Lung microbiome is defined as the specific microbiota of lung. Lung microbiome can make the lung in a state of chronic inflammation through direct destruction, activation of inflammatory cells and release of inflammatory factors, and then progress to lung cancer. There are significant differences in lung microbiome between lung cancer patients and healthy people. Some specific microbial flora can be used as a diagnostic marker of lung cancer. Specific microbial communities are related to the efficacy of immunotherapy, and microbial composition may be used as a marker of immune-related adverse events. There are both challenges and opportunities for research on the relationship between lung microbiome and lung cancer. This review will focus on the significance and value of lung microbiome in the occurrence, diagnosis and immunotherapy of lung cancer, in order to provide a reference for basic and clinical researchers in related fields.
Behcet's Disease (BD) is a multisystem vasculitis characterized by disease alternated with recurrent episodes and remissions, involving genital, oral, ocular uvea, cutaneous, and articular manifestations. The nuclear factor (NF)-κB signaling pathway paly an important role in the BD progression. It encompasses diverse gene, protein, and cellular regulatory mechanisms operating across various levels, alongside microbiological and experimental studies involving animals and cells. At the protein research findings, activation of the NF-κB pathway in BD patients is marked by elevated plasma levels of soluble CD40 ligand, which stimulates neutrophils to release reactive oxygen species and extracellular traps, thereby promoting inflammation. At the cellular research findings, macrophages in BD patients polarize towards classically activated macrophages phenotype through the NF-κB pathway, exacerbating the inflammatory response. The activation of NF-κB is associated with increased expression of anti-apoptotic proteins in T cells, leading to prolonged inflammation. Microbiological investigations reveal that the decreased gut microbiota diversity in BD patients compromises intestinal barrier integrity. NF-κB pathway involvement in regulating neutrophil and type 1 helper T cell (Th) 1/Th17 cell function worsens inflammation. Genetically, BD patients exhibit polymorphisms in immune regulatory genes, which contribute to inflammation through the NF-κB pathway. Mutations in NF-κB-associated genes elevate the risk of BD, while mutations in the endogenous inhibitor A20 lead to abnormal NF-κB activity, sustaining inflammation. Animal experiments and in vitro experiments corroborate the efficacy of NF-κB inhibitors in attenuating inflammation. Targeting upstream inflammatory factors within the NF-κB pathway yields positive outcomes in BD patients. In summary, the NF-κB signaling pathway plays a pivotal role in the development of BD. Developing NF-κB inhibitors may open new avenues for treating BD. Further research is necessary to comprehensively elucidate the precise mechanisms by which NF-κB operates in the pathogenesis of BD, as well as its potential clinical applications in therapy.
Childhood obesity is a global public health problem that seriously affects the normal growth and development of children. In recent years, a large number of studies have pointed out that the intestinal microbiome is closely related to childhood obesity, and the treatment strategies targeting the intestinal microbiome have a certain improvement effect on childhood obesity. This article elaborates on the establishment and development of intestinal microbiome, intestinal microbiome characteristics, the mechanisms of intestinal microbiome involvement in the occurrence and development of childhood obesity, and potential intervention strategies, so as to provide more ideas for basic and clinical research on childhood obesity.
Microorganism distributes in the organs of human body which connect with external environment, especially those organs in the gastrointestinal tracts, and it also plays a fundamental role in the physiopathology of the host's body. Because the microorganism is very small and has a great variety, it is difficult to reveal the significance of microorganism in the human physiopathology comprehensively and deeply. With the development of molecular biology, genomics, bioinformatics and other disciplines, the microbiome research will be more possible and easier. There are two key contents of microecology. One of these is to identify and quantify the diversity of microorganism, and the other is to reveal activity and the physiopathological function of microorganism in the host. Microbiome research methods, therefore, can be summarized as the traditional detection methods, construction of gene library, the genetic fingerprint analysis and molecular hybridization techniques and so on.
ObjectiveTo explore gut microbiome influences on anastomotic healing following gastrointestinal surgery and its mechanism.MethodThe relevant literatures about gut microbiome and its impact on healing of gastrointestinal anastomosis and their mechanisms were reviewed.ResultsSeveral symbiotic intestinal microbiota such as the Enterococcus faecalis, Pseudomonas aeruginosa, Serratia marcescens, etc. could transform into the pathogenic bacteria with high toxic phenotype in an inflammatory environment in the body, and dissolve the extracellular matrix by degrading collagen or activating matrix metalloproteinase 9, resulting in the anastomotic leak.ConclusionIn general, exploring of effect of intestinal microbiome on healing process of anastomotic stoma is just beginning, conditions and mechanisms for transformation of bacteria from symbiotic to pathogenic still need to be explored.
