The efficacy of coaxial percutaneous iodine-125 seed implantation combined with arterial infusion chemotherapy for advanced pancreatic cancer: a randomized clinical trial.

BACKGROUND: This study aimed to evaluate the clinical efficacy of coaxial percutaneous Iodine-125 (125I) seed implantation in combination with arterial infusion chemotherapy for the treatment of advanced pancreatic cancer (PC) through a randomized controlled trial. METHODS: A total of 101 patients with advanced PC were randomized into two groups: control group treated with systemic intravenous chemotherapy and experimental group that received 125I seed implantation in combination with arterial infusion chemotherapy. Outcomes, including tumor control, abdominal pain relief, and survival time were compared between these two groups (Trial Registration No. KYKT2018-65). RESULTS: Pretreatment abdominal pain scores were comparable between the two groups, whereas the abdominal pain scores at 1- and 3-month post-treatment were significantly lower in the control group than those in the experimental group (1-month: 3.74 ± 1.54 vs. 4.48 ± 1.46, p = .015; 3-month: 3.64 ± 2.21 vs. 5.40 ± 1.56, p < .001). At 3-month post-treatment, computed tomography (CT) scan revealed a significantly higher disease control rate in the experimental group than that in the control group (94.0% vs. 74.5%, p = .007). The median survival time in the experimental group was significantly longer than that in the control group (15-month vs. 9-month, p < .001). CONCLUSION: The combination of coaxial percutaneous 125I seed implantation with arterial infusion chemotherapy could significantly alleviate abdominal pain, improve tumor control rates, and prolong survival time in patients with advanced PC.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation.

Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents. Areas Covered: Established clinically relevant biomarkers are used in surveillance, as prognostic indicators, and sometimes for treatment planning; however, the inability to intercept early recurrence or predict upfront resistance to treatment remains a significant challenge that limits the selection of patients for adjuvant therapy. We discuss attempts at intercepting early failure. We examine biomarkers that have made it into the clinic where they are used for treatment monitoring and management alteration, and novel biomarkers that lead the field with targeted adjuvant therapy seeking to harness these. Expert Opinion: Given the growth of data correlating interventions with omic analysis toward identifying biomarkers of radiation resistance, more robust markers of recurrence that link to biology will increasingly be leveraged toward targeted adjuvant therapy to make a successful transition to the clinic in the coming years.

pH-Sensitive Nanoparticles for Colonic Delivery Anti-miR-301a in Mouse Models of Inflammatory Bowel Diseases.

Though the anti-miR-301a (anti-miR) is a promising treatment strategy for inflammatory bowel disease (IBD), the degradability and the poor targeting of the intestine are a familiar issue. This study aimed to develop a multifunctional oral nanoparticle delivery system loaded with anti-miR for improving the targeting ability and the therapeutic efficacy. The HA-CS/ES100/PLGA nanoparticles (HCeP NPs) were prepared using poly (lactic-co-glycolic acid) copolymer (PLGA), enteric material Eudragit®S100 (ES100), chitosan (CS), and hyaluronic acid (HA). The toxicity of nanoparticles was investigated via the Cell Counting Kit-8, and the cellular uptake and inflammatory factors of nanoparticles were further studied. Moreover, we documented the colon targeting and pharmacodynamic properties of nanoparticles. The nanoparticles with uniform particle size exhibited pH-sensitive release, favorable gene protection, and storage stability. Cytology experiments showed that anti-miR@HCeP NPs improved the cellular uptake through HA and reduced pro-inflammatory factors. Administering anti-miR@HCeP NPs orally to IBD mice markedly reduced their pro-inflammatory factors levels and disease activity indices. We also confirmed that anti-miR@HCeP NPs mostly accumulated in the colon site, and effectively repaired the intestinal barrier, as well as relieved intestinal inflammation. The above nanoparticle is a candidate of the treatment for IBD due to its anti-inflammatory properties.

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics.

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

The value of the wireless stethoscope in patients with COVID-19 infection in a makeshift hospital.

