Gastroesophageal reflux disease (GERD) is one of the most commonly encountered disorders in clinical practice nowadays, with an increasing burden on healthcare systems worldwide. GERD-related respiratory symptoms such as unexplained chronic cough, bronchial asthma or chronic obstructive pulmonary disease (COPD) with frequent exacerbations often pose diagnostic and therapeutic challenges and may require a multidisciplinary approach. Moreover, a potential role of GERD as a risk factor has been proposed for chronic rejection in patients who underwent lung transplantation. Pepsin has gained considerable attention from the scientific community in the last few years as a possible surrogate biomarker for GERD. The aim of this narrative review was to provide an overview of the potential utility of pepsin detection as a marker of micro-aspiration in various biological fluids retrieved from patients with suspected GERD-induced respiratory manifestations and in lung transplant patients with allograft dysfunction. Data on the subject remains highly contradictory, and while certain studies support its applicability in investigating atypical GERD manifestations, at the moment, it would be realistic to accept a modest utility at best. A major lack of consensus persists regarding topics such as the optimal timeframe for fluid collection and cut-off values. Further research is warranted in order to address these issues.

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.

Patients undergoing ablation for atrial fibrillation may be at increased risk of developing gastroesophageal reflux disease. We prospectively studied the presence of symptomatic gastroesophageal reflux disease in naïve patients who underwent atrial fibrillation ablation. Methods: The presence of typical symptoms suggestive of gastroesophageal reflux disease was clinically assessed by the gastroenterologist at baseline and at 3 months after ablation. In addition to that, all patients underwent upper gastrointestinal endoscopy. Results: Seventy-five patients were included in two groups: 46 patients who underwent atrial fibrillation ablation (study group) and 29 patients without ablation (control group). Patients with atrial fibrillation ablation were younger (57.76 ± 7.66 years versus 67.81 ± 8.52 years; p = 0.001), predominantly male (62.2% versus 33.3%; p = 0.030) and with higher body mass index (28.96 ± 3.12 kg/m² versus 26.81 ± 5.19 kg/m²; p = 0.046). At three months after the ablation, in the study and control groups, there were 88.9% and 57.1% patients in sinus rhythm, respectively, (p = 0.009). Symptomatic gastroesophageal reflux disease was not more frequent in the study group (42.2% versus 61.9%; p = 0.220). There was no difference in terms of sinus rhythm prevalence in patients with versus without symptomatic gastroesophageal reflux disease (89.5% versus 88.5%; p = 0.709). Conclusion: In this small prospective study, typical symptoms suggestive of gastroesophageal reflux disease were not more frequent three months following atrial fibrillation ablation.

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.

Hepatic and splenic flexures are important anatomical landmarks in colonoscopy, supporting segmental orientation, lesion localisation, and bowel preparation assessment. This exploratory study investigated whether liver- and spleen-related shadow patterns visible on the colonic wall could serve as indirect cues for flexure localisation. We developed a preliminary workflow combining colour-based annotation and deep learning semantic segmentation. Representative shadow and mucosal regions were used to extract colour characteristics, while lumen/borders and artefacts were identified using luminance-based criteria. Based on these rules, 500 colonoscopy frames with visible shadows were automatically annotated and used to train a segmentation network for four classes: shadows, intestinal mucosa, lumen/borders, and artefacts. The trained model was then applied frame by frame to colonoscopy videos to generate temporal profiles of the segmented regions. In a representative test video, shadow clusters appeared near expert-indicated flexure regions after empirical filtering of frames not matching several criteria. These preliminary findings suggest that liver- and spleen-related shadows may provide complementary cues for flexure localisation in selected cases. However, the method remains exploratory and requires validation on larger, independently annotated datasets.