART experienced patients (the cut-off value of 11 had a Youden's J-Index of 0.1). [ABSTRACT FROM AUTHOR]

Background: Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results: As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions: The two-way AIC performs high specificity for operon gene detection on the microarray metadataset. This method can also be applied to estimation of mutual gene interactions. [ABSTRACT FROM AUTHOR]

Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics. We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although "combinatorial explosion" occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/. [ABSTRACT FROM AUTHOR]

Judgment periodicity of biological time series data is important and done widely to find the circadian expression of genes, monthly change of hormones, etc. To keep complete reproducibility of judgment is a problem because popular judgment methods such as curve fitting, Fourier analysis, etc. need judgment criteria determined by analysts considering experimental conditions and results (level, S/N, distribution, etc.) based on their experience. Judgment results are often affected by analysts' subjects. Reproducible criterion determination is therefore strongly needed. We propose introducing the information criterion to replace analysts' criteria. A judgment algorithm by combining Bayesian information criterion (BIC) and discrete Fourier transform (DFT) has been developed and has proved its ability through application to mice microarray data and finding of circadian genes. Our method, named "Piccolo", shows higher sensitivity than the simple DFT (without BIC) method with reproducibility, and can be fully automated. [ABSTRACT FROM AUTHOR]

Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway. [ABSTRACT FROM AUTHOR]

Recent studies have shown that mutation at Ser522 causes inhibition of collapsin response mediator protein 2 (CRMP2) phosphorylation and induces axon elongation and partial recovery of the lost sensorimotor function after spinal cord injury (SCI). We aimed to reveal the intracellular mechanism in axotomized neurons in the CRMP2 knock-in (CRMP2KI) mouse model by performing transcriptome analysis in mouse sensorimotor cortex using micro-dissection punching system. Prior to that, we analyzed the structural pathophysiology in axotomized or neighboring neurons after SCI and found that somatic atrophy and dendritic spine reduction in sensorimotor cortex were suppressed in CRMP2KI mice. Further analysis of the transcriptome has aided in the identification of four hemoglobin genes Hba-a1, Hba-a2, Hbb-bs, and Hbb-bt that are significantly upregulated in wild-type mice with concomitant upregulation of genes involved in the oxidative phosphorylation and ribosomal pathways after SCI. However, we observed substantial upregulation in channel activity genes and downregulation of genes regulating vesicles, synaptic function, glial cell differentiation in CRMP2KI mice. Moreover, the transcriptome profile of CRMP2KI mice has been discussed wherein energy metabolism and neuronal pathways were found to be differentially regulated. Our results showed that CRMP2KI mice displayed improved SCI pathophysiology not only via microtubule stabilization in neurons, but also possibly via the whole metabolic system in the central nervous system, response changes in glial cells, and synapses. Taken together, we reveal new insights on SCI pathophysiology and the regenerative mechanism of central nervous system by the inhibition of CRMP2 phosphorylation at Ser522. All these experiments were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee at Waseda University, Japan (2017-A027 approved on March 21, 2017; 2018-A003 approved on March 25, 2018; 2019-A026 approved on March 25, 2019)., Competing Interests: None

The factors that lead to the improvement of gait function in patients with diseases of the central nervous system (CNS) who use a hybrid assistive limb (HAL) are not yet fully understood. The purpose of the present study was to analyze these factors to determine the prognosis of the patients' gait function. Patients whose CNS disease was within 180 days since onset were designated as the subacute-phase patients, and patients whose disease onset had occurred more than 180 days previously were designated as chronic-phase patients. Fifteen subacute-phase patients and 15 chronic-phase patients were given HAL training. The study analyzed how post-training walking independence in these patients was affected by the following factors: age, disease, lesion area, lower limb function, balance, period until the start of training, number of training sessions, additional rehabilitation, higher-order cognitive dysfunction, HAL model, and the use of a non-weight-bearing walking-aid. In subacute-phase patients, walking independence was related to lower limb function (r s = 0.35). In chronic-phase patients, there was a statistically significant correlation between post-training walking independence and balance (r s = 0.78). In addition, in patients with a severe motor dysfunction that was accompanied by inattention and global cognitive dysfunction, little improvement occurred, even with double-leg model training, because they had difficulty wearing the device. The results demonstrated that the factors that improved walking independence post HAL training differed between patients with subacute- and chronic-stage CNS diseases. The findings may serve as valuable information for future HAL training of patients with CNS diseases.

