Mitochondrial Glutathione in Cellular Redox Homeostasis and Disease Manifestation.

Mitochondria are critical for providing energy to maintain cell viability. Oxidative phosphorylation involves the transfer of electrons from energy substrates to oxygen to produce adenosine triphosphate. Mitochondria also regulate cell proliferation, metastasis, and deterioration. The flow of electrons in the mitochondrial respiratory chain generates reactive oxygen species (ROS), which are harmful to cells at high levels. Oxidative stress caused by ROS accumulation has been associated with an increased risk of cancer, and cardiovascular and liver diseases. Glutathione (GSH) is an abundant cellular antioxidant that is primarily synthesized in the cytoplasm and delivered to the mitochondria. Mitochondrial glutathione (mGSH) metabolizes hydrogen peroxide within the mitochondria. A long-term imbalance in the ratio of mitochondrial ROS to mGSH can cause cell dysfunction, apoptosis, necroptosis, and ferroptosis, which may lead to disease. This study aimed to review the physiological functions, anabolism, variations in organ tissue accumulation, and delivery of GSH to the mitochondria and the relationships between mGSH levels, the GSH/GSH disulfide (GSSG) ratio, programmed cell death, and ferroptosis. We also discuss diseases caused by mGSH deficiency and related therapeutics.

Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm.

In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required.

Detection of weak micro-scratches on aspherical lenses using a Gabor neural network and transfer learning.

Surface defect detection is a crucial step in ensuring the quality of lenses. One method to check for surface defects is to use an optical system integrated with an industrial camera to magnify and highlight the position of a defect on the surface of a lens. Therefore, automatic optical inspection systems are applied to detect micro-defects. In this study, we propose an automatic inspection platform based on a deep neural network for automatically imaging and examining the surface of a lens. High-resolution images of 2448×2048 pixels are acquired using a hybrid lighting system. A convolutional neural network integrated with a trainable Gabor filter is used as a machine vision algorithm to perform image classification and defect segmentation tasks. The experimental results show that the proposed method effectively performed with noise in the background, achieving a segmentation accuracy of 98%.

Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging.

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique.

Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 µm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%.

Optical and Material Characteristics of MoS2/Cu2O Sensor for Detection of Lung Cancer Cell Types in Hydroplegia.

In this study, n-type MoS2 monolayer flakes are grown through chemical vapor deposition (CVD), and a p-type Cu2O thin film is grown via electrochemical deposition. The crystal structure of the grown MoS2 flakes is analyzed through transmission electron microscopy. The monolayer structure of the MoS2 flakes is verified with Raman spectroscopy, multiphoton excitation microscopy, atomic force microscopy, and photoluminescence (PL) measurements. After the preliminary processing of the grown MoS2 flakes, the sample is then transferred onto a Cu2O thin film to complete a p-n heterogeneous structure. Data are confirmed via scanning electron microscopy, SHG, and Raman mapping measurements. The luminous energy gap between the two materials is examined through PL measurements. Results reveal that the thickness of the single-layer MoS2 film is 0.7 nm. PL mapping shows a micro signal generated at the 627 nm wavelength, which belongs to the B2 excitons of MoS2 and tends to increase gradually when it approaches 670 nm. Finally, the biosensor is used to detect lung cancer cell types in hydroplegia significantly reducing the current busy procedures and longer waiting time for detection. The results suggest that the fabricated sensor is highly sensitive to the change in the photocurrent with the number of each cell, the linear regression of the three cell types is as high as 99%. By measuring the slope of the photocurrent, we can identify the type of cells and the number of cells.

