Prediction Of Rupture Research Articles

Prediction of Rupture by Complete Blood Count in Tubal Ectopic Pregnancies Treated with a Single-Dose Methotrexate Protocol.

The availability of reliable and inexpensive markers that can be used to determine the risk of rupture during methotrexate (MTX) treatment in ectopic pregnancies (EPs) is considerable. The aim of the present study is to investigate the role of systemic inflammatory markers such as leukocytes (or white blood cells, WBCs), the neutrophil-to-lymphocyte ratio (NLR), and platelet distribution width (PDW), which are among the parameters of the complete blood count (CBC), in the prediction of rupture of EPs under MTX treatment. A total of 161 patients with tubal EP who underwent a single-dose methotrexate (MTX) protocol were retrospectively analyzed, and the control group (n = 83) included patients cured by MTX, while the ruptured group (n = 78) included patients who were operated on for tubal rupture during the MTX treatment. The features of EP, beta-human chorionic gonadotropin (β-hCG) levels, sonographic findings, and CBC-derived markers such as WBC, NLR, and PDW, were investigated by comparing both groups. The NLR was found to be higher in the ruptured group, of 2.92 ± 0.86%, and significantly lower in the control group, of 2.09 ± 0.6%. Similarly, the PDW was higher (51 ± 9%) in the ruptured group, and it was significantly lower a (47 ± 13%) in the control group (p < 0.05). Other CBC parameters were similar in both groups (p > 0.05). Systemic inflammation markers derived from CBC can be easily applied to predict the risk of tubal rupture in Eps, since the CBC is an inexpensive and easy-to-apply test, which is first requested from each patient during hospitalization.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Clinical Scales in Aneurysm Rupture Prediction

The rate of incidentally discovered unruptured intracranial aneurysms has increased with the broad availability of neuroimaging. The determination of the risk of rupture of brain aneurysms is challenging. Several clinical scales for aneurysm rupture prediction have been developed. The most common scales are PHASES, ELAPSS, and UIATS. These scales are not routinely used in clinical practice due to inherent shortcomings. In this review, we analyze the risk factors used in generating these scales and the performance of these scales in clinical studies. We also discuss new potential biomarkers and tools to predict aneurysm rupture.

Computational fluid dynamics-based prediction of aortic aneurysm rupture in a patient with chronic aortic dissection

BackgroundThe indication of additional aortic arch surgery for residual aortic dissection remains controversial as some patients experience aneurysm rupture at a smaller diameter of 55 mm.MethodAn 84-year-old woman, who underwent total arch replacement for chronic dissection, developed rupture of a residual aneurysm of with a diameter of 48 mm. Computational fluid dynamics simulated pressure and wall shear stress comparing pre- and post-total arch replacement.ResultsAfter total arch replacement, false lumen pressure in the distal aortic arch increased (pre, 138.5 mmHg; post, 148.2 mmHg). Wall shear stress also increased in the distal aortic arch (pre, 10.5 Pa; post, 16.9 Pa).ConclusionComputational fluid dynamics could retrospectively predict a significant postoperative increase in false lumen pressure and wall shear stress of chronic dissections after total arch replacement, potentially leading to aneurysm rupture.

Probabilistic creep with the Wilshire–Cano–Stewart model

AbstractCreep behavior is susceptible to uncertainty. Replicated creep data for steel can exhibit logarithm decades of uncertainty, which necessitates the development of probabilistic creep models for reliability‐based design. The deterministic Wilshire–Cano–Stewart (WCS) model is transformed into a probabilistic creep deformation, damage, and rupture prediction model via injecting the uncertainty of test conditions, pre‐existing damage, and material constants. The WCS model is calibrated to replicated quintuplicate creep data from a “single heat” of 304 stainless steel. The Markov chain Monte Carlo (MCMC) method with the Metropolis–Hastings (MH) algorithm is employed to introduce the sources of uncertainties into the model via calibrated probability distribution functions (pdfs). The probabilistic predictions encapsulate most of the experimental outliers across isostresses‐isotherms. Interpolative and extrapolative assessments reveal that minimum‐creep‐strain‐rate (MCSR) and stress rupture (SR) predictions are consistent across isotherms. The WCS probabilistic model captures the range of creep behavior of a single heat of material using short‐term data.

