The effect of exercise on the risk of metabolic syndrome associated with sleep insufficiency: a cross-sectional study.

Introduction: Sleep disturbance and insufficient sleep have been linked to metabolic syndrome, increasing cardiovascular disease and mortality risk. However, few studies investigate the joint effect of sleep and exercise on metabolic syndrome. We hypothesized that regular exercise can mitigate the exacerbation of metabolic syndrome by sleep insufficiency. Objective: The aim of this study was to investigate whether exercise can attenuate or eliminate the relationship between sleep insufficiency and metabolic syndrome. Method: A total of 6,289 adults (mean age = 33.96 years; women: 74.81%) were included in the study, a cross-sectional study conducted based on the results of employee health screening questionnaires and databases from a large healthcare system in central Taiwan. Participants reported sleep insufficiency or not. Self-reported exercise habits were classified into 3 levels: no exercise, exercise <150 min/week, and exercise ≧150 min/week. Multiple logistic regression and sensitivity analyses were conducted to understand the joint associations of sleep patterns and exercise with metabolic syndrome with exposure variables combining sleep duration/disturbances and PA. Results: Compared with the reference group (sufficient sleep), individuals with sleep insufficiency had a higher risk for metabolic syndrome [adjusted odds ratio (AOR) = 1.40, 95% confidence interval (95% CI): 1.01-1.94, p < 0.05] in females aged 40-64 years, but not in other populations. Sleep insufficiency was not associated with the risk of metabolic syndrome among individuals achieving an exercise level of <150 min/week, and in particular among those achieving ≧150 min/week in all populations in our study. Conclusion: Sleep insufficiency was related to a higher risk of metabolic syndrome in female healthcare staff aged 40-64 years. Being physically active with exercise habits in these individuals, the risk of metabolic syndrome was no longer significant.

Comparison of In-Hospital Major Adverse Cardiovascular Events in Patients with Acute Myocardial Infarction Treated with Ticagrelor or Clopidogrel in the Emergency Department: A Propensity Score Matched Retrospective Cohort Study.

BACKGROUND: Dual antiplatelet therapy (DAPT) is a standard treatment option for acute myocardial infarction (AMI). The difference between the efficacy of ticagrelor and clopidogrel in the emergency department (ED) before percutaneous coronary intervention (PCI) remains unknown. The present study compared the in-hospital major adverse cardiovascular event (MACE) rates between patients with AMI treated with clopidogrel and those treated with ticagrelor in the ED before PCI. METHODS: We retrospectively collected the data of patients diagnosed as having AMI in the ED. Patients were only included if they had successfully received complete DAPT with aspirin and ticagrelor/clopidogrel in the ED and had undergone PCI. The patients were divided into two groups according to their DAPT regimen. The primary outcome was the rate of in-hospital MACEs. The secondary outcomes included an unexpected return to the ED within 72 h, readmission within 14 d, and revascularization. RESULTS: A total of 1836 patients were enrolled. Patients in the ticagrelor group had a lower in-hospital MACE rate (3.01% versus 7.51%, p < 0.001) and in-hospital mortality rate (2.15% versus 5.70%, p < 0.001) than those in the clopidogrel group. Multivariate logistic regression analysis revealed ticagrelor was independently associated with a lower risk of in-hospital MACEs (odds ratio [OR]: 0.53, 95% CI: 0.32-0.88, p = 0.013). After propensity score matching, the risk of in-hospital MACEs remained significantly lower in the ticagrelor group (OR 0.42, 95% CI: 0.21-0.85, p = 0.016). CONCLUSION: DAPT with ticagrelor and aspirin in the ED before PCI is associated with a lower in-hospital MACE rate among patients with AMI.

Association of Low Back Pain with Shift Work: A Meta-Analysis.

Shift work (SW) is the main working schedule worldwide, and it may cause sleep disorders, breast cancer, and cardiovascular disease. Low back pain (LBP) is a common problem in the workplace; however, the association between LBP and SW remains unclear. Therefore, we conducted a meta-analysis to determine the association between SW and LBP. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The PubMed, Embase, and Web of Science databases using a set of associated keywords were queried. The inclusion criteria were as follows: (1) adult employees hired by a company or organization; (2) SW exposure; and (3) the outcome of LBP according to examination or assessment. A total of 40 studies were included that met the inclusion criteria for the meta-analysis. SW was significantly associated with LBP (odds ratio [OR]: 1.31, 95% confidence interval [CI]: 1.18−1.47, p < 0.00001). Furthermore, it was observed that LBP was significantly associated with night shift (NS) (OR: 1.49, 95% CI: 1.24−1.82, p < 0.0001) but not with rotating shift (RS) (OR: 0.96, 95% CI: 0.76−1.22, p = 0.49). Moreover, LBP was significantly associated with SW in health care workers (HCWs) (OR: 1.40, 95% CI: 1.20−1.63, p < 0.0001) but not in non-HCWs (OR: 1.19, 95% CI: 0.94−1.50, p = 0.14). SW was significantly associated with LBP. Furthermore, the subgroup analysis showed that NS, but not RS, was associated with LBP. Compared with SW in non-HCWs, SW in HCWs was significantly associated with LBP.

Video Pivoting Unsupervised Multi-Modal Machine Translation.

