Hybrid Nanoparticle/DNAzyme Electrochemical Biosensor for the Detection of Divalent Heavy Metal Ions and Cr3.

A hybrid noble nanoparticle/DNAzyme electrochemical biosensor is proposed for the detection of Pb2+, Cd2+, and Cr3+. The sensor takes advantage of a well-studied material that is known for its selective interaction with heavy metal ions (i.e., DNAzymes), which is combined with metallic nanoparticles. The double-helix structure of DNAzymes is known to dissociate into smaller fragments in the presence of specific heavy metal ions; this results in a measurable change in device resistance due to the collapse of conductive inter-nanoparticle DNAzyme bridging. The paper discusses the effect of DNAzyme anchoring groups (i.e., thiol and amino functionalization groups) on device performance and reports on the successful detection of all three target ions in concentrations that are well below their maximum permitted levels in tap water. While the use of DNAzymes for the detection of lead in particular and, to some extent, cadmium has been studied extensively, this is one of the few reports on the successful detection of chromium (III) via a sensor incorporating DNAzymes. The sensor showed great potential for its future integration in autonomous and remote sensing systems due to its low power characteristics, simple and cost-effective fabrication, and easy automation and measurement.

Impacts of dietary exposure to pesticides on faecal microbiome metabolism in adult twins.

BACKGROUND: Dietary habits have a profound influence on the metabolic activity of gut microorganisms and their influence on health. Concerns have been raised as to whether the consumption of foodstuffs contaminated with pesticides can contribute to the development of chronic disease by affecting the gut microbiome. We performed the first pesticide biomonitoring survey of the British population, and subsequently used the results to perform the first pesticide association study on gut microbiome composition and function from the TwinsUK registry. METHODS: Dietary exposure of 186 common insecticide, herbicide, or fungicide residues and the faecal microbiome in 65 twin pairs in the UK was investigated. We evaluated if dietary habits, geographic location, or the rural/urban environment, are associated with the excretion of pesticide residues. The composition and metabolic activity of faecal microbiota was evaluated using shotgun metagenomics and metabolomics respectively. We performed a targeted urine metabolomics analysis in order to evaluate whether pesticide urinary excretion was also associated with physiological changes. RESULTS: Pyrethroid and/or organophosphorus insecticide residues were found in all urine samples, while the herbicide glyphosate was found in 53% of individuals. Food frequency questionnaires showed that residues from organophosphates were higher with increased consumption of fruit and vegetables. A total of 34 associations between pesticide residue concentrations and faecal metabolite concentrations were detected. Glyphosate excretion was positively associated with an overall increased bacterial species richness, as well as to fatty acid metabolites and phosphate levels. The insecticide metabolite Br2CA, reflecting deltamethrin exposure, was positively associated with the phytoestrogens enterodiol and enterolactone, and negatively associated with some N-methyl amino acids. Urine metabolomics performed on a subset of samples did not reveal associations with the excretion of pesticide residues. CONCLUSIONS: The consumption of conventionally grown fruit and vegetables leads to higher ingestion of pesticides with unknown long-term health consequences. Our results highlight the need for future dietary intervention studies to understand effects of pesticide exposure on the gut microbiome and possible health consequences.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors.

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Plasma lipid biomarkers in relation to BMI, lung function, and airway inflammation in pediatric asthma.

INTRODUCTION: There is limited understanding of how plasma fatty acid levels affect pulmonary function in pediatric years. It has been speculated that polyunsaturated fatty acids influence asthma via anti or pro-inflammatory mechanisms. Metabolomics presents a new and promising resource for identifying molecular processes involved in asthma pathology. OBJECTIVES: We investigated the relationship of plasma fatty acid metabolites as biomarkers of the 'mild-asthma' phenotype and lung function including airway inflammation in children. METHODS: This cross-sectional study involved 64 children (5-12 years, 33 male) with mild-asthma phenotype attending an outpatient pediatric clinic in Athens, Greece. Clinical examination included spirometry (FVC, FEV1, FEV1/FVC, PEF, FEF25-75%) and Fractional exhaled Nitric Oxide (FeNO). Targeted metabolomic profiling was used to quantify plasma fatty acid composition. Associations between lipids and pulmonary function indices were investigated applying linear regression. RESULTS: Targeted GC-MS identified 25 unique plasma fatty acids in mild-asthmatic children. Linear regression revealed significant associations between linoleic, oleic, erucic, cis-11-eicosenoic, arachidic acids and FEV1, FVC, FEV1/FVC, PEF, FEF25-75% and FeNO in the overweight/obese group, adjusting for age and sex; and in the normo-weight between stearic and arachidic acids versus FEV1 and FEV1/FVC respectively. No associations were observed for arachidonic, α-linolenic, EPA and DHA. CONCLUSION: Metabolomics is a novel science that is useful to discover metabolic signatures specific to disease. Evaluation of fatty acid status could assist clinicians in decision-making about a dietary modification that can be used for personalized nutrition therapies to achieve better asthma control, optimum lung function, and therapeutic response in children.

