Controlled silver electrodeposition on gold nanoparticle antibody tags for ultrasensitive prostate specific antigen sensing using electrochemical and optical smartphone detection.

One of the current challenges in medicine is to achieve a rapid and unequivocal detection and quantification of extremely low levels of disease biomarkers in complex biological samples. Here, we present the development and analytical evaluation of a low-cost smartphone-based system designed for ultrasensitive detection of the prostate-specific antigen (PSA) using two detection alternatives: electrochemical or optical, by coupling the smartphone with a portable potentiostat or magnifying lenses. An antibody tagged with gold nanoparticles (AuNPs), and indium tin oxide coated polyethylene terephthalate platform (ITO-PET) have been used to develop a sandwich-type immunoassay. Then, a controlled silver electrodeposition on the AuNPs surface is carried out, enhancing their size greatly. Due to such strong nanoparticle-size amplification (from nm to µm), the final detection can be dual, by measuring current intensity or the number of silver-enlarged microstructures generated. The proposed strategies exhibited limit detections (LOD) of 102 and 37 fg/mL for electrochemical and optical detection respectively. The developed immunosensor reaches excellent selectivity and performance characteristics to quantify biomarkers at clinically relevant values without any pretreatment. These proposed procedures could be useful to check and verify possible recurrence after clinical treatment of tumors or even report levels of disease serum biomarkers in early stages.

Progress and Challenge of Sensors for Dairy Food Safety Monitoring.

One of the most consumed foods is milk and milk products, and guaranteeing the suitability of these products is one of the major concerns in our society. This has led to the development of numerous sensors to enhance quality controls in the food chain. However, this is not a simple task, because it is necessary to establish the parameters to be analyzed and often, not only one compound is responsible for food contamination or degradation. To attempt to address this problem, a multiplex analysis together with a non-directed (e.g., general parameters such as pH) analysis are the most relevant alternatives to identifying the safety of dairy food. In recent years, the use of new technologies in the development of devices/platforms with optical or electrochemical signals has accelerated and intensified the pursuit of systems that provide a simple, rapid, cost-effective, and/or multiparametric response to the presence of contaminants, markers of various diseases, and/or indicators of safety levels. However, achieving the simultaneous determination of two or more analytes in situ, in a single measurement, and in real time, using only one working 'real sensor', remains one of the most daunting challenges, primarily due to the complexity of the sample matrix. To address these requirements, different approaches have been explored. The state of the art on food safety sensors will be summarized in this review including optical, electrochemical, and other sensor-based detection methods such as magnetoelastic or mass-based sensors.

Functionalized heteroatom-doped carbon dots for biomedical applications: A review.

Carbon nanoparticles (CDs) have recently drawn a great attention in (bio)chemical analysis, sensing and bioimaging owing to their photostability, water stability, minimal toxicity, biocompatibility and ease of surface functionalization. While the vast majority of CDs applications rely on exploiting their fluorescent properties, doping such nanomaterials with various elements has recently received increasing attention as an effective approach to modify their optoelectronic characteristics, introducing novel improved optical features such as phosphorescence, upconversion luminescence or multimodal imaging capabilities. This review article focuses in the recent advances on the synthesis of heteroatom-doped CDs, exhibiting distinctive features of high value for sensing and imaging, as well as various functionalization schemes developed for guided analyte labeling. Relevant applications in chemical sensing, bioimaging and disease therapy are here presented. A final section intends to provide an overview towards future developments of such emerging light-emitting nanomaterials in the design of future devices and strategies for (bio)analytical chemistry.

Industrial impact on sustainable dairy farms: Essential elements, hazardous metals and polycyclic aromatic hydrocarbons in forage and cow's milk.

