Background: The gut is thought to play an important role in the pathogenesis of sepsis. Changes in the gut microbiota are closely related to the occurrence and development of human diseases, but few studies have focused on taxonomic composition of gut microbiota in septic patients. Knowledge of changes in the gut microbiota is a key issue in intensive care. Clinicians must understand how an altered gut microbiota affects the susceptibility and prognosis of septic patients.Measurements and Main Results: In the single-center case control study, 20 septic patients and 20 healthy children were recruited. The taxonomic composition of gut microbiota was determined via 16S rRNA gene sequencing. Gut microbiota diversity in children with sepsis was significantly reduced compared with that in healthy children. The taxonomic composition of gut microbiota can effectively distinguish children with sepsis from healthy children. Thirteen taxa of gut microbiota were significantly increased in the guts of children with sepsis compared with those of healthy children. The increased abundances of Enterococcaceae, Enterococcus, and Enterococcus durans in gut of septic patients were significantly positively correlated with blood inflammation indicators CRP and WBC. The abundances of seven bacteria were significantly decreased in the guts of septic children compared with those of healthy children. The decreased abundance of Bifidobacteriales in gut of septic patients is significantly negatively correlated with blood inflammation index WBC. A machine-learning classifier was built for distinguishing sepsis and achieved the AUC value of 81.25%. It shows that the composition of gut microbiota has certain potential for diagnosis of sepsis.Conclusions: Gut microbiota alterations in septic patients exhibit proliferation of opportunistic pathogenic bacteria, the massive reduction of the commensal flora, and the significant decrease in the diversity of the gut microbiota. Dysbiosis may also account for some changes in the inflammation indexes.

Background: Severe pneumonia due to lower respiratory tract infections (LRTIs) is a significant cause of morbidity and mortality in children. Noninfectious respiratory syndromes resembling LRTIs can complicate the diagnosis and may also make targeted therapy difficult because of the difficulty of identifying LRTI pathogens. In the present study, a highly sensitive metagenomic next-generation sequencing (mNGS) approach was used to characterize the microbiome of bronchoalveolar lavage fluid (BALF) in children with severe lower pneumonia and identify pathogenic microorganisms that may cause severe pneumonia. The purpose of this study was to use mNGS to explore the potential microbiomes of children with severe pneumonia in a PICU.Methods: We enrolled patients meeting diagnostic criteria for severe pneumonia admitted at PICU of the Children’s Hospital of Fudan University, China, from February 2018 to February 2020. In total, 126 BALF samples were collected, and mNGS was performed at the DNA and/or RNA level. The pathogenic microorganisms in BALF were identified and correlated with serological inflammatory indicators, lymphocyte subtypes, and clinical symptoms.Results: mNGS of BALF identified potentially pathogenic bacteria in children with severe pneumonia in the PICU. An increased BALF bacterial diversity index was positively correlated with serum inflammatory indicators and lymphocyte subtypes. Children with severe pneumonia in the PICU had the potential for coinfection with viruses including Epstein–Barr virus, Cytomegalovirus, and Human betaherpesvirus 6B, the abundance of which was positively correlated with immunodeficiency and pneumonia severity, suggesting that the virus may be reactivated in children in the PICU. There was also the potential for coinfection with fungal pathogens including Pneumocystis jirovecii and Aspergillus fumigatus in children with severe pneumonia in the PICU, and an increase in potentially pathogenic eukaryotic diversity in BALF was positively associated with the occurrence of death and sepsis.Conclusions: mNGS can be used for clinical microbiological testing of BALF samples from children in the PICU. Bacterial combined with viral or fungal infections may be present in the BALF of patients with severe pneumonia in the PICU. Viral or fungal infections are associated with greater disease severity and death.

Background: Notch signaling is critical for regulating the function of vascular endothelial cells (ECs). However, the effect of the intracellular domain of Notch1 (NICD) on EC injury in sepsis remains unclear.Methods: We established a cell model of vascular endothelial dysfunction and induced sepsis in a mouse model via lipopolysaccharide (LPS) injection and cecal ligation and puncture (CLP). Endothelial barrier function and expression of endothelial-related proteins were determined using CCK-8, permeability, flow cytometry, immunoblot, and immunoprecipitation assays. The effect of NICD inhibition or activation on endothelial barrier function was evaluated in vitro. Melatonin was used for NICD activation in sepsis mice. The survival rate, Evans blue dye of organs, vessel relaxation assay, immunohistochemistry, ELISA, immunoblot were used to explore the specific role of melatonin for sepsis induced vascular dysfunction in vivo.Results: We found that LPS, interleukin 6, and serum collected from septic children could inhibit the expression of NICD and its downstream regulator Hes1, which impaired endothelial barrier function and led to EC apoptosis through the AKT pathway. Mechanistically, LPS decreased the stability of NICD by inhibiting the expression of a deubiquitylating enzyme, ubiquitin-specific proteases 8 (USP8). Melatonin, however, upregulated USP8 expression, thus maintaining the stability of NICD and Notch signaling, which ultimately reduced EC injury in our sepsis model and elevated the survival rate of septic mice.Conclusions: We found a previously uncharacterized role of Notch1 in mediating vascular permeability during sepsis, and we showed that inhibition of NICD resulted in vascular EC dysfunction in sepsis, which was reversed by melatonin. Thus, the Notch1 signaling pathway is a potential target for the treatment of sepsis.

