The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion: We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion: The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Waste Coffee Ground-Derived Porous Carbon for Neurochemical Detection.

We present an optimized synthetic method for repurposing coffee waste to create controllable, uniform porous carbon frameworks for biosensor applications to enhance neurotransmitter detection with fast-scan cyclic voltammetry. Harnessing porous carbon structures from biowastes is a common practice for low-cost energy storage applications; however, repurposing biowastes for biosensing applications has not been explored. Waste coffee ground-derived porous carbon was synthesized by chemical activation to form multivoid, hierarchical porous carbon, and this synthesis was specifically optimized for porous uniformity and electrochemical detection. These materials, when modified on carbon-fiber microelectrodes, exhibited high surface roughness and pore distribution, which contributed to significant improvements in electrochemical reversibility and oxidative current for dopamine (3.5 ± 0.4-fold) and other neurochemicals. Capacitive current increases were small, showing evidence of small increases in electroactive surface area. Local trapping of dopamine within the pores led to improved electrochemical reversibility and frequency-independent behavior. Overall, we demonstrate an optimized biowaste-derived porous carbon synthesis for neurotransmitter detection for the first time and show material utility for viable neurotransmitter detection within a tissue matrix. This work supports the notion that controlled surface nanogeometries play a key role in electrochemical detection.

Selenophene Substitution Enabled High-Performance n-Type Polymeric Mixed Ionic-Electronic Conductors for Organic Electrochemical Transistors and Glucose Sensors.

High-performance n-type polymeric mixed ionic-electronic conductors (PMIECs) are essential for realizing organic electrochemical transistors (OECTs)-based low-power complementary circuits and biosensors, but their development still remains a great challenge. Herein, by devising two novel n-type polymers (f-BTI2g-SVSCN and f-BSeI2g-SVSCN) containing varying selenophene contents together with their thiophene-based counterpart as the control, it is demonstrated that gradually increasing selenophene loading in polymer backbones can simultaneously yield lowered lowest unoccupied molecular orbital levels, boosted charge-transport properties, and improved ion-uptake capabilities. Therefore, a remarkable volumetric capacitance (C*) of 387.2 F cm-3 and a state-of-the-art OECT electron mobility (µe,OECT ) of 0.48 cm2 V-1 s-1 are synchronously achieved for f-BSeI2g-SVSCN having the highest selenophene content, yielding an unprecedented geometry-normalized transconductance (gm,norm ) of 71.4 S cm-1 and record figure of merit (µC*) value of 191.2 F cm-1 V-1 s-1 for n-type OECTs. Thanks to such excellent performance of f-BSeI2g-SVSCN-based OECTs, a glucose sensor with a remarkably low detection limit of 10 nMm and decent selectivity is further implemented, demonstrating the power of selenophene substitution strategy in enabling high-performance n-type PMIECs for biosensing applications.

Calibration-free electrochemical sensor to monitor factor-Xa inhibitors at the point-of-care anticoagulation therapy.

This article presents a novel proof of concept for the blood plasma quantification of clinically relevant concentrations of direct oral anticoagulants, DOACs, including rivaroxaban and edoxaban, as well as low-molecular-weight heparins, LMWHs, such as enoxaparin and dalteparin, utilising a calibration-free disposable electrochemical sensor with co-facing electrodes. A dose-response curve was generated for rivaroxaban and edoxaban to demonstrate the sensor's ability to detect ≥9.00 ng mL-1 rivaroxaban and quantify it in the 11.0-140 ng mL-1 range. Similarly, the lower detection limit for edoxaban was 12.9 ng mL-1, with a quantification range of 16.8-140 ng mL-1. The significance of this sensor lies in its ability to quantify rivaroxaban and edoxaban below 30 ng mL-1, which is crucial in emergency care centres when patients undergoing DOAC therapy require emergency surgery or reversal of DOACs due to bleeding or ischemic stroke. Furthermore, the sensor can detect ≥0.016 IU mL-1 enoxaparin and ≥0.013 IU mL-1 dalteparin and quantify them in the 0.025-0.75 and 0.019-0.75 IU mL-1 range, respectively. Additionally, a dose-response curve was presented to demonstrate the potential ability of this sensor to quantify factor-Xa inhibitors independently of which DOACs or LMWHs are used. With the assay completed in less than 30 s using a minimal volume of 7 µL sample, the possibility to work at physiological pH and under calibration-free format makes this assay an excellent candidate for point-of-care testing.

Low-Cost Plant-Based Metal and Metal Oxide Nanoparticle Synthesis and Their Use in Optical and Electrochemical (Bio)Sensors.

