Research progress of KL-6 in respiratory system diseases.

This article comprehensively elucidates the discovery of Krebs von den Lungen-6 (KL-6), its structural features, functional mechanisms, and the current research status in various respiratory system diseases. Discovered in 1985, KL-6 was initially considered a tumor marker, but its elevated levels in interstitial lung disease (ILD) led to its recognition as a relevant serum marker for ILD. KL-6 is primarily produced by type 2 alveolar epithelial cell regeneration. Over the past 30 years since the discovery of KL-6, the number of related research papers has steadily increased annually. Following the coronavirus disease 2019 (COVID-19) pandemic, there has been a sudden surge in relevant literature. Despite KL-6's potential as a biomarker, its value in the diagnosis, treatment, and prognosis varies across different respiratory diseases, including ILD, idiopathic pulmonary fibrosis (IPF), COVID-19, and lung cancer. Therefore, as an important serum biomarker in respiratory system diseases, the value of KL-6 still requires further investigation.

Overall and subgroup prevalence of self-reported asthma in US adults: a nationally representative cross-sectional study.

BACKGROUND: The purpose of this study is to determine the variations in the prevalence of self-reported asthma among the adult population in the United States of America (USA), analyzing demographic characteristics, physical indicators, living habits, and sarcopenia. METHODS: 10,566 participants from the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) of the USA who were 20 years of age or older and not pregnant were included in the study. RESULTS: The prevalence of patients with asthma varies by age, gender, and race. The weighted prevalence is 15.5%, estimated to represent 19.36 million people in the USA (95% CI, 14.5% to 16.6%). The prevalence of self-reported asthma decreases with age, with the highest prevalence among young adults aged 20-25 for both males and females. Females were also more susceptible to asthma compared to males. The increase in asthma prevalence attributed to smoking was most pronounced among African American and Caucasian participants (p < 0.05), while its effect on Mexican American and Asian participants was relatively minor. Notably, the prevalence of asthma was significantly higher in African American and Caucasian participants with sarcopenia compared to those without sarcopenia. CONCLUSIONS: The prevalence of asthma is associated to varying degrees with factors such as age, gender, smoking, and the presence of sarcopenia. The elevated prevalence of asthma among young people and females warrants attention. Intensifying efforts toward smoking cessation and the scientific management of sarcopenia could be instrumental in reducing the incidence of asthma.

[Structural design of "fitness" and "feasibility" of body-fitted stent and its mechanical analysis].

To address the conflict between the "fitness" and "feasibility" of body-fitted stents, this paper investigates the impact of various smoothing design strategies on the mechanical behaviour and apposition performance of stent. Based on the three-dimensional projection method, the projection region was fitted with the least squares method (fitting orders 1-6 corresponded to models 1-6, respectively) to achieve the effect of smoothing the body-fitted stent. The simulation included the crimping and expansion process of six groups of stents in stenotic vessels with different degrees of plaque calcification. Various metrics were analyzed, including bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction, and contact volume fraction. The study findings showed that the bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction and contact volume fraction increased with the fitting order's increase. Model 1 had the smallest contact area fraction and contact volume fraction, 77.63% and 83.49% respectively, in the incompletely calcified plaque environment. In the completely calcified plaque environment, these values were 72.86% and 82.21%, respectively. Additionally, it had the worst "fitness". Models 5 and 6 had similar values for stent ruggedness, with 32.15% and 32.38%, respectively, which indicated the worst "feasibility" for fabrication and implantation. Models 2, 3, and 4 had similar area residual stenosis rates in both plaque environments. In conclusion, it is more reasonable to obtain the body-fitted stent by using 2nd to 4th order fitting with the least squares method to the projected region. Among them, the body-fitted stent obtained by the 2nd order fitting performs better in the completely calcified environment.

A dimer-monomer transition captured by the crystal structures of cyanobacterial apo flavodoxin.

