What is known and objective Edaravone is a new antioxidant and hydroxyl radical scavenger. Although there is evidence that it improves clinical outcomes of patients with acute ischaemic stroke (AIS), it is not yet widely accepted for treatment of AIS in Western countries. We further investigated the efficacy and safety of edaravone through this meta‐analysis of randomized controlled clinical trials (RCTs). Method Pubmed, Embase, Web of Science and Cochrane Library were screened up to December 2020 for original articles from SCI journals that published in English. RCTs that compared edaravone versus placebo or no intervention in adult patients and reported the efficacy or safety of edaravone were regarded as eligible. Mortality was regarded as the primary outcome and the improvement of neurological impairment was regarded as the secondary outcome. Safety evaluation was conducted according to the incidence of adverse events. Review Manager 5.3 was employed to perform the assessment of the risk of bias and data synthesis. The Cochrane risk of bias tool for randomized controlled trials was employed to assess the risk of bias. Results and discussion Seven randomized controlled trials with 2069 patients were included. For the incidence of mortality, the pooled RR for studies that evaluated edaravone after three‐month follow‐up was 0.55 (95% Cl, 0.43‐0.7, I2 = 0, P < 0.01). The pooled RR for improvement of neurological impairment at the three months follow‐up was 1.54 (95% CI, 1.27‐1.87, I2 = 0, P < 0.01) in four RCTs. On subgroup analysis of studies that were conducted in Asia, the RR was 1.56 (95% CI, 1.27‐1.90, I2 = 0%; P < 0.01); the pooled RR for studies that conducted in Europe was 1.32 (95% CI, 0.64‐2.72; P = 0.45); the pooled RR for studies that used edaravone for two weeks was 1.42 (95% CI, 1.10 to 1.83, I2 = 0%; P < 0.01); the pooled RR for studies that used edaravone for one week was 1.64 (95% CI, 1.24‐2.16, I2 = 0%; P < 0.01); the pooled RR for studies that conducted in patients with mean age equal to or over 60 years was 1.52 (95% CI, 1.24‐1.87, I2 = 0%; P < 0.01); and the pooled RR for studies that conducted in patients with mean age less than 60 was 1.80 (95% CI, 1.05‐3.08, I2 = 0%; P = 0.03). For the incidence of any treatment‐related adverse events, the pooled RR for studies that evaluated edaravone during treatment was 0.83 (95% CI, 0.51‐1.34, I2 = 0, P = 0.43). The difference of the incidence of any treatment‐related adverse events between two groups was not statistically significant. What is new and conclusion The limited studies indicate that edaravone can improve neurological impairment with a survival benefit at three‐month follow‐up, regardless of the mean age and course of treatment. It is worthy of promotion in the clinical treatment of AIS in Asian countries. More well‐designed RCTs with larger sample sizes are needed to determine the benefits of edaravone in patients from Western countries.

In this work, a quartz crystal microbalance (QCM) sensor has been fabricated using immunoassay for sensitive determination of Bifidobacterium bifidum. Au nanoparticle has been used for amplifying sandwich assays. The proposed immunosensor exhibited a linear detection range between 10 3 and 10 5 CFU/mL with a limit of detection of 2.1 × 10 2 CFU/mL. The proposed immunosensor exhibited good selectivity for B. bifidum sensing with low cross reactivity for other foodborne pathogens such as Lactobacillus acidophilus, Listeria monocytogenes, and Escherichia coli. In addition, the proposed immunosensor has been successfully used for B. bifidum detection in feces samples and food samples. The frequency decreases of 12, 17, and 10 Hz were observed from the milk samples consisting of the mixtures of L. acidophilus, L. monocytogenes, and E. coli. The frequency decreases of 8, 15, and 7 Hz were observed from the feces samples consisting of the mixtures of L. acidophilus, L. monocytogenes, and E. coli.

In deep face recognition, the commonly-used softmax loss and its newly proposed variations are not yet sufficiently effective to handle the class imbalance and softmax saturation issues during the training process, while extracting discriminative features. In this brief paper, to address both issues, we propose a class-variant margin (CVM) normalized softmax loss, by introducing a true-class margin and a false-class margin into the cosine space of the angle between the feature vector and the class-weight vector. The true-class margin alleviates the class imbalance problem and the false-class margin postpones the early individual saturation of softmax. With negligible computational complexity increment during training, the new loss function is easy to implement in the common deep learning frameworks. Comprehensive experiments on the LFW, YTF and MegaFace protocols demonstrate the effectiveness of the proposed CVM loss function.