Objective To evaluate the effect and safety of infantile femoral vein blood sampling with vacuum versus disposable needle. Methods Such databases as VIP, CNKI, CBM, Google Academic and Wanfang data were searched to collect the randomized controlled trials (RCTs) about infantile femoral vein blood sampling with vacuum versus disposable needle published from January 2000 to July 2010. The studies were screened according to the inclusive and exclusive criteria, the data were extracted, the methodology quality was assessed, and meta-analysis was conducted by using RevMan 5.0 software. Results A total of 15 RCTs were included. Of 3 490 patients in all, 1 770 were in the treatment group and 1 726 were in the control group. The baseline conditions were reported in 14 studies, and the random methods were mentioned in 11 RCTs. All studies didn’t report the allocation concealment and blind method. Only 2 RCTs reported separately that, the degree of neonatal pain was lower in the treatment group (Plt;0.01), and the satisfaction of parents was higher in the treatment group (Plt;0.01). Four RCTs compared the sampling time between the two groups without meta-analysis mentioned due to the disunity of standard, only the descriptive outcomes showed a shorter time in the treatment group. The meta-analysis showed that, compared with the control group, the reject rate of sample quality was lower (RR=0.20, 95%CI 0.15 to 0.26), the success rate of one time sampling was higher (RR=1.20, 95%CI 1.16 to 1.24), the injury of local tissue was slighter (RR=0.62, 95%CI 0.45 to 0.86), and the iatrogenic contamination was lower (RR=0.62, 95%CI 0.45 to 0.86) in the treatment group. Conclusion This review shows that the vacuum sampling is superior to the disposable needle sampling for domestic infantile femoral vein blood collection. Due to the low quality of the included studies with high possibility of bias, this conclusion needs to be further verified by performing more high-quality studies.
Abdominal imaging is one of the important clinical applications of magnetic resonance imagining, but image degradation due to respiratory motion remains a major problem. Retrospective respiratory navigator gating technique is an effective approach to alleviate such degradation but is subject to long scan time and low signal-to-noise ratio (SNR) efficiency. In this study, a modified retrospective navigator gating technique with variable over-sampling ratio acquisition and weighted average reconstruction algorithm is presented. Experiments in phantom and the imaging results of seven volunteers demonstrated that the proposed method provided an enhanced SNR and reduced ghost-to-image ratio compared to the conventional method. The proposed method can also be used to reduce imaging time while maintaining comparable image quality.
Traditional speech detection methods regard the noise as a jamming signal to filter, but under the strong noise background, these methods lost part of the original speech signal while eliminating noise. Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise. According to stochastic resonance theory, a new method based on adaptive stochastic resonance to extract weak speech signals is proposed. This method, combined with twice sampling, realizes the detection of weak speech signals from strong noise. The parameters of the system a, b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal, and then the weak speech signal is optimally detected. Experimental simulation analysis showed that under the background of strong noise, the output signal-to-noise ratio increased from the initial value-7 dB to about 0.86 dB, with the gain of signal-to-noise ratio is 7.86 dB. This method obviously raises the signal-to-noise ratio of the output speech signals, which gives a new idea to detect the weak speech signals in strong noise environment.
Reconstruction of gene regulatory networks (GRNs) from large-scale expression data can mine the potential causality relationship among the genes and help understand the complex regulatory mechanisms. It is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. For the past decades, numerous theoretical and computational approaches have been introduced for inferring the GRNs. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but are seldom taken into account simultaneously in one computational method. Some information theory algorithms do not recover the true positive edges that may have been deleted in an earlier computing process. These interaction relationships may reflect the actual relationship of genes. To overcome these disadvantages and to further enhance the precision and robustness of inferred GRNs, we presented an ensemble method, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. In this algorithm, the jackknife resampling procedure was first employed to form a series of sub-datasets of gene expression data, then the conditional mutual information was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with those of the state-of-the-art algorithm on the benchmark synthetic GRNs datasets from the DREAM3 challenge and a real SOS DNA repair network, the results show that our method outperforms significantly LP, LASSO and ARANCE methods, and has a high and robust performance.
Real-time updates of metrical data can not generally be realized in the commonly used methods for calculating the pulse wave of blood oxygen saturation. Based on the hardware platform of pulse wave signal from NJL5501R, and high linear correlation of the red laser and infrared light collected in pulse wave signal measurement, an approach to determine the value of the blood oxygen saturation is proposed in the present paper by establishing the linear regression model of the red laser and infrared light. The effect of the sampling number of pulse wave signal in calculation on the characteristic parameters of pulse wave is also analyzed. The experimental results showed that the approach could guarantee the measuring accuracy and realize the fast updates of blood oxygen saturation data. This paper provides an effective method for real-time and accurate monitoring of pulse blood oxygen saturation in human body.
