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Introduction: Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems., Methods: Baidu search engine data on the keyword "fever" were extracted from 255 cities in China from November 2022 to January 2023. Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days., Results: The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average, balancing stability and precision., Discussion: The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts., (Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2024.)

Introduction: Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems., Methods: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency., Results: ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 ( P <0.05)., Discussion: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation., Competing Interests: No conflicts of interest., (Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2024.)

Exogenous supplementation of antioxidants with ROS scavenging activity would be a potential therapy to cerebral ischemia-reperfusion injury in stroke. In the present study, a series of NDGA analogues with attenuation oxidative stress by directly scavenging ROS and indirectly through keap1/Nrf2/ARE pathway activation were designed and synthesized. All analogues were found to effectively remove ROS directly by DPPH radical scavenging assay, and compound 3a conferred potent protection from the oxidative injury in PC12 cells via promoting Nrf2 to translocate into nucleus and increasing expression of heme oxygenase-1(HO-1), where strongly reduced intracellular ROS level indirectly. More importantly, 3a significantly reduced brain infarction after cerebral ischemia-reperfusion injury in rats subjected to transient middle cerebral artery occlusion (MCAO). Overall, our findings shown compound 3a could serve as a promising compound for the treatment of stroke., (Copyright © 2017. Published by Elsevier Masson SAS.)