Card Database Research Articles

Molecular genetic characteristics of <i>Vibrio cholerae</i> nono1/nono139 strain, the causative agent of a new case of acute intestinal infection in Rostov-on-Don

During more than 50 years, a heterogeneous population of Vibrio cholerae non-O1/non-O139 (NAGs) exists in Rostov region of Russia, whose representatives periodically cause acute human intestinal infections; the last case was registered in 2018. In 2022, a sick child was again revealed in Rostov, from whom 2 NAG subcultures were isolated. Aim of the study: bioinformatics analysis of whole genome sequences (WGSs) of the 2022 clinical NAG isolates, genetic determinants of pathogenicity factors and relevant products. Materials and methods. Isolation, identification of cultures, as well as antibiotic sensitivity were carried out according to the MUK 4.2.3745-22. SNP analysis of WGSs obtained on the MiSeq Illumina platform were analyzed by means of designed software; BioEdit 7.2.5, Vector NTI Advance 11 package, CARD database were used to identify and analyze genes and relevant deduced products. Results. The isolated cultures with identical phenotypic features, PCR-genotypes and results of SNP analysis, were identified as a non-toxigenic NAG. The agent showed sensitivity to chloramphenicol, ceftriaxone, streptomycin, gentamicin, nalidixic acid, ciprofloxacin, ampicillin, amikacin and intermediate resistance to tetracycline, doxycycline, furazolidon and co-trimoxazole. WGSs lacked CTX and preCTX prophages, pathogenicity island VPI, thermostable and cholix toxin genes, pandemic islands VSP-I and VSP-II. The determinants of the type 6 secretion system (T6SS) were not presented completely, which evidenced in favor of the loss of its functionality. An intact T3SS cluster was revealed, which was adjacent to the nan-nag region of the VPI-2 pathogenicity island in the absence of the proximal and distal parts of the latter. Other determinants of pathogenicity factors were revealed: RTX cluster responsible for synthesis of MARTX cytotoxin, genes for hemolysin HlyA, cytotonic toxin Cef, several serine and metalloproteases. The nucleotide compositions of most determinants differed from the prototypes, but their putative products preserved characteristic active domains. The adhesive activity might be provided by mannose-insensitive MSHA-like pili, since the msh-cluster included all the genes necessary for their production, as well as flagella, as both clusters responsible for their assembling were 99% identical to the prototype. From antibiotic resistance determinants only -lactamase VarG gene was found. Conclusion. The NAG strain examined here contains a sufficient set of intact virulence-associated genetic determinants, expression of which could contribute to the development of the disease.

423. Molecular Epidemiology of ESBL E. coli Over a 2 Year Period in Worcester Massachusetts USA

Abstract Background Extended-spectrum beta-lactamase (ESBL) producing Escherichia coli are an increasing health burden contributing to a pandemic of resistant bacteria. Methods To better understand the population genomics of ESBL E. coli, 129 phenotypically identified ESBL isolates collected between 2017-04 to 2018-11 from a single academic medical center underwent whole genome sequencing with the presence of antibiotic resistance genes identified by screening against the CARD database. The odds of phenotypic resistance to beta-lactam agents were calculated for each known resistance gene. A novel approach based on k-mer-enrichment among antibiotic resistance gene groups was developed to predict genes associated with phenotypic resistance. Results Genomic analysis revealed that 60% of the ESBL isolates collected were pandemic strain ST131. All isolates contained at least 1 gene in the beta-lactamase family with a median of 3 different beta-lactamase genes being present. Ninety-five percent of isolates contained a CTX-M gene with 52% CTX-M-15, 43% CTX-M-27, 5% other CTX-M; 24% had OXA and 34% had TEM. The presence of CTX-M-15 significantly increased the odds of resistance to ampicillin-sulbactam (95% confidence interval 5.9-116.5, p-value < 1e-7), ceftazidime (4.2-25.9, p< 1e-6) and cefepime (3.6-20.1, p< 1e-5) but not to piperacillin-tazobactam (0.9-6.3, p 1). CTX-M-27 decreased the odds of resistance to these 4 beta-lactams (0.003-0.07, p< 1e-13; 0.04-0.3, p< 1e-05; 0.06-0.4, p< 0.001; 0.03-0.6, p 0.11). TEM-1 significantly increased the odds for ampicillin-sulbactam resistance (3.8-1036.7, p< 1e-3) but not for other beta-lactams. Our de novo sequence approach was able to identify the known genes listed above and also identified a 683 bp open reading frame with unknown function that had increased odds of resistance to piperacillin-tazobactam resistance (2.1-43.6, p< 1e-3). This 683 bp open reading frame did not share any homology with known beta-lactamases. Conclusion The majority of phenotypic ESBL isolates contain multiple beta-lactamase genes. The presence of CTX-M-15 appears to increase the odds of resistance to key beta-lactam agents. Our novel k-mer-enrichment approach identified a novel marker of piperacillin-tazobactam resistance. Disclosures All Authors: No reported disclosures.

