The Antiviral Effect of Berdazimer Sodium on Molluscum Contagiosum Virus Using a Novel In Vitro Methodology.

Molluscum contagiosum (MC) is characterized by skin lesions containing the highly contagious molluscum contagiosum poxvirus (MCV). MCV primarily infects children, with one US Food and Drug Administration (FDA)-approved drug-device treatment in use but no approved medications. Assessing antivirals is hindered by the inability of MCV to replicate in vitro. Here, we use vaccinia virus as a surrogate to provide evidence of the anti-poxvirus properties of berdazimer sodium, a new chemical entity, and the active substance in berdazimer gel, 10.3%, a nitric oxide-releasing topical in phase 3 development for the treatment of MC. We show that berdazimer sodium reduced poxvirus replication and, through a novel methodology, demonstrate that cells infected with drug-treated MCV virions have reduced early gene expression. Specifically, this is accomplished by studying the nuclear factor kappa-light-chain-enhancer of activated B cell (NF-kB)-blocking protein MC160 as an example of an early gene. The results provide a plausible unique antiviral mechanism of action supporting increased MCV resolution observed in patients treated with berdazimer gel, 10.3% and describe a novel methodology that overcomes limitations in investigating MCV response in vitro to a potential new MC topical medication.

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/.

Hierarchical graph representations in digital pathology.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.

Nitric oxide loading reduces sickle red cell adhesion and vaso-occlusion in vivo.

Sickle red blood cells (SSRBCs) are adherent to the endothelium, activate leukocyte adhesion, and are deficient in bioactive nitric oxide (NO) adducts such as S-nitrosothiols (SNOs), with reduced ability to induce vasodilation in response to hypoxia. All these pathophysiologic characteristics promote vascular occlusion, the hallmark of sickle cell disease (SCD). Loading hypoxic SSRBCs in vitro with NO followed by reoxygenation significantly decreased epinephrine-activated SSRBC adhesion to the endothelium, the ability of activated SSRBCs to mediate leukocyte adhesion in vitro, and vessel obstruction in vivo. Because transfusion is frequently used in SCD, we also determined the effects of banked (SNO-depleted) red blood cells (RBCs) on vaso-occlusion in vivo. Fresh or 14-day-old normal RBCs (AARBCs) reduced epinephrine-activated SSRBC adhesion to the vascular endothelium and prevented vaso-occlusion. In contrast, AARBCs stored for 30 days failed to decrease activated SSRBC adhesivity or vaso-occlusion, unless these RBCs were loaded with NO. Furthermore, NO loading of SSRBCs increased S-nitrosohemoglobin and modulated epinephrine's effect by upregulating phosphorylation of membrane proteins, including pyruvate kinase, E3 ubiquitin ligase, and the cytoskeletal protein 4.1. Thus, abnormal SSRBC NO/SNO content both contributes to the vaso-occlusive pathophysiology of SCD, potentially by affecting at least protein phosphorylation, and is potentially amenable to correction by (S)NO repletion or by RBC transfusion.

BACH: Grand challenge on breast cancer histology images.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

A New Unsupervised Approach for Segmenting and Counting Cells in High-Throughput Microscopy Image Sets.

New technological advances in automated microscopy have given rise to large volumes of data, which have made human-based analysis infeasible, heightening the need for automatic systems for high-throughput microscopy applications. In particular, in the field of fluorescence microscopy, automatic tools for image analysis are making an essential contribution in order to increase the statistical power of the cell analysis process. The development of these automatic systems is a difficult task due to both the diversification of the staining patterns and the local variability of the images. In this paper, we present an unsupervised approach for automatic cell segmentation and counting, namely CSC, in high-throughput microscopy images. The segmentation is performed by dividing the whole image into square patches that undergo a gray level clustering followed by an adaptive thresholding. Subsequently, the cell labeling is obtained by detecting the centers of the cells, using both distance transform and curvature analysis, and by applying a region growing process. The advantages of CSC are manifold. The foreground detection process works on gray levels rather than on individual pixels, so it proves to be very efficient. Moreover, the combination of distance transform and curvature analysis makes the counting process very robust to clustered cells. A further strength of the CSC method is the limited number of parameters that must be tuned. Indeed, two different versions of the method have been considered, CSC-7 and CSC-3, depending on the number of parameters to be tuned. The CSC method has been tested on several publicly available image datasets of real and synthetic images. Results in terms of standard metrics and spatially aware measures show that CSC outperforms the current state-of-the-art techniques.

Automatic segmentation of pigment deposits in retinal fundus images of Retinitis Pigmentosa.

Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images. Fundus image analysis is a suitable tool in daily practice for an early detection of retinal diseases and the monitoring of their progression. Moreover, the fundus camera represents a low-cost and easy-access diagnostic system, which can be employed in resource-limited regions and countries. The fundus images of a patient suffering from retinitis pigmentosa are characterized by an attenuation of the vessels, a waxy disc pallor and the presence of pigment deposits. Considering that several methods have been proposed for the analysis of retinal vessels and the optic disk, this work focuses on the automatic segmentation of the pigment deposits in the fundus images. The image distortions are attenuated by applying a local pre-processing. Next, a watershed transformation is carried out to produce homogeneous regions. Working on regions rather than on pixels makes the method very robust to the high variability of pigment deposits in terms of color and shape, so allowing the detection even of small pigment deposits. The regions undergo a feature extraction procedure, so that a region classification process is performed by means of an outlier detection analysis and a rule set. The experiments have been performed on a dataset of images of patients suffering from retinitis pigmentosa. Although the images present a high variability in terms of color and illumination, the method provides a good performance in terms of sensitivity, specificity, accuracy and the F-measure, whose values are 74.43, 98.44, 97.90, 59.04, respectively.

Wearable Improved Vision System for Color Vision Deficiency Correction.

Color vision deficiency (CVD) is an extremely frequent vision impairment that compromises the ability to recognize colors. In order to improve color vision in a subject with CVD, we designed and developed a wearable improved vision system based on an augmented reality device. The system was validated in a clinical pilot study on 24 subjects with CVD (18 males and 6 females, aged 37.4 ± 14.2 years). The primary outcome was the improvement in the Ishihara Vision Test score with the correction proposed by our system. The Ishihara test score significantly improved ([Formula: see text]) from 5.8 ± 3.0 without correction to 14.8 ± 5.0 with correction. Almost all patients showed an improvement in color vision, as shown by the increased test scores. Moreover, with our system, 12 subjects (50%) passed the vision color test as normal vision subjects. The development and preliminary validation of the proposed platform confirm that a wearable augmented-reality device could be an effective aid to improve color vision in subjects with CVD.

S-Nitrosylated fetal hemoglobin in neonatal human blood.

BACKGROUND: Nitric oxide (NO) and its derivatives play important roles in the cardiopulmonary transition upon birth and in other oxygen-sensitive developmental milestones. One mechanism for the coupling of oxygen sensing and signaling by NO species is via the formation of an S-nitrosothiol (SNO) moiety on hemoglobin (Hb, forming SNO-Hb) and its release from the red blood cell in hypoxia. Although SNO-Hb formed on adult-type Hb (HbA, forming SNO-HbA) has been documented in physiological and pathophysiological human states, the fetal variant, SNO-HbF, has thus far not been isolated or characterized in human blood. METHODS AND RESULTS: We developed a technique capable of separating Hbs A and F under conditions that preserve SNO. We then measured SNO-HbF in the blood of healthy and premature or otherwise ill neonates using the gold standard for SNO measurement, mercury-coupled photolysis-chemiluminescence. SNO-HbF levels were in the range of those previously reported for HbA in adults. We found that SNO-HbF was more abundant at earlier gestational age (<30 weeks), even when accounting for the absolute HbF level. CONCLUSIONS: The ability to monitor SNO-HbF could provide new insights into fetal development and the perinatal transition, and has potential as a biomarker relevant to the management of neonatal diseases.

Renitrosylation of banked human red blood cells improves deformability and reduces adhesivity.

BACKGROUND: Transfusion of red blood cells (RBCs) is a frequent health care practice. However, unfavorable consequences may occur from transfusions of stored RBCs and are associated with RBC changes during storage. Loss of S-nitrosohemoglobin (SNO-Hb) and other S-nitrosothiols (SNOs) during storage is implicated as a detriment to transfusion efficacy. It was hypothesized that restoring SNOs within banked RBCs would improve RBC functions relevant to successful transfusion outcomes, namely, increased deformability and decreased adhesivity. STUDY DESIGN AND METHODS: Stored human RBCs were incubated with nitric oxide (NO) donors PROLI/NO and DEA/NO (disodium 1-[2-(carboxylato)-pyrrolidin-1-yl]diazen-1-ium-1,2-diolate and diethylammonium (Z)-1-(N,N-diethylamino)diazen-1-ium-1,2-diolate) under varying experimental conditions (e.g., aerobic/anaerobic incubation, NO donor to RBC ratio). SNO restoration was evaluated in vitro and in vivo as a means to improve RBC function after storage. RESULTS: Incubation of RBCs with the NO donors resulted in 10-fold greater levels of SNO-Hb versus untreated control or sham RBCs, with significantly higher Hb-bound NO yields from an NO dose delivered by DEA/NO. RBC incubation with DEA/NO at a stoichiometry of 1:62.5 NO:Hb significantly increased RBC deformabilty and reduced adhesion to cultured endothelial cells. RBC incubation with DEA/NO also increased S-nitrosylation of RBC cytoskeletal and membrane proteins, including the ß-spectrin chain. Renitrosylation attenuated both RBC sequestration in the lung and the mild blood oxygen saturation impairments seen with banked RBCs in a mouse model of transfusion. CONCLUSIONS: RBC renitrosylation using NO donors has promise for correcting deficient properties (e.g., adhesivity, rigidity, and SNO loss) of banked RBCs and in turn improving transfusion outcomes.

