Robust and multiplexed colorimetric immunoassay for cardiovascular disease biomarkers detection in serum with high specificity

Optical Bionanosensors for Sepsis Diagnostics

Sepsis is a global health challenge, characterized by a dysregulated immune response, leading to organ dysfunction and death. Despite advances in medical care, sepsis continues to claim a significant toll on human lives, with mortality rates from 10-25% for sepsis and 30-50% for septic shock, making it a leading cause of death worldwide. Current diagnostic methods rely on clinical signs, laboratory parameters, or microbial cultures and suffer from delays and inaccuracies. Therefore, there is a pressing need for novel diagnostic tools that can rapidly and accurately identify sepsis. This review highlights advances in biosensor development that could ultimately lead to faster and more accurate sepsis diagnostics. The focus is on nanomaterial-based optical approaches that promise rapid diagnostics without the need for large equipment or trained personnel. An overview of sepsis is provided, highlighting potential molecular targets and the challenges they present for assay development. The requirements for an ideal point-of-care test (POC) are discussed, including speed, simplicity, and cost-effectiveness. Different nanomaterials suitable for various optical detection methods are reviewed and innovative nanosensors are discussed for sepsis diagnostics, focusing on chemical design and approaches to increase selectivity by multiplexing.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Rapid and Highly Sensitive Detection of C-Reaction Protein Using Robust Self-Compensated Guided-Mode Resonance BioSensing System for Point-of-Care Applications

The rapid and sensitive detection of human C-reactive protein (CRP) in a point-of-care (POC) may be conducive to the early diagnosis of various diseases. Biosensors have emerged as a new technology for rapid and accurate detection of CRP for POC applications. Here, we propose a rapid and highly stable guided-mode resonance (GMR) optofluidic biosensing system based on intensity detection with self-compensation, which substantially reduces the instability caused by environmental factors for a long detection time. In addition, a low-cost LED serving as the light source and a photodetector are used for intensity detection and real-time biosensing, and the system compactness facilitates POC applications. Self-compensation relies on a polarizing beam splitter to separate the transverse-magnetic-polarized light and transverse-electric-polarized light from the light source. The transverse-electric-polarized light is used as a background signal for compensating noise, while the transverse-magnetic-polarized light is used as the light source for the GMR biosensor. After compensation, noise is drastically reduced, and both the stability and performance of the system are enhanced over a long period. Refractive index experiments revealed a resolution improvement by 181% when using the proposed system with compensation. In addition, the system was successfully applied to CRP detection, and an outstanding limit of detection of 1.95 × 10-8 g/mL was achieved, validating the proposed measurement system for biochemical reaction detection. The proposed GMR biosensing sensing system can provide a low-cost, compact, rapid, sensitive, and highly stable solution for a variety of point-of-care applications.

Aspects of Point-of-Care Diagnostics for Personalized Health Wellness

Advancements in analytical diagnostic systems for point-of-care (POC) application have gained considerable attention because of their rapid operation at the site required to manage severe diseases, even in a personalized manner. The POC diagnostic devices offer easy operation, fast analytical outcome, and affordable cost, which promote their advanced research and versatile adoptability. Keeping advantages in view, considerable efforts are being made to design and develop smart sensing components such as miniaturized transduction, interdigitated electrodes-based sensing chips, selective detection at low level, portable packaging, and sustainable durability to promote POC diagnostics according to the needs of patient care. Such effective diagnostics systems are in demand, which creates the challenge to make them more efficient in every aspect to generate a desired bio-informatic needed for better health access and management. Keeping advantages and scope in view, this mini review focuses on practical scenarios associated with miniaturized analytical diagnostic devices at POC application for targeted disease diagnostics smartly and efficiently. Moreover, advancements in technologies, such as smartphone-based operation, paper-based sensing assays, and lab-on-a-chip (LOC) which made POC more sensitive, informative, and suitable for major infectious disease diagnosis, are the main focus here. Besides, POC diagnostics based on automated patient sample integration with a sensing platform is continuously improving therapeutics interventions against specific infectious disease. This review also discussed challenges associated with state-of-the-art technology along with future research opportunities to design and develop next generation POC diagnostic systems needed to manage infectious diseases in a personalized manner.