Sensitivity Study of Cancer Antigens (CA-125) Detection Using Interdigitated Electrodes Under Microfluidic Flow Condition

Micro-interdigitated electrodes genosensor based on Au-deposited nanoparticles for early detection of cervical cancer

Genosensor-based electrodes mediated with nanoparticles (NPs) have tremendously developed in medical diagnosis. Herein, we report a facile, rapid, low cost and highly sensitive biosensing strategy for early detection of HPV 18 using gold-nanoparticles (AuNPs) deposited on micro-IDEs. This study represents surface charge transduction of micro-interdigitated electrodes (micro-IDE) alumina insulated with silica, independent and mini genosensor modified with colloidal gold NPs (AuNPs), and determination of gene hybridization for early detection of cervical cancer. The surface of AuNPs deposited micro-IDE functionalized with optimized 3-aminopropyl-triethoxysilane (APTES) followed by hybridization with deoxyribonucleic acid (DNA) virus to develop DNA genosensor. The results of ssDNA hybridization with the ssDNA target of human papillomavirus (HPV) 18 have affirmed that micro-IDE functionalized with colloidal AuNPs resulted in the lowest detection at 0.529 aM. Based on coefficient regression, micro-IDE functionalized with AuNPs produces better results in the sensitivity test (R2 = 0.99793) than unfunctionalized micro-IDE.

Application of Various Optical and Electrochemical Nanobiosensors for Detecting Cancer Antigen 125 (CA-125): A Review

Nowadays, diagnosing early-stage cancers can be vital for saving patients and dramatically decreases mortality rates. Therefore, specificity and sensitivity in the detection of cancer antigens should be elaborately ensured. Some early-stage cancers can be diagnosed via detecting the cancer antigen CA-125, such as ovarian cancer, and required treatments can be applied more efficiently. Thus, detection of CA-125 by employing various optical or electrochemical biosensors is a preliminary and crucial step to treating cancers. In this review, a diverse range of optical and electrochemical means of detecting CA-125 are reviewed. Furthermore, an applicable comparison of their performance and sensitivity is provided, several commercial detection kits are investigated, and their applications are compared and discussed to determine whether they are applicable and accurate enough.

Designing DNA probe from HPV 18 and 58 in the E6 region for sensing element in the development of genosensor-based gold nanoparticles

The E6 region has higher protuberant probability annealing than consensus probe focusing on another region in the human papillomavirus (HPV) genome in terms of detection and screening method. Here, we designed the first multiple virus single-stranded deoxyribonucleic acid (ssDNA) for multiple detections in an early phase of screening for cervical cancer in the E6 region and became a fundamental evolution of detection electrochemical HPV biosensor. Gene profiling of the virus ssDNA sequences has been carried by high-end bioinformatics tools such as GenBank, Basic Local Alignment Searching Tools (BLAST), and Clustal OMEGA in a row. The output from bioinformatics tools resulted in 100% of similarities between our virus ssDNA probe and HPV complete genome in the databases. The cross-validation between HPV genome and our designed virus ssDNA provided high specificity and selectivity during screening methods compared with Pap smear. The DNA probe for HPV 18, 5' COOH-GAT CCA GAA GGT ACA GAC GGG GAG GGC ACG 3', while 5'COOH-GGG CGC TGT GCA GTG TGT TGG AGA CCC CGA3' as DNA probe for HPV 58 designed with 66.77% guanine (G) and cytosine (C) content for both. Our virus ssDNA probe for the HPV biosensor promises high sensitivity, specificity, selectivity, repeatability, low fluid consumption, and will be useful in mini-size diagnostic devices for cervical cancer detection.

COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection.

Blood Plasma Self-Separation Technologies during the Self-Driven Flow in Microfluidic Platforms

Blood plasma is the most commonly used biofluid in disease diagnostic and biomedical analysis due to it contains various biomarkers. The majority of the blood plasma separation is still handled with centrifugation, which is off-chip and time-consuming. Therefore, in the Lab-on-a-chip (LOC) field, an effective microfluidic blood plasma separation platform attracts researchers' attention globally. Blood plasma self-separation technologies are usually divided into two categories: active self-separation and passive self-separation. Passive self-separation technologies, in contrast with active self-separation, only rely on microchannel geometry, microfluidic phenomena and hydrodynamic forces. Passive self-separation devices are driven by the capillary flow, which is generated due to the characteristics of the surface of the channel and its interaction with the fluid. Comparing to the active plasma separation techniques, passive plasma separation methods are more considered in the microfluidic platform, owing to their ease of fabrication, portable, user-friendly features. We propose an extensive review of mechanisms of passive self-separation technologies and enumerate some experimental details and devices to exploit these effects. The performances, limitations and challenges of these technologies and devices are also compared and discussed.

Modeling Focused-Ultrasound Response for Non-Invasive Treatment Using Machine Learning

The interactions between body tissues and a focused ultrasound beam can be evaluated using various numerical models. Among these, the Rayleigh-Sommerfeld and angular spectrum methods are considered to be the most effective in terms of accuracy. However, they are computationally expensive, which is one of the underlying issues of most computational models. Typically, evaluations using these models require a significant amount of time (hours to days) if realistic scenarios such as tissue inhomogeneity or non-linearity are considered. This study aims to address this issue by developing a rapid estimation model for ultrasound therapy using a machine learning algorithm. Several machine learning models were trained on a very-large dataset (19,227 simulations), and the performance of these models were evaluated with metrics such as Root Mean Squared Error (RMSE), R-squared (R²), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The resulted random forest provides superior accuracy with an R² value of 0.997, an RMSE of 0.0123, an AIC of -82.56, and a BIC of -81.65 on an external test dataset. The results indicate the efficacy of the random forest-based model for the focused ultrasound response, and practical adoption of this approach will improve the therapeutic planning process by minimizing simulation time.

Biosensors-on-Chip: An Up-to-Date Review

Generally, biosensors are designed to translate physical, chemical, or biological events into measurable signals, thus offering qualitative and/or quantitative information regarding the target analytes. While the biosensor field has received considerable scientific interest, integrating this technology with microfluidics could further bring significant improvements in terms of sensitivity and specificity, resolution, automation, throughput, reproducibility, reliability, and accuracy. In this manner, biosensors-on-chip (BoC) could represent the bridging gap between diagnostics in central laboratories and diagnostics at the patient bedside, bringing substantial advancements in point-of-care (PoC) diagnostic applications. In this context, the aim of this manuscript is to provide an up-to-date overview of BoC system development and their most recent application towards the diagnosis of cancer, infectious diseases, and neurodegenerative disorders.