Longitudinal trends in produce purchasing behavior: a descriptive study of transaction level data from loyalty card households

BackgroundHousehold food purchases (HFP) are in the pathway between the community food environment and the foods available in households for consumption. As such, HFP data have emerged as alternatives to monitor population dietary trends over-time. In this paper, we investigate the use of loyalty card datasets as unexplored sources of continuously collected HFP data to describe temporal trends in household produce purchases.MethodsWe partnered with a grocery store chain to obtain a loyalty card database with grocery transactions by household from January 2016-October 2018. We included households in an urban county with complete observations for head of household age group, household income group, and family size. Data were summarized as weighted averages (95% CI) of percent produce purchased out of all foods purchased by household per month. We modeled seasonal and linear trends in the proportion of produce purchases by age group and income while accounting for repeated observations per household using generalized estimating equations.ResultsThere are 290,098 households in the database (88% of all county households). At baseline, the smallest and largest percent produce purchases are observed among the youngest and lowest income (12.2%, CI 11.1; 13.3) and the oldest and highest income households (19.3, CI 18.9; 19.6); respectively. The seasonal variations are consistent in all age and income groups with an April-June peak gradually descending until December. However, the average linear change in percent produce purchased per household per year varies by age and income being the steepest among the youngest households at each income level (from 1.42%, CI 0.98;1.8 to 0.69%, CI 0.42;0.95) while the oldest households experience almost no annual change.Conclusions We explored the potential of a collaboration with a food retailer to use continuously collected loyalty card data for public health nutrition purposes. Our findings suggest a trend towards a healthier pattern in long-term food purchases and household food availability among the youngest households that may lessen the population chronic disease burden if sustained. Understanding the foods available for consumption within households allows public health advocates to develop and evaluate policies and programs promoting foods and nutrients along the life course.

Comparative genomics analysis of the multidrug-resistant Aeromonas hydrophila MX16A providing insights into antibiotic resistance genes.

In this paper, the whole genome of the multidrug-resistant Aeromonas hydrophila MX16A was comprehensively analyzed and compared after sequencing by PacBio RS II.To shed light on the drug resistance mechanism of A. hydrophila MX16A, aKirby-Bauer disk diffusion method was used to assess the phenotypic drug susceptibility. Importantly, resistance against β-lactam, sulfonamides, rifamycins, macrolides, tetracyclines and chloramphenicols was largely consistent with the prediction analysis results of drug resistance genes in the CARD database.The varied types of resistance genes identified from A. hydrophila MX16A revealed multiple resistance mechanisms, including enzyme inactivation, gene mutation and active effusion.The publicly available complete genomes of 35 Aeromonas hydrophila strains on NCBI, including MX16A, were downloaded for genomic comparison and analysis. The analysis of 33 genomes with ANI greater than 95% showed that the pan-genome consisted of 9556 genes, and the core genes converged to 3485 genes. In summary, the obtained results showed that A. hydrophila exhibited a great genomic diversity as well as diverse metabolic function and it is believed that frequent exchanges between strains lead to the horizontal transfer of drug resistance genes.

Exploring the Active Ingredients and Mechanism of Action of Huanglian Huazhuo Capsule for the Treatment of Obese Type-2 Diabetes Mellitus Based on Using Network Pharmacology and Molecular Docking.

