Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation.

PURPOSE: To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). METHODS: Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis. RESULTS: A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality. CONCLUSIONS: The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases.

Objectives: To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed. Methods: This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared. Results: Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%). Conclusion: This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.

Gasdermin D-dependent platelet pyroptosis exacerbates NET formation and inflammation in severe sepsis.

Platelets have emerged as key inflammatory cells implicated in the pathology of sepsis, but their contributions to rapid clinical deterioration and dysregulated inflammation have not been defined. Here, we show that the incidence of thrombocytopathy and inflammatory cytokine release was significantly increased in patients with severe sepsis. Platelet proteomic analysis revealed significant upregulation of gasdermin D (GSDMD). Using platelet-specific Gsdmd-deficient mice, we demonstrated a requirement for GSDMD in triggering platelet pyroptosis in cecal ligation and puncture (CLP)-induced sepsis. GSDMD-dependent platelet pyroptosis was induced by high levels of S100A8/A9 targeting toll-like receptor 4 (TLR4). Pyroptotic platelet-derived oxidized mitochondrial DNA (ox-mtDNA) potentially promoted neutrophil extracellular trap (NET) formation, which contributed to platelet pyroptosis by releasing S100A8/A9, forming a positive feedback loop that led to the excessive release of inflammatory cytokines. Both pharmacological inhibition using Paquinimod and genetic ablation of the S100A8/A9-TLR4 signaling axis improved survival in mice with CLP-induced sepsis by suppressing platelet pyroptosis.

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass nodules (GGNs) and compare the diagnostic performance of it with that of radiologists. METHODS: A total of 1946 patients with solitary and histopathologically confirmed GGNs with maximum diameter less than 3 cm were retrospectively enrolled. The training dataset containing 1704 GGNs was augmented by resampling, scaling, random cropping, and so forth, to generate new training data. A multimodal data fusion model based on residual learning architecture and two multilayer perceptron with attention mechanism combining CT images with patient general data and serum tumor markers was built. The distance-based confidence scores (DCS) were calculated and compared among multimodal data models with different combinations. An observer study was conducted and the prediction performance of the fusion algorithms was compared with that of the two radiologists by an independent testing dataset with 242 GGNs. RESULTS: Among the whole GGNs, 606 GGNs are confirmed as invasive adenocarcinoma (IA) and 1340 are non-IA. The proposed novel multimodal data fusion model combining CT images, patient general data, and serum tumor markers achieved the highest accuracy (88.5%), area under a ROC curve (0.957), F1 (81.5%), F1weighted (81.9%), and Matthews correlation coefficient (73.2%) for classifying between IA and non-IA GGNs, which was even better than the senior radiologist's performance (accuracy, 86.1%). In addition, the DCSs for multimodal data suggested that CT image had a stronger influence (0.9540) quantitatively than general data (0.6726) or tumor marker (0.6971). CONCLUSION: This study demonstrated that the feasibility of integrating different types of data including CT images and clinical variables, and the multimodal data fusion model yielded higher performance for distinguishing IA from non-IA GGNs.

Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction.

BACKGROUND: Accurate segmentation of pulmonary nodules is important for image-driven nodule analysis and nodule malignancy risk prediction. However, due to interobserver variability caused by manual segmentation, an accurate and robust automatic segmentation method has become an essential task. Therefore, the aim of the present study was to construct an accurate segmentation and malignant risk prediction algorithm for pulmonary nodules. METHODS: In the present study, we proposed a coarse-to-fine 2-stage framework consisting of the following 2 convolutional neural networks: a 3D multiscale U-Net used for localization and a 2.5D multiscale separable U-Net (MSU-Net) used for segmentation refinement. A multitask framework was proposed for nodules' malignancy risk prediction. Features from encoding and decoding paths of MSU-Net were integrated for pathology or morphology characteristic classification. RESULTS: Experimental results showed that our method achieved state-of-art results on the Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed method achieved a Dice similarity coefficient (DSC) of 83.04% and an overlapping error of 27.47% on the dataset. Our method achieved accuracy of 77.8% and area under the receiver-operating characteristic curve of 84.3% for malignancy risk prediction. Moreover, we compared our method with the inter-radiologist agreement, and the average DSC difference was only 0.39%. CONCLUSIONS: The results showed the effectiveness of the multitask end-to-end framework. The coarse-to-fine 2.5D strategy increased the accuracy and efficiency of pulmonary nodule segmentation and malignancy risk prediction of the computer-aided diagnosis system. In clinical practice, doctors can obtain accurate morphological characteristics and quantitative information of nodules by using the proposed method, so as to make future treatment plan.

