Stem cells hold great promise for regenerative medicine because of their ability to self-renew and to differentiate into various cell types. Although embryonic stem cells (BSC) have greater differentiation potential than adult stem cells, the former is lagging in reaching clinical applications because of ethical concerns and governmental restrictions. Bone marrow stem cells (BMSC) are the best-studied adult stem cells (ASC) and have the potential to treat a wide variety of diseases, including erectile dysfunction (ED) and male infertility. More recently discovered adipose tissue-derived stem cells (ADSC) are virtually identical to bone marrow stem cells in differentiation and therapeutic potential, but are easier and safer to obtain, can be harvested in larger quantities, and have the associated benefit of reducing obesity. Therefore, ADSC appear to be a better choice for future clinical applications. We have previously shown that ESC could restore the erectile function of neurogenic ED in rats, and we now have evidence that ADSC could do so as well. We are also investigating whether ADSC can differentiate into Leydig, Sertoli and male germ cells. The eventual goal is to use ADSC to treat male infertility and testosterone deficiency.
Adult Stem Cells ; Animals ; Cell Transplantation ; Embryonic Stem Cells ; Erectile Dysfunction ; therapy ; Humans ; Infertility, Male ; therapy ; Male ; Research

Objective To understand the status quo of midwives' core competency in Fujian Province and analyze its influencing factors.Methods A total of 374 midwives from 13 hospitals in Fujian Province were surveyed by the midwife core competency scale.Results The average score of midwives' core competency were(3.96±0.54).The scores of vocational literacy,postnatal care skills and health care skills during pregnancy were relatively high.The scores of public health care knowledge,public health care skills and women's health knowledge were relatively low.The scores of core competence for junior,senior and expert abilities for midwives were (4.03±0.55),(3.78±0.59)and (3.54±0.75),respectively.In seven dimensions,the scores for skills were all higher than those for knowledge.Multivariate stepwise regression analysis showed that years of working,hospital grade,marital status and form of employment were influencing factors of midwives' core competency (P＜0.05).Conclusion The core competence of midwives in 13 hospitals in Fujian Province was generally at the middle level.The junior ability was satisfactory,and the senior and expert abilities should be improved.Midwives who were newly employed,working in primary institutes,non-institution personnels should be paid more attention in order to promote midwives' core competency holistically.

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
Algorithms ; Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis