Spectral fiber photometry derives hemoglobin concentration changes for accurate measurement of fluorescent sensor activity.
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ABSTRACT:
SUBMITTER: Zhang WT
PROVIDER: S-EPMC9308135 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This study examines the use of a conductive carbon fiber to construct a flexible biosensing platform for monitoring biomarkers in sweat. Cortisol was chosen as a model analyte. Functionalization of the conductive carbon yarn (CCY) with ellipsoidal Fe2O3 has been performed to immobilize the antibodies specific to cortisol. 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and N-Hydroxysuccinimide (NHS) chemistry has been used to immobilize the antibodies onto the Fe2O3 modified CCY. Crystallinity, structure, morphology, flexibility, surface area, and elemental analysis were studied using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, Field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FE-SEM/EDS) and Brunauer-Emmett-Teller (BET) analysis. Mechanical properties of the fiber such as tensile strength, young's modulus have also been investigated. Under optimal parameters, the fabric sensor exhibited a good linearity (r2?=?0.998) for wide a linear range from 1 fg to 1??g with a detection limit of 0.005 fg/mL for the sensitive detection of cortisol. Repeatability, reliability, reproducibility, and anti-interference properties of the current sensor have been investigated. Detection of cortisol levels in human sweat samples has also been investigated and the results were validated with commercial chemiluminescence immunoassay (CLIA) method.
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Project description:Ammonium (NH??) is the most common N-source for yeast fermentations, and N-limitation is frequently applied to reduce growth and increase product yields. While there is significant molecular knowledge on NH?? transport and assimilation, there have been few attempts to measure the in vivo concentration of this metabolite. In this article, we present a sensitive and accurate analytical method to quantify the in vivo intracellular ammonium concentration in Saccharomyces cerevisiae based on standard rapid sampling and metabolomics techniques. The method validation experiments required the development of a proper sample processing protocol to minimize ammonium production/consumption during biomass extraction by assessing the impact of amino acid degradation-an element that is often overlooked. The resulting cold chloroform metabolite extraction method, together with quantification using ultra high performance liquid chromatography-isotope dilution mass spectrometry (UHPLC-IDMS), was not only more sensitive than most of the existing methods but also more accurate than methods that use electrodes, enzymatic reactions, or boiling water or boiling ethanol biomass extraction because it minimized ammonium consumption/production during sampling processing and interference from other metabolites in the quantification of intracellular ammonium. Finally, our validation experiments showed that other metabolites such as pyruvate or 2-oxoglutarate (?KG) need to be extracted with cold chloroform to avoid measurements being biased by the degradation of other metabolites (e.g., amino acids).
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Project description:BACKGROUND:Hand hygiene is a crucial and cost-effective method to prevent health care-associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation. OBJECTIVE:We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing. METHODS:We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping. RESULTS:Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance. CONCLUSIONS:A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.
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Project description:We have modified the periplasmic Escherichia coli glucose/galactose binding protein (GBP) and labelled with environmentally sensitive fluorophores to further explore its potential as a sensor for the evaluation of glucose concentration in airway surface liquid (ASL). We identified E149C/A213R GBP labelled with N,N'-Dimethyl-N-(iodoacetyl)-N'-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)ethylenediamine (IANBD, emission wavelength maximum 536nm) with a Kd for D-glucose of 1.02mM and a fluorescence dynamic range of 5.8. This sensor was specific for D-glucose and exhibited fluorescence stability in experiments for several hours. The use of E149C/A213R GBP-IANBD in the ASL of airway cells grown at air-liquid-interface (ALI) detected an increase in glucose concentration 10 minutes after raising basolateral glucose from 5 to 15mM. This sensor also reported a greater change in ASL glucose concentration in response to increased basolateral glucose in H441 airway cells compared to human bronchial epithelial cells (HBEC) and there was less variability with HBEC data than that of H441 indicating that HBEC more effectively regulate glucose movement into the ASL. The sensor detected glucose in bronchoalveolar lavage fluid (BALf) from diabetic db/db mice but not normoglycaemic wildtype mice, indicating limited sensitivity of the sensor at glucose concentrations <50μM. Using nasal inhalation of the sensor and spectral unmixing to generate images, E149C/A213R GBP-IANBD fluorescence was detected in luminal regions of cryosections of the murine distal lung that was greater in db/db than wildtype mice. In conclusion, this sensor provides a useful tool for further development to measure luminal glucose concentration in models of lung/airway to explore how this may change in disease.
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