Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

OBJECTIVES: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. METHODS: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). RESULTS: The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). CONCLUSIONS: Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. KEY POINTS: • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

[Effects of Banxia Xiexin Decoction Containing Serum on Proliferation, Invasion and Metastasis of Gastric Cancer Peritoneal Metastasis Cell Line GC9811-P].

Objective To observe the effects of Banxia Xiexin Decoction (BXD) containing ser- um on proliferation, invasion and metastasis of in vitro human gastric cancer peritoneum cell strain GC9811-P (which has high metastatic potential). Methods BXD containing serum was prepared. GC9811-P cells were inoculated in the E-Plate 96 and CIM Plate 16, and then 0, 25, 50, 100 µL/mL BXD containing serums were added respectively. Meanwhile, GC9811-P cells were stained by Diff-Quik stai- ning method. Inhibition of BXD containing serum on cell index (CI) for proliferation of GC9811-P cells, invasion and metastasis were observed by real time cellular analysis (RTCA) and Diff-Quik staining method. Results BXD containing serum could obviously inhibit the proliferation of GC9811-P cells. The Cl approximated to 0 after 70 h. Most stained Diff-Quik cells died. Cell migration curve showed that 25, 50, 100 µL/mL BXD containing serums could obviously inhibit the capacities for cell migration of GC9811-P cells in concentration dependent manner. The number of migration cells was reduced more obviously, as com- pared with 0 µL/mL BXD containing serum (P <0. 05). Conclusion BXD containing serums could inhibit the proliferation, invasion and metastasis of GC9811-P cells, which might be associated with blocking peritoneal metastasis of gastric cancer.

Gut microbiota affects obesity susceptibility in mice through gut metabolites.

Introduction: It is well-known that different populations and animals, even experimental animals with the same rearing conditions, differ in their susceptibility to obesity. The disparity in gut microbiota could potentially account for the variation in susceptibility to obesity. However, the precise impact of gut microbiota on gut metabolites and its subsequent influence on susceptibility to obesity remains uncertain. Methods: In this study, we established obesity-prone (OP) and obesity-resistant (OR) mouse models by High Fat Diet (HFD). Fecal contents of cecum were examined using 16S rDNA sequencing and untargeted metabolomics. Correlation analysis and MIMOSA2 analysis were used to explore the association between gut microbiota and intestinal metabolites. Results: After a HFD, gut microbiota and gut metabolic profiles were significantly different between OP and OR mice. Gut microbiota after a HFD may lead to changes in eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), a variety of branched fatty acid esters of hydroxy fatty acids (FAHFAs) and a variety of phospholipids to promote obesity. The bacteria g_Akkermansia (Greengene ID: 175696) may contribute to the difference in obesity susceptibility through the synthesis of glycerophosphoryl diester phosphodiesterase (glpQ) to promote choline production and the synthesis of valyl-tRNA synthetase (VARS) which promotes L-Valine degradation. In addition, gut microbiota may affect obesity and obesity susceptibility through histidine metabolism, linoleic acid metabolism and protein digestion and absorption pathways.

Appraising causal risk and protective factors for rheumatoid arthritis.

Aims: Mendelian randomization (MR) is considered to overcome the bias of observational studies, but there is no current meta-analysis of MR studies on rheumatoid arthritis (RA). The purpose of this study was to summarize the relationship between potential pathogenic factors and RA risk based on existing MR studies. Methods: PubMed, Web of Science, and Embase were searched for MR studies on influencing factors in relation to RA up to October 2022. Meta-analyses of MR studies assessing correlations between various potential pathogenic factors and RA were conducted. Random-effect and fixed-effect models were used to synthesize the odds ratios of various pathogenic factors and RA. The quality of the study was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines. Results: A total of 517 potentially relevant articles were screened, 35 studies were included in the systematic review, and 19 studies were eligible to be included in the meta-analysis. Pooled estimates of 19 included studies (causality between 15 different risk factors and RA) revealed that obesity, smoking, coffee intake, lower education attainment, and Graves' disease (GD) were related to the increased risk of RA. In contrast, the causality contribution from serum mineral levels (calcium, iron, copper, zinc, magnesium, selenium), alcohol intake, and chronic periodontitis to RA is not significant. Conclusion: Obesity, smoking, education attainment, and GD have real causal effects on the occurrence and development of RA. These results may provide insights into the genetic susceptibility and potential biological pathways of RA.

