Hypoxic Culture Conditions as a Solution for Mesenchymal Stem Cell Based Regenerative Therapy

Cell-based regenerative therapies, based on in vitro propagation of stem cells, offer tremendous hope to many individuals suffering from degenerative diseases that were previously deemed untreatable. Due to the self-renewal capacity, multilineage potential, and immunosuppressive property, mesenchymal stem cells (MSCs) are considered as an attractive source of stem cells for regenerative therapies. However, poor growth kinetics, early senescence, and genetic instability during in vitro expansion and poor engraftment after transplantation are considered to be among the major disadvantages of MSC-based regenerative therapies. A number of complex inter-and intracellular interactive signaling systems control growth, multiplication, and differentiation of MSCs in their niche. Common laboratory conditions for stem cell culture involve ambient O-2 concentration (20%) in contrast to their niche where they usually reside in 2-9% O-2. Notably, O-2 plays an important role in maintaining stem cell fate in terms of proliferation and differentiation, by regulating hypoxia-inducible factor-1 (HIF-1) mediated expression of different genes. This paper aims to describe and compare the role of normoxia (20% O-2) and hypoxia (2-9% O-2) on the biology of MSCs. Finally it is concluded that a hypoxic environment can greatly improve growth kinetics, genetic stability, and expression of chemokine receptors during in vitro expansion and eventually can increase efficiency of MSC-based regenerative therapies.Article Link: http://www.hindawi.com/journals/tswj/2013/632972

Dynamic importance sampling for uniformly recurrent markov chains

Importance sampling is a variance reduction technique for efficient estimation of rare-event probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, however, has suggested that such schemes do not work well in many situations. In this paper we consider dynamic importance sampling in the setting of uniformly recurrent Markov chains. By ``dynamic'' we mean that in the course of a single simulation, the change of measure can depend on the outcome of the simulation up till that time. Based on a control-theoretic approach to large deviations, the existence of asymptotically optimal dynamic schemes is demonstrated in great generality. The implementation of the dynamic schemes is carried out with the help of a limiting Bellman equation. Numerical examples are presented to contrast the dynamic and standard schemes.Comment: Published at http://dx.doi.org/10.1214/105051604000001016 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Adaptive Randomized Distributed Space-Time Coding in Cooperative MIMO Relay Systems

An adaptive randomized distributed space-time coding (DSTC) scheme and algorithms are proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receivers and an amplify-and-forward (AF) cooperation strategy are considered. In the proposed DSTC scheme, a randomized matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions are devised to compute the parameters of the adaptive randomized matrix and the linear receive filter. A stochastic gradient algorithm is also developed to compute the parameters of the adaptive randomized matrix with reduced computational complexity. We also derive the upper bound of the error probability of a cooperative MIMO system employing the randomized space-time coding scheme first. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.Comment: 4 figure

Challenges and strategies in the repair of ruptured annulus fibrosus

Lumbar discectomy is the surgical procedure most frequently performed for patients suffering from low back pain and sciatica. Disc herniation as a consequence of degenerative or traumatic processes is commonly encountered as the underlying cause for the painful condition. While discectomy provides favourable outcome in a majority of cases, there are conditions where unmet requirements exist in terms of treatment, such as large disc protrusions with minimal disc degeneration; in these cases, the high rate of recurrent disc herniation after discectomy is a prevalent problem. An effective biological annular repair could improve the surgical outcome in patients with contained disc herniations but otherwise minor degenerative changes. An attractive approach is a tissue-engineered implant that will enable/stimulate the repair of the ruptured annulus. The strategy is to develop three-dimensional scaffolds and activate them by seeding cells or by incorporating molecular signals that enable new matrix synthesis at the defect site, while the biomaterial provides immediate closure of the defect and maintains the mechanical properties of the disc. This review is structured into (1) introduction, (2) clinical problems, current treatment options and needs, (3) biomechanical demands, (4) cellular and extracellular components, (5) biomaterials for delivery, scaffolding and support, (6) pre-clinical models for evaluation of newly developed cell- and material-based therapies, and (7) conclusions. This article highlights that an interdisciplinary approach is necessary for successful development of new clinical methods for annulus fibrosus repair. This will benefit from a close collaboration between research groups with expertise in all areas addressed in this review

Minimally traumatic implantation and bone regeneration: a systematic review

Introduction: Dental implant procedures have reached about one million dental implants per year worldwide. In this context, it is necessary to establish the state of the art of minimally traumatic procedures for dental implants, especially after bone grafting procedures and/or use of biomaterials for bone elevation. Objective: Conducted a systematic review of the main considerations for optimizing techniques for dental implants by proposing minimally invasive procedures, especially after bone recovery or elevation processes through the use of biomaterials such as FRP and Bio-Oss®. Methods: The survey was conducted from June 2021 to July 2021 and developed based on Scopus, PubMed, Science Direct, Scielo and Google Scholar, following the rules of Systematic Review-PRISMA. Study quality was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: Studies have shown that guided preoperative planning and dental implant-guided jaw reconstruction provide high success rates for implant and dental rehabilitation. Furthermore, the concept of using personalized implants with the help of 3D virtual treatment planning can greatly improve mandibular restoration and help to achieve good facial profile, esthetics and dental rehabilitation, avoiding complications with grafts in general

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

高次視覚情報処理機能を有する三次元積層人工網膜チップの研究

要約のみTohoku University田中徹課