Bacterial driver-passenger model in biofilms: a new mechanism in the development of colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease of the intestinal epithelium and ranks the third largest diagnosed malignancy in the world. Many studies have shown that the high risk of CRC is believed to be related to the formation of biofilms. To prove causation, it will be significant to decipher which specific bacteria in biofilms initiate and maintain CRC and fully describe their underlying mechanisms. Here we introduce a bacterial driver-passenger model. This model added a novel and compelling angle to the role of microorganisms, putting more emphasis on the transformation of bacterial composition in biofilms which play different roles in the development of CRC. In this model, bacterial drivers can initiate the formation of CRC through genotoxicity, while bacterial passengers maintain the CRC process through metabolites. On the basis of these pathogens, we further turned our attention to strategies that can inhibit and eradicate these pathogenic biofilms, with the aim of finding new ways to hinder colorectal carcinogenesis.

Melanoma secretion of transforming growth factor-β2 leads to loss of epidermal AMBRA1 threatening epidermal integrity and facilitating tumour ulceration

BACKGROUND

For patients with early American Joint Committee on Cancer (AJCC)-stage melanoma the combined loss of the autophagy regulatory protein AMBRA1 and the terminal differentiation marker loricrin in the peritumoral epidermis is associated with a significantly increased risk of metastasis.

OBJECTIVES

The aim of the present study was to evaluate the potential contribution of melanoma paracrine transforming growth factor (TGF)-β signalling to the loss of AMBRA1 in the epidermis overlying the primary tumour and disruption of epidermal integrity.

METHODS

Immunohistochemistry was used to analyse AMBRA1 and TGF-β2 in a cohort of 109 AJCC all-stage melanomas, and TGF-β2 and claudin-1 in a cohort of 30 or 42 AJCC stage I melanomas, respectively, with known AMBRA1 and loricrin (AMLo) expression. Evidence of pre-ulceration was analysed in a cohort of 42 melanomas, with TGF-β2 signalling evaluated in primary keratinocytes.

RESULTS

Increased tumoral TGF-β2 was significantly associated with loss of peritumoral AMBRA1 (P < 0·05), ulceration (P < 0·001), AMLo high-risk status (P < 0·05) and metastasis (P < 0·01). TGF-β2 treatment of keratinocytes resulted in downregulation of AMBRA1, loricrin and claudin-1, while knockdown of AMBRA1 was associated with decreased expression of claudin-1 and increased proliferation of keratinocytes (P < 0·05). Importantly, we show loss of AMBRA1 in the peritumoral epidermis was associated with decreased claudin-1 expression (P < 0·05), parakeratosis (P < 0·01) and cleft formation in the dermoepidermal junction (P < 0·05).

CONCLUSIONS

Collectively, these data suggest a paracrine mechanism whereby TGF-β2 causes loss of AMBRA1 overlying high-risk AJCC early-stage melanomas and reduced epidermal integrity, thereby facilitating erosion of the epidermis and tumour ulceration.

Optimization of Left Ventricle Pace Maker Location Using Echo-Based Fluid-Structure Interaction Models

Introduction

Cardiac pacing has been an effective treatment in the management of patients with bradyarrhythmia and tachyarrhythmia. Different pacemaker location has different responses, and pacemaker effectiveness to each individual can also be different. A novel image-based ventricle animal modeling approach was proposed to optimize ventricular pacemaker site for better cardiac outcome.

Method

One health female adult pig (weight 42.5 kg) was used to make a pacing animal model with different ventricle pacing locations. Ventricle surface electric signal, blood pressure and echo image were acquired 15 min after the pacemaker was implanted. Echo-based left ventricle fluid-structure interaction models were constructed to perform ventricle function analysis and investigate impact of pacemaker location on cardiac outcome. With the measured electric signal map from the pig associated with the actual pacemaker site, electric potential conduction of myocardium was modeled by material stiffening and softening in our model, with stiffening simulating contraction and softening simulating relaxation. Ventricle model without pacemaker (NP model) and three ventricle models with the following pacemaker locations were simulated: right ventricular apex (RVA model), posterior interventricular septum (PIVS model) and right ventricular outflow tract (RVOT model). Since higher peak flow velocity, flow shear stress (FSS), ventricle stress and strain are linked to better cardiac function, those data were collected for model comparisons.

