Background & Aim: Patatin-like phospholipase domain-containing protein 3 (PNPLA3) rs738409 polymorphism is associated with NAFLD severity and the PNPLA3 gene is expressed in the kidneys,but whether PNPLA3 rs738409 polymorphism is also associated with renal tubular injury is uncertain. We assessed the effect of PNPLA3 genotypes on biomarkers of renal tubular injury (RTI) and glomerular function in subjects with NAFLD who had either normal (nALT) or abnormal (abnALT) alanine aminotransaminase levels. Methods: 217 patients with histologically-proven NAFLD, of which 75 had persistently nALT (below upper limit of normal for 3 months) were included. Multivariable regression analyses were undertaken to test associations between PNPLA3 genotype and biomarkers of kidney dysfunction. Results: The nALT patient group had higher urinary neutrophil gelatinase-associated lipocalin levels (u-NGAL, a biomarker of RTI) (P <0.001), higher albuminuria (P =0.039) and greater prevalence of chronic kidney disease (CKD) (P =0.046) than the abnALT group. The association between PNPLA3 GG genotype and risk of CKD and abnormal albuminuria remained significant after adjustment for kidney risk factors and severity of NAFLD histology, mostly in the nALT group. Similarly, PNPLA3 GG 6 genotype was associated with higher u-NGAL levels in the nALT group, even after adjustment for the aforementioned risk factors and glomerular filtration-based markers (β-coefficient: 22.29, 95% CI: 0.99-43.60, P =0.041). Conclusion: Patients with NAFLD and persistently nALT, who carry the PNPLA3 rs738409 G allele, are at higher risk of early glomerular and tubular damage. We suggest PNPLA3 genotyping may help identify patients with NAFLD at higher risk of RTI.

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Background and Aim Patatin‐like phospholipase domain‐containing protein 3 (PNPLA3) I148M (rs738409) genotype influences clinical/biochemical characteristics in patients with nonalcoholic fatty liver disease (NAFLD), but whether PNPLA3‐I148M (rs738409) genotype also influences the diagnostic performance of noninvasive diagnostic tests for NAFLD is uncertain. Our aim was to investigate the differences in diagnostic performance of noninvasive diagnostic tests for NAFLD according to PNPLA3‐I148M (rs738409) genotype. Methods Fifty‐eight healthy controls and 349 patients with biopsy‐proven NAFLD were included. Areas under the receiver operating characteristic curve (AUROCs) were calculated to predict hepatic steatosis (fatty liver index and hepatic steatosis index), nonalcoholic steatohepatitis (cytokeratin‐18 M30 and M65), and significant fibrosis (≥F2 fibrosis) (fibrosis‐4 and BARD), stratifying by rs738409 genotypes (CC and CG + GG groups). Results Fatty liver index and hepatic steatosis index showed good diagnostic performance for diagnosing steatosis only in the CG + GG group with AUROCs ranging from 0.819 to 0.832. Cytokeratin‐18 M30 (AUROC = 0.688) and M65 (AUROC = 0.678) had suboptimal performance for diagnosing nonalcoholic steatohepatitis in the CG + GG group, whereas both had good performance (AUROC = 0.814 and 0.813, respectively) in the CC group. BARD score showed good performance in the CG + GG group compared with the CC group (AUROC = 0.805 and 0.532, respectively). Fibrosis‐4 had suboptimal performance in the CG + GG group and good performance in the CC group (AUROC = 0.662 and 0.801, respectively). Conclusions Diagnostic performance of noninvasive tests for NAFLD varied markedly according to PNPLA3 genotypes. Clinicians should be aware that PNPLA3 genotype limits the clinical utility of noninvasive diagnostic tests for diagnosing NAFLD.

Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) infected pneumonia (NCP) and influenza A infected pneumonia (IAP), a seasonal disease that coincided with the outbreak. We aim to develop a machine-learning algorithm based on radiomics to distinguish NCP from IAP by texture analysis based on computed tomography (CT) imaging. Forty-one NCP and 37 IAP patients admitted from January to February 6, 2019 admitted to two hospitals in Wenzhou, China. All patients had undergone chest CT examination and blood routine tests prior to receiving medical treatment. NCP was diagnosed by real-time RT-PCR assays. Eight of 56 radiomic features extracted by LIFEx were selected by least absolute shrinkage and selection operator regression to develop a radiomics score and subsequently constructed into a nomogram to predict NCP with area under the operating characteristics curve of 0.87 (95% confidence interval: 0.77-0.93). The nomogram also showed excellent calibration with Hosmer-Lemeshow Abbreviations: ASA, American Society of Anesthesiologists; AUC, area under the operating characteristics curve; CT, computed tomography; IAP, influenza A virus infected pneumonia; LASSO, least absolute shrinkage and selection operator; NGLDM, neighborhood gray-level dependence matrix; ROI, region of interest; SARS-CoV-2, severe acute respiratory syndrome-associated coronavirus 2; SVM, support vector machine This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Background:The presence of significant liver fibrosis is a key determinant of longterm prognosis in non-alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non-invasive fibrosis biomarkers. Methods: We used a cohort of 553 adults with biopsy-proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F ≥ 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Results: In the training cohort, the variables selected by LASSO algorithm were body mass index, pro-collagen type III, collagen type IV, aspartate aminotransferase and albumin-to-globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869-0.904) for identifying fibrosis F ≥ 2. The LRM AUROC was 0.764,