Sustainable dairy farms are characterised by the self-production of forage for animal feed. These farms are sometimes located near industrial areas, entailing a risk of food chain contamination with hazardous metals and polycyclic aromatic hydrocarbons (PAHs). Accordingly, evaluating the impact of pollution on forage and milk is of great interest. In this study, the effects of industrial factors on sustainable forage from 43 dairy farms and possible correlations between inorganic elements and PAHs were studied. Spearman's correlation and principal component analysis (PCA) were performed for the forage and milk. Most of the inorganic elements in the forage were below the maximum residual limits for cadmium (Cd) and lead (Pb), established in EU 2013/1275 and EU 2019/1869, respectively. However, arsenic (As) and mercury (Hg) levels were above their respective limits in the forage (EU 2019/1869). No milk samples exceeded the maximum residual limits for Pb (EU 488/2014) or Cd (EU 1881/2006) in dairy products. Heavy-weight PAHs (HW-PAHs, four or more aromatic rings) were detected in forage but not in milk. In the forage samples, HW-PAHs were positively correlated with Zn and Cd. In addition, some hazardous metals (chromium (Cr), iron (Fe), As, Hg, and Pb) also were positively correlated with Zn and Cd. Interestingly, no correlations were found between forage pollutants and milk, suggesting that these pollutants have a low transfer rate to milk. The PCA results highlighted the predominant contribution of PAHs to the global variance in forage samples collected at different distances from industrial areas. In milk, the contributions of hazardous metals and PAHs were more balanced than in forages. Finally, when distances to potential pollution sources were included in the PCA of forage samples, a negative correlation was observed between the former and the concentrations of HW-PAHs, Cd, and Zn, suggesting that thermal power plants and steel factory emissions were the main sources of polluting forage in this area.

MNAzymes and gold nanoparticles as isothermal signal amplification strategy for visual detection of miRNA.

MicroRNAs (miRNAs) represent a class of small noncoding RNAs that are considered a novel emerging class of disease biomarkers in a variety of afflictions. Sensitive detection of miRNA is typically achieved using hybridization-based methods coupled with genetic amplification techniques. Although their sensitivity has improved, amplification techniques often present erroneous results due to their complexity. In addition, the use of these techniques is usually linked to the application of protein enzymes, the activity of which is dependent on the temperature and pH of the medium. To address these drawbacks, an alternative genetic enzyme for the highly sensitive detection of miRNAs is proposed in this work. Multicomponent nucleic acid enzymes (MNAzymes), coupled with the use of DNA-functionalized gold nanoparticles (AuNPs), were used in this study to develop an isothermal signal amplification strategy for visual genetic detection. miR146a, a biomarker of bovine mastitis present in milk, was selected as a model analyte. The developed methodology is easily carried out in 80 min at 50 °C, generating a low visual limit of detection of 250 pM based on the observation of a color change. The methodology was successfully applied to the detection of miR146a in raw cow milk samples.

Impact of Potentially Toxic Compounds in Cow Milk: How Industrial Activities Affect Animal Primary Productions.

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) frequently coexist in soils near industrial areas and sometimes in environmental compartments directly linked to feed (forage) and food (milk) production. However, the distribution of these pollutants along the dairy farm production chain is unclear. Here, we analyzed soil, forage, and milk samples from 16 livestock farms in Spain: several PTEs and PAHs were quantified. Farms were compared in terms of whether they were close to (<5 km) or far away from (>5 km) industrial areas. The results showed that PTEs and PAHs were enriched in the soils and forages from farms close to industrial areas, but not in the milk. In the soil, the maximum concentrations of PTEs reached 141, 46.1, 3.67, 6.11, and 138 mg kg-1 for chromium, arsenic, cadmium, mercury, and lead, respectively, while fluoranthene (172.8 µg kg-1) and benzo(b)fluoranthene (177.4 µg kg-1) were the most abundant PAHs. Principal component analysis of the soil PTEs suggested common pollution sources for iron, arsenic, and lead. In the forage, the maximum contents of chromium, arsenic, cadmium, mercury, and lead were 32.8, 7.87, 1.31, 0.47, and 7.85 mg kg-1, respectively. The PAH found in the highest concentration in the feed forage was pyrene (120 µg kg-1). In the milk, the maximum PTE levels were much lower than in the soil or the feed forages: 74.1, 16.1, 0.12, 0.28, and 2.7 µg kg-1 for chromium, arsenic, cadmium, mercury, and lead, respectively. Neither of the two milk samples exceeded the 20 µg kg-1 limit for lead set in EU 1881/2006. Pyrene was the most abundant PAH found in the milk (39.4 µg kg-1), while high molecular weight PAHs were not detected. For PTEs, the results showed that soil-forage transfer factors were higher than forage-milk ratios. Our results suggest that soils and forages around farms near industries, as well as the milk produced from those farms, have generally low levels of PTE and PAH contaminants.