Results

At the peak of filling, velocity magnitude, FSS, stress and strain for RVOT and PIVS models were 13%, 45%, 18%, 13% and 5%, 30%, 10%, 5% higher than NP model, respectively. At the peak of ejection, velocity magnitude, FSS, stress and strain for RVOT and PIVS models were 50%, 44%, 54%, 59% and 23%, 36%, 39%, 53% higher than NP model, respectively. RVA model had lower velocity, FSS, stress and strain than NP model. RVOT model had higher peak flow velocity and stress/strain than PIVS model. It indicated RVOT pacemaker site may be the best location.

Conclusion

This preliminary study indicated that RVOT model had the best performance among the four models compared. This modeling approach could be used as “virtual surgery” to try various pacemaker locations and avoid risky and dangerous surgical experiments on real patients.

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.

Hemodynamic Mimic Shear Stress for Platelet Membrane Nanobubbles Preparation and Integrin αIIbβ3 Conformation Regulation

Platelet (PLT) membrane biomimetic nanomaterials have become promising theranostic platforms due to their good biocompatibility and effectiveness. However, in order to achieve precise regulation of cell membrane components, novel controllable construction approaches need to be developed. Inspired by the interaction mechanism among platelet production, activation, and dynamic biomechanical signals in blood circulation, here a platelet nanobubbles (PNBs) with reassembled platelet membrane with ideal echogenicity was fabricated using an adjustable pressure-induced shear stress method. The results demonstrate that the high shear stress during PNBs fabrication led to the enrichment of platelet membrane lipid rafts and proteins, as well as their reassembly on the gas-liquid interface. More importantly, the conformation of platelet integrin α_IIbβ₃ was transformed into a shear stress-induced intermediate affinity state, which gives PNBs enhanced adhesion ability to the vascular endothelial injury. Taken together, these PNBs have great application potential in the specifically targeted ultrasound diagnosis of vascular endothelial injury.

Design, Synthesis, and Characterization of I-BET567, a Pan-Bromodomain and Extra Terminal (BET) Bromodomain Oral Candidate

Through regulation of the epigenome, the bromodomain and extra terminal (BET) family of proteins represent important therapeutic targets for the treatment of human disease. Through mimicking the endogenous N-acetyl-lysine group and disrupting the protein-protein interaction between histone tails and the bromodomain, several small molecule pan-BET inhibitors have progressed to oncology clinical trials. This work describes the medicinal chemistry strategy and execution to deliver an orally bioavailable tetrahydroquinoline (THQ) pan-BET candidate. Critical to the success of this endeavor was a potency agnostic analysis of a data set of 1999 THQ BET inhibitors within the GSK collection which enabled identification of appropriate lipophilicity space to deliver compounds with a higher probability of desired oral candidate quality properties. SAR knowledge was leveraged via Free-Wilson analysis within this design space to identify a small group of targets which ultimately delivered I-BET567 (27), a pan-BET candidate inhibitor that demonstrated efficacy in mouse models of oncology and inflammation.

Optical Coherence Tomography-Derived Changes in Plaque Structural Stress Over the Cardiac Cycle: A New Method for Plaque Biomechanical Assessment

