Prognostic role and predictors of lymph node involvement in pancreatic neuroendocrine tumors.

PURPOSE: To define the prognostic role of lymph node involvement (LNI) in patients with pancreatic neuroendocrine tumors (PNETs) and identify predictors of LNI using a comprehensive multifactor analysis focusing on preoperative radiological features. METHODS: This study included 236 patients with preoperative computed tomography who underwent radical surgical resection of PNETs at our hospital between 2009 and 2019. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with LNI and tumor recurrence. The disease-free survival (DFS) rates with and without LNI were compared. RESULTS: Forty-four of the 236 patients (18.6%) had LNI. Biliopancreatic duct dilatation (odds ratio [OR], 2.295; 95% confidence interval [CI], 1.046-5.035; p = 0.038), tumor margin (OR, 2.189; 95% CI, 1.034-4.632; p = 0.041), and WHO grade (G2: OR, 2.923; 95% CI, 1.005-8.507; p = 0.049; G3: OR, 12.067; 95% CI, 3.057-47.629; p < 0.001) were independent risk factors of LNI in PNETs. Multivariable analysis showed that LNI (OR, 2.728; 95% CI, 1.070-6.954; p = 0.036), G3 (OR, 4.894; 95% CI, 1.047-22.866; p = 0.044), and biliopancreatic duct dilatation (OR, 2.895; 95% CI, 1.124-7.458; p = 0.028) were associated with PNET recurrence in patients after surgery. Patients with LNI had a significantly worse DFS than those without LNI (3-year DFS: 85.9 vs. 96.7%; p < 0.001; 5-year DFS: 65.1 vs. 93.9%; p < 0.001). CONCLUSION: LNI was associated with decreased DFS. Biliopancreatic duct dilatation, irregular tumor margins, and grades G2 and G3 were independent risk factors for LNI.

Clinicopathological features and long-term prognosis of purely cystic pancreatic neuroendocrine tumors: A single-center experience.

BACKGROUND: Pancreatic neuroendocrine tumors (pNETs) are heterogenous neoplasms, of which the prognosis varies widely. Purely cystic pancreatic neuroendocrine tumors (C-pNETs) are a small subset of pNETs in which data are extremely rare. This study aimed to compare clinicopathological and long-term survival differences between C-pNETs and solid pNETs (S-pNETs). METHODS: A retrospective review of 242 patients with pNETs underwent resection in our institution from 2009 to 2019 was conducted. Demography characteristics, clinicopathological features and long-term outcomes of them were analyzed. RESULTS: Sixteen out of 242 patients (6.6%) were identified as C-pNETs. Compared with S-pNETs, C-pNETs were more frequently non-functional (75% vs 45%, P = 0.02), and the median tumor diameter of C-pNETs was smaller (36 mm vs. 47 mm, P = 0.001). And the accuracy of preoperative diagnosis of C-pNETs was significantly lower (31% vs 78%, P = 0.001). Of note, the majority of C-pNETs were well-differentiated with G1 (81% vs 35%, P = 0.001). And there were no G3 (0 vs 7%, P = 0.001) in C-pNETs. No T4 stage or R1/R2 surgical margin detected in C-pNETs. And only one C-pNETs (6%) had regional lymph node metastasis (N) or synchronous distant metastasis (M). Additionally, only one patient with C-pNETs (6%) suffered tumor recurrence, compared with 24 (13%) for S-pNETs. And survival analysis showed the patients with C-pNETs seemed to be with better disease-free survival (P = 0.26). CONCLUSION: C-pNETs are rare subtype with possibly less aggressive behavior comparing with their solid counterparts. Recurrence and tumor-related death still occurs in patients with resected C-pNETs, although they tend to be with more favorable prognosis.

HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.