Simulation tool for predicting and optimizing the performance of nanoparticle based strain sensors.

In this work a Monte-Carlo tool simulating platinum nanoparticle (NP) based strain-sensors, on flexible substrates, is presented. The tool begins by randomly placing the NPs on the simulation area, with the ability to tune the NP surface coverage. After the calculation of the conductive paths that were generated in the previous step, the whole system is represented with an equivalent circuit; the NPs and the NP clusters act as nodes and the inter-particle gaps as resistances. The effective resistance is then calculated with the use of a Laplacian Matrix, which has proven extremely effective in significantly reducing the overall computational time. The simulation results are then benchmarked with experimental measurements from actual strain-sensing devices. The software is capable of predicting the strain-sensitivity for different NP sizes as well as surface coverages, emerging as a powerful computational tool for design-optimization of NP based devices in polymeric substrates, while it could well be extended to other nanocomposite materials used in flexible or stretchable electronic applications.

Identification of Two Commercial Pesticides by a Nanoparticle Gas-Sensing Array.

This study presents the experimental testing of a gas-sensing array, for the detection of two commercially available pesticides (i.e., Chloract 48 EC and Nimrod), towards its eventual use along a commercial smart-farming system. The array is comprised of four distinctive sensing devices based on nanoparticles, each functionalized with a different gas-absorbing polymeric layer. As discussed herein, the sensing array is able to identify as well as quantify three gas-analytes, two pesticide solutions, and relative humidity, which acts as a reference analyte. All of the evaluation experiments were conducted in close to real-life conditions; specifically, the sensors response towards the three analytes was tested in three relative humidity backgrounds while the effect of temperature was also considered. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analyzed using the common statistical analysis tool Principal Component Analysis (PCA). The sensing array, being compact, low-cost, and highly sensitive, can be easily integrated with pre-existing crop-monitoring solutions. Given that there are limited reports for effective pesticide gas-sensing solutions, the proposed gas-sensing technology would significantly upgrade the added-value of the integrated system, providing it with unique advantages.

Investigating the origins of ultra-short relaxation times of silver filaments in forming-free SiO2-based conductive bridge memristors.

The threshold switching effect is considered of outmost importance for a variety of applications ranging from the reliable operation of crossbar architectures to emulating neuromorphic properties with artificial neural networks. This property is strongly believed to be associated with the rich inherit dynamics of a metallic conductive filament (CF) formation and its respective relaxation processes. Understanding the origin of these dynamics is very important in order to control the degree of volatility and design novel electronic devices. Here, we present a synergistic numerical and experimental approach in order to deal with that issue. The distribution of relaxation time is addressed through time-resolved pulse measurements whereas the entire switching behavior is modeled through a 2D dynamical model by taking into account the destructive interference of the drift/diffusion transport mechanisms and the Soret diffusion flux due to the intense local Joule heating. The proposed mechanism interprets successfully both the threshold to bipolar switching transition as well as the self-rectifying effects in SiO2-based memories. The model incorporates the effect of electrode materials on the switching pattern and provides a different perception of the ionic transport processes, shading light into the ultra-small lifetimes of the CF and explaining the different behavior of the silver or copper active materials in a conductive bridge random access memory architecture.

Thin Film Protected Flexible Nanoparticle Strain Sensors: Experiments and Modeling.

In this work, the working performance of Platinum (Pt), solvent-free nanoparticle (NP)-based strain sensors made on a flexible substrate has been studied. First, a new model has been developed in order to explain sensor behaviour under strain in a more effective manner than what has been previously reported. The proposed model also highlights the difference between sensors based on solvent-free and solvent-based NPs. As a second step, the ability of atomic layer deposition (ALD) developed Al2O3 (alumina) thin films to act as protective coatings against humidity while in adverse conditions (i.e., variations in relative humidity and repeated mechanical stress) has been evaluated. Two different alumina thicknesses (5 and 11 nm) have been tested and their effect on protection against humidity is studied by monitoring sensor resistance. Even in the case of adverse working conditions and for increased mechanical strain (up to 1.2%), it is found that an alumina layer of 11 nm provides sufficient sensor protection, while the proposed model remains valid. This certifies the appropriateness of the proposed strain-sensing technology for demanding applications, such as e-skin and pressure or flow sensing, as well as the possibility of developing a comprehensive computational tool for NP-based devices.

