Enhancement of Immune Functions by Limosilactobacillus reuteri KBL346: In Vitro and In Vivo Studies.

Lactobacilli have been widely used as probiotics because of their benefits for intestinal health and physiological functions. Among a variety of Lactobacillus genera, Limosilactobacillus reuteri has been studied for its ability to exert anti-inflammatory functions and its role in controlling metabolic disorders, as well as the production of the antimicrobial compound reuterin. However, the effects and mechanisms of L. reuteri on enhancing immune responses in the immunosuppressed states have been relatively understudied. In this study, we isolated an immunomodulatory strain, namely, L. reuteri KBL346 (KBL346), from a fecal sample of a 3-month-old infant in Korea. We evaluated the immunostimulatory activity and hematopoietic function of KBL346 in macrophages and cyclophosphamide (CPA)-induced immunosuppressed mice. KBL346 increased the phagocytic activity against Candida albicans MYA-4788 in macrophages, and as biomarkers for this, increased secretions of nitric oxide (NO) and prostaglandin E2 (PGE2) were confirmed. Also, the secretions of innate cytokines (TNF-α, IL-1ß, and IL-6) were increased. In CPA-induced immunosuppressed mice, KBL346 at a dosage of 1010 CFU/kg protected against spleen injury and suppressed levels of immune-associated parameters, including NK cell activity, T and B lymphocyte proliferation, CD4+ and CD8+ T cell abundance, cytokines, and immunoglobulins in vivo. The effects were comparable or superior to those in the Korean red ginseng positive control group. Furthermore, the safety assessment of KBL346 as a probiotic was conducted by evaluating its antibiotic resistance, hemolytic activity, cytotoxicity, and metabolic characteristics. This study demonstrated the efficacy and safety of KBL346, which could potentially be used as a supplement to enhance the immune system.

Flow Field-Flow Fractionation with a Thickness-Tapered Channel.

This study introduces the thickness-tapered channel design for flow field-flow fractionation (FlFFF) for the first time. In this design, the channel thickness linearly decreases along the channel axis such that the flow velocity increases down the channel. Channel thickness is an important variable for controlling retention time and resolution in field-flow fractionation. Especially, in the steric/hyperlayer mode of FlFFF, in which particles (>1 µm) migrate at elevated heights above the channel wall owing to hydrodynamic lift forces, the migration of long-retaining smaller-sized particles can be enhanced in a relatively thin channel or by increasing the migration flow rate; however, an upper size limit that can be resolved is simultaneously sacrificed. A thickness-tapered channel was constructed without a channel spacer by carving the surface of a channel block such that the channel inlet was deeper than the outlet (w = 400 → 200 µm). The performance of a thickness-tapered channel was evaluated using polystyrene standards and compared to that of a channel of uniform thickness (w = 300 µm) with a similar effective channel volume in terms of sample recovery, dynamic size range of separation, and steric transition under different flow rate conditions. The thickness-tapered channel can be an alternative to maintain the resolving power for particles with an upper large-diameter limit, faster separation of particles with a lower limit, and higher elution recovery without implementing the additional field-programming option.

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. METHODS: In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). RESULTS: We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. CONCLUSIONS: The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. KEY POINTS: • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates.

Laboratory mouse, Mus musculus, is one of the most important animal tools in biomedical research. Functional characterization of the mouse genes, hence, has been a long-standing goal in mammalian and human genetics. Although large-scale knockout phenotyping is under progress by international collaborative efforts, a large portion of mouse genome is still poorly characterized for cellular functions and associations with disease phenotypes. A genome-scale functional network of mouse genes, MouseNet, was previously developed in context of MouseFunc competition, which allowed only limited input data for network inferences. Here, we present an improved mouse co-functional network, MouseNet v2 (available at http://www.inetbio.org/mousenet), which covers 17 714 genes (>88% of coding genome) with 788 080 links, along with a companion web server for network-assisted functional hypothesis generation. The network database has been substantially improved by large expansion of genomics data. For example, MouseNet v2 database contains 183 co-expression networks inferred from 8154 public microarray samples. We demonstrated that MouseNet v2 is predictive for mammalian phenotypes as well as human diseases, which suggests its usefulness in discovery of novel disease genes and dissection of disease pathways. Furthermore, MouseNet v2 database provides functional networks for eight other vertebrate models used in various research fields.

