Therapeutic potential of mesenchymal stem cell-derived exosomes in skeletal diseases.

Skeletal diseases impose a considerable burden on society. The clinical and tissue-engineering therapies applied to alleviate such diseases frequently result in complications and are inadequately effective. Research has shifted from conventional therapies based on mesenchymal stem cells (MSCs) to exosomes derived from MSCs.

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.

Introduction: Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods.

Methods: In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission.

Application of Metal-Based Nanomaterials in In Vitro Diagnosis of Tumor Markers: Summary and Prospect.

Cancer, which presents with high incidence and mortality rates, has become a significant health threat worldwide. However, there is currently no effective solution for rapid screening and high-quality treatment of early-stage cancer patients. Metal-based nanoparticles (MNPs), as a new type of compound with stable properties, convenient synthesis, high efficiency, and few adverse reactions, have become highly competitive tools for early cancer diagnosis.

Chemotherapy-Sensitized Vaccination for Malignant Osteosarcoma Enabled by Bioinspired Calcium Phosphonate Nanoagents.

How to effectively treat malignant osteosarcoma remains clinically challenging. Programmed delivery of chemotherapeutic agents and immunostimulants may offer a universal strategy for killing osteosarcoma cells while simultaneously eliciting antitumor immunity. However, targeted chemoimmunotherapy lacks a reliable delivery system.

Gadolinium-Bisphosphonate Nanoparticle-Based Low-Dose Radioimmunotherapy for Osteosarcoma.

Osteosarcoma is a malignant osteogenic tumor with a high metastatic rate commonly occurring in adolescents. Although radiotherapy is applied to treat unresectable osteosarcoma with radiation resistance, a high dose of radiotherapy is required, which may weaken the immune microenvironment. Therefore, there is an urgent need to develop novel agents to maximize the radiotherapeutic effects by eliciting immune activation effects.

A gradient boosting tree model for multi-department venous thromboembolism risk assessment with imbalanced data.

Venous thromboembolism (VTE) is the world's third most common cause of vascular mortality and a serious complication from multiple departments. Risk assessment of VTE guides clinical intervention in time and is of great importance to in-hospital patients. Traditional VTE risk assessment methods based on scaling tools, which always require rules carefully designed by human experts, are difficult to apply to large-population scenarios since the manually designed rules are not guaranteed to be accurate to all populations.

Preclinical safety and hepatotoxicity evaluation of biomineralized copper sulfide nanoagents.

Albumin-biomineralized copper sulfide nanoparticles (CuS NPs) have attracted much attention as an emerging phototheranostic agent due to their advantages of facile preparation method and high biocompatibility. However, comprehensive preclinical safety evaluation is the only way to meet its further clinical translation. We herein evaluate detailedly the safety and hepatotoxicity of bovine serum albumin-biomineralized CuS (BSA@CuS) NPs with two different sizes in rats.

A novel hierarchical machine learning model for hospital-acquired venous thromboembolism risk assessment among multiple-departments.

Venous thromboembolism (VTE) is a common vascular disease and potentially fatal complication during hospitalization, and so the early identification of VTE risk is of significant importance. Compared with traditional scale assessments, machine learning methods provide new opportunities for precise early warning of VTE from clinical medical records. This research aimed to propose a two-stage hierarchical machine learning model for VTE risk prediction in patients from multiple departments.

Hepatotoxicity of copper sulfide nanoparticles towards hepatocyte spheroids using a novel multi-concave agarose chip method.

To explore the hepatotoxicity of copper sulfide nanoparticles (CuSNPs) toward hepatocyte spheroids. Other than the traditional agarose method to generate hepatocyte spheroids, we developed a multi-concave agarose chip (MCAC) method to investigate changes in hepatocyte viability, morphology, mitochondrial membrane potential, reactive oxygen species and hepatobiliary transporter by CuSNPs. The MCAC method allowed a large number of spheroids to be obtained per sample.

Artificial Intelligence for Screening Chinese Electronic Medical Record and Biobank Information.

To establish a structured and integrated platform of clinical data and biobank data, and a client to retrieve these data. Initially, the hospital information system (HIS) and biobank information system (BIS) were integrated through the patients' ID numbers. Then, natural language processing (NLP) was used to process the integrated unstructured clinical information.