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming.

The increased use of pesticides across the globe has a major impact on public health. Advanced sensing methods are considered of significant importance to ensure that pesticide use on agricultural products remains within safety limits. This study presents the experimental testing of a hybrid, nanomaterial based gas-sensing array, for the detection of a commercial organophosphate pesticide, towards its integration in a holistic smart-farming tool such as the "gaiasense" system. The sensing array utilizes nanoparticles (NPs) as the conductive layer of the device while four distinctive polymeric layers (superimposed on top of the NP layer) act as the gas-sensitive layer. The sensing array is ultimately called to discern between two gas-analytes: Chloract 48 EC (a chlorpyrifos based insecticide) and Relative Humidity (R.H.) which acts as a reference analyte since is anticipated to be present in real-field conditions. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analysed using a common statistical analysis tool, namely Principal Component Analysis (PCA). PCA has validated the ability of the array to detect, quantify as well as to differentiate between R.H. and Chloract. The sensing array being compact, low-cost and highly sensitive (LOD in the order of ppb for chlorpyrifos) can be effectively integrated with pre-existing crop-monitoring solutions such as the gaiasense.

Weight Status and Respiratory Health in Asthmatic Children.

In this study,we explored the effect of adiposity as measured by BMI on lung function in 72 asthmatic school children (5-12 years) using baseline data from the Mediterranean diet enriched with fatty fish intervention study. Bronchial function was assessed using spirometry and fractional exhaled nitric oxide (FeNO). BMI categories were classified as normal and overweight/obese based on International Obesity Task Force cut-offs. Weak correlations were observed between BMI and FVC (p = 0.013) and FEV1 (p = 0.026). Median FeNO was lower in the overweight/obese as compared to normal weight group (p = 0.027). Linear regression showed an increment in FEF25-75% in the overweight/obese group as compared to normal weight after controlling for confounders namely age, height, sex, regular physical activity, medication and KIDMED score (p = 0.043; ß = 11.65 units, 95% CI 0.36-22.94), although with no effect on FeNO. In conclusion, the findings of this study suggest that excess body weight could impact pulmonary dynamics in childhood asthma.

Atomic layer deposited Al2O3 thin films as humidity barrier coatings for nanoparticle-based strain sensors.

Al2O3 thin films deposited by atomic layer deposition on Pt nanoparticle-based strain sensors were studied as humidity barrier coatings for sensor protection. The effect of two deposition parameters-film thickness and growth temperature-is discussed in relation to an Al2O3 coating's ability to isolate the nanoparticle surface and protect strain sensitivity from humidity variations. It is shown that transmission electron microscopy images cannot confirm the effective protection of a nanoparticle surface, thus x-ray photoelectron spectroscopy and electrical measurements have been employed. The existence of a critical thickness of the Al2O3 protective film above which resistance and gauge factor variations are suppressed during humidity change was observed at different deposition temperatures. This ability is linked with the existence of incorporated pinholes and intrinsic hydroxyl groups in the Al2O3 thin film, which are responsible for humidity transport through the oxide.

Vibration Sensors on Flexible Substrates Based on Nanoparticle Films Grown by Physical Vapor Deposition.

Flexible electronics have gained a lot of attention in recent years due to their compatibility with soft robotics, artificial arms, and many other applications. Meanwhile, the detection of acoustic frequencies is a very useful tool for applications ranging from voice recognition to machine condition monitoring. In this work, the dynamic response of Pt nanoparticles (Pt NPs)-based strain sensors on flexible substrates is investigated. the nanoparticles were grown in a vacuum by magnetron-sputtering inert-gas condensation. Nanoparticle sensors made on cracked alumina deposited by atomic layer deposition on the flexible substrate and reference nanoparticle sensors, without the alumina layer, were first characterized by their response to strain. The sensors were then characterized by their dynamic response to acoustic frequency vibrations between 20 Hz and 6250 Hz. The results show that alumina sensors outperformed the reference sensors in terms of voltage amplitude. Sensors on the alumina layer could accurately detect frequencies up to 6250 Hz, compared with the reference sensors, which were sensitive to frequencies up to 4250 Hz, while they could distinguish between two neighboring frequencies with a difference of no more than 2 Hz.

