Using Raman Spectroscopy to Detect and Diagnose Skin Cancer In Vivo

Novel imaging techniques for tumor margin detection in basal cell carcinoma: a systematic scoping review of FDA and EMA-approved imaging modalities

Mohs micrographic surgery (MMS) is the gold standard for removing basal cell carcinomas (BCCs) due to its ability to guarantee 100% margin evaluation through frozen section histopathology, offering the highest cure rate among current treatments. However, noninvasive imaging technologies have emerged as promising alternatives to clinical assessment for defining presurgical margins. This systematic scoping review examines the efficacy of these imaging modalities, focusing on those approved for clinical use by the United States Food and Drug Administration (FDA) or the European Medicines Agency (EMA). A systematic search of EMBASE, Scopus, PubMed, and the Cochrane Public Library databases identified 11 relevant studies out of 2123 records, encompassing 644 lesions across five imaging techniques. The findings suggest that dermoscopy, high-frequency ultrasound (HFUS), optical coherence tomography (OCT), line-field optical coherence tomography (LC-OCT), and reflectance confocal microscopy (RCM) show potential in detecting BCC margins, which could enhance MMS by providing better preoperative planning, informing patients of expected defect size, aiding in reconstruction decisions, and reducing overall procedure costs. This review discusses the benefits and limitations of each technique, offering insights into how these innovations could influence the future of BCC management. Emerging imaging techniques could enhance MMS by improving BCC margin assessment and reducing costs. Their adoption will depend on price and ease of use.

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Tumors: A Systematic Review and Meta-Analysis

A line-field confocal optical coherence tomography (LC-OCT) combines confocal microscopy and optical coherence tomography into a single, rapid, easy-to-use device. This meta-analysis was performed to determine the reliability of LC-OCT for diagnosing malignant skin tumors. PubMed, EMBASE, Web of Science databases, and the Cochrane Library were searched for research studies in the English language from inception till December 2023. To assess quality and the risk of bias, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. The sensitivity and specificity of each study were calculated. The bivariate summary sensitivity and specificity were calculated using the linear mixed model. Five studies with 904 reported per lesion analyses in our study; the specificity and sensitivity ranged from 67% to 97% and 72% to 92%, respectively. The pooled specificity and sensitivity were 91% (95% CI: 76-97%) and 86.9% (95% CI: 81.8-90.8%), respectively. The summary sensitivity and specificity from the bivariate approach are 86.9% (95% CI: 81.8-90.8%) and 91.1% (95% CI: 76.7-97.0%), respectively. The area under the curve is 0.914. LC-OCT shows great sensitivity and specificity in diagnosing malignant skin tumors. However, due to the limited number of studies included in our meta-analysis, it is premature to elucidate the true potential of LC-OCT.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation

Background

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy.

Patients and methods

Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN.

Results

The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test).

Conclusions

Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Cutaneous Oncology: Strategies for Melanoma Prevention, Diagnosis, and Therapy

Skin cancer comprises one-third of all diagnosed cancer cases and remains a major health concern. Genetic and environmental parameters serve as the two main risk factors associated with the development of skin cancer, with ultraviolet radiation being the most common environmental risk factor. Studies have also found fair complexion, arsenic toxicity, indoor tanning, and family history among the prevailing causes of skin cancer. Prevention and early diagnosis play a crucial role in reducing the frequency and ensuring effective management of skin cancer. Recent studies have focused on exploring minimally invasive or non-invasive diagnostic technologies along with artificial intelligence to facilitate rapid and accurate diagnosis. The treatment of skin cancer ranges from traditional surgical excision to various advanced methods such as phototherapy, radiotherapy, immunotherapy, targeted therapy, and combination therapy. Recent studies have focused on immunotherapy, with the introduction of new checkpoint inhibitors and personalized immunotherapy enhancing treatment efficacy. Advancements in multi-omics, nanotechnology, and artificial intelligence have further deepened the understanding of the mechanisms underlying tumoral growth and their interaction with therapeutic effects, which has paved the way for precision oncology. This review aims to highlight the recent advancements in the understanding and management of skin cancer, and provide an overview of existing and emerging diagnostic, prognostic, and therapeutic modalities, while highlighting areas that require further research to bridge the existing knowledge gaps.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

