Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.

High-throughput metabolomics identifies new biomarkers for cervical cancer.

BACKGROUND: Cervical cancer (CC) is a danger to women's health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions. METHOD: In this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO). RESULT: Here, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration. CONCLUSION: Our study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage. IMPACT: These findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers.

A transferrin receptor targeting dual-modal MR/NIR fluorescent imaging probe for glioblastoma diagnosis.

The prognosis of glioblastoma (GBM) remains challenging, primarily due to the lack of a precise, effective imaging technique for comprehensively characterization. Addressing GBM diagnostic challenges, our study introduces an innovative dual-modal imaging that merges near-infrared (NIR) fluorescent imaging with magnetic resonance imaging (MRI). This method employs superparamagnetic iron oxide nanoparticles coated with NIR fluorescent dyes, specifically Cyanine 7, and targeted peptides. This synthetic probe facilitates MRI functionality through superparamagnetic iron oxide nanoparticles, provides NIR imaging capability via Cyanine 7 and enhances tumor targeting trough peptide interactions, offering a comprehensive diagnostic tool for GBM. Notably, the probe traverses the blood-brain barrier, targeting GBM in vivo via peptides, producing clear and discernible images in both modalities. Cytotoxicity and histopathology assessments confirm the probe's favorable safety profile. These findings suggest that the dual-modal MR\NIR fluorescent imaging probe could revolutionize GBM prognosis and survival rates, which can also be extended to other tumors type.

Activation of GPR81 by lactate drives tumour-induced cachexia.

Cachexia affects 50-80% of patients with cancer and accounts for 20% of cancer-related death, but the underlying mechanism driving cachexia remains elusive. Here we show that circulating lactate levels positively correlate with the degree of body weight loss in male and female patients suffering from cancer cachexia, as well as in clinically relevant mouse models. Lactate infusion per se is sufficient to trigger a cachectic phenotype in tumour-free mice in a dose-dependent manner. Furthermore, we demonstrate that adipose-specific G-protein-coupled receptor (GPR)81 ablation, similarly to global GPR81 deficiency, ameliorates lactate-induced or tumour-induced adipose and muscle wasting in male mice, revealing adipose GPR81 as the major mediator of the catabolic effects of lactate. Mechanistically, lactate/GPR81-induced cachexia occurs independently of the well-established protein kinase A catabolic pathway, but it is mediated by a signalling cascade sequentially activating Gi-Gßγ-RhoA/ROCK1-p38. These findings highlight the therapeutic potential of targeting GPR81 for the treatment of this life-threatening complication of cancer.

Proteomic characteristics of the treatment trajectory of patients with COVID-19.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has prompted global concern due to its profound impact on public health and the economy. Effective treatment of COVID-19 patients in the acute phase or of those with long COVID is a major challenge. Using data-independent acquisition (DIA) technology, we performed proteomic profiling on plasma samples from 22 COVID-19 patients and six healthy controls at Dazhou Central Hospital. Random forest and least absolute shrinkage and selection operator algorithms were used for analysis at various COVID-19 treatment stages. We identified 79 proteins that were differentially expressed between COVID-19 patients and healthy controls, mainly involving pathways associated with cell processes and binding. Across different treatment stages of COVID-19, five proteins-PI16, GPLD1, IGFBP3, KRT19, and VCAM1-were identified as potential molecular markers for dynamic disease monitoring. Furthermore, the proteins BTD, APOM, IGKV2-28, VWF, C4BPA, and C7 were identified as candidate biomarkers for distinguishing between SARS-CoV-2 positivity and negativity. Analysis of protein change profiles between the follow-up and healthy control groups highlighted cardiovascular changes as a concern for patients recovering from COVID-19. Our study revealed the infection profiles of SARS-CoV-2 at the protein expression level comparing different phases of COVID-19. DIA mass spectrometry analysis of plasma samples from COVID-19 patients undergoing treatment identified key proteins involved in signaling pathways that might be used as markers of the recovery phase. These findings provide insight for the development of therapy options and suggest potential blood biomarkers for COVID-19.

