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.

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.

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.

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.

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.

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 [...].

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.

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.

Low dose of esketamine combined with propofol in painless fibronchoscopy in elderly patients

To explore the effects and safety of low dose of esketamine combined with propofol in elderly patients undergoing fibronchoscopy. Eighty elderly patients who underwent painless fibronchoscopy in our hospital from June 2021 to September 2021 were recruited,and randomly divided into experimental group (esketamine 0.15mg/ kg + propofol 1mg/ kg) and control group (sufentanil 0.1 μg/ kg + propofol 1mg/ kg), with 40 cases in each group. There were significant differences in MAP, HR and SpO2 of T2, T3 and T4 between the experimental and control groups (P < .05). Besides, there were significantly differences on the trend of change between the 2 groups, with a small and relatively stable fluctuation in the experimental group (P < .05). Compared with the control group, the total dosage of propofol in the experimental group was significantly lower, and the number of vasoactive drugs, the incidence of respiratory depression and bronchospasm were significantly lower (P < .05). There was no significant difference in microscopic examination time, wake-up time, visual analogue score, and agitation, mental symptoms, increased secretion, nausea and vomiting, choking cough and laryngeal spasm during awakening period between the 2 groups. The incidence of total adverse reactions in the experimental group were strongly lower than those in control group. (P < .05). Low dose of esketamine combined with propofol can be safely used for fibronchoscopy in elderly patients, with good effects, more stable respiration and circulation, and low incidence of adverse reactions.

Effect of the accordion technique on bone regeneration during distraction osteogenesis: A computational study

BACKGROUND AND OBJECTIVE

Distraction osteogenesis (DO), a bone lengthening technique, is widely employed to treat congenital and acquired limb length discrepancies and large segmental bone defects. However, a major issue of DO is the prolonged consolidation phase (10-36 months) during which patients must wear a cumbersome external fixator. Attempts have been made to accelerate the healing process of DO by an alternating distraction and compression mode (so-called "accordion" technique or AT). However, it remains unclear how varied AT parameters affect DO outcomes and what the most effective AT mode is.

METHODS

Based on an experimentally-verified mechanobiological model, we performed a parametric analysis via in silico simulation of the bone regeneration process of DO under different AT modes, including combinations of varied application times (AT began at week 1-8 of the consolidation phase), durations (AT was used continuously for 1 week, 2 weeks or 4 weeks) and rates (distraction or compression at 0.25, 0.5, 0.75, and 1 mm/12 h). The control group had no AT applied during the consolidation phase.

RESULTS

Compared with the control group (no AT), AT applied at an early consolidation stage (e.g. week 1 of the consolidation phase) significantly enhanced bone formation and reduced the overall healing time. However, the effect of AT on bone healing was dependent on its duration and rate. Specifically, a moderate rate of AT (e.g. 0.5 mm/12 h) lasting for two weeks promoted blood perfusion recovery and bone regeneration, ultimately shortening the healing time. Conversely, over-high rates (e.g. 1 mm/12 h) and longer durations (e.g. 4 weeks) of AT adversely affected bone regeneration and blood perfusion recovery, thereby delaying bone bridging.

CONCLUSIONS

These results suggest that the therapeutic effects of AT on DO are highly dependent of the AT parameters of choice. Under appropriate durations and rates, the AT applied at an early consolidation phase is beneficial for blood recovery and bone regeneration. These results may provide a basis for selecting effective AT modes to accelerate consolidation and reduce the overall treatment period of DO.

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.

Zinc-Based Biodegradable Materials for Orthopaedic Internal Fixation

Traditional inert materials used in internal fixation have caused many complications and generally require removal with secondary surgeries. Biodegradable materials, such as magnesium (Mg)-, iron (Fe)- and zinc (Zn)-based alloys, open up a new pathway to address those issues. During the last decades, Mg-based alloys have attracted much attention by researchers. However, the issues with an over-fast degradation rate and release of hydrogen still need to be overcome. Zn alloys have comparable mechanical properties with traditional metal materials, e.g., titanium (Ti), and have a moderate degradation rate, potentially serving as a good candidate for internal fixation materials, especially at load-bearing sites of the skeleton. Emerging Zn-based alloys and composites have been developed in recent years and in vitro and in vivo studies have been performed to explore their biodegradability, mechanical property, and biocompatibility in order to move towards the ultimate goal of clinical application in fracture fixation. This article seeks to offer a review of related research progress on Zn-based biodegradable materials, which may provide a useful reference for future studies on Zn-based biodegradable materials targeting applications in orthopedic internal fixation.

Finite Element Analysis of the Non-Uniform Degradation of Biodegradable Vascular Stents

Most of the studies on the finite element analysis (FEA) of biodegradable vascular stents (BVSs) during the degradation process have limited the accuracy of the simulation results due to the application of the uniform degradation model. This paper aims to establish an FEA model for the non-uniform degradation of BVSs by considering factors such as the dynamic changes of the corrosion properties and material properties of the element, as well as the pitting corrosion and stress corrosion. The results revealed that adjusting the corrosion rate according to the number of exposed surfaces of the element and reducing the stress threshold according to the corrosion status accelerates the degradation time of BVSs by 26% and 25%, respectively, compared with the uniform degradation model. The addition of the pitting model reduces the service life of the BVSs by up to 12%. The effective support of the stent to the vessel could reach at least 60% of the treatment effect before the vessel collapsed. These data indicate that the proposed non-uniform degradation model of BVSs with multiple factors produces different phenomena compared with the commonly used models and make the numerical simulation results more consistent with the real degradation scenario.

In-situ mineralized homogeneous collagen-based scaffolds for potential guided bone regeneration

For guided bone regeneration (GBR) in clinical orthopedics, the importance of a suitable scaffold which can provide the space needed for bone regeneration and simultaneously promotes the new bone formation cannot be overemphasized. Due to its excellent biocompatibility, mechanical strength, and similarity in structure and composition to natural bone, the mineralized collagen-based scaffolds have been increasingly considered as promising GBR scaffolds. Herein, we propose a novel method to fabricate an in-situ mineralized homogeneous collagen-based scaffold (IMHCS) with excellent osteogenic capability for GBR by electrospinning the collagen solution in combination with essential mineral ions. The IMHCS exhibited homogeneous distribution of apatite crystals in electrospun fibers, which helped to achieve a significantly higher tensile strength than the pure collagen scaffold (CS) and the scaffold with directly added nano-hydroxyapatite particles (HAS). Furthermore, the IMHCS had significantly better cell compatibility, cell migration ratio, and osteogenic differentiation property than the HAS and CS. Therefore, the IMHCS not only retains traditional function of inhibiting fibroblast invasion, but also possesses excellent osteogenic differentiation property, indicating a robust alternative for GBR applications.

Biomechanics and mechanobiology of the bone matrix

The bone matrix plays an indispensable role in the human body, and its unique biomechanical and mechanobiological properties have received much attention. The bone matrix has unique mechanical anisotropy and exhibits both strong toughness and high strength. These mechanical properties are closely associated with human life activities and correspond to the function of bone in the human body. None of the mechanical properties exhibited by the bone matrix is independent of its composition and structure. Studies on the biomechanics of the bone matrix can provide a reference for the preparation of more applicable bone substitute implants, bone biomimetic materials and scaffolds for bone tissue repair in humans, as well as for biomimetic applications in other fields. In providing mechanical support to the human body, bone is constantly exposed to mechanical stimuli. Through the study of the mechanobiology of the bone matrix, the response mechanism of the bone matrix to its surrounding mechanical environment can be elucidated and used for the health maintenance of bone tissue and defect regeneration. This paper summarizes the biomechanical properties of the bone matrix and their biological significance, discusses the compositional and structural basis by which the bone matrix is capable of exhibiting these mechanical properties, and studies the effects of mechanical stimuli, especially fluid shear stress, on the components of the bone matrix, cells and their interactions. The problems that occur with regard to the biomechanics and mechanobiology of the bone matrix and the corresponding challenges that may need to be faced in the future are also described.

A computational analysis of potential aortic dilation induced by the hemodynamic effects of bicuspid aortic valve phenotypes

BACKGROUND AND OBJECTIVES

The bicuspid aortic valve (BAV) is a major risk factor for the progression of aortic dilation (AD) because of the induced abnormal blood flow environment in aorta. The differences in the development of AD induced by BAV phenotypes remains unclear. Therefore, the objective of this study was to assess the potential locations of AD induced by different phenotypes of BAV. The different effects of opening orifice area and leaflet orientation on ascending aortic hemodynamics in Type-1 BAV was investigated by means of numerical simulation.