Effect of COVID-19 Pandemic on Acute Coronary Syndrome Clinical Practice Patterns: Findings from a Multicenter Clinician Survey in China.

Background: The coronavirus disease 2019 (COVID-19) pandemic has severely affected healthcare systems around the world. This study aimed to investigate the perceptions of cardiologists regarding how the COVID-19 pandemic has affected the clinical practice patterns for acute coronary syndrome (ACS). Methods: A multicenter clinician survey was sent to 300 cardiologists working in 22 provinces in China. The survey collected demographic information and inquired about their perceptions of how the COVID-19 pandemic has affected ACS clinical practice patterns. Results: The survey was completed by 211 (70.3%) cardiologists, 82.5% of whom were employed in tertiary hospitals, and 52.1% reported more than 10 years of clinical cardiology practice. Most respondents observed a reduction in ACS inpatients and outpatients in their hospitals during the pandemic. Only 29.9% of the respondents had access to a dedicated catheter room for the treatment of COVID-19-positive ACS patients. Most respondents stated that the COVID-19 pandemic had varying degrees of effect on the treatment of acute ST-segment elevation myocardial infarction (STEMI), acute non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Compared with the assumed non-pandemic period, in the designed clinical questions, the selection of coronary interventional therapy for STEMI, NSTEMI, and unstable angina during the COVID-19 pandemic was significantly decreased (all p < 0.05), and the selection of pharmacotherapy was increased (all p < 0.05). The selection of fibrinolytic therapy for STEMI during the pandemic was higher than in the assumed non-pandemic period (p < 0.05). Conclusions: The COVID-19 pandemic has profoundly affected ACS clinical practice patterns. The use of invasive therapies significantly decreased during the pandemic period, whereas pharmacotherapy was more often prescribed by the cardiologists.

Osteoprotegerin prompts cardiomyocyte hypertrophy via autophagy inhibition mediated by FAK/BECLIN1 pathway.

AIM: It has been reported that Osteoprotegerin (OPG) induces cardiomyocyte hypertrophy, but the mechanism remains unclear. This study was to investigate the role of Focal Adhesion Kinase (FAK) pathway in the OPG induced hypertrophy in cultured cardiomyocytes. METHODS: The H9C2 line of rat cardiomyocytes were treated with OPG at different concentrations and the cellular hypertrophy was evaluated. Meanwhile, the activity of FAK and other the phosphorylation kinases were detected. Autophagy flux assay was performed in absence and presence OPG. The interaction between proteins was analyses using Co-Immunoprecipitation assay. RESULTS: We found that OPG induced cardiomyocyte hypertrophic response, indicated by increased cellular size and protein content per cell. OPG increases the heart/body weight ratio in vivo. Also OPG inhibits autophagy and induces FAK phosphorylation. FAK silencing using si-RNA abrogates the effect of OPG on autophagy and cellular hypertrophy. Furthermore, Co-immunoprecipitation assay reveals that OPG inhibits autophagy through enhancing the binding of FAK and Beclin1. CONCLUSION: The FAK/Beclin1 signal pathway is essential for the OPG induced autophagy inhibition and hypertrophic response in cultured H9C2 cells.

Chromosomal mosaicism detected by karyotyping and chromosomal microarray analysis in prenatal diagnosis.

