ON RESISTANCE REDUCTION OF A HULL BY TRIM OPTIMIZATION

The paper aims at conducting trim optimization for a hull to reveal the influence of trim on wave resistance by a potential-based panel method coupled with a response surface method. First, a numerical program for solving the linear free-surface flow problem of a hull moving with a uniform speed in calm water is built by the panel method. The S60 hull model is used to validate the numerical procedure. Next, calculation for hull is performed with two different trims at a wide range of Froude number; resistance test is conducted to validate the numerical method in demonstrating the influence of trim on wave resistance. Finally, a response surface of wave resistance is constructed with respect to variations of trim and Froude number, using the database of wave resistance calculated by the surface method. In this way, a framework is developed to perform trim optimization. The optimum trim point for the present hull shows a significant improvement in both wave resistance and total resistance, compared with that of even keel and the worst trim point. The optimization framework is proved to be effective in energy saving due to resistance reduction

Construction and validation of a glioblastoma prognostic model based on immune-related genes

BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Trim Optimization of Ship by a Potential-Based Panel Method

A surface panel method for a boundary-value problem with the free surface is proposed to predict ship wave resistance under different trim conditions based on a so-called double-model solution. The free surface boundary condition is linearized with respect to the oncoming flow and computed by a four-point finite difference scheme. Sample computation for Wigley hull is carried out to demonstrate the effectiveness and the robustness of the method. A hull model is taken into account at two different displacements with respect to trim conditions of lower wave resistance. It is demonstrated by calculation and experiment that the wave resistance under the trim conditions provided by the proposed method is lower than that under the initial conditions

Numerical Simulations of Seaplane Ditching on Calm Water and Uniform Water Current Coupled with Wind

In this paper, the ditching performance of a seaplane model on calm water and a uniform water current coupled with wind was numerically investigated. The overset grid technique was applied to treat the large amplitude of the body motions of the seaplane without leading to mesh distortion. The effects of the initial velocity and the initial pitch angle on the slamming loads and motion responses were investigated for the seaplane’s ditching on calm water. A good agreement with the experimental data on the velocity and angle was obtained. Besides ditching on calm water without the water current and wind, three more-complicated conditions were adopted, including the seaplane’s ditching on calm water with wind, a water current without wind, and a water current coupled with wind. The accelerations and impact pressures of the seaplane can be influenced by the wind or current. Water splashing and overwashing could be observed during the water entry process, with water overtopping the seaplane head or nose and flowing over the body surface. It can be concluded that the relative motion between the water and the seaplane model should be carefully controlled to avoid possible damages caused by the occurrence of overwashing

Data_Sheet_1_The prognostic value of ASPECTS in specific regions following mechanical thrombectomy in patients with acute ischemic stroke from large-vessel occlusion.XLSX

ObjectiveThe aim of this study is to investigate the relationship between the volume of specific regional infarction and the prognosis of patients who undergo mechanical thrombectomy (MT) for acute large vessel occlusion.MethodsIn this study, we collected the clinical and imaging features of patients with unilateral acute anterior circulation ischemic stroke from January 2021 to June 2023 in the Second Affiliated Hospital of Nanchang University. All patients underwent CT perfusion and non-contrast CT scan before MT. The ASPECTS was assessed based on imaging data, and artificial intelligence was used to obtain the percentage of infarction in each of the 10 regions of ASPECTS. According to the modified Rankin Scale, the patients were divided into the good prognosis group and poor prognosis group at the 90-day follow-up. Various indicators in the two groups were compared. Multivariable logistic regression was used to assess the risk factors for poor prognosis. The relationship between core infarction volume and the probability of poor prognosis was plotted to analyze the trend of poor prognosis with changes in the proportion of infarction volume. Finally, a receiver operating characteristic curve was constructed to analyze the predictive ability on poor prognosis.ResultsA total of 91 patients were included, with 58 patients having a good prognosis (mRS ≤ 2) and 33 patients having a poor prognosis (mRS ≥ 3). Multivariate analysis showed that NIHSS score and core infarction involving the internal capsule and M6 region were independent risk factors for poor prognosis. According to the linear correlation, a higher ratio of core infarction volume in the internal capsule or M6 region was linked to an increased risk of a poor prognosis. However, the non-linear analysis revealed that the prognostic impact of core infarction volume was significant when the ratio was greater than 69.7%. The ROC curve indicated that the combination of NIHSS score, infarct location, and the ratio of infarct volume has an AUC of 0.87, with a sensitivity of 84.8% and a specificity of 84.5%.ConclusionIt is important to examine the location and volume of the infarct in the internal capsule and M6 when deciding whether to do a MT.</p

Endovascular treatment of acute basilar artery occlusion caused by vertebral artery stump syndrome: A clinical analysis of 37 cases

Introduction: Acute basilar artery occlusion (ABAO) caused by vertebral artery stump syndrome (VASS) has a low incidence and is always underestimated. Due to the occlusion of the origin of the vertebral artery (VA), it is often combined with basilar artery (BA) endovascular diseases or non-dominant contralateral vertebral artery, making the endovascular treatment (EVT) challenging to implement. Objective: This article focuses on whether EVT and two interventional route options could bring clinical benefits to this group of patients: basilar artery thrombectomy through the occluded lateral vertebral artery and implementing revascularization of the occluded vertebral artery (dirty-road-path); thrombectomy through the non-occluded lateral vertebral artery (clean-road-path). Methods: We collected six cases of acute embolic basilar artery occlusion (ABAO) due to VASS from January 2020 to December 2021 at our hospital and retrospectively analyzed 31 patients previously reported in the literature and applied statistical analysis to investigate the treatment options and clinical prognosis of these patients. Results: The clean-road-path surgical protocol was applied in 4 of 37 patients, the dirty-road-path protocol was applied in 29 patients, and 4 patients did not recanalized the basilar artery. By statistical analysis we found that successful recanalization of the basilar artery was clinically significant in reducing the modified Rankin Scale (mRS) scores in these patients, the statistical difference in the benefit of the two surgical protocols was negative. There was a significant positive correlation between preoperative National Institute of Health Stroke Scale (NIHSS) and postoperative 90-day mRS scores. Conclusion: Endovascular treatment can benefit patients with ABAO due to VASS, and patients with higher preoperative NIHSS scores are more vulnerable to getting a poor prognosis. Comparison between the two endovascular options did not yield statistically significant results, but the dirty-road-path option may be superior to using the clean-road-path

Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China

Introduction Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS).Methods and analysis A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated.Ethics and dissemination This study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences.Trial registration number ChiCTR2200055209

Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia

Abstract Purpose To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. Methods Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. Results Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759–0.936) and validation (AUC = 0.830, 95% CI 0.758–0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. Conclusion The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility