[Establishment and efficiency test of a clinical prediction model of bronchopulmonary dysplasia associated pulmonary hypertension in very premature infants]

Objective: To develop a risk prediction model for identifying bronchopulmonary dysplasia (BPD) associated pulmonary hypertension (PH) in very premature infants. Methods: This was a retrospective cohort study. The clinical data of 626 very premature infants whose gestational age <32 weeks and who suffered from BPD were collected from October 1st, 2015 to December 31st, 2021 of the Seventh Medical Center of the People's Liberation Army General Hospital as a modeling set. The clinical data of 229 very premature infants with BPD of Hunan Children's Hospital from January 1 st, 2020 to December 31st, 2021 were collected as a validation set for external verification. The very premature infants with BPD were divided into PH group and non PH group based on the echocardiogram after 36 weeks' corrected age in the modeling set and validation set, respectively. Univariate analysis was used to compare the basic clinical characteristics between groups, and collinearity exclusion was carried out between variables. The risk factors of BPD associated PH were further screened out by multivariate Logistic regression, and the risk assessment model was established based on these variables. The receiver operating characteristic (ROC) area under curve (AUC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the model's discrimination and calibration power, respectively. And the calibration curve was used to evaluate the accuracy of the model and draw the nomogram. The bootstrap repeated sampling method was used for internal verification. Finally, decision curve analysis (DCA) to evaluate the clinical practicability of the model was used. Results: A total of 626 very premature infants with BPD were included for modeling set, including 85 very premature infants in the PH group and 541 very premature infants in the non PH group. A total of 229 very premature infants with BPD were included for validation set, including 24 very premature infants in the PH group and 205 very premature infants in the non PH group. Univariate analysis of the modeling set found that 22 variables, such as artificial conception, fetal distress, gestational age, birth weight, small for gestational age, 1 minute Apgar score ≤7, antenatal corticosteroids, placental abruption, oligohydramnios, multiple pulmonary surfactant, neonatal respiratory distress syndrome (NRDS)>stage Ⅱ, early pulmonary hypertension, moderate-severe BPD, and hemodynamically significant patent ductus arteriosus (hsPDA) all had statistically significant influence between the PH group and the non PH group (all P<0.05). Antenatal corticosteroids, fetal distress, NRDS >stage Ⅱ, hsPDA, pneumonia and days of invasive mechanical ventilation were identified as predictive variables and finally included to establish the Logistic regression model. The AUC of this model was 0.86 (95%CI 0.82-0.90), the cut-off value was 0.17, the sensitivity was 0.77, and the specificity was 0.84. Hosmer-Lemeshow goodness-of-fit test showed that P>0.05. The AUC for external validation was 0.88, and the Hosmer-Lemeshow goodness-of-fit test suggested P>0.05. Conclusions: A high sensitivity and specificity risk prediction model of PBD associated PH in very premature infants was established. This predictive model is useful for early clinical identification of infants at high risk of BPD associated PH.

A bibliometric analysis and visualization of retinopathy of prematurity from 2001 to 2021

OBJECTIVE

Retinopathy of prematurity (ROP) is an eye disease with the potential to cause blindness, primarily affecting premature infants with low birth weight. This study analyzed the etiology, primary location, and research advances in ROP.

MATERIALS AND METHODS

We used bibliometric techniques and searched the Web of Science Core Collection for "retinopathy of prematurity." We found 4,018 original articles and reviews with 69,819 references. We analyzed the data using HistCite (12.03.17), VOSviewer (1.6.16), CiteSpace (6.1. R5), and the Bibliometrix Package (4.1.0).

RESULTS

The amount of literature in this area has increased between 2001-2021. An analysis of references and journal co-citations highlights this field's most influential articles and related topics. Hellström, from the University of Gothenburg (Sweden), is the most prolific researcher; Harvard University is the most prolific research institution, and the USA is the most productive country. "Threshold ROP" and "cryotherapy" are the keywords with the highest burst strength. The future research hotspots are artificial intelligence, zone II, ROP development, ranibizumab, and type 1 retinopathy.

CONCLUSIONS

This article offers a comprehensive review of the present status of ROP research, along with insights into emerging concepts and potential international collaborations in this field.

Alzheimer's Disease and Aging Association: Identification and Validation of Related Genes

BACKGROUND

Aging is considered a key risk factor for Alzheimer's disease (AD). This study aimed to identify and validate potential aging-related genes associated with AD using bioinformatics analysis.

METHODS

Datasets GSE36980 and GSE5281 were selected to screen differentially expressed genes (DEGs), and the immune cell correlation analysis and GSEA analysis of DEGs were performed. The intersection with senescence genes was taken as differentially expressed senescence-related genes (DESRGs), and the GSE44770 dataset was used for further validation. The potential biological functions and signaling pathways were determined by GO and KEGG, and the hub genes were identified by 12 algorithms in Cytohubba. The expression of 10 hub genes in different brain regions was determined and single-cell sequencing analysis was performed, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed.

RESULTS

A total of 2137 DEGs were screened from the GSE36980 and GSE5281 datasets, and 278 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 29 DESRGs, including 14 senescence suppressor genes and 15 senescence inducible genes. The top 10 hub genes, including MDH1, CKB, PSMD14, SMARCA4, PEBP1, DDB2, ITPKB, ATF7IP, YAP1, and EWSR1 were screened. Furthermore, four diagnostic genes were identified: PMSD14, PEBP1, ITPKB, and ATF7IP. The ROC analysis showed that the respective area under the curves (AUCs) of PMSD14, PEBP1, ITPKB, and ATF7IP were 0.732, 0.701, 0.747, and 0.703 in the GSE36980 dataset and 0.870, 0.817, 0.902, and 0.834 in the GSE5281 dataset. In the GSE44770 dataset, PMSD14 (AUC, 0.838) and ITPKB (AUC, 0.952) had very high diagnostic values in the early stage of AD. Finally, based on these diagnostic genes, we found that the drug Abemaciclib is a targeted drug for the treatment of age-related AD. Flutamide can aggravate aging-related AD.

CONCLUSION

The results of this study suggest that cellular SRGs might play an important role in AD. PMSD14, PEBP1, ITPKB, and ATF7IP have the potential as specific biomarkers for the early diagnosis of AD.

TNFSF14 mediates the impact of docosahexaenoic acid on atopic dermatitis: a Mendelian randomization study

OBJECTIVE

While current research suggests potential value for docosahexaenoic acid (DHA) in the prevention and management of atopic dermatitis (AD), the causal relationship between DHA and AD remains unclear, and the underlying mechanisms are not well understood.

MATERIALS AND METHODS

To investigate the potential causal relationship between DHA and AD, as well as to explore potential mediating mechanisms, we employed the Mendelian randomization (MR) methods. To study these potential relationships, we conducted MR analysis using publicly available Genome-Wide Association Studies (GWAS) data. Effect estimates were computed using the random-effects inverse-variance weighted method.

RESULTS

Our study demonstrates a negative correlation between DHA levels and AD risk (OR: 0.915, 95% CI: 0.858-0.975, p=0.007). Furthermore, in MR analysis using tumor necrosis factor ligand superfamily member 14 (TNFSF14) levels as an outcome, DHA levels also show a negative association with TNFSF14 levels (OR: 0.933, 95% CI: 0.879-0.990, p=0.022). Subsequently, we performed further analysis to explore the relationship between TNFSF14 and AD risk, revealing a positive correlation (OR: 1.069, 95% CI: 1.005-1.137, p=0.033). This suggests a potential mediating role of TNFSF14 in the impact of DHA on AD risk.

CONCLUSIONS

In summary, our study employs MR analysis to offer genetic evidence indicating a potential role of DHA in reducing the risk of AD, as well as opening avenues for further in-depth investigation into potential mechanisms. These findings emphasize the importance of ongoing research in this field.