Technological progress has led to the development of analytical tools that promise a huge socio-economic impact on our daily lives and an improved quality of life for all. The use of plant extract synthesized nanoparticles in the development and fabrication of optical or electrochemical (bio)sensors presents major advantages. Besides their low-cost fabrication and scalability, these nanoparticles may have a dual role, serving as a transducer component and as a recognition element, the latter requiring their functionalization with specific components. Different approaches, such as surface modification techniques to facilitate precise biomolecule attachment, thereby augmenting recognition capabilities, or fine tuning functional groups on nanoparticle surfaces are preferred for ensuring stable biomolecule conjugation while preserving bioactivity. Size optimization, maximizing surface area, and tailored nanoparticle shapes increase the potential for robust interactions and enhance the transduction. This article specifically aims to illustrate the adaptability and effectiveness of these biosensing platforms in identifying precise biological targets along with their far-reaching implications across various domains, spanning healthcare diagnostics, environmental monitoring, and diverse bioanalytical fields. By exploring these applications, the article highlights the significance of prioritizing the use of natural resources for nanoparticle synthesis. This emphasis aligns with the worldwide goal of envisioning sustainable and customized biosensing solutions, emphasizing heightened sensitivity and selectivity.

Design of Remote Real-Time Monitoring and Biofeedback System to Connect Patients and Clinicians During Orthopaedic Rehabilitation.

The ORME (Orthopaedic Rehabilitation for ME) is a comprehensive solution developed by NTR Biosensors to address the issue of uncertain weight monitoring during orthopaedic rehabilitation. It consists of multiple ultra-thin force resistance sensors-equipped insole, microelectronics for data processing and transmission, a dedicated smartphone app called ORME control app, and a cloud platform called ORME PRO for remote monitoring by clinicians. The system alerts patients and clinicians to overload events by offering real-time biofeedback and providing haptic and audible cues to correct gait during telemonitoring or telerehabilitation. This minimizes the risk of new injuries and prevents overloading-related setbacks during rehabilitation, while also enabling gait analysis and plantar pressure monitoring. The ORME & ORME PRO systems present an innovative solution to enhance home care, telemonitoring, and tele-orthopaedic rehabilitation outcomes, empowering patients and specialists with an effective tool to monitor and manage the telerehabilitation process and patient-reported outcomes.

Determination of Fluoxetine in Weight Loss Herbal Medicine Using an Electrochemical Sensor Based on rGO-CuNPs.

The rising popularity of herbal medicine as a weight loss remedy, fueled by misleading propaganda, raises concerns about the manufacturing processes and potential inclusion of controlled substances such as fluoxetine (FLU). The objective of this work is to develop and evaluate the performance of an electrochemical device by modifying a glassy carbon electrode (GC) with a nanocomposite based on reduced graphene oxide (rGO) and copper nanoparticles (CuNPs) for detecting FLU in manipulated herbal medicines. Scanning electron microscopy (FEG-SEM) and cyclic voltammetry (CV) were applied for morphological and electrochemical characterization and analysis of the composite's electrochemical behavior. Under optimized conditions, the proposed sensor successfully detected FLU within the range of 0.6 to 1.6 µmol L-1, showing a limit of detection (LOD) of 0.14 µmol L-1. To determine the presence of FLU in herbal samples, known amounts of the analytical standard were added to the sample, and the analyses were performed using the standard addition method, yielding recoveries between -2.13 and 2.0%.

Magnetic Properties of Amorphous Ta/CoFeB/MgO/Ta Thin Films on Deformable Substrates with Magnetic Field Angle and Tensile Strain.

Recently, the application of cobalt iron boron (CoFeB) thin films in magnetic sensors has been widely studied owing to their high magnetic moment, anisotropy, and stability. However, most of these studies were conducted on rigid silicon substrates. For diverse applications of magnetic and angle sensors, it is important to explore the properties of ferromagnetic thin films grown on nonrigid deformable substrates. In this study, representative deformable substrates (polyimide (PI), polyethylene naphthalate (PEN), and polydimethylsiloxane (PDMS)), which can be bent or stretched, were used to assess the in-plane magnetic field angle-dependent properties of amorphous Ta/CoFeB/MgO/Ta thin films grown on deformable substrates. The effects of substrate roughness, tensile stress, deformable substrate characteristics, and sputtering on magnetic properties, such as the coercive field (Hc), remanence over saturation magnetization (Mr/Ms), and biaxial characteristics, were investigated. This study presents an unconventional foundation for exploring deformable magnetic sensors capable of detecting magnetic field angles.