[Effects of Baicalin on an orthotopic transplantation mouse model of mismatch repair gene deficient colorectal cancer].

OBJECTIVE: To study the anticancer effects of Baicalin on an orthotopic transplantation mouse model of mismatch repair gene deficient colorectal cancer. METHODS: Sixty orthotopic transplantation mice model of human colon cancer cell line HCT-116 expressing eGFP were established, which were divided randomly into negative controlled group (5% NaHCO3) and 50, 100, 200 mg/kg Baicalin groups. The nude mice were treated with intragastric infusion twice a day. Nude mice growth state, average weigh, inhibition rate of transplanted tumor, tumor metastasis and survival state were observed. RESULTS: At 14, 21 and 28 days after treatment with different dose of Baicalin, tumor growth velocity was significantly slower in the treatment groups, and tumor volume was significantly smaller than the controlled group (there were (832 ± 637), (2012 ± 1566) and (2494 ± 1557) mm(3) respectively in 14, 21 and 28 days) (F = 4.433, P < 0.05). At the end point of study, survival state of 100 mg/kg group (13/15) was superior to controlled group (8/15) and 200 mg/kg group (8/15) (χ(2) = 4.665 and 3.980, P < 0.05).However, there were no significant differences in tumor metastasis and tumor surface vessel density. CONCLUSIONS: Baicalin has statistically significant effects in inhibiting tumor growth in an orthotopic transplantation mouse model of mismatch repair gene deficient colorectal cancer, and 100 mg/kg may be an ideal treatment dose.

Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics.

The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.

CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation.

Retinal vessels play an important role in judging many eye-related diseases, so accurate segmentation of retinal vessels has become the key to auxiliary diagnosis. In this paper, we present a Cascaded Residual Attention U-Net (CRAUNet) that can be regarded as a set of U-Nets, that allows coarse-to-fine representations. In the CRAUNet, we introduce a DropBlock regularization similar to the frequently-used dropout, which greatly reduces the overfitting problem. In addition, we propose a multi-scale fusion channel attention (MFCA) module to explore helpful information, and then merge this information instead of using a direct skip-connection. Finally, to prove the effectiveness of our method, we conduct extensive experiments on DRIVE and CHASE_DB1 datasets. The proposed CRAUNet achieves area under the receiver operating characteristic curve (AUC) of 0.9830 and 0.9865, respectively, for the two datasets. Compared to other state-of-the-art methods, the experimental results demonstrate that the performance of the proposed method is superior to that of others.

Segmentation and classification of thyroid follicular neoplasm using cascaded convolutional neural network.

In this paper, we present a segmentation and classification method for thyroid follicular neoplasms based on a combination of the prior-based level set method and deep convolutional neural network. The proposed method aims to discriminate thyroid follicular adenoma (TFA) and follicular thyroid carcinoma (FTC) in ultrasound images. In their appearance, these two kinds of tumours have similar shapes, sizes and contrasts. Therefore, it is difficult for even ultrasound specialists to distinguish them. Because of the complex background in thyroid ultrasound images, before distinguishing TFA and FTC, we need to segment the lesions from the whole image for each patient. The main challenge of segmentation is that the images often have weak edges and heterogeneous regions. The main issue of classification is that the accuracy depends on the features extracted from the segmentation results. To solve these problems, we conduct the two tasks, i.e. segmentation and classification, by a cascaded learning architecture. For segmentation, to obtain more accurate results, we exploit the Res-U-net framework and the prior-based level set method to enhance their respective abilities. Then, the classification network is trained by sharing shallow layers of the segmentation network. Testing the proposed method on real patient data shows that it is able to segment the lesion areas in thyroid ultrasound images with a Dice score of 92.65% and to distinguish TFA and FTC with a classification accuracy of 96.00%.

[Establishment and applications of the spatial database of land resources information in Anshun, Guizhou Province].

For the purpose of managing and processing land resources and environment information, a spatial database of land information in Anshun City, Guizhou Province, which involved present data and the investigated information by remote sensing, was established under the supporting of PAMAP GIS 4.2. The spatial database could provide the functionality of querying for spatial data and its associated attributes, conversion between the different data formats, mapping the digital terrain, fast and accurate analysis on soil resources, land assessment and management application, and soil erosion risk mapping. The establishment of this spatial database was expected to play an important role in territorial planning, agricultural sustainable development, spatial assessment of ecological environment, and so on.