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Fulltext Development of biomass waste-based carbon quantum dots and their potential application as non-toxic bioimaging agents

Abu N, Chinnathambi S, Kumar M, Etezadi F, Bakhori NM, Zubir ZA, et al.

RSC Adv, 2023 Sep 18;13(40):28230-28249.
PMID: 37753403 DOI: 10.1039/d3ra05840a

Over recent years, carbon quantum dots (CQDs) have advanced significantly and gained substantial attention for their numerous benefits. These benefits include their simple preparation, cost-effectiveness, small size, biocompatibility, bright luminescence, and low cytotoxicity. As a result, they hold great potential for various fields, including bioimaging. A fascinating aspect of synthesizing CQDs is that it can be accomplished by using biomass waste as the precursor. Furthermore, the synthesis approach allows for control over the physicochemical characteristics. This paper unequivocally examines the production of CQDs from biomass waste and their indispensable application in bioimaging. The synthesis process involves a simple one-pot hydrothermal method that utilizes biomass waste as a carbon source, eliminating the need for expensive and toxic reagents. The resulting CQDs exhibit tunable fluorescence and excellent biocompatibility, making them suitable for bioimaging applications. The successful application of biomass-derived CQDs has been demonstrated through biological evaluation studies in various cell lines, including HeLa, Cardiomyocyte, and iPS, as well as in medaka fish eggs and larvae. Using biomass waste as a precursor for CQDs synthesis provides an environmentally friendly and sustainable alternative to traditional methods. The resulting CQDs have potential applications in various fields, including bioimaging.
Fulltext Calcium lignosulfonate modulates physiological and biochemical responses to enhance shoot multiplication in Vanilla planifolia Andrews

Thye KL, Wan Abdullah WMAN, Ong-Abdullah J, Lamasudin DU, Wee CY, Mohd Yusoff MHY, et al.

Physiol Mol Biol Plants, 2023 Mar;29(3):377-392.
PMID: 37033764 DOI: 10.1007/s12298-023-01293-w

Utilisation of calcium lignosulfonate (CaLS) in Vanilla planifolia has been reported to improve shoot multiplication. However, mechanisms responsible for such observation remain unknown. Here, we elucidated the underlying mechanisms of CaLS in promoting shoot multiplication of V. planifolia via comparative proteomics, biochemical assays, and nutrient analysis. The proteome profile of CaLS-treated plants showed enhancement of several important cellular metabolisms such as photosynthesis, protein synthesis, Krebs cycle, glycolysis, gluconeogenesis, and carbohydrate synthesis. Further biochemical analysis recorded that CaLS increased Rubisco activity, hexokinase activity, isocitrate dehydrogenase activity, total carbohydrate content, glutamate synthase activity and total protein content in plant shoot, suggesting the role of CaLS in enhancing shoot growth via upregulation of cellular metabolism. Subsequent nutrient analysis showed that CaLS treatment elevated the contents of several nutrient ions especially calcium and sodium ions. In addition, our study also revealed that CaLS successfully maintained the cellular homeostasis level through the regulation of signalling molecules such as reactive oxygen species and calcium ions. These results demonstrated that the CaLS treatment can enhance shoot multiplication in V. planifolia Andrews by stimulating nutrient uptake, inducing cell metabolism, and regulating cell homeostasis.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-023-01293-w.
Mutation analysis of BCR-ABL1 kinase domain in chronic myeloid leukemia patients with tyrosine kinase inhibitors resistance: a Malaysian cohort study

Seman ZA, Ahid F, Kamaluddin NR, Sahid ENM, Esa E, Said SSM, et al.

BMC Res Notes, 2024 Apr 20;17(1):111.
PMID: 38643202 DOI: 10.1186/s13104-024-06772-1

OBJECTIVE: Mutational analysis of BCR::ABL1 kinase domain (KD) is a crucial component of clinical decision algorithms for chronic myeloid leukemia (CML) patients with failure or warning responses to tyrosine kinase inhibitor (TKI) therapy. This study aimed to detect BCR::ABL1 KD mutations in CML patients with treatment resistance and assess the concordance between NGS (next generation sequencing) and Sanger sequencing (SS) in detecting these mutations.
RESULTS: In total, 12 different BCR::ABL1 KD mutations were identified by SS in 22.6% (19/84) of patients who were resistant to TKI treatment. Interestingly, NGS analysis of the same patient group revealed an additional four different BCR::ABL1 KD mutations in 27.4% (23/84) of patients. These mutations are M244V, A344V, E355A, and E459K with variant read frequency below 15%. No mutation was detected in 18 patients with optimal response to TKI therapy. Resistance to TKIs is associated with the acquisition of additional mutations in BCR::ABL1 KD after treatment with TKIs. Additionally, the use of NGS is advised for accurately determining the mutation status of BCR::ABL1 KD, particularly in cases where the allele frequency is low, and for identifying mutations across multiple exons simultaneously. Therefore, the utilization of NGS as a diagnostic platform for this test is very promising to guide therapeutic decision-making.
Fulltext Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review

Islam MS, Al Farid F, Shamrat FMJM, Islam MN, Rashid M, Bari BS, et al.

PeerJ Comput Sci, 2024;10:e2517.
PMID: 39896401 DOI: 10.7717/peerj-cs.2517

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.