Development and validation of a TAAbs and TAAs based non-invasive model for diagnosing lung cancer.

Background: Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection. Methods: 1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis. Results: The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis. Conclusions: This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.

Structures and organizations of PSI-AcpPCI supercomplexes from red tidal and coral symbiotic photosynthetic dinoflagellates.

Marine photosynthetic dinoflagellates are a group of successful phytoplankton that can form red tides in the ocean and also symbiosis with corals. These features are closely related to the photosynthetic properties of dinoflagellates. We report here three structures of photosystem I (PSI)-chlorophylls (Chls) a/c-peridinin protein complex (PSI-AcpPCI) from two species of dinoflagellates by single-particle cryoelectron microscopy. The crucial PsaA/B subunits of a red tidal dinoflagellate Amphidinium carterae are remarkably smaller and hence losing over 20 pigment-binding sites, whereas its PsaD/F/I/J/L/M/R subunits are larger and coordinate some additional pigment sites compared to other eukaryotic photosynthetic organisms, which may compensate for the smaller PsaA/B subunits. Similar modifications are observed in a coral symbiotic dinoflagellate Symbiodinium species, where two additional core proteins and fewer AcpPCIs are identified in the PSI-AcpPCI supercomplex. The antenna proteins AcpPCIs in dinoflagellates developed some loops and pigment sites as a result to accommodate the changed PSI core, therefore the structures of PSI-AcpPCI supercomplex of dinoflagellates reveal an unusual protein assembly pattern. A huge pigment network comprising Chls a and c and various carotenoids is revealed from the structural analysis, which provides the basis for our deeper understanding of the energy transfer and dissipation within the PSI-AcpPCI supercomplex, as well as the evolution of photosynthetic organisms.

Sequence-Guided Localization of DNA Hybridization Enables Highly Selective and Robust Genotyping.

Genetic variations are always related to human diseases or susceptibility to therapies. Nucleic acid probes that precisely distinguish closely related sequences become an indispensable requisite both in research and clinical applications. Here, a Sequence-guided DNA LOCalization for leaKless DNA detection (SeqLOCK) is introduced as a technique for DNA hybridization, where the intended targets carrying distinct "guiding sequences" act selectively on the probes. In silicon modeling, experimental results reveal considerable agreement (R2  = 0.9228) that SeqLOCK is capable of preserving high discrimination capacity at an extraordinarily wide range of target concentrations. Furthermore, SeqLOCK reveals high robustness to various solution conditions and can be directly adapted to nucleic acid amplification techniques (e.g., polymerase chain reaction) without the need for laborious pre-treatments. Benefiting from the low hybridization leakage of SeqLOCK, three distinct variations with a clinically relevant mutation frequency under the background of genomic DNA can be discriminated simultaneously. This work establishes a reliable nucleic acid hybridization strategy that offers great potential for constructing robust and programmable systems for molecular sensing and computing.

Iterated Residual Graph Convolutional Neural Network for Personalized Three-Dimensional Reconstruction of Left Myocardium from Cardiac MR Images.

Three-dimensional reconstruction of the left myocardium is of great significance for the diagnosis and treatment of cardiac diseases. This paper proposes a personalized 3D reconstruction algorithm for the left myocardium using cardiac MR images by incorporating a residual graph convolutional neural network. The accuracy of the mesh, reconstructed using the model-based algorithm, is largely affected by the similarity between the target object and the average model. The initial triangular mesh is obtained directly from the segmentation result of the left myocardium. The mesh is then deformed using an iterated residual graph convolutional neural network. A vertex feature learning module is also built to assist the mesh deformation by adopting an encoder-decoder neural network to represent the skeleton of the left myocardium at different receptive fields. In this way, the shape and local relationships of the left myocardium are used to guide the mesh deformation. Qualitative and quantitative comparative experiments were conducted on cardiac MR images, and the results verified the rationale and competitiveness of the proposed method compared to related state-of-the-art approaches.

