Whole-genome sequence and resistance determinants of four Elizabethkingia anophelis clinical isolates collected in Hanoi, Vietnam.

Four isolates of the opportunistic pathogen Elizabethkingia anophelis were identified for the first time in a Vietnamese hospital and underwent antimicrobial susceptibility testing and genomic characterization by whole-genome sequencing. Complete, fully circularized genome sequences were obtained for all four isolates. Average Nucleotide Identity analysis and single nucleotide polymorphism phylogenetic analysis on the core genome showed that three of the four isolates were genetically distinct, ruling out the hypothesis of a single strain emergence. Antibiotic susceptibility testing highlighted multi-resistant phenotypes against most antimicrobial families, including beta-lactams, carbapenems, aminoglycosides, quinolones, macrolides, amphenicols, rifamycins and glycopeptides. Additionally, in silico genomic analysis was used to correlate the phenotypic susceptibility to putative resistance determinants, including resistance genes, point mutations and multidrug efflux pumps. Nine different resistance genes were located inside a single resistance pocket predicted to be a putative Integrative and Conjugative Element (ICE). This novel ICE was shared by three isolates from two different lineages and displayed similarity with ICEs previously reported in various Elizabethkingia and Chryseobacterium species. The role of such ICEs in pathogenicity, genome plasticity and antimicrobial resistance gene spread within the Flavobacteriaceae family needs to be further elucidated.

Genomic insights into an extensively drug-resistant and hypervirulent Burkholderia dolosa N149 isolate of a novel sequence type (ST2237) from a Vietnamese patient hospitalised for stroke.

OBJECTIVES: Burkholderia dolosa is a clinically important opportunistic pathogen in inpatients. Here we characterised an extensively drug-resistant and hypervirulent B. dolosa isolate from a patient hospitalised for stroke. METHODS: Resistance to 41 antibiotics was tested with the agar disc diffusion, minimum inhibitory concentration, or broth microdilution method. The complete genome was assembled using short-reads and long-reads and the hybrid de novo assembly method. Allelic profiles obtained by multilocus sequence typing were analysed using the PubMLST database. Antibiotic-resistance and virulence genes were predicted in silico using public databases and the 'baargin' workflow. B. dolosa N149 phylogenetic relationships with all available B. dolosa strains and Burkholderia cepacia complex strains were analysed using the pangenome obtained with Roary. RESULTS: B. dolosa N149 displayed extensive resistance to 31 antibiotics and intermediate resistance to 4 antibiotics. The complete genome included three circular chromosomes (6 338 630 bp in total) and one plasmid (167 591 bp). Genotypic analysis revealed various gene clusters (acr, amr, amp, emr, ade, bla and tet) associated with resistance to 35 antibiotic classes. The major intrinsic resistance mechanisms were multidrug efflux pump alterations, inactivation and reduced permeability of targeted antibiotics. Moreover, 91 virulence genes (encoding proteins involved in adherence, formation of capsule, biofilm and colony, motility, phagocytosis inhibition, secretion systems, protease secretion, transmission and quorum sensing) were identified. B. dolosa N149 was assigned to a novel sequence type (ST2237) and formed a mono-phylogenetic clade separated from other B. dolosa strains. CONCLUSIONS: This study provided insights into the antimicrobial resistance and virulence mechanisms of B. dolosa.

Pretomanid resistance: An update on emergence, mechanisms and relevance for clinical practice.

Pretomanid (PA-824), a novel anti-tuberculosis (TB) nitroimidazoxazine, has been approved for multi-drug-resistant TB treatment for a few years. Pretomanid has been demonstrated to be highly active against Mycobacterium tuberculosis when combined with other anti-TB drugs. This review provides an update of the current knowledge on the modes of action, resistance mechanisms, emergence of drug resistance, and status of antimicrobial susceptibility testing for pretomanid and its relevance for clinical practice. Pretomanid resistance has been reported in in-vitro and animal models but not yet in clinical trials. Pretomanid-resistance-associated mutations have been reported in the fbiA, fbiB, fbiC, fbiD, ddn and fgd1 genes. However, understanding of in-vivo molecular resistance mechanisms remains limited, and complicates the development of accurate antimicrobial susceptibility testing methods for pretomanid. As such, no reference method for antimicrobial susceptibility testing of pretomanid has been established to guide clinical use. Further studies linking specific mutations, in-vitro susceptibility, drug exposure and resistance mechanisms to treatment failure with pretomanid should be prioritized.

