Endothelial Autophagy Promotes Atheroprotective Communication Between Endothelial and Smooth Muscle Cells via Exosome-Mediated Delivery of miR-204-5p.

BACKGROUND: Cellular communication among different types of vascular cells is indispensable for maintaining vascular homeostasis and preventing atherosclerosis. However, the biological mechanism involved in cellular communication among these cells and whether this biological mechanism can be used to treat atherosclerosis remain unknown. We hypothesized that endothelial autophagy mediates the cellular communication in vascular tissue through exosome-mediated delivery of atherosclerosis-related genes. METHODS: Rapamycin and adeno-associated virus carrying Atg7 short hairpin RNA under the Tie (TEK receptor tyrosine kinase) promoter were used to activate and inhibit vascular endothelial autophagy in high-fat diet-fed ApoE-/- mice, respectively. miRNA microarray, in vivo and in vitro experiments, and human vascular tissue were used to explore the effects of endothelial autophagy on endothelial function and atherosclerosis and its molecular mechanisms. Quantitative polymerase chain reaction and miRNA sequencing were performed to determine changes in miRNA expression in exosomes. Immunofluorescence and exosome coculture experiments were conducted to examine the role of endothelial autophagy in regulating the communication between endothelial cells and smooth muscle cells (SMCs) via exosomal miRNA. RESULTS: Endothelial autophagy was inhibited in thoracic aortas of high-fat diet-fed ApoE-/- mice. Furthermore, rapamycin alleviated high-fat diet-induced atherosclerotic burden and endothelial dysfunction, while endothelial-specific Atg7 depletion aggravated the atherosclerotic burden. miRNA microarray, in vivo and in vitro experiments, and human vascular tissue analysis revealed that miR-204-5p was significantly increased in endothelial cells after high-fat diet exposure, which directly targeted Bcl2 to regulate endothelial cell apoptosis. Importantly, endothelial autophagy activation decreased excess miR-204-5p by loading miR-204-5p into multivesicular bodies and secreting it through exosomes. Moreover, exosomal miR-204-5p can effectively transport to SMCs, alleviating SMC calcification by regulating target proteins such as RUNX2 (runt-related transcription factor 2). CONCLUSIONS: Our study revealed the exosomal pathway by which endothelial autophagy protects atherosclerosis: endothelial autophagy activation transfers miR-204-5p from endothelial cells to SMCs via exosomes, both preventing endothelial apoptosis and alleviating SMC calcification. REGISTRATION: URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR2200064155.

Comparing the Efficacy of Robotic Versus Laparoscopic Sleeve Gastrectomy: A Systematic Review and Meta-Analysis.

BACKGROUND: Laparoscopic sleeve gastrectomy (LSG) has emerged as the predominant metabolic bariatric surgery. With a growing number of studies evaluating the feasibility of robotic sleeve gastrectomy (RSG), it becomes imperative to ascertain whether the outcomes of both techniques are comparable. This study endeavors to synthesize existing evidence and juxtapose the surgical outcomes of LSG and RSG. METHODS: We collected articles comparing LSG and RSG published between 2011 and 2024. The compiled data included author names, study duration, sample size, average age, gender distribution, geographical location, preoperative body mass index (BMI), bougie diameter, duration of hospitalization, surgical duration, readmission rates, conversion rates, costs, postoperative percentage of excess weight loss (%EWL), postoperative BMI, mortality rates, and complications. RESULTS: We incorporated 21 articles. Both the RSG and LSG cohorts exhibited comparable rates of readmission, conversion, mortality, and incidence of complications (p > 0.05). Moreover, the efficacy of weight loss was similar between RSG and LSG. Nonetheless, RSG was linked to longer operative duration (WMD, -27.50 minutes; 95% confidence interval [CI], -28.82 to -26.18; p < 0.0001), prolonged hospitalization (WMD, -0.15 days; 95% CI, -0.25 to -0.04; p = 0.006), and elevated expenses (WMD, -5830.9 dollars; 95% CI, -8075.98 to -3585.81; p < 0.0001). CONCLUSIONS: While both RSG and LSG demonstrated positive postoperative clinical outcomes, RSG patients experienced extended hospital stays, longer operative times, and increased hospitalization costs compared to LSG patients. Using the robotic platform for sleeve gastrectomy (SG) in patients with obesity did not appear to offer any clear benefits.

[Advances in bioinformatics-based protein function prediction].

