ObjectivePneumonia accounts for more deaths than any other infectious disease worldwide. The intestinal microbiota supports local mucosal immunity and is increasingly recognised as an important modulator of the systemic immune system. The precise role of the gut microbiota in bacterial pneumonia, however, is unknown. Here, we investigate the function of the gut microbiota in the host defence against Streptococcus pneumoniae infections.DesignWe depleted the gut microbiota in C57BL/6 mice and subsequently infected them intranasally with S. pneumoniae. We then performed survival and faecal microbiota transplantation (FMT) experiments and measured parameters of inflammation and alveolar macrophage whole-genome responses.ResultsWe found that the gut microbiota protects the host during pneumococcal pneumonia, as reflected by increased bacterial dissemination, inflammation, organ damage and mortality in microbiota-depleted mice compared with controls. FMT in gut microbiota-depleted mice led to a normalisation of pulmonary bacterial counts and tumour necrosis factor-α and interleukin-10 levels 6 h after pneumococcal infection. Whole-genome mapping of alveolar macrophages showed upregulation of metabolic pathways in the absence of a healthy gut microbiota. This upregulation correlated with an altered cellular responsiveness, reflected by a reduced responsiveness to lipopolysaccharide and lipoteichoic acid. Compared with controls, alveolar macrophages derived from gut microbiota-depleted mice showed a diminished capacity to phagocytose S. pneumoniae.ConclusionsThis study identifies the intestinal microbiota as a protective mediator during pneumococcal pneumonia. The gut microbiota enhances primary alveolar macrophage function. Novel therapeutic strategies could exploit the gut–lung axis in bacterial infections.

Key Points Severe thrombocytopenia is associated with a strongly impaired host defense during pneumonia-derived Klebsiella pneumoniae sepsis. Platelet counts between 5 and 13 × 109/L of normal prevent bleeding and confer protection against distant organ damage during gram-negative sepsis.

Background99mTc-mebrofenin hepatobiliary scintigraphy (HBS) was used as a quantitative method to evaluate liver function. The aim of this study was to compare future remnant liver function assessed by 99mTc-mebrofenin hepatobiliary scintigraphy with future remnant liver volume in the prediction of liver failure after major liver resection.MethodsComputed tomography (CT) volumetry and 99mTc-mebrofenin hepatobiliary scintigraphy were performed prior to major resection in 55 high-risk patients, including 30 patients with parenchymal liver disease. Liver volume was expressed as percentage of total liver volume or as standardized future remnant liver volume. Receiver operating characteristic (ROC) curve analysis was performed to identify a cutoff value for future remnant liver function in predicting postoperative liver failure.ResultsPostoperative liver failure occurred in nine patients. A liver function cutoff value of 2.69%/min/m2 was calculated by ROC curve analysis. 99mTc-mebrofenin hepatobiliary scintigraphy demonstrated better sensitivity, specificity, and positive and negative predictive value compared to future remnant liver volume. Using 99mTc-mebrofenin hepatobiliary scintigraphy, one cutoff value suffices in both compromised and noncompromised patients.ConclusionPreoperative 99mTc-mebrofenin hepatobiliary scintigraphy is a valuable technique to estimate the risk of postoperative liver failure. Especially in patients with uncertain quality of the liver parenchyma, 99mTc-mebrofenin HBS proved of more value than CT volumetry.

BackgroundThe development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid–Schiff (PAS).MethodsWe trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network’s glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies.ResultsThe weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was “glomeruli” in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by “tubuli combined” and “interstitium.” The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures.ConclusionsThis study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.