Glutathione and Xanthine Metabolic Changes in Tamoxifen Resistant Breast Cancer Cell Lines are Mediated by Down-Regulation of GSS and XDH and Correlated to Poor Prognosis.

Background: Tamoxifen is commonly used in the treatment of hormonal-positive breast cancer. However, 30%-40% of tumors treated with tamoxifen develop resistance; therefore, an important step to overcome this resistance is to understand the underlying molecular and metabolic mechanisms. In the present work, we used metabolic profiling to determine potential biomarkers of tamoxifen resistance, and gene expression levels of enzymes important to these metabolites and then correlated the expression to the survival of patients receiving tamoxifen. Methods: Tamoxifen-resistant cell lines previously developed and characterized in our laboratory were metabolically profiled with nuclear magnetic resonance spectroscopy (NMR) using cryogenic probe, and the findings were correlated with the expression of genes that encode the key enzymes of the significant metabolites. Moreover, the effect of significantly altered genes on the overall survival of patients was assessed using the Kaplan-Meier plotter web tool. Results: We observed a significant increase in the levels of glutamine, taurine, glutathione, and xanthine, and a significant decrease in the branched-chain amino acids, valine, and isoleucine, as well as glutamate and cysteine in the tamoxifen-resistant cells compared to tamoxifen sensitive cells. Moreover, xanthine dehydrogenase and glutathione synthase gene expression were downregulated, whereas glucose-6-phosphate dehydrogenase was upregulated compared to control. Additionally, increased expression of xanthine dehydrogenase was associated with a better outcome for breast cancer patients. Conclusion: Overall, this study sheds light on metabolic pathways that are dysregulated in tamoxifen-resistant cell lines and the potential role of each of these pathways in the development of resistance.

NMR-based metabolomics identification of potential serum biomarkers of disease progression in patients with multiple sclerosis.

Multiple sclerosis (MS) is a chronic and progressive neurological disorder, characterized by neuroinflammation and demyelination within the central nervous system (CNS). The etiology and the pathogenesis of MS are still unknown. Till now, no satisfactory treatments, diagnostic and prognostic biomarkers are available for MS. Therefore, we aimed to investigate metabolic alterations in patients with MS compared to controls and across MS subtypes. Metabolic profiles of serum samples from patients with MS (n = 90) and healthy control (n = 30) were determined by Nuclear Magnetic Resonance (1H-NMR) Spectroscopy using cryogenic probe. This approach was also utilized to identify significant differences between the metabolite profiles of the MS groups (primary progressive, secondary progressive, and relapsing-remitting) and the healthy controls. Concentrations of nine serum metabolites (adenosine triphosphate (ATP), tryptophan, formate, succinate, glutathione, inosine, histidine, pantothenate, and nicotinamide adenine dinucleotide (NAD)) were significantly higher in patients with MS compared to control. SPMS serum exhibited increased pantothenate and tryptophan than in PPMS. In addition, lysine, myo-inositol, and glutamate exhibited the highest discriminatory power (0.93, 95% CI 0.869-0.981; 0.92, 95% CI 0.859-0.969; 0.91, 95% CI 0.843-0.968 respectively) between healthy control and MS. Using NMR- based metabolomics, we identified a set of metabolites capable of classifying MS patients and controls. These findings confirmed untargeted metabolomics as a useful approach for the discovery of possible novel biomarkers that could aid in the diagnosis of the disease.

Cytotoxic and molecular differences of anticancer agents on 2D and 3D cell culture.

BACKGROUND: Cancer and multidrug resistance are regarded as concerns related to poor health outcomes. It was found that the monolayer of 2D cancer cell cultures lacks many important features compared to Multicellular Tumor Spheroids (MCTS) or 3D cell cultures which instead have the ability to mimic more closely the in vivo tumor microenvironment. This study aimed to produce 3D cell cultures from different cancer cell lines and to examine the cytotoxic activity of anticancer medications on both 2D and 3D systems, as well as to detect alterations in the expression of certain genes levels. METHOD: 3D cell culture was produced using 3D microtissue molds. The cytotoxic activities of colchicine, cisplatin, doxorubicin, and paclitaxel were tested on 2D and 3D cell culture systems obtained from different cell lines (A549, H1299, MCF-7, and DU-145). IC50 values were determined by MTT assay. In addition, gene expression levels of PIK3CA, AKT1, and PTEN were evaluated by qPCR. RESULTS: Similar cytotoxic activities were observed on both 3D and 2D cell cultures, however, higher concentrations of anticancer medications were needed for the 3D system. For instance, paclitaxel showed an IC50 of 6.234 µM and of 13.87 µM on 2D and 3D H1299 cell cultures, respectively. Gene expression of PIK3CA in H1299 cells also showed a higher fold change in 3D cell culture compared to 2D system upon treatment with doxorubicin. CONCLUSION: When compared to 2D cell cultures, the behavior of cells in the 3D system showed to be more resistant to anticancer treatments. Due to their shape, growth pattern, hypoxic core features, interaction between cells, biomarkers synthesis, and resistance to treatment penetration, the MCTS have the advantage of better simulating the in vivo tumor conditions. As a result, it is reasonable to conclude that 3D cell cultures may be a more promising model than the traditional 2D system, offering a better understanding of the in vivo molecular changes in response to different potential treatments and multidrug resistance development.

