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Objective: The ancient classical Chinese formulation, Xie-Bai-San (XBS), was studied qualitatively and quantitatively, and its main components were characterized. Materials and Methods: The chemical compounds in XBS were identified using a combination of ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry and molecular networking (MN) analysis. In addition, a multiple reaction monitoring (MRM) quantification method was established using ultra-high-performance liquid chromatography–triple quadrupole mass spectrometry. Mulberroside A, kukoamine B, liquiritin, and glycyrrhizic acid were quantitatively analyzed in ten batches of XBS using this method. Results: Using clusters of MN and databases, 91 compounds were rapidly identified, including stilbene glycosides, saponins, flavonoids, and alkaloids. Of these, 30, 33, and 28 compounds were from Mori Cortex, Lycii Cortex, and Glycyrrhizae Radix et Rhizoma, respectively. The MRM scanning mode was used for the simultaneous sensitive quantitative determination of multiple components in a wide range of complex samples. The four main components were quantified using this method, which has good linearity. Conclusions: This study provides a comprehensive and reliable method for the identification and quantitative analysis of XBS.

The early warning of fires is pivotal for preventing substantial economic losses and ecological damage. However, the enhancement of fire model generalization performance still faces challenges due to limitations in existing fire datasets, such as image quantity and heterogeneity. Taking the advantage of rapid advancements in remote sensing technology and artificial intelligence, we introduce the Flame And Smoke Detection Dataset (FASDD), a pioneering collection comprising over 120,000 heterogeneous images covering various fire scenarios, aiming to promote the evolution of fire detection models. FASDD serves as a challenging benchmark for object detection tasks and features three sub-datasets: FASDD_CV, FASDD_UAV, and FASDD_RS. These sub-datasets comprise cross-domain images from ground-based, airborne, and spaceborne sensors, respectively. Extensive experiments using advanced Swin Transformer models demonstrate satisfactory fire detection performance, with mAP scores of 84.9%, 89.7%, and 74.0% on the respective sub-datasets. Notably, the application case in wildfire localization highlights the proficiency of FASDD_RS models for monitoring and tracking forest fires. Trained on FASDD_RS, FASDD_UAV, and FASDD_CV, deep learning models can be individually deployed on edge devices including satellites, drones, and terrestrial sensors, thus empowering collaborative fire warning within the space-air-ground integrated observation network. Additionally, a standardized procedure for data preparation, data procedure, and quality assurance is established, providing off-the-shelf annotations with four common configurations to facilitate machine learning model training. FASDD stands as a pivotal benchmark, providing valuable resources for training and evaluating deep learning fire detection models, especially Large Vision Models (LVM). It holds the potential to accelerate research progress in urban firefighting or forest fire detection, providing vital security measures for early fire warning and emergency response tasks.

Abstract Numerous studies establish a significant correlation between autoimmune disorders (AIDs) and prostate cancer (PCa). Our Mendelian randomization (MR) analysis investigates the potential connection between rheumatoid arthritis (RA) and PCa, aiming to confirm causal links between systemic lupus erythematosus (SLE), hyperthyroidism, and PCa. Summary statistics from genome-wide association studies provided data on PCa and three AIDs. MR analysis, using IVW as the main approach, assessed causal relationships, validated by sensitivity analysis. IVW revealed a correlation between genetically anticipated RA and PCa, notably in Europeans (OR = 1.03; 95% CI 1.01–1.04, p = 2*10−5). Evidence supported a lower PCa risk in individuals with SLE (OR = 0.94; 95% CI 0.91–0.97, p = 2*10−4) and hyperthyroidism (OR = 0.02; 95% CI 0.001–0.2, p = 2*10−3). Weighted mode and median confirmed these findings. No pleiotropic effects were observed, and MR heterogeneity tests indicated dataset homogeneity. Our study establishes a causal link between RA, SLE, hyperthyroidism, and PCa.

ObjectiveThe level of mitochondrial DNA copy number (mtDNA-CN) in peripheral blood cells had been identified to be involved in several immune and cardiovascular diseases. Thus, the aim of this study is to evaluate the levels of mtDNA-CN in Kawasaki disease (KD) and to construct a nomogram prediction for coronary artery lesions in children with KD.MethodsOne hundred and forty-four children with KD diagnosed from March 2020 to March 2022 were involved in the study. The clinical features and laboratory test parameters of these children were assessed between the KD and normal groups. Univariable and multivariable analyses were performed sequentially to identify the essential risk factors. Subsequently, a nomogram prediction was constructed.ResultsA total of 274 children were included in the analysis. Of these, 144 (52.6%) represented the KD group. Peripheral blood DNA mtDNA qPCR showed that the -log value of mtDNA-CN in the KD group (6.67 ± 0.34) was significantly higher than that in the healthy group (6.40 ± 0.18) (P