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Objectives: To compare the efficacy and safety of levetiracetam and phenytoin for the treatment of established status epilepticus.Methods: In this systematic review, we searched Medline, Embase, and Cochrane databases from their inception with no language restrictions until May 8, 2019 and updated on February 5, 2020, for randomized controlled trials comparing the efficacy and safety of levetiracetam and phenytoin for the treatment of established status epilepticus. A Meta-analysis was conducted to calculate the risk ratio (RR) using random-effects models.Results: We identified 7 trials with a total of 1028 participants. Levetiracetam was not associated with an increased rate of clinical seizure cessation within 60 min compared with phenytoin (RR, 1.02; 95 %CI, 0.92-1.13; I2 = 3%; 60.0 % [309/515] vs 59.3 % [275/463];12 more events [95 % CI, -48 to 77] per 1000 participants; moderate-quality evidence). Results were similar in the subgroup analysis of adults and children. The sample size met the optimum size in trial sequential analysis. There were also no statistically significant effects on good functional outcome (RR, 1.05; 95 % CI, 0.90-1.23), admission to critical care (RR, 1.09; 95 % CI, 0.95-1.24), or all-cause mortality (RR, 1.09; 95 % CI, 0.55-2.16).Conclusions: Moderate-quality evidence suggested that levetiracetam was not significantly superior to phenytoin in seizure cessation in patients with established status epilepticus. [ABSTRACT FROM AUTHOR]

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learning-based model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus , Staphylococcus aureus , Bacillus cereus , Salmonella typhi , and Streptococcus hemolyticus , with accuracy rates of 90%–100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection. [ABSTRACT FROM AUTHOR]