Activity Monitoring Research Articles

Intracellularly manipulable aggregation of the aggregation-induced emission luminogens

Biophotonics has seen significant advancements with the development of optical imaging techniques facilitating the noninvasive detection of biologically relevant species. Aggregation-induced emission (AIE) materials have emerged as a novel class of luminogens exhibiting enhanced luminescence or photodynamic efficiency in the aggregated state, making them ideal for biomedical applications. The intracellularly controlled aggregation of aggregate-induced emission luminogens (AIEgens) enables high-resolution imaging of intracellular targets and diagnosis of related diseases, and enables disease therapy by exploiting the novel properties of aggregates. This review provides an in-depth analysis of the strategies employed to modulate the aggregation of AIEgens, focusing on the importance of molecular modifications to improve hydrophilicity and achieve precise control over the intercellular aggregation of AIEgens. Furthermore, the representative applications of AIEgens in bioimaging, such as enzyme activity monitoring, protein tracking, organelle function monitoring, and in vivo tumor-specific therapeutics, are reviewed. Additionally, we outline the challenges and future opportunities for AIE research, emphasizing the importance of the strategies for realizing the precisely controllable aggregation of AIEgens inside cells and the need for extending AIEgens’ absorption and emission wavelengths. This review aims to elucidate the rational development of responsive AIEgens for advanced biomedical applications.

Visualizing PIEZO1 Localization and Activity in hiPSC-Derived Single Cells and Organoids with HaloTag Technology.

PIEZO1 is critical to numerous physiological processes, transducing diverse mechanical stimuli into electrical and chemical signals. Recent studies underscore the importance of