Bio-inspired hemocompatible surface modifications for biomedical applications.

When blood first encounters the artificial surface of a medical device, a complex series of biochemical reactions is triggered, potentially resulting in clinical complications such as embolism/occlusion, inflammation, or device failure. Preventing thrombus formation on the surface of blood-contacting devices is crucial for maintaining device functionality and patient safety. As the number of patients reliant on blood-contacting devices continues to grow, minimizing the risk associated with these devices is vital towards lowering healthcare-associated morbidity and mortality. The current standard clinical practice primarily requires the systemic administration of anticoagulants such as heparin, which can result in serious complications such as post-operative bleeding and heparin-induced thrombocytopenia (HIT). Due to these complications, the administration of antithrombotic agents remains one of the leading causes of clinical drug-related deaths. To reduce the side effects spurred by systemic anticoagulation, researchers have been inspired by the hemocompatibility exhibited by natural phenomena, and thus have begun developing medical-grade surfaces which aim to exhibit total hemocompatibility via biomimicry. This review paper aims to address different bio-inspired surface modifications that increase hemocompatibility, discuss the limitations of each method, and explore the future direction for hemocompatible surface research.

The generation of diverse traveling pulses and its solution scheme in an excitable slow-fast dynamics.

In this article, we report on the generation and propagation of traveling pulses in a homogeneous network of diffusively coupled, excitable, slow-fast dynamical neurons. The spatially extended system is modeled using the nearest neighbor coupling theory, in which the diffusion part measures the spatial distribution of coupling topology. We derive analytically the conditions for traveling wave profiles that allow the construction of the shape of traveling nerve impulses. The analytical and numerical results are used to explore the nature of propagating pulses. The symmetric or asymmetric nature of traveling pulses is characterized, and the wave velocity is derived as a function of system parameters. Moreover, we present our results for an extended excitable medium by considering a slow-fast biophysical model with a homogeneous, diffusive coupling that can exhibit various traveling pulses. The appearance of series of pulses is an interesting phenomenon from biophysical and dynamical perspective. Varying the perturbation and coupling parameters, we observe the propagation of activities with various amplitude modulations and transition phases of different wave profiles that affect the speed of pulses in certain parameter regimes. We observe different types of traveling pulses, such as envelope solitons and multi-bump solutions, and show how system parameters and coupling play a major role in the formation of different traveling pulses. Finally, we obtain the conditions for stable and unstable plane waves.

COVID-19 prognosis using limited chest X-ray images.

The COrona VIrus Disease 2019 (COVID-19) pandemic is an ongoing global pandemic that has claimed millions of lives till date. Detecting COVID-19 and isolating affected patients at an early stage is crucial to contain its rapid spread. Although accurate, the primary viral test 'Reverse Transcription Polymerase Chain Reaction' (RT-PCR) for COVID-19 diagnosis has an elaborate test kit, and the turnaround time is high. This has motivated the research community to develop CXR based automated COVID-19 diagnostic methodologies. However, COVID-19 being a novel disease, there is no annotated large-scale CXR dataset for this particular disease. To address the issue of limited data, we propose to exploit a large-scale CXR dataset collected in the pre-COVID era and train a deep neural network in a self-supervised fashion to extract CXR specific features. Further, we compute attention maps between the global and the local features of the backbone convolutional network while finetuning using a limited COVID-19 CXR dataset. We empirically demonstrate the effectiveness of the proposed method. We provide a thorough ablation study to understand the effect of each proposed component. Finally, we provide visualizations highlighting the critical patches instrumental to the predictive decision made by our model. These saliency maps are not only a stepping stone towards explainable AI but also aids radiologists in localizing the infected area.

Spatiotemporal characteristics in systems of diffusively coupled excitable slow-fast FitzHugh-Rinzel dynamical neurons.

In this paper, we study an excitable, biophysical system that supports wave propagation of nerve impulses. We consider a slow-fast, FitzHugh-Rinzel neuron model where only the membrane voltage interacts diffusively, giving rise to the formation of spatiotemporal patterns. We focus on local, nonlinear excitations and diverse neural responses in an excitable one- and two-dimensional configuration of diffusively coupled FitzHugh-Rinzel neurons. The study of the emerging spatiotemporal patterns is essential in understanding the working mechanism in different brain areas. We derive analytically the coefficients of the amplitude equations in the vicinity of Hopf bifurcations and characterize various patterns, including spirals exhibiting complex geometric substructures. Furthermore, we derive analytically the condition for the development of antispirals in the neighborhood of the bifurcation point. The emergence of broken target waves can be observed to form spiral-like profiles. The spatial dynamics of the excitable system exhibits two- and multi-arm spirals for small diffusive couplings. Our results reveal a multitude of neural excitabilities and possible conditions for the emergence of spiral-wave formation. Finally, we show that the coupled excitable systems with different firing characteristics participate in a collective behavior that may contribute significantly to irregular neural dynamics.

