Znaczenie badania krwi pobranej z żył tarczowych górnych i dolnych w rozpoznawaniu raka tarczycy

Introduction: We aimed to determine whether levels of thyroglobulin measured in blood from the inferior-superior thyroid veins and the peripheral antecubital vein could predict the presence of thyroid carcinoma in patients undergoing surgery for thyroid diseases. Material and methods: Sixty-one patients were prospectively enrolled in the study. Levels of thyroglobulin were analysed. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) of these markers were investigated. Results: Twenty-six out of 61 patients (42.6%) with malignancy were diagnosed. The levels of thyroglobulin in the inferior-superior thyroid veins were higher than those in the peripheral antecubital vein (p = 0.001). The levels of thyroglobulin in the blood taken from the antecubital vein and the inferior-superior thyroid veins did not differ between benign and malignant thyroid disorders. For thyroglobulin, sensitivity was 33.3%, specificity 60.6%, PPV 27.8%, and NPV 66.7% respectively. Conclusion: Thyroglobulin levels in the antecubital vein compared to the inferior-superior thyroid veins were not significant either in benign or malignant disorders.Wstęp: Celem badania było ustalenie, czy oznaczenie stężeń tyreoglobuliny we krwi pobranej z żył tarczowych górnych i dolnych oraz z żyły przedłokciowej ma wartość prognostyczną w wykrywaniu nowotworów tarczycy u chorych poddawanych zabiegom chirurgicznym z powodu chorób tarczycy. Materiał i metody: Do tego prospektywnego badania włączono 61 chorych. Analizowano wartości stężeń tyreoglobuliny. Badano czułość, swoistość oraz wartość prognostyczną dodatnią (PPV) i ujemną (NPV) tych wskaźników. Wyniki: U 26 spośród 61 chorych (42,6%) rozpoznano chorobę nowotworową. Stężenia tyreoglobuliny w żyłach tarczowych dolnych i górnych były wyższe niż stężenia mierzone we krwi pobranej z żyły przedłokciowej (p = 0,001). Stężenia tyreoglobuliny w próbkach krwi pobranych z żyły przedłokciowej i żył tarczowych nie różniły się istotnie między osobami z łagodnymi i złośliwymi guzami tarczycy. Oznaczenie tyreoglobuliny cechowało się czułością wynoszącą 33,3%, swoistością równą 60,6%, PPV — 27,8% i NPV — 66,7%. Wnioski: Porównanie stężeń tyreoglobuliny w żyle przedłokciowej i w żyłach tarczowych górnych i dolnych nie miało istotnego znaczenia diagnostycznego w wykrywaniu zarówno zmian łagodnych, jak i złośliwych

Money, Banks, and Savings: A Comparative Analysis of Turkish Laypeople's Social Representations over Five Periods (1999-2017)

The way and context-specific scope of how money and banks are mirrored in citizens’ minds is an expanding area of research in relation to economic psychology. Through the analysis of data collected from salaried employees, self-employed professionals, and small/medium enterprise [SME] owners, lay people’s social representations for money, saving and banks in Turkey are comparatively investigated and analysed over time. Grounded in a previous study by authors (undisclosed), with respective samples from the years of 1999, 2001, 2007, 2015, a new fifth dataset for 2017 is introduced here. Changing priorities, understanding and cognitive constructs related to money, banks and savings were particularly analysed to be substantially interconnected with the evolving socioeconomic dynamics and conditions of the crisis periods. Negative evocations and lack of trust became prominent as to the findings such that banks are seen as contemporary pawnshops; money is mirrored as a symbol of power, prestige and reputation, and cannot be achieved through hard work

Canonical Wnt signaling is antagonized by noncanonical Wnt5a in hepatocellular carcinoma cells

<p>Abstract</p> <p>Background</p> <p>β-catenin mutations that constitutively activate the canonical Wnt signaling have been observed in a subset of hepatocellular carcinomas (HCCs). These mutations are associated with chromosomal stability, low histological grade, low tumor invasion and better patient survival. We hypothesized that canonical Wnt signaling is selectively activated in well-differentiated, but repressed in poorly differentiated HCCs. To this aim, we characterized differentiation status of HCC cell lines and compared their expression status of Wnt pathway genes, and explored their activity of canonical Wnt signaling.</p> <p>Results</p> <p>We classified human HCC cell lines into "well-differentiated" and "poorly differentiated" subtypes, based on the expression of hepatocyte lineage, epithelial and mesenchymal markers. Poorly differentiated cell lines lost epithelial and hepatocyte lineage markers, and overexpressed mesenchymal markers. Also, they were highly motile and invasive. We compared the expression of 45 Wnt pathway genes between two subtypes. TCF1 and TCF4 factors, and LRP5 and LRP6 co-receptors were ubiquitously expressed. Likewise, six Frizzled receptors, and canonical Wnt3 ligand were expressed in both subtypes. In contrast, canonical ligand Wnt8b and noncanonical ligands Wnt4, Wnt5a, Wnt5b and Wnt7b were expressed selectively in well- and poorly differentiated cell lines, respectively. Canonical Wnt signaling activity, as tested by a TCF reporter assay was detected in 80% of well-differentiated, contrary to 14% of poorly differentiated cell lines. TCF activity generated by ectopic mutant β-catenin was weak in poorly differentiated SNU449 cell line, suggesting a repressive mechanism. We tested Wnt5a as a candidate antagonist. It strongly inhibited canonical Wnt signaling that is activated by mutant β-catenin in HCC cell lines.</p> <p>Conclusion</p> <p>Differential expression of Wnt ligands in HCC cells is associated with selective activation of canonical Wnt signaling in well-differentiated, and its repression in poorly differentiated cell lines. One potential mechanism of repression involved Wnt5a, acting as an antagonist of canonical Wnt signaling. Our observations support the hypothesis that Wnt pathway is selectively activated or repressed depending on differentiation status of HCC cells. We propose that canonical and noncanonical Wnt pathways have complementary roles in HCC, where the canonical signaling contributes to tumor initiation, and noncanonical signaling to tumor progression.</p

