The Safety of Fecal Microbiota Transplantation for Crohn's Disease: Findings from A Long-Term Study

INTRODUCTION

Fecal microbiota transplantation (FMT) has been used as a potential treatment option for Crohn's disease (CD). However, there is still lack of safety and efficacy evidence based on large samples of CD undergoing FMT. This study aimed to evaluate the risk factors of adverse event (AE) in the long term and the efficacy of FMT in the short term for patients with CD.

METHODS

FMT via mid-gut for mild to severe CD in a single center trial (NCT01793831) was performed from October 2012 to December 2016. The possible factors with AE and efficacy after FMT were prospectively recorded.

RESULTS

A total of 184 frequencies of FMT were performed for 139 patients who received FMT. During 1 month after FMT, 13.6% of mild AEs occurred, including increased frequency of defecation, fever, abdominal pain, flatulence, hematochezia, vomiturition, bloating and herpes zoster. No AE beyond 1 month was observed. Therefore, a 1 month cut-off could be suggested to define short-term and long-term AEs of FMT. Among the possible risk factors, only fecal microbiota purification methods were closely associated with the occurrence of AEs. The rate of AEs in patients undergoing manual methods for the preparation of fecal microbiota was 21.7%, which was significantly higher than the 8.7% in those experiencing an automatic method. The manual or automatic purification of fecal microbiota had no correlation with the efficacy of FMT.

CONCLUSION

This cohort study based on the largest size of cases demonstrated that improved fecal microbiota preparation reduced the rates of AEs, but did not affect the clinical efficacy in patients with CD.

Advances in lung cancer screening and early detection

Lung cancer is associated with a heavy cancer-related burden in terms of patients' physical and mental health worldwide. Two randomized controlled trials, the US-National Lung Screening Trial (NLST) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON), indicated that low-dose CT (LDCT) screening results in a statistically significant decrease in mortality in patients with lung cancer, LDCT has become the standard approach for lung cancer screening. However, many issues in lung cancer screening remain unresolved, such as the screening criteria, high false-positive rate, and radiation exposure. This review first summarizes recent studies on lung cancer screening from the US, Europe, and Asia, and discusses risk-based selection for screening and the related issues. Second, an overview of novel techniques for the differential diagnosis of pulmonary nodules, including artificial intelligence and molecular biomarker-based screening, is presented. Third, current explorations of strategies for suspected malignancy are summarized. Overall, this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.

Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis

BACKGROUND

The sensitivity and specificity of minimal residual disease detected by circulating tumor DNA profiling (ctDNA MRD) in lung cancer, with particular attention to the distinction between landmark strategy and surveillance strategy, for predicting relapse in lung cancer patients after definitive therapy has yet to be determined.

METHODS

The prognostic value of ctDNA MRD by landmark strategy and surveillance strategy was evaluated in a large cohort of patients with lung cancer who received definitive therapy using a systemic literature review and meta-analysis. Recurrence status stratified by ctDNA MRD result (positive or negative) was extracted as the clinical endpoint. We calculated the area under the summary receiver operating characteristic curves, and pooled sensitivities and specificities. Subgroup analyses were conducted based on histological type and stage of lung cancer, types of definitive therapy, and ctDNA MRD detection methods (detection technology and strategy such as tumor-informed or tumor-agnostic).

RESULTS

This systematic review and meta-analysis of 16 unique studies includes 1251 patients with lung cancer treated with definitive therapy. The specificity of ctDNA MRD in predicting recurrence is high (0.86-0.95) with moderate sensitivity (0.41-0.76), whether shortly after treatment or during the surveillance. The landmark strategy appears to be more specific but less sensitive than the surveillance strategy.

CONCLUSIONS

Our study suggests that ctDNA MRD is a relatively promising biomarker for relapse prediction among lung cancer patients after definitive therapy, with a high specificity but suboptimal sensitivity, whether in landmark strategy or surveillance strategy. Although surveillance ctDNA MRD analysis decreases specificity compared with the landmark strategy, the decrease is minimal compared to the increase in sensitivity for relapse prediction of lung cancer.