OBJECTIVE: When COVID-19 sweeps the world, traditional stethoscopes are seen as infectious agents and then the use of stethoscopes is limited especially when health providers were in their personal protective equipment. These reasons led to the ignoring of the values of stethoscopes during pandemics. This study aims to explore the value of wireless stethoscopes in patients of a makeshift hospital. MATERIAL AND METHODS: 200 consecutive hospitalized patients with confirmed SARS-CoV-2 at Lingang Makeshift Hospital in Shanghai, China, were enrolled from April 10 to May 10, 2022 (Trial Registration Number: ChiCTR2000038272,2020/9/15). They were randomly divided into two groups. In group A (n = 100), patients were examined without a stethoscope. In group B (n = 100), lung breath sounds and heart sounds were examined with a wireless stethoscope, and positive signs were recorded. The duration of cough and tachycardia symptoms, as well as emergency cases, were compared between the two groups. In addition, the pressure, anxiety, and depression of patients in the two groups were investigated using the DAS-21 questionnaire scale, to observe the psychological impact of the stethoscope-based doctor-patient communication on patients in the makeshift hospital. RESULTS: There was no significant difference in baseline characteristics between the two groups. In group B, some significant positive signs were detected by wireless stethoscopes, including pulmonary rales and tachycardia, etc. Moreover, the therapeutic measures based on these positive signs effectively alleviated the symptoms of cough and tachycardia, which showed that the duration of symptoms was significantly shorter than that of group A (cough: 2.8 ± 0.9 vs. 3.6 ± 0.9; palpitation: 1.4 ± 0.7 vs. 2.6 ± 0.7). In particular, the number of emergency cases in group B is less than that in group A (1% vs. 3%), and the severity is lower. Notably, stethoscope-based doctor-patient communication was found to be effective in alleviating psychological measures of group B patients. CONCLUSION: Wireless stethoscopes in makeshift hospitals can avoid cross-infections and detect more valuable positive signs, which can help health providers make accurate decisions and relieve patients' symptoms more quickly. Moreover, stethoscope-based doctor-patient communication can diminish the psychological impacts of the epidemic on isolated patients in makeshift hospitals. Trial registration This study was registered in the Chinese Clinical Trial (ChiCTR2000038272) at http://www.chictr.org.cn . http://www.chinadrugtrials.org.cn/clinicaltrials.searchlistdetail.dhtml .

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.

The serum soluble scavenger with 5 domains levels: A novel biomarker for individuals with heart failure.

Background: We aimed to explore the relationship between the serum Soluble Scavenger with 5 Domains (SSC5D) levels and heart failure (HF). Methods and Results: We retrospectively enrolled 276 patients diagnosed with HF or normal during hospitalization in Shanghai General Hospital between September 2020 and December 2021. Previously published RNA sequencing data were re-analyzed to confirm the expression profile of SSC5D in failing and non-failing human and mouse heart tissues. Quantitative real-time polymerase chain reaction assay was used to quantify Ssc5d mRNA levels in murine heart tissue after myocardial infarction and transverse aortic constriction surgery. To understand the HF-induced secreted proteins profile, 1,755 secreted proteins were investigated using human dilated cardiomyopathy RNA-seq data, and the results indicated that SSC5D levels were significantly elevated in failing hearts compared to the non-failing. Using single-cell RNA sequencing data, we demonstrated that Ssc5d is predominantly expressed in cardiac fibroblasts. In a murine model of myocardial infarction or transverse aortic constriction, Ssc5d mRNA levels were markedly increased compared with those in the sham group. Similarly, serum SSC5D levels were considerably elevated in the HF group compared with the control group [15,789.35 (10,745.32-23,110.65) pg/mL, 95% CI (16,263.01-19,655.43) vs. 8,938.72 (6,154.97-12,778.81) pg/mL, 95% CI (9,337.50-11,142.93); p < 0.0001]. Moreover, serum SSC5D levels were positively correlated with N-terminal pro-B-type natriuretic peptide (R = 0.4, p = 7.9e-12) and inversely correlated with left ventricular ejection fraction (R = -0.46, p = 9.8e-16). Conclusion: We concluded that SSC5D was a specific response to HF. Serum SSC5D may function as a novel biomarker and therapeutic target for patients with HF.