Raman Spectroscopy of Emulsions and Emulsion Chemistry

Emulsions are dispersed systems widely used in various industries. In recent years, Raman spectroscopy (RS), as a spectroscopic technique, has gained much attention for measuring and monitoring emulsions. In this review, we explore the use of RS on emulsion structures and emulsification, important reactions that use emulsions such as emulsion polymerization, catalysis and cascading reactions, as well as various applications of emulsions. We explore how RS is used in emulsions, reactions and applications. RS is a powerful and versatile tool for studying emulsions, but there are also challenges in using RS to monitor emulsion processes, especially if they are rapid or volatile. We also explore these challenges and difficulties, as well as possible designs that can be used to overcome them.

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

The growing incidence of skin cancer, with its associated mortality and morbidity, has in recent years led to the developing of new non-invasive technologies, which allow an earlier and more accurate diagnosis. Some of these, such as digital photography, 2D and 3D total-body photography and dermoscopy are now widely used and others, such as reflectance confocal microscopy and optical coherence tomography, are limited to a few academic and referral skin cancer centers because of their cost or the long training period required. Health care professionals involved in the treatment of patients with skin cancer need to know the implications and benefits of new non-invasive technologies for dermatological oncology. In this article we review the characteristics and usability of the main diagnostic imaging methods available today.

Diagnosis accuracy of Raman spectroscopy in the diagnosis of breast cancer: a meta-analysis

To investigate the diagnostic efficiency of Raman spectroscopy for the diagnosis of breast cancer, we searched PubMed, Web of Science, Cochrane Library, and Embase for articles published from the database establishment to May 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the receiver pooled operating characteristic curve were derived for the included studies as outcome measures. The methodological quality was assessed according to the questionnaires and criteria suggested by the Diagnostic Accuracy Research Quality Assessment-2 tool. Sixteen studies were included in this meta-analysis. The pooled sensitivity and specificity of Raman spectroscopy for breast cancer diagnosis were 0.97 (95% CI, [0.92-0.99]) and 0.96 (95% CI, [0.91-0.98]). The diagnostic odds ratio was 720.89 (95% CI, [135.73-3828.88]) and the area under the curve of summary receiver operating characteristic curves was 0.99 (95% CI, [0.98-1]). Subgroup analysis revealed that all subgroup types in our analysis, including different races, sample types, diagnostic algorithms, number of spectra, instrument types, and laser wavelengths, turned out to have a sensitivity and specificity greater than 0.9. Significant heterogeneity was found between studies. Deeks' funnel plot demonstrated that publication bias was acceptable. This meta-analysis suggests that Raman spectroscopy may be an effective and accurate tool to differentiate breast cancer from normal breast tissue, which will help us diagnose and treat breast cancer.

Novel aspects of Raman spectroscopy in skin research

The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.

Application and Progress of Raman Spectroscopy in Male Reproductive System

Raman spectroscopy is a fast-developing, unmarked, non-invasive, non-destructive technique which allows for real-time scanning and sampling of biological samples in situ, reflecting the subtle biochemical composition alterations of tissues and cells through the variations of spectra. It has great potential to identify pathological tissue and provide intraoperative assistance in clinic. Raman spectroscopy has made many exciting achievements in the study of male reproductive system. In this review, we summarized literatures about the application and progress of Raman spectroscopy in male reproductive system from PubMed and Ovid databases, using MeSH terms associated to Raman spectroscopy, prostate, testis, seminal plasma and sperm. The existing challenges and development opportunities were also discussed and prospected.