How to better select SARS-CoV-2 preservation solution of virus nucleic acid testing.

BACKGROUND: Sampling and testing for SARS-CoV-2 is a widely recognized method for identifying patients with COVID-19. However, there is limited research available on the stability of nucleic acids in viral storage solutions. METHODS: This paper investigates the components that provide better protection for virus and nucleic acid detection. The study utilized real-time quantitative fluorescent PCR to detect SARS-CoV-2 and evaluate the preservation effect and stability of SARS-CoV-2 viral storage solution under various conditions, including different guanidinium salts, brands, and storage conditions. RESULTS: All brands of inactivated virus preservation solutions demonstrated effective preservation and stability. However, 0.5 mol/L guanidine hydrochloride and guanidine isothiocyanate solutions exhibited poor antiseptic effects. Additionally, refrigerated storage showed better preservation compared to room temperature storage. CONCLUSIONS: We recommend using inactivated virus collection solution to preserve and transport samples and testing preferably within 6 hours to reduce false negatives of NAT results.

Genomic insight into domestication of rubber tree.

Understanding the genetic basis of rubber tree (Hevea brasiliensis) domestication is crucial for further improving natural rubber production to meet its increasing demand worldwide. Here we provide a high-quality H. brasiliensis genome assembly (1.58 Gb, contig N50 of 11.21 megabases), present a map of genome variations by resequencing 335 accessions and reveal domestication-related molecular signals and a major domestication trait, the higher number of laticifer rings. We further show that HbPSK5, encoding the small-peptide hormone phytosulfokine (PSK), is a key domestication gene and closely correlated with the major domestication trait. The transcriptional activation of HbPSK5 by myelocytomatosis (MYC) members links PSK signaling to jasmonates in regulating the laticifer differentiation in rubber tree. Heterologous overexpression of HbPSK5 in Russian dandelion (Taraxacum kok-saghyz) can increase rubber content by promoting laticifer formation. Our results provide an insight into target genes for improving rubber tree and accelerating the domestication of other rubber-producing plants.

The rs9402373 polymorphism of CTGF gene may not be related to inflammatory bowel disease susceptibility in Chinese population based on ARMS-PCR genotyping.

Background: It has been confirmed that the connective tissue growth factor (CTGF) gene rs9402373 polymorphism is associated with fibrotic and inflammatory diseases. However, studies on the relationship between polymorphisms in CTGF rs9402373 and inflammatory bowel disease (IBD) remain rare. Therefore, the aim of this study was to assess the association between the CTGF rs9402373 polymorphism and IBD susceptibility in a Chinese population. Materials and methods: To establish an amplification refractory mutation system (ARMS) PCR technology for genotyping CTGF gene rs9402373 polymorphism, we designed two specific forward primers for the wild and mutant types by placing the allele-specific nucleotide at the penultimate position of the '3' end of the primer. Then, 10 samples were randomly selected and rechecked by DNA sequencing to verify the accuracy of this method. We further used the established method to detect specimens collected from 191 patients with inflammatory bowel disease, including 120 Crohn's disease (CD) and 71 ulcerative colitis (UC), and 110 healthy Han Chinese individuals. Results: We successfully established the ARMS-PCR method for genotyping, and the results of 10 randomly selected samples were completely consistent with DNA sequencing. The rs9402373 G allele frequencies in UC and CD cases were 38.03% and 43.75%, respectively, and in controls, they were 41.82%. No significant difference was found in minor allele frequencies between the UC or CD and control groups (P = 0.473, P = 0.676). Genotype analysis demonstrated that there was no relationship between CTGF rs9402373 polymorphism and the risk of IBD regardless of the inheritance mode (P > 0.05). Conclusions: In this preliminary study, we successfully developed a simple, efficient and cost-effective method for genotyping CTGF rs9402373 polymorphism. The polymorphism may not be related to IBD susceptibility in the Chinese Han population.