METHODS

Finite element dynamic analysis was performed on tricuspid aortic valve (TAV) and BAV models to simulate the motion of the leaflets and obtain the geometrical characteristics of AV at peak systole as a reference, which were used for aortic models. Then, four sets of aortic fluid models were designed according to the leaflet fusion types [TAV; BAV (left-right-coronary cusp fusion, LR; right-non-coronary cusp fusion, RN; left-non-coronary cusp fusion, LN)], and the computational fluid dynamics method was applied to compare the hemodynamic differences within the aorta at peak systole.

RESULTS

The maximum opening area of BAV was significantly reduced, resulting in alterations in aortic hemodynamics compared with TAV. The velocity streamlines were essentially parallel to the aortic wall in TAV. The average pressure and wall shear stress in aorta tend to be stable. In contrary, the eccentricity of BAV orifice jet resulted in high-velocity flow directed toward the ascending aorta (AA) wall and aortic arch for LR and LN; RN features an asymmetrical velocity distribution toward the outer bend of the middle AA, and eccentric flow tends to impact the distal AA. As the flow angle is associated with distinct flow impingement locations, different degrees of WSS and pressure concentration occur along the aortic wall from the AA to the aortic arch in three BAV types.

CONCLUSIONS

The BAV morphotype affects the aortic hemodynamics, and the abnormal blood flow associated with BAV may play a role in AD. The different BAV phenotypes determine the direction of blood flow jet and change the expression of dilation. LR is likely to cause dilation of the tubular AA; RN results in dilation of the middle AA to proximal aortic arch; and LN causes an increased incidence of the tubular AA and the proximal aortic arch.

Interactive effects of various loading parameters on the fluid dynamics within the lacunar-canalicular system for a single osteocyte

The osteocyte lacunar-canalicular system (LCS) serves as a mechanotransductive core where external loading applied to the skeleton is transduced into mechanical signals (e.g., fluid shear) that can be sensed by mechanosensors (osteocytes). The fluid velocity and shear stress within the LCS are affected by various loading parameters. However, the interactive effect of distinct loading parameters on the velocity and shear stress in the LCS remains unclear. To address this issue, we developed a multiscale modeling approach, combining a poroelastic finite element (FE) model with a single osteocytic LCS unit model to calculate the flow velocity and shear stress within the LCS. Next, a sensitivity analysis was performed to investigate individual and interactive effects of strain magnitude, strain rate, number of cycles, and intervening short rests between loading cycles on the velocity and shear stress around the osteocyte. Lastly, we developed a relatively simple regression model to predict those outcomes. Our results demonstrated that the strain magnitude or rate alone were the main factors affecting the velocity and shear stress; however, the combination of these two was not directly additive, and addition of a short rest between cycles could enhance the combination of these two related factors. These results show highly interactive effects of distinct loading parameters on fluid velocity and shear stress in the LCS. Specifically, our results suggest that an enhanced fluid dynamics environment in the LCS can be achieved with a brief number of load cycles combined with short rest insertion and high strain magnitude and rate.

Accurate Calculation of FFR Based on a Physics-Driven Fluid‐Structure Interaction Model

Background: The conventional FFRct numerical calculation method uses a model with a multi-scale geometry based upon CFD, and rigid walls. Therefore, important interactions between the elastic vessel wall and blood flow are not routinely considered. Changes in the resistance of coronary microcirculation during hyperaemia are likewise not typically incorporated using a fluid–structure interaction (FSI) algorithm. It is likely that both have resulted in FFRct calculation errors.

Objective: In this study we incorporated both the influence of vascular elasticity and coronary microcirculatory structure on FFR, to improve the accuracy of FFRct calculation. Thus, in this study, a physics-driven 3D–0D coupled model including fluid–structure interaction was established to calculate accurate FFRct values.

Methods: Based upon a novel geometric multi-scale modeling technology, a FSI simulation approach was used. A lumped parameter model (0D) was used as the outlet boundary condition for the 3D FSI coronary artery model to incorporate physiological microcirculation, with bidirectional coupling between the two models.

Results: The accuracy, sensitivity, specificity, and both positive and negative predictive values of FFR_DC calculated based upon the coupled 3D–0D model were 86.7, 66.7, 84.6, 66.7, and 91.7%, respectively. Compared to the calculated value using the basic CFD model (MSE = 5.9%, accuracy rate = 80%), the FFR_CFD calculated based on the coupled 3D–0D model has a smaller MSE of 1.9%.