An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil.

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.

Advances in quantum dots as diagnostic tools.

Quantum dots (QDs) are crystalline inorganic semiconductor nanoparticles a few nanometers in size that possess unique optical electronic properties vs those of larger materials. For example, QDs usually exhibit a strong and long-lived photoluminescence emission, a feature dependent on size, shape and composition. These special optoelectronic properties make them a promising alternative to conventional luminescent dyes as optical labels in biomedical applications including biomarker quantification, biomolecule targeting and molecular imaging. A key parameter for use of QDs is to functionalize their surface with suitable (bio)molecules to provide stability in aqueous solutions and efficient and selective tagging biomolecules of interest. Researchers have successfully developed biocompatible QDs and have linked them to various biomolecule recognition elements, i.e., antibodies, proteins, DNA, etc. In this chapter, QD synthesis and characterization strategies are reviewed as well as the development of nanoplatforms for luminescent biosensing and imaging-guided targeting. Relevant biomedical applications are highlighted with a particular focus on recent progress in ultrasensitive detection of clinical biomarkers. Finally, key future research goals to functionalize QDs as diagnostic tools are explored.

Near-Infrared Sensors for Onsite and Noninvasive Quantification of Macronutrients in Breast Milk.

Breast milk is an optimal food that covers all the nutritional needs of the newborn. It is a dynamic fluid whose composition varies with lactation period. The neonatal units of hospitals have human milk banks, a service that analyzes, stores, and distributes donated human milk. This milk is used to feed premature infants (born before 32 weeks of gestation or weighing less than 1500 g) whose mothers, for some reason, cannot feed them with their own milk. Here, we aimed to develop near-infrared spectroscopy (NIRS) measures for the analysis of breast milk. For this purpose, we used a portable NIRS instrument scanning in the range of 1396-2396 nm to collect the spectra of milk samples. Then, different chemometrics were calculated to develop 18 calibration models with and without using derivatives and the standard normal variate. Once the calibration models were developed, the best treatments were selected according to the correlation coefficients (r2) and prediction errors (SECVs). The best results for the assayed macronutrients were obtained when no pre-treatment was applied to the NIR spectra of fat (r2 = 0.841, SECV = 0.51), raw protein (r2 = 0.512, SECV = 0.21), and carbohydrates (r2 = 0.741, SECV = 1.35). SNV plus the first derivative was applied to obtain satisfactory results for energy (r2 = 0.830, SECV = 9.60) quantification. The interpretation of the obtained results showed the richness of the NIRS spectra; moreover, the presence of specific bands for fat provided excellent statistics in quantitative models. These results demonstrated the ability of portable NIRS sensors in a methodology developed for the quality control of macronutrients in breast milk.

Absolute quantification of proteins using element mass spectrometry and generic standards.

Elemental mass spectrometry is a powerful analytical technique widely established in inorganic analysis. However, despite its quantitative capabilities, it is not yet fully integrated or considered in Life Sciences fields like proteomics. Whereas it is true that ICP-MS has suffered from several instrumental and analytical limitations that have hindered its applicability in protein analysis, significant developments during the last decades have turned ICP-MS into an interesting and, in our opinion, a powerful tool to consider for accurate protein quantification without recourse to specific protein standards. Herein we will try to discuss how these traditional limitations in ICP-MS have been overcome, what further improvements are yet necessary (some of which are shared with MS-based proteomics platforms) and enlighten some of the already existing and potential applications of ICP-MS in absolute quantitative proteomics. SIGNIFICANCE: ICP-MS has the potential to become a complementary tool to help molecular mass spectrometry cope with existing limitations, especially those related to standardization and accuracy, in the absolute proteomics field. It can provide absolute quantification of diverse proteoforms using a single generic compound containing sulfur and/or another target element (e.g., phosphorous). Moreover, its applications in quantitative proteomics are no longer limited to protein standards certification or quantification of simple or purified mixtures. Interestingly, absolute quantification of proteins using ICP-MS is favored when carried out at the intact level, making it very compatible with top-down proteomics approaches. Recent instrumental and methodological advances enable synergic combination of ICP-MS with stablished LC-MS proteomics methodologies, setting the basis for its implementation in quantitative proteomics workflows.

The Dairy Cow Slurry Composition Used as Organic Fertilizer Is Influenced by the Level and Origin of the Dietary Protein.

Less than 30% of dairy cattle's nitrogen ingested is retained in milk. Therefore, large amounts of nitrogen can be excreted in manure and urine with a potential environmental impact. In addition, some legume forages can be more susceptible to proteolysis during the silage process than grasses, and dairy cows fed these legume silages would excrete a larger quantity of nitrogen in slurry. The objectives of this work were to evaluate the amount of nitrogen excretion in dairy cows fed different protein levels and legume silages with a view to improve the slurry quality as a co-product that can be used as fertilizer. Two double 3 × 3 Latin square trials were carried out in order to study three different protein levels (high, medium, and low) and three different silages (grass, faba bean, and field pea). Dry matter intake, milk production, and composition were not affected by treatments. The excretion of ammonia-N in the urine was almost four times lower in the diet with the lowest protein level. The ammonia-N in the urine was twice as high with the pea silage than faba bean and grass silages. In conclusion, the diet containing 13% of protein meets the protein requirement for lactating cows producing 31 kg daily, with low nitrogen excretion in the urine, and the main pathway for the excretion of surplus nitrogen from legume silages is through urine and the metabolization of pea silage protein goes toward ammonia-N.

Visual detection of microRNA146a by using RNA-functionalized gold nanoparticles.

Gold nanoparticles of different sizes have been synthesized and surface-functionalized with selected RNA probes in order to develop a rapid, low-cost and sensitive method for detection of microRNA146a (miR146a). The strategy is based on the change of colour that can be observed visually after aggregation of the RNA modified-gold nanoparticles (AuNPs) in presence of miR146a. Experimental conditions have been carefully selected in order to obtain a good sensitivity that allows to perform visual detection of microRNA at the nM level, achieving a detection limit of 5 nM. Good repeatability and selectivity versus other sequences that only differ from miR146a in 3 bases was achieved. miR146a has been described as one of the main microRNA involved in the immune response of bovine mastitis, being expressed in tissue, blood and milk samples. The method was successfully applied to the detection of miR146a in raw cow milk samples. The present scheme constitutes a rapid and low-cost alternative to perform highly sensitive detection of microRNA without the need of instrumentation and amplification steps for the early detection of bovine mastitis in the agrofood industry. Graphical abstract Schematic representation of the assay based on aggregation of RNA-modified gold nanoparticles (blue) in presence of microRNA146a generating a dark blue spot onto a solid support, versus a pink spot observed in absence of miR146a due to dispersed gold nanoparticles (red).

Electrochemical quantification of Ag2S quantum dots: evaluation of different surface coating ligands for bacteria determination.

