Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm.

PURPOSE: To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort. METHODS: A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system. RESULTS: A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97. CONCLUSION: The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

A novel data-driven decision model based on extended belief rule base to predict China's carbon emissions.

Global warming and climate change are gaining traction in recent years. As a major cause of global warming, carbon emissions were centered to China's climate change policy initiatives. Nevertheless, the existing policy discourse has yet reached a consensus on the optimal modeling method for carbon emissions prediction that is well-informed of both policy goals and the time-series pattern of carbon emissions. This paper fills the gap by promoting a novel data-driven decision model for carbon emissions prediction that is based on the extended belief rule base (EBRB) inference model. The new decision model consists of three components: 1) an indicator integration method, which aims to generate a few group indicators from a large number of statistical indicators; 2) a new EBRB construction method, which aims to consider the management policy goals for constructing EBRB; 3) a new ER-based inference method, which aims to predict carbon emissions based on time series change of relevant factors. The effectiveness of the proposed decision model has been tested against carbon emissions management data from 30 provinces in China. Experimental results demonstrate that the model will offer powerful reference value in the policy decision-making process, which will help to meet policy requirements for carbon emissions.

A novel immune-related long non-coding RNAs risk model for prognosis assessment of lung adenocarcinoma.

BACKGROUND: The abundant immune-related long non-coding RNA (IRLNRs) in immune cells and immune microenvironment have the potential to forecast prognosis and evaluate the effect of immunotherapy. IRLNRs analysis will provide a new perspective for LUAC research. METHODS: We calculated the immune score of each sample according to the expression levels of immune-related genes (IRGs) and screened the survival-related IRLNRs (sIRLNRs) by Cox regression analysis. The expression levels of AC068338.3 and AL691432.2 in tissues and cell lines were confirmed by RT-qPCR. RESULTS: 36 IRLNRs were selected by Pearson correlation analysis. Ten sIRLNRs were significantly correlated with the clinical outcomes of LUAC patients. Five sIRLNRs were identified by multivariate COX regression analysis to establish the immune-related risk score model (IRRS). The overall survival (OS) in the high-risk group was shorter than that in the low-risk group. IRRS could be an independent prognostic factor with significant survival correlation The distributions of immune gene concentrations were different between high-risk group and low-risk group. Furthermore, we further verified that the expression levels of AC068338.3 and AL691432.2 in different LUAC cell lines and tumor tissues were lower than that in Human bronchial epithelial cell (HBE) and adjacent tissues respectively. The lower expression levels of AC068338.3 and AL691432.2 were detected with the more advance T-stages. CONCLUSIONS: Our results highlighted some sIRLNRs with significant clinical correlations and demonstrated their monitored and prognostic values for LUAC patients. The results of this study may provide a new perspective for immunological research and immunotherapy strategies.

Tropical Cellulolytic Bacteria: Potential Utilization of Sugarcane Bagasse as Low-Cost Carbon Source in Aquaculture.

Sugarcane bagasse (SB), as a major by-product of sugarcane, is one of the most abundant organic matter and characterized by cheap and easily available carbon source in Hainan Island, China. The objective of this study was to isolate tropical cellulolytic bacteria from Hainan Island and demonstrate their prospects of utilization of SB as a low-cost carbon source to greatly reduce the cost of aquaculture. A total of 97 cellulolytic marine bacteria were isolated, of which, 58 cellulolytic marine bacteria displayed the hydrolysis capacity (HC) of more than 1, while 28 cellulolytic marine bacteria displayed more than 2. Of the 28 tropical cellulolytic bacterial strains with HC more than 2, Microbulbifer sp. CFW-C18 and Vibrio sp. MW-M19 exhibited excellent SB decomposition in a small-scale laboratory simulation of shrimp aquaculture, up to 75.31 and 74.35%, respectively, and both of them were safe for shrimps. Meanwhile, both of CFW-C18 and MW-M19 besides displaying low multiple antibiotic resistance (MAR) index, also increased the C/N ratio (CFW-C18: C/N ratio of 14.34; MW-M19: C/N ratio of 14.75) of the small-scale laboratory simulation of shrimp aquaculture by decreasing the nitrogen content after a supplement of SB for 15 days. More importantly, CFW-C18 and MW-M19 displayed a relatively low MAR index, 0.47 and 0.1, respectively, especially MW-M19, with the lowest MAR index (0.1), which was resistant to only three antibiotics, streptomycin, amikacin, and levofloxacin, indicating that this strain was safe and non-drug resistance for further use. Overall, tropical cellulolytic bacteria isolated from Hainan Island, especially CFW-C18 and MW-M19, will provide the proficient candidates as probiotics for further construction of the recirculating aquaculture system based on the supplement of low-cost external carbon source-SB.

Nondestructive Assessment of Glycosaminoglycans in Engineered Cartilages Using Hexabrix-Enhanced Micro-Computed Tomography.

It is very useful to evaluate the content and 3D distribution of extracellular matrix non-destructively in tissue engineering. This study evaluated the feasibility of using micro-computed tomography (µCT) with Hexabrix to measure quantitatively sulfated glycosaminoglycans (GAGs) of engineered cartilage. Rabbit chondrocytes at passage 2 were used to produce artificial cartilages in polyglycolic acid scaffolds in vitro. Engineered cartilages were incubated with Hexabrix 320 for 20 min and analyzed via µCT scanning. The number of voxels in the 2D and 3D scanning images were counted to estimate the amount of sulfated GAGs. The optimal threshold value for quantification was determined by regression analysis. The 2D µCT images of an engineered cartilage showed positive correlation with the histological image of Safranin-O staining. Quantitative data obtained with the 3D µCT images of 14 engineered cartilages showed strong correlation with sulfated GAGs contents obtained by biochemical analysis (R2 = 0.883, p < 0.001). Repeated exposure of engineered cartilages to Hexabrix 320 and µCT scanning did not significantly affect cell viability, total DNA content, or the total content of sulfated GAGs. We conclude that µCT imaging using Hexabrix 320 provides high spatial resolution and sensitivity to assess the content and 3D distribution of sulfated GAGs in engineered cartilages. It is expected to be a valuable tool to evaluate the quality of engineered cartilage for commercial development in the future.