Conclusion: The physics-driven coupled 3D–0D model that incorporates fluid–structure interactions not only consider the influence of the elastic vessel wall on blood flow, but also provides reliable microvascular resistance boundary conditions for the 3D FSI model. This allows for a calculation that is based upon conditions that are closer to the physiological environment, and thus improves the accuracy of FFRct calculation. It is likely that more accurate information will provide an enhanced recommendation regarding percutaneous coronary intervention (PCI) in the clinic.

In vivo and in silico monitoring bone regeneration during distraction osteogenesis of the mouse femur

BACKGROUND AND OBJECTIVE

Distraction osteogenesis (DO) is a mechanobiological process of producing new bone by gradual and controlled distraction of the surgically separated bone segments. Mice have been increasingly used to study the role of relevant biological factors in regulating bone regeneration during DO. However, there remains a lack of in silico DO models and related mechano-regulatory tissue differentiation algorithms for mouse bone. This study sought to establish an in silico model based on in vivo experimental data to simulate the bone regeneration process during DO of the mouse femur.

METHODS

In vivo micro-CT, including time-lapse morphometry was performed to monitor the bone regeneration in the distraction gap. A 2D axisymmetric finite element model, with a geometry originating from the experimental data, was created. Bone regeneration was simulated with a fuzzy logic-based two-stage (distraction and consolidation) mechano-regulatory tissue differentiation algorithm, which was adjusted from that used for fracture healing based on our in vivo experimental data. The predictive potential of the model was further tested with varied distraction frequencies and distraction rates.

RESULTS

The computational simulations showed similar bone regeneration patterns to those observed in the micro-CT data from the experiment throughout the DO process. This consisted of rapid bone formation during the first 10 days of the consolidation phase, followed by callus reshaping via bone remodeling. In addition, the computational model predicted a faster and more robust bone healing response as the model's distraction frequency was increased, whereas higher or lower distraction rates were not conducive to healing.

CONCLUSIONS

This in silico model could be used to investigate basic mechanobiological mechanisms involved in bone regeneration or to optimize DO strategies for potential clinical applications.

Orthophosphate and alkaline phosphatase induced the formation of apatite with different multilayered structures and mineralization balance

Mineralized collagen is a natural organic-inorganic composite. The combination of organic collagen and inorganic apatite to form different nanostructures is the key to producing bone substitutes with biomechanical properties that are as identical to normal bone as possible. However, the formation of apatite with different nanostructures during collagen mineralization is unexplored. Here, pyrophosphate (Pyro-P), as an important hydrolysate of adenosine triphosphate in the body, was introduced to prepare mineralized collagen under the regulation of alkaline phosphatase (ALP) and orthophosphate (Ortho-P). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) results showed that mineralized collagen, which combined with different crystallinities and multilayered structured apatite, was successfully prepared. A combination of ion chromatography (IC), Fourier transform infrared (FTIR) spectroscopy, circular dichroism (CD), and thermogravimetry (TG) analyses revealed the crucial role of Ortho-P in the formation of multilayered flower-shaped apatite with different crystallinities and in the maintenance of mineralization balance. Mineralization balance is of great significance for maintaining normal bone morphology during bone regeneration. Overall, our results provide a promising method to produce new bone substitute materials for the repair of large bone defects and a deeper insight into the mechanisms of biomineralization.

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.

Training and validation of a deep learning architecture for the automatic analysis of coronary angiography

BACKGROUND

In recent years, the use of deep learning has become more commonplace in the biomedical field and its development will greatly assist clinical and imaging data interpretation. Most existing machine learning methods for coronary angiography analysis are limited to a single aspect.

AIMS

We aimed to achieve an automatic and multimodal analysis to recognise and quantify coronary angiography, integrating multiple aspects, including the identification of coronary artery segments and the recognition of lesion morphology.

METHODS

A data set of 20,612 angiograms was retrospectively collected, among which 13,373 angiograms were labelled with coronary artery segments, and 7,239 were labelled with special lesion morphology. Trained and optimised by these labelled data, one network recognised 20 different segments of coronary arteries, while the other detected lesion morphology, including measures of lesion diameter stenosis as well as calcification, thrombosis, total occlusion, and dissection detections in an input angiogram.

RESULTS

For segment prediction, the recognition accuracy was 98.4%, and the recognition sensitivity was 85.2%. For detecting lesion morphologies including stenotic lesion, total occlusion, calcification, thrombosis, and dissection, the F1 scores were 0.829, 0.810, 0.802, 0.823, and 0.854, respectively. Only two seconds were needed for the automatic recognition.

