Microbiota Emergencies in the Diagnosis of Lung Diseases: A Meta-Analysis.
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ABSTRACT:
SUBMITTER: Ruan R
PROVIDER: S-EPMC8490768 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveLung microbiota is increasingly implicated in multiple types of respiratory diseases. However, no study has drawn a consistent conclusion regarding the relationship between changes in the microbial community and lung diseases. This study verifies the association between microbiota level and lung diseases by performing a meta-analysis.MethodsLiterature databases, including PubMed, ISI Web of Science, Embase, Google Scholar, PMC, and CNKI, were used to collect related articles published before March 20, 2021. The standard mean deviation (SMD) and related 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup, sensitivity, and publication bias analyses were also conducted.ResultsSix studies, comprising 695 patients with lung diseases and 176 healthy individuals, were included in this meta-analysis. The results indicated that the microbiota level was higher in patients with lung diseases than in healthy individuals (SMD = 0.39, 95% CI = 0.22-0.55, I 2 = 91.5%, P < 0.01). Subgroup analysis based on country demonstrated that the microbiota level was significantly higher in Chinese (SMD = 1.90, 95% CI = 0.87-2.93, I 2 = 62.3%, P < 0.01) and Korean (SMD = 0.24, 95% CI = 0.13-0.35, I 2 = 78.7%, P < 0.01) patients with lung diseases. The microbiota level of patients with idiopathic pulmonary fibrosis (IPF) (SMD = 1.40, 95% CI = 0.42-2.38, I 2 = 97.3%, P = 0.005), chronic obstructive pulmonary disease (COPD) (SMD = 0.30, 95% CI = 0.09-0.50, I 2 = 83.9%, P = 0.004), and asthma (SMD = 0.19, 95% CI = 0.06-0.32, I 2 = 69.4%, P = 0.004) were significantly higher than those of the healthy group, whereas a lower microbiota level was found in patients with chronic hypersensitivity pneumonitis (CHP). The microbiota level significantly increased when the disease sample size was >50. Subgroup analysis based on different microbiota genera, indicated that Acinetobacter baumannii and Pseudomonas aeruginosa were significantly increased in COPD and asthma diseases.ConclusionWe observed that patients with IPF, COPD, and asthma had a higher microbiota level, whereas patients with CHP had a lower microbiota level compared to the healthy individuals. The level of A. baumannii and P. aeruginosa were significantly higher in patients with COPD and asthma, and thus represented as potential microbiota markers in the diagnosis and treatment of lung diseases.
| S-EPMC8795898 | biostudies-literature
Project description:BackgroundWhile there seems to be a consensus that a decrease in gut microbiome diversity is related to a decline in health status, the associations between respiratory microbiome diversity and chronic lung disease remain a matter of debate. We provide a systematic review and meta-analysis of studies examining lung microbiota alpha-diversity in patients with asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF) or bronchiectasis (NCFB), in which a control group based on disease status or healthy subjects is provided for comparison.ResultsWe reviewed 351 articles on title and abstract, of which 27 met our inclusion criteria for systematic review. Data from 24 of these studies were used in the meta-analysis. We observed a trend that CF patients have a less diverse respiratory microbiota than healthy individuals. However, substantial heterogeneity was present and detailed using random-effects models, which limits the comparison between studies.ConclusionsKnowledge on respiratory microbiota is under construction, and for the moment, it seems that alpha-diversity measurements are not enough documented to fully understand the link between microbiota and health, excepted in CF context which represents the most studied chronic respiratory disease with consistent published data to link alpha-diversity and lung function. Whether differences in respiratory microbiota profiles have an impact on chronic respiratory disease symptoms and/or evolution deserves further exploration.
