Awareness of having hypertension, diabetes and dyslipidaemia among US adults: The 2011-2018 NHANES data.

AIM: This study aimed to investigate awareness of having hypertension, diabetes and dyslipidaemia and their associated factors among US adults. METHODS: Data from the National Health and Nutrition Examination Survey, including 21,399 adults aged ⩾20 years (pregnant women excluded) collected between 2011 and 2018, were used. Blood pressure was measured using a Baumanometer calibrated mercury true gravity wall model sphygmomanometer. Serum total cholesterol levels were measured using enzymatic assays. The percentage of haemoglobin A1C (HbA1c), which reflects long-term blood glucose levels, was measured and used to identify diabetes. Participants self-reported whether they were told by a doctor that they have hypertension, dyslipidaemia and diabetes. Awareness was defined as alignment between objective and self-reported measures for having the conditions. Sampling weights and the Taylor series linearisation variance estimation method were used in the analyses. RESULTS: The findings showed that 64.06% of people with hypertension, 54.71% of those with dyslipidaemia and 78.40% of those with diabetes were aware of having the respective condition. Age, sex and health insurance were associated with awareness of having all three conditions, but marital status was not associated with any outcome. Weight status was associated with awareness of having hypertension and dyslipidaemia, whereas ethnicity was associated with awareness of having hypertension and diabetes. Relative family income was only associated with awareness of having hypertension. CONCLUSIONS: Large proportions of US adults with hypertension, dyslipidaemia and diabetes are not aware of having the conditions. Interventions targeting groups at higher risk of being unaware of these conditions are needed.

Circularly permuted LOV2 as a modular photoswitch for optogenetic engineering.

Plant-based photosensors, such as the light-oxygen-voltage sensing domain 2 (LOV2) from oat phototropin 1, can be modularly wired into cell signaling networks to remotely control protein activity and physiological processes. However, the applicability of LOV2 is hampered by the limited choice of available caging surfaces and its preference to accommodate the effector domains downstream of the C-terminal Jα helix. Here, we engineered a set of LOV2 circular permutants (cpLOV2) with additional caging capabilities, thereby expanding the repertoire of genetically encoded photoswitches to accelerate the design of optogenetic devices. We demonstrate the use of cpLOV2-based optogenetic tools to reversibly gate ion channels, antagonize CRISPR-Cas9-mediated genome engineering, control protein subcellular localization, reprogram transcriptional outputs, elicit cell suicide and generate photoactivatable chimeric antigen receptor T cells for inducible tumor cell killing. Our approach is widely applicable for engineering other photoreceptors to meet the growing need of optogenetic tools tailored for biomedical and biotechnological applications.

Contextualized medication event extraction with levitated markers.

Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must also be able to predict event context, such as negation, uncertainty, and time of occurrence, in order to construct accurate patient timelines. Towards this goal, we introduce Levitated Context Markers (LCMs), a novel transformer-based model for contextualized event extraction. LCMs are an adaptation of levitated markers -originally developed for relation extraction- that allow pretrained transformer models to utilize global input representations while also focusing on event-related subspans using a sparse attention mechanism. In addition to outperforming a strong baseline model on the Contextualized Medication Event Dataset, we show that LCMs' sparse attention can provide interpretable predictions by detecting relevant context cues in an unsupervised manner.

GENA: A knowledge graph for nutrition and mental health.

While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental illnesses, even though a large amount of knowledge about these relationships is available in the form of unstructured text in biomedical literature articles. To address this limitation, this article describes the development of GENA - (Graph of mEntal-health and Nutrition Association), a knowledge graph that represents relations between nutrition and mental health, extracted from biomedical abstracts. GENA is constructed from PubMed abstracts that contain keywords relating to chemicals, food, and health. A hybrid named entity recognition (NER) model is firstly applied to these abstracts to identify various entities of interest. Subsequently, a deep syntax-based relation extraction model is used to detect binary relations between the identified entities. Finally, the resulting relations are used to populate the GENA knowledge graph, whose relationships can be accessed in an intuitive and interpretable manner using the Neo4J Database Management System. To evaluate the reliability of GENA, two annotators manually assessed a subset of the extracted relations. The evaluation results show that our methods obtain high precision for the NER task and acceptable precision and relative recall for the relation extraction task. GENA consists of 43,367 relationships that encode information about nutrition and health, of which 94.04% are new relations that are not present in existing ontologies of food and diseases. GENA is constructed based on scientific principles, and has the potential to be used within further applications to contribute towards scientific research within the domain. It is a pioneering knowledge graph in nutrition and mental health, containing a diverse range of relationship types. All of our source code and results are publicly available at https://github.com/ddlinh/gena-db.

Energetic preference and topological constraint effects on the formation of DNA twisted toroidal bundles.

DNA toroids are compact torus-shaped bundles formed by one or multiple DNA molecules being condensed from the solution due to various condensing agents. It has been shown that the DNA toroidal bundles are twisted. However, the global conformations of DNA inside these bundles are still not well understood. In this study, we investigate this issue by solving different models for the toroidal bundles and performing replica-exchange molecular dynamics (REMD) simulations for self-attractive stiff polymers of various chain lengths. We find that a moderate degree of twisting is energetically favorable for toroidal bundles, yielding optimal configurations of lower energies than for other bundles corresponding to spool-like and constant radius of curvature arrangements. The REMD simulations show that the ground states of the stiff polymers are twisted toroidal bundles with the average twist degrees close to those predicted by the theoretical model. Constant-temperature simulations show that twisted toroidal bundles can be formed through successive processes of nucleation, growth, quick tightening, and slow tightening of the toroid, with the two last processes facilitating the polymer threading through the toroid's hole. A relatively long chain of 512 beads has an increased dynamical difficulty to access the twisted bundle states due to the polymer's topological constraint. Interestingly, we also observed significantly twisted toroidal bundles with a sharp U-shaped region in the polymer conformation. It is suggested that this U-shaped region makes the formation of twisted bundles easier by effectively reducing the polymer length. This effect can be equivalent to having multiple chains in the toroid.

Identification of molecular determinants that govern distinct STIM2 activation dynamics.

The endoplasmic reticulum (ER) Ca2+ sensors stromal interaction molecule 1 (STIM1) and STIM2, which connect ER Ca2+ depletion with extracellular Ca2+ influx, are crucial for the maintenance of Ca2+ homeostasis in mammalian cells. Despite the recent progress in unraveling the role of STIM2 in Ca2+ signaling, the mechanistic underpinnings of its activation remain underexplored. We use an engineering approach to direct ER-resident STIMs to the plasma membrane (PM) while maintaining their correct membrane topology, as well as Förster resonance energy transfer (FRET) sensors that enabled in cellulo real-time monitoring of STIM activities. This allowed us to determine the calcium affinities of STIM1 and STIM2 both in cellulo and in situ, explaining the current discrepancies in the literature. We also identified the key structural determinants, especially the corresponding G residue in STIM1, which define the distinct activation dynamics of STIM2. The chimeric E470G mutation could switch STIM2 from a slow and weak Orai channel activator into a fast and potent one like STIM1 and vice versa. The systemic dissection of STIM2 activation by protein engineering sets the stage for the elucidation of the regulation and function of STIM2-mediated signaling in mammals.

Single-Atom Fluorescence Switch: A General Approach toward Visible-Light-Activated Dyes for Biological Imaging.

Photoactivatable fluorophores afford powerful molecular tools to improve the spatial and temporal resolution of subcellular structures and dynamics. By performing a single sulfur-for-oxygen atom replacement within common fluorophores, we have developed a facile and general strategy to obtain photoactivatable fluorogenic dyes across a broad spectral range. Thiocarbonyl substitution within fluorophores results in significant loss of fluorescence via a photoinduced electron transfer-quenching mechanism as suggested by theoretical calculations. Significantly, upon exposure to air and visible light residing in their absorption regime (365-630 nm), thio-caged fluorophores can be efficiently desulfurized to their oxo derivatives, thus restoring strong emission of the fluorophores. The effective photoactivation makes thio-caged fluorophores promising candidates for super-resolution imaging, which was realized by photoactivated localization microscopy (PALM) with low-power activation light under physiological conditions in the absence of cytotoxic additives (e.g., thiols, oxygen scavengers), a feature superior to traditional PALM probes. The versatility of this thio-caging strategy was further demonstrated by multicolor super-resolution imaging of lipid droplets and proteins of interest.

