Molecularly Imprinted Polymer Biosensor Based on Nitrogen-Doped Electrochemically Exfoliated Graphene/Ti3 CNTX MXene Nanocomposite for Metabolites Detection.

Rapid and accurate quantification of metabolites in different bodily fluids is crucial for a precise health evaluation. However, conventional metabolite sensing methods, confined to centralized laboratory settings, suffer from time-consuming processes, complex procedures, and costly instrumentation. Introducing the MXene/nitrogen-doped electrochemically exfoliated graphene (MXene@N-EEG) nanocomposite as a novel biosensing platform in this work addresses the challenges associated with conventional methods, leveraging the concept of molecularly imprinted polymers (MIP) enables the highly sensitive, specific, and reliable detection of metabolites. To validate our biosensing technology, we utilize agmatine as a significant biologically active metabolite. The MIP biosensor incorporates electrodeposited Prussian blue nanoparticles as a redox probe, facilitating the direct electrical signaling of agmatine binding in the polymeric matrix. The MXene@N-EEG nanocomposite, with excellent metal conductivity and a large electroactive specific surface area, effectively stabilizes the electrodeposited Prussian blue nanoparticles. Furthermore, increasing the content of agmatine-imprinted cavities on the electrode enhances the sensitivity of the MIP biosensor. Evaluation of the designed MIP biosensor in buffer solution and plasma samples reveals a wide linear concentration range of 1.0 nM-100.0 µM (R2 = 0.9934) and a detection limit of 0.1 nM. Notably, the developed microfluidic biosensor offers low cost, rapid response time to the target molecule (10 min of sample incubation), good recovery results for detecting agmatine in plasma samples, and acceptable autonomous performance for on-chip detection. Moreover, its high reliability and sensitivity position this MIP-based biosensor as a promising candidate for miniaturized microfluidic devices with the potential for scalable production for point-of-care applications.

Sensitive, Real-time and Non-Intrusive Detection of Concentration and Growth of Pathogenic Bacteria using Microfluidic-Microwave Ring Resonator Biosensor.

Infection diagnosis and antibiotic susceptibility testing (AST) are time-consuming and often laborious clinical practices. This paper presents a microwave-microfluidic biosensor for rapid, contactless and non-invasive device for testing the concentration and growth of Escherichia Coli (E. Coli) in medium solutions of different pH to increase the efficacy of clinical microbiology practices. The thin layer interface between the microfluidic channel and the microwave resonator significantly enhanced the detection sensitivity. The microfluidic chip, fabricated using standard soft lithography, was injected with bacterial samples and incorporated with a microwave microstrip ring resonator sensor with an operation frequency of 2.5 GHz and initial quality factor of 83 for detecting the concentration and growth of bacteria. The resonator had a coupling gap area on of 1.5 × 1.5 mm2 as of its sensitive region. The presence of different concentrations of bacteria in different pH solutions were detected via screening the changes in resonant amplitude and frequency responses of the microwave system. The sensor device demonstrated near immediate response to changes in the concentration of bacteria and maximum sensitivity of 3.4 MHz compared to a logarithm value of bacteria concentration. The minimum prepared optical transparency of bacteria was tested at an OD600 value of 0.003. The sensor's resonant frequency and amplitude parameters were utilized to monitor bacteria growth during a 500-minute time frame, which demonstrated a stable response with respect to detecting the bacterial proliferation. A highly linear response was demonstrated for detecting bacteria concentration at various pH values. The growth of bacteria analyzed over the resonator showed an exponential growth curve with respect to time and concurred with the lag-log-stationary-death model of cell growth. This biosensor is one step forward to automate the complex AST workflow of clinical microbiology laboratories for rapid and automated detection of bacteria as well as screening the bacteria proliferation in response to antibiotics.

Translational models of tumor angiogenesis: A nexus of in silico and in vitro models.

Emerging evidence shows that endothelial cells are not only the building blocks of vascular networks that enable oxygen and nutrient delivery throughout a tissue but also serve as a rich resource of angiocrine factors. Endothelial cells play key roles in determining cancer progression and response to anti-cancer drugs. Furthermore, the endothelium-specific deposition of extracellular matrix is a key modulator of the availability of angiocrine factors to both stromal and cancer cells. Considering tumor vascular network as a decisive factor in cancer pathogenesis and treatment response, these networks need to be an inseparable component of cancer models. Both computational and in vitro experimental models have been extensively developed to model tumor-endothelium interactions. While informative, they have been developed in different communities and do not yet represent a comprehensive platform. In this review, we overview the necessity of incorporating vascular networks for both in vitro and in silico cancer models and discuss recent progresses and challenges of in vitro experimental microfluidic cancer vasculature-on-chip systems and their in silico counterparts. We further highlight how these two approaches can merge together with the aim of presenting a predictive combinatorial platform for studying cancer pathogenesis and testing the efficacy of single or multi-drug therapeutics for cancer treatment.