Introduction

Lactose intolerance is defined as an impaired lactose absorption associated with symptoms including diarrhea, abdominal pain and bloating. It is caused by a genetically determined reduced expression of the enzyme lactase in the small intestine; although, several studies underline that symptoms do not depend exclusively on genetics. Among different possible causes, few information exists on microbiota signatures.

Aims & Methods

In this study, we aimed to investigate specific gut microbiota features associated with lactose intolerance and respective gastrointestinal (GI) symptoms.
Adult patients presenting GI symptoms compatible with lactose intolerance were enrolled and underwent a) a validated questionnaire for self-assessment of gastrointestinal symptoms; b) lactose breath test to confirm lactose malabsorption; c) lactulose/mannitol breath test to assess intestinal permeability and intestinal transit; d) 16S rRNA gut microbiota profiling.
Healthy controls without GI symptoms or diseases were enrolled.
Patients with inflammatory bowel disease, recent infectious enteritis, active neoplasia, pregnant women, with recent history of medications were excluded.

Results

According to the results of the tests, patients were divided into three groups: 28 patients with lactose intolerance (group 1), 7 patients with GI symptoms and negative lactose breath test (group 2), 29 healthy controls (group 3).
GI symptoms assessed by questionnaire were prevalent in patients with lactose malabsorption in comparison to patients without lactose malabsorption and in comparison to healthy controls (average GSRS group 1 14.6, group 2 9.8, group 3 2.6). Increased intestinal permeability (group 1 88.8% vs group 2 100% vs group 3 6.6%) and altered intestinal transit (group 1 50%, group 2 50%, group 3 27%) were observed in patients with GI symptoms, independently from lactose malabsorption.
Gut microbiota profiling showed an increased biodiversity between patients with lactose intolerance (group 1) and healthy controls (group 3), both in terms of alpha-diversity (p<0.05 according to Shannon and Simpson index) and beta-diversity (p=0.001). According to taxonomic analysis, Proteobacteria and Bacteroidetes phyla were significantly higher in patients with lactose intolerance in comparison to healthy controls (p<0.05). At the species level, significant markers of dysbiosis in lactose intolerant individuals appear to be represented by Dialister, Staphylococcus, Streptococcus, Enterobacteriacae, Bacteroides (p<0.05).

Conclusion

Specific gut microbiota signatures are associated with lactose intolerance, suggesting a role as a prebiotic for lactose, targeting differential microbial elements. Increased intestinal permeability and altered intestinal transit are common features of patients presenting GI symptoms, independently from lactose malabsorption. For this reason, further microbiota characterization is required to elucidate mechanisms leading to symptoms development and treatment.

Introduction

Endoscopy is essential for the early detection and management of upper gastrointestinal (UGI) tumors, yet variability in diagnostic expertise and workload-related fatigue often compromise the quality of report documentation. These challenges underscore the urgent need for innovative technological approaches to assist endoscopists in optimizing the report generation process.
Multimodal large language models (MLLMs) enable the synergistic integration of visual and semantic representations, demonstrating significant clinical value in medical text generation. This study introduces MERGS, an innovative endoscopy report generation system powered by MLLM. MERGS annotates medical terminology and generates natural language descriptions based on endoscopic images, ultimately producing structured reports that conform to clinical standards.

Aims & Methods

MERGS is built on the pre-trained MLLM Qwen-VL-2.5-7B. It adopts a multimodal architecture integrating visual encoding and cross-modal alignment to enable medical term annotation and natural language generation from endoscopic images. A pathological classification module further enhances lesion characterization. The language model synthesizes image-derived descriptions to generate a comprehensive report with a diagnostic conclusion.
UGI endoscopy reports were collected from the Sun Yat-sen University Cancer Center between 2019 and 2021. A standardized terminology dictionary was developed based on clinical guidelines, supporting a three-tier annotation framework: endoscopic image → standardized medical terminology → natural language text. Model-assisted pre-annotation combined with expert validation substantially reduced manual effort. The resulting dataset was split into training and test sets at an 8:2 ratio.
Model performance was assessed via multi-label classification metrics (accuracy, specificity, F1-score), as well as semantic similarity measures (Jaccard index) to evaluate its ability to recognize anatomical sites and endoscopic features. Natural language generation quality was assessed using BLEU-2 and ROUGE-L scores, and expert satisfaction ratings were collected for 100 randomly sampled outputs to assess readability and clinical relevance. A human–AI comparison was conducted on 50 test reports, comparing system-generated outputs with those authored by experienced endoscopists.

