Elucidation of molecular basis of osteolytic bone lesions in advanced multiple myeloma.

Osteolytic bone lesion is a major cause of lower quality of life and poor prognosis in patients with multiple myeloma (MM), but molecular pathogenesis of the osteolytic process in MM remains elusive. Fms-like tyrosine kinase 3 ligand (FLT3L) was reported to be elevated in bone marrow (BM) and blood of patients with advanced MM who often show osteolysis. Here, we investigated a functional link of FLT3L to osteolytic process in MM. We recruited 86, 306, and 52 patients with MM, acute myeloid leukemia (AML), and acute lymphoblastic leukemia (ALL), respectively. FLT3L levels of patients with hematologic malignancies were measured in BM-derived plasma and found to be significantly higher in MM than in AML or ALL, which rarely show osteolysis. FLT3L levels were further elevated in MM patients with bone lesion compared with patients without bone lesion. In vitro cell-based assays showed that the administration of FLT3L to HEK293T, HeLa, and U2OS cells led to an increase in the DKK1 transcript level through STAT3 phosphorylation at tyrosine 705. WNT reporter assay showed that FLT3L treatment reduced WNT signaling and nuclear translocation of ß-catenin. These results collectively show that the FLT3L-STAT3-DKK1 pathway inhibits WNT signaling-mediated bone formation in MM, which can cause osteolytic bone lesion. Finally, transcriptomic profiles revealed that FLT3L and DKK1 were predominantly elevated in the hyperdiploidy subtype of MM. Taken together, FLT3L can serve as a promising biomarker for predicting osteolytic bone lesion and also a potential therapeutic target to prohibit the progression of the osteolytic process in MM with hyperdiploidy.

Lower Plasma Amyloid Beta - 42 Levels Associated With Worse Survival in Patients With Glioma.

BACKGROUND/AIM: Glioma is often refractory. The accumulation of amyloid beta (Aß) in the brain is commonly associated with Alzheimer's disease (AD), but there are studies suggesting that Aß has tumor suppressor potential. The aim of this study was to identify a novel, non-invasive candidate biomarker for histological prediction and prognostic assessment of glioma. PATIENTS AND METHODS: Serum was prepared from blood samples collected preoperatively from 48 patients with WHO grade II-IV glioma between October 2004 and December 2017 at a single tertiary institution. The concentration of Aß42 was measured using the SMCxPRO immunoassay (Merck). The clinical and histological characteristics of the patients, including molecular subtypes, were reviewed. RESULTS: The mean age of the patients was 52.2±12.5 years. The mean value of serum Aß42 concentration was 7.6±7.8 pg/ml in the anaplastic astrocytoma (WHO grade III) group and 6.4±6.5 pg/ml in the glioblastoma multiforme (WHO grade IV) group. The Negative epidermal growth factor receptor (EGFR) expression was associated with higher serum Aß42 levels (p=0.020). Kaplan-Meier analysis demonstrated that patients with high serum Aß42 (>11.78 pg/ml) had significantly longer progression-free survival (PFS) (p=0.038) and overall survival (OS) (p=0.018). CONCLUSION: This study investigated serum Aß42 levels as a potential biomarker for glioma. The results showed that low serum Aß42 levels were associated with EGFR expression and poor PFS and OS. Overall, these findings suggest a potential role of Aß42 as a prognostic marker in astrocytomas.

A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing.

Microbial community profiling using 16S rRNA amplicon sequencing allows for taxonomic characterization of diverse microorganisms. While amplicon sequence variant (ASV) methods are increasingly favored for their fine-grained resolution of sequence variants, they often discard substantial portions of sequencing reads during quality control, particularly in datasets with large number samples. We present a streamlined pipeline that integrates FastP for read trimming, HmmUFOtu for operational taxonomic units (OTU) clustering, Vsearch for chimera checking, and Kraken2 for taxonomic assignment. To assess the pipeline's performance, we reprocessed two published stool datasets of normal Korean populations: one with 890 and the other with 1,462 independent samples. In the first dataset, HmmUFOtu retained 93.2% of over 104 million read pairs after quality trimming, discarding chimeric or unclassifiable reads, while DADA2, a commonly used ASV method, retained only 44.6% of the reads. Nonetheless, both methods yielded qualitatively similar ß-diversity plots. For the second dataset, HmmUFOtu retained 89.2% of read pairs, while DADA2 retained a mere 18.4% of the reads. HmmUFOtu, being a closed-reference clustering method, facilitates merging separately processed datasets, with shared OTUs between the two datasets exhibiting a correlation coefficient of 0.92 in total abundance (log scale). While the first two dimensions of the ß-diversity plot exhibited a cohesive mixture of the two datasets, the third dimension revealed the presence of a batch effect. Our comparative evaluation of ASV and OTU methods within this streamlined pipeline provides valuable insights into their performance when processing large-scale microbial 16S rRNA amplicon sequencing data. The strengths of HmmUFOtu and its potential for dataset merging are highlighted.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.