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

Chromosome-level genome assembly of an oligophagous leaf beetle Ophraella communa (Coleoptera: Chrysomelidae).

The leaf beetle Ophraella communa LeSage (Coleoptera: Chrysomelidae) is an effective biological control agent of the common ragweed. Here, we assembled a chromosome-level genome of the O. communa by combining Illumina, Nanopore, and Hi-C sequencing technologies. The genome size of the final genome assembly is 733.1 Mb, encompassing 17 chromosomes, with an improved contig N50 of 7.05 Mb compared to the original version. Genome annotation reveals 25,873 protein-coding genes, with functional annotations available for 22,084 genes (85.35%). Non-coding sequence annotation identified 204 rRNAs, 626 tRNAs, and 1791 small RNAs. Repetitive elements occupy 414.41 Mb, constituting 57.76% of the genome. This high-quality genome is fundamental for advancing biological control strategies employing O. communa.

Computational analysis of phytocompounds in Centella asiatica for its antifibrotic and drug-likeness properties - Herb to drug study.

Oral submucous fibrosis (OSMF) is a potentially malignant disorder with no permanent cure that affects the quality of life due to trismus. Computational pharmacology has accelerated the discovery of drug candidates for the treatment of incurable diseases. The present study aimed to screen the compounds of the miracle herb Centella asiatica with drug-likeness properties based on the absorption, distribution, metabolism, and excretion (ADME) properties. The pharmacological actions of these screened compounds against OSMF were identified by network pharmacology, gene ontology, pathway enrichment analysis, molecular docking, and simulation. Fifteen drug-like ligands were identified after virtual screening viz; asiatic acid, kaempferol, quercetin, luteolin, apigenin, bayogenin, gallic acid, isothankunic acid, madecassic acid, madasiatic acid, arjunolic acid, terminolic acid, catechin, epicatechin, and nobiletin. 850 potential targets were predicted for the ligands, which were analyzed against 354 proteins associated with OSMF. Compound pathway analysis and disease pathway analysis identified 53 common proteins. The GO enrichment analysis identified 472 biological process terms, 76 molecular function terms, and 44 cellular component terms. Pathway enrichment analysis predicted 142 KEGG pathways, 35 Biocarta pathways, and 236 Reactome pathways for the target proteins. The analysis revealed that the herb targets crucial events of fibrosis such as inflammation, oxidative stress, apoptosis, collagen deposition, and epithelial-mesenchymal transition. The common 53 proteins were used for protein-protein interaction (PPI) network analysis, which revealed 4 key proteins interacting with the phytocompounds viz; transforming growth factor-ß1 (TGF-ß1), mothers against decapentaplegic-3 (SMAD-3), mitogen-activated protein kinase-1 (MAPK-1) and proto-oncogene tyrosine-protein kinase (SRC). Molecular docking revealed that all ligands had a good binding affinity to the target proteins. Bayogenin had the highest binding affinity towards MAPK-1 (-9.7 kcal/mol), followed by isothankunic acid towards SRC protein (-9.3 kcal/mol). Madasiatic acid had the highest binding affinity to SMAD-3 (-7.6 kcal/mol) and TGF-ß1 (-7.1 kcal/mol). Molecular dynamics simulation demonstrated stable ligand protein interactions of bayogenin and MAPK complex, isothankunic acid and SRC complex. This in silico study is the first to identify potential phytochemicals present in Centella asiatica and their target molecules, which might be responsible for reversing OSMF.

E3 ubiquitin ligase TRIM31: A potential therapeutic target.

Ubiquitination is a key mechanism for post-translational protein modification, affecting protein localization, metabolism, degradation and various cellular physiological processes. Dysregulation of ubiquitination is associated with the pathogenesis of various diseases, such as tumors and cardiovascular diseases, making it a primary area of interest in biochemical research and drug development endeavors. E3 ubiquitin ligases play a pivotal role in modulating the ubiquitination of substrate proteins through their unique recognition functions. TRIM31, a member of the TRIM family of E3 ubiquitin ligases, is aberrantly expressed in different pathophysiological conditions. The biological function of TRIM31 is associated with the occurrence and development of diverse diseases. TRIM31 has been demonstrated to inhibit inflammation by promoting ubiquitin-proteasome-mediated degradation of the sensing protein NLRP3 in the inflammasome. TRIM31 mediates ubiquitination of MAVS, inducing the formation of prion-like aggregates, and triggering innate antiviral immune responses. TRIM31 is also implicated in tumor pathophysiology through its ability to promote ubiquitination of the tumor suppressor protein p53. These findings indicate that TRIM31 is a potential therapeutic target, and subsequent in-depth research of TRIM31 is anticipated to provide information on its clinical application in therapy.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

MOTIVATION: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION: https://github.com/XuLew/MIDTI.

BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil.

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Quantitative assessment and correlational analysis of subjective and objective indicators in patients with allergic rhinitis.

Background: The diagnosis of allergic rhinitis is mainly based on the typical medical history, clinical manifestations, and corresponding allergen test results of the patients. However, there are often clinical inconsistencies among the 3. Objective: To study the clinical characteristics of patients with allergic rhinitis from both subjective and objective aspects to determine the correlations between the quantitative assessment outcomes of subjective and objective indicators. Methods: A total of 111 patients with allergic rhinitis who visited our outpatient clinic from June 2022 to December 2022 were selected. The 22-item sino-nasal outcome test (SNOT-22) and the visual analog scale (VAS) for the severity of the disease were used to score the subjective indicators of allergic rhinitis. The objective indicators of allergic rhinitis were evaluated by serum inhalant allergens immunoglobulin E test, nasal endoscopy modified Lund-Kennedy (MLK) scoring method, and acoustic rhinometry. Results: SNOT-22 score, total VAS score for symptoms, and the VAS score for nasal itching were positively correlated with the number of positive allergens (r = 0.266, P = 0.005, r = 0.576, P < 0.001, and r = 0.271, P = 0.004, respectively). No differences were found in all subjective indicators scores between the total immunoglobulin E positive and negative groups (P > 0.05). SNOT-22 score, total VAS score for symptoms, and the VAS score for nasal congestion were positively correlated with MLK total score of nasal endoscopy (r = 0.343, P < 0.001, r = 0.438, P < 0.001, and r = 0.225, P = 0.018, respectively). Parameters of acoustic rhinometry were not correlated with the subjective indicators scores of allergic rhinitis (P > 0.05). Conclusion: A multifaceted quantitative assessment of allergic rhinitis using a combination of subjective and objective methods can help physicians make an accurate diagnosis and create reasonable treatment plans.

A nomogram of anastomotic stricture after rectal cancer: a retrospective cohort analysis.

BACKGROUND: Anastomotic stricture significantly impacts patients' quality of life and long-term prognosis. However, current clinical practice lacks accurate tools for predicting anastomotic stricture. This study aimed to develop a nomogram to predict anastomotic stricture in patients with rectal cancer who have undergone anterior resection. METHODS: A total of 1542 eligible patients were recruited for the study. Least absolute shrinkage selection operator (Lasso) analysis was used to preliminarily select predictors. A prediction model was constructed using multivariate logistic regression and presented as a nomogram. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration diagrams, and decision curve analysis (DCA). Internal validation was conducted by assessing the model's performance on a validation cohort. RESULTS: 72 (4.7%) patients were diagnosed with anastomotic stricture. Participants were randomly divided into training (n = 1079) and validation (n = 463) sets. Predictors included in this nomogram were radiotherapy, diverting stoma, anastomotic leakage, and anastomotic distance. The area under the ROC curve (AUC) for the training set was 0.889 [95% confidence interval (CI) 0.840-0.937] and for the validation set, it was 0.930 (95%CI 0.879-0.981). The calibration curve demonstrated a strong correlation between predicted and observed outcomes. DCA results showed that the nomogram had clinical value in predicting anastomotic stricture in patients after anterior resection of rectal cancer. CONCLUSION: We developed a predictive model for anastomotic stricture following anterior resection of rectal cancer. This nomogram could assist clinicians in predicting the risk of anastomotic stricture, thus improving patients' quality of life and long-term prognosis.

In-line imaging and recognition of flip chip fabrication defects by real-time photoacoustic remote sensing system.

Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.

Adaptive Proton Therapy Using CBCT-Guided Digital Twins.

This study aims to develop a digital twin (DT) framework to enhance adaptive proton stereotactic body radiation therapy (SBRT) for prostate cancer. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertainties using DT concept, with the goal of improving treatment quality, potentially revolutionizing prostate radiotherapy to offer personalized treatment solutions. Our study presented a pioneering approach that leverages DT technology to enhance adaptive proton SBRT. The framework improves treatment plans by utilizing patient-specific CTV setup uncertainty, which is usually smaller than conventional clinical setups. This research contributes to the ongoing efforts to enhance the efficiency and efficacy of prostate radiotherapy, with ultimate goals of improving patient outcomes and life quality.

