Structural remodeling of AAA+ ATPase p97 by adaptor protein ASPL facilitates posttranslational methylation by METTL21D.

p97 is an essential AAA+ ATPase that extracts and unfolds substrate proteins from membranes and protein complexes. Through its mode of action, p97 contributes to various cellular processes, such as membrane fusion, ER-associated protein degradation, DNA repair, and many others. Diverse p97 functions and protein interactions are regulated by a large number of adaptor proteins. Alveolar soft part sarcoma locus (ASPL) is a unique adaptor protein that regulates p97 by disassembling functional p97 hexamers to smaller entities. An alternative mechanism to regulate the activity and interactions of p97 is by posttranslational modifications (PTMs). Although more than 140 PTMs have been identified in p97, only a handful of those have been described in detail. Here we present structural and biochemical data to explain how the p97-remodeling adaptor protein ASPL enables the metastasis promoting methyltransferase METTL21D to bind and trimethylate p97 at a single lysine side chain, which is deeply buried inside functional p97 hexamers. The crystal structure of a heterotrimeric p97:ASPL:METTL21D complex in the presence of cofactors ATP and S-adenosyl homocysteine reveals how structural remodeling by ASPL exposes the crucial lysine residue of p97 to facilitate its trimethylation by METTL21D. The structure also uncovers a role of the second region of homology (SRH) present in the first ATPase domain of p97 in binding of a modifying enzyme to the AAA+ ATPase. Investigation of this interaction in the human, fish, and plant reveals fine details on the mechanism and significance of p97 trimethylation by METTL21D across different organisms.

Exploiting document graphs for inter sentence relation extraction.

BACKGROUND: Most previous relation extraction (RE) studies have focused on intra sentence relations and have ignored relations that span sentences, i.e. inter sentence relations. Such relations connect entities at the document level rather than as relational facts in a single sentence. Extracting facts that are expressed across sentences leads to some challenges and requires different approaches than those usually applied in recent intra sentence relation extraction. Despite recent results, there are still limitations to be overcome. RESULTS: We present a novel representation for a sequence of consecutive sentences, namely document subgraph, to extract inter sentence relations. Experiments on the BioCreative V Chemical-Disease Relation corpus demonstrate the advantages and robustness of our novel system to extract both intra- and inter sentence relations in biomedical literature abstracts. The experimental results are comparable to state-of-the-art approaches and show the potential by demonstrating the effectiveness of graphs, deep learning-based model, and other processing techniques. Experiments were also carried out to verify the rationality and impact of various additional information and model components. CONCLUSIONS: Our proposed graph-based representation helps to extract ∼50% of inter sentence relations and boosts the model performance on both precision and recall compared to the baseline model.

Flavanonol Glycosides from the Stems of Myrsine seguinii and Their Neuroprotective Activities.

The accumulation of amyloid beta (Aß) peptides is common in the brains of patients with Alzheimer's disease, who are characterized by neurological cognitive impairment. In the search for materials with inhibitory activity against the accumulation of the Aß peptide, seven undescribed flavanonol glycosides (1-7) and five known compounds (8-12) were isolated from stems of Myrsine seguinii by HPLC-qTOF MS/MS-based molecular networking. Interestingly, this plant has been used as a folk medicine for the treatment of various inflammatory conditions. The chemical structures of the isolated compounds (1-12) were elucidated based on spectroscopic data, including 1D and 2D nuclear magnetic resonance (NMR), high-resolution electrospray ionization mass spectrometry (HRESIMS) and electronic circular dichroism (ECD) data. Compounds 2, 6 and 7 showed neuroprotective activity against Aß-induced cytotoxicity in Aß42-transfected HT22 cells.

Serum Concentration of the Phytohormone Abscisic Acid Is Associated With Immune-Regulatory Mediators and Is a Potential Biomarker of Disease Severity in Chronic Obstructive Pulmonary Disease.

COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography-tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.

