Chronopotentiometric sensors for antimicrobial peptide-based biosensing of Staphylococcus aureus.

Charged antimicrobial peptides can be used for direct potentiometric biosensing, but have never been explored. We report here a galvanostatically-controlled potentiometric sensor for antimicrobial peptide-based biosensing. Solid-state pulsed galvanostatic sensors that showed excellent stability under continuous galvanostatic polarization were prepared by utilizing reduced graphene oxide/poly (3,4-ethylenedioxythiophene): poly (4-styrenesulfonate) (rGO/PEDOT: PSS) as a solid contact. More importantly, the chronopotentiometric sensor can be made sensitive to antimicrobial peptides with intrinsic charge on demand via a current pulse. In this study, a positively charged antimicrobial peptide that can bind to Staphylococcus aureus with high affinity and good selectivity was designed as a model. Two arginine residues with positive charges were linked to the C-terminal of the peptide sequence to increase its potentiometric responses on the electrode. The bacteria binding-induced charge or charge density change of the antimicrobial peptide enables the direct chronopotentiometric detection of the target. Under the optimized conditions, the concentration of Staphylococcus aureus can be determined in the linear range 10-1.0 × 105 CFU mL-1 with a detection limit of 10 CFU mL-1. It is anticipated that such a chronopotentiometric sensing platform is readily adaptable to detect other bacteria by choosing the peptides.

COVID-19-related secretory otitis media in the omicron era: a case series.

OBJECTIVES: Increased numbers of patients with secretory otitis media appeared in outpatient clinics after the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Omicron pandemic; however, the relationship between SARS-CoV-2 Omicron variant infection and secretory otitis media is uncertain. METHODS: We performed tympanocentesis and used reverse transcription-polymerase chain reaction (RT-PCR) testing to examine middle ear effusion (MEE) and nasopharyngeal secretions from 30 patients with secretory otitis media associated with SARS-CoV-2 infection. RT-PCR was performed using the open reading frame 1ab and nucleocapsid protein gene kit from Shanghai Berger Medical Technology Co., Ltd., as the sole assay method, in accordance with the manufacturer's instructions. RESULTS: MEEs from 5 of the 30 patients tested positive for SARS-CoV-2, including one patient with positive results for both the nasopharyngeal secretion and MEE. We report and discuss the medical records of six patients, including these five MEE-positive patients and a MEE-negative patient. CONCLUSION: SARS-CoV-2 RNA can be detected in MEE caused by coronavirus disease 2019-related secretory otitis media even when a patient's nasopharyngeal secretion tests PCR-negative for SARS-CoV-2. The virus can remain in the MEE for a long time after SARS-CoV-2 infection.

A Comparative Study of Nanobio Interaction of Zn-Doped CdTe Quantum Dots with Lactoferrin Using Different Spectroscopic Methods.

In this paper, glutathione (GSH)-coated Zn-doped CdTe quantum dots (QDs) with different particle sizes were synthesized using the "reflow method", and the interaction mechanism between the two QDs and lactoferrin (LF) was investigated systemically with different spectroscopic methods. The steady-state fluorescence spectra showed that the LF formed a tight complex with the two QDs through static bursting and that the electrostatic force was the main driving force between the two LF-QDs systems. The complex generation process was found to be spontaneous (ΔG < 0) and accompanied by exothermic and increasing degrees of freedom (ΔH < 0, ΔS > 0) by using the temperature-dependent fluorescence spectroscopy. The critical transfer distance (R0) and donor-acceptor distance (r) of the two LF-QDs systems were obtained based on the fluorescence resonance energy transfer theory. In addition, it was observed that the QDs changed the secondary and tertiary structures of LF, leading to an increase in the hydrophobicity of LF. Further, the nano-effect of orange QDs on LF is much larger than that of green QDs. The above results provide a basis for metal-doped QDs with LF in safe nano-bio applications.

Electrochemiluminescence Sensor Based on CeO2 Nanocrystalline for Hg2+ Detection in Environmental Samples.

