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Introduction: Health systems are confronted with not only the growing worldwide childhood obesity epidemic but also associated comorbidities. These subsequently cause variations in distinct metabolic pathways, leading to metabolic dysfunction-associated steatotic liver disease (MASLD). The aim of this evidence map is to systematically evaluate the evidence and to identify research gaps on glucagon-induced amino acid (AA) turnover and its metabolic interaction with MASLD., Methodology: A systematic literature search was conducted up to April 2023 in three electronic databases. Studies were required to include at least two of the main research areas, glucagon, AA metabolism and MASLD. Two independent reviewers screened titles and abstracts according to prespecified eligibility criteria, as well as full-text articles. Results are summarized in tables stratified by human and animal studies and study population age., Results: Thirty-four references were ultimately included. The publication years dated back to 1965 showed a great increase from 2012 to 2023. In total, there were 19 animal studies and 15 human studies. Among the human studies, except for two studies in adolescents, all the studies were conducted in adults. In human studies, the methods used to evaluate metabolic changes differed among hyperinsulinemic-euglycemic clamp and oral glucose tolerance tests. Thirteen studies focused on the metabolic effects of MASLD, while only two studies explored the interaction between MASLD, glucagon and AA metabolism in humans. The other 19 studies focused on metabolomics, beta cell function or just one topic of a research area and not on interactions between one another., Conclusion: Research on the interaction between MASLD, glucagon and AA metabolism in humans is sparse and complete lacking in pediatrics. Furthermore, longitudinal studies with a focus on hyperglucagonemia independent of diabetes but related to MASLD present an unambiguous research gap.

Background: The aim of this retrospective observational study was to examine the relationship between SCORE2 and the occurrence of colonic diverticula in a screening population without cardiovascular or gastrointestinal symptoms. SCORE2, recognized and supported by the European Society of Cardiology for cardiovascular risk assessment, served as the primary metric for the analysis in this investigation., Methods: We studied 3935 asymptomatic individuals undergoing screening colonoscopy. SCORE2 was calculated for each participant and categorized into three groups based on the following projected 10-year cardiovascular disease risk: SCORE2 0-4.9%, SCORE2 5-9.9%, and SCORE2 ≥ 10%. Logistic regression was used to assess the relationship between SCORE2 and diverticulosis., Results: SCORE2 was associated with the presence of diverticulosis (OR 1.09, 95%CI 1.07-1.10; p < 0.001) in univariable logistic regression, translating into an RR of 1.07 per unit increase. The association persisted after multivariable adjusting for metabolic syndrome (aOR 1.08; 95%CI 1.06-1.10; p < 0.001). Patients with high cardiovascular risk had higher rates of diverticulosis compared to those with lower risk: high risk (OR 2.00, 95%CI 1.71-2.33; p < 0.001); very high risk (OR 2.53, 95%CI 2.10-3.05; p < 0.001). This association remained after adjusting for metabolic syndrome: high risk (aOR 1.86, 95%CI 1.59-2.18; p < 0.001); very high risk (aOR 2.27, 95%CI 1.88-2.75; p < 0.001)., Conclusions: A higher SCORE2 was found to be a suitable screening parameter for diverticular disease. This suggests a potential link between cardiovascular risk factors and colon diverticula development, warranting further research on whether optimizing cardiovascular risk factors could positively influence diverticular disease.

