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Background Aortic dissection (AD) is a rare disease with severe morbidity and high mortality. Presently, the pathogenesis of aortic dissection is still not completely clear, and studying its pathogenesis will have important clinical significance. Methods We downloaded 28 samples from the Gene Expression Omnibus (GEO) database (Accession numbers: GSE147026 and GSE190635), including 14 aortic dissection samples and 14 healthy controls (HC) samples. The Limma package was used to screen differentially expressed genes. The StarBasev2.0 tool was used to predict the upstream molecular circRNA of the selected miRNAs, and Cytoscape software was used to process the obtained data. STRING database was used to analyze the interacting protein pairs of differentially expressed genes under medium filtration conditions. The R package \"org.hs.eg.db\" was used for functional enrichment analysis. Results Two hundred genes associated with aortic dissection were screened. Functional enrichment analysis was performed based on these 200 genes. At the same time, 2720 paired miRNAs were predicted based on these 200 genes, among which hsa-miR -650, hsa-miR -625-5p, hsa-miR -491-5p and hsa-miR -760 paired mRNAs were the most. Based on these four miRNAs, 7106 pairs of circRNAs were predicted to be paired with them. The genes most related to these four miRNAs were screened from 200 differentially expressed genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1). Conclusions The study demonstrates that miRNA-associated circRNA-mRNA networks are altered in AD, implying that miRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.

To investigate the predictive value of radiomics based on T1-weighted contrast-enhanced MRI (CE-MRI) in forecasting the recurrence of acute pancreatitis (AP). A total of 201 patients with first-episode of acute pancreatitis were enrolled retrospectively (140 in the training cohort and 61 in the testing cohort), with 69 and 30 patients who experienced recurrence in each cohort, respectively. Quantitative image feature extraction was obtained from MR contrast-enhanced late arterial-phase images. The optimal radiomics features retained after dimensionality reduction were used to construct the radiomics model through logistic regression analysis, and the clinical characteristics were collected to construct the clinical model. The nomogram model was established by linearly integrating the clinically independent risk factor with the optimal radiomics signature. The five best radiomics features were determined by dimensionality reduction. The radiomics model had a higher area under the receiver operating characteristic curve (AUC) than the clinical model for estimating the recurrence of acute pancreatitis for both the training cohort (0.915 vs. 0.811, p  = 0.020) and testing cohort (0.917 vs. 0.681, p  = 0.002). The nomogram model showed good performance, with an AUC of 0.943 in the training cohort and 0.906 in the testing cohort. The radiomics model based on CE-MRI showed good performance for optimizing the individualized prediction of recurrent acute pancreatitis, which provides a reference for the prevention and treatment of recurrent pancreatitis.

Modern warfare demands weapons capable of penetrating substantial structures, which presents significant challenges to the reliability of the electronic devices that are crucial to the weapon's performance. Due to miniaturization of electronic components, it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions. To address this issue, an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element (FE) predictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration. For predicting the high-strain-rate penetration process, an FE model was established with elasto-plastic constitutive models of the representative packaging materials (that is, solder material and epoxy molding compound) in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar. As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration, the corner solder joints in the edge regions experience the highest S11 and strain, making them more prone to failure. Based on FE predictions at different structural scales, an improved Bayesian method based on augmented Tikhonov regularization was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints. By incorporating a wavelet thresholding technique, the method resolves the problem of poor load identification accuracy at high noise levels. The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures, while significantly balancing the smoothness of response curves with the accuracy of peak identification. At medium and low noise levels, the relative error is less than 6%, while it is less than 10% at high noise levels. The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process, and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.

A high-precision camera intrinsic parameters calibration method based on concentric circles was proposed. Different from Zhang’s method, its feature points are the centers of concentric circles. First, the collinearity of the projection of the center of concentric circles and the centers of two ellipses which are imaged from the concentric circles was proved. Subsequently, a straight line passing through the center of concentric circles was determined with four tangent lines of concentric circles. Finally, the projection of the center of concentric circles was extracted with the intersection of the straight line and the line determined by the two ellipse centers. Simulation and physical experiments are carried out to analyze the factors affecting the accuracy of circle center coordinate extraction and the results show that the accuracy of the proposed method is higher. On this basis, several key parameters of the calibration target design are determined through simulation experiments and then the calibration target is printed to calibrate a binocular system. The results show that the total reprojection error of the left camera is reduced by 17.66% and that of the right camera is reduced by 21.58% compared with those of Zhang’s method. Therefore, the proposed calibration method has higher accuracy.

