A Logistic Regression Model for Predicting the Risk of Subsequent Surgery among Patients with Newly Diagnosed Crohn's Disease Using a Brute Force Method.

Surgery avoidance is an important goal in Crohn's disease (CD) treatment and predicting the risk of subsequent surgery is important to determine adequate therapeutic strength for patients with newly diagnosed CD. Herein, we aimed to construct a prediction model for the risk of subsequent surgery based on disease characteristics at the patients' initial visit. We retrospectively collected disease characteristic data from 93 patients with newly diagnosed CD. A logistic regression model with a brute force method was used to maximize the area under the receiver operating characteristic curve (auROC) by employing a combination of potential predictors from 14 covariates (16,383). The auROC remained almost constant when one to 12 covariates were considered, reaching a peak of 0.89 at four covariates (small-bowel patency, extensive small-bowel lesions, main lesions, and the number of poor prognostic factors), and it decreased with increasing covariate size. The most significant predictors were small-bowel patency, extensive small-bowel lesions, and age or major lesions. Therefore, this prediction model using covariates may be helpful in determining the likelihood that a patient with newly diagnosed CD will require surgery, which can aid in appropriate treatment selection for high-risk patients.

Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50.

Capsule endoscopy has been widely used as a non-invasive diagnostic tool for small or large intestinal lesions. In recent years, automated lesion detection systems using machine learning have been devised. This study aimed to develop an automated system for capsule endoscopic severity in patients with ulcerative colitis along the entire length of the colon using ResNet50. Capsule endoscopy videos from patients with ulcerative colitis were collected prospectively. Each single examination video file was partitioned into four segments: the cecum and ascending colon, transverse colon, descending and sigmoid colon, and rectum. Fifty still pictures (576 × 576 pixels) were extracted from each partitioned video. A patch (128 × 128 pixels) was trimmed from the still picture at every 32-pixel-strides. A total of 739,021 patch images were manually classified into six categories: 0) Mayo endoscopic subscore (MES) 0, 1) MES1, 2) MES2, 3) MES3, 4) inadequate quality for evaluation, and 5) ileal mucosa. ResNet50, a deep learning framework, was trained using 483,644 datasets and validated using 255,377 independent datasets. In total, 31 capsule endoscopy videos from 22 patients were collected. The accuracy rates of the training and validation datasets were 0.992 and 0.973, respectively. An automated evaluation system for the capsule endoscopic severity of ulcerative colitis was developed. This could be a useful tool for assessing topographic disease activity, thus decreasing the burden of image interpretation on endoscopists.

Analysis of lead distribution in avian organs by LA-ICP-MS: Study of experimentally lead-exposed ducks and kites.

Lead poisoning of wild birds by ingestion of lead ammunition occurs worldwide. Histopathological changes in organs of lead-intoxicated birds are widely known, and lead concentration of each organ is measurable using mass spectrometry. However, detailed lead localization at the suborgan level has remained elusive in lead-exposed birds. Here we investigated the detailed lead localization in organs of experimentally lead-exposed ducks and kites by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). In both the ducks and kites, lead accumulated diffusely in the liver, renal cortex, and brain. Lead accumulation was restricted to the red pulp in the spleen. With regard to species differences in lead distribution patterns, it is noteworthy that intensive lead accumulation was observed in the arterial walls only in the kites. In addition, the distribution of copper in the brain was altered in the lead-exposed ducks. Thus, the present study shows suborgan lead distribution in lead-exposed birds and its differences between avian species for the first time. These findings will provide fundamental information to understand the cellular processes of lead poisoning and the mechanisms of species differences in susceptibility to lead exposure.