Introduction: Cyclic plaque structural stress has been hypothesized as a mechanism for plaque fatigue and eventually plaque rupture. A novel approach to derive cyclic plaque stress in vivo from optical coherence tomography (OCT) is hereby developed. Materials and Methods: All intermediate lesions from a previous OCT study were enrolled. OCT cross-sections at representative positions within each lesion were selected for plaque stress analysis. Detailed plaque morphology, including plaque composition, lumen and internal elastic lamina contours, were automatically delineated. OCT-derived vessel and plaque morphology were included in a 2-dimensional finite element analysis, loaded with patient-specific intracoronary pressure tracing data, to calculate the changes in plaque structural stress (ΔPSS) on vessel wall over the cardiac cycle. Results: A total of 50 lesions from 41 vessels were analyzed. A significant ΔPSS gradient was observed across the plaque, being maximal at the proximal shoulder (45.7 [32.3, 78.6] kPa), intermediate at minimal lumen area (MLA) (39.0 [30.8, 69.1] kPa) and minimal at the distal shoulder (35.1 [28.2, 72.3] kPa; p = 0.046). The presence of lipidic plaques were observed in 82% of the diseased segments. Larger relative lumen deformation and ΔPSS were observed in diseased segments, compared with normal segments (percent diameter change: 8.2 ± 4.2% vs. 6.3 ± 2.3%, p = 0.04; ΔPSS: 59.3 ± 48.2 kPa vs. 27.5 ± 8.2 kPa, p < 0.001). ΔPSS was positively correlated with plaque burden (r = 0.37, p < 0.001) and negatively correlated with fibrous cap thickness (r = -0.25, p = 0.004). Conclusions: ΔPSS provides a feasible method for assessing plaque biomechanics in vivo from OCT images, consistent with previous biomechanical and clinical studies based on different methodologies. Larger ΔPSS at proximal shoulder and MLA indicates the critical sites for future biomechanical assessment.

Quantifying Patient-Specific in vivo Coronary Plaque Material Properties for Accurate Stress/Strain Calculations: An IVUS-Based Multi-Patient Study

Introduction: Mechanical forces are closely associated with plaque progression and rupture. Precise quantifications of biomechanical conditions using in vivo image-based computational models depend heavily on the accurate estimation of patient-specific plaque mechanical properties. Currently, mechanical experiments are commonly performed on ex vivo cardiovascular tissues to determine plaque material properties. Patient-specific in vivo coronary material properties are scarce in the existing literature.

Methods:In vivo Cine intravascular ultrasound and virtual histology intravascular ultrasound (IVUS) slices were acquired at 20 plaque sites from 13 patients. A three-dimensional thin-slice structure-only model was constructed for each slice to obtain patient-specific in vivo material parameter values following an iterative scheme. Effective Young's modulus (YM) was calculated to indicate plaque stiffness for easy comparison purposes. IVUS-based 3D thin-slice models using in vivo and ex vivo material properties were constructed to investigate their impacts on plaque wall stress/strain (PWS/PWSn) calculations.

Results: The average YM values in the axial and circumferential directions for the 20 plaque slices were 599.5 and 1,042.8 kPa, respectively, 36.1% lower than those from published ex vivo data. The YM values in the circumferential direction of the softest and stiffest plaques were 103.4 and 2,317.3 kPa, respectively. The relative difference of mean PWSn on lumen using the in vivo and ex vivo material properties could be as high as 431%, while the relative difference of mean PWS was much lower, about 3.07% on average.

Conclusion: There is a large inter-patient and intra-patient variability in the in vivo plaque material properties. In vivo material properties have a great impact on plaque stress/strain calculations. In vivo plaque material properties have a greater impact on strain calculations. Large-scale-patient studies are needed to further verify our findings.

[Causal graph model and its application in nutritional epidemiologic research]

Suboptimal diet is one of the most important controllable risk factors for non-communicable diseases. However, randomized controlled trials make it difficult to quantify the causal association between specific dietary factors and health outcomes. In recent years, the rapid development of causal inference has provided a robust theoretical and methodological tool for making full use of observational research data and producing high-quality nutritional epidemiologic research evidence. The causal graph model visualizes the complex causal relationship system by integrating a large amount of prior knowledge and provides a basic framework for identifying confounding and determining causal effect estimation strategies. Different analysis strategies such as adjusting confounders, instrumental variables, or mediation analysis can be created based on other causal graphs. This paper introduces the idea of the causal graph model and the characteristics of various analysis strategies and their application in nutritional epidemiology research, aiming to promote the application of the causal graph model in nutrition and provide references and suggestions for the follow-up research.

Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models

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

A Novel Pulmonary Valve Replacement Surgery Strategy Using Contracting Band for Patients With Repaired Tetralogy of Fallot: An MRI-Based Multipatient Modeling Study

Patients with repaired Tetralogy of Fallot (ToF), a congenital heart defect which includes a ventricular septal defect and severe right ventricular outflow obstruction, account for the majority of cases with late-onset right ventricle (RV) failure. Current surgery procedures, including pulmonary valve replacement (PVR) with right ventricle remodeling, yield mixed results. PVR with active band insertion was hypothesized to be of clinical usage on improving RV function measured by ejection fraction (EF). In lieu of risky open-heart surgeries and experiments on animal and human, computational biomechanical models were adapted to study the impact of PVR with five band insertion options. Cardiac magnetic resonance (CMR) images were acquired from seven TOF patients before PVR surgery for model construction. For each patient, five different surgery plans combined with passive and active contraction band with contraction ratio of 20, 15, and 10% were studied. Those five plans include three single-band plans with different band locations; one plan with two bands, and one plan with three bands. Including the seven no-band models, 147 computational bi-ventricle models were constructed to simulate RV cardiac functions and identify optimal band plans. Patient variations with different band plans were investigated. Surgery plan with three active contraction bands and band active contraction ratio of 20% had the best performance on improving RV function. The mean ± SD RV ejection fraction value from the seven patients was 42.90 ± 5.68%, presenting a 4.19% absolute improvement or a 10.82% relative improvement, when compared with the baseline models (38.71 ± 5.73%, p = 0.016). The EF improvements from the seven patients varied from 2.87 to 6.01%. Surgical procedures using active contraction bands have great potential to improve RV function measured by ejection fraction for patients with repaired ToF. It is possible to have higher right ventricle ejection fraction improvement with more bands and higher band active contraction ratio. Our findings with computational models need to be further validated by animal experiments before clinical trial could become possible.

Optical Coherence Tomography-Based Patient-Specific Residual Multi-Thrombus Coronary Plaque Models With Fluid-Structure Interaction for Better Treatment Decisions: A Biomechanical Modeling Case Study

Intracoronary thrombus from plaque erosion could cause fatal acute coronary syndrome (ACS). A conservative antithrombotic therapy has been proposed to treat ACS patients in lieu of stenting. It is speculated that the residual thrombus after aspiration thrombectomy would influence the prognosis of this treatment. However, biomechanical mechanisms affecting intracoronary thrombus remodeling and clinical outcome remain largely unknown. in vivo optical coherence tomography (OCT) data of a coronary plaque with two residual thrombi after antithrombotic therapy were acquired from an ACS patient with consent obtained. Three OCT-based fluid-structure interaction (FSI) models with different thrombus volumes, fluid-only, and structure-only models were constructed to simulate and compare the biomechanical interplay among blood flow, residual thrombus, and vessel wall mimicking different clinical situations. Our results showed that residual thrombus would decrease coronary volumetric flow rate by 9.3%, but elevate wall shear stress (WSS) by 29.4% and 75.5% at thrombi 1 and 2, respectively. WSS variations in a cardiac cycle from structure-only model were 12.1% and 13.5% higher at the two thrombus surfaces than those from FSI model. Intracoronary thrombi were subjected to compressive forces indicated by negative thrombus stress. Tandem intracoronary thrombus might influence coronary hemodynamics and solid mechanics differently. Computational modeling could be used to quantify biomechanical conditions under which patients could receive patient-specific treatment plan with optimized outcome after antithrombotic therapy. More patient studies with follow-up data are needed to continue the investigation and better understand mechanisms governing thrombus remodeling process.

Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Background

Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability.

Methods

Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results.

Results

For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847.