The excessive concentration of heavy-metal mercury ions (Hg2+) in the environment seriously affects the ecological environment and even threatens human health. Therefore, it is necessary to develop rapid and low-cost determination methods to achieve trace detection of Hg2+. In this paper, an Electrochemiluminescence (ECL) sensing platform using a functionalized rare-earth material (cerium oxide, CeO2) as the luminescent unit and an aptamer as a capture unit was designed and constructed. Using the specific asymmetric matching between Hg2+ and thymine (T) base pairs in the deoxyribonucleic acid (DNA) single strand, the "T-Hg-T" structure was formed to change the ECL signal, leading to a direct and sensitive response to Hg2+. The results show a good linear relationship between the concentration and the response signal within the range of 10 pM-100 µM for Hg2+, with a detection limit as low as 0.35 pM. In addition, the ECL probe exhibits a stable ECL performance and excellent specificity for identifying target Hg2+. It was then successfully used for spiked recovery tests of actual samples in the environment. The analytical method solves the problem of poor Hg2+ recognition specificity, provides a new idea for the efficient and low-cost detection of heavy-metal pollutant Hg2+ in the environment, and broadens the prospects for the development and application of rare-earth materials.

A novel electrochemiluminescence sensor based on a molecular imprinting technique and UCNPs@ZIF-8 nanocomposites for sensitive determination of imidacloprid.

As an efficient and easily available pesticide, imidacloprid (IM) has been widely used in agriculture to kill pests. However, threats to the ecological environment are becoming increasingly prominent, caused by abuse and pesticide residues. This paper reports a sensitive electrochemiluminescence (ECL) sensor based on upconverting nanoparticle functional zeolite imidazolate framework (UCNPs@ZIF-8) nanocomposites combined with molecularly imprinted polymers (MIPs) to successfully achieve the quantitative detection of IM. The composites exhibited a certain multi-faceted prismatic structure and the effective binding of UCNPs was demonstrated by characterization technology. In addition, a sensor with MIPs/UCNPs@ZIF-8/GCE as the working electrode exhibited outstanding ECL performance, including a strong and stable signal and excellent selectivity under optimal conditions. This sensor showed a good linear response to IM over a wide concentration range (0.1 ng L-1-1 mg L-1), with a limit of detection as low as 0.01 ng L-1. More significantly, it was successfully applied to the determination of IM concentration levels in food, which provides broad application prospects for the construction of pesticide ECL sensors.

An ultrasensitive electrochemiluminescence biosensor for the detection of total bacterial count in environmental and biological samples based on a novel sulfur quantum dot luminophore.

In this paper, the electrochemiluminescence (ECL) of sulfur quantum dots (SQDs) in a potassium persulfate cathodic co-reactant was studied. Based on the selective quenching of the ECL emission from the SQDs by ß-nicotinamide adenine dinucleotide (NADH), an ultrasensitive ECL biosensor with NADH as an important parameter was established for the highly sensitive and selective detection of total bacterial count (TBC). A linear response between the ECL intensity and the NADH concentration in the range of 1 pM to 10 µM was obtained, thus achieving a detection limit of 1 pM. As the content of NADH in cells is positively correlated with the TBC, a sensor has been successfully applied to detect the TBC in actual water samples with a good recovery rate of 103-107 CFU mL-1. This study provides a green and feasible method for TBC detection in the environment.

Electrochemiluminescent determination of CYFRA21-1 serum levels using Ti-Fe-O nanotubes immunoassay.

