The present and future of gastroenterology and hepatology: an international SWOT analysis (the GASTROSWOT project)

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors

INTRODUCTION

High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation).

METHODS

A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated.

RESULTS

The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00.

DISCUSSION

The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.

First insights into multidisciplinary and multispecialty long COVID networks-a SWOT analysis from the perspective of ambulatory health care professionals

Introduction

Multidisciplinary and multispecialty approaches with central integration of primary care, individualized long-term rehabilitative care, and multidisciplinary care pathways are recommended by international consortia to face the challenges of care of long COVID. Two regional long COVID networks-Rhein-Neckar (RN) and Ludwigsburg (LU) have emerged as ad hoc examples of best practice in Southern Germany. The aim of the community case study is to provide first insights into the experiences of the networks.

Methods

The exploratory observational study was conducted between April and June 2023, focusing on an observation period of just under 24 months and using a document analysis supported by MAXQDA and SWOT analysis with ambulatory health care professionals in two online group discussions.

Results

The document analysis revealed that both networks have defined network participants who have agreed on common goals and patient pathways and have established ways of communicating, organizing, and collaborating. Both networks agreed on a primary care-based, multidisciplinary and multispecialty approach. The main differences in realization emerged in LU as a focus on the ambulatory setting and very concrete application to individual patients, while RN showed a focus on an intersectoral character with participation of the specialized university hospital sector, knowledge transfer and a supra-regional approach with the involvement of the meso and macro level. The SWOT analysis (n = 14 participants, n = 6 male, 7 physicians (4 disciplines), 7 therapists (5 professions)) showed strengths such as resulting collaboration, contribution to knowledge transfer, and improvement of care for individual patients. As barriers, e.g., lack of reimbursement, high efforts of care, and persistent motivation gaps became apparent. Potentials mentioned were, e.g., transferability to other diseases such as Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, promotion of addressing a "difficult topic" and promotion of intersectoral care concepts; risks mentioned were, e.g., limited network resources and negative effects on the development of other structures.

Conclusion

Resulting implications for practice and research address a call to policy makers and funders to support further research to find out what generalizable results regarding usefulness, effectiveness, and efficiency including transferability to other post-infectious diseases can be derived.

Genetics and athletic performance: a systematic SWOT analysis of non-systematic reviews

Exercise genetics/genomics is a growing research discipline comprising several Strengths and Opportunities but also deals with Weaknesses and Threats. This "systematic SWOT overview of non-systematic reviews" (sSWOT) aimed to identify the Strengths, Weaknesses, Opportunities, and Threats linked to exercise genetics/genomics. A systematic search was conducted in the Medline and Embase databases for non-systematic reviews to provide a comprehensive overview of the current literature/research area. The extracted data was thematically analyzed, coded, and categorized into SWOT clusters. In the 45 included reviews five Strengths, nine Weaknesses, six Opportunities, and three Threats were identified. The cluster of Strengths included "advances in technology", "empirical evidence", "growing research discipline", the "establishment of consortia", and the "acceptance/accessibility of genetic testing". The Weaknesses were linked to a "low research quality", the "complexity of exercise-related traits", "low generalizability", "high costs", "genotype scores", "reporting bias", "invasive methods", "research progress", and "causality". The Opportunities comprised of "precision exercise", "omics", "multicenter studies", as well as "genetic testing" as "commercial"-, "screening"-, and "anti-doping" detection tool. The Threats were related to "ethical issues", "direct-to-consumer genetic testing companies", and "gene doping". This overview of the present state of the art research in sport genetics/genomics indicates a field with great potential, while also drawing attention to the necessity for additional advancement in methodological and ethical guidance to mitigate the recognized Weaknesses and Threats. The recognized Strengths and Opportunities substantiate the capability of genetics/genomics to make significant contributions to the performance and wellbeing of athletes.

Effects of thiacloprid exposure on microbiota-gut-liver axis: Multiomics mechanistic analysis in Japanese quails

Neonicotinoid insecticides (NNIs) are the most widely used class of pesticides globally. However, NNIs may cause adverse health effects, including chronic liver disease, and perturbation of the gut microbiota. Thiacloprid (THI) is one of the NNIs widely used in agriculture. Therefore, it is essential to elucidate effects of THI on the microbiota-gut-liver axis to assess the risk of chronic liver disease following exposure to NNIs. This study aimed at investigating whether THI exposure promoted liver injury by altering the gut microbiota and related metabolites. In this study, healthy male quails were exposed to 2 or 4 mg/kg THI or 0.75 % (w/v) saline once daily for 6 weeks, respectively. Metabolomics, 16S rRNA sequencing, and transcriptomic methods were performed to analyze the toxic mechanisms of THI in Japanese quails. We found that THI evoked damage and disruption to intestinal barrier function, leading to increased harmful substances such as lipopolysaccharide (LPS) and phenylacetic acid entering the liver. Besides, our results showed significantly altered hepatic bile acid and cholesterol metabolism in THI-exposed quails, with abnormal liver lipid metabolism, showing severe liver injury, fibrosis, and steatosis compared with the control quails. In conclusion, THI exposure aggravates liver injury via microbiota-gut-liver axis.

SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD can progress to liver cirrhosis and hepatocellular carcinoma (HCC). Recently, the prognosis of NAFLD/NASH has been reported to be dependent on liver fibrosis degree. Liver biopsy remains the gold standard, but it has several issues that must be addressed, including its invasiveness, cost, and inter-observer diagnosis variability. To solve these issues, a variety of noninvasive tests (NITs) have been in development for the assessment of NAFLD progression, including blood biomarkers and imaging methods, although the use of NITs varies around the world. The aim of the Japan NASH NIT (JANIT) Forum organized in 2020 is to advance the development of various NITs to assess disease severity and/or response to treatment in NAFLD patients from a scientific perspective through multi-stakeholder dialogue with open innovation, including clinicians with expertise in NAFLD/NASH, companies that develop medical devices and biomarkers, and professionals in the pharmaceutical industry. In addition to conventional NITs, artificial intelligence will soon be deployed in many areas of the NAFLD landscape. To discuss the characteristics of each NIT, we conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis in this study with the 36 JANIT Forum members (16 physicians and 20 company representatives). Based on this SWOT analysis, the JANIT Forum identified currently available NITs able to accurately select NAFLD patients at high risk of NASH for HCC surveillance/therapeutic intervention and evaluate the effectiveness of therapeutic interventions.

Diagnostics of Gastrointestinal Motility and Function: Update for Clinicians

Disorders of gastrointestinal (GI) tract motility and function are prevalent in the general population and negatively affect quality of life [...]

Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management

Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.