Applying artificial intelligence and digital health technologies, Viet Nam.

Problem: Direct application of digital health technologies from high-income settings to low- and middle-income countries may be inappropriate due to challenges around data availability, implementation and regulation. Hence different approaches are needed. Approach: Within the Viet Nam ICU Translational Applications Laboratory project, since 2018 we have been developing a wearable device for individual patient monitoring and a clinical assessment tool to improve dengue disease management. Working closely with local staff at the Hospital for Tropical Diseases, Ho Chi Minh City, we developed and tested a prototype of the wearable device. We obtained perspectives on design and use of the sensor from patients. To develop the assessment tool, we used existing research data sets, mapped workflows and clinical priorities, interviewed stakeholders and held workshops with hospital staff. Local setting: In Viet Nam, a lower middle-income country, the health-care system is in the nascent stage of implementing digital health technologies. Relevant changes: Based on patient feedback, we are altering the design of the wearable sensor to increase comfort. We built the user interface of the assessment tool based on the core functionalities selected by workshop attendees. The interface was subsequently tested for usability in an iterative manner by the clinical staff members. Lessons learnt: The development and implementation of digital health technologies need an interoperable and appropriate plan for data management including collection, sharing and integration. Engagements and implementation studies should be conceptualized and conducted alongside the digital health technology development. The priorities of end-users, and understanding context and regulatory landscape are crucial for success.

Compound K Production: Achievements and Perspectives.

Compound K (CK) is one of the major metabolites found in mammalian blood and organs following oral administration of Panax plants. CK, also known as minor ginsenoside, can be absorbed in the systemic circulation. It has garnered significant attention in healthcare and medical products due to its pharmacological activities, such as antioxidation, anticancer, antiproliferation, antidiabetics, neuroprotection, and anti-atherogenic activities. However, CK is not found in natural ginseng plants but in traditional chemical synthesis, which uses toxic solvents and leads to environmental pollution during the harvest process. Moreover, enzymatic reactions are impractical for industrial CK production due to low yield and high costs. Although CK could be generated from major ginsenosides, most ginsenosides, including protopanaxatriol-oleanane and ocotillol-type, are not converted into CK by catalyzing ß-glucosidase. Therefore, microbial cell systems have been used as a promising solution, providing a safe and efficient approach to CK production. This review provides a summary of various approaches for the production of CK, including chemical and enzymatic reactions, biotransformation by the human intestinal bacteria and endophytes as well as engineered microbes. Moreover, the approaches for CK production have been discussed to improve the productivity of target compounds.

Large-scale analysis of putative plasmids in clinical multidrug-resistant Escherichia coli isolates from Vietnamese patients.

Introduction: In the past decades, extended-spectrum beta-lactamase (ESBL)-producing and carbapenem-resistant (CR) Escherichia coli isolates have been detected in Vietnamese hospitals. The transfer of antimicrobial resistance (AMR) genes carried on plasmids is mainly responsible for the emergence of multidrug-resistant E. coli strains and the spread of AMR genes through horizontal gene transfer. Therefore, it is important to thoroughly study the characteristics of AMR gene-harboring plasmids in clinical multidrug-resistant bacterial isolates. Methods: The profiles of plasmid assemblies were determined by analyzing previously published whole-genome sequencing data of 751 multidrug-resistant E. coli isolates from Vietnamese hospitals in order to identify the risk of AMR gene horizontal transfer and dissemination. Results: The number of putative plasmids in isolates was independent of the sequencing coverage. These putative plasmids originated from various bacterial species, but mostly from the Escherichia genus, particularly E. coli species. Many different AMR genes were detected in plasmid contigs of the studied isolates, and their number was higher in CR isolates than in ESBL-producing isolates. Similarly, the blaKPC-2, blaNDM-5, blaOXA-1, blaOXA-48, and blaOXA-181 ß-lactamase genes, associated with resistance to carbapenems, were more frequent in CR strains. Sequence similarity network and genome annotation analyses revealed high conservation of the ß-lactamase gene clusters in plasmid contigs that carried the same AMR genes. Discussion: Our study provides evidence of horizontal gene transfer in multidrug-resistant E. coli isolates via conjugative plasmids, thus rapidly accelerating the emergence of resistant bacteria. Besides reducing antibiotic misuse, prevention of plasmid transmission also is essential to limit antibiotic resistance.