The role of KNTC1 in the regulation of proliferation, migration and tumorigenesis in colorectal cancer.

BACKGROUND: Current findings have revealed that kinetochore-associated protein 1 (KNTC1) plays a pivotal role in the carcinogenesis of numerous types of cancer. This study was undertaken to inspect the role and probable underlying mechanisms of KNTC1 during the genesis and progression of colorectal cancer. METHODS: Immunohistochemistry was implemented to determine KNTC1 expression levels in colorectal cancer tissues and para-carcinoma tissues. The association between KNTC1 expression profiles and several clinicopathological traits of colorectal cancer cases was examined employing Mann-Whitney U, Spearman, and Kaplan-Meier analyses. To track the proliferation, apoptosis, cell cycle, migration and in vivo carcinogenesis of colorectal cancer cells, KNTC1 was knocked down in colorectal cell line via RNA interference. To investigate the potential mechanism, the expression profile alterations of associated proteins were detected using human apoptosis antibody arrays, and verified by Western blot analysis. RESULTS: In colorectal cancer tissues, KNTC1 was substantially expressed, and it was associated with the pathological grade as well as overall survival rate of the disease. The knockdown of KNTC1 was able to inhibit proliferation, cell cycle, migration and in vivo tumorigenesis of colorectal cancer cells, but promote apoptosis. CONCLUSIONS: KNTC1 is a key player in the emergence of colorectal cancer and may serve as an early diagnostic indicator of precancerous lesions.

Canine model of electrical conduction recurrence after radiofrequency catheter ablation constructed by CARTO3 and preliminary application evaluation of DOX-L.

BACKGROUND: Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical conduction after ablation is essential before we can explore the mechanisms involved or develop new therapeutic strategies. METHODS: Eighteen beagles aged 12 to 24 months were randomly assigned to this study. RFCA ablation of the right atrial free wall was performed. Then, electrical block and conduction recovery in the ablation area were evaluated using voltage mapping and pacing tests assisted by CARTO3 system. Finally, liposome doxorubicin (DOX-L) was intravenously injected after ablation to investigate the effect of DOX-L on this animal model. RESULTS: The conduction block (CB) rates at 5 min after ablation were 16.7%, 83.3%, and 100%, corresponding to 30w, 35w, and 40w power, respectively. However, after 20 min, the rate of CB was 0%, 33.3%, and 75%; thus, the combined success rate of CB and conduction recurrence was 16.7%, 50%, and 25%, respectively. The optimal ablation parameter is 35 W for 20 s, based on the CB rate, REC rate. After 10 days of ablation, the residual conduction recurrence rate was as high as 83.3% in the RFCA alone group, whereas there was no recurrence with RFCA combined with DOX-L treatment. CONCLUSIONS: The novel model accurately simulated the electrical conduction recurrence after cardiac radiofrequency ablation. RFCA combined with DOX-L treatment dramatically reduces the recurrence rate of electrical conduction after ablation.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future.

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.

Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.

Machine learning based survival prediction in Glioma using large-scale registry data.

Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.

The Next Frontier in Health Disparities-A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back.

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women's data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

DNA Walkers for Biosensing Development.

The ability to design nanostructures with arbitrary shapes and controllable motions has made DNA nanomaterials used widely to construct diverse nanomachines with various structures and functions. The DNA nanostructures exhibit excellent properties, including programmability, stability, biocompatibility, and can be modified with different functional groups. Among these nanoscale architectures, DNA walker is one of the most popular nanodevices with ingenious design and flexible function. In the past several years, DNA walkers have made amazing progress ranging from structural design to biological applications including constructing biosensors for the detection of cancer-associated biomarkers. In this review, the key driving forces of DNA walkers are first summarized. Then, the DNA walkers with different numbers of legs are introduced. Furthermore, the biosensing applications of DNA walkers including the detection- of nucleic acids, proteins, ions, and bacteria are summarized. Finally, the new frontiers and opportunities for developing DNA walker-based biosensors are discussed.

Automated glioma grading on conventional MRI images using deep convolutional neural networks.