Integrated Plastic Microfluidic Device for Heavy Metal Ion Detection.

The presence of heavy metal ions in soil, air and water constitutes an important global environmental threat, as these ions accumulate throughout the food chain, contributing to the rise of chronic diseases, including, amongst others, cancer and kidney failure. To date, many efforts have been made for their detection, but there is still a need for the development of sensitive, low-cost, and portable devices able to conduct on-site detection of heavy metal ions. In this work, we combine microfluidic technology and electrochemical sensing in a plastic chip for the selective detection of heavy metal ions utilizing DNAzymes immobilized in between platinum nanoparticles (PtNPs), demonstrating a reliable portable solution for water pollution monitoring. For the realization of the microfluidic-based heavy metal ion detection device, a fast and easy-to-implement fabrication method based on the photolithography of dry photosensitive layers is proposed. As a proof of concept, we demonstrate the detection of Pb2+ ions using the prototype microfluidic device.

Psoriasis immunometabolism: progress on metabolic biomarkers and targeted therapy.

Psoriasis is a common inflammatory disease that affects mainly the skin. However, the moderate to severe forms have been associated with several comorbidities, such as psoriatic arthritis, Crohn's disease, metabolic syndrome and cardiovascular disease. Keratinocytes and T helper cells are the dominant cell types involved in psoriasis development via a complex crosstalk between epithelial cells, peripheral immune cells and immune cells residing in the skin. Immunometabolism has emerged as a potent mechanism elucidating the aetiopathogenesis of psoriasis, offering novel specific targets to diagnose and treat psoriasis early. The present article discusses the metabolic reprogramming of activated T cells, tissue-resident memory T cells and keratinocytes in psoriatic skin, presenting associated metabolic biomarkers and therapeutic targets. In psoriatic phenotype, keratinocytes and activated T cells are glycolysis dependent and are characterized by disruptions in the TCA cycle, the amino acid metabolism and the fatty acid metabolism. Upregulation of the mammalian target of rapamycin (mTOR) results in hyperproliferation and cytokine secretion by immune cells and keratinocytes. Metabolic reprogramming through the inhibition of affected metabolic pathways and the dietary restoration of metabolic imbalances may thus present a potent therapeutic opportunity to achieve long-term management of psoriasis and improved quality of life with minimum adverse effects.

A novel nutraceutical formulation increases telomere length and activates telomerase activity in middle­aged rats.

Telomeres are major contributors to cell fate and aging through their involvement in cell cycle arrest and senescence. The accelerated attrition of telomeres is associated with aging­related diseases, and agents able to maintain telomere length (TL) through telomerase activation may serve as potential treatment strategies. The aim of the present study was to assess the potency of a novel telomerase activator on TL and telomerase activity in vivo. The administration of a nutraceutical formulation containing Centella asiatica extract, vitamin C, zinc and vitamin D3 in 18­month­old rats for a period of 3 months reduced the telomere shortening rate at the lower supplement dose and increased mean the TL at the higher dose, compared to pre­treatment levels. TL was determined using the Q­FISH method in peripheral blood mononuclear cells collected from the tail vein of the rats and cultured with RPMI­1640 medium. In both cases, TLs were significantly longer compared to the untreated controls (P≤0.001). In addition, telomerase activity was increased in the peripheral blood mononuclear cells of both treatment groups. On the whole, the present study demonstrates that the nutraceutical formulation can maintain or even increase TL and telomerase activity in middle­aged rats, indicating a potential role of this formula in the prevention and treatment of aging­related diseases.

Does BMI Modify the Association between Vitamin D and Pulmonary Function in Children of the Mild Asthma Phenotype?

