A fluorescence AuNPs-LISA: A new approach for Opisthorchis viverrini (Ov) antigen detection with a simple fluorescent enhancement strategy by surfactant micelle in urine samples

Clonorchiasis and opisthorchiasis: epidemiology, transmission, clinical features, morbidity, diagnosis, treatment, and control

Clonorchis sinensis, Opisthorchis viverrini, and Opisthorchis felineus are important liver flukes that cause a considerable public health burden in eastern Asia, southeastern Asia, and eastern Europe, respectively. The life cycles are complex, involving humans, animal reservoirs, and two kinds of intermediate hosts. An interplay of biological, cultural, ecological, economic, and social factors drives transmission. Chronic infections are associated with liver and biliary complications, most importantly cholangiocarcinoma. With regard to diagnosis, stool microscopy is widely used in epidemiologic surveys and for individual diagnosis. Immunologic techniques are employed for screening purposes, and molecular techniques facilitate species differentiation in reference laboratories. The mainstay of control is preventive chemotherapy with praziquantel, usually combined with behavioral change through information, education and communication, and environmental control. Tribendimidine, a drug registered in the People's Republic of China for soil-transmitted helminth infections, shows potential against both C. sinensis and O. viverrini and, hence, warrants further clinical development. Novel control approaches include fish vaccine and biological control. Considerable advances have been made using multi-omics which may trigger the development of new interventions. Pressing research needs include mapping the current distribution, disentangling the transmission, accurately estimating the disease burden, and developing new diagnostic and treatment tools, which would aid to optimize control and elimination measures.

Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks

Background

Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist's abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid.

Methods

Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection.

Results

The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947.

Conclusion

This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model's performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.

Nanoparticle-mediated therapeutic management in cholangiocarcinoma drug targeting: Current progress and future prospects

Patients with cholangiocarcinoma (CCA) often have an unfavorable prognosis because of its insidious nature, low resectability rate, and poor response to anticancer drugs and radiotherapy, which makes early detection and treatment difficult. At present, CCA has a five-year overall survival rate (OS) of only 5%, despite advances in therapies. New an increasing number of evidence suggests that nanoplatforms may play a crucial role in enhancing the pharmacological effects and in reducing both short- and long-term side effects of cancer treatment. This document reviews the advantages and shortcomings of nanoparticles such as liposomes, polymeric nanoparticle，inorganic nanoparticle, nano-metals and nano-alloys, carbon dots, nano-micelles, dendrimer, nano-capsule, bio-Nanomaterials in the diagnosis and treatment of CCA and discuss the current challenges in of nanoplatforms for CCA.

Effects of day-to-day variation of Opisthorchis viverrini antigen in urine on the accuracy of diagnosing opisthorchiasis in Northeast Thailand

Antigen detection in urine using an enzyme-linked immunosorbent assay (ELISA) is more sensitive than fecal examination for diagnosis of opisthorchiasis and for assessment of the effects of drug treatment. It is not known whether day-to-day variation of urine composition, including levels of Opisthorchis viverrini antigen, influences the urine assay. We investigated this topic with the cooperation of participants from two localities in Northeast Thailand. Project participants were screened for parasite infections for three consecutive days using the quantitative formalin-ethyl acetate concentration technique (FECT) to detect O. viverrini eggs and the urine ELISA for detection of O. viverrini antigen. A subset of participants (n = 801) with matched fecal and urine samples were analyzed for comparison of inter-day prevalence estimates and the performance of the urine assay compared against FECT for diagnosis of opisthorchiasis. The daily prevalence measured by the urine assay ranged between 29.0%-30.2% while those by FECT ranged between 11.9%-20.2%. The cumulative three-day prevalence estimate determined by the urine antigen assay was 30.3%, which was significantly higher than that by FECT (20.2%, p < 0.05). A significant positive correlation was found between the concentration of antigen in urine and fecal egg counts (p < 0.001). Overall, the urine assay had better diagnostic performance for opisthorchiasis than fecal examination by FECT. The high sensitivity plus negligible daily variation of O. viverrini antigen in urine indicates the utility of the urine assay for diagnosis, as well as population screening, of opisthorchiasis.

Nanobody and Nanozyme-Enabled Immunoassays with Enhanced Specificity and Sensitivity

Immunoassay as a rapid and convenient method for detecting a variety of targets has attracted tremendous interest with its high specificity and sensitivity. Among the commonly used immunoassays, enzyme-linked immunosorbent assay has been widely used as a gold standard method in various fields that consists of two main components including a recognition element and an enzyme label. With the rapid advances in nanotechnology, nanobodies and nanozymes enable immunoassays with enhanced specificity and sensitivity compared with conventional antibodies and natural enzymes. This review is focused on the applications of nanobodies and nanozymes in immunoassays. Nanobodies advantage lies in their small size, high specificity, mass expression, and high stability. Nanozymes with peroxidase, phosphatase, and oxidase activities and their applications in immunoassays are highlighted and discussed in detail. In addition, the challenges and outlooks in terms of the use of nanobodies and the development of novel nanozymes in practical applications are discussed.

Smartphone-based portable fluorescence sensor with gold nanoparticle mediation for selective detection of nitrite ions

A simple, portable device for the detection of NO₂^- via a fluorescence method was developed. The proposed device consisted of a dark box containing a blue LED as a low-power excitation light source and a smartphone with a mobile application for RGB analysis as a light detector. Detection was mediated by using synthesized cetyltrimethylammonium bromide-stabilized gold nanoparticles (CTAB-AuNPs). The CTAB-AuNPs were etched with NO₂^- to yield Au³⁺, which catalyzes the oxidation of o-phenylenediamine (OPD) in the presence of H₂O₂ to generate 2,3-diaminophenazine (DAP). Triton X-100 (TX-100) micelles were introduced to improve the DAP fluorescence emission. The fluorescence intensity of DAP was recorded by the smartphone in terms of RGB intensity, which was correlated with the NO₂^- concentration. This method provided a wide linear working concentration range (0.5-100 μM), a limit of detection of 0.17 μM and excellent selectivity for NO₂^- over other anions.

A simple strategy to enhance the sensitivity of fluorescent sensor-based CdS quantum dots by using a surfactant for Hg2+ detection

A simple strategy to enhance the detection sensitivity of fluorescent sensor-based CdS quantum dots (CdS QDs) for the detection of mercury ions (Hg²⁺) was demonstrated. L-Cysteine-capped CdS QDs (L-Cyst-CdS QDs) were synthesized and utilized as a probe for selective detection of Hg²⁺. The fluorescence intensity of the L-Cyst-CdS QDs was quenched in the presence of Hg²⁺. However, the detection sensitivity was unsatisfactory. Upon the addition of sodium dodecyl sulfate (SDS), the fluorescence intensity of L-Cyst-CdS QDs can be effectively enhanced. On the other hand, the fluorescence intensity of the L-Cyst-CdS QDs in the presence of SDS (SDS@L-Cyst-CdS QDs) was able to be dramatically decreased with the addition of Hg²⁺. Furthermore, the proposed sensor displayed excellent selectivity towards Hg²⁺ compared to other cations. Under optimized conditions, the proposed sensor could be applied to detect trace amounts of Hg²⁺ with a limit of detection of approximately 36 nM. The applicability of this sensor was demonstrated by the determination of Hg²⁺ in real water samples, and the results agreed with those obtained from cold vapor atomic absorption spectrometry (CVAAS).