Osteosarcopenia(OS) is the coexistence of sarcopenia(SP) and osteoporosis(OP). SP is a decrease in the number and strength of muscle fibers, causing impaired skeletal muscle function, and OP manifests itself as bone loss, decreased density, and degradation of bone microarchitecture. Mechanical loading is an important factor in maintaining the skeletal muscle-skeletal units in the interaction between skeletal muscle and bone. Increased muscle mass promotes bone growth and development and improves bone density and strength. As we age, skeletal muscle mass progressively decreases, leading to reduced skeletal loading which triggers wasting atrophy of the skeleton. Hormonal imbalance, chronic inflammation, oxidative stress, imbalance between protein degradation and synthesis, decreased physical activity and malnutrition are all closely associated with the development of OS. Interleukin(IL)-6 and tumor necrosis factor α(TNF-α)are important regulators of bone metabolism, and their elevated levels are negatively correlated with bone mineral density. IL-6 and TNF-α also inhibit protein synthesis in muscle by interfering with PI3K/Akt signaling pathways. NOD-like receptor protein 3(NLRP3) causes up-regulation of the NF-κB(nuclear factor-kappa B) pathway by activating damage-related molecules, and NLRP3 recruits pro-Caspase-1 to promote the release of IL-1β and IL-18, leading to increased chronic inflammation and inducing OS. The interdependence between skeletal muscle and bone and the interaction of multiple biological factors combine to contribute to the development of OS. As global aging increases, the incidence of OS will continue to rise, and in-depth investigation of its mechanisms is urgently needed to provide a theoretical basis for OS prevention and treatment.

Objective To explore the correlation between plasma asymmetric dimethylarginin(ADMA) and essential hyper‐tention(EH) by comparing the difference of plasma asymmetric dimethylarginine levels between Kazak and Han patients with EH in Xinjiang .Methods 91 Kazak and 112 Han patients with EH were selected .81 Kazak and 110 Han healthy people were selected as healthy control groups .The plasma ADMA levels in EH groups and the control groups were measured by using the reverse phase‐high performance liquid chromatography (RP‐HPLC) .Meanwhile the liver function ,renal function ,blood lipids ,blood glucose and fructosamine were measured .Results Kazak and Han patients with EH had higher levels of plasma ADMA than the control groups (P<0 .01);there was a positive correlation between the plasma ADMA and blood pressure levels of EH patients in two na‐tionalities(r=0 .715 ,P<0 .01 for Kazak ;r=0 .645 ,P<0 .01 for Han) .Conclusion Both Kazak and Han patients with EH have higher levels of ADMA than the respective healthy control group in Xinjiang .The correlation between the plasma levels of ADMA and EH existed ,which indicate that ADMA might be involved in the occurrence and development of EH .

The endpoint detection of cough signal in continuous speech has been researched in order to improve the efficiency and veracity of manual recognition or computer-based automatic recognition. First, using the short time zero crossing ratio(ZCR) for identifying the suspicious coughs and getting the threshold of short time energy based on acoustic characteristics of cough. Then, the short time energy is combined with short time ZCR in order to implement the endpoint detection of cough in continuous speech. To evaluate the effect of the method, first, the virtual number of coughs in each recording was identified by two experienced doctors using the graphical user interface (GUI). Second, the recordings were analyzed by automatic endpoint detection program under Matlab7.0. Finally, the comparison between these two results showed: The error rate of undetected cough is 2.18%, and 98.13% of noise, silence and speech were removed. The way of setting short time energy threshold is robust. The endpoint detection program can remove most speech and noise, thus maintaining a lower rate of error.
Algorithms ; Artificial Intelligence ; Cough ; physiopathology ; Endpoint Determination ; Humans ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted ; Sound