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Background: Endometriosis (EM) is a hormone-dependent condition marked by progressively severe secondary dysmenorrhea, significantly impacting patients' quality of life and overall health. Wen Jing Tang (WJT), a traditional Chinese medicinal formulation derived from the Synopsis of the Golden Chamber, has proven to be an effective therapeutic agent for EM. However, the precise molecular mechanisms underlying its efficacy remain unclear. Objective: This study aims to elucidate the underlying mechanisms of WJT in the treatment of EM by integrating network pharmacology analysis with experimental validation. Methods: The chemical constituents and target sites of WJT were obtained from the TCMSP database, while EM-related target genes were sourced from OMIM, TTD, GeneCards, and the DrugBank databases. A ''herbs-components-targets'' network was constructed using Cytoscape 3.9.1. The intersecting target genes of WJT and EM were then uploaded to the STRING database for protein-protein interaction (PPI) analysis. Subsequently, the common target genes were subjected to GO and KEGG enrichment analysis via the DAVID database. Molecular docking were employed to analyze the binding affinities between the top five core components and their respective targets. Additionally, ELISA were used to quantify the serum levels of IL-6, IL-1β, E2, and P in EM model rats. The expression levels of TNF-α, HIF1A, STAT3, and EGFR mRNA and proteins in ectopic endometrial tissue were assessed using q-PCR and Western blotting. Results: A total of 250 chemical components and 553 targets were identified in WJT, while 3491 EM-related targets were screened from multiple databases. Among these, 187 common targets between WJT and EM were found, with quercetin, kaempferol, and beta-sitosterol emerging as the core chemical components, and AKT1, IL6, TNF, and IL1B identified as the key targets. These core components demonstrated strong binding affinities to the targets. GO and KEGG enrichment analyses revealed that the shared targets were primarily involved in the HIF1 signaling pathway. Furthermore, compared to the control group, the EM model rats exhibited an increased ectopic endometrial area, disordered glandular and stromal cells, and notable inflammatory infiltration. Serum levels of IL-6, IL-1β, E2, and P were significantly elevated (P

Recent studies have attempted to characterize the layer-specific mechanical and microstructural properties of the aortic tissues in either normal or pathological state to understand its structural-mechanical property relationships. However, layer-specific tissue mechanics and compositions of normal and dissected ascending aortas have not been thoroughly compared with a statistical conclusion obtained. Eighteen ascending aortic specimens were harvested from 13 patients with type A aortic dissection and 5 donors without aortic diseases, with each specimen further excised to obtain three tissue samples including an intact wall, an intima-media layer and an adventitia layer. For each tissue sample, biaxial tensile testing was performed to obtain the experimental stress-stretch ratio data, which were further fed into the Fung-type model to quantify the tissue stiffness, and Elastin Van Gieson stain and Masson's trichrome stain were employed to quantify the elastic and collagen fiber densities. Statistical analyses were performed to determine whether any significant differences exist in mechanical properties and compositions between diseased and normal aortic tissues. The tissue stiffness of intima-media samples was significant higher in diseased group than that of normal group in longitudinal direction at the stretch ratio 1.30 (p = 0.0068), while no significant differences were found in the other direction or other tissue types. Even though there was no significant difference in elastic or collagen fiber densities between two groups, the diseased group generally had lower elastic fiber density, but higher collagen fiber density for all three tissue layers. Compared to normal aortic tissues, the elastic fiber density of the intima-media layer in the dissected aortic tissue was lower, while its tissue stiffness was significantly higher, indicating the tissue stiffness of the intima-media layer could be a potential indicator for aortic dissection.