Cyclic Stretch Promotes Cellular Reprogramming Process through Cytoskeletal-Nuclear Mechano-Coupling and Epigenetic Modification.

Advancing the technologies for cellular reprogramming with high efficiency has significant impact on regenerative therapy, disease modeling, and drug discovery. Biophysical cues can tune the cell fate, yet the precise role of external physical forces during reprogramming remains elusive. Here the authors show that temporal cyclic-stretching of fibroblasts significantly enhances the efficiency of induced pluripotent stem cell (iPSC) production. Generated iPSCs are proven to express pluripotency markers and exhibit in vivo functionality. Bulk RNA-sequencing reveales that cyclic-stretching enhances biological characteristics required for pluripotency acquisition, including increased cell division and mesenchymal-epithelial transition. Of note, cyclic-stretching activates key mechanosensitive molecules (integrins, perinuclear actins, nesprin-2, and YAP), across the cytoskeletal-to-nuclear space. Furthermore, stretch-mediated cytoskeletal-nuclear mechano-coupling leads to altered epigenetic modifications, mainly downregulation in H3K9 methylation, and its global gene occupancy change, as revealed by genome-wide ChIP-sequencing and pharmacological inhibition tests. Single cell RNA-sequencing further identifies subcluster of mechano-responsive iPSCs and key epigenetic modifier in stretched cells. Collectively, cyclic-stretching activates iPSC reprogramming through mechanotransduction process and epigenetic changes accompanied by altered occupancy of mechanosensitive genes. This study highlights the strong link between external physical forces with subsequent mechanotransduction process and the epigenetic changes with expression of related genes in cellular reprogramming, holding substantial implications in the field of cell biology, tissue engineering, and regenerative medicine.

Identification of novel Carnobacterium maltaromaticum strains in bone marrow samples of patients with acute myeloid leukemia using a metagenomic binning approach.

The aim of this study aimed to examine the existence of a bacterial metagenome in the bone marrow of patients with acute myeloid leukemia (AML). We re-examined whole-genome sequencing data from the bone marrow samples of seven patients with AML, four of whom were remitted after treatment, for metagenomic analysis. After the removal of human reads, unmapped reads were used to profile the species-level composition. We used the metagenomic binning approach to confirm whether the identified taxon was a complete genome of known or novel strains. We observed a unique and novel microbial signature in which Carnobacterium maltaromaticum was the most abundant species in five patients with AML or remission. The complete genome of C. maltaromaticum "BMAML_KR01," which was observed in all samples, was 100% complete with 8.5% contamination and closely clustered with C. maltaromaticum strains DSM20730 and SF668 based on single nucleotide polymorphism variations. We identified five unique proteins that could contribute to cancer progression and 104 virulent factor proteins in the BMAML_KR01 genome. To our knowledge, this is the first report of a new strain of C. maltaromaticum in patients with AML. The presence of C. maltaromaticum and its new strain in patients indicates an urgent need to validate the existence of this bacterium and evaluate its pathophysiological role.

Aberrant Methylation of Somatostatin Receptor 2 Gene Is Initiated in Aged Gastric Mucosa Infected with Helicobacter pylori and Consequential Gene Silencing Is Associated with Establishment of Inflammatory Microenvironment In Vitro Study.

