Machine learning-based exosome profiling of multi-receptor SERS sensors for differentiating adenocarcinoma in situ from early-stage invasive adenocarcinoma.

Exosomes, extracellular vesicles released by cells, hold potential as diagnostic markers for the early detection of lung cancer. Despite their clinical promise, current technologies lack rapid and effective means to discriminate between exosomes derived from adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC). This challenge arises from the intrinsic structural heterogeneity of exosomes, necessitating the development of advanced methodologies for precise differentiation. Here, we demonstrate a novel approach for plasma exosome detection utilizing multi-receptor surface-enhanced Raman spectroscopy (SERS) technology to differentiate between AIS and early-stage IAC. To accomplish this, we synthesized a stable and uniform two-dimensional SERS substrate (BC/Au NPs film) by fabricating gold nanoparticles onto bacterial cellulose. We then enhanced its capabilities by introducing multi-receptor SERS functionality via modifying the substrate with both low-specificity and physicochemical-selective molecules. Furthermore, by strategically combining all capturer-exosome SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of the exosome. Combining these features with partial least squares regression-discriminant analysis (PLS-DA) modeling significantly improved discriminatory accuracy, achieving 90% sensitivity and 95% specificity in distinguishing AIS from early-stage IAC. Our developed SERS sensor provides an effective method for early detection of lung cancer, thereby paving a new way for innovative advancements in diagnosing lung cancer.

Early diagnosis of thyroid-associated ophthalmopathy using label-free Raman spectroscopy and multivariate analysis.

Thyroid-associated ophthalmopathy (TAO) is the most common orbital disease in adults, with complex clinical manifestations and significant impacts on the life quality of patients. The current diagnosis of TAO lacks reliable biomarkers for early and non-invasive screening and detection, easily leading to poor prognosis. Therefore, it is essential to explore new methods for accurately predicting TAO development in its early stage. In this study, Raman spectroscopy, with non-destructive, label-free, and high-sensitivity characteristics, was used to analyze the differences in biochemical components of orbital adipocyte and tear samples between TAO and control groups. Furthermore, a multivariate analysis method (i.e., Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA)) was applied for data processing and analysis. Compared with controls, PCA-LDA yielded TAO diagnostic accuracies of 72.7% and 75.0% using orbital adipocytes and tears, respectively. Our proof-of-concept results suggest that Raman spectroscopy holds potential for exploring the underlying pathogenesis of TAO, and its potential application in early screening of other thyroid-associated diseases can be further expanded.

Smartphone-based low-cost and rapid quantitative detection of urinary creatinine with the Tyndall effect.

This research aims to develop a robust and quantitative method for measuring creatinine levels by harnessing the enhanced Tyndall effect (TE) phenomenon. The envisioned sensing assay is designed for practical deployment in resource-limited settings or homes, where access to advanced laboratory facilities is limited. Its primary objective is to enable regular and convenient monitoring of renal healthcare, particularly in cases involving elevated creatinine levels. The creatinine sensing strategy is achieved based on the aggregation of gold nanoparticles (AuNPs) triggered via the direct crosslinking reaction between creatinine and AuNPs, where an inexpensive laser pointer was used as a handheld light source and a smartphone as a portable device to record the TE phenomenon enhanced by the creatinine-induced aggregation of AuNPs. After evaluation and optimization of parameters such as AuNP concentrations and TE measurement time, the subsequent proof-of-concept experiments demonstrated that the average gray value change of TE images was linearly related to the logarithm of creatinine concentrations in the range of 1-50 µM, with a limit of detection of 0.084 µM. Meanwhile, our proposed creatinine sensing platform exhibited highly selective detection in complex matrix environments. Our approach offers a straightforward, cost-effective, and portable means of creatinine detection, presenting an encouraging signal readout mechanism suitable for point-of-care (POC) applications. The utilization of this assay as a POC solution exhibits potential for expediting timely interventions and enhancing healthcare outcomes among individuals with renal health issues.

Machine learning-assisted global DNA methylation fingerprint analysis for differentiating early-stage lung cancer from benign lung diseases.