A Novel Experiment-free Site-specific TDoA Localization Performance-Evaluation Approach

Time difference of arrival (TDoA) technology is widely utilized for source localization, which stimulates many studies on performance-evaluation approaches for TDoA localization systems. Some approaches using simulations are designed merely for a simple Line-of-Sight (LoS) scenario while some other ones using experiments show high cost and inefficiency. This paper proposes an integrated approach to evaluate a TDoA localization system in an area with a complicated environment. Radio propagation graph is applied through a simulation to obtain channel impulse responses (CIRs) between a source to be located and the TDoA sensors for the area. Realistic signals received by the sensors in baseband are emulated combining the source transmitted signal and the CIRs. A hardware unit takes charge of sending the radio emulated received signals to the system under test, which is consistent with real experimental measurements. Statistical analysis of the system is allowed based on localization errors obtained comparing the system's estimates with the ground truth of the source location. Verified results for LoS and non-LoS scenarios with variable transmitted signal bandwidths and signal-to-noise ratios, as well as for three variations of the sensor locations in an automobile circuit, show the usability of the proposed experiment-free performance-evaluation approach.

Construction and Validation of a Prognostic Model Based on Novel Senescence-Related Genes in Non-Small Cell Lung Cancer Patients with Drug Sensitivity and Tumor Microenvironment

Cellular senescence contributes to cancer pathogenesis and immune regulation. Using the LASSO Cox regression, we developed a 12-gene prognostic signature for lung adenocarcinoma (LUAD) from The Cancer Genome Atlas (TCGA) and a Gene Expression Omnibus (GEO) dataset. We assessed gene expression, drug sensitivity, immune infiltration, and conducted cell line experiments. High-risk LUAD patients showed increased mortality risk and shorter survival (P < 0.001). Senescence-related gene analysis indicated differences in protein phosphorylation and DNA methylation between normal individuals and LUAD patients. The high-risk group showed a positive association with PD-L1 expression (P = 0.003). Single-cell sequencing data suggested PEBP1 might significantly impact T cell infiltration. We predicted potential sensitive compounds for 12 senescence genes and found GAPDH promoted cell line proliferation. We established a novel prognostic system based on a newly identified senescence gene. High-risk patients had elevated immunosuppressive markers, and PEBP1 might influence T cell infiltration significantly. GAPDH, expressed at higher levels in tumors, could affect cancer progression. Our drug prediction model may guide treatment selection.

Risk mapping of lung cancer: a comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies

INTRODUCTION

A comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies was performed to map the different risk factors and assess the causality for lung cancer.

METHODS

Systematic reviews and meta-analyses of observational and interventional studies were reviewed based on PubMed, Embase, Web of Science, and Cochrane Library. Mendelian randomization analyses were conducted to validate the causal associations of those various exposures with lung cancer using summary statistics from 10 genome-wide association studies (GWAS) consortia and other GWAS databases in MR-Base platform.

RESULTS

In the review of meta-analyses, 105 risk factors associated with lung cancer were identified from 93 articles. It was found that 72 risk factors were nominally significant (P < 0.05) associated with lung cancer. Mendelian randomization analyses were performed to analyze 36 exposures based on 551 SNPs and 4,944,052 individuals, finding that 3 exposures had a consistent risk/protective effect on lung cancer with the results of the meta-analysis. In Mendelian randomization anaylses, smoking (OR 1.44, 95% CI 1.18-1.75; P = 0.001) and blood copper (OR 1.14, 95% CI 1.01-1.29; P = 0.039) significantly associated with increased risk of lung cancer, whereas aspirin use (OR 0.67, 95% CI 0.50-0.89; P = 0.006) showed protective effects.

