Omega-3 polyunsaturated fatty acids (PUFAs) play a vital role in human health, well-being, and the management of inflammatory diseases. Insufficient intake of omega-3 is linked to disease development. Specialized pro-resolving mediators (SPMs) are derived from omega-3 PUFAs and expedite the resolution of inflammation. They fall into categories known as resolvins, maresins, protectins, and lipoxins. The actions of SPMs in the resolution of inflammation involve restricting neutrophil infiltration, facilitating the removal of apoptotic cells and cellular debris, promoting efferocytosis and phagocytosis, counteracting the production of pro-inflammatory molecules like chemokines and cytokines, and encouraging a pro-resolving macrophage phenotype. This is an experimental pilot study in which ten healthy subjects were enrolled and received a single dose of 6 g of an oral SPM-enriched marine oil emulsion. Peripheral blood was collected at baseline, 3, 6, 9, 12, and 24 h post-administration. Temporal increases in plasma and serum SPM levels were found by using LC-MS/MS lipid profiling. Additionally, we characterized the temporal increases in omega-3 levels and established fundamental pharmacokinetics in both aforementioned matrices. These findings provide substantial evidence of the time-dependent elevation of SPMs, reinforcing the notion that oral supplementation with SPM-enriched products represents a valuable source of essential bioactive SPMs.

Background/Objective: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies. Methods: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease. Results: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical–demographics models significantly increased discriminatory ability in both diagnostic and prognostic models. Conclusions: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.

The risk for gastrointestinal bleeding from dual antiplatelet therapy (DAPT) with new antiplatelets (prasugrel/ticagrelor) compared to clopidogrel is unclear. Aim: To determine the risk and type of major (gastrointestinal bleeding requiring hospitalization) and minor (anemia and iron deficiency) gastrointestinal events with different types of DAPT. Methods: Retrospective observational cohort study of patients who started DAPT after percutaneous coronary intervention. Follow-up was censored after 12 months of DAPT, when a major gastrointestinal event occurred, or when DAPT was discontinued. Results: Among 1,327 patients (54.03% were treated with clopidogrel-based DAPT, 38.13% with ticagrelor-based DAPT, and 7.84% with prasugrel-based DAPT), 29.5% had at least one gastrointestinal event. Patients taking clopidogrel-DAPT were older, with more comorbidities, and higher gastrointestinal risk compared to those taking other DAPT regimens. Adjusted hazard ratios (HRs) showed no between-group differences in the risk for major (clopidogrel vs. new antiplatelets: HR 0.996; 95% confidence interval 0.497–1.996) and minor (HR 0.920; 0.712–1.189) gastrointestinal events. Most patients received proton pump inhibitors while on DAPT (93.3%) and after withdrawal (83.2%). Conclusion: Prasugrel- or ticagrelor-based DAPT was not associated with increased gastrointestinal bleeding risk when compared to clopidogrel-DAPT. New antiplatelets do not necessarily need to be restricted to patients with low gastrointestinal risk.

The management and effectiveness of the treatment of Helicobacter pylori infection are heterogeneous worldwide, despite the publication of international consensus conferences and guidelines, which have been widely available for years. The aim of the study was to describe the clinical management and the eradication rates in a region of Southern Europe (Spain). Between 2010 and 2019, we conducted a retrospective analysis of patients with H. pylori infection attended by gastroenterologists in two defined areas of the National Health System in Aragón. We compared the appropriateness of therapies according to guidelines, and described the effectiveness of each treatment. A total of 1644 penicillin non-allergic patients were included. The most prescribed therapy between 2010 and 2013 was the ‘classic’ triple therapy PCA (80%), whereas the ’concomitant’ therapy PCAM was chosen by 90% of the gastroenterologists in 2015. After 2016, the use of the quadruple bismuth-containing therapy in a single capsule (Pylera^®) quickly increased, representing almost half of the overall prescriptions in 2019. Throughout the decade, adherence to guidelines was 76.4% and global efficacy was 70.7% (ITT). Triple therapies’ eradication rates were lower than 70% (ITT), whereas eradication rates with quadruple therapies achieved or were over 80% (ITT). In conclusion, despite the use of quadruple therapies and optimized treatments, the effectiveness of H. pylori management in daily clinical practice is far from the target of 90%.

Background: The COVID pandemic has forced the closure of many colorectal cancer (CRC) screening programs. Resuming these programs is a priority, but fewer colonoscopies may be available. We developed an evidence-based tool for decision-making in CRC screening programs, based on a fecal hemoglobin immunological test (FIT), to optimize the strategy for screening a population for CRC.Methods: We retrospectively analyzed data collected at a regional CRC screening program between February/2014 and November/2018. We investigated two different scenarios: not modifying vs. modifying the FIT cut-off value. We estimated program outcomes in the two scenarios by evaluating the numbers of cancers and adenomas missed or not diagnosed in due time (delayed).Results: The current FIT cut-off (20-μg hemoglobin/g feces) led to 6,606 colonoscopies per 100,000 people invited annually. Without modifying this FIT cut-off value, when the optimal number of individuals invited for colonoscopies was reduced by 10–40%, a high number of CRCs and high-risk adenomas (34–135 and 73–288/100.000-people invited, respectively) will be undetected every year. When the FIT cut-off value was increased to where the colonoscopy demand matched the colonoscopy availability, the number of missed lesions per year was remarkably reduced (9–36 and 29–145/100.000 people, respectively). Moreover, the unmodified FIT scenario outcome was improved by prioritizing the selection process based on sex (males) and age, rather than randomly reducing the number invited.Conclusions: Assuming a mismatch between the availability and demand for annual colonoscopies, increasing the FIT cut-off point was more effective than randomly reducing the number of people invited. Using specific risk factors to prioritize access to colonoscopies should be also considered.

Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Methods: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. Results: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658–0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655–0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596–0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727–0.953); p ≤ 0.001). Conclusions: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice.