Disordered gut microbiota and alterations in the serum metabolome are associated with venous thromboembolism.

INTRODUCTION: The gut microbiome plays a crucial role in various diseases, and its regulation is a potential treatment option for these conditions. However, the relationship between the gut microbiome and venous thromboembolism (VTE) remains poorly explored. METHODS: In this study, we collected feces and serum samples from 8 VTE patients and 7 healthy controls. The gut microbiota and serum metabolites were analyzed using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Additionally, a combined analysis of microbiota and metabolome was performed. RESULTS: The alpha and beta diversity between the VTE and control groups were significantly different. Patients with VTE exhibited an overgrowth of Blautia, Roseburia, Coprococcus, and Ruminococcus. Moreover, serum metabolomics analysis revealed altered levels of choline and lithocholic acid. Pathway enrichment analysis indicated a significant upregulation of bile secretion pathways. In addition, a positive correlation was observed between the levels of serum choline and lithocholic acid and the abundance of gut flora enriched in the VTE group. CONCLUSION: This study provided novel insights into the disordered gut microbiota and serum metabolome associated with VTE, suggesting potential common pathological mechanisms between VTE and arterial thrombosis. Targeted modulation of the gut microbiome may hold promise as a preventive and therapeutic approach for VTE.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Improvement and the relationship between chemical properties and microbial communities in secondary salinization of soils induced by rotating vegetables.

Choosing a good crop rotation plan helps maintain soil fertility and creates a healthy soil ecosystem. However, excessive fertilization and continuous cultivation of vegetables in a greenhouse results in secondary salinization of the soil. It remains unclear how crop rotation affects Yunnan's main place for vegetable growing in the greenhouse. Six plant cultivation patterns were chosen to determine how different rotation patterns affect the chemical properties and the soil microbial communities with secondary salinization, including lettuce monoculture, lettuce-large leaf mustard, lettuce-red leaf beet, lettuce-cabbage, lettuce-romaine lettuce, and lettuce-cilantro (DZ, A1, A2, A3, A4, and A5). The results showed that all treatments increased the proportion of nutrients available in the soil, and the effect of the A1 treatment was the most significant compared to the monoculture mode. The high-throughput sequencing findings revealed that distinct crop rotation patterns exerted varying effects on the microbial communities. Microbial community diversity was significantly lower in the monoculture than in the other treatments. The number of microbial operational taxonomic units OTUs was significantly higher in the crop rotation modes (P < 0.05), and the A1 treatment had larger numbers and diversity of bacterial and fungal OTUs (Shannon's and Simpson's) than other treatments (P < 0.05). Prominent bacterial and fungal communities were readily observable in the soils planted with rotational crops. Proteobacteria had the highest relative abundance of bacteria, whereas Ascomycota was the most abundant fungus. The principal coordinate analysis at the OTU level separated soil bacterial and fungal growth communities under the different treatments. Among the six treatments, The first two axes (PC1 and PC2) described 46.44 % and 42.42 % of the bacterial and fungal communities, respectively. Network-based analysis showed that Bacteroidota and Gemmatimonadota members of the genus Bacteroidota were positively correlated with Proteobacteria. Members of Ascomycota and Chytridiomycota exhibited positive relationships. These results extend the theoretical understanding of how various crop rotation patterns affect soil chemical properties, microbial community diversity, and metabolic functions. They reveal the beneficial effects of crop rotation patterns on enhanced soil quality. This study provides theoretical guidance for the future enhancement of sustainable agriculture and soil management planning.

Physiological and Biochemical Mechanisms of Wood Vinegar-Induced Stress Response against Tomato Fusarium Wilt Disease.

