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  1. Fulltext Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning
    Ali A, Ali H, Saeed A, Ahmed Khan A, Tin TT, Assam M, et al.
    Sensors (Basel), 2023 Sep 07;23(18).
    PMID: 37765797 DOI: 10.3390/s23187740
    The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
  2. Fulltext A multicentre study to determine the in vitro efficacy of flomoxef against extended-spectrum beta-lactamase producing Escherichia coli in Malaysia
    Yap PSX, Chong CW, Ponnampalavanar S, Ramli R, Harun A, Tengku Jamaluddin TZM, et al.
    PeerJ, 2023;11:e16393.
    PMID: 38047021 DOI: 10.7717/peerj.16393
    BACKGROUND: The high burden of extended-spectrum beta-lactamase-producing (ESBL)-producing Enterobacterales worldwide, especially in the densely populated South East Asia poses a significant threat to the global transmission of antibiotic resistance. Molecular surveillance of ESBL-producing pathogens in this region is vital for understanding the local epidemiology, informing treatment choices, and addressing the regional and global implications of antibiotic resistance.

    METHODS: Therefore, an inventory surveillance of the ESBL-Escherichia coli (ESBL-EC) isolates responsible for infections in Malaysian hospitals was conducted. Additionally, the in vitro efficacy of flomoxef and other established antibiotics against ESBL-EC was evaluated.

    RESULTS: A total of 127 non-repetitive ESBL-EC strains isolated from clinical samples were collected during a multicentre study performed in five representative Malaysian hospitals. Of all the isolates, 33.9% were isolated from surgical site infections and 85.8% were hospital-acquired infections. High rates of resistance to cefotaxime (100%), cefepime (100%), aztreonam (100%) and trimethoprim-sulfamethoxazole (100%) were observed based on the broth microdilution test. Carbapenems remained the most effective antibiotics against the ESBL-EC, followed by flomoxef. Antibiotic resistance genes were identified by PCR. The blaCTX-M-1 was the most prevalent ESBL gene, with 28 isolates (22%) harbouring blaCTX-M-1 only, 27 isolates (21.3%) co-harbouring blaCTX-M-1 and blaTEM, and ten isolates (7.9%) co-harbouring blaCTX-M-1, blaTEM and blaSHV. A generalised linear model showed significant antibacterial activity of imipenem against different types of infection. Besides carbapenems, this study also demonstrated a satisfactory antibacterial activity of flomoxef (81.9%) on ESBL-EC, regardless of the types of ESBL genes.

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