The loss-of-function variants are thought to be associated with inflammation in the stomach. We here aimed to evaluate the extent and role of methylation at the SSTR2 promoter in inflammation and gastric tumor formation. A whole-genome bisulfite sequencing analysis revealed that the SSTR2 promoter was significantly hypermethylated in gastric tumors, dysplasia, and intestinal metaplasia compared to non-tumor tissues from patients with gastric cancer. Using public data, we confirmed SSTR2 promoter methylation in primary gastric tumors and intestinal metaplasia, and even aged gastric mucosae infected with Helicobacter pylori, suggesting that aberrant methylation is initiated in normal gastric mucosa. The loss-of-function of SSTR2 in SNU638 cell-induced cell proliferation in vitro, while stable transfection of SSTR2 in AGS and MKN74 cells inhibited cell proliferation and tumorigenesis in vitro and in vivo. As revealed by a comparison of target genes differentially expressed in these cells with hallmark molecular signatures, inflammation-related pathways were distinctly induced in SSTR2-KO SNU638 cell. By contrast, inflammation-related pathways were inhibited in AGS and MKN74 cells ectopically expressing SSTR2. Collectively, we propose that SSTR2 silencing upon promoter methylation is initiated in aged gastric mucosae infected with H. pylori and promotes the establishment of an inflammatory microenvironment via the intrinsic pathway. These findings provide novel insights into the initiation of gastric carcinogenesis.

Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images.

To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MRIs of gliomas and augmenting our meningioma training set with normal brain MRIs. Pre-operative MRIs of 91 meningioma patients (171 MRIs) and 10 non-meningioma patients (normal brains) were collected between 2016 and 2019. Three-dimensional (3D) U-Net was used as the base architecture. The model was pre-trained with BraTS 2019 data, then fine-tuned with our datasets consisting of 154 meningioma MRIs and 10 normal brain MRIs. To increase the utility of the normal brain MRIs, a novel balanced Dice loss (BDL) function was used instead of the conventional soft Dice loss function. The model performance was evaluated using the Dice scores across the remaining 17 meningioma MRIs. The segmentation performance of the model was sequentially improved via the pre-training and inclusion of normal brain images. The Dice scores improved from 0.72 to 0.76 when the model was pre-trained. The inclusion of normal brain MRIs to fine-tune the model improved the Dice score; it increased to 0.79. When employing BDL as the loss function, the Dice score reached 0.84. The proposed learning strategy for U-net showed potential for use in segmenting meningioma lesions.

Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using Transcriptome Imputed from Genotypes.

Almost half of patients show no primary or secondary response to monoclonal anti-tumor necrosis factor α (anti-TNF) antibody treatment for inflammatory bowel disease (IBD). Thus, the exact mechanisms of a non-durable response (NDR) remain inadequately defined. We used our genome-wide genotype data to impute expression values as features in training machine learning models to predict a NDR. Blood samples from various IBD cohorts were used for genotyping with the Korea Biobank Array. A total of 234 patients with Crohn's disease (CD) who received their first anti-TNF therapy were enrolled. The expression profiles of 6294 genes in whole-blood tissue imputed from the genotype data were combined with clinical parameters to train a logistic model to predict the NDR. The top two and three most significant features were genetic features (DPY19L3, GSTT1, and NUCB1), not clinical features. The logistic regression of the NDR vs. DR status in our cohort by the imputed expression levels showed that the ß coefficients were positive for DPY19L3 and GSTT1, and negative for NUCB1, concordant with the known eQTL information. Machine learning models using imputed gene expression features effectively predicted NDR to anti-TNF agents in patients with CD.

Effect of Lactobacillus dominance modified by Korean Red Ginseng on the improvement of Alzheimer's disease in mice.

Background: Gut microbiota influence the central nervous system through gut-brain-axis. They also affect the neurological disorders. Gut microbiota differs in patients with Alzheimer's disease (AD), as a potential factor that leads to progression of AD. Oral intake of Korean Red Ginseng (KRG) improves the cognitive functions. Therefore, it can be proposed that KRG affect the microbiota on the gut-brain-axis to the brain. Methods: Tg2576 were used for the experimental model of AD. They were divided into four groups: wild type (n = 6), AD mice (n = 6), AD mice with 30 mg/kg/day (n = 6) or 100 mg/kg/day (n = 6) of KRG. Following two weeks, changes in gut microbiota were analyzed by Illumina HiSeq4000 platform 16S gene sequencing. Microglial activation were evaluated by quantitative Western blot analyses of Iba-1 protein. Claudin-5, occludin, laminin and CD13 assay were conducted for Blood-brain barrier (BBB) integrity. Amyloid beta (Aß) accumulation demonstrated through Aß 42/40 ratio was accessed by ELISA, and cognition were monitored by Novel object location test. Results: KRG improved the cognitive behavior of mice (30 mg/kg/day p < 0.05; 100 mg/kg/day p < 0.01), and decreased Aß 42/40 ratio (p < 0.01) indicating reduced Aß accumulation. Increased Iba-1 (p < 0.001) for reduced microglial activation, and upregulation of Claudin-5 (p < 0.05) for decreased BBB permeability were shown. In particular, diversity of gut microbiota was altered (30 mg/kg/day q-value<0.05), showing increased population of Lactobacillus species. (30 mg/kg/day 411%; 100 mg/kg/day 1040%). Conclusions: KRG administration showed the Lactobacillus dominance in the gut microbiota. Improvement of AD pathology by KRG can be medicated through gut-brain axis in mice model of AD.

