Intradermal injection of Cutibacterium acnes and staphylococcus: A pustular acne-like murine model.

BACKGROUND: Skin 16S microbiome diversity analysis indicates that the Staphylococcus genus, especially Staphylococcus aureus (S. aureus), plays a crucial role in the inflammatory lesions of acne. However, current animal models for acne do not fully replicate human diseases, especially pustular acne, which limits the development of anti-acne medications. AIMS: The aim is to develop a mouse model for acne, establishing an animal model that more closely mimics the clinical presentation of pustular acne. This will provide a new research platform for screening anti-acne drugs and evaluating the efficacy of clinical anti-acne experimental treatments. METHODS: Building upon the existing combination of acne-associated Cutibacterium acnes (C. acnes) with artificial sebum, we will inject a mixture of S. aureus and C. acnes locally into the dermis in a 3:7 ratio. RESULTS: We found that the acne animal model with mixed bacterial infection better replicates the dynamic evolution process of human pustular acne. Compared to the infection with C. acnes alone, mixed bacterial infection resulted in pustules with a distinct yellowish appearance, resembling pustular acne morphology. The lesions exhibited redness, vascular dilation, and noticeable congestion, along with evident infiltration of inflammatory cells. This induced higher levels of inflammation, as indicated by a significant increase in the secretion of inflammatory factors such as IL-1ß and TNF-α. CONCLUSION: This model can reflect the clinical symptoms and development of human pustular acne, overcoming the limitations of animal models commonly used in basic research to study this situation. It provides support for foundational research and the development of new acne medications.

PIPER: A logic-driven deep contrastive optimization pipeline for event temporal reasoning.

Event temporal relation extraction is an important task for information extraction. The existing methods usually rely on feature engineering and require post-process to achieve optimization, though inconsistent optimization may occur in the post-process module and main neural network due to their independence. Recently, a few works start to incorporate the temporal logic rules into the neural network and achieve joint optimization. However, these methods still suffer from two shortcomings: (1) Although the joint optimization is applied, the differences between rules are neglected in the unified design of rule losses and further the interpretability and flexibility of the design of model are reduced. (2) Because of lacking abundant syntactic connections between events and rule-match features, the performance of the model may be suppressed by the inefficient interaction in training between features and rules. To tackle these issues, this paper proposes PIPER, a logic-driven deep contrastive optimization pipeline for event temporal reasoning. Specifically, we apply joint optimization (including multi-stage and single-stage joint paradigms) by combining independent rule losses (i.e., flexibility) to make PIPER more interpretable. Also, by proposing a hierarchical graph distillation network to obtain more abundant syntactic information, the designed rule-match features can effectively aid in the interaction between low-level features and high-level rules during training. The final experiments on TB-Dense and MATRES demonstrate that the proposed model can achieve competitive performance compared with the recent advances.

Pilose antler extract restores type I and III collagen to accelerate wound healing.

Granulation tissue has supporting and filling functions in wound healing. The collagen produced by fibroblast acts as a cell scaffold in the granulation tissue to facilitate the formation of new blood vessels and epithelial coverage. Previously, we extracted protein components from the pilose antler that was involved in the biological process of collagen fibril organization. They were also found to contain abundant extracellular matrix(ECM) components. Therefore, in this experiment, we used a rat model of full-thickness skin excision and fibroblasts to perform an experiment for determination of the effects of pilose antler protein extract (PAE) on collagen content and fiber synthesis during wound healing. Additionally, we further analyzed its pharmacological effects on wound healing and the possible regulatory mechanisms. We found that PAE accelerated synthesis of type I and III collagen, promoted the formation of type III collagen fibers, and reduced collagen degradation by recruiting fibroblasts. Furthermore, the extract upregulated the expression of TGF ß R1 and Smad2, and initiated the entry of Smad2/Smad3 into the nucleus. After adding SB431542 to inhibit TGF-ß type I receptor activity, PAE's ability to promote Smad2/Smad3 nuclear localization was weakened. These data indicate that local PAE therapy can promote the proliferation of fibroblasts, dynamically regulate the expression of TGF-ß, and increase the amount of collagen and the synthesis of type III collagen fibers by promoting smad2 activity in the proliferation period, thus accelerating the regenerative healing of wounds.

