Search

Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that is able to identify the molecular fingerprint of various samples making use of the inelastic scattering of monochromatic light. Because of its advantages of non-destructive and accurate detection, RS is finding more and more use for benign and malignant tissues, tumor differentiation, tumor subtype classification, and section pathology diagnosis, operating either in vivo or in vitro. However, the high specificity of RS comes at a cost. The acquisition rate is low, depth information cannot be directly accessed, and the sampling area is limited. Such limitations can be contained if data pre- and post-processing methods are combined with current methods of Artificial Intelligence (AI), essentially, Machine Learning (ML) and Deep Learning (DL). The latter is modifying the approach to cancer diagnosis currently used to automate many cancer data analyses, and it has emerged as a promising option for improving healthcare accuracy and patient outcomes by abiliting prediction diseases tools. In a very broad context, Artificial Intelligence applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. The application of autonomous methods to datasets generated by RS analysis of benign and malignant tissues could make RS a rapid and stand-alone technique to help pathologists diagnose cancer with very high accuracy. This review describes the current milestones achieved by applying AI-based algorithms to RS analysis, grouped according to seven major types of cancers (Pancreatic, Breast, Skin, Brain, Prostate, Ovarian and Oral cavity). Additionally, it provides a theoretical foundation to tackle both present and forthcoming challenges in this domain. By exploring the current achievements and discussing the relative methodologies, this review offers recapitulative insights on recent and ongoing efforts to position RS as a rapid and effective cancer screening tool for pathologists. Accordingly, we aim to encourage future research endeavors and to facilitate the realization of the full potential of RS and AI applications in cancer grading.

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost.

AITA is an international conference that has run since 1992 and is aimed at assessing the state of the art of the infrared spectral range technology and to present its most interesting applications [...]

Abstract In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.

Osteosarcoma is the most common malignant tumour of the bone. Complete surgical excision is critical to achieve optimal outcomes and lower recurrence rates. However, accurate assessment of tumour margins remains a challenge and multiple technologies are employed for this purpose. The aim of this study is to highlight current and emerging technologies and their efficacy in detecting clear bone margins intraoperatively, through a systematic review of the literature.The following databases were searched using the OVID platform: Medline, Embase, Global Health and Google Scholar. Studies were screened using predetermined eligibility criteria. Data was extracted based on study and patient characteristics, modes of detection, and commercial availability, followed by quality assessment.A total of 17 studies were included. The primary diagnosis varied, with osteosarcoma being reported by 9 studies. Three studies reported relapse, ranging between 17.6%−48%. Twelve studies reported non-invasive imaging as the mode of detection used, while 4 studies reported the use of frozen section. MRI and CT were found to have an accuracy of up to 93 %. Raman spectroscopy was reported to have an accuracy, sensitivity, and specificity of 69%, 58.8% and 83.3% respectively. CT had a sensitivity and specificity up to 83% and 100%, respectively.In conclusion, there seems to be high potential for the use of multimodal technologies to increase the accuracy of intraoperative margin assessment. Although imaging modalities possess a fair level of accuracy, they carry the risk of radiation exposure, are expensive, and cannot be used in-situ. Future clinical trials are needed to test the effectiveness of these technologies to measure the diagnostic accuracy and overall patient survival.

Melanoma is one of the most aggressive forms of cancer. Early-stage diagnosis is therefore a landmark for the success of the therapies and to improve the prognosis. Raman spectroscopy represents a powerful and label-free approach for the molecular characterization of biological samples. Due to its level of detail, when applied to cancer tissues, Raman spectroscopy can help the classification of cancer-related malignant degrees. However, there is a high similarity between Raman spectra related to different cancerous tissues, which requires the use of sophisticated techniques for the treatment of Raman data. In this work, we coupled Confocal Raman Microscopy and Machine Learning techniques for the automatic classification of ex vivo melanoma tissues. In particular, we compared the performance of a PCA+LDA routine with a Random Forest Classifier. The work demonstrated excellent Machine Learning performances in classifying the tissues under investigation.