Oxygen Reduction by Iron Porphyrins with Covalently Attached Pendent Phenol and Quinol.

Phenols and quinols participate in both proton transfer and electron transfer processes in nature either in distinct elementary steps or in a concerted fashion. Recent investigations using synthetic heme/Cu models and iron porphyrins have indicated that phenols/quinols can react with both ferric superoxide and ferric peroxide intermediates formed during O2 reduction through a proton coupled electron transfer (PCET) process as well as via hydrogen atom transfer (HAT). Oxygen reduction by iron porphyrins bearing covalently attached pendant phenol and quinol groups is investigated. The data show that both of these can electrochemically reduce O2 selectively by 4e-/4H+ to H2O with very similar rates. However, the mechanism of the reaction, investigated both using heterogeneous electrochemistry and by trapping intermediates in organic solutions, can be either PCET or HAT and is governed by the thermodynamics of these intermediates involved. The results suggest that, while the reduction of the FeIII-O2̇- species to FeIII-OOH proceeds via PCET when a pendant phenol is present, it follows a HAT pathway with a pendant quinol. In the absence of the hydroxyl group the O2 reduction proceeds via an electron transfer followed by proton transfer to the FeIII-O2̇- species. The hydrogen bonding from the pendant phenol group to FeIII-O2̇- and FeIII-OOH species provides a unique advantage to the PCET process by lowering the inner-sphere reorganization energy by limiting the elongation of the O-O bond upon reduction.

Extremely low dark current and detection range extension of Ga2O3 UV photodetector using Sn alloyed nanostructures.

A unique metal-semiconductor-metal (MSM) photodetector has been fabricated using Sn incorporation in Ga2O3 forming Sn x Ga1-x O nanostructures (Ns) with platinum (Pt) metal as contacts. The mixed nanostructures (MNs) has been attributed to an increment in the detection range of UV (254-302 nm) with ultra-low dark current, hence a potential device in the field of long range deep-UV detector. Sn x Ga1-x O Ns are deposited on c-plane sapphire using low-pressure chemical vapour deposition. From the x-ray diffraction (XRD) results, existence of both Sn x Ga1-x O and tetragonal SnO2 MNs are confirmed. The XRD peak shifts in Sn x Ga1-x O are attributed to the integration of Sn with Ga forming a Sn x Ga1-x O alloy with x to be ∼7.3% determined from the Vegard's law. The field effect scanning eletron microscope images show the thick diameter wire-shaped nanostructures. The absorption spectra show a trace of two absorption edges corresponding to both Sn x Ga1-x O and SnO2 Ns. Photo to dark current ratio (PDCR) of the fabricated photodetector is large (103) at 2 V bias with fast fall time of 0.18 s. The detector reveals self-powered behaviour also with PDCR >104 at 0 V bias. The dark current is ultra-low (13 pA at 5 V) due to high barrier height of Pt and the UV detection range has been extended from 254-302 nm with a very small drop in PDCR owing to incorporation of Sn.

Time dependent ultrastructural alterations on the skin, eye, barbel and fins of the spawn of Clarias batrachus (Linn. 1758) exposed to UV-B radiation.

