Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy.

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic ß-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

Operating an eHealth System for Prehospital and Emergency Health Care Support in Light of Covid-19.

Introduction: The support of prehospital and emergency call handling and the impact of Covid-19 is discussed throughout this study. The initial purpose was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. This system was expanded to facilitate needed operation changes for Covid-19. Materials and Methods: An effort to reform the procedures followed for emergency call handling and Ambulance dispatch started on the Island of Cyprus in 2016; along that direction, a central call centre was created. The electronic system presented in this work was designed for this call centre and the new organization of the ambulance services. The main features are the support for ambulance fleet handling, the support for emergency call evaluation and triage procedure, and the improvement of communication between the call centre and the ambulance vehicles. This system started regular operation at the end of 2018. One year later, when Covid-19 period started, we expanded it with the addition of several new features in order to support the handling of patients infected with the new virus. Results: This system has handled 112,414 cases during the last 25 months out of which 4,254 were Covid-19 cases. These cases include the transfer of patients from their house to the reference hospital, or the transfer of critical patients from the reference hospital to another hospital with an intensive care unit or transfer of patients from one hospital to another one for other reasons, like the number of admissions. Conclusion: The main purpose of this study was to create an electronic system (eEmergency system) in order to support, improve, and help the procedure of handling emergency calls. The main components and the architecture of this system are outlined in this paper. This system is being successfully used for 25 months and has been a useful tool from the beginning of the pandemic period of Covid-19.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.