Dental Stem Cell-Derived Exosomes: A Review of Their Isolation, Classification, Functions, and Mechanisms.

The scientific field concerned with the study of regeneration has developed rapidly in recent years. Stem cell therapy is a highly promising therapeutic modality for repairing tissue defects; however, several limitations exist, such as cytotoxicity, potential immune rejection, and ethical issues. Exosomes secreted by stem cells are cell-specific secreted vesicles that play a regulatory role in many biological functions in the human body; they not only have a series of functional roles of stem cells and exert the expected therapeutic effects, but they can also overcome the mass limitations of stem cells and are thus considered in the research as an alternative treatment strategy for stem cells. Since dental stem cell-derived exosomes (DSC-Exos) are easy to acquire and present modulating effects in several fields, including neurovascular regeneration and craniofacial soft and hard tissue regeneration processes, they are served as an emerging cell-free therapeutic strategy in various systematic diseases. There is a growing body of research on various types of DSC-Exos; however, they lack systematic elaboration and tabular summarization. Therefore, this review presents the isolation, characterization, and phenotypes of DSC-Exos and focuses on their current status of functions and mechanisms, as well as the multiple challenges prior to clinical applications.

The functional effects of Piezo channels in mesenchymal stem cells.

Mesenchymal stem cells (MSCs) are widely used in cell therapy, tissue engineering, and regenerative medicine because of their self-renewal, pluripotency, and immunomodulatory properties. The microenvironment in which MSCs are located significantly affects their physiological functions. The microenvironment directly or indirectly affects cell behavior through biophysical, biochemical, or other means. Among them, the mechanical signals provided to MSCs by the microenvironment have a particularly pronounced effect on their physiological functions and can affect osteogenic differentiation, chondrogenic differentiation, and senescence in MSCs. Mechanosensitive ion channels such as Piezo1 and Piezo2 are important in transducing mechanical signals, and these channels are widely distributed in sites such as skin, bladder, kidney, lung, sensory neurons, and dorsal root ganglia. Although there have been numerous studies on Piezo channels in MSCs in recent years, the function of Piezo channels in MSCs is still not well understood, and there has been no summary of their relationship to illustrate which physiological functions of MSCs are affected by Piezo channels and the possible underlying mechanisms. Therefore, based on the members, structures, and functions of Piezo ion channels and the fundamental information of MSCs, this paper focused on summarizing the advances in Piezo channels in MSCs from various tissue sources to provide new ideas for future research and practical applications of Piezo channels and MSCs.

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction. RESULTS: Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems. CONCLUSIONS: A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset.

CNN-based ranking for biomedical entity normalization.

BACKGROUND: Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. RESULTS: The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. CONCLUSIONS: We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V.

In this article, an end-to-end system was proposed for the challenge task of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction in BioCreative V, where DNER includes disease mention recognition (DMR) and normalization (DN). Evaluation on the challenge corpus showed that our system achieved the highest F1-scores 86.93% on DMR, 84.11% on DN, 43.04% on CID relation extraction, respectively. The F1-score on DMR is higher than our previous one reported by the challenge organizers (86.76%), the highest F1-score of the challenge.Database URL: http://database.oxfordjournals.org/content/2016/baw077.

CMedTEX: A Rule-based Temporal Expression Extraction and Normalization System for Chinese Clinical Notes.

Time is an important aspect of information and is very useful for information utilization. The goal of this study was to analyze the challenges of temporal expression (TE) extraction and normalization in Chinese clinical notes by assessing the performance of a rule-based system developed by us on a manually annotated corpus (including 1,778 clinical notes of 281 hospitalized patients). In order to develop system conveniently, we divided TEs into three categories: direct, indirect and uncertain TEs, and designed different rules for each category of them. Evaluation on the independent test set shows that our system achieves an F-score of93.40% on TE extraction, and an accuracy of 92.58% on TE normalization under "exact-match" criterion. Compared with HeidelTime for Chinese newswire text, our system is much better, indicating that it is necessary to develop a specific TE extraction and normalization system for Chinese clinical notes because of domain difference.

An automatic system to identify heart disease risk factors in clinical texts over time.

Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk factors associated with heart disease; however, none have attempted to identify all risk factors. In 2014, the National Center of Informatics for Integrating Biology and Beside (i2b2) issued a clinical natural language processing (NLP) challenge that involved a track (track 2) for identifying heart disease risk factors in clinical texts over time. This track aimed to identify medically relevant information related to heart disease risk and track the progression over sets of longitudinal patient medical records. Identification of tags and attributes associated with disease presence and progression, risk factors, and medications in patient medical history were required. Our participation led to development of a hybrid pipeline system based on both machine learning-based and rule-based approaches. Evaluation using the challenge corpus revealed that our system achieved an F1-score of 92.68%, making it the top-ranked system (without additional annotations) of the 2014 i2b2 clinical NLP challenge.

Automatic de-identification of electronic medical records using token-level and character-level conditional random fields.

De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informatics for Integrating Biology and Bedside) clinical natural language processing (NLP) challenge sets up a track for de-identification (track 1). In this study, we propose a hybrid system based on both machine learning and rule approaches for the de-identification track. In our system, PHI instances are first identified by two (token-level and character-level) conditional random fields (CRFs) and a rule-based classifier, and then are merged by some rules. Experiments conducted on the i2b2 corpus show that our system submitted for the challenge achieves the highest micro F-scores of 94.64%, 91.24% and 91.63% under the "token", "strict" and "relaxed" criteria respectively, which is among top-ranked systems of the 2014 i2b2 challenge. After integrating some refined localization dictionaries, our system is further improved with F-scores of 94.83%, 91.57% and 91.95% under the "token", "strict" and "relaxed" criteria respectively.