Head acupuncture combined with exercise therapy for nonspecific low back pain：a randomized controlled trial / 中国针灸

OBJECTIVE@#To compare the clinical effect between head acupuncture combined with exercise therapy and conventional acupuncture for nonspecific low back pain.@*METHODS@#A total of 64 patients with nonspecific low back pain were randomized into an observation group (32 cases, 2 cases dropped off) and a control group (32 cases, 2 cases dropped off). In the control group, conventional acupuncture was applied at Jiaji (EX-B 2) of L1 to L3, ashi point, Shenshu (BL 23), Dachangshu (BL 25), Yaoyangguan (GV 3) and Weizhong (BL 40). The observation group was treated with head acupuncture combined with exercise therapy, head acupuncture was applied at foot-motor-sensory area on the healthy side and Cuanzhu (BL 2), Tongziliao (GB 1) on the affected side, and McKenzie therapy was performed during retention. The needles were retained for 40 min, once a day, continuous treatment for 6 days with the interval of 1 day, 14 days were required in the two groups. Before and after treatment, the pain visual analogue scale (VAS) score, Oswestry disability index (ODI) score and infrared thermography temperature of pain area in the low back were compared in the two groups.@*RESULTS@#Compared before treatment, the VAS and ODI scores after treatment were decreased in the two groups (P<0.01), and those in the observation group were lower than the control group (P<0.01). Compared before treatment, the infrared thermography temperature of pain area in the low back after treatment was increased in the two groups (P<0.01), and that in the observation group was higher than the control group (P<0.01).@*CONCLUSION@#Head acupuncture combined with exercise therapy could relieve pain, improve dysfunction and increase the local temperature of pain area in patients with nonspecific low back pain, and its curative effect is better than conventional acupuncture.

Current progress of computational modeling for guiding clinical atrial fibrillation ablation.

Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.

Fully Automatic Scar Segmentation for Late Gadolinium Enhancement MRI Images in Left Ventricle with Myocardial Infarction.

Numerous methods have been published to segment the infarct tissue in the left ventricle, most of them either need manual work, post-processing, or suffer from poor reproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue in left ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 human hearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundaries of the ventricles in every 2D slice of the cardiac magnetic resonance with late gadolinium enhancement images were manually segmented. The subsequent pipeline of infarct tissue segmentation is fully automatic. The segmentation results with the automatic algorithm proposed in this paper were compared to the consensus ground truth. The median of Dice overlap between our automatic method and the consensus ground truth is 0.79. We also compared the automatic method with the consensus ground truth using different image sources from different centers with different scan parameters and different scan machines. The results showed that the Dice overlap with the public dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method is robust with respect to different MRI image sources, which were scanned by different centers with different image collection parameters. The segmentation accuracy we obtained is comparable to or better than that of the conventional semi-automatic methods. Our segmentation method may be useful for processing large amount of dataset in clinic.

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling.

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

Inhibition of autophagy induced by PTEN loss promotes intrinsic breast cancer resistance to trastuzumab therapy.

This study aims to explore the effects of the phosphatase and tension homolog (PTEN) expression level on autophagic status and on the resistance of breast cancer to trastuzumab treatment. PTEN and LC3I/II were knocked down with shRNA expression vectors, which were transfected into estrogen receptor (ER)-positive breast cancer cell lines. After trastuzumab treatment, the changes in the autophagy signal transduction pathways and autophagic proteins (LC3I/II, p62, LAMP, and cathepsin B) in these stably transfected cells were detected using western blot. The cells were also orthotopically implanted into nude mice to explore the influence of PTEN knockdown on tumor size, cell viability, and autophagic proteins after trastuzumab treatment. Similar determinations were performed using the LC3I/II overexpressed shPTEN breast cancer cells (LC3I/II-shPTEN). Downregulation of PTEN and autophagic proteins LC3-I and LC3-II was observed in resistant human breast cancer samples. Knockdown of PTEN and PTEN+ LC3I/II with shRNA in breast cancer cells resulted in increased resistance to trastuzumab. Consistently, trastuzumab treatment could not effectively reduce tumor size. Significant decreases in the levels of autophagic proteins LC3I/II, LAMP, p62, cathepsin B, and PI3K-Akt-mTOR and the signaling pathway protein Akt were found in PTEN knockdown cells, compared to the PTEN normal group, after trastuzumab administration, both in vitro and in vivo. However, these findings were reversed with the LC3I/II-shPTEN treatment. Therefore, the loss of PTEN may promote the development of primary resistance to trastuzumab in breast cancer via autophagy defects.