Wood vinegar, a by-product of charcoal biomass pyrolysis, has been used as a biofungicide in plant disease management because of its antimicrobial properties. However, the physiological and biochemical mechanisms through which wood vinegar alleviates biotic stress are poorly understood. In this study, pot experiments were conducted to investigate the resistance and regulation mechanism of wood vinegar prepared from different raw materials (ZM) and from a single raw material (SM) in controlling tomato (Solanum lycopersicum "Bonny Best") Fusarium wilt at different concentrations (0.3%, 0.6%, 0.9%, 1.2%, and 1.5%). The results showed that ZM and SM had significant control effects on tomato fusarium wilt under different concentrations in the same growth cycle. Under biotic stress, the two kinds of wood vinegar significantly increased the plant height, stem diameter, leaf area and yield of tomato under the concentration of 0.3%, 0.6%, 0.9% and 1.2%, and significantly reduced the content of malondialdehyde (MDA) and hydrogen peroxide (H2O2) in tomato leaves. The effect of 0.9% treatment was the most significant, ZM and SM significantly increased tomato yield by 122% and 74%, respectively, compared with CK under 0.9% treatment. However, the plant height, stem diameter and leaf area of tomato were significantly reduced under 1.5% treatment, but the content of soluble sugar, soluble protein and vitamin C in tomato fruit was the best. Compared with CK, ZM significantly increased by 14%, 193% and 67%, respectively, and SM significantly increased by 28%, 300% and 159%, respectively. Except for 0.3% treatment, both significantly increased the activities of catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) in tomato leaves. The response intensity of two kinds of wood vinegar-physiological and biochemical-to tomato disease resistance, growth and development, showed ZM > SM. The disease index of tomato showed highly significant negative correlation with plant height, stem thickness, leaf area and antioxidant physiology CAT, and highly significant positive correlation with MDA and H2O2 content. In conclusion, ZM was more effective than SM in enhancing tomato disease resistance by promoting tomato growth and development, decreasing leaf MDA and H2O2 content, and inducing antioxidant enzyme activity in leaves at moderate concentrations.

A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma.

It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.

Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study.

OBJECTIVES: Accuracy in the detection of recurrent nasopharyngeal carcinoma (NPC) on follow-up magnetic resonance (MR) scans needs to be improved. MATERIAL AND METHODS: A total of 5 035 follow-up MR scans from 5 035 survivors with treated NPC between April 2007 and July 2020 were retrospectively collected from three cancer centers for developing and evaluating the deep learning (DL) model MODERN (MR-based Deep learning model for dEtecting Recurrent Nasopharyngeal carcinoma). In a reader study with 220 scans, the accuracy of two radiologists in detecting recurrence on scans with vs without MODERN was evaluated. The performance was measured using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy with a 95% confidence interval (CI). RESULTS: MODERN exhibited sound performance in the validation cohort (internal: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 1: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 2: ROC-AUC, 0.85, 95% CI, 0.82-0.88). In a reader study, MODERN alone achieved reliable accuracy compared to that of radiologists (MODERN: 84.1%, 95% CI, 79.3%-88.9%; competent: 80.9%, 95% CI, 75.7%-86.1%, P < 0.001; expert: 85.9%, 95% CI, 81.3%-90.5%, P < 0.001). The accuracy of radiologists was boosted by the MODERN score (competent with MODERN score: 84.6%, 95% CI, 79.8%-89.3%, P < 0.001; expert with MODERN score: 87.7%, 95% CI, 83.4%-92.1%, P < 0.001). CONCLUSION: We developed a DL model for recurrence detection with reliable performance. Computer-human collaboration has the potential to refine the workflow in interpreting surveillant MR scans among patients with treated NPC.

An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling.

PURPOSE: This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions. METHODS: In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors. RESULTS: Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model. CONCLUSION: The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.

BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images.

BACKGROUND: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth. METHODS: To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest. RESULTS: Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve's control points to refine the result. CONCLUSIONS: Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.

Circular RNAs Regulate Vascular Remodelling in Pulmonary Hypertension.

Circular RNAs (circRNAs) are a newly identified type of noncoding RNA molecule with a unique closed-loop structure. circRNAs are widely expressed in different tissues and developmental stages of many species, participating in many important pathophysiological processes and playing an important role in the occurrence and development of diseases. This article reviews the discovery, characteristics, formation, and biological function of circRNAs. The relationship between circRNAs and vascular remodelling, as well as the current status of research and potential application value in pulmonary hypertension (PH), is discussed to promote a better understanding of the role of circRNAs in PH. circRNAs are closely related to the remodelling of vascular endothelial cells and vascular smooth muscle cells. circRNAs have potential application prospects for in-depth research on the possible pathogenesis and mechanism of PH. Future research on the role of circRNAs in the pathogenesis and mechanism of PH will provide new insights and promote screening, diagnosis, prevention, and treatment of this disease.

The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area.

