Coordinated Dispersion and Aggregation of Gold Nanorod in Aptamer-Mediated Gestational Hypertension Analysis

A Gold Nanoparticle-Based Cortisol Aptasensor for Non-Invasive Detection of Fish Stress

Cortisol is a key stress biomarker in humans and animals, including fishes. In aquafarming, stress monitoring using cortisol quantification can help to optimize aquaculture practices for welfare and productivity enhancement. However, most current methods for cortisol detection rely on invasive tissue sampling. In this work, we developed a gold nanoparticle (AuNP)-based cortisol sensor to address the demand of detecting picomolar ranges of cortisol from complex fish tank water matrices as a non-invasive alternative for more effective stress monitoring. We first identified a DNA aptamer with effective binding to cortisol and then conjugated the thiol-labelled aptamer to AuNPs together with a blocker molecule (CALNN) to form an Au-Apt-CALNN conjugate that is stable in fish tank water. The cortisol detection principle is based on magnesium chloride (MgCl2)-induced particle aggregation, where the cortisol-bound aptamer on the AuNPs folds into a tertiary structure and provides greater protection for Au-Apt-CALNN against MgCl2-induced aggregation due to steric stabilization. At an optimum MgCl2 concentration, the differential stability of particles with and without cortisol binding offers a limit of detection (LOD) of 100 pM for cortisol within a 35 min reaction. The aptasensor has been validated on recirculating aquaculture system (RAS) fish tank water samples by the HPLC method and was able to detect changes in water cortisol induced by two different stress paradigms. This on-site deployable and non-invasive sensor offers opportunities for more efficient and real-time fish stress monitoring for the optimization of aquaculture practices.

Sensitive osteoarthritis sensing by salt-induced aggregation and dispersion of gold nanoparticles

Osteoarthritis occurs in any joints, and identification in its earlier stages helps to treat the disease and increase the recovery rate. The radiography method and imaging techniques are traditionally used to identify osteoarthritis. But these methods are expensive, and with the complicated steps. Researchers are working toward developing a highly sensitive biosensor in identifying the osteoarthritis biomarker. This research was focused on developing a C-terminal telopeptide of type II collagen (CTX-II) colorimetric sensor with gold nanoparticle (AuNP) for diagnosing osteoarthritis. Anti-CTX-II was conjugated with AuNP and then added with CTX-II and sodium chloride for the color change. In the presence of CTX-II, antibody releases from AuNP then binds with CTX-II, and the color of AuNP changed to purple. Without the CTX-II, AuNP remains its red color (dispersed). This easier colorimetric assay detected the CTX-II as low as 2 ng/mL on linear regression [y = 0.0131x - 0.0051; R2 = 0.9205]. Furthermore, control performances with the relevant proteins osteopontin, IL-6, and nonimmune antibody failed to change the color confirming the specific identification of CTX-II.

Role of First Trimester Screening Biochemical Markers to Predict Hypertensive Pregnancy Disorders and SGA Neonates-A Narrative Review

Early recognition of high-risk pregnancies through biochemical markers may promote antenatal surveillance, resulting in improved pregnancy outcomes. The goal of this study is to evaluate the possibilities of using biochemical markers during the first trimester of pregnancy in the prediction of hypertensive pregnancy disorders (HPD) and the delivery of small-for-gestational-age (SGA) neonates. A comprehensive search was conducted on key databases, including PubMed, Scopus, and Web of Science, for articles relating to the use of biochemical markers in the prediction of HPD and SGA. The findings show that changes in the levels of biomarkers in the early pregnancy phases could be an important indicator of adverse pregnancy outcomes. The literature shows that low PAPP-A (pregnancy-associated plasma protein A) and PlGF (placental growth factor) levels, low alkaline phosphatase (AP), higher sFlt-1 (soluble fms-like Tyrosine Kinase-1) levels, higher AFP (alfa fetoprotein) levels, and elevated levels of inflammatory markers such as β-HGC (free beta human chorionic gonadotropin), interferon-gamma (INF-γ), and tumor necrosis factor-α (TNF-α) may be associated with risks including the onset of HPD, fetal growth restriction (FGR), and delivery of SGA neonates. Comparatively, PAPP-A and PlGF appear to be the most important biochemical markers for the prediction of SGA and HPD.

One-step hydrothermal synthesis of near-infrared emission carbon quantum dots as fluorescence aptamer sensor for cortisol sensing and imaging

Fluorescence carbon quantum dots (CQDs) have been widely applied to sensing and bioimaging. In this paper, near-infrared carbon quantum dots (NIR-CQDs) were prepared through a simple one-step hydrothermal approach using reduced glutathione and formamide as raw materials. Based on NIR-CQDs, aptamer (Apt) and graphene oxide (GO) has been applied to fluorescence sensing cortisol. NIR-CQDs-Apt adsorbed to the surface of GO through π-π stacking and an inner filter effect (IFE) occurred between NIR-CQDs-Apt and GO leading to NIR-CQDs-Apt fluorescence "off". The IFE process is disrupted in the presence of cortisol, allowing NIR-CQDs-Apt fluorescence "on". This led us to construct a detection method with excellent selectivity over other cortisol sensors. The sensor can detect cortisol from 0.4 to 500 nM and has a detection limit as low as 0.13 nM. Importantly, this sensor can be used to detect intracellular cortisol with excellent biocompatibility and cellular imaging capabilities, which is promising for biosensing.