Background Obese type 2 diabetes mellitus (obese T2DM) is one of the prime diseases that endangers human health. Clinical studies have confirmed the ability of the Huanglian Huazhuo capsule to treat obese T2DM; however, its mechanism of action is still unclear. In this study, effects and mechanisms of the Huanglian Huazhuo capsule in obese T2DM were systematically investigated using network pharmacology and molecular docking techniques. Methods The active ingredients and targets of the Huanglian Huazhuo capsule were extracted from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Obese T2DM diabetes-related targets were retrieved from a geographic dataset combined with a gene card database. A protein-protein interaction (PPI) network was constructed to screen core targets. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using Database for Annotation Visualization and Integrated Discovery (DAVID). Interactions between potential targets and active compounds were assessed using molecular docking. Molecular docking was performed on the best core protein complexes obtained using molecular docking. Results A total of 89 and 108 active ingredients and targets, respectively, were identified. Seven core targets were obtained using a topological analysis of the PPI network. The GO and KEGG pathway enrichment analyses showed that the effects of the Huanglian Huazhuo capsules were mediated by inflammation, lipid response, oxidative stress-related genes, and HIF-1 and IL-17 signaling pathways. Good binding ability was observed between the active compounds and screened targets using molecular docking. Conclusions The active ingredients, potential targets, and pathways of the Huanglian Huazhuo capsule for the treatment of obese T2DM were successfully predicted, providing a new strategy for further investigation of its molecular mechanisms. In addition, the potential active ingredients provide a reliable source for drug screening in obese T2DM.

Mechanism of Shishiwei Wendan Decoction in the Prevention and Treatment of Lung Adenocarcinoma Using Network Pharmacology and Molecular Docking.

Objective This study used network pharmacology and molecular docking technology to elucidate the mechanism of action of Shishiwei Wendan Decoction against lung adenocarcinoma. Methods By using the world's largest TCM System Pharmacology Database and Analysis Technology Platform (TCMSP) system to conduct in-depth mining analysis and data collection of the main active components of the medicinal components in Shishiwei Wendan Decoction and using the human gene card database (GeneCards), Human Mendelian Inheritance Online System (OMIM), and Human Disease-Related Gene and Mutation Information Database (DisGeNET) to collect the pathogenic targets of lung adenocarcinoma and build a PPI network; for the core drug targets, use GO enrichment analysis and KEGG pathway analysis; use Cytoscape software to build relevant network maps; and use AutoDock to achieve molecular docking. Results Shishiwei Wendan Decoction screened 144 active ingredients and 384 drug targets; 7680 lung adenocarcinoma disease targets were obtained, including 380 targets for Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma. GO enrichment analysis demonstrated 2,299 downstream genes, and key target genes were closely related to nutrient levels, membrane rafts, and protein serine/threonine kinase activity; KEGG functional enrichment analysis yielded 179 related pathways, including tumor necrosis factor signaling pathway which is related to the target gene. Molecular docking showed that the core active ingredients and key targets could be well combined. Conclusion Through the network pharmacology analysis and molecular docking experiments of Shishiwei Wendan Decoction against lung adenocarcinoma, it is found that Shishiwei Wendan Decoction has multidimensional effects on the treatment of lung adenocarcinoma, and it is the first Shiwei Wendan Decoction to treat lung adenocarcinoma. Decoction in the treatment of lung adenocarcinoma provides biointellectual support and the oretical support.

Network pharmacology-based study on apigenin present in the methanolic fraction of leaves extract of Cestrum nocturnum L. to uncover mechanism of action on hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth most common type of cancer in the world. It is the third leading cause of cancer-related mortality. In more than 80% of people liver cancer-related death is due to its poor prognosis. The flavonoids obtained from natural sources have potent therapeutic effects on HCC. The flavonoid rich methanolic fraction obtained from ethyl acetate extract of leaf of Cestrum nocturnum (MFLCN) was analyzed by UPLC-QTOFMS/MS for the presence of different flavonoids. The physiochemical and pharmacokinetics properties of the identified flavonoids were performed by absorption, distribution, metabolism, excretion, and toxicity (ADMET). It was selected on the basis of Lipinski rule and hepatotoxicity evaluations. The potential gene dataset of HCC were taken from gene card database and targets compounds were selected from target net prediction. Gene ontology and pathway enrichment analysis of HCC was performed via enricher and David web tools. Cytoscape was used to visualize targets and network pathways. MFLCN contains 33 flavonoids. Among these flavonoids, apigenin was selected as principal active compound on the basis of their pharmacokinetic and ADMET properties. Apigenin has 92 targets out of 627 total targets related to HCC, while there was13 pathways in the target-pathway network. Results revealed that apigenin regulates cell proliferation and survival, primarily through different signaling pathways like estrogen, VEGF, PI3K/AKT1, TNF, FoXO, and Ras signaling pathways. Thus, integrating network pharmacology prediction with m-RNA and human protein atlas validation could be an effective method for understanding the molecular mechanism of apigenin on HCC.