CONCLUSIONS

Our deep learning architecture automatically provides a coronary diagnostic map by integrating multiple aspects. This helps cardiologists to flag and diagnose lesion severity and morphology during the intervention.

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods. To overcome such limitations, we have developed a deep learning algorithm, referred to as adaptive convolutional neural networks for k-space data interpolation (ACNN-k-Space), which adopts a residual Encoder-Decoder network architecture to interpolate the undersampled k-space data by integrating spatially contiguous slices as multi-channel input, along with k-space data from multiple coils if available. The network is enhanced by self-attention layers to adaptively focus on k-space data at different spatial frequencies and channels. We have evaluated our method on two public datasets and compared it with state-of-the-art existing methods. Ablation studies and experimental results demonstrate that our method effectively reconstructs images from undersampled k-space data and achieves significantly better image reconstruction performance than current state-of-the-art techniques. Source code of the method is available at https://gitlab.com/qgpmztmf/acnn-k-space.

Bioinspired mineralized collagen scaffolds for bone tissue engineering

Successful regeneration of large segmental bone defects remains a major challenge in clinical orthopedics, thus it is of important significance to fabricate a suitable alternative material to stimulate bone regeneration. Due to their excellent biocompatibility, sufficient mechanical strength, and similar structure and composition of natural bone, the mineralized collagen scaffolds (MCSs) have been increasingly used as bone substitutes via tissue engineering approaches. Herein, we thoroughly summarize the state of the art of MCSs as tissue-engineered scaffolds for acceleration of bone repair, including their fabrication methods, critical factors for osteogenesis regulation, current opportunities and challenges in the future. First, the current fabrication methods for MCSs, mainly including direct mineral composite, in-situ mineralization and 3D printing techniques, have been proposed to improve their biomimetic physical structures in this review. Meanwhile, three aspects of physical (mechanics and morphology), biological (cells and growth factors) and chemical (composition and cross-linking) cues are described as the critical factors for regulating the osteogenic feature of MCSs. Finally, the opportunities and challenges associated with MCSs as bone tissue-engineered scaffolds are also discussed to point out the future directions for building the next generation of MCSs that should be endowed with satisfactorily mimetic structures and appropriately biological characters for bone regeneration.

A Numerical Model for Simulating the Hemodynamic Effects of Enhanced External Counterpulsation on Coronary Arteries

Traditional enhanced external counterpulsation (EECP) used for the clinical treatment of patients with coronary heart disease only assesses diastolic/systolic blood pressure (Q = D/S > 1.2). However, improvement of the hemodynamic environment surrounding vascular endothelial cells of coronary arteries after long-term application of EECP is the basis of the treatment. Currently, the quantitative hemodynamic mechanism is not well understood. In this study, a standard 0D/3D geometric multi-scale model of the coronary artery was established to simulate the hemodynamic effects of different counterpulsation modes on the vascular endothelium. In this model, the neural regulation caused by counterpulsation was thoroughly considered. Two clinical trials were carried out to verify the numerical calculation model. The results demonstrated that the increase in counterpulsation pressure amplitude and pressurization duration increased coronary blood perfusion and wall shear stress (WSS) and reduced the oscillatory shear index (OSI) of the vascular wall. However, the impact of pressurization duration was the predominant factor. The results of the standard model and the two real individual models indicated that a long pressurization duration would cause more hemodynamic risk areas by resulting in excessive WSS, which could not be reflected by the change in the Q value. Therefore, long-term pressurization during each cardiac cycle therapy is not recommended for patients with coronary heart disease and clinical treatment should not just pay attention to the change in the Q value. Additional physiological indicators can be used to evaluate the effects of counterpulsation treatment.

Highly aligned hierarchical intrafibrillar mineralization of collagen induced by periodic fluid shear stress

Periodic fluid shear stress (FSS) is one of the main mechanical microenvironments in mineralization of bone matrix. To elucidate the mechanism of periodic FSS in collagen mineralization, a mechanical loading induced mineralization system is developed and compared with traditional polyacrylic acid (PAA) induced mineralization. Fourier transform infrared (FTIR) spectroscopy, calcium-to-phosphorus molar ratio and transmission electron microscopy (TEM) demonstrate that both periodic FSS and PAA can control the size of amorphous calcium phosphate (ACP) to avoid aggregation and help the formation of intrafibrillar mineralization. Differently, periodic FSS under a proper cycle and range can accelerate the conversion of ACP to apatite crystals and alleviate the reduced transformation caused by PAA. Under the action of template analogues, periodic FSS can also promote the formation of highly oriented hierarchical intrafibrillar mineralized (HIM) collagen. These findings are helpful for understanding the mechanism of collagen mineralization in natural bone matrix and contribute to the design of novel bone substitute materials with hierarchical structures.

