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.

MicroSweat: A Wearable Microfluidic Patch for Noninvasive and Reliable Sweat Collection Enables Human Stress Monitoring.

Stress affects cognition, behavior, and physiology, leading to lasting physical and mental illness. The ability to detect and measure stress, however, is poor. Increased circulating cortisol during stress is mirrored by cortisol release from sweat glands, providing an opportunity to use it as an external biomarker for monitoring internal emotional state. Despite the attempts at using wearable sensors for monitoring sweat cortisol, there is a lack of reliable wearable sweat collection devices that preserve the concentration and integrity of sweat biomolecules corresponding to stress levels. Here, a flexible, self-powered, evaporation-free, bubble-free, surfactant-free, and scalable capillary microfluidic device, MicroSweat, is fabricated to reliably collect human sweat from different body locations. Cortisol levels are detected corresponding to severe stress ranging from 25 to 125 ng mL-1 averaged across multiple body regions and 100-1000 ng mL-1 from the axilla. A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS). Moreover, owing to the sweat variation in response to environmental effects and physiological differences, the longitudinal and personalized profile of sweat cortisol is acquired, for the first time, for various body locations. The obtained sweat cortisol data is crucial for analyzing human stress in personalized and clinical healthcare sectors.

Immuno-affinity Potent Strip with Pre-Embedded Intermixed PEDOT:PSS Conductive Polymers and Graphene Nanosheets for Bio-Ready Electrochemical Biosensing of Central Nervous System Injury Biomarkers.

Future point-of-care (PoC) and wearable electrochemical biosensors explore new technology solutions to eliminate the need for multistep electrode modification and functionalization, overcome the limited reproducibility, and automate the sensing steps. In this work, a new screen-printed immuno-biosensor strip is engineered and characterized using a hybrid graphene nanosheet intermixed with the conductive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) polymers, all embedded within the base carbon matrix (GiPEC) of the screen-printing ink. This intermixed nanocomposite ink is chemically designed for self-containing the "carboxyl" functional groups as the most specific chemical moiety for protein immobilization on the electrodes. The GiPEC ink enables capturing the target antibodies on the electrode without any need for extra surface preparation. As a proof of concept, the performance of the non-functionalized ready-to-immobilize strips was assessed for the detection of glial fibrillary acidic protein (GFAP) as a known central nervous system injury blood biomarker. This immuno-biosensor exhibits the limit of detection of 281.7 fg mL-1 (3 signal-to-noise ratio) and the sensitivity of 322.6 Ω mL pg-1 mm-2 within the clinically relevant linear detection range from 1 pg mL-1 to 10 ng mL-1. To showcase its potential PoC application, the bio-ready strip is embedded inside a capillary microfluidic device and automates electrochemical quantification of GFAP spiked in phosphate-buffered saline and the human serum. This new electrochemical biosensing platform can be further adapted for the detection of various protein biomarkers with the application in realizing on-chip immunoassays.

Autonomous electrochemical biosensing of glial fibrillary acidic protein for point-of-care detection of central nervous system injuries.

The integration of electrochemical biosensors into fluid handling units such as paper-based, centrifugal, and capillary microfluidic devices has been explored with the purpose of developing point-of-care platforms for quantitative detection of bodily fluid markers. However, the present fluidic device designs largely lack the capacity of full assay automation, needing manual loading of one or multiple reagents or requiring external devices for liquid manipulation. Such fluidic handing platforms also require universality for detecting various biomarkers. These platforms are also largely produced using materials unsuitable for scalable manufacturing and with a high production cost. The mechanism of fluid flow also often induces noise to the embedded biosensors which adversely impacts the accuracy of biosensing. This work addresses these challenges by presenting a reliable design of a fully automated and universal capillary-driven microfluidic platform that automates several steps of label-free electrochemical biosensing assays. These steps include sample aliquoting, controlled incubation, removal of non-specific bindings, reagent mixing and delivery to sensing electrodes, and electrochemical detection. The multilayer architecture of the microfluidic device is made of polymeric and adhesive materials commercially used for the fabrication of point-of-care devices. The design and geometry of different components of the device (e.g., sampling unit, mixer, resistances, delay valves, interconnecting components) were optimized using a combined experimental testing and numerical fluid flow modeling to reach high reproducibility and minimize the noise-induced to the biosensor. As a proof of concept, the performance of this on-chip immunosensing platform was demonstrated for rapid and autonomous detection of glial fibrillary acidic proteins (GFAP) in phosphate-buffered saline (PBS). The microfluidic immunosensing device exhibited a linear detection range of 10-1000 pg mL-1 for the detection of GFAP within 30 min, with a limit of detection (LoD) and sensitivity of 3 pg mL-1 and 39 mL pg-1 mm-2 in PBS, respectively. Owing to its simplicity, sample-to-result performance, universality for handing different biofluids, low cost, high reproducibility, compatibility with scalable production, and short analysis time, the proposed biosensing platform can be further adapted for the detection of other biomarkers in different clinical bodily fluids for rapid diagnostic and prognostic applications.

