Monitoring Live Mycobacteria in Real-Time Using a Microfluidic Acoustic-Raman Platform.

Tuberculosis (TB) is the most common cause of death from an infectious disease. Although treatment has been available for more than 70 years, it still takes too long and many patients default risking relapse and the emergence of resistance. It is known that lipid-rich, phenotypically antibiotic-tolerant, bacteria are more resistant to antibiotics and may be responsible for relapse necessitating extended therapy. Using a microfluidic system that acoustically traps live mycobacteria, M. smegmatis, a model organism for M. tuberculosis we can perform optical analysis in the form of wavelength-modulated Raman spectroscopy (WMRS) on the trapped organisms. This system can allow observations of the mycobacteria for up to 8 h. By adding antibiotics, it is possible to study the effect of antibiotics in real-time by comparing the Raman fingerprints in comparison to the unstressed condition. This microfluidic platform may be used to study any microorganism and to dynamically monitor its response to many conditions including antibiotic stress, and changes in the growth media. This opens the possibility of understanding better the stimuli that trigger the lipid-rich downregulated and phenotypically antibiotic-resistant cell state.

RPCA-based thermoacoustic imaging for microwave ablation monitoring.

Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA's effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.

High-temperature PTT/CDT coordination nanoplatform realizing exacerbated hypoxia for enhancing hypoxia-activated chemotherapy to overcome tumor drug resistance.

BACKGROUND: Hypoxia-activated prodrugs present new opportunities for safe and effective tumor drug resistance therapy due to their high selectivity for hypoxic cells. However, the uneven distribution of oxygen in solid tumor and insufficient hypoxia in the tumor microenvironment greatly limit its therapeutic efficacy. RESULTS: In this paper, a novel AQ4N-Mn(II)@PDA coordination nanoplatform was designed and functionalized with GMBP1 to target drug-resistant tumor cells. Its excellent photothermal conversion efficiency could achieve local high-temperature photothermal therapy in tumors, which could not only effectively exacerbate tumor hypoxia and thus improve the efficacy of hypoxia-activated chemotherapy of AQ4N but also significantly accelerate Mn2+-mediated Fenton-like activity to enhance chemodynamic therapy. Moreover, real-time monitoring of blood oxygen saturation through photoacoustic imaging could reflect the hypoxic status of tumors during treatment. Furthermore, synergistic treatment effectively inhibited tumor growth and improved the survival rate of mice bearing orthotopic drug-resistant tumors. CONCLUSIONS: This study not only provided a new idea for PTT combined with hypoxia-activated chemotherapy and CDT for drug-resistant tumors but also explored a vital theory for real-time monitoring of hypoxia during treatment.

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines.

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System.

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

Establishment of a stress granule reporter system for evaluating in vitro colon toxicity.

Exposure to toxic molecules from food or oral medications induces toxicity in colon cells that cause various human diseases; however, in vitro monitoring systems for colon cell toxicity are not well established. Stress granules are nonmembranous foci that form in cells exposed to cellular stress. When cells sense toxic environments, they acutely and systemically promote stress granule formation, with Ras GTPase-activating protein-binding protein 1 (G3BP1) acting as a core component to protect their mRNA from abnormal degradation. Here, we knocked in green fluorescent protein (GFP)-coding sequences into the C-terminal region of the G3BP1 gene in a human colon cell line through CRISPR-Cas9-mediated homologous recombination and confirmed the formation of stress granules with the G3BP1-GFP protein in these cells under cellular stress exposure. We demonstrated the formation and dissociation of stress granules in G3BP1-GFP expressing colon cells through real-time monitoring using a fluorescence microscope. Furthermore, we validated the toxicity monitoring system in the established colon cell line by observing stress granule formation following exposure to dihydrocapsaicin, bisphenol A, and sorbitol. Taken together, we established a stress granule reporter system in a colon cell line, providing a novel assessment for the real-time monitoring of colon toxicity in response to various chemicals.

Spectroscopic detection of bioaerosols with the wibs-4+: Anthropogenic and meteorological impacts.