Identifying disease-related miRNAs can improve the understanding of complex diseases. However, experimentally finding the association between miRNAs and diseases is expensive in terms of time and resources. The computational screening of reliable miRNA-disease associations has thus become a necessary tool to guide biological experiments. "Similar miRNAs will be associated with the same disease" is the assumption on which most current miRNA-disease association prediction methods rely; however, biased prior knowledge, and incomplete and inaccurate miRNA similarity data and disease similarity data limit the performance of the model. Here, we propose heuristic learning based on graph neural networks to predict microRNA-disease associations (HLGNN-MDA). We learn the local graph topology features of the predicted miRNA-disease node pairs using graph neural networks. In particular, our improvements to the graph convolution layer of the graph neural network enable it to learn information among homogeneous nodes and among heterogeneous nodes. We illustrate the performance of HLGNN-MDA by performing tenfold cross-validation against excellent baseline models. The results show that we have promising performance in multiple metrics. We also focus on the role of the improvements to the graph convolution layer in the model. The case studies are supported by evidence on breast cancer, hepatocellular carcinoma and renal cell carcinoma. Given the above, the experiments demonstrate that HLGNN-MDA can serve as a reliable method to identify novel miRNA-disease associations.

Mindfulness-Based Movement Intervention to Improve Sleep Quality: A Meta-Analysis and Moderator Analysis of Randomized Clinical Trials.

(1) Background: Given that the most effective dose, optimal type, and most beneficial population for improving sleep with mindfulness-based movement (MBM) remains unknown, we conducted a systematic review and meta-analysis with moderator analysis of randomized controlled trials (RCTs) to assess these effects. (2) Methods: Three electronic databases (PubMed, Web of Science, and EBSCO) were systematically searched for RCTs published through August 2021 for analysis. The risk of bias of the included studies was assessed with Review Manager 5.3, and the meta-analysis was performed in Stata 16.0. (3) Results: A meta-analysis of 61 RCTs with 2697 participants showed that MBM significantly improved sleep quality compared to controls (SMD = −0.794; 95% CI: −0.794 to −0.994, p < 0.001, I2 = 90.7%). Moderator analysis showed that a long-term MBM (SMD = −0.829; 95% CI: 0.945 to 0.712; p < 0.001) had a larger effect size on sleep than a short-term MBM (SMD = −0.714; 95% CI: 0.784 to 0.644; p < 0.001). Practicing at least twice per week (SMD = −0.793; 95% CI: −0.868 to −0.718; p < 0.001) was more effective compared to practicing once per week (SMD = −0.687; 95% CI: −0.804 to −0.570; p < 0.001). Studies with a total intervention time of more than 24 h also revealed better sleep quality improvement (SMD = −0.759; 95% CI: −0.865 to −0.653; p < 0.001). In addition, the healthy population and older adults gained more from MBM than the patients and younger adults. (4) Conclusions: MBM can effectively improve subjective sleep quality, and the optimal intervention dose of MBM can be utilized in future intervention studies to treat or improve sleep disturbance (MBM more than twice a week for more than three months, with a total intervention time of more than 24 h).

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

MOTIVATION: Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. RESULTS: Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug-drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. AVAILABILITY AND IMPLEMENTATION: Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.

Backgroud: Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited. Methods: The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram. Results: A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883). Conclusions: Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

Enhancing overall properties of epoxy-based composites using polydopamine-coated edge-carboxylated graphene prepared via one-step high-pressure ball milling.

