A Facile NMR Method for Pre-MRI Evaluation of Trigger-Responsive T1 Contrast Enhancement.

There is a growing interest in developing paramagnetic nanoparticles as responsive magnetic resonance imaging (MRI) contrast agents, which feature switchable T1 image contrast of water protons upon biochemical cues for better discerning diseases. However, performing an MRI is pragmatically limited by its cost and availability. Hence, a facile, routine method for measuring the T1 contrast is highly desired in early-stage development. This work presents a single-point inversion recovery (IR) nuclear magnetic resonance (NMR) method that can rapidly evaluate T1 contrast change by employing a single, optimized IR pulse sequence that minimizes water signal for "off-state" nanoparticles and allows for sensitively measuring the signal change with "switch-on" T1 contrast. Using peptide-induced liposomal gadopentetic acid (Gd3+ -DTPA) release and redox-sensitive manganese oxide (MnO2 ) nanoparticles as a demonstration of generality, this method successfully evaluates the T1 shortening of water protons caused by liposomal Gd3+ -DTPA release and Mn2+ formation from MnO2 reduction. Furthermore, the NMR measurement is highly correlated to T1 -weighted MRI scans, suggesting its feasibility to predict the MRI results at the same field strength. This NMR method can be a low-cost, time-saving alternative for pre-MRI evaluation for a diversity of responsive T1 contrast systems.

Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop.

Accurate classification and localization of abnormalities in chest X-rays play an important role in clinical diagnosis and treatment planning. Building a highly accurate predictive model for these tasks usually requires a large number of manually annotated labels and pixel regions (bounding boxes) of abnormalities. However, it is expensive to acquire such annotations, especially the bounding boxes. Recently, contrastive learning has shown strong promise in leveraging unlabeled natural images to produce highly generalizable and discriminative features. However, extending its power to the medical image domain is under-explored and highly non-trivial, since medical images are much less amendable to data augmentations. In contrast, their prior knowledge, as well as radiomic features, is often crucial. To bridge this gap, we propose an end-to-end semi-supervised knowledge-augmented contrastive learning framework, that simultaneously performs disease classification and localization tasks. The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation. Specifically, we first apply an image encoder to classify the chest X-rays and to generate the image features. We next leverage Grad-CAM to highlight the crucial (abnormal) regions for chest X-rays (even when unannotated), from which we extract radiomic features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray. In this way, our framework constitutes a feedback loop for image and radiomic features to mutually reinforce each other. Their contrasting yields knowledge-augmented representations that are both robust and interpretable. Extensive experiments on the NIH Chest X-ray dataset demonstrate that our approach outperforms existing baselines in both classification and localization tasks.

Neutrophil extracellular traps participate in the development of cancer-associated thrombosis in patients with gastric cancer.

BACKGROUND: The development of venous thromboembolism (VTE) is associated with high mortality among gastric cancer (GC) patients. Neutrophil extracellular traps (NETs) have been reported to correlate with the prothrombotic state in some diseases, but are rarely reported in GC patients. AIM: To investigate the effect of NETs on the development of cancer-associated thrombosis in GC patients. METHODS: The levels of NETs in blood and tissue samples of patients were analyzed by ELISA, flow cytometry, and immunofluorescence staining. NET generation and hypercoagulation of platelets and endothelial cells (ECs) in vitro were observed by immunofluorescence staining. NET procoagulant activity (PCA) was determined by fibrin formation and thrombin-antithrombin complex (TAT) assays. Thrombosis in vivo was measured in a murine model induced by flow stenosis in the inferior vena cava (IVC). RESULTS: NETs were likely to form in blood and tissue samples of GC patients compared with healthy individuals. In vitro studies showed that GC cells and their conditioned medium, but not gastric mucosal epithelial cells, stimulated NET release from neutrophils. In addition, NETs induced a hypercoagulable state of platelets by upregulating the expression of phosphatidylserine and P-selectin on the cells. Furthermore, NETs stimulated the adhesion of normal platelets on glass surfaces. Similarly, NETs triggered the conversion of ECs to hypercoagulable phenotypes by downregulating the expression of their intercellular tight junctions but upregulating that of tissue factor. Treatment of normal platelets or ECs with NETs augmented the level of plasma fibrin formation and the TAT complex. In the models of IVC stenosis, tumor-bearing mice showed a stronger ability to form thrombi, and NETs abundantly accumulated in the thrombi of tumor-bearing mice compared with control mice. Notably, the combination of deoxyribonuclease I, activated protein C, and sivelestat markedly abolished the PCA of NETs. CONCLUSION: GC-induced NETs strongly increased the risk of VTE development both in vitro and in vivo. NETs are potential therapeutic targets in the prevention and treatment of VTE in GC patients.

A Toolkit for Engineering Proteins in Living Cells: Peptide with a Tryptophan-Selective Ru-TAP Complex to Regioselectively Photolabel Specific Proteins.

