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Abstract Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8–140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

Abstract Red blood cells (RBCs) become sickle‐shaped and stiff under hypoxia as a consequence of hemoglobin (Hb) polymerization in sickle cell anemia. Distinguishing between sickle cell disease and trait is crucial during the diagnosis of sickle cell disease. While genetic analysis or high‐performance liquid chromatography (HPLC) can accurately differentiate between these two genotypes, these tests are unsuitable for field use. Here, we report a novel microscopy‐based diagnostic test called ShapeDx™ to distinguish between disease and trait blood in less than 1 h. This is achieved by mixing an unknown blood sample with low and high concentrations of a chemical oxygen scavenger and thereby subjecting the blood to slow and fast hypoxia, respectively. The different rates of Hb polymerization resulting from slow and fast hypoxia lead to two distinct RBC shape distributions in the same blood sample, which allows us to identify it as healthy, trait, or disease. The controlled hypoxic environment necessary for differential Hb polymerization is generated using an imaging microchamber, which also reduces the sickling time of trait blood from several hours to just 30 min. In a single‐blinded proof‐of‐concept study conducted on a small cohort of clinical samples, the results of the ShapeDx™ test were 100% concordant with HPLC results. Additionally, our field studies have demonstrated that ShapeDx™ is the first reported microscopy test capable of distinguishing between sickle cell disease and trait samples in resource‐limited settings with the same accuracy as a gold standard test.

Monitoring the presence of viruses and bacteria in environmental reservoirs such as wastewater, lake water etc. is a very useful tool for early detection and controlling the spread of diseases, while also helping estimate the severity of infections at a community level. While several DNA sensors have been demonstrated for nucleic acid sensing for a variety of applications, most such demonstrations require sophisticated instrumentation or assays that need to be controllably performed in a well-equipped laboratory. In this work, we demonstrate a low-cost, portable device to detect DNA in wastewater and lake water, based on the color change in samples due to the intercalation of DNA with methylene blue (MB) dye. The optical sensor consists of a phase-sensitive detection circuit constructed with low-cost semiconductor ICs, red LED, and a silicon PIN photodiode. The phase-sensitive detection circuit has a resolution of 68μV, and the sensor has a limit of detection of 25.68 nM for DNA sensing. The utility of this sensor is demonstrated through the detection of bacteria (E. coli) and viruses (bacteriophage phi6) from wastewater and lake-water samples, respectively.

Abstract Eccrine sweat is an ideal surrogate diagnostic biofluid for physiological and metabolic biomarkers for wearable biosensor design. Its periodic and non-invasive availability for candidate analytes such as glucose and cortisol along with limited correlation with blood plasma is of significant research interest. An insilico model of eccrine sweat can assist in the development of such wearable biosensors. In this regard, molecular modelling can be employed to observe the most fundamental interactions. Here, we determine a suitable molecular model for building eccrine sweat. The basic components of sweat are water and sodium chloride, in which glucose and other analytes are present in trace quantities. Given the wide range of water models available in the molecular dynamics space, in this study, we first validate the water models. We use three compounds to represent the base to build bulk sweat fluid and validate the force fields. We compare the self-diffusivity of water, glucose, sodium, and chloride ions as well as bulk viscosity values and present the results which are > 90% accurate as compared with the available literature. This validated insilico eccrine sweat model can serve as an aid to expedite the development de novo biosensors by addition of other analytes of interest e.g. cortisol, uric acid etc., simulate various temperatures and salt concentrations, expand search space for screening candidate target receptors by their binding affinity and assess the interference between competing species via simulations.

The detection of Circulating Tumor Cells (CTCs) is of profound importance for cancer diagnosis and monitoring in diseased patients. In the presented work, an aptasensor for CTC detection was developed using AS1411 aptamer specific for nucleolin on cancer cells’ surface as receptor on Gold NanoParticles (GNPs) decorated U-bent optical fiber. The aptamer binds to target upon folding into a parallel G-quadruplex structure, and therefore, components of folding and running buffer were optimized to 50 mM tris buffer and 100 mM KCl with and without 5 mM Mg+2 based on Circular Dichroism spectroscopy (CD) and bright field microscopy respectively. Further, the binding of aptamer and MCF-7 cells to GNP coated U-bent fiber were validated by fluorescence microscopy. The aptasensor showed significantly higher absorbance for 104 cancer cells (retinoblastoma, meningioma, breast, cervical, and colon cancer cells) as compared to non-cancer cells (MCF-10A and WBCs) with a t-test p-value of 0.0012. On an optical bench, aptasensor was able to detect as low as 500 cells/mL. To develop a portable and small CTC detection system, aptasensor was integrated with a hand-held opto-electronic device μSens (custom developed in our lab) and was able to detect 50 cells/mL. This is the first time a portable sensing system based on U-bent optical fiber was tested for sensing cancer cells. The ability to detect diverse types of cancer cells by AS1411 aptamer is promising and paves the way for the detection of circulating tumor cells in blood samples, with relative ease and low cost.