The Role of TGF-ß/SMAD Signaling in Hepatocellular Carcinoma: from Mechanism to Therapy and Prognosis.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with high incidence and mortality, accounting for approximately 90% of liver cancer. The development of HCC is a complex process involving the abnormal activation or inactivation of multiple signaling pathways. Transforming growth factor-ß (TGF-ß)/Small mothers against decapentaplegic (SMAD) signaling pathway regulates the development of HCC. TGF-ß activates intracellular SMADs protein through membrane receptors, resulting in a series of biological cascades. Accumulating studies have demonstrated that TGF-ß/SMAD signaling plays multiple regulatory functions in HCC. However, there is still controversy about the role of TGF-ß/SMAD in HCC. Because it involves different pathogenic factors, disease stages, and cell microenvironment, as well as upstream and downstream relationships with other signaling pathways. This review will summary the regulatory mechanism of the TGF-ß/SMAD signaling pathway in HCC, involving the regulation of different pathogenic factors, different disease stages, different cell populations, microenvironments, and the interaction with microRNAs. In addition, we also introduced small molecule inhibitors, therapeutic vaccines, and traditional Chinese medicine extracts based on targeting the TGF-ß/SMAD signaling pathway, which will provide future research direction for HCC therapy targeting the TGF-ß/SMAD signaling pathway.

A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer.

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.

Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development.

OBJECTIVES: Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis. METHODS: A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects. RESULTS: Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort. CONCLUSIONS: These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

Manganese regulation of COPII condensation controls circulating lipid homeostasis.

Precise control of circulating lipids is instrumental in health and disease. Bulk lipids, carried by specialized lipoproteins, are secreted into the circulation, initially via the coat protein complex II (COPII). How the universal COPII machinery accommodates the abundant yet unconventional lipoproteins remains unclear, let alone its therapeutic translation. Here we report that COPII uses manganese-tuning, self-constrained condensation to selectively drive lipoprotein delivery and set lipid homeostasis in vivo. Serendipitously, adenovirus hijacks the condensation-based transport mechanism, thus enabling the identification of cytosolic manganese as an unexpected control signal. Manganese directly binds the inner COPII coat and enhances its condensation, thereby shifting the assembly-versus-dynamics balance of the transport machinery. Manganese can be mobilized from mitochondria stores to signal COPII, and selectively controls lipoprotein secretion with a distinctive, bell-shaped function. Consequently, dietary titration of manganese enables tailored lipid management that counters pathological dyslipidaemia and atherosclerosis, implicating a condensation-targeting strategy with broad therapeutic potential for cardio-metabolic health.

A Sedentary Lifestyle Changes the Composition and Predicted Functions of the Gut Bacterial and Fungal Microbiota of Subjects from the Same Company.

A sedentary lifestyle affects the diversity and composition of the gut microbiota, but previous studies have mainly focused on bacteria instead of fungi. Here, we compared both the fecal bacterial and fungal microbiota compositions and functions in sedentary persons and controls. Subjects from the China Railway Corporation, including 99 inspectors and 88 officials, were enrolled in our study. Fecal microbiota communities were analyzed using 16S rRNA gene sequencing for bacteria and ITS sequencing for fungi. We found that the diversity of the gut microbiota of the sedentary group was significantly lower than that of the control group (P < 0.05). The sedentary group had a higher abundance of Firmicutes, a lower abundance of Actinobacteria and Proteobacteria and a higher abundance of Ascomycota, and a lower abundance of Basidiomycota. Furthermore, functional prediction analysis of the fungal microbiota revealed more L-tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, more phospholipid remodeling (phosphatidylethanolamine, yeast), and more L-tyrosine degradation I, as well as less pentose phosphate pathway (non-oxidative branch), less adenosine nucleotide biosynthesis and less L-valine biosynthesis in the sedentary group (P < 0.05). Thus, a sedentary lifestyle changes the composition and function of the gut microbiota. It may change the pentose phosphate pathway (non-oxidative branch), nucleic acid and amino acid biosynthesis and phospholipid metabolism in fungi.

Blocking IL-6 signaling improves glucose tolerance via SLC39A5-mediated suppression of glucagon secretion.

