In this study, we applied a unique fiberoptic Raman spectroscopy probe to access the post-treatment NPC treatment efficacy follow-up and accurate recurrent NPC diagnosis. Significant Raman feature differences are discovered among normal, NPC, and non-recurring post-treatment patients. By incorporating with partial-least-squares linear-discriminant-analysis (PLS-LDA), the in vivo fingerprint and high-wavenumber tissue Raman spectra provide high diagnostic accuracy for detecting recurrent NPC from both early post-treatment inflammation and long-term post-treatment fibrosis. We further investigate the major biochemicals associated with NPC tissue compared to normal nasopharyngeal tissue through quantitative modeling. In this work, we demonstrate that fiberoptic Raman spectroscopy is an effective diagnostic modality for real-time, label-free post-treatment surveying and recurrent tumor detection in NPC patients.
We report the utility of fiberoptic Raman spectroscopy for realizing post-treatment NPC patients surveying and accurate detection of tumor recurrence. Distinct Raman spectral differences are observed among the tissue Raman spectra of normal, NPC, and non-recurring post-treatment patients. The classification models using the in vivo fingerprint and high-wavenumber (FP/HW) tissue Raman spectra together with the partial-least-squares linear-discriminant-analysis (PLS-LDA) provide the high diagnostic accuracy for detecting recurrent NPC from both inflammation and long-term post-treatment fibrosis. We further quantitatively analyze the major biochemicals related to the NPC malignancy (e.g., triolein, elastin, keratin, fibrillar collagen, and type IV collagen, etc.). This study suggests that fiberoptic Raman spectroscopy can enable real-time in-vivo post-treatment patients surveying and tumor recurrence detection with high biomolecular sensitiveness.
Antibiotic resistance is a burgeoning global public health threats of our time. Antibiotic resistance is a multifactorial and complex problem which cannot be solved by only developing stronger and better antibiotic compounds. Rapid detection and characterization of pathogenic bacteria are critical for effectively treating bacterial infections without exacerbating the resistance problem. Here, we present a novel highly-sensitive and label-free platform, Rapid-Ultra-Sensitive-Detector (RUSD), that utilizes the high reflectance coefficient of light at the interface between low-refractive-index and high-refractive-index media. The sensitivity of RUSD is three to four orders of magnitude higher than conventional optical density-based methods. Utilizing RUSD, we can detect as low as ~20 bacterial cells or a single fungal cell. This technique does not require any sophisticated signal processing steps and it enables growth rate measurements in less than an hour. Finally, we can now measure antibiotics resistance of several gram-negative and gram-positive bacteria, including Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, within two hours.
The development of rapid and objective diagnostic techniques with high accuracy is highly desirable for real-time in vivo cancer diagnosis and characterization during endoscopic examination. This work reports a deep learning-based fiberoptic Raman technique for improving in vivo cancer detection of nasopharyngeal carcinoma (NPC) in clinical settings. We have developed a robust cancer diagnostic platform based on deep neural network (DNN) model in combination with fiberoptic Raman endoscopic technique for effectively extracting latent discriminative features contained in in vivo tissue Raman spectra. We applied the platform onto the tasks of predicting new NPC patients as well as follow-up of post-irradiated patients at endoscopy. A better diagnostic performance was achieved in the testing dataset by using this diagnostic platform as compared to the classic chemometric classification methods such as partial least squares-discriminate analysis (PLSDA). This work demonstrates that DNN-based fiberoptic Raman technique is more effective and reliable for NPC classification, particularly robust for clinical prediction of new NPC patients and post-irradiated patients surveillance.
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