Iron colloidal transport mechanisms and sequestration of As, Ni, and Cu along AMD-induced environmental gradients

Colloids are common in mine waters and their chemistry and interactions are critical aspects of metal(loid)s cycling. Previous studies mostly focus on the colloidal transport of metal(loid)s in zones where rivers and soil profiles receive acid mine drainage (AMD). However, there is limited knowledge of the colloid and the associated toxic element behavior as the effluent flows through the coal waste dump, where a geochemical gradient is produced due to AMD reacting with waste rocks which have high acid-neutralization effects. Here, we investigated the geochemistry of Fe and co-occurring elements As, Ni, and Cu along the coal waste dump, in aqueous, colloidal, and precipitate phases, using micro/ultrafiltration combined with STEM, AFM-nanoIR, SEM-EDS, XRD, and FTIR analysis. The results demonstrated that a fast attenuation of H+, SO42-, and metal(loid)s happened as the effluent flowed through the waste-rock dump. The Fe, As, Ni, and Cu were distributed across all colloidal sizes and primarily transported in the nano-colloidal phase (3 kDa-0.1 μm). An increasing pH induced a higher percentage of large Fe colloid fractions (> 0.1 μm) associated with greater sequestration of trace metals, and the values for As from 39.5 % to 54.4 %, Ni from 40.8 % to 75.7 %, and Cu from 43.7 % to 56.0 %, respectively. The Fe-bearing colloids in AMD upstream (pH ≤ 3.0) were primarily composed of Fe-O-S and Fe-O-C with minor Al-Si-O and Ca-O-S, while in less acidic and alkaline sections (pH ≥ 4.1), they were composed of Fe-O with minor Ca-O-S. The iron colloid agglomerates associated with As, Ni, and Cu precipitated coupling the transformation of jarosite, and schwertmannite to ferrihydrite, goethite, and gypsum. These results demonstrate that the formation and transformation of Fe-bearing colloids response to this unique geochemical gradient help to understand the natural metal(loid)s attenuation along the coal waste dump.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

An investigation of the flexural behaviour of large-span prestressed and steel-reinforced concrete slabs

The prestressed and steel-reinforced concrete slab (PSRCS) is an innovative composite structural member offering high load capacity and stiffness and exceptional anti-crack performance, making it a leading trend in composite structures. This paper presents the derived calculation formulas for bearing capacity, section stiffness, mid-span deflection of PSRCS. Additionally, a numerical analysis of PSRCS is conducted using ABAQUS software, with several models created to systematically investigate bearing capacity, section stiffness, anti-crack performance, and failure mode. Concurrently, PSRCS member parameters are analyzed for optimal design, and the results of finite element (FE) calculations are compared with theoretical formula calculations. The results demonstrate that PSRCS exhibits superior load capacity, section stiffness, and anti-crack performance comparing to conventional slabs. The parametric analysis offers optimal design for each parameter and presents the corresponding recommended span-to-depth ratios for various spans in PSRCS applications.

[Focusing on timing selection and whole-course management of liver transplantation treatment for patients with acute-on-chronic liver failure]

Acute-on-chronic liver failure (ACLF) is a clinical syndrome of acute decompensation accompanied by organ failure that occurs on the basis of chronic liver disease and has a high short-term mortality rate. Currently, there are still differences in relation to the definition of ACLF; thus, baseline characteristics and dynamic changes are important bases for clinical decision-making in patients with liver transplantation and others. The basic strategies for treating ACLF currently include internal medicine treatment, artificial liver support systems, and liver transplantation. Multidisciplinary active collaborative management throughout the whole course is of great significance for further improving the survival rate in patients with ACLF.

Correlation of pericoronary adipose tissue CT attenuation values of plaques and periplaques with plaque characteristics

AIM

To investigate the relationship between different plaque characteristics and pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation values for plaques and periplaques.

MATERIALS AND METHODS

The data from 188 eligible patients with stable coronary heart disease (280 lesions) who underwent coronary CT angiography between March 2021 and November 2021 were collected retrospectively. All PCAT CT attenuation values of plaques and periplaques (the area within 5 and 10 mm proximal and distal to the plaque) were calculated, and multiple linear regression was used to assess their correlation with different plaque characteristics.

RESULTS

PCAT CT attenuation of plaques and periplaques was higher in non-calcified plaques (-73.38 ± 10.41 HU, -76.77 ± 10.86 HU, 79.33 ± 11.13 HU, -75.67 ± 11.24 HU, -78.63 ± 12.09 HU) and mixed plaques (-76.83 ± 8.11 HU, -79 [-85, -68.5] HU, -78.55 ± 11 HU, -78.76 ± 9.9 HU, -78.79 ± 11.06 HU) than in calcified plaques (-86.96 ± 10 HU, -84 [-92, -76] HU, -84.14 ± 11.08 HU, -84.91 ± 11.41 HU, -84.59 ± 11.69 HU; all p<0.05) and higher in distal segment plaques than in proximal segment plaques (all p<0.05). Plaque PCAT CT attenuation was lower in plaques with minimal stenosis than in plaques with mild or moderate stenosis (p<0.05). The significant determinants of PCAT CT attenuation values of plaques and periplaques were non-calcified plaques, mixed plaques, and plaques located in the distal segment (all p<0.05).

CONCLUSIONS

PCAT CT attenuation values in both plaques and periplaques were related to plaque type and location.

Deep-learning based broadband reflection reduction metasurface

Reflection reduction metasurface (RRM) has been drawing much attention due to its potential application in stealth technology. However, the traditional RRM is designed mainly based on trial-and-error approaches, which is time-consuming and leads to inefficiency. Here, we report the design of a broadband RRM based on deep-learning methodology. On one hand, we construct a forward prediction network that can forecast the polarization conversion ratio (PCR) of the metasurface in a millisecond, demonstrating a higher efficiency than traditional simulation tools. On the other hand, we construct an inverse network to immediately derive the structure parameters once a target PCR spectrum is given. Thus, an intelligent design methodology of broadband polarization converters has been established. When the polarization conversion units are arranged in chessboard layout with 0/1 form, a broadband RRM is achieved. The experimental results show that the relative bandwidth reaches 116% (reflection<-10 dB) and 107.4% (reflection<-15 dB), which demonstrates a great advantage in bandwidth compared with the previous designs.

[Risk factors of gastrointestinal polypectomy concurrent with bleeding in patients with liver cirrhosis]

Objective: To investigate and analyze the occurrence and the related risk factors of gastrointestinal polypectomy accompanied by bleeding in patients with liver cirrhosis. Methods: 127 cases of gastrointestinal polyps with cirrhosis who had endoscopy at the Endoscopic Center of Tianjin Third Central Hospital between November 2017 and November 2020 were collected. At the same time, 127 cases of gastrointestinal polyps with non-cirrhosis that were treated by endoscopy were collected for comparison. The occurrence of hemorrhagic complications between the two groups was compared. The effects of age, sex, liver function, peripheral blood leukocytes, hemoglobin, platelets, blood glucose, the international normalized ratio (INR), polyp resection method, polyp location, size, number, endoscopic morphology, pathology, the presence or absence of diabetes, portal vein thrombosis, and esophageal varices on polypectomy bleeding in the cirrhosis group were analyzed. The measurement data between groups were compared using the t-test and rank sum test. The χ (2) test or Fisher's exact probability method, and multivariate logistic regression analysis were used for the comparison of categorical data between groups. Results: The number of polypectomy bleeding cases in the cirrhotic group was 21, with a bleeding rate of 16.5%. The number of bleeding cases in the non-cirrhotic group was 3, with a bleeding rate of 2.4%. The bleeding rate was higher in the cirrhosis group when polypectomy was performed (χ (2) = 14.909, P < 0.001). A univariate analysis of the risk factors for gastrointestinal polypectomy associated with bleeding in patients with liver cirrhosis showed that liver function grading, platelets, INR, hemoglobin, degree of esophageal and gastric varices, and the location, shape, size, and pathology of the polyps had a statistically significant impact on bleeding (P < 0.05). Multivariate logistic regression analysis showed that liver function grade, degree of varicose veins, and polyp location were independent risk factors for bleeding. Patients with Child-Pugh B or C grade liver function were more likely to bleed than those with Child-Pugh A grade (OR = 4.102, 95% CI 1.133 ~ 14.856), gastric polyps were more likely to bleed than colorectal polyps (OR = 27.763, 95% CI 5.567 ~ 138.460), and severe esophagogastric varices were more likely to bleed than no varices or mild to moderate varices (OR = 7.183, 95% CI 1.384 ~ 37.275). Conclusion: Cirrhotic population has higher risk of bleeding during endoscopic gastrointestinal polypectomy than the non-cirrhotic population. Cirrhotic patients with Child-Pugh grades B or C liver function, polyps located in the stomach, severe esophagogastric varices, and other high-risk factors should be listed as a relative contraindication for endoscopic polypectomy.