In cyanobacteria and algae (but not plants), flavodoxin (Fld) replaces ferredoxin (Fd) under stress conditions to transfer electrons from photosystem I (PSI) to ferredoxin-NADP+ reductase (FNR) during photosynthesis. Fld constitutes a small electron carrier noncovalently bound to flavin mononucleotide (FMN), and also an ideal model for revealing the protein/flavin-binding mechanism because of its relative simplicity compared to other flavoproteins. Here, we report two crystal structures of apo-Fld from Synechococcus sp. PCC 7942, one dimeric structure of 2.09 Å and one monomeric structure of 1.84 Å resolution. Analytical ultracentrifugation showed that in solution, apo-Fld exists both as monomers and dimers. Our dimer structure contains two ligand-binding pockets separated by a distance of 45 Å, much longer than the previous structures of FMN-bound dimers. These results suggested a potential dimer-monomer transition mechanism of cyanobacterial apo-Fld. We further propose that the dimer represents the "standby" state to stabilize itself, while the monomer constitutes the "ready" state to bind FMN. Furthermore, we generated a new docking model of cyanobacterial Fld-FNR complex based on the recently reported cryo-EM structures, and mapped the special interactions between Fld and FNR in detail.

Automatic construction of coronary artery tree structure based on vessel blood flow tracking.

We sought to propose an innovative vessel blood flow tracking (VBFT) method to extract coronary artery tree (CAT) and to assess the effectiveness of this VBFT versus the single-frame method. Construction of a CAT from a segmented artery is the basis of artificial intelligence-aided angiographic diagnosis. However, construction of a CAT using a single frame remains challenging, due to bifurcations and overlaps in two-dimensional angiograms. Overall, 13,222 angiograms, including 28,539 vessels, were retrospectively collected from 3275 patients and were then annotated. Coronary arteries were automatically segmented by a previously established deep neural networks (DNNs), and the skeleton lines were then extracted from segmentation images to construct CAT using the single-frame method and the VBFT method. Additionally, 1322 angiograms with 2201 vessels were used to test these two methods. Compared to the single-frame method, the VBFT method can significantly improve the accuracy of CAT as (84.3% vs. 72.3%; p < 0.001). Overlap (OV) was higher in the VBFT group than that in the Single-Frame group (91.1% vs. 87.5%; p < 0.001). The VBFT method significantly reduced the incidence of the lack of branching (7.30% vs. 13.9%, p < 0.001), insufficient length (6.70% vs. 11.0%, p < 0.001), and redundant branches (1.60% vs. 3.10%, p < 0.001). The VBFT method improved the extraction of a CAT structure, which will facilitate the development of artificial intelligence-aided angiographic diagnosis. Cardiologists can efficiently diagnose CAD using this method.

Enhancing the Efficiency of Distraction Osteogenesis through Rate-Varying Distraction: A Computational Study.

Distraction osteogenesis (DO) is a mechanobiological process of producing new bone and overlying soft tissues through the gradual and controlled distraction of surgically separated bone segments. The process of bone regeneration during DO is largely affected by distraction parameters. In the present study, a distraction strategy with varying distraction rates (i.e., "rate-varying distraction") is proposed, with the aim of shortening the distraction time and improving the efficiency of DO. We hypothesized that faster and better healing can be achieved with rate-varying distractions, as compared with constant-rate distractions. A computational model incorporating the viscoelastic behaviors of the callus tissues and the mechano-regulatory tissue differentiation laws was developed and validated to predict the bone regeneration process during DO. The effect of rate-varying distraction on the healing outcomes (bony bridging time and bone formation) was examined. Compared to the constant low-rate distraction, a low-to-high rate-varying distraction provided a favorable mechanical environment for angiogenesis and bone tissue differentiation, throughout the distraction and consolidation phase, leading to an improved healing outcome with a shortened healing time. These results suggest that a rate-varying clinical strategy could reduce the overall treatment time of DO and decrease the risk of complications related to the external fixator.

The first deep-snouted tyrannosaur from Upper Cretaceous Ganzhou City of southeastern China.

Tyrannosaurids were the most derived group of Tyrannosauroidea and are characterized by having two body plans: gracile, long-snouted and robust, deep-snouted skulls. Both groups lived sympatrically in central Asia. Here, we report a new deep-snouted tyrannosaurid, Asiatyrannus xui gen. et sp. nov., from the Upper Cretaceous of Ganzhou City, southeastern China, which has produced the large-bodied and long-snouted Qianzhousaurus. Based on histological analysis, the holotype of Asiatyrannus xui is not a somatically mature adult, but it already passed through the most rapid growth stages. Asiatyrannus is a small to medium-sized tyrannosaurine, with a skull length of 47.5 cm and an estimated total body length of 3.5-4 m; or around half the size of Qianzhousaurus and other large-bodied tyrannosaurines in similar growth stages. Asiatyrannus and Qianzhousaurus are sympatric tyrannosaurid genera in the Maastrichtian of southeastern China. Asiatyrannus differs from Qianzhousaurus in that it has a proportionally deeper snout, longer premaxilla, deeper maxilla, and deeper dentary, and the cornual process of the lacrimal is inflated without developing a discrete horn. The different skull proportions and body sizes suggest that Asiatyrannus and Qianzhousaurus likely had different feeding strategies and occupied different ecological niches.