Effects of Luteolin on Biofilm of Trueperella pyogenes and Its Therapeutic Effect on Rat Endometritis.

Trueperella pyogenes is an opportunistic pathogen that causes suppurative infections in animals. The development of new anti-biofilm drugs will improve the current treatment status for controlling T. pyogenes infections in the animal husbandry industry. Luteolin is a naturally derived flavonoid compound with antibacterial properties. In this study, the effects and the mechanism of luteolin on T. pyogenes biofilm were analyzed and explored. The MBIC and MBEC of luteolin on T. pyogenes were 156 µg/mL and 312 µg/mL, respectively. The anti-biofilm effects of luteolin were also observed by a confocal laser microscope and scanning electron microscope. The results indicated that 312 µg/mL of luteolin could disperse large pieces of biofilm into small clusters after 8 h of treatment. According to the real-time quantitative PCR detection results, luteolin could significantly inhibit the relative expression of the biofilm-associated genes luxS, plo, rbsB and lsrB. In addition, the in vivo anti-biofilm activity of luteolin against T. pyogenes was studied using a rat endometritis model established by glacial acetic acid stimulation and T. pyogenes intrauterine infusion. Our study showed that luteolin could significantly reduce the symptoms of rat endometritis. These data may provide new opinions on the clinical treatment of luteolin and other flavonoid compounds on T. pyogenes biofilm-associated infections.

In vivo Visualization of Collagen Transdermal Absorption by Second-Harmonic Generation and Two-Photon Excited Fluorescence Microscopy.

The transdermal administration of collagen is an important method used for wound healing and skin regeneration. However, due to the limitations of previous approaches, the process and degree of collagen transdermal absorption could only be quantitatively and qualitatively evaluated in vitro. In the present study, we introduced a novel approach that combines second-harmonic generation with two-photon excited fluorescence to visualize the dynamics of collagen transdermal absorption in vivo. High-resolution images showed that exogenous recombinant human collagen permeated the epidermis through hair follicles and sebaceous glands reached the dermis, and formed reticular structures in real time. We also validated these findings through traditional in vitro skin scanning and histological examination. Thus, our approach provides a reliable measurement for real-time evaluation of collagen absorption and treatment effects in vivo.

Temporal information extraction with the scalable cross-sentence context for electronic health records.

Temporal information is essential for accurate understanding of medical information hidden in electronic health record texts. In the absence of temporal information, it is even impossible to distinguish whether the mentioned symptom is a current condition or past medical history. Hence, identifying the relationship between medical events and document creation time (DCT) is a critical component for medical language comprehension, which can link the mentioned medical information to the time dimension by marking temporal tags. Existing natural language processing (NLP) systems are typically based on the sentence where the medical event is located to extract the DCT relationship. Inevitably, the limited textual context can be insufficient as it is difficult to contain adequate document information. Introducing the surrounding sentences into models is a fitting way to enrich the information. However, in addition to document information, the added context can also bring noise to confuse the models. For effective utilization of the context, we design the DCDR (Dynamic Context and Dynamic Representation) model. Our model consists of two modules, i.e. the dynamic context mechanism and dynamic representation mechanism. The dynamic context mechanism is employed to bring the related texts into our model via the sliding windows and a scoring calculation. For the dynamic representation mechanism, a modified dynamic routing algorithm is adopted to filter the noise and generate an integrated representation for the whole context. Besides, the mentioned medical information is led into the routing process to enhance the dynamic representation module. The experiments show that our proposed model achieves improvement over existing models and achieves an F-score of 85.7% on the commonly used THYME corpus.

