Objective To detect the expressions of nm23-H1 and heat shock protein 27 (HSP27) and their clinical significance on development and metastasis in non-small cell lung carcinoma (NSCLC).Methods 75 tumor tissues from patients with NSCLC were included as experimental group and 28 pulmonary benign lesion tissues were as control group.The expressions of nm23-H1 and HSP27 in patients with different clinical and pathological characters were detected by immunohistochemistry.Results nm23-H1 and HSP27 were mainly expressed in cytoplasm,the positive rates of nm23-H1 and HSP27 were significantly higher in the experimental group than that in control group [41.3 ％ (31/75) vs 7.1％ (2/28),x2 =10.946,P =0.001,80.0 ％ (60/75) vs 46.4 ％ (13/28),x2 =11.131,P =0.001].Compared with control group,the positive rate of HSP27 was correlated with the degree of tumor differentiation (x2 =4.191,P =0.041).nm23-H1 was related with HSP27 in lung cancer (r =0.284,P =0.013).Conclusion nm23-H1 and HSP27 are related to the occurrence and development of NSCLC.The joint detection of nm23-H1 and HSP27 should be helpful to the diagnosis and judge the biological behavior of NSCLC.

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Algorithms ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices