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Methanol poisoning causes blindness, organ failure or even death when recognized too late. Currently, there is no methanol detector for quick diagnosis by breath analysis or for screening of laced beverages. Typically, chemical sensors cannot distinguish methanol from the much higher ethanol background. Here, we present an inexpensive and handheld sensor for highly selective methanol detection. It consists of a separation column (Tenax) separating methanol from interferants like ethanol, acetone or hydrogen, as in gas chromatography, and a chemoresistive gas sensor (Pd-doped SnO 2 nanoparticles) to quantify the methanol concentration. This way, methanol is measured within 2 min from 1 to 1000 ppm without interference of much higher ethanol levels (up to 62,000 ppm). As a proof-of-concept, we reliably measure methanol concentrations in spiked breath samples and liquor. This could enable the realization of highly selective sensors in emerging applications such as breath analysis or air quality monitoring. Methanol poisoning is frequent and dangerous, but selective sensors able to work in the presence of an ethanol background are missing. Here the authors propose an easy to operate sensor incorporating a separation column, able to sense toxic methanol levels in alcoholic beverages and human breath.

Gadolinium(III) complexes have been widely utilised as magnetic resonance imaging (MRI) contrast agents for decades. In recent years however, concerns have developed about their toxicity, believed to derive from demetallation of the complexes in vivo, and the relatively large quantities of compound required for a successful scan. Recent efforts have sought to enhance the relaxivity of trivalent gadolinium complexes without sacrificing their stability. This review aims to examine the strategic design of ligands synthesised for this purpose, provide an overview of recent successes in gadolinium-based contrast agent development and assess the requirements for clinical translation. Gadolinium(III) complexes are strong enhancers of magnetic resonance imaging (MRI) signals, thus are widely used as contrast agents despite their potential toxicity. Here, the authors review ligand design approaches aimed at improving the stability of Gd(III)-based MRI contrast agents.

The ability to detect low numbers of microbial cells in food and clinical samples is highly valuable but remains a challenge. Here we present a detection system (called ‘APC-Cas’) that can detect very low numbers of a bacterial pathogen without isolation, using a three-stage amplification to generate powerful fluorescence signals. APC-Cas involves a combination of nucleic acid-based allosteric probes and CRISPR-Cas13a components. It can selectively and sensitively quantify Salmonella Enteritidis cells (from 1 to 10 5 CFU) in various types of samples such as milk, showing similar or higher sensitivity and accuracy compared with conventional real-time PCR. Furthermore, APC-Cas can identify low numbers of S . Enteritidis cells in mouse serum, distinguishing mice with early- and late-stage infection from uninfected mice. Our method may have potential clinical applications for early diagnosis of pathogens. The detection of pathogens in food and clinical samples remains a challenge. Here, Shen et al. present a detection system, involving a combination of nucleic acid-based allosteric probes and CRISPR-Cas13a components, that can detect very low numbers of a bacterial pathogen in milk and serum samples without isolation.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, which is characterised by degeneration of distinct neuronal populations, including dopaminergic neurons of the substantia nigra. Here, we use a metabolomics profiling approach to identify changes to lipids in PD observed in sebum, a non-invasively available biofluid. We used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Pathway enrichment analysis shows alterations in lipid metabolism related to the carnitine shuttle, sphingolipid metabolism, arachidonic acid metabolism and fatty acid biosynthesis. This study shows sebum can be used to identify potential biomarkers for PD. Studies of metabolites in neurodegeneration have not yet used sebum as a source fluid. Here the authors demonstrate the potential of metabolomics of sebum samples from individuals with Parkinson’s disease and controls.

Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples. SARS-CoV-2 is reliably detected in nasal swab samples using mass spectrometry and machine learning analysis.

Blood stream infection or sepsis is a major health problem worldwide, with extremely high mortality, which is partly due to the inability to rapidly detect and identify bacteria in the early stages of infection. Here we present a new technology termed ‘Integrated Comprehensive Droplet Digital Detection’ (IC 3D) that can selectively detect bacteria directly from milliliters of diluted blood at single-cell sensitivity in a one-step, culture- and amplification-free process within 1.5–4 h. The IC 3D integrates real-time, DNAzyme-based sensors, droplet microencapsulation and a high-throughput 3D particle counter system. Using Escherichia coli as a target, we demonstrate that the IC 3D can provide absolute quantification of both stock and clinical isolates of E. coli in spiked blood within a broad range of extremely low concentration from 1 to 10,000 bacteria per ml with exceptional robustness and limit of detection in the single digit regime. Early detection of blood stream infections is essential for providing effective treatments. Here the authors present a system integrating DNAzyme sensors, droplet microfluidics and a high-throughput 3D particle counter that can detect specific, single bacterial cells in blood within a few hours.

Surface-enhanced Raman scattering (SERS) is of interest for biomedical analysis and imaging because of its sensitivity, specificity and multiplexing capabilities. The successful application of SERS for in vivo biosensing requires probes to be biocompatible and procedures to be minimally invasive, challenges that have respectively been met by developing new nanoprobes and instrumentation. This Review presents recent developments in these areas, describing case studies in which sensors have been implemented, as well as outlining shortcomings that must be addressed before SERS sees clinical use. Surface-enhanced Raman scattering (SERS) is a physical phenomenon first discovered in 1974. SERS has since been exploited for bioanalysis because of its high sensitivity and multiplexing capabilities. This Review describes the progress made and problems faced with respect to using in vivo SERS in humans.

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers. Biomonitoring of sweat from fingertips overcomes current limitations in time-resolved metabolomic profiling of humans and may prove to become a powerful, noninvasive tool for precision medicine. Here, in a feasibility study of short interval sampling of sweat from fingertips, the authors assay individual dynamic metabolic patterns of endogenous and exogenous molecules.

Portable, low-cost and quantitative detection of a broad range of targets at home and in the field has the potential to revolutionize medical diagnostics and environmental monitoring. Despite many years of research, very few such devices are commercially available. Taking advantage of the wide availability and low cost of the pocket-sized personal glucose meter—used worldwide by diabetes sufferers—we demonstrate a method to use such meters to quantify non-glucose targets, ranging from a recreational drug (cocaine, 3.4 µM detection limit) to an important biological cofactor (adenosine, 18 µM detection limit), to a disease marker (interferon-gamma of tuberculosis, 2.6 nM detection limit) and a toxic metal ion (uranium, 9.1 nM detection limit). The method is based on the target-induced release of invertase from a functional-DNA–invertase conjugate. The released invertase converts sucrose into glucose, which is detectable using the meter. The approach should be easily applicable to the detection of many other targets through the use of suitable functional-DNA partners (aptamers, DNAzymes or aptazymes). Portable sensors for the rapid quantitation of a variety of analytical targets could revolutionize both medical diagnostics and environmental monitoring. Here, functional DNA sensors that release the enzyme invertase in response to an analyte of choice are described. The enzyme converts sucrose to glucose which can then be easily detected using a widely available personal glucose meter.

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.