Effect of Exercise Therapy on Cancer-Related Fatigue in Patients With Breast Cancer: A Systematic Review and Network Meta-analysis.

OBJECTIVE: This study aimed to explore the efficacy of different exercise therapies in reducing fatigue in patients with breast cancer. DESIGN: PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, China Biology Medicine, China National Knowledge Infrastructure, Database of Chinese Sci-tech Periodicals, and Wanfang databases were searched from their inception to March 2022. The authors independently screened all randomized controlled trials of exercise therapy in patients with breast cancer. A network meta-analysis was performed using Stata 16.0 software. RESULTS: Seventy-eight studies were analyzed, with 167 comparisons and 6235 patients. The network results showed that stretching (standardized mean difference = -0.74, confidence interval = -1.43 to -0.06), yoga (standardized mean difference = -0.49, confidence interval = -0.75 to -0.22), combined exercise (standardized mean difference = -0.47, confidence interval = -0.70 to -0.24), aerobic exercise (standardized mean difference = -0.46, confidence interval = -0.66 to -0.26), and resistance exercise (standardized mean difference = -0.42, confidence interval = -0.77 to -0.08) significantly reduced fatigue. Pairwise comparisons confirmed that yoga, combined exercise, aerobic exercise, and resistance exercise were positively associated with fatigue relief. However, no significant association was identified between reduced fatigue and traditional Chinese exercises or stretching. CONCLUSIONS: The most effective exercise therapy to relieve cancer-related fatigue in patients with breast cancer was yoga, followed by combined aerobic and resistance exercises. It is expected that more randomized controlled trials will be conducted to further explore the efficacy and mechanisms of exercise.

Prevalence and associated factors of mortality after percutaneous coronary intervention for adult patients with ST-elevation myocardial infarction: A systematic review and meta-analysis.

Background: There is a paucity of systematic reviews on the associated factors of mortality among ST-elevation myocardial infarction (STEMI) patients after percutaneous coronary intervention (PCI). This meta-analysis was designed to synthesize available evidence on the prevalence and associated factors of mortality after PCI for adult patients with STEMI. Materials and Methods: Databases including the Cochrane Library, PubMed, Web of Science, Embase, Ovid, Scopus, ProQuest, MEDLINE, and CINAHL Complete were searched systematically to identify relevant articles published from January 2008 to March 2020 on factors affecting mortality after PCI in STEMI patients. Meta-analysis was conducted using Stata 12.0 software package. Results: Our search yielded 91 cohort studies involving a total of 199, 339 participants. The pooled mortality rate for STEMI patients after PCI was 10%. After controlling for grouping criteria or follow-up time, the following 17 risk factors were significantly associated with mortality for STEMI patients after PCI: advanced age (odds ratio [OR] = 3.89), female (OR = 2.01), out-of-hospital cardiac arrest (OR = 5.55), cardiogenic shock (OR = 4.83), renal dysfunction (OR = 3.50), admission anemia (OR = 3.28), hyperuricemia (OR = 2.71), elevated blood glucose level (OR = 2.00), diabetes mellitus (OR = 1.8), chronic total occlusion (OR = 2.56), Q wave (OR = 2.18), without prodromal angina (OR = 2.12), delay in door-to-balloon time (OR = 1.72), delay in symptom onset-to-balloon time (OR = 1.43), anterior infarction (OR = 1.66), ST-segment resolution (OR = 1.40), and delay in symptom onset-to-door time (OR = 1.29). Conclusion: The pooled prevalence of mortality after PCI for STEMI patients was 10%, and 17 risk factors were significantly associated with mortality for STEMI patients after PCI.

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach.

The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.

Comparative proteomics reveals the physiological differences between winter tender shoots and spring tender shoots of a novel tea (Camellia sinensis L.) cultivar evergrowing in winter.

BACKGROUND: A recently discovered tea [Camellia sinensis (L.) O. Kuntze] cultivar can generate tender shoots in winter. We performed comparative proteomics to analyze the differentially accumulated proteins between winter and spring tender shoots of this clonal cultivar to reveal the physiological basis of its evergrowing character during winter. RESULTS: We extracted proteins from the winter and spring tender shoots (newly formed two leaves and a bud) of the evergrowing tea cultivar "Dongcha11" respectively. Thirty-three differentially accumulated high-confidence proteins were identified by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF / TOF MS). Among these, 24 proteins had increased abundance while nine showed were decreased abundance in winter tender shoots as compared with the spring tender shoots. We categorized the differentially accumulated proteins into eight critical biological processes based on protein function annotation including photosynthesis, cell structure, protein synthesis & destination, transporters, metabolism of sugars and polysaccharides, secondary metabolism, disease/defense and proteins with unknown functions. Proteins with increased abundance in winter tender shoots were mainly related to the processes of photosynthesis, cytoskeleton and protein synthesis, whereas those with decreased abundance were correlated to metabolism and the secondary metabolism of polyphenolic flavonoids. Biochemical analysis showed that the total contents of soluble sugar and amino acid were higher in winter tender shoots while tea polyphenols were lower as compared with spring tender shoots. CONCLUSIONS: Our study suggested that the simultaneous increase in the abundance of photosynthesis-related proteins rubisco, plastocyanin, and ATP synthase delta chain, metabolism-related proteins eIF4 and protease subunits, and the cytoskeleton-structure associated proteins phosphatidylinositol transfer protein and profilin may be because of the adaptation of the evergrowing tea cultivar "Dongcha11" to low temperature and light conditions. Histone H4, Histone H2A.1, putative In2.1 protein and protein lin-28 homologs may also regulate the development of winter shoots and their response to adverse conditions.