Dissociation Kinetics and Antimicrobial Activity of Ofloxacin Antibiotic in Artificial Tears Via 1H-NMR, Raman, and UV-Vis Spectroscopic Analysis.

Introduction: The hydrogen-bonded networks play a significant role in influencing several physicochemical properties of ofloxacin in artificial tears (ATs), including density, pH, viscosity, and self-diffusion coefficients. The activities of the ofloxacin antibiotic with Ats mixtures are not solely determined by their concentration but are also influenced by the strength of the hydrogen bonding network which highlight the importance of considering factors such as excessive tear production and dry eye conditions when formulating appropriate dosages of ofloxacin antibiotics for eye drops. Objectives: Investigating the physicochemical properties of ofloxacin-ATs mixtures, which serve as a model for understanding the impact of hydrogen bonding on the antimicrobial activity of ofloxacin antibiotic eye drops. Determine the antimicrobial activities of the ofloxacin-Ats mixture with different concentration of ofloxacin. Methods: The ofloxacin-ATs mixtures were analyzed using 1H-NMR, Raman, and UV-Vis spectroscopies, with variation of ofloxacin concentration to study its dissociation kinetics in ATs, mimicking its behavior in human eye tears. The investigation includes comprehensive analysis of 1H-NMR spectral data, self-diffusion coefficients, Raman spectroscopy, UV-Vis spectroscopy, liquid viscosity, and acidity, providing a comprehensive assessment of the physicochemical properties. Results: Analysis of NMR chemical shifts, linewidths, and self-diffusion coefficient curves reveals distinct patterns, with peaks or minima observed around 0.6 ofloxacin mole fraction dissociated in ATs, indicating a strong correlation with the hydrogen bonding network. Additionally, the pH data exhibits a similar trend to viscosity, suggesting an influence of the hydrogen bonding network on protonic ion concentrations. Antibacterial activity of the ofloxacin-ATs mixtures is evaluated through growth rate analysis against Salmonella typhimurium, considering varying concentrations with mole fractions of 0.1, 0.4, 0.6, 0.8, and 0.9. Conclusions: The antibiotic-ATs mixture with a mole fraction of 0.6 ofloxacin exhibited lower activity compared to mixtures with mole fractions of 0.1 and 0.4, despite its lower concentration. The activities of the mixtures are not solely dependent on concentration but are also influenced by the strength of the hydrogen bonding network. These findings emphasize the importance of considering tear over-secretion and dry eye problems when designing appropriate doses of ofloxacin antibiotics for eye drop formulations.

Surface plasmon coupling between wide-field SPR microscopy and gold nanoparticles.

The coupling behavior of the wide field surface plasmon microscopy (WF-SPRM) with single-, two-, and multiple-gold nanoparticles (AuNPs) with different AuNPs sizes is investigated using theoretical, simulation, and experimental approaches. The signal intensity of a single AuNP increases from 208 a.u. to 583 a.u. as particle size increases from 40 to 80 nm, which evidences the signal-building mechanism of Rayleigh scattering theory. A discrete particle model of SPR is used to understand the interaction between an Au-layer and a single AuNP. The calculated intensity profile of the single AuNP from the discrete particle model is accepted with the experimental data. In addition, the superposition between 2-AuNPs surface plasmon waves is studied using the finite element method as well as experimental data from WF-SPRM. The surface plasmon waves around the two particles generate an interference pattern. Finally, it is demonstrated that plasmonic multiple particles scattering can be represented by an effective media, which is described by Maxwell-Garnet equations.

Low-field NMR with multilayer Halbach magnet and NMR selective excitation.

