Biosensor Technologies in Medicine: from Detection of Biochemical Markers to Research into Molecular Targets (Review)

Recent Advances in Hydrogel-Based Biosensors for Cancer Detection

Early cancer detection is crucial for effective treatment, but current methods have limitations. Novel biomaterials, such as hydrogels, offer promising alternatives for developing biosensors for cancer detection. Hydrogels are three-dimensional and cross-linked networks of hydrophilic polymers that have properties similar to biological tissues. They can be combined with various biosensors to achieve high sensitivity, specificity, and stability. This review summarizes the recent advances in hydrogel-based biosensors for cancer detection, their synthesis, their applications, and their challenges. It also discusses the implications and future directions of this emerging field.

Cortisol: Biosensing and detection strategies

Cortisol, a crucial steroid hormone synthesized by the adrenal glands, has diverse impacts on multiple physiological processes, such as metabolism, immune function, and stress management. Disruption in cortisol levels can result in conditions like Cushing's syndrome and Addison's disease. This review provides an in-depth exploration of cortisol, covering its structure, various forms in the body, detection methodologies, and emerging trends in cancer treatment and detection. Various techniques for cortisol detection, including electrochemical, chromatographic, and immunoassay methods were discussed and highlighted for their merits and applications. Electrochemical immunosensing emerges as a promising approach, which offered high sensitivity and low detection limits. Moreover, the review delves into the intricate relationship between cortisol and cancer, emphasizing cortisol's role in cancer progression and treatment outcomes. Lastly, the utilization of biomarkers, in-silico modeling, and machine learning for electrochemical cortisol detection were explored, which showcased innovative strategies for stress monitoring and healthcare advancement.

Recent Advances in Metallic Nanostructures-assisted Biosensors for Medical Diagnosis and Therapy

The field of nanotechnology has witnessed remarkable progress in recent years, particularly in its application to medical diagnosis and therapy. Metallic nanostructures-assisted biosensors have emerged as a powerful and versatile platform, offering unprecedented opportunities for sensitive, specific, and minimally invasive diagnostic techniques, as well as innovative therapeutic interventions. These biosensors exploit the molecular interactions occurring between biomolecules, such as antibodies, enzymes, aptamers, or nucleic acids, and metallic surfaces to induce observable alterations in multiple physical attributes, encompassing electrical, optical, colorimetric, and electrochemical signals. These interactions yield measurable data concerning the existence and concentration of particular biomolecules. The inherent characteristics of metal nanostructures, such as conductivity, plasmon resonance, and catalytic activity, serve to amplify both sensitivity and specificity in these biosensors. This review provides an in-depth exploration of the latest advancements in metallic nanostructures-assisted biosensors, highlighting their transformative impact on medical science and envisioning their potential in shaping the future of personalized healthcare.

Optical Sensors for Bacterial Detection

Analytical devices for bacterial detection are an integral part of modern laboratory medicine, as they permit the early diagnosis of diseases and their timely treatment. Therefore, special attention is directed to the development of and improvements in monitoring and diagnostic methods, including biosensor-based ones. A promising direction in the development of bacterial detection methods is optical sensor systems based on colorimetric and fluorescence techniques, the surface plasmon resonance, and the measurement of orientational effects. This review shows the detecting capabilities of these systems and the promise of electro-optical analysis for bacterial detection. It also discusses the advantages and disadvantages of optical sensor systems and the prospects for their further improvement.

Intelligent Health: Progress and Benefit of Artificial Intelligence in Sensing-Based Monitoring and Disease Diagnosis

Technology has progressed and allows people to go further in multiple fields related to social issues. Medicine cannot be the exception, especially nowadays, when the COVID-19 pandemic has accelerated the use of technology to continue living meaningfully, but mainly in giving consideration to people who remain confined at home with health issues. Our research question is: how can artificial intelligence (AI) translated into technological devices be used to identify health issues, improve people's health, or prevent severe patient damage? Our work hypothesis is that technology has improved so much during the last decades that Medicine cannot remain apart from this progress. It must integrate technology into treatments so proper communication between intelligent devices and human bodies could better prevent health issues and even correct those already manifested. Consequently, we will answer: what has been the progress of Medicine using intelligent sensor-based devices? Which of those devices are the most used in medical practices? Which is the most benefited population, and what do physicians currently use this technology for? Could sensor-based monitoring and disease diagnosis represent a difference in how the medical praxis takes place nowadays, favouring prevention as opposed to healing?

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

Artificial cell design: reconstructing biology for life science applications

Artificial cells are developed to redesign novel biological functions in a programmable and tunable manner. Although it aims to reconstitute living cell features and address 'origin of life' related questions, rapid development over the years has transformed artificial cells into an engineering tool with huge potential in applied biotechnology. Although the application of artificial cells was introduced decades ago as drug carriers, applications in other sectors are relatively new and could become possible with the technological advancement that can modulate its designing principles. Artificial cells are non-living system that includes no prerequisite designing modules for their formation and therefore allow freedom of assembling desired biological machinery within a physical boundary devoid of complex contemporary living-cell counterparts. As stimuli-responsive biomimetic tools, artificial cells are programmed to sense the surrounding, recognise their target, activate its function and perform the defined task. With the advantage of their customised design, artificial cells are being studied in biosensing, drug delivery, anti-cancer therapeutics or artificial photosynthesis type fields. This mini-review highlights those advanced fields where artificial cells with a minimalistic setup are developed as user-defined custom-made microreactors, targeting to reshape our future 'life'.

Double-Clad Fiber-Based Multifunctional Biosensors and Multimodal Bioimaging Systems: Technology and Applications

Optical fibers have been used to probe various tissue properties such as temperature, pH, absorption, and scattering. Combining different sensing and imaging modalities within a single fiber allows for increased sensitivity without compromising the compactness of an optical fiber probe. A double-clad fiber (DCF) can sustain concurrent propagation modes (single-mode, through its core, and multimode, through an inner cladding), making DCFs ideally suited for multimodal approaches. This study provides a technological review of how DCFs are used to combine multiple sensing functionalities and imaging modalities. Specifically, we discuss the working principles of DCF-based sensors and relevant instrumentation as well as fiber probe designs and functionalization schemes. Secondly, we review different applications using a DCF-based probe to perform multifunctional sensing and multimodal bioimaging.