BACKGROUND AND OBJECTIVES: Administration of contrast is not desirable for all cases in clinical setting, and no consensus in sequence selection for deep learning model development has been achieved, thus we aim to explore whether contrast-enhanced magnetic resonance imaging (ceMRI) can be substituted in the identification and segmentation of nasopharyngeal carcinoma (NPC) with the aid of deep learning models in a large-scale cohort. METHODS: A total of 4478 eligible individuals were randomly split into training, validation and test sets, and self-constrained 3D DenseNet and V-Net models were developed using axial T1-weighted imaging (T1WI), T2WI or enhanced T1WI (T1WIC) images separately. The differential diagnostic performance between NPC and benign hyperplasia were compared among models using chi-square test. Segmentation evaluation metrics, including dice similarity coefficient (DSC) and average surface distance (ASD), were compared using paired student's t-test between T1WIC and T1WI or T2WI models or M_T1/T2, a merged output of malignant region derived from T1WI and T2WI models. RESULTS: All models exhibited similar satisfactory diagnostic performance in discriminating NPC from benign hyperplasia, all attaining overall accuracy over 99.00% in all T stages of NPC. And T1WIC model exhibited similar average DSC and ASD with those of M_T1/T2 (DSC, 0.768±0.070 vs 0.764±0.070; ASD, 1.573±10.954 mm vs 1.626±10.975 mm 1.626±0.975 mm vs 1.573±0.954 mm, all p > 0.0167) in primary NPC using DenseNet, but yielded a significantly higher DSC and lower ASD than either T1WI model or T2WI model (DSC, 0.759±0.065 or 0.755±0.071; ASD, 1.661±0.898 mm or 1.722±1.133 mm, respectively, all p < 0.01) in the entire test set of NPC cohort. Moreover, the average DSCs and ASDs were not statistically significant between T1WIC model and M_T1/T2 in both.

Research advances in Dieulafoy's disease of the bronchus (Review).

Dieulafoy's disease is characterized by abnormal submucosal arteries and results in acute luminal hemorrhage. Dieulafoy's lesions can also be found in the submucosa of the bronchus. Due to its low incidence rate and non-specific clinical symptoms, Dieulafoy's disease is easy to overlook, but can lead to massive bleeding and high rates of mortality. Therefore, improvements in the understanding of the disease are necessary. The awareness of the disease and associated diagnostic and treatment techniques have continued to improve, and thus, an increasing number of cases of Dieulafoy's disease of the bronchus have been reported. In the present review, 74 cases of Dieulafoy's disease are summarized. New technologies such as endobronchial ultrasound, narrow-band imaging, angiography and argon plasma treatment have been found to be increasingly applied to diagnose and treat Dieulafoy's disease of the bronchus. Therefore, the primary focus of this systematic review is to highlight advances in the diagnosis and treatment of bronchial Dieulafoy's disease.

Impact of renal function on the prognosis of acute pulmonary embolism patients: a systematic review and meta-analysis.

OBJECTIVES: We conduct a study to explore the influence of impaired renal function on prognosis in Acute pulmonary embolism (APE) patients. METHODS: A meta-analysis was performed using the EMBASE and PubMed databases for relevant publications reporting the impact of impaired renal function on the clinical outcomes in patients with APE. RESULTS: Eventually, 17 articles were included in our analysis. The results suggested that renal insufficiency (RI) is a predictor of poor prognosis in APE patients(short-term mortality: pooled OR = 2.83, 95%CI: 2.20-3.63; long-term mortality: pooled OR = 2.30, 95%CI: 1.72-3.08; adverse outcomes: pooled OR = 3.02, 95%CI: 2.60-3.51). The short-term and long-term mortality rates of APE patients with RI were both higher than those in patients without RI. In addition, acute kidney injury(AKI) could serve as a predictive factor of poor prognosis (pooled OR = 2.75, 95%CI: 2.45-3.08), and it doubles the overall mortality rate in APE patients. However, chronic kidney disease (CKD) did not predict poor prognosis in APE patients (pooled OR = 1.94, 95%CI: 0.99-3.81), although it could slightly increase the overall mortality rate in APE patients. CONCLUSIONS: RI and AKI could be included in the prognosis evaluation for APE, but the impact of CKD in APE patients has yet to be determined.

Construction and analysis of the abnormal lncRNA-miRNA-mRNA network in hypoxic pulmonary hypertension.

The incidence of hypoxic pulmonary hypertension (HPH) is increasing. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) play an important role in HPH, but the functions and mechanism have yet to be fully elucidated. In the present study, we established a HPH rat model with 8 h of hypoxia exposure (10% O2) per day for 21 days. High-throughput sequencing identified 60 differentially expressed (DE) lncRNAs, 20 DE miRNAs and 695 DE mRNAs in rat lung tissue. qRT-PCR verified the accuracy of the results. The DE mRNAs were significantly enriched in immune response, inflammatory response, leukocyte migration, cell cycle, cellular response to interleukin-1, IL-17 signalling pathway, cytokine-cytokine receptor interaction and Toll-like receptor signalling pathway. According to the theory of competing endogenous RNA (ceRNA) networks, lncRNA-miRNA-mRNA network was constructed by Cytoscape software, 16 miRNAs and 144 mRNAs. The results suggested that seven DE lncRNAs (Ly6l, AABR07038849.2, AABR07069008.2, AABR07064873.1, AABR07001382.1, AABR07068161.1 and AABR07060341.2) may serve as molecular sponges of the corresponding miRNAs and play a major role in HPH.

Regulation of circular RNAs act as ceRNA in a hypoxic pulmonary hypertension rat model.

To explore potential critical genes and identify circular RNAs (circRNAs) that act as the competitive endogenous RNA (ceRNA) in a hypoxic pulmonary hypertension (HPH) rat model. Constructed rat model, and a bioinformatics method was used to analyse differentially expressed (DE) genes and construct a circRNA-miRNA-mRNA ceRNA regulatory network. Then, qRT-PCR was used to verify. The significant DEcircRNAs/DEmiRNAs/DEmRNAs was showed, and a ceRNA network with 8 DEcircRNAs, 9 DEmiRNAs and 46 DEmRNAs were constructed. The functional enrichment suggested the inflammatory response, NF-κB signalling, MAPK cascade and Toll-like receptor were associated with HPH. Further assessment confirmed that circ_002723, circ_008021, circ_016925 and circ_020581 could have a potential ceRNA mechanism by sponging miR-23a or miR-21 to control downstream target gene and be involved in the pathophysiology of HPH. The qRT-PCR validation results were consistent with the RNA-Seq results. This study revealed potentially important genes, pathways and ceRNA regulatory networks in HPH.

Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs.

BACKGROUND: Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients. OBJECTIVE: To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients. METHODS: In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient. RESULT: A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610). CONCLUSIONS: The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.

Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images.

OBJECTIVES: We aimed to develop a dual-task model to detect and segment nasopharyngeal carcinoma (NPC) automatically in magnetic resource images (MRI) based on deep learning method, since the differential diagnosis of NPC and atypical benign hyperplasia was difficult and the radiotherapy target contouring of NPC was labor-intensive. MATERIALS AND METHODS: A self-constrained 3D DenseNet (SC-DenseNet) architecture was improved using separated training and validation sets. A total of 4100 individuals were finally enrolled and split into the training, validation and test sets at a proximate ratio of 8:1:1 using simple randomization. The diagnostic metrics of the established model against experienced radiologists was compared in the test set. The dice similarity coefficient (DSC) of manual and model-defined tumor region was used to evaluate the efficacy of segmentation. RESULTS: Totally, 3142 nasopharyngeal carcinoma (NPC) and 958 benign hyperplasia were included. The SC-DenseNet model showed encouraging performance in detecting NPC, attained a higher overall accuracy, sensitivity and specificity than those of the experienced radiologists (97.77% vs 95.87%, 99.68% vs 99.24% and 91.67% vs 85.21%, respectively). Moreover, the model also exhibited promising performance in automatic segmentation of tumor region in NPC, with an average DSC at 0.77 ± 0.07 in the test set. CONCLUSIONS: The SC-DenseNet model showed competence in automatic detection and segmentation of NPC in MRI, indicating the promising application value as an assistant tool in clinical practice, especially in screening project.

Red cell distribution width for prognosis in patients with pulmonary embolism: A systematic review and meta-analysis.

OBJECTIVES: Patients with pulmonary embolism (PE) have a higher red cell distribution width (RDW), which may complicate the interpretation of the prognosis of PE. We aim to evaluate the diagnostic utility of the RDW in patients with PE. METHODS: Eligible studies were identified by searching PubMed and EMBASE from inception to December 23, 2018. The data were pooled using Stata 12 to evaluate the prognostic value. The sources of heterogeneity were detected. RESULTS: For the analysis of prognosis, 7 studies with 2146 patients were included, with median RDW values ranging from 13.4% to 15.7% and a median cut-off point of 18.27%. A total of 247 patients (11.5% mortality) among the 2146 participants died. Overall, RDW was positively associated with the prognosis of PE in the overall analysis (HR = 1.19, 95% CI = 1.10-1.29, I2  = 73.4%; P = 0.000). Subgroup analyses by study design, sex and follow-up time indicated that they were not sources of heterogeneity. CONCLUSIONS: An elevated RDW has the potential to be a biomarker for prognosis in PE patients.

