Label-Free Protein Detection by Micro-Acoustic Biosensor Coupled with Electrical Field Sorting. Theoretical Study in Urine Models

"Three-in-one" Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning

Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of urine. As a critical component of routine urinalysis, urine protein testing measures the levels and types of proteins, enabling the early diagnosis of diseases. Traditional methods require three separate steps including strip testing, protein/creatinine ratio measurement, and electrophoresis respectively to achieve qualitative, quantitative, and classification analyses of proteins in urine with long time and cumbersome operations. Herein, this work demonstrates a "three-in-one" protocol to analyze the urine composition by combining multifunctional nanoparticles with machine learning. This work constructs a sensor array to analyze proteinuria by employing nanoparticles with unique optical properties, outstanding catalytic activity, diverse composition, and tunable structure as probes. Different proteins interacted with nanoprobes differently and are classified by this sensor array based on their physicochemical heterogeneities. With the aid of machine learning, the urine composition is precisely detected for the diagnosis of bladder cancer. This protocol enables quantification and classification of 5 proteinuria in 10 min without any tedious pretreatment, showing proimise for the comprehensive analysis of body fluid for early disease diagnosis.

Bio-inspired polydopamine layer as a versatile functionalisation protocol for silicon-based photonic biosensors

Photonic biosensors have made major advances in recent years, achieving very high sensitivity, and progressing towards point-of-care deployment. By using photonic resonances, sensors can be label-free, which is particularly attractive for a low-cost technological realisation. A key remaining issue is the biological interface and the efficient and reliable immobilisation of binder molecules such as antibodies; many protocols are currently in use that have led to widely varying sensor performance. Here, we study a very simple and robust surface functionalisation protocol for silicon photonics, which is based on polydopamine, and we demonstrate both its simplicity and its high performance. The use of polydopamine (PDA) is inspired by molluscs, especially mussels, that employ dopamine to adhere to virtually any surface, especially in an aqueous environment. We studied the versatility of the PDA protocol by showing compatibility with 5 different disease biomarkers (Immunoglobulin (IgG), C-reactive protein (CRP), Tumour Necrosis factor-α (TNF-α), Interleukin-6 (IL-6), Matrix metalloproteinase (MMP-9) and show that the protocol is resistant to hydrolysis during incubation; the loss of functionality due to hydrolysis is a major issue for many of the functionalisation protocols commonly used for silicon-based sensors. The study using guided mode resonance-based sensors highlights the wide dynamic range of the protocol (0.01 ng/mL to 1 μg/mL), using IgG, CRP and MMP-9 protein biomarkers as exemplars. In addition, we show that the surface chemistry allows performing measurements in 10% human serum with a sensitivity as low as 10 ng/mL for IgG. We suggest that adopting this protocol will make it easier for researchers to achieve biofunctionalisation and that the biosensor community will be able to achieve more consistent results.

Label-Free, Impedance-Based Biosensor for Kidney Disease Biomarker Uromodulin

We demonstrate the development of a label-free, impedance-based biosensor by using a passivation layer of 50-nm tantalum pentoxide (Ta2O5) on interdigitated electrodes (IDE). This layer was fabricated by atomic layer deposition (ALD) and has a high dielectric constant (high-κ), which improves the capacitive property of the IDE. We validate the biosensor's performance by measuring uromodulin, a urine biomarker for kidney tubular damage, from artificial urine samples. The passivation layer is functionalized with uromodulin antibodies for selective binding. The passivated IDE enables the non-faradaic impedance measurement of uromodulin concentrations with a measurement range from 0.5 ng/mL to 8 ng/mL and with a relative change in impedance of 15 % per ng/mL at a frequency of 150 Hz (log scale). This work presents a concept for point-of-care biosensing applications for disease biomarkers.

Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks

BACKGROUND

Early diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically.

RESULTS

Experiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers.

CONCLUSION

A new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.

Development of a textile based protein sensor for monitoring the healing progress of a wound

This article focuses on the design and fabrication of flexible textile-based protein sensors to be embedded in wound dressings. Chronic wounds require continuous monitoring to prevent further complications and to determine the best course of treatment in the case of infection. As proteins are essential for the progression of wound healing, they can be used as an indicator of wound status. Through measuring protein concentrations, the sensor can assess and monitor the wound condition continuously as a function of time. The protein sensor consists of electrodes that are directly screen printed using both silver and carbon composite inks on polyester nonwoven fabric which was deliberately selected as this is one of the common backing fabric types currently used in wound dressings. These sensors were experimentally evaluated and compared to each other by using albumin protein solution of pH 7. A comprehensive set of cyclic voltammetry measurements was used to determine the optimal sensor design the measurement of protein in solution. As a result, the best sensor design is comprised of silver conductive tracks but a carbon layer as the working and counter electrodes at the interface zone. This design prevents the formation of silver dioxide and protects the sensor from rapid decay, which allows for the recording of consecutive measurements using the same sensor. The chosen printed protein sensor was able to detect bovine serum albumin at concentrations ranging from 30 to 0.3 mg/mL with a sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.0026 \mu $$\end{document}0.0026μA/M. Further testing was performed to assess the sensor’s ability to identify BSA from other interferential substances usually present in wound fluids and the results show that it can be distinguishable.

Effects of Buffer Concentration on the Sensitivity of Silicon Nanobelt Field-Effect Transistor Sensors

In this work, a single-crystalline silicon nanobelt field-effect transistor (SiNB FET) device was developed and applied to pH and biomolecule sensing. The nanobelt was formed using a local oxidation of silicon technique, which is a self-aligned, self-shrinking process that reduces the cost of production. We demonstrated the effect of buffer concentration on the sensitivity and stability of the SiNB FET sensor by varying the buffer concentrations to detect solution pH and alpha fetoprotein (AFP). The SiNB FET sensor was used to detect a solution pH ranging from 6.4 to 7.4; the response current decreased stepwise as the pH value increased. The stability of the sensor was examined through cyclical detection under solutions with different pH; the results were stable and reliable. A buffer solution of varying concentrations was employed to inspect the sensing capability of the SiNB FET sensor device, with the results indicating that the sensitivity of the sensor was negatively dependent on the buffer concentration. For biomolecule sensing, AFP was sensed to test the sensitivity of the SiNB FET sensor. The effectiveness of surface functionalization affected the AFP sensing result, and the current shift was strongly dependent on the buffer concentration. The obtained results demonstrated that buffer concentration plays a crucial role in terms of the sensitivity and stability of the SiNB FET device in chemical and biomolecular sensing.