This study introduces a low-field NMR spectrometer (LF-NMR) featuring a multilayer Halbach magnet supported by a combined mechanical and electrical shimming system. This setup offers improved field homogeneity and sensitivity compared to spectrometers relying on typical Halbach and dipole magnets. The multilayer Halbach magnet was designed and assembled using three nested cylindrical magnets, with an additional inner Halbach layer that can be rotated for mechanical shimming. The coils and shim-kernel of the electrical shimming system were constructed and coated with layers of zirconia, thermal epoxy, and silver-paste resin to facilitate passive heat dissipation and ensure mechanical and thermal stability. Furthermore, the 7-channel shim coils were divided into two parts connected in parallel, resulting in a reduction of joule heating temperatures from 96.2 to 32.6 °C. Without the shimming system, the Halbach magnet exhibits a field inhomogeneity of approximately 140 ppm over the sample volume. The probehead was designed to incorporate a solenoidal mini coil, integrated into a single planar board. This design choice aimed to enhance sensitivity, minimize [Formula: see text] inhomogeneity, and reduce impedance discrepancies, transmission loss, and signal reflections. Consequently, the resulting linewidth of water within a 3 mm length and 2.4 mm inner diameter sample volume was 4.5 Hz. To demonstrate the effectiveness of spectral editing in LF-NMR applications at 29.934 MHz, we selectively excited hydroxyl and/or methyl protons in neat acetic acid using optimal control pulses calculated through the Krotov algorithm.

Switchable Polyacrylic Acid Polyelectrolyte Brushes for Surface Plasmon Resonance Applications.

Imaging wide-field surface plasmon resonance (SPR) microscopy sensors based on polyacrylic acid polyelectrolyte brushes (PAA PEBs) were designed to enhance the sensitivity of nano-object detection. The switching behavior of the PAA PEBs against changes in the pH values was investigated by analyzing the chemical, morphological, optical, and electrical properties. At pH ~1, the brushes collapse on the surface with the dominance of carboxylic groups (COOH). Upon the increase in the pH to nine, the switching process completes, and the brushes swell from dissociating most of the COOH groups and converting them into COO- groups. The domination of the negatively charged COO- groups increases the electrostatic repulsion in the polymer chains and stretches the brushes. The sensitivity of the SPR sensing device was investigated using a theoretical approach, as well as experimental measurements. The signal-to-noise ratio for a Au layer increases from six to eighteen after coating with PAA PEBs. In addition, the linewidth of the recorded image decreases from six pixels to five pixels by using the Au-PAA layers, which results from the enhanced spatial resolution of the recorded images. Coating a Au-layer with PAA PEBs enhances the sensitivity of the SPR sensing device, and improves the spatial resolution of the recorded image.

The Employment of the Surface Plasmon Resonance (SPR) Microscopy Sensor for the Detection of Individual Extracellular Vesicles and Non-Biological Nanoparticles.

A wide-field surface plasmon resonance (SPR) microscopy sensor employs the surface plasmon resonance phenomenon to detect individual biological and non-biological nanoparticles. This sensor enables the detection, sizing, and quantification of biological nanoparticles (bioNPs), such as extracellular vesicles (EVs), viruses, and virus-like particles. The selectivity of bioNP detection does not require biological particle labeling, and it is achieved via the functionalization of the gold sensor surface by target-bioNP-specific antibodies. In the current work, we demonstrate the ability of SPR microscopy sensors to detect, simultaneously, silica NPs that differ by four times in size. Employed silica particles are close in their refractive index to bioNPs. The literature reports the ability of SPR microscopy sensors to detect the binding of lymphocytes (around 10 µm objects) to the sensor surface. Taken together, our findings and the results reported in the literature indicate the power of SPR microscopy sensors to detect bioNPs that differ by at least two orders in size. Modifications of the optical sensor scheme, such as mounting a concave lens, help to achieve homogeneous illumination of a gold sensor chip surface. In the current work, we also characterize the improved magnification factor of the modified SPR instrument. We evaluate the effectiveness of the modified and the primary version of the SPR microscopy sensors in detecting EVs isolated via different approaches. In addition, we demonstrate the possibility of employing translation and rotation stepper motors for precise adjustments of the positions of sensor optical elements-prism and objective-in the primary version of the SPR microscopy sensor instrument, and we present an algorithm to establish effective sensor-actuator coupling.

Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing 1H-1H TOCSY NMR.