Using a chemical approach to crosslink functionally versatile bioeffectors (such as peptides) to native proteins of interest (POI) directly inside a living cell is a useful toolbox for chemical biologists. However, this goal has not been reached due to unsatisfactory chemoselectivity, regioselectivity, and protein selectivity in protein labeling within living cells. Herein, we report the proof of concept of a cytocompatible and highly selective photolabeling strategy using a tryptophan-specific Ru-TAP complex as a photocrosslinker. Aside from the high selectivity, the photolabeling is blue light-driven by a photoinduced electron transfer (PeT) and allows the bioeffector to bear an additional UV-responsive unit. The two different photosensitivities are demonstrated by blue light-photocrosslinking a UV-sensitive peptide to POI. Our visible light photolabeling can generate photocaged proteins for subsequent activity manipulation by UV light. Cytoskeletal dynamics regulation is demonstrated in living cells via the unprecedented POI photomanipulation and proves that our methodology opens a new avenue to endogenous protein modification.

Comprehensive Expression Profile Analysis of Neutrophil Extracellular Trap-Affected Genes in Gastric Cancer Cells and the Clinical Significance of lncRNA NEAT1-Related Signaling.

Background: Gastric cancer (GC) is the fifth most common malignant tumor and the third leading cause of cancer-related deaths worldwide. Neutrophil extracellular traps (NETs) can enhance the invasion of GC cells and are associated with poor prognosis in patients. However, its mechanism of action is not completely understood. Methods: The content of NETs in the peripheral blood of patients with GC was detected by enzyme-linked immunosorbent assay. GC AGS cells were treated with or without NETs for 24 h. High-throughput RNA sequencing was performed to screen differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). Real-time polymerase chain reaction (PCR) was used to verify gene expression. A competing endogenous RNA (ceRNA) regulatory network was constructed. Modules were screened using the molecular complex detection (MCODE) plug-in. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the genes in the network. The role and clinical significance of the lncRNA NEAT1-related signaling pathway were validated. Results: The content of NETs in the patients with GC was significantly higher than that in healthy controls and was also higher in patients with high-grade (stages III and IV) GC. NETs promoted the invasion of AGS cells. A total of 1,340 lncRNAs, 315 miRNAs, and 1,083 mRNAs were differentially expressed after NET treatment. The expression of five genes was validated using real-time PCR, which were in accordance with the RNA sequencing results. A ceRNA regulatory network was constructed with 1,239 lncRNAs, 310 miRNAs, and 1,009 mRNAs. Four genes (RAB3B, EPB41L4B, ABCB11, and CCDC88A) in the ceRNA network were associated with patient prognosis, with RAB3B being the most prominent and with signaling among the lncRNA NEAT1, the miRNA miR-3158-5p, and RAB3B. NEAT1 was upregulated in AGS cells after NET treatment. RNA interference of NEAT1 inhibited the invasion of AGS cells induced by NETs, inhibited miR-3158-5p expression, and promoted RAB3B expression. NEAT1 and RAB3B expression were positively correlated in patients with GC. Furthermore, RAB3B was upregulated and miR-3158-5p was downregulated in GC tissues compared with adjacent normal tissues, which was also associated with cancer stage. Conclusion: This study provides a comprehensive analysis of differentially expressed genes in NET-treated GC cells and validated the clinical significance of NEAT1-related signaling.

Pandemics are catalysts of scientific novelty: Evidence from COVID-19.

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

Liposomal PHD2 Inhibitors and the Enhanced Efficacy in Stabilizing HIF-1α.

Prolyl hydroxylase domain-containing protein 2 (PHD2) inhibition, which stabilizes hypoxia-inducible factor (HIF)-1α and thus triggers adaptation responses to hypoxia in cells, has become an important therapeutic target. Despite the proven high potency, small-molecule PHD2 inhibitors such as IOX2 may require a nanoformulation for favorable biodistribution to reduce off-target toxicity. A liposome formulation for improving the pharmacokinetics of an encapsulated drug while allowing a targeted delivery is a viable option. This study aimed to develop an efficient loading method that can encapsulate IOX2 and other PHD2 inhibitors with similar pharmacophore features in nanosized liposomes. Driven by a transmembrane calcium acetate gradient, a nearly 100% remote loading efficiency of IOX2 into liposomes was achieved with an optimized extraliposomal solution. The electron microscopy imaging revealed that IOX2 formed nanoprecipitates inside the liposome's interior compartments after loading. For drug efficacy, liposomal IOX2 outperformed the free drug in inducing the HIF-1α levels in cell experiments, especially when using a targeting ligand. This method also enabled two clinically used inhibitors-vadadustat and roxadustat-to be loaded into liposomes with a high encapsulation efficiency, indicating its generality to load other heterocyclic glycinamide PHD2 inhibitors. We believe that the liposome formulation of PHD2 inhibitors, particularly in conjunction with active targeting, would have therapeutic potential for treating more specifically localized disease lesions.