Pan-cancer landscape of abnormal ctDNA methylation across human tumors

BACKGROUND

The aim of this paper is to explore the correlation between circulating tumor DNA (ctDNA) methylation and mutations and its value in clinical early cancer screening.

METHODS

We performed target region methylation sequencing and genome sequencing on plasma samples. Methylation models to distinguish cancer from healthy individuals have been developed using hypermethylated genes in tumors and validated in training set and prediction set.

RESULTS

We found that patients with cancer had higher levels of ctDNA methylation compared to healthy individuals. The level of ctDNA methylation in cell cycle, p53, Notch pathway in pan-cancer was significantly correlated with the number of mutations, and mutation frequency. Methylation burden in some tumors was significantly correlated with tumor mutational burden (TMB), microsatellite instability (MSI) and PD-L1. The ctDNA methylation differences in cancer patients were mainly concentrated in the Herpes simplex virus 1 infection pathway. The area under curve (AUC) of the training and prediction sets of the methylation model distinguishing cancer from healthy individuals were 0.93 and 0.92, respectively.

CONCLUSION

Our study provides a landscape of methylation levels of important pathways in pan-cancer. ctDNA methylation significantly correlates with mutation type, frequency and number, providing a reference for clinical application of ctDNA methylation in early cancer screening.

Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis

AIM

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM).

MATERIALS AND METHODS

Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours.

RESULTS

The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10^-6 mm²/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively.

CONCLUSION

Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.

Abstract 5150: Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection

Introduction: Genomic scale copy number, methylation, and fragmentation aberrations are proven genetic and epigenetic biomarkers of circulating tumor DNA (ctDNA). We developed a novel cell-free DNA (cfDNA) methylome sequencing assay that allows for integrative analysis of these genomic features for sensitive detection of multiple types of cancer.

Methods: Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA. Low-pass (~2X) paired-end NGS sequencing was performed on WMS libraries and paired whole genome sequencing (WGS) libraries of unconverted cfDNA for technical comparison and analytical validation. For development of cancer detection models, we profiled the genome-wide methylation density (MD), fragment size index (FSI), fragment end motif (motif) and chromosome instability (CIN) based on WMS data from a discovery cohort of 352 healthy controls and 559 newly diagnosed cancer patients (45 breast, 105 colorectal, 44 esophageal, 79 gastric, 79 liver, 110 lung, 83 pancreatic, and 14 others), 34.5% of which were at stage I or II. Machine learning models, including KNN, SVM, LR, GBDT, and random forest were trained and tested for individual biomarker types, with a final ensemble classifier to integrate all biomarkers. Performance of the predictive model was confirmed on an independent validation cohort consisting of 145 healthy controls and 236 cancer patients (21 breast, 45 colorectal, 18 esophageal, 35 gastric, 34 liver, 47 lung, and 36 pancreatic), among which 31.8% were at early stages (I or II).

Results: WMS and WGS data from 512 cfDNA samples showed high concordance in CIN (R=0.988, 95% CI: 0.986-0.990) and FSI (R=0.961, 0.954-0.967) profiles. On the independent validation cohort, the optimal model selected for each of individual genomic features achieved following area under the ROC curve (AUC) values for cancer detection: MD-KNN, 0.830 (0.789-0.870); FSI-SVM, 0.904 (0.874-0.933); motif-SVM, 0.943 (0.920-0.966); and CIN-PAscore, 0.812 (0.770-0.854). The ensemble classifier based on linear SVM outperformed individual biomarkers, with an AUC value of 0.952 (0.934-0.971), which translated to, at 95% specificity, detection sensitivity of 66.7% for breast, 77.8% for colorectal, 83.3% for esophageal, 62.9% for gastric, 82.4% for liver, 66.0% for lung, and 77.8% for pancreatic cancers. Noteworthily, the overall sensitivity on early-stage cancer was 74.7%.

Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers on the same WMS technical platform. Low-pass WMS on plasma cfDNA from 10ml of blood with integrative multimodal analysis of methylation, fragmentation, and CNV profiles yields in satisfactory sensitivity and specificity for detection of multiple types of cancer, warranting a forthcoming prospective study to further assess its clinical performance in a larger cohort.

Citation Format: Yulong Li, Fenglong Bie, Fengwei Tan, Tiancheng Han, Shunli Yang, Fang Lv, Peiyao Nie, Qi Zhang, Yuanyuan Hong, Zhijie Wang, Ji He, Weizhi Chen, Liang Zhao, Shugeng Gao. Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5150.

Early detection of colorectal adenocarcinoma by decoding epigenetic and DNA fragmentation fingerprints of plasma cell-free DNA

3550

Background: Cell-free DNA (cfDNA) methylation, fragmentation patterns, chromosome instability, and chromatin accessiblity have been previously shown to be valid plasma biomarkers for non-invasive cancer detection. However, conventional whole-genome bisulfite sequencing (WGBS) is unable to simultaneously profile all these biomarkers due to bisulfite-induced DNA damages. Here we developed a machine learning approach to comprehensively integrate multiple types of cancer genomic markers from enzyme-conversion-based low-pass whole-methylome sequencing (WMS) of plasma cfDNA to non-invasively detect colorectal adenocarcinoma. Methods: Plasma cfDNA sampels from 215 colorectal adenocarcinoma patients and 568 healthy individuals were collected and were split into the discovery and independent testing cohort. The discovery cohort includes 150 cancer patients and 398 healthy individuals and the independent testing cohort includes 65 cancer patients and 170 healthy individuals. Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA and were subsequently paired-end sequenced at ̃2X coverage. The genome-wide methylation density, fragmentation fingerprints, chromosome instability, and chromatin accessibility were extracted from the WMS data and individually modelled via machine learning methods such as SVM, LR, GBDT, random forest. The final predictive model is an ensemble model integrating all uni-modal models. All models were trained and fitted on the discovery cohort. Results: Data of different modalities provide complementary information in separating the cancer patients from the healthy individuals. Unsupervised clustering of the individuals showed clear separation between cancer patients and healthy individuals. The final predictive model achieved AUC =0.982 in the discovery cohort and AUC =0.9664 in the independent testing cohort. Under a specificity of 94.71% (CI: 90% - 98.82%), sensitivity was 78.46% (CI: 60% - 92.31%) in the independent testing cohort. Separating the cancer patients into different stages, we found that the detection power is usuaul lower for early-stage cancer patients. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers to non-invasively detect colorectal adenocarcinoma from WMS plasma cell-free DNA. A large prospective cohort study is planned to further validate its clinical performance.