A framework for intracranial aneurysm detection and rupture analysis on DSA

BackgroundIntracranial aneurysm is a severe cerebrovascular disease that can result in subarachnoid hemorrhage (SAH), leading to high incidence and mortality rates. Computer-aided detection of aneurysms can assist doctors in enhancing diagnostic accuracy. The analysis of aneurysm imaging holds considerable predictive value for aneurysm rupture. This paper presents a method for the detection of aneurysms and analysis of ruptures using digital subtraction angiography (DSA). MethodsA total of 263 aneurysms were analyzed, with 125 being ruptured and 138 being unruptured. Firstly, a filter based on the eigenvalues of the Hessian matrix was proposed for aneurysm detection. The filter's detection parameters can be automatically obtained through Bayesian optimization. Aneurysms were detected based on their structure and the response of the filter. Secondly, considering the variations in blood flow and morphology among aneurysms in DSA, intensity, texture, and blood perfusion features were extracted from the ruptured aneurysms and unruptured aneurysms. Subsequently, a sparse representation (SR) method was utilized to classify unruptured and ruptured aneurysms. ResultsThe experimental results for aneurysm detection showed that the F1-score was 94.1%. In the classification of ruptured and unruptured aneurysms, the accuracy, sensitivity, specificity, and area under curve (AUC) were 96.1%, 94.4%, 97.5%, and 0.982, respectively. ConclusionThis paper presents a scheme combining an aneurysm detection filter and machine learning, offering a reliable solution for the diagnosis and prediction of aneurysm rupture.

The risk of abdominal aortic aneurysm rupture prediction according to computed tomography data

Background. Abdominal aortic aneurysm – is a widespread disease, which in some cases does not have any specific clinical symptoms, but possible complications of this pathology are characterized by high mortality. Aortic aneurism rupture is an extremely serious condition that requires emergency surgical treatment. A timely diagnosis of abdominal aortic aneurysm’s rupture risk remains extremely important, since the patient mortality rate is up to 7 times higher when performing an emergency surgery, than when performing a planned surgery. Aim of study: to improve the diagnosis of abdominal aortic aneurysms instability by creating an assessing scale of aortic rupture risk according to computed tomography data. Materials and methods. CT studies were performed by a 160-slice CT scanner and included the native and arterial phases of the scanning. The MSCT data of 179 patients with diagnosed abdominal aortic aneurysm were retrospectively studied, in 46 of whom the condition was complicated by an aortic rupture. CT scans can reveal a number of specific structural changes in the aorta and also surrounding tissues, which can be regarded as aortic wall rupture prediction. That specific structural changes were stratified by severity and the preliminary results were compared with the treatment tactics and the outcome of the disease. Results. The MSCT data of 179 patients with diagnosed abdominal aortic aneurysm shows that the minimum and sufficient set of abdominal aortic aneurysm rupture predictions are: aneurysm size, a sickle sign, aortic wall draping sign, paraaortic fat induration, the thickness of parietal thrombotic masses. The obtained parameters give the possibility to predict the risks of AAA rupture. The prognostic scale is represented by a 4-point scale for assessing aneurysm parameters. Depending on the influence degree of each parameter, it is assigned a number of points corresponding to the level of prognostic significance. The maximum number of risk factors is 17 points. Finding. The assessing scale of the abdominal aortic aneurysm rupture’s risk according to computed tomography data was developed.