Present study highlighted the ultramicroscopic (SEM) alterations of the skin, eye, barbel, and fins of spawn of an air-breathing teleost (Clarias batrachus, Linn. 1758) induced by UV-B radiation (280-320 nm) at a dose (@4.07 × 10-20J/photon/m2) under the time-frame of 5, 10 and 15 min/d in the laboratory condition for the periods of 5 and 10 days. Limnological parameters revealed no significant changes throughout the period of experimentation which were measured by PCS Testr 35 Multi-Parameter. Morphometric analysis revealed that during the extended exposure period of 10 days the spawn size and weight were reduced as analysed through Specific Growth Rate (SGR). SGR values in terms of weight for 5 and 10 days under 3 time-frames were 17.12%, 12.52%, 11.46% and 9.09%, 6.43%, 6.09% respectively, which revealed a declined trend along with the exposure days. In the skin of C. batrachus, the compact regular orientation of the stratified epithelial cells and mucous cells became distorted and the microridges and double-ridged structures showed destruction and fragmentations. The body striations and microfolds became shrinked and swollen and finally degenerated to form a mass. The distribution of mucous cells throughout the epidermis was disorganised and releasing secretory contents on the surface through small pores. Appearance of huge quantity of biogenic semi-hexagonal plate like crystals (guanine platelets) on the skin surface of the body was the most significant observations during UV-B radiation. In the developmental phases the eyeball showed shrinkage loosing normal regular concave structure and to become a dome-shaped one. The supportive connective infoldings became loosened. The choroid coat displayed deformities and the iris deformed the pupil. The fibroblast on the epithelium and melanocytes depicted dispersed arrangement. The pairs of ventral barbels near the mouth depicted the presence of taste buds that became severely damaged exposing the sensory as well as neuroepithelial cells. Compact regular arrangement of the SECs was completely destroyed leaving long and deep channels inbetween them; the disintegrated concentric MRs also showed a mass.

Dynamics of a modified excitable neuron model: Diffusive instabilities and traveling wave solutions.

We examine the dynamics of a spatially extended excitable neuron model between phase state and stable/unstable equilibrium point depending on the parameter regimes. The solitary wave profiles in the excitable medium are characterized by an improved Hindmarsh-Rose (H-R) spiking-bursting neuron model with an injected decaying current function. Linear stability and the nature of deterministic system dynamics are analyzed. Further investigation for the existence of wave using the reaction-diffusion H-R system and the criteria for diffusion-driven instabilities are performed. An approximation method is introduced to analyze traveling wave profiles for the oscillatory neuron model that allows the explicit analytical treatment of both the speed equations and shape of the traveling wave solution. The solitary wave profiles exhibited by the system are explored. The analytical expression for the solution scheme is validated with good accuracy in a wide range of the biophysical parameters of the system. The traveling wave fronts and speed equations control the variations of the information transmission, and the speed of signal transmission may be affected by the injection of certain drugs.

N-Acetyl Cysteine-Decorated Nitric Oxide-Releasing Interface for Biomedical Applications.

Biomedical devices are vulnerable to infections and biofilm formation, leading to extended hospital stays, high expenditure, and increased mortality. Infections are clinically treated via the administration of systemic antibiotics, leading to the development of antibiotic resistance. A multimechanistic strategy is needed to design an effective biomaterial with broad-spectrum antibacterial potential. Recent approaches have investigated the fabrication of innately antimicrobial biomedical device surfaces in the hope of making the antibiotic treatment obsolete. Herein, we report a novel fabrication strategy combining antibacterial nitric oxide (NO) with an antibiofilm agent N-acetyl cysteine (NAC) on a polyvinyl chloride surface using polycationic polyethylenimine (PEI) as a linker. The designed biomaterial could release NO for at least 7 days with minimal NO donor leaching under physiological conditions. The proposed surface technology significantly reduced the viability of Gram-negative Escherichia coli (>97%) and Gram-positive Staphylococcus aureus (>99%) bacteria in both adhered and planktonic forms in a 24 h antibacterial assay. The composites also exhibited a significant reduction in biomass and extra polymeric substance accumulation in a dynamic environment over 72 h. Overall, these results indicate that the proposed combination of the NO donor with mucolytic NAC on a polymer surface efficiently resists microbial adhesion and can be used to prevent device-associated biofilm formation.

Emission inventory of inorganic trace gases from solid residential fuels over the National Capital Territory of India.

