Cu2O/PEDOT:PSS/ZnO Nanocomposite Material Biosensor for Esophageal Cancer Detection

Enhanced sensing of bacteria biomarkers by ZnO and graphene oxide decorated PEDOT film

Developing a biofilm biomarker detector and inhibitor will immensely benefit efforts geared at curbing infectious diseases and microbiologically induced corrosion of medical implants, marine vessels and buried steel pipelines. N-Acyl homoserine lactones (AHLs) are important biomarkers gram-negative bacteria use for communication. In this work, we investigated the interactions between three AHL molecules and graphene oxide (GO) and ZnO nanomaterials embedded in conjugated poly(3,4-ethylenedioxythiophene) (PEDOT) film. The results show that PEDOT/GO/ZnO detected AHLs to a considerable extent with adsorption enthalpies of -4.02, -4.87 and -4.97 KJ/mol, respectively, for N-(2-oxotetrahydrofuran-3-yl)heptanamide (AHL1), 2-hydroxy-N-(2-oxotetrahydrofuran-3-yl)nonanamide (AHL2) and (E)-3-(3-hydroxyphenyl)-N-(2-oxotetrahydrofuran-3-yl)acrylamide (AHL3) molecules. The ZnO nanoparticles facilitated charge redistribution and transfer, thereby enhancing the conductivity and overall sensitivity of the substrate toward the AHLs. The adsorption distance and sites of interactions further tuned the charge migration and signal generation by the substrate, thus affirming the suitability of the modeled thin film as a sensor material. Excellent stability and conductivity were maintained before and after the adsorption of each AHL molecule. Moreover, the desorption time for each AHL molecule was calculated, and the result affirmed that the modeled film materials are promising for developing highly sensitive biosensors.Communicated by Ramaswamy H. Sarma.

Lung cancer cells detection by a photoelectrochemical MoS2 biosensing chip

This research aims to explore the potential application of this approach in the production of biosensor chips. The biosensor chip is utilized for the identification and examination of early-stage lung cancer cells. The findings of the optical microscope were corroborated by the field emission scanning electron microscopy, which provided further evidence that the growth of MoS2 is uniform and that there is minimal disruption in the electrode, hence minimizing the likelihood of an open circuit creation. Furthermore, the bilayer structure of the produced MoS2 has been validated through the utilization of Raman spectroscopy. A research investigation was undertaken to measure the photoelectric current generated by three various types of clinical samples containing lung cancer cells, specifically the CL1, NCI-H460, and NCI-H520 cell lines. The findings from the empirical analysis indicate that the coefficient of determination (R-Square) for the linear regression model was approximately 98%. Furthermore, the integration of a double-layer MoS2 film resulted in a significant improvement of 38% in the photocurrent, as observed in the device's performance.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Nanostructured Graphdiyne: Synthesis and Biomedical Applications

Graphdiyne (GDY) is a 2D carbon allotrope that features a one-atom-thick network of sp- and sp²-hybridized carbon atoms with high degrees of π conjugation. Due to its distinct electronic, chemical, mechanical, and magnetic properties, GDY has attracted great attention and shown great potential in various fields, such as catalysis, energy storage, and the environment. Preparation of GDY with various nanostructures, including 0D quantum dots, 1D nanotubes/nanowires/nanoribbons, 2D nanosheets/nanowalls/ordered stripe arrays, and 3D nanospheres, greatly improves its function and has propelled its applications forward. High biocompatibility and stability make GDY a promising candidate for biomedical applications. This review introduces the latest developments in fabrication of GDY-based nanomaterials with various morphologies and summarizes their propective use in the biomedical domain, specifically focusing on their potential advantages and applications for biosensing, cancer diagnosis and therapy, radiation protection, and tissue engineering.

Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging

In this study, the combination of hyperspectral imaging (HSI) technology and band selection was coupled with color reproduction. The white-light images (WLIs) were simulated as narrow-band endoscopic images (NBIs). As a result, the blood vessel features in the endoscopic image became more noticeable, and the prediction performance was improved. In addition, a single-shot multi-box detector model for predicting the stage and location of esophageal cancer was developed to evaluate the results. A total of 1780 esophageal cancer images, including 845 WLIs and 935 NBIs, were used in this study. The images were divided into three stages based on the pathological features of esophageal cancer: normal, dysplasia, and squamous cell carcinoma. The results showed that the mean average precision (mAP) reached 80% in WLIs, 85% in NBIs, and 84% in HSI images. This study′s results showed that HSI has more spectral features than white-light imagery, and it improves accuracy by about 5% and matches the results of NBI predictions.

Identification of Early Esophageal Cancer by Semantic Segmentation

Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder–decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study.

Design of a Lab-On-Chip for Cancer Cell Detection through Impedance and Photoelectrochemical Response Analysis

In this study, a biochip was fabricated using a light-absorbing layer of a silicon solar element combined with serrated, interdigitated electrodes and used to identify four different types of cancer cells: CE81T esophageal cancer, OE21 esophageal cancer, A549 lung adenocarcinoma, and TSGH-8301 bladder cancer cells. A string of pearls was formed from dielectrophoretic aggregated cancer cells because of the serrated interdigitated electrodes. Thus, cancer cells were identified in different parts, and electron-hole pairs were separated by photo-excited carriers through the light-absorbing layer of the solar element. The concentration catalysis mechanism of GSH and GSSG was used to conduct photocurrent response and identification, which provides the fast, label-free measurement of cancer cells. The total time taken for this analysis was 13 min. Changes in the impedance value and photocurrent response of each cancer cell were linearly related to the number of cells, and the slope of the admittance value was used to distinguish the location of the cancerous lesion, the slope of the photocurrent response, and the severity of the cancerous lesion. The results show that the number of cancerous cells was directly proportional to the admittance value and the photocurrent response for all four different types of cancer cells. Additionally, different types of cancer cells could easily be differentiated using the slope value of the photocurrent response and the admittance value.

Characteristics of P-Type and N-Type Photoelectrochemical Biosensors: A Case Study for Esophageal Cancer Detection

P-type and N-type photoelectrochemical (PEC) biosensors were established in the laboratory to discuss the correlation between characteristic substances and photoactive material properties through the photogenerated charge carrier transport mechanism. Four types of human esophageal cancer cells (ECCs) were analyzed without requiring additional bias voltage. Photoelectrical characteristics were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), UV–vis reflectance spectroscopy, and photocurrent response analyses. Results showed that smaller photocurrent was measured in cases with advanced cancer stages. Glutathione (L-glutathione reduced, GSH) and Glutathione disulfide (GSSG) in cancer cells carry out redox reactions during carrier separation, which changes the photocurrent. The sensor can identify ECC stages with a certain level of photoelectrochemical response. The detection error can be optimized by adjusting the number of cells, and the detection time of about 5 min allowed repeated measurement.