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the cellular pathways of development, and enhances our understanding of best practices for in vitro differentiation to guide cellular therapies. In this work, the dynamic evolution of adipose-tissue-derived human Mesenchymal stem cells (AT-derived hMSCs) after fourteen days of cultivation, adipocyte and osteocyte differentiation, was inspected based on 1H-1H TOCSY using machine learning. Multi-class classification in addition to the novelty detection of metabolites was established based on a control hMSC sample after four days' cultivation and we successively detected the changes of metabolites in differentiated MSCs following a set of 1H-1H TOCSY experiments. The classifiers Kernel Null Foley-Sammon Transform and Kernel Density Estimation achieved a total classification error between 0% and 3.6% and false positive and false negative rates of 0%. This approach was successfully able to automatically reveal metabolic changes that accompanied MSC cellular evolution starting from their undifferentiated status to their prolonged cultivation and differentiation into adipocytes and osteocytes using machine learning supporting the research in the field of metabolic pathways of stem cell differentiation.

Bortezomib advanced mechanisms of action in multiple myeloma, solid and liquid tumors along with its novel therapeutic applications.

Bortezomib (BTZ) is a first-in-class reversible and selective proteasome inhibitor. It inhibits the ubiquitin proteasome pathway that leads to the degradation of many intracellular proteins. Initially, BTZ was FDA approved for the treatment of refractory or relapsed multiple myeloma (MM) in 2003. Later, its usage was approved for patients with previously untreated MM. In 2006, BTZ was approved for the treatment of relapsed or refractory Mantle Cell Lymphoma (MCL) and, in 2014, for previously untreated MCL. BTZ has been extensively studied either alone or in combination with other drugs for the treatment of different liquid tumors especially in MM. However, limited data evaluated the efficacy and safety of using BTZ in patients with solid tumors. In this review, we will discuss the advanced and novel mechanisms of action of BTZ documented in MM, solid tumors and liquid tumors. Moreover, we will shed the light on the newly discovered pharmacological effects of BTZ in other prevalent diseases.

Surface Plasmon Resonance Sensitivity Enhancement Based on Protonated Polyaniline Films Doped by Aluminum Nitrate.

Complex composite films based on polyaniline (PANI) doped hydrochloric acid (HCl) incorporated with aluminum nitrate (Al(NO3)3) on Au-layer were designed and synthesized as a surface plasmon resonance (SPR) sensing device. The physicochemical properties of (PANI-HCl)/Al(NO3)3 complex composite films were studied for various Al(NO3)3 concentrations (0, 2, 4, 8, 16, and 32 wt.%). The refractive index of the (PANI-HCl)/Al(NO3)3 complex composite films increased continuously as Al(NO3)3 concentrations increased. The electrical conductivity values increased from 5.10 µS/cm to 10.00 µS/cm as Al(NO3)3 concentration increased to 32 wt.%. The sensitivity of the SPR sensing device was investigated using a theoretical approach and experimental measurements. The theoretical system of SPR measurement confirmed that increasing Al(NO3)3 in (PANI-HCl)/Al(NO3)3 complex composite films enhanced the sensitivity from about 114.5 [Deg/RIU] for Au-layer to 159.0 [Deg/RIU] for Au-((PANI-HCl)/Al(NO3)3 (32 wt.%)). In addition, the signal-to-noise ratio for Au-layer was 3.95, which increased after coating by (PANI-HCl)/Al(NO3)3 (32 wt.%) complex composite layer to 8.82. Finally, we conclude that coating Au-layer by (PANI-HCl)/Al(NO3)3 complex composite films enhances the sensitivity of the SPR sensing device.

Glycolytic flux control by drugging phosphoglycolate phosphatase.

Targeting the intrinsic metabolism of immune or tumor cells is a therapeutic strategy in autoimmunity, chronic inflammation or cancer. Metabolite repair enzymes may represent an alternative target class for selective metabolic inhibition, but pharmacological tools to test this concept are needed. Here, we demonstrate that phosphoglycolate phosphatase (PGP), a prototypical metabolite repair enzyme in glycolysis, is a pharmacologically actionable target. Using a combination of small molecule screening, protein crystallography, molecular dynamics simulations and NMR metabolomics, we discover and analyze a compound (CP1) that inhibits PGP with high selectivity and submicromolar potency. CP1 locks the phosphatase in a catalytically inactive conformation, dampens glycolytic flux, and phenocopies effects of cellular PGP-deficiency. This study provides key insights into effective and precise PGP targeting, at the same time validating an allosteric approach to control glycolysis that could advance discoveries of innovative therapeutic candidates.

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets.

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public "The Cancer Genome Atlas" (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong "Warburg effect", glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR).

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from 1H-1H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the 1H-1H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.

In Vitro Spatio-Temporal NMR Metabolomics of Living 3D Cell Models.

