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The availability of high-frequency, real-time measurements of the concentrations of specific metabolites in cell culture systems will enable a deeper understanding of cellular metabolism and facilitate the application of good laboratory practice standards in cell culture protocols. However, currently available approaches to this end either are constrained to single-time-point and single-parameter measurements or are limited in the range of detectable analytes. Electrochemical aptamer-based (EAB) biosensors have demonstrated utility in real-time monitoring of analytes in vivo in blood and tissues. Here, we characterize a pH-sensing capability of EAB sensors that is independent of the specific target analyte of the aptamer sequence. We applied this dual-purpose EAB to the continuous measurement of pH and phenylalanine in several in vitro cell culture settings. The miniature EAB sensor that we developed exhibits rapid response times, good stability, high repeatability, and biologically relevant sensitivity. We also developed and characterized a leak-free reference electrode that mitigates the potential cytotoxic effects of silver ions released from conventional reference electrodes. Using the resulting dual-purpose sensor, we performed hourly measurements of pH and phenylalanine concentrations in the medium superfusing cultured epithelial tumor cell lines (A549, MDA-MB-23) and a human fibroblast cell line (MRC-5) for periods of up to 72 h. Our scalable technology may be multiplexed for high-throughput monitoring of pH and multiple analytes in support of the broad metabolic qualification of microphysiological systems.

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This paper proposes a novel multicomponent gas-sensing optical fiber probe system. It utilizes a precisely engineered Platinum-coated capillary fabricated via Atomic Layer Deposition (ALD) technology as the core for enhanced Raman spectroscopy, marking the first application of ALD in creating such a structure for gas Raman sensing. The noble metal capillary gas Raman probe demonstrates a low detection limit of 55 ppm for CO2 with a 30 s exposure time and good repeatability in multicomponent gas sensing. The capillary exhibits excellent stability, environmental resistance, and a large core diameter, enabling a rapid gas exchange rate and making it suitable for practical applications.

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The surface symmetry of the substrate plays an important role in the epitaxial high-quality growth of 2D materials; however, in-depth and in situ studies on these materials during growth are still limited due to the lack of effective in situ monitoring approaches. In this work, taking the growth of MoSe2 as an example, the distinct growth processes on Al2O3 (112¯0) and Al2O3 (0001) are revealed by parallel monitoring using in situ reflectance anisotropy spectroscopy (RAS) and differential reflectance spectroscopy (DRS), respectively, highlighting the dominant role of the surface symmetry. In our previous study, we found that the RAS signal of MoSe2 grown on Al2O3 (112¯0) initially increased and decreased ultimately to the magnitude of bare Al2O3 (112¯0) when the first layer of MoSe2 was fully merged, which is herein verified by the complementary DRS measurement that is directly related to the film coverage. Consequently, the changing rate of reflectance anisotropy (RA) intensity at 2.5 eV is well matched with the dynamic changes in differential reflectance (DR) intensity. Moreover, the surface-dominated uniform orientation of MoSe2 islands at various stages determined by RAS was further investigated by low-energy electron diffraction (LEED) and atomic force microscopy (AFM). By contrast, the RAS signal of MoSe2 grown on Al2O3 (0001) remains at zero during the whole growth, implying that the discontinuous MoSe2 islands have no preferential orientations. This work demonstrates that the combination of in situ RAS and DRS can provide valuable insights into the growth of unidirectional aligned islands and help optimize the fabrication process for single-crystal transition metal dichalcogenide (TMDC) monolayers.

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Current skin sensors or wound dressings fall short in addressing the complexities and challenges encountered in real-world scenarios, lacking adequate capability to facilitate wound repair. The advancement of methodologies enabling early diagnosis, real-time monitoring, and active regulation of drug delivery for timely comprehensive treatment holds paramount significance for complex chronic wounds. In this study, a nanocomposite hydrogel is devised for real-time monitoring of wound condition and comprehensive treatment. Tannins and siRNA containing matrix metalloproteinase-9 gene siRNA interference are self-assembled to construct a degradable nanogel and modified with bovine serum albumin. The nanogel and pH indicator are encapsulated within a dual-crosslinking hydrogel synthesized with norbornene dianhydride-modified paramylon. The hydrogel exhibited excellent shape adaptability due to borate bonding, and the click polymerization reaction led to rapid in situ curing of the hydrogel. The system not only monitors pH, temperature, wound exudate alterations, and peristalsis during wound healing but also exhibits hemostatic, antimicrobial, anti-inflammatory, and antioxidant properties, modulates macrophage polarization, and facilitates vascular tissue regeneration. This therapeutic approach, which integrates the monitoring of pathological parameters with comprehensive treatment, is anticipated to address the clinical issues and challenges associated with chronic diabetic wounds and infected wounds, offering broad prospects for application.