Prominent electrochemiluminescence (ECL) in Ti-Fe-O nanotube arrays (Ti-Fe-O NTs) with K2S2O8 as the cathode coreactant is reported for the first time. Compared with pure titanium dioxide nanotubes (TiO2 NTs), this heterojunction could effectively reduce the band gap, facilitate electronic transitions, and move the ECL potential to a positive direction. The ECL performance motivated the development of an ultrasensitive ECL immunosensor for detecting cytokeratin fragment 21-1 (CYFRA21-1). Magnetic beads loaded with conductive carbon black (CCB/MNTs) were used to efficiently quench the ECL signal of a Ti-Fe-O NTs electrode and were combined with an ECL immunoassay to realize sensitive detection of CYFRA21-1. Over a CYFRA21-1 concentration range of 1.0 pg·mL-1 ~ 100 ng·mL-1, the change in the ECL signal was highly linear with the logarithm of the CYFRA21-1 concentration, and the limit of detection (LOD) was 0.114 pg·mL-1. This ECL immunosensor was used to successfully determine the CYFRA21-1 content in serum. The recovery of CYFRA21-1 in actual serum was 88.6 - 104.4%, and the RSD was 1.4 - 3.0%. The coreaction solution used in this work was PBS (0.1 M, pH = 7.4) containing 0.05 M K2S2O8, the scanning range was -1.0 - 0 V, the photomultiplier tube (PMT) was set to 750 V, and the scanning rate was 100 mV·s-1.

Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors.

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure-activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.

Sensitive and Facile HCOOH Fluorescence Sensor Based on Highly Active Ir Complexes' Catalytic Transfer Hydrogen Reaction.

With several major polarity and weak optical properties, the sensitive detection of HCOOH remains a major challenge. Given the special role of HCOOH in assisting in the catalytic hydrogenation process of Ir complexes, HCOOH (as a hydrogen source) could rapidly activate Ir complexes as catalysts and further reduce the substrates. This work developed a facile and sensitive HCOOH fluorescence sensor utilizing an optimal catalytic fluorescence generation system, which consists of the phenyl-pyrazole-type Ir-complex PP-Ir-Cl and the coumarin-type fluorescence probe P-coumarin. The sensor demonstrates excellent sensitivity and specificity for HCOOH and formates; the limits of detection for HCOOH, HCOONa, and HCOOEt3N were tested to be 50.6 ppb, 68.0 ppb, and 146.0 ppb, respectively. Compared to previous methods, the proposed sensor exhibits good detection accuracy and excellent sensitivity. Therefore, the proposed HCOOH sensor could be used as a new detection method for HCOOH and could provide a new design path for other sensors.

Facile synthesis of a cyclodextrin-metal organic framework decorated with Ketjen Black and platinum nanoparticles and its application in the electrochemical detection of ofloxacin.

A facile and novel method was developed to improve the conductivity of a cyclodextrin-metal organic frameworks (CD-MOFs) by modifying it with Ketjen Black (KB) and platinum nanoparticles (PtNPs). The structural morphology and composition of this PtNPs/KB/CD-MOFs nanocomposite was characterized by transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, elemental mapping, and Raman spectroscopy. The sensor was then evaluated for its ability to detect ofloxacin using cyclic voltammetry, and the results showed that the PtNPs/KB/CD-MOF had high sensitivity, satisfactory stability and good reproducibility. In addition, a great linear range was obtained with a concentration range of 0.08-100 µM and a low detection limit of 0.037 µM (S/N = 3). Therefore, the prepared electrochemical sensor could be successfully used to detect ofloxacin in serum.

FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction.

The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in the era of intelligence. As a nonlinear network, the stochastic configuration network (SCN) is considered to be an effective method for regression prediction due to its good performance in learning and generalization. Therefore, in this paper, we adapt the SCN to regression analysis, and design and verify the field programmable gate array (FPGA) framework to implement SCN model for the first time. In addition, in order to improve the performance of the SCN model based on the FPGA, the implementation of the nonlinear activation function on the FPGA is optimized, which effectively improves the prediction accuracy while considering the utilization rate of hardware resources. Experimental results based on the simulation data set and the real data set prove that the proposed FPGA framework successfully implements the SCN regression prediction model, and the improved SCN model has higher accuracy and a more stable performance. Compared with the extreme learning machine (ELM), the prediction performance of the proposed SCN implementation model based on the FPGA for the simulation data set and the real data set is improved by 56.37% and 17.35%, respectively.

Interaction of Coumarin Phytoestrogens with ERα and ERß: A Molecular Dynamics Simulation Study.

