Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

A lymphoepithelial cyst (LEC) is a rare pancreatic lesion, histologically showing squamous epithelia, dense lymphoid tissues, and a keratin substance. Cross-section images of the pancreatic LEC typically show a well demarcated unilocular or multilocular cyst without a solid component. Here we report a rare case of pancreatic LEC in which multiple floating ball-like components were depicted via endoscopic ultrasound. The ball-like components were also depicted by various imaging methods such as computed tomography (CT) showing low-density components, T1-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) showing high-intensity components, and T2-weighted MRI showing low-intensity components. The ball-like components in all images were not well enhanced. Laparotomic cyst resection was performed, and the surgical material revealed keratin balls inside the pancreatic LEC. Keratin components of a pancreatic LEC can take a liquid, sludge, or solid form. Clinicians must be aware of the variations in imaging to facilitate the differentiation and management of pancreatic cystic lesions. Abbreviations: CA 19-9: carbohydrate antigen 19-9; CEA: carcinoembryonic antigen; DWI: diffusion-weighted image; LEC: lymphoepithelial cyst; IPMN: intraductal papillary neoplasm; MCN: mucinous cystic neoplasm.

The Gant-Miwa-Thiersch method (GMT) is regarded as a basic and safe surgical procedure for rectal prolapse. We report two cases of rectal perforations while performing a GMT repair. Case 1 involved a 75-year-old female and case 2 involved a 93-year-old female. The patients underwent emergency surgeries with colostomies for rectal perforation. The perforation rate during the GMT method in our hospital is 0.5 ; however, the perforation rate is increased in patients with relapse. The possibility of serious complications during a GMT repair should be kept in mind, and the best surgical approach for rectal prolapse should always be selected.Key words: Gant-Miwa-Thiersch method, rectal perforation after operation of rectal prolapse, complication after operation of rectalprolapse

No abstract

No abstract