Toward a Coronavirus Knowledge Graph.

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

Analyzing knowledge entities about COVID-19 using entitymetrics.

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays.

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. In this paper, we develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. Specifically, ChexRadiNet first applies a light-weight but efficient triplet-attention mechanism to classify the chest X-rays and highlight the abnormal regions. Then it uses the generated class activation map to extract radiomic features, which further guides our model to learn more robust image features. After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR. We find that ChexRadiNet outperforms the state-of-the-art on both disease detection (0.843 in AUC) and localization (0.679 in T(IoU) = 0.1). We make the code publicly available at https://github. com/bionlplab/lung_disease_detection_amia2021, with the hope that this method can facilitate the development of automatic systems with a higher-level understanding of the radiological world.

PNEUMONIA DETECTION ON CHEST X-RAY USING RADIOMIC FEATURES AND CONTRASTIVE LEARNING.

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still has been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model's interpretability.

Building a PubMed knowledge graph.

PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.

A nano-molar fluorescent turn-on probe for copper(II) detection in living cells.

The development of fluorescent probes to detect trace metal ions in biological samples has been in great need. Herein, a fluorescence turn-on sensor (PHC) was designed for highly selective detection of Cu2+ ions. The probe PHC shows weak fluorescence due to imine isomerization. With Cu2+, a significant blue emission due to Cu2+-induced oxidation of imine to a carboxylate group is observed. The turn-on process is observed with a 63-fold increase of fluorescence quantum yield (from 0.004 to 0.252). The emission intensity has a good linear relation at Cu2+ concentrations of 0-40 µM. The detection limit is estimated as 8 nM (S/N = 3). The maximum emission change induced by Cu2+ is found in the pH range of 6.5-8.0. The probe PHC can be applied in detecting Cu2+ in living cells monitored by confocal fluorescence microscopy imaging.

Clinical characteristics, treatment patterns and survival outcome of hepatocellular carcinoma patients aged 70 years or older: a single-center retrospective study from China.

BACKGROUND AND AIMS: The information about clinical presentation and outcome of elderly hepatocellular carcinoma (HCC) patients is limited. We performed this study to assess the impact of age on potential differences in clinical characteristics, treatment patterns and outcome in HCC patients. METHODS: Clinical data of 164 "elderly" (≥70 years old) and 531 "younger" (<70 years old) HCC patients treated at a Chinese tertiary university-affiliated medical center between April 2004 and April 2012 were collected and compared using various parameters. RESULTS: Compared with younger patients, the elderly patients had a higher proportion of females (32.9 % vs. 18.1 %, p < 0.001), less hepatitis B virus (HBV) infection (40.9 % vs. 76.6 %, p < 0.001), more hepatitis C virus (HCV) infection (23.8 % vs. 5.6 %, p < 0.001), less liver cirrhosis (68.3 % vs. 76.8 %, p = 0.03) and massive tumors (12.8 % vs. 21.8 %, p = 0.01). There was no significant difference between the two groups in Child-Pugh class and tumor stages. The elderly patients received less surgical resection (14.6 % vs. 29.6 %, p < 0.001) and more supportive care (48.8 % vs. 37.9 %, p = 0.01) than younger patients. The overall survival was not significantly different between the two groups (26.2 mo. vs. 28.3 mo., p = 0.75). CONCLUSION: Characteristics that distinguish elderly from younger HCC patients included more female, less HBV infection, more HCV infection, less liver cirrhosis and massive tumors. Significant differences were observed in therapeutic strategies utilized with the two groups, but the overall survival was not significantly different.

The association between common genetic variant of microRNA-499 and cancer susceptibility: a meta-analysis.

Published data on the association between microRNA-499 (miR-499) rs3746444 T>C polymorphism and cancer susceptibility are inconclusive. To derive a more precise estimation of this relationship, a comprehensive meta-analysis was performed on nine published studies, with a total sample of 4,794 cases and 5,971 controls. Overall, no significant association was found between miR-499 polymorphism and cancer risk after all studies were pooled into the meta-analysis. However, in the subgroup analysis by ethnicity, significant association with an increased risk was found in Asian (CC vs. TT: OR = 1.439, 95 % CI = 1.118-1.852, P = 0.005, p-heterogeneity = 0.116). Moreover, in the the subgroup analysis by cancer type, this SNP was associated with an increased risk of breast cancer in the recessive model (OR = 1.077, 95 % CI = 1.008-1.151, P = 0.028, p-heterogeneity = 0.125). Our findings support the view that miR-499 rs3746444 T>C polymorphism is associated with breast cancer and the C allele can increase cancer susceptibility in Asian.