The effect of nitric oxide surface flux on the foreign body response to subcutaneous implants.

Although the release of nitric oxide (NO) from biomaterials has been shown to reduce the foreign body response (FBR), the optimal NO release kinetics and doses remain unknown. Herein, polyurethane-coated wire substrates with varying NO release properties were implanted into porcine subcutaneous tissue for 3, 7, 21 and 42 d. Histological analysis revealed that materials with short NO release durations (i.e., 24 h) were insufficient to reduce the collagen capsule thickness at 3 and 6 weeks, whereas implants with longer release durations (i.e., 3 and 14 d) and greater NO payloads significantly reduced the collagen encapsulation at both 3 and 6 weeks. The acute inflammatory response was mitigated most notably by systems with the longest duration and greatest dose of NO release, supporting the notion that these properties are most critical in circumventing the FBR for subcutaneous biomedical applications (e.g., glucose sensors).

Nitric oxide release: part I. Macromolecular scaffolds.

The roles of nitric oxide (NO) in physiology and pathophysiology merit the use of NO as a therapeutic for certain biomedical applications. Unfortunately, limited NO payloads, too rapid NO release, and the lack of targeted NO delivery have hindered the clinical utility of NO gas and low molecular weight NO donor compounds. A wide-variety of NO-releasing macromolecular scaffolds has thus been developed to improve NO's pharmacological potential. In this tutorial review, we provide an overview of the most promising NO release scaffolds including protein, organic, inorganic, and hybrid organic-inorganic systems. The NO release vehicles selected for discussion were chosen based on their enhanced NO storage, tunable NO release characteristics, and potential as therapeutics.

Examination of bacterial resistance to exogenous nitric oxide.

While much research has been directed to harnessing the antimicrobial properties of exogenous NO, the possibility of bacteria developing resistance to such therapy has not been thoroughly studied. Herein, we evaluate potential NO resistance using spontaneous and serial passage mutagenesis assays. Specifically, Staphylococcus aureus, Methicillin-resistant S. aureus (MRSA), Staphylococcus epidermidis, Escherichia coli, and Pseudomonas aeruginosa were systematically exposed to NO-releasing 75mol% MPTMS-TEOS nitrosothiol particles at or below minimum inhibitory concentration (MIC) levels. In the spontaneous mutagenesis assay, bacteria that survived exposure to lethal concentrations of NO showed no increase in MIC. Similarly, no increase in MIC was observed in the serial passage mutagenesis assay after exposure of these species to sub-inhibitory concentrations of NO through 20 d.

Photoinitiated nitric oxide-releasing tertiary S-nitrosothiol-modified xerogels.

The synthesis of a tertiary thiol-bearing silane precursor (i.e., N-acetyl penicillamine propyltrimethoxysilane or NAPTMS) to enable enhanced NO storage stability at physiological temperature is described. The novel silane was co-condensed with alkoxy- or alkylalkoxysilanes under varied synthetic parameters (e.g., water to silane ratio, catalyst and solvent concentrations, and reaction time) to evaluate systematically the formation of stable xerogel films. The resulting xerogels were subsequently nitrosated to yield tertiary RSNO-modified coatings. Total NO storage ranged from 0.87 to 1.78 µmol cm(-2) depending on the NAPTMS concentration and xerogel coating thickness. Steric hindrance near the nitroso functionality necessitated the use of photolysis to liberate NO. The average NO flux for irradiated xerogels (20% NAPTMS balance TEOS xerogel film cast using 30 µL) in physiological buffer at 37 °C was ∼23 pmol cm(-2) s(-1). The biomedical utility of the photoinitiated NO-releasing films was illustrated by their ability to both reduce Pseudomonas aeruginosa adhesion by ∼90% relative to control interfaces and eradicate the adhered bacteria.

Visible photolysis and amperometric detection of S-nitrosothiols.