An ultrasensitive approach for cancer screening and tissue of origin prediction based on targeted methylation sequencing of cell-free DNA

10553

Background: Cancer-specific therapy requires the identification of the tumor origin. Detecting cancer and identifying the tumor origin in early stage can improve the survival and prognosis of cancer patients. However, early diagnosis of cancer is challenging as the early symptoms of cancer are mild or even absent. In order to improve the diagnostic efficacy and the patient compliance, we developed a two-stage model for cancer diagnosis and tissue of origin (TOO) classification, using DNA methylation profile evaluated through non-invasive blood-based testing as biomarker. Methods: cfDNA samples from 302 healthy and 598 cancer patients of 8 cancer types (colorectal, lung, liver, pancreatic, gastric, esophagus, breast and ovarian) were sequenced by our in-house-designed DNA methylation panel and randomly split into a training dataset (213:426) and a testing dataset (89:172). The testing dataset was reserved to evaluate the performance of models and would not be used during the feature selection and model development process. Methylation levels of cfDNA samples were measured by the methylated fragment ratios (MFRs) in methylated-correlated blocks (MCBs). We first selected pan-cancer markers by comparing the MFR values of MCBs between the healthy and the diseased individuals and developed a binary classifier (hyper-methylation score). Sample correctly predicted by the hyper-methylation score in the cancer group were then used for TOO marker selection and model fitting. TOO biomarkers were obtained by a series of pairwise comparisons of MFR values between any two cancer types. A deconvolution model was built to estimate the composition of ctDNA, which implied the tumor origin. Results: The hyper-methylation score model based on 135 MCB biomarkers achieved an area under the curve (AUC) of 0.89 in the training dataset and 0.85 in the testing dataset. Under a specificity of 94.8%, sensitivity was 66.2% in the training dataset. Using the 282 cases successfully being predicted, 583 TOO markers were selected to build the deconvolution model. In the testing dataset, the specificity was 95.5%, and the overall sensitivity was 66.3%. With increasing stage, sensitivity increased in all cancer types: 34.6%, 57.1%, 62.5% and 84.8% in stage I, II, III and IV respectively. In stage I-III, sensitivity was 52.3%. TOO classes were predicted in the 114 true positives from the hyper-methylation score model, producing a top 1 accuracy (true class matched the most probable class) of 75.4% and a top 2 accuracy (true class matched the first or the second most probable class) of 84.2%. Top 1 accuracy was 71.1% in stage I-III, vs. 84.0% in stage IV. Top 2 accuracy was 82.2% in stage I-III, vs. 90.0% in stage IV. Conclusions: Our cfDNA-based epigenetic method achieved outstanding performance either in pan-cancer detection or in TOO classification, and is a promising tool for early-stage cancer diagnosis.

Non-invasive detection of esophageal carcinoma by integrative analysis of low-pass whole methylome sequencing of plasma cell-free DNA

4067

Background: Cell-free DNA (cfDNA) methylation, fragmentation patterns, chromosome instability, and chromatin accessiblity have been previously shown to be valid plasma biomarkers for non-invasive cancer detection. However, conventional whole-genome bisulfite sequencing (WGBS) is unable to simultaneously profile all these biomarkers due to bisulfite-induced DNA damages. Here we developed a machine learning approach to comprehensively integrate multiple types of cancer genomic markers from enzyme-conversion-based low-pass whole-methylome sequencing (WMS) of plasma cfDNA to non-invasively detect esophageal carcinoma. Methods: Plasma cfDNA sampels from 85 patients and 568 healthy individuals were collected and were split into the discovery and independent testing cohort. The discovery cohort includes 60 cancer patients and 398 healthy individuals and the independent testing cohort includes 25 cancer patients and 170 healthy individuals. Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA and were subsequently paired-end sequenced at ̃2X coverage. The genome-wide methylation density, fragmentation fingerprints, chromosome instability, and chromatin accessibility were extracted from the WMS data and individually modelled via machine learning methods such as SVM, LR, GBDT, random forest. The final predictive model is an ensemble model integrating all uni-modal models. All models were trained and fitted on the discovery cohort. Results: Data of different modalities provide complementary information in separating the cancer patients from the healthy individuals. Unsupervised clustering of the individuals showed clear separation between cancer patients and healthy individuals. The final predictive model achieved AUC =96.25% in the discovery cohort and AUC =91.15% in the independent testing cohort. Under a specificity of 95.29% (CI: 87.64% - 100.00%), sensitivity was 72.00% (CI: 56.00% - 92.00%) in the independent testing cohort. Separating the cancer patients into different stages, we found that the detection power is usuaul lower for early-stage cancer patients. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers to non-invasively detect esophageal carcinoma from WMS plasma cell-free DNA. A large prospective cohort study is planned to further validate its clinical performance.

Sensitive detection of pancreatic adenocarcinoma using plasma cell-free DNA methylomes

e16277

Background: Cell-free DNA (cfDNA) methylation, fragmentation patterns, chromosome instability, and chromatin accessiblity have been previously shown to be valid plasma biomarkers for non-invasive cancer detection. However, conventional whole-genome bisulfite sequencing (WGBS) is unable to simultaneously profile all these biomarkers due to bisulfite-induced DNA damages. Here we developed a machine learning approach to comprehensively integrate multiple types of cancer genomic markers from enzyme-conversion-based low-pass whole-methylome sequencing (WMS) of plasma cfDNA to non-invasively detect pancreatic adenocarcinoma. Methods: Plasma cfDNA sampels from 139 cancer patients and 568 healthy individuals were collected and were split into the discovery and independent testing cohort. The discovery cohort includes 99 cancer patients and 398 healthy individuals and the independent testing cohort includes 40 cancer patients and 170 healthy individuals. Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA and were subsequently paired-end sequenced at ̃2X coverage. The genome-wide methylation density, fragmentation fingerprints, chromosome instability, and chromatin accessibility were extracted from the WMS data and individually modelled via machine learning methods such as SVM, LR, GBDT, random forest. The final predictive model is an ensemble model integrating all uni-modal models. All models were trained and fitted on the discovery cohort. Results: Data of different modalities provide complementary information in separating the cancer patients from the healthy individuals. Unsupervised clustering of the individuals showed clear separation between cancer patients and healthy individuals. The final predictive model achieved AUC =0.982 in the discovery cohort and AUC =0.986 in the independent testing cohort. Under a specificity of 96.23% (CI: 87% - 88%), sensitivity was 95% (CI: 93% - 96%) in the independent testing cohort. Separating the cancer patients into different stages, we found that the detection power is usuaul lower for early-stage cancer patients. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers to non-invasively detect pancreatic adenocarcinoma from WMS plasma cell-free DNA. A large prospective cohort study is planned to further validate its clinical performance.