UENet: A Novel Generative Adversarial Network for Angiography Image Segmentation

Convolutional neural networks (CNNs) have been widely used in medical image segmentation. Vessel segmentation in coronary angiography remains a challenging task. It is a great challenge to extract fine features of coronary artery for segmentation due to the poor opacification, numerous overlap of different artery segments and high similarity between artery segments and soft tissues in an angiography image, which results in a sub-optimal segmentation performance. In this paper, we propose an adapted generative adversarial networks (GANs) to complete the conversion from coronary angiography image to semantic segmentation image. We implemented an adapted U-net as the generator, and a novel 3-layer pyramid structure as the discriminator. During the training period, multi-scale inputs were fed into the discriminator to optimize the objective functions, producing high-definition segmentation results. Due to the generative adversarial mechanism, both generator and discriminator can extract fine feature of coronary artery. Our method effectively solves the problems of segmentation discontinuity and intra-class inconsistencies. Experiment shows that our method improves the segmentation accuracy effectively comparing to other vessel segmentation methods.

Multiple Slice k-space Deep Learning for Magnetic Resonance Imaging Reconstruction

Magnetic resonance imaging (MRI) has been one of the most powerful and valuable imaging methods for medical diagnosis and staging of disease. Due to the long scan time of MRI acquisition, k-space under-samplings is required during the acquisition processing. Thus, MRI reconstruction, which transfers undersampled k-space data to high-quality magnetic resonance imaging, becomes an important and meaningful task. There have been many explorations on k-space interpolation for MRI reconstruction. However, most of these methods ignore the strong correlation between target slice and its adjacent slices. Inspired by this, we propose a fully data-driven deep learning algorithm for k-space interpolation, utilizing the correlation information between the target slice and its neighboring slices. A novel network is proposed, which models the inter-dependencies between different slices. In addition, the network is easily implemented and expended. Experiments show that our methods consistently surpass existing image-domain and k-space-domain magnetic resonance imaging reconstructing methods.

Detection and Classification of Chronic Total Occlusion lesions using Deep Learning

Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern society, while chronic total occlusion (CTO) is the initial factor that influences the success rate of percutaneous coronary intervention (PCI), which is one of the most common treatments for CVD. In this work, novel deep convolutional neural networks (CNNs) are proposed to detect the entry point of CTO and classify its morphology according to the coronary angiography automatically. Specifically, feature pyramid networks (FPN) module and model fusion technique are applied to the detection network, and data augmentation and attentive regularization loss by reciprocative learning algorithm are used in classification network. Extracted from contrary angiography, the dataset consists of 2059 cases annotated by professional cardiologists. Experiment results show that the recall of CTO detection can reach up to 89.3%, and the sensitivity and specificity of CTO classification can reach up to 94.5% and 89.1% respectively.

Coronary Artery Vascular Segmentation on Limited Data via Pseudo-Precise Label

The scale of training data is significant in segmentation task, especially in segmenting the medical coronary artery angiograms. Traditional semantic segmentation networks have been restricted in this field, due to the particularity of cardiac coronary angiography data, that is, it is very difficult to balance the manual labeling costs and network accuracy. On the basis of these observations, we propose a new method to generate the so-called `pseudo-precise' label and a complementary training pipeline, which can improve the performance of the networks on the premise of reducing labor costs as much as possible. Our method can thus increase the f1-score by 4%-11% with the same amount of precisely labeled data.

