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Background and Aim Biliary tract infection (BTI) is an inflammatory disease and commonly associated with bacteremia. Delays in diagnosis or treatment of BTI cause high morbidity and mortality. However, an early diagnosis depends on appropriate clinical investigations. Appropriate biomarkers are urgently needed to improve the BTI diagnostic rate. We hypothesized that intestinal fatty acid‐binding protein (I‐FABP) might be a potential biomarker for BTI diagnosis. Methods We examined data from subjects aged ≥18 years diagnosed with BTI, including cholangitis and cholecystitis, whose blood samples were adequate for I‐FABP and zonulin assessment. We also collected blood samples from healthy volunteers as the control group. We excluded subjects in both groups who received steroids, antibiotics, or probiotics within 1 month before hospital admission (BTI cohort) or participation in this research (controls). The main study endpoint was to compare the diagnostic ability of I‐FABP to detect BTI in comparison with high‐sensitivity C‐reactive protein (hs‐CRP) and zonulin. Results The study collected the data of 51 patients with BTI and 35 healthy subjects. The receiver operating characteristic (ROC) area under the curve (AUC) for I‐FABP was 0.884 (95% confidence interval [CI]: 0.814–0.954), numerically higher than that for hs‐CRP (0.880; 0.785–0.976) and zonulin (0.570; 0.444–0.697). We estimated that the optimal cutoff value of I‐FABP was 2.1 ng/mL (sensitivity: 0.804; specificity: 0.829) for the diagnosis of BTI. Conclusions In summary, this study suggests that I‐FABP may be a potential alternative biomarker to hs‐CRP for diagnosing BTI. Further research should verify the use of I‐FABP as a marker for BTI diagnosis, but also for other inflammatory diseases., Our study revealed the diagnostic performance of intestinal fatty acid‐binding protein (I‐FABP) was comparable to that of C‐reactive protein (CRP) and superior to zonulin for detecting biliary tract infection (BTI).

Pyogenic liver abscess (PLA) is an infectious disease and causes high in-hospital mortality. It has no specific symptoms and is difficult to be diagnosed early in the emergency department. Therefore, early diagnosis is an urgent need for clinicians. Ultrasound has been widely used to detect lesions of PLA. However, the sensitivity of ultrasound is affected by the size and location of lesions and clinician’s experience. We conducted a retrospective study to assess the effects of early adoption of non-enhanced computed tomography (CT) scanning (i.e., receiving CT scanning within 48 hours after admission) on treatment and disease status of patients with PLA. This study enrolled 76 hospitalized patients with PLA in department of digestive disease, who received CT examination during 2014 to 2021. Of the enrolled patients, 56 received an early CT scan and 20 received a late CT scan. Results showed that the length of hospitalization in the early CT group was significantly shorter than the late CT group (15.0 days vs. 20.5 days; p = 0.035). Besides, the median time to initiating drainage after admission was also significantly shorter in the early CT group than the late CT group (1.0 days vs. 4.5 days; p

Adequate bowel preparation is key to a successful colonoscopy, which is necessary for detecting adenomas and preventing colorectal cancer. We developed an artificial intelligence (AI) platform using a convolutional neural network (CNN) model (AI-CNN model) to evaluate the quality of bowel preparation before colonoscopy.This was a colonoscopist-blinded, randomized study. Enrolled patients were randomized into an experimental group, in which our AI-CNN model was used to evaluate the quality of bowel preparation (AI-CNN group), or a control group, which performed self-evaluation per routine practice (control group). The primary outcome was the consistency (homogeneity) between the results of the 2 methods. The secondary outcomes included the quality of bowel preparation according to the Boston Bowel Preparation Scale (BBPS), polyp detection rate, and adenoma detection rate.A total of 1,434 patients were enrolled (AI-CNN, n = 730; control, n = 704). No significant difference was observed between the evaluation results ("pass" or "not pass") of the groups in the adequacy of bowel preparation as represented by BBPS scores. The mean BBPS scores, polyp detection rate, and adenoma detection rate were similar between the groups. These results indicated that the AI-CNN model and routine practice were generally consistent in the evaluation of bowel preparation quality. However, the mean BBPS score of patients with "pass" results were significantly higher in the AI-CNN group than in the control group, indicating that the AI-CNN model may further improve the quality of bowel preparation in patients exhibiting adequate bowel preparation.The novel AI-CNN model, which demonstrated comparable outcomes to the routine practice, may serve as an alternative approach for evaluating bowel preparation quality before colonoscopy.

Background/aims Given the increased incidence of obstructive sleep apnea (OSA) among patients with nonalcoholic fatty liver disease (NAFLD), noninvasive screening methods are urgently needed to screen for OSA risk in these patients when conducting an office-based assessment of hepatic steatosis. Therefore, we investigated the controlled attenuation parameter (CAP) and hepatic steatosis index (HSI) in patients with and without OSA and developed screening models to detect OSA. Methods We retrospectively reviewed the medical records of all adult snorers with suspected NAFLD undergoing liver sonography between June 2017 and June 2020. Records encompassed CAP and HSI data as well as data collected during in-hospital full-night polysomnography. The multivariate logistic regression models were constructed to explore the predictors of OSA risk. Furthermore, model validation was performed based on the medical records corresponding to the July 2020–June 2021 period. Results A total of 59 patients were included: 81.4% (48/59) were men, and the mean body mass index (BMI) was 26.4 kg/m2. Among the patients, 62.7% (37/59) and 74.6% (44/59) (detected by the HSI and CAP, respectively) had NAFLD, and 78% (46/59) were diagnosed with OSA on the basis of polysomnography. Three screening models based on multivariate analysis were established. The model combining male sex, a BMI of > 24.8, and an HSI of > 38.3 screened for OSA risk the most accurately, with an area under the receiver operating characteristic curve of 0.81 (sensitivity: 78%; specificity: 85%; and positive and negative predictive values: 95% and 52%, respectively) in the modeling cohort. An accuracy of 70.0% was achieved in the validation group. Conclusions The combination screening models proposed herein provide a convenient, noninvasive, and rapid screening tool for OSA risk and can be employed while patients receive routine hepatic check-ups. These models can assist physicians in identifying at-risk OSA patients and thus facilitate earlier detection and timely treatment initiation.