Iodinated BSA Nanoparticles for Macrophage-Mediated CT Imaging and Repair of Gastritis.

The development of a specific and noninvasive technology for understanding gastritic response together with efficient therapy is an urgent clinical issue. Herein, we fabricated a novel iodinated bovine serum albumin (BSA) nanoparticle based on gastritic microenvironment for computed tomography (CT) imaging and repair of acute gastritis. Derived from the characteristic mucosa defect and inflammatory cell (e.

Correction to: Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods.

[This corrects the article PMC7467127.].

Detection of medical text semantic similarity based on convolutional neural network.

Background: Imaging examinations, such as ultrasonography, magnetic resonance imaging and computed tomography scans, play key roles in healthcare settings. To assess and improve the quality of imaging diagnosis, we need to manually find and compare the pre-existing reports of imaging and pathology examinations which contain overlapping exam body sites from electrical medical records (EMRs). The process of retrieving those reports is time-consuming.

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

Background: The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming.

Objective: To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard.

Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach.

Background: The vocabulary gap between consumers and professionals in the medical domain hinders information seeking and communication. Consumer health vocabularies have been developed to aid such informatics applications. This purpose is best served if the vocabulary evolves with consumers' language.

"We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.

Patients discuss complementary and alternative medicine (CAM) in online health communities. Sometimes, patients' conflicting opinions toward CAM-related issues trigger debates in the community. The objectives of this paper are to identify such debates, identify controversial CAM therapies in a popular online breast cancer community, as well as patients' stances towards them.

A cascaded approach for Chinese clinical text de-identification with less annotation effort.

With rapid adoption of Electronic Health Records (EHR) in China, an increasing amount of clinical data has been available to support clinical research. Clinical data secondary use usually requires de-identification of personal information to protect patient privacy. Since manually de-identification of free clinical text requires significant amount of human work, developing an automated de-identification system is necessary.

Cataloguing Treatments Discussed and Used in Online Autism Communities.

A large number of patients discuss treatments in online health communities (OHCs). One research question of interest to health researchers is whether treatments being discussed in OHCs are eventually used by community members in their real lives. In this paper, we rely on machine learning methods to automatically identify attributions of mentions of treatments from an online autism community.

EliIE: An open-source information extraction system for clinical trial eligibility criteria.

Objective: To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0.

Materials And Methods: EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring.

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks.

Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation.

Factors Contributing to Dropping-out in an Online Health Community: Static and Longitudinal Analyses.

Dropping-out, which refers to when an individual abandons an intervention, is common in Internet-based studies as well as in online health communities. Community facilitators and health researchers are interested in this phenomenon because it usually indicates dissatisfaction towards the community and/or its failure to deliver expected benefits. In this study, we propose a method to identify dropout members from a large public online breast cancer community.

Detecting negation and scope in Chinese clinical notes using character and word embedding.

Background And Objectives: Researchers have developed effective methods to index free-text clinical notes into structured database, in which negation detection is a critical but challenging step. In Chinese clinical records, negation detection is particularly challenging because it may depend on upstream Chinese information processing components such as word segmentation [1]. Traditionally, negation detection was carried out mostly using rule-based methods, whose comprehensiveness and portability were usually limited.

Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods.

Expression of emotion has been linked to numerous critical and beneficial aspects of human functioning. Accurately capturing emotional expression in text grows in relevance as people continue to spend more time in an online environment. The Linguistic Inquiry and Word Count (LIWC) is a commonly used program for the identification of many constructs, including emotional expression.

Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact.

Objectives: The Internet and social media are revolutionizing how social support is exchanged and perceived, making online health communities (OHCs) one of the most exciting research areas in health informatics. This paper aims to provide a framework for organizing research of OHCs and help identify questions to explore for future informatics research. Based on the framework, we conceptualize OHCs from a social support standpoint and identify variables of interest in characterizing community members.

Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.

Speculations represent uncertainty toward certain facts. In clinical texts, identifying speculations is a critical step of natural language processing (NLP). While it is a nontrivial task in many languages, detecting speculations in Chinese clinical notes can be particularly challenging because word segmentation may be necessary as an upstream operation.