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Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19
The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.
Journal Article
Characterizing immune profiles in hepatocellular carcinoma patients benefiting from pembrolizumab and lenvatinib using machine learning
Background
Combination immunotherapies, such as pembrolizumab plus lenvatinib (PL), are commonly used in treatment for unresectable hepatocellular carcinoma (uHCC). However, it remains challenging to predict which patients will benefit from this therapy. This study aimed to address this issue by comparing immune cell profiles (ICPs) between uHCC patients with objective response (responders, R) and those with tumor progression (non-responders, NR) following PL therapy, and to identify the key contributors to ICPs.
Methods
We prospectively enrolled 51 uHCC patients between July 2019 and July 2023. Peripheral blood samples were collected prior to initiating PL therapy, and ICPs were analyzed according to tumor response according to RECIST 1.1 criteria. A machine learning (ML) model was developed to differentiate R from NR using baseline ICP data.
Results
16 patients achieved objective tumor responses, while 11 experienced disease progression following PL therapy. Responders exhibited higher levels of total T cells, CD8 T cells, and PD-1
+
subpopulations of CD4 T cells, CD8 T cells, and NK cells. In contrast, NR had higher proportions of PD-L1
+
monocytes. The trained ICP-based ML model accurately discriminated between the two groups, achieving 100% sensitivity and 66.7% specificity, with CD8 T cells, PD-1
+
CD8 NK cells, and PD-L1
+
monocytes contributing significantly to the classification.
Conclusion
This study recognized distinct ICPs between uHCC patients with and without tumor response to PL therapy and identified key contributing immune subpopulations. These findings provide a foundation for developing predictive tools for clinical outcomes before initiating combination immunotherapy.
Journal Article
The impact of China's Belt and Road Initiative in Southeast Asia : evaluating risks and benefits
Drawing insights from intensive empirical investigation of the Belt and Road Initiative (BRI) projects in various Southeast Asian countries, this book assesses and analyzes the continuing impact of China's BRI on Thailand and ASEAN at a critical period of Southeast Asian history.
BOOK
Validation of Acute Myocardial Infarction Cases in the National Health Insurance Research Database in Taiwan
Background: The aim of this study was to determine the validity of acute myocardial infarction (AMI) diagnosis coding in the National Health Insurance Research Database (NHIRD) by cross-comparisons of discharge diagnoses listed in the NHIRD with those in the medical records obtained from a medical center in Taiwan. Methods: This was a cross-sectional study comparing records in the NHIRD and discharge notes in one medical center (DNMC) in the year 2008. Positive predictive values (PPVs) for AMI diagnoses were evaluated by reviewing the relevant clinical and laboratory data recorded in the discharge notes of the medical center. Agreement in comorbidities, cardiac procedures, and antiplatelet agent (aspirin or clopidogrel) prescriptions between the two databases was evaluated. Results: We matched 341 cases of AMI hospitalizations from the two databases, and 338 cases underwent complete chart review. Of these 338 AMI cases, 297 were confirmed with clinical and lab data, which yielded a PPV of 0.88. The consistency rate for coronary intervention, stenting, and antiplatelet prescription at admission was high, yielding a PPV over 0.90. The percentage of consistency in comorbidity diagnoses was 95.9% (324/338) among matched AMI cases. Conclusions: The NHIRD appears to be a valid resource for population research in cardiovascular diseases.
Journal Article
Two-phase modelling of submarine granular flows with shear-induced volume change and pore-pressure feedback
Two important issues that arise when using an Eulerian–Eulerian two-phase model to simulate submarine granular flows have not been addressed well: the shear-induced volume change and the resultant pore-pressure feedback. This study develops a multiphase model with a novel evolution equation to determine the static solid pressure resulting from prolonged contact between particles. The evolution equation can effectively describe the relaxation process of the static solid pressure and the shear-induced volume change in plane-shear configurations. Additionally, the evolution equation allows the present model to capture two typical phenomena associated with pore-pressure feedback: the time delay of the initiating submarine granular flows and the different collapse processes for differently packed columns.
Journal Article
Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities
The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.
Journal Article