Pyrrolizines are bicyclic fused azaheterocycles with a bridgehead nitrogen contained in a core skeleton and are often found in biologically active compounds. Despite their importance, there have been few reports on concise and flexible syntheses of pyrrolizines. A novel one-pot, convergent method is described for pyrrolizines by simple mixing of iminoesters, acetylenes, and dipolarophiles in the presence of a cationic gold catalyst and an acid additive. This domino process affords multisubstituted pyrrolizines without handling unstable intermediates.

In terms of their internal (genetic) and external (phenotypic) states, living cells are always changing at varying rates. Periods of stable or low rate of change are often called States, Stages, or Phases, whereas high-rate periods are called Transitions or Transients. While states and transitions are observed phenotypically, such as cell differentiation, cancer progression, for example, are related with gene expression levels. On the other hand, stages of gene expression are definable based on changes of expression levels. Analyzing relations between state changes of phenotypes and stage transitions of gene expression levels is a general approach to elucidate mechanisms of life phenomena. Herein, we propose an algorithm to detect stage transitions in a time series of expression levels of a gene by defining statistically optimal division points. The algorithm shows detecting ability for simulated datasets. An annotation based analysis on detecting results for a dataset of initial development of Caenorhabditis elegans agrees with that are presented in the literature., (Copyright © 2014 The Author. Published by Elsevier Inc. All rights reserved.)

HIV-associated neurocognitive disorders (HAND) are one of major comorbidities in patients with HIV-1 infection. There are currently no standardized tests for screening HAND in such patients. The sensitivity of the cognitive function tests routinely used in clinical practice, such as the Mini-Mental State Examination and the Revised Hasegawa's Dementia Scale, is inadequate to rule out HAND, even in patients with clear abnormal behavior. We report a 41-year-old man with HIV-associated dementia, the most severe form of HAND, in whom the simplified methods did not show abnormal results, and a comprehensive battery of neuropsychological tests which covering several cognitive domains was needed to detect cognitive impairment., (Copyright © 2014 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Background: Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design., Results: Here, we propose a novel method for identifying MoRFs, named as MFSPSSMpred (Masked, Filtered and Smoothed Position-Specific Scoring Matrix-based Predictor). Firstly, a masking method is used to calculate the average local conservation scores of residues within a masking-window length in the position-specific scoring matrix (PSSM). Then, the scores below the average are filtered out. Finally, a smoothing method is used to incorporate the features of flanking regions for each residue to prepare the feature sets for prediction. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the PSSM of sequence only. Experimental results show that, comparing with other methods tested on the same datasets, our method achieves the best performance: achieving 0.004~0.079 higher AUC than other methods when tested on TEST419, and achieving 0.045~0.212 higher AUC than other methods when tested on TEST2012. In addition, when tested on an independent membrane proteins-related dataset, MFSPSSMpred significantly outperformed the existing predictor MoRFpred., Conclusions: This study suggests that: 1) amino acid composition and physicochemical properties in the flanking regions of MoRFs are very different from those in the general non-MoRF regions; 2) MoRFs contain both highly conserved residues and highly variable residues and, on the whole, are highly locally conserved; and 3) combining contextual information with local conservation information of residues facilitates the prediction of MoRFs.

Background: For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value parameters must be optimized to identify the models. They also have many 'hidden variables', which cannot be observed directly through experimentation. Calculation of the values of the hidden variables is generally very difficult, if not impossible in some special cases. Identification of the ODE models is also quite difficult., Results: We show that the simplified logistic Lotka-Volterra model, a well-established ODE model for biological and biochemical phenomena, can represent RNAi dynamics as a predator-prey system. Although a hidden variable exists in the model, its values can be determined and made visible as dynamic profiles of RNA-decomposing effects of siRNAs. Correlation analysis shows that the model parameters correlate highly with the total effect of the siRNA., Conclusions: The results suggest that analyses using our model are useful to estimate dynamic profiles of siRNA effects on apoptosis and to score siRNA by its effects on apoptosis, namely 'phenotypic scoring'.