Improving segmentation and detection of lesions in CT scans using intensity distribution supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% → 47.8%, 74.2% → 76.0%, and 26.4% → 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% → 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% -> 47.8%, 74.2% -> 76.0%, and 26.4% -> 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% -> 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

BACKGROUND: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase the sensitivity. PURPOSE: We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of our knowledge. METHODS: Thirty-three preoperative CT scans of 33 unique patients with surgically-proven carcinoid tumors within the small bowel were collected. Ground-truth segmentation of tumors was drawn on CT scans by referring to available 18 F-DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch-based classification, and segmentation-based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free-response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion- and patient-level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were also performed. RESULTS: The detection network method performed the best among the compared methods. For lesion-level detection, the detection network method, without the small bowel segmentation-based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per-scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance ( p = 0.742 $p=0.742$ ). For patient-level detection, again the detection network method, but with the small bowel segmentation-based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youden point. CONCLUSIONS: The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion-level detection. It also achieved a promising AUC for patient-level detection. The method may have clinical application in patients with this rare and difficult to detect disease.

A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans.

We present a novel graph-theoretic method for small bowel path tracking. It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection. We observed that a trivial solution with many short-cuts is easily made even with the wall detection, where the tracked path penetrates indistinct walls around the contact between different parts of the small bowel. Thus, we propose to include must-pass nodes in finding the path to better cover the entire course of the small bowel. The proposed method does not entail training with ground-truth paths while the previous methods do. We acquired ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans, which enables the evaluation of the path tracking for the entire course of the small bowel. The proposed method showed clear improvements in terms of several metrics compared to the baseline method. The maximum length of the path that is tracked without an error per scan, by the proposed method, is above 800mm on average.

GRAPH-BASED SMALL BOWEL PATH TRACKING WITH CYLINDRICAL CONSTRAINTS.

We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the contact between parts of itself along its course, which makes the path tracking difficult together with the indistinct appearance of the wall. It causes the tracked path to easily cross over the walls when relying on low-level features like the wall detection. To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions. It is implemented as soft constraints using a new cost function. The proposed method is evaluated against ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans. The proposed method showed clear improvements compared to the baseline method in tracking the path without making an error. Improvements of 6.6% and 17.0%, in terms of the tracked length, were observed for two different settings related to the small bowel segmentation.

Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations.

Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT) path of the small bowel in 3D. In this work, we propose to train a deep reinforcement learning tracker using datasets with different types of annotations. Specifically, we utilize CT scans that have only GT small bowel segmentation as well as ones with the GT path. It is enabled by designing a unique environment that is compatible for both, including a reward definable even without the GT path. The performed experiments proved the validity of the proposed method. The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.

The Effects of Persimmon (Diospyros kaki L.f.) Oligosaccharides on Features of the Metabolic Syndrome in Zebrafish.

Metabolic syndrome has become a global health care problem since it is rapidly increasing worldwide. The search for alternative natural supplements may have potential benefits for obesity and diabetes patients. Diospyros kaki fruit extract and its oligosaccharides, including gentiobiose, melibiose, and raffinose, were examined for their anti-insulin resistance and obesity-preventing effect in zebrafish larvae. The results show that D. kaki oligosaccharides improved insulin resistance and high-fat-diet-induced obesity in zebrafish larvae, evidenced by enhanced ß-cell recovery, decreased abdominal size, and reduced the lipid accumulation. The mechanism of the oligosaccharides, molecular docking, and enzyme activities of PTP1B were investigated. Three of the oligosaccharides had a binding interaction with the catalytic active sites of PTP1B, but did not show inhibitory effects in an enzyme assay. The catalytic residues of PTP1B were typically conserved and the cellular penetration of the cell membrane was necessary for the inhibitors. The results of the mechanism of action study indicate that D. kaki fruit extract and its oligosaccharides affected gene expression changes in inflammation- (TNF-α, IL-6, and IL-1ß), lipogenesis- (SREBF1 and FASN), and lipid-lowering (CPT1A)-related genes. Therefore, D. kaki fruit extract and its oligosaccharides may have a great potential for applications in metabolic syndrome drug development and dietary supplements.

