Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction.

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

There is a growing interest in developing paramagnetic nanoparticles as responsive magnetic resonance imaging (MRI) contrast agents, which feature switchable T 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 T contrast is highly desired in early-stage development.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 Cu ions. The probe PHC shows weak fluorescence due to imine isomerization.

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.

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.