Graphene (GN) nanofillers have been widely used to enhance the overall performance of polymer composites due to their various superior properties, which strongly rely on the uniform dispersion and strong interfacial bonding of GN with high-quality polymer matrices. In the present study, the strengthening and functional effects of polydopamine-coated edge-carboxylated graphene (p-ECG) on the mechanical, moisture-barrier and electromagnetic properties of epoxy (EP)-based composites were systematically evaluated. p-ECG was successfully prepared via one-step high-pressure ball milling through the edge-selective functionalization and exfoliation of pristine graphite in the presence of dry ice, followed by synchronous reduction and coating via the mild oxidative polymerization of mussel-inspired dopamine. p-ECG showed prominent advantages of a small sheet size, excellent dispersibility and high chemical reactivity in the EP matrix. Obvious enhancements were achieved in the tensile and flexural properties and moisture-barrier performance of EP composites as well as the interlaminar shear strength (ILSS) and transverse fiber bundle tensile (TFBT) strength of carbon fiber (CF)/EP composites, which confirmed the excellent dispersion and chemically strengthened interfacial bonding of p-ECG in the EP matrix. More importantly, p-ECG introduced onto the surface of desized CF led to significant enhancement in the electromagnetic interference (EMI) shielding capability of CF/EP composites, which was primarily ascribed to the polarization relaxation effect induced by the defects and functional groups in p-ECG as well as the increase in electrical conductivity derived from the "bridging effect" of p-ECG. Specifically, with p-ECG content of 0.5 wt%, the increments in tensile strength, TFBT strength, shielding effectiveness (total, SET) and shielding effectiveness (reflection loss, SER) were as high as 33.3, 34.3, 31.3 and 71.0%, respectively.

[Stoichiometric characteristics of leaf carbon, nitrogen, and phosphorus of 102 dominant species in forest ecosystems along the North-South Transect of East China].

One hundred and twelve sampling sites in the forest ecosystems along the North-South Transect of Eastern China (NSTEC) were selected to study the stoichiometric characteristics and variability of leaf carbon (C), nitrogen (N), and phosphorous (P) of 102 dominant species. The contents of leaf C (Cmass), leaf N (Nmass), and leaf P (Pmass) ranged in 374.1-646.5 mg x g(-1), 8.4-30.5 mg x g(-1), and 0.6-6.2 mg x g(-1), with the arithmetic mean (AM) being 480.1, 18.3 and 2.0 mg x g(-1), and the variation coefficient (CV) being 11.1%, 27.5%, and 56.4%, respectively. The leaf C/N, C/P and N/P ranged from 14.1 to 64.1, from 70.9 to 838.6, and from 1.5 to 21.2, with the AM being 29.1, 313.9 and 11.5, and the CV being 32.8%, 48.3% and 44.1%, respectively. The mass ratio of C:N:P was 313.9:11.5:1, and the atom ratio was 810.9:25.4:1. As compared with those at global scale, the tree leaf Cmass and C/N in the study area were significantly higher, Nmass and N/P were significantly lower, while Pmass and C/P had less differences.

[Leaf nitrogen and phosphorus stoichiometry across 654 terrestrial plant species in NSTEC].

Stoichiometry of leaf N and P is a characteristic of plant to adapt to environment, and can provide data for process-based modeling at large scale. Leaf nitrogen and phosphorus stoichiometry of NSTEC terrestrial plants were studied based on a NSTEC data set including leaf nitrogen and phosphorus observations for 654 plant species at 168 sites. The results showed that leaf nitrogen and phosphorus stoichiometry in NSTEC exhibited large variations, primarily ranging 2.17-52.61 mg x g(-1) for N, 0.10-10.27 mg x g(-1) for P, 1.7-74.6 for N/P ratio. Geometric means for all plant species were 17.55 mg x g(-1), 1.28 mg x g(-1) and 13.5, respectively. Leaf P of NSTEC (ever across China) was lower than global level, therefore plant growth was more limited by P in China region comparing to global environment. For all functional groups, the difference of leaf N was largest, but that of N/P ratio was smallest, since leaf N and P closely related in most of functioning groups; for leaf N and P of the different phylogenic functional groups, the difference was largest for farthest relative (seed vs fern), least for closest relative (monocotyledon vs. dicotyledon) . There were obvious correlation between leaf N (or P) and latitude (or mean annual temperature, MAT). Leaf N and P significantly increased with latitude increasing (or MAT decreasing), but the relationship between N/P ratio and latitude (or MAT) wasn't significant (p = 0.386 and p = 0.342), and the reason maybe include, leaf N and P had the same tendencies and large variations, and region in this research was smaller than global research.