Exploring the Role of Gut-Lung Interactions in COPD Pathogenesis: A Comprehensive Review on Microbiota Characteristics and Inflammation Modulation.

Chronic obstructive pulmonary disease (COPD) is a paramount contributor to global morbidity and mortality. Over the past decade, the concept of the "gut-lung axis" has emerged, offering a lens through which to examine the intricate interplay between the host, microbiome, and respiratory diseases, including COPD. An expanding body of evidence underscores that the composition of both the gastrointestinal and respiratory microbiome deviates in COPD patients compared to healthy individuals, leading to distinct host immune responses and clinical manifestations. The objective of this review is to provide a concise overview of the role both gut and respiratory microbiome play in the development of COPD. This was accomplished by compiling current literature on the microbiome profile in stable and exacerbated cases of COPD, as well as exploring the biological mechanisms through a discussion of relevant experiments conducted on murine models. Hallmark characteristics of the microbial profile in COPD encompass reduced Prevotella species in the respiratory microbiome, culminating in a loss of anti-inflammatory protection, and diminished Bacteroidetes in the gut microbiome, leading to a decrease in protective short-chain fatty acids. The proliferation of Proteobacteria, particularly the Haemophilus species, Moraxellaspecies, and Pseudomonas species contribute to COPD pathologies via recognition of proinflammatory lipopolysaccharide via Toll-like receptors. As a consequence, deteriorated pulmonary function, enhanced severity, increased onset of exacerbations, and elevated mortality were observed.

Prognostic value of inflammation-related biomarkers in patients with gastroenteropancreatic neuroendocrine neoplasms: A systematic review and meta-analysis.

Hematological indicators of chronic systemic inflammation are significant biomarkers for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). We performed a systematic review and meta-analysis to assess the impact of certain factors on the overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) of patients with GEP-NENs. These factors include the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), and C-reactive protein (CRP) levels. After searching the Medline, Embase, and Cochrane Library databases from January 1, 2000 to October 20, 2022 and the American Society of Clinical Oncology conference proceedings from January 1, 2017, hazard ratios (HRs) and 95% confidence intervals (CIs) were extracted. Subgroup analyses were conducted to identify the origins of heterogeneity and examine the impact of factor grouping. The effects of the cut-off values and sample size were assessed by meta-regression. The results revealed that higher NLRs, PLRs, and CRP levels were associated with shorter OS (HR = 2.09, 95% CI = 1.55-2.8; HR = 1.79, 95% CI = 1.40-2.28; and HR = 2.88, 95% CI = 2.09-3.95, respectively; all p < 0.001). Higher NLRs and lower LMRs were associated with shorter DFS (HR = 3.34, 95% CI = 2.11-5.29 and HR = 2.71, 95% CI = 2.27-3.24, respectively; both p < 0.001). Higher PLRs and CRP levels were correlated with shorter PFS (HR = 3.48, 95% CI = 1.34-9.03, p = 0.01 and HR = 3.14, 95% CI = 1.63-6.08, p = 0.001). As demonstrated in the research, hematological indicators of systemic inflammation are promising biomarkers for GEP-NEN assessment.

Genetic Insights into the Gut-Lung Axis: Mendelian Randomization Analysis on Gut Microbiota, Lung Function, and COPD.

Background: Chronic obstructive pulmonary disease (COPD) is a respiratory disorder with a complex etiology involving genetic and environmental factors. The dysbiosis of gut microbiota has been implicated in COPD. Mendelian Randomization (MR) provides a tool to investigate causal links using genetic variants as instrumental variables. This study aims to employ MR analysis to explore the causal relationship between gut microbiota, lung function, and COPD. Methods: We utilized genome-wide association study (GWAS) data from MiBioGen, UK Biobank and FinnGen, which were related to gut microbial taxa, lung function parameters including forced vital capacity in one second (FEV1), forced vital capacity (FVC), and percentage of predicted FEV1 (FEV1%pred), as well as GWAS data for COPD. MR analysis was conducted to assess the causal effects of gut microbiota on lung function and the risk of COPD. Sensitivity analysis was utilized to examine the stability of the causal relationships. Multiple testing and reverse analysis were employed to evaluate the robustness of these relationships. Results: Using the IVW method, 64 causal correlations were identified. Through conducting sensitivity analysis, multiple testing, and reverse analysis, we identified 14 robust and stable causal relationships. The bacterial taxa that showed a positive association with lung function included Desulfovibrionaceae, Erysipelotrichales, Desulfovibrionales, Clostridiales, Clostridia, Deltaproteobacteria and Erysipelotrichia, while Selenomonadales and Negativicutes showed a negative association with lung function. The abundance of Holdemanella were positively correlated with the risk of COPD, while FamilyXIII exhibited a negative correlation with the risk of COPD. Conclusion: Several microbial taxa were discovered to have a positive causal correlation with lung function, offering potential insights into the development of probiotics. The presence of microbial taxa negatively correlated with lung function and positively correlated with COPD emphasized the potential impact of gut microbiota dysbiosis on respiratory health.