Coumarin phytoestrogens, as one of the important classes of phytoestrogens, have been proved to play an important role in various fields of human life. In this study, molecular simulation method including molecular docking and molecular dynamics methods were performed to explore the various effects between four classical coumarin phytoestrogens (coumestrol, 4-methoxycoumestrol, psoralen and isopsoralen), and estrogen receptors (ERα, ERß), respectively. The calculated results not only proved that the four coumarin phytoestrogens have weaker affinity than 17ß-estradiol to both ERα, and ERß, but also pointed out that the selective affinity for ERß is greater than ERα. In addition, the binding mode indicated that the formation of hydrogen bond and hydrophobic interaction have an important effect on the stability of the complexes. Further, the calculation and decomposition of binding free energy explored the main contribution interactions to the total free energy.

Understanding the interaction of single-walled carbon nanotube (SWCNT) on estrogen receptor: A combined molecular dynamics and experimental study.

Considering the large-scale production of diversified nanomaterials, it is paramount importance to unravel the structural details of interactions between nanoparticles and biological systems, and thus to explore the potential adverse impacts of nanoparticles. Estrogen receptors (ER) is one of the most important receptor of human reproductive system and the binding of carbon nanotubes to estrogen receptors was the possible trigger leading to the reproductive toxicity of carbon nanotubes. Thus, with single-walled carbon nanotube (SWCNT) treated as model nanomaterials, a combination of in vivo experiments, spectroscopy assay and molecular dynamic modeling was applied to help us unravel some important issues on the binding characterization between SWCNT and the ligand binding domain (LBD) of ER alpha (ERα). The fluorescence assay and molecular dynamics simulations together validated the binding of SWCNT to ERα, suggesting the possible molecular initiating event. As a consequence, SWCNT binding led to a conformational change on tertiary structure levels and hydrophobic interaction was recognized as the driving force governing the binding behavior between SWCNT and LBD of ERα. A in vivo process presented that the exposure of SWCNT increased ERα expression from 26.43 pg/ml to 259.01 pg/ml, suggesting a potential estrogen interference effects of SWCNT. Our study offers insight on the binding of SWCNT and ERα LBD at atomic level, helpful to accurately evaluate the potential health risks of SWCNT.

Simultaneous voltammetric determination of guanine and adenine using MnO2 nanosheets and ionic liquid-functionalized graphene combined with a permeation-selective polydopamine membrane.

Guanine and adenine in blood samples can be detected by using an electrochemical sensor based on the use of manganese dioxide (MnO2) nanosheets and ionic liquid functionalized graphene (IL-GR) bound to a polydopamine (PDA) membrane. Both guanine and adenine undergo a redox reaction on the surface of the modified electrode. Cyclic voltammetry and differential pulse voltammetry were used to evaluate the electrochemical behavior of a glassy carbon electrode (GCE) modified with PDA/MnO2/IL-GR. The sensor allows for individual as well as simultaneous determination of guanine and adenine. The working voltage of differential pulse voltammetry at which data were acquired to establish the calibration plot: 0.6-1.2 V for guanine, 0.8-1.4 V for adenine, 0.4-1.4 V for mixture of guanine and adenine. A wide detection range (10-300 µM), low detection limits (guanine: 0.25 µM; adenine: 0.15 µM), selectivity and reproducibility are demonstrated. The modified GCE was successfully applied to the analysis of guanine and adenine in spiked fetal bovine serum and mouse whole blood samples. Graphical abstract An electrochemical sensor is presented for the determination of guanine (G) and adenine (A) based on MnO2 nanosheets, ionic liquid functionalized graphene (IL-graphene) and polydopamine membrane.

Preparation of gold nanoparticles supported on graphene oxide with flagella as the template for nonenzymatic hydrogen peroxide sensing.