Conclusion

This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

A prediction tool for plaque progression based on patient-specific multi-physical modeling

Atherosclerotic plaque rupture is responsible for a majority of acute vascular syndromes and this study aims to develop a prediction tool for plaque progression and rupture. Based on the follow-up coronary intravascular ultrasound imaging data, we performed patient-specific multi-physical modeling study on four patients to obtain the evolutional processes of the microenvironment during plaque progression. Four main pathophysiological processes, i.e., lipid deposition, inflammatory response, migration and proliferation of smooth muscle cells (SMCs), and neovascularization were coupled based on the interactions demonstrated by experimental and clinical observations. A scoring table integrating the dynamic microenvironmental indicators with the classical risk index was proposed to differentiate their progression to stable and unstable plaques. The heterogeneity of plaque microenvironment for each patient was demonstrated by the growth curves of the main microenvironmental factors. The possible plaque developments were predicted by incorporating the systematic index with microenvironmental indicators. Five microenvironmental factors (LDL, ox-LDL, MCP-1, SMC, and foam cell) showed significant differences between stable and unstable group (p < 0.01). The inflammatory microenvironments (monocyte and macrophage) had negative correlations with the necrotic core (NC) expansion in the stable group, while very strong positive correlations in unstable group. The inflammatory microenvironment is strongly correlated to the NC expansion in unstable plaques, suggesting that the inflammatory factors may play an important role in the formation of a vulnerable plaque. This prediction tool will improve our understanding of the mechanism of plaque progression and provide a new strategy for early detection and prediction of high-risk plaques.

Besides the traditional systematic factors, the influences of the local microenvironmental factors on atherosclerotic plaque progression have been demonstrated. Mathematical and computational modeling is an important tool to investigate the complex interplay between plaque progression and the microenvironment, and provides a potential way toward the prediction of plaque vulnerability according to the comprehensive evaluation of both morphological and/or biochemical factors in tissue level with microenvironmental factors in cellular level. We performed patient-specific multi-physical modeling study on four patients to obtain the evolutional processes of the microenvironment during plaque progression and predicted the possible plaque developments. A scoring table integrating the dynamic microenvironmental indicators with the classical risk index was proposed to differentiate their progression to stable and unstable plaques. Based on patient-specific imaging data, the mathematical model will provide a novel method to predict the changes of plaque microenvironment and improve ability to access the personal therapeutic strategy for atherosclerotic plaque.

Comparisons of simulation results between passive and active fluid structure interaction models for left ventricle in hypertrophic obstructive cardiomyopathy

BACKGROUND

Patient-specific active fluid-structure interactions (FSI) model is a useful approach to non-invasively investigate the hemodynamics in the heart. However, it takes a lot of effort to obtain the proper external force boundary conditions for active models, which heavily restrained the time-sensitive clinical applications of active computational models.

METHODS

The simulation results of 12 passive FSI models based on 6 patients' pre-operative and post-operative CT images were compared with corresponding active models to investigate the differences in hemodynamics and cardiac mechanics between these models.

RESULTS

In comparing the passive and active models, it was found that there was no significant difference in pressure difference and shear stress on mitral valve leaflet (MVL) at the pre-SAM time point, but a significant difference was found in wall stress on the inner boundary of left ventricle (endocardium). It was also found that pressure difference on the coapted MVL and the shear stress on MVL were significantly decreased after successful surgery in both active and passive models.

CONCLUSION

Our results suggested that the passive models may provide good approximated hemodynamic results at 5% RR interval, which is crucial for analyzing the initiation of systolic anterior motion (SAM). Comparing to active models, the passive models decrease the complexity of the modeling construction and the difficulty of convergence significantly. These findings suggest that, with proper boundary conditions and sufficient clinical data, the passive computational model may be a good substitution model for the active model to perform hemodynamic analysis of the initiation of SAM.

Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study

Background

Detecting coronary vulnerable plaques in vivo and assessing their vulnerability have been great challenges for clinicians and the research community. Intravascular ultrasound (IVUS) is commonly used in clinical practice for diagnosis and treatment decisions. However, due to IVUS limited resolution (about 150–200 µm), it is not sufficient to detect vulnerable plaques with a threshold cap thickness of 65 µm. Optical Coherence Tomography (OCT) has a resolution of 15–20 µm and can measure fibrous cap thickness more accurately. The aim of this study was to use OCT as the benchmark to obtain patient-specific coronary plaque cap thickness and evaluate the differences between OCT and IVUS fibrous cap quantifications. A cap index with integer values 0–4 was also introduced as a quantitative measure of plaque vulnerability to study plaque vulnerability.