Efflux Pump Inhibitors in Controlling Antibiotic Resistance: Outlook under a Heavy Metal Contamination Context.

Multi-drug resistance to antibiotics represents a growing challenge in treating infectious diseases. Outside the hospital, bacteria with the multi-drug resistance (MDR) phenotype have an increased prevalence in anthropized environments, thus implying that chemical stresses, such as metals, hydrocarbons, organic compounds, etc., are the source of such resistance. There is a developing hypothesis regarding the role of metal contamination in terrestrial and aquatic environments as a selective agent in the proliferation of antibiotic resistance caused by the co-selection of antibiotic and metal resistance genes carried by transmissible plasmids and/or associated with transposons. Efflux pumps are also known to be involved in either antibiotic or metal resistance. In order to deal with these situations, microorganisms use an effective strategy that includes a range of expressions based on biochemical and genetic mechanisms. The data from numerous studies suggest that heavy metal contamination could affect the dissemination of antibiotic-resistant genes. Environmental pollution caused by anthropogenic activities could lead to mutagenesis based on the synergy between antibiotic efficacy and the acquired resistance mechanism under stressors. Moreover, the acquired resistance includes plasmid-encoded specific efflux pumps. Soil microbiomes have been reported as reservoirs of resistance genes that are available for exchange with pathogenic bacteria. Importantly, metal-contaminated soil is a selective agent that proliferates antibiotic resistance through efflux pumps. Thus, the use of multi-drug efflux pump inhibitors (EPIs) originating from natural plants or synthetic compounds is a promising approach for restoring the efficacy of existing antibiotics, even though they face a lot of challenges.

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.

Background: Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. Methods: We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. Results: The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). Conclusion: This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Green Analytical Method for Simultaneous Determination of Glucosamine and Calcium in Dietary Supplements by Capillary Electrophoresis with Capacitively Coupled Contactless Conductivity Detection.

The need for analytical methods that are fast, affordable, and ecologically friendly is expanding. Because of its low solvent consumption, minimal waste production, and speedy analysis, capillary electrophoresis is considered a "green" choice among analytical separation methods. With these "green" features, we have utilized the capillary electrophoresis method with capacitively coupled contactless conductivity detection (CE-C4D) to simultaneously determine glucosamine and Ca2+ in dietary supplements. The CE analysis was performed in fused silica capillaries (50 µm inner diameter, 40 cm total length, 30 cm effective length), and the analytical time was around 5 min. After optimization, the CE conditions for selective determination of glucosamine and Ca2+ were obtained, including a 10 mM tris (hydroxymethyl) aminomethane/acetic acid (Tris/Ace) buffer of pH 5.0 as the background electrolyte; separation voltage of 20 kV; and hydrodynamic injection (siphoning) at 25 cm height for 30 s. The method illustrated good linearity over the concentration range of 5.00 to 200 mg/L of for glucosamine (R 2 = 0.9994) and 1.00 to 100 mg/L for Ca2+ (R 2 = 0.9994). Under the optimum conditions, the detection limit of glucosamine was 1.00 mg/L, while that of Ca2+ was 0.05 mg/L. The validated method successfully analyzed glucosamine and Ca2+ in seven dietary supplement samples. The measured concentrations were generally in line with the values of label claims and with cross-checking data from reference methods (HPLC and ICP-OES).

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools.

BACKGROUND: Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation. METHODS: We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers. RESULTS: Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools. CONCLUSIONS: The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

Graph Transformer for Drug Response Prediction.

BACKGROUND: Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models have shown drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features was up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP and GraOmicDRP.

Essential Oils from the Leaves, Stem, and Roots of Blumea lanceolaria (Roxb.) Druce in Vietnam: Determination of Chemical Composition, and In Vitro, In Vivo, and In Silico Studies on Anti-Inflammatory Activity.

