Association between triglyceride-glucose related indices and mortality among individuals with non-alcoholic fatty liver disease or metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: The prognostic value of triglyceride-glucose (TyG) related indices in non-alcoholic fatty liver disease (NAFLD) or metabolic dysfunction-associated steatotic liver disease (MASLD) is still unclear. This study aimed to determine the associations between TyG-related indices and long-term mortality in this population. METHODS: The data came from the National Health and Nutrition Examination Survey (NHANES III) and National Death Index (NDI). Baseline TyG, TyG combining with body mass index (TyG-BMI), and TyG combining with waist circumference (TyG-WC) indices were calculated, and mortality status was determined through 31 December 2019. Multivariate Cox and restricted cubic spline (RCS) regression models were performed to evaluate the relationship between TyG-related indices and long-term mortality among participants with NAFLD/MASLD. In addition, we examined the association between TyG-related indices and all-cause mortality within subgroups defined by age, sex, race/ethnicity, and fibrosis-4 index (FIB-4). RESULTS: There were 10,390 participants with completed ultrasonography and laboratory data included in this study. NAFLD was diagnosed in 3672/10,390 (35.3%) participants, while MASLD in 3556/10,390 (34.2%) amongst the overall population. The multivariate Cox regression analyses showed high levels of TyG-related indices, particularly in TyG-BMI and TyG-WC indices were significantly associated with the all-cause mortality, cardiovascular mortality, and diabetes mortality in either NAFLD or MASLD. The RCS curves showed a nonlinear trend between three TyG-related indices with all-cause mortality in either NAFLD or MASLD. Subgroup analyses showed that TyG-BMI and TyG-WC indices were more suitable for predicting all-cause mortality in patients without advanced fibrosis. CONCLUSION: Our study highlights the clinical value of TyG-related indices in predicting the survival of the NAFLD/MASLD population. TyG-BMI and TyG-WC indices would be the surrogate biomarkers for the follow-up of the population without advanced fibrosis.

Clinical application value of metagenomic next-generation sequencing in the diagnosis of central nervous system infections.

Objectives: The purpose of this study is to evaluate the clinical application value of metagenomic next-generation sequencing (mNGS) in central nervous system (CNS) infections. Methods: Both mNGS and routine examination of cerebrospinal fluid (CSF) samples from patients with CNS infections retrospectively analyzed the efficacy of mNGS in this cohort and were ultimately compared with a clinical diagnosis. Results: A total of 94 cases consistent with CNS infections were included in the analysis. The positive rate for mNGS is 60.6% (57/94), which is significantly higher than 20.2% (19/94; p < 0.01) detected using conventional methods. mNGS detected 21 pathogenic strains that could not be detected by routine testing. Routine tests were positive for two pathogens but negative for mNGS. The sensitivity and specificity of mNGS in the diagnosis of central nervous system infections were 89.5% and 44%, respectively, when compared with traditional tests. At discharge, 20 (21.3%) patients were cured, 55 (58.5%) patients showed improvements, five (5.3%) patients did not recover, and two (2.1%) patients died. Conclusion: mNGS has unique advantages in the diagnosis of central nervous system infections. mNGS tests can be performed when patients are clinically suspected of having a central nervous system infection but no pathogenic evidence.

Brain-gut microbiota multimodal predictive model in patients with bipolar depression.

BACKGROUND: The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction. METHODS: At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders. RESULTS: The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified，Porphyromonas_asaccharolytica，Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality. LIMITATIONS: Lack post-treatment and external validation datasets; size of HCs is modest. CONCLUSIONS: Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.

Metagenomic next-generation sequencing provides prognostic warning by identifying mixed infections in nocardiosis.

Nocardia is an opportunistic pathogen that mainly involves immunosuppressed patients and causes a high mortality rate. As an emerging approach to detect infectious pathogens, metagenomic next-generation sequencing (mNGS) was reported in the detection of Nocardia. However, there is no evidence demonstrating the effect of mNGS on the prognosis of Nocardia infection. In this retrospective study, we included 18 nocardiosis patients. Nocardia species were detected by mNGS from their clinical samples. All the patients were diagnosed with nocardiosis by clinical experts through a comprehensive evaluation. Of these 18 patients, fever is the most frequent initial symptom. Compared to traditional culture methods, mNGS provides a faster turnaround time (TAT) and higher sensitivity. Pulmonary nocardiosis was the most common clinical presentation in the study. mNGS detected 13 types of Nocardia species, of which Nocardia abscessus and Nocardia cyriacigeorgica were the most common species. The study's most noteworthy discovery is that mNGS outperforms culture at detecting mixed infections (more than one pathogen detected in one clinical specimen, including bacteria, fungi, and excluding virus), and number of infectious species was an independent risk factor for nocardiosis patients' prognostics after adjusting age, ICU days, gender and underlying diseases (adjusted HR = 1.47, 95% CI: 1.09-1.98, p = 0.011). As a result, we believe that by detecting mixed infections (more than one pathogenic species), mNGS can provide a clinical risk warning for the prognosis of nocardiosis.

Application of Metagenomic Next-Generation Sequencing in Mycobacterium tuberculosis Infection.