Plasma cell-free DNA integrative analysis for early detection of hepatocellular carcinoma

e16173

Background: Cell-free DNA (cfDNA) methylation, fragmentation patterns, chromosome instability, and chromatin accessiblity have been previously shown to be valid plasma biomarkers for non-invasive cancer detection. However, conventional whole-genome bisulfite sequencing (WGBS) is unable to simultaneously profile all these biomarkers due to bisulfite-induced DNA damages. Here we developed a machine learning approach to comprehensively integrate multiple types of cancer genomic markers from enzyme-conversion-based low-pass whole-methylome sequencing (WMS) of plasma cfDNA to non-invasively detect hepatocellular carcinoma. Methods: Plasma cfDNA sampels from 127 cancer patients and 568 healthy individuals were collected and were split into the discovery and independent testing cohort. The discovery cohort includes 90 cancer patients and 398 healthy individuals and the independent testing cohort includes 37 cancer patients and 170 healthy individuals. Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA and were subsequently paired-end sequenced at ̃2X coverage. The genome-wide methylation density, fragmentation fingerprints, chromosome instability, and chromatin accessibility were extracted from the WMS data and individually modelled via machine learning methods such as SVM, LR, GBDT, random forest. The final predictive model is an ensemble model integrating all uni-modal models. All models were trained and fitted on the discovery cohort. Results: Data of different modalities provide complementary information in separating the cancer patients from the healthy individuals. Unsupervised clustering of the individuals showed clear separation between cancer patients and healthy individuals. The final predictive model achieved AUC =0.979 in the discovery cohort and AUC =0.98 in the independent testing cohort. Under a specificity of 96.23%(CI: 86% - 87%), sensitivity was 86% (CI: 85% - 88%) in the independent testing cohort. Separating the cancer patients into different stages, we found that the detection power is usuaul lower for early-stage cancer patients. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers to non-invasively detect hepatocellular carcinoma from WMS plasma cell-free DNA. A large prospective cohort study is planned to further validate its clinical performance.

Efficacy of mirror therapy and virtual reality therapy in alleviating phantom limb pain: a meta-analysis and systematic review

INTRODUCTION

Amputations result from trauma, war, conflict, vascular diseases and cancer. Phantom limb pain (PLP) is a potentially debilitating form of chronic pain affecting around 100 million amputees across the world. Mirror therapy and virtual reality (VR) are two commonly used treatments, and we evaluated their respective success rates.

METHODS

A meta-analysis and systematic review was undertaken to investigate mirror therapy and VR in their ability to reduce pain levels. A mean difference (MD) model to compare group pain levels pretreatment and post-treatment via aggregating these results from numerous similar studies was employed. Meta-analysis was conducted using RevMan (V.5.4) and expressed in MD for visual analogue scale (VAS) score.

RESULTS

A total of 15 studies met our search criteria; they consisted of eight mirror therapy with 214 participants and seven VR including 86 participants, totalling 300 participants. Mean age ranged from 36 to 63 years, 77% male, of which 61% were lower body amputees. Both led to a VAS reduction (mirror therapy mean reduction VAS score was 2.54, 95% CI 1.42 to 3.66; p<0.001; VR 2.24, 95% CI 1.28 to 3.20; p<0.001). There was no statistically significant difference in pain alleviation between mirror therapy and VR (p=0.69).

CONCLUSIONS

Mirror therapy and VR are both equally efficacious in alleviating PLP, but neither is more effective than the other. However, due to small sample size and limited number of studies, factors such as gender, cause of amputation, site of limb loss or length of time from amputation, which may influence treatment success, could not be explored.

[Changes of serum uric acid levels in patients with chronic hepatitis C after using direct antiviral agents therapy]

Objective: To observe the changes of serum uric acid levels and clinical characteristic in patients with chronic hepatitis C combined with hyperuricemia after direct antiviral agents (DAA) therapy. Methods: A prospective cohort study was used to investigate the risk of hyperuricemia in patients with chronic hepatitis C who received DAA treatment to obtain sustained virological response. The changes and factors influencing serum uric acid levels after 12 weeks of DAA treatment were observed. Comparisons between groups were performed using χ (2) test or Fisher's exact test, analysis of variance, Student's t test, or the non-parametric Mann-Whitney U test. Serum uric acid (SUA) changes, liver and kidney function indexes before and after treatment were compared by repeated measurement and paired t-test. Uric acid reduction was defined as a decrease in SUA from baseline at 12 weeks after treatment. Rates of change in eGFR, aspartate aminotransferase/platelet ratio, alanine aminotransferase and controlled attenuation parameter were defined from baseline (baseline to 12 weeks after treatment). Binary logistic regression analysis was used to compare the risk factors and factors influencing high and low uric acid level. Results: 161 cases with chronic hepatitis C who received DAA treatment were included, of which 19.3% patients were hyperuricemic. eGFR < 60 ml/(min·1.73 m(2)) and body mass index were independent risk factors for hyperuricemia in patients with chronic hepatitis C (eGFR: OR = 0.123, P = 0.002; body mass index: OR = 1.220, P = 0.002). SUA levels was changed significantly before treatment, at the end of treatment and at 12 weeks after treatment (327.96 vs. 320.76 vs. 314.92, F = 3.272, P = 0.042). At 12 weeks after treatment, SUA, liver stiffness, alanine aminotransferase and control attenuation parameters were all significantly lower than baseline (P < 0.05). The rate of increase in eGFR from baseline and the rate of decrease in controlled attenuation parameter during treatment were the factors influencing SUA reduction (eGFR: OR = 5124, P = 0.000; controlled attenuation index: OR = 0.010, P = 0.039). Conclusion: In chronic hepatitis C, reduced eGFR and body mass index are the risk factors for the development of hyperuricemia and a significant reduction in serum uric acid levels after DAA treatment can eradicate the virus.

[Risk factors and nutritional status analysis in patients with liver cirrhosis and concomitant chronic periodontitis]

Objective: To study and explore the prevalence, characteristics, preliminary risk factors, as well as their relationship with nutritional scores in liver cirrhotic patient with chronic periodontitis. Methods: 163 patients with liver cirrhosis who were hospitalized in the Hepatology Division, Department of Internal Medicine at Tianjin Third Central Hospital from June to September 2018 were enrolled as the case group, while the control group consisted 140 healthy individuals enrolled during the same period. Periodontal examination, biochemical examination and oral hygiene habits were investigated. The prevalence of periodontitis in the two groups was compared, and the risk factors of severe periodontitis were conducted by multivariate regression analysis. Results: The prevalence of chronic periodontitis was significantly higher in patients with liver cirrhosis than healthy control population, and the differences were statistically significant (P < 0.05). The prevalence of severe periodontitis and full edentulous jaws was significantly higher in patients with liver cirrhosis than healthy control group, and the differences were statistically significant (P < 0.05 and P < 0.001). Compared with the healthy control group, the depth of periodontal pocket and the degree of attachment loss were significantly increased in the liver cirrhosis group (P < 0.001). Multivariate regression analysis showed that liver cirrhosis was the independent risk factors for both groups of patients with severe periodontitis (χ (2) = 11.046, P < 0.001). Univariate and multivariate regression analysis showed that toothbrushing frequency, nutritional risk score, prealbumin level and Child-Pugh grade were independent risk factors for occurrence of severe periodontitis in liver cirrhotic patient (χ (2) = 5.252, P = 0.022; χ (2) = 24.162, P < 0.001; χ (2) = 4.159, P = 0.041; χ (2) = 9.249, P = 0.002). Conclusion: The prevalence of periodontitis is significantly higher in patients with liver cirrhosis than healthy individuals, and liver cirrhosis is an independent risk factor for the occurrence of severe periodontitis. Toothbrushing frequency, nutritional risk score, prealbumin level and Child-Pugh grade are risk factors for severe periodontitis in patients with liver cirrhosis.

An ultrasensitive method for noninvasive pan-cancer early detection based on targeted methylation sequencing of cell-free DNA

10544

Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.

[Impact of cardiovascular metabolic diseases on COVID-19: review of recent progress]

The high comorbidity between cardiovascular and metabolic diseases (CVMD) and coronavirus disease 2019 (COVID-19) and the consequent high mortality and the potential risk of cardiovascular damage have brought great challenges to the clinical diagnosis and treatment of the condition. The latest studies found that advanced age, immune function defects, inflammatory factor storms and oxidative stress damage all potentially contribute to the high comorbidity of the two. Direct virus invasion, myocardial oxygen supply and demand imbalance and vascular endothelial and coagulation dysfunction may be important mechanisms for cardiovascular injury in COVID-19 patients. In addition, the expression level of ACE2 (the cell membrane receptor of SARS-CoV-2) in various organs and the peripheral blood not only mediates the direct invasion and damage of the organs, but also participates in regulation of the balance of systematic inflammation and oxidative stress, thus affecting the susceptibility and outcomes of the patients. Herein we review the recent research progress in the comorbidity between COVID-19 and CVMD and explore the mechanisms of cardiovascular damage caused by SARS-CoV-2, thus to provide a theoretical basis for the clinical diagnosis and treatment of COVID-19 with underlying CVMD.