Blumea lanceolaria (Roxb.) Druce, a flowering plant, is used for treating cancer and inflammatory diseases. In this study, we determined the chemical composition of the EOs extracted from the leaves (LBEO), stem (SBEO), and roots (RBEO) of B. lanceolaria and analyzed their anti-inflammation potential. Overall, 30 compounds representing 99.12%, 98.44%, and 96.89% of total EO constituents of the leaves, stem, and roots, respectively, were identified using GC-MS. ELISA, Western blotting, and qRT-PCR studies showed that LBEO, SBEO, and RBEO inhibited multiple steps in the inflammatory responses in the RAW 264.7 cell model, including NO production; TNF-α, IL-6, iNOS, and COX-2 transcription and translation; and phosphorylation of IκBα and p65 of the NF-κB pathway. In the carrageenan-induced paw edema model, all three EOs inhibited paw edema at both early and delayed phases. Molecular docking studies indicated that the main components of B. lanceolaria EOs (BEOs) targeted and inhibited major components of inflammation-related pathways, including the arachidonic acid metabolic pathway, NF-κB pathway, and MAPK pathway. We present the first study to characterize the chemical composition of BEOs and confirm their potent anti-inflammatory effects in in vitro, in vivo, and in silico analysis. These results can facilitate the development of effective anti-inflammatory drugs with limited side effects in the future.

Genome-wide comparison deciphers lifestyle adaptation and glass biodeterioration property of Curvularia eragrostidis C52.

Glass biodeterioration by fungi has caused irreversible damage to valuable glass materials such as cultural heritages and optical devices. To date, knowledge about metabolic potential and genomic profile of biodeteriorative fungi is still scarce. Here, we report for the first time the whole genome sequence of Curvularia eragrostidis C52 that strongly degraded silica-based glasses coated with fluorine and hafnium, as expressed by the hyphal surface coverage of 46.16 ± 3.3% and reduced light transmission of 50.93 ± 1.45%. The genome of C. eragrostidis C52 is 36.9 Mb long with a GC content of 52.1% and contains 14,913 protein-coding genes, which is the largest genome ever recorded in the genus Curvularia. Phylogenomic analysis revealed C. eragrostidis C52 formed a distinct cluster with Curvularia sp. IFB-Z10 and was not evolved from compared genomes. Genome-wide comparison showed that strain C52 harbored significantly higher proportion of proteins involved in carbohydrate-active enzymes, peptidases, secreted proteins, and transcriptional factors, which may be potentially attributed to a lifestyle adaptation. Furthermore, 72 genes involved in the biosynthesis of 6 different organic acids were identified and expected to be crucial for the fungal survival in the glass environment. To form biofilm against stress, the fungal strain utilized 32 genes responsible for exopolysaccharide production. These findings will foster a better understanding of the biology of C. eragrostidis and the mechanisms behind fungal biodeterioration in the future.

Curcumin and Paclitaxel Co-loaded Heparin and Poloxamer P403 Hybrid Nanocarrier for Improved Synergistic Efficacy in Breast Cancer.

INTRODUCTION: Multi-drug nanosystem has been employed in several therapeutic models due to the synergistic effect of the drugs and/or bioactive compounds, which help in tumor targeting and limit the usual side effects of chemotherapy. METHODS: In this research, we developed the amphiphilic Heparin-poloxamer P403 (HSP) nanogel that could load curcumin (CUR) and Paclitaxel (PTX) through the hydrophobic core of Poloxamer P403. The features of HSP nanogel were assessed through Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), differential light scattering (DLS), and critical micelle concentration (CMC). Nanogel and its dual drug-loaded platform showed high stability and spherical morphology. RESULTS: The drug release profile indicated fast release at pH 5.5, suggesting effective drug distribution at the tumor site. In vitro research confirms lower cytotoxicity of HSP@CUR@PTX compared to free PTX and higher inhibition effect with MCF-7 than HSP@PTX. These results support the synergism between PTX and CUR. CONCLUSION: HSP@CUR@PTX suggests a prominent strategy for achieving the synergistic effect of PTX and CUR to circumvent undesirable effects in breast cancer treatment.

DrGA: cancer driver gene analysis in a simpler manner.

BACKGROUND: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. RESULTS: This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. CONCLUSIONS: DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA .

Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas.

