Lactobacillus acidophilus KBL409 Ameliorates Atopic Dermatitis in a Mouse Model.

Atopic dermatitis (AD) is a chronic inflammatory skin disease with repeated exacerbations of eczema and pruritus. Probiotics can prevent or treat AD appropriately via modulation of immune responses and gut microbiota. In this study, we evaluated effects of Lactobacillus acidophilus (L. acidophilus) KBL409 using a house dust mite (Dermatophagoides farinae)-induced in vivo AD model. Oral administration of L. acidophilus KBL409 significantly reduced dermatitis scores and decreased infiltration of immune cells in skin tissues. L. acidophilus KBL409 reduced in serum immunoglobulin E and mRNA levels of T helper (Th)1 (Interferon-γ), Th2 (Interleukin [IL]-4, IL-5, IL-13, and IL-31), and Th17 (IL-17A) cytokines in skin tissues. The anti-inflammatory cytokine IL-10 was increased and Foxp3 expression was up-regulated in AD-induced mice with L. acidophilus KBL409. Furthermore, L. acidophilus KBL409 significantly modulated gut microbiota and concentrations of short-chain fatty acids and amino acids, which could explain its effects on AD. Our results suggest that L. acidophilus KBL409 is the potential probiotic for AD treatment by modulating of immune responses and gut microbiota of host.

Lactobacillus rhamnosus KBL2290 Ameliorates Gut Inflammation in a Mouse Model of Dextran Sulfate Sodium-Induced Colitis.

Ulcerative colitis, a major form of inflammatory bowel disease (IBD) associated with chronic colonic inflammation, may be induced via overreactive innate and adaptive immune responses. Restoration of gut microbiota abundance and diversity is important to control the pathogenesis. Lactobacillus spp., well-known probiotics, ameliorate IBD symptoms via various mechanisms, including modulation of cytokine production, restoration of gut tight junction activity and normal mucosal thickness, and alterations in the gut microbiota. Here, we studied the effects of oral administration of Lactobacillus rhamnosus (L. rhamnosus) KBL2290 from the feces of a healthy Korean individual to mice with DSS-induced colitis. Compared to the dextran sulfate sodium (DSS) + phosphate-buffered saline control group, the DSS + L. rhamnosus KBL2290 group evidenced significant improvements in colitis symptoms, including restoration of body weight and colon length, and decreases in the disease activity and histological scores, particularly reduced levels of pro-inflammatory cytokines and an elevated level of anti-inflammatory interleukin-10. Lactobacillus rhamnosus KBL2290 modulated the levels of mRNAs encoding chemokines and markers of inflammation; increased regulatory T cell numbers; and restored tight junction activity in the mouse colon. The relative abundances of genera Akkermansia, Lactococcus, Bilophila, and Prevotella increased significantly, as did the levels of butyrate and propionate (the major short-chain fatty acids). Therefore, oral L. rhamnosus KBL2290 may be a useful novel probiotic.

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.