DNA methylation plays a critical role in the development of human tumors. However, routine characterization of DNA methylation can be time-consuming and labor-intensive. We herein describe a sensitive, simple surface-enhanced Raman spectroscopy (SERS) approach for identifying the DNA methylation pattern in early-stage lung cancer (LC) patients. By comparing SERS spectra of methylated DNA bases or sequences with their counterparts, we identified a reliable spectral marker of cytosine methylation. To move toward clinical applications, we applied our SERS strategy to detect the methylation patterns of genomic DNA (gDNA) extracted from cell line models as well as formalin-fixed paraffin-embedded tissues of early-stage LC and benign lung diseases (BLD) patients. In a clinical cohort of 106 individuals, our results showed distinct methylation patterns in gDNA between early-stage LC (n = 65) and BLD patients (n = 41), suggesting cancer-induced DNA methylation alterations. Combined with partial least square discriminant analysis, early-stage LC and BLD patients were differentiated with an area under the curve (AUC) value of 0.85. We believe that the SERS profiling of DNA methylation alterations, together with machine learning could potentially offer a promising new route toward the early detection of LC.

Shifted-excitation Raman difference spectroscopy for improving in vivo detection of nasopharyngeal carcinoma.

A strong fluorescence background is one of the common interference factors of Raman spectroscopic analysis in biological tissue. This study developed an endoscopic shifted-excitation Raman difference spectroscopy (SERDS) system for real-time in vivo detection of nasopharyngeal carcinoma (NPC) for the first time. Owing to the use of the SERDS method, the high-quality Raman signals of nasopharyngeal tissue could be well extracted and characterized from the complex raw spectra by removing the fluorescence interference signals. Significant spectral differences relating to proteins, phospholipids, glucose, and DNA were found between 42 NPC and 42 normal tissue sites. Using linear discriminant analysis, the diagnostic accuracy of SERDS for NPC detection was 100%, which was much higher than that of raw Raman spectroscopy (75.0%), showing the great potential of SERDS for improving the accurate in vivo detection of NPC.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

BACKGROUND/PURPOSE OF THE STUDY: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. METHODS: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. RESULTS: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. CONCLUSIONS: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications.

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma.

Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.

Ratiometric SERS quantitative analysis of tyrosinase activity based on gold-gold hybrid nanoparticles with Prussian blue as an internal standard.

Tyrosinase (TYR) is a polyphenol oxidase that regulates melanin biosynthesis. Abnormal levels of TYR have been confirmed closely associated with melanoma cancer and other diseases, making the establishment of highly sensitive and accurate quantitative detection of TYR is thus essential for fundamental research and clinical applications. Herein, we proposed a new strategy that combines surface-enhanced Raman scattering (SERS) with Dopamine (DA) and Prussian blue (PB) functionalized gold-gold hybrid nanoparticles for TYR detection. DA is oxidized to dopaquinone with the presence of TYR, leading to the consumption of DA in the reaction solution and the corresponding decrease in DA characteristic peak intensity at 1480 cm-1. Our SERS quantitative assay of TYR with "on-off" sensing strategy yields an excellent limit of detection (LOD) of 3 × 10-3 U mL-1 in a linear range of 10-3 to 100 U mL-1. In particular, the intensity ratio of 1480 cm-1 to 2121 cm-1 allows us to achieve remarkably reliable quantitative detection of TYR, with the 2121 cm-1 peak intensity of the standard internal (IS) molecule PB being used to correct SERS signal fluctuations. Furthermore, our proposed assay has been successfully demonstrated to quantify TYR spiked in human serum samples, with excellent percentage recovery of 90.0-110.6 %. The potential of our ratiometric SERS strategy for the performance evaluation of TYR inhibitors has also been verified. Our work is therefore expected to provide a new route for accurate and reliable monitoring of TYR activity in the complex biological environment.

Analysis of the interaction between A1 R and A2A R proteins in living cells based on FRET imaging and batch processing method.

The quantitative FRET analysis of living cells is a tedious and time-consuming task for freshman lacks technical training. In this study, FRET imaging and batch processing method were combined to analyze reagents-induced interactions of A1 R and A2A R on cell membranes. Results showed that the method had taken less time than if cell-by-cell was analyzed. The accuracy and repeatability of FRET efficiency values were likewise improved by removing the interference from anthropogenic factors. Then this method was applied to rapidly analyze acetaldehyde-induced interactions, which analyzed hundreds of single-cell trends by one operation, and the results revealed that interactions were consistently attenuated in LX-2 cells, and statistical differences appeared after 30 min. Combined with batch processing method, procedures of cells FRET analysis have been greatly simplified without additional technical work, which has broad prospects in large-scale analysis of cellar protein interaction.

High spatio-temporal resolution measurement of A1 R and A2A R interactions combined with Iem-spFRET and E-FRET methods.