Extracting chemical-induced disease relation by integrating a hierarchical concentrative attention and a hybrid graph-based neural network.

Extracting the chemical-induced disease relation from literatures is important for biomedical research. On one hand, it is challenging to capture the interactions among remote words and the long-distance information is not adequately exploited by existing systems for document-level relation extraction. On the other hand, there is some information particularly important to the target relations in documents, which should attract more attention than the less relevant information for the relation extraction. However, this issue is not well addressed in existing methods. In this paper, we present a method that integrates a hybrid graph and a hierarchical concentrative attention to overcome these problems. The hybrid graph is constructed by synthesizing the syntactic graph and Abstract Meaning Representation graph to acquire the long-distance information for document-level relation extraction. Meanwhile, the concentrative attention is used to focus on the most important information, and alleviate the disturbance brought by the less relevant items in the document. The experimental results demonstrate that our model yields competitive performance on the dataset of chemical-induced disease relations.

Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions.

Drug-drug interactions (DDIs) extraction is one of the important tasks in the field of biomedical relation extraction, which plays an important role in the field of pharmacovigilance. Previous neural network based models have achieved good performance in DDIs extraction. However, most of the previous models did not make good use of the information of drug entity names, which can help to judge the relation between drugs. This is mainly because drug names are often very complex, leading to the fact that neural network models cannot understand their semantics directly. To address this issue, we propose a DDIs extraction model using multiple entity-aware attentions with various entity information. We use an output-modified bidirectional transformer (BioBERT) and a bidirectional gated recurrent unit layer (BiGRU) to obtain the vector representation of sentences. The vectors of drug description documents encoded by Doc2Vec are used as drug description information, which is an external knowledge to our model. Then we construct three different kinds of entity-aware attentions to get the sentence representations with entity information weighted, including attentions using the drug description information. The outputs of attention layers are concatenated and fed into a multi-layer perception layer. Finally, we get the result by a softmax classifier. The F-score is used to evaluate our model, which is also adopted by most previous DDIs extraction models. We evaluate our proposed model on the DDIExtraction 2013 corpus, which is the benchmark corpus of this domain, and achieves the state-of-the-art result (80.9% in F-score).

Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks.

Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an F1 score of 80.27 percent in trigger identification and an F1 score of 59.65 percent in biomedical event extraction, which performs better than other state-of-the-art methods.

Integrating Language Model and Reading Control Gate in BLSTM-CRF for Biomedical Named Entity Recognition.

Biomedical named entity recognition (Bio-NER) is an important preliminary step for many biomedical text mining tasks. The current mainstream methods for NER are based on the neural networks to avoid the complex hand-designed features derived from various linguistic analyses. However, these methods ignore some potential sentence-level semantic information and general features of semantic and syntactic. Therefore, we propose a novel Long Short Term Memory (LSTM) Networks model integrating language model and sentence-level reading control gate (LS-BLSTM-CRF) for Bio-NER. In our model, a sentence-level reading control gate (SC) is inserted into the networks to integrate the implicit meaning of an entire sentence and the language model is integrated to our model to learn richer potential features. Besides, character-level embeddings are introduced as the input to deal with out-of-vocabulary words. The experimental results conducted on the BioCreative II GM corpus show that our method can achieve an F-score of 89.94 percent, which outperforms all state-of-the-art systems and is 1.33 percent higher than the best performing neural networks.

Associative attention networks for temporal relation extraction from electronic health records.

