Batch injection analysis towards auxiliary diagnosis of periodontal diseases based on indirect amperometric detection of salivary α-amylase on a cupric oxide electrode

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient's condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

Development of enzyme-inorganic hybrid nanoflower-modified electrodes and a smartphone-controlled electrochemical analyzer for point-of-care testing of salivary amylase in saliva

Quantitation of salivary alpha-amylase (sAA) plays a significant role in not only theoretical studies but also clinical practice. This study reports a quantitative point-of-care testing (POCT) system for sAA quantitation anywhere, anytime and by anyone, which consists of customized electrodes and a smartphone-controlled electrochemical analyzer. Organic-inorganic hybrid nanoflowers (NFs) encapsulating α-glucosidase (AG) and glucose dehydrogenase (GDH) have been synthesized and modified onto screen-printed electrodes (SPCEs) to fabricate the customized electrodes. The SPCEs integrated with the smartphone-controlled electrochemical analyzer exhibit good analytical performance for sAA with a low detection limit of 5.02 U mL-1 and a wide dynamic range of 100-2000 U mL-1 using chronoamperometry. The reported POCT system has been successfully demonstrated for quantitation of sAA in clinical saliva samples, and the quantitation results correlated well with those of the Bernfeld method which is extensively used in clinics. More importantly, this study reveals the great potential of sAA as an early warning indicator of abnormal glucose metabolism in obese individuals. Considering the non-invasive saliva sampling process as well as the easy-to-use and cost-effectiveness features of this quantitative POCT system, quantitation of salivary sAA at home by laypersons might become an appealing choice for obese individuals to monitor their glucose metabolism status anytime.

3D-printed electrochemical platform with multi-purpose carbon black sensing electrodes

The 3D printing is described of a complete and portable system comprising a batch injection analysis (BIA) cell and an electrochemical platform with eight sensing electrodes. Both BIA and electrochemical cells were printed within 3.4 h using a multimaterial printer equipped with insulating, flexible, and conductive filaments at cost of ca. ~ U$ 1.2 per unit, and their integration was based on a threadable assembling without commercial component requirements. Printed electrodes were exposed to electrochemical/Fenton pre-treatments to improve the sensitivity. Scanning electron microscopy and electrochemical impedance spectroscopy measurements upon printed materials revealed high-fidelity 3D features (90 to 98%) and fast heterogeneous rate constants ((1.5 ± 0.1) × 10⁻³ cm s⁻¹). Operational parameters of BIA cell were optimized using a redox probe composed of [Fe(CN)₆]^4−/3− under stirring and the best analytical performance was achieved using a dispensing rate of 9.0 µL s⁻¹ and an injection volume of 2.0 µL. The proof of concept of the printed device for bioanalytical applications was evaluated using adrenaline (ADR) as target analyte and its redox activities were carefully evaluated through different voltammetric techniques upon multiple 3D-printed electrodes. The coupling of BIA system with amperometric detection ensured fast responses with well-defined peak width related to the oxidation of ADR applying a potential of 0.4 V vs Ag. The fully 3D-printed system provided suitable analytical performance in terms of repeatability and reproducibility (RSD ≤ 6%), linear concentration range (5 to 40 µmol L⁻¹; R² = 0.99), limit of detection (0.61 µmol L⁻¹), and high analytical frequency (494 ± 13 h⁻¹). Lastly, artificial urine samples were spiked with ADR solutions at three different concentration levels and the obtained recovery values ranged from 87 to 118%, thus demonstrating potentiality for biological fluid analysis. Based on the analytical performance, the complete device fully printed through additive manufacturing technology emerges as powerful, inexpensive, and portable tool for electroanalytical applications involving biologically relevant compounds.

Batch injection analysis in tandem with electrochemical detection: the recent trends and an overview of the latest applications (2015–2020)

The purpose of the proposed review is to refer the contemporary capability of automated analytical systems, in particular batch injection analysis (BIA) in connection with electrochemical detection, for widespread applications in analytical chemistry. This combination recently represents an efficient tool for improvement of method parameters, such as speed, selectivity, and sampling rate for sensing of miscellaneous organic and inorganic substances. The review is focused on conception and usage of BIA in tandem with electrochemical detection utilizing various techniques, namely amperometry, voltammetry, and multiple pulse amperometry, as well as design of electrochemical cells constructed for BIA systems is discussed. Finally, this paper also summarizes the comprehensive overview of works published from 2015 to 2020 dealing with the electrochemical determination of different analytes by BIA in various matrices.

Self-Attention-Guided Recurrent Neural Network and Motion Perception for Intelligent Prediction of Chronic Diseases

Parkinson's disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson's disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson's disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model's performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson's disease, which is better than the compared methods by 2.45% to 12.07%.

A facile SERS strategy to detect glucose utilizing tandem enzyme activities of Au@Ag nanoparticles

Surface-Enhanced Raman Scattering (SERS) is a powerful analysis technology, attracting more and more attention due to its high sensitivity and selectivity. Herein, we report a simple seed-mediated method to synthesize Au@Ag nanoparticles (NPs) as a multifunctional biosensor for the label-free detection of hydrogen peroxide (H₂O₂) and glucose by SERS. Au@Ag NPs, as an ultrasensitive SERS substrate, show the dual activities (peroxidase-like and GOx-like activities). Under the condition of pH 4.0 NaAc buffer solution, the glucose and H₂O can be catalyzed by Au@Ag NPs to produce glucose acid and H₂O₂, and then H₂O₂ can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to form a blue oxidation product oxidic TMB (oxTMB) which exhibits strong SERS signals at 1188, 1330, 1605 cm^-1. Thus, we have developed a new SERS strategy for analysis of glucose with a detection limit of 5 × 10^-10molL^-1, suggesting that Au@Ag NPs have the potential for biosensor, immunoassay and medical treatment.