Technical Acoustic Measurements Combined with Clinical Parameters for the Differential Diagnosis of Nonalcoholic Steatohepatitis.

Background and aim: Diagnosing nonalcoholic steatohepatitis (NASH) is challenging. This study intended to explore the diagnostic value of multiple technical acoustic measurements in the diagnosis of NASH, and to establish a diagnostic model combining technical acoustic measurements with clinical parameters to improve the diagnostic efficacy of NASH. Methods: We consecutively enrolled 75 patients with clinically suspected nonalcoholic fatty liver disease (NAFLD) who underwent percutaneous liver biopsy in our hospital from June 2020 to December 2021. All cases underwent multiple advanced acoustic measurements for liver such as shear wave dispersion (SWD), shear wave speed (SWS), attenuation imaging (ATI), normalized local variance (NLV), and liver-kidney intensity ratio (Ratio) examination before liver biopsies. A nomogram prediction model combining the technical acoustic measurements and clinical parameters was established and the model is proposed to improve the diagnostic performance of NASH. Results: A total of 75 cases were included in this study. The classification of pathological grade for NASH was as follows: normal liver, (n = 15, 20%), nonalcoholic fatty liver (NAFL), (n = 44, 58.7%), and NASH, (n = 16, 21.3%). There were statistically significant differences in SWS (p = 0.002), acoustic coefficient (AC) (p = 0.018), NLV (p = 0.033), age (p = 0.013) and fasting blood glucose (Glu) (p = 0.049) between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH, and the calibration curves showed good calibrations in both training and validation sets. The AUCs of the combined nomogram model for the training set and validation set were 0.8597 and 0.7794, respectively. Conclusion: There were statistically significant differences in SWS, AC, NLV, age and Glu between NASH and non-NASH. A nomogram model which includes SWS, AC, NLV, age and Glu was built to predict NASH. The predictive model has a higher diagnostic performance than a single factor model in the diagnosis of NASH and has good clinical application prospects.

Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes.

Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.

Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification.

Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (magnetic resonance imaging) slices, low discriminative and large spanning organs. To handle these problems, a two-stage CNN (convolutional neural network) segmentation method based on the combination of Log-Gabor filter attention mechanism and metric classification is proposed. The Log-Gabor filterbank is applied to selectively enhance the texture information and contour information of each tissue and organ, and the spatial and channel attention mechanism jointly with the varying kernel size of Log-Gabor filterbank is incorporated into the codec structure to adaptively extract target features of different sizes and focus on the discriminative features in the network. To solve the problem of insufficient segmentation on subtle and adherent edges involving different tissues, a metric classification network is incorporated to finely optimize the hard-to-be-segmented boundaries. The proposed method was tested on cardiac MRI data set to segment 7 cardiac tissues, and the rationality and effectiveness of the method were verified. In comparison to a series of deep learning-based segmentation models, the proposed method achieves competitive performance.

Identification and Validation of Three Hub Genes Involved in Cell Proliferation and Prognosis of Castration-Resistant Prostate Cancer.

Background: The acquisition of castration resistance is lethal and inevitable in most prostate cancer patients under hormone therapy. However, effective biomarkers and therapeutic targets for castration-resistant prostate cancer remain to be defined. Methods: Comprehensive bioinformatics tools were used to screen hub genes in castration-resistant prostate cancer and were verified in androgen-dependent prostate cancer and castration-resistant prostate cancer in TCGA and the SU2C/PCF Dream Team database, respectively. Gene set enrichment analysis and in vitro experiments were performed to determine the potential functions of hub genes involved in castration-resistant prostate cancer progression. Results: Three hub genes were screened out by bioinformatics analysis: MCM4, CENPI, and KNTC1. These hub genes were upregulated in castration-resistant prostate cancer and showed high diagnostic and prognostic value. Moreover, the expression levels of the hub genes were positively correlated with neuroendocrine prostate cancer scores, which represent the degree of castration-resistant prostate cancer aggression. Meanwhile, in vitro experiments confirmed that hub gene expression was increased in castration-resistant prostate cancer cell lines and that inhibition of hub genes hindered cell cycle transition, resulting in suppression of castration-resistant prostate cancer cell proliferation, which confirmed the gene set enrichment analysis results. Conclusions: MCM4, CENPI, and KNTC1 could serve as candidate diagnostic and prognostic biomarkers of castration-resistant prostate cancer and may provide potential preventive and therapeutic targets.