Highly Stable Buffer-Based Zinc Oxide/Reduced Graphene Oxide Nanosurface Chemistry for Rapid Immunosensing of SARS-CoV-2 Antigens.

The widespread and long-lasting effect of the COVID-19 pandemic has called attention to the significance of technological advances in the rapid diagnosis of SARS-CoV-2 virus. This study reports the use of a highly stable buffer-based zinc oxide/reduced graphene oxide (bbZnO/rGO) nanocomposite coated on carbon screen-printed electrodes for electrochemical immuno-biosensing of SARS-CoV-2 nuelocapsid (N-) protein antigens in spiked and clinical samples. The incorporation of a salt-based (ionic) matrix for uniform dispersion of the nanomixture eliminates multistep nanomaterial synthesis on the surface of the electrode and enables a stable single-step sensor nanocoating. The immuno-biosensor provides a limit of detection of 21 fg/mL over a linear range of 1-10 000 pg/mL and exhibits a sensitivity of 32.07 ohms·mL/pg·mm2 for detection of N-protein in spiked samples. The N-protein biosensor is successful in discriminating positive and negative clinical samples within 15 min, demonstrating its proof of concept used as a COVID-19 rapid antigen test.

A novel design and implementation of wheelchair navigation system using Leap Motion sensor.

PURPOSE: In this paper, a novel design for a leap motion wheelchair navigation system is proposed, and the suggested model is implemented on a prototype. The behaviour of the created prototype is closely observed during the different performance tests carried out, and the results are presented throughout this manuscript. MATERIALS AND METHODS: In the prototype, a Leap Motion sensor is implemented to acquire navigation data through hand gestures of the users. This navigations system design is specifically implemented to facilitate wheelchair use for amputee users and stroke patients as it does not rely on the movement of the fingers. Through this design, wheelchair movement can be controlled through detection of finger, fist, palm or wrist (for amputees) movement by the leap motion sensor. Bluetooth connection is used as the navigation system's communication means, removing the need for constant internet connection and providing freedom of movement outside of internet-covered territory. Additionally, two Dynamixel motors are used as movement force, which yield optimal computational time and minimal delay. RESULTS: The performance of the designed prototype is tested by considering response time and speed resolution as evaluation metrics. Results suggest that the designed wheelchair will give movement independence to users who cannot use their fingers to control the movement of their wheelchairs, while reducing delay, being independent of internet connection, providing high resolution and minimising detection error. CONCLUSIONS: The promising results obtained from prototype testing suggest the possibility of real-life application of this wheelchair navigation system, which can greatly assist amputee users and rehabilitation patients.Implications for rehabilitationA novel wheelchair navigations system designed to facilitate amputee users, stroke patients and rehabilitation patients.The proposed system eliminates the reliance on finger movements, is gaze independent, and does not require voice or gesture control, creating much more freedom for users undergoing specific medical conditions or still under rehabilitation or treatment.Results demonstrate very low delay time in wheelchair command to action, allowing improved control for users and reducing the occurrence of control-related accidents.The designed wheelchair navigation system is independent of internet connection, allowing more freedom in range for wheelchair users compared to available cloud based models.

Assessment of skin dose in breast cancer radiotherapy: on-phantom measurement and Monte Carlo simulation.