Calcium store refilling and STIM activation in STIM- and Orai-deficient cell lines.

Mediated through the combined action of STIM proteins and Orai channels, store-operated Ca2+ entry (SOCE) functions ubiquitously among different cell types. The existence of multiple STIM and Orai genes has made it difficult to assign specific roles of each STIM and Orai homolog in mediating Ca2+ signals. Using CRISPR/Cas9 gene editing tools, we generated cells with both STIM or all three Orai homologs deleted and directly monitored store Ca2+ and Ca2+ signals. We found that unstimulated, SOCE null KO cells still retain 50~70% of ER Ca2+ stores of wildtype (wt) cells. After brief exposure to store-emptying conditions, acute refilling of ER Ca2+ stores was totally blocked in KO cells. However, after 24 h in culture, stores were eventually refilled. Thus, SOCE is critical for immediate refilling of ER Ca2+ but is dispensable for the maintenance of long-term ER Ca2+ homeostasis. Using the Orai null background triple Orai-KO cells, we examined the plasma membrane translocation properties of a series of truncated STIM1 variants. FRET analysis reveals that, even though PM tethering of STIM1 expedites the activation of STIM1 by facilitating its oligomerization, migration, and accumulation in ER-PM junctions, it is not required for the conformational switch, oligomerization, and clustering of STIM1. Even without overt puncta formation at ER-PM junctions, STIM11-491 and STIM11-666 could still rescue SOCE when expressed in STIM KO cells. Thus, ER-PM trapping and clustering of STIM molecules only facilitates the process of SOCE activation, but is not essential for the activation of Orai channels.

Mutations in DDX58, which encodes RIG-I, cause atypical Singleton-Merten syndrome.

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.Glu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373Ala) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies.

Use of Colistin and Other Critical Antimicrobials on Pig and Chicken Farms in Southern Vietnam and Its Association with Resistance in Commensal Escherichia coli Bacteria.

UNLABELLED: Antimicrobial resistance (AMR) is a global health problem, and emerging semi-intensive farming systems in Southeast Asia are major contributors to the AMR burden. We accessed 12 pig and chicken farms at key stages of production in Tien Giang Province, Vietnam, to measure antimicrobial usage and to investigate the prevalence of AMR to five critical antimicrobials (ß-lactams, third-generation cephalosporins, quinolones, aminoglycosides, and polymyxins) and their corresponding molecular mechanisms among 180 Escherichia coli isolates. Overall, 94.7 mg (interquartile range [IQR], 65.3 to 151.1) and 563.6 mg (IQR, 398.9 to 943.6) of antimicrobials was used to produce 1 kg (live weight) of chicken and pig, respectively. A median of 3 (out of 8) critical antimicrobials were used on pig farms. E. coli isolates exhibited a high prevalence of resistance to ampicillin (97.8% and 94.4% for chickens and pigs, respectively), ciprofloxacin (73.3% and 21.1%), gentamicin (42.2% and 35.6%), and colistin (22.2% and 24.4%). The prevalence of a recently discovered colistin resistance gene, mcr-1, was 19 to 22% and had strong agreement with phenotypic colistin resistance. We conducted plasmid conjugation experiments with 37 mcr-1 gene-positive E. coli isolates and successfully observed transfer of the gene in 54.0% of isolates through a plasmid of approximately 63 kb, consistent with one recently identified in China. We found no significant correlation between total use of antimicrobials at the farm level and AMR. These data provide additional insight into the role of mcr-1 in colistin resistance on farms and outline the dynamics of phenotypic and genotypic AMR in semi-intensive farming systems in Vietnam. IMPORTANCE: Our study provides accurate baseline information on levels of antimicrobial use, as well as on the dynamics of phenotypic and genotypic resistance for antimicrobials of critical importance among E. coli over the different stages of production in emerging pig and poultry production systems in Vietnam. E. coli isolates showed a high prevalence of resistance (>20%) to critically important antimicrobials, such as colistin, ciprofloxacin, and gentamicin. The underlying genetic mechanisms identified for colistin (the mcr-1 gene) and quinolone (gyrA gene mutations) are likely to play a major role in AMR to those compounds. Conjugation experiments led to the identification of a 63-kb plasmid, similar to one recently identified in China, as the potential carrier of the mcr-1 gene. These results should encourage greater restrictions of such antimicrobials in Southeast Asian farming systems.