Results

A multimodal dataset was constructed after quality control, comprising 22,373 reports and 135,503 associated images from 21,619 patients. Internal validation demonstrated that MERGS achieved robust performance in automated image interpretation, with 97.3% accuracy and 98.6% specificity for standard anatomical site recognition. In feature identification, it showed high clinical relevance (F1 scores: smooth mucosa 0.915, polyp 0.857, tumor 0.878, erythematous mucosa 0.766).
Generated reports showed strong alignment with reference reports (BLEU-2 = 0.587, ROUGE-L = 0.661) and received high expert satisfaction ratings (mean score: 4.34 ± 0.63 on a 5-point scale). In a human–AI comparison, MERGS-generated reports were considered non-inferior to those authored by endoscopists (scores: MERGS 4.52 ± 0.81 vs. manual 4.68 ± 0.72), with the upper limit of the one-sided 95% CI (0.37) below the non-inferiority margin (0.5, p = 0.0045).

Conclusion

This study developed an automated system for UGI endoscopy reporting powered by an MLLM. Both internal and clinical validation demonstrated the system's high performance in endoscopic image feature recognition and report generation, underscoring its strong potential for clinical deployment.

References

Luo H, Xu G, Li C, He L, Luo L, Wang Z, Jing B, Deng Y, Jin Y, Li Y, Li B, Tan W, He C, Seeruttun S R, Wu Q, Huang J, Huang D W, Chen B, Lin S B, Chen Q M, Yuan C M, Chen H X, Pu H Y, Zhou F, He Y, Xu R H. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study[J]. Lancet Oncol, 2019,20(12):1645-1654.

Introduction

Clostridioides difficile infection (CDI) is among of the most common causes of nosocomial diarrhea with high morbidity and mortality. Recurrent and refractory disease courses in particular represent a special challenge in the management of these patients. The identification of patients at risk for recurrent courses could improve management, e.g. through the use of new and effective therapies in patients with CDI.

Aims & Methods

The aim of this study was to investigate the relationship between the presence of different comorbidities and the course of CDI.
In a retrospective, monocentric cohort study of a tertiary centre over a period of 10 years, patients with CDI-related hospitalizations were examined. The study included 336 patients with available information on comorbidities (diverticulosis, biliary diseases (cholecysto-, docholithiasis, cholecystitis, cholangitis or cholangiectasis) inflammatory bowel diseases (IBD), liver cirrhosis and malignancies) and the diagnosis of the recurrent CDI (rCDI) vs. non-recurrent course of CDI (nrCDI). Patients on dialysis or immunosuppressed patients were also considered as comorbid.

Results

Among 336 patients included in the study, 227 (67.6%) presented with at least one comorbidity. The most common were malignancy (30.4%), biliary diseases (19.4%), diverticulosis (15.8%), liver cirrhosis (14.3%), and inflammatory bowel disease (IBD, 8.9%). rCDI occurred more frequently in patients with diverticulosis and biliary disease. Diverticulosis was present in 24.4% of rCDI cases vs. 12.6% in nrCDI cases (OR 2.24, 95% CI 1.22–4.17). Biliary diseases were observed in 26.7% of rCDI vs. 16.7% of nrCDI patients (OR 1.82, 95% CI 1.01–3.26). No significant associations with rCDI were found for IBD (OR 0.66, 95% CI 0.27–1.65), malignancy (OR 1.13, 95% CI 0.67–1.88), or liver cirrhosis (OR 0.79, 95% CI 0.38–1.61).

Conclusion

These findings suggest diverticulosis and biliary disease may be independent risk factors for rCDI, whereas other common comorbidities are not significantly associated with recurrence. Structured assessment of risk conditions may help to select patients for tailored management to prevent rCDI disease course.

Introduction

Upper gastrointestinal bleeding (UGIB) remains a common and potentially life-threatening condition in emergency medicine. Early identification of patients requiring urgent esophagogastroduodenoscopy (EGD) is crucial to improve outcomes and optimize healthcare resources management. Traditional risk stratification tools, such as the Glasgow-Blatchford Score (GBS), have been widely used to guide clinical decision-making. However, the growing availability of artificial intelligence (AI) offers new perspectives in diagnostic and therapeutic strategies. Recent advancements in large language models (LLMs), such as ChatGPT® and Google GEMINI®, suggest potential applications in clinical triage.