Therapeutic potential of iron chelators in retinal vascular diseases.

Iron is one of the necessary metal elements in the human body. There are numerous factors that control the balance of iron metabolism, and its storage and transportation mechanisms are intricate. As one of the most energy-intensive tissues in the body, the retina is susceptible to iron imbalance. The occurrence of iron overload in the retina leads to the generation of a significant quantity of reactive oxygen species. This will aggravate local oxidative stress and inflammatory reactions and even lead to ferroptosis, eventually resulting in retinal dysfunction. The blood-retina-retinal barrier is eventually harmed by oxidative stress and elevated inflammation, which are characteristics of retinal vascular disorders. The pathophysiology of retinal vascular disorders may be significantly influenced by iron. Recently, iron-chelating agents have been found to have antioxidative and anti-inflammatory actions in addition to iron chelating. Therefore, iron neutralization is considered to be a new and potentially useful therapeutic strategy. This article reviews the iron overload in retinal vascular diseases and discusses the therapeutic potential of iron-chelating agents.

Genetic evidence on the causality between gut microbiota and various asthma phenotypes: a two-sample Mendelian randomization study.

Introduction: Asthma is a multifarious disease that manifests in various phenotypes. Among the various factors that contribute to the development of asthma, the gut microbiota has recently emerged as a compelling area of investigation. This study aims to investigate the causal relationships between gut microbiota and distinct asthma phenotypes. Methods: The genome-wide association study (GWAS) summary statistics for 211 gut microbial taxa were used as study exposure. Five traits pertaining to various asthma phenotypes (asthma, allergic asthma, childhood asthma, suggestive for eosinophilic asthma and obesity-related asthma) were included as study outcome. We conducted Mendelian randomization (MR) analysis and sensitivity analysis for each bacterial taxa and asthma phenotypes. Result: We discovered a total of 58 associations that exhibited evidence of causality. Out of these, 4 associations remained significant even after applying multiple correction. An increased risk of asthma was causally associated with higher abundance of genus Holdemanella (OR = 1.11; CI: 1.05-1.17; p = 0.027), genus Oxalobacter (OR = 1.09; CI: 1.04-1.15; p = 0.025) and genus Butyricimonas (OR = 1.14; CI: 1.06-1.22; p = 0.027). Order NB1n was causally linked with an increased risk of obesity-related asthma (OR = 1.17; CI: 1.07-1.29; p = 0.015). There was limited overlap among the taxa that exhibited potential causal relationships with distinct asthma phenotypes. Conclusion: Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and distinct asthma phenotypes, supporting the role of the gut microbiota in various asthma phenotypes. It is possible that different taxa play a role in the development of distinct asthma phenotypes. The causal relationships identified in this study require further investigation.

Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma.

Objectives: Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC). Methods: We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method. Results: Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29-65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28-88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750-0.949) in the training set and 0.819 (95% CI: 0.693-0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035). Conclusions: In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.

Advances in medical treatment for pancreatic neuroendocrine neoplasms.

Pancreatic neuroendocrine neoplasms (PanNENs) are rare neoplasms with strong heterogeneity that have experienced an increasing incidence rate in recent years. For patients with locally advanced or distant metastatic PanNENs, systemic treatment options vary due to the different differentiations, grades and stages. The available options for systemic therapy include somatostatin analogs, mole-cularly targeted agents, cytotoxic chemotherapeutic agents, immune checkpoint inhibitors, and peptide receptor radionuclide therapy. In addition, the development of novel molecularly targeted agents is currently in progress. The sequence of selection between different chemotherapy regimens has been of great interest, and resistance to chemotherapeutic agents is the major limitation in their clinical application. Novel agents and high-level clinical evidence continue to emerge in the field of antiangiogenic agents. Peptide receptor radionuclide therapy is increasingly employed for the treatment of advanced neuroendocrine tumors, and greater therapeutic efficacy may be achieved by emerging radio-labeled peptides. Since immune checkpoint inhibitor monotherapies for PanNENs appear to have limited antitumor activity, dual immune checkpoint inhibitor therapies or combinations of antiangiogenic therapies and immune checkpoint inhibitors have been applied in the clinic to improve clinical efficacy. Combining the use of a variety of agents with different mechanisms of action provides new possibilities for clinical treatments. In the future, the study of systemic therapies will continue to focus on the screening of the optimal benefit population and the selection of the best treatment sequence strategy with the aim of truly achieving individualized precise treatment of PanNENs.

Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase.

OBJECTIVE: To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS AND METHODS: This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model. RESULTS: Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant. CONCLUSION: The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer. KEY POINTS: • Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer.

PURPOSE: To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer. METHODS: In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29-85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student's t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson's chi-square test or Fisher's exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests. RESULTS: A statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029). CONCLUSIONS: Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.