A theoretical model to estimate inactivation effects of OH radicals on marine Vibrio sp. in bubble-shock interaction

A theoretical model for estimating inactivation effects on marine Vibrio sp. is developed from the viewpoint of the chemical action of the OH radicals induced by interaction of bubbles with shock waves. It consists of a biological probability model for cell viability and a bubble dynamic model for its collapsing motion due to the shock pressures. The biological probability model is built by defining a sterilized space of the OH radicals. To determine the radius of the sterilized space, the Herring equation is solved in the bubble dynamic model in consideration of the effect of the heat conductivity and mass transportation. Furthermore, the pressure waveform of incident shock wave used in the model is obtained with the pressure measurement. On the other hand, a bio-experiment of marine Vibrio sp. is carried out using a high-voltage power supply in a cylindrical water chamber. Finally, the viability ratio of marine bacteria estimated by the theoretical model is examined under the experimental conditions of this study. In addition, we also discuss the influence of bubble initial size for predicting the inactivation effects.

Apatite minerals derived from collagen phosphorylation modification induce the hierarchical intrafibrillar mineralization of collagen fibers

Collagen is the critical organic component of bone matrix, which is the template for bone biomineralization. Phosphorylation modification of collagen plays an important role in the process of biomineralization in vivo, but its mechanism on in vitro biomimetic mineralization of bone matrix remains unclear at the molecular level. Sodium tripolyphosphate is used to phosphorylate collagen in this study and new phosphate groups appear on collagen fibrils after phosphorylation modification. The chelating amount of calcium is improved linearly with increasing the phosphorylation degree of collagen fibrils, which demonstrates that the introduced phosphate groups serve as new nucleation sites and participate in the formation of apatite minerals inside the collagen fibers. Stabilized nanosized amorphous calcium phosphate by polyacrylic acid can also permeate into collagen fibers and further transform into another layer of hydroxyapatite minerals. Both layers of apatite minerals eventually induce the formation of hierarchical intrafibrillar mineralization structure within the phosphorylated collagen fibers. The present research enriches the previous biomineralization mechanism of bone matrix, provides a facile strategy for biomimetic mineralization of collagen, and offers the basis for future investigation of the advanced bone substitute materials.

Abstract P1-03-03: Invasive lobular carcinoma and invasive ductal carcinoma differ in immune response, translation efficiency and metabolic rate

Background

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer after invasive ductal carcinoma (IDC). ILC differs from IDC in pathologic, molecular, and clinical features. ILC tumors are most often characterized as luminal A by PAM50 analysis, suggestive of an indolent disease. Yet, when matched for receptor status and tumor grade, patients with ILC tend to have worse long-term outcomes than patients with IDC. The main distinguishing molecular feature of ILC is the loss of functional E-cadherin, and yet, beyond that loss, the mechanisms underlying the differences between ILC and IDC are largely unknown. We examined the RNA expression profiles of ILC and IDC tumors to assess if there may be underlying vulnerabilities of ILC tumors to novel therapeutic strategies.

Methods

Differential expression analysis was performed on 159 luminal A (LumA) ILC tumors versus 311 LumA IDC tumors from The Cancer Genome Atlas (TCGA). The METABRIC cohort (65 LumA ILC and 533 LumA IDC) was used as a validation dataset. Pathway enrichment analysis was performed to identify potential differences in biological processes, and these potential differences were then tested in a series of in vitro experiments, using 3 ER+ ILC (MDA-MB-134VI, SUM44PE, and MDA-MB-330) and 3 ER+ IDC (MCF7, T47D, and ZR75.1) cell lines.

Results

Pathway analysis led to the identification of three main signaling differences between LumA ILC and LumA IDC: immune regulation, translation, and metabolism. A series of immune pathways, including Immunological Synapse, Biocarta IL17 pathway, and Response to Wounding were up-regulated in ILC tumors. We examined specific cell type markers, and found that ILC tumors have a higher activity of nearly all immune cell types, including CD4+ T cells, CD8+ T cells, B cells, NK cells, dendritic cells, M1 macrophages, and M2 macrophages. These results were surprising, since ILC tumors have a lower incidence of stromal inflammation, as defined by H&E staining, suggesting a unique immune regulatory mechanism in ILC.

Next, we examined the translational regulation in ILC vs IDC tumors by comparing RNA expression and protein quantities as determined by RPPA analysis. ILC tumors have a lower protein:RNA ratio, suggesting a lower translation efficiency. This was reflected in the RPPA data by lower protein expression of eIF4G, ribosome protein S6 (S6) and p70-S6K in ILC tumors. Phosphorylation of 4E-BP1 (Ser65), eEF2, S6 (Ser235/236, Ser240/244), and mTOR (Ser2448) were also significantly lower in LumA ILCs. This lower translation efficiency was then validated in cell lines by O-propargyl-puromycin treatment.

