The role of blood testing in prevention, diagnosis, and management of chronic diseases: A review

Nanosensors and Microsensors for Body Fluid Monitoring: Various Analyte Detection and Construction Solutions

This review provides a comprehensive overview of the recent advancements in nanosensors and microsensors for body fluid monitoring. The principles behind sensor technologies, their applications in healthcare, and the types of body fluids that they analyze are described in the scope of this paper. Additionally, this review discusses emerging trends, challenges, and future perspectives in this field. The first two sections explore various body fluids and their diagnostic significance and discuss the fundamentals and classification of nanosensors and microsensors. The main aim of this paper is to highlight recent advancements in nanosensors for body fluid monitoring and to examine the role of microsensors in healthcare diagnostics. Innovative solutions such as microfluidic-based sensors, lab-on-a-chip systems, MEMS-based sensors, and wearable and implantable sensors are discussed in this section. Various construction solutions for microsensors and nanosensors have also been compiled and compared based on their target analytes, which are widely present in body fluids. The following sections review technologies and trends, including AI integration and flexible sensors, and discuss challenges and future perspectives in the development and application of sensors. The conclusion includes a summary of key findings and the future outlook for nanosensors and microsensors in personalized medicine.

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry's gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO.

The Power of Antibodies: Advancing Biomarker-Based Disease Detection and Surveillance

BACKGROUND

Antibodies have long served as fundamental tools in disease diagnosis and surveillance. Their utility as biomarkers has expanded beyond infectious diseases to encompass a wide range of health conditions.

OBJECTIVES

This review aims to explore recent advancements in antibody biomarker discovery and their applications in diagnosing and monitoring diverse health conditions. It also examines the role of antibody surveillance in public health and epidemiological studies.

METHODS

A comprehensive analysis of recent literature was conducted, focusing on studies that identify and characterize disease-specific antibodies. Particular attention was given to their relevance in autoimmune diseases, infections, cancers, and neurological disorders.

CONTENT

The review highlights disease-specific antibody biomarkers and their clinical significance. It also discusses the utility and challenges of antibody-based surveillance in assessing disease prevalence, tracking immunity trends, and supporting One Health strategies.

CONCLUSIONS

Recent advancements in antibody biomarker discovery demonstrate significant potential in improving early diagnosis, personalized treatment, and population-level health management. Antibody surveillance continues to play a pivotal role in guiding public health responses and understanding disease dynamics.

The Effect of Environmental Enrichment on Selected Physiological and Immunological Stress-Related Markers in Dairy Goats

Homeostasis preservation is essential for animal survival, and any event that causes a disturbance in homeostasis is defined as a stressor. Here, we aimed to evaluate the effect of scratch brushes and stages as an environmental enrichment to alleviate stress in dairy goats. Twenty-four mixed-breed goats were divided into two groups according to common physiological conditions in breeding farms: milking and dry (milk-producing and non-milk-producing, respectively). Ten days after exposure to environmental enrichment treatment or not (control), blood was sampled. Following the enrichment, we observed a reduction in reactive oxidative stress metabolites, advanced glycation end products (AGEs), and their binding protein (transferrin) in the dry goats, as determined by an ELISA. In contrast, no change in AGEs, along with an increase in transferrin levels, was observed in the milking goats. Moreover, oxytocin levels decreased in the dry and increased in the milking goats, while serotonin levels increased in the dry and remained unchanged in the milking goats. Additionally, gene expression of the cytokines, IL-6 and IL-1ß, and anti-oxidative proteins, lysozyme and transferrin (in peripheral blood leukocytes), as determined by qPCR, presented the same pattern: down-regulation in the dry or up-regulation in the milking goats. In conclusion, a reliable methodology was developed for measuring husbandry stress in goats and to improve dairy goats' husbandry practice. Current environmental enrichment produced different responsiveness in goats correlated to their physiological status: beneficial effect in dry goats, detrimental effect in milking goats.

Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine

The diagnosis of diseases is a complex process involving the integration of multiple parameters obtained from various sources, including laboratory findings. The interpretation of laboratory data is inherently comparative, necessitating reliable references for accurate assessment. Different types of references, such as reference intervals, decision limits, action limits, and reference change values, are essential tools in the interpretation of laboratory data. Although these references are used to interpret individual laboratory data, they are typically derived from population data, which raises concerns about their reliability and consequently the accuracy of interpretation of individuals' laboratory data. The accuracy of diagnosis is critical to all subsequent steps in medical practice, making the estimate of reliable references a priority. For more precise interpretation, references should ideally be derived from an individual's own data rather than from population averages. This manuscript summarizes the current sources of references used in laboratory data interpretation, examines the references themselves, and discusses the transition from population-based laboratory medicine to personalized laboratory medicine.