The Influence of Nonlinear High-Intensity Dynamic Processes on the Standing Wave Precession of a Non-Ideal Hemispherical Resonator.

The properties of small size, low noise, high performance and no wear-out have made the hemispherical resonator gyroscope a good choice for high-value space missions. To enhance the precision of the hemispherical resonator gyroscope for use in tasks with large angular velocities and angular accelerations, this paper investigates the standing wave precession of a non-ideal hemispherical resonator under nonlinear high-intensity dynamic conditions. Based on the thin shell theory of elasticity, a dynamic model of a hemispherical resonator is established by using Lagrange's second kind equation. Then, the dynamic model is equivalently transformed into a simple harmonic vibration model of a point mass in two-dimensional space, which is analyzed using a method of averaging that separates the slow variables from the fast variables. The results reveal that taking the nonlinear terms about the square of the angular velocity and the angular acceleration in the dynamic equation into account can weaken the influence of the 4th harmonic component of a mass defect on standing wave drift, and the extent of this weakening effect varies with the dimensions of the mass defects, which is very important for steering the development of the high-precision hemispherical resonator gyroscope.

Influencing factors of hospitalization cost of hypertension patients in traditional Chinese medicine hospitals.

Objectives: This study aimed to analyze the influencing factors of hospitalization cost of hypertensive patients in TCM (traditional Chinese medicine, TCM) hospitals, which can provide a scientific basis for hospitals to control the hospitalization cost of hypertension. Methods: In this study, 3,595 hospitalized patients with a primary diagnosis of tertiary hypertension in Tianshui City Hospital of TCM, Gansu Province, China, from January 2017 to June 2022, were used as research subjects. Using univariate analysis to identify the relevant variables of hospitalization cost, followed by incorporating the statistically significant variables of univariate analysis as independent variables in multiple linear regression analysis, and establishing the path model based on the results of the multiple linear regression finally, to explore the factors influencing hospitalization cost comprehensively. Results: The results showed that hospitalization cost of hypertension patients were mainly influenced by length of stay, age, admission pathways, payment methods of medical insurance, and visit times, with length of stay being the most critical factor. Conclusion: The Chinese government should actively exert the characteristics and advantages of TCM in the treatment of chronic diseases such as hypertension, consistently optimize the treatment plans of TCM, effectively reduce the length of stay and steadily improve the health literacy level of patients, to alleviate the illnesses pain and reduce the economic burden of patients.

S1PR3 suppresses the inflammatory response and extracellular matrix degradation in human nucleus pulposus cells.

Sphingosine 1-phosphate receptor 3 (S1PR3) participates in the inflammatory response in multiple types of diseases. However, the biological role of S1PR3 in intervertebral disc degeneration and the underlying mechanism are unclear. The aim of the present study was to investigate the functional role and the mechanism of S1PR3 in lipopolysaccharide (LPS)-induced human nucleus pulposus cells. The expression of S1PR3 and Toll-like receptor (TLR) 2 in LPS-induced nucleus pulposus (NP) cells was investigated using western blotting. The Cell Counting Kit-8 assay was used to detect cell proliferation, and the levels of inflammatory factors were detected using ELISA. Flow cytometry and western blotting were used for the assessment of apoptosis. The deposition of extracellular matrix (ECM) proteins was investigated using reverse transcription-quantitative PCR and western blotting. In addition, western blotting was used to investigate the protein expression levels of phosphorylated (p)-STAT3, STAT3, p-JNK, JNK, p-ERK, ERK, p-p38 and p38associated with STAT3 and MAPK signaling. S1PR3 expression was reduced, while TLR2 expression was elevated in LPS-induced human nucleus pulposus cells (HNPC). S1PR3 overexpression increased HNPC viability, inhibited the inflammatory response and suppressed apoptosis. Meanwhile, S1PR3 overexpression regulated the expression of ECM-related proteins. Additionally, overexpression of S1PR3 inhibited the expression of the TLR2-regulated STAT3 and MAPK pathways in LPS-induced HNPCs. Furthermore, TLR2 overexpression partially offset the impacts of S1PR3 overexpression on HNPC viability, apoptosis level, inflammation and as ECM degradation. In conclusion, STAT3 overexpression suppressed viability injury, the inflammatory response and the level of apoptosis and alleviated ECM protein deposition in HNPCs through the TLR2/STAT3 and TLR2/MAPK pathways, which may offer a promising candidate for the amelioration of intervertebral disc degeneration.