Three-dimensional cell cultures are of growing importance in biochemical research as they represent tissue features more accurately than standard two-dimensional systems, but to investigate these challenging new models an adaptation of established analytical techniques is required. Spatially resolved data for living organoids are needed to gain insight into transport processes and biochemical characteristics of domains with different nutrient supply and waste product removal. Within this work, we present an NMR-based approach to obtain dynamically radial metabolite profiles for cell spheroids, one of the most frequently used 3D models. Our approach combines an easy to reproduce custom-made measurement design, maintaining physiological conditions without inhibition of the NMR experiment, with spatially selective NMR pulse sequences. To overcome the inherently low sensitivity of NMR spectroscopy we excited slices instead of smaller cube-like voxels in combination with an efficient interleaved measurement approach and employed a commercially available cryogenic NMR probe. Finally, radial metabolite profiles could be obtained via double Abel inversion of the measured one-dimensional intensity profiles. Applying this method to Ty82 cancer cell spheroids demonstrates the achieved spatial resolution, for instance confirming exceedingly high lactic acid and strongly decreased glucose concentrations in the oxygen-depleted core of the spheroid. Furthermore, our approach can be employed to investigate fast and slow metabolic changes in single spheroids simultaneously, which is shown as an example of a spheroid degrading over several days after stopping the nutrient supply.

Features of Sizing and Enumeration of Silica and Polystyrene Nanoparticles by Nanoparticle Tracking Analysis (NTA).

Nanoparticle Tracking Analysis (NTA) allows for the simultaneous determination of both size and concentration of nanoparticles in a sample. This study investigates the accuracy of particle size and concentration measurements performed on an LM10 device. For experiments, standard nanoparticles of different sizes composed of two materials with different refractive indices were used. Particle size measurements were found to have a decent degree of accuracy. This fact was verified by the manufacturer-reported particle size-determined by transmission electron microscopy (TEM)-as well as by performed scanning electron microscopy (SEM) measurements. On the other hand, concentration measurements resulted in overestimation of the particle concentration in majority of cases. Thus, our findings confirmed the accuracy of nanoparticle sizing performed by the LM10 instrument and highlighted the overestimation of particle concentration made by this device. In addition, an approach of swift correction of the results of concentration measurements received for samples is suggested in the presented study.

Surface Plasmon Resonance (SPR)-Based Biosensors as Instruments with High Versatility and Sensitivity.

Surface plasmon resonance (SPR), as a physical phenomenon, is not restricted only to events occurring in thin planar metal films [...].

Tuning the Surface Chemistry of Melanin-Mimetic Polydopamine Nanoparticles Drastically Enhances Their Accumulation into Excised Human Skin.

Melanin-mimetic polydopamine nanoparticles (PDA NPs) are emerging as promising candidates for topical and transdermal drug delivery because they mimic melanin, a naturally occurring skin pigment. However, our knowledge of their interactions with human skin remains limited. Hence, we set out to investigate the role of PDA NP surface chemistry in modulating their skin deposition. PDA NPs were synthesized by base-catalyzed oxidative self-polymerization of dopamine and functionalized with poly(ethylene glycol) (PEG) bearing different termini to obtain neutral, anionic, cationic, and hydrophobic PEGylated NPs. NPs were characterized by dynamic light scattering, transmission electron microscopy, Fourier transform-infrared spectroscopy, and X-ray photoelectron spectroscopy. The NPs were then labeled with rhodamine B, and their skin interactions were investigated both in vitro, using a Strat-M membrane, and ex vivo, using excised whole thickness human skin. In vitro diffusion studies revealed that the NPs did not permeate transdermally, rather the NPs accumulated in the Strat-M membrane after 24 h of incubation. Membrane deposition of the NPs showed a strong dependence on surface chemistry, with anionic (unmodified and carboxyl-terminated PEGylated) NPs achieving the highest accumulation, followed by neutral and cationic NPs, whereas hydrophobic NPs achieved the lowest degree of accumulation. In ex vivo permeation studies, we observed that surface modification of PDA NPs with PEG serving as an antifouling coating is essential to maintaining colloidal stability upon skin contact. Moreover, anionic PEGylated NPs were able to achieve 78% skin accumulation, which was significantly higher than neutral and cationic NPs (51 and 34% accumulation, respectively). Our findings provide important insights into the role of surface chemistry in enhancing the skin accumulation of melanin-mimetic PDA NPs as potential sunscreens and carriers for skin-targeted treatments.

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms.

A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 µ s per image for the Fourier features and 17 µ s for the Haar wavelet features. Although the CNN-based method scores 1-2.5 percentage points higher in classification accuracy, it takes 3370 µ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.