Phenotypic and genomic characterization for Salmonella isolates recovered from meat products in Beijing wholesale markets, 2014-2015

Objective: To analyze the phenotypic and genomic characteristics of Salmonella isolates recovered from meat products in Beijing wholesale markets. Methods: A total of 336 Salmonella strains from meat products collected from wholesale markets in Beijing were tested for antimicrobial resistance to 25 antimicrobial compounds by micro-broth dilution method; whole genome data were sequenced, followed by the serotype and ST type prediction by Seqsero2 and SISTR software, and the drug resistance genes and virulence factors were also predicted with CARD and VFDB databases of Abricate software; Salmonella serotyping assay kit and serum agglutination method were used for serotype confirmation of some isolates with different genome prediction results. Results: The resistance rates to Nalidixic acid and Ampicillin were 62.5% (210/336) and 55.1% (185/336), respectively, and all isolates were susceptible to Tigecyclin, Cefoxitin and Carbapenem antimicrobial compounds; 207 isolates (61.6%, 207/336) were multi-drug resistant, some could even be resistant to ten categories of drugs at the same time, and the most common antimicrobial resistance spectrum was NAL-AMP-SAM. A total of 24 serotypes were detected with predominant serotypes of Enteritidis (34.5%, 116/336), Derby (17.3%, 58/336) and Indiana (10.4%, 35/336). A total of 27 ST types were detected, the dominant type was ST11; ST types were in good consistency with serotypes; The detection rates of resistant genes referred to aminoglycosides, fluoroquinolones, β-lactams, sulfonamides and tetracyclines are more than 48%, and the first two reached 100%. The prediction of drug resistance genes was consistent with the results of antimicrobial resistance phenotype. A total of 122 virulence genes were predicted, 74 of which existing among all isolates. Conclusion: Salmonella in meat from the wholesale markets of Beijing has a high proportion of multiple drug resistance, a complex drug resistance spectrum, a variety of serotypes and ST types, and a high carrying rate of drug resistance gene and virulence gene; drug resistance phenotype and genotype are relatively consistent.

DiGiVoter: An Online Voting System

Abstract: To design a system 1. Allows the voters to login with aadhaar no, which is then matched with an already saved within a database that is retrieved from aadhaar card database of the government. 2.The voting system is managed in a simpler way as all the users must login by aadhaar card number and One Time password and click on his/her favourable candidates to cast the vote. 3. Increase the voting percentage in India and reduces the cost of voting process. By using unique identification no it provides enough security which reduces the false votes. Keywords: Online voting system, Face recognition, OTP

Comparative Genomics of Lactiplantibacillus plantarum: Insights Into Probiotic Markers in Strains Isolated From the Human Gastrointestinal Tract and Fermented Foods.

Lactiplantibacillus (Lpb.) plantarum is a versatile species commonly found in a wide variety of ecological niches including dairy products and vegetables, while it may also occur as a natural inhabitant of the human gastrointestinal tract. Although Lpb. plantarum strains have been suggested to exert beneficial properties on their host, the precise mechanisms underlying these microbe–host interactions are still obscure. In this context, the genome-scale in silico analysis of putative probiotic bacteria represents a bottom–up approach to identify probiotic biomarkers, predict desirable functional properties, and identify potentially detrimental antibiotic resistance genes. In this study, we characterized the bacterial genomes of three Lpb. plantarum strains isolated from three distinct environments [strain IMC513 (from the human GIT), C904 (from table olives), and LT52 (from raw-milk cheese)]. A whole-genome sequencing was performed combining Illumina short reads with Oxford Nanopore long reads. The phylogenomic analyses suggested the highest relatedness between IMC513 and C904 strains which were both clade 4 strains, with LT52 positioned within clade 5 within the Lpb. plantarum species. The comparative genome analysis performed across several Lpb. plantarum representatives highlighted the genes involved in the key metabolic pathways as well as those encoding potential probiotic features in these new isolates. In particular, our strains varied significantly in genes encoding exopolysaccharide biosynthesis and in contrast to strains IMC513 and C904, the LT52 strain does not encode a Mannose-binding adhesion protein. The LT52 strain is also deficient in genes encoding complete pentose phosphate and the Embden–Meyerhof pathways. Finally, analyses using the CARD and ResFinder databases revealed that none of the strains encode known antibiotic resistance loci. Ultimately, the results provide better insights into the probiotic potential and safety of these three strains and indicate avenues for further mechanistic studies using these isolates.

