Exosomes and Paper-Based Biosensors for Early Oral  Cancer Screening

Dinesh, Y; Ramani, Pratibha; Ramalingam, Karthikeyan

doi:10.47750/pnr.2022.13.s07.036

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…Several novel methods are evolving in the field of diagnosis of cancers such as machine learning, exosomes and paper-based biosensors [ 10 ]. Oncology is being revolutionized by cutting-edge machine learning algorithms and provides rapid and cutting-edge methods for the diagnosis of potential lesions with an accuracy comparable to that of medical specialists.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images

Ramalingam,

Ramani

et al. 2023

Cureus

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Introduction: Artificial intelligence in oncology has gained a lot of interest in recent years. Early detection of Oral squamous cell carcinoma (OSCC) is crucial for early management to attain a better prognosis and overall survival. Machine learning (ML) has also been used in oral cancer studies to explore the discrimination between clinically normal and oral cancer. Materials and methods: A dataset comprising 360 clinical intra-oral images of OSCC, Oral Potentially Malignant Disorders (OPMDs) and clinically healthy oral mucosa were used. Clinicians trained the machine learning model with the clinical images (n=300). Roboflow software (Roboflow Inc, USA) was used to classify and annotate images along with Multi-class annotation and object detection models trained by two expert oral pathologists. The test dataset (n=60) of new clinical images was again evaluated by two clinicians and Roboflow. The results were tabulated and Kappa statistics was performed using SPSS v23.0 (IBM Corp., Armonk, NY). Results: Training dataset clinical images (n=300) were used to train the clinicians and Roboflow algorithm. The test dataset (n=60) of new clinical images was again evaluated by the clinicians and Roboflow. The observed outcomes revealed that the Mean Average Precision (mAP) was 25.4%, precision 29.8% and Recall 32.9%. Based on the kappa statistical analysis the 0.7 value shows a moderate agreement between the clinicians and the machine learning model. The test dataset showed the specificity and sensitivity of the Roboflow machine learning model to be 75% and 88.9% respectively. Conclusion: In conclusion, machine learning showed promising results in the early detection of suspected lesions using clinical intraoral images and aids general dentists and patients in the detection of suspected lesions such as OPMDs and OSCC that require biopsy and immediate treatment.

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Section: Discussionmentioning

confidence: 99%

Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images

Ramalingam,

Ramani

et al. 2023

Cureus

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“…We have planned to identify the role of our novel nanogel in animal studies and ultimately implement it as an oral aid for patients. Nanoparticles could be used for diagnostic and treatment modalities [ 25 ] and also in the development of paper-based biosensors for oral cancer screening [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Cytocompatibility and Wound Healing Activity of Chitosan Thiocolchicoside Lauric Acid Nanogel in Human Gingival Fibroblast Cells

Ramalingam

et al. 2023

Cureus

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Aim: To investigate the cytocompatibility effect and wound healing activity of chitosan thiocolchicoside lauric acid (CTL) nanogel using human gingival fibroblast (hGF) cells. Materials and methods: hGF cells were established from gingival tissue as per the standard cell isolation protocol. The cytocompatibility effect was assessed using an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide) assay. A scratch wound healing assay was performed to assess the wound-healing potential of CTL nanogel. For the nuclear morphological changes analysis, acridine orange staining was used in gingival fibroblast cells. The stained nuclei were viewed under a fluorescent microscope. ANOVA with posthoc analysis was performed using GraphPad Prism 5 software (Dotmatics, Boston, Massachusetts). The significance level (p-value) was expressed as <0.05. Results: CTL nanogel did not show any significant cytotoxicity at concentrations 10-80 µl/ml (p<0.05). CTL nanogel at a concentration of 40µl/ml has a cytocompatibility effect on hGF cells and increases cell viability. In vitro scratch wound healing assay resulted in faster wound healing and cell migration with CTL nanogel when compared to the control group. Conclusion: CTL nanogel has a significant effect on cell proliferation at various concentrations, which suggests its use as a safe and effective drug delivery system.

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Microfluidic Chips Fabrication and Integration for Tumor‐Derived Exosomes Detection

Sun

Gao

et al. 2023

Adv Materials Technologies

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Exosomes are a type of small extracellular vesicles carrying proteins, nucleic acids, and lipids. Because their expression levels are highly correlated with tumor development, exosomes are regarded as non‐invasive biomarkers for cancer diagnosis. Therefore, many exosome detection techniques are developed to provide a new way for cancer diagnosis. However, conventional techniques require the isolation of exosomes from body fluids before analysis, which usually relies on complex equipment and cumbersome operations. In addition, complex procedures may introduce impurities and thus affect the accuracy of the test. As an emerging analysis technology, microfluidic overcomes these shortcomings by integrating a microchannel for sample transport isolation and an exosome analysis module, showing great prospects in cancer diagnosis. This review will provide an overview of microchannel fabrication and analysis methods with outstanding compatibility for different microfluidic types, with a particular focus on microfluidic‐based exosome analysis methods that have the potential for clinical application.

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Exosomes and Paper-Based Biosensors for Early Oral Cancer Screening

Cited by 3 publications

References 53 publications

Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images

Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images

Cytocompatibility and Wound Healing Activity of Chitosan Thiocolchicoside Lauric Acid Nanogel in Human Gingival Fibroblast Cells

Microfluidic Chips Fabrication and Integration for Tumor‐Derived Exosomes Detection

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