The concentration of S-nitrosothiols (RSNOs), endogenous transporters of the signaling molecule nitric oxide (NO), fluctuate greatly in physiology often as a function of disease state. RSNOs may be measured indirectly by cleaving the S-N bond and monitoring the liberated NO. While ultraviolet photolysis and reductive-based cleavage both decompose RSNOs to NO, poor selectivity and the need for additional reagents preclude their utility clinically. Herein, we report the coupling of visible photolysis (i.e., 500-550 nm) and amperometric NO detection to quantify RSNOs with greater selectivity and sensitivity. Enhanced sensitivity (up to 1.56 nA µM(-1)) and lowered theoretical detection limits (down to 30 nM) were achieved for low molecular weight RSNOs (i.e., S-nitrosoglutathione, S-nitrosocysteine) by tuning the irradiation exposure. Detection of nitrosated proteins (i.e., S-nitrosoalbumin) was also possible, albeit at a decreased sensitivity (0.11 nA µM(-1)). This detection scheme was used to measure RSNOs in plasma and illustrate the potential of this method for future physiological studies.

Fabrication of nitric oxide-releasing polyurethane glucose sensor membranes.

Despite clear evidence that polymeric nitric oxide (NO) release coatings reduce the foreign body response (FBR) and may thus improve the analytical performance of in vivo continuous glucose monitoring devices when used as sensor membranes, the compatibility of the NO release chemistry with that required for enzymatic glucose sensing remains unclear. Herein, we describe the fabrication and characterization of NO-releasing polyurethane sensor membranes using NO donor-modified silica vehicles embedded within the polymer. In addition to demonstrating tunable NO release as a function of the NO donor silica scaffold and polymer compositions and concentrations, we describe the impact of the NO release vehicle and its release kinetics on glucose sensor performance.

Stöber Synthesis of Nitric Oxide-Releasing S-Nitrosothiol-Modified Silica Particles.

We report the synthesis of S-nitrosothiol-modified silica particles capable of nitric oxide (NO) release. The thiol precursor modification to form S-nitrosothiol NO donors was introduced into the silica network via co-condensation of mercaptosilane and alkoxysilane precursors. Both the concentrations of reactants (i.e., water, ammonia, and silane) and the silane feed rate into the reaction proved important in the yield of monodisperse, spherical particles with tunable diameters ranging from 241-718 nm. Subsequent nitrosation resulted in NO storage approaching ~4.40 µmol NO mg(-1), as determined by total NO release. Behaving similar to low molecular weight S-nitrosothiol NO donors, the NO release from the macromolecular silica vehicles was influenced by light, temperature, and metal ions.

Nitric oxide-releasing S-nitrosothiol-modified xerogels.

The synthesis, material characterization, and in vitro biocompatibility of S-nitrosothiol (RSNO)-modified xerogels are described. Thiol-functionalized xerogel films were formed by hydrolysis and co-condensation of 3-mercaptopropyltrimethoxysilane (MPTMS) and methyltrimethoxysilane (MTMOS) sol-gel precursors at varying concentrations. Subsequent thiol nitrosation via acidified nitrite produced RSNO-modified xerogels capable of generating nitric oxide (NO) for up to 2 weeks under physiological conditions. Xerogels also exhibited NO generation upon irradiation with broad-spectrum light or exposure to copper, with NO fluxes proportional to wattage and concentration, respectively. Xerogels were capable of storing up to approximately 1.31 micromol NO mg(-1), and displayed negligible fragmentation over a 2-week period. Platelet and bacterial adhesion to nitrosated films was reduced compared to non-nitrosated controls, confirming the antithrombotic and antibacterial properties of the NO-releasing materials. Fibroblast cell viability was maintained on the xerogel surfaces illustrating the promise of RSNO-modified xerogels as biomedical device coatings.

Xerogel optical sensor films for quantitative detection of nitroxyl.

Xerogel sensing films were synthesized via sol-gel chemistry were used to fabricate optical nitroxyl (HNO) sensors [corrected] Selective detection of HNO in solution was achieved by monitoring the rates of manganese(III) meso-tetrakis(4-sulfonatophenyl) porphyrinate (MnIIITPPS) reductive nitrosylation in the anaerobic interior of aminoalkoxysilane-derived xerogel films. Nitroxyl permeability in sensor films deposited in round-bottom 96-well plates was enhanced via incorporation of trimethoxysilyl-terminated poly(amidoamine-organosilicon) dendrimers in the xerogel network. The selectivity of MnIIITPPS for HNO, the overall sensitivity, and the working dynamic range of the resulting sensors were characterized. The HNO-sensing microtiter plates were used to quantify pH-dependent HNO generation by the recently described HNO-donor sodium 1-(isopropylamino)diazene-1-ium-1,2-diolate (IPA/NO), and compare HNO production efficiency between IPA/NO and Angeli's salt, a traditional HNO-donor.

A range/domain approximation error-based approach for fractal image compression.

Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range\slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.