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

A highly sensitive photoelectrochemical (PEC) biosensor without external bias was developed in this study. The biosensor was configured with a p-Cu2O and n-ZnO heterostructure. Hexamethylenetetramine (HMTA) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) was used to improve the crystal structure of Cu2O and ZnO and reduce the defects in the Cu2O/ZnO interface. This fabrication method provided the highly crystallized Cu2O/ZnO structure with excellent electrical property and photoresponse in visible light. The structure was applied to a biosensor for detecting two different cancerous levels of esophageal cells, namely, OE21 and OE21-1, with a high gain in photocurrent (5.8 and 6.2 times, respectively) and a low detection limit (3000 cells in 50 µL). We believe that such a p-n heterojunction PEC biosensor could advance biosensor development and provide a promising candidate for biomedical applications.

Time-evolution of the electrical characteristics of MoS2 field-effect transistors after electron beam irradiation.

As the feature sizes of devices decrease to the nanoscale, electron microscopy and lithography will become increasingly essential techniques for fabrication and inspection. In this study, we probed the memory effects of MoS2 field-effect transistors (FETs) subjected to electron beam (e-beam) irradiation; after fabricating the devices on 300 nm SiO2/Si substrates, we irradiated the MoS2 FETs with various doses of irradiation from a 30 kV e-beam. The threshold voltage shifted to the negative side and the mobility increased-a so-called memory effect-upon increasing the e-beam dose. These changes resulted from positively charged oxide traps, formed upon e-beam irradiation, in the gate oxide layer. Interestingly, the electrical characteristics of the MoS2 FETs after e-beam irradiation continued to change upon aging: the threshold voltage shifted toward the positive side and the mobility decreased, suggesting that the dominant mechanism changed from the presence of positively charged oxide traps to the presence of negatively charged interface traps. Notably, the threshold voltage shifts of the MoS2 FETs could be retained for one or two days. This behavior should be useful for preparing property-adjustable nanodevices, with particular potential for applications in multi-level memory devices.

Endoscopic screening for synchronous esophageal neoplasia among patients with incident head and neck cancer: Prevalence, risk factors, and outcomes.

Esophageal squamous-cell neoplasia (ESCN) is a common second primary neoplasia found in patients with head-and-neck squamous-cell carcinoma (HNSCC). This study sought to identify the risk factors for synchronous ESCN and how they influence survival in HNSCC patient. Eight hundred and fifteen incident HNSCC patients were prospectively recruited for endoscopy screening for ESCN using white-light imaging, narrow-band imaging, Lugol chromoendoscopy, and pathological confirmation. Associated lifestyle and clinicopathological data were collected. The interquartile follow-up period cutoffs were 11.3, 20.5 and 34.9 months. 124 patients (15.2%) were diagnosed as having synchronous ESCN (66 low-grade dysplasia, 29 high-grade dysplasia, and 29 esophageal squamous-cell carcinoma). Consumption of alcohol, but not betel nut or cigarette, was significantly associated with the presence of synchronous ESCN (adjusted odds ratio [aOR] = 7.1 and 10.9 for former and current drinkers, respectively). There was an interaction between cumulative dose of alcohol consumption and alcohol flushing response on the development of ESCN. High-dose drinkers with flush response were 16.9 times more likely to have esophageal high-grade dysplasia/SCC than non-drinkers. Compared with oral cavity cancer patients, those with hypopharyngeal, laryngeal and oropharyngeal cancer were 6.8, 4.6 and 2.8 times more likely to have esophageal high-grade dysplasia/SCC. HNSCC patients with synchronous ESCN had lower overall survival than those without (p < 0.0001). In conclusion, surveillance of ESCN is strongly recommended for the high-risk subpopulation of HNSCC patients, especially drinkers who have a flush response to alcohol, and those with distant metastasis of index cancer and cancers in hypopharynx, oropharynx and larynx.

Nano-structure ZnO/Cu2O photoelectrochemical and self-powered biosensor for esophageal cancer cell detection.