In this work, novel silver sulphide quantum dots (Ag2S QD) are electrochemically quantified for the first time. The method is based on the electrochemical reduction of Ag+ to Ag0 at -0.3 V on screen-printed carbon electrodes (SPCEs), followed by anodic stripping voltammetric oxidation that gives a peak of currents at +0.06 V which represents the analytical signal. The optimized methodology allows the quantification of water-stabilized Ag2S QD in the range of approximately 2 × 109-2 × 1012 QD·mL-1 with a good reproducibility (RSD: 5%). Moreover, as proof-of-concept of relevant biosensing application, Ag2S QD are evaluated as tags for Escherichia coli (E. coli) bacteria determination. Bacteria tagged with QD are separated by centrifugation from the sample solution and placed on the SPCE surface for quantitative analysis. The effect of two different Ag2S QD surface coating/stabilizing agents on both the voltammetric response and the bacteria sensing is also evaluated. 3-mercaptopropionic acid (3-MPA) is studied as model of short length coating ligand with no affinity for the bacteria, while boronic acid (BA) is evaluated as longer length ligand with chemical affinity for the polysaccharides present in the peptidoglycan layer on the bacteria cells surface. The biosensing system allows to detect bacteria in the range 10-1-103 bacteria·mL-1 with a limit of detection as low as 1 bacteria·mL-1. This methodology is a promising proof-of-concept alternative to traditional laboratory-based tests, with good sensitivity and short time and low cost of analysis. Graphical abstractNovel silver sulphide quantum dots (Ag2S QD) are electrochemically quantified for the first time. Moreover, Ag2S QD are evaluated as tags for Escherichia coli bacteria determination. The effect of two different QD surface coating ligands is also evaluated.

Near-infrared fluorescent nanoprobes for highly sensitive cyanide quantification in natural waters.

Near infrared (NIR) emitting Ag2S quantum dots have been synthesized, characterized and evaluated for chemical sensing applications. After their optical characterization, it was observed that the Ag2S quantum dots present both, excitation and emission in the NIR region, and an excellent quantum yield of 33.2%. These features are of great value for many biological applications, since autofluorescence of biological tissues or cells is minimized, and also for environmental applications, where other fluorescent concomitant species with excitation and emission in the ultraviolet-visible region might be present. Different purification procedures were evaluated in order to obtain a stable and homogeneous population of nanoparticles, which is necessary to perform quantitative analysis (e.g.: mass spectrometry-based applications), as well as to obtain a narrow NIR emission spectrum for optical applications. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and asymmetric flow field flow fractionation coupled to inductively coupled plasma-mass spectrometry has been performed to obtain parameters not easily achieved and of great interest in different research areas, such as the nanoparticle concentration NIR-emitting nanoparticles, and the surface ligand density, which directly affects to the interactions of the nanoparticles with their close environment, including unspecific adsorptions, cellular uptake, macrophage interaction, etc. Finally, the capability for sensing analytes of environmental interest based on direct-interactions of a reactive compound with the surface of the nanoparticle has been also evaluated. Quenching of the NIR emission upon interaction of the Ag2S quantum dots with cyanide ions was observed. Hence, a rapid, selective and highly sensitive methodology was developed for the detection of cyanide in natural waters.

Handheld NIRS sensors for routine compound feed quality control: Real time analysis and field monitoring.

Significant advances achieved in different sensor technologies and computer processing data have made possible to respond the needs of livestock sector, providing precise and rapid information on feed composition, being an alternative to real time quality control on compound feed the use of handheld NIRS sensors. This work aimed to evaluate two hand-held portable NIR spectrophotometers for on-site and real time analysis of nutritive parameters in raw compound feed: Phazir 1624 Polychromix Inc (PhIR) and MicroNIRTM 1700 by JDSU (MICRO). For computing data, different combinations of pre-treatments and multivariate statistical methods have been assayed to extract the valuable information of spectra data and to develop appropriate calibrations. The calibration models displayed greatest predictive capacity for Crude Protein (CP), Crude Fiber (CF) and Starch (STCH) and the determination coefficients of cross validation were 0.90-0.88 for CP, 0.85-0.91 for CF, 0.89-0.88 and 0.89-0.91 for STCH using PhIR and MICRO instruments respectively. Dry Matter showed the lowest determination coefficients of cross validation 0.67-0.73. Accuracy achieved 99-101% for both NIRS instruments and no differences were found when applying tstudent-test comparing reference and predicted data. Results obtained with both instruments were compared by using standard deviation and not significant differences were observed at the 5% level. Results so far have demonstrated the potential of these handheld NIRS instruments proposed here to estimate the individual compound feeds composition changes at farms level instantly, time avoiding the disadvantage of moving the samples to the lab.

