AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

A Novel Piggyback Strategy for mRNA Delivery Exploiting Adenovirus Entry Biology.

Molecular therapies exploiting mRNA vectors embody enormous potential, as evidenced by the utility of this technology for the context of the COVID-19 pandemic. Nonetheless, broad implementation of these promising strategies has been restricted by the limited repertoires of delivery vehicles capable of mRNA transport. On this basis, we explored a strategy based on exploiting the well characterized entry biology of adenovirus. To this end, we studied an adenovirus-polylysine (AdpL) that embodied "piggyback" transport of the mRNA on the capsid exterior of adenovirus. We hypothesized that the efficient steps of Ad binding, receptor-mediated entry, and capsid-mediated endosome escape could provide an effective pathway for transport of mRNA to the cellular cytosol for transgene expression. Our studies confirmed that AdpL could mediate effective gene transfer of mRNA vectors in vitro and in vivo. Facets of this method may offer key utilities to actualize the promise of mRNA-based therapeutics.

Advanced genetic engineering to achieve in vivo targeting of adenovirus utilizing camelid single domain antibody.

For the developing field of gene therapy the successful address of the basic requirement effective gene delivery has remained a critical barrier. In this regard, the "Holy Grail" vector envisioned by the field's pioneers embodied the ability to achieve efficient and specific in vivo gene delivery. Functional linkage of antibody selectivity with viral vector efficiency represented a logical strategy but has been elusive. Here we have addressed this key issue by developing the technical means to pair antibody-based targeting with adenoviral-mediated gene transfer. Our novel method allows efficient and specific gene delivery. Importantly, our studies validated the achievement of this key vectorology mandate in the context of in vivo gene delivery. Vectors capable of effective in vivo delivery embody the potential to dramatically expand the range of successful gene therapy cures.

Targeting Tumor Neoangiogenesis via Targeted Adenoviral Vector to Achieve Effective Cancer Gene Therapy for Disseminated Neoplastic Disease.

The application of cancer gene therapy has heretofore been restricted to local, or locoregional, neoplastic disease contexts. This is owing to the lack of gene transfer vectors, which embody the requisite target cell selectivity in vivo required for metastatic disease applications. To this end, we have explored novel vector engineering paradigms to adapt adenovirus for this purpose. Our novel strategy exploits three distinct targeting modalities that operate in functional synergy. Transcriptional targeting is achieved via the hROBO4 promoter, which restricts transgene expression to proliferative vascular endothelium. Viral binding is modified by incorporation of an RGD4C peptide in the HI loop of the fiber knob for recognition of cellular integrins. Liver sequestration is mitigated by ablation of factor X binding to the major capsid protein hexon by a serotype swap approach. The combination of these technologies into the context of a single-vector agent represents a highly original approach. Studies in a murine model of disseminated cancer validated the in vivo target cell selectivity of our vector agent. Of note, clear gains in therapeutic index accrued these vector modifications. Whereas there is universal recognition of the value of vector targeting, very few reports have validated its direct utility in the context of cancer gene therapy. In this regard, our article validates the direct gains that may accrue these methods in the stringent delivery context of disseminated neoplastic disease. Efforts to improve vector targeting thus represent a critical direction to fully realize the promise of cancer gene therapy.

Soluble RAGE attenuates AngII-induced endothelial hyperpermeability by disrupting HMGB1-mediated crosstalk between AT1R and RAGE.