This research builds upon a previous study that explored the potential of the modified WIBS-4+ to selectively differentiate and detect different bioaerosol classes. The current work evaluates the influence of meteorological and air quality parameters on bioaerosol concentrations, specifically pollen and fungal spore dynamics. Temperature was found to be the most influential parameter in terms of pollen production and release, showing a strong positive correlation. Wind data analysis provided insights into the potential geographic origins of pollen and fungal spore concentrations. Fungal spores were primarily shown to originate from a westerly direction, corresponding to agricultural land use, whereas pollen largely originated from a North-easterly direction, corresponding to several forests. The influence of air quality was also analysed to understand its potential impact on the WIBS fluorescent parameters investigated. Most parameters had a negative association with fungal spore concentrations, whereas several anthropogenic influences showed notable positive correlations with daily pollen concentrations. This is attributed to similar driving forces (meteorological parameters) and geographical origins. In addition, the WIBS showed a significant correlation with anthropogenic pollutants originating from combustion sources, suggesting the potential for such modified spectroscopic instruments to be utilized as air quality monitors. By combining all meteorological and pollution data along with WIBS-4+ channel data, a set of Multiple Linear Regression (MLR) analyses were completed. Successful results with R2 values ranging from 0.6 to 0.8 were recorded. The inclusion of meteorological parameters was dependent on the spore or pollen type being examined.

Expediting online liquid chromatography for real-time monitoring of product attributes to advance process analytical technology in downstream processing of biopharmaceuticals.

The application of Process Analytical Technology (PAT) principles for manufacturing of biotherapeutics proffers the prospect of ensuring consistent product quality along with increased productivity as well as substantial cost and time savings. Although this paradigm shift from a traditional, rather rigid manufacturing model to a more scientific, risk-based approach has been advocated by health authorities for almost two decades, the practical implementation of PAT in the biopharmaceutical industry is still limited by the lack of fit-for-purpose analytical methods. In this regard, most of the proposed spectroscopic techniques are sufficiently fast but exhibit deficiencies in terms of selectivity and sensitivity, while well-established offline methods, such as (ultra-)high-performance liquid chromatography, are generally considered as too slow for this task. To address these reservations, we introduce here a novel online Liquid Chromatography (LC) setup that was specifically designed to enable real-time monitoring of critical product quality attributes during time-sensitive purification operations in downstream processing. Using this online LC solution in combination with fast, purpose-built analytical methods, sampling cycle times between 1.30 and 2.35 min were achieved, without compromising on the ability to resolve and quantify the product variants of interest. The capabilities of our approach are ultimately assessed in three case studies, involving various biotherapeutic modalities, downstream processes and analytical chromatographic separation modes. Altogether, our results highlight the expansive opportunities of online LC based applications to serve as a PAT tool for biopharmaceutical manufacturing.

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Real-time monitoring of dephosphorylation process of phosphopeptide and rapid assay of PTP1B activity based on a 100 MHz QCM biosensing platform.

The misregulation of protein phosphatases is a key factor in the development of many human diseases, notably cancers. Here, based on a 100 MHz quartz crystal microbalance (QCM) biosensing platform, the dephosphorylation process of phosphopeptide (P-peptide) caused by protein tyrosine phosphatase 1B (PTP1B) was monitored in real time for the first time and PTP1B activity was assayed rapidly and sensitively. The QCM chip, coated with a gold (Au) film, was used to immobilized thiol-labeled single-stranded 5'-phosphate-DNAs (P-DNA) through Au-S bond. The P-peptide, specific to PTP1B, was then connected to the P-DNA via chelation between Zr4+ and phosphate groups. When PTP1B was injected into the QCM flow cell where the P-peptide/Zr4+/MCH/P-DNA/Au chip was placed, the P-peptide was dephosphorylated and released from the Au chip surface, resulting in an increase in the frequency of the QCM Au chip. This allowed the real-time monitoring of the P-peptide dephosphorylation process and sensitive detection of PTP1B activity within 6 min with a linear detection range of 0.01-100 pM and a detection limit of 0.008 pM. In addition, the maximum inhibitory ratios of inhibitors were evaluated using this proposed 100 MHz QCM biosensor. The developed 100 MHz QCM biosensing platform shows immense potential for early diagnosis of diseases related to protein phosphatases and the development of drugs targeting protein phosphatases.