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Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r2 of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a 'Smart Aquaculture Analytics' web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

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Traditional methods for measuring chemical exposure have challenges in terms of obtaining sufficient data; therefore, improved methods for better assessing occupational exposure are needed. One possible approach to mitigate these challenges is to use self-monitoring methods such as sensors, diaries, or biomarkers. In the present study, a self-monitored method for measuring soot exposure, which included real-time air monitoring, a work diary, and the collection of urine samples, was evaluated. To validate the method, exposure measurements during the workday and diary entries were compared with velocities calculated from GPS tracking and the expected polycyclic aromatic hydrocarbon (PAH) metabolite patterns in urine. The method was applied with chimney sweeps, an occupational group at a high risk of many severe health outcomes and for whom effective control measures for reducing exposure are needed. In the study, 20 chimney sweeps followed a self-monitoring protocol for 8 consecutive workdays. Personal exposure to soot was measured as black carbon (BC) using micro-aethalometers. A diary was used to record the work tasks performed, and urine samples were collected and analysed for PAH metabolites. From the expected 160 full day measurements, 146 (91%) BC measurements and 149 (93%) diaries were collected. From the expected 320 urine samples, 304 (95%) were collected. The tasks noted in the diaries overlapped with information obtained from the GPS tracking of the chimney sweeps, which covered 96% of the measurement time. The PAH metabolites in urine increased during the work week. Factors believed to have positively influenced the sample collection and task documentation were the highly motivated participants and the continuous presence of trained occupational hygiene professionals during the planning of the study and throughout the measurement stage, during which they were available to inform, instruct, and address questions. In conclusion, the self-monitored protocol used in this study with chimney sweeps is a valuable and valid method that can be used to collect larger numbers of samples. This is especially valuable for occupations in which the employees are working independently and the exposure is difficult to monitor with traditional occupational hygiene methods.

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The precise prediction of hypotension is vital for advancing preemptive patient care strategies. Traditional machine learning approaches, while instrumental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative application of deep learning technologies, utilizing a sophisticated end-to-end architecture grounded in XResNet. This architecture is further enhanced by the integration of contrastive learning and a value attention mechanism, specifically tailored to analyze arterial blood pressure (ABP) waveform signals. Our approach improves the performance of hypotension prediction over the existing state-of-theart ABP model [7]. This research represents a step towards optimizing patient care, embodying the next generation of AI-driven healthcare solutions. Through our findings, we demonstrate the promise of deep learning in overcoming the limitations of conventional prediction models, thereby offering an avenue for enhancing patient outcomes in clinical settings.

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Maintaining consistent and accurate temperature is critical for the safe and effective storage of vaccines. Traditional monitoring methods often lack real-time capabilities and may not be sensitive enough to detect subtle anomalies. This paper presents a novel deep learning-based system for real-time temperature fault detection in refrigeration systems used for vaccine storage. Our system utilizes a semi-supervised Convolutional Autoencoder (CAE) model deployed on a resource-constrained ESP32 microcontroller. The CAE is trained on real-world temperature sensor data to capture temporal patterns and reconstruct normal temperature profiles. Deviations from the reconstructed profiles are flagged as potential anomalies, enabling real-time fault detection. Evaluation using real-time data demonstrates an impressive 92% accuracy in identifying temperature faults. The system's low energy consumption (0.05 watts) and memory usage (1.2 MB) make it suitable for deployment in resource-constrained environments. This work paves the way for improved monitoring and fault detection in refrigeration systems, ultimately contributing to the reliable storage of life-saving vaccines.

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Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

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Monitoring while drilling (MWD) is a crucial task in mining operations. Accurately measuring drill and rock-related operating parameters can significantly reduce the cost of drilling operations. This study explores the potential of monitoring drilling specific energy (SE) and optimizing drilling operations by processing vibroacoustic signals generated while drilling. For this purpose, 30 samples of different rocks, are used for drilling tests. During the drilling process, the acoustic and vibration signals are recorded and analyzed in the time, frequency, and time-frequency domains., and parameters related to the resulting spectra are extracted. After obtaining the vibroacoustic parameters for drilling, the relationship between them and the drilling SE was investigated. There is evidence that the progression of SE contributes to the magnitude of rock drilling vibroacoustic features, which could be employed to indicate energy conditions during drilling. Results obtained in this study have the potential to be used as the basis for an industrial monitoring system that can detect excessive energy consumption and advise the user of the end of the bit's useful life. This method can be an intelligent technique for measuring the behavior of real-time drilling operations based on the SE simply by installing vibroacoustic sensors on the drilling machines.