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.

Lomens-P0 (mixed extracts of Hordeum vulgare and Chrysanthemum zawadskii) regulate the expression of factors affecting premenstrual syndrome symptoms.

BACKGROUND/OBJECTIVES: Premenstrual syndrome (PMS) is a disorder characterized by repeated emotional, behavioral, and physical symptoms before menstruation, and the exact cause and mechanism are uncertain. Hyperprolactinemia interferes with the normal production of estrogen and progesterone, leading to PMS symptoms. Thus, we judged that the inhibition of prolactin hypersecretion could mitigate PMS symptoms. MATERIALS/METHODS: Hordeum vulgare L. extract (HVE), Chrysanthemum zawadskii var. latilobum extract (CZE), and Lomens-P0 the mixture of these extracts were tested in subsequent experiments. The effect of extracts on prolactin secretion at the in vitro level was measured in GH3 cells. Nitric oxide and pro-inflammatory mediator expression were measured in RAW 264.7 cells to confirm the anti-inflammatory effect. Also, the hyperprolactinemic Institute for Cancer Research (ICR) mice model was used to measure extract effects on prolactin and hormone secretion and uterine inflammation. RESULTS: Anti-inflammatory effects of and prolactin secretion suppress by HVE and CZE were confirmed through in vitro experiments (P < 0.05). Treatment with Lomens-P0 inhibited prolactin secretion (P < 0.05) and restored normal sex hormone secretion in the hyperprolactinemia mice model. In addition, extracts significantly inhibited the expression of pro-inflammatory biomarkers, including interleukin-1ß, and -6, tumor necrosis factor-α, inducible nitric oxide synthase, and cyclooxygenase-2 (P < 0.01). We used high-performance liquid chromatography analyses to identify tricin and chlorogenic acid as the respective components of HVE and CZE that inhibit prolactin secretion. The Lomens-P0, which includes tricin and chlorogenic acid, is expected to be effective in improving PMS symptoms in the human body. CONCLUSIONS: The Lomens-P0 suppressed the prolactin secretion in hyperprolactinemia mice, normalized the sex hormone imbalance, and significantly suppressed the expression of inflammatory markers in uterine tissue. This study suggests that Lomens-P0 may have the potential to prevent or remedy materials to PMS symptoms.

Deep Small Bowel Segmentation with Cylindrical Topological Constraints.

We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could break the applied constraint, we propose to augment a network with an additional branch to predict an inner cylinder of the small bowel. Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation. For strict evaluation, we achieved an abdominal computed tomography dataset with dense segmentation ground-truths. The proposed method showed clear improvements in terms of four different metrics compared to the baseline method, and also showed the statistical significance from a paired t-test.

Evaluation of genetic toxicity, acute and sub-chronic oral toxicity and systemic safety of Agrimonia pilosa and Rhus gall 50% ethanolic extract mixture (APRG64) in vitro and in vivo (rodent and non-rodent animal models).

Agrimonia pilosa (AP) and Rhus gall (RG) are traditional medicinal plants. The bioflavonoid composition standardized by HPLC analysis was named APRG64. Despite many studies reported to beneficial bioactivities of AP and RG, very limited range of toxicity tests have documented. So, we did experiment diversely on the toxicity tests of the substance APRG64. Genotoxicity (mammalian chromosomal aberration test, micronoucleus test) against APRG64, acute and sub-chronic toxicity test from rodent/non-rodent, and systemic safety pharmacology test were conducted. As a result of the test, genotoxicity against APRG64 was not observed. The NOAEL of rodents was confirmed as 2000 mg/kg/day and non-rodents was confirmed as 500 mg/kg/day. In addition, systemic safety pharmacological toxicity (effects on respiratory system, central nervous system, cardiovascular system) following administration of APRG64 was not observed. Finally, we accomplished ten potential toxicity tests and evaluated extensive safety of APRG64. Consequently, APRG64 may be a promising material for nutraceuticals and natural medicines.