Objective: Education often reflects socioeconomic status. Research indicates that lower socioeconomic status may increase the risk of diverticulosis, and according to data from the USA, diverticular disease is a significant and costly health problem. Our study explores the link between educational level and colonic diverticula occurrence., Subject and Methods: We conducted a cohort study on 5,532 asymptomatic Austrian patients who underwent colonoscopy, categorizing them by education level using the updated Generalized International Standard Classification of Education (GISCED). Logistic regression models, adjusting for age, gender, metabolic syndrome, diet, and activity, were used to determine the association between education and diverticulosis., Results: Overall, 39% of the patients had low educational status, while 53% had medium, and 8% had high educational status. Colon diverticula were less frequent in patients with medium (OR 0.73) and high (aOR 0.62) educational status. Medium educational level remained associated with lower rates of diverticulosis after adjustment for age and sex (aOR 0.85) and further metabolic syndrome, dietary habits, and physical activity (aOR 0.84). In higher education status, this phenomenon was only seen by trend., Conclusion: Low education correlated with higher colon diverticula risk, while medium education showed lower rates even after adjustments. This trend persisted at higher education levels, highlighting the potential for strategies for cost reduction tailored to socioeconomic conditions., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Objective: Over the years, non-alcoholic fatty liver (NAFLD) disease has progressed to become the most frequent chronic liver disease in children and adolescents. The full pathology is not yet known, but disease progression leads to cirrhosis and hepatocellular carcinoma. Risk factors included hypercaloric diet, obesity, insulin resistance and genetics. Hyperglucagonemia appears to be a pathophysiological consequence of hepatic steatosis, thus, the hypothesis of the study is that hepatic fat accumulation leads to increased insulin resistance and impaired glucagon metabolism leading to hyperglucagonemia in pediatric NAFLD., Methods: 132 children and adolescents between 10 and 18 years, with varying degrees of obesity, were included in the study. Using Magnetic Resonance Imaging (MRI) average liver fat was determined, and patients were stratified as NAFLD (>5% liver fat content) and non-NAFLD (<5%). All patients underwent a standardized oral glucose tolerance test (OGTT). Additionally, anthropometric parameters (height, weight, BMI, waist circumference, hip circumference) such as lab data including lipid profile (triglycerides, HDL, LDL), liver function parameters (ALT, AST), uric acid, glucose metabolism (fasting insulin and glucagon, HbA1c, glucose 120 min) and indices evaluating insulin resistance (HIRI, SPISE, HOMA-IR, WBISI) were measured., Results: Children and adolescents with NAFLD had significantly higher fasting glucagon values compared to the non-NAFLD cohort (p=0.0079). In the NAFLD cohort univariate analysis of fasting glucagon was associated with BMI-SDS (p<0.01), visceral adipose tissue volume (VAT) (p<0.001), average liver fat content (p<0.001), fasting insulin concentration (p<0.001), triglycerides (p<0.001) and HDL (p=0.034). This correlation equally applied to all insulin indices HOMA-IR, WBISI, HIRI (all p<0.001) and SPISE (p<0.002). Multivariate analysis (R² adjusted 0.509) for the same subgroup identified HIRI (p=0.003) and VAT volume (p=0.017) as the best predictors for hyperglucagonemia. Average liver fat content is predictive in pediatric overweight and obesity but not NAFLD., Conclusions: Children and adolescents with NAFLD have significantly higher fasting plasma glucagon values, which were best predicted by hepatic insulin resistance and visceral adipose tissue, but not average liver fat content., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Maruszczak, Radzikowski, Schütz, Mangge, Bergsten, Forslund, Manell, Pixner, Ahlström, Kullberg, Mörwald and Weghuber.)

Objectives: The European Society of Cardiology endorsed SCORE2 to assess cardiovascular risk. The aim of this observational, retrospective study was to assess whether SCORE2 is associated with colorectal neoplasia in an asymptomatic screening population. Further, we evaluated if SCORE2 predicts tumor-related mortality. Methods: We included 3408 asymptomatic patients who underwent a screening colonoscopy. We calculated SCORE2 for each participant and stratified patients according to their predicted 10-year risk of cardiovascular disease: SCORE2 0−4.9%, SCORE2 5−9.9%, and SCORE2 ≥ 10%. We assessed the association between SCORE2 as a continuous variable, the presence of colorectal neoplasia using multilevel logistic regression, and SCORE2 and mortality using Cox regression. Results: In total, 1537 patients had a SCORE2 of 0−4.9%, 1235 a SCORE2 of 5−9.9%, and 636 a SCORE2 ≥ 10%. The respective rates of colorectal neoplasia were 20%, 37%, and 44%. SCORE2 was associated with the presence of any (OR 1.11 95%CI 1.09−1.12; p < 0.001) and advanced colorectal neoplasia (OR 1.06 95%CI 1.08−1.13; p < 0.001) in univariate analysis. After multivariable adjustment (age, sex, family history, and metabolic syndrome) a higher SCORE2 remained associated with higher odds for any (aOR 1.04 95%CI 1.02−1.06; p = 0.001) and advanced (aOR 1.06 95%CI 1.03−1.10; p < 0.001) colorectal neoplasia. SCORE2 was associated with both all-cause (HR 1.11 95%CI 1.09−1.14; p < 0.001) and tumor-related mortality (HR 1.10 95%CI 1.05−1.14; p < 0.001). Conclusions: We found that SCORE2 is associated with the presence of colorectal neoplasia. Clinicians could kill two birds with one stone calculating SCORE2. In patients with a high SCORE2, screening colonoscopy aside from cardiovascular risk mitigation could improve outcomes.

Models for keyword spotting in continuous recordings can significantly improve the experience of navigating vast libraries of audio recordings. In this paper, we describe the development of such a keyword spotting system detecting regions of interest in Polish call centre conversations. Unfortunately, in spite of recent advancements in automatic speech recognition systems, human-level transcription accuracy reported on English benchmarks does not reflect the performance achievable in low-resource languages, such as Polish. Therefore, in this work, we shift our focus from complete speech-to-text conversion to acoustic similarity matching in the hope of reducing the demand for data annotation. As our primary approach, we evaluate Siamese and prototypical neural networks trained on several datasets of English and Polish recordings. While we obtain usable results in English, our models' performance remains unsatisfactory when applied to Polish speech, both after mono- and cross-lingual training. This performance gap shows that generalisation with limited training resources is a significant obstacle for actual deployments in low-resource languages. As a potential countermeasure, we implement a detector using audio embeddings generated with a generic pre-trained model provided by Google. It has a much more favourable profile when applied in a cross-lingual setup to detect Polish audio patterns. Nevertheless, despite these promising results, its performance on out-of-distribution data are still far from stellar. It would indicate that, in spite of the richness of internal representations created by more generic models, such speech embeddings are not entirely malleable to cross-language transfer.

Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.

Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.