[Assessment and intervention strategies for sarcopenia in patients with liver cirrhosis]

Sarcopenia is a common complication in patients with liver cirrhosis, which has an adverse effect on the clinical outcome and prognosis. Attention must be paid to early detection and active diagnosis and treatment. Clinically, the diagnosis process of finding suspicious cases can be traced through screening-assessment-diagnosis-severity evaluation. On the ground of treating liver cirrhosis and its complications, reasonable nutritional intervention and exercise are currently important measures for the treatment of liver cirrhosis with sarcopenia, and the role of hormone supplementation and drug therapy for skeletal muscle metabolism needs to be further investigated.

Experimental demonstration of an ultra-thin radar-infrared bi-stealth rasorber

We propose a radar-infrared bi-stealth rasorber that not only provides broad microwave absorptivity and low infrared emissivity but also possesses a microwave transmission window at low frequency. It is composed of three functional layers, which are carefully designed to independently control the infrared emission, microwave absorption, and transmission, respectively. The structure exhibits broadband (8.1-19.3 GHz) and high-efficiency (>90%) absorption. A transmission window appears at low frequency with a transmission peak of 80% at 2.68 GHz. The thermal emissivity of the structure is about 0.27 in the atmosphere window, which is close to that of metal. Moreover, the total thickness of the proposed structure is only 3.713 mm. The low-infrared-emissivity, high-microwave-absorption and frequency-selective-transmission properties promise it will find potential applications in various stealth fields.

IGF-1 concentrations after weaning in young sows fed different pre-mating diets are positively associated with piglet mean birth weight at subsequent farrowing

Pre-mating diets can influence piglet birth weight and within-litter birth weight variation and thereby piglet survival and development. The major objective of this study was to evaluate the litter characteristics of young sows whose pre-mating diets received different supplementation. The supplements included a top-dressing of 200 g, consisting of either wheat (CON) or wheat plus microfibrillated cellulose, _L-carnitine or _L-arginine at one of two supplementation levels (low and high) in late lactation and during the weaning-to-oestrus interval (WEI). The second objective was to investigate the role of body condition loss and IGF-1 concentration during the WEI for subsequent litter characteristics. In total, sows after their first (N =41) and second (N =15) lactation were used. One week before weaning, the sows were allocated to the seven treatments based on the number of piglets and BW loss from farrowing until 1 week before weaning. Pre-mating diets did not affect litter characteristics at subsequent farrowing. However, at subsequent farrowing, sows after their first lactation had a lower total number of piglets born per litter (18.3 v. 20.3), higher mean piglet birth weight (1365 v. 1253 g), lower CV of birth weight (20.0 v. 26.1%) and lower percentage of piglets <1000 g (11.5 v. 24.4%) than sows after their second lactation. Litter weight at second parturition was positively related to IGF-1 during the WEI after first lactation (P <0.04). Within parity, piglet mean birth weight was positively related to IGF-1 at oestrus (P <0.02). Surprisingly, within parity, a higher relative loin muscle depth loss during previous lactation was related to lower CV and SD of birth weight (P <0.05, for both). In conclusion, pre-mating diets did not affect litter characteristics at subsequent birth. However, a higher IGF-1 concentration during the WEI was positively associated with subsequent litter weight and piglet mean birth weight. Further studies should elucidate the role of IGF-1 during the WEI for subsequent litter characteristics and dietary interventions to stimulate IGF-1.

Path-Dependent Thermal Metadevice beyond Janus Functionalities

Janus metamaterials, metasurfaces, and monolayers have received intensive attention in nanophotonics and 2D materials. Their core concept is to introduce asymmetry along the wave propagation direction, by stacking different materials or layers of meta-atoms, or breaking out-of-plane mirror asymmetry with external biases. Nevertheless, it has been hitherto elusive to realize a diffusive Janus metadevice, since scalar diffusion systems such as heat conduction normally operate in the absence of polarization control, spin manipulation, or electric-field stimuli, which all are widely used in achieving optical Janus devices. It is even more challenging, if not impossible, for a single diffusive metadevice to exhibit more than two thermal functions. Here a path-dependent thermal metadevice beyond Janus characteristics is proposed, which can exhibit three distinct thermal behaviors (cloaking, concentrating, and transparency) under different directions of heat flow. The rotation transformation mechanism of thermal conductivity provides a robust platform to assign a specific thermal behavior in any direction. The proof-of-concept experiment of anisotropic in-plane conduction successfully validates such a path-dependent trifunction thermal metamaterial device. It is anticipated that this path-dependent strategy can provide a new dimension for multifunctional metamaterial devices in the thermal field, as well as for a more general diffusion process.

LncRNA H19 promotes the development of hepatitis B related hepatocellular carcinoma through regulating microRNA-22 via EMT pathway

OBJECTIVE

To explore the relationship between long non-coding RNA (lncRNA) H19 expression and prognosis of hepatitis B-related hepatocellular carcinoma (HBV-related HCC), and its underlying mechanism.

PATIENTS AND METHODS

Expression level of lncRNA H19 in 36 HBV-related HCC tissues and para-cancerous tissues was detected by quantitative Real-time polymerase chain reaction (qRT-PCR). The relationship between lncRNA H19 expression and prognosis of HBV-related HCC was analyzed by Kaplan-Meier method. Serum DNA levels of HBV were detected by fluorescence quantitative polymerase chain reaction (FQ-PCR). For in vitro experiments, lncRNA H19 expression in HCC cell line, HBV-related HCC cell line and normal liver cell line was detected by qRT-PCR. After plasmids construction, the effects of lncRNA H19 on cell viability, migration, and invasion were detected by cell counting kit-8 (CCK-8), colony formation and transwell assay, respectively. Finally, protein levels of epithelial-mesenchymal transition (EMT) pathway-related genes were detected by Western blot.

RESULTS

LncRNA H19 was highly expressed in HBV-related HCC tissues. The expression of lncRNA H19 was positively correlated with lymph node metastasis and distant metastasis, whereas negatively correlated with the overall survival of HBV-related HCC patients. Results of in vitro experiments showed that lncRNA H19 knockdown significantly downregulated cell proliferation and invasion. However, lncRNA H19 knockdown significantly upregulated apoptosis of HBV-related HCC cells. Western blot results demonstrated that lncRNA H19 remarkably decreased the protein expressions of EMT pathway-related genes, including N-cadherin, Vimentin, β-catenin and MMP-9. In addition, rescue experiments demonstrated that lncRNA H19 remarkably promoted malignant development of HBV-related HCC via regulating microRNA-22.

CONCLUSIONS

LncRNA H19 promotes malignant development of HBV-related HCC through regulating microRNA-22 via EMT pathway.