Methods for improving the properties of zinc for the application of biodegradable vascular stents.

Biodegradable stents can support vessels for an extended period, maintain vascular patency, and progressively degrade once vascular remodeling is completed, thereby reducing the constraints of traditional metal stents. An ideal degradable stent must have good mechanical properties, degradation behavior, and biocompatibility. Zinc has become a new type of biodegradable metal after magnesium and iron, owing to its suitable degradation rate and good biocompatibility. However, zinc's poor strength and ductility make it unsuitable as a vascular stent material. Therefore, this paper reviewed the primary methods for improving the overall properties of zinc. By discussing the mechanical properties, degradation behavior, and biocompatibility of various improvement strategies, we found that alloying is the most common, simple, and effective method to improve mechanical properties. Deformation processing can further improve the mechanical properties by changing the microstructures of zinc alloys. Surface modification is an important means to improve the biological activity, blood compatibility and corrosion resistance of zinc alloys. Meanwhile, structural design can not only improve the mechanical properties of the vascular stents, but also endow the stents with special properties such as negative Poisson 's ratio. Manufacturing zinc alloys with excellent degradation properties, improved mechanical properties and strong biocompatibility and exploring their mechanism of interaction with the human body remain areas for future research.

A quantitative study of the effects of a dual layer coating drug-eluting stent on safety and efficacy.

A key strategy for increasing drug mass (DM) while maintaining good safety is to improve the drug release profile (RP). We designed a dual layer coating drug-eluting stent (DES) that exhibited smaller concentration gradients between the coating and the artery wall and significantly impacted the drug RP. However, a detailed understanding of the effects of the DES designed by our team on safety and efficacy is still lacking. The objective of this study was to provide a comprehensive multiscale computational framework that would allow us to probe the safety and efficacy of the DES we designed. This framework consisted of four coupled modules, namely (1) a mechanical stimuli module, simulating mechanical stimuli caused by percutaneous coronary intervention through a finite element analysis, (2) an inflammation module, simulating inflammation of vascular smooth muscle cells (VSMCs) induced by mechanical stimuli through an agent-based model (ABM), (3) a drug transport module, simulating drug transport through a continuum-based approach, and (4) a mitosis module, simulating VSMC mitosis through an ABM. Our results indicated that when the DM increased to two times the initial DM value, the DES we designed had higher safety and lower efficacy values than a conventional DES. When the DM increased to five times the initial DM value, the DES we designed had higher safety than a conventional DES, and negligible differences in efficacy compared with a conventional DES. In summary, the DES we designed exhibited a significant advantage in safety, but a slightly reduced efficacy compared with that of a conventional DES.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage.

BACKGROUND AND OBJECTIVE: Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets. METHODS: This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods. RESULTS: This study conducts numerous experiments using classical machine learning and deep learning methods, demonstrating the differences in various segmentation and detection methods on the PHE-SICH-CT-IDS. The highest precision achieved in semantic segmentation is 76.31%, while object detection attains a maximum precision of 97.62%. The experimental results on radiomic feature extraction and analysis prove the suitability of PHE-SICH-CT-IDS for evaluating image features and highlight the predictive value of these features for the prognosis of SICH patients. CONCLUSION: To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Background: Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. Method: This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. Result: The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Conclusion: Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation.

BACKGROUND AND OBJECTIVE: The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current research on computer-aided detection of adipose distribution is hampered by the lack of open-source large abdominal adipose datasets. METHODS: In this study, a benchmark Abdominal Adipose Tissue CT Image Dataset (AATCT-IDS) containing 300 subjects is prepared and published. AATCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. RESULTS: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATCT-IDS reveals three adipose distributions in the subject population. CONCLUSION: AATCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Biomechanical Characteristics and Analysis Approaches of Bone and Bone Substitute Materials.