Effects of cigarette smoking and nicotine dependence on adherence to antiretroviral therapy among HIV-positive patients in Vietnam.

Cigarette smoking is increasingly recognized as an indicator for inferior adherence to antiretroviral therapy (ART) among HIV-positive patients. Given the limited body of work on this issue, we aimed to explore the relations between cigarette smoking, nicotine dependence, and ART adherence in Vietnam. A cross-sectional study of 1050 HIV-positive people was conducted from January to September 2013 in Hanoi (the capital) and Nam Dinh (a rural city). Adherence to ART during the last 30 days was measured by the 100-point visual analog scale (VAS). Smoking history and nicotine dependence (Fagerstrom Test of Nicotine Dependence) were self-reported by participants. Multiple logistic regression was performed to examine the association of current smoking and nicotine dependence with ART nonadherence. Using the established VAS cut point of 95 to indicate adequate adherence, the prevalence of ART nonadherence was 30.9%. Approximately 35.5% of the sample reported current smoking. No association between smoking status and ART nonadherence was found. However, participants with greater nicotine dependence (OR = 1.1, 95%CI = 1.0-1.2 per unit increase) were more likely to be nonadherent. Also, individuals who were female (OR = 1.70, 95%CI = 1.19-2.42), receiving ART in Nam Dinh (OR = 1.6, 95%CI = 1.1-2.4), and currently feeling anxiety (OR = 1.6, 95% CI = 1.2-2.1) had a higher likelihood of ART nonadherence. Additionally, current smokers reporting current pain (OR = 1.9, 95%CI = 1.2-3.1) were more likely to be nonadherent. Conversely, protective factors included living with a spouse/partner (OR = 0.5, 95%CI = 0.3-0.7) and having more than a high school education (OR = 0.4, 95%CI = 0.1-1.0). Given the high prevalence of suboptimal adherence and current smoking among HIV-positive patients, screening for smoking status and nicotine dependence during ART treatment may help to improve patients' adherence to medication. More efforts should be targeted to women, patients with mental health problems, and ART clinics in rural areas.

Mixed-Metal Zeolitic Imidazolate Frameworks and their Selective Capture of Wet Carbon Dioxide over Methane.

A presynthesized, square planar copper imidazole complex, [Cu(imidazole)4](NO3)2, was utilized as a precursor in the synthesis of a new series of zeolitic imidazolate frameworks, termed ZIF-202, -203, and -204. The structures of all three members were solved by single-crystal X-ray diffraction analysis, which revealed ZIF-203 and -204 having successfully integrated square planar units within the backbones of their respective frameworks. As a result of this unit, the structures of both ZIF-203 and -204 were found to adopt unprecedented three-dimensional nets, namely, ntn and thl, respectively. One member of this series, ZIF-204, was demonstrated to be highly porous, exhibit exceptional stability in water, and selectively capture CO2 over CH4 under both dry and wet conditions without any loss in performance over three cycles. Remarkably, the regeneration of ZIF-204 was performed under the mild conditions of flowing a pure N2 gas through the material at ambient temperature.

Wide-coverage relation extraction from MEDLINE using deep syntax.

BACKGROUND: Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework. RESULTS: Our system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems. CONCLUSION: PASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.

Three-Dimensional Metal-Catecholate Frameworks and Their Ultrahigh Proton Conductivity.