Metagenomics Reveals the Diversity and Taxonomy of Carbohydrate-Active Enzymes and Antibiotic Resistance Genes in Suancai Bacterial Communities.

Suancai, as a traditional fermented food in China with reputed health benefits, has piqued global attention for many years. In some circumstances, the microbial-driven fermentation may confer health (e.g., probiotics) or harm (e.g., antibiotic resistance genes) to the consumers. To better utilize beneficial traits, a deeper comprehension of the composition and functionality of the bacterial species harboring enzymes of catalytically active is required. On the other hand, ingestion of fermented food increases the likelihood of microbial antibiotic resistance genes (ARGs) spreading in the human gastrointestinal tract. Besides, the diversity and taxonomic origin of ARGs in suancai are little known. In our study, a metagenomic approach was employed to investigate distribution structures of CAZymes and ARGs in main bacterial species in suancai. Functional annotation using the CAZy database identified a total of 8796 CAZymes in metagenomic data. A total of 83 ARGs were detected against the CARD database. The most predominant ARG category is multidrug-resistant genes. The ARGs of antibiotic efflux mechanism are mostly in Proteobacteria. The resistance mechanism of ARGs in Firmicutes is primarily antibiotic inactivation, followed by antibiotic efflux. Due to the abundance of species with different ARGs, strict quality control including microbial species, particularly those with lots of ARGs, is vital for decreasing the risk of ARG absorption via consumption. Ultimately, we significantly widen the understanding of suancai microbiomes by using metagenomic sequencing to offer comprehensive information on the microbial functional potential (including CAZymes and ARGs content) of household suancai.

Genomic analysis of virulence factors and antimicrobial resistance of group B Streptococcus isolated from pregnant women in northeastern Mexico.

Group B Streptococcus (GBS) causes infections in women during pregnancy and puerperium and invasive infections in newborns. The genes lmb, cylE, scpB, and hvgA are involved with increased virulence of GBS, and hypervirulent clones have been identified in different regions. In addition, increasing resistance of GBS to macrolides and lincosamides has been reported, so knowing the patterns of antibiotic resistance may be necessary to prevent and treat GBS infections. This study aimed to identify virulence genes and antibiotic resistance associated with GBS colonization in pregnant women from northeastern Mexico. Pregnant women with 35-37 weeks of gestation underwent recto-vaginal swabbing. One swab was inoculated into Todd-Hewitt broth supplemented with gentamicin and nalidixic acid, a second swab was inoculated into LIM enrichment broth, and a third swab was submerged into a transport medium. All samples were subcultured onto blood agar. After overnight incubation, suggestive colonies with or without hemolysis were analyzed to confirm GBS identification by Gram staining, catalase test, hippurate hydrolysis, CAMP test, and incubation in a chromogenic medium. We used latex agglutination to confirm and serotype GBS isolates. Antibiotic resistance patterns were assessed by Vitek 2 and disk diffusion. Periumbilical, rectal and nasopharyngeal swabs were collected from some newborns of colonized mothers. All colonized women and their newborns were followed up for three months to assess the development of disease attributable to GBS. Draft genomes of all GBS isolates were obtained by whole-genome sequencing. In addition, bioinformatic analysis to identify genes encoding capsular polysaccharides and virulence factors was performed using BRIG, while antibiotic resistance genes were identified using the CARD database. We found 17 GBS colonized women out of 1154 pregnant women (1.47%). None of the six newborns sampled were colonized, and no complications due to GBS were detected in pregnant women or newborns. Three isolates were serotype I, 5 serotype II, 3 serotype III, 4 serotype IV, and 2 serotype V. Ten distinct virulence gene profiles were identified, being scpB, lmb, fbsA, acp, PI-1, PI-2a, cylE the most common (3/14, 21%). The virulence genes identified were scpB, lmb, cylE, PI-1, fbsA, PI-2a, acp, fbsB, PI-2b, and hvgA. We identified resistance to tetracycline in 65% (11/17) of the isolates, intermediate susceptibility to clindamycin in 41% (7/17), and reduced susceptibility to ampicillin in 23.5% (4/17). The tetM gene associated to tetracyclines resistance was found in 79% (11/14) and the mel and mefA genes associated to macrolides resistance in 7% (1/14). The low prevalence of colonization and the non-occurrence of mother-to-child transmission suggest that the intentional search for GBS colonization in this population is not justified. Our results also suggest that risk factors should guide the use of intrapartum antibiotic prophylaxis. The detection of strains with genes coding virulence factors means that clones with pathogenic potential circulates in this region. On the other hand, the identification of decreased susceptibility to antibiotics from different antimicrobial categories shows the importance of adequately knowing the resistance patterns to prevent and to treat GBS perinatal infection.