BACKGROUND AND AIMS: Hyperinsulinemia, hyperglucagonemia, and low-grade inflammation are frequently presented in obesity and type 2 diabetes (T2D). The pathogenic regulation between hyperinsulinemia/insulin resistance (IR) and low-grade inflammation is well documented in the development of diabetes. However, the cross-talk of hyperglucagonemia with low-grade inflammation during diabetes progression is poorly understood. In this study, we investigated the regulatory role of proinflammatory cytokine interleukin-6 (IL-6) on glucagon secretion. METHODS: The correlations between inflammatory cytokines and glucagon or insulin were analyzed in rhesus monkeys and humans. IL-6 signaling was blocked by IL-6 receptor-neutralizing antibody tocilizumab in obese or T2D rhesus monkeys, glucose tolerance was evaluated by intravenous glucose tolerance test (IVGTT). Glucagon and insulin secretion were measured in isolated islets from wild-type mouse, primary pancreatic α-cells and non-α-cells sorted from GluCre-ROSA26EYFP (GYY) mice, in which the enhanced yellow fluorescent protein (EYFP) was expressed under the proglucagon promoter, by fluorescence-activated cell sorting (FACS). Particularly, glucagon secretion in α-TC1 cells treated with IL-6 was measured, and RNA sequencing was used to screen the mediator underlying IL-6-induced glucagon secretion. SLC39A5 was knocking-down or overexpressed in α-TC1 cells to determine its impact in glucagon secretion and cytosolic zinc density. Dual luciferase and chromatin Immunoprecipitation were applied to analyze the signal transducer and activator of transcription 3 (STAT3) in the regulation of SLC39A5 transcription. RESULTS: Plasma IL-6 correlate positively with plasma glucagon levels, but not insulin, in rhesus monkeys and humans. Tocilizumab treatment reduced plasma glucagon, blood glucose and HbA1c in spontaneously obese or T2D rhesus monkeys. Tocilizumab treatment also decreased glucagon levels during IVGTT, and improved glucose tolerance. Moreover, IL-6 significantly increased glucagon secretion in isolated islets, primary pancreatic α-cells and α-TC1 cells. Mechanistically, we found that IL-6-activated STAT3 downregulated the zinc transporter SLC39A5, which in turn reduced cytosolic zinc concentration and ATP-sensitive potassium channel activity and augmented glucagon secretion. CONCLUSIONS: This study demonstrates that IL-6 increases glucagon secretion via the downregulation of zinc transporter SLC39A5. This result revealed the molecular mechanism underlying the pathogenesis of hyperglucagonemia and a previously unidentified function of IL-6 in the pathophysiology of T2D, providing a potential new therapeutic strategy of targeting IL-6/glucagon to preventing or treating T2D.

GABNet: global attention block for retinal OCT disease classification.

Introduction: The retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments. Methods: This study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases. Results: Notably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models. Discussion: With the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels. METHODS: Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects. RESULTS: Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort. CONCLUSION: Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

Lymphatic vessels in bone support regeneration after injury.

Blood and lymphatic vessels form a versatile transport network and provide inductive signals to regulate tissue-specific functions. Blood vessels in bone regulate osteogenesis and hematopoiesis, but current dogma suggests that bone lacks lymphatic vessels. Here, by combining high-resolution light-sheet imaging and cell-specific mouse genetics, we demonstrate presence of lymphatic vessels in mouse and human bones. We find that lymphatic vessels in bone expand during genotoxic stress. VEGF-C/VEGFR-3 signaling and genotoxic stress-induced IL6 drive lymphangiogenesis in bones. During lymphangiogenesis, secretion of CXCL12 from proliferating lymphatic endothelial cells is critical for hematopoietic and bone regeneration. Moreover, lymphangiocrine CXCL12 triggers expansion of mature Myh11+ CXCR4+ pericytes, which differentiate into bone cells and contribute to bone and hematopoietic regeneration. In aged animals, such expansion of lymphatic vessels and Myh11-positive cells in response to genotoxic stress is impaired. These data suggest lymphangiogenesis as a therapeutic avenue to stimulate hematopoietic and bone regeneration.

Randomized Trial of First-Line Tyrosine Kinase Inhibitor With or Without Radiotherapy for Synchronous Oligometastatic EGFR-Mutated Non-Small Cell Lung Cancer.

BACKGROUND: Adding radiotherapy (RT) to systemic therapy improves progression-free survival (PFS) and overall survival (OS) in oligometastatic non-small cell lung cancer (NSCLC). Whether these findings translate to epidermal growth factor receptor (EGFR)-mutated NSCLC remains unknown. The SINDAS trial (NCT02893332) evaluated first-line tyrosine kinase inhibitor (TKI) therapy for EGFR-mutated synchronous oligometastatic NSCLC and randomized to upfront RT vs no RT; we now report the prespecified interim analysis at 68% accrual. METHODS: Inclusion criteria were biopsy-proven EGFR-mutated adenocarcinoma (per amplification refractory mutation system or next generation sequencing), with synchronous (newly diagnosed, treatment naïve) oligometastatic (≤5 metastases; ≤2 lesions in any one organ) NSCLC without brain metastases. All patients received a first-generation TKI (gefitinib, erlotinib, or icotinib), and randomization was between no RT vs RT (25-40 Gy in 5 fractions depending on tumor size and location) to all metastases and the primary tumor/involved regional lymphatics. The primary endpoint (intention to treat) was PFS. Secondary endpoints included OS and toxicities. All statistical tests were 2-sided. RESULTS: A total of 133 patients (n = 65 TKI only, n = 68 TKI with RT) were enrolled (2016-2019). The median follow-up was 23.6 months. The respective median PFS was 12.5 months vs 20.2 months (P < .001), and the median OS was 17.4 months vs 25.5 months (P < .001) for TKI only vs TKI with RT. Treatment yielded no grade 5 events and a 6% rate of symptomatic grade 3-4 pneumonitis in the TKI with RT arm. Based on the efficacy results of this prespecified interim analysis, the ethics committee recommended premature cessation of this trial. CONCLUSIONS: As compared with a first-line TKI alone, addition of upfront local therapy using RT statistically significantly improved PFS and OS for EGFR-mutated NSCLC.