Hydroxyoleoside-type seco-iridoids from Symplocos cochinchinensis and their insulin mimetic activity.

As part of an ongoing study of new insulin mimetic agents from medicinal plants, the 70% EtOH extract of Symplocos cochinchinensis was found to have a stimulatory effect on glucose uptake in 3T3-L1 adipocyte cells. The intensive targeted isolation of this active extract resulted in ten new hydroxyoleoside-type compounds conjugated with a phenolic acid and monoterpene (1-6 and 8-11), as well as four known compounds (7 and 12-14). The chemical structures of the new compounds were determined based on spectroscopic data analysis (1H and 13C NMR, HSQC, HMBC, NOESY and MS). The absolute configurations of the isolated compounds were determined by electronic circular dichroism (ECD) analysis of derivatives obtained after a series of reactions, such as those with dirhodium (ІІ) tetrakis (trifluoroacetate) and dimolybdenum (ІІ) tetraacetate. In vitro, compounds 3, 7 and 8 moderately increased the 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) uptake level in differentiated 3T3-L1 adipocytes. For further studies, we evaluated their effects on the expression of glucose transporter-4 (GLUT4), its translocation, protein tyrosine phosphatase 1B (PTP1B) inhibition and expression of phosphorylated Akt. Our results strongly suggest that the traditional uses of this plant can be described as active constituents by hydroxyoleoside-type compounds.

D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information.

Motivation: Recognition of biomedical named entities in the textual literature is a highly challenging research topic with great interest, playing as the prerequisite for extracting huge amount of high-valued biomedical knowledge deposited in unstructured text and transforming them into well-structured formats. Long Short-Term Memory (LSTM) networks have recently been employed in various biomedical named entity recognition (NER) models with great success. They, however, often did not take advantages of all useful linguistic information and still have many aspects to be further improved for better performance. Results: We propose D3NER, a novel biomedical named entity recognition (NER) model using conditional random fields and bidirectional long short-term memory improved with fine-tuned embeddings of various linguistic information. D3NER is thoroughly compared with seven very recent state-of-the-art NER models, of which two are even joint models with named entity normalization (NEN), which was proven to bring performance improvements to NER. Experimental results on benchmark datasets, i.e. the BioCreative V Chemical Disease Relation (BC5 CDR), the NCBI Disease and the FSU-PRGE gene/protein corpus, demonstrate the out-performance and stability of D3NER over all compared models for chemical, gene/protein NER and over all models (without NEN jointed, as D3NER) for disease NER, in almost all cases. On the BC5 CDR corpus, D3NER achieves F1 of 93.14 and 84.68% for the chemical and disease NER, respectively; while on the NCBI Disease corpus, its F1 for the disease NER is 84.41%. Its F1 for the gene/protein NER on FSU-PRGE is 87.62%. Availability and implementation: Data and source code are available at: https://github.com/aidantee/D3NER. Supplementary information: Supplementary data are available at Bioinformatics online.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530).

Erratum: Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

[This corrects the article DOI: 10.1093/database/baw102.].

Unexpected diversity of Anopheles species in Eastern Zambia: implications for evaluating vector behavior and interventions using molecular tools.

The understanding of malaria vector species in association with their bionomic traits is vital for targeting malaria interventions and measuring effectiveness. Many entomological studies rely on morphological identification of mosquitoes, limiting recognition to visually distinct species/species groups. Anopheles species assignments based on ribosomal DNA ITS2 and mitochondrial DNA COI were compared to morphological identifications from Luangwa and Nyimba districts in Zambia. The comparison of morphological and molecular identifications determined that interpretations of species compositions, insecticide resistance assays, host preference studies, trap efficacy, and Plasmodium infections were incorrect when using morphological identification alone. Morphological identifications recognized eight Anopheles species while 18 distinct sequence groups or species were identified from molecular analyses. Of these 18, seven could not be identified through comparison to published sequences. Twelve of 18 molecularly identified species (including unidentifiable species and species not thought to be vectors) were found by PCR to carry Plasmodium sporozoites - compared to four of eight morphological species. Up to 15% of morphologically identified Anopheles funestus mosquitoes in insecticide resistance tests were found to be other species molecularly. The comprehension of primary and secondary malaria vectors and bionomic characteristics that impact malaria transmission and intervention effectiveness are fundamental in achieving malaria elimination.