[Abstract]The pathogenesis of aortic disease is not fully understood. Gut dysbiosis may play a role in the occurrence and development of aortic diseases. Several studies showed that the diversity of microbiota in abdominal aortic aneurysms significantly decreases and is correlated with the diameter of the aneurysm. Characteristic microbial communities associated with abdominal aortic aneurysm, such as Roseburia, Bifidobacterium, Ruminococcus, Akkermansia have been found in human and animal studies. The gut microbiota of patients with aortic dissection varies greatly. Characteristic microbial communities like Lachnospiraceae and Ruminococcus present a potential impact on the pathogenesis of aortic dissection. Bifidobacterium may be associated with Takayasu arteritis and thoracic aortic aneurysm. The gut microbiota affects the physiological functions of the host by synthesizing bioactive metabolites, which causes aortic diseases, mainly involving metabolites such as trimethylamine N-oxide (TMAO), lipopolysaccharides (LPS), tryptophan, and short chain fatty acids. More and more evidence supports the causal relationship between gut microbiota dysbiosis and aortic disease. Clarifying abnormal changes in gut microbiota may provide clues for finding potential therapeutic targets.
Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. MethodsA total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. ResultsThere were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.
Objective To investigate the characteristics of micro-biology in the respiratory tract in the patients who were suffering acute exacerbation of chronic obstructive pulmonary disease (AECOPD) with/without their respiratory failures as well as with the high/low frequency of exacerbation. MethodsSixty confirmed subjects in the Department of Respiratory and Critical Care in Guizhou Provincial Hospital from Nov. 2021 to Mar. 2022 were chosen and then divided them into two pairs of sub-groups randomly. Sub-group pairs one were based on the frequency of AECOPD: higher frequency and lower frequency. Sub-group pairs two were based on whether the patients were once with respiratory failure or not. 16S rRNA high-throughput sequencing method was used to detect sputum microecology. The Alpha and Beta diversity of each subgroup, and the differences in bacterial composition and relative abundance, were compared. Results For the AECOPD group with low-frequent of exacerbation, its diversity and abundance of microbiology were higher than those group with high-frequent of exacerbation. The group of AECOPD with respiratory failure had lower bacteria micro diversity but abundancy was higher than those group without respiratory failure. ConclusionThe frequency of AECOPD and whether it is with respiratory failure is related to the change of micro-biology in respiratory tract, so such change plays a great role in this disease.
There is increasing evidence that microorganisms play a complex and important role in human health and disease, and that the in vivo microbiome can directly or indirectly affect the host’s immune system, endocrine system, and nervous system. Therefore, a relatively stable equilibrium between the host and the microbiome is crucial in human health. However, in the special pathophysiological state of the perioperative period, preoperative anxiety and sleep deprivation, anesthesia intervention and surgical injury, postoperative medication and complications may all have different effects on the microbial composition of various organs in the body, resulting in pathogenic microorganisms, and the balance between beneficial microorganisms is altered. This may affect patient the outcomes and prognosis in a direct or indirect manner. This paper will provide a systematic review of key studies to understand the impact of perioperative stress on the commensal microbiome, provide a fresh perspective on optimizing perioperative management strategies, and discuss possible potential interventions to restore microbiome-mediated steady state.
Objective To investigate the potential causal relationship between specific oral microbiota and peptic ulcer disease (PUD) using a Mendelian randomization (MR) approach. Methods The genome-wide association study (GWAS) data from East Asian populations was utilized to perform a two-sample MR analysis to determine the causal relationship between oral microbiota and PUD. The MR analysis was primarily conducted using the inverse-variance weighted (IVW) method, supplemented by MR-Egger and weighted median methods. Heterogeneity and pleiotropy were assessed, and the leave-one-out method was employed to evaluate the stability of the MR results. Results There was a complex association between specific bacterial genera of the oral microbiota and PUD. Prevotella was found to potentially promote duodenal ulcers while exerting a protective effect against gastric ulcers. Campylobacter and Streptococcus demonstrated differing effects on gastric and duodenal ulcers. Furthermore, Fusobacterium and Haemophilus_A were positively associated with peptic ulcers, suggesting an increased risk of gastroduodenal ulcer development. Conclusion This study explores the causal relationship between oral microbiota and PUD, providing new insights into the prevention and treatment of PUD mediated by oral microbiota.