A Novel Automatic Rapid Diagnostic Test Reader Platform

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

Transforming Rapid Diagnostic Tests for Precision Public Health: Open Guidelines for Manufacturers and Users

BACKGROUND

Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.

OBJECTIVE

We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system.

METHODS

We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia.

RESULTS

The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems.

CONCLUSIONS

Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

Developing Gold Nanoparticles-Conjugated Aflatoxin B1 Antifungal Strips

Lateral flow immunochromatographic assays are a powerful diagnostic tool for point-of-care tests, based on their simplicity, specificity, and sensitivity. In this study, a rapid and sensitive gold nanoparticle (AuNP) immunochromatographic strip is produced for detecting aflatoxin B1 (AFB1) in suspicious fungi-contaminated food samples. The 10 nm AuNPs were encompassed by bovine serum albumin (BSA) and AFB1 antibody. Thin-layer chromatography, gel electrophoresis and nuclear magnetic resonance spectroscopy were employed for analysing the chemical complexes. Various concentrations of AFB1 antigen (0-16 ng/mL) were tested with AFB1 antibody-BSA-AuNPs (conjugated AuNPs) and then analysed by scanning electron microscopy, ultraviolet-visible spectroscopy, and Zetasizer. The results showed that the AFB1 antibody was coupled to BSA by the N-hydroxysuccinimide ester method. The AuNPs application has the potential to contribute to AFB1 detection by monitoring a visible colour change from red to purple-blue, with a detection limit of 2 ng/mL in a 96-well plate. The lateral flow immunochromatographic strip tests are rapid, taking less than 10 min., and they have a detection capacity of 10 ng/g. The smartphone analysis of strips provided the results in 3 s, with a detection limit of 0.3 ng/g for AFB1 when the concentration was below 10 ng/g. Excellent agreement was found with AFB1 determination by high-performance liquid chromatography in the determination of AFB1 among 20 samples of peanuts, corn, rice, and bread.

Development of a smartphone-based rapid dual fluorescent diagnostic system for the simultaneous detection of influenza A and H5 subtype in avian influenza A-infected patients

Accurate and rapid diagnosis of highly pathogenic avian influenza A H5N1 is of critical importance for the effective clinical management of patients. Here, we developed a rapid and simultaneous detection toolkit for influenza A H5 subtype viruses in human samples based on a bioconjugate of quantum dots (QDs) assembly and a smartphone-based rapid dual fluorescent diagnostic system (SRDFDS). Methods: Two types of QDs were assembled on a latex bead to enhance the detection sensitivity and specificity of influenza A infection (QD580) and H5 subtype (QD650). The dual signals of influenza A and H5 subtype of H5N1-infected patients were detected simultaneously and quantified separately by SRDFDS equipped with two emission filters. Results: Our results showed a high sensitivity of 92.86% (13/14) and 78.57% (11/14), and a specificity of 100% (38/38, P < 0.0001) and 97.37% (37/38) for influenza A and H5 subtype detection, respectively. Conclusion: Therefore, our multiplex QD bioconjugates and SRDFDS-based influenza virus detection toolkit potentially provide accurate and meaningful diagnosis information with improved detection accuracies and sensitivities for H5N1 patients.

Smartphone-Based Dual-Modality Imaging System for Quantitative Detection of Color or Fluorescent Lateral Flow Immunochromatographic Strips

Nowadays, lateral flow immunochromatographic assays are increasingly popular as a diagnostic tool for point-of-care (POC) test based on their simplicity, specificity, and sensitivity. Hence, quantitative detection and pluralistic popular application are urgently needed in medical examination. In this study, a smartphone-based dual-modality imaging system was developed for quantitative detection of color or fluorescent lateral flow test strips, which can be operated anywhere at any time. In this system, the white and ultra-violet (UV) light of optical device was designed, which was tunable with different strips, and the Sobel operator algorithm was used in the software, which could enhance the identification ability to recognize the test area from the background boundary information. Moreover, this technology based on extraction of the components from RGB format (red, green, and blue) of color strips or only red format of the fluorescent strips can obviously improve the high-signal intensity and sensitivity. Fifty samples were used to evaluate the accuracy of this system, and the ideal detection limit was calculated separately from detection of human chorionic gonadotropin (HCG) and carcinoembryonic antigen (CEA). The results indicated that smartphone-controlled dual-modality imaging system could provide various POC diagnoses, which becomes a potential technology for developing the next-generation of portable system in the near future.

Diagnosis of Helicobacter pylori infection

Important progress is being made in endoscopic methods which allow clinicians to predict the presence of Helicobacter pylori based on characteristics of gastric mucosa and to obtain targeted biopsies. There are also important developments in molecular methods with various techniques derived from standard PCR, applied both on gastric biopsies and stool specimens. Progress is being made in microfluidic systems to get a reliable diagnosis in a very short time. The interest of the ¹³ C urea breath test has been confirmed as well as stool antigen tests. Attempts are being made to find biological markers of premalignant conditions by serology, other than pepsinogens.