Molecular diagnosis of pancreatobiliary tract cancer by detecting mutations and methylation changes in bile samples.

Background: Patients with pancreatobiliary tract cancer usually have a poor clinical outcome, with a 5-year overall survival rate below 20%. This is mainly associated with the late diagnosis. In addition, the standard-of-care for patients with malignant biliary stenosis involves a major surgery, the Whipple procedure. An accurate preoperative diagnosis, including differentiation from benign diseases, is critical to avoid unnecessary treatment. Here we developed BileScreen, a sensitive detection modality for the diagnosis of pancreatobiliary tract cancer based on massively parallel sequencing mutation and methylation changes in bile samples. Methods: A total of 338 patients, from five hospitals in China, with pancreatobiliary system disorders were enrolled in this study between November 2018 and October 2020, and 259 were included for the analysis of BileScreen. We profiled 23 gene mutations and 44 genes with methylation modifications in parallel from bile samples, and set up a model for the detection of malignancy based on multi-level biomarkers. Findings: We applied the BileScreen assay in a training cohort (n = 104) to set up the model and algorithm. The model was further evaluated in a validation cohort (n = 105), resulting in 92% sensitivity and 98% specificity. The performance of BileScreen was further assessed in a prospective test cohort (n = 50) of patients diagnosed with suspicious or negative pathology by endoscopic retrograde cholangiopancreatography and were confirmed in follow-up. BileScreen yielded 90% sensitivity and 80% specificity, and outcompeted serum carbohydrate antigen 19-9 in detecting pancreatobiliary tract cancer in all three cohorts, especially in terms of specificity. Interpretation: Taken together, BileScreen has the ability to interrogate mutations and methylation changes in bile samples in parallel, thus rendering it a potentially sensitive detection method to help in the diagnosis of pancreatobiliary tract cancer in a safe, convenient and less-invasive manner. Funding: This study was supported by the Capital's Funds for Health Improvement and Research (2020-2-4025 to S.H.), the National Natural Science Foundation of China (81972311 to H.Z.), CAMS Innovation Fund for Medical Sciences (CIFMS) (2017-12M-4-002 to H.Z.), the CAMS Innovation Fund for Medical Sciences(CIFMS) (2021-I2M-1-066 to CJQ), the Non-profit Central Research Institution Fund of Chinese Academy of Medical Sciences (2019PT310026 to H.Z.) and Sanming Project of Medicine in Shenzhen (SZSM202011010 to H.Z.).

Preoperative prediction model of microvascular invasion in patients with hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) is an adverse factor for the prognosis of patients with hepatocellular carcinoma (HCC). We aimed to construct a preoperative prediction model for MVI, thereby providing a reference for clinicians in formulating treatment options for HCC. METHODS: A total of 360 patients with non-metastatic HCC were retrospectively enrolled. We used logistic regression analysis to screen out independent risk factors for MVI and further constructed a predictive model for MVI. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: Logistic regression analysis revealed that fibrinogen (>4 g/L) (OR: 6.529), alpha-fetoprotein (≥ 400 ng/mL) (OR: 2.676), cirrhosis (OR: 2.25), tumor size (OR: 1.239), and poor tumor border (OR: 3.126) were independent risk factors of MVI. The prediction model of MVI had C-index of 0.746 and 0.772 in the training and validation cohorts, respectively. The calibration curves showed good agreement between actual and predicted MVI risk. Finally, DCA reveals that this model has good clinical utility. CONCLUSION: The nomogram-based model we established can predict the preoperative MVI well and provides reference for surgeons to make clinical treatment decisions.

Identification of key genes and pathways associated with sex differences in rheumatoid arthritis based on bioinformatics analysis.

INTRODUCTION: Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women. METHODS: First, the Gene Expression Omnibus database's gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses. RESULTS: According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy. CONCLUSION: The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women.

The change of plasma metabolic profile and gut microbiome dysbiosis in patients with rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA. Methods: Metabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively. Results: There were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion. Conclusion: The plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.