To investigate the incidence and clinical significance of chromosomal mosaicism (CM) in prenatal diagnosis by G-banding karyotyping and chromosomal microarray analysis (CMA). This is a single-centre retrospective study of invasive prenatal diagnosis for CM. From 5758 karyotyping results and 6066 CMA results, 104 foetal cases with CM were selected and analysed further. In total, 50% (52/104) of foetal cases with CM were affected by ultrasound-detectable phenotypes. Regardless of whether they were singleton or twin pregnancies, isolated structural defects in one system (51.35%, 19/37 in singletons; 86.67%, 13/15 in twins) and a single soft marker (18.92%, 7/37 in singletons; 13.33%, 2/15 in twins) were the most common ultrasound anomalies. Mosaic autosomal trisomy (19.23%, 20/104) was the most frequent type, and its rate was higher in phenotypic foetuses (28.85%, 15/52) than in non-phenotypic foetuses (9.62%, 5/52). There was no difference in mosaic fractions between phenotypic and non-phenotypic foetuses based on specimen sources or overall classification. Discordant mosaic results were observed in 16 cases (15.38%, 16/104) from different specimens or different testing methods. Genetic counselling and clinical management regarding CM in prenatal diagnosis remain challenging due to the variable phenotypes and unclear significance. Greater caution should be used in prenatal counselling, and more comprehensive assays involving serial ultrasound examinations, different specimens or testing methods verifications and follow-up should be applied.

The Invasiveness Classification of Ground-Glass Nodules Using 3D Attention Network and HRCT.

The early stage lung cancer often appears as ground-glass nodules (GGNs). The diagnosis of GGN as preinvasive lesion (PIL) or invasive adenocarcinoma (IA) is very important for further treatment planning. This paper proposes an automatic GGNs' invasiveness classification algorithm for the adenocarcinoma. 1431 clinical cases and a total of 1624 GGNs (3-30 mm) were collected from Shanghai Cancer Center for the study. The data is in high-resolution computed tomography (HRCT) format. Firstly, the automatic GGN detector which is composed by a 3D U-Net and a 3D multi-receptive field (multi-RF) network detects the location of GGNs. Then, a deep 3D convolutional neural network (3D-CNN) called Attention-v1 is used to identify the GGNs' invasiveness. The attention mechanism was introduced to the 3D-CNN. This paper conducted a contract experiment to compare the performance of Attention-v1, ResNet, and random forest algorithm. ResNet is one of the most advanced convolutional neural network structures. The competition performance metrics (CPM) of automatic GGN detector reached 0.896. The accuracy, sensitivity, specificity, and area under curve (AUC) value of Attention-v1 structure are 85.2%, 83.7%, 86.3%, and 92.6%. The algorithm proposed in this paper outperforms ResNet and random forest in sensitivity, accuracy, and AUC value. The deep 3D-CNN's classification result is better than traditional machine learning method. Attention mechanism improves 3D-CNN's performance compared with the residual block. The automatic GGN detector with the addition of Attention-v1 can be used to construct the GGN invasiveness classification algorithm to help the patients and doctors in treatment.

Aberrant hydroxymethylation of ANGPTL4 is associated with selective intrauterine growth restriction in monochorionic twin pregnancies.

Selective intrauterine growth restriction (sIUGR) is a severe complication in monochorionic (MC) twin pregnancies, and it carries increased risks of poor prognosis. Current data suggest that vascular anastomoses and unequal placental sharing may be the key contributor to discordant foetal growth. While MC twins derive from a single zygote and have almost identical genetic information, the precise mechanisms remain unknown. DNA hydroxymethylation is a newly discovered epigenetic feature associated with gene regulation and modification. Here, we investigate discordant hydroxymethylation patterns between two placental shares of sIUGR and analyse the potential role of aberrant hydroxymethylation of angiopoietin-like 4 (ANGPTL4) in placental dysplasia. Hydroxymethylation DNA immunoprecipitation (hMeDIP)-chip and mRNA sequencing were performed to identify hydroxymethylation-associated genes. Real-time qPCR, western blotting, and immunohistochemistry were used to confirm ANGPTL4 expression. The mechanisms regulating ANGPTL4 were investigated by cell migration assay, invasion assay, viability assay, and apoptotic ratio assays, western blotting and hMeDIP-qPCR. Decreased ANGPTL4 was detected in the smaller placental shares of sIUGR. ANGPTL4 knockdown suppressed trophoblast invasiveness and migration, which possibly occurred through hypoxia inducible factor 1α (HIF-1α) and HIF-1 signalling pathway. Hypoxia leads to aberrant expression of ANGPTL4 and HIF-1α, positively correlated with their aberrant hydroxymethylation levels in promoter regions. Aberrant hydroxymethylation of ANGPTL4 may contribute to placental impairment by the HIF-1 signalling pathway in smaller placental shares of sIUGR.

Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports.