A series of three-dimensional (3D) extended metal catecholates (M-CATs) was synthesized by combining the appropriate metal salt and the hexatopic catecholate linker, H6THO (THO(6-) = triphenylene-2,3,6,7,10,11-hexakis(olate)) to give Fe(THO)·Fe(SO4) (DMA)3, Fe-CAT-5, Ti(THO)·(DMA)2, Ti-CAT-5, and V(THO)·(DMA)2, V-CAT-5 (where DMA = dimethylammonium). Their structures are based on the srs topology and are either a 2-fold interpenetrated (Fe-CAT-5 and Ti-CAT-5) or noninterpenetrated (V-CAT-5) porous anionic framework. These examples are among the first catecholate-based 3D frameworks. The single crystal X-ray diffraction structure of the Fe-CAT-5 shows bound sulfate ligands with DMA guests residing in the pores as counterions, and thus ideally suited for proton conductivity. Accordingly, Fe-CAT-5 exhibits ultrahigh proton conductivity (5.0 × 10(-2) S cm(-1)) at 98% relative humidity (RH) and 25 °C. The coexistence of sulfate and DMA ions within the pores play an important role in proton conductivity as also evidenced by the lower conductivity values found for Ti-CAT-5 (8.2 × 10(-4) S cm(-1) at 98% RH and 25 °C), whose structure only contained DMA guests.

Identifying synonymy between relational phrases using word embeddings.

Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Most of the previous work that has addressed this task of synonymy resolution uses similarity metrics between relational phrases based on textual strings or dependency paths, which, for the most part, ignore the context around the relations. To overcome this shortcoming, we employ a word embedding technique to encode relational phrases. We then apply the k-means algorithm on top of the distributional representations to cluster the phrases. Our experimental results show that this approach outperforms state-of-the-art statistical models including latent Dirichlet allocation and Markov logic networks.

From toroidal to rod-like condensates of semiflexible polymers.

The competition between toroidal and rod-like conformations as possible ground states for DNA condensation is studied as a function of the stiffness, the length of the DNA, and the form of the long-range interactions between neighboring molecules, using analytical theory supported by Monte Carlo simulations. Both conformations considered are characterized by a local nematic order with hexagonal packing symmetry of neighboring DNA molecules, but differ in global configuration of the chain and the distribution of its curvature as it wraps around to form a condensate. The long-range interactions driving the DNA condensation are assumed to be of the form pertaining to the attractive depletion potential as well as the attractive counterion induced soft potential. In the stiffness-length plane we find a transition between rod-like to toroid condensate for increasing stiffness at a fixed chain length L. Strikingly, the transition line is found to have a L(1/3) dependence irrespective of the details of the long-range interactions between neighboring molecules. When realistic DNA parameters are used, our description reproduces rather well some of the experimental features observed in DNA condensates.

Selective capture of carbon dioxide under humid conditions by hydrophobic chabazite-type zeolitic imidazolate frameworks.

Hydrophobic zeolitic imidazolate frameworks (ZIFs) with the chabazite (CHA) topology are synthesized by incorporating two distinct imidazolate links. Zn(2-mIm)0.86 (bbIm)1.14 (ZIF-300), Zn(2-mIm)0.94 (cbIm)1.06 (ZIF-301), and Zn(2-mIm)0.67 (mbIm)1.33 (ZIF-302), where 2-mIm = 2-methylimidazolate, bbIm = 5(6)-bromobenzimidazolate, cbIm = 5(6)-chlorobenzimidazolate, and mbIm = 5(6)-methylbenzimidazolate, were prepared by reacting zinc nitrate tetrahydrate and 2-mIm with the respective bIm link in a mixture of N,N-dimethylformamide (DMF) and water. Their structures were determined by single-crystal X-ray diffraction and their permanent porosity shown. All of these structures are hydrophobic as confirmed by water adsorption isotherms. All three ZIFs are equally effective at the dynamic separation of CO2 from N2 under both dry and humid conditions without any loss of performance over three cycles and can be regenerated simply by using a N2 flow at ambient temperature.