Biosensing epidemic and pandemic respiratory viruses: Internet of Things with Gaussian noise channel algorithmic model

The current world condition is dire due to epidemics and pandemics as a result of novel viruses, such as influenza and the coronavirus, causing acute respiratory syndrome. To overcome these critical situations, the current research seeks to generate a common surveillance system with the assistance of a controlled Internet of Things operated under a Gaussian noise channel. To create the model system, a study with an analysis of H1N1 influenza virus determination on an interdigitated electrode (IDE) sensor was validated by current-volt measurements. The preliminary data were generated using hemagglutinin as the target against gold-conjugated aptamer/antibody as the probe, with the transmission pattern showing consistency with the Gaussian noise channel algorithm. A good fit with the algorithmic values was found, displaying a similar pattern to that output from the IDE, indicating reliability. This study can be a model for the surveillance of varied pathogens, including the emergence and reemergence of novel strains.

Aptamer-Based Biosensors for the Colorimetric Detection of Blood Biomarkers: Paving the Way to Clinical Laboratory Testing

Clinical diagnostics for human diseases rely largely on enzyme immunoassays for the detection of blood biomarkers. Nevertheless, antibody-based test systems have a number of shortcomings that have stimulated a search for alternative diagnostic assays. Oligonucleotide aptamers are now considered as promising molecular recognizing elements for biosensors (aptasensors) due to their high affinity and specificity of target binding. At the moment, a huge variety of aptasensors have been engineered for the detection of various analytes, especially disease biomarkers. However, despite their great potential and excellent characteristics in model systems, only a few of these aptamer-based assays have been translated into practice as diagnostic kits. Here, we will review the current progress in the engineering of aptamer-based colorimetric assays as the most suitable format for clinical lab diagnostics. In particular, we will focus on aptasensors for the detection of blood biomarkers of cardiovascular, malignant, and neurodegenerative diseases along with common inflammation biomarkers. We will also analyze the main obstacles that have to be overcome before aptamer test systems can become tantamount to ELISA for clinical diagnosis purposes.

Identification of Mycoplasma pneumoniae by DNA-modified Gold Nanomaterials in a Colorimetric Assay

Mycoplasma pneumoniae (M. pneumoniae) is a highly infectious bacterium and the major cause of pneumonia, especially in school children. Mycoplasma pneumoniae affects the respiratory tract, and 25% of patients experience health-related problems. It is important to have a suitable method to detect M. pneumoniae, and gold nanoparticle (GNP)-based colorimetric biosensing was used in this study to identify the specific target DNA for M. pneumoniae. The color of GNPs changes due to negatively charged GNPs in the presence of positively charged monovalent (Na⁺ ) ions from NaCl. This condition is reversed in the presence of a single-stranded oligonucleotide, as it attracts GNPs, but not in the presence of double-stranded DNA. Single standard capture DNA was mixed with optimal target DNA that cannot be adsorbed by GNPs; under this condition, GNPs are not stabilized and aggregate at high ionic strength (from 100 mM). Without capture DNA, the GNPs were stabilized by capture DNA (from 1 μM), becoming more stable under high ionic conditions and retaining their red color. The GNPs turned blue in the presence of target DNA at concentrations of 1 pM, and the GNPs retained a red color when there was no target in the solution. This method is useful for the simple, easy, and accurate identification of M. pneumoniae target DNA at higher discrimination and without involving sophisticated equipment, and this method provides a diagnostic for M. pneumoniae. This article is protected by copyright. All rights reserved.

Predicting fetal weight by three-dimensional limb volume ultrasound (AVol/TVol) and abdominal circumference

BACKGROUND

Fetal weight is an important parameter to ensure maternal and child safety. The purpose of this study was to use three-dimensional (3D) limb volume ultrasound combined with fetal abdominal circumference (AC) measurement to establish a model to predict fetal weight and evaluate its efficiency.

METHODS

A total of 211 participants with single pregnancy (28-42 weeks) were selected between September 2017 and December 2018 in the Beijing Obstetrics and Gynecology Hospital of Capital Medical University. The upper arm (AVol)/thigh volume (TVol) of fetuses was measured by the 3D limb volume technique. Fetal AC was measured by two-dimensional ultrasound. Nine cases were excluded due to incomplete information or the interval between examination and delivery >7 days. The enrolled 202 participants were divided into a model group (134 cases, 70%) and a verification group (68 cases, 30%) by mechanical sampling method. The linear relationship between limb volume and fetal weight was evaluated using Pearson Chi-squared test. The prediction model formula was established by multivariate regression with data from the model group. Accuracy of the model formula was evaluated with verification group data and compared with traditional formulas (Hadlock, Lee2009, and INTERGROWTH-21st) by paired t-test and residual analysis. Receiver operating characteristic curves were generated to predict macrosomia.

RESULTS

AC, AVol, and TVol were linearly related to fetal weight. Pearson correlation coefficient was 0.866, 0.862, and 0.910, respectively. The prediction model based on AVol/TVol and AC was established as follows: Y = -481.965 + 12.194TVol + 15.358AVol + 67.998AC, R2adj = 0.868. The scatter plot showed that when birth weight fluctuated by 5% (i.e., 95% to 105%), the difference between the predicted fetal weight by the model and the actual weight was small. A paired t-test showed that there was no significant difference between the predicted fetal weight and the actual birth weight (t = -1.015, P = 0.314). Moreover, the residual analysis showed that the model formula's prediction efficiency was better than the traditional formulas with a mean residual of 35,360.170. The combined model of AVol/TVol and AC was superior to the Lee2009 and INTERGROWTH-21st formulas in the diagnosis of macrosomia. Its predictive sensitivity and specificity were 87.5% and 91.7%, respectively.

CONCLUSION

Fetal weight prediction model established by semi-automatic 3D limb volume combined with AC is of high accuracy, sensitivity, and specificity. The prediction model formula shows higher predictive efficiency, especially for the diagnosis of macrosomia.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03002246; https://clinicaltrials.gov/ct2/show/NCT03002246?recrs=e&cond=fetal&draw=8&rank=67.