COMMENTS ON THE HOLISTIC ACTION OF ACUPUNCTURE IN VIEW OF ITS INFLUENCE ON NERVE-ENDOCRINE-IMMUNE NETWORK

In the present paper, the author makes some comments on the holistic action of acupuncture from the regulative effect on the nerve-endocrine-immune network, and the close relationship between the nerve-endocrine-immune network and the meridian-collateral system of TCM. The wholism concept of TCM refers to the organism being an integrated entirety, and the regulatory effect of acupuncture on functional activities of the organism relies on the integral connection of the acupoint-meridian-collateral-zangfu-organ system. When stimulated with acupuncture, the human body will brings its potential force into full play in preventing and treating diseases.

Stem cells therapy for regenerative medicine:Principles of present and future practice

The characterization and isolation of various stem cell populations, from embryonic to tissue-derived stem cells and induced pluripotent stem cells (iPSCs), have led to a rapid growth in the field of stem cell research and its potentially clinical application in the field of regenerative medicine and tissue repair. Stem cell therapy has recently progressed from the preclinical to the early clinical trial arena for a variety of diseases states, although further knowledge on action mechanisms, long-term safety issues, and standardization and characterization of the therapeutic cell products remains to be thoroughly elucidated. In this paper we summarize the current state of the art of basic and clinical research that were highlighted at the 2012 meeting of the Spanish Cell Therapy Network. This includes the current research involving in genomic and transcriptomic characterization of selected stem cell populations, studies of the role of resident and transplanted stem cells during tissue regeneration and their mechanism of action, improved new strategies of tissue engineering, transplantation of mesenchymal stem cells (MSCs) in different animal models of disease, disease correction by iPSCs, and preliminary results of cell therapy in human clinical trials.

Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor

Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.