[Establishment of clinical features and prognostic scoring model in early-stage hepatitis B-related acute-on-chronic liver failure]

Objective: To explore the clinical characteristics and establish a corresponding prognostic scoring model in patients with early-stage clinical features of hepatitis B-induced acute-on-chronic liver failure (HBV-ACLF). Methods: Clinical characteristics of 725 cases with hepatitis B-related acute-on-chronic hepatic dysfunction (HBV-ACHD) were retrospectively analyzed using Chinese group on the study of severe hepatitis B (COSSH). The independent risk factors associated with 90-day prognosis to establish a prognostic scoring model was analyzed by multivariate Cox regression, and was validated by 500 internal and 390 external HBV-ACHD patients. Results: Among 725 cases with HBV-ACHD, 76.8% were male, 96.8% had cirrhosis base,66.5% had complications of ascites, 4.1% had coagulation failure in respect to organ failure, and 9.2% had 90-day mortality rate. Multivariate Cox regression analysis showed that TBil, WBC and ALP were the best predictors of 90-day mortality rate in HBV-ACHD patients. The established scoring model was COSS-HACHADs = 0.75 × ln(WBC) + 0.57 × ln(TBil)-0.94 × ln(ALP) +10. The area under the receiver operating characteristic curve (AUROC) of subjects was significantly higher than MELD, MELD-Na, CTP and CLIF-C ADs(P < 0.05). An analysis of 500 and 390 cases of internal random selection group and external group had similar verified results. Conclusion: HBV-ACHD patients are a group of people with decompensated cirrhosis combined with small number of organ failure, and the 90-day mortality rate is 9.2%. COSSH-ACHDs have a higher predictive effect on HBV-ACHD patients' 90-day prognosis, and thus provide evidence-based medicine for early clinical diagnosis and treatment.

Influence of post-core material and cement peculiarities on stress of post-cores under ultrasonic vibration: a three-dimensional finite element analysis

AIM

To analyse the effect of post-core, and cement materials and thickness of the cement lute on the stress in post-core systems under ultrasonic vibration at different frequencies and amplitudes using three-dimensional finite element analysis.

METHODOLOGY

Eight three-dimensional finite element models of a maxillary central incisor with post-cores were established. Two post-core materials (Au and Ni-Cr alloys), two cements (glass ionomer (GI) and zinc phosphate (ZP)) and two cement layer thicknesses (50 and 150 μm) were considered. Vibration loads were applied near the neck on the buccal side of the core at frequencies of 10-40 kHz and amplitudes of 10-50 μm. The maximum shear stress of the cement layer and maximum principal stress of the roots and their distributions were investigated.

RESULTS

The stresses on cements and roots increased with an increase in the frequency and amplitude of the vibration load and elastic modulus of the cements, and decreased with increasing thickness of the cement layer and elastic modulus of the post-core. Maximum cement stress was observed on the contralateral upper part of the loading side, whereas the maximum root stress was found on the ferrule where the load was applied.

CONCLUSIONS

In this simulated model, the frequency and amplitude of ultrasound needed to remove a post-core were positively related to the elastic modulus of the post-core and thickness of the cement layer and negatively related to the elastic modulus of the cements.

DNA methylation profile in unknown cancer origin identification: A multi-class classification model

e13554

Background: Approximately 3-5% of human cancers are cancer of unknown primary (CUP). Treatment of a cancer patient is largely dependent on the tumor origin. Therefore, identification of the tumor origin can improve the survival of patients with CUP. We developed a multi-class classification model using DNA methylation profile as biomarker to determine the primary site of CUP. Methods: We split 7,082 primary tumor samples of 19 cancers and 679 normal samples of 15 tissues from TCGA into a 75% training set and a 25% testing set to develop the classification model. We started with multiple support vector machine (SVM) models, and then combined them into an optimal multi-class ensemble model. Predictors included tumor-specific markers and tissue-specific markers, which were filtered by comparing between groups. Only the training samples were used for feature selection and model development. A validation dataset consisting of 150 primary tissues, 54 metastasis tissues, 105 plasma samples with known cancer site origins from 12 classes was generated in house by a self-designed panel. Performance was measured by area under the curve (AUC) using the one-vs-all approach. Results: 7,453 tumor-specific and 1,533 tissue-specific markers were selected for model construction. AUCs of all cancer types were high in TCGA training and testing set (AUC≥0.96 for all classes). In our validation tissues, esophageal cancer, pancreatic cancer, colorectal cancer, lung adenocarcinoma, breast cancer and liver cancer achieved high AUC in both primary (0.83, 0.83, 0.82, 0.82, 0.80 and 0.79 respectively) and metastasis (0.74, 0.92, 0.86, 0.61, 0.92 and 0.65 respectively). Lung adenocarcinoma, colorectal cancer, liver cancer, breast cancer and esophageal cancer even achieved high AUC in the plasmas. Conclusions: Performance of our model in tissue and plasma samples indicated the potential clinical application of DNA methylation profile in unknown cancer origin identification.

[Diagnosis and therapeutic strategies for hepatopulmonary syndrome]

Hepatopulmonary syndrome (HPS) is a common pulmonary complication in patients with liver disease and / or portal hypertension, and is characterized by abnormal arterial oxygenation caused by intrapulmonary vascular dilatation. The pathogenesis of HPS is complex, with a low clinical early diagnosis rate and poor prognosis. HPS currently lacks effective therapeutic drugs; therefore, liver transplantation is the only fundamental treatment. This article summarizes the pathogenesis, clinical manifestations, diagnosis and treatment of HPS in order to further improve the level of clinical screening and diagnosis and treatment of HPS.

A noninvasive multi-analytic approach for lung cancer screening of patients with pulmonary nodules

1550

Background: Low-dose computed tomography (LDCT) is an effective approach for lung cancer screening of high-risk patients with pulmonary nodules, however with varying false positive rates depending on the somewhat subjective judgement of the practice professional. Artificial intelligence derived from machine learning of comprehensive patient profiles, including multi-omics and clinical data, has the potential to provide more objective assessment of patient’s risk in order to aid clinician’s decision making. We have developed a multi-analyte algorithm-based assay (MAAA) that incorporates ctDNA mutation, ctDNA methylation, and protein biomarker profiles evaluated through non-invasive blood-based testing, as well as patient’s clinical information, to improve the diagnostic efficacy of lung cancer. Methods: 98 high-risk patients with pulmonary nodules were enrolled in two independent cohorts (68 for training/testing and 30 for independent validation). The malignancy of the pulmonary nodules were established through pathology of surgical-removed nodules. Prior to surgery, each patient was also subject to cell-free DNA-based sequencing for DNA mutation and DNA methylation profiling, as well as serum protein biomarker profiling. On the training/testing patient cohort, machine-learning-based predictive models were first built for malignancy status prediction based on each type of molecular or clinical features. A final ensemble model was then constructed to incorporate the measurements based on molecular and clinical markers to provide the ultimate recommendation on the malignancy of the pulmonary nodule. The performance of each individual model and the final ensemble model was benchmarked on the training/testing cohort, and also validated on the independent validation cohort. Results: On the 30-patient independent validation cohort, individual prediction models based on clinical information, protein marker, ctDNA mutation, and ctDNA methylation profiles achieved predictive AUC of 0.59, 0.48, 0.71, and 0.84, respectively. The final ensemble model achieved predictive AUC of 0.86, which has strongly indicated that an integrative, algorithm-based approach of multi-analytic molecular and clinical profiles greatly outperforms any single-analytic profiling. Conclusions: Multi-analyte algorithm-based approach can be utilized to assist in lung cancer screening for patients with pulmonary nodules. It has demostrated a high accuracy through independent validation, and has outperformed any single-analyte testing in our study.