Abstract Acute appendicitis is one of the common acute abdominal diseases in pediatrics. However, the implementation of radiological examination guided endoscopic retrograde appendicitis therapy (ERAT) in adults is limited in children. Our previous research explored the non-invasive guidance of high-frequency ultrasound (HFUS) for ERAT and achieved good therapeutic effects. This study mainly focuses on exploring the application value of HFUS in the feasibility assessment of ERAT in children with appendicitis. 163 children with appendicitis received ERAT guided by HFUS were analyzed retrospectively. According to the parameters evaluated by HFUS before and during ERAT, the results indicated that the distance between the appendix orifice and the ileocecal valve significantly affected the time required for the guidewire to enter the appendix cavity (P

Mechanical mismatch between native aortas and aortic grafts can induce graft failure. This study aims to compare the mechanical and microstructural properties of different graft materials used in aortic repair surgeries with those of normal and dissected human ascending aortas. Five types of materials including normal aorta (n = 10), dissected aorta (n = 6), human pericardium (n = 8), bovine pericardium (n = 8) and Dacron graft (n = 5) were collected to perform uniaxial tensile testing to determine their material stiffness, and ultimate strength/stretch. The elastin and collagen contents in four tissue groups except for Dacron were quantified by histological examinations, while the material ultrastructure of five material groups was visualized by scanning electron microscope. Statistical results showed that three graft materials including Dacron, human pericardium and bovine pericardium had significantly higher ultimate strength and stiffness than both normal and dissected aortas. Human and bovine pericardia had significantly lower ultimate stretch than native aortas. Histological examinations revealed that normal and diseased aortic tissues had a significantly higher content of elastic fiber than two pericardial tissues, but less collagen fiber content. All four tissue groups exhibited lamellar fiber ultrastructure, with aortic tissues possessing thinner lamella. Dacron was composed of densely coalesced polyethylene terephthalate fibers in thick bundles. Aortic graft materials with denser fiber ultrastructure and/or higher content of collagen fiber than native aortic tissues, exhibited higher ultimate strength and stiffness. This information provides a basis to understand the mechanical failure of aortic grafts, and inspire the design of biomimetic aortic grafts.

Purpose: Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment. Methods: This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared. Results: Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89–6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35–9.48%). Conclusions: The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.

Tumor vaccines trigger tumor-specific immune responses to prevent or treat tumors by activating the hosts’ immune systems, and therefore, these vaccines have potential clinical applications. However, the low immunogenicity of the tumor antigen itself and the low efficiency of the vaccine delivery system hinder the efficacy of tumor vaccines that cannot produce high-efficiency and long-lasting antitumor immune effects. Here, we constructed a nanovaccine by integrating CD47KO/CRT dual-bioengineered B16F10 cancer cell membranes and the unmethylated cytosine-phosphate-guanine (CpG) adjuvant. Hyperbranched PEI25k was used to load unmethylated cytosine-phosphate-guanine (CpG) through electrostatic adsorption to prepare PEI25k/CpG nanoparticles (PEI25k/CpG-NPs). CD47KO/CRT dual-bioengineered cells were obtained by CRISPR-Cas9 gene editing technology, followed by the cell surface translocation of calreticulin (CRT) to induce immunogenic cell death (ICD) in vitro. Finally, the extracted cell membranes were coextruded with PEI25k/CpG-NPs to construct the CD47KO/CRT dual-bioengineered cancer cell membrane-coated nanoparticles (DBE@CCNPs). DBE@CCNPs could promote endocytosis of antigens and adjuvants in murine bone marrow derived dendritic cells (BMDCs) and induce their maturation and antigen cross-presentation. To avoid immune checkpoint molecule-induced T cell dysfunction, the immune checkpoint inhibitor, the anti-PD-L1 antibody, was introduced to boost tumor immunotherapy through a combination with the DBE@CCNPs nanovaccine. This combination therapy strategy can significantly alleviate tumor growth and may open up a potential strategy for clinical tumor immunotherapy.

