The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units.

This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (P = 0.002) and distinct compositions (P = 0.001) compared to non-septic patients. Corynebacterium, Staphylococcus, Acinetobacter, and Pseudomonas were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine.IMPORTANCEThe important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.

Association Between Diet-Related Inflammation and COPD: Findings From NHANES III.

Background and Aims: Little is known about diet-related inflammation in chronic obstructive pulmonary disease (COPD). In this study, we aimed to explore the association between COPD and dietary inflammatory index (DII) scores in adults over 40 years old. Methods: Data were obtained from the 2013 to 2018 National Health and Nutrition Examination Survey (NHANES). In the present study, 9,929 participants were included and analyzed. The DII score was calculated and divided into tertiles. Logistic regression analysis was performed to determine the odds ratios of DII tertiles. Results: Participants were categorized into COPD (565, 5.69%) and non-COPD groups (9,364, 94.31%) according to interview information. COPD individuals had higher DII scores than non-COPD individuals (0.429 ± 1.809 vs. -0.191 ± 1.791, p < 0.001). The highest DII score tertile included 46.55% of COPD individuals was associated with lower family incomes and education and a higher smoking rate (p < 0.01). The odds ratios (95% CIs) of COPD according to logistic regression were 0.709 (0.512-0.982) for T1 and 0.645 (0.475-0.877) for T2 of the DII score (p = 0.011). Conclusion: Higher DII scores were positively correlated with COPD in participants over 40 years old. These results further support that diet can be used as an intervention strategy for COPD management.

Septic patients in the intensive care unit present different nasal microbiotas.

AIM: The primary objective of this study was to evaluate correlations among mortality, intensive care unit (ICU) length of stay and airway microbiotas in septic patients. MATERIALS & METHODS: A deep-sequencing analysis of the 16S rRNA gene V4 region was performed. RESULTS: The nasal microbiota in septic patients was dominated by three nasal bacterial types (Corynebacterium, Staphylococcus and Acinetobacter). The Acinetobacter type was associated with the lowest diversity and longest length of stay (median: 9 days), and the Corynebacterium type was associated with the shortest length of stay. We found that the Acinetobacter type in the >9-day group was associated with the highest mortality (33%). CONCLUSION: Septic patients have three nasal microbiota types, and the nasal microbiota is related to the length of stay and mortality.

[A machine learning model based on initial gut microbiome data for predicting changes of Bifidobacterium after prebiotics consumption].

OBJECTIVE: To investigate the effects of prebiotics supplementation for 9 days on gut microbiota structure and function and establish a machine learning model based on the initial gut microbiota data for predicting the variation of Bifidobacterium after prebiotic intake. METHODS: With a randomized double-blind self-controlled design, 35 healthy volunteers were asked to consume fructo-oligosaccharides (FOS) or galacto-oligosaccharides (GOS) for 9 days (16 g per day). 16S rRNA gene high-throughput sequencing was performed to investigate the changes of gut microbiota after prebiotics intake. PICRUSt was used to infer the differences between the functional modules of the bacterial communities. Random forest model based on the initial gut microbiota data was used to identify the changes in Bifidobacterium after 5 days of prebiotic intake and then to build a continuous index to predict the changes of Bifidobacterium. The data of fecal samples collected after 9 days of GOS intervention were used to validate the model. RESULTS: Fecal samples analysis with QIIME revealed that FOS intervention for 5 days reduced the intestinal flora alpha diversity, which rebounded on day 9; in GOS group, gut microbiota alpha diversity decreased progressively during the intervention. Neither FOS nor GOS supplement caused significant changes in ß diversity of gut microbiota. The area under the curve (AUC) of the prediction model was 89.6%. The continuous index could successfully predict the changes in Bifidobacterium (R=0.45, P=0.01), and the prediction accuracy was verified by the validation model (R=0.62, P=0.01). CONCLUSION: Short-term prebiotics intervention can significantly decrease α-diversity of the intestinal flora. The machine learning model based on initial gut microbiota data can accurately predict the changes in Bifidobacterium, which sheds light on personalized nutrition intervention and precise modulation of the intestinal flora.

Sputum Bacterial and Fungal Dynamics during Exacerbations of Severe COPD.

The changes in the microbial community structure during acute exacerbations of severe chronic obstructive pulmonary disease (COPD) in hospitalized patients remain largely uncharacterized. Therefore, further studies focused on the temporal dynamics and structure of sputum microbial communities during acute exacerbation of COPD (AECOPD) would still be necessary. In our study, the use of molecular microbiological techniques provided insight into both fungal and bacterial diversities in AECOPD patients during hospitalization. In particular, we examined the structure and varieties of lung microbial community in 6 patients with severe AECOPD by amplifying 16S rRNA V4 hyper-variable and internal transcribed spacer (ITS) DNA regions using barcoded primers and the Illumina sequencing platform. Sequence analysis showed 261 bacterial genera representing 20 distinct phyla, with an average number of genera per patient of >157, indicating high diversity. Acinetobacter, Prevotella, Neisseria, Rothia, Lactobacillus, Leptotrichia, Streptococcus, Veillonella, and Actinomyces were the most commonly identified genera, and the average total sequencing number per sputum sample was >10000 18S ITS sequences. The fungal population was typically dominated by Candia, Phialosimplex, Aspergillus, Penicillium, Cladosporium and Eutypella. Our findings highlight that COPD patients have personalized structures and varieties in sputum microbial community during hospitalization periods.

[An analysis of the characteristics and etiology of hand, foot and mouth disease in a Guangzhou sentinel hospital from May to December, 2008].

OBJECTIVE: To analyze the characteristics and etiology of hand, foot and mouth disease (HFMD) in a sentinel hospital of Guangzhou. METHODS: The epidemiological data and clinical specimens were collected from May to December, 2008 for virological investigations (viral isolation, RT-PCR and molecular identification) and phylogenetic analysis. RESULTS: A total of 309 clinical cases were reported, and the incidence was the highest in 2-4-year-old children, among whom only 15 developed complications, with human enterovirus 71 (HEV71) as the main pathogen (64.7%). Phylogenetic analysis indicated that ten Guangzhou EV71 isolates belonged to Cluster C4a. CONCLUSION: HFMD is an important infectious disease in children resulting from infections by HEV71 as the main pathogen in 2008, and the Guangzhou C4a strains co-evolved with the isolates from other provinces in China and the neighboring countries.