Deep learning data augmentation for Raman spectroscopy cancer tissue classification

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Emerging Minimally Invasive Technologies for the Detection of Skin Cancer

With the increasing incidence of skin cancer, many noninvasive technologies to detect its presence have been developed. This review focuses on reflectance confocal microscopy (RCM), optical coherence tomography (OCT), high-frequency ultrasound (HFUS), electrical impedance spectroscopy (EIS), pigmented lesion assay (PLA), and Raman spectroscopy (RS) and discusses the basic principle, clinical applications, advantages, and disadvantages of each technology. RCM provides high cellular resolution and has high sensitivity and specificity for the diagnosis of skin cancer. OCT provides lower resolution than RCM, although its evaluable depth is deeper than that of RCM. RCM and OCT may be useful in reducing the number of unnecessary biopsies, evaluating the tumor margin, and monitoring treatment response. HFUS can be mainly used to delineate tumor depths or margins and monitor the treatment response. EIS provides high sensitivity but low specificity for the diagnosis of skin malignancies. PLA, which is based on the genetic information of lesions, is applicable for the detection of melanoma with high sensitivity and moderate-to-high specificity. RS showed high accuracy for the diagnosis of skin cancer, although more clinical studies are required. Advances in these technologies for the diagnosis of skin cancer can lead to the realization of optimized and individualized treatments.

Bioreactor design and validation for manufacturing strategies in tissue engineering

The fields of regenerative medicine and tissue engineering offer new therapeutic options to restore, maintain or improve tissue function following disease or injury. To maximize the biological function of a tissue-engineered clinical product, specific conditions must be maintained within a bioreactor to allow the maturation of the product in preparation for implantation. Specifically, the bioreactor should be designed to mimic the mechanical, electrochemical and biochemical environment that the product will be exposed to in vivo. Real-time monitoring of the functional capacity of tissue-engineered products during manufacturing is a critical component of the quality management process. The present review provides a brief overview of bioreactor engineering considerations. In addition, strategies for bioreactor automation, in-line product monitoring and quality assurance are discussed.

Diagnostic accuracy of Raman spectroscopy for prostate cancer: a systematic review and meta-analysis

Background

Although various studies have been conducted to demonstrate the possibility of Raman spectroscopy (RS) as a diagnostic tool for prostate cancer (PC), it is difficult to use it in the real clinical area because of imitations in various research processes. Therefore, we did a systematic review and meta-analysis about the accuracy in diagnostic use of RS for PC.

Methods

A literature search was done using PubMed, Embase, and Cochrane library databases in March 2019 to analyze the accuracy of RS for diagnosis of PC. The accuracy of RS for diagnosis of PC was evaluated by means of pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC).

Results

Five studies were included for qualitative analysis by screening the remaining articles according to the inclusion and exclusion criteria by means of a systematic review. The pooled sensitivity and specificity of RS were 0.89 (95% CI: 0.87-0.91) and 0.91 (95% CI: 0.89-0.93), respectively. The overall PLR and NLR were 9.12 (95% CI: 4.15-20.08) and 0.14 (95% CI: 0.07-0.29), respectively. The DOR of RS demonstrated high accuracy (73.32; 95% CI: 18.43-291.73). The area under the curves (AUCs) of SROC curves was 0.93.

Conclusions

RS is an optical diagnostic method with high potential for diagnosis and grading of PC and has advantages of real-time and convenient use. In order to consider real-time use of RS in an actual clinical setting, more studies for standardization and generalization of RS performance and analytical method must be conducted.

Multimodal imaging of skin lesions by using methylene blue as cancer biomarker

This study aimed the optical imaging of malignant and normal skin tissues with multimodal wide-field fluorescence polarization imaging (WF) technique, by using methylene blue as fluorescence dye. We present optical imaging of skin tissues by different techniques, including reflectance, fluorescence, and polarization imaging for early detection of skin cancer. We collected the reflectance confocal images at 390 and 500 nm. For wide-field fluorescence images, specimens were stimulated at 640 nm and images were collected between 670 and 710 nm. The correlation of the regarded optical modalities with histopathology (H&E), their potentials, capabilities, and limitations to detect skin lesions are discussed. The advantages of multimodal imaging of skin tissues are analyzed to divulge possibilities for precise tumor boundary detection and their classification for malignant and nonmalignant skin tissues. Prior to imaging, the cells were stained in aqueous MB (a dye approved by FDA).