Abstract Background Rosai-Dorfman disease (RDD) is a rare benign non-langerhans cell histiocytosis, mainly involving lymph nodes and skin. It is even rarer occurring only in central airway of lung and in diffuse form. Central airway RDD is similar to malignant tumor in imaging by radiological method and in bronchoscopy features. It is difficult to differentiate it from primary airway malignant tumor and to diagnose correctively in time. Case presentation Here we present a rare case of 18-year-old male diagnosed with primary diffuse RDD in central airway. Although the features examined by enhanced chest computed tomography, positron emission tomography/computed tomography, diffusion-weighted imaging of enhanced chest MRI and bronchoscopy indicate to be malignant tumor, the patient was definitely confirmed by multiple transbronchial biopsies and immunohistochemistry. After two transbronchial resections, the patient's symptoms of paroxysmal cough, whistle sound and shortness of breath were significantly reduced, as well as the airway stenosis was significantly improved. After 5 months of follow-up, the patient had no symptoms and the central airway were unobstructed. Conclusions Primary diffuse RDD in central airway is characterized by intratracheal neoplasm, which is usually suspected as malignant tumor according to radiological image and bronchoscopy. Pathology and immunohistochemistry are necessary for definite diagnosis. Transbronchial resection is effective and safe for patients with primary diffuse RDD in central airway.

The tissue-engineered blood vessel (TEBV) has been developed and used in cardiovascular disease modeling, preclinical drug screening, and for replacement of native diseased arteries. Increasing attention has been paid to biomechanical cues in TEBV and other tissue-engineered organs to better recapitulate the functional properties of the native organs. Currently, computational fluid dynamics models were employed to reveal the hydrodynamics in TEBV-on-a-chip. However, the biomechanical wall stress/strain conditions in the TEBV wall have never been investigated. In this paper, a straight cylindrical TEBV was placed into a polydimethylsiloxane-made microfluidic device to construct the TEBV-on-a-chip. The chip was then perfused with cell culture media flow driven by a peristaltic pump. A three-dimensional fluid–structure interaction (FSI) model was generated to simulate the biomechanical conditions in TEBV and mimic both the dynamic TEBV movement and pulsatile fluid flow. The material stiffness of the TEBV wall was determined by uniaxial tensile testing, while the viscosity of cell culture media was measured using a rheometer. Comparison analysis between the perfusion experiment and FSI model results showed that the average relative error in diameter expansion of TEBV from both approaches was 10.0% in one period. For fluid flow, the average flow velocity over a period was 2.52 cm/s from the FSI model, 10.5% higher than the average velocity of the observed cell clusters (2.28 mm/s) in the experiment. These results demonstrated the facility to apply the FSI modeling approach in TEBV to obtain more comprehensive biomechanical results for investigating mechanical mechanisms of cardiovascular disease development.

BackgroundAlthough asthma and chronic obstructive pulmonary disease (COPD) are two well-defined and distinct diseases, some patients present combined clinical features of both asthma and COPD, particularly in smokers and the elderly, a condition termed as asthma-COPD overlap (ACO). However, the definition of ACO is yet to be established and clinical guidelines to identify and manage ACO remain controversial. Therefore, in this study, inflammatory biomarkers were established to distinguish asthma, ACO, and COPD, and their relationship with the severity of patients’ symptoms and pulmonary function were explored.Materials and methodsA total of 178 patients, diagnosed with asthma (n = 38), ACO (n = 44), and COPD (n = 96) between January 2021 to June 2022, were enrolled in this study. The patients’ pulmonary function was examined and routine blood samples were taken for the analysis of inflammatory indexes. Logistic regression analysis was used to establish inflammatory biomarkers for distinguishing asthma, ACO, and COPD; linear regression analysis was used to analyze the relationship between inflammatory indexes and symptom severity and pulmonary function.ResultThe results showed that, compared with ACO, the higher the indexes of platelet, neutrophil-lymphocyte ratio (NLR) and eosinophil-basophil ratio (EBR), the more likely the possibility of asthma and COPD in patients, while the higher the eosinophils, the less likely the possibility of asthma and COPD. Hemoglobin and lymphocyte-monocyte ratio (LMR) were negatively correlated with the severity of patients’ symptoms, while platelet-lymphocyte ratio (PLR) was negatively correlated with forced expiratory volume in the 1 s/forced vital capacity (FEV1/FVC) and FEV1 percent predicted (% pred), and EBR was positively correlated with FEV1% pred.ConclusionInflammatory indexes are biomarkers for distinguishing asthma, ACO, and COPD, which are of clinical significance in therapeutic strategies and prognosis evaluation.