CONCLUSION

This study mapped putative associations of risk factors for lung cancer, revealing the causal hazard effect of smoking, blood copper, and the protective effect of aspirin use in the development of lung cancer.

CLINICAL TRIAL REGISTRY

This study is registered with PROSPERO (CRD42020159082).

Landscape of C-MET overexpression in non-small cell lung cancer: a large-scale study of clinicomolecular features and prognosis based on Chinese data

Background

Real-world data on C-MET protein overexpression in non-small cell lung cancer (NSCLC) patients, particularly among the Asian Chinese population, are limited.

Objectives

This study aimed to evaluate the clinicomolecular characteristics and prognosis of C-MET overexpression in Chinese NSCLC patients, focusing on those with positive C-MET overexpression (immunohistochemistry (IHC) 3+).

Design

A retrospective and observational study.

Methods

Data were collected from NSCLC patients diagnosed at the First Affiliated Hospital of Guangzhou Medical University between November 2006 and April 2021. We identified C-MET overexpression using IHC and C-MET overexpression positivity was defined as IHC 3+ with ⩾50% tumor cells. Additionally, patient genotypes were collected for subgroup analysis.

Results

Data from 9785 NSCLC patients were collected. C-MET (-) accounted for 5% (503/9785), C-MET (+) for 27% (2654/9785), C-MET (++) for 36% (3464/9785), and C-MET (+++) for 32% (3164/9785). Genetic testing was available for 4326 patients. Wild-type was observed in 37% (1591 cases), with epidermal growth factor receptor (EGFR) abnormalities being the most common at 49% (2127 cases). Positive C-MET overexpression correlated significantly with women (p < 0.001), early-stage (p = 0.003), adenocarcinoma (p < 0.001), and driver mutations (p < 0.001). Patients with anaplastic lymphoma kinase (ALK) alterations had a higher occurrence of C-MET overexpression positivity (57.1%). Positive C-MET overexpression was significantly associated with EGFR (p < 0.001), ALK (p < 0.001), and KRAS alterations (p = 0.024). Compared to C-MET overexpression (IHC 0), C-MET overexpression (IHC 2+) (hazard ratio (HR) = 0.455, p < 0.001) and C-MET overexpression (IHC 3+) (HR = 0.569, p < 0.001) were correlated with better overall survival in overall NSCLC patients, especially for C-MET overexpression (IHC 2+).

Conclusion

Our study elucidates the clinicomolecular characteristics and prognosis of C-MET overexpression in NSCLC patients, particularly those with positive C-MET overexpression (IHC 3+). This provides insight into the prevalence of C-MET overexpression in Chinese NSCLC patients and offers a basis for considering C-MET overexpression as a prognostic and predictive marker in NSCLC.

Interpretable machine learning for predicting chronic kidney disease progression risk

Objective

Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnostic and decision-making.

Methods

A cohort of 491 patients with clinical data was collected for this study. The dataset was randomly split into an 80% training set and a 20% testing set. To achieve the first objective, we developed four ML algorithms (logistic regression, random forests, neural networks, and eXtreme Gradient Boosting (XGBoost)) to classify patients into two classes-those who progressed to CKD stages 3-5 during follow-up (positive class) and those who did not (negative class). For the classification task, the area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance in discriminating between the two classes. For survival analysis, Cox proportional hazards regression (COX) and random survival forests (RSFs) were employed to predict CKD progression, and the concordance index (C-index) and integrated Brier score were used for model evaluation. Furthermore, variable importance, partial dependence plots, and restrict cubic splines were used to interpret the models' results.