The p-n heterojunction photoelectrochemical biosensor, which comprises a p-type Cu2O film formed by electrochemical deposition and n-type ZnO nanorods formed by the hydrothermal method, is prone to photoelectrochemical reactions and self-powered. Four types of human esophageal cancer cells (ECCs) were detected by this biosensor without requiring an extra bias voltage. The measured photocurrent values of high invasion capacity cancer cells was consistently 2 times higher than those measured by a slight invasion capacity cancer cells. The response time, which was about 0.5 s, allowed repeated measurement.

Characterizing Esophageal Cancerous Cells at Different Stages Using the Dielectrophoretic Impedance Measurement Method in a Microchip.

Analysis of cancerous cells allows us to provide useful information for the early diagnosis of cancer and to monitor treatment progress. An approach based on electrical principles has recently become an attractive technique. This study presents a microdevice that utilizes a dielectrophoretic impedance measurement method for the identification of cancerous cells. The proposed biochip consists of circle-on-line microelectrodes that are patterned using a standard microfabrication processes. A sample of various cell concentrations was introduced in an open-top microchamber. The target cells were collectively concentrated between the microelectrodes using dielectrophoresis manipulation, and their electrical impedance properties were also measured. Different stages of human esophageal squamous cell carcinoma lines could be distinguished. This result is consistent with findings using hyperspectral imaging technology. Moreover, it was observed that the distinguishing characteristics change in response to the progression of cancer cell invasiveness by Raman spectroscopy. The device enables highly efficient cell collection and provides rapid, sensitive, and label-free electrical measurements of cancerous cells.

All-reflective RGB LED flashlight design for effective color mixing.

Red, green, and blue (RGB) light-emitting diode (LED) is a narrow-band light source that can improve visual contrast, and thus, can be used for special illumination. In this study, three RGB LEDs, each provided with two reflective mirrors, are used to design an all-reflective color temperature-adjustable LED flashlight. The LED flashlight features an adjustable color temperature ranging from 2000 K to 6500 K, a uniformity of illuminance of 0.68, an average difference of uniformity of approximately 25%, and a color uniformity of 0.0042.

Anti-glare LED lamps with adjustable illumination light field.

We introduce a type of LED light-gauge steel frame lamp with an adjustable illumination light field that does not require a diffusion plate. Base on the Monte Carlo ray tracing method, this lamp has a good glare rating (GR) of 17.5 at 3050 lm. Compared with the traditional LED light-gauge steel frame lamp (without diffusion plate), the new type has low GR. The adjustability of the illumination light field could improve the zebra effect caused by the inadequate illumination light field of the lamp. Meanwhile, we adopt the retinal image analysis to discuss the influence of GR on vision. High GR could reflect stray light on the retinal image, which will reduce vision clarity and hasten the feeling of eye fatigue.

Identification of Skin Lesions by Snapshot Hyperspectral Imaging.

This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer.

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

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.

Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration.

Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.

Influence of catalyst choices on transport behaviors of InAs NWs for high-performance nanoscale transistors.

The influence of the catalyst materials on the electron transport behaviors of InAs nanowires (NWs) grown by a conventional vapor transport technique is investigated. Utilizing the NW field-effect transistor (FET) device structure, ~20% and ~80% of Au-catalyzed InAs NWs exhibit strong and weak gate dependence characteristics, respectively. In contrast, ~98% of Ni-catalyzed InAs NWs demonstrate a uniform n-type behavior with strong gate dependence, resulting in an average OFF current of ~10(-10) A and a high I(ON)/I(OFF) ratio of >10(4). The non-uniform device performance of Au-catalyzed NWs is mainly attributed to the non-stoichiometric composition of the NWs grown from a different segregation behavior as compared to the Ni case, which is further supported by the in situ TEM studies. These distinct electrical characteristics associated with different catalysts were further investigated by the first principles calculation. Moreover, top-gated and large-scale parallel-array FETs were fabricated with Ni-catalyzed NWs by contact printing and channel metallization techniques, which yield excellent electrical performance. The results shed light on the direct correlation of the device performance with the catalyst choice.

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.