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In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use the attention mechanism for fact verification, they have not considered imposing attentional constraints on important related words in the claim and evidence sentences, resulting in inaccurate attention for some irrelevant words. In this paper, we propose a syntactic evidence network (SENet) model which incorporates entity keywords, syntactic information and sentence attention for fact verification. The SENet model extracts entity keywords from claim and evidence sentences, and uses a pre-trained syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic information into the attention mechanism for language-driven word representation. In addition, the sentence attention mechanism is applied to obtain a richer semantic representation. We have conducted experiments on the FEVER and UKP Snopes datasets for performance evaluation. Our SENet model has achieved 78.69% in Label Accuracy and 75.63% in FEVER Score on the FEVER dataset. In addition, our SENet model also has achieved 65.0% in precision and 61.2% in macro F1 on the UKP Snopes dataset. The experimental results have shown that our proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification., Competing Interests: Declaration of competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Detecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context. Given its success in image classification, label representation learning is a promising approach to enhancing biomedical event trigger detection models by leveraging the rich semantics of pre-defined event type labels., Results: In this paper, we propose the Biomedical Label-based Synergistic representation Learning (BioLSL) model, which effectively utilizes event type labels by learning their correlation with trigger words and enriches the representation contextually. The BioLSL model consists of three modules. Firstly, the Domain-specific Joint Encoding module employs a transformer-based, domain-specific pre-trained architecture to jointly encode input sentences and pre-defined event type labels. Secondly, the Label-based Synergistic Representation Learning module learns the semantic relationships between input texts and event type labels, and generates a Label-Trigger Aware Representation (LTAR) and a Label-Context Aware Representation (LCAR) for enhanced semantic representations. Finally, the Trigger Classification module makes structured predictions, where each label is predicted with respect to its neighbours. We conduct experiments on three benchmark BioNLP datasets, namely MLEE, GE09, and GE11, to evaluate our proposed BioLSL model. Results show that BioLSL has achieved state-of-the-art performance, outperforming the baseline models., Conclusions: The proposed BioLSL model demonstrates good performance for biomedical event trigger detection without using any external resources. This suggests that label representation learning and context-aware enhancement are promising directions for improving the task. The key enhancement is that BioLSL effectively learns to construct semantic linkages between the event mentions and type labels, which provide the latent information of label-trigger and label-context relationships in biomedical texts. Moreover, additional experiments on BioLSL show that it performs exceptionally well with limited training data under the data-scarce scenarios., (© 2024. The Author(s).)

Aims: Children with severe needle phobia find vaccination extremely distressing and can remain unvaccinated, which puts them at an increased risk of contracting and transmitting vaccine preventable disease. Referral to a specialist or hospital service may occur when they cannot be safely vaccinated in the community, but engagement of allied health services can be inconsistent. The aim of the study was to assess the impact of a multidisciplinary, consumer-oriented model of care on vaccinations for needle phobic children., Methods: Needle phobic children aged between 6 and 16 years attended multidisciplinary consultation, as part of a care package, to assess previous experiences and determine the level of intervention that was required to support vaccination. A multidisciplinary case meeting followed this appointment and an individualised plan formulated for each patient. The main outcome of the project was rate of successful vaccination., Results: The care package resulted in a successful vaccination rate of 83% (n = 20) with 69 vaccines administered across three clinics. Of those successful, 90% required multiple injections per visit. The majority of patients indicated moderate to high level of anxiety. Supportive care was escalated and de-escalated as tolerated., Conclusions: Results demonstrate the diversity of patients presenting with needle phobia and indicate an individualised, collaborative approach is preferable to a 'one size fits all' model of care. The study highlights a need for the development of guidelines that streamline the assessment and individualisation of procedural anxiety plans to meet patient needs and embed these processes into standard care., (© 2022 The Authors. Journal of Paediatrics and Child Health published by John Wiley & Sons Australia, Ltd on behalf of Paediatrics and Child Health Division (The Royal Australasian College of Physicians).)

Prostate cancer (PCa), an extremely common malignancy in males, is the most prevalent disease in several countries. Norcantharidin (NCTD) has antiproliferation, antimetastasis, apoptosis, and autophagy effects in various tumor cells. Nevertheless, the antitumor effect of NCTD combined with paclitaxel (PTX), a chemotherapeutic drug, in PCa remains unknown. The cell growth, proliferative rate, cell cycle distribution, and cell death were determined by 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyltetrazolium bromide, colony formation assay, PI staining, and Annexin V/PI staining by flow cytomertry, whereas the mitochondrial membrane potential (MMP) and endoplasmic reticulum (ER) stress was evaluated using the MitoPotential assay and ER-ID red assay. We also evaluated the protein and mRNA expression of SIRTs by Western blotting and qRTPCR assay. Overexpression effectivity was measured by DNA transfection assay. Our study showed that cell viability and proliferative PC3 and DU145 rates were effectively inhibited after NCTD-PTX combination. We also found that NCTD-PTX combination treatment significantly enhance G2/M phase arrest, induction of cell death and ER stress, loss of MMP, and ER- or apoptotic-related protein expression. Furthermore, NCTD-PTX combination treatment was significantly decreasing the protein and mRNA expression of SIRT7 in PCa cells. Combination therapy effectively reduced cell viability, ER stress-mediated apoptosis and p-eIF2α/ATF4/CHOP/cleaved-PARP expression inhibition in SIRT7 overexpression of PCa cells. These results indicate that NCTD combined with PTX induces ER stress-mediated apoptosis of PCa cells by regulating the SIRT7 expression axis. Moreover, combination therapy may become a potential therapeutic strategy against human PCa., (© 2021 Wiley Periodicals LLC.)