Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscopy frames. Methods: We collected thousands of colonoscopy frames extracted from a set of video colonoscopy files. A color-based image processing method was used to extract color features from specific regions of each colonoscopy frame, namely, the intestinal mucosa, residues, artifacts, and lumen. With these features, we automatically annotated all the colonoscopy frames and then selected the best of them to train a semantic segmentation network. This trained network was used to classify the four region types in a different set of test colonoscopy frames and extract pixel statistics that are relevant to quality evaluation. The test colonoscopies were also evaluated by colonoscopy experts using the Boston scale. Results: The deep learning semantic segmentation method obtained good results, in terms of classifying the four key regions in colonoscopy frames, and produced pixel statistics that are efficient in terms of objective quality assessment. The Spearman correlation results were as follows: BBPS vs. pixel scores: 0.69; BBPS vs. mucosa pixel percentage: 0.63; BBPS vs. residue pixel percentage: −0.47; BBPS vs. Artifact Pixel Percentage: −0.65. The agreement analysis using Cohen’s Kappa yielded a value of 0.28. The colonoscopy evaluation based on the extracted pixel statistics showed a fair level of compatibility with the experts’ evaluations. Conclusions: Our proposed deep learning semantic segmentation approach is shown to be a promising tool for evaluating the overall quality of colonoscopies and goes beyond the Boston Bowel Preparation Scale in terms of assessing colonoscopy quality. In particular, while the Boston scale focuses solely on the amount of residual content, our method can identify and quantify the percentage of colonic mucosa, residues, and artifacts, providing a more comprehensive and objective evaluation.

Background/Objectives: In cardiovascular-kidney-metabolic (CKM) syndrome, left atrium (LA) involvement begins early: reservoir function declines before the chamber dilates, and the resulting atrial cardiomyopathy (ACM) precedes atrial fibrillation (AF), heart failure with preserved ejection fraction (HFpEF), and cardioembolic stroke. How ACM is distributed across CKM stages has not yet been characterized in routine clinical practice. ATRIO-CKM aims to characterize ACM non-invasively across the range of CKM stages in patients assessed in an internal medicine and cardiology department and to identify the parameters most strongly associated with ACM within CKM syndrome. Methods: Single-center, prospective, observational study with cross-sectional phenotyping at the index visit. We will enroll consecutive adults at their first assessment in our department (target, 200; minimum, 150). The primary objective is to estimate the stage-specific prevalence of ACM among participants in sinus rhythm without documented AF, who form the main cohort and are grouped as CKM with versus without ACM. Patients in sinus rhythm are phenotyped with 12-lead electrocardiogram (P-wave duration, Bayés interatrial block), echocardiography with speckle-tracking of the left ventricle (LV), LA (biplane reservoir, conduit, and contractile strain), and right ventricular (RV) free wall, and biomarkers including MR-proANP, Fetuin-A, FGF-23, and NT-proBNP, with cardiac magnetic resonance in an exploratory subset. Participants with AF documented at any time, including previously documented paroxysmal AF, are allocated to a separate established-AF cohort irrespective of the rhythm at the index visit. Results: We will report ACM prevalence by CKM stage with confidence intervals, the profile separating the two groups, LA volume index across three groups of increasing severity (no ACM, ACM in sinus rhythm, and established AF), and the parameters associated with ACM. Conclusions: Using only measurements already available in routine cardiology, ATRIO-CKM will describe how ACM is distributed across CKM stages and identify the parameters most strongly associated with ACM within CKM syndrome, as candidates for a classification index to be developed and validated in a larger cohort.

Background: Pepsin, a component of gastric refluxate, has been investigated as a potential salivary biomarker for gastro-esophageal reflux disease (GERD), although its diagnostic accuracy remains uncertain. This proof-of-concept study aimed to characterize the effects of pepsin on the human salivary peptidome using matrix-assisted laser desorption/ionization–time of flight (MALDI-ToF) mass spectrometry. Methods: Whole saliva samples were collected from ten healthy adult volunteers under fasting conditions and divided into untreated controls and aliquots digested with pepsin at acidic pH. MALDI-ToF MS was used to profile digestion-induced changes in peptide mass patterns. Spectral data were analyzed using multivariate statistical approaches, including principal component analysis (PCA), linear discriminant analysis (LDA), and hierarchical clustering. Results: Pepsin digestion increased peptide signal intensity and spectral complexity compared with controls. PCA demonstrated clear separation between native and digested samples along the first principal component. Ten peptide m/z features showed the strongest association with pepsin exposure based on PCA loadings. LDA and hierarchical clustering further supported this distinction, with the top 15 discriminative m/z features showing consistent enrichment in digested samples despite inter-individual variability. Conclusions: Pepsin exposure induces reproducible remodeling of the salivary peptidome detectable by MALDI‑ToF MS. Although this peptide‑level approach cannot resolve the full diversity of salivary proteoforms, the resulting signatures support the feasibility of identifying markers of reflux‑associated enzymatic activity and provide a basis for future validation in clinical GERD cohorts.

Cardiovascular disease often requires multidisciplinary collaboration in order to achieve an appropriate diagnosis and optimal patient outcomes