In developing nations, solid residential fuels are the major sources of primary energy for various domestic activities. To date, the emission inventory of inorganic trace gases over National Capital Territory (NCT) was prepared using either default or country-specific emission factors. In this paper, we report (for the first time) the spatial variation of emission factors (EFs) of inorganic trace gases (SO2, NO, NO2, CO, CO2, and CH4) from the residential fuels used in slums and rural areas of NCT determined using dilution chamber in the laboratory. 147 residential fuel samples, including fuelwood, dung cake, crop residues, coal, etc., were collected at 149 NCT locations out of 675 slum clusters and 146 rural villages. The range of EF(s) of SO2 (0.02 ± 0.01 to 0.04 ± 0.01 g kg-1), CH4 (0.10 to 0.34 g kg-1), NO2 (0.01 to 0.02 g kg-1) is lower than the CO (3.55 ± 1.72 to 6.07 ± 1.53 g kg-1) and CO2 (0 to 129.45 ± 46.94 g kg-1). The north and north west districts of NCT are emission hotspots for CH4, NO, and NO2 emissions, whereas, the southern and northern areas of NCT are for CO2. These citywide emission inventories (0.05° × 0.05°) of inorganic trace gases are prepared using laboratory-determined EFs and available consumption data determined by recent survey information. Among solid residential fuels, fuel wood, and dung cake are two major contributors to inorganic trace gases in NCT.

Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary methodology that helps to analyze transcriptome or genome information from a single cell. However, high dimensionality and sparsity in data due to dropout events pose computational challenges for existing state-of-the-art scRNA-seq clustering methods. Learning efficient representations becomes even more challenging due to the presence of noise in scRNA-seq data. To overcome the effect of noise and learn effective representations, this paper proposes sc-INDC (Single-Cell Information Maximized Noise-Invariant Deep Clustering), a deep neural network that facilitates learning of informative and noise-invariant representations of scRNA-seq data. Furthermore, the time complexity of the proposed sc-INDC is significantly lower compared to state-of-the-art scRNA-seq clustering methods. Extensive experimentation on fourteen publicly available scRNA-seq datasets illustrates the efficacy of the proposed model. Additionally, visualizations of t-SNE plots and several ablation studies are also conducted to provide insights into the improved representation ability of sc-INDC. Code of the proposed sc-INDC will be available at: https://github.com/arnabkmondal/sc-INDC.

Biomimetic catheter surface with dual action NO-releasing and generating properties for enhanced antimicrobial efficacy.

Infection of indwelling catheters is a common healthcare problem, resulting in higher morbidity and mortality. The vulnerable population reliant on catheters post-surgery for food and fluid intake, blood transfusion, or urinary incontinence or retention is susceptible to hospital-acquired infection originating from the very catheter. Bacterial adhesion on catheters can take place during the insertion or over time when catheters are used for an extended period. Nitric oxide-releasing materials have shown promise in exhibiting antibacterial properties without the risk of antibacterial resistance which can be an issue with conventional antibiotics. In this study, 1, 5, and 10 wt % selenium (Se) and 10 wt % S-nitrosoglutathione (GSNO)-incorporated catheters were prepared through a layer-by-layer dip-coating method to demonstrate NO-releasing and NO-generating capability of the catheters. The presence of Se on the catheter interface resulted in a 5 times higher NO flux in 10% Se-GSNO catheter through catalytic NO generation. A physiological level of NO release was observed from 10% Se-GSNO catheters for 5 d, along with an enhanced NO generation via the catalytic activity as Se was able to increase NO availability. The catheters were also found to be compatible and stable when subjected to sterilization and storage, even at room temperature. Additionally, the catheters showed a 97.02% and 93.24% reduction in the adhesion of clinically relevant strains of Escherichia coli and Staphylococcus aureus, respectively. Cytocompatibility testing of the catheter with 3T3 mouse fibroblast cells supports the material's biocompatibility. These findings from the study establish the proposed catheter as a prospective antibacterial material that can be translated into a clinical setting to combat catheter-related infections.

Non-Trivial Dynamics in the FizHugh-Rinzel Model and Non-Homogeneous Oscillatory-Excitable Reaction-Diffusions Systems.

This article focuses on the qualitative analysis of complex dynamics arising in a few mathematical models in neuroscience context. We first discuss the dynamics arising in the three-dimensional FitzHugh-Rinzel (FHR) model and then illustrate those arising in a class of non-homogeneous FitzHugh-Nagumo (Nh-FHN) reaction-diffusion systems. FHR and Nh-FHN models can be used to generate relevant complex dynamics and wave-propagation phenomena in neuroscience context. Such complex dynamics include canards, mixed-mode oscillations (MMOs), Hopf-bifurcations and their spatially extended counterpart. Our article highlights original methods to characterize these complex dynamics and how they emerge in ordinary differential equations and spatially extended models.

Catalytic effect of transition metal-doped medical grade polymer on S-nitrosothiol decomposition and its biological response.