Bone has a special structure that is both stiff and elastic, and the composition of bone confers it with an exceptional mechanical property. However, bone substitute materials that are made of the same hydroxyapatite (HA) and collagen do not offer the same mechanical properties. It is important for bionic bone preparation to understand the structure of bone and the mineralization process and factors. In this paper, the research on the mineralization of collagen is reviewed in terms of the mechanical properties in recent years. Firstly, the structure and mechanical properties of bone are analyzed, and the differences of bone in different parts are described. Then, different scaffolds for bone repair are suggested considering bone repair sites. Mineralized collagen seems to be a better option for new composite scaffolds. Last, the paper introduces the most common method to prepare mineralized collagen and summarizes the factors influencing collagen mineralization and methods to analyze its mechanical properties. In conclusion, mineralized collagen is thought to be an ideal bone substitute material because it promotes faster development. Among the factors that promote collagen mineralization, more attention should be given to the mechanical loading factors of bone.

Biomechanical Study and Analysis for Cardiovascular/Skeletal Materials and Devices.

The Special Issue entitled "Biomechanical Study and Analysis for Cardiovascular/Skeletal Materials and Devices" addresses biological functional materials and devices relevant to cardiovascular diseases and orthopedic conditions [...].

Numerical Simulation of Dynamic Degradation and Fatigue Damage of Degradable Zinc Alloy Stents.

Current research on the fatigue properties of degradable zinc alloy stents has not yet considered the issue of the fatigue life changing with material properties during the dynamic degradation process. Therefore, in this paper, we established a fatigue damage algorithm to study the fatigue problem affected by the changing of material properties during the dynamic degradation process of the stent under the action of pulsating cyclic loading. Three models: the dynamic degradation model, the dynamic degradation model under pulsating cyclic loading, and the coupled model of fatigue damage and dynamic degradation, were developed to verify the effect of fatigue damage on stent life. The results show that fatigue damage leads to a deeper degree of inhomogeneous degradation of the stent, which affects the service life of the stent. Fatigue damage is a factor that cannot be ignored. Therefore, when studying the mechanical properties and lifetime of degradable stents, incorporating fatigue damage into the study can help more accurately assess the lifetime of the stents.

A comprehensive survey of intestine histopathological image analysis using machine vision approaches.

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.

Effects of dynamization timing and degree on bone healing of different fracture types.

Dynamization, that is, increasing interfragmentary movement (IFM) by reducing fixation stiffness from a rigid to a more flexible state, has been successfully used in clinical practice to promote fracture healing. However, it remains unclear how dynamization timing and degree affect bone healing of different fracture types. Finite element models of tibial fractures based on the OTA/AO classification (Simple: A1-Spiral, A2-Oblique, A3-Transverse; Wedge: B2-Spiral, B3-Fragmented; Complex: C2-Segment, C3-Irregular), in combination with fuzzy logic-based mechano-regulatory tissue differentiation algorithms, were used to simulate the healing process when dynamization of varied degrees (dynamization coefficient or DC = 0-0.9; 0.9 represents 90% reduction in the fixation stiffness relative to a rigid fixation) were applied at different time points after fracture. The fuzzy logic-based algorithms have been validated with a preclinical animal model. The results showed that the healing responses of type A fractures were more sensitive to the changes in dynamization degree and timing comparing with type B or C fractures. Additionally, the optimal dynamization regime for each fracture type was different. For type A fractures, a moderate dynamization degree (e.g., DC = 0.5) applied after Week 1 promoted the recovery of biomechanical integrity. For type B and C fractures, the effective dynamization included a greater dynamization degree (DC = 0.7) applied after Week 2. Our results further demonstrated that the fracture morphology affected interfragmentary strain environments within the callus, leading to varied healing results for different fracture types. These results suggest that the effects of dynamization are highly dependent of the fracture types. Therefore, specific dynamization strategies should be chosen for different fracture types to achieve optimal healing outcomes.

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.

