Effect of Aerobic Exercise on Inflammatory Markers in Healthy Middle-Aged and Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Chronic inflammation plays a significant role in accelerating the aging process and is closely associated with the initiation and progression of a broad range of age-related diseases. Physical exercise is considered beneficial in alleviating these conditions, but the effects of aerobic exercise on inflammatory markers in a healthy population should be furtherly clarified.Objective: The purpose of this systematic review and meta-analysis was to evaluate the effect of aerobic exercise on inflammatory markers in middle-aged and older adults.Methods: The literature search was conducted utilizing PubMed, Web of Science, Embase, and the Cochrane Library from their inception through April 2018, and the reference lists were screened to identify appropriate studies. Only randomized controlled trials that investigated the effect of aerobic exercise on inflammatory markers in middle-aged and older adults were eligible for this review.Results: Eleven studies involving 1,250 participants were retrieved from the databases for analysis. The pooled results showed that aerobic exercise significantly reduced inflammatory markers (C-reactive protein (CRP): SMD = 0.53, 95% CI 0.26–0.11, p = 0.0002; tumor necrosis factor-alpha (TNF-α): SMD = 0.75, 95% CI 0.31–1.19, p = 0.0007; interleukin 6 (IL-6): SMD = 0.75, 95% CI 0.31–1.19, p = 0.0007). No significant improvement was found in relation to interleukin 4 (IL-4).Conclusions: Aerobic exercise may have a positive effect on reduction of CRP, TNF-α, and IL-6 in middle-aged and older adults. Further randomized controlled trials (RCTs) need to be conducted to determine the effect of aerobic exercise on additional inflammatory markers in the population of middle-aged and older adults

An Artificial Intelligence-Based Noninvasive Solution to Estimate Pulmonary Artery Pressure

Aims: Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs. Methods and results: We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 18 (10%) patients, and tested the performance of the method using 18 (10%) patients, respectively. After comparing all multi-stage algorithms on the testing cohort, the combination of the residual neural network model and wavelet scattering transform data preprocessing method attained the highest coefficient of determination R2 of 90.78% as well as the following other performance metrics and corresponding 95% confidence intervals (CIs): mean square error of 11.55 (10.22–13.5), mean absolute error of 2.42 (2.06–2.85), mean absolute percentage error of 0.91 (0.76–1.13), and explained variance score of 90.87 (85.32–93.31). Conclusion: The proposed analytical approach that combines data preprocessing, sampling method, and AI algorithm can precisely predict PAP waveform using three input signals obtained by noninvasive approaches

GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology

BACKGROUND: Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge make it promising for systematic understanding of complex human diseases at functional modules level. Based on Gene Ontology, a large number of automatic tools have been developed for the functional analysis and biological interpretation of the high-throughput microarray data. RESULTS: Different from the existing tools such as Onto-Express and FatiGO, we develop a tool named GO-2D for identifying 2-dimensional functional modules based on combined GO categories. For example, it refines biological process categories by sorting their genes into different cellular component categories, and then extracts those combined categories enriched with the interesting genes (e.g., the differentially expressed genes) for identifying the cellular-localized functional modules. Applications of GO-2D to the analyses of two human cancer datasets show that very specific disease-relevant processes can be identified by using cellular location information. CONCLUSION: For studying complex human diseases, GO-2D can extract functionally compact and detailed modules such as the cellular-localized ones, characterizing disease-relevant modules in terms of both biological processes and cellular locations. The application results clearly demonstrate that 2-dimensional approach complementary to current 1-dimensional approach is powerful for finding modules highly relevant to diseases

A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy. Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts. Results: For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%. Conclusion: In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts

Primary prevention for risk factors of ischemic stroke with Baduanjin exercise intervention in the community elder population: study protocol for a randomized controlled trial

BACKGROUND: Stroke is a major cause of death and disability in the world, and the prevalence of stroke tends to increase with age. Despite advances in acute care and secondary preventive strategies, primary prevention should play the most significant role in the reduction of the burden of stroke. As an important component of traditional Chinese Qigong, Baduanjin exercise is a simple, safe exercise, especially suitable for older adults. However, current evidence is insufficient to inform the use of Baduanjin exercise in the prevention of stroke. The aim of this trail is to systematically evaluate the prevention effect of Baduanjin exercise on ischemic stroke in the community elder population with high risk factors. METHODS: A total of 170 eligible participants from the community elder population will be randomly allocated into the Baduanjin exercise group and usual physical activity control group in a 1:1 ratio. Besides usual physical activity, participants in the Baduanjin exercise group will accept a 12-week Baduanjin exercise training with a frequency of five days a week and 40 minutes a day. Primary and secondary outcomes will be measured at baseline, 13 weeks (at end of intervention) and 25 weeks (after additional 12-week follow-up period). DISCUSSION: This study will be the randomized trial to evaluate the effectiveness of Baduanjin exercise for primary prevention of stroke in community elder population with high risk factors of stroke. The results of this trial will help to establish the optimal approach for primary prevention of stroke. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR-TRC-13003588. Registration date: 24 July, 2013

The mechanism of high contents of oil and oleic acid revealed by transcriptomic and lipidomic analysis during embryogenesis in Carya cathayensis Sarg.