BackgroundThe PTPN11 gene, located at 12q24. 13, encodes protein tyrosine phosphatase 2C. Mutations in the PTPN11 gene can lead to various phenotypes, including Noonan syndrome and LEOPARD syndrome. The SEC24D gene is located at 4q26 and encodes a component of the COPII complex, and is closely related to endoplasmic reticulum protein transport. Mutations in SEC24D can lead to Cole-Carpenter syndrome-2. To date, dual mutations in these two genes have not been reported in the literature.MethodsWe report a patient with short stature and osteogenesis imperfecta as the primary clinical manifestation. Other clinical features were peculiar facial features, deafness, and a history of recurrent fractures. Whole exome sequencing was performed on this patient.ResultsAfter whole-exome sequencing, three mutations in two genes were identified that induced protein alterations associated with the patient's phenotype. One was a de novo variant c.1403C>T (p.Thr468Met) on exon 12 of the PTPN11 gene, and the other was a compound heterozygous mutation in the SEC24D gene, a novel variant c.2609_2610delGA (p.Arg870Thrfs*10) on exon 20 and a reported variant c.938G>A (p.Arg313His) on exon 8.ConclusionsConcurrent mutations in PTPN11 and SEC24D induced a phenotype that was significantly different from individual mutations in either PTPN11 or SEC24D gene. Personalized genetic analysis and interpretation could help us understand the patient's etiology and hence develop treatments and improve the prognosis of these patients.

Abstract Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models.Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up.Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively.Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression.

Hydraulic slotting has become one of the most common technologies adopted to increase permeability in low permeability in coal field seams. There are many factors affecting the rock breaking effects of water jets, among which the impact force cannot be ignored. To study the influencing effects of contact surface shapes on jet flow patterns and impact force, this study carried out experiments involving water jet impingement planes and boreholes under different pressure conditions. The investigations included numerical simulations under solid boundary based on gas–liquid coupling models and indoor experiments under high-speed camera observations. The results indicated that when the water jets impinged on different contact surfaces, obvious reflection flow occurred, and the axial velocity had changed through three stages during the development process. Moreover, the shapes of the contact surfaces, along with the outlet pressure, were found to have impacts on the angles and velocities of the reflected flow. The relevant empirical formulas were summarized according to this study's simulation results. In addition, the flow patterns and shapes of the contact surfaces were observed to have influencing effects on the impact force. An impact force model was established in this study based on the empirical formula, and the model was verified using both the simulation and experimental results. It was confirmed that the proposed model could provide important references for the optimization of the technical parameters water jet systems, which could provide theoretical support for the further intelligent and efficient transformation of coal mine drilling water jet technology.

Abstract Background Detecting coronary vulnerable plaques in vivo and assessing their vulnerability have been great challenges for clinicians and the research community. Intravascular ultrasound (IVUS) is commonly used in clinical practice for diagnosis and treatment decisions. However, due to IVUS limited resolution (about 150–200 µm), it is not sufficient to detect vulnerable plaques with a threshold cap thickness of 65 µm. Optical Coherence Tomography (OCT) has a resolution of 15–20 µm and can measure fibrous cap thickness more accurately. The aim of this study was to use OCT as the benchmark to obtain patient-specific coronary plaque cap thickness and evaluate the differences between OCT and IVUS fibrous cap quantifications. A cap index with integer values 0–4 was also introduced as a quantitative measure of plaque vulnerability to study plaque vulnerability. Methods Data from 10 patients (mean age: 70.4; m: 6; f: 4) with coronary heart disease who underwent IVUS, OCT, and angiography were collected at Cardiovascular Research Foundation (CRF) using approved protocol with informed consent obtained. 348 slices with lipid core and fibrous caps were selected for study. Convolutional Neural Network (CNN)-based and expert-based data segmentation were performed using established methods previously published. Cap thickness data were extracted to quantify differences between IVUS and OCT measurements. Results For the 348 slices analyzed, the mean value difference between OCT and IVUS cap thickness measurements was 1.83% (p = 0.031). However, mean value of point-to-point differences was 35.76%. Comparing minimum cap thickness for each plaque, the mean value of the 20 plaque IVUS-OCT differences was 44.46%, ranging from 2.36% to 91.15%. For cap index values assigned to the 348 slices, the disagreement between OCT and IVUS assignments was 25%. However, for the OCT cap index = 2 and 3 groups, the disagreement rates were 91% and 80%, respectively. Furthermore, the observation of cap index changes from baseline to follow-up indicated that IVUS results differed from OCT by 80%. Conclusions These preliminary results demonstrated that there were significant differences between IVUS and OCT plaque cap thickness measurements. Large-scale patient studies are needed to confirm our findings.