Deep vessel segmentation by learning graphical connectivity.

We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective use of the strong relationship that exists between vessel neighborhoods can help improve the vessel segmentation accuracy. To this end, we incorporate a graph neural network into a unified CNN architecture to jointly exploit both local appearances and global vessel structures. We extensively perform comparative evaluations on four retinal image datasets and a coronary artery X-ray angiography dataset, showing that the proposed method outperforms or is on par with current state-of-the-art methods in terms of the average precision and the area under the receiver operating characteristic curve. Statistical significance on the performance difference between the proposed method and each comparable method is suggested by conducting a paired t-test. In addition, ablation studies support the particular choices of algorithmic detail and hyperparameter values of the proposed method. The proposed architecture is widely applicable since it can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance.

A Novel Cascade Classifier for Automatic Microcalcification Detection.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (µC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual µCs, where non-µC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for µC candidates determined in the RF stage, which automatically learns the detailed morphology of µC appearances for improved discriminative power; and iii) a detector to detect clusters of µCs from the individual µC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish µCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual µCs and free-response receiver operating characteristic (FROC) curve for detection of clustered µCs.

Methyl p-Hydroxycinnamate Suppresses Lipopolysaccharide-Induced Inflammatory Responses through Akt Phosphorylation in RAW264.7 Cells.

Derivatives of caffeic acid have been reported to possess diverse pharmacological properties such as anti-inflammatory, anti-tumor, and neuroprotective effects. However, the biological activity of methyl p-hydroxycinnamate, an ester derivative of caffeic acid, has not been clearly demonstrated. This study aimed to elucidate the anti-inflammatory mechanism of methyl p-hydroxycinnamate in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophage cells. Methyl p-hydroxycinnamate significantly inhibited LPS-induced excessive production of pro-inflammatory mediators such as nitric oxide (NO) and PGE2 and the protein expression of iNOS and COX-2. Methyl p-hydroxycinnamate also suppressed LPS-induced overproduction of pro-inflammatory cytokines such as IL-1ß and TNF-α. In addition, methyl p-hydroxycinnamate significantly suppressed LPS-induced degradation of IκB, which retains NF-κB in the cytoplasm, consequently inhibiting the transcription of pro-inflammatory genes by NF-κB in the nucleus. Methyl p-hydroxycinnamate exhibited significantly increased Akt phosphorylation in a concentration-dependent manner. Furthermore, inhibition of Akt signaling pathway with wortmaninn abolished methyl p-hydroxycinnamate-induced Akt phosphorylation. Taken together, the present study clearly demonstrates that methyl p-hydroxycinnamate exhibits anti-inflammatory activity through the activation of Akt signaling pathway in LPS-stimulated RAW264.7 macrophage cells.

Anesthetic management of an 8-month-old infant with osteogenesis imperfecta undergoing liver transplantation: a case report.

Anesthetic management of pediatric liver transplantation in a patient with osteogenesis imperfecta (OI) requires tough decisions and comprehensive considerations of the cascade of effects that may arise and the required monitoring. Total intravenous anesthesia (TIVA) with propofol and remifentanil was chosen as the main anesthetic strategy. Malignant hyperthermia (MH), skeletal fragility, anhepatic phase during liver transplantation, uncertainties of TIVA in children, and propofol infusion syndrome were considered and monitored. There were no adverse events during the operation. Despite meticulous precautions with regard to the risk of MH, there was an episode of high fever (40℃) in the ICU a few hours after the operation, which was initially feared as MH. Fortunately, MH was ruled out as the fever subsided soon after hydration and antipyretics were given. Although the delivery of supportive care and the administration of dantrolene are the core principles in the management of MH, perioperative fever does not always mean a MH in patients at risk for MH, and other common causes of fever should also be considered.