Risk factors and prediction of ectopic pregnancy rupture following methotrexate treatment: A retrospective cohort study

ObjectiveEctopic pregnancy (EP) rupture after methotrexate (MTX) treatment can have severe consequences. We examined clinical characteristics and beta-hCG trends that may predict EP rupture after MTX treatment. Study DesignIn this 10-year retrospective study of 277 women with an EP, we compared clinical, sonographic and beta-hCG trends, before and after MTX treatment, between those who did and did not have an EP rupture after MTX treatment. ResultsEP rupture was diagnosed in 41 women (15.1%) within 25 days of MTX treatment, and was correlated with higher parity and advanced pregnancy age: 2(0–5) vs. 1(0–6), P = 0.027 and 6.6(4.2–9.8) vs. 6.1(4–9.5), P = 0.045. EP rupture was also correlated with higher beta-hCG levels on days 0, 4 and 7 of MTX treatment: (2063 vs. 920 mIU/ml), (3221 vs. 921 mIU/ml) and (2368 vs. 703 mIU/ml), respectively, P < 0.001, for all. An increase of beta-hCG by>14% during days 0–4 showed a sensitivity of 71.4% CI 95% [55.4%-84.3%] and a specificity of 67.5% CI 95% [61.1%-73.6%] for predicting EP rupture after MTX treatment. Beta-hCG > 910 mIU/ml on day 0 showed a sensitivity of 80.9% CI 95% [66.7%-90.8%] and a specificity of 70.4% CI 95% [64.1%-76.3%] for predicting EP rupture after MTX treatment.A beta-hCG increase by>14% during days 0–4, and a beta-hCG value > 910 mUI/mL on day 0 were associated with increased risks of EP rupture after MTX treatment; the odds ratios were 6.4 and 10.5, respectively. Odds ratios were 8.06 [CI 95% (3.70–17.56)], P < 0.001 for every percent rise in beta-hCG during days 0–4; 1.37 [CI 95% (1.06–1.86)], P = 0.046 for every week change in gestational age; and 1.001 [CI 95% (1.000–1.001)], P < 0.001 for every unit rise in beta-hCG at day 0. ConclusionBeta-hCG > 910 mIU/ml at day 0, a rise in beta-hCG by>14% during days 0–4, and more advanced gestational age were associated with EP rupture after MTX treatment.

Influence of abdominal aortic aneurysm shape on hemodynamics in human aortofemoral arteries: A transient open-loop study

New imaging methods have enabled the detection of unruptured abdominal aortic aneurysms (AAA). It is necessary to develop appropriate mathematical models for rupture prediction to allow a proper patient treatment plan. To provide valid hemodynamic parameters, high-fidelity numerical models with patient-specific boundary conditions are needed. Researchers have pointed out in recent research articles and reviews that those morphological parameters, such as shape, dilation ratio, neck angle, common iliac bifurcation angle, and AAA type, consistently correlate with the rupture mechanism. However, it is unclear how morphological indicators affect hemodynamics-based computational fluid dynamics predictions. The present work investigates the influence of AAA shape on local and global hemodynamics parameters and rupture predictions. Five cases of magnetic resonance imaging scan-based data for patient-specific aortofemoral artery modeling are explored. The inflow conditions are patient-specific, and an open loop system has been considered to model all five cases. Hemodynamics parameters in pulsating conditions, such as wall shear stress (WSS), velocity contour, time average WSS (TAWSS), oscillatory shear index (OSI), vorticity, and streamlines, are computed and investigated. Both maximum dilation diameter and aneurysm neck angle are found to have substantial effects on local hemodynamics parameters. The magnitude of WSS, TAWSS, and OSI increases and decreases non-linearly with a change in maximum diameter during the cardiac process. Also, aneurysms with doubly titled and completely saccular shape show complex streamlines, low WSS, and high residence time in the sac area of the wall.

Comprehensive analysis of atherosclerotic plaques reveals crucial genes and molecular mechanisms associated with plaque progression and rupture.