| S-EPMC9396807 | biostudies-literature
Project description:Diffuse cystic lung diseases are characterized by cysts in more than one lung lobe, the cysts originating from various mechanisms, including the expansion of the distal airspaces due to airway obstruction, necrosis of the airway walls, and parenchymal destruction. The progression of these diseases is variable. One essential tool in the evaluation of these diseases is HRCT, because it improves the characterization of pulmonary cysts (including their distribution, size, and length) and the evaluation of the regularity of the cyst wall, as well as the identification of associated pulmonary and extrapulmonary lesions. When combined with clinical and laboratory findings, HRCT is often sufficient for the etiological definition of diffuse lung cysts, avoiding the need for lung biopsy. The differential diagnoses of diffuse cystic lung diseases are myriad, including neoplastic, inflammatory, and infectious etiologies. Pulmonary Langerhans cell histiocytosis, lymphangioleiomyomatosis, lymphocytic interstitial pneumonia, and follicular bronchiolitis are the most common diseases that produce this CT pattern. However, new diseases have been included as potential determinants of this pattern. RESUMO As doenças pulmonares císticas difusas se caracterizam pela presença de cistos envolvendo mais de um lobo pulmonar, que se originam por diversos mecanismos, incluindo dilatação dos espaços aéreos distais por obstrução, necrose das paredes das vias aéreas e destruição do parênquima. Essas doenças apresentam evolução variável. A TCAR é fundamental na avaliação dessas doenças uma vez que permite uma melhor caracterização dos cistos pulmonares, incluindo sua distribuição, tamanho, extensão e regularidade das paredes, assim como a determinação de outras lesões pulmonares e extrapulmonares associadas. Frequentemente a TCAR é suficiente para a definição etiológica dos cistos pulmonares difusos, associada a achados clínicos e laboratoriais, sem a necessidade de realização de biópsia pulmonar. O diagnóstico diferencial das doenças pulmonares císticas difusas é extenso, incluindo etiologias neoplásicas, inflamatórias e infecciosas, sendo as mais frequentes determinantes desse padrão tomográfico a histiocitose pulmonar de células de Langerhans, a linfangioleiomiomatose, a pneumonia intersticial linfocitária e a bronquiolite folicular. Novas etiologias foram incluídas como potenciais determinantes desse padrão.
| S-EPMC5474378 | biostudies-other
Project description:Inflammatory rheumatic diseases (IRDs), encompassing a broad spectrum of chronic disorders, typically necessitate prolonged therapeutic intervention. Nevertheless, these diseases can sometimes manifest as severe emergencies requiring prompt and extensive medical intervention. Urgent intervention is essential for effectively recognizing and managing these situations, as they have the potential to be life-threatening and can result in severe morbidity and mortality. Emergencies in IRDs can occur with different frequencies and manifestations, including nervous system issues, severe infections, thrombosis-emboli, renal crises, gastrointestinal issues, and cardiovascular events. The fact that these events can occur across different IRDs underscores the necessity for heightened awareness and readiness among healthcare professionals. The pathophysiologic mechanisms that cause rheumatic emergencies are complex and involve multiple factors. These emergencies frequently arise due to the interplay between the inflammatory characteristics of rheumatic diseases and different systemic triggers. Early detection and treatment can have a substantial impact on an individual's prognosis in cases of severe and life-threatening disorders that require prompt recognition. Rapid decision-making and urgent care are required to effectively address rheumatic emergencies, as well as the implementation of a diagnostic flowchart. This article provides an overview of the emergencies linked to IRDs, classifying and assessing them individually. This article aims to enhance healthcare professionals' knowledge and awareness of critical situations by examining current recommendations and pathophysiological information. Implementing standardized diagnostic and treatment methods, providing patient education, and conducting continuing research into the underlying mechanisms are essential for enhancing the management of these critical situations and improving patient outcomes.
| S-EPMC11343811 | biostudies-literature
Project description:Numerous lines of evidence, ranging from recent studies back to those in the 1920s, have demonstrated that the lungs are NOT bacteria-free during health. We have recently proposed that the entire respiratory tract should be considered a single ecosystem extending from the nasal and oral cavities to the alveoli, which includes gradients and niches that modulate microbiome dispersion, retention, survival and proliferation. Bacterial exposure and colonization of the lungs during health is most likely constant and transient, respectively. Host microanatomy, cell biology and innate defenses are altered during chronic lung disease, which in turn, alters the dynamics of bacterial turnover in the lungs and can lead to longer term bacterial colonization, as well as blooms of well-recognized respiratory bacterial pathogens. A few new respiratory colonizers have been identified by culture-independent methods, such as Pseudomonas fluorescens; however, the role of these bacteria in respiratory disease remains to be determined.