Nitric oxide (NO)-release from polymer metal composites is achieved through the incorporation of NO donors such as S-nitrosothiols (RSNO). Several studies have shown that metal nanoparticles catalytically decompose RSNO to release NO. In polymer composites, the NO surface flux from the surface can be modulated by the application of metal nanoparticles with a varying degree of catalytic activity. In this study, we compare the NO-releasing polymer composite design strategy - demonstrating how different ways of incorporating RSNO and metal nanoparticles can affect NO flux, donor leaching, or biological activity of the films. The first approach included blending both the RSNO and metal nanoparticle in the matrix (non-layered), while the second approach involved dip-coating metal nanoparticle/polymer layer on the RSNO-containing polymer composite (layered). Secondly, we compare both designs with respect to metal nanoparticles, including iron (Fe), copper (Cu), nickel (Ni), zinc (Zn), and silver (Ag). Differential NO surface flux is observed for each metal nanoparticle, with the Cu-containing polymer composites showing the highest flux for layered composites, whereas Fe demonstrated the highest NO flux for non-layered composites in 24 h. Additionally, a comparative study on NO flux modulation via the choice of metal nanoparticles is shown. Furthermore, mouse fibroblast cell viability when exposed to leachates from the polymer metal composites was dependent on (1) the design of the polymer composite where the layered approach performed better than non-layered composites (2) diffusion of metal nanoparticles from the composites plays a key role. Antibacterial activity on methicillin-resistant Staphylococcus aureus was also dependent on individual metal nanoparticles and flux levels in a 24 h in vitro CDC bioreactor study. Therefore, the study establishes the need for a layered polymer metal composite strategy that synergizes NO flux without negatively affecting biocompatibility.

Superhydrophobic and Conductive Foams with Antifouling and Oil-Water Separation Properties.

Hybrid organic-inorganic materials are attracting enormous interest in materials science due to the combination of multiple advantageous properties of both organic and inorganic components. Taking advantage of a simple, scalable, solvent-free hard-sacrificial method, we report the successful fabrication of three-dimensional hybrid porous foams by integrating two types of fillers into a poly(dimethylsiloxane) (PDMS) framework. These fillers consist of hydrophobic electrically conductive graphene (GR) nanoplatelets and hydrophobic bactericidal copper (Cu) microparticles. The fillers were utilized to create the hierarchical rough structure with low-surface-energy properties on the PDMS foam surfaces, leading to remarkable superhydrophobicity/superoleophilicity with contact angles of 158 and 0° for water and oil, respectively. The three-dimensional interconnected porous foam structures facilitated high oil adsorption capacity and excellent reusability as well as highly efficient oil/organic solvent-water separation in turbulent, corrosive, and saline environments. Moreover, the introduction of the fillers led to a significant improvement in the electrical conductivity and biofouling resistance (vs whole blood, fibrinogen, platelet cells, and Escherichia coli) of the foams. We envision that the developed composite strategy will pave a facile, scalable, and effective way for fabricating novel multifunctional hybrid materials with ideal properties that may find potential use in a broad range of biomedical, energy, and environmental applications.

Nanoarchitectonics of nitric oxide releasing supramolecular structures for enhanced antibacterial efficacy under visible light irradiation.

Light-controlled therapies offer a promising strategy to prevent and suppress infections caused by numerous bacterial pathogens. Excitation of exogenously supplied photosensitizers (PS) at specific wavelengths elicits levels of reactive oxygen intermediates toxic to bacteria. Porphyrin-based supramolecular nanostructure frameworks (SNF) are effective PS with unique physicochemical properties that have led to their widespread use in photomedicine. Herein, we developed a nitric oxide (NO) releasing, biocompatible, and stable porphyrin-based SNF (SNF-NO), which was achieved through a confined noncovalent self-assembly process based on π-π stacking. Characterization of the SNFs via scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analysis showed the formation of three-dimensional, well-defined octahedral structures. These SNF-NO were shown to exhibit a red shift due to the noncovalent self-assembly of porphyrins, which also show extended light absorption to broadly cover the entire visible light spectrum to enhance photodynamic therapy (PDT). Under visible light irradiation (46 J cm-2), the SNF generates high yields of singlet oxygen (1O2) radicals, hydroxyl radicals (HO), superoxide radicals (O2), and peroxynitrite (ONOO-) radicals that have shown potential to enhance antimicrobial photodynamic therapy (APDT) against Gram-positive methicillin-resistant Staphylococcus aureus (MRSA) and Gram-negative Escherichia coli (E. coli). The resulting SNFs also exhibit significant biofilm dispersion and a decrease in biomass production. The combination of robust photosensitizer SNFs with nitric oxide-releasing capabilities is dynamic in its ability to target pathogenic infections while remaining nontoxic to mammalian cells. The engineered SNFs have enormous potential for treating and managing microbial infections.

scRAE: Deterministic Regularized Autoencoders With Flexible Priors for Clustering Single-Cell Gene Expression Data.