Plaque rupture and acute atherothrombosis, resulting from continued progression of atherosclerotic plaques (APs), are major contributors to acute clinical events such as stroke or myocardial infarction. This article aimed to explore the gene signatures and potential molecular mechanisms in the progression and instability of APs and to identify novel biomarkers and interventional targets for AP rupture. The microarray data were downloaded from the Gene Expression Omnibus (GEO) database and grouped into discovery and validation cohorts. In the discovery cohort, Weighted Gene Co-Expression Network Analysis was performed for finding co-expression modules, and the Metascape database was used to perform functional enrichment analysis. Differential Expression Genes analysis subsequently was performed in the validation cohort for verification of the obtained results. Common genes were introduced into Metascape database for protein-protein interaction and functional enrichment analysis. We constructed the miRNAs-mRNAs network with the hub genes. Moreover, gene expression profiles of peripheral blood mononuclear cells (PBMCs) from peripheral blood of patients with plaque rupture were analyzed by high-throughput sequencing, and the diagnostic power of hub genes was verified by receiver operating characteristic (ROC) analysis. In the discovery cohort, the brown module in GSE28829 and the turquoise module in GSE163154 were the most significant co-expression modules. Functional enrichment analysis of shared genes suggested that "Neutrophil degranulation" was the most significantly enriched pathway. These conclusions were also demonstrated by the validation cohort. A total of 16 hub genes were identified. The miRNA-mRNA network revealed that hsa-miR-665 and hsa-miR-512-3p might regulate the "Neutrophil degranulation" pathway through PLAU and SIRPA, which might play a significant role in AP progression and instability. Five hub genes, including PLAUR, FCER1G, PLAU, ITGB2, and SLC2A5, showed significantly increased expression in PBMCs from patients with plaque rupture compared with controls. ROC analysis finally identified three hub genes PLAUR, FCER1G, and PLAU that could effectively distinguish patients with APs rupture from controls. The present study demonstrated that the "neutrophil degranulation" signaling pathways and identified novel mRNA and miRNA candidates are closely associated with plaque progression and instability. The hub genes FCER1G, PLAUR, and PLAU may serve as biomarkers for the prospective prediction of AP rupture.

First-trimester prediction of preterm prelabour rupture of membranes incorporating cervical length measurement

ObjectivesTo examine early pregnancy risk factors for preterm prelabour rupture of membranes (PPROM) and develop a predictive model. Study designRetrospective analysis of a cohort of mixed-risk singleton pregnancies screened in the first and second trimesters in three Danish tertiary fetal medicine centres, including a cervical length measurement at 11–14 weeks, at 19–21 weeks and at 23–24 weeks of gestation. Univariable and multivariable logistic regression analyses were employed to identify predictive maternal characteristics, biochemical and sonographic factors. Receiver operating characteristic (ROC) curve analysis was used to determine predictors for the most accurate model. ResultsOf 3477 screened women, 77 (2.2%) had PPROM. Maternal factors predictive of PPROM in univariable analysis were nulliparity (OR 2.0 (95% CI 1.2–3.3)), PAPP-A < 0.5 MoM (OR 2.6 (1.1–6.2)), previous preterm birth (OR 4.2 (1.9–8.9)), previous cervical conization (OR 3.6 (2.0–6.4)) and cervical length ≤ 25 mm on transvaginal imaging (first-trimester OR 15.9 (4.3–59.3)). These factors all remained statistically significant in a multivariable adjusted model with an AUC of 0.72 in the most discriminatory first-trimester model. The detection rate using this model would be approximately 30% at a false-positive rate of 10%. Potential predictors such as bleeding in early pregnancy and pre-existing diabetes mellitus affected very few cases and could not be formally assessed. ConclusionsSeveral maternal characteristics, placental biochemical and sonographic features are predictive of PPROM with moderate discrimination. Larger numbers are required to validate this algorithm and additional biomarkers, not currently used for first-trimester screening, may improve model performance.