The prevalence of undiagnosed diabetes mellitus and the association of baseline glycemic control on mortality in the intensive care unit: a prospective observational study.

PURPOSE: Hyperglycemia is common during critical illness and can adversely affect clinical outcomes. We sought to determine the prevalence of undiagnosed diabetes among medical intensive care unit (MICU) patients with stress hyperglycemia and the association between baseline glycemic control and mortality. MATERIALS AND METHODS: A prospective, observational cohort study was performed at a tertiary care MICU. Hemoglobin A1c (HbA1c) levels were obtained from any patient who developed hyperglycemia and all known diabetic patients. We assessed the prevalence of undiagnosed diabetes (defined by HbA1c) among patients with stress hyperglycemia, and the association between baseline glycemic control and mortality. RESULTS: We enrolled 299 patients. One hundred two (34.1%) had no history and 197 (65.9%) had a history of diabetes. Of the nondiabetic patients, 14 (13.7%) had an HbA1c of at least 6.5%. There was a significant difference in mortality between patients with HbA1c less than 6.5% and those with HbA1c of at least 6.5% (19.3% vs 11.7%, P=.038), despite similar Acute Physiology and Chronic Health Evaluation II scores. There was no significant difference in demographic characteristics between these groups. Multivariable logistic regression revealed lower HbA1c levels to be significantly associated with increased hospital mortality (odds ratio, 1.92; 95% confidence interval, 1.30-2.85; P=.001). CONCLUSION: A significant number of MICU patients with stress hyperglycemia have undiagnosed diabetes. Hyperglycemia with lower baseline HbA1c was associated with increased mortality.

SLIPTA e-Tool improves laboratory audit process in Vietnam and Cambodia.

BACKGROUND: The Stepwise Laboratory Quality Improvement Process Towards Accreditation (SLIPTA) checklist is used worldwide to drive quality improvement in laboratories in developing countries and to assess the effectiveness of interventions such as the Strengthening Laboratory Management Toward Accreditation (SLMTA) programme. However, the paper-based format of the checklist makes administration cumbersome and limits timely analysis and communication of results. DEVELOPMENT OF E-TOOL: In early 2012, the SLMTA team in Vietnam developed an electronic SLIPTA checklist tool. The e-Tool was pilot tested in Vietnam in mid-2012 and revised. It was used during SLMTA implementation in Vietnam and Cambodia in 2012 and 2013 and further revised based on auditors' feedback about usability. OUTCOMES: The SLIPTA e-Tool enabled rapid turn-around of audit results, reduced workload and language barriers and facilitated analysis of national results. Benefits of the e-Tool will be magnified with in-country scale-up of laboratory quality improvement efforts and potential expansion to other countries.

Model-based safety analysis of human-robot interactions: the MIRAS walking assistance robot.

Robotic systems have to cope with various execution environments while guaranteeing safety, and in particular when they interact with humans during rehabilitation tasks. These systems are often critical since their failure can lead to human injury or even death. However, such systems are difficult to validate due to their high complexity and the fact that they operate within complex, variable and uncertain environments (including users), in which it is difficult to foresee all possible system behaviors. Because of the complexity of human-robot interactions, rigorous and systematic approaches are needed to assist the developers in the identification of significant threats and the implementation of efficient protection mechanisms, and in the elaboration of a sound argumentation to justify the level of safety that can be achieved by the system. For threat identification, we propose a method called HAZOP-UML based on a risk analysis technique adapted to system description models, focusing on human-robot interaction models. The output of this step is then injected in a structured safety argumentation using the GSN graphical notation. Those approaches have been successfully applied to the development of a walking assistant robot which is now in clinical validation.