Objective: Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides to assist in the determination of epidermal growth factor receptor (EGFR) mutation status in patients. Methods: We obtained enhanced MRI data from patients with breast cancer before treatment and selected diffusion-weighted imaging (DWI), T1 fast-spin echo (T1 FSE), and T2 fast-spin echo (T2 FSE) as the data sources for extracting texture information. Imaging physicians manually outlined the 3D regions of interest (ROIs) and extracted texture features according to the gray level cooccurrence matrix (GLCM) of the images. For the HE staining images of the patients, we adopted a specific normalization algorithm to simulate the images dyed with only hematoxylin or eosin and extracted textures. We extracted texture features to predict the expression of EGFR. After evaluating the predictive power of each model, the models from the two data sources were combined for remodeling. Results: For enhanced MRI data, the modeling of texture information of T1 FSE had a good predictive effect for EGFR mutation status. For pathological images, eosin-stained images can achieve a better prediction effect. We selected these two classifiers as the weak classifiers of the final model and obtained good results (training group: AUC, 0.983; 95% CI, 0.95-1.00; accuracy, 0.962; specificity, 0.936; and sensitivity, 0.979; test group: AUC, 0.983; 95% CI, 0.94-1.00; accuracy, 0.943; specificity, 1.00; and sensitivity, 0.905). Conclusion: The EGFR mutation status of patients with breast cancer can be well predicted based on enhanced MRI data and pathological data. This helps hospitals that do not test the EGFR mutation status of patients with breast cancer. The technology gives clinicians more information about breast cancer, which helps them make accurate diagnoses and select suitable treatments.

Habitat Analysis of Breast Cancer-Enhanced MRI Reflects BRCA1 Mutation Determined by Immunohistochemistry.

Objective: To use habitat analysis (also termed habitat imaging) for classifying untreated breast cancer-enhanced magnetic resonance imaging (MRI) in women. Moreover, we intended to obtain clustering parameters to predict the BReast CAncer gene 1 (BRCA1) gene mutation and to determine the use of MRI as a noninvasive examination tool. Methods: We obtained enhanced MRI data of patients with breast cancer before treatment and selected some sequences as the source of habitat imaging. We used the k-means clustering to classify these images. According to the formed subregions, we calculated several parameters to evaluate the clustering. We used immunohistochemistry to detect BRCA1 mutations. Moreover, we separately determined the ability of these parameters through independent modeling or multiple parameter joint modeling to predict these mutations. Results: Of all extracted values, separation (SP) demonstrated the best prediction performance for a single parameter (area under the receiver operating characteristic curve (AUC), 0.647; 95% confidence interval (CI), 0.557-0.731). Simultaneously, models based on the Calinski-Harabasz Index and sum of square error performed better in the training (AUC, 0.903; 95% CI, 0.831-0.96) and verification (AUC, 0.845; 95% CI, 0.723-0.942) sets for multiparameter joint modeling. Conclusion: Based on the enhanced MRI of breast tumors and the subregions generated according to the habitat imaging theory, the parameters extracted to describe the clustering effect could reflect the BRCA1 status. Differences between clusters, including the general differences of cluster centers and clusters and the similarity of samples within clusters, were the embodiment of this mutation. We propose an algorithm to predict the BRCA1 mutation of a patient according to the enhanced MRI of the breast tumor.

Computational simulation of stent thrombosis induced by various degrees of stent malapposition.

Percutaneous coronary intervention with stent implantation is one of the most commonly used approaches to treat coronary artery stenosis. Stent malapposition (SM) can increase the incidence of stent thrombosis, but the quantitative association between SM distance and stent thrombosis is poorly clarified. The objective of this study is to determine the biomechanical reaction mechanisms underlying stent thrombosis induced by SM and to quantify the effect of different SM severity grades on thrombosis. The thrombus simulation was performed in a continuous model based on the diffusion-convection response of blood substance transport. Simulated models included well-apposed stents and malapposed stents with various severities where the detachment distances ranged from 0 to 400 µm. The abnormal shear stress induced by SM was considered a critical contributor affecting stent thrombosis, which was dependent on changing SM distances in the simulation. The results illustrate that the proportion of thrombus volume was 1.88% at a SM distance of 75 µm (mild), 3.46% at 150 µm, and 3.93% at 400 µm (severe), but that a slight drop (3.18%) appeared at the detachment distance of 225 µm (intermediate). The results indicate that when the SM distance was less than 150 µm, the thrombus rose notably as the gap distance increased, whereas the progression of thrombogenicity weakened when it exceeded 150 µm. Therefore, more attention should be paid when SM is present at a gap distance of 150 µm. Moreover, when the SM length of stents are the same, thrombus tends to accumulate downstream towards the distal end of the stent as the SM distance increases.