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Background: Hepatitis B (HepB) is a major public health concern in Malaysia yet little is known about knowledge and awareness of this infection in the country. Such information is essential for designing effective intervention strategies for HepB prevention and control. The aim of this study was to characterize knowledge and awareness regarding HepB in Malaysia and to identify their associated sociodemographic determinants.
Methods: A community-based cross-sectional survey was conducted between January and May 2016 in Selangor state of Malaysia. A two-stage cluster random sampling design was used and one adult member of selected households was interviewed face-to-face. Logistic regression was used to estimate the differences in knowledge and awareness between groups.
Results: A total of 764 households completed the interviews and were included in the final analysis. Only 36.9 and 38.8% of the participants had good knowledge and awareness, respectively. The factors associated with good knowledge were being in the 35–44 year age group, Malay ethnicity, high educational attainment and high family income. Being Chinese, being older and having high educational attainment were determinants of having good awareness towards HepB. Participants who had good knowledge were 2.5 times more likely to also have good awareness (OR: 2.41, 95% CI: 1.78–3.26, p < 0.001).
Conclusions: This study reveals a low level of knowledge and awareness of HepB among households in Malaysia. This finding highlights the need to improve public knowledge and awareness through well-designed programs targeting vulnerable groups in order to reduce hepatitis B virus transmission and achieve the governmental target of eliminating viral hepatitis as a public health concern by 2030.
Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve reproducibility by offering a wide range of common easy-to-use functions for table and vector manipulation. Vectools also offers a number of vector related functions to speed up workflow development, such as simple machine learning and common statistics functions.
Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve reproducibility by offering a wide range of common easy-to-use functions for table and vector manipulation. Vectools also offers a number of vector related functions to speed up workflow development, such as simple machine learning and common statistics functions.
LSTMVoter : chemical named entity recognition using a conglomerate of sequence labeling tools
(2019)
Background: Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier.
Results: We introduce LSTMVoter, a bidirectional long short-term memory (LSTM) tagger that utilizes a conditional random field layer in conjunction with attention-based feature modeling. Our approach explores information about features that is modeled by means of an attention mechanism. LSTMVoter outperforms each extractor integrated by it in a series of experiments. On the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus, LSTMVoter achieves an F1-score of 90.04%; on the BioCreative V.5 chemical entity mention in patents corpus, it achieves an F1-score of 89.01%.
Availability and implementation: Data and code are available at https://github.com/texttechnologylab/LSTMVoter.
Adjuvanted influenza vaccines constitute a key element towards inducing neutralizing antibody responses in populations with reduced responsiveness, such as infants and elderly subjects, as well as in devising antigen-sparing strategies. In particular, squalene-containing adjuvants have been observed to induce enhanced antibody responses, as well as having an influence on cross-reactive immunity. To explore the effects of adjuvanted vaccine formulations on antibody response and their relation to protein-specific immunity, we propose different mathematical models of antibody production dynamics in response to influenza vaccination. Data from ferrets immunized with commercial H1N1pdm09 vaccine antigen alone or formulated with different adjuvants was instrumental to adjust model parameters. While the affinity maturation process complexity is abridged, the proposed model is able to recapitulate the essential features of the observed dynamics. Our numerical results suggest that there exists a qualitative shift in protein-specific antibody response, with enhanced production of antibodies targeting the NA protein in adjuvanted versus non-adjuvanted formulations, in conjunction with a protein-independent boost that is over one order of magnitude larger for squalene-containing adjuvants. Furthermore, simulations predict that vaccines formulated with squalene-containing adjuvants are able to induce sustained antibody titers in a robust way, with little impact of the time interval between immunizations.
Regular exercise has widespread health benefits. Fundamental to these beneficial effects is the ability of the heart to intermittently and substantially increase its performance without incurring damage, but the underlying homeostatic mechanisms are unclear. We identify the ROS-generating NADPH oxidase-4 (Nox4) as an essential regulator of exercise performance in mice. Myocardial Nox4 levels increase during acute exercise and trigger activation of the transcription factor Nrf2, with the induction of multiple endogenous antioxidants. Cardiomyocyte-specific Nox4-deficient (csNox4KO) mice display a loss of exercise-induced Nrf2 activation, cardiac oxidative stress and reduced exercise performance. Cardiomyocyte-specific Nrf2-deficient (csNrf2KO) mice exhibit similar compromised exercise capacity, with mitochondrial and cardiac dysfunction. Supplementation with an Nrf2 activator or a mitochondria-targeted antioxidant effectively restores cardiac performance and exercise capacity in csNox4KO and csNrf2KO mice respectively. The Nox4/Nrf2 axis therefore drives a hormetic response that is required for optimal cardiac mitochondrial and contractile function during physiological exercise.
