Background Ezetimibe is a widely used medication to reduce the plasma cholesterol level, particularly low-density lipoprotein level. However, its impact on cancer remains controversial. Here, its impacts on risks of various types of cancers were meta-analyzed. Methods PubMed and Cochrane Library electronic databases were searched and randomized controlled trials with followed up for at least 24 weeks were selected and included. The experimental group was defined as those patients treated with ezetimibe alone or with other medications, and the control group was defined as those who received a placebo or the matched medication. The number of new cancer cases or cancer-related deaths was extracted. Statistical analysis was performed using Review Manager (version 5.3). Results Nine trials enrolling 35 222 patients were included in the analyses. Compared with the control group, ezetimibe increased the number of new intestine cancer patients [relative risk (RR), 1.30; 95% confidence interval (CI), 1.02–1.67; P = 0.03] and had a trend to increase the number of new breast cancer patients (RR, 1.39; 95% CI, 0.98–1.98; P = 0.07). There was no significant difference in new hepatobiliary cancer, prostate cancer, skin cancer or cancer of other sites. Ezetimibe did not significantly increase the risk of new cancer in total (RR, 1.03; 95% CI, 0.96–1.11; P = 0.38), cancer-related death (RR, 1.11; 95% CI, 0.98–1.26; P = 0.10) or cancer events (RR, 1.04; 95% CI, 0.97–1.12; P = 0.30). In terms of lipid-lowering effect, ezetimibe significantly reduced total cholesterol and low-density lipoprotein cholesterol, increased high-density lipoprotein cholesterol. Conclusion Ezetimibe may increase the risk of intestine cancer and has a trend of increasing the risk of breast cancer. There is no evidence to support that it increases or decreases the risk of other types.
Background: Up to 90% of patients who are under the active treatment suffer from cancer-related fatigue (CRF). CRF can persist about 10 years after diagnosis and/or treatment. Accumulating reports support that ginseng and ginseng injections are both potential drugs for the treatment of CRF but few studies put them together for analysis. Methods: Two reviewers independently extracted data in 3 databases (PubMed, Cochrane Library and China National Knowledge Infrastructure) from their inception to May 24, 2021. The primary outcome was the effect of ginseng in alleviating CRF. The secondary outcome was ginseng in alleviating emotional or cognitive fatigue. Standardized mean difference (SMD) was employed. Results: Twelve studies were included to evaluate efficacy of ginseng oral administration and ginseng injections on CRF. The pooled SMD was 0.40 (95% confidence Interval [95% CI] [0.29–0.51], P < .00001). Six studies were included to evaluate efficacy of ginseng oral administration on CRF and the SMD was 0.29 (95% CI [0.15–0.42], P < .0001). The order was 2000 mg/d, 3000 mg/d, 1000 mg/d and placebo from high efficacy to low. Ten studies were included to evaluate efficacy of ginseng injections on CRF and the SMD was 0.74 (95% CI [0.59–0.90], P < .00001). Emotional fatigue was reported in 4 studies, ginseng oral administration in 2 and ginseng injections in 2. The pooled SMD was 0.12 (95% CI [−0.04 to 0.29], P = .15). Cognitive fatigue was reported in 4 studies focusing on ginseng injections and the SMD was 0.72 (95% CI [0.48–0.96], P < .00001). Conclusion: Ginseng can improve CRF. Intravenous injection might be better than oral administration. Ginseng injections may alleviate cognitive fatigue. No evidence was found to support that ginseng could alleviate emotional fatigue.
Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classification. Protoformer features a selection mechanism for embedding samples that allows us to efficiently extract and utilize anomalies prototypes and difficult class prototypes. We demonstrated such capabilities on datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also applied the framework to several models. The results indicate that Protoformer can improve current Transformers in various empirical settings.
This study assessed the psychometric properties of the culturally inclusive instructional design (CIID) scale with 31 items on a 7-point Likert scale. The data were collected from the training (N = 55) and validating samples (N = 80) of K-20 educators. Data analysis employed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA results revealed a clear five-factor structure, and the CFA results indicated good factor loadings. The reliability indices were .95 and .94 for the training and validation samples, respectively. The significant correlations among the factors indicated the five subscales measuring on the same CIID construct. In contrast, a non-perfect correlation presented a discriminating power for each subscale measuring the unique dimension of the construct. The study results established the validity and reliability of the instrument to measure culturally inclusive instructional design with implications for the design and development of online learning for cultural inclusivity.
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