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Biopolymer gels have gained tremendous potential for therapeutic applications due to their biocompatibility, biodegradability, and ability to adsorb and bind biological fluids, making them attractive for drug delivery and therapy. In this study, the versatility of biopolymer gels is explored in theranostic backgrounds, with a focus on integrating imaging features and facilitating real-time monitoring of drug delivery. Different methods of delivery are explored for incorporating imaging agents into biopolymer gels, including encapsulation, surface functionalization, nanoparticle encapsulation, and layer-by-layer assembly techniques. These methods exhibit the integration of agents and real-time monitoring drug delivery. We summarize the synthesis methods, general properties, and functional mechanisms of biopolymer gels, demonstrating their broad applications as multimodal systems for imaging-based therapeutics. These techniques not only enable multiple imaging but also provide signal enhancement and facilitate imaging targets, increasing the diagnostic accuracy and therapeutic efficacy. In addition, current techniques for incorporating imaging agents into biopolymer gels are discussed, as well as their role in precise drug delivery and monitoring.

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BACKGROUND: We report the results of a retrospective analysis of localized prostate cancer (LPCa) treated with transperineal ultrasound image-guided radiotherapy (TPUS-IGRT). METHODS: A total of 124 patients (median age: 74 y, 46-84 y) with LPCa who underwent TPUS-IGRT (Clarity Autoscan system; CAS, Elekta; Stockholm, Sweden) between April 2016 and October 2021 for curative/after hormone induction were enrolled. The number of patients by risk (National Comprehensive Cancer Network 2019) was 7, 25, 42, and 50 for low (LR), good intermediate (good IR), poor intermediate (poor IR), and high (HR)/very high (VHR), respectively. Ninety-five patients were given neoadjuvant hormonal therapy. The planning target volume margin setting was 3 mm for rectal in most cases, 5-7 mm for superior/inferior, and 5 mm for anterior/right/left. The principle prescribed dose is 74 Gy (LR), 76 Gy (good IR), and 76-78 Gy (poor IR or above). CAS was equipped with a real-time prostate intrafraction monitoring (RTPIFM) system. When a displacement of 2-3 mm or more was detected, irradiation was paused, and the patients were placed on standby for prostate reinstatement/recorrection. Of the 3135 fractions in 85 patients for whom RTPIFM was performed, 1008 fractions (32.1%) were recorrected at least once after starting irradiation. RESULTS: A total of 123 patients completed the radiotherapy course. The 5-year overall survival rate was 95.9%. The 5-year biological prostate-specific antigen relapse-free survival rate (bPFS) was 100% for LR, 92.9% for intermediate IR, and 93.2% for HR/VHR (Phoenix method). The 5-year late toxicity rate of Grade 2+ was 7.4% for genitourinary (GU) and 6.5% for gastrointestinal (GI) organs. Comparing the ≤ 76 Gy group to the 78 Gy group for both GU and GI organs, the incidence was higher in the 78 Gy group for both groups. CONCLUSION: These results suggest that TPUS-IGRT is well tolerated, as the bPFS and incidence of late toxicity are almost comparable to those reported by other sources of image-guided radiotherapy.

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Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

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The urgent need for real-time and noninvasive monitoring of health-associated biochemical parameters has motivated the development of wearable sweat sensors. Existing electrochemical sensors show promise in real-time analysis of various chemical biomarkers. These sensors often rely on labels and redox probes to generate and amplify the signals for the detection and quantification of analytes with limited sensitivity. In this study, we introduce a molecularly imprinted polymer (MIP)-based biochemical sensor to quantify a molecular biomarker in sweat using electrochemical impedance spectroscopy, which eliminates the need for labels or redox probes. The molecularly imprinted biosensor can achieve sensitive and specific detection of cortisol at concentrations as low as 1 pM, 1000-fold lower than previously reported MIP cortisol sensors. We integrated multimodal electrochemical sensors with an iontophoresis sweat extraction module and paper microfluidics for real-time sweat analysis. Several parameters can be simultaneously quantified, including sweat volume, secretion rate, sodium ion, and cortisol concentration. Paper microfluidic modules not only quantify sweat volume and secretion rate but also facilitate continuous sweat analysis without user intervention. While we focus on cortisol sensing as a proof-of-concept, the molecularly imprinted wearable sensors can be extended to real-time detection of other biochemicals, such as protein biomarkers and therapeutic drugs.