Recent progress in online detection methods of bioaerosols.

The aerosol transmission of coronavirus disease in 2019, along with the spread of other respiratory diseases, caused significant loss of life and property; it impressed upon us the importance of real-time bioaerosol detection. The complexity, diversity, and large spatiotemporal variability of bioaerosols and their external/internal mixing with abiotic components pose challenges for effective online bioaerosol monitoring. Traditional methods focus on directly capturing bioaerosols before subsequent time-consuming laboratory analysis such as culture-based methods, preventing the high-resolution time-based characteristics necessary for an online approach. Through a comprehensive literature assessment, this review highlights and discusses the most commonly used real-time bioaerosol monitoring techniques and the associated commercially available monitors. Methods applied in online bioaerosol monitoring, including adenosine triphosphate bioluminescence, laser/light-induced fluorescence spectroscopy, Raman spectroscopy, and bioaerosol mass spectrometry are summarized. The working principles, characteristics, sensitivities, and efficiencies of these real-time detection methods are compared to understand their responses to known particle types and to contrast their differences. Approaches developed to analyze the substantial data sets obtained by these instruments and to overcome the limitations of current real-time bioaerosol monitoring technologies are also introduced. Finally, an outlook is proposed for future instrumentation indicating a need for highly revolutionized bioaerosol detection technologies.

Biomimetic Materials for Skin Tissue Regeneration and Electronic Skin.

Biomimetic materials have become a promising alternative in the field of tissue engineering and regenerative medicine to address critical challenges in wound healing and skin regeneration. Skin-mimetic materials have enormous potential to improve wound healing outcomes and enable innovative diagnostic and sensor applications. Human skin, with its complex structure and diverse functions, serves as an excellent model for designing biomaterials. Creating effective wound coverings requires mimicking the unique extracellular matrix composition, mechanical properties, and biochemical cues. Additionally, integrating electronic functionality into these materials presents exciting possibilities for real-time monitoring, diagnostics, and personalized healthcare. This review examines biomimetic skin materials and their role in regenerative wound healing, as well as their integration with electronic skin technologies. It discusses recent advances, challenges, and future directions in this rapidly evolving field.

A Comprehensive Exploration of Caspase Detection Methods: From Classical Approaches to Cutting-Edge Innovations.

The activation of caspases is a crucial event and an indicator of programmed cell death, also known as apoptosis. These enzymes play a central role in cancer biology and are considered one promising target for current and future advancements in therapeutic interventions. Traditional methods of measuring caspase activity such as antibody-based methods provide fundamental insights into their biological functions, and are considered essential tools in the fields of cell and cancer biology, pharmacology and toxicology, and drug discovery. However, traditional methods, though extensively used, are now recognized as having various shortcomings. In addition, these methods fall short of providing solutions to and matching the needs of the rapid and expansive progress achieved in studying caspases. For these reasons, there has been a continuous improvement in detection methods for caspases and the network of pathways involved in their activation and downstream signaling. Over the past decade, newer methods based on cutting-edge state-of-the-art technologies have been introduced to the biomedical community. These methods enable both the temporal and spatial monitoring of the activity of caspases and their downstream substrates, and with enhanced accuracy and precision. These include fluorescent-labeled inhibitors (FLIs) for live imaging, single-cell live imaging, fluorescence resonance energy transfer (FRET) sensors, and activatable multifunctional probes for in vivo imaging. Recently, the recruitment of mass spectrometry (MS) techniques in the investigation of these enzymes expanded the repertoire of tools available for the identification and quantification of caspase substrates, cleavage products, and post-translational modifications in addition to unveiling the complex regulatory networks implicated. Collectively, these methods are enabling researchers to unravel much of the complex cellular processes involved in apoptosis, and are helping generate a clearer and comprehensive understanding of caspase-mediated proteolysis during apoptosis. Herein, we provide a comprehensive review of various assays and detection methods as they have evolved over the years, so to encourage further exploration of these enzymes, which should have direct implications for the advancement of therapeutics for cancer and other diseases.

