Knowledge atlas of antibody-drug conjugates on CiteSpace and clinical trial visualization analysis

ObjectiveAntibody-drugs conjugates (ADCs) are novel drugs with highly targeted and tumor-killing abilities and developing rapidly. This study aimed to evaluate drug discovery and clinical trials of and explore the hotspots and frontiers from 2012 to 2022 using bibliometric methods.MethodsPublications on ADCs were retrieved between 2012 and 2022 from Web of Science (WoS) and analyzed with CiteSpace 6.1.R2 software for the time, region, journals, institutions, etc. Clinical trials were downloaded from clinical trial.org and visualized with Excel software.ResultsA total of 696 publications were obtained and 187 drug trials were retrieved. Since 2012, research on ADCs has increased year by year. Since 2020, ADC-related research has increased dramatically, with the number of relevant annual publications exceeding 100 for the first time. The United States is the most authoritative and superior country and region in the field of ADCs. The University of Texas MD Anderson Cancer Center is the most authoritative institution in this field. Research on ADCs includes two clinical trials and one review, which are the most influential references. Clinical trials of ADCs are currently focused on phase I and phase II. Comprehensive statistics and analysis of the published literature and clinical trials in the field of ADCs, have shown that the most studied drug is brentuximab vedotin (BV), the most popular target is human epidermal growth factor receptor 2 (HER2), and breast cancer may become the main trend and hotspot for ADCs indications in recent years.ConclusionAntibody-drug conjugates have become the focus of targeted therapies in the field of oncology. The innovation of technology and combination application strategy will become the main trend and hotspots in the future

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Abstract Background Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge. Results Here, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc. Conclusions ConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system

Identifying Cancer Specific Driver Modules Using a Network-Based Method

Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git

Ion Transport Behavior through Thermally Reduced Graphene Oxide Membrane for Precise Ion Separation

The cation transport behavior of thermally treated reduced graphene oxide membranes (GOMs) is reported. The GOMs were reduced by heat treatment at 25, 80, and 120 °C and then characterized by Fourier transform infrared spectroscopy, X-ray powder diffraction, and X-ray photoelectron spectroscopy to determine oxygen group content, C/O ratio, and layer spacing. The permeation rates of various cations with different sizes and charge numbers through these membranes were measured to understand the effect of the cations on transport behavior. The results indicated that the cation transport through the membranes depended on the layer spacing of the membrane and ion size and charge. Cations of the same valence permeating through the same GOM could be differentiated by their hydration radius, whereas the same type of cation passing through different GOMs could be determined by the spacing of the GOM layers. The cation valence strongly affected permeation behavior. The GOM that was prepared at 120 °C exhibited a narrow layer spacing and high separation factors for Mg/Ca, Mg/Sr, K/Ca, and K/Fe. The cations moving through the membrane could insert into the membrane lamellas, which neutralized the negative charge of the membrane, enlarged the layer spacing of the GOMs, and affected cation permeation

Amino Acid Cross-Linked Graphene Oxide Membranes for Metal Ions Permeation, Insertion and Antibacterial Properties

Graphene oxide (GO) and its composite membranes have exhibited great potential for application in water purification and desalination. This article reports that a novel graphene oxide membrane (GOM) of ~5 µm thickness was fabricated onto a nylon membrane by vacuum filtration and cross-linked by amino acids (L-alanine, L-phenylalanine, and serine). The GOM cross-linked by amino acids (GOM-A) exhibits excellent stability, high water flux, and high rejection to metal ions. The rejection coefficients to alkali and alkaline earth metal ions through GOM-A were over 94% and 96%, respectively. The rejection coefficients decreased with an increasing H+ concentration. Metal ions (K+, Ca2+, and Fe3+) can be inserted into GOM-A layers, which enlarges the interlayer spacing of GOM-A and neutralizes the electronegativity of the membrane, resulting in the decease in the rejection coefficients to metal ions. Meanwhile, GOM-A showed quite high antibacterial efficiency against E. coli. With the excellent performance as described above, GOM-A could be used to purify and desalt water