Centrifugal microfluidic platforms are becoming increasing popular in many research and diagnostic applications. A major challenge in centrifugal microfluidics is continuous handling of large sample volumes. Keeping the flow rate constant during sample inflow is difficult without a pump. We report an affordable (

Understanding the fluid flow on paperfluidic substrates is crucial due to an increasing interest in affordable sensors for diagnosis, environmental testing, food testing, etc. Charged dyes are the most commonly used model analytes for characterizing the fluid flow in paperfluidic devices. Here, we propose a framework to quantify the concentration-dependent loss during transport of four charged dyes (e.g., rhodamine B, methylene blue, tartrazine and amaranth). At concentrations lower than a critical value (Cc), electrostatic interaction of the dye molecules with the filter paper retards the flow of cationic dyes rhodamine B and methylene blue, resulting in 75%–99% sample loss. We determined Ccto be 20 μM for rhodamine B and 5 mM for methylene blue. The large difference in their Ccvalues is explained by the molecular structures of these dyes. While anionic dyes (e.g., tartrazine and amaranth) are not retarded during flow on filter paper due to negligible electrostatic interaction with the substrate, they form strong coffee ring patterns in the detection zone when the dye concentration is below Cc. We introduced a new parameter called the ‘coffee ring index’ (CRI) to quantify the non-uniform sample distribution in the detection zone, and from this, determined Ccto be 250 μM for amaranth and 1.25 mM for tartrazine. Finally, we demonstrated that an increase in the dye viscosity by adding 30% glycerol can lower Ccof tartrazine from 1.25 mM to 100 μM. These results provide useful guidelines for the paperfluidics community to design more effective sensors.

The identification of biomarkers from blood plasma is at the heart of many diagnostic tests. These tests often need to be conducted frequently and quickly, but the logistics of sample collection and processing not only delays the test result, but also puts a strain on the healthcare system due to the sheer volume of tests that need to be performed. The advent of microfluidics has made the processing of samples quick and reliable, with little or no skill required on the user’s part. However, while several microfluidic devices have been demonstrated for plasma separation, none of them have validated the chemical integrity of the sample post-process. Here, we present Haemoprocessor: a portable, robust, open-fluidic system that utilizes Travelling Surface Acoustic Waves (TSAW) with the expression of overtones to separate plasma from 20× diluted human blood within a span of 2 min to achieve 98% RBC removal. The plasma and red blood cell separation quality/integrity was validated through Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy and multivariate analyses to ascertain device performance and reproducibility when compared to centrifugation (the prevailing gold-standard for plasma separation). Principal Component Analysis (PCA) showed a remarkable separation of 92.21% between RBCs and plasma components obtained through both centrifugation and Haemoprocessor methods. Moreover, a close association between plasma isolates acquired by both approaches in PCA validated the potential of the proposed system as an eminent cell enrichment and plasma separation platform. Thus, compared to contemporary acoustic devices, this system combines the ease of operation, low sample requirement of an open system, the versatility of a SAW device using harmonics, and portability.

This paper reports for the first time printed-circuit-board (PCB)-based label-free electrochemical detection of bacteria. The demonstrated immunosensor was implemented on a PCB sensing platform which was designed and fabricated in a standard PCB manufacturing facility. Bacteria were directly captured on the PCB sensing surface using a specific, pre-immobilized antibody. Electrochemical impedance spectra (EIS) were recorded and used to extract the charge transfer resistance (Rct) value for the different bacteria concentrations under investigation. As a proof-of-concept, Streptococcus mutans (S. mutans) bacteria were quantified in a phosphate buffered saline (PBS) buffer, achieving a limit of detection of 103 CFU/mL. Therefore, the proposed biosensor is an attractive candidate for the development of a simple and robust point-of-care diagnostic platform for bacteria identification, exhibiting good sensitivity, high selectivity, and excellent reproducibility.