Review of the Real-Time Monitoring Technologies for Lithium Dendrites in Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) have the advantage of high energy density, which has attracted the wide attention of researchers. Nevertheless, the growth of lithium dendrites on the anode surface causes short life and poor safety, which limits their application. Therefore, it is necessary to deeply understand the growth mechanism of lithium dendrites. Here, the growth mechanism of lithium dendrites is briefly summarized, and the real-time monitoring technologies of lithium dendrite growth in recent years are reviewed. The real-time monitoring technologies summarized here include in situ X-ray, in situ Raman, in situ resonance, in situ microscopy, in situ neutrons, and sensors, and their representative studies are summarized. This paper is expected to provide some guidance for the research of lithium dendrites, so as to promote the development of LIBs.

Development of an online hydrogen fuel quality analyzer with gas chromatography-pulsed discharge helium ionization detector for applying hydrogen infrastructures.

Hydrogen fuel, which is essential for the hydrogen economy, including hydrogen cell vehicles, must be of high quality for optimal hydrogen cell use. Currently, hydrogen fuel quality control is mainly done by offline analysis with periodic sampling. However, with the anticipated surge in hydrogen charging stations, there's a pressing need for cost-effective, high-throughput online analysis systems. Additionally, the miniaturization of these analytical instruments for field application is also a challenge. In this study, we present a compact, real-time hydrogen fuel analyzer based on gas chromatography with a pulsed discharge helium ionization detector. Its dual-column system efficiently analyzes major impurities in hydrogen fuel in less than 30 min. Indicator species (CO, CO2, CH4, O2, N2, and additional hydrogen sulfide [H2S]) are determined by examining hydrogen production and supply processes. The analyzer's measurement capability is consistent with µmol/mol-level analysis, providing valuable real-time information for hydrogen infrastructure managers. Additionally, it can analyze H2S, a crucial marker of sulfur compounds acting as catalytic poisons in fuel cells. This real-time analyzer offers efficient, informed decision-making support for hydrogen infrastructure managers, enhancing the overall reliability of hydrogen fuel in fuel-cell electric vehicles.

Intelligent electrospinning nanofibrous membranes for monitoring and promotion of the wound healing.

The incidence of chronic wound healing is promoted by the growing trend of elderly population, obesity, and type II diabetes. Although numerous wound dressings have been studied over the years, it is still challenging for many wound dressings to perfectly adapt to the healing process due to the dynamic and complicated wound microenvironment. Aiming at an optimal reproduction of the physiological environment, multifunctional electrospinning nanofibrous membranes (ENMs) have emerged as a promising platform for the wound treatment owing to their resemblance to extracellular matrix (ECM), adjustable preparation processes, porousness, and good conformability to the wound site. Moreover, profiting from the booming development of human-machine interaction and artificial intelligence, a next generation of intelligent electrospinning nanofibrous membranes (iENMs) based wound dressing substrates that could realize the real-time monitoring of wound proceeding and individual-based wound therapy has evoked a surge of interest. In this regard, general wound-related biomarkers and process are overviewed firstly and representative iENMs stimuli-responsive materials are briefly summarized. Subsequently, the emergent applications of iENMs for the wound healing are highlighted. Finally, the opportunities and challenges for the development of next-generation iENMs as well as translating iENMs into clinical practice are evaluated.

Real-time monitoring of the pH of white wine and beer with colorimetric sensor arrays (CSAs).

