Liver Cancer Detection by a Simple, Inexpensive and Effective Immunosensor with Zinc Oxide Nanoparticles.

Regular monitoring of blood α-fetoprotein (AFP) and/or carcino-embryonic antigen (CEA) levels is important for the routine screening of liver cancer. However, AFP and CEA have a much lower specificity than des-γ-carboxyprothrombin (DCP) to detect liver cancer. Therefore, the study reported here was designed, to develop a screen-printed DCP immunosensor incorporating zinc oxide nanoparticles, for accurate determination of DCP. The designed immunosensor shows low detection limits for the detection of DCP: 0.440 ng/mL (based on impedance measurement), 0.081 ng/mL (based on real part of impedance measurement) and 0.078 ng/mL (based on imaginary part of impedance measurement), within the range of 3.125 ng/mL to 2000 ng/mL. In addition, there was little interference to DCP determination by molecules such as Na⁺, K⁺, Ca(2+), Cl(-), glucose, urea, and uric acid. It is therefore concluded that the DCP immunosensor developed and reported here is simple, inexpensive and effective, and shows promise in the rapid screening of early-stage liver cancer at home with a point-of-care approach.

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis.

BACKGROUND: The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis. METHODS: The web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail. RESULTS: Between 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years. CONCLUSIONS: This study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

BACKGROUND: There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics. METHODS: The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R. RESULTS: The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US. CONCLUSION: The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

A Mechanical Sensor Designed for Dynamic Joint Angle Measurement.

Background. The measurement of the functional range of motion (FROM) of lower limb joints is an essential parameter for gait analysis especially in evaluating rehabilitation programs. Aim. To develop a simple, reliable, and affordable mechanical goniometer (MGR) for gait analysis, with six-degree freedom to dynamically assess lower limb joint angles. Design. Randomized control trials, in which a new MGR was developed for the measurements of FROM of lower limb joints. Setting. Reliability of the designed MGR was evaluated and validated by a motion analysis system (MAS). Population. Thirty healthy subjects participated in this study. Methods. Reliability and validity of the new MGR were tested by intraclass correlation coefficient (ICC), Bland-Altman plots, and linear correlation analysis. Results. The MGR has good inter- and intrarater reliability and validity with ICC ≥ 0.93 (for both). Moreover, measurements made by MGR and MAS were comparable and repeatable with each other, as confirmed by Bland-Altman plots. Furthermore, a very high degree of linear correlation (R ≥ 0.92 for all joint angle measurements) was found between the lower limb joint angles measured by MGR and MAS. Conclusion. A simple, reliable, and affordable MGR has been designed and developed to aid clinical assessment and treatment evaluation of gait disorders.