Vitamin D deficiency and obesity are global health problems that are associated with increased asthma risk in children. The purpose of this study was to investigate whether BMI modifies pulmonary function across vitamin D tertiles in pediatric asthma patients of the mild asthma phenotype. This cross-sectional study conducted from November 2016-September 2017 compared lung function variability as assessed by spirometry and nitric oxide in exhaled breath (FeNO) among 35 normal-weight and 26 overweight/obese Greek schoolchildren (5-12 years old) with mild asthma. Serum 25 (OH)D levels ≥ 30 ng/mL were defined as 'sufficient', 20-30 ng/mL 'insufficient', and <20 ng/mL 'deficient'. Stratification by BMI category, linear regression showed positive associations between D, % FVC (ß = 0.49, 95%CI: 0.05, 0.94), and % FEV1 (ß = 0.48, 95%CI: -0.01, 0.95) in the normal-weight only, adjusted for age, sex, regular exercise, and medication. FEV1 was 10% higher in the normal-weight D-sufficient group compared to those D-deficient (ß = 10.43, 95%CI: 0.54, 20.32). No associations were observed for the overweight/obese group or FeNO. In conclusion, BMI modified associations of vitamin D on airway mechanics in children of the mild asthma phenotype. Serum 25 (OH)D concentrations ≥ 30 ng/mL were associated with higher ventilation in central airways of normal-weight asthmatic children.

Resistive crack-based nanoparticle strain sensors with extreme sensitivity and adjustable gauge factor, made on flexible substrates.

In this paper, we report the demonstration of highly sensitive flexible strain sensors formed by a network of metallic nanoparticles (NPs) grown under vacuum on top of a cracked thin alumina film which has been deposited by atomic layer deposition. It is shown that the sensor sensitivity depends on the surface density of NPs as well as on the thickness of alumina thin films that can both be well controlled via the deposition techniques. This method allows reaching a record strain sensitivity value of 2.6 × 108 at 7.2% strain, while exhibiting high sensitivity in a large strain range from 0.1% to 7.2%. The demonstration is followed by a discussion enlightening the physical understanding of sensor operation, which enables the tuning of its performance according to the above process parameters.

The snapshot of metabolic health in evaluating micronutrient status, the risk of infection and clinical outcome of COVID-19.

COVID-19 has re-established the significance of analyzing the organism through a metabolic perspective to uncover the dynamic interconnections within the biological systems. The role of micronutrient status and metabolic health emerge as pivotal in COVID-19 pathogenesis and the immune system's response. Metabolic disruption, proceeding from modifiable factors, has been proposed as a significant risk factor accounting for infection susceptibility, disease severity and risk for post-COVID complications. Metabolomics, the comprehensive study and quantification of intermediates and products of metabolism, is a rapidly evolving field and a novel tool in biomarker discovery. In this article, we propose that leveraging insulin resistance biomarkers along with biomarkers of micronutrient deficiencies, will allow for a diagnostic window and provide functional therapeutic targets. Specifically, metabolomics can be applied as: a. At-home test to assess the risk of infection and propose nutritional support, b. A screening tool for high-risk COVID-19 patients to develop serious illness during hospital admission and prioritize medical support, c(i). A tool to match nutritional support with specific nutrient requirements for mildly ill patients to reduce the risk for hospitalization, and c(ii). for critically ill patients to reduce recovery time and risk of post-COVID complications, d. At-home test to monitor metabolic health and reduce post-COVID symptomatology. Metabolic rewiring offers potential virtues towards disease prevention, dissection of high-risk patients, taking actionable therapeutic measures, as well as shielding against post-COVID syndrome.

A Clinical Trial for the Identification of Metabolic Biomarkers in Hashimoto's Thyroiditis and in Psoriasis: Study Protocol.

Hashimoto's thyroiditis and psoriasis are inflammatory disorders that significantly impact patients' quality of life, stressing the need for novel biomarkers of early diagnosis. This randomized clinical trial (NCT04693936) aims to identify Hashimoto's thyroiditis' and psoriasis' metabolic biomarkers and to investigate the effect of environmental factors on the disease-related metabolic imprint and quality of life. Patients with Hashimoto's thyroiditis, patients with psoriasis, and healthy individuals aged 18-60 will be recruited, enrolled according to eligibility criteria (medical history, clinical thyroid markers and the PASI score) and randomized to two groups. The intervention group will receive a combination of nutraceuticals for 6 months as part of a Mediterranean diet, and the control group will follow their usual diet. Data will be collected at baseline and the end of the study, including metabolite levels, lifestyle and anthropometric measurements, adherence to the Mediterranean diet (through the Mediterranean Diet Score) and disease-specific quality of life (through the Thyroid Patient Report Outcome for Hashimoto's group, and the Dermatology Life Quality Index for the psoriasis group). This study will investigate metabolic biomarkers and related changes in Hashimoto's thyroiditis and psoriasis and evaluate the association of metabolic changes with dietary factors and quality of life.

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles.

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.