Learning to recognize phenotype candidates in the auto-immune literature using SVM re-ranking.

The identification of phenotype descriptions in the scientific literature, case reports and patient records is a rewarding task for bio-medical text mining. Any progress will support knowledge discovery and linkage to other resources. However because of their wide variation a number of challenges still remain in terms of their identification and semantic normalisation before they can be fully exploited for research purposes. This paper presents novel techniques for identifying potential complex phenotype mentions by exploiting a hybrid model based on machine learning, rules and dictionary matching. A systematic study is made of how to combine sequence labels from these modules as well as the merits of various ontological resources. We evaluated our approach on a subset of Medline abstracts cited by the Online Mendelian Inheritance of Man database related to auto-immune diseases. Using partial matching the best micro-averaged F-score for phenotypes and five other entity classes was 79.9%. A best performance of 75.3% was achieved for phenotype candidates using all semantics resources. We observed the advantage of using SVM-based learn-to-rank for sequence label combination over maximum entropy and a priority list approach. The results indicate that the identification of simple entity types such as chemicals and genes are robustly supported by single semantic resources, whereas phenotypes require combinations. Altogether we conclude that our approach coped well with the compositional structure of phenotypes in the auto-immune domain.

Determinants of antiretroviral treatment adherence among HIV/AIDS patients: a multisite study.

INTRODUCTION: Adherence to antiretroviral treatment (ART) is vital in achieving virological treatment success. This study assessed the prevalence of optimal ART adherence and its determinants among HIV/AIDS patients in Vietnam. METHOD: A cross-sectional survey was conducted with 1,016 HIV/AIDS patients at seven hospitals and health centers providing antiretroviral treatment services in three provinces, including Hanoi, Hai Phong, and Ho Chi Minh City. Self-reported medication adherence was measured using a 30-day visual analog scale (VAS) and 7-day missed-doses questions. RESULTS: The mean adherence VAS-score was 94.5 out of 100 (SD=8.2), ranging from 40 to 100%. The rate of suboptimal adherence was 25.9%. The rate of missed-doses was 25.2%. In multivariate analysis, increased perceived self-efficacy, use of mobile phone alarms, and reminders from family members were associated with optimal adherence; higher CD4 level, single status, and unstable employment were associated with suboptimal adherence. CONCLUSION: High rate of suboptimal adherence observed in this study highlights the importance of adherence support interventions during ART. The use of mobile phone reminders, involvement of relatives, and HIV self-management training programs have the potential to improve ART adherence in Vietnam.

Pediatric overuse injuries in sports.

Overuse injuries in the pediatric and adolescent population are a growing problem in the United States as more children participate in recreational and organized sports. It is not uncommon for children and adolescents to play on multiple teams simultaneously or to be involved in sports year-round. Without adequate rest, the demands of exercise can exceed the body's ability to repair tissues, leading to repetitive microtrauma and overuse injury. Unlike in adults, the consequences of overuse injury in the pediatric and adolescent athlete are far more serious because the growing bones are vulnerable to stress. The ability to identify individuals who are at risk of overuse injuries is key so that education, prevention, and early diagnosis and treatment can occur. Preventive measures of modifying training factors (ie, magnitude, intensity, and frequency of sports participation) and correcting improper biomechanics (alignment, laxity, inflexibility, and muscle imbalance) should always be part of the management plan.

A case control investigation of hepatitis C risk factors in Hawaii.

This case control investigation of hepatitis C risk factors in Hawaii showed that IV drug use, blood transfusion, tattoos, incarceration, acupuncture, prior dental or oral surgery, and HIV infection were associated with HCV Future public health efforts in Hawaii should focus on developing effective and appropriate community interventions targeting those with well-established risk factors for HCV