CBD2: A functional biomarker database for colorectal cancer.

The rapidly evolving landscape of biomarkers for colorectal cancer (CRC) necessitates an integrative, updated repository. In response, we constructed the Colorectal Cancer Biomarker Database (CBD), which collected and displayed the curated biomedicine information for 870 CRC biomarkers in the previous study. Building on CBD, we have now developed CBD2, which includes information on 1569 newly reported biomarkers derived from different biological sources (DNA, RNA, protein, and others) and clinical applications (diagnosis, treatment, and prognosis). CBD2 also incorporates information on nonbiomarkers that have been identified as unsuitable for use as biomarkers in CRC. A key new feature of CBD2 is its network analysis function, by which users can investigate the visible and topological network between biomarkers and identify their relevant pathways. CBD2 also allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multidrug, multitarget, multipathway analyses, toward facilitating the design of polypharmacological treatments for CRC. CBD2 is freely available at http://www.eyeseeworld.com/cbd.

High-Order line graphs of fMRI data in major depressive disorder.

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. PURPOSE: This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. METHODS: Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. RESULTS: The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. CONCLUSIONS: This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Analysis of the influencing factors of vitality and built environment of shopping centers based on mobile-phone signaling data.

Nowadays, shopping centers not only provides commercial function but also serve as a public space. In this article, we take Nanshan district of Shenzhen as an example, based on the characteristics information of people activities provided by Mobile-phone Signaling Data, using the standard deviation ellipse method to classify the direction of people in shopping centers, and then applying the entropy weighting method to analyze the vitality factors of shopping centers from three perspectives: visitors' density, revisit rate, and the average length of stay. Finally, we analyzed the influence factors of the surrounding built environment based on correlation analysis to discuss the results with field survey data. The results show that (1) shopping centers in Nanshan District are classified into wide-area type and geo-regional type according to the gathering of visitors. The shopping centers with high comprehensive vitality are basically wide-area type. (2) The factors influencing the vitality of shopping centers are different between wide-area type and geo-regional type. The vitality of wide-area type is mainly influenced by the traffic accessibility and whether they are located in adjacent to large public spaces such as squares and green public; the vitality of geo-regional type shopping centers is mainly influenced by the number of people within a 15-minute walking circle, and the high-vitality of geo-regional shopping centers are generally located in densely populated areas.

MRI-based brain age prediction model for children under 3 years old using deep residual network.

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

EBD: an eye biomarker database.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

Enhanced antiproliferative effect of resveratrol in head and neck squamous cell carcinoma using GE11 peptide conjugated liposome.

The present study describes the preparation of a dodecapeptide YHWYGYTPQNVI (GE11)­conjugated liposome bound with polyethylene glycol to enhance the therapeutic effect of resveratrol (RSV) in head and neck cancer cells. The results indicated that (RSV)­loaded GE11­conjugated liposomes (RSV­GL) exhibited a high entrapment efficiency of >95%, with an active drug loading level of 19.5% w/w. Release kinetics revealed that RSV was released in a slow and sustained manner from the RSV­GL and RSV­loaded liposome (RSV­L) nanoparticulate systems. The epidermal growth factor receptor (EGFR)­overexpressing squamous cell carcinoma HN cells specifically internalized GE11 surface­conjugated liposome in a manner that was markedly increased compared with that of the non­targeted carrier. Consistently, RSV­GL exhibited a significantly increased cytotoxic effect compared with that of the non­targeted nanoparticles. Notably, RSV­GL induced significantly increased proportions of early (~60%) and late (~10%) apoptotic cells in head and neck cancer cell populations. To the best of our knowledge, the application and development of EGFR­targeted peptide­conjugated liposome system for RSV delivery has not been studied previously in the treatment of head and neck cancer. In addition, RSV­GL exhibited the greatest antitumor efficacy compared with any other group. RSV­GL exhibited a 2­fold decrease in tumor volume compared with the free RSV and a 3­fold decrease in volume compared with the control. Overall, the nanomedicine strategy described in the present study may potentially advance the chemotherapy­based treatment of head and neck cancer, with promising applications in other EGFR­overexpressing tumors.

The Preparation of Ag Nanoparticle and Ink Used for Inkjet Printing of Paper Based Conductive Patterns.

Ag nanoparticles were successfully prepared using a liquid reduction method with a suitable mixture reductant of polyethylene glycol (PEG) and ethylene glycol (EG). OP-10 as a dispersing agent, was used to prepare the conductive Ag ink. Ag nanoparticles with an average particle size of 40 nm were prepared while the ratio of PEG to EG was 1:2. Meanwhile, the Ag particles had a narrow size distribution and great dispersion performance. The effects of paper substrates, sintering temperature, and sintering time on the conductivity of the printed Ag ink pattern were also studied. It was found that Lucky porous high glossy photo paper was a good candidate as the printing substrate. The resistivity of the printed pattern could reach 5.1 × 10-3 Ω·cm after heated at 100 °C for 2 h. Hence, the printed pattern showed good conductivity which led to the LED light being on. Furthermore, the Ag nanoparticle ink could be printed to form any pattern as required that still showed good electrical conductivity after being sintered under low-temperature. This could provide new possibilities for the preparation of flexible electrodes.