Abstract Number ‐ 59: Study on Rupture Prediction Model for Small Unruptured Cerebral Aneurysms Using Random Forest

Introduction Hemodynamic, morphological, and clinical factors have been reported to be involved in aneurysm rupture. Hemodynamic factors could be calculated by computational fluid dynamics (CFD) analysis. In addition, machine learning has been used in various medical fields in recent years, and it is expected to be applicable to the prediction of aneurysm rupture. In this study, we built a rupture prediction model for the small aneurysms (3∼10 mm) using Random Forest, which is one of the machine learning algorithms. This model included hemodynamic parameters from CFD simulations, morphological parameters of each aneurysm, and clinical information. The model was applied to the other patient dataset, and we verified the model’s prediction accuracy. Methods In this study, the ruptured case was defined as an aneurysm that ruptured during the follow‐up term. On the other hand, the unruptured case was defined as an aneurysm that remained stable during the follow‐up for more than two years (average follow‐up term is about nine years), and registered in the database of Jikei Hospital between January 1, 2003 and April 30, 2020. Only small aneurysms (3∼10 mm) were considered in the present study. Based on the criteria, we identified 507 aneurysms (ruptured: 41, unruptured: 466), in which CFD analysis has been completed. Among these aneurysms, 405 aneurysms (ruptured: 32, unruptured: 373) were used to build the rupture prediction model as Training data, and 102 aneurysms (ruptured: 9, unruptured: 93) were used to verify the prediction accuracy as Test data. We used 3‐dimensional arterial geometries that were reconstructed from computed tomography angiography images for conducting CFD simulations and morphological measurements. For the ruptured aneurysms, the image acquired before the rupture was used. We obtained 138 hemodynamic parameters, 7 morphological parameters, and 6 clinical information in each case. From the Training data, we proposed the rupture prediction model with these parameters using Random Forest. Then, we introduced the test data into the model, and its sensitivity and specificity were estimated. Results The sensitivity and specificity of this model for the Test data were 88.9% and 83.9%, respectively. In addition, aspect ratio (AR), the maximum height of the cerebral aneurysm (Hmax), and the spatially maximum values of the oscillatory shear index on the aneurysm wall (OSImax) were obtained as the top three of the important features to predict aneurysm rupture. Furthermore, the values of these parameters of ruptured aneurysms are higher than those of unruptured aneurysms. For unruptured and ruptured cases, average AR were 0.770±0.260 [‐] and 1.02±0.382 [‐], average Hmax were 3.39±1.22 [mm] and 4.96±1.96 [mm], and average OSImax were 0.399±0.105 [‐] and 0.460±0.0328 [‐], respectively. These results imply that cerebral aneurysms with high Hmax, high AR, and high OSImax are more likely to rupture. Conclusions Ruptured aneurysms tend to have high AR, high Hmax, and high OSImax. The sensitivity and specificity of our prediction model were 88.9% and 83.9%, respectively. The rupture prediction model obtained from this study may predict the aneurysm rupture in advance.

First trimester prediction of uterine rupture in cesarean scar pregnancy

First trimester prediction of uterine rupture in cesarean scar pregnancy

Parâmetros que influenciam na resistência ao cisalhamento de vigas de concreto armado sem estribos

abstract: Experimental investigations have been commonly used to improve the existing knowledge about structural design, presenting accurate conclusions regarding structure behavior. However, considering the limitations of experimental studies, such as restricted amount of strain gauges, unexpected pathologies, difficulties to foresee the correct position of cracks, or simply saving in costs, the use of numerical analysis can present some innovative approaches to understand the process of failure in concrete elements, presenting easier results where experimental programs can hardly report. This study presents the numerical analysis of eight beams experimented by Sherwood [26], with two different sizes and variable aggregate sizes, seeking to understand the influence of coarse aggregate size in shear strength of beams without stirrups. The numerical approach was used to derive the influence of each internal shear mechanism and to identify the specific amount of dowel force, shear transfer by uncracked compression zone and aggregate interlock portion. The results showed that fair results can be obtained by 2D smeared crack approaches, enabling the identification of major aggregate interlock portion in beams with bigger coarse aggregate sizes. Comparing the size effect in beams allowed us to conclude that a higher contribution of aggregate interlock contribution can be obtained in large beams, with almost 64% of the total contribution, whereas smaller beams had only 43%. To evaluate the accuracy of the studied mechanisms, the results of Sherwood [26] were compared with the standards Model Code [23], CSA-A.23.3 [24], NBR 6118 [38], and ACI 318 [39]. Regarding the prediction of element rupture by concrete internal mechanisms, the normative instructions Model Code [23] and CSA A.23.3 [24] were the closest to the experimental results.