Temporal relations are crucial in constructing a timeline over the course of clinical care, which can help medical practitioners and researchers track the progression of diseases, treatments and adverse reactions over time. Due to the rapid adoption of Electronic Health Records (EHRs) and high cost of manual curation, using Natural Language Processing (NLP) to extract temporal relations automatically has become a promising approach. Typically temporal relation extraction is formulated as a classification problem for the instances of entity pairs, which relies on the information hidden in context. However, EHRs contain an overwhelming amount of entities and a large number of entity pairs gathering in the same context, making it difficult to distinguish instances and identify relevant contextual information for a specific entity pair. All these pose significant challenges towards temporal relation extraction while existing methods rarely pay attention to. In this work, we propose the associative attention networks to address these issues. Each instance is first carved into three segments according to the entity pair to obtain the differentiated representation initially. Then we devise the associative attention mechanism for a further distinction by emphasizing the relevant information, and meanwhile, for the reconstruction of association among segments as the final representation of the whole instance. In addition, position weights are utilized to enhance the performance. We validate the merit of our method on the widely used THYME corpus and achieve an average F1-score of 64.3% over three runs, which outperforms the state-of-the-art by 1.5%.

Extracting chemical-protein interactions from biomedical literature via granular attention based recurrent neural networks.

BACKGROUND AND OBJECTIVE: The extraction of interactions between chemicals and proteins from biomedical literature is important for many biomedical tasks such as drug discovery and precision medicine. In the existing systems, the methods achieving competitive results are combined of several models or implemented in multi-stage, and they are challenged by high cost because numerous external features are employed. These problems can be avoided by deep learning algorithms, but the performance of the deep learning based models is limited by inadequate exploration of the information. Our goal is to devise a system to improve the performance of the automatic extraction between chemical entities and protein entities from biomedical literature. METHODS: In this paper, we propose a model based on recurrent neural networks integrating granular attention mechanism. The granular attention can explore the inner information of the context vectors, which are represented in multiple dimensions that play different roles in the extraction of the interactions. Furthermore, we employ Swish activation function in the neural networks for the chemical-protein interactions extraction task for the first time. RESULTS: The proposed method is evaluated on BioCreative VI chemical-protein track test corpus. The experimental results show that this method achieves an F-score of 65.14%, which is 1.04% higher than the state-of-the-art system. CONCLUSIONS: The model synthesizing recurrent neural networks and granular attention mechanism, exploring the inner information of the context vectors, can improve the extraction performance without extra hand-crafted features. The experimental results demonstrate that the proposed model is promising for further study on the interaction extraction between chemicals and proteins.

Contextual label sensitive gated network for biomedical event trigger extraction.

Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in previous methods: (1) The association among contextual trigger labels which can provide significant clues is ignored. (2)The weight between word embeddings and contextual features needs to be adjusted dynamically according to the trigger candidate. In this paper, we propose a novel contextual label sensitive gated network for biomedical event trigger extraction to solve the above two problems, which can mix the two parts dynamically and capture the contextual label clues automatically. Furthermore, we also introduce the dependency-based word embeddings to represent dependency-based semantic information as well as attention mechanism to get more focused representations. Experimental results show that our approach advances state-of-the-arts and achieves the best F1-score on the commonly used Multi-Level Event Extraction (MLEE) corpus.

Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space.

BACKGROUND: In biomedical information extraction, event extraction plays a crucial role. Biological events are used to describe the dynamic effects or relationships between biological entities such as proteins and genes. Event extraction is generally divided into trigger detection and argument recognition. The performance of trigger detection directly affects the results of the event extraction. In general, the traditional method is used to address the trigger detection as a classification task, as well as the use of machine learning or rules method, which construct many features to improve the classification results. Moreover, the classification model only recognizes triggers composed of single words, whereas for multiple words, the result is unsatisfactory. RESULTS: The corpus of our model is MLEE. If we were to only use the biomedical LSTM and CRF model without other features, the F-score would reach about 78.08%. Comparing entity to part of speech (POS), we find the entity features more conducive to the improvement of performance of detection, with the F-score potentially reaching about 80%. Furthermore, we also experiment on the other three corpora (BioNLP 2009, BioNLP 2011, and BioNLP 2013) to verify the generalization of our model. Hence, F-scores can reach more than 60%, which are better than the comparative experiments. CONCLUSIONS: The trigger recognition method based on the sequence annotation model does not require initial complex feature engineering, and only requires a simple labeling mechanism to complete the training. Therefore, generalization of our model is better compared to other traditional models. Secondly, this method can identify multi-word triggers, thereby improving the F-scores of trigger recognition. Thirdly, details on the entity have a crucial impact on trigger detection. Finally, the combination of character-level word embedding and word-level word embedding provides increasingly effective information for the model; therefore, it is a key to the success of the experiment.

