Publications by authors named "H Hamzah"

Breast cancer, a leading cause of mortality among women, has been recognized as requiring improved diagnostic methods. Exosome proteins, found in small extracellular vesicles, have emerged as a promising solution, reflecting the state of their cell of origin and playing key roles in cancer progression. This review examines their potential in breast cancer diagnosis, discussing advanced isolation and characterization techniques such as ultracentrifugation and microfluidic-based approaches.

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Sulfuric acid is commonly used to electrochemically activate gold electrodes in a variety of electrochemical applications. This work provides the first evaluations of the electrochemical behaviors and a 3D image of an activated screen-printed gold electrode (SPGE, purchased commercially) through electrochemical and imaging analyses. The activated SPGE surface appears rougher than the unactivated SPGE surface when viewed through microtopography images using scanning electron microscopy (SEM) and atomic force microscopy (AFM).

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The bovine leukocyte antigen (BoLA) gene is a significant genetic part of the immune system and has been used as a disease marker in cattle. In this study, we detected Theileria orientalis, T. sinensis, Anaplasma marginale, Anaplasma platys, Candidatus Mycoplasma haemobos and Trypanosoma evansi by PCR amplification and sequencing of the amplicons.

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Colitis-associated colon cancer (CAC) arises from prolonged inflammation of the inner colon lining. An alternative approach to treating or preventing CAC involves the use of natural products such as (L.) P.

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Article Synopsis
  • The study investigated the use of deep learning techniques to assess the severity of cervical spinal cord injuries (SCI) from MRI scans, addressing limitations in the standard assessment method (ASIA Impairment Scale).
  • Patients with cervical SCI from 2019 to 2022 had their MRI images labeled by physicians, and a deep convolutional neural network was trained for image segmentation and classification.
  • The model demonstrated high accuracy, achieving impressive Dice and IoU scores for spinal cord segmentation and a decent F1 score for classification, indicating potential for more advanced predictive models in future research.
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