The main challenge in the field of unsupervised machine translation (UMT) is to associate source-target sentences in the latent space. As people who speak different languages share biologically similar visual systems, various unsupervised multi-modal machine translation (UMMT) models have been proposed to improve the performances of UMT by employing visual contents in natural images to facilitate alignment. Commonly, relation information is the important semantic in a sentence. Compared with images, videos can better present the interactions between objects and the ways in which an object transforms over time. However, current state-of-the-art methods only explore scene-level or object-level information from images without explicitly modeling objects relation; thus, they are sensitive to spurious correlations, which poses a new challenge for UMMT models. In this paper, we employ a spatial-temporal graph obtained from videos to exploit object interactions in space and time for disambiguation purposes and to promote latent space alignment in UMMT. Our model employs multi-modal back-translation and features pseudo-visual pivoting, in which we learn a shared multilingual visual-semantic embedding space and incorporate visually pivoted captioning as additional weak supervision. Experimental results on the VATEX Translation 2020 and HowToWorld datasets validate the translation capabilities of our model on both sentence-level and word-level and generalizes well when videos are not available during the testing phase.

Evaluation of and the prognostic factors for cats with big kidney-little kidney syndrome.

BACKGROUND: The term big kidney-little kidney syndrome in cats has been used for many years, but the definitions are not consistent and relevant research is limited. OBJECTIVE: To determine the factors that differ between normal and BKLK cats, as well as to develop models for predicting the 30-day survival of cats with ureteral obstruction (UO). ANIMALS: Sixteen healthy cats and 64 cats with BKLK. METHODS: Retrospective study. To define BKLK by reference to data from clinically healthy cats. The demographic and clinicopathological data among groups were statistically analyzed. RESULTS: Big kidney-little kidney syndrome cats had higher blood urea nitrogen (BUN) (median [interquartile range] 69 [28-162] vs 21 [19-24] mg/dL, P < .001), creatinine (5.6 [1.9-13.3] vs 1.3 [1.05-1.40] mg/dL, P < .001), and white blood cells (10 800 [7700-17 500] vs 6500 [4875-9350] /µL, P < .001) and lower hematocrit (32.8 [27.1-38.4] vs 39.1 [38.1-40.4]%, P < .001), urine specific gravity (1.011 [1.009-1.016] vs 1.049 [1.044-1.057], P < .001) and pH (5.88 [5.49-6.44] vs 6.68 [6.00-7.18], P = .001) compared to the control cats. A lower body temperature (BT; 38.1 [37.9-38.2] vs 38.7 [38.3-39.2]°C, P = .009), higher BUN (189 [150-252] vs 91 [36-170] mg/dL, P = .04), and creatinine (15.4 [13.3-17.4] vs 9.0 [3.1-14.2] mg/dL, P = .03) were found among the UO cats that were not 30-day survivors. A combination of BUN, phosphorus, and BT can predict 30-day survival among UO cats with an area under receiver operating characteristic curve of 0.863. (P = .01). CONCLUSION: An increase in the length difference between kidneys can indicate UO, but cannot predict outcome for BKLK cats.

Self-Supervised Deep Correlation Tracking.

The training of a feature extraction network typically requires abundant manually annotated training samples, making this a time-consuming and costly process. Accordingly, we propose an effective self-supervised learning-based tracker in a deep correlation framework (named: self-SDCT). Motivated by the forward-backward tracking consistency of a robust tracker, we propose a multi-cycle consistency loss as self-supervised information for learning feature extraction network from adjacent video frames. At the training stage, we generate pseudo-labels of consecutive video frames by forward-backward prediction under a Siamese correlation tracking framework and utilize the proposed multi-cycle consistency loss to learn a feature extraction network. Furthermore, we propose a similarity dropout strategy to enable some low-quality training sample pairs to be dropped and also adopt a cycle trajectory consistency loss in each sample pair to improve the training loss function. At the tracking stage, we employ the pre-trained feature extraction network to extract features and utilize a Siamese correlation tracking framework to locate the target using forward tracking alone. Extensive experimental results indicate that the proposed self-supervised deep correlation tracker (self-SDCT) achieves competitive tracking performance contrasted to state-of-the-art supervised and unsupervised tracking methods on standard evaluation benchmarks.

Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size.

Deep neural networks have gained immense popularity in the Big Data problem; however, the availability of training samples can be relatively limited in specific application domains, particularly medical imaging, and consequently leading to overfitting problems. This "Small Data" challenge may need a mindset that is entirely different from the existing Big Data paradigm. Here, under the small data scenarios, we examined whether the network structure has a substantial influence on the performance and whether the optimal structure is predominantly determined by sample size or data nature. To this end, we listed all possible combinations of layers given an upper bound of the VC-dimension to study how structural hyperparameters affected the performance. Our results showed that structural optimization improved accuracy by 27.99%, 16.44%, and 13.11% over random selection for a sample size of 100, 500, and 1,000 in the MNIST dataset, respectively, suggesting that the importance of the network structure increases as the sample size becomes smaller. Furthermore, the optimal network structure was mostly determined by the data nature (photographic, calligraphic, or medical images), and less affected by the sample size, suggesting that the optimal network structure is data-driven, not sample size driven. After network structure optimization, the convolutional neural network could achieve 91.13% accuracy with only 500 samples, 93.66% accuracy with only 1000 samples for the MNIST dataset and 94.10% accuracy with only 3300 samples for the Mitosis (microscopic) dataset. These results indicate the primary importance of the network structure and the nature of the data in facing the Small Data challenge.