Assessment and prediction of vulnerable plaque progression and rupture risk are of utmost importance for diagnosis, management and treatment of cardiovascular diseases and possible prevention of acute cardiovascular events such as heart attack and stroke. However, accurate assessment of plaque vulnerability assessment and prediction of its future changes require accurate plaque cap thickness, tissue component and structure quantifications and mechanical stress/strain calculations. Multi-modality intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography image data with follow-up were acquired from ten patients to obtain accurate and reliable plaque morphology for model construction. Three-dimensional thin-slice finite element models were constructed for 228 matched IVUS + OCT slices to obtain plaque stress/strain data for analysis. Quantitative plaque cap thickness and stress/strain indices were introduced as substitute quantitative plaque vulnerability indices (PVIs) and a machine learning method (random forest) was employed to predict PVI changes with actual patient IVUS + OCT follow-up data as the gold standard. Our prediction results showed that optimal prediction accuracies for changes in cap-PVI (C-PVI), mean cap stress PVI (meanS-PVI) and mean cap strain PVI (meanSn-PVI) were 90.3% (AUC = 0.877), 85.6% (AUC = 0.867) and 83.3% (AUC = 0.809), respectively. The improvements in prediction accuracy by the best combination predictor over the best single predictor were 6.6% for C-PVI, 10.0% for mean S-PVI and 8.0% for mean Sn-PVI. Our results demonstrated the potential using multi-modality IVUS + OCT image to accurately and efficiently predict plaque cap thickness and stress/strain index changes. Combining mechanical and morphological predictors may lead to better prediction accuracies.

Introduction: Mechanical forces are closely associated with plaque progression and rupture. Precise quantifications of biomechanical conditions using in vivo image-based computational models depend heavily on the accurate estimation of patient-specific plaque mechanical properties. Currently, mechanical experiments are commonly performed on ex vivo cardiovascular tissues to determine plaque material properties. Patient-specific in vivo coronary material properties are scarce in the existing literature.Methods:In vivo Cine intravascular ultrasound and virtual histology intravascular ultrasound (IVUS) slices were acquired at 20 plaque sites from 13 patients. A three-dimensional thin-slice structure-only model was constructed for each slice to obtain patient-specific in vivo material parameter values following an iterative scheme. Effective Young's modulus (YM) was calculated to indicate plaque stiffness for easy comparison purposes. IVUS-based 3D thin-slice models using in vivo and ex vivo material properties were constructed to investigate their impacts on plaque wall stress/strain (PWS/PWSn) calculations.Results: The average YM values in the axial and circumferential directions for the 20 plaque slices were 599.5 and 1,042.8 kPa, respectively, 36.1% lower than those from published ex vivo data. The YM values in the circumferential direction of the softest and stiffest plaques were 103.4 and 2,317.3 kPa, respectively. The relative difference of mean PWSn on lumen using the in vivo and ex vivo material properties could be as high as 431%, while the relative difference of mean PWS was much lower, about 3.07% on average.Conclusion: There is a large inter-patient and intra-patient variability in the in vivo plaque material properties. In vivo material properties have a great impact on plaque stress/strain calculations. In vivo plaque material properties have a greater impact on strain calculations. Large-scale-patient studies are needed to further verify our findings.

