Detection of Cytomegalovirus Antibodies Using a Biosensor Based on Imaging Ellipsometry

Impact of cytomegalovirus (CMV) seroconversion pre-allogeneic hematopoietic cell transplantation on posttransplant outcomes

Cytomegalovirus (CMV) reactivation post-allogeneic hematopoietic cell transplantation (post-alloHCT) increases morbidity and mortality. We sought to determine the frequency of CMV seroconversion in patients pre-alloHCT and to investigate the impact on posttransplant outcomes. We retrospectively investigated 752 adult patients who underwent alloHCT at our center from January 2015 to February 2020 before the adoption of letermovir prophylaxis. CMV serology was assessed at consult and pretransplant. The cohort was divided into four groups based on pretransplant CMV seroconversion: negative to positive (Group 1), positive to negative (Group 2), consistently negative (Group 3), and consistently positive (Group 4). Eighty-nine patients (12%) had seroconverted from negative to positive, 17 (2%) from positive to negative, 151 (20%) were consistently seronegative, and 495 (66%) were consistently seropositive pretransplant. For the four CMV serostatus groups, cumulative incidence of CMV reactivation at 6 months posttransplant was 4.5%, 47.1%, 6.6%, and 76.6% for Groups 1, 2, 3, and 4, respectively (p < .0001). No differences between groups were seen regarding Grade III-IV acute graft-versus-host disease (GVHD) (p = .91), moderate/severe chronic GVHD (p = .41), or graft failure (p = .28). On multivariable analysis, there was no impact of CMV serostatus group on overall survival (p = .67), cumulative incidence of relapse (p = .83) or non-relapse mortality. alloHCT patients who demonstrate CMV seroconversion pretransplant from negative to positive have a very low risk of CMV reactivation posttransplant. The observed seroconversion may be due to passive CMV immunity acquired through blood products. Quantitative CMV immunoglobulin G/immunoglobulin M pretransplant may help differentiate between true seroconversion and passively transmitted CMV immunoglobulin.

CA19-9 and CEA biosensors in pancreatic cancer

Cancer is a complex pathophysiological condition causing millions of deaths each year. Early diagnosis is essential especially for pancreatic cancer. Existing diagnostic tools rely on circulating biomarkers such as Carbohydrate Antigen 19-9 (CA19-9) and Carcinoembryonic Antigen (CEA). Unfortunately, these markers are nonspecific and may be increased in a variety of disorders. Accordingly, diagnosis of pancreatic cancer generally involves more invasive approaches such as biopsy as well as imaging studies. Recent advances in biosensor technology have allowed the development of precise diagnostic tools having enhanced analytical sensitivity and specificity. Herein we examine these advances in the detection of cancer in general and in pancreatic cancer specifically. Furthermore, we highlight novel technologies in the measurement of CA19-9 and CEA and explore their future application in the early detection of pancreatic cancer.

State of the Art in Smart Portable, Wearable, Ingestible and Implantable Devices for Health Status Monitoring and Disease Management

Several illnesses that are chronic and acute are becoming more relevant as the world's aging population expands, and the medical sector is transforming rapidly, as a consequence of which the need for "point-of-care" (POC), identification/detection, and real time management of health issues that have been required for a long time are increasing. Biomarkers are biological markers that help to detect status of health or disease. Biosensors' applications are for screening for early detection, chronic disease treatment, health management, and well-being surveillance. Smart devices that allow continual monitoring of vital biomarkers for physiological health monitoring, medical diagnosis, and assessment are becoming increasingly widespread in a variety of applications, ranging from biomedical to healthcare systems of surveillance and monitoring. The term "smart" is used due to the ability of these devices to extract data with intelligence and in real time. Wearable, implantable, ingestible, and portable devices can all be considered smart devices; this is due to their ability of smart interpretation of data, through their smart sensors or biosensors and indicators. Wearable and portable devices have progressed more and more in the shape of various accessories, integrated clothes, and body attachments and inserts. Moreover, implantable and ingestible devices allow for the medical diagnosis and treatment of patients using tiny sensors and biomedical gadgets or devices have become available, thus increasing the quality and efficacy of medical treatments by a significant margin. This article summarizes the state of the art in portable, wearable, ingestible, and implantable devices for health status monitoring and disease management and their possible applications. It also identifies some new technologies that have the potential to contribute to the development of personalized care. Further, these devices are non-invasive in nature, providing information with accuracy and in given time, thus making these devices important for the future use of humanity.

Fast and Sensitive Ellipsometry-Based Biosensing

In this work, a biosensing method based on in situ, fast, and sensitive measurements of ellipsometric parameters (Ψ, ∆) is proposed. Bare silicon wafer substrate is functionalized and used to bind biomolecules in the solution. Coupled with a 45° dual-drive symmetric photoelastic modulator-based ellipsometry, the parameters Ψ and ∆ of biolayer arising due to biomolecular interactions are determined directly, and the refractive index (RI) of the solution and the effective thickness and surface mass density of the biolayer for various interaction time can be further monitored simultaneously. To illustrate the performance of the biosensing method, immunosensing for immunoglobulin G (IgG) was taken as a case study. The experiment results show that the biosensor response of the limit of detection for IgG is 15 ng/mL, and the data collection time is in milliseconds. Moreover, the method demonstrates a good specificity. Such technique is a promising candidate in developing a novel sensor which can realize fast and sensitive, label-free, easy operation, and cost-effective biosensing.