Deconstructing Pancreatic Cancer Using Next Generation-Omic Technologies–From Discovery to Knowledge-Guided Platforms for Better Patient Management

Comprehensive molecular landscaping studies reveal a potentially brighter future for pancreatic ductal adenocarcinoma (PDAC) patients. Blood-borne biomarkers obtained from minimally invasive “liquid biopsies” are now being trialled for early disease detection and to track responses to therapy. Integrated genomic and transcriptomic studies using resectable tumour material have defined intrinsic patient subtypes and actionable genomic segments that promise a shift towards genome-guided patient management. Multimodal mapping of PDAC using spatially resolved single cell transcriptomics and imaging techniques has identified new potentially therapeutically actionable cellular targets and is providing new insights into PDAC tumour heterogeneity. Despite these rapid advances, defining biomarkers for patient selection remain limited. This review examines the current PDAC cancer biomarker ecosystem (identified in tumour and blood) and explores how advances in single cell sequencing and spatially resolved imaging modalities are being used to uncover new targets for therapeutic intervention and are transforming our understanding of this difficult to treat disease.

Usage and Analysis of Big Data in E-Health Domain

Big data analytics is a growth area with the potential to provide useful insight in healthcare. Big Data can unify all patient related data to get a 360-degree view of the patient to analyze and predict outcomes. It can improve clinical practices, new drug development and health care financing process. It offers a lot of benefits such as early disease detection, fraud detection and better healthcare quality and efficiency. This chapter introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. These issues include Big Data benefits, its applications and opportunities in medical areas and health care. Methods and technology progress about Big Data are presented in this study. Big Data challenges in medical applications and health care are also discussed. While many dimensions of big data still present issues in its use and adoption, such as managing the volume, variety, velocity, veracity, and value, the accuracy, integrity, and semantic interpretation are of greater concern in clinical application.

The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.

Malaysia Healthcare Early Response in Combatting COVID-19 Pandemic in 2020

The year 2020 saw the emergence of a novel coronavirus—the severe acute respiratory syndrome coronavirus 2— which has led to an unprecedented pandemic that has shaken the entire world. The pandemic has been a new experience for Malaysia, especially during the implementation of large-scale public health and social measures called the Movement Control Order (MCO). This paper seeks to describe the experiences of the Malaysian healthcare system thus far in combatting the pandemic. The Malaysian healthcare system comprises two main arms: public health and medicine. The public health arm focuses on early disease detection, contact tracing, quarantines, the MCO, and risk stratification strategies in the community. The medical arm focuses on the clinical management of coronavirus disease 2019 (COVID-19) patients; it encompasses laboratory services, the devising of clinical setting adjustments, and hospital management for COVID-19 and non-COVID-19 patients. Malaysia experienced intense emotions at the beginning of the pandemic, with great uncertainty regarding the pandemic’s outcome, as the world saw a frighteningly high COVID-19 mortality. As of writing (May 30, 2020), Malaysia has passed the peak of its second wave of infections. The experience thus far has helped in preparing the country’s healthcare system to be vigilant and more prepared for future COVID-19 waves. To date, the pandemic has changed many aspects of Malaysia’s life, and people are still learning to adapt to new norms in their lives.

Serum Biomarkers in Carotid Artery Disease

Carotid artery disease is considered a major cause of strokes and there is a need for early disease detection and management. Although imaging techniques have been developed for the diagnosis of carotid artery disease and different imaging-based markers have been proposed for the characterization of atherosclerotic plaques, there is still need for a definition of high-risk plaques in asymptomatic patients who may benefit from surgical intervention. Measurement of circulating biomarkers is a promising method to assist in patient-specific disease management, but the lack of robust clinical evidence limits their use as a standard of care. The purpose of this review paper is to present circulating biomarkers related to carotid artery diagnosis and prognosis, which are mainly provided by statistical-based clinical studies. The result of our investigation showed that typical well-established inflammatory biomarkers and biomarkers related to patient lipid profiles are associated with carotid artery disease. In addition to this, more specialized types of biomarkers, such as endothelial and cell adhesion, matrix degrading, and metabolic biomarkers seem to be associated with different carotid artery disease outputs, assisting vascular specialists in selecting patients at high risk for stroke and in need of intervention.

Disease Diagnostic System Using Deep Learning

Early disease detection cannot be neglected in the healthcare domain and especially in the diseases where a person's life is at stake. According to the WHO, if the diseases are predicted on time, then the death rates could reduce. The paper's goal is to find out how to detect Breast Cancer, Skin Cancer, Lung Cancer, and Brain Tumor at the early stages with the help of Deep Learning techniques. The authors of different papers have used different techniques and Algorithms like Adaptive Median Filters, Gaussian Filters, CNN algorithms, etc. Keywords: Breast Cancer, Skin Cancer, Brain Tumor, Lung Cancer, Deep Learning, CNN, SVM, Random Forest

Current Technologies for RNA-Directed Liquid Diagnostics

There is unequivocal acceptance of the variety of enormous potential liquid nucleic acid-based diagnostics seems to offer. However, the existing controversies and the increased awareness of RNA-based techniques in society during the current global COVID-19 pandemic have made the readiness of liquid nucleic acid-based diagnostics for routine use a matter of concern. In this regard—and in the context of oncology—our review presented and discussed the status quo of RNA-based liquid diagnostics. We summarized the technical background of the available assays and benchmarked their applicability against each other. Herein, we compared the technology readiness level in the clinical context, economic aspects, implementation as part of routine point-of-care testing as well as performance power. Since the preventive care market is the most promising application sector, we also investigated whether the developments predominantly occur in the context of early disease detection or surveillance of therapy success. In addition, we provided a careful view on the current biotechnology investment activities in this sector to indicate the most attractive strategies for future economic success. Taken together, our review shall serve as a current reference, at the interplay of technology, clinical use and economic potential, to guide the interested readers in this rapid developing sector of precision medicine.