Biomedical event extraction based on GRU integrating attention mechanism.

BACKGROUND: Biomedical event extraction is a crucial task in biomedical text mining. As the primary forum for international evaluation of different biomedical event extraction technologies, BioNLP Shared Task represents a trend in biomedical text mining toward fine-grained information extraction (IE). The fourth series of BioNLP Shared Task in 2016 (BioNLP-ST'16) proposed three tasks, in which the Bacteria Biotope event extraction (BB) task has been put forward in the earlier BioNLP-ST. Deep learning methods provide an effective way to automatically extract more complex features and achieve notable results in various natural language processing tasks. RESULTS: The experimental results show that the presented approach can achieve an F-score of 57.42% in the test set, which outperforms previous state-of-the-art official submissions to BioNLP-ST 2016. CONCLUSIONS: In this paper, we propose a novel Gated Recurrent Unit Networks framework integrating attention mechanism for extracting biomedical events between biotope and bacteria from biomedical literature, utilizing the corpus from the BioNLP'16 Shared Task on Bacteria Biotope task. The experimental results demonstrate the potential and effectiveness of the proposed framework.

A Two-Stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings.

Extracting biomedical events from biomedical literature plays an important role in the field of biomedical text mining, and the trigger detection is a key step in biomedical event extraction. We propose a two-stage method for trigger detection, which divides trigger detection into recognition stage and classification stage, and different features are selected in each stage. In the first stage, we select the features which are more suitable for recognition, and in the second stage, the features that are more helpful to classification are adopted. Furthermore, we integrate word embeddings to represent words semantically and syntactically. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 79.75 percent, which outperforms the state-of-the-art systems.

Parallel gene selection and dynamic ensemble pruning based on Affinity Propagation.

Gene selection and sample classification based on gene expression data are important research areas in bioinformatics. Selecting important genes closely related to classification is a challenging task due to high dimensionality and small sample size of microarray data. Extended rough set based on neighborhood has been successfully applied to gene selection, as it can select attributes without redundancy and deal with numerical attributes directly. However, the computation of approximations in rough set is extremely time consuming. In this paper, in order to accelerate the process of gene selection, a parallel computation method is proposed to calculate approximations of intersection neighborhood rough set. Furthermore, a novel dynamic ensemble pruning approach based on Affinity Propagation clustering and dynamic pruning framework is proposed to reduce memory usage and computational cost. Experimental results on three Arabidopsis thaliana biotic and abiotic stress response datasets demonstrate that the proposed method can obtain better classification performance than ensemble method with gene pre-selection.

Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings.

Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments. The proposed system consists of four components: trigger detector, argument detector, jointly inference with dual decomposition, and rule-based semantic post-processing, and outperforms the state-of-the-art systems. On the development set of BioNLP'09, the F-score is 59.77 percent on the primary task, which is 0.96 percent higher than the best system. On the test set of BioNLP'11, the F-score is 56.09 and 0.89 percent higher than the best published result that do not adopt additional techniques. On the test set of BioNLP'13, the F-score reaches 53.19 percent which is 2.22 percent higher than the best result.

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words using deeper information, we make explicit the semantic regularity to emerge in word relations, including dependency relations and context relations, and propose a novel architecture for computing continuous vector representation by leveraging those relations. The performance of our model is measured on word analogy task and Protein-Protein Interaction Extraction (PPIE) task. Experimental results show that our method performs overall better than other word representation models on word analogy task and have many advantages on biomedical text mining.

An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data.

Protein-Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html.