Transvaginal Ultrasonographic Assessment of Lower Uterine Segment Thickness and Prediction of Uterine Rupture In Cases of Vaginal Birth After Caesarean Delivery

Background: The rise in elective CSs, which make about one-third of all CSs, is primarily to blame for the rise in the Cesarean birth rate. Objective: To assess the strength of the association between transvaginal ultrasonographic measurement of the lower uterine segment in women with prior one CS undergoing a trial of vaginal birth and uterine scar defect at delivery. Patients and Methods: A retrospective observational study was conducted at Al Ahrar Zagazig Teaching Hospital during the period from June 2018 through June 2020. The lower uterine segment thickness was measured transvaginal ultrasonography in 60 gravid as (36 week – 40 week) with previous one CS undergoing VBAC, and the scar was evaluated during delivery. Results: Uterine dehiscence was found in 4 cases (6.7%). There were no cases of uterine rupture. There was significant correlation between the grade of scar and sonographic measurement of LUS thickness using trans-vaginal ultrasound (p value= 0.001).The scar thickness in the third trimester (36-40 week) had a significant relation with the mode of delivery. 2.4 mm was considered the critical cutoff value of the LUS thickness above which safe vaginal delivery can be achieved. This critical cutoff value was derived from the ROC curve with sensitivity, specificity, PPV, and NPV 75.0%, 85.7%, 27.3%, and 98.0%, respectively. Conclusion: Measurement of the lower uterine segment thickness by TVS seems to be a good screening test with its obviously high sensitivity and negative predictive value. LUS thickness of

Application of Logistic Regression and Artificial Intelligence in the Risk Prediction of Acute Aortic Dissection Rupture.

Logistic regression (LR) and artificial intelligence algorithms were used to analyze the risk factors for the early rupture of acute type A aortic dissection (ATAAD). Data from electronic medical records of 200 patients diagnosed with ATAAD from the Department of Emergency of Guangdong Provincial People’s Hospital from April 2012 to March 2017 were collected. Logistic regression and artificial intelligence algorithms were used to establish prediction models, and the prediction effects of four models were analyzed. According to the LR models, we elucidated independent risk factors for ATAAD rupture, which included age > 63 years (odds ratio (OR) = 1.69), female sex (OR = 1.77), ventilator assisted ventilation (OR = 3.05), AST > 80 U/L (OR = 1.59), no distortion of the inner membrane (OR = 1.57), the diameter of the aortic sinus > 41 mm (OR = 0.92), maximum aortic diameter > 48 mm (OR = 1.32), the ratio of false lumen area to true lumen area > 2.12 (OR = 1.94), lactates > 1.9 mmol/L (OR = 2.28), and white blood cell > 14.2 × 109 /L (OR = 1.23). The highest sensitivity and accuracy were found with the convolutional neural network (CNN) model. Its sensitivity was 0.93, specificity was 0.90, and accuracy was 0.90. In this present study, we found that age, sex, select biomarkers, and select morphological parameters of the aorta are independent predictors for the rupture of ATAAD. In terms of predicting the risk of ATAAD, the performance of random forests and CNN is significantly better than LR, but the performance of the support vector machine (SVM) is worse than LR.

Acute hemicranial pain accompanied with a pearl and string type dissection of intracranial vertebral artery: Consideration for the time when to finish the medical observation.