The fight against Mycobacterium tuberculosis (MTB) has been going on for thousands of years, while it still poses a threat to human health. In addition to routine detections, metagenomic next-generation sequencing (mNGS) has begun to show presence as a comprehensive and hypothesis-free test. It can not only detect MTB without isolating specific pathogens but also suggest the co-infection pathogens or underlying tumor simultaneously, which is of benefit to assist in comprehensive clinical diagnosis. It also shows the potential to detect multiple drug resistance sites for precise treatment. However, considering the cost performance compared with conventional assays (especially Xpert MTB/RIF), mNGS seems to be overqualified for patients with mild and typical symptoms. Technology optimization of sequencing and analyzing should be conducted to improve the positive rate and broaden the applicable fields.

Invasive Pulmonary Aspergillosis Diagnosis via Peripheral Blood Metagenomic Next-Generation Sequencing.

Invasive pulmonary aspergillosis (IPA) is one of the major causes of morbidity and mortality in immunocompromised patients such as hematological malignancies, hematopoietic stem cell transplantation, and solid organ transplantation. The diagnosis of IPA in these patients is still difficult because it has no obvious specificity in clinical symptoms, signs and imaging, and test sensitivity of blood 1,3-ß-d-glucan test, galactomannan are low. Therefore, we still need to explore more diagnostic methods. In our study, via peripheral blood metagenomic next-generation sequencing (mNGS), five patients were tested positive for Aspergillus DNA and then quickly diagnosed as IPA. Out of the 5 cases, 1 was proven and 4 were probable IPA. The underlying diseases of the 5 patients were myelodysplastic syndrome (2 cases), acute myeloid leukemia (2 cases), and renal transplantation (1 case). Then they were diagnosed as IPA using other methods such as lung histopathology, bronchoalveolar lavage fluid (BALF) mNGS, and sputum culture or sputum mNGS. In case 1, sputum culture suggested Aspergillus flavus. In case 2, both Grocott methenamine silver (GMS) stain of lung histopathology and lung tissue mNGS suggested Aspergillus infection. In cases 3 and 4, BALF-mNGS suggested Aspergillus infection. In case 5, sputum mNGS suggested Aspergillus infection. In conclusion, detecting the cfDNA of Aspergillus via peripheral blood mNGS can be used to diagnose IPA and is a rapid and non-invasive diagnosis method.

Alcohol-Related Elevation of Liver Transaminase Is Associated With Gut Microbiota in Male.

Alcoholic liver damage has become a widespread health problem as alcohol consumption increases and is usually identified by elevated liver transaminase. We conducted this study to investigate the role of the gut microbiome in the individual susceptibility to alcoholic liver injury. We divided the participants into four groups based on alcohol consumption and liver transaminase elevation, which were drinking case group, drinking control group, non-drinking case group, and non-drinking control group. The drinking case group meant participants who were alcohol consumers with elevated liver transaminase. We found that alpha and beta diversities of the drinking case group differed from the other three groups. Species Faecalibacterium prausnitzii and Roseburia hominis were significantly in lower abundance in the drinking case group and were proved the protective effect against inflammatory liver damage in the former study. Ruminococcus gnavus exhibited the most positive association to alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and contributed to liver inflammation.

Effects of Smoking on Inflammatory Markers in a Healthy Population as Analyzed via the Gut Microbiota.

The number of people who smoke has increased in recent years, and the incidence of smoking-related diseases increases annually. This study was conducted to explore whether smoking affects diseases via changes in the gut microbiota. We enrolled 33 smokers and 121 non-smokers. We collected fecal samples from all participants and performed whole-genome sequencing. Smoking significantly affected the gut microbiota. At the phylum through genus levels, the smokers' microbiotas showed slight changes compared with those of the non-smokers. The α- and ß-diversities differed significantly between the smokers and non-smokers, and the smokers' gut microbiota compositions differed significantly from those of the non-smokers. At the species level, the relative abundances of Ruminococcus gnavus (P=0.00197) and Bacteroides vulgatus (P=0.0468) were significantly greater in the smokers than in the non-smokers, while the relative abundances of Faecalibacterium prausnitzii (P=0.0000052) and Akkermansia muciniphila (P=0.0057) were significantly lower in the smokers. Smoking increases inflammation in the body by inducing an increased abundance of proinflammatory bacteria. Non-smokers had higher abundances of anti-inflammatory microorganisms than did smokers; these microorganisms can produce short-chain fatty acids, which inhibit inflammation.

Case Report: A Severe and Multi-Site Nocardia farcinica Infection Rapidly and Precisely Identified by Metagenomic Next-Generation Sequencing.

Nocardia genus is an aerobic, gram-positive, and opportunistic pathogen, which mainly affects cell-mediated immunosuppressed patients. Early diagnosis and treatment greatly improve prognosis. However, the limitation of golden standard-bacterial culture exists. Here, we report a 61-year-old male with pneumonia, sepsis and intermuscular abscesses induced by Nocardia farcinica. Venous blood culture reported negative results. Former improper diagnosis and treatment did not improve his condition. With the assistant of metagenomic next-generation sequencing, the pathogen was identified as Nocardia farcinica. He was then applied with accurate treatment and had a remarkable clinical and radiological improvement.

Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model.

Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP, and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0.907 (95% CI 0.831-0.983). Mixed model constructed with combination of age, demographics, symptoms, and laboratory findings at admission had better performance (p = 0.021) with a generalized AUC of 0.9852 (95% CI 0.961-1). We chose 0.441 as death prediction threshold (with 0.85 sensitivity and 0.987 specificity) and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.