Helicobacter pylori infection alters gastric microbiota structure and biological functions in patients with gastric ulcer or duodenal ulcer.

BACKGROUND: Helicobacter pylori (H. pylori) infection is closely associated with gastrointestinal diseases. Our preliminary studies have indicated that H. pylori infection had a significant impact on the mucosal microbiome structure in patients with gastric ulcer (GU) or duodenal ulcer (DU). AIM: To investigate the contributions of H. pylori infection and the mucosal microbiome to the pathogenesis and progression of ulcerative diseases. METHODS: Patients with H. pylori infection and either GU or DU, and healthy individuals without H. pylori infection were included. Gastric or duodenal mucosal samples was obtained and subjected to metagenomic sequencing. The compositions of the microbial communities and their metabolic functions in the mucosal tissues were analyzed. RESULTS: Compared with that in the healthy individuals, the gastric mucosal microbiota in the H. pylori-positive patients with GU was dominated by H. pylori, with significantly reduced biodiversity. The intergroup differential functions, which were enriched in the H. pylori-positive GU patients, were all derived from H. pylori, particularly those concerning transfer RNA queuosine-modification and the synthesis of demethylmenaquinones or menaquinones. A significant enrichment of the uibE gene was detected in the synthesis pathway. There was no significant difference in microbial diversity between the H. pylori-positive DU patients and healthy controls. CONCLUSION: H. pylori infection significantly alters the gastric microbiota structure, diversity, and biological functions, which may be important contributing factors for GU.

Lens structure segmentation from AS-OCT images via shape-based learning.

BACKGROUND AND OBJECTIVES: The lens is one of the important refractive media in the eyeball. Abnormality of the nucleus or cortex in the lens can lead to ocular disorders such as cataracts and presbyopia. To achieve an accurate diagnosis, segmentation of these ocular structures from anterior segment optical coherence tomography (AS-OCT) is essential. However, weak-contrast boundaries of the object in the images present a challenge for accurate segmentation. The state-of-the-art (SOTA) methods, such as U-Net, treat segmentation as a binary classification of pixels, which cannot handle pixels on weak-contrast boundaries well. METHODS: In this paper, we propose to incorporate shape prior into a deep learning framework for accurate nucleus and cortex segmentation. Specifically, we propose to learn a level set function, whose zero-level set represents the object boundary, through a convolutional neural network. Moreover, we design a novel shape-based loss function, where the shape prior knowledge can be naturally embedded into the learning procedure, leading to improvement in performance. We collect a high-quality AS-OCT image dataset with precise annotations to train our model. RESULTS: Abundant experiments are conducted to verify the effectiveness of the proposed framework and the novel shape-based loss. The mean Intersection over Unions (MIoUs) of the proposed method for lens nucleus and cortex segmentation are 0.946 and 0.957, and the mean Euclidean Distance (MED) measure, which can reflect the accuracy of the segmentation boundary, are 6.746 and 2.045 pixels. In addition, the proposed shape-based loss improves the SOTA models on the nucleus and cortex segmentation tasks by an average of 0.0156 and 0.0078 in the MIoU metric and 1.394 and 0.134 pixels in the MED metric. CONCLUSION: We transform the segmentation from a classification task to a regression task by making the model learn the level set function, and embed shape information in deep learning by designing loss functions. This allows the proposed method to be more efficient in the segmentation of the object with weak-contrast boundaries. CONCISE ABSTRACT: We propose to incorporate shape priors into a deep learning framework for accurate nucleus and cortex segmentation from AS-OCT images. Specifically, we propose to learn a level set function, where the zero-level set represents the boundary of the target. Meanwhile, we design a novel shape-based loss function in which additional convex shape prior can be embedded in the learning process, leading to an improvement in performance. The IOUs for nucleus and cortex segmentation are 0.946 and 0.957, while the MED that reflects the accuracy of the boundary are 6.746 and 2.045 pixels. The proposed shape-based loss improves the SOTA model for nucleus and cortex segmentation by an average of 0.0156 and 0.0078 in IOU, and 1.394 and 0.134 pixels in MED. We transform segmentation from classification to regression by making the model learn a level set function, resulting in improved performance at the boundary with weak contrast.