PURPOSE: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). METHODS: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. RESULTS: The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. CONCLUSIONS: Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.

Inhibition of circHIPK3 prevents angiotensin II-induced cardiac fibrosis by sponging miR-29b-3p.

BACKGROUND: Circular RNAs (circRNAs) are emerging as powerful regulators of cardiac development and disease. Nevertheless, detailed studies describing circRNA-mediated regulation of cardiac fibroblasts (CFs) biology and their role in cardiac fibrosis remain limited. METHODS: PCR and Sanger sequencing were performed to identify the expression of circHIPK3 in CFs. Edu corporation assays, Transwell migration assays, and immunofluorescence staining assays were conducted to detect the function of circHIPK3 in CFs in vitro. Bioinformatics analysis, dual luciferase activity assays, RNA immunoprecipitation, and fluorescent in situ hybridization experiments were conducted to investigate the mechanism of circHIPK3-mediated cardiac fibrosis. Echocardiographic analysis, Sirius Red staining and immunofluorescence staining were performed to investigate the function of circHIPK3 in angiotensin II (Ang II) induced cardiac fibrosis in vivo. RESULTS: circHIPK3 expression markedly increased in CFs and heart tissues after the treatment of Ang II. circHIPK3 silencing attenuates CFs proliferation, migration and the upregulation of a-SMA expression levels induced by Ang II in vitro. circHIPK3 acted as a miR-29b-3p sponge and overexpression of circHIPK3 effectively reverses miR-29b-3p-induced inhibition of CFs proliferation and migration and alters the expression levels of miR-29b-3p targeting genes (a-SMA, COL1A1, COL3A1) in vitro. Combination of circHIPK3 silencing and miR-29b-3p overexpression had a stronger effect on cardiac fibrosis suppression in vivo than did circHIPK3 silencing or miR-29b-3p overexpression alone. CONCLUSIONS: Our data suggest that circHIPK3 serves as a miR-29b-3p sponge to regulate CF proliferation, migration and development of cardiac fibrosis, revealing a potential new target for the prevention of Ang II-induced cardiac fibrosis.

A data-efficient method for local noise power spectrum (NPS) estimation in FDK-reconstructed 3D cone-beam CT.

PURPOSE: For computed tomography (CT) systems in which noise is nonstationary, a local noise power spectrum (NPS) is often needed to characterize its noise property. We have previously developed a data-efficient radial NPS method to estimate the two-dimensional (2D) local NPS for filtered back projection (FBP)-reconstructed fan-beam CT utilizing the polar separability of CT NPS. In this work, we extend this method to estimate three-dimensional (3D) local NPS for feldkamp-davis-kress (FDK)-reconstructed cone-beam CT (CBCT) volumes. METHODS: Starting from the 2D polar separability, we analyze the CBCT geometry and FDK image reconstruction process to derive the 3D expression of the polar separability for CBCT local NPS. With the polar separability, the 3D local NPS of CBCT can be decomposed into a 2D radial NPS shape function and a one-dimensional (1D) angular amplitude function with certain geometrical transforms. The 2D radial NPS shape function is a global function characterizing the noise correlation structure, while the 1D angular amplitude function is a local function reflecting the varying local noise amplitudes. The 3D radial local NPS method is constructed from the polar separability. We evaluate the accuracy of the 3D radial local NPS method using simulated and real CBCT data by comparing the radial local NPS estimates to a reference local NPS in terms of normalized mean squared error (NMSE) and a task-based performance metric (lesion detectability). RESULTS: In both simulated and physical CBCT examples, a very small NMSE (<5%) was achieved by the radial local NPS method from as few as two scans, while for the traditional local NPS method, about 20 scans were needed to reach this accuracy. The results also showed that the detectability-based system performances computed using the local NPS estimated with the NPS method developed in this work from two scans closely reflected the actual system performance. CONCLUSIONS: The polar separability greatly reduces the data dimensionality of the 3D CBCT local NPS. The radial local NPS method developed based on this property is shown to be capable of estimating the 3D local NPS from only two CBCT scans with acceptable accuracy. The minimum data requirement indicates the potential utility of local NPS in CBCT applications even for clinical situations.