Gene transcription in lipid synthesis. (XLSX 55 kb

Serum Procalcitonin Correlates with Renal Function in Hepatitis B Virus-Related Acute-on-Chronic Liver Failure

Background/Aims: To investigate the relationship between elevated serum procalcitonin (PCT) and renal function in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). Methods: HBV-ACLF patients (n = 201) presenting to the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University, from January 2013 to November 2016 were categorized into three groups according to serum PCT levels: (i) normal group (n = 74) had PCT of ≤ 0.5 ng/mL; (ii) elevated group (n = 85) had PCT in the range 0.5–1.0 ng/mL; and (iii) highly elevated group (n = 42) had PCT of > 1.0 ng/mL. Thirty-five cases received standard care after admission. Serum PCT levels and renal function were determined during a two-week follow-up. Results: Significant increases in serum creatinine (Cr) were recorded in male and female patients in the elevated group and highly elevated group compared with the normal group (P < 0.05). In addition, serum Cr levels in male and female patients were significantly higher in the highly elevated group than in the elevated group (P < 0.05). The glomerular filtration rate (GFR) was significantly lower in the highly elevated group (P < 0.05) and this group had the highest risk of altered Cr (45.9% in males; 80% in females) and abnormal GFR (37.5%). Serum PCT levels correlated significantly with all renal function parameters including homocysteine (Hcy), GFR, Cr, blood urea nitrogen, uric acid, and cystatin C at baseline and during treatment. Univariate and multivariate analyses indicated that serum PCT was a strong predictor of renal dysfunction. Conclusion: Serum PCT is closely related to renal dysfunction in HBV-ACLF

Modelling biological age based on plasma peptides in Han Chinese adults

Age-related disease burdens increased over time, and whether plasma peptides can be used to accurately predict age in order to explain the variation in biological indicators remains inadequately understood. Here we first developed a biological age model based on plasma peptides in 1890 Chinese Han adults. Based on mass spectrometry, 84 peptides were detected with masses in the range of 0.6-10.0 kDa, and 13 of these peptides were identified as known amino acid sequences. Five of these thirteen plasma peptides, including fragments of apolipoprotein A-I (m/z 2883.99), fibrinogen alpha chain (m/z 3060.13), complement C3 (m/z 2190.59), complement C4-A (m/z 1898.21), and breast cancer type 2 susceptibility protein (m/z 1607.84) were finally included in the final model by performing a multivariate linear regression with stepwise selection. This biological age model accounted for 72.3% of the variation in chronological age. Furthermore, the linear correlation between the actual age and biological age was 0.851 (95% confidence interval: 0.836-0.864) and 0.842 (95% confidence interval: 0.810-0.869) in the training and validation sets, respectively. The biological age based on plasma peptides has potential positive effects on primary prevention, and its biological meaning warrants further investigation

A capsule network-based method for identifying transcription factors

Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers

Titanium-containing high entropy oxide (Ti-HEO): A redox expediting electrocatalyst towards lithium polysulfides for high performance Li-S batteries

Since lithium sulfur (Li-S) energy storage devices are anticipated to power portable gadgets and electric vehicles owing to their high energy density (2600 Wh·kg–1); nevertheless, their usefulness is constrained by sluggish sulfur reaction kinetics and soluble lithium polysulfide (LPS) shuttling effects. High electrically conductive bifunctional electrocatalysts are urgently needed for Li-S batteries, and high-entropy oxide (HEO) is one of the most promising electrocatalysts. In this work, we synthesize titanium-containing high entropy oxide (Ti-HEO) (TiFeNiCoMg)O with enhanced electrical conductivity through calcining metal-organic frameworks (MOF) templates at modest temperatures. The resulting single-phase Ti-HEO with high conductivity could facilitate chemical immobilization and rapid bidirectional conversion of LPS. As a result, the Ti-HEO/S/KB cathode (with 70 wt.% of sulfur) achieves an initial discharge capacity as high as ~1375 mAh·g–1 at 0.1 C, and a low-capacity fade rate of 0.056% per cycle over 1000 cycles at 0.5 C. With increased sulfur loading (~5.0 mg·cm–2), the typical Li-S cell delivered a high initial discharge capacity of ~607 mAh·g–1 at 0.2 C and showcased good cycling stability. This work provides better insight into the synthesis of catalytic Ti-containing HEOs with enhanced electrical conductivity, which are effective in simultaneously enhancing the LPS-conversion kinetics and reducing the LPS shuttling effect