Fluorescence-guided surgical system using holographic display: from phantom studies to canine patients.

Significance: Holographic display technology is a promising area of research that can lead to significant advancements in cancer surgery. We present the benefits of combining bioinspired multispectral imaging technology with holographic goggles for fluorescence-guided cancer surgery. Through a series of experiments with 43D-printed phantoms, small animal models of cancer, and surgeries on canine patients with head and neck cancer, we showcase the advantages of this holistic approach. Aim: The aim of our study is to demonstrate the feasibility and potential benefits of utilizing holographic display for fluorescence-guided surgery through a series of experiments involving 3D-printed phantoms and canine patients with head and neck cancer. Approach: We explore the integration of a bioinspired camera with a mixed reality headset to project fluorescent images as holograms onto a see-through display, and we demonstrate the potential benefits of this technology through benchtop and in vivo animal studies. Results: Our complete imaging and holographic display system showcased improved delineation of fluorescent targets in phantoms compared with the 2D monitor display approach and easy integration into the veterinarian surgical workflow. Conclusions: Based on our findings, it is evident that our comprehensive approach, which combines a bioinspired multispectral imaging sensor with holographic goggles, holds promise in enhancing the presentation of fluorescent information to surgeons during intraoperative scenarios while minimizing disruptions.

Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands.

This study presents the results of the long-term monitoring of PM10 and PM2.5 concentrations using a low-cost particle sensor installed in a suburban environment in the Canary Islands. A laser-scattering Nova Fitness SDS011 sensor was operated continuously for approximately three and a half years, which is longer than most other studies using this type of sensor. The impact of African dust outbreaks on the aerosol concentrations was assessed, showing a significant increase in both PM10 and PM2.5 concentrations during the outbreaks. Additionally, a good correlation was found with a nearby reference instrument of the air quality network of the Canary Islands' government. The correlation between the PM10 and PM2.5 concentrations, the effect of relative humidity, and the stability of the sensor were also investigated. This study highlights the potential of this kind of sensor for long-term air quality monitoring with a view to developing extensive and dense low-cost air quality networks that are complementary to official air quality networks.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System.

The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.

Synthesis of ZnO Nanoflower Arrays on a Protrusion Sapphire Substrate and Application of Al-Decorated ZnO Nanoflower Matrix in Gas Sensors.

In this study, we utilized a sapphire substrate with a matrix protrusion structure as a template. We employed a ZnO gel as a precursor and deposited it onto the substrate using the spin coating method. After undergoing six cycles of deposition and baking, a ZnO seed layer with a thickness of 170 nm was formed. Subsequently, we used a hydrothermal method to grow ZnO nanorods (NRs) on the aforementioned ZnO seed layer for different durations. ZnO NRs exhibited a uniform outward growth rate in various directions, resulting in a hexagonal and floral morphology when observed from above. This morphology was particularly evident in ZnO NRs synthesized for 30 and 45 min. Due to the protrusion structure of ZnO seed layer, the resulting ZnO nanorods (NRs) displayed a floral and matrix morphology on the protrusion ZnO seed layer. To further enhance their properties, we utilized Al nanomaterial to decorate the ZnO nanoflower matrix (NFM) using a deposition method. Subsequently, we fabricated devices using both undecorated and Al-decorated ZnO NFMs and deposited an upper electrode using an interdigital mask. We then compared the gas-sensing performance of these two types of sensors towards CO and H2 gases. The research findings indicate that sensors based on Al-decorated ZnO NFM exhibit superior gas-sensing properties compared to undecorated ZnO NFM for both CO and H2 gases. These Al-decorated sensors demonstrate faster response times and higher response rates during the sensing processes.

Retinal Prostheses: Engineering and Clinical Perspectives for Vision Restoration.

