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Abstract Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease. It integrates causal thinking, deep learning, and biological priors to handle non-causal confounding factors, through a contrastive scheme and a spatial-relation regularization that reduces interferences from individual characteristics and noises, respectively. The comprehensive reliability of the proposed method was verified by simulations and applications involving various human diseases, sample origins, and sequencing technologies, highlighting its universal biomedical significance. Overall, this study offers a causal-deep-learning-based perspective with a compatible tool to identify reliable DNAm biomarker candidates, promoting resource-efficient biomarker discovery.

Abstract Due to its decade‐long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high‐performance CRC screening tool based on the promising specific terminal‐mediated polymerase chain reaction (STEM‐PCR) strategy is developed. CRC‐related methylation‐specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self‐developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC‐mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case‐control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early‐stage CRC. Taken together, ColoC‐mSTEM, based on a single methylation‐specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions.

Abstract Microfluidic 3D cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost‐effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, an approach to pattern fluid guides inside microchannels is introduced by establishing differential hydrophilicity using pressure‐sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. Optimal coating conditions are identified using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open‐chip prototype and the fully bonded device containing additional features for medium perfusion. The suitability of the device for dynamic 3D cell culture is tested by growing human hepatocytes in the device under controlled fluid flow for a 14‐day period. Additionally, the study demonstrated the potential of using the device for pharmaceutical high‐throughput screening applications, such as predicting drug‐induced liver injury. The approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials.

Abstract Objectives For children with Kawasaki disease (KD) at high risk of developing coronary artery lesions and requiring retreatment with intravenous immunoglobulin (IVIG), the availability of accurate prediction models remains limited because of inconsistent variables and unsatisfactory prediction results. We aimed to construct models to predict patient's probability of IVIG retreatment combining children's individual inflammatory characteristics. Methods Clinical manifestations and laboratory examinations of 266 children with KD were retrospectively analysed to build a development cohort data set (DC) and a validation cohort data set (VC). In the DC, binary logistic regression analyses were performed using R language. Nomograms and receiver operating curves were plotted. The concordance index (C index), net reclassification index, integrated discrimination improvement index and confusion matrix were applied to evaluate and validate the models. Results Models_5V and _9V were established. Both contained variables including the percentages of CD8+ T cells, CD4+ T cells, CD3+ T cells, levels of interleukin (IL)‐2R and CRP. Model_9V additionally included variables for IL‐6, TNF‐α, NT‐proBNP and sex, with a C index of 0.86 (95% CI 0.79–0.92). When model_9V was compared with model_5V, the NRI and IDI were 0.15 (95% CI 0.01–0.30, P

Abstract Background To investigate the value of 18F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm). Methods A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA). Results According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM. Conclusions The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Detecting nucleic acids often requires choosing between different amplification mechanisms. Here the authors present a generalisable and programmable isothermal methodology, demonstrated in clinical applications, including for multiplexed detection of short miRNAs.

ObjectiveTo develop and validate an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma (PDAC).Background“Radiomics” enables the investigation of huge amounts of radiological features in parallel by extracting high-throughput imaging data. MRI provides better tissue contrast with no ionizing radiation for PDAC.MethodsThere were 78 PDAC patients enrolled in this study. In total, there were 386 radiomics features extracted from MRI scan, which were screened by the least absolute shrinkage and selection operator algorithm to develop a risk score. Cox multivariate regression analysis was applied to develop the radiomics-based nomogram. The performance was assessed by discrimination and calibration.ResultsThe radiomics-based risk-score was significantly associated with PDAC overall survival (OS) (P < 0.05). With respect to survival prediction, integrating the risk score, clinical data and TNM information into the nomogram exhibited better performance than the TNM staging system, radiomics model and clinical model. In addition, the nomogram showed fine discrimination and calibration.ConclusionsThe radiomics nomogram incorporating the radiomics data, clinical data and TNM information exhibited precise survival prediction for PDAC, which may help accelerate personalized precision treatment.Clinical trial registrationclinicaltrials.gov, identifier NCT05313854.

Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice.