[Corrigendum] RON and c­Met facilitate metastasis through the ERK signaling pathway in prostate cancer cells.

Subsequently to the publication of the above article, the authors have discovered that the version of Fig. 5 included in the paper was an incorrect version, and that two pairs of data panels were inadvertently included in Fig. 6D (the data panels for the NC+migration and NC+HGF+U0126+invasion experiments for the PC3 cells, and the data panels for the NC+invasion and NC+HGF+U0126+invasion experiments for the DU145 cells) that contained overlapping data derived from the same source. These data were intended to represent the results obtained under different experimental conditions. Furthermore, the GAPDH control bands in Fig. 4A (DU145 cells) and the p­ERK1/2 bands in Fig. 6A (PC3 cells) were incorrectly chosen for these figures. After having consulted the original data, the authors discovered that unintended errors were made in assembling the data for these graphs. In uploading the corrected version of Fig. 5, Fig. 3C and D and Fig. 4C and D were adjusted accordingly. The corrected versions of Figs. 3, 4, 5, and 6 are shown on the subsequent pages. The authors regret the errors that were made during the preparation of the published figures, and confirm that these errors did not affect the conclusions reported in the study. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish a Corrigendum, and all the authors agree to this Corrigendum. Furthermore, they apologize to the readership for any inconvenience caused. [Oncology Reports 37: 3209­3218, 2017; DOI: 10.3892/or.2017.5585].

Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer's Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma.

Background: Predicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer's disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aß1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid ß positron emission tomography (PET) biomarkers. Methods: We developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making. Results: Under optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort). Conclusion: The developed assay and a satisfactory nomogram model hold promising clinical potential for early diagnosis of aMCI and AD participants.

Toward Understanding the Alginate Catabolism in Microbulbifer sp. ALW1 by Proteomics Profiling.

The bacterial strain of Microbulbifer sp. ALW1 has demonstrated visible ability of degrading the cell wall of Laminaria japonica, and biochemical characterization has been performed on some individual enzymes to elucidate its genetic basis. However, it still remains elusive how strain ALW1 successfully breaks down the major cell wall component alginate polysaccharide and colonizes on its marine host. In this study, a mass spectrometry-based quantitative analysis of the extracellular and intracellular proteomes was introduced to elucidate the alginate degradation pathway in ALW1 strain. Mass spectrometry and biochemical assays indicated that strain ALW1 could effectively degrade alginate polysaccharide into disaccharides and trisaccharides within 12 h. Proteome analysis identified 156 and 1,047 proteins exclusively localized in extracellular and intracellular compartments, respectively, with 1,086 protein identities of dual localization. Functional annotation of the identified proteins suggested the involvement of diverse catalytic enzymes and non-catalytic molecules for the cleavage and metabolism of alginate polysaccharide. A simplified pathway was constructed to demonstrate the extracellular digestion, active transport, and intracellular conversion of alginate polysaccharide and its fragmented oligosaccharides, casting a picture of genetic loci controlling alginate catabolism by ALW1 strain. This study aims to provide a guide for utilization and genetic manipulation of the bacterial strain ALW1 for efficient alginate oligosaccharides production by fermentation.

Serum oncostatin M is a potential biomarker of disease activity and infliximab response in inflammatory bowel disease measured by chemiluminescence immunoassay.

