Introduction: Population-based eradication of Helicobacter pylori has been suggested to be cost-effective and is recommended by international guidelines. However, the potential adverse effects of widespread antibiotic use that this would entail have not been sufficiently studied. An alternative way to decrease gastric cancer mortality is by non-invasive search for precancerous lesions, in particular gastric atrophy; pepsinogen tests are the best currently available alternative. The primary objective of GISTAR is to determine whether H pylori eradication combined with pepsinogen testing reduces mortality from gastric cancer among 40–64-year-old individuals. The secondary objectives include evaluation of H pylori eradication effectiveness in gastric cancer prevention in patients with precancerous lesions and evaluation of the potential adverse events, including effects on microbiome. Methods and analysis: Individuals are recruited from general population (50% men) in areas with high gastric cancer risk in Europe and undergo detailed lifestyle and medical history questionnaire before being randomly allocated to intervention or control groups. The intervention group undergoes H pylori testing and is offered eradication therapy if positive; in addition, pepsinogen levels are detected in plasma and those with decreased levels are referred for upper endoscopy. All participants are offered faecal occult blood testing as an incentive for study participation. Effectiveness of eradication and the spectrum of adverse events are evaluated in study subpopulations. A 35% difference in gastric cancer mortality between the groups is expected to be detectable at 90% power after 15 years if 30 000 individuals are recruited. Biological materials are biobanked for the main and ancillary studies. The study procedure and assumptions will be tested during the pilot phase. Ethics and dissemination: The study was approved by the respective ethics committees. An independent Data Safety and Monitoring Board has been established. The findings will be published in peer-reviewed journals and presented at scientific meetings. Trial registration number: NCT02047994

Background and Aims: The prevalence of Helicobacter pylori (H. pylori) infection is higher in developing countries and is often linked to lower socioeconomic status. Few studies have investigated the association between H. pylori and individual level characteristics in Europe, where several countries have a high prevalence of H. pylori infection. The study aimed to identify risk factors for H. pylori infection among adults in a large clinical trial in Latvia. Methods: 1,855 participants (40-64 years) of the “Multicenter randomized study of H. pylori eradication and pepsinogen testing for prevention of gastric cancer mortality” (GISTAR study) in Latvia tested for H. pylori IgG antibodies were included in a cross-sectional analysis. Sociodemographic, lifestyle and medical factors were compared for participants seropositive (H. pylori+) and seronegative. Mutually adjusted odds ratios (OR) were calculated for H. pylori+ and factors significant in univariate analysis (education, smoking, binge drinking, several dietary habits, history of H. pylori eradication and disease), adjusting for age, gender and income. Results: Of the participants 1,044 (55.4%) were H. pylori seropositive. The infection was associated with current (OR: 1.34, 95%CI: 1.01-1.78) and former (OR: 1.38; 95%CI: 1.03-1.85) smoking, binge drinking (OR: 1.35; 95%CI: 1.03-1.78), having ≥200g dairy daily (OR: 1.37; 95%CI: 1.11-1.69), and very hot food/drinks (OR: 1.32; 95%CI: 1.03-1.69) and inversely with ≥400g vegetables/fruit daily (OR: 0.76; 95%CI: 0.60-0.96), history of H. pylori eradication (OR: 0.57; 95%CI: 0.39-0.84), peptic ulcer (OR: 0.55; 95%CI: 0.38-0.80) and cardiovascular disease (OR: 0.78; 95%CI: 0.61-0.99). Conclusions: After mutual adjustment, H. pylori seropositivity was associated with lifestyle and in particular dietary factors rather than socioeconomic indicators in contrast to the majority of other studies.

Introduction––Serum pepsinogen tests for gastric cancer screening have been debated for decades. We assessed the performance of two pepsinogen assays with or without gastrin-17 for the detection of different precancerous lesions alone or as a composite endpoint in a Latvian cohort. Methods––Within the intervention arm of the GISTAR population-based study, participants with abnormal pepsinogen values by ELISA or latex-agglutination tests, or abnormal gastrin-17 by ELISA and a subset of subjects with all normal biomarker values were referred for upper endoscopy with biopsies. Performance of biomarkers, corrected by verification bias, to detect five composite outcomes based on atrophy, intestinal metaplasia, dysplasia or cancer was explored. Results––Data from 1045 subjects were analysed, of those 273 with normal biomarker results. Both pepsinogen assays showed high specificity (>93%) but poor sensitivity (range: 18.4–31.1%) that slightly improved when lesions were restricted to corpus location (40.5%) but decreased when dysplasia and prevalent cancer cases were included (23.8%). Adding gastrin-17 detection, sensitivity reached 33–45% while specificity decreased (range: 61.1–62%) and referral rate for upper endoscopy increased to 38.6%. Conclusions––Low sensitivity of pepsinogen assays is a limiting factor for their use in population-based primary gastric cancer screening, however their high specificity could be useful for triage.

The paper presents a possible enhancement of entropy-based classifiers to handle problems, caused by the class imbalance in the original dataset. The proposed method was tested on synthetic data in order to analyse its robustness in the controlled environment with different class proportions. As also the proposed method was tested on the real medical data with imbalanced classes and compared to the original classification algorithm results. The medical field was chosen for testing due to frequent situations with uneven class ratios.

The Optimization of COCOMO Model Coefficients Using Genetic Algorithms

A joint analysis of continuous (time series demand observations) and discrete (well-describing parameters) data is studied. Such data mining techniques as data collection, preprocessing, clustering analysis, and classification are considered. Upon continuous data preprocessing and clustering, images of possible sales development are constructed. A new product’s demand is searched for using inductive decision trees built on well-describing data.