Prognostic impact of red blood cell distribution width in pulmonary hypertension patients: A systematic review and meta-analysis.

Elevated red blood cell distribution width (RDW) may correlate with a worse prognosis in pulmonary hypertension (PH), though results to date are inconsistent. The goal of this study is to detect the impact of RDW on the prognosis of PH.PubMed and EMBASE databases were searched from their inception to July 22, 2019 for relevant publications reporting the relationship between RDW and the prognosis of PH. A meta-analysis was performed, and the heterogeneity across the included studies was evaluated using I and Q statistics. We conducted sensitivity and subgroup analyses to detect sources of heterogeneity. In addition, potential publication bias was evaluated by Begg's and Egger's tests.In total, 1236 publications were retrieved, and 7 eligible publications with 666 PH patients were included in our meta-analysis. The results suggested that increased RDW can predict worse prognosis in PH (hazard ratio (HR) = 1.27, 95% confidence interval (CI) 1.11-1.45). According to subgroup analysis, study design, region, various endpoints, time of follow-up, and patient age were not sources of heterogeneity. In addition, RDW showed prognostic value in retrospective studies (HR = 1.32, 95%CI 1.15-1.51) but not in prospective studies (HR = 1.14, 95%CI 0.78-1.67). Additionally, RDW may serve as a predictive biomarker of PH in Europe (HR = 1.33, 95%CI 1.18-1.49) but not in Asia (HR = 1.20, 95%CI 0.90-1.58). Further analysis indicated that the prognostic value of RDW was influenced by patient age (>44 years: HR = 1.34, 95%CI 1.17-1.55; ≤44 years: HR = 1.20, 95%CI 0.90-1.58) and follow-up (<3 years, HR = 1.36, 95%CI 0.53-3.47; ≥3 years, HR = 1.29, 95%CI 1.14-1.45).RDW provides important prognostic information for PH patients, and this measure may be used to optimize patient management and guide clinical treatment.PROSPERO registration number: CRD42019122636.

Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.

BACKGROUND: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies. METHODS: This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. FINDINGS: 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952-0·957) in the internal validation set, 0·927 (0·925-0·929) in the prospective set, and ranged from 0·915 (0·913-0·917) to 0·977 (0·977-0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924-0·957] vs 0·945 [0·927-0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832-0·880], p<0·0001) and trainee (0·722 [0·691-0·752], p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788-0·838) for GRAIDS, 0·932 (0·913-0·948) for the expert endoscopist, 0·974 (0·960-0·984) for the competent endoscopist, and 0·824 (0·795-0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971-0·984) for GRAIDS, 0·980 (0·974-0·985) for the expert endoscopist, 0·951 (0·942-0·959) for the competent endoscopist, and 0·904 (0·893-0·916) for the trainee endoscopist. INTERPRETATION: GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. FUNDING: The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities.

CTPA, DECT, MRI, V/Q Scan, and SPECT/CT V/Q for the noninvasive diagnosis of chronic thromboembolic pulmonary hypertension: A protocol for systemic review and network meta-analysis of diagnostic test accuracy.

BACKGROUND: To determine the diagnostic accuracy of techniques with chronic thromboembolic pulmonary hypertension (CTEPH) patients via a protocol for systemic review and network meta-analysis. METHODS: We will search PubMed, EMBASE, Web of Science, and Google Scholar from inception to October 1, 2018. The reference lists of the retrieved articles are also consulted. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) will be used to assess the risk of bias in each study. The direct meta-analyses, network meta-analyses, and ranking of competing diagnostic tests will be used by STATA 12.0 and WINBUGS 1.4. Heterogeneity and inconsistency are assessed. RESULTS: This study is ongoing, will be submitted to a peer-reviewed journal publication once completed. CONCLUSION: This study will provide a comprehensive evidence summary of diagnostic test accuracy in detecting the CTEPH, and can help patients and clinicians to select appropriate or best diagnostic test. ETHICS AND COMMUNICATION: No ethical approval and patient consent are required, because it is based on published researches. PROSPERO REGISTRATION NUMBER: CRD42019121279.