Aim of the study: Hepatocyte transplantation has been discussed as an alternative to liver transplantation in selected cases of acute and chronic liver failure and metabolic diseases. Immediately after infusion of hepatocytes, hypoxia-related cell injury is inevitable. N-acetylcysteine (NAC) has been suggested to attenuate hypoxic damage. This study’s objective was to evaluate NAC’s protective effect in a model of hypoxia-related hepatocyte injury.
Material and methods: HepG2 cells were used as a model for hepatocytes and were cultured under standardized hypoxia or normoxia for 24 hours with or without NAC. Growth kinetics were monitored using trypan blue staining. The activation of apoptotic pathways was measured using quantitative real-time PCR for Bcl-2/Bax and p53. The proportions of vital, apoptotic and necrotic cells were verified by fluorescence activated cell sorting using annexin V-labelling. The expression of hypoxia inducible factor 1 (HIF-1) was measured indirectly using its downstream target vascular endothelial growth factor A (VEGF-A).
Results: After NAC, cell proliferation increased under both hypoxia and normoxia by 528% and 320% (p < 0.05), while VEGF-A expression decreased under normoxia by 67% and 37% (p < 0.05). Compared to cells treated without NAC under hypoxia, the Bcl-2/Bax ratio increased significantly in cells treated with NAC. This finding was confirmed by an increased number of vital cells in FACS analysis.
Conclusions: NAC protects hepatocytes from hypoxic injury and ultimately activates anti-apoptotic pathways.
Neuropogonoid species in the lichen-forming fungal genus Usnea exhibit great morphological variation that can be misleading for delimitation of species. We specifically focused on the species delimitation of two closely-related, predominantly Antarctic species differing in the reproductive mode and representing a so-called species pair: the asexual U. antarctica and the sexual U. aurantiacoatra. Previous studies have revealed contradicting results. While multi-locus studies based on DNA sequence data provided evidence that these two taxa might be conspecific, microsatellite data suggested they represent distinct lineages. By using RADseq, we generated thousands of homologous markers to build a robust phylogeny of the two species. Furthermore, we successfully implemented these data in fine-scale population genomic analyses such as DAPC and fineRADstructure. Both Usnea species are readily delimited in phylogenetic inferences and, therefore, the hypothesis that both species are conspecific was rejected. Population genomic analyses also strongly confirmed separated genomes and, additionally, showed different levels of co-ancestry and substructure within each species. Lower co-ancestry in the asexual U. antarctica than in the sexual U. aurantiacoatra may be derived from a wider distributional range of the former species. Our results demonstrate the utility of this RADseq method in tracing population dynamics of lichens in future analyses.
An oroantral fistula (OAF) is a pathological abnormal communication between the oral cavity and the maxillary sinus which may arise as a result of failure of primary healing of an OAF, dental infections, osteomyelitis, radiation therapy, trauma, or iatrogenic complications. With the presence of a fistula, the maxillary sinus is permanently open. Microbial flora passes from the oral cavity into the maxillary sinus, and the inflammation of the sinus occurs with all potential consequences. In literature, various techniques have been proposed for closure of OAFs. Due to the heterogeneity of the data and techniques found, we opted for a narrative review to highlight the variety of techniques discussed in the literature. Techniques of particular interest include the bone sandwich with resorbable guided tissue regeneration (GTR) membrane and platelet-rich fibrin (PRF) used alone as both a clot and a membrane. The great advantage of these techniques is that no donor site surgery is necessary, making the outcome valuable in terms of time savings, cost and, more importantly, less discomfort to the patient. Additionally, both bony and soft tissue closure is performed for OAF, in contrast to flaps, which are typically used for procedures in the sinus area. The reconstructed bony tissue regenerated from these techniques will also be appropriate for endosseous dental implantation.
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.