Peripheral nerve injury (PNI) is one of the most common concerns in trauma patients. Despite significant advances in repair surgeries, the outcome can still be unsatisfactory, resulting in morbidities such as loss of sensory or motor function and reduced quality of life. This highlights the need for more supportive strategies for nerve regrowth and adequate recovery. Multifunctional cytokine transforming growth factor-β (TGF-β) is essential for the development of the nervous system and is known for its neuroprotective functions. Accumulating evidence indicates its involvement in multiple cellular and molecular responses that are critical to peripheral nerve repair. Following PNI, TGF-β is released at the site of injury where it can initiate a series of phenotypic changes in Schwann cells (SCs), modulate immune cells, activate neuronal intrinsic growth capacity, and regulate blood nerve barrier (BNB) permeability, thus enhancing the regeneration of the nerves. Notably, TGF-β has already been applied experimentally in the treatment of PNI. These treatments with encouraging outcomes further demonstrate its regeneration-promoting capacity. Herein, we review the possible roles of TGF-β in peripheral nerve regeneration and discuss the underlying mechanisms, thus providing new cues for better treatment of PNI.

Heart rate variability and motion artifact are two major obstacles to the Photoplethysmographic (PPG) signal analysis, which is a very promising tool to derive useful information about the hemodynamics as well as autonomic nerve system. This paper suggests employing a truncation/extrapolation method on the PPG signal or its first order derivative to overcome the heart rate variability problem while extract a refined mean PPG pulse waveform. Meanwhile, the cross-correlation detection method is employed to remove the motion artifacts. Test results indicate that the proposed approach can effectively enhance the signal to noise ratio of PPG waveform and therefore verify the effectiveness of the proposed preprocessing scheme.

The Radiology report is the fundamental means by which radiologists communicate with clinicians and patients. The traditional method of generating reports is time consuming and expensive. Recent advances in computer hardware and software technology have improved Speech Recognition systems used for radiology reporting. In this paper, we design a continuous Mandarin Speech Recognition dictation system for radiology reporting in Radiology Information System based on the CMU Sphinx recognition engine. We compare our system with IBM ViaVoice pro v9.1. The experiment results show that both systems can achieve a high accuracy rate when using recognition for Brain Skull CT report generation in the laboratory environment. Furthermore, our system performs slightly better than ViaVoice when training and testing data are both randomly selected from the same text corpora. We conclude that it is technically feasible to employ Speech Recognition for report generating in Radiology Information System. In summary, the Speech Recognition technology has matured to the point where it is now viable as a mean of providing more timely delivery and better quality of medical services to patients, and this technology will be widely applied in the healthcare industry in the near future.

A new tool of wavelet entropy (WE) is applied to characterize the dynamical properties of EEGs for purpose of evaluating the depth of anesthesia (DOA). 31 cases are analysed with WE, and the result shows the WE measure for EEGs can distinguish awake and asleep state in anesthesia with a high accuracy of 95%. Compared with C(n) complexity, the WE demonstrates the same good performance, and needs shorter data length for analysis. These remind that WE is a potential quantifier of DOA.