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  1. Fulltext Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models
    Yuan CJ, Varathan KD, Suhaimi A, Ling LW
    J Rehabil Med, 2023 Jan 09;55:jrm00348.
    PMID: 36306152 DOI: 10.2340/jrm.v54.2432
    OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.

    SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.

    METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.

    RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.

    CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

  2. Fulltext Development of the Sensing Platform for Protein Tyrosine Kinase Activity
    Wei LY, Lin W, Leo BF, Kiew LV, Chang CC, Yuan CJ
    Biosensors (Basel), 2021 Jul 15;11(7).
    PMID: 34356711 DOI: 10.3390/bios11070240
    A miniature tyrosinase-based electrochemical sensing platform for label-free detection of protein tyrosine kinase activity was developed in this study. The developed miniature sensing platform can detect the substrate peptides for tyrosine kinases, such as c-Src, Hck and Her2, in a low sample volume (1-2 μL). The developed sensing platform exhibited a high reproducibility for repetitive measurement with an RSD (relative standard deviation) of 6.6%. The developed sensing platform can detect the Hck and Her2 in a linear range of 1-200 U/mL with the detection limit of 1 U/mL. The sensing platform was also effective in assessing the specificity and efficacies of the inhibitors for protein tyrosine kinases. This is demonstrated by the detection of significant inhibition of Hck (~88.1%, but not Her2) by the Src inhibitor 1, an inhibitor for Src family kinases, as well as the significant inhibition of Her2 (~91%, but not Hck) by CP-724714 through the platform. These results suggest the potential of the developed miniature sensing platform as an effective tool for detecting different protein tyrosine kinase activity and for accessing the inhibitory effect of various inhibitors to these kinases.
  3. Fulltext Development of flexible electrochemical impedance spectroscopy-based biosensing platform for rapid screening of SARS-CoV-2 inhibitors
    Kiew LV, Chang CY, Huang SY, Wang PW, Heh CH, Liu CT, et al.
    Biosens Bioelectron, 2021 Jul 01;183:113213.
    PMID: 33857754 DOI: 10.1016/j.bios.2021.113213
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters the cells through the binding of its spike protein (S-protein) to the cell surface-expressing angiotensin-converting enzyme 2 (ACE2). Thus, inhibition of S-protein-ACE2 binding may impede SARS-CoV-2 cell entry and attenuate the progression of Coronavirus disease 2019 (COVID-19). In this study, an electrochemical impedance spectroscopy-based biosensing platform consisting of a recombinant ACE2-coated palladium nano-thin-film electrode as the core sensing element was fabricated for the screening of potential inhibitors against S-protein-ACE2 binding. The platform could detect interference of small analytes against S-protein-ACE2 binding at low analyte concentration and small volume (0.1 μg/mL and ~1 μL, estimated total analyte consumption 
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