[Risk factors of cirrhosis combined with sarcopenia and their impact on clinical outcomes]

Objective: To study the occurrence of sarcopenia in patients with liver cirrhosis, and to explore their risk factors and impact on clinical outcomes. Methods: 199 hospitalized cases with liver cirrhosis were collected for nutritional risk screening, anthropometric measurement and blood biochemical examination. The body composition analysis was measured based on the skeletal muscle content of the four limbs to calculate the appendicular skeletal muscle mass index (ASMI). Patients were divided into sarcopenia and non-sarcopenia group and the relevant indexes of both groups were compared to screen for factors affecting the occurrence of sarcopenia. During the follow-up of 48 months, the survival and complications of the both groups were compared. Statistical analysis was performed using t-test, χ(2) test and logistic regression analysis in terms of different data. Results: The incidence of sarcopenia in cirrhosis was 36.7%, with the highest prevalence in patients with recurrent hepatic encephalopathy (62.5%), followed by patients with abdominal ascites / pleural effusion (37.6%). The incidence of sarcopenia was significantly higher in those with nutritional risk than in those without nutritional risk (P < 0.05). However, even among those without nutritional risk, 14.8% had combined sarcopenia. The body mass index (BMI), upper arm muscle circumference (AMC), and body cell mass (BCM) of the sarcopenia group were lower than those of the non-sarcopenia group (P < 0.05), and the edema index (ECW/TBW) was higher than the latter (P < 0.05). Multivariate analysis showed that age, gender, BMI, and complications of hepatic encephalopathy were the main influencing factors of cirrhosis combined with sarcopenia (P < 0.05). During the follow-up period, the sarcopenia group had a higher mortality rate than non-sarcopenia goup (P < 0.05), and the incidence of recurrent abdominal ascites/pleural effusion, hepatic encephalopathy, and infection was also significantly elevated (P < 0.05). Conclusion: Sarcopenia is one of the manifestations of malnutrition in patients with liver cirrhosis, which increases the risk of mortality and other complications, and has adverse impact on the clinical outcome. Additionally, older age, male sex, low BMI and recurrent hepatic encephalopathy has higher risk for developing sarcopenia.

[Research advances of sarcopenia in chronic liver disease]

Sarcopenia is the main constituent of malnutrition and is a frequent complication of chronic liver diseases, which affects up to 70% of patients with advanced liver diseases. It has been associated with adverse clinical outcomes and prognosis, including poor quality of life, development of other complications and reduction in survival rate of non-transplant patients and transplant recipients. Chronic liver disease causes alteration in glucose metabolism, lipid oxidation, ketogenesis and protein catabolism, leading to the loss of adipose and muscle tissue. In addition, inadequate nutrients intake and limited or lack of physical activity perpetuate the reduction of muscle mass. Recently, the roles and mechanisms of muscle growth-related hormones, hyperammonemia-mediated signaling pathways and gut microbiota have been recognized. In view of its impact in chronic liver disease, sarcopenia can be considered as a powerful prognostic factor and a useful additional tool in the global assessment of patients with advanced liver disease. Rational nutritional intervention, appropriate physical exercise, effective ammonia lowering strategies, hormone supplements and targeted molecular therapy (use of myostatin blockers), and liver transplantation, may improve sarcopenia, but still needs more studies for validation.

[Advances in new type of biomolecular markers for liver fibrosis]

Liver fibrosis is a common pathological process of chronic liver disease, and the number of deaths from liver cirrhosis, liver failure and liver cancer is increasing year-by-year worldwide. Presently, the detection methods to evaluate hepatic fibrosis mainly include hepatic histological examination, imaging and serum markers, but all these have many limitations in clinical aspects. Recently, there have been more and more studies related to the development of non-coding RNA, exosomes and liver fibrosis that are considered as a new type of biomolecular markers with potential clinical application. Herein, we did a preliminary assessment in conjunction with relevant advances to provide a reference for the early diagnosis and treatment of liver fibrosis.

A new method towards calculating the cancer cell fraction in cell-free DNA

e13053

Background: Circulating tumor DNA (ctDNA) has been applied and showed potential in cancer early/late-stage detection, tumor genotyping and post-operation recurrence monitoring. The fraction of ctDNA in cell-free DNA (noted as ccf hereby), in addition to standard SNV/INDEL/CNV analysis, has also been showed to associate with the tumor progression and prognosis. In theory, accurate ccf can further be useful in correcting and improving given SNV/INDEL/CNV results. Existing tools capable for calculating ccf (PureCN, FACETS, Sequenza, etc.) use coverage data in targeted regions and SNP allele frequency to calculate the tumor fraction, which fail to give accurate estimation at relatively low ctDNA concentrations. Methods: A Maximum Likelihood model was built to estimate ccf. We first select informative SNPs with significantly different VAF in the case and paired-control samples. The mutation type of an informative SNP is determined by the variant allele frequency (VAF) in the paired samples and the copy number of the case sample. Likelihood of each SNP given a specific ccf was then calculated. After clustering SNPs into clones, the ccf of each clone was estimated using a global likelihood. Results: Performance of the method was validated by ctDNA dilution series analysis. 6 cfDNA from cancer patient was diluted (concentrations: 1/3 - 1/81). Detection limit of the method is ~2%, and correlation between estimated and expected ccf ranged from 0.93 to 0.98. Conclusions: We have developed a novel method to better estimate cancer cell fractions in cell-free DNA. Results showed our method is able to calculate ccf at lower ctDNA concentrations with higher accuracy and stability than benchmarked tools. We describe here a method for target-sequencing data that is more sensible, accurate and stable than currently available tools.

A novel computational tool for copy number variation detection in targeted circulating tumor DNA

e13051

Background: Targeted sequencing of circulating tumor DNA (ctDNA) has been used in early tumor detection and guidance of clinical cancer treatment. Accurate somatic copy number variation (SCNV) estimation is useful for better decisions in clinics, but remain challenging due to low percentage of tumor-cell-released DNA in circulating blood, in addition to the low signal-noise ratio and usual lack of normal control. Methods: To overcome these challenges and call SCNV at gene level, we develop a novel bioinformatics tool ctCNV. After correction for GC content, target region length and read counts, genome local scores at the gene level (GCS) were calculated, using 30 normal blood samples as control. Statistically significant cutoffs were determined using control. Results: The new method has been benchmarked with other SCNV calling tools such as CNVkit, which have been developed mainly for whole exome sequencing instead of targeted DNAseq. The validation experiment involves 6 mixed samples consisting of different proportions of known cell lines and real blood samples. ROC curve obtained through comparing the SCNV calling results with Droplet Digital PCR results show improvement of our method comparing to existing methods. Conclusions: As a summary, our study has three main results/contributions. First, we developed a novel computational tool for SCNV calling in targeted sequencing of ctDNA, with good sensitivity and specificity. Second, we thoroughly evaluated the performance of current available CNV calling tools in targeted sequenced ctDNA. Third, the raw data generated from our six real samples of gradient mixture of different cell lines and human’s samples can serve as an evaluation standard to other further computational tools for CNV calling in targeted sequenced ctDNA.

Comparing the efficacy of targeted next-generation sequencing in the identification of somatic mutations in circulating tumor DNA from different stages of lung cancer

This study aims to assess the potential clinical application of targeted next generation sequencing (NGS)-based deep sequencing for the detection of clinically relevant mutations in circulating tumor DNA (ctDNA) obtained from non-small cell lung cancer (NSCLC) patients. Targeted deep sequencing was performed to identify High Confidence Somatic Variants (HCSVs) in matched tumor tissue DNA (tDNA) and ctDNA in 50 NSCLC patients. Our results demonstrated that NSCLC patients with Stage IV (61.5%) exhibited a higher concordance rate at the mutation level between plasma ctDNA and tDNA samples than those with Stage I-III (14.5%). Moreover, it is noteworthy that the allele frequency of these detected HCSVs in ctDNA increased with the advance in tumor stage. Besides, using tDNA as a reference, the sensitivity of plasma ctDNA analyzed by deep NGS for actionable EGFR was much higher in patients with Stage IV (66.6%) than those with Stage I-III (7.7%). In conclusion, it appears that ctDNA NGS-based deep sequencing is a feasible approach to identify mutations in patients with Stage IV NSCLC. However, additional methods with higher sensitivity and specificity are needed to improve the successful application of this platform in the earlier stages of NSCLC.