Background: This study aimed to develop an ultrathin nanofibrous membrane able to, firstly, mimic the natural fibrous architecture of human Bruch’s membrane (BM) and, secondly, promote survival of retinal pigment epithelial (RPE) cells after surface functionalization of fibrous membranes. Methods: Integrin-binding peptides (IBPs) that specifically interact with appropriate adhesion receptors on RPEs were immobilized on Bruch’s-mimetic membranes to promote coverage of RPEs. Surface morphologies, Fourier-transform infrared spectroscopy spectra, contact angle analysis, Alamar Blue assay, live/dead assay, immunofluorescence staining, and scanning electron microscopy were used to evaluate the outcome. Results: Results showed that coated membranes maintained the original morphology of nanofibers. After coating with IBPs, the water contact angle of the membrane surfaces varied from 92.38 ± 0.67 degrees to 20.16 ± 0.81 degrees. RPE cells seeded on IBP-coated membranes showed the highest viability at all time points (Day 1, p < 0.05; Day 3, p < 0.01; Days 7 and 14, p < 0.001). The proliferation rate of RPE cells on uncoated poly(ε-caprolactone) (PCL) membranes was significantly lower than that of IBP-coated membranes (p < 0.001). SEM images showed a well-organized hexa/polygonal monolayer of RPE cells on IBP-coated membranes. RPE cells proliferated rapidly, contacted, and became confluent. RPE cells formed a tight adhesion with nanofibers under high-magnification SEM. Our findings confirmed that the IBP-coated PCL membrane improved the attachment, proliferation, and viability of RPE cells. In addition, in this study, we used serum-free culture for RPE cells and short IBPs without immunogenicity to prevent graft rejection and immunogenicity during transplantation. Conclusions: These results indicated that the biomimic BM-IBP-RPE nanofibrous graft might be a new, practicable approach to increase the success rate of RPE cell transplantation.

Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ 2  = 0.038, P  = 0.845, McNemar’s test), higher sensitivity (75.90% vs. 63.86%, χ 2  = 4.500, P  = 0.034, McNemar’s test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ 2  = 6.125, P  = 0.013, McNemar’s test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.

Purpose To observe long-term prognosis of anterior segment ischemia (ASI) following hyaluronic acid (HA) injection, propose a severity grading system for ASI and a predictive model for phthisis bulbi (PB) based on long-term secretion dysfunction of ciliary process. Methods This is a retrospective case–control study. All enrolled 20 patients were divided into two groups and followed for at least 6 months to observe the formation and transformation characteristics of ASI and long-term prognosis based on the degrees of ciliary function damage. Results The severity of ASI following HA injection could be subdivided into 4 grades according to the degrees of ciliary function damage, comprising ASI grades 0, 1, 2 and 3. In 20 patients, ophthalmoplegia at 1-month follow-up, ASI within 1 month, ASI at 1-month follow-up, hypotony within 6 months were all significantly more common in study group than in control group (60% vs. 0%, P  = 0.011; 100% vs. 20%, P  = 0.001; 100% vs. 0%, P  < 0.001; 80% vs. 0%, P  = 0.001, respectively). Sensitivity, specificity and the area under the receiver operating characteristic curve (AUC) for predicting subsequent PB at 2-year follow-up through the co-occurrence of ophthalmoplegia at 1-month follow-up and hypotony within 6 months was 100%, 100% and 1.00, respectively. Conclusions The new grading system for ASI and novel predictive model for PB we proposed could predict the long-term prognosis and probability of subsequent PB due to ASI following HA injection through several dynamic assessments within 6 months. Level of Evidence Level IV, observational prognostic study.