BACKGROUND: Although endoscopy is the gold standard to assess disease activity and infliximab efficacy in inflammatory bowel disease (IBD), the invasive, costly, and time-consuming procedure limits its routine applications. We aimed to investigate the clinical value of serum oncostatin M (OSM) as a surrogate biomarker. METHODS: Fifty healthy controls, 34 non-IBD patients, and 189 IBD patients who were pre-infliximab treatment (n = 122) or in infliximab maintenance (n = 67) were enrolled. A chemiluminescence immunoassay (CLIA) was constructed to quantify serum OSM concentrations. Receiver operator characteristic (ROC) curve analysis was used to evaluate the performance of blood biomarkers for IBD management. RESULTS: The methodology of CLIA exhibited great analytical performance with a wide linear range of 31.25-25000 pg/mL, a low detection limit of 23.2 pg/mL, acceptable precision, and applicable accuracy. Patients with IBD (121.5 [43.3-249.4] pg/mL, p < 0.001) and non-IBD (72.4 [51.4-129.6] pg/mL, p = 0.005) had higher serum OSM levels than healthy controls (35.8 [23.2-56.4] pg/mL). In the analysis of clinical and endoscopic activity, serum OSM levels were elevated in moderate and severe patients compared to those in remission. IBD patients without mucosal healing had higher serum OSM levels than those with mucosal healing (AUC = 0.843). Besides, serum OSM levels were increased in clinical non-responders (287.3 [127.9-438] pg/mL) compared to responders (24.1 [23.2-53.4] pg/mL, p < 0.001), and showed great recognition ability with an AUC of 0.898. CONCLUSIONS: The newly developed methodology of CLIA had great potential for use in the clinic. Elevated serum OSM expression was a promising biomarker of severe disease and infliximab non-response in IBD patients.

Proteomics analysis of the effects for different salt ions in leaves of true halophyte Sesuvium portulacastrum.

Sesuvium portulacastrum is a true halophyte and shows an optimal development under moderate salinity with large amounts of salt ions in its leaves. However, the specific proteins in response to salt ions are remained unknown. In this study, comparative physiological and proteomic analyses of different leaves subject to NaCl, KCl, NaNO3 and KNO3 were performed. Chlorophyll content was decreased under the above four kinds of salt treatments. Starch and soluble sugar contents changed differently under different salt treatments. A total of 53 differentially accumulated proteins (DAPs) were identified by mass spectrometry. Among them, 13, 25, 26 and 25 DAPs were identified after exposure to KCl, NaCl, KNO3, and NaNO3, respectively. These DAPs belong to 47 unique genes, and 37 of them are involved in protein-protein interactions. These DAPs displayed different expression patterns after treating with different salt ions. Functional annotation revealed they are mainly involved in photosynthesis, carbohydrate and energy metabolism, lipid metabolism, and biosynthesis of secondary metabolites. Genes and proteins showed different expression profiles under different salt treatments. Enzyme activity analysis indicated P-ATPase was induced by KCl, NaCl and NaNO3, V-ATPase was induced by KCl and NaCl, whereas V-PPase activity was significantly increased after application of KNO3, but sharply inhibited by NaCl. These results might deepen our understanding of responsive mechanisms in the leaves of S. portulacastrum upon different salt ions.

A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis.

