Physiological and transcriptomic profiles reveal key regulatory pathways involved in cold resistance in sunflower seedlings.

During sunflower growth, cold waves often occur and impede plant growth. Therefore, it is crucial to study the underlying mechanism of cold resistance in sunflowers. In this study, physiological analysis revealed that as cold stress increased, the levels of ROS, malondialdehyde, ascorbic acid, and dehydroascorbic acid and the activities of antioxidant enzymes increased. Transcriptomics further identified 10,903 DEGs between any two treatments. Clustering analysis demonstrated that the expression of MYB44a, MYB44b, MYB12, bZIP2 and bZIP4 continuously upregulated under cold stress. Cold stress can induce ROS accumulation, which interacts with hormone signals to activate cold-responsive transcription factors regulating target genes involved in antioxidant defense, secondary metabolite biosynthesis, starch and sucrose metabolism enhancement for improved cold resistance in sunflowers. Additionally, the response of sunflowers to cold stress may be independent of the CBF pathway. These findings enhance our understanding of cold stress resistance in sunflowers and provide a foundation for genetic breeding.

Development of a knowledge-based healthcare-associated infections surveillance system in China.

BACKGROUND: In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. METHODS: We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. RESULTS: We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. CONCLUSIONS: Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.