BACKGROUND: Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assisting clinicians to locate and assimilate the key information of reports is of great significance for improving the efficiency of reading reports. There are few studies on information extraction from Chinese medical texts and its application in radiology information systems (RIS) for efficiency improvement. OBJECTIVES: The purpose of this study was to explore methods for extracting, grouping, ranking, delivering, and displaying medical-named entities in radiology reports which can yield efficiency improvement in RISs. METHODS: A total of 5,000 reports were obtained from two medical institutions for this study. We proposed a neural network model called Multi-Embedding-BGRU-CRF (bidirectional gated recurrent unit-conditional random field) for medical-named entity recognition and rule-based methods for entity grouping and ranking. Furthermore, a methodology for delivering and displaying entities in RISs was presented. RESULTS: The proposed neural named entity recognition model has achieved a good F1 score of 95.88%. Entity ranking achieved a very high accuracy of 99.23%. The weakness of the system is the entity grouping approach which yield accuracy of 91.03%. The effectiveness of the overall solution was proved by an evaluation task performed by two clinicians based on the setup of actual clinical practice. CONCLUSIONS: The neural model shows great potential in extracting medical-named entities from radiology reports, especially for languages, that lack lexicons and natural language processing tools. The pipeline of extracting, grouping, ranking, delivering, and displaying medical-named entities could be a feasible solution to enhance RIS functionality by information extraction. The integration of information extraction and RIS has been demonstrated to be effective in improving the efficiency of reading radiology reports.

[Screening and verification of plasma biomarkers for stable angina pectoris: a differential proteomic analysis].

OBJECTIVE: To compare and analyze the differentially expressed plasma proteome between patients with stable angina pectoris (SAP) and healthy donors to identify the biomarkers for early diagnosis of SAP. METHODS: Plasma samples from 60 patients with SAP and 60 healthy controls were collected. Twenty samples (100 mL each) randomly selected from each group were pooled and after removing high-abundance proteins from the pooled plasma, two-dimensional gel electrophoresis (2DE) was performed to isolate the total proteins. The protein spots with more than 2 fold changes were selected after 2D analysis using software, and the differentially expressed proteins were identified by MALDI TOF/TOF mass spectrometer. ELISA was performed to detect hemoglobin subunit delta (HBD) levels in 40 randomly selected samples from each group for verification of the results of 2DE. RESULTS: A total of 7 differentially expressed proteins were found in plasma samples from patients with SAP, including 3 up regulated proteins (serum albumin, hemoglobin subunit alpha and hemoglobin subunit delta,) and 4 down?regulated ones (apolipoprotein L1, apolipoprotein C3, apolipoprotein E and complement C4B). ELISA results showed that HBD level was increased in SAP plasma, which was consistent with the results of 2DE. CONCLUSION: Patients with SAP have different plasma protein profiles from those of healthy controls, and HBD may serve as a potential specific biomarker for early diagnosis of SAP.

[Effects of polyethylene oxide on blood perfusion in the hind limbs of rats with chronic hindlimb ischemia].

OBJECTIVE: To evaluate the effects of polyethylene oxide (PEO) on blood perfusion in hind limb skeletal muscles in a rat model of chronic hind limb ischemia. METHDOS: Twelve rat models of chronic hind limb ischemia established by unilateral femoral artery ligation were randomized into PEO and control groups (n=6) and treated with intravenous infusion of PEO and saline through the internal jugular vein every other day for 2 weeks. Contrast-enhanced ultrasonography was performed after the treatments to evaluate the blood flow in the skeletal muscles at different time points and blood flow reserve in the ischemic hind limbs on day 28. RESULTS: Starting from 7 days after femoral artery ligation, blood flow in the ischemic hind limb skeletal muscles was significantly higher in PEO group than in the control group (P<0.05). On day 28, blood flow reserve in the ischemic hind limb was significantly higher (P=0.012), and blood volume was significantly increased in PEO group as compared that in the control group (P=0.024). CONCLUSIONS: PEO can increase blood flow, blood flow reserve and vascular volume in the hind limb skeletal muscles in rats with chronic hind limb ischemia, suggesting that PEO can promote angiogenesis and arterial formation by increasing blood flow shear stress to improve blood supply of ischemic hind limbs.

The Application of THz Spectroscopy and GA-BP in Methanol Concentration Detection.