| S-EPMC5081074 | biostudies-literature
Project description:BackgroundIn order to review the epidemiologic evidence concerning previous lung diseases as risk factors for lung cancer, a meta-analysis and systematic review was conducted.MethodsRelevant studies were identified through MEDLINE searches. Using random effects models, summary effects of specific previous conditions were evaluated separately and combined. Stratified analyses were conducted based on smoking status, gender, control sources and continent.ResultsA previous history of COPD, chronic bronchitis or emphysema conferred relative risks (RR) of 2.22 (95% confidence interval (CI): 1.66, 2.97) (from 16 studies), 1.52 (95% CI: 1.25, 1.84) (from 23 studies) and 2.04 (95% CI: 1.72, 2.41) (from 20 studies), respectively, and for all these diseases combined 1.80 (95% CI: 1.60, 2.11) (from 39 studies). The RR of lung cancer for subjects with a previous history of pneumonia was 1.43 (95% CI: 1.22-1.68) (from 22 studies) and for subjects with a previous history of tuberculosis was 1.76 (95% CI=1.49, 2.08), (from 30 studies). Effects were attenuated when restricting analysis to never smokers only for COPD/emphysema/chronic bronchitis (RR=1.22, 0.97-1.53), however remained significant for pneumonia 1.36 (95% CI: 1.10, 1.69) (from 8 studies) and tuberculosis 1.90 (95% CI: 1.45, 2.50) (from 11 studies).ConclusionsPrevious lung diseases are associated with an increased risk of lung cancer with the evidence among never smokers supporting a direct relationship between previous lung diseases and lung cancer.
| S-EPMC3069026 | biostudies-literature
Project description:BackgroundEach lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.Objective and methodsIn this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions.FindingsBy employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations.ConclusionFor the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases.
| S-EPMC4589383 | biostudies-literature
Project description:Given the high comorbidity in patients on hemodialysis and the complexity of the dialysis treatment, it is remarkable how rarely a life-threatening complication occurs during dialysis. The low rate of dialysis emergencies can be attributed to numerous safety features in modern dialysis machines; meticulous treatment and testing of the dialysate solution to prevent exposure to trace elements, toxins, and pathogens; adherence to detailed treatment protocols; and extensive training of dialysis staff to handle medical emergencies. Most hemodialysis emergencies can be attributed to human error. A smaller number are due to rare idiosyncratic reactions. In this review, we highlight major emergencies that may occur during hemodialysis treatments, describe their pathogenesis, offer measures to minimize them, and provide specific interventions to prevent catastrophic consequences on the rare occasions when such emergencies arise. These emergencies include dialysis disequilibrium syndrome, venous air embolism, hemolysis, venous needle dislodgement, vascular access hemorrhage, major allergic reactions to the dialyzer or treatment medications, and disruption or contamination of the dialysis water system. Finally, we describe root cause analysis after a dialysis emergency has occurred to prevent a future recurrence.
| S-EPMC5293333 | biostudies-literature
Project description:The lungs, as a place of gas exchange, are continuously exposed to environmental stimuli, such as allergens, microbes, and pollutants. The development of the culture-independent technique for microbiological analysis, such as 16S rRNA sequencing, has uncovered that the lungs are not sterile and, in fact, colonized by diverse communities of microbiota. The function of intestinal microbiota in modulating mucosal homeostasis and defense has been widely studied; however, the potential function of lung microbiota in regulating immunity and homeostasis has just begun. Increasing evidence indicates the relevance of microbiota to lung homeostasis and disease. In this review, we describe the distribution and composition of microbiota in the respiratory system and discuss the potential function of lung microbiota in both health and acute/chronic lung disease. In addition, we also discuss the recent understanding of the gut-lung axis, because several studies have revealed that the immunological interaction among the gut, the lung, and the microbiota was involved in this issue.
| S-EPMC7089139 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).AimWe performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.MethodsWe searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.ResultsFor the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.ConclusionsAI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
| S-EPMC9305819 | biostudies-literature