Development of a Machine Learning Model to Distinguish between Ulcerative Colitis and Crohn's Disease Using RNA Sequencing Data.

Crohn's disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Reversion of Gut Microbiota during the Recovery Phase in Patients with Asymptomatic or Mild COVID-19: Longitudinal Study.

Patients with COVID-19 have been reported to experience gastrointestinal symptoms as well as respiratory symptoms, but the effects of COVID-19 on the gut microbiota are poorly understood. We explored gut microbiome profiles associated with the respiratory infection of SARS-CoV-2 during the recovery phase in patients with asymptomatic or mild COVID-19. A longitudinal analysis was performed using the same patients to determine whether the gut microbiota changed after recovery from COVID-19. We applied 16S rRNA amplicon sequencing to analyze two paired fecal samples from 12 patients with asymptomatic or mild COVID-19. Fecal samples were selected at two time points: during SARS-CoV-2 infection (infected state) and after negative conversion of the viral RNA (recovered state). We also compared the microbiome data with those from 36 healthy controls. Microbial evenness of the recovered state was significantly increased compared with the infected state. SARS-CoV-2 infection induced the depletion of Bacteroidetes, while an abundance was observed with a tendency to rapidly reverse in the recovered state. The Firmicutes/Bacteroidetes ratio in the infected state was markedly higher than that in the recovered state. Gut dysbiosis was observed after infection even in patients with asymptomatic or mild COVID-19, while the composition of the gut microbiota was recovered after negative conversion of SARS-CoV-2 RNA. Modifying intestinal microbes in response to COVID-19 might be a useful therapeutic alternative.

Selective estrogen receptor modulator lasofoxifene suppresses spondyloarthritis manifestation and affects characteristics of gut microbiota in zymosan-induced SKG mice.

Ankylosing spondylitis is a male-predominant disease and previous study revealed that estrogens have an anti-inflammatory effect on the spondyloarthritis (SpA) manifestations in zymosan-induced SKG mice. This study aimed to evaluate the effect of selective estrogen receptor modulator (SERM) lasofoxifene (Laso) on disease activity of SpA. Mice were randomized into zymosan-treated, zymosan + 17ß-estradiol (E2)-treated, and zymosan + Laso-treated groups. Arthritis was assessed by 18F-fluorodeoxyglucose (18F-FDG) small-animal positron emission tomography/computed tomography and bone mineral density (BMD) was measured. Fecal samples were collected and 16S ribosomal RNA gene sequencing was used to determine gut microbiota differences. Both zymosan + E2-treated mice and zymosan + Laso-treated mice showed lower arthritis clinical scores and lower 18F-FDG uptake than zymosan-treated mice. BMD was significantly higher in zymosan + E2-treated mice and zymosan + Laso-treated mice than zymosan-treated mice, respectively. Fecal calprotectin levels were significantly elevated at 8 weeks after zymosan injection in zymosan-treated mice, but it was not significantly changed in zymosan + E2-treated mice and zymosan + Laso-treated mice. Gut microbiota diversity of zymosan-treated mice was significantly different from zymosan + E2-treated mice and zymosan + Laso-treated mice, respectively. There was no significant difference in gut microbiota diversity between zymosan + E2-treated mice and zymosan + Laso -treated mice. Laso inhibited joint inflammation and enhanced BMD in SKG mice, a model of SpA. Laso also affected the composition and biodiversity of gut microbiota. This study provides new knowledge regarding that selected SpA patients could benefit from SERM treatment.

Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn's Disease: Results from the IMPACT Study.

Early intestinal resection in patients with Crohn's disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical-genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD.

Deep Neural Network-Based Prediction of the Risk of Advanced Colorectal Neoplasia.

Background/Aims: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). Conclusions: Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.

Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults.

BACKGROUND/AIMS: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized. METHODS: We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split validation group. RESULTS: In comparison with the LR model (AUC, 0.724; 95% confidence interval [CI], 0.684 to 0.765), the DNN model (AUC, 0.760; 95% CI, 0.724 to 0.795) demonstrated significantly improved performance with respect to the prediction of ACRN (p < 0.001). At a sensitivity of 90%, the specificity significantly increased with the application of the DNN model (41.0%) in comparison with the LR model (26.5%) (p < 0.001), indicating that the colonoscopy workload required to detect the same number of ACRNs could be reduced by 20%. CONCLUSION: The application of DNN to big clinical data could significantly improve the prediction of ACRNs in comparison with the LR model, potentially realizing further customization by utilizing large quantities and various types of biomedical information.

Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema.

BACKGROUND: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals. METHODS: Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images were used. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (> 0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. The optic neuropathy group included cases of ischemic optic neuropathy (177), optic neuritis (48), diabetic optic neuropathy (17), papilledema (22), and retinal disorders (31). We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)). Accuracy and area under receiver operating characteristic curve (AUROC) were analyzed. RESULTS: The accuracy of machine learning classifiers ranged from 95.89 to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999). CONCLUSIONS: Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies.

ERK-dependent IL-6 positive feedback loop mediates resistance against a combined treatment using danusertib and BKM120 in Burkitt lymphoma cell lines.

This study was conducted to define the synergistic effect of the PI3K inhibitor BKM120 with the pan-Aurora kinase inhibitor danusertib and the potential mechanism of resistance to the combined inhibitor treatment in Burkitt lymphoma cell lines. The combination of danusertib and BKM120 showed a synergistic effect on Namalwa cells but not on BJAB cells. The combined treatment led to ERK hyperactivation and induced IL-6 secretion in BJAB cells but not in Namalwa cells. A blockade of ERK signaling with trametinib suppressed the combination treatment-induced ERK activation, reduced IL-6 mRNA expression, and downregulated IL-6R mRNA expression, resulting in an improvement in the antitumor effect. We stepwise treated Namalwa cells with both inhibitors using on-and-off treatment cycles and found that Namalwa cells gained chemoresistance by activating the ERK/IL-6 feedback loop, suggesting that the ERK-dependent IL-6 positive feedback loop can compensate for AKT inactivation and is closely associated with adaptive resistance and relapse.

Correction: A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

[This corrects the article DOI: 10.1371/journal.pone.0207982.].

Fecal Microbiota Differences According to the Risk of Advanced Colorectal Neoplasms.

GOALS AND BACKGROUND: This study aimed to compare differences in the fecal microbiota according to the risk of advanced colorectal neoplasia (ACN) based on a risk-score model in a large Korean cohort. STUDY: Stool samples were collected from 1122 health screening recipients: 404 enrolled in the average risk (AR) group, 514 in the moderate risk (MR) group, and 204 in the high risk (HR) group, in accordance with their risk of ACN. The fecal microbiota was characterized using pyrosequencing of the V3-V4 region of the 16S rRNA genes. RESULTS: The overall microbial diversity was significantly reduced with an increased risk of ACN [false discovery rate (FDR), P<0.001], and the composition was significantly different between the risk groups (Bonferroni corrected, P<0.05). On taxonomic comparison, 6 of 11 phyla and 39 of 88 genera were significantly different among the risk groups (all FDR P<0.05). These included under-representation of Bacteroides, Ruminococcus, and Bifidobacterium, and over-representation of Prevotella and Fusobacterium with an increased risk of ACN. In particular, we observed that the unknown genus of Ruminococcaceae were relatively abundant (16.2%) in the AR group and significantly depleted with an increased risk of ACN (13.5% in the HR group; FDR P<0.001). CONCLUSIONS: These findings support the hypothesis that the fecal microbiota is different according to the risk of ACN. An unknown genus of Ruminococcaceae, as novel potential butyrate producers, might have a possible role in colorectal tumorigenesis in the Korean population.