AIM: The main purpose of the present study is assessment of skin dose in breast cancer radiotherapy. BACKGROUND: Accurate assessment of skin dose in radiotherapy can provide useful information for clinical considerations. MATERIALS AND METHODS: A RANDO phantom was irradiated using a 6 MV Siemens Primus linac with medial and tangential radiotherapy fields for simulating breast cancer treatment. Dosimetry was also performed on various positions across the fields using an EBT3 radiochromic film. Similar conditions of measurement on the RANDO phantom including field size, irradiation angle, number of fields, etc. were subsequently simulated via the Monte Carlo N-Particle Transport code (MCNP). Ultimately, dose values for corresponding points from both methods were compared. RESULTS: Considering dosimetry using radiochromic films on the RANDO phantom, there were points having underdose and overdose based on the prescribed dose and skin tolerance levels. In this respect, 81.25% and 18.75% of the points had underdose and overdose, respectively. In some cases, several differences were observed between the measurement and the MCNP simulation results associated with skin dose. CONCLUSION: Based on the results of the points which had underdose, it was suggested that a bolus should be used for the given points. With regard to overdose points, it was advocated to consider skin tolerance dose in treatment planning. Differences between the measurement and the MCNP simulation results might be due to voxel size of tally cells in simulations, effect of beam's angle of incidence, validation time of linac's head, lack of electronic equilibrium in the build-up region, as well as MCNP tally type.

Corrigendum: Corrigendum to 'Enhanced Hybrid Copper Conductive Ink for Low Power Selective Laser Sintering'.

[This corrects the article DOI: 10.1016/j.promfg.2020.05.108.].

A semi-supervised machine learning framework for microRNA classification.

BACKGROUND: MicroRNAs (miRNAs) are a family of short, non-coding RNAs that have been linked to critical cellular activities, most notably regulation of gene expression. The identification of miRNA is a cross-disciplinary approach that requires both computational identification methods and wet-lab validation experiments, making it a resource-intensive procedure. While numerous machine learning methods have been developed to increase classification accuracy and thus reduce validation costs, most methods use supervised learning and thus require large labeled training data sets, often not feasible for less-sequenced species. On the other hand, there is now an abundance of unlabeled RNA sequence data due to the emergence of high-throughput wet-lab experimental procedures, such as next-generation sequencing. RESULTS: This paper explores the application of semi-supervised machine learning for miRNA classification in order to maximize the utility of both labeled and unlabeled data. We here present the novel combination of two semi-supervised approaches: active learning and multi-view co-training. Results across six diverse species show that this multi-stage semi-supervised approach is able to improve classification performance using very small numbers of labeled instances, effectively leveraging the available unlabeled data. CONCLUSIONS: The proposed semi-supervised miRNA classification pipeline holds the potential to identify novel miRNA with high recall and precision while requiring very small numbers of previously known miRNA. Such a method could be highly beneficial when studying miRNA in newly sequenced genomes of niche species with few known examples of miRNA.

A novel dual and triple shifted RSVP paradigm for P300 speller.

BACKGROUND: A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen. NEW METHOD: Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character twice and three times respectively, compared to the one-time appearance in the single paradigm. To compare the named paradigms, three subjects participated in experiments using all three paradigms. RESULTS: Offline results demonstrate an average character detection accuracy of 97% for the single and double protocols, and 80% for the Triple paradigm. In addition, average ITR is calculated to be 5.45, 7.62 and 7.90 bit/min for the single, Dual and Triple paradigms respectively. Results identify the Dual RSVP paradigm as the most suitable approach that provides the best balance between ITR and character detection accuracy. COMPARISON WITH EXISTING METHODS: The novel speller system (the Dual paradigm) suggested in this paper demonstrates improved performance compared to existing methods, and overcomes the gaze dependency issue. CONCLUSIONS: Overall, our novel method is a reliable alternative that both removes limitations for users suffering from impaired oculomotor control and improves performance.

Multi-view Co-training for microRNA Prediction.

MicroRNA (miRNA) are short, non-coding RNAs involved in cell regulation at post-transcriptional and translational levels. Numerous computational predictors of miRNA been developed that generally classify miRNA based on either sequence- or expression-based features. While these methods are highly effective, they require large labelled training data sets, which are often not available for many species. Simultaneously, emerging high-throughput wet-lab experimental procedures are producing large unlabelled data sets of genomic sequence and RNA expression profiles. Existing methods use supervised machine learning and are therefore unable to leverage these unlabelled data. In this paper, we design and develop a multi-view co-training approach for the classification of miRNA to maximize the utility of unlabelled training data by taking advantage of multiple views of the problem. Starting with only 10 labelled training data, co-training is shown to significantly (p < 0.01) increase classification accuracy of both sequence- and expression-based classifiers, without requiring any new labelled training data. After 11 iterations of co-training, the expression-based view of miRNA classification experiences an average increase in AUPRC of 15.81% over six species, compared to 11.90% for self-training and 4.84% for passive learning. Similar results are observed for sequence-based classifiers with increases of 46.47%, 39.53% and 29.43%, for co-training, self-training, and passive learning, respectively. The final co-trained sequence and expression-based classifiers are integrated into a final confidence-based classifier which shows improved performance compared to both the expression (1.5%, p = 0.021) and sequence (3.7%, p = 0.006) views. This study represents the first application of multi-view co-training to miRNA prediction and shows great promise, particularly for understudied species with few available training data.