Results

XGBOOST demonstrated the best predictive performance for CKD progression in the classification task, with an AUC-ROC of 0.867 (95% confidence interval (CI): 0.728-0.100), outperforming the other ML algorithms. In survival analysis, RSF showed slightly better discrimination and calibration on the test set compared to COX, indicating better generalization to new data. Variable importance analysis identified estimated glomerular filtration rate, age, and creatinine as the most important predictors for CKD survival analysis. Further analysis revealed non-linear associations between age and CKD progression, suggesting higher risks in patients aged 52-55 and 65-66 years. The association between cholesterol levels and CKD progression was also non-linear, with lower risks observed when cholesterol levels were in the range of 5.8-6.4 mmol/L.

Conclusions

Our study demonstrated the effectiveness of interpretable ML models for predicting CKD progression. The comparison between COX and RSF highlighted the advantages of ML in survival analysis, particularly in handling non-linearity and high-dimensional data. By leveraging interpretable ML for unraveling risk factor relationships, contrasting predictive techniques, and exposing non-linear associations, this study significantly advances CKD risk prediction to enable enhanced clinical decision-making.

A possible mechanism for neurofilament slowing down in myelinated axon: Phosphorylation-induced variation of NF kinetics

Neurofilaments(NFs) are the most abundant intermediate filaments that make up the inner volume of axon, with possible phosphorylation on their side arms, and their slow axonal transport by molecular motors along microtubule tracks in a “stop-and-go” manner with rapid, intermittent and bidirectional motion. The kinetics of NFs and morphology of axon are dramatically different between myelinate internode and unmyelinated node of Ranvier. The NFs in the node transport as 7.6 times faster as in the internode, and the distribution of NFs population in the internode is 7.6 folds as much as in the node of Ranvier. We hypothesize that the phosphorylation of NFs could reduce the on-track rate and slow down their transport velocity in the internode. By modifying the ‘6-state’ model with (a) an extra phosphorylation kinetics to each six state and (b) construction a new ‘8-state’ model in which NFs at off-track can be phosphorylated and have smaller on-track rate, our model and simulation demonstrate that the phosphorylation-induced decrease of on-track rate could slow down the NFs average velocity and increase the axonal caliber. The degree of phosphorylation may indicate the extent of velocity reduction. The Continuity equation used in our paper predicts that the ratio of NFs population is inverse proportional to the ratios of average velocity of NFs between node of Ranvier and internode. We speculate that the myelination of axon could increase the level of phosphorylation of NF side arms, and decrease the possibility of NFs to get on-track of microtubules, therefore slow down their transport velocity. In summary, our work provides a potential mechanism for understanding the phosphorylation kinetics of NFs in regulating their transport and morphology of axon in myelinated axons, and the different kinetics of NFs between node and internode.

A new Schizophyllum commune strain as a potential biocontrol agent against blueberry root rot

In recent years, blueberry root rot has been caused mainly by Fusarium commune, and there is an urgent need for a green and efficient method to control this disease. To date, research on Schizophyllum commune has focused on antioxidant mechanisms, reactive dye degradation, etc., but the mechanism underlying the inhibition of pathogenic microorganisms is still unclear. Here, the control effects of S. commune on F. commune and blueberry root rot were studied using adversarial culture, tissue culture, and greenhouse pot experiments. The results showed that S. commune can dissolve insoluble phosphorus and secrete various extracellular hydrolases. The results of hyphal confrontation and fermentation broth antagonism experiments showed that S. commune had a significant inhibitory effect on F. commune, with inhibition rates of 70.30% and 22.86%, respectively. Microscopy results showed distortion of F. commune hyphae, indicating that S. commune is strongly parasitic. S. commune had a significant growth-promoting effect on blueberry tissue-cultured seedlings. After inoculation with S. commune, inoculation with the pathogenic fungus, or inoculation at a later time, the strain significantly reduced the root rot disease index in the potted blueberry seedlings, with relative control effects of 79.14% and 62.57%, respectively. In addition, S. commune G18 significantly increased the antioxidant enzyme contents in the aboveground and underground parts of potted blueberry seedlings. We can conclude that S. commune is a potential biocontrol agent that can be used to effectively control blueberry root rot caused by F. commune in the field.