Methods

Data from 10 patients (mean age: 70.4; m: 6; f: 4) with coronary heart disease who underwent IVUS, OCT, and angiography were collected at Cardiovascular Research Foundation (CRF) using approved protocol with informed consent obtained. 348 slices with lipid core and fibrous caps were selected for study. Convolutional Neural Network (CNN)-based and expert-based data segmentation were performed using established methods previously published. Cap thickness data were extracted to quantify differences between IVUS and OCT measurements.

Results

For the 348 slices analyzed, the mean value difference between OCT and IVUS cap thickness measurements was 1.83% (p = 0.031). However, mean value of point-to-point differences was 35.76%. Comparing minimum cap thickness for each plaque, the mean value of the 20 plaque IVUS-OCT differences was 44.46%, ranging from 2.36% to 91.15%. For cap index values assigned to the 348 slices, the disagreement between OCT and IVUS assignments was 25%. However, for the OCT cap index = 2 and 3 groups, the disagreement rates were 91% and 80%, respectively. Furthermore, the observation of cap index changes from baseline to follow-up indicated that IVUS results differed from OCT by 80%.

Conclusions

These preliminary results demonstrated that there were significant differences between IVUS and OCT plaque cap thickness measurements. Large-scale patient studies are needed to confirm our findings.

Identification and application of the Pigm-1 gene in rice disease resistance breeding

Rice blast, caused by Magnaporthe oryzae, is one of the most destructive diseases of rice worldwide. The identification and utilization of resistance genes are crucial and significant for breeding resistant rice cultivars. We identified a resistance gene from Shuangkang77009, which is highly resistant to the rice blast isolate Guy11 using map-based cloning. We performed bulked segregant analysis combined with specific length amplified fragment sequencing. We also performed association analysis, candidate gene prediction and cDNA sequencing to identify the candidate gene. The resistance gene is located on chromosome 6, and we ultimately mapped the resistance locus to a 92-kb region. The resistance gene in Shuangkang77009 was allelic to PigmR, hereafter referred to as Pigm-1. The Pigm-1 protein had one amino acid change: serine (Ser) residue 860 was replaced by tyrosine (Tyr) in Pigm-1 compared with the previously identified Pigm protein, which significantly changed the structure of the Pigm-1 protein based on 3-D structure simulation. In addition, using the developed molecular marker linked to the Pigm-1 gene and molecular marker-assisted selection technology, we introduced the Pigm-1 gene into Minghui86, a widely used and excellent restorer. We generated 11 stable homozygous rice lines with desirable agronomic traits and strong resistance to rice blast. In conclusion, Pigm-1, a natural allelic variant of PigmR, was responsible for blast resistance in Shuangkang77009 rice. The molecular marker-assisted breeding strategy for Pigm-1 was highly efficient.

IDENTIFICATION OF A NOVEL MUTATION IN THE MMAA GENE IN A CHINESE BOY WITH ISOLATED METHYLMALONIC ACIDEMIA

Background

Isolated methylmalonic acidemia refers to a group of inborn errors of metabolism characterized by elevated methylmalonic acid concentrations in the blood and urine. It occurs in approximately one to three out of every 100 thousand Chinese newborns. Mutations in the MMAA gene cause isolated methylmalonic acidemia.

Case presentation

A 13-month-old boy was diagnosed with isolated methylmalonic acidemia. We identified two mutations in the MMAA gene in this case: c.491G>A and c.650T>A. The c.491G>A is a novel mutation in the MMAA gene. The boy is a heterozygous carrier of both mutations. The boy was treated with intravenous sodium benzoate and fluids. His sensorium gradually improved and he recovered from the acute illness. Other family members are heterozygous carriers of either mutations but with no symptoms.

Conclusions

We identified a novel c.491G>A mutation in the MMAA gene. Heterozygous carriers of both c.491G>A and c.650T>A mutations are associated with isolated methylmalonic acidemia.