At ambient temperature and atmospheric pressure, making use of a photoconductive-antenna terahertz time-domain spectrograph and a self-designed air chamber, the terahertz time-domain spectroscopy (THz-TDS) technique test of methanol gas in the range of 0.1~3.0 THz shows that the methanol gas has no obvious absorption peaks in the range of 0.1~3.0 THz and has obvious absorption peaks in the range of 0.1~1.0 THz. In order to improve the determination accuracy of the concentration of the methanol gas, the author detected 15 groups of methanol gas with different concentrations on the basis of the relationship between the strengths of 15 characteristic absorption peaks of different locations and the concentration of the methanol gas, and obtained the difference curve of the of the characteristic absorption peaks. Based on the function approximation of BP neural network, the author optimized the initial weights and biases of the BP neural network by using the GA the genetic algorithm, which has higher rate of convergence to prevent from getting into local optimum easily, and constructed the mathematical model with the purpose of predicting the methanol gas concentration. The test results show that the neural network is applicable to predict methanol gas in the volume concentration range of 0.028 3~0.424 6 m3·L(-1), the average relative standard deviation of the 2 sets of samples is 1.7%, the average recovery rate is 98%, the error precision of the neural network is 10(-1), and correlation coefficient of the measured values and the predicted values is 0.996 77. The test basically achieved ideal predicted results. The research results obtained experimental data of methanol gas in the terahertz frequency band and found that the method of combining terahertz time-domain spectroscopy with GA-BP neural network can effectively detect the volume concentration of methanol gas, and provided a new method for the detection of concentration of methanol gas.

Role of osteoprotegerin and its gene polymorphisms in the occurrence of left ventricular hypertrophy in essential hypertensive patients.

The aim of the study was to investigate the role of osteoprotegerin (OPG) in left ventricular hypertrophy (LVH) development in patients with essential hypertension (EH). A total of 1092 patients diagnosed with EH were recruited. The LVHs were determined and OPG gene polymorphisms were genotyped. Patients with LVH had a significantly higher mean serum OPG level than those without LVH. The 1181CC genotype carriers had significantly lower risk for LVH compared with GC and GG genotype carriers. The serum OPG level and OPG 1181 G>C polymorphism were found to be independent risk factors for the occurrence of LVH in hypertensive patients. In vitro study shows that OPG overexpression upregulates cell surface size, protein synthesis per cell, and hypertrophy- and fibrosis-related proteins in both cardiomyocytes and cardiac fibroblasts, whereas OPG inhibition can abolish the above-mentioned changes. Consistent with the in vitro data, our in vivo study revealed that the OPG administration induced the LVH in hypertensive rats. This study is the first to report the close association between OPG and LVH development in EH patients and the regulatory effect of OPG on cardiomyocytes and cardiac fibroblasts.

[Associations of circadian blood pressure rhythm with autonomic nervous system and myocardial energy expenditure level in patients with primary hypertension].

OBJECTIVE: To investigate the relationship among circadian blood pressure rhythm, autonomic nervous system and myocardial energy expenditure (MEE) level in patients with primary hypertension. METHODS: A total of 102 hypertensive and 45 normotensive subjects were recruited. According to blood pressure reduction rate at night, the hypertensive patients were divided into non-dipper group (NDH group, n=54) with a reduction rate of <10% and dipper group (DH group, n=48) with a reduction rate of ≥10%. The circadian blood pressure rhythm and heart rate variability were measured with ambulatory blood pressure monitoring and 24-hour electrocardiograph monitoring, respectively, and MEE was measured by Doppler echocardiography to analyze their correlations. RESULTS: SDNN, SDANN, SDNNindex, RMSSD, PNN50, and HF were significantly lower in the hypertensive patients than in the control group (P<0.05); these parameters, except for PNN50, were all significantly lower in NDH group than in DH group (P<0.05). The hypertensive patients had significantly higher MEE than the control group (P<0.05), and MEE was significantly higher in NDH group than in DH group (P<0.05). Bivariate correlation analysis showed significant correlations of MEE with SDANN in the hypertensive patients (P<0.01). CONCLUSION: Patients with primary hypertension, especially those in NDH group, have impaired autonomic nervous system function. The hypertensive patients in NDH group show a more prominent increase in MEE in relation to sympathetic activation, suggesting the importance of restoring circadian blood pressure rhythm in the treatment of hypertension.