Optical coherence tomography angiography (OCTA) has been a frequently used diagnostic method in neovascular age-related macular degeneration (nAMD) because it is non-invasive and provides a comprehensive view of the characteristic lesion, choroidal neovascularization (CNV). In order to study its characteristics, an automated method is needed to identify and quantify CNV. Here, we have developed a deep learning model that can automatically segment CNV regions from OCTA images. Specifically, we use the ResNeSt block as our basic backbone, which learns better feature representations through group convolution and split-attention mechanisms. In addition, considering the varying sizes of CNVs, we developed a spatial pyramid pooling module, which uses different receptive fields to enable the model to extract contextual information at different scales to better segment CNVs of different sizes, thus further improving the segmentation performance of the model. Experimental results on a clinical OCTA dataset containing 116 OCTA images show that the CNV segmentation model has an AUC of 0.9476 (95% CI 0.9473–0.9479), with specificity and sensitivity of 0.9950 (95% CI 0.9945–0.9955) and 0.7271 (95% CI 0.7265–0.7277), respectively. In summary, the model has satisfactory performance in extracting CNV regions from the background of OCTA images of nAMD patients.

To compare the efficacy and safety of triple therapy combining intravitreal injection of anti-vascular endothelial growth factor, trabeculectomy, and pan-retinal photocoagulation via binocular indirect ophthalmoscopy, with that of transscleral cyclophotocoagulation (TCP) to treat neovascular glaucoma in the angle-closure stage. Eighteen triple therapy patients and 25 TCP patients between May 2014 and May 2016 were retrospectively analysed. Anterior chamber puncture and anti-VEGF intravitreal injection were performed on the first day of sequential therapy. Trabeculectomy was performed 3–5 d after injection; pan-retinal laser photocoagulation via binocular indirect ophthalmoscopy was initiated 5–7 d later. The IOP of the triple therapy group was lower than that of the TCP group (15.2 ± 2.2 vs. 20.0 ± 8.5 mmHg) and fewer anti-glaucoma drugs were used (0.5 ± 1.0 vs. 0.6 ± 1.0) after treatment. The success rates of the two groups were 89% and 60% respectively (P = 0.032). The visual function of 94% of triple therapy patients was preserved or improved compared to 64% of TCP patients with statistical significance (P = 0.028). No patient in the triple therapy group showed hypotony or eyeball atrophy. Compared to TCP, triple therapy shows higher success rate, fewer complications, and attributes to visual function preservation.

To explore the diagnostic value of radiomics model based on magnetic resonance T2-weighted imaging for predicting the recurrence of acute pancreatitis. We retrospectively collected 190 patients with acute pancreatitis (AP), including 122 patients with initial acute pancreatitis (IAP) and 68 patients with recurrent acute pancreatitis (RAP). At the same time, the clinical characteristics of the two groups were collected. They were randomly divided into training group and validation group in the ratio of 7:3. One hundred thirty-four cases in the training group, including 86 cases of IAP and 48 cases of RAP. There were 56 cases in the validation group, including 36 cases of IAP and 20 cases of RAP. Least absolute shrinkage and selection operator (LASSO) were used for feature screening. Logistic regression was used to establish the radiomics model, clinical model and combined model for predicting AP recurrence. The predictive ability of the three models was evaluated by the area under the curve (AUC). The recurrence risk in patients with AP was assessed using the nomogram. The AUCs of radiomics model in training group and validation group were 0.804 and 0.788, respectively. The AUCs of the combined model in the training group and the validation group were 0.833 and 0.799, respectively. The AUCs of the clinical model in training group and validation group were 0.677 and 0.572, respectively. The sensitivities of the radiomics model, combined model, and clinical model were 0.646, 0.691, and 0.765, respectively. The specificities of the radiomics model, combined model, and clinical model were 0.791, 0.828, and 0.590, respectively. There was no significant difference in AUC between the radiomics model and the combined model for predicting RAP ( = 0.067). The AUCs of the radiomics model and combined model were greater than those of the clinical model ( = 0.008 and = 0.007, respectively). Radiomics features based on magnetic resonance T2WI could be used as biomarkers to predict the recurrence of AP, and radiomics model and combined model can provide new directions for predicting recurrence of acute pancreatitis.