Multicommutated Flow Analysis System for Determination of Horseradish Peroxidase and Its Inhibitors

A fully mechanized multicommutated flow analysis (MCFA) system dedicated to determining horseradish peroxidase (HRP) activity was developed. Detection was conducted using a flow-through optoelectronic detector-constructed of paired LEDs operating according to the paired emitter-detector diode (PEDD) principle. The PEDD-MCFA system is dedicated to monitoring the enzyme-catalyzed oxidation of p-phenylenediamine (pPD) by a hydrogen peroxide. Under optimized conditions, the presented bioanalytical system was characterized by a linear response range (33.47-200 U/L) with a detection limit at 10.54 U/L HRP activity and 1.66 mV·L/U sensitivity, relatively high throughput (12 signals recordings per hour), and acceptable precision (RSD below 6%). Additionally, the utility of the developed PEDD-MCFA system for the determination of HRP inhibitors allowing the detection of selected thiols at micromolar levels, is demonstrated. The practical utility of the flow system was illustrated by the analysis of some dietary supplements containing L-cysteine, N-acetylcysteine, and L-glutathione.

Bioanalytical insight into the life of microbial populations: A chemical monitoring of ureolytic bacteria growth

In this publication an alternative approach to investigations of bacterial growth is proposed. Contrary to the conventional physical methods it is based on enzyme activity detection. The procedure for real-time and on-line monitoring of microbial ureolytic activity (applied as a model experimental biosystem) in the flow analysis format is presented. The developed fully-mechanized bioanalytical flow system is composed of solenoid micropumps and microvalves actuated by Arduino microcontroller. The photometric detection based on Nessler reaction is performed using dedicated flow-through optoelectronic detector made of paired light emitting diodes. The developed bioanalytical system allows discrete assaying of microbial urease in the wide range of activity up to 5.4 U mL^-1 with detection limit below 0.44 U mL^-1, a high sensitivity in the linear range of response (up to 200 mV U^-1 mL and relatively high throughput (9 detection per hour). The proposed differential procedure of measurements (i.e. a difference between peaks register for sample with and without external addition of urea is treated as an analytical signal) allows elimination of interfering effects from substrate and products of biocatalysed reaction as well as other components of medium used for microbial growth. The developed bioanalytical system was successfully applied for the control of growth of urease-positive bacteria strains (Proteus vulgaris, Klebsiella pneumoniae and Paracoccus yeei) including examination of effects from various microbial cultivation conditions like temperature, composition of culture medium and amount of substrate required for induction of bacterial enzymatic activity. The developed bioanalytical flow system can be applied for metabolic activity-based estimation of parameters of lag and log phases of microbial growth as well as for detection of decline phase.

Recent advances in graphene-based nanobiosensors for salivary biomarker detection

As biosensing research is rapidly advancing due to significant developments in materials, chemistry, and electronics, researchers strive to build cutting-edge biomedical devices capable of detecting health-monitoring biomarkers with high sensitivity and specificity. Biosensors using nanomaterials are highly promising because of the wide detection range, fast response time, system miniaturization, and enhanced sensitivity. In the recent development of biosensors and electronics, graphene has rapidly gained popularity due to its superior electrical, biochemical, and mechanical properties. For biomarker detection, human saliva offers easy access with a large variety of analytes, making it a promising candidate for its use in point-of-care (POC) devices. Here, we report a comprehensive review that summarizes the most recent graphene-based nanobiosensors and oral bioelectronics for salivary biomarker detection. We discuss the details of structural designs of graphene electronics, use cases of salivary biomarkers, the performance of existing sensors, and applications in health monitoring. This review also describes current challenges in materials and systems and future directions of the graphene bioelectronics for clinical POC applications. Collectively, the main contribution of this paper is to deliver an extensive review of the graphene-enabled biosensors and oral electronics and their successful applications in human salivary biomarker detection.

Hand-held Colorimetry Sensor Platform for Determining Salivary α-Amylase Activity and Its Applications for Stress Assessment

This study develops a hand-held stress assessment meter with a chemically colorimetric strip for determining salivary α-amylase activity, using a 3,5 dinitrosalicylic acid (DNS) assay to quantify the reducing sugar released from soluble starch via α-amylase hydrolysis. The colorimetric reaction is produced by heating the strip with a mini polyester heater plate at boiling temperature to form a brick red colored product, which measured at 525 nm wavelength. This investigation describes in detail the design, construction, and performance evaluation of a hand-held α-amylase activity colorimeter with a light emitted diode (LED) and photo-detector with built-in filters. The dimensions and mass of the proposed prototype are only 120 × 60 × 60 mm³ and 200 g, respectively. This prototype has an excellent correlation coefficient (>0.995), comparable with a commercial ultraviolet–visible spectroscope, and has a measurable α-amylase activity range of 0.1–1.0 U mL⁻¹. The hand-held device can measure the salivary α-amylase activity with only 5 μL of saliva within 12 min of testing. This sensor platform effectively demonstrates that the level of salivary α-amylase activity increases more significantly than serum cortisol, the other physiological stressor biomarker, under physiologically stressful exercise conditions. Thus, this work demonstrates that the hand-held α-amylase activity meter is an easy to use and cost-effective stress assessment tool for psychoneuroendocrinology research.