Subclinical ketosis on dairy cows in transition period in farms with contrasting butyric acid contents in silages.

This study examines the relationship between subclinical ketosis (SCK) in dairy cows and the butyric acid content of the silage used in their feeding. Twenty commercial farms were monitored over a period of 12 months. The feed at each farm and the silages used in its ration were sampled monthly for proximal analysis and for volatile fatty acid analysis. A total of 2857 urine samples were taken from 1112 cows to examine the ketonuria from about 30 days prepartum to 100 postpartum. Wide variation was recorded in the quality of silages used in the preparation of diets. Approximately 80% of the urine samples analyzed had no detectable ketone bodies, 16% returned values indicative of slight SCK, and the remainder, 4%, showed symptoms of ketosis. Most of the cases of hyperkenuria were associated with the butyric acid content of the silage used (r2=0.56; P<0.05). As the metabolizable energy content of the feed was similar, no relationship was observed between the proportion of cows with SCK and the energy content of the feed. In our study, the probability of dairy cows suffering SCK is higher when they are eating feed made from silage with a high butyric acid content (35.2 g/kg DM intake).

Effect of total mixed ration composition and daily grazing pattern on milk production, composition and fatty acids profile of dairy cows.

The possibilities of using high quality pastures in conjunction with total mixed ration (TMR) during the grazing season have been examined. An experiment with sixteen Holstein cows blocked and randomly assigned to four treatments in a factorial arrangement was conducted in order to evaluate the influence of grazing time of day (day or night) and type of silage (maize or Italian ryegrass) included in the TMR of dairy cows grazing 12 h daily on milk yield, composition and fatty acid profile. The silage type had no effect on the dry matter intake, milk yield and fat and protein proportions. However, cows grazing during the night ate more grass than cows grazing during the day (8·53 vs. 5·65 kg DM/d; P<0·05). No differences were seen between grazing-time with respect to milk production, fat and protein contents. However, the proportion of polyunsaturated fatty acid was higher in milk of dairy cows grazing at night-time than grazing at day-time, especially 18:2n-6 (2·37 vs. 2·12 g/100 g FA respectively, P<0·05) and 18:2cis9trans11 (2·08 vs. 1·74 g/100 g FA respectively, P<0·05).

Improving the fatty acid profile of dairy cow milk by combining grazing with feeding of total mixed ration.

Grazing cows could produce milk with a higher proportion of polyunsaturated fatty acids, which is beneficial to human health, compared with non-grazing cows, though grazing alone could compromise milk production. Under oceanic climate conditions, a study involving 15 dairy cows, fed total mixed ration (TMR) ad libitum in combination with different grazing times of 12 h (TMR12), 6 h (TMR06) and zero grazing time (TMR00) with the aim to evaluate different strategies on the fatty acids profile of milk and milk production. No differences were seen between the treatments with respect to milk yield (34.4+/-6.3 kg/d) or milk protein content (30.4+/-1.8 g/kg). The milk produced by the TMR12 cows had less total fat (36.2 vs. 38.2 g/kg) and saturated fatty acid (FA, 69.39 vs. 71.44 g/100 g FA) than that produced by the TMR00 cows. The concentration of vaccenic acid in the TMR06 and TMR12 milk was twice that of the TMR00 milk (4.22, 4.09 and 2.26 g/100 g FA respectively). Linear increases in conjugated linoleic (CLA) and linolenic acids were observed with increasing grazing time. Pasture was an important source of FA especially C18:3 for TMR06 and TMR12 cows. Under oceanic climatic conditions, the grazing of dairy cows as a complement to feeding with TMR can improve the FA profile of milk and increase its CLA content.