Electrospinning is an easy and versatile technique to obtain nanofibrous membranes with nanosized fibers, high porosity, and pore interconnectivity. Metal nanoparticles (e.g., Ag, Cu, ZnO) exhibit excellent biocide properties due to their size, shape, release of metal ions, or reactive oxygen species production, and thus are often used as antimicrobial agents. In this study, a combined electrospinning/spray technique was employed to fabricate electrospun polyurethane membranes loaded with copper nanoparticles at different surface densities (10, 20, 25, or 30 μg/cm2). This method allows particle deposition onto the surface of the membranes without the use of chemical agents. SEM images showed that polyurethane fibers own homogeneous thickness (around 650 nm), and that spray-deposited copper nanoparticles are evenly distributed. STEM-EDX demonstrated that copper nanoparticles are deposited onto the surface of the fibers and are not covered by polyurethane. Moreover, a uniaxial rupture test showed that particles are firmly anchored to the electrospun fibers. Antibacterial tests against model microorganisms Escherichia coli indicated that the prepared electrospun membranes possess good bactericidal effect. Finally, the antiviral activity against SARS-CoV-2 was about 90% after 1 h of direct contact. The obtained results suggested that the electrospun membranes possess antimicrobial activities and can be used in medical and industrial applications.

Face masks are an effective protection tool to prevent bacterial and viral transmission. However, commercial face masks contain filters made of materials that are not capable of inactivating either SARS-CoV-2. In this regard, we report the development of an antiviral coating of polyurethane and Copper nanoparticles on a face mask filter fabricated with a spray technology that is capable of inactivating more than 99% of SARS-CoV-2 particles in 30 min of contact.

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

The occurrence of plasmon resonances on metallic nanometer-scale structures is an intrinsically nanoscale phenomenon, given that the two resonance conditions (i.e., negative dielectric permittivity and large free-space wavelength in comparison with system dimensions) are realized at the same time on the nanoscale. Resonances on surface metallic nanostructures are often experimentally found by probing the structures under investigation with radiation of various frequencies following a trial-and-error method. A general technique for the tuning of these resonances is highly desirable. In this paper we address the issue of the role of local surface patterns in the tuning of these resonances as a function of wavelength and electric field polarization. The effect of nanoscale roughness on the surface plasmon polaritons of randomly patterned gold films is numerically investigated. The field enhancement and relation to specific roughness patterns is analyzed, producing many different realizations of rippled surfaces. We demonstrate that irregular patterns act as metal–dielectric–metal local nanogaps (cavities) for the resonant plasmonic field. In turn, the numerical results are compared to experimental data obtained via aperture scanning near-field optical microscopy.

Need for regeneration and repair of nasal tissues occurs as a consequence of several pathologies affecting the nose, including, but not limited to infective diseases, traumas and tumor resections. A platform for nasal tissue regeneration was set up using poly(vinyl alcohol)/gelatin sponges with 20%–30% (w/w) gelatin content to be used as scaffolds, for their intrinsic hydrophilic, cell adhesive and shape recovery properties. We propose mesodermal progenitor cells (MPCs) isolated from the bone marrow as a unique stem cell source for obtaining different connective tissues of the nose, including vascular tissue. Finally, epithelial cell immune response to these scaffolds was assessed in vitro in an environment containing inflammatory molecules. The results showed that mesenchymal stromal cells (MSCs) deriving from MPCs could be used to differentiate into cartilage and fibrous tissue; whereas, in combination with endothelial cells still deriving from MPCs, into pre-vascularized bone. Finally, the scaffold did not significantly alter the epithelial cell response to inflammatory insults derived from interaction with bacterial molecules.