Skin Tissue Substitutes and Biomaterial Risk Assessment and Testing.

Tremendous progress has been made over the past few decades to develop skin substitutes for the management of acute and chronic wounds. With the advent of tissue engineering and the ability to combine advanced manufacturing technologies with biomaterials and cell culture systems, more biomimetic tissue constructs have been emerged. Synthetic and natural biomaterials are the main constituents of these skin-like constructs, which play a significant role in tissue grafting, the body's immune response, and the healing process. The act of implanting biomaterials into the human body is subject to the body's immune response, and the complex nature of the immune system involves many different cell types and biological processes that will ultimately determine the success of a skin graft. As such, a large body of recent studies has been focused on the evaluation of the performance and risk assessment of these substitutes. This review summarizes the past and present advances in in vitro, in vivo and clinical applications of tissue-engineered skins. We discuss the role of immunomodulatory biomaterials and biomaterials risk assessment in skin tissue engineering. We will finally offer a roadmap for regulating tissue engineered skin substitutes.

Controlling Differentiation of Stem Cells for Developing Personalized Organ-on-Chip Platforms.

Organ-on-chip (OOC) platforms have attracted attentions of pharmaceutical companies as powerful tools for screening of existing drugs and development of new drug candidates. OOCs have primarily used human cell lines or primary cells to develop biomimetic tissue models. However, the ability of human stem cells in unlimited self-renewal and differentiation into multiple lineages has made them attractive for OOCs. The microfluidic technology has enabled precise control of stem cell differentiation using soluble factors, biophysical cues, and electromagnetic signals. This study discusses different tissue- and organ-on-chip platforms (i.e., skin, brain, blood-brain barrier, bone marrow, heart, liver, lung, tumor, and vascular), with an emphasis on the critical role of stem cells in the synthesis of complex tissues. This study further recaps the design, fabrication, high-throughput performance, and improved functionality of stem-cell-based OOCs, technical challenges, obstacles against implementing their potential applications, and future perspectives related to different experimental platforms.

Evaluation of the accuracy of various dose calculation algorithms of a commercial treatment planning system in the presence of hip prosthesis and comparison with Monte Carlo.

PURPOSE: High atomic number elements are commonly used in a hip prosthesis which can cause uncertainty in accurate dose calculations in radiation therapy. The aim of this study is to assess the accuracy of the three various algorithms of ISOgray treatment planning system in the presence of hip prosthesis by Monte Carlo (MC). MATERIALS AND METHODS: A MC model of Siemens PRIMUS linear accelerator has been built and verified by the measured data of the different algorithms of ISOgray treatment planning systems (TPS) in 6 and 15 MV photon beam energies. Two types of hip prosthesis have been used: stainless steel and titanium. The accuracy of mentioned dose calculation algorithms in the presence of hip prosthesis was evaluated. RESULTS: There were 24.78%, 27.68%, and 27.72% errors in fast Fourier transform (FFT) Convolution, collapsed cone (CC), and superposition in 6 MV photon beam and 26.45%, 30.45%, and 28.63% in 15 MV photon beam for titanium type, respectively. However, there were 32.84%, 35.89%, and 35.57% in 6 MV photon beam and 38.81%, 47.31%, and 39.91% errors in 15 MV photon beam in steel type, respectively. In addition, the ISOgray TPS algorithms are not able to predict the dose enhancement and reduction at the proximal and distal prosthesis interfaces, respectively. CONCLUSIONS: Hip prosthesis creates a considerable disturbance in dose distribution which cannot be predicted accurately by the FFT convolution, CC, and superposition algorithms. It is recommended to use of MC-based TPS for the treatment fields including the hip prosthesis.