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor (81.1 to 93.8%, 93.7 to 96.2%, 93.1 to 94.8%, 95.9 to 97.2% and 97.1 to 98.2% for ethambutol, rifampicin, pyrazinamide, isoniazid and ofloxacin respectively). We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.

A Comparative Safety Analysis of Medicines Based on the UK Pharmacovigilance and General Practice Prescribing Data in England.

Adverse drug reactions (ADRs) represent a major concern leading to significant increases in both morbidity and mortality globally. Providing healthcare professionals (HCPs) and patients with real-world data on drug safety is imperative to facilitate informed decision-making. The study aimed to determine the feasibility of creating comparative safety charts for medicines by mapping ADR reporting onto prescribing data. Data on serious and fatal ADR reports from the Yellow Card database was mapped onto general practice prescription data in England. The rate of serious and fatal ADR reports per million items prescribed was calculated for commonly-prescribed medicines. Quantitative comparative analyses for 137 medicines belonging to 26 therapeutic classes were conducted. Significant differences were observed within most therapeutic classes for the rate of serious and fatal ADR reports per prescribing unit. Despite the limitations of ADR reporting and prescribing databases, the study provides a proof-of-concept for the feasibility of mapping ADR reporting onto prescribing data to create comparative safety charts that could support evidence-based decision-making around formulary choices.

Consumo de antibióticos en atención primaria en población adulta de Asturias durante el periodo 2014-2020

Trend study of the consumption of systemic antibiotics in the adult population in of Primary Care of the Health Service of the Principality of Asturias (SESPA) during the period 2014̶2020. Retrospective observational study. SESPA, Primary Care. Population from the Individual Health Card database. Data were collected on the prescription of antibiotics, carried out in the family medicine consultations, dispensed in the pharmacy offices with charge of SESPA. Antibiotic use and consumption variables were analyzed using linear regression models. Prevalence of antibiotic use (population percentage); consumption rate of systemic antibiotics (DTD), relative consumption of narrow-spectrum antibiotics (percentage DDD). The average prevalence of the use of antibiotics for the 2014̶2019 period was 32.2% and 23.9% in 2020. The rate of consumption of systemic antibiotics decreased from 21.4 DTD in 2014 to 12.7 DTD in 2020. The consumption of narrow-spectrum antibiotics remained stable (19.4% DDD in 2014 and 19.3% DDD in 2020) (CI95: −0.10, 0.26). In the period from March to December 2020, the consumption of antibiotics decreased by 28.6% compared to the same period in 2019. In 2014̶2020, the consumption of antibiotics decreased, especially since the COVID-19 pandemic, with stabilization of the consumption of narrow-spectrum antibiotics compared to the total. There is variability in consumption by therapeutic subgroups.

Microbiome Compositions and Resistome Levels after Antibiotic Treatment of Critically Ill Patients: An Observational Cohort Study.

Hospitalization and treatment with antibiotics increase the risk of acquiring multidrug-resistant bacteria due to antibiotic-mediated changes in patient microbiota. This study aimed to investigate how broad- and narrow-spectrum antibiotics affect the gut microbiome and the resistome in antibiotic naïve patients during neurointensive care. Patients admitted to the neurointensive care unit were treated with broad-spectrum (meropenem or piperacillin/tazobactam) or narrow-spectrum antibiotic treatment (including ciprofloxacin, cefuroxime, vancomycin and dicloxacillin) according to clinical indications. A rectal swab was collected from each patient before and after 5–7 days of antibiotic therapy (N = 34), respectively. Shotgun metagenomic sequencing was performed and the composition of metagenomic species (MGS) was determined. The resistome was characterized with CARD RGI software and the CARD database. As a measure for selection pressure in the patient, we used the sum of the number of days with each antibiotic (antibiotic days). We observed a significant increase in richness and a tendency for an increase in the Shannon index after narrow-spectrum treatment. For broad-spectrum treatment the effect was more diverse, with some patients increasing and some decreasing in richness and Shannon index. This was studied further by comparison of patients who had gained or lost >10 MGS, respectively. Selection pressure was significantly higher in patients with decreased richness and a decreased Shannon index who received the broad treatment. A decrease in MGS richness was significantly correlated to the number of drugs administered and the selection pressure in the patient. Bray–Curtis dissimilarities were significant between the pre- and post-treatment of samples in the narrow group, indicating that the longer the narrow-spectrum treatment, the higher the differences between the pre- and the post-treatment microbial composition. We did not find significant differences between pre- and post-treatment for both antibiotic spectrum treatments; however, we observed that most of the antibiotic class resistance genes were higher in abundance in post-treatment after broad-spectrum treatment.

