Association between consumption of flavonol and its subclasses and chronic kidney disease in US adults: an analysis based on National Health and Nutrition Examination Survey data from 2007-2008, 2009-2010, and 2017-2018.

Background: There is little research on the relationship between flavonol consumption and chronic kidney disease (CKD). This study aimed to examine the link between flavonol consumption and the risk of CKD among US adults, using data from the 2007-2008, 2009-2010 and 2017-2018 National Health and Nutrition Examination Survey (NHANES). Methods: A cross-sectional approach was used, drawing on data from three NHANES cycles. The flavonol consumption of the participants in this study was assessed using a 48 h dietary recall interview. CKD was diagnosed based on an estimated glomerular filtration rate below 60 mL/min/1.73 m2 or a urine albumin-to-creatinine ratio of 30 mg/g or higher. Results: Compared to the lowest quartile of flavonol intake (Q1), the odds ratios for CKD were 0.598 (95% CI: 0.349, 1.023) for the second quartile (Q2), 0.679 (95% CI: 0.404, 1.142) for the third quartile (Q3), and 0.628 (95% CI: 0.395, 0.998) for the fourth quartile (Q4), with a p value for trend significance of 0.190. In addition, there was a significant trend in CKD risk with isorhamnetin intake, with the odds ratios for CKD decreasing to 0.860 (95% CI: 0.546, 1.354) in the second quartile, 0.778 (95% CI: 0.515, 1.177) in the third quartile, and 0.637 (95% CI: 0.515, 1.177) in the fourth quartile (p for trend = 0.013). Conclusion: Our analysis of the NHANES data spanning 2007-2008, 2009-2010, and 2017-2018 suggests that high consumption of dietary flavonol, especially isorhamnetin, might be linked to a lower risk of CKD in US adults. These findings offer new avenues for exploring strategies for managing CKD.

Spontaneous resolution or antibiotic effectiveness? Reflection on a case of pediatric urinary tract infections caused by Enterococcus raffinosus.

Enterococcus raffinosus, named by Collins et al. in 1989, is a cocci-shaped bacterium that typically appears in pairs or short chains. As a Gram-positive and non-motile bacterium, it grows at 10°C-45°C, exhibiting negative peroxidase activity [1]. It is a normal flora in the oropharynx and gastrointestinal tract of domestic cats [2] and can also be isolated from human rectal swabs [3], it belongs to the same genus Enterococcus as Enterococcus faecalis and Enterococcus faecium. Enterococcus faecalis and Enterococcus faecium constitute 90% of clinically isolated strains. However, the incidence of other enterococci, excluding E. faecalis and E. faecium, is on the rise [4]. In this case report, a patient with pediatric urinary tract infections caused by E. raffinosus was presented, and a summary of relevant literature was provided.

Peritoneal dialysis-associated peritonitis caused by Neisseria.sicca: A case report and literature review.

Neisseria sicca, a Gram-negative diplococcus commonly found in the nasopharynx as part of normal bacterial flora, is typically non-pathogenic but has been associated with various diseases including endocarditis, conjunctivitis, pneumonia and meningitis (Jeurissen et al., 2006; Kozlova et al., 2020; Alcid, 1980; Carter et al., 2007). In this report, we present a case of peritonitis in a patient undergoing peritoneal dialysis caused by N. sicca and review the literature on Neisseria-associated peritonitis.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

TGM1/3-mediated transamidation of Exo70 promotes tumor metastasis upon LKB1 inactivation.

Exo70, a key exocyst complex component, is crucial for cell motility and extracellular matrix (ECM) remodeling in cancer metastasis. Despite its potential as a drug target, Exo70's post-translational modifications (PTMs) are poorly characterized. Here, we report that Exo70 is transamidated on Gln5 with Lys56 of cystatin A by transglutaminases TGM1 and TGM3, promoting tumor metastasis. This modification enhances Exo70's association with other exocyst subunits, essential for secreting matrix metalloproteinases, forming invadopodia, and delivering integrins to the leading edge. Tumor suppressor liver kinase B1 (LKB1), whose inactivation accelerates metastasis, phosphorylates TGM1 and TGM3 at Thr386 and Thr282, respectively, to inhibit their interaction with Exo70 and the following transamidation. Cantharidin, a US Food and Drug Administration (FDA)-approved drug, inhibits Exo70 transamidation to restrain tumor cell migration and invasion. Together, our findings highlight Exo70 transamidation as a key molecular mechanism and target and propose cantharidin as a therapeutic strategy with direct clinical translational value for metastatic cancers, especially those with LKB1 loss.