Background: Metagenomic next-generation sequencing (mNGS) is a powerful method for pathogen detection in various infections. In this study, we assessed the value of mNGS in the pathogen diagnosis and microbiome analysis of pneumonia in pediatric intensive care units (PICU) using bronchoalveolar lavage fluid (BALF) samples.Methods: A total of 104 pediatric patients with pneumonia who were admitted into PICU between June 2018 and February 2020 were retrospectively enrolled. Among them, 101 subjects who had intact clinical information were subject to parallel comparison of mNGS and conventional microbiological tests (CMTs) for pathogen detection. The performance was also evaluated and compared between BALF-mNGS and BALF-culture methods. Moreover, the diversity and structure of all 104 patients’ lung BALF microbiomes were explored using the mNGS data.Results: Combining the findings of mNGS and CMTs, 94.06% (95/101) pneumonia cases showed evidence of causative pathogenic infections, including 79.21% (80/101) mixed and 14.85% (15/101) single infections. Regarding the pathogenesis of pneumonia in the PICU, the fungal detection rates were significantly higher in patients with immunodeficiency (55.56% vs. 25.30%, P =0.025) and comorbidities (40.30% vs. 11.76%, P=0.007). There were no significant differences in the α-diversity either between patients with CAP and HAP or between patients with and without immunodeficiency. Regarding the diagnostic performance, the detection rate of DNA-based BALF-mNGS was slightly higher than that of the BALF-culture although statistically insignificant (81.82% vs.77.92%, P=0.677) and was comparable to CMTs (81.82% vs. 89.61%, P=0.211). The overall sensitivity of DNA-based mNGS was 85.14% (95% confidence interval [CI]: 74.96%-92.34%). The detection rate of RNA-based BALF-mNGS was the same with CMTs (80.00% vs 80.00%, P>0.999) and higher than BALF-culture (80.00% vs 52.00%, P=0.045), with a sensitivity of 90.91% (95%CI: 70.84%-98.88%).Conclusions: mNGS is valuable in the etiological diagnosis of pneumonia, especially in fungal infections, and can reveal pulmonary microecological characteristics. For pneumonia patients in PICU, the mNGS should be implemented early and complementary to CMTs.

Background: The mortality prediction scores were widely used in pediatric intensive care units. However, their performances were unclear in Chinese patients and there were also no reports based on large sample sizes in China. This study aims to evaluate the performances of three existing severity assessment scores in predicting PICU mortality and to identify important determinants. Methods: This prospective observational cohort study was carried out in eight multidisciplinary, tertiary-care PICUs of teaching hospitals in China. All eligible patients admitted to the PICUs between Aug 1, 2016, and Jul 31, 2017, were consecutively enrolled, among whom 3,957 were included for analysis. We calculated PCIS, PRISM IV, and PELOD-2 scores based on patient data collected in the first 24 h after PICU admission. The in-hospital mortality was defined as all-cause death within 3 months after admission. The discrimination of mortality was assessed using the area under the receiver-operating characteristics curve (AUC) and calibrated using the Hosmer–Lemeshow goodness-of-fit test. Results: A total of 4,770 eligible patients were recruited (median age 18.2 months, overall mortality rate 4.7%, median length of PICU stay 6 days), and 3,957 participants were included in the analysis. The AUC (95% confidence intervals, CI) were 0.74 (0.71–0.78), 0.76 (0.73–0.80), and 0.80 (0.77–0.83) for PCIS, PRISM IV, and PELOD-2, respectively. The Hosmer–Lemeshow test gave a chi-square of 3.16 for PCIS, 2.16 for PRISM IV and 4.81 for PELOD-2 (p ≥ 0.19). Cox regression identified five predictors from the items of scores better associated with higher death risk, with a C-index of 0.83 (95%CI 0.79–0.86), including higher platelet (HR = 1.85, 95% CI 1.59–2.16), invasive ventilation (HR = 1.40, 1.26–1.55), pupillary light reflex (HR = 1.31, 95% CI 1.22–1.42) scores, lower pH (HR 0.89, 0.84–0.94), and extreme PaO₂ (HR 2.60, 95% CI 1.61–4.19 for the 1st quantile vs. 4th quantile) scores. Conclusions: Performances of the three scores in predicting PICU mortality are comparable, and five predictors were identified with better prediction to PICU mortality in Chinese patients.