Environmental conditions contribute to plant growth and metabolism. This study aimed to determine a suitable environment and climate for large-scale artificial cultivation of an endangered plant, Tetrastigma hemsleyanum, by investigating the seasonal variations influencing the flavonoid biosynthetic selectivity and antioxidant activity of its major metabolites. Under conditions of precipitation (2.0~6.6 mm), temperature (17.5~24.1°C), humidity (67.3~80.2%), and sunshine duration (3.4~5.8 h) from April to May, the total flavonoid content in T. hemsleyanum reached higher levels between 281.3 and 392.8 μg/g. In the second half of April, the production selectivity (PS) of isoorientin (IsoO), orientin (Or), rutin (Rut), isoquercitin (IsoQ), kaempferol-3-O-rutinoside (Km3rut), astragalin (Ast), quercetin (Qu), apigenin (Ap), and kaempferol (Km) were 0.30, 0.06, 0.07, 0.07, 0.00, 0.04, 0.38, 0.05, and 0.03, respectively. Naringenin was dehydrogenated or hydroxylated to initiate two parallel reaction pathways for flavonoid biosynthesis in T. hemsleyanum: path I subsequently generated flavone derivatives including apigenin, luteolin, orientin, and isoorientin, and path II subsequently generated flavonol derivatives including Km, Qu, IsoQ, Rut, Ast, and Km3rut. The reaction selectivity of path II (RPSII) from January 1 to September 30 was considerably higher than that of path I (RPSI), except for March 16-31. In addition, either the content or antioxidant activity of three major metabolites in T. hemsleyanum followed the order of phenolic compounds > polysaccharides > sterols, and exhibited dynamic correlations with environmental factors. Naringenin favored hydroxylation and derived six flavonol compounds from January to September, and favored dehydrogenation and derived three flavone compounds from October to December. In most months of a year, Km preferentially favored hydroxylation rather than glucosylation.

Primary cardiac tumors in the right ventricular outflow tract are often misdiagnosed as pulmonary embolism due to rarity and inadequate imaging characterization. Multimodality imaging offers advantages and facilitates subsequent diagnostics and management. We present a case of a woman with suspected submassive pulmonary embolism who was found to have pleomorphic leiomyosarcoma. (Level of Difficulty: Intermediate.)

Background: While microRNAs (miRNAs) were widely considered to repress target genes at mRNA and/or protein levels, emerging evidence from in vitro experiments has shown that miRNAs can also activate gene expression in particular contexts. However, this counterintuitive observation has rarely been reported or interpreted in in vivo conditions. Methods: We systematically explored the positive correlation between miRNA and gene expressions and its potential implications in tumorigenesis, based on 8375 patient samples across 31 major human cancers from The Cancer Genome Atlas (TCGA). Findings: We found that positive miRNA-gene correlations are surprisingly prevalent and consistent across cancer types, and show distinct patterns than negative correlations. The top-ranked positive correlations are significantly involved in the immune cell differentiation and cell membrane signaling related processes, and display strong power in stratifying patients in terms of survival rate. Although intragenic miRNAs generally tend to co-express with their host genes, a substantial portion of miRNAs shows no obvious correlation with their host gene plausibly due to non-conservation. A miRNA can upregulate a gene by inhibiting its upstream suppressor, or shares transcription factors with that gene, both leading to positive correlation. The miRNA/gene sites associated with the top-ranked positive correlations are more likely to form super-enhancers compared to randomly chosen pairs. Wet-lab experiments revealed that positive correlations partially remain in in vitro condition. Interpretation: Our study brings new insights into the critical role of miRNA in gene regulation and the complex mechanisms underlying miRNA functions, and reveals both biological and clinical significance of miRNA-associated gene activation. Keywords: Pan-cancer miRNA, miRNA activation, Intragenic miRNA, Super-enhancer

ObjectivesThis study assessed the preoperative prediction of TP53 status based on multiparametric magnetic resonance imaging (mpMRI) radiomics extracted from two-dimensional (2D) and 3D images.Methods57 patients with pancreatic cancer who underwent preoperative MRI were included. The diagnosis and TP53 gene test were based on resections. Of the 57 patients included 37 mutated TP53 genes and the remaining 20 had wild-type TP53 genes. Two radiologists performed manual tumour segmentation on seven different MRI image acquisition sequences per patient, including multi-phase [pre-contrast, late arterial phase (ap), portal venous phase, and delayed phase] dynamic contrast enhanced (DCE) T1-weighted imaging, T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC). PyRadiomics-package was used to generate 558 two-dimensional (2D) and 994 three-dimensional (3D) image features. Models were constructed by support vector machine (SVM) for differentiating TP53 status and DX score method were used for feature selection. The evaluation of the model performance included area under the curve (AUC), accuracy, calibration curves, and decision curve analysis.ResultsThe 3D ADC-ap-DWI-T2WI model with 11 selected features yielded the best performance for differentiating TP53 status, with accuracy = 0.91 and AUC = 0.96. The model showed the good calibration. The decision curve analysis indicated that the radiomics model had clinical utility.ConclusionsA non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients and contribute to the precision treatment.

ObjectivesAnaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.MethodsFive hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.ResultsA total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.ConclusionsThis study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

In this paper, we propose a novel method to estimate the disparity maps from a light field image pair captured by a pair of light field cameras. Our method integrates two types of critical depth cues, which are separately inferred from the epipolar plane images and binocular stereo vision into a global solution. At the same time, in order to produce highly accurate disparity maps, we adopt a generative model, which can estimate a light field image only with the central subaperture view and corresponding hypothesized disparity map. The objective function of our method is formulated to minimize two energy terms/differences. One is the difference between the two types of previously extracted disparity maps and the target disparity maps, directly optimized in the gray-scale disparity space. The other indicates the difference between the estimated light field images and the input light field images, optimized in the RGB color space. Comprehensive experiments conducted on real and virtual scene light field image pairs demonstrate the effectiveness of our method.

Purpose: The purpose of this study was to identify measles virus in Shanghai in 2012 and study the genotype trend of measles virus epidemic strains during 2000–2012. Methods: Nose and throat swab specimens were collected from 34 suspected measles cases in Shanghai. Measles virus was isolated using Vero-SLAM cells (African green monkey kidney cells/lymphoid signal activating factor-transfected African green monkey kidney cells). The 450 bp of C terminus of the N gene and the entire hemagglutinin gene sequence was amplified using RT-PCR. Phylogenetic analysis was performed by comparing the seven measles strains in Shanghai with the reference strains for H1a, H1b and D8 genotypes, as well as the Chinese measles virus vaccine strain. Results: Seven measles viruses strains were isolated from the 34 throat swap specimens. Six strains were genotype H1a, which is the predominant strain in China and one strain was genotype D8, which is the first imported strain since 2000. All these seven strains maintained most of the glycosylation sites except subtype H1a, which lost one glycosylation site. Conclusion: Since 2000, measles virus strains in Shanghai are consistent with measles virus from other provinces in China with H1a being the predominant genotype. This study is also the first report of genotype D8 strain in Shanghai. All strains maintained their glycosylation sites except H1a that lost one glycosylation site. These strains could still be neutralized by the Chinese measles vaccine. We suggest that Shanghai Center for Disease Control laboratories should strengthen their approaches to monitor measles cases to prevent further spread of imported strains. Keywords: D8 genotype, Importation, Measles virus, Molecular epidemiology

Wave and current energy can be harnessed in the East China Sea and South China Sea; however, both areas are subject to high frequencies of typhoon events. To improve the safety of the ocean energy conversion device, a Floating Ocean Energy Conversion Device (FOECD) with a single mooring system is proposed, which can be towed to avoid severe ocean conditions or for regular maintenance. In this paper, the structure of the FOECD is introduced, and it includes a catamaran platform, an oscillating buoy part, a current turbine blade, hydraulic energy storage and an electrical generation part. The numerical study models the large catamaran platform as a single, large buoy, while the four floating buoys were modeled simply as small buoys. Theoretical models on wave energy power capture and efficiency were established. To improve the suitability of the buoy for use in the FOECD and its power harvesting capability, a numerical simulation of the four buoy geometries was undertaken. The shape profiles examined in this paper are cylindrical, turbinate (V-shaped and U-shaped cone with cylinder), and combined cylinder-hemisphere buoys. Simulation results reveal that the suitability of a turbinate buoy is the best of the four types. Further simulation models were carried out by adjusting the tip radius of the turbinate buoy. Three performance criteria including suitability, power harvesting capability and energy capture efficiency were analyzed. It reveals that the turbinate buoy has almost the same power harvesting capabilities and energy capture efficiency, while its suitability is far better than that of a cylindrical buoy.