Objectives To systematically review the efficacy and safety of non-systemic lymph dissection (NSMLD) vs. systemic lymph dissection (SMLD) for early stage non-small cell lung cancer (NSCLC). Methods PubMed, EMbase, Web of Science and The Cochrane Library databases were searched online to collect randomized controlled trials (RCTs) and non-randomized controlled studies (NRCTs) of NSMLD vs. SMLD for NSCLC patients from inception to October, 2016. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 software. Results A total of 16 studies (4 RCTs and 12 NRCTs) involving 4 718 patients were included. The results of meta-analysis showed that: Compared with the SMLD group, the NSMLD group had higher mortality (HR=1.23, 95%CI 1.11 to 1.37, P<0.000 1). There were no significant differences in disease-free survival, local recurrence rate, distant metastasis rate, and safety between two groups. In addition, the NSMLD group had shorter operation time, and lower drainage and blood loss. Subgroup analysis was performed according to operation methods. The results showed that: NSMLD group by lymph node sampling (LN-S) had higher mortality than SMLD group (HR=1.43, 95%CI 1.17 to 1.75,P=0.004), NSMLD group by lobe-specific lymph node dissection (L-SLD) did not have higher mortality. Conclusions Current evidence shows that: compared with SMLD, NSMLD by L-SLD do not have higher mortality in early stage NSCLC patients, while NSMLD by LN-S have higher mortality. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusion.
Objective To compare lymph node sampling (LN-S) and lobe-specific lymph node dissection (L-SLD) in the clinical efficacy and safety for early-stage non-small cell lung cancer (NSCLC). Methods PubMed, Medline, EMbase, Web of Science and The Cochrane Library databases were searched up to March 2017 for English language studies. We collected randomized controlled trials (RCTs) and cohort studies (CS) which used the systematic mediastinal lymph node dissection (SMLD) and LN-S or L-SLD for the treatment of NSCLC. Direct meta-analysis was performed using RevMan 5.3 software and indirect meta-analysis with ITC software after two researchers screened the literature, extracted the data and evaluated the risk of bias independently. Results A total of 18 articles were included (4 RCTs and 14 CS, and 10 714 patients). Meta-analysis results showed that in the CS, compared with the the SMLD group, overall survival increased in the L-SLD group (HR=0.99, 95%CI 0.78 to 1.25, P=0.92), and overall survival decreased in the LN-S group with significant difference in CS (HR=1.43, 95%CI 1.17 to 1.75, P=0.000 4), but was not statistically significant in RCT (P=0.35). In terms of disease-free survival, there was no significant difference between the SMLD group and the LN-S group (HR=1.25, 95%CI 0.90, 1.62, P=0.10) as well as the L-SLD group (HR=1.15, 95%CI 0.92 to 1.43, P=0.23) in the CS. There was no significant difference in the local recurrence rate or distant metastasis rate between the non-systematic lymph node dissection (NSMLD) and SMLD in CS and RCTs (CS: P=0.43, P=0.39; RCT: P=0.43, P=0.10). There was no significant difference in the postoperative complications between NSMLD and SMLD in the CS (OR=0.79, 95%CI0.58 to 1.09, P=0.15) and RCTs (OR=0.36, 95%CI 0.09 to 1.45, P=0.15). Indirect meta-analysis showed that risk of death decreased by 31% and risk of recurrence by 35% in the L-SLD group compared with the LN-S group (HR=0.69, 95% CI 0.51 to 0.95, P=0.46; HR=0.65, 95% CI 0.65 to 1.30, P=0.72), but the difference was not statistically significant. Conclusion For early-stage NSCLC, L-SLD is not statistically different from SMLD in terms of survival; however, the overall survival of LN-S is lower than that of systematic lymphadenectomy. Indirect meta-analysis shows that L-SLD reduces the risk of death and recurrence risk compared with LN-S. There is no evidence to support both direct comparison of the prognosis of LN-S and L-SLD, therefore further prospective studies are still needed to verify.
Lymph node metastasis in non-small cell lung cancer is an independent risk factor for poor prognosis. Resection of lymph nodes can improve the prognosis of patients. Although surgical techniques are progressing, there is still much controversy about the way of lymph node resection for non-small cell lung cancer. The research progress of hot topics such as the choice of lymph node resection methods for non-small cell lung cancer is discussed and summarized.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.