Clinical and Experimental Cell Therapy in Parkinson’s Disease

Parkinson’s disease (PD), a chronic neurodegenerative disorder, is characterized as a movement disorder with resting tremor, dyskinesia, gait disturbance, etc. The main pathology is based on the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain. These motor symptoms can be treated by dopaminergic drugs, but over time, the drug’s effect has less efficacy, and side effects develop such as involuntary movements. As there is no gold standard long-term treatment for this condition, there is a strong need to develop new drugs and therapies. The clinical and experimental findings of successful intrastriatal transplantation of fetal mesencephalic dopaminergic neurons into the brains of patients with PD have been well established. The development of human stem cell technology including embryonic stem (ES) cells or induced pluripotent stem (iPS) cells opened a new field called clinical cell therapy, especially for PD. In this chapter, we cover the scientific progress of the clinical and experimental trials of cell therapy for patients with PD. It also contains the recent advances in the clinical application of stem cells including neural stem cells, mesencephalic stem cell, ESC, and iPS cells and unsolved problems in the clinical setting. The combination of gene therapy and gene-manipulated stem cell application in PD therapy will be the most discussed in this area

Multiplex Real-Time Polymerase Chain Reaction-Based Method for the Rapid Detection of gyrA and parC Mutations in Quinolone-Resistant Escherichia coli and Shigella spp.

AbstractTwo real-time polymerase chain reaction assays were developed to detect mutations in codons 83 and 87 in gyrA and in codons 80 and 91 in parC, the main sites that causes quinolone resistance in pathogenic Escherichia coli and Shigella spp. isolates. These assays can be employed as a useful method for controlling infections caused by quinolone-resistant E coli and Shigella isolates

In vivo fluorescence imaging of conjunctival goblet cells

Conjunctival goblet cells (GCs) are specialized epithelial cells that secrete mucins onto the ocular surface to maintain the wet environment. Assessment of GCs is important because various ocular surface diseases are associated with their loss. Although there are GC assessment methods available, the current methods are either invasive or difficult to use. In this report, we developed a simple and non-invasive GC assessment method based on fluorescence imaging. Moxifloxacin ophthalmic solution was used to label GCs via topical administration, and then various fluorescence microscopies could image GCs in high contrasts. Fluorescence imaging of GCs in the mouse conjunctiva was confirmed by both confocal reflection microscopy and histology with Periodic acid-Schiff (PAS) labeling. Real-time in-vivo conjunctival GC imaging was demonstrated in a rat model by using both confocal fluorescence microscopy and simple wide-field fluorescence microscopy. Different GC densities were observed in the forniceal and bulbar conjunctivas of the rat eye. Moxifloxacin based fluorescence imaging provides high-contrast images of conjunctival GCs non-invasively and could be useful for the study or diagnosis of GC related ocular surface diseases.11Ysciescopu

Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text—without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of ≥10 sentences

An underwater superoleophobic nanofibrous cellulosic membrane for oil/water separation with high separation flux and high chemical stability

Oil spills and an increasing demand for the treatment of industrial oily wastewater are driving the need for continuous large-scale oil/water separation processes. Herein, we report a nanofibrous cellulosic membrane (NFC membrane) for the continuous high-flux separation of large amounts of oil/water mixtures. The NFC membrane was fabricated using wet electrospinning, a facile yet effective method for stacking nanofibrous membranes with uniform porous structures on a substrate. Owing to its cellulosic nature, the membrane showed excellent underwater superoleophobicity along with robust chemical stability and was able to separate oil/water mixtures at efficiencies exceeding 99%. Repetitive oil/water separations could be performed using a single membrane, during which the oil content in the filtrate remained extremely low (30 kPa) that allowed not only gravity-driven but also pressure-driven separation of oil/water mixtures. The separation flux reached 120 000 L m−2 h−1 during pressure-driven separations, which is a very promising feature for actual applications such as the large-scale treatment of industrial oily wastewater.116Ysciescopu

A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents

The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled investigations into optimal context windows are urgently needed given that the amount of dialogue history and corresponding representations can play an important role in the overall performance of a conversational system. This paper studies the amount of history required by conversational agents for reliably predicting dialogue rewards. The task of dialogue reward prediction is chosen for investigating the effects of varying amounts of dialogue history and their impact on system performance. Experimental results using a dataset of 18K human-human dialogues report that lengthy dialogue histories of at least 10 sentences are preferred (25 sentences being the best in our experiments) over short ones, and that lengthy histories are useful for training dialogue reward predictors with strong positive correlations between target dialogue rewards and predicted ones

Amyloid Precursor Protein Binding Protein-1 Modulates Cell Cycle Progression in Fetal Neural Stem Cells

Amyloid precursor protein binding protein-1 (APP-BP1) binds to the carboxyl terminus of the amyloid precursor protein (APP) and serves as the bipartite activation enzyme for the ubiquitin-like protein, NEDD8. In the present study, we explored the physiological role of APP-BP1 in the cell cycle progression of fetal neural stem cells. Our results show that cell cycle progression of the cells is arrested at the G1 phase by depletion of APP-BP1, which results in a marked decrease in the proliferation of the cells. This action of APP-BP1 is antagonistically regulated by the interaction with APP. Consistent with the evidence that APP-BP1 function is critical for cell cycle progression, the amount of APP-BP1 varies depending upon cell cycle phase, with culminating expression at S-phase. Furthermore, our FRET experiment revealed that phosphorylation of APP at threonine 668, known to occur during the G2/M phase, is required for the interaction between APP and APP-BP1. We also found a moderate ubiquitous level of APP-BP1 mRNA in developing embryonic and early postnatal brains; however, APP-BP1 expression is reduced by P12, and only low levels of APP-BP1 were found in the adult brain. In the cerebral cortex of E16 rats, substantial expression of both APP-BP1 and APP mRNAs was observed in the ventricular zone. Collectively, these results indicate that APP-BP1 plays an important role in the cell cycle progression of fetal neural stem cells, through the interaction with APP, which is fostered by phopshorylation of threonine 668