A retinal prosthesis, also known as a bionic eye, is a device that can be implanted to partially restore vision in patients with retinal diseases that have resulted in the loss of photoreceptors (e.g., age-related macular degeneration and retinitis pigmentosa). Recently, there have been major breakthroughs in retinal prosthesis technology, with the creation of numerous types of implants, including epiretinal, subretinal, and suprachoroidal sensors. These devices can stimulate the remaining cells in the retina with electric signals to create a visual sensation. A literature review of the pre-clinical and clinical studies published between 2017 and 2023 is conducted. This narrative review delves into the retinal anatomy, physiology, pathology, and principles underlying electronic retinal prostheses. Engineering aspects are explored, including electrode-retina alignment, electrode size and material, charge density, resolution limits, spatial selectivity, and bidirectional closed-loop systems. This article also discusses clinical aspects, focusing on safety, adverse events, visual function, outcomes, and the importance of rehabilitation programs. Moreover, there is ongoing debate over whether implantable retinal devices still offer a promising approach for the treatment of retinal diseases, considering the recent emergence of cell-based and gene-based therapies as well as optogenetics. This review compares retinal prostheses with these alternative therapies, providing a balanced perspective on their advantages and limitations. The recent advancements in retinal prosthesis technology are also outlined, emphasizing progress in engineering and the outlook of retinal prostheses. While acknowledging the challenges and complexities of the technology, this article highlights the significant potential of retinal prostheses for vision restoration in individuals with retinal diseases and calls for continued research and development to refine and enhance their performance, ultimately improving patient outcomes and quality of life.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Flexible Pressure Sensors for Integration into Karate Body Protector.

The increasing interest in karate has also attracted the attention of researchers, especially in combining the equipment used by practitioners with technology to prevent injuries, improve technical skills and provide appropriate scoring. Contrary to the sport of taekwondo, the development of a smart body protector in the sport of karate is still a niche field to be researched. This study focused on developing piezoresistive, textile-based pressure sensors using piezoresistive film, conductive fabric as well as different bonding materials and methods. Primarily, small-scale sensors were produced using ultrasonic welding, hot press welding and oven curing. These were characterized using a universal testing machine and specific conditioning and data-acquisition hardware combined with custom processing software. Large-scale sensors were then manufactured to be placed inside the karate body protector and characterized using cyclic testing. The conditioning circuit allows flexible gain adjustment, and it was possible to obtain a stable signal with an output of up to 0.03 V/N, an adequate signal for the tested force range. The transfer function shows some drift over the cycles, in addition to the expected hysteresis and slight nonlinearity, which can be compensated for. Finally, the configuration with the best results was tested in real practice tests; during these tests the body protector was placed on a dummy as well as on a person. The results showed that the piezoresistive textile-based pressure sensor produced is able to detect and quantify the impact of even light punches, providing an unobtrusive means for performance monitoring and score calculation for competitive practice of this sport.

CMOS Image Sensor for Broad Spectral Range with >90% Quantum Efficiency.

Even though the recent progress made in complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) has enabled numerous applications affecting our daily lives, the technology still relies on conventional methods such as antireflective coatings and ion-implanted back-surface field to reduce optical and electrical losses resulting in limited device performance. In this work, these methods are replaced with nanostructured surfaces and atomic layer deposited surface passivation. The results show that such surface nanoengineering applied to a commercial backside illuminated CIS significantly extends its spectral range and enhances its photosensitivity as demonstrated by >90% quantum efficiency in the 300-700 nm wavelength range. The surface nanoengineering also reduces the dark current by a factor of three. While the photoresponse uniformity of the sensor is seen to be slightly better, possible scattering from the nanostructures can lead to increased optical crosstalk between the pixels. The results demonstrate the vast potential of surface nanoengineering in improving the performance of CIS for a wide range of applications.

Corrigendum: The amino acids sensing and utilization in response to dietary aromatic amino acid supplementation in LPS-induced inflammation piglet model.

[This corrects the article DOI: 10.3389/fnut.2021.819835.].

Field-Deployable Determinations of Peroxide Index and Total Phenolic Content in Olive Oil Using a Promising Portable Sensor System.

Useful information about the oxidative stability of a virgin olive oil in terms of oxidation products and antioxidant compounds can be obtained by analyzing the peroxide index (PI) and total phenolic content (TPC), respectively. These quality parameters are usually determined in a chemical laboratory using expensive equipment, toxic solvents, and well-trained personnel. This paper presents a novel portable sensor system for in the field and rapid determination of PI and TPC that is particularly suited in the case of small production environments that cannot afford an internal laboratory for quality control analysis. The system is small, can be powered by both USB ports and batteries, is easy to operate, and integrates a Bluetooth module for wireless data transmission. It estimates the PI and TPC in olive oil from the measurement of the optical attenuation of an emulsion between a reagent and the sample under test. The system has been tested on a set of 12 olive oil samples (eight for calibration and four for validation), and the results have shown how the considered parameters can be estimated with good accuracy. The maximum deviation from the results obtained with the reference analytical techniques is 4.7 meq O2/kg in the case of PI and 45.3 ppm in the case of TPC for the calibration set, while it is 14.8 meq O2/kg in the case of PI and 55 ppm in the case of TPC for the validation set.