Severity predictors for multisystemic inflammatory syndrome in children after SARS-CoV-2 infection in Vietnam.

Multisystemic inflammatory syndrome in children (MIS-C) might manifest in a broad spectrum of clinical scenarios, ranging from mild features to multi-organ dysfunction and mortality. However, this novel entity has a heterogenicity of data regarding prognostic factors associated with severe outcomes. The present study aimed to identify independent predictors for severity by using multivariate regression models. A total of 391 patients (255 boys and 136 girls) were admitted to Vietnam National Children's Hospital from January 2022 to June 2023. The median age was 85 (range: 2-188) months, and only 12 (3.1%) patients had comorbidities. 161 (41.2%) patients required PICU admission, and the median PICU LOS was 4 (2-7) days. We observed independent factors related to PICU admission, including CRP ≥ 50 (mg/L) (OR 2.52, 95% CI 1.39-4.56, p = 0.002), albumin ≤ 30 (g/L) (OR 3.18, 95% CI 1.63-6.02, p = 0.001), absolute lymphocyte count ≤ 2 (× 109/L) (OR 2.18, 95% CI 1.29-3.71, p = 0.004), ferritin ≥ 300 (ng/mL) (OR 2.35, 95% CI 1.38-4.01), p = 0.002), and LVEF < 60 (%) (OR 2.48, 95% CI 1.28-4.78, p = 0.007). Shock developed in 140 (35.8%) patients, especially for those decreased absolute lymphocyte ≤ 2 (× 109/L) (OR 2.48, 95% CI 1.10-5.61, p = 0.029), albumin ≤ 30 (g/L) (OR 2.53, 95% CI 1.22-5.24, p = 0.013), or LVEF < 60 (%) (OR 2.24, 95% CI 1.12-4.51, p = 0.022). In conclusion, our study emphasized that absolute lymphocyte count, serum albumin, CRP, and LVEF were independent predictors for MIS-C severity. Further well-designed investigations are required to validate their efficacy in predicting MIS-C severe cases, especially compared to other parameters. As MIS-C is a new entity and severe courses may progress aggressively, identifying high-risk patients optimizes clinicians' follow-up and management to improve disease outcomes.

Evaluation of screening algorithms to detect rectal colonization with carbapenemase-producing Enterobacterales in a resource-limited setting.

Objectives: To improve and rationalize the detection of carbapenemase-producing Enterobacterales (CPE) in rectal swabs in a high-prevalence and resource-constrained setting, addressing surveillance challenges typically encountered in laboratories with limited resources. Methods: A point prevalence survey (PPS) was conducted on 15 August 2022, in a provincial children's hospital in northern Vietnam. Rectal swab samples of all admitted children were collected and plated on a selective medium for carbapenem-resistant Enterobacterales (CRE). Species identification and antimicrobial susceptibility testing (AST) were performed by MALDI-TOF, and VITEK2 XL and interpreted according to CLSI breakpoints (2022). Carbapenemases were detected by the carbapenem inactivation method (CIM) and quantitative real-time PCR (qRT-PCR). Results: Rectal swab samples were obtained from 376 patients. Of 178 isolates growing on the CRE screening agar, 140 isolates were confirmed as Enterobacterales of which 118 (84.3%) isolates were resistant to meropenem and/or ertapenem. CIM and PCR showed that 90/118 (76.3%) were carbapenemase producers. Overall, 83/367 (22.6%) were colonized by CPE. Klebsiella pneumoniae, Escherichia coli and Enterobacter cloacae complex were the most common CPE detected, with NDM as the predominant carbapenemase (78/90; 86.7%). Phenotypic resistance to meropenem was the best predictor of CPE production (sensitivity 85.6%, specificity 100%) compared with ertapenem resistance (95.6% sensitivity, 36% specificity). CIM was 100% concordant with PCR in detecting carbapenemases. Conclusions: These findings underscore the effectiveness of meropenem resistance as a robust indicator of the production of carbapenemases and the reliability of the CIM method to detect such carbapenemases in resource-limited settings where the performance of molecular methods is not possible.

A metric learning-based method for biomedical entity linking.

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here.