Mechanical properties of the arterial walls could provide meaningful information for the diagnosis, management and treatment of cardiovascular diseases. Classically, various experimental approaches were conducted on dissected arterial tissues to obtain their stress–stretch relationship, which has limited value clinically. Therefore, there is a pressing need to obtain biomechanical behaviors of these vascular tissues in vivo for personalized treatment. This paper reviews the methods to quantify arterial mechanical properties in vivo. Among these methods, we emphasize a novel approach using image-based finite element models to iteratively determine the material properties of the arterial tissues. This approach has been successfully applied to arterial walls in various vascular beds. The mechanical properties obtained from the in vivo approach were compared to those from ex vivo experimental studies to investigate whether any discrepancy in material properties exists for both approaches. Arterial tissue stiffness values from in vivo studies generally were in the same magnitude as those from ex vivo studies, but with lower average values. Some methodological issues, including solution uniqueness and robustness; method validation; and model assumptions and limitations were discussed. Clinical applications of this approach were also addressed to highlight their potential in translation from research tools to cardiovascular disease management.

Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings.

BackgroundImage-based computational models are widely used to determine atherosclerotic plaque stress/strain conditions and investigate their association with plaque progression and rupture. However, patient-specific vessel material properties are in general lacking in those models, limiting the accuracy of their stress/strain measurements. A noninvasive approach of combining in vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) and computational modeling was introduced to quantify patient-specific carotid plaque material properties for potential plaque model improvements. Vessel material property variation in patients, along vessel segment, and between baseline and follow up were investigated.MethodsIn vivo 3D multi-contrast and Cine MRI carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. 3D thin-layer models and an established iterative procedure were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaque samples. Effective Young's Modulus (YM) values were calculated for comparison and analysis.ResultsAverage Effective Young's Modulus (YM) and circumferential shrinkage rate (C-Shrink) value of the 81 slices was 411kPa and 5.62%, respectively. Slice YM value varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 311% (149 kPa vs. 613 kPa). The average slice YM variation rate for the 16 vessels was 134%. The average variation of YM values for all patients from baseline to follow up was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For plaque progression study, YM at follow-up showed negative correlation with plaque progression measured by wall thickness increase (WTI) (r = -0.7764, p = 0.0235). Wall thickness at baseline correlated with WTI negatively, with r = -0.5253 (p = 0.1813). Plaque burden at baseline correlated with YM change between baseline and follow-up, with r = 0.5939 (p = 0.1205).ConclusionIn vivo carotid vessel material properties have large variations from patient to patient, along the diseased segment within a patient, and with time. The use of patient-specific, location specific and time-specific material properties in plaque models could potentially improve the accuracy of model stress/strain calculations.

We report a systematic molecular design in BF2bdk-based afterglow emitters with photoluminescence quantum yields up to 46.3% and lifetimes around 1 s. Suitable excited-state types, diverse excited state species, relatively small singlet–triplet energy gaps and strong dipole–dipole interactions are critical in determining the afterglow properties. [ABSTRACT FROM AUTHOR]

In today's world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel fractal wavelet decomposition (FWD) is used to investigate the time-frequency representation of the input signal. Based on the sparsity of the PIC model in the Hilbert envelope spectrum, a method for evaluating PIC energy ratio (PICER) is defined based on an over-complete Fourier dictionary. A compound indicator considering kurtosis and PICER of dynamic signal is designed. Using this index, evaluations of the impulsiveness of the cycle-stationary process can be enabled, thus avoiding serious interference from the sporadic impact during measurements. The robustness of the proposed approach to noise is demonstrated via numerical simulations, and an engineering application is employed to validate its effectiveness. [ABSTRACT FROM AUTHOR]