Risk factors in gastric cancer

STATE OF THE ART: Gastric cancer (GC) is still a major health problem worldwide due to its frequency, poor prognosis and limited treatment options. At present prevention is likely to be the most effective means of reducing the incidence and mortality from this disease. The most important etiological factors implicated in gastric carcinogenesis are diet and Helicobacter pylori (H. pylori) infection. High intake of salted, pickled or smoked foods, as well as dried fish and meat and refined carbohydrates significantly increased the risk of developing GC while fibers, fresh vegetables and fruit were found to be inversely associated with GC risk. Epidemiological investigations (retrospective, case-control and prospective) and several meta-analyses have demonstrated that concurrent or previous H. pylori infection is associated with an increased risk of GC in respect to uninfected people. H. pylori colonizes gastric mucosa where it induces a complex inflammatory and immune reaction that on time leads to a severe mucosal damage i.e., atrophy, intestinal metaplasia (IM) and dysplasia. The risk of GC is closely related to the grade and extension of gastric atrophy, IM and dysplasia. PERSPECTIVES AND CONCLUSIONS: Today a plausible program for GC prevention means: (1) a correct dietary habit since childhood increasing vegetables and fruit intake, (2) a decrease of H. pylori spread improving family and community sanitation and hygiene, (3) a search and treat H. pylori strategy in offspring of GC, (4) a search and treat H. pylori strategy in patients with chronic atrophic gastritis and intestinal metaplasia (IM), (5) a careful endoscopic and histologic follow-up if precancerous lesions persist irrespective of H. pylori eradication

Buddhist-Derived Loving-Kindness and Compassion Meditation for the Treatment of Psychopathology: a Systematic Review

Although clinical interest has predominantly focused on mindfulness meditation, interest into the clinical utility of Buddhist-derived loving-kindness meditation (LKM) and compassion meditation (CM) is also growing. This paper follows the PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines and provides an evaluative systematic review of LKM and CM intervention studies. Five electronic academic databases were systematically searched to identify all intervention studies assessing changes in the symptom severity of Diagnostic and Statistical Manual of Mental Disorders (text revision fourth edition) Axis I disorders in clinical samples and/or known concomitants thereof in sub-clinical/healthy samples. The comprehensive database search yielded 342 papers and 20 studies (comprising a total of 1,312 participants) were eligible for inclusion. The Quality Assessment Tool for Quantitative Studies was then used to assess study quality. Participants demonstrated significant improvements across five psychopathology-relevant outcome domains: (i) positive and negative affect, (ii) psychological distress, (iii) positive thinking, (iv) interpersonal relations, and (v) empathic accuracy. It is concluded that LKM and CM interventions may have utility for treating a variety of psychopathologies. However, to overcome obstacles to clinical integration, a lessons-learned approach is recommended whereby issues encountered during the (ongoing) operationalization of mindfulness interventions are duly considered. In particular, there is a need to establish accurate working definitions for LKM and CM

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

Evaluating maintenance policies by quantitative modeling and analysis

International audienceThe growing importance of maintenance in the evolving industrial scenario and the technological advancements of the recent years have yielded the development of modern maintenance strategies such as the condition-based maintenance (CBM) and the predictive maintenance (PrM). In practice, assessing whether these strategies really improve the maintenance performance becomes a funda-mental issue. In the present work, this is addressed with reference to an example concerning the stochastic crack growth of a generic mechanical component subject to fatigue degradation. It is shown that modeling and analysis provide information useful for setting a maintenance policy

Modeling the Effects of Maintenance on the degradation of a Water-feeding Turbo-pump of a Nuclear Power Plant

International audienceThis work addresses the modelling of the effects of maintenance on the degradation of an electric power plant component. This is done within a modelling framework previously proposed by the authors, of which the distinguishing feature is the characterization of the component living conditions by influencing factors (IFs), i.e. conditioning aspects of the component life that influence its degradation. The original fuzzy logic-based modelling framework includes maintenance as an IF; this requires one to jointly model its effects on the component degradation together with those of the other influencing factors. This may not come natural to the experts who are requested to provide the if-then linguistic rules at the basis of the fuzzy model linking the IFs with the component degradation state. An alternative modelling approach is proposed in this work, which does not consider maintenance as an IF that directly impacts on the degradation but as an external action that affects the state of the other IFs. By way of an example regarding the propagation of a crack in a water-feeding turbo-pump of a nuclear power plant, the approach is shown to properly model the maintenance actions based on information that can be more easily elicited from experts

Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

International audienceParticle Filtering (PF) is a model-based, filtering technique, which has drawn the attention of the Prognostic and Health Management (PHM) community due to its applicability to nonlinear models with non-additive and non-Gaussian noise. When multiple physical models can describe the evolution of the degradation of a component, the PF approach can be based on Multiple Swarms (MS) of particles, each one evolving according to a different model, from which to select the most accurate a posteriori distribution. However, MS are highly computational demanding due to the large number of particles to simulate. In this work, to tackle the problem we have developed a PF approach based on the introduction of an augmented discrete state identifying the physical model describing the component evolution, which allows to detect the occurrence of abnormal conditions and identifying the degradation mechanism causing it. A crack growth degradation problem has been considered to prove the effectiveness of the proposed method in the detection of the crack initiation and the identification of the occurring degradation mechanism. The comparison of the obtained results with that of a literature MS method and of an empirical statistical test has shown that the proposed method provides both an early detection of the crack initiation, and an accurate and early identification of the degradation mechanism. A reduction of the computational cost is also achieved.

A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction

The human gastric microbiota: Is it time to rethink the pathogenesis of stomach diseases?

NTRODUCTION: Although long thought to be a sterile organ, due to its acid production, the human stomach holds a core microbiome. AIM: To provide an update of findings related to gastric microbiota and its link with gastric diseases. METHODS: We conducted a systematic review of the literature. RESULTS: The development of culture-independent methods facilitated the identification of many bacteria. Five major phyla have been detected in the stomach: Firmicutes, Bacteroidites, Actinobacteria, Fusobacteria and Proteobacteria. At the genera level, the healthy human stomach is dominated by Prevotella, Streptococcus, Veillonella, Rothia and Haemophilus; however, the composition of the gastric microbiota is dynamic and affected by such factors as diet, drugs and diseases. The interaction between the pre-existing gastric microbiota and Helicobacter pylori infection might influence an individual's risk of gastric disease, including gastric cancer. CONCLUSIONS: The maintenance of bacterial homeostasis could be essential for the stomach's health and highlights the chance for therapeutic interventions targeting the gastric microbiota, even if gastric pH, peristalsis and the mucus layer may prevent bacteria colonization; and the definition of gastric microbiota of the healthy stomach is still an ongoing challenging task