Personality and psychological capital as indicators of future job success? : A multicultural comparison between three European countries

The main goal of this research was to study psychological capital and its relationship with personality across cultures. This was achieved by comparing the main variables across three distinct cultural settings: Eastern Europe (Bulgaria), Nordic Europe (Finland), and South Europe (Portugal). Altogether 231 people answered the questionnaires. Results indicated that personality and psychological capital were connected. In particular, Extraverted (p < 0.01), iNtuitive (p < 0.01) and Thinking people (p < 0.01) revealed higher scores in all psychological capital dimensions than their counterparts: Introverted, Sensing and Feeling people. There were also significant differences concerning the level of psychological capital in different countries. The Portuguese sample scored highest in all the dimensions of psychological capital, whereas Finnish indicated the lowest scores of the three countries. When all variables are taken together, results show that the highest psychological capital scores are observed in the “Portuguese perceiving” group; the lowest psychological capital scores are found in the “Finnish introverted” group. Bulgarians did not differ significantly in their scores. These results illustrate important and previously unidentified relationships between psychological capital and personality in distinct cultures. All together, and from a theoretical standpoint, the findings point to the need to explore the effect of culture on psychological capital; the relationships between personality and psychological capital also need further exploration. There are also practical implications, which are discussed at the end of the text. The fact that the questionnaires were collected from students in distinct scientific areas in the three countries may represent a drawback. Studies of psychological capital are very recent. After a first phase of instrument development, the next step is to build knowledge regarding the relationships between psychological capital and other well-established individual, social and organizational constructs. The current research aimed at contributing to this stream of works.info:eu-repo/semantics/publishedVersio

Investigating the impact of poverty on colonization and infection with drug-resistant organisms in humans: a systematic review

Background Poverty increases the risk of contracting infectious diseases and therefore exposure to antibiotics. Yet there is lacking evidence on the relationship between income and non-income dimensions of poverty and antimicrobial resistance. Investigating such relationship would strengthen antimicrobial stewardship interventions. Methods A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Ovid, MEDLINE, EMBASE, Scopus, CINAHL, PsychINFO, EBSCO, HMIC, and Web of Science databases were searched in October 2016. Prospective and retrospective studies reporting on income or non-income dimensions of poverty and their influence on colonisation or infection with antimicrobial-resistant organisms were retrieved. Study quality was assessed with the Integrated quality criteria for review of multiple study designs (ICROMS) tool. Results Nineteen articles were reviewed. Crowding and homelessness were associated with antimicrobial resistance in community and hospital patients. In high-income countries, low income was associated with Streptococcus pneumoniae and Acinetobacter baumannii resistance and a seven-fold higher infection rate. In low-income countries the findings on this relation were contradictory. Lack of education was linked to resistant S. pneumoniae and Escherichia coli. Two papers explored the relation between water and sanitation and antimicrobial resistance in low-income settings. Conclusions Despite methodological limitations, the results suggest that addressing social determinants of poverty worldwide remains a crucial yet neglected step towards preventing antimicrobial resistance

Matching Schur complement approximations for certain saddle-point systems

The solution of many practical problems described by mathematical models requires approximation methods that give rise to linear(ized) systems of equations, solving which will determine the desired approximation. This short contribution describes a particularly effective solution approach for a certain class of so-called saddle-point linear systems which arises in different contexts

Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013–2022: Data from the European Registry on H. pylori Management (Hp-EuReg)

The segmentation of patients into homogeneous groups could help to improve eradication therapy effectiveness. Our aim was to determine the most important treatment strategies used in Europe, to evaluate first-line treatment effectiveness according to year and country. Data collection: All first-line empirical treatments registered at AEGREDCap in the European Registry on Helicobacter pylori management (Hp-EuReg) from June 2013 to November 2022. A Boruta method determined the “most important” variables related to treatment effectiveness. Data clustering was performed through multi-correspondence analysis of the resulting six most important variables for every year in the 2013–2022 period. Based on 35,852 patients, the average overall treatment effectiveness increased from 87% in 2013 to 93% in 2022. The lowest effectiveness (80%) was obtained in 2016 in cluster #3 encompassing Slovenia, Lithuania, Latvia, and Russia, treated with 7-day triple therapy with amoxicillin–clarithromycin (92% of cases). The highest effectiveness (95%) was achieved in 2022, mostly in Spain (81%), with the bismuth–quadruple therapy, including the single-capsule (64%) and the concomitant treatment with clarithromycin–amoxicillin–metronidazole/tinidazole (34%) with 10 (69%) and 14 (32%) days. Cluster analysis allowed for the identification of patients in homogeneous treatment groups assessing the effectiveness of different first-line treatments depending on therapy scheme, adherence, country, and prescription year

