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Ruochen Zhu

Bio: Ruochen Zhu is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Myeloid leukemia. The author has co-authored 1 publications.

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Journal ArticleDOI
TL;DR: In this paper, the authors developed a timely and accurate method for predicting acute myeloid leukemia (AML) prognosis after chemotherapy treatment by surfaceenhanced Raman spectroscopy (SERS).

4 citations


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Journal ArticleDOI
TL;DR: In this article , the serum SERS technique was investigated as a potential prognostic tool for early assessment of hepatocellular carcinoma (HCC) chemotherapeutic response.

3 citations

Journal ArticleDOI
TL;DR: In this paper , a review summarizes studies on the potential of Raman spectroscopy (RS) in pediatric cancers, focusing on early diagnosis, prognosis prediction, and treatment improvement.
Abstract: Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.
Journal ArticleDOI
TL;DR: A detailed overview of the benefits and limitations of Raman spectroscopy and its use in hematological cancers can be found in this article , where the authors provided an overview of their work.
Abstract: Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.
Journal ArticleDOI
TL;DR: In this paper , a Raman spectroscopic method for fast and label-free genotype screening was proposed and validated using spontaneous Raman measurements of wild-type Cryptococcus neoformans and its six mutants.
Abstract: It is now understood that genes and their various mutations are associated with the onset and progression of diseases. However, routine genetic testing techniques are limited by their high cost, time consumption, susceptibility to contamination, complex operation, and data analysis difficulties, rendering them unsuitable for genotype screening in many cases. Therefore, there is an urgent need to develop a rapid, sensitive, user-friendly, and cost-effective method for genotype screening and analysis. In this study, we propose and investigate a Raman spectroscopic method for achieving fast and label-free genotype screening. The method was validated using spontaneous Raman measurements of wild-type Cryptococcus neoformans and its six mutants. An accurate identification of different genotypes was achieved by employing a one-dimensional convolutional neural network (1D-CNN), and significant correlations between metabolic changes and genotypic variations were revealed. Genotype-specific regions of interest were also localized and visualized using a gradient-weighted class activation mapping (Grad-CAM)-based spectral interpretable analysis method. Furthermore, the contribution of each metabolite to the final genotypic decision-making was quantified. The proposed Raman spectroscopic method demonstrated huge potential for fast and label-free genotype screening and analysis of conditioned pathogens.