Use of imaging techniques for melanocytic naevi and basal cell carcinoma in integrative analysis (Review)

Early detection of skin cancer is essential in order to obtain an improved prognosis. Clinicians need more objective and non-invasive examination methods to support their decision whether to biopsy or not tumoral lesions. These may include several imaging techniques such as dermoscopy, videodermoscopy, also known as sequential digital dermoscopy (SDD), computer-aided diagnosis (CAD), total body photography, imaging and high-frequency ultrasonography (HFUS), reflectance confocal microscopy, multiphoton tomography, electrical impedance spectroscopy, Raman spectroscopy, stepwise two-photon-laser spectroscopy and quantitative dynamic infrared. This review summarizes the current developments in the field of melanocytic lesions, such as naevi and basal cell carcinoma (BCC) imaging techniques. The aim was to collect and analyze data concerning types, indications, advantages and disadvantages of modern imaging techniques for in vivo skin tumor diagnosis. Two main methods were focused on, namely videodermoscopy and HFUS, which can be included in daily dermatologists' practice. In skin tumors HFUS allows the assessment of tumoral lesions with depth smaller than 1.5 cm, being described a correlation between ultrasonographic depth and the histologic index.

Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues

The differences in the biochemistry of normal and cancerous tissue could be better exploited by Raman spectroscopy when the spectral information from normal tissue is subtracted from the abnormal tissues. In this study, we evaluated the use of the normal-subtracted spectra to evidence the biochemical differences in the pre-cancerous and cancerous skin tissues compared with normal skin, and to discriminate the groups with altered tissues with respect to the normal sites. Raman spectra from skin tissues [normal (Normal), benign (dermatitis-BEN), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and actinic keratosis (KER)] were obtained in vivo (Silveira et al., 2015, doi: https://doi.org/10.1002/lsm.22318) and used to develop the spectral model. The mean spectrum of the normal sites (circumjacent to each lesion) from each subject was calculated and subtracted from each individual spectrum of that particular subject independently of the group (Normal, BEN, BCC, SCC, KERAT). The mean spectra of each altered group and the mean spectra of the differences were firstly evaluated in terms of biochemical contribution or differentiation comparing the normal site. Then, the normal-subtracted spectra were submitted to discriminant models based on partial least squares and principal components regression (PLS-DA and PCR-DA), and the discrimination were compared with the model using non-subtracted spectra. Results showed that the peaks of nucleic acids, lipids (triolein) and proteins (elastin and collagens I, III, and IV) were significantly different in the lesions, higher for the pre- and neoplastic lesions compared with normal and benign. The PLS-DA showed that the groups could be discriminated with 90.3% accuracy when the mean-subtracted spectra were used, contrasting with 75.1% accuracy when the non-subtracted spectra were used. Also, when discriminating non-neoplastic tissue (Normal + BEN) from pre- and neoplastic sites (BCC + SCC + KERAT), the accuracy increases to 92.5% for the normal-subtracted compared with 85.3% for the non-subtracted. The subtraction of the mean normal spectrum from the subject obtained circumjacent to each lesion could significantly increase the diagnostic capability of the Raman-based discrimination algorithm.

Early diagnosis of skin cancer by ultrasound frequency analysis

The diagnosis of cancer by modern computer tools, at the very first stages of the incident, is a very important issue that has involved many researchers. In the meantime, skin cancer is a great deal of research because many people are involved with it. The purpose of this paper is to introduce an innovative method based on tissue frequency analyzes to obtain the accurate and real-time evaluation of skin cancers. According to the Biological resonance theory, body cells have natural and unique frequencies based on their biological fluctuations, which, if the structure, profile and cellular status change, its frequency also varies. This concept and theory is considered as the basis for analyzing skin tissue health in the proposed method. Reflected ultrasound waves from tissue have been processed and studied based on frequency analysis as a new method for early detection and diagnosis of accurate location and type of skin diseases. The developed algorithm was approved through 400 patients from CRED; its ability to evaluate benign and malignant skin lesions was shown (AUC = 0.959), with comparable clinical precision; as for the selected threshold, sensitivity, and specificity were 93.8% and 97.3%, respectively. Therefore, this method can detect skin malignancy with an accurate, noninvasive and real-time procedure.