It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A-lncRNAs in light of three state-of-the-art tools WGCNA, iWGCNA, and oCEM, and interrogated their prognostic values in brain low-grade gliomas (LGG). Of the 13 hub m6A-lncRNAs, we further detected three hub m6A-lncRNAs as independent prognostic risk factors, including HOXB-AS1, ELOA-AS1, and FLG-AS1. Then, the m6ALncSig model was built based on these three hub m6A-lncRNAs. Patients with LGG next were divided into two groups, high- and low-risk, based on the median m6ALncSig score. As predicted, the high-risk group was more significantly related to mortality. The prognostic signature of m6ALncSig was validated using internal and external cohorts. In summary, our work introduces a high-confidence prognostic prediction signature and paves the way for using m6A-lncRNAs in the signature as new targets for treatment of LGG.

Health and safety risks faced by delivery riders during the Covid-19 pandemic.

OBJECTIVES: Delivery riders have been front-line workers throughout the pandemic but little is known about their own health and safety during this time. This study explores the health and safety issues facing delivery riders in Ho Chi Minh City, Vietnam, during the Covid-19 pandemic, in particular during the second lockdown (May-October 2021). METHOD: A web-based survey of more than 800 riders was conducted in August-September 2021. Following descriptive statistics, four logit models were fitted to examine the factors associated with (a) sanitizing one's hands, (b) using a face shield, (c) contracting a new health issue, and (d) engaging in riskier traffic behaviors during the lockdown. RESULTS: The riders who were less consistent in adopting health and safety measures tended to be male, older, less-educated, and vaccinated. Also, they were under greater financial pressure and had suffered a larger loss of income during the pandemic. To recover the loss, they worked longer hours and felt under more intense pressure at work. The job pressure, long working hours, and financial burdens led many drivers to adopt risky traffic behaviors, such as speeding. Conversely, where the companies and co-workers were more supportive, riders tended to adopt health prevention measures more often. Fear of Covid-19 also acted as a facilitator. Job and financial pressure combined with the fear of contracting the virus contributed to the occurrence of new heath issues during the pandemic. Again, support from the company and co-workers helped to reduce the risk of new health problems emerging. CONCLUSION: In Ho Chi Minh City and other Global South megacities that employ tens of thousands of riders, ensuring their health and safety is important to support both private businesses and public health. Overall, companies should assume a much larger responsibility here.

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules.

BACKGROUND: When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. RESULTS: This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. CONCLUSIONS: oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM .

African swine fever virus vaccine candidate ASFV-G-ΔI177L efficiently protects European and native pig breeds against circulating Vietnamese field strain.

The recent spread of African swine fever (ASF) in the People's Republic of China and neighbouring countries in Asia has had significant economic consequences with an estimated direct cost of $55-$130 billion. This pandemic, originally detected in Republic of Georgia in 2007, has devastated the swine industry in large geographical areas of Southeast Asia with 14 countries reporting ASF outbreaks since the first documented case was confirmed in the city of Shenyang, Liaoning Province, China, on 3 August 2018. In the absence of any available vaccines, the control of ASF relies on the detection and culling of infected animals. The United States Department of Agriculture recently developed a recombinant experimental vaccine candidate, ASFV-G-ΔI177L, by deleting the I177L gene from the genome of the highly virulent pandemic ASFV strain Georgia, which efficaciouly protects pigs from the parental virus. Here, the initial studies were extended demonstrating that ASFV-G-ΔI177L is able to protect pigs against the virulent ASFV isolate currently circulating and producing disease in Vietnam with similar efficacy as reported against the Georgia strain. Comparative studies performed using a large number of pigs of European and Vietnamese origin demonstrated that a minimum protective dose of 102 HAD50 of ASFV-G-ΔI177L equally protects animals of both breeds. In concurrence with those results, the onset of immunity in these animal breed showed appearance of protection in approximately one-third of the animals by the second week post vaccination, with full protection achieved by the fourth week post vaccination. Therefore, results presented here demonstrated that ASFV-G-ΔI177L is able to induce protection against virulent Vietnameese ASFV field strains and is effective in protecting local breeds of pigs as efficiently as previously shown for European cross-bred pigs. To our knowledge, this is the first report showing the efficacy of a Georgia 2007 based vaccine candidate in Asian breed of pigs or challenged with an Asian ASFV strain.