INTRODUCTION: Prostate cancer (PCa) is one of the most frequently diagnosed cancers and the leading cause of cancer death in males worldwide. Although prostate-specific antigen (PSA) screening has considerably improved the detection of PCa, it has also led to a dramatic increase in overdiagnosing indolent disease due to its low specificity. This study aimed to develop and validate a multivariate diagnostic model based on the urinary epithelial cell adhesion molecule (EpCAM)-CD9-positive extracellular vesicles (EVs) (uEVEpCAM-CD9) to improve the diagnosis of PCa. METHODS: We investigated the performance of uEVEpCAM-CD9 from urine samples of 193 participants (112 PCa patients, 55 benign prostatic hyperplasia patients, and 26 healthy donors) to diagnose PCa using our laboratory-developed chemiluminescent immunoassay. We applied machine learning to training sets and subsequently evaluated the multivariate diagnostic model based on uEVEpCAM-CD9 in validation sets. RESULTS: Results showed that uEVEpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEVEpCAM-CD9 was observed after prostatectomy. We further used a training set (N = 116) and constructed an exclusive multivariate diagnostic model based on uEVEpCAM-CD9, PSA, and other clinical parameters, which showed an enhanced diagnostic sensitivity and specificity and performed excellently to diagnose PCa [area under the curve (AUC) = 0.952, P < 0.0001]. When applied to a validation test (N = 77), the model achieved an AUC of 0.947 (P < 0.0001). Moreover, this diagnostic model also exhibited a superior diagnostic performance (AUC = 0.917, P < 0.0001) over PSA (AUC = 0.712, P = 0.0018) at the PSA gray zone. CONCLUSIONS: The multivariate model based on uEVEpCAM-CD9 achieved a notable diagnostic performance to diagnose PCa. In the future, this model may potentially be used to better select patients for prostate transrectal ultrasound (TRUS) biopsy.

Combined Use of Fecal Biomarkers in Inflammatory Bowel Diseases: Oncostatin M and Calprotectin.

BACKGROUND: Fecal biomarkers have emerged as one of the most useful tools for clinical management of inflammatory bowel disease (IBD). Oncostatin M (OSM), like fecal calprotectin (FC), is highly expressed in the inflamed intestinal mucosa which may have potential usefulness. We aimed to evaluate the additional utility of these two fecal biomarkers for IBD diagnosis, activity, and prediction of infliximab response over FC alone. METHODS: In group 1, 236 IBD patients (145 Crohn's disease, 91 ulcerative colitis), 50 disease controls, and 32 healthy controls were recruited for IBD diagnosis and activity. In group 2, baseline stool samples were collected from 62 patients to predict infliximab response at week 28 and 52. The performance of fecal biomarkers for IBD management was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: Fecal OSM and FC levels were increased in IBD patients and were positively correlated with clinical and endoscopic activity. Their combination showed a better ability for disease diagnosis (AUC = 0.93) and slightly improved the capability to identify mucosal healing (AUC = 0.923). Baseline OSM and FC levels were elevated in non-responders at week 28 and 52. The AUCs of OSM, FC, and their combination to predict therapeutic response were 0.763, 0.834, and 0.859 at week 28, 0.638, 0.661, and 0.704 at week 52, respectively. Combined use of fecal and blood biomarkers improved predictive accuracy with an AUC of 0.919 at week 28 and 0.887 at week 52. CONCLUSION: In addition to FC, OSM is a novel fecal biomarker, and their combination is more beneficial for disease diagnosis and prediction of infliximab response but not for disease activity in IBD patients. Further larger-scale studies are required to confirm our findings.

Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma.

Metabolic pattern reconstruction is an important factor in tumor progression. Metabolism of tumor cells is characterized by abnormal increase in anaerobic glycolysis, regardless of high oxygen concentration, resulting in a significant accumulation of energy from glucose sources. These changes promotes rapid cell proliferation and tumor growth, which is further referenced a process known as the Warburg effect. The current study reconstructed the metabolic pattern in progression of cancer to identify genetic changes specific in cancer cells. A total of 12 common types of solid tumors were included in the current study. Gene set enrichment analysis (GSEA) was performed to analyze 9 glycolysis-related gene sets, which are implicated in the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes showed high area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). The findings of the current study showed that 8 genes (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were correlated with overall survival (OS) and recurrence-free survival (RFS). Further analysis showed that the prediction model accurately distinguished between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics showed good discrimination based on internal and external cohorts. These findings indicate that changes in expression level of metabolic genes implicated in glycolysis can contribute to reconstruction of tumor-related microenvironment.

A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.