The real-time monitoring of the pH values of alcoholic beverages was performed with a compact wireless device based on a colorimetric detection method with the Hue (H) as the analytical signal working in a pH range of 2.50-6.50. This device represents the first colorimetric pH meter reported in the literature monitoring in real-time the pH value of colored solutions. This pH meter consists of I) a nitrocellulose membrane impregnated with a pH-sensitive gel; II) a CCD camera for color acquisition; III) an electronic board with the calibration profiles of H vs. pH, and IV) a display to read the measured pH. It was applied to the pH determination of a white wine, a prosecco white wine, and a double malt beer leading to the values of pHwine= 3.30, pHprosecco= 3.33, pHbeer = 4.29. The analytical performance is comparable to the glass electrode with an accuracy error ≤ 0.05 pH units.

A remote-controlled portable workstation for highly sensitive and real-time chemiluminescent detection of cadmium.

The prevention of pollution requires real-time monitoring of cadmium (Cd2+) concentration in the food, as it has a dramatic impact on poultry and can pose a threat to human health. Here, we fabricate a portable workstation integrating a microfluidic chip that facilitates real-time monitoring of Cd2+ levels in real samples by utilizing the Luminol-KMnO4 chemiluminescence (CL) system. Interestingly, Cd2+ can significantly enhance the CL signal, resulting in sensitive detection of Cd2+ in the range of 0-0.18 mg/L with the limit of detection (LOD) of 0.207 µg/L. Furthermore, a remote-controlled unit is integrated into the portable workstation to form a remote-controlled portable workstation (RCPW) performing automated point-of-care testing (POCT) of Cd2+. The as-prepared strategy allows remote control of RCPW to avoid long-distance transportation of samples to achieve real-time target monitoring. Consequently, this system furnishes RCPW for monitoring Cd2+ levels in real samples, thereby holding potential for applications in preventing food pollution.

Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.

Detecting and quantifying changes in growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing impact. Substantial changes in SARS-CoV-2 prevalence with emergence of variants provides opportunity to investigate different methods to do this. We included PCR results from all participants in the UK's COVID-19 Infection Survey between August 2020-June 2022. Change-points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalised additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8,799,079 visits, 147,278 (1.7%) were PCR-positive. Change-points associated with emergence of major variants were estimated to occur a median 4 days earlier (IQR 0-8) in GAMs versus ISR. When estimating recent change-points using successive data periods, four change-points (4/96) identified by GAMs were not found when adding later data or by ISR. Change-points were detected 3-5 weeks after they occurred in both methods but could be detected earlier within specific subgroups. Change-points in growth rates of SARS-CoV-2 can be detected in near real-time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories both methods could be run in parallel.

Dynamic monitoring soft tissue healing via visualized Gd-crosslinked double network MRI microspheres.

By integrating magnetic resonance-visible components with scaffold materials, hydrogel microspheres (HMs) become visible under magnetic resonance imaging(MRI), allowing for non-invasive, continuous, and dynamic monitoring of the distribution, degradation, and relationship of the HMs with local tissues. However, when these visualization components are physically blended into the HMs, it reduces their relaxation rate and specificity under MRI, weakening the efficacy of real-time dynamic monitoring. To achieve MRI-guided in vivo monitoring of HMs with tissue repair functionality, we utilized airflow control and photo-crosslinking methods to prepare alginate-gelatin-based dual-network hydrogel microspheres (G-AlgMA HMs) using gadolinium ions (Gd (III)), a paramagnetic MRI contrast agent, as the crosslinker. When the network of G-AlgMA HMs degrades, the cleavage of covalent bonds causes the release of Gd (III), continuously altering the arrangement and movement characteristics of surrounding water molecules. This change in local transverse and longitudinal relaxation times results in variations in MRI signal values, thus enabling MRI-guided in vivo monitoring of the HMs. Additionally, in vivo data show that the degradation and release of polypeptide (K2 (SL)6 K2 (KK)) from G-AlgMA HMs promote local vascular regeneration and soft tissue repair. Overall, G-AlgMA HMs enable non-invasive, dynamic in vivo monitoring of biomaterial degradation and tissue regeneration through MRI, which is significant for understanding material degradation mechanisms, evaluating biocompatibility, and optimizing material design.