A1 R-A2A R heterodimers regulate striatal glutamatergic neurotransmission. However, few researches about kinetics have been reported. Here, we combined Iem-spFRET and E-FRET to investigate the kinetics of A1 R and A2A R interaction. Iem-spFRET obtains the energy transfer efficiency of the whole cell. E-FRET gets energy transfer efficiency with high spatial resolution, whereas, it was prone to biases because background was easily selected due to manual operation. To study the interaction with high spatio-temporal resolution, Iem-spFRET was used to correct the deviation of E-FRET. In this paper, A1 R and A2A R interaction was monitored, and the changes of FRET efficiency of the whole or/and partial cell membrane were described. The results showed that activation of A1 R or A2A R leads to rapid aggregation, inhibition of A1 R or A2A R leads to slow segregation, and the interaction is reversible. These results demonstrated that combination of Iem-spFRET and E-FRET could measure A1 R and A2A R interaction with high spatio-temporal resolution.

Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy.

Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.

Interference-free SERS tags for ultrasensitive quantitative detection of tyrosinase in human serum based on magnetic bead separation.

Tyrosinase (TYR) expression and activity determine the rate and yield of melanin production. Studies have shown that TYR is a potential biomarker for melanoma and highly sensitive detection of TYR benefits early diagnosis of melanoma-related diseases. In this study, we developed a method that combines surface-enhanced Raman scattering (SERS) and sandwich-type immunity for sensitive detection of TYR, in which 4-mercaptobenzonitrile (4 MB) embedded between the Au core and Au shell (Au4MB @ Au) core-shell structure was employed as a SERS probe for quantitative detection of TYR while the magnetic bead serves as a capture substrate. Our results demonstrated that under magnetic separation, the specific SERS signal obtained is highly correlated with TYR concentrations. Furthermore, the combination of magnetic beads and Au4MB @ Au core-shell structure significantly improved the sensitivity of the sensing platform, resulting in detection limits of 0.45 ng mL-1. More importantly, the detection and analysis of TYR concentration in human serum samples showed good accuracy and an excellent recovery rate. Accuracy of the system was investigated from % recovery of spiked TYR standard solutions and found to be in the range of 90-104%, which further verified the feasibility and reliability of our method applied in a complex environment. We anticipate this SERS-based immunoassay method to be applied to TYR detection in the clinical setting and to be extended to other promising related fields.

Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma.

OBJECTIVE: The objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma. PATIENTS AND METHODS: We proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer. RESULTS: SERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%. CONCLUSION: Our results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.

Silver nanocube coupling with a nanoporous silver film for dual-molecule recognition based ultrasensitive SERS detection of dopamine.

Dopamine (DA) is one of the catecholamine neurotransmitters used for the treatment of neural disorders. In this study, a novel sensor based on surface-enhanced Raman scattering (SERS) with dual molecule-recognition for ultrasensitive detection of DA was presented, with a limit of detection (LOD) of 40 fM, without any pretreatment of clinical samples. To realize the sensitive and selective detection of DA in complex samples, the nanoporous silver film (AgNF) surfaces were functionalized with mercaptopropionic acid (MPA) to accurately capture DA, while silver nanocubes (AgNCs) were modified with 4-mercaptobenzene boronic acid (4-MPBA) as a Raman reporter for the quantitative detection of DA. The nanogaps between AgNCs and the AgNF led to the generation of an abundance of hot spots for the SERS signal and thus effectively improved the sensitivity of DA detection. Measurements of DA concentrations in clinical body fluids such as human serum and urine samples are also demonstrated, showing excellent performance for DA detection in a complex environment. Our results demonstrate the promising potential for the ultrasensitive detection of DA for the potential diagnosis of DA-related diseases.

Rapid, Label-free Optical Spectroscopy Platform for Diagnosis of Heparin-Induced Thrombocytopenia.

The use of surface-enhanced Raman spectroscopy (SERS) to determine spectral markers for the diagnosis of heparin-induced thrombocytopenia (HIT), a difficult-to-diagnose immune-related complication that often leads to limb ischemia and thromboembolism, is proposed. The ability to produce distinct molecular signatures without the addition of labels enables unbiased inquiry and makes SERS an attractive complementary diagnostic tool. A capillary-tube-derived SERS platform offers ultrasensitive, label-free measurement as well as efficient handling of blood serum samples. This shows excellent reproducibility, long-term stability and provides an alternative diagnostic rubric for the determination of HIT by leveraging machine-learning-based classification of the spectroscopic data. We envision that a portable Raman instrument could be combined with the capillary-tube-based SERS analytical tool for diagnosis of HIT in the clinical laboratory, without perturbing the existing diagnostic workflow.

Surface-enhanced Raman spectroscopy analysis of mast cell degranulation induced by low-intensity laser.