Raman spectroscopic discrimination of normal and cancerous lung tissues

Raman spectroscopy is non-destructive method that allows monitoring of biological tissues with minimal intervention. FT-Raman (λ_ex 1064 nm) and NIR-Vis-Raman (λ_ex 785 nm) spectroscopic measurements were used in ex vivo analysis of normal, non-cancerous abnormal and cancerous lung tissues. Spectroscopic discrimination of the lung tissue samples was made by the use of the ratio of characteristic bands and multivariate statistical methods (PCA, LDA). The combination of Raman spectroscopy and multivariate statistics may have a diagnostic potential for recognizing of cancer lesions in lung.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Assessment of treatment efficacy using surface-enhanced Raman spectroscopy analysis of urine in rats with kidney transplantation or kidney disease

BACKGROUND

Individuals who have kidney disease or kidney transplants need routine assessment of their kidney damage and function, which are largely measured based on histological examination of kidney biopsies, blood test, and urinalysis. These methods are practically difficult or inconvenient, and expensive. The objective of this study was to develop a model to estimate the kidney damage and function by surface-enhanced Raman spectroscopy (SERS).

METHODS

Urine samples were collected from two previous studies: renal allograft recipient Lewis rats receiving anti-TGF-β antibody or control antibody treatment and obese diabetic ZSF1 rats with kidney disease fed with whole grape powder-containing chow or control chow. Silver nanoparticle-based SERS spectra of urine were measured. SERS spectra were analyzed using principal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squires (PLS) analysis.

RESULTS

PCA/LDA separated anti-TGF-β antibody-treated group from control group with 90% sensitivity and 70% specificity in kidney transplants, and grape-fed group from controls with 72.7% sensitivity and 60% specificity in diabetic kidneys. The receiver operating characteristic curves showed that the integration area under the curve was 0.850 ± 0.095 (p = 0.008) in kidney transplant groups and 0.800 ± 0.097 (p = 0.02) in diabetic kidney groups. PLS predicted the biochemical parameters of kidney function using the SERS spectra, resulting in R² = 0.8246 (p < 0.001,urine protein), R² = 0.8438 (p < 0.001, urine creatinine), R² = 0.9265 (p < 0.001, urea), R² = 0.8719 (p < 0.001, serum creatinine), and R² = 0.6014 (p < 0.001, urine protein to creatinine ratio).

CONCLUSION

Urine SERS spectral analysis suggesting that it may become a convenient method for rapid assessment of renal impairment.

Application of atomic force microscopy in cancer research

Atomic force microscopy (AFM) allows for nanometer-scale investigation of cells and molecules. Recent advances have enabled its application in cancer research and diagnosis. The physicochemical properties of live cells undergo changes when their physiological conditions are altered. These physicochemical properties can therefore reflect complex physiological processes occurring in cells. When cells are in the process of carcinogenesis and stimulated by external stimuli, their morphology, elasticity, and adhesion properties may change. AFM can perform surface imaging and ultrastructural observation of live cells with atomic resolution under near-physiological conditions, collecting force spectroscopy information which allows for the study of the mechanical properties of cells. For this reason, AFM has potential to be used as a tool for high resolution research into the ultrastructure and mechanical properties of tumor cells. This review describes the working principle, working mode, and technical points of atomic force microscopy, and reviews the applications and prospects of atomic force microscopy in cancer research.

Emerging imaging technologies in dermatology: Part II: Applications and limitations

Clinical examination is critical for the diagnosis and identification of response to treatment. It is fortunate that technologies are continuing to evolve, enabling augmentation of classical clinical examination with noninvasive imaging modalities. This article discusses emerging technologies with a focus on digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional developing modalities. The most readily adopted technologies to date include total-body digital photography and dermoscopy, with some practitioners beginning to use confocal microscopy. In this article, applications and limitations are addressed. For a detailed discussion of the principles involved in these technologies, please refer to the first part of this review article.

Emerging imaging technologies in dermatology: Part I: Basic principles

Dermatologists rely primarily on clinical examination in combination with histopathology to diagnose conditions; however, clinical examination alone might not be sufficient for accurate diagnosis and skin biopsies have associated morbidity. With continued technological advancement, there are emerging ancillary imaging technologies available to dermatologists to aid in diagnosis and management. This 2-part review article will discuss these emerging technologies including: digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional modalities in development. In this first installment, the authors describe the breadth of technologies available and the science behind them. Then, in the second article, the authors discuss the applications and limitations of these technologies and future directions.

