Adaptive metabolic pattern biomarker for disease monitoring and staging of lung cancer with liquid biopsy

Serum metabolomics reveals an innovative diagnostic model for salivary gland tumors

An early diagnosis of salivary gland tumors (SGTs) and determination of their malignancy are conducive to developing individualized therapeutic strategies and thus improving prognosis. The aim of this study was to investigate the difference of serum metabolic profiles in patients with SGTs to better understand the mechanism of this disease and disease risk stratification. We used ultrahigh-performance liquid chromatography Q Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of serum metabolites in a population with normal control and SGTs. 32 differentially expressed metabolites were identified, while the level of serine and lactic acid were investigated to gradually upregulate in benign SGTs and malignant SGTs. Then, the expression of serine and lactic acid were assessed in validation cohort using multiple reaction monitoring (MRM) based targeted metabolite analysis. A risk score formula based on the amount of serine and lactic acid was developed and explored to be significantly related to benign SGTs and malignant SGTs in discovery and validation cohort. Our work highlights the possible use of the risk score assessment based on the serum metabolites not only reveal in the early diagnosis of SGTs but also assist in enhancing current therapeutic strategies in the clinic.

Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

Paper-based plasmonic substrates as surface-enhanced Raman scattering spectroscopy platforms for cell culture applications

The engineering of advanced materials capable of mimicking the cellular micro-environment while providing cells with physicochemical cues is central for cell culture applications. In this regard, paper meets key requirements in terms of biocompatibility, hydrophilicity, porosity, mechanical strength, ease of physicochemical modifications, cost, and ease of large-scale production, to be used as a scaffold material for biomedical applications. Most notably, paper has demonstrated the potential to become an attractive alternative to conventional biomaterials for creating two-dimensional (2D) and three-dimensional (3D) biomimetic cell culture models that mimic the features of in vivo tissue environments for improving our understanding of cell behavior (e.g. growth, cell migration, proliferation, differentiation and tumor metastasis) in their natural state. On the other hand, integration of plasmonic nanomaterials (e.g. gold nanoparticles) within the fibrous structure of paper opens the possibility to generate multifunctional scaffolds equipped with biosensing tools for monitoring different cell cues through physicochemical signals. Among different plasmonic based detection techniques, surface-enhanced Raman scattering (SERS) spectroscopy emerged as a highly specific and sensitive optical tool for its extraordinary sensitivity and the ability for multidimensional and accurate molecular identification. Thus, paper-based plasmonic substrates in combination with SERS optical detection represent a powerful future platform for monitoring cell cues during cell culture processes. To this end, in this review, we will describe the different methods for fabricating hybrid paper-plasmonic nanoparticle substrates and their use in combination with SERS spectroscopy for biosensing and, more specifically, in cell culture applications.

Assessment of extracranial metastatic disease in patients with brain metastases: How much effort is needed in the context of evolving survival prediction models?

Survival prediction models may serve as decision-support tools for clinicians who have to assign the right treatment to each patient, in a manner whereby harmful over- or undertreatment is avoided as much as possible. Current models differ regarding their components, the overall number of components and the weighting of individual components. Some of the components are easy to assess, such as age or primary tumor type. Others carry the risk of inter-assessor inconsistency and time-dependent variation. The present publication focuses on issues related to assessment of extracranial metastases and potential surrogates, e.g. blood biomarkers. It identifies areas of controversy and provides recommendations for future research projects, which may contribute to prognostic models with improved accuracy.

Present and Future of Surface-Enhanced Raman Scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Surface-Enhanced Raman Spectroscopy in Cancer Diagnosis, Prognosis and Monitoring

As medicine continues to advance our understanding of and knowledge about the complex and multifactorial nature of cancer, new major technological challenges have emerged in the design of analytical methods capable of characterizing and assessing the dynamic heterogeneity of cancer for diagnosis, prognosis and monitoring, as required by precision medicine. With this aim, novel nanotechnological approaches have been pursued and developed for overcoming intrinsic and current limitations of conventional methods in terms of rapidity, sensitivity, multiplicity, non-invasive procedures and cost. Eminently, a special focus has been put on their implementation in liquid biopsy analysis. Among optical nanosensors, those based on surface-enhanced Raman scattering (SERS) have been attracting tremendous attention due to the combination of the intrinsic prerogatives of the technique (e.g., sensitivity and structural specificity) and the high degree of refinement in nano-manufacturing, which translate into reliable and robust real-life applications. In this review, we categorize the diverse strategic approaches of SERS biosensors for targeting different classes of tumor biomarkers (cells, nucleic acids and proteins) by illustrating key recent research works. We will also discuss the current limitations and future research challenges to be addressed to improve the competitiveness of SERS over other methodologies in cancer medicine.