Fast and Ultrasensitive Electrochemical Detection for Antiviral Drug Tenofovir Disoproxil Fumarate in Biological Matrices.

Tenofovir disoproxil fumarate (TDF) is an antiretroviral medication with significant curative effects, so its quantitative detection is important for human health. At present, there are few studies on the detection of TDF by electrochemical sensors. This work can be a supplement to the electrochemical detection of TDF. Moreover, bare electrodes are susceptible to pollution, and have high overvoltage and low sensitivity, so it is crucial to find a suitable electrode material. In this work, zirconium oxide (ZrO2) that has a certain selectivity to phosphoric acid groups was synthesized by a hydrothermal method with zirconyl chloride octahydrate as the precursor. A composite modified glassy carbon electrode for zirconium oxide-chitosan-multiwalled carbon nanotubes (ZrO2-CS-MWCNTs/GCE) was used for the first time to detect the TDF, and achieved rapid, sensitive detection of TDF with a detection limit of sub-micron content. The ZrO2-CS-MWCNTs composite was created using sonication of a mixture of ZrO2 and CS-MWCNTs solution. The composite was characterized using scanning electron microscopy (SEM) and cyclic voltammetry (CV). Electrochemical analysis was performed using differential pulse voltammetry (DPV). Compared with single-material electrodes, the ZrO2-CS-MWCNTs/GCE significantly improves the electrochemical sensing of TDF due to the synergistic effect of the composite. Under optimal conditions, the proposed method has achieved good results in linear range (0.3~30 µM; 30~100 µM) and detection limit (0.0625 µM). Moreover, the sensor has the merits of simple preparation, good reproducibility and good repeatability. The ZrO2-CS-MWCNTs/GCE has been applied to the determination of TDF in serum and urine, and it may be helpful for potential applications of other substances with similar structures.

Zirconium Molybdate Nanocomposites' Sensing Platform for the Sensitive and Selective Electrochemical Detection of Adefovir.

Adefovir (ADV) is an anti-retroviral drug, which can be used to treat acquired immune deficiency syndrome (AIDS) and chronic hepatitis B (CHB), so its quantitative analysis is of great significance. In this work, zirconium molybdate (ZrMo2O8) was synthesized by a wet chemical method, and a composite with multi-walled carbon nanotubes (MWCNTs) was made. ZrMo2O8-MWCNTs composite was dropped onto the surface of a glassy carbon electrode (GCE) to prepare ZrMo2O8-MWCNTs/GCE, and ZrMo2O8-MWCNTs/GCE was used in the electrochemical detection of ADV for the first time. The preparation method is fast and simple. The materials were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and cyclic voltammetry (CV). It was electrochemically analysed by differential pulse voltammetry (DPV). Compared with single-material modified electrodes, ZrMo2O8-MWCNTs/GCE showed a vastly improved electrochemical response to ADV. Moreover, the sensor complements the study of the electrochemical detection of ADV. Under optimal conditions, the proposed electrochemical method showed a wide linear range (from 1 to 100 µM) and a low detection limit (0.253 µM). It was successfully tested in serum and urine. In addition, the sensor has the advantages of a simple preparation, fast response, good reproducibility and repeatability. It may be helpful in the potential applications of other substances with similar structures.

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers.

Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. However, when building a population-level description, it can be easy to lose sight of each individual and how they contribute to the larger picture. In this paper, we present a novel transformer architecture for learning from time-varying data that builds descriptions of both the individual as well as the collective population dynamics. Rather than combining all of our data into our model at the onset, we develop a separable architecture that operates on individual time-series first before passing them forward; this induces a permutation-invariance property and can be used to transfer across systems of different size and order. After demonstrating that our model can be applied to successfully recover complex interactions and dynamics in many-body systems, we apply our approach to populations of neurons in the nervous system. On neural activity datasets, we show that our model not only yields robust decoding performance, but also provides impressive performance in transfer across recordings of different animals without any neuron-level correspondence. By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

Simultaneous and sensitive determination of ascorbic acid, dopamine and uric acid via an electrochemical sensor based on PVP-graphene composite.

A method with high sensitivity, good accuracy and fast response is of ever increasing importance for the simultaneous detection of AA, DA and UA. In this paper, a simple and sensitive electrochemical sensor, which based on the polyvinylpyrrolidone (PVP)-graphene composite film modified glassy carbon electrode (PVP-GR/GCE), was presented for detecting ascorbic acid (AA), dopamine (DA) and uric acid (UA) simultaneously. The PVP-GR/GCE has excellent electrocatalytic activity for the oxidation of AA, DA and UA. The second-order derivative linear sweep voltammetry was used for the electrochemical measurements. The peak potential differences of DA-AA, DA-UA, and UA-AA (measured on the PVP-GR/GCE) were 212, 130 and 342 mV respectively. Besides, the over potential of AA, DA and UA reduced obviously, so did the peak current increase. Under the optimum conditions, the linear ranges of AA, DA and UA were 4.0 µM-1.0 mM, 0.02-100 µM, and 0.04-100 µM, respectively. The detection limits were 0.8 µM, 0.002 µM and 0.02 µM for AA, DA, and UA. The electrochemical sensor presented the advantages of high sensitivity and selectivity, excellent reproducibility and long-term stability. Furthermore, the sensor was successfully applied to the analysis of real samples.