Gold nanoparticles supported on graphene oxide with flagella as the template were developed as an electrochemical sensor for the detection of hydrogen peroxide (H2O2) in serum. The flagella-Au nanoparticles composite and graphene oxide were dropped onto a glassy carbon electrode (GCE) to form a new H2O2 electrochemical sensor. The structure morphology of the prepared sensor was characterized by transmission electron microscopy (TEM), and the electrocatalytic performance towards H2O2 reduction was evaluated by cyclic voltammetry (CV) and amperometric methods. The response current of the sensor showed a good linear relationship with the concentration of H2O2 in the range of 10-1000 µM (R2 = 0.9916). The minimum detection limit of 1 µM was obtained (S/N = 3). Finally, the sensor was applied to the detection of H2O2 in serum, and the recoveries were satisfactory. As the sensor is sensitive, fast, and easy to make, it is expected to be used for rapid detection of H2O2. Graphical abstract ᅟ.

Reduced graphene oxide functionalized with a CoS2/ionic liquid composite and decorated with gold nanoparticles for voltammetric sensing of dopamine.

A nanohybrid electrode was prepared by functionalizing reduced graphene oxide nanosheets (GNs) with gold nanoparticles (AuNP), CoS2, and an ionic liquid. It is shown to enable voltammetric determination of dopamine (DA). The AuNPs were electrodeposited onto the electrode that was first modified with CoS2 and the IL-GNs to obtain a well-defined 3-dimensional and porous structure. The nanohybrid material displays high catalytic activity and an ultrasensitive cyclic voltammetric response to DA. The peak current (best measured at a working voltage of 0.17 V vs. Ag/AgCl) increases linearly in the 0.1 to 400 µM DA concentration range, with a 40 nM detection limit (at S/N = 3). The electrode was successfully applied to the determination of DA in spiked serum samples. Graphical abstract Schematic of the preparation of a nanohybrid electrode odified with graphene oxide nanosheets functionalized with gold nanoparticles/CoS2/ionic liquid by electrodeposition. The electrode exhibits excellent electrocatalytic performance with respect to the electrochemical detection of dopamine. The determination of the DPV curves provides satisfactory results which can be characterized by a linear response for DA concentrations ranging from 0.1 to 400.0 µM with a 40 nM detection limit (at S/N = 3).

Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors.

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure⁻activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R² (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq² (leave-one-out correlation coefficient) = 0.864, F (Fisher's test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R² = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R² = 0.925, LOOq² = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R² = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach.

Nowadays, quantitative structure⁻activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R² = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R² = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R² = 0.803, RMS = 0.222, RBFNN: n = 60, R² = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R² = 0.852, RMS = 0.192; RBFNN: n = 31, R² = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.

Hollow core anti-resonant fiber with split cladding.

An improved design for hollow core anti-resonant fibers (HAFs) is presented. A split cladding structure is introduced to reduce the fabrication distortion within design tolerance. We use numerical simulations to compare the Kagome fibers (KFs) and the proposed split cladding fibers (SCFs) over two normalized transmission bands. It reveals that SCFs are able to maintain the desired round shape of silica cladding walls, hence improving the confinement loss (CL) compared to the KF and is comparable to that of the nested antiresonant nodeless fiber (NANF) with the same core size. In addition, the SCF allows stacking multiple layers of cladding rings to control the CL. The influences of the number of cladding layers and the cladding gap width on the CL of the SCFs have been studied. SCF with three cladding rings is fabricated by the stack-and-draw technique. A measured attenuation spectrum matches well with the calculation prediction. The measured near field mode patterns also prove the feasibility of our fiber design.

Controlled excitation of higher radial order whispering gallery modes with metallic diffraction grating.

Metallic diffraction grating coupler is investigated for controlled excitation of whispering gallery modes (WGMs) of different radial orders. Based on effective mode index calculations and finite difference time domain method, it is found that higher radial order WGMs can be separated from the fundamental modes by sending them into the opposite propagation direction. By phase-matching designs, the metallic diffraction grating provides extra freedom to switch propagation directions, and is able to selectively enhance or suppress different radial-order WGMs. Such structure offers a simple and practical configuration for various WGM applications including liquid sensing, band pass filtering and fiber lasers.