Keratose Hydrogel Drives Differentiation of Cardiac Vascular Smooth Muscle Progenitor Cells: Implications in Ischemic Treatment.

Peripheral artery disease (PAD) is a common vascular disorder in the extremity of limbs with limited clinical treatments. Stem cells hold great promise for the treatment of PAD, but their therapeutic efficiency is limited due to multiple factors, such as poor engraftment and non-optimal selection of cell type. To date, stem cells from a variety of tissue sources have been tested, but little information is available regarding vascular smooth muscle cells (VSMCs) for PAD therapy. The present study examines the effects of keratose (KOS) hydrogels on c-kit+/CD31- cardiac vascular smooth muscle progenitor cell (cVSMPC) differentiation and the therapeutic potential of the resultant VSMCs in a mouse hindlimb ischemic model of PAD. The results demonstrated that KOS but not collagen hydrogel was able to drive the majority of cVSMPCs into functional VSMCs in a defined Knockout serum replacement (SR) medium in the absence of differentiation inducers. This effect could be inhibited by TGF-ß1 antagonists. Further, KOS hydrogel increased expression of TGF-ß1-associated proteins and modulated the level of free TGF-ß1 during differentiation. Finally, transplantation of KOS-driven VSMCs significantly increased blood flow and vascular densities of ischemic hindlimbs. These findings indicate that TGF-ß1 signaling is involved in KOS hydrogel-preferred VSMC differentiation and that enhanced blood flow are likely resulted from angiogenesis and/or arteriogenesis induced by transplanted VSMCs.

A flexible three-dimensional heterophase computed tomography hepatocellular carcinoma detection algorithm for generalizable and practical screening.

Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios (e.g., fully dynamic contrast-enhanced [DCE] studies, noncontrast [NC] plus venous phase [VP] abdominal studies, or NC-only studies). Each scenario presents its own clinical challenges that could benefit from computer-aided detection (CADe) tools. We investigate whether a single CADe model can be made flexible enough to handle different contrast protocols and whether this flexibility imparts performance gains. We developed a flexible three-dimensional deep algorithm, called heterophase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs with adjustable sensitivity depending on the clinical purpose. We trained HPVD on 771 DCE CT scans to detect HCCs and evaluated it on 164 positives and 206 controls. We compared performance against six clinical readers, including two radiologists, two hepatopancreaticobiliary surgeons, and two hepatologists. The area under the curve of the localization receiver operating characteristic for NC-only, NC plus VP, and full DCE CT yielded 0.71 (95% confidence interval [CI], 0.64-0.77), 0.81 (95% CI, 0.75-0.87), and 0.89 (95% CI, 0.84-0.93), respectively. At a high-sensitivity operating point of 80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measured physician performance. We also demonstrated performance improvements over more typical and less flexible nonheterophase detectors. Conclusion: A single deep-learning algorithm can be effectively applied to diverse HCC detection clinical scenarios, indicating that HPVD could serve as a useful clinical aid for at-risk and opportunistic HCC surveillance.

Interleukin Receptor Associated Kinase 1 Signaling and Its Association with Cardiovascular Diseases.

Toll-like receptors (TLRs) and interleukin-1 receptor (IL-1R) directly interact with intracellular interleukin receptor associated kinase (IRAK) family members to initialize innate immune and inflammatory responses following activation by pathogen-associated or host-derived elements. Although four IRAK family members [IRAK1, 2, 3 (i.e., IRAK-M), and 4] are involved in TLR and IL-1R signaling pathways, IL-1R > IRAK1 signaling appears to be the most studied pathway, with sufficient evidence to support its central role linking the innate immune response to the pathogenesis of various diseases, including cancers, metabolic disorders, and non-infectious immune disorders. However, IRAK1's involvement in cardiovascular diseases was only recently revealed and the detailed mechanism underling the pathogenesis of cardiovascular diseases, such as atherosclerosis, myocardial infarction, and heart failure (all non-infectious disorders), remains largely unknown with very limited publications to date. This review aims to summarize the overall roles of the IRAK family, especially IRAK1, in mediating the development of cardiovascular diseases.

Pathogenic Mechanisms of Trimethylamine N-Oxide-induced Atherosclerosis and Cardiomyopathy.

Trimethylamine N-oxide (TMAO) is a gut microbiota metabolite derived from trimethylamine- containing nutrient precursors such as choline, L-carnitine, and betaine, which are rich in many vegetables, fruits, nuts, dairy products, and meats. An increasing number of clinical studies have demonstrated a strong relationship between elevated plasma TMAO levels and adverse cardiovascular events. It is commonly agreed that TMAO acts as an independent risk factor and a prognostic index for patients with cardiovascular disease. Although most animal (mainly rodent) data support the clinical findings, the mechanisms by which TMAO modulates the cardiovascular system are still not well understood. In this context, we provide an overview of the potential mechanisms underlying TMAO-induced cardiovascular diseases at the cellular and molecular levels, with a focus on atherosclerosis. We also address the direct effects of TMAO on cardiomyocytes (a new and under-researched area) and finally propose TMAO as a potential biomarker and/or therapeutic target for diagnosis and treatment of patients with cardiovascular disease.

Common differentially expressed genes and pathways correlating both coronary artery disease and atrial fibrillation.

