Objective: Pneumonia 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. Design: We 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. Results: We 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 . Conclusions: This 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.

Matrix metalloproteinase (MMP)-9 is involved in extracellular matrix degradation and leukocyte migration. To determine the role of MMP-9 in the innate immune response to peritonitis, MMP-9 gene-deficient (MMP-9^−/−) and normal wild-type mice were i.p. infected with Escherichia coli. MMP-9 mRNA and pro-MMP-9 protein levels increased rapidly upon induction of peritonitis. Although MMP-9^−/− neutrophils showed a normal phagocytosis of E. coli in vitro, MMP-9^−/− mice displayed a reduced resistance against E. coli peritonitis, as indicated by an enhanced bacterial outgrowth in the peritoneal cavity and increased dissemination of the infection. Furthermore, the cytokine response to LPS was not influenced by MMP-9 deficiency. However, during E. coli peritonitis, MMP-9^−/− mice showed much higher peritoneal chemokine and cytokine levels compared with wild-type mice. Despite the increased local chemokine concentrations, MMP-9^−/− mice displayed a diminished recruitment of leukocytes to the site of infection, indicating that cellular migration was impaired. Moreover, MMP-9^−/− mice developed more severe distant organ damage during infection. These data suggest that MMP-9 is an essential component of an effective host response to E. coli peritonitis.

Significance Statement: Histopathologic assessment of kidney tissue currently relies on manual scoring or traditional image-processing techniques to quantify and classify tissue features, time-consuming approaches that have limited reproducibility. The authors present an alternative approach, featuring a convolutional neural network for multiclass segmentation of kidney tissue in sections stained by periodic acid–Schiff. Their findings demonstrate applicability of convolutional neural networks for tissue from multiple centers, for biopsies and nephrectomy samples, and for the analysis of both healthy and pathologic tissues. In addition, they validated the network’s results with components from the Banff classification system. Their convolutional neural network may have utility for quantitative studies involving kidney histopathology across centers and potential for application in routine diagnostics. Background: The 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). Methods: We 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. Results: The 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. Conclusions: This 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.

Background ^99mTc-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. Methods Computed 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. Results Postoperative 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. Conclusion Preoperative ^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.

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.

Introduction: Mechanical ventilation (MV) may cause ventilator-induced lung injury (VILI). Present models of VILI use exceptionally large tidal volumes, causing gross lung injury and haemodynamic shock. In addition, animals are ventilated for a relative short period of time and only after a 'priming' pulmonary insult. Finally, it is uncertain whether metabolic acidosis, which frequently develops in models of VILI, should be prevented. To study VILI in healthy mice, the authors used a MV model with clinically relevant ventilator settings, avoiding massive damage of lung structures and shock, and preventing metabolic acidosis. Methods: Healthy C57Bl/6 mice (n = 66) or BALB/c mice (n = 66) were ventilated (tidal volume = 7.5 ml/kg or 15 ml/kg; positive end-expiratory pressure = 2 cmH₂O; fraction of inspired oxygen = 0.5) for five hours. Normal saline or sodium bicarbonate were used to correct for hypovolaemia. Lung histopathology, lung wet-to-dry ratio, bronchoalveolar lavage fluid protein content, neutrophil influx and levels of proinflammatory cytokines and coagulation factors were measured. Results: Animals remained haemodynamically stable throughout the whole experiment. Lung histopathological changes were minor, although significantly more histopathological changes were found after five hours of MV with a larger tidal volume. Lung histopathological changes were no different between the strains. In both strains and with both ventilator settings, MV caused higher wet-to-dry ratios, higher bronchoalveolar lavage fluid protein levels and more influx of neutrophils, and higher levels of proinflammatory cytokines and coagulation factors. Also, with MV higher systemic levels of cytokines were measured. All parameters were higher with larger tidal volumes. Correcting for metabolic acidosis did not alter endpoints. Conclusions: MV induces VILI, in the absence of a priming pulmonary insult and even with use of relevant (least injurious) ventilator settings. This model offers opportunities to study the pathophysiological mechanisms behind VILI and the contribution of MV to lung injury in the absence of pre-existing lung injury.