Mechanism research of Salvia miltiorrhiza on treating myocardial ischemia reperfusion injury according to network pharmacology combined with molecular docking technique.

Myocardial ischemia reperfusion injury (MIRI) is a kind of complicated disease with an increasing incidence all over the world. Danshen was shown to exert therapeutic effect on MIRI. However, its chemical and pharmacological profiles remain to be elucidated. Network pharmacology was applied to characterize the mechanisms of Danshen on MIRI.The active compounds were screened from the online database according to their oral bioavailability and drug-likeness. The potential proteins of Danshen were collected from the TCMSP database, whereas the potential genes of MIRI were obtained from Gene Card database. The function of gene and pathways involved were researched by GO and KEGG enrichment analysis. The compounds-targets and protein–protein interaction networks were constructed by Cytoscape software. The affinity between active components and potential targets was detected by molecular docking simulation.A total of 202 compounds in Danshen were obtained, and 65 were further selected as active components for which conforming to criteria. Combined the network analysis and molecular docking simulation, the results firstly demonstrated that the effect of Danshen on MIRI may be realized through the targeting of vascular endothelial growth factor A, interleukin-6, and AKT1 by its active components tanshinone IIA, cryptotanshinone, and luteolin. The main regulatory pathways involved may include PI3K/ Akt signaling pathway, HIF-1 signaling pathway, and interleukin-17 signaling pathway. The present study firstly researched the mechanism of Danshen on MIRI based on network pharmacology.The results revealed the multicomponents and multi-targets effects of Danshen in the treatment of MIRI. Importantly, the study provides objective basis for further experimental research.

Colistin resistance in Gram-negative bacteria analysed by five phenotypic assays and inference of the underlying genomic mechanisms

BackgroundColistin is used against multi-drug resistant pathogens, yet resistance emerges through dissemination of plasmid-mediated genes (mcr) or chromosomal mutation of genes involved in lipopolysaccharide synthesis (i.e. mgrB, phoPQ, pmrCAB). Phenotypic susceptibility testing is challenging due to poor diffusion of colistin in agar media, leading to an underestimation of resistance. Performance of five phenotypic approaches was compared in the context of different molecular mechanisms of resistance. We evaluated Vitek 2® (bioMérieux, AST N242), Colistin MIC Test Strip (Liofilchem Diagnostici), UMIC (Biocentric), and Rapid Polymyxin™ NP test (ELITechGroup) against the standard broth microdilution (BMD) method. We used whole genome sequencing (WGS) to infer molecular resistance mechanisms. We analysed 97 Enterobacterales and non-fermenting bacterial isolates, largely clinical isolates collected up to 2018. Data was analysed by comparing susceptibility categories (susceptible or resistant) and minimal inhibitory concentrations (MIC). Susceptibility category concordance is the percentage of test results sharing the same category to BMD. MIC concordance was calculated similarly but considering ±1 MIC titre error range. We determined genomic diversity by core genome multi locus sequencing typing (cgMLST) and identified putative antimicrobial resistance genes using NCBI and CARD databases, and manual annotation.ResultsOf 97 isolates, 54 (56%) were resistant with standard BMD. Highest susceptibility category concordance was achieved by Rapid Polymyxin™ NP (98.8%) followed by UMIC (97.9%), Colistin E-test MIC strip (96.9%) and Vitek 2® (95.6%). Highest MIC concordance was achieved by UMIC (80.4%), followed by Vitek 2® (72.5%) and Colistin E-test MIC strip (62.9%). Among resistant isolates, 23/54 (43%) were intrinsically resistant to colistin, whereas 31/54 (57%) isolates had acquired colistin resistance. Of these, mcr-1 was detected in four isolates and mcr-2 in one isolate. Non-synonymous mutations in mgrB, phoQ, pmrA, pmrB, and pmrC genes were encountered in Klebsiella pneumoniae, Escherichia coli, and Acinetobacter bereziniae resistant isolates. Mutations found in mgrB and pmrB were only identified in isolates exhibiting MICs of ≥16 mg/L.ConclusionsThe Rapid Polymyxin™ NP test showed highest categorical concordance and the UMIC test provided MIC values with high concordance to BMD. We found colistin resistance in diverse species occurred predominantly through spontaneous chromosomal mutation rather than plasmid-mediated resistance.