Finally, the pathway analysis suggested lower rates of metabolism in lobular tumors. Comparative studies of OXPHOS and glycolysis with a Seahorse analyzer confirmed this finding.

Conclusions

ILC tumors have a higher immune activity than IDC tumors, even with lower rates of stromal inflammation, suggesting a potential for differential response to immunotherapy. The lower rates of translation and metabolism, which are general identifiers of tumor dormancy, could enable ILC to escape from cytotoxic therapies, and may play an important role in the late recurrence of ILC.

Citation Format: Levine KM, Du T, Zhu L, Tasdemir N, Lee AV, Van Houten B, Tseng GC, Oesterreich S. Invasive lobular carcinoma and invasive ductal carcinoma differ in immune response, translation efficiency and metabolic rate [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-03-03.

HOXA10 promotes nasopharyngeal carcinoma cell proliferation and invasion via inducing the expression of ZIC2

OBJECTIVE

In this study, we aimed to explore the dysregulated genes in nasopharyngeal carcinoma (NPC) and to investigate the regulative effect of HOXA10 on ZIC2 expression and their involvement in NPC cell proliferation and invasion.

MATERIALS AND METHODS

Microarray data that compared the transcription profile of NPC tissues and normal tissues was searched in GEO datasets and was re-analyzed. The expression of HOXA10 and ZIC2 mRNA were retrieved in TCGA database. CNE1 and CNE2 cells were used as an in-vitro cell model. Luciferase reporters carrying truncated ZIC2 promoter sequences were generated to verify the predicted HOXA10 binding site. CCK-8 assay and transwell assay were applied to assess cell proliferation and invasion respectively.

RESULTS

HOXC6, HOXA3, and HOXA10 were upregulated in NPC tissues. Data mining in TCGA database showed that HOXA10, but not HOXC6 or HOXA3 is positively correlated to ZIC2 expression. Enforced HOXA10 expression significantly elevated ZIC2 expression at both mRNA and protein levels in both CNE1 and CNE2 cells. HOXA10 can directly bind to the promoter of ZIC2 and upregulate ZIC2 transcription. ZIC2 knockdown significantly reduced cell proliferation and invasion capability of CNE1 cells and also partly abrogated the effect of HOXA10 overexpression on enhancing cell proliferation and invasion.

CONCLUSIONS

Both HOXA10 and ZIC2 are upregulated in NPC tissues compared to the normal tissues. HOXA10 can increase ZIC2 expression via binding to the ZIC2 promoter. Functionally, the HOXA10-ZIC2 axis can enhance NPC cell proliferation and invasion.

Shear-mediated orientational mineralization of bone apatite on collagen fibrils

Intrafibrillar mineralization of collagen under a 1.5 Pa FSS environment versus the serious extrafibrillar mineralization of collagen under no FSS.

Sex differences in the impact of nonalcoholic fatty liver disease on cardiovascular risk factors

BACKGROUND AND AIMS

Information on sex differences in the association of nonalcoholic fatty liver disease (NAFLD) with cardiovascular disease (CVD) risk factors is scarce. We examined whether men exhibit greater differences in established CVD risk factors between NAFLD and non-NAFLD than women.

METHODS AND RESULTS

We conducted a cross-sectional analysis using a cohort of 10761 apparently healthy Chinese adults who underwent comprehensive health checkups including abdominal ultrasonography. In the setting of NAFLD and non-NAFLD, although men had significantly higher levels of atherogenic lipids as indicated by higher levels of triglyceride, triglyceride/HDL-cholesterol, and lower levels of HDL-cholesterol and worsen renal function as indicated by higher levels of creatinine and lower levels of estimated glomerular filtration rate (eGFR) than female counterparts, men with NAFLD showed greater relative differences in atherogenic lipids and deteriorated renal function than women with NAFLD when compared with their non-NAFLD counterparts. The interactions between sex and NAFLD on triglyceride, HDL-cholesterol, triglyceride/HDL-cholesterol, creatinine, and eGFR were statistically significant (P < 0.05). In the multivariate Logistic regression analyses, we observed a stronger association of TG with NAFLD and comparable associations of eGFR or HDL-C with NAFLD in men compared with women.

CONCLUSION

There was greater adverse influence of NAFLD per se on triglyceride, and triglyceride/HDL-cholesterol in men compared with women. The greater adverse influence of NAFLD per se on HDL-C and eGFR in men compared with women probably related to the gender differences in TG levels.