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  1. Fulltext An adaptive data-driven architecture for mental health care applications
    Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad Nor A
    PeerJ, 2024;12:e17133.
    PMID: 38563009 DOI: 10.7717/peerj.17133
    BACKGROUND: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders.

    OBJECTIVE: Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated.

    METHOD: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale.

    RESULTS: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.

  2. A new chromanone acid from the stem bark of Calophyllum teysmannii
    Lim CK, Subramaniam H, Say YH, Jong VY, Khaledi H, Chee CF
    Nat Prod Res, 2015;29(21):1970-7.
    PMID: 25716662 DOI: 10.1080/14786419.2015.1015020
    A new chromanone acid, namely caloteysmannic acid (1), along with three known compounds, calolongic acid (2), isocalolongic acid (3) and stigmasterol (4) were isolated from the stem bark of Calophyllum teysmannii. All these compounds were evaluated for their cytotoxic and antioxidant activities in the MTT and DPPH assays, respectively. The structure of compound 1 was determined by means of spectroscopic methods including 1D and 2D NMR experiments as well as HR-EIMS spectrometry. The stereochemical assignment of compound 1 was done based on the NMR results and X-ray crystallographic analysis. The preliminary assay results revealed that all the test compounds displayed potent inhibitory activity against HeLa cancer cell line, in particular with compound 1 which exhibited the highest cytotoxic activity comparable to the positive control used, cisplatin. However, no significant antioxidant activity was observed for all the test compounds in the DPPH radical scavenging capacity assay.
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