Shotgun whole genome sequencing of drug-resistance Streptococcus anginosus strain 47S1 isolated from a patient with pharyngitis in Saudi Arabia

BackgroundStreptococcus anginosus is an emergence opportunistic pathogen that colonize the human upper respiratory tract (URT), S. anginosus alongside with S. intermedius and S. constellatus, members of S. anginosus group, are implicated in several human infections. However, our understanding this bacterium to the genotype level with determining the genes associated with pathogenicity and antimicrobial resistance (AMR) is scarce. S. anginosus 47S1 strain was isolated from sore throat infection, the whole genome was characterized and the virulence & AMR genes contributing in pathogenicity were investigated. MethodologyThe whole genome of 47S1 was sequenced by Illumina sequencing technology. Strain 47S1 genome was de novo assembled with different strategies and annotated via PGAP, PROKKA and RAST pipelines. Identifying the CRISPR-Cass system and prophages sequences was performed using CRISPRloci and PhiSpy tools respectively. Prediction the virulence genes were performed with the VFDB database. AMR genes were detected in silico using NCBI AMRFinderPlus pipeline and CARD database and compared with in vitro AST findings. Resultsβ-hemolytic strain 47S1 was identified with conventional microbiology techniques and confirmed by the sequences of 16S rRNA gene. Genome of 47S1 comprised of 1981512 bp. Type I-C CRISPR-Cas system and 4 prophages were detected among the genome of 47S1. Several virulence genes were predicted, most of these genes are found in other pathogenic streptococci, mainly lmb, pavA, htrA/degP, eno, sagA, psaA and cpsI which play a significant role in colonizing, invading host tissues and evade form immune system. In silico AMR findings showed that 47S1 gnome harbors (tetA, tetB &tet32), (aac(6′)-I, aadK &aph(3′)-IVa), fusC, and PmrA genes that mediated-resistance to tetracyclines, aminoglycosides, fusidic acid, and fluoroquinolone respectively which corresponds with in vitro AST obtained results. In conclusion, WGS is a key approach to predict the virulence and AMR genes, results obtained in this study may contribute for a better understanding of the opportunistic S. anginosus pathogenicity.

Vascular age in a sample of general population of the sanitary area of Toledo (Spain). RICARTO study

ObjectiveTo know the vascular age (VA) of a sample of general population included in the RICARTO study. Patients and methodEpidemiological study of the general population aged ≥ 18 from the Health Area of Toledo, based on the health card database. VA was calculated from the absolute cardiovascular risk (CVR) estimated with the Framingham and SCORE equations (type 2 diabetes increased CVR in SCORE 2-fold in men and 4-fold in women). Patients with cardiovascular or renal disease were excluded. An ANCOVA analysis was conducted to adjust and compare the mean of VA by age and sex. Results1,496 subjects (53.54% women) were analyzed. Mean (SD) age was 48.77 (14.89) years old and. Mean VA was 51.37 (19.13) with Framingham equation and 57.09 (17.63) years old with SCORE equation. VA was significantly higher in men, low education level, arterial hypertension, dyslipidemia, hypertriglyceridemia, diabetes mellitus, abdominal obesity, general obesity, smoking and in individuals with 5 CVR factors vs none (P < .001 in all). Higher differences (Cohen’s D > 0.5) were found in non-diabetic vs diabetic people (1.58 Framingham; 2.44 SCORE), normotensive vs hypertensive subjects (1.64 Framingham; 1.19 SCORE), and non-dyslipidemia vs presence of dyslipidemia (0.95 Framingham; 0.66 SCORE). ConclusionsVA of our sample is two and a half years older than chronological one with Framingham equation and more than eight years with SCORE equation. Control of CVR factors is the key to get a VA closer to real and to obtain a better cardiovascular health in the population.