Electrochemically deposited silver detection substrate for surface-enhanced Raman spectroscopy cancer diagnostics

Surface-enhanced Raman spectroscopy (SERS) of blood plasma on an electrochemically prepared silver surface has been studied as a label-free, noninvasive diagnostic test for colorectal cancer. Indium tin oxide glass substrates were modified with 0.01  mol dm  -  3 silver nitrate using the pulsed double-potentiostatic method. The prepared silver substrates were tested with Rhodamine 6G as a model analyte and the surface with the highest signal enhancement was selected. This silver dendritic surface was used as a diagnostic substrate for SERS measurements of human blood plasma. A group of oncological patients with declared colorectal carcinoma (n  =  15) and the control group of healthy volunteers (n  =  15) were compared. The biomolecular changes in chemical composition in the cancer samples were detected by statistical processing of the resulting SERS spectra. About 94% specificity and 100% sensitivity were achieved for the analysis by the ratio of the SERS peak intensity at 725  cm  -  1 for adenine to the peak intensity at 638  cm  -  1 for tyrosine and 100% specificity and sensitivity by using principal component analysis. This method of SERS diagnostics of colorectal cancer, which does not require the nanoparticle preparation, mixing, and incubation of plasma with a colloidal solution as in conventional tests, is a rapid, inexpensive method, which could be introduced as a primary diagnostic test.

Biochemistry and biomedicine of quantum dots: from biodetection to bioimaging, drug discovery, diagnostics, and therapy

According to recent research, nanotechnology based on quantum dots (QDs) has been widely applied in the field of bioimaging, drug delivery, and drug analysis. Therefore, it has become one of the major forces driving basic and applied research. The application of nanotechnology in bioimaging has been of concern. Through in vitro labeling, it was found that luminescent QDs possess many properties such as narrow emission, broad UV excitation, bright fluorescence, and high photostability. The QDs also show great potential in whole-body imaging. The QDs can be combined with biomolecules, and hence, they can be used for targeted drug delivery and diagnosis. The characteristics of QDs make them useful for application in pharmacy and pharmacology. This review focuses on various applications of QDs, especially in imaging, drug delivery, pharmaceutical analysis, photothermal therapy, biochips, and targeted surgery. Finally, conclusions are made by providing some critical challenges and a perspective of how this field can be expected to develop in the future.

STATEMENT OF SIGNIFICANCE

Quantum dots (QDs) is an emerging field of interdisciplinary subject that involves physics, chemistry, materialogy, biology, medicine, and so on. In addition, nanotechnology based on QDs has been applied in depth in biochemistry and biomedicine. Some forward-looking fields emphatically reflected in some extremely vital areas that possess inspiring potential applicable prospects, such as immunoassay, DNA analysis, biological monitoring, drug discovery, in vitro labelling, in vivo imaging, and tumor target are closely connected to human life and health and has been the top and forefront in science and technology to date. Furthermore, this review has not only involved the traditional biochemical detection but also particularly emphasized its potential applications in life science and biomedicine.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

SERS study of riboflavin on green-synthesized silver nanoparticles prepared by reduction using different flavonoids: What is the role of flavonoid used?

Spectroscopy of surface-enhanced Raman scattering (SERS) is nowadays widely used in the field of bio-science and medicine. These applications require new enhancing substrates with special properties. They should be non-toxic, environmentally friendly and (bio-) compatible with examined samples. Flavonoids are natural antioxidants with many positive effects on human health. Simultaneously, they can be used as reducing agent in preparation procedure of plasmonic enhancing substrate for SERS spectroscopy. The best amplifiers of Raman vibrational spectroscopic signal are generally silver nanoparticles (AgNPs). In this study, several flavonoids (forming a logical set) were used as reducing agent in AgNPs preparation procedures. Reactivity of 10 structurally arranged flavonoids (namely flavone, chrysin, apigenin, luteolin, tricetin, 3-hydroxyflavone, galangin, kaempferol, quercetin and myricetin) was compared and SERS-activity of prepared AgNPs was tested using model analyte riboflavin. Riboflavin was detected down to concentration 10^-9mol/l.