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

MR2: A Two-stage Feature Selection Algorithm in High-throughput Methylation Data for Max-relevance and Min-redundancy

Recent advances reveal that DNA methylation plays an important role in regulating different genome functions where anomalous methylation levels are associated with various cancer types. Feature selection algorithms are geared towards high-throughput analysis of DNA methylation to help identify idiosyncratic DNA methylation profiles associated with cancer types and subtypes. In high dimensional and highly correlated DNA methylation data, feature selection algorithms aim at selecting an efficient and comprehensive feature set to better capture characteristics of phenotypes. In this work, we introduce a two-stage feature selection algorithm (MR2) based on maximum relevance and minimum redundancy criteria. The features that satisfy the relevance conditions are filtered in the first stage, in the second stage, the final subset of loci is selected to reach minimal redundancy by using a k-medoids clustering algorithm that embeds a succinct uncertainty measure score. The performance of the proposed feature selection algorithm is benchmarked against those of the principal component analysis and four other commonly used filtering methods using lung and breast cancer datasets obtained from Gene Expression Omnibus in terms of their classification errors in support vector machine classifiers. Our MR2 algorithm outperforms these filtering based algorithms while at the same time providing more interpretable results

Estimating gene expression from high-dimensional DNA methylation levels in cancer data: A bimodal unsupervised dimension reduction algorithm

•A bi-modal dimension reduction is proposed for unsupervised methylation analysis → 83 characters.•A curve-fitting based filtering is developed to identify informative biomarkers → 82 characters.•Pearson correlation is used to eliminate redundant biomarkers → 65 characters.•The performance of our algorithm is shown using nine real cancer datasets → 76 characters.•Our algorithm outperforms two benchmark algorithms in eight datasets out of nine → 83 characters. Recent molecular and genetic studies have revealed the importance of DNA methylation, a key epigenetic mark, in regulating gene expression and the abnormal profiles of DNA methylation in various diseases including cancer. Here, unsupervised learning methods that are geared towards high-throughput DNA methylation analysis are used to extract useful information from high-dimensional genome wide methylation data in order to provide crucial insights for accurate early diagnosis and treatment of cancer. Herein, these methods are highly dependent on the performance of an earlier step of dimension reduction that aims to find the best subset of attributes to be retained for learning. Widely used algorithms in the literature commonly suffer from resulting in trivial cluster structures and failing to shed light on the relationship between DNA methylation and cancer types due to their myopic and arbitrary search mechanisms. Addressing this issue, we introduce a bimodal unsupervised dimension reduction algorithm (BOUNDER) that identifies the best subset of loci for downstream analysis considering the variability and redundancy across all the samples using bimodal modeling before it feeds into the learning method. BOUNDER models each locus as a bimodal representation using a piecewise linear function with two segments and filters the informative loci based on the fitted line characteristics. To the best of our knowledge, the work presented here is the first study that uses bimodal modeling in unsupervised learning in DNA methylation analysis. BOUNDER is tailored for DNA methylation analysis using a detailed parameter tuning analysis. The performance of BOUNDER is benchmarked against those of widely used conventional algorithms using real lung, breast, kidney, and urological cancer datasets obtained from Gene Expression Omnibus in terms of their accuracies in hierarchical clustering and k-means clustering. Computational experiments reveal that BOUNDER outperforms the PCA and filtering based approach by providing the highest accuracy in 6 out of 9 datasets while providing more interpretable results through a correlation analysis. The BOUNDER algorithm is also shown to be more robust when compared to multiple other conventional dimension reduction algorithms across different datasets

Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative kk-Medoids Clustering Algorithm (T-CLUST)

Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k k -means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed. In this study, we introduce three related algorithms for a complete high-throughput methylation analysis: a variance-based dimension reduction algorithm to handle high-dimensionality in data, an outlier detection algorithm to identify the outliers of data, and an advanced Tabu-based iterative k k -medoids clustering algorithm (T-CLUST) to reduce the impact of initial solutions on the performance of conventional k k -medoids algorithm. The performance of the proposed algorithms is demonstrated on nine different real DNA methylation datasets obtained from the Gene Expression Omnibus DataSets database. The accuracy of the cluster identification obtained by our proposed algorithms is higher than those of hierarchical and k k -means clustering, as well as the conventional methods. The algorithms are implemented in MATLAB, and available at: http://www.coe.miami.edu/simlab/tclust.html