Coronary artery disease (CAD) and atrial fibrillation (AF) share common risk factors, such as hypertension and diabetes. The patients with CAD often suffer concomitantly AF, but how two diseases interact with each other at cellular and molecular levels remain largely unknown. The present study aims to dissect the common differentially expressed genes (DEGs) that are concurrently associated with CAD and AF. Two datasets [GSE71226 for CAD) and GSE31821 for AF] were analyzed with GEO2R and Venn Diagram to identify the DEGs. Signaling pathways, gene enrichments, and protein-protein interactions (PPI) of the identified common DEGs were further analyzed with Kyoto Encyclopedia of Gene and Genome (KEGG), Database for Annotation, Visualization and Integrated Discovery (DAVID), and Search Toll for the Retrieval of Interacting Genes (STRING). 565 up- and 1367 down-regulated genes in GSE71226 and 293 up- and 68 down-regulated genes in GSE31821 were identified. Among those, 21 common DEGs were discovered from both datasets, which lead to the findings of 4 CAD and 21 AF pathways, 3 significant gene enrichments (intracellular cytoplasm, protein binding, and vascular labyrinthine layer), and 3 key proteins (membrane metallo-endopeptidase (MME), transferrin receptor 1 (TfR1), and Lysosome-associated membrane glycoprotein 1 (LAMP1)). Together, these data implied that these three proteins may play a central role in development of both CAD and AF.

Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT.

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.1 1https://github.com/viggin/DeepLesion_manual_test_set.

Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale.

The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester-a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.

PET Index of Bone Glucose Metabolism (PIBGM) Classification of PET/CT Data for Fever of Unknown Origin Diagnosis.

OBJECTIVES: Fever of unknown origin (FUO) remains a challenge in clinical practice. Fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is helpful in diagnosing the etiology of FUO. This paper aims to develop a completely automatic classification method based on PET/CT data for the computer-assisted diagnosis of FUO. METHODS: We retrospectively analyzed the FDG PET/CT scan of 175 FUO patients, 79 males and 96 females. The final diagnosis of all FUO patients was achieved through pathology or clinical evaluation, including 108 normal patients and 67 FUO patients. CT anatomic information was used to acquire bone functional information from PET images. The skeletal system of FUO patients was classified by analyzing the standardized uptake value (SUV) and the PET index of bone glucose metabolism (PIBGM). The SUV distributions in the bone marrow and the bone cortex were also studied in detail. RESULTS: The SUV and PIBGM of the bone marrow only slightly differed between the FUO patients and normal people, whereas the SUV of whole bone structures and the PIBGM of the bone cortex significantly differed between the normal people and FUO patients. The method detected 43 patients from 67 FUO patients, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 64.18%, 95%, 93.48%, 72.73%, and 83.33%, respectively. CONCLUSION: The experimental results demonstrate that the study can achieve automatic classification of FUO patients by the proposed novel biomarker of PIBGM, which has the potential to be utilized in clinical practice.

Kinetic analysis of dynamic (11)C-acetate PET/CT imaging as a potential method for differentiation of hepatocellular carcinoma and benign liver lesions.

OBJECTIVE: The kinetic analysis of (11)C-acetate PET provides more information than routine one time-point static imaging. This study aims to investigate the potential of dynamic (11)C-acetate hepatic PET imaging to improve the diagnosis of hepatocellular carcinoma (HCC) and benign liver lesions by using compartmental kinetic modeling and discriminant analysis. METHODS: Twenty-two patients were enrolled in this study, 6 cases were with well-differentiated HCCs, 7 with poorly-differentiated HCCs and 9 with benign pathologies. Following the CT scan, all patients underwent (11)C-acetate dynamic PET imaging. A three-compartment irreversible dual-input model was applied to the lesion time activity curves (TACs) to estimate the kinetic rate constants K1-k3, vascular fraction (VB) and the coefficient α representing the relative hepatic artery (HA) contribution to the hepatic blood supply on lesions and non-lesion liver tissue. The parameter Ki (=K1×k3/(k2 + k3)) was calculated to evaluate the local hepatic metabolic rate of acetate (LHMAct). The lesions were further classified by discriminant analysis with all the above parameters. RESULTS: K1 and lesion to non-lesion standardized uptake value (SUV) ratio (T/L) were found to be the parameters best characterizing the differences among well-differentiated HCC, poorly-differentiated HCC and benign lesions in stepwise discriminant analysis. With discriminant functions consisting of these two parameters, the accuracy of lesion prediction was 87.5% for well-differentiated HCC, 50% for poorly-differentiated HCC and 66.7% for benign lesions. The classification was much better than that with SUV and T/L, where the corresponding classification accuracy of the three kinds of lesions was 57.1%, 33.3% and 44.4%. CONCLUSION: (11)C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis. The discriminant analysis using static and kinetic parameters appears to be a very helpful method for clinical liver masses diagnosis and staging.

Diffuse Hepatic FDG Activity From Nonmalignant Etiologies.

Diffuse increased ¹8F-FDG activity is commonly caused by malignant involvement. We describe here cases of diffuse, uniformly increased ¹8F-FDG activity due to nonmalignant illnesses in 2 patients: one had parvovirus B19 infection, whereas the other had porphyria, a heme synthesis disorder.