Aims & Methods

This study aims to evaluate the performance of these AI tools in determining the appropriate timing for endoscopy in UGIB patients, compared to standard clinical scoring systems.
The medical records of 100 patients who presented to the emergency department with upper gastrointestinal bleeding (UGIB) between April and September 2024 were reviewed retrospectively. Each case was evaluated by two AI models: Google GEMINI® (September 2024 version) and ChatGPT® (version 3.5). These models, acting as virtual gastroenterology experts, were asked to recommend whether urgent or delayed esophagogastroduodenoscopy (EGD) was necessary. For each patient, clinical data such as age, medical history, clinical examination findings, and laboratory results at admission were provided. The AI recommendations were recorded in an Excel file and compared with the Glasgow-Blatchford Score (GBS), which was calculated by a gastroenterologist at the time of admission.
An urgent endoscopy was deemed necessary for a GBS ≥ 3. Statistical analysis was performed using Jamovi® software (version 2.3), and the agreement between the AI models and human evaluations was assessed using Cohen’s kappa coefficient (κ).

Results

The epidemiological characteristics of the patient cohort revealed a median age of 63 years (range: 37.5 to 78), with a male-to-female ratio of 1.7. Among the patients, 73% had at least one comorbidity, including hypertension (33%), diabetes (22%), ischemic heart disease (17%), and chronic liver disease (12%). At the time of admission, melena was the predominant symptom, reported in 78% of patients. The average Glasgow-Blatchford Score (GBS) was 7.07 ± 3.47.
In terms of AI recommendations, ChatGPT suggested urgent endoscopy in 48% of cases, while Google GEMINI recommended it in 49%. A moderate level of agreement was observed between the two AI models for urgent endoscopy recommendations (kappa = 0.540, p < 0.001). However, the agreement between the AI models and the human evaluator was weak. The level of agreement between the gastroenterologist’s assessment and ChatGPT’s recommendation was weak (kappa = 0.222, p < 0.001), with similar results for Google GEMINI (kappa = 0.231, p < 0.001).

Conclusion

Artificial intelligence is a promising tool that is already beginning to transform various fields of medicine. However, our study demonstrates that the publicly available AI models currently in use are not yet capable of effectively triaging patients in emergency settings, particularly in cases of upper gastrointestinal bleeding. A model specifically trained on clinical gastroenterology cases, using validated scientific guidelines, could produce more accurate results and potentially revolutionize patient triage in emergency departments.

Introduction

Pre-cut sphincterotomy are most often used as a salvage procedure after failure of conventional methods of bile duct cannulation although its success is not guaranteed.　
When we performed initial pre-cut sphincterotomy, papilla is incited, with needle knife or sphincterotome, until the opening of bile duct is recognized to gain access to its lumen.
Our technique, in cases of not recognized the opening of bile duct, involves engaging the exposing underlying submucosal tissue layer of the papilla with the catheter and then seeking bile duct with the hydrophilic guide wire, completely dependent on fluoroscopic view.
The purpose of this study was to assess the efficacy and safety of our technique in cases of failed pre-cut.

Aims & Methods

We retrospectively analyzed the clinical records of consecutive patients undergoing ERCP between 1 January 2009 and 31 October 2024 at Mitsui Memorial Hospital, Tokyo, Japan.
Patients who underwent an ERCP plus pre-cut were included.
Patients were divided into two groups, in whom the common bile duct could be cannulated (Group A), and the common bile duct could not be cannulated and our technique was performed(Group B), after initial pre-cut.
The primary objective was a successful biliary cannulation, and the secondary objective was the complication in group B.
The complications and patient characteristics were compared between group A and B using Fisher’s exact test.

Results

Over a period of fourteen years ten months, 262 Pre-cut sphincterotomy (Needle knife sphincterotomy 108, Transpancreatic septotomy 126, Both 28) were performed. After initial pre-cut, we could achieve biliary access in 183 (Group A), and could not in 79. We performed our technique in 77 among these 79(group B).
In Group B, the therapeutic indications for ERCP were choledocholithiasis in 44 (57.1%) of cases, malignant and benign biliary obstruction in 29(37.7%) and 4(5.2%). The average age of the patients was 73 years (range 29-96) and 45 (58.4%) were male. Among 77 patients, a successful biliary cannulation was achieved in 72 (93.5 %). Complications were 15(19.5%). 2 patients presented bleeding (2.6 %) and required endoscopic treatment and transfusion of red blood cells. 9 (11.7 %) patients developed post-ERCP pancreatitis (1 severe, 8 mild pancreatitis). Guidewire related perforations that were resolved with conservative management occurred in 4(5.2%).
Alerted anatomy status was more frequently observed in group B than group A (p < 0.01). The other characteristics of both groups were similar. Total complications were more frequently observed in group B (p ＝ 0.03).

Conclusion

Our technique in cases of failed pre-cut sphincterotomy is found to be highly effective method to get biliary access, but carried a higher adverse event rate.