Iohexol Clearance for Determination of Glomerular Filtration Rate in Cynomolgus Monkeys (Macaca fascicularis).

The purpose of this study was to validate a method for determine the glomerular filtration rate (GFR) in healthy cynomolgus monkeys by using iohexol. Eighteen healthy cynomolgus macaque monkeys (age, 4 to 6 y [mean, 5 y]; weight, 2 to 6 kg [mean, 4 kg]) were randomly entered into 3 different doses groups (3 male and 3 female macaques per group) of 30, 60, 90 mg I/kg to receive an intravenous bolus injection of iohexol. Serum iohexol concentrations were determined by using liquid chromatography-tandem mass spectrometry, and clearance rate were determined by using WinNonlin software. The GFR value (mean ± SD) of each dose group was 2.50 ± 0.321, 2.65 ± 0.529, and 2.75 ± 0.385 mL/min/kg. These values did not differ significantly between dose levels or sexes. Iohexol clearance is a simple, precise method that is suitable for the determination of GFR in cynomolgus monkeys.

Quantification of ß-cell insulin secretory function using a graded glucose infusion with C-peptide deconvolution in dysmetabolic, and diabetic cynomolgus monkeys.

BACKGROUND: Quantitation of ß-cell function is critical in better understanding of the dynamic interactions of insulin secretion, clearance and action at different phases in the progression of diabetes. The present study aimed to quantify ß-cell secretory function independently of insulin sensitivity in the context of differential metabolic clearance rates of insulin (MCRI) in nonhuman primates (NHPs). METHODS: Insulin secretion rate (ISR) was derived from deconvolution of serial C-peptide concentrations measured during a 5 stage graded glucose infusion (GGI) in 12 nondiabetic (N), 8 prediabetic or dysmetabolic (DYS) and 4 overtly diabetic (DM) cynomolgus monkeys. The characterization of the monkeys was based on the fasting glucose and insulin concentrations, glucose clearance rate measured by intravenous glucose tolerance test, and insulin resistance indices measured in separate experiments. The molar ratio of C-peptide/insulin (C/I) was used as a surrogate index of hepatic MCRI. RESULTS: Compared to the N monkeys, the DYS with normal glycemia and hyperinsulinemia had significantly higher basal and GGI-induced elevation of insulin and C-peptide concentrations and lower C/I, however, each unit of glucose-stimulated ISR increment was not significantly different from that in the N monkeys. In contrast, the DM monkeys with ß-cell failure and hyperglycemia had a depressed GGI-stimulated ISR response and elevated C/I. CONCLUSIONS: The present data demonstrated that in addition to ß-cell hypersecretion of insulin, reduced hepatic MCRI may also contribute to the development of hyperinsulinemia in the DYS monkeys. On the other hand, hyperinsulinemia may cause the saturation of hepatic insulin extraction capacity, which in turn reduced MCRI in the DYS monkeys. The differential contribution of ISR and MCRI in causing hyperinsulinemia provides a new insight into the trajectory of ß-cell dysfunction in the development of diabetes. The present study was the first to use the GGI and C-peptide deconvolution method to quantify the ß-cell function in NHPs.

Mechanism of the plant cytochrome P450 for herbicide resistance: a modelling study.

Plant cytochrome P450 is a key enzyme responsible for the herbicide resistance but the molecular basis of the mechanism is unclear. To understand this, four typical plant P450s and a widely resistant herbicide chlortoluron were analysed by carrying out homology modelling, molecular docking, molecular dynamics simulations and binding free energy analysis. Our results demonstrate that: (i) the putative hydrophobic residues located in the F-helix and polar residues in I-helix are critical in the herbicide resistance; (ii) the binding mode analysis and binding free energy calculation indicate that the distance between catalytic site of chlortoluron and heme of P450, as well as the binding affinity are key elements affecting the resistance for plants. In conclusion, this work provides a new insight into the interactions of plant P450s with herbicide from a molecular level, offering valuable information for the future design of novel effective herbicides which also escape from the P450 metabolism.

A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.