The International Classification of Headache Disorder (ICHD) clearly guides the suspicion of intracranial vertebral artery dissection (ICVAD) in headache patients, but guidelines on how observational or imaging studies should be performed to detect dangerous progression early are unclear. Fifty-six cases with pearl and string type intracranial vertebral artery dissection were divided into 3 groups: 39 in the headache group, 6 in the infarction group, and 11 in the hemorrhagic group. Clinical and angiographic data were analyzed and compared. Most headaches resolved within 2 weeks and did not exceed 8 weeks. Of the 33 patients (84.6%) who underwent continuous follow-up imaging, 18 (54.5%) returned to normal, but 3 (9%) had deteriorated. All the patients survived without subsequent bleeding or infarction. Image changes started before 3rd month and ended after 6 to 7 months. In acute ICVADs, image changes occur at the same time as the headache resolves and continue for several months after the headache has subsided. Since the dissection is likely to worsen even after the headache disappears, the image changes continue over several months, and prediction of rupture of unruptured ICVAD is unpredictable, it is desirable to conduct continuous imaging studies regularly after the initiation of dissection until stabilization is confirmed.

Experimental study of dewetting instability in polymer multilayer shear flow

<h2>Abstract</h2> The interfacial stability of multilayer Couette flow is investigated experimentally. A better understanding of this flow is critical for the processing of multilayer polymeric structures, such as multilayer coextrusion. We first look at the liquid film rupture predictions from numerical simulations of the thin film equation that considers capillary and van der Waals forces. With the addition of shear, three regimes have then been evidenced as a function of the shear rate. In the regime of low shear rates the rupture is delayed when compared to the no-shear problem, while at higher shear rates it is suppressed. Then, experiments investigating the effect of shear on the dewetting of thin polymer films at different temperatures by using an optical microscope coupled with a shearing hotstage will be presented. The dewetting dynamics, i.e. the growth of dewetting holes is monitored over time at various shear rates. It is observed that their circularity is modified by shear and that for all temperatures and thicknesses studied, the growth speed of the formed holes rapidly increases with increasing shear rate. A model balancing capillary forces and viscous dissipation while taking into account shear-thinning is then proposed and captures the main features of the experimental data, such as the ellipsoid shape of the holes and the faster dynamics in the direction parallel to the shear.

Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25

Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.

Hemodynamics and bio-mechanics of morphologically distinct saccular intracranial aneurysms at bifurcations: Idealised vs Patient-specific geometries

Background and Objective.Understanding the factors that influence the rupture of aneurysms is of primary concern to the clinicians, who are grappled with patient management. It is important to know how the relation between morphological features of the cerebral aneurysm, and the mechanical stresses on the containing arterial walls are influenced by the hemodynamic forces. Present study investigates three different shapes, which have been identified correspondingly in patient-specific scenarios as well. The primary objective is to categorize the bifurcation aneurysms into standard shapes such as, spherical, beehive and pear-shaped, based on patient-specific clinical studies and further compare and contrast the model aneurysms with the patient specific configurations, for their hemodynamic factors as well as the attendant stresses on the wall.MethodsComputational fluid dynamic simulations are performed accounting for the fluid-structure interaction (FSI) effects between the flowing fluid and the containing vessel wall. Blood is assumed to be Newtonian, while the arterial walls are assumed to be linearly elastic. A commercial solver is used for performing detailed calculations. Hemodynamic and bio-mechanical rupture predictions are carried out for the three different shapes. Observations derived from the idealised simulations are compared and contrasted against their patient-specific counterparts.ResultsFrom detailed numerical simulations, it was observed that pear-shaped aneurysms exhibit large re-circulation bubble and flow stagnation zone, with higher residence time for the particles, which may lead to atherosclerotic lesions. Beehive shape allows for maximum flow into the aneurysmal sac with concentrated jet impinging on the dome, leading to high values of maximum WSS (MWSS) resulting in great propensity to form a secondary bleb. However, flow field inside a spherical aneurysm is found to be stable with fewer vortices, and nearly uniform distribution of wall stresses are observed though-out the sac, which perhaps signifies hemodynamically and bio-mechanically stable condition.ConclusionCategorizing patient-specific intracranial aneurysms into standard shapes viz, spherical, beehive and pear could generalize the process of prediction of hemodynamic and bio-mechanical rupture indicators. Comparative assessment of the flow field and stresses reported from the simulations on idealised models, with corresponding patient-specific simulations reveal that, these studies could aid in understanding the generalised shape dependence of hemodynamic and bio-mechanical behaviour of aneurysms.