Clustering single-cell RNA sequence (scRNA-seq) data poses statistical and computational challenges due to their high-dimensionality and data-sparsity, also known as 'dropout' events. Recently, Regularized Auto-Encoder (RAE) based deep neural network models have achieved remarkable success in learning robust low-dimensional representations. The basic idea in RAEs is to learn a non-linear mapping from the high-dimensional data space to a low-dimensional latent space and vice-versa, simultaneously imposing a distributional prior on the latent space, which brings in a regularization effect. This paper argues that RAEs suffer from the infamous problem of bias-variance trade-off in their naive formulation. While a simple AE wita latent regularization results in data over-fitting, a very strong prior leads to under-representation and thus bad clustering. To address the above issues, we propose a modified RAE framework (called the scRAE) for effective clustering of the single-cell RNA sequencing data. scRAE consists of deterministic AE with a flexibly learnable prior generator network, which is jointly trained with the AE. This facilitates scRAE to trade-off better between the bias and variance in the latent space. We demonstrate the efficacy of the proposed method through extensive experimentation on several real-world single-cell Gene expression datasets. The code for our work is available at https://github.com/arnabkmondal/scRAE.

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

Nitric Oxide-Releasing Lock Solution for the Prevention of Catheter-Related Infection and Thrombosis.

Although frequently used, venous catheters are often associated with serious complications such as infection and thrombosis. Lock solution therapies are clinically used to deter these issues but generally address only infection or thrombosis with limited success. Here, we report the development of a dual-functional lock therapy using nitric oxide (NO) donor molecule, S-nitrosoglutathione (GSNO). NO is a potent, broad-spectrum antimicrobial agent that also temporarily inhibits platelet activation, preventing thrombosis. Furthermore, NO has antibiofilm actions, an ability that traditional antibiotic lock solutions lack, thus limiting their efficacy. In this work, different concentrations of GSNO were characterized via NO analysis to determine a range of NO-releasing lock solution (NOreLS) concentrations to investigate and to demonstrate prolonged potential efficacy. Tested against clinically used vancomycin and gentamicin lock solutions, GSNO-based NOreLS repeatedly outperformed in models of different stages of catheter infections. NOreLS also prevented clot formation when exposed to whole blood, showing increased efficacy compared to a heparin lock solution. Moreover, NOreLS was demonstrated to be biocompatible via hemolysis and cytotoxicity assays. NOreLS has excellent potential for safely and effectively preventing infection and thrombosis related to catheter usage.

Biomimetic gasotransmitter-releasing alginate beads for biocompatible antimicrobial therapy.

HYPOTHESIS: Alginate is widely used in biomedical applications due to its high biocompatibility as well as structural and mechanical similarities to human tissue. Further, simple ionic crosslinking of alginate allows for the formation of alginate beads capable of drug delivery. S-nitrosoglutathione is a water-soluble molecule that releases nitric oxide in physiological conditions, where it acts as a potent antimicrobial gas, among other functions. As macrophages and endothelial cells endogenously produce nitric oxide, incorporating nitric oxide donors into polymers and hydrogels introduces a biomimetic approach to mitigate clinical infections, including those caused by antibiotic-resistant microorganisms. The incorporation of S-nitrosoglutathione into macro-scale spherical alginate beads is reported for the first time and shows exciting potential for biomedical applications. EXPERIMENTS: Herein, nitric oxide-releasing crosslinked alginate beads were fabricated and characterized for surface and cross-sectional morphology, water uptake, size distribution, and storage stability. In addition, the NO release was quantified by chemiluminescence and its biological effects against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus were investigated. The biocompatibility of the alginate beads was tested against 3T3 mouse fibroblast cells. FINDINGS: Overall, nitric oxide-releasing alginate beads demonstrate biologically relevant activities without eliciting a cytotoxic response, revealing their potential use as an antimicrobial material with multiple mechanisms of bacterial killing.