Introduction

Endoscopic retrograde cholangiopancreatography (ERCP) is a widely utilized procedure for managing biliary and pancreatic disorders (1). ERCP is associated with complications, and potential factors influencing the ERCP outcomes is a critical area of research (2). Among these factors, obesity has emerged as a variable of interest due to its increasing prevalence globally and its known associations with a range of health conditions (3). Previous studies have yielded conflicting results on the influence of obesity on ERCP outcomes, with some reporting higher complication rates, while others found no significant associations (4, 5). The interplay between weight and procedural outcomes across a spectrum of BMI categories remains underexplored. This study aimed to assess the impact of body mass index (BMI) on clinical aspects and procedural outcomes of ERCP.

Aims & Methods

A total of 2143 patients who underwent ERCP at Hitit University Hospital between January 2020 and February 2024 were initially included. Of these, 1315 patients were excluded due to incomplete clinical information, previous ERCP history, altered anatomy and pregnancy, resulting in a final cohort of 828 patients for analysis.
The included patients were stratified into BMI categories: ≤30 kg/m² and >30 kg/m². Additionally, subgroup analyses were performed across finer BMI classifications: ≤20, 20 to ≤25, 25 to ≤30, 30 to ≤35, and >35 kg/m². Clinical and procedural parameters evaluated included admission diagnoses of acute pancreatitis and acute cholangitis (according to Tokyo cholangitis grade), American Society of Anesthesiologists (ASA) scores, gender, history of cholecystectomy, cannulation success, cannulation time, post-ERCP complications including post-ERCP pancreatitis and other complications according to modified Cotton criteria and 30-day readmission rates. The relationships between BMI and the clinical aspects as well as procedural outcomes of ERCP were investigated.

Results

The study cohort consisted of 828 patients, with 75.8% (n=628) having a BMI ≤30 kg/m² and 24.2% (n=200) having a BMI >30 kg/m². Within the finer BMI categories, 3.7% (n=31) of patients had a BMI ≤20 kg/m², 24.2% (n=200) were in the 20 to ≤25 kg/m² range, 47.9% (n=397) were in the 25 to ≤30 kg/m² range, 19.7% (n=163) were in the 30 to ≤35 kg/m² range, and 4.5% (n=37) had a BMI >35 kg/m². The majority of patients (47.9%) were within the BMI range of 25–30 kg/m², reflecting a significant proportion of overweight individuals in the cohort. Patients with a BMI >35 kg/m² constituted the smallest group, accounting for only 4.5% of the total population.
The study found no statistically significant differences between the groups concerning admission rates for acute pancreatitis and acute cholangitis, ASA scores, cannulation success, post-ERCP complications, and readmission within 30 days after the procedure (p>0.05). Notably, women were found to be significantly more obese (p<0.001), and a higher weight was associated with an increased history of cholecystectomy (p=0.013).
Additionally, there were no significant differences in average age, Charlson comorbidity index, cannulation time, length of hospital stay, or white blood cell count at admission between the BMI groups (p>0.05). However, CRP and total bilirubin levels were significantly higher in the non-obese group compared to the obese group (p=0.01 and p=0.011, respectively).

Conclusion

BMI did not influence the incidence of acute pancreatitis or acute cholangitis on admission, ASA scores, cannulation success or time, post-ERCP complications, length of hospital stays, or 30-day readmission rates. Interestingly, higher CRP and total bilirubin levels were observed in patients without obesity at admission, warranting further investigation into their clinical implications.

References

1. Tokmak S TS. History of ERCP. The Turkish Journal of Academic Gastroenterology 2021;20: 1-2.
2. Duzenli T, Koseoglu H. Endoscopic Retrograde Cholangiopancreatography During the COVID-19 Pandemic: Effects of Enhanced Personal Protective Equipment. Dig Dis Sci 2021;66: 1845-1851.
3. Alfaris N, Alqahtani AM, Alamuddin N, Rigas G. Global Impact of Obesity. Gastroenterol Clin North Am2023;52: 277-293.
4. Deenadayalu VP, Blaut U, Watkins JL, Barnett J, Freeman M, Geenen J, et al. Does obesity confer an increased risk and/or more severe course of post-ERCP pancreatitis?: a retrospective, multicenter study. J Clin Gastroenterol2008;42: 1103-1109.
5. Fujisawa T, Kagawa K, Hisatomi K, Kubota K, Sato H, Nakajima A, et al. Obesity with abundant subcutaneous adipose tissue increases the risk of post-ERCP pancreatitis. J Gastroenterol 2016;51: 931-938.