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Real-time monitoring and tracking of extreme toxins that penetrate into living cells by using biocompatible, low-cost visual detection via fluorescent monitors are vitally essential to reduce health hazards. Herein, we report a simple engineering design of biocompatible and fluorescent sensors/trackers for real-time monitoring and ultra-trace tracking (up to ppb) of extremely toxic substances (such as arsenic species) in living cells. The biocompatible As(V) sensor (BAS) design is fabricated via successful dressing/decoration process of 2-hydroxy 5-methyl isophthalaldehyde fluorescent receptor into hierarchical organic-inorganic carriers that have micro-hollow geodes, swirled caves and nest-shaped cages, and uniform cubic structures. The BAS monitors show evidence for the selective trapping/detecting/tracking of As(V) species in biological cells (i.e., HeLa cells) despite the coexistence of highly competitive and interfered species. Our simple batch-contact sensing assays shows real-space evidence of the continuous monitoring of As(V) species in HeLa cells with ultra-sensitive detection (i.e., with a low detection limit of 0.149 ppb) and rapid recognition (i.e., in the order of seconds). Significantly, the BAS monitors did not affect the cell population and achieved low cytotoxicity and high cell viability during the monitoring/tracking process inside HeLa cells. The high biocompatibility of BAS remarkably allows precise quantification and real-time monitoring/tracking of toxicant targets in living cells.

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Although biocides are important materials in modern society and help protect human health and the environment, increasing exposure to combined biocides can cause severe side effects in the human body, such as lung fibrosis. In this study, we developed a receptonics system to screen for biocides in combined household chemical products based on biocides. The system contains transient receptor potential ankyrin 1 (TRPA1) nanovesicles (NVs) to sense biocides based on pain receptors and a side-gated field-effect transistor (SGFET) using a single-layer graphene (SLG) micropattern channel. The binding affinities between the TRPA1 receptor and the various biocides were estimated by performing biosimulation and using a calcium ion (Ca2+) assay, and the sensitivity of the system was compared with that of TRPA1 NV receptonics systems. Based on the results of the TRPA1 NV receptonics system, the antagonistic and potentiation effects of combined biocides and household chemical products depended on the concentration. Finally, the TRPA1 NV receptonics system was applied to screen for biocides in real products, and its performance was successful. Based on these results, the TRPA1 NV receptonics system can be utilized to perform risk evaluations and identify biocides in a simple and rapid manner.

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Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology's complexity, costs, fabrication and implementation challenges often hinder engineers' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-ß and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.

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Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. STATEMENT OF SIGNIFICANCE: An intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials.

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Monitoring and quantifying ATP levels in vivo is essential to understanding its role as a signaling molecule in tumor progression and therapy. Nevertheless, the real-time monitoring and quantitative assessment of lysosomal ATP remains challenging due to the lack of accurate tools in deep tissues. In this study, based on the crosslinking enhanced emission (CEE) effect, we successfully synthesized red carbon dots (R-CDs) with dual emission properties for efficient quantification of intracellular ATP. The R-CDs emit in the near-infrared range and target lysosomes with rapid detection capabilities, rendering them exceptionally well-suited for directly observing and analyzing the dynamics of lysosomal ATP through live cell imaging techniques. Importantly, R-CDs have proven their efficacy in real-time monitoring of drug stimulus-induced fluctuations in endogenous lysosomal ATP concentration and have also been employed for quantifying and distinguishing lysosomal ATP levels among normal and cancer cell lines. These noteworthy findings emphasize the versatility of the R-CD as a valuable imaging tool for elucidating the functional role of lysosomal ATP in drug screening and cancer diagnostics and hold the promise of becoming a reference tool for deepening our understanding of drug mechanisms of action.

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Water constitutes the most prevalent impurity in organic solvents, exerting significant influence on chemical reactions and potentially leading to fires and explosions, even in minute quantities. Thus, the development of convenient, rapid, and cost effective fluorescent probes for real-time monitoring of water content in organic solvents is imperative. Although some fluorescent materials have been synthesized for this purpose, most suffer from laborious preparation processes and poor cycling performance, constraining their practical application. This study investigates the impact of hydrogen bonding on the aggregation-induced emission (AIE) properties of quinoline derivatives, leveraging quinoline as the foundational scaffold and its nitrogen atom as the hydrogen bond acceptor. Research findings elucidate that intermolecular hydrogen bonding of quinoline is the primary determinant of their AIE behaviors. By harnessing the phenomenon of water molecules forming intermolecular hydrogen bonds with quinoline nitrogen atoms, we devised a straightforward and rapid method to fabricate a fluorescent test paper for real-time monitoring of water content in organic solvents. Experimental results demonstrate that even minute changes in water content, down to concentrations as low as 0.5 % by volume in organic solvents, can induce fluorescence changes in the test paper, which also exhibits favorable cycling performance. This study not only explores the influence of hydrogen bonds on the AIE properties of quinoline derivatives but also pioneers the development of a cost-effective, rapid, and recyclable test paper for real-time monitoring of water content fluctuations in organic solvents.