The real-time environmental surveillance of large areas requires the ability to dislocate sensor networks. Generally, the probability of the occurrence of a pollution event depends on the burden of possible sources operating in the areas to be monitored. This implies a challenge for devising optimal real-time dislocation of wireless sensor networks. This challenge involves both hardware solutions and algorithms optimizing the displacements of mobile sensor networks in large areas with a vast number of sources of pollutant factors based mainly on diffusion mechanisms. In this paper, we present theoretical and simulated results inherent to a Voronoi partition approach for the optimized dislocation of a set of mobile wireless sensors with circular (radial) sensing power on large areas. The optimal deployment was found to be a variation of the generalized centroidal Voronoi configuration, where the Voronoi configuration is event-driven, and the centroid set of the corresponding generalized Voronoi cells changes as a function of the pollution event. The initial localization of the pollution events is simulated with a Poisson distribution. Our results could improve the possibility of reducing the costs for real-time surveillance of large areas, and other environmental monitoring when wireless sensor networks are involved.

Nanoscale rippling induced by an atomic force microscope (AFM) tip can be observed after performing one or many scans over the same area on a range of materials, namely ionic salts, metals, and semiconductors. However, it is for the case of polymer films that this phenomenon has been widely explored and studied. Due to the possibility of varying and controlling various parameters, this phenomenon has recently gained a great interest for some technological applications. The advent of AFM cantilevers with integrated heaters has promoted further advances in the field. An alternative method to heating up the tip is based on solvent-assisted viscoplastic deformations, where the ripples develop upon the application of a relatively low force to a solvent-rich film. An ensemble of AFM-based procedures can thus produce nanoripples on polymeric surfaces quickly, efficiently, and with an unprecedented order and control. However, even if nanorippling has been observed in various distinct modes and many theoretical models have been since proposed, a full understanding of this phenomenon is still far from being achieved. This review aims at summarizing the current state of the art in the perspective of achieving control over the rippling process on polymers at a nanoscale level.

In the last decade, surface-enhanced Raman spectroscopy (SERS) met increasing interest in the detection of chemical and biological agents due to its rapid performance and ultra-sensitive features. SERS is a combination of Raman spectroscopy and nanotechnology; it includes the advantages of Raman spectroscopy, providing rapid spectra collection, small sample sizes, and characteristic spectral fingerprints for specific analytes. In this paper, we detected label-free SERS signals for arbitrarily configurations of dimers, trimers, etc., composed of gold nanoshells (AuNSs) and applied to the mapping of osteosarcoma intracellular components.

Raman spectroscopy is a technique based on inelastic scattering of molecular systems when illuminated by monochromatic radiation. Owing to its high chemical specificity and noninvasive detection capability, in the last decade, Raman scattering has found wide application in cancer screening and diagnosis. In this paper; we describe recent results obtained by applying Raman spectroscopy to osteosarcoma and chondrosarcoma; the two main bone tumors. The results show the remarkable potential of Thermo Scientific™ DXR™2 microscope to discriminate between subcellular components inside osteo-differentiated osteoblasts and osteosarcoma cells or; at level of tissues; to discriminate chondrogenic tumors giving the possibility to grade the level of malignancy the cartilaginous tumors under investigation.

Biologically inspired to mammalian olfactory system, electronic noses became popular during the last three decades. In literature, as well as in daily practice, a wide range of applications are reported. Nevertheless, the most pioneering one has been (and still is) the assessment of the human breath composition. In this study, we used a prototype of electronic nose, called Wize Sniffer (WS) and based it on an array of semiconductor gas sensor, to detect ammonia in the breath of patients suffering from severe liver impairment. In the setting of severely impaired liver, toxic substances, such as ammonia, accumulate in the systemic circulation and in the brain. This may result in Hepatic Encephalopathy (HE), a spectrum of neuro−psychiatric abnormalities which include changes in cognitive functions, consciousness, and behaviour. HE can be detected only by specific but time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. In the presented proof-of-concept study, we aimed at investigating the possibility of discriminating the severity degree of liver impairment on the basis of the detected breath ammonia, in view of the detection of HE at its early stage.