Testing multiplexed anti-ASFV CRISPR-Cas9 in reducing African swine fever virus.

African swine fever (ASF) is a highly fatal viral disease that poses a significant threat to domestic pigs and wild boars globally. In our study, we aimed to explore the potential of a multiplexed CRISPR-Cas system in suppressing ASFV replication and infection. By engineering CRISPR-Cas systems to target nine specific loci within the ASFV genome, we observed a substantial reduction in viral replication in vitro. This reduction was achieved through the concerted action of both Type II and Type III RNA polymerase-guided gRNA expression. To further evaluate its anti-viral function in vivo, we developed a pig strain expressing the multiplexable CRISPR-Cas-gRNA via germline genome editing. These transgenic pigs exhibited normal health with continuous expression of the CRISPR-Cas-gRNA system, and a subset displayed latent viral replication and delayed infection. However, the CRISPR-Cas9-engineered pigs did not exhibit a survival advantage upon exposure to ASFV. To our knowledge, this study represents the first instance of a living organism engineered via germline editing to assess resistance to ASFV infection using a CRISPR-Cas system. Our findings contribute valuable insights to guide the future design of enhanced viral immunity strategies. IMPORTANCE: ASFV is currently a devastating disease with no effective vaccine or treatment available. Our study introduces a multiplexed CRISPR-Cas system targeting nine specific loci in the ASFV genome. This innovative approach successfully inhibits ASFV replication in vitro, and we have successfully engineered pig strains to express this anti-ASFV CRISPR-Cas system constitutively. Despite not observing survival advantages in these transgenic pigs upon ASFV challenges, we did note a delay in infection in some cases. To the best of our knowledge, this study constitutes the first example of a germline-edited animal with an anti-virus CRISPR-Cas system. These findings contribute to the advancement of future anti-viral strategies and the optimization of viral immunity technologies.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.

BACKGROUND: Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection. METHODS: A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors. RESULTS: The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649-0.923] vs. 0.822 [0.692-0.952] vs. 0.733 [0.573-0.892] vs. 0.511 [0.359-0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance. CONCLUSIONS: The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population. TRIAL REGISTRATION: Not applicable.

Clinical prediction factors of nonchronic total occlusion lesion progression in patients with unstable angina receiving percutaneous coronary intervention for chronic total occlusion lesions.

Background: In this study, we investigated clinical prediction factors of nonchronic total occlusion lesion (NCTOL) progression in patients who underwent percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) lesions. Methods: In total, 450 patients with unstable angina (mean age = 57.1 ± 9.2 years) who underwent PCI for CTO lesions between January 2016 and December 2018 at Beijing Anzhen Hospital were enrolled in this study. A clinical and angiographic follow-up examination was performed 12 months postoperatively. The patients were divided into NCTOL progression (145 cases) and control (305 cases) groups based on the outcome of the 12-month angiographic follow-up. The clinical and angiographic features of the participants were analyzed. Results: The adenosine diphosphate-induced platelet aggregation (ADP-IPA) rate and levels of lipoprotein (a) (Lp(a)) in the NCTOL progression group were significantly higher than those in the control group (51.89 ± 14.81 vs. 39.63 ± 17.12, P < 0.01; 0.22 ± 0.26 vs. 0.14 ± 0.18, P < 0.05, respectively). Logistic regression showed that the ADP-IPA rate (odds ratio = 1.047, 95 % confidence interval: 1.014-1.082, P = 0.005) and Lp(a) (odds ratio = 11.972, 95 % confidence interval: 1.230-116.570, P = 0.033) were independent predictors of NCTOL progression. Partial correlation analysis demonstrated that the ADP-IPA rate was positively correlated with NCTOL progression (r = 0. 351, P < 0.001). Receiver operating characteristic curve showed that the boundary point of the ADP-IPA rate to predict NCTOL progression was 30 % (sensitivity, 86.2 %; specificity, 68.9 %). Conclusions: NCTOL progression is an important cause of recurrent PCI in patients with coronary artery disease after PCI for CTO lesions. The ADP-IPA rate is a useful predictor for NCTOL progression in patients with unstable angina who undergo PCI for CTO lesions.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.