Mast cell (MC) degranulation is an important step in the healing process. In this study, silver-nanoparticles-based surface-enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of degranulation of MCs activated by low-intensity laser. The significant spectral changes, such as Raman peak intensities, suggested the concentration variation of some degranulated substances. The Raman intensity ratio of 799-554 cm-1 could be used as a potential internal indicator for the degranulation degree of MCs. Principal component analysis (PCA) was employed to reduce the high dimension of spectra into a few principal components (PCs) while retaining the most diagnostically significant information for sample differentiation. Using the diagnostically significant PC scores (P < 0.05), linear discriminate analysis (LDA) was applied to identify different cell degranulation groups with high sensitivity, specificity and accuracy. This exploratory work demonstrates that SERS technique combined with a PCA-LDA algorithm possesses great potential for developing a label-free, comprehensive, non-invasive and accurate method for measuring MC degranulation.

Interference-free and high precision biosensor based on surface enhanced Raman spectroscopy integrated with surface molecularly imprinted polymer technology for tumor biomarker detection in human blood.

The reliable quantitative analysis of tumor biomarkers in circulating blood is crucial for cancer early screening, therapy monitoring and prognostic prediction. Herein, a novel biosensor combing surface-enhanced Raman spectroscopy (SERS) and surface molecularly imprinted polymer (SMIP) technology was developed for quantitative detection of carcinoembryonic antigen (CEA) that is closely related to several common cancers. Owing to the use of SMIP, recognition sites with high affinity to the target of interest can be well imprinted on the surface of SERS substrate, leading to a more stable and specific capture ability. In addition, two layers of core-shell nanoparticles were integrated to this SERS substrate to form highly efficient electromagnetic enhancement for SERS measurement via the generation of lots of "hot spot". Besides, a unique Raman reporter (CC) with silent Raman signals at 2024 cm-1 was capsulated in the nanoparticles to avoid the optical noises originating from endogenous molecules at fingerprint region (300-1800 cm-1). Meanwhile, we employed an internal standard molecular (CN) to real time correct the fluctuating signals of Raman reporter when performing the quantitative analysis. Due to these features, a limit of detection (LOD) of 0.064 pg mL-1 with the detection range of 0.1 pg mL-1 - 10 µg mL-1 can be achieved by this assay. Excitingly, this technology even showed wonderful performances for CEA detection in real blood from cancer patients, demonstrating great potential for biomarker-based cancer screening.

LKB1 suppresses androgen synthesis in a mouse model of hyperandrogenism via IGF-1 signaling.

Polycystic ovary syndrome (PCOS) is a major cause of anovulatory sterility in women, and most PCOS patients exhibit hyperandrogenism (HA). Liver kinase b1 (LKB1) is a tumor suppressor that has recently been reported to be involved in PCOS. However, the mechanism by which LKB1 affects HA has not previously been elucidated. We report here that ovarian LKB1 levels are significantly decreased in a female mouse model of HA. Moreover, we report that LKB1 expression is inhibited by elevated androgens via activation of androgen receptors. In addition, LKB1 treatment was observed to suppress androgen synthesis in theca cells and promote estrogen production in granulosa cells by regulating steroidogenic enzyme expression. As expected, LKB1 knockdown inhibited estrogen levels and enhanced androgen levels, and LKB1-transgenic mice were protected against HA. The effect of LKB1 appears to be mediated via IGF-1 signaling. In summary, we describe here a key role for LKB1 in controlling sex hormone levels.

Quantitative assessment of microenvironment characteristics and metabolic activity in glioma via multiphoton microscopy.

Tumor microenvironment and metabolic activity in gliomas are the important biomarkers to evaluate the progression of gliomas. Many evidences have suggested that the targeting of metabolic activity and tumor microenvironment simultaneously can be more effective to take the tumor therapy. Therefore, the noninvasive, accurate assessment of tumor microenvironment and metabolic activity is quite important in clinical practice. Multiphoton microscopy (MPM), based on two-photon-excited fluorescence and second harmonic generation was performed on unstained glioma tissues. With our combined image analysis approaches, our research findings indicate that MPM is able to qualitatively and quantitatively describe the microenvironment characteristics in gliomas, such as collage deposition in extracellular matrix, lymphocyte infiltration and tumor angiogenesis, etc. Meanwhile, the metabolic activity can also be quantitatively evaluated by optical redox ratio, NADH and FAD intensity. With the microendoscope and fiberscope are portable, MPM technique can be used to perform in-vivo studies and clinical examinations in gliomas.