Increased endothelial permeability, one of the earliest signs of endothelial dysfunction, is associated with the development of cardiovascular diseases such as hypertension and atherosclerosis. Recent studies suggest that the receptor for advanced glycation end products (RAGE) regulates endothelial permeability in inflammation. In the present study, we investigated the regulatory mechanism of RAGE in endothelial hyperpermeability induced by angiotensin II (Ang II), a well-known inflammatory mediator, and the potential therapeutic effect of soluble RAGE (sRAGE), a decoy receptor for RAGE ligands. For in vitro studies, Ang II-treated human umbilical vein endothelial cells (HUVECs) were treated with siRNA specific to either RAGE or sRAGE to disrupt RAGE-mediated signaling. Endothelial permeability was estimated using FITC-labeled dextran 40 and a resistance meter. To evaluate intercellular junction disruption, VE-cadherin expression was examined by western blotting and immunocytochemistry. Ang II increased the expression of the Ang II type 1 receptor (AT1R) and RAGE, and this increase was inhibited by sRAGE. sRAGE prevented Ang II-induced VE-cadherin disruption in HUVECs. For in vivo studies, Ang II-infused, atherosclerosis-prone apolipoprotein E knockout mice were utilized. Endothelial permeability was assessed by Evans blue staining of the aorta. Ang II increased endothelial barrier permeability, and this effect was significantly attenuated by sRAGE. Our data demonstrate that blockade of RAGE signaling using sRAGE attenuates Ang II-induced endothelial barrier permeability in vitro and in vivo and indicate the therapeutic potential of sRAGE in controlling vascular permeability under pathological conditions.

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

OBJECTIVE: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MATERIALS AND METHODS: MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. RESULTS: Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. CONCLUSION: The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

A Novel Inhibitor IDPP Interferes with Entry and Egress of HCV by Targeting Glycoprotein E1 in a Genotype-Specific Manner.

Despite recent advances in curing chronic hepatitis C (CHC), the high economic burden to therapy, viral drug resistance, difficult to treat hepatitis C virus (HCV) genotypes and patient groups are still of concern. To address this unmet medical needs, we devised strategies to identify novel viral interventions through target-free high-throughput screening of small molecules utilizing a phenotypic-based HCV infection assay. Thereby, a very potent (EC50 46 ± 26 pM) iminodipyridinopyrimidine (IDPP) drug candidate was selected, and confirmed in primary human hepatocytes (EC50 0.5 nM). IDPP mainly targets a post-attachment step of HCV without affecting endosomal acidification, prevents the secretion of infectious particles and viral cell-to-cell spread. The putative molecular target of IDPP is glycoprotein E1, as revealed by selection for viral drug resistance (Gly-257-Arg). IDPP was synergistic in combination with FDA-approved HCV drugs and inhibited pre-existing resistant HCV strains induced by today's therapies. Interestingly, IDPP exclusively inhibited HCV genotype 2. However, we identified the genotype-specificity determining region in E1 and generated HCV genotype 1 susceptible to IDPP by changing one amino acid in E1 (Gln-257-Gly). Together, our results indicate an opportunity to provide an alternative treatment option for CHC and will shed light on the poorly understood function of HCV glycoprotein E1.

Micrococcin P1, a naturally occurring macrocyclic peptide inhibiting hepatitis C virus entry in a pan-genotypic manner.

Hepatitis C virus (HCV) is considered a major public health concern worldwide. Despite recent advances in curing chronic hepatitis C, unmet medical needs still remain, especially due to the high economic burden of therapies. Accordingly, our study aimed to identify affordable novel HCV inhibitors by screening of natural product compound libraries. We identified micrococcin P1, a macrocyclic peptide antibiotic, inhibiting HCV entry in a pan-genotypic manner with an EC50 range of 0.1-0.5 µM. Micrococcin P1 interfered with HCV entry at an attachment step. Furthermore, micrococcin P1 efficiently inhibited HCV spread by blocking cell-free infection as well as cell-to-cell transmission, without affecting the secretion of infectious virions. Interestingly, the putative molecular target of micrococcin P1 is glycoprotein E2 (IIe-630-Thr), as revealed by selection for viral drug resistance. In addition, micrococcin P1 inhibited sofosbuvir-resistant HCV strains and showed synergy in combination with selected HCV drugs, suggesting an alternative treatment paradigm for patients. In conclusion, we identified micrococcin P1 as specifically inhibiting entry of all HCV genotypes and demonstrated that micrococcin P1 potentially could add value to therapies in combination with current HCV interventions.

Segmentation of interest region in medical volume images using geometric deformable model.

In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccard's measure score (72.52%-94.17%) and lower Hausdorff distance (1.2654 mm-3.1527 mm) than the other methods such as mean speed (67.67%-93.36% and 1.3361mm-3.4463 mm), mean-variance speed (63.44%-94.72% and 1.3361 mm-3.4616 mm), and edge-based speed (0.76%-42.44% and 3.8010 mm-6.5389 mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.