European Registry on Helicobacter pylori management (Hp-EuReg): Patterns and trends in first-line empirical eradication prescription and outcomes of 5 years and 21 533 patients

Objective The best approach for Helicobacter pylori management remains unclear. An audit process is essential to ensure clinical practice is aligned with best standards of care. Design International multicentre prospective non-interventional registry starting in 2013 aimed to evaluate the decisions and outcomes in H. pylori management by European gastroenterologists. Patients were registered in an e-CRF by AEG-REDCap. Variables included demographics, previous eradication attempts, prescribed treatment, adverse events and outcomes. Data monitoring was performed to ensure data quality. Time-trend and geographical analyses were performed. Results 30 394 patients from 27 European countries were evaluated and 21 533 (78%) first-line empirical H. pylori treatments were included for analysis. Pretreatment resistance rates were 23% to clarithromycin, 32% to metronidazole and 13% to both. Triple therapy with amoxicillin and clarithromycin was most commonly prescribed (39%), achieving 81.5% modified intention-to-treat eradication rate. Over 90% eradication was obtained only with 10-day bismuth quadruple or 14-day concomitant treatments. Longer treatment duration, higher acid inhibition and compliance were associated with higher eradication rates. Time-trend analysis showed a region-dependent shift in prescriptions including abandoning triple therapies, using higher acid-inhibition and longer treatments, which was associated with an overall effectiveness increase (84%-90%). Conclusion Management of H. pylori infection by European gastroenterologists is heterogeneous, suboptimal and discrepant with current recommendations. Only quadruple therapies lasting at least 10 days are able to achieve over 90% eradication rates. European recommendations are being slowly and heterogeneously incorporated into routine clinical practice, which was associated with a corresponding increase in effectiveness

Analysis of antimicrobial susceptibility and virulence factors in Helicobacter pylori clinical isolates

BACKGROUND: In this study, we evaluated the prevalence of primary resistance of Brazilian H. pylori isolates to metronidazole, clarithromycin, amoxicillin, tetracycline, and furazolidone. In addition, the vacA, iceA, cagA and cagE genotypes of strains isolated from Brazilian patients were determined and associated with clinical data in an effort to correlate these four virulence markers and antibiotic resistance. METHODS: H. pylori was cultured in 155 H. pylori-positive patients and MICs for metronidazole, clarithromycin, amoxicillin, tetracycline, and furazolidone were determined by the agar dilution method. Genomic DNA was extracted, and allelic variants of vacA, iceA, cagA and cagE were identified by the polymerase chain reaction. RESULTS: There was a strong association between the vacA s1/cagA -positive genotype and peptic ulcer disease (OR = 5.42, 95% CI 2.6–11.3, p = 0.0006). Additionally, infection by more virulent strains may protect against GERD, since logistic regression showed a negative association between the more virulent strain, vacA s1/cagA-positive genotype and GERD (OR = 0.26, 95% CI 0.08–0.8, p = 0.03). Resistance to metronidazole was detected in 75 patients (55%), to amoxicillin in 54 individuals (38%), to clarithromycin in 23 patients (16%), to tetracycline in 13 patients (9%), and to furazolidone in 19 individuals (13%). No significant correlation between pathogenicity and resistance or susceptibility was detected when MIC values for each antibiotic were compared with different vacA, iceA, cagA and cagE genotypes. CONCLUSION: The analysis of virulence genes revealed a specific association between H. pylori strains and clinical outcome, furthermore, no significant association was detected among pathogenicity and resistance or susceptibility

Systems biology of platelet-vessel wall interactions

Platelets are small, anucleated cells that participate in primary hemostasis by forming a hemostatic plug at the site of a blood vessel's breach, preventing blood loss. However, hemostatic events can lead to excessive thrombosis, resulting in life-threatening strokes, emboli, or infarction. Development of multi-scale models coupling processes at several scales and running predictive model simulations on powerful computer clusters can help interdisciplinary groups of researchers to suggest and test new patient-specific treatment strategies

Functional module search in protein networks based on semantic similarity improves the analydsis of proteomics data

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc