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Objectives: To investigate the role of the oral and gut microbiome related to systemic metabolism and clinical parameters in various types of kidney stone disease. Patients and methods: We conducted a case–control study by analyzing 16S rRNA and untargeted metabolomics profiling of 76 fecal, 68 saliva, 73 urine, and 43 serum samples from 76 participants aged 18–75 years old. The participants included 15 patients with uric acid stones, 41 patients with calcium oxalate stones, and 20 healthy controls. Correlations among microbiome, metabolism, and clinical parameters were identified through Spearman’s correlation analysis. (Clinical trial No. ChiCTR2200055316). Results: Patients with uric acid stones exhibited reduced richness and diversity in their microbiome, as well as altered composition in both oral and gut microbiome. Furthermore, their fecal samples showed lower relative abundances of Bacteroides and Lachnospiraceae, while their saliva samples showed higher relative abundances of Porphyromonas and Neisseria. Predicted KEGG metabolism pathways, including amino acid and fatty acid metabolisms, were significantly altered in subjects with uric acid stones. Oral, gut microbiota, and metabolism were also associated with low water intake and urine pH. The area under the curve (AUC) of the specific microbiota and metabolite prediction models was over 0.85. Conclusion: The structure and composition of the oral and gut microbiome in different types of kidney stone disease, the correlations between oral and gut microbiome, and the associations among oral and gut microbiota, systemic metabolism and clinical parameters imply an important role that the oral and gut microbiome may play in kidney stone disease. [ABSTRACT FROM AUTHOR]

In the field of multi-target tracking, the widely embraced tracking-by-detection paradigm has rapidly progressed with the refinement of detectors and matching techniques. However, the paradigm of joint detection and tracking is relatively limited, and it is difficult to model complex scenes, such as the complexities introduced by camera motion and occlusion. In this work, a hierarchical joint detection and tracking framework is proposed, namely MSPNet. From a temporal concern, a motion-guided feature aggregation module is proposed to address the complexities of multi-frame variations. From a spatial concern, an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion. Extensive experiments on MOT challenging benchmarks demonstrate that the MSPNet can effectively reduce false negatives and improve the accuracy of tracking while outperforming a wide range of existing methods. • An enhanced MOT model through motion-guided spatial perception is proposed, namely MSPNet, which optimizes detection and tracking jointly. • To capture temporal variations and ensure spatial motion consistency of instances, a motion-guided cross-temporal feature aggregation module (MFA) is proposed. • To handle the cases of occlusion, occlusion-aware head (OAH) and spatial hierarchical association (SHA) are proposed. The OAH and SHA improves the tracking performance of the network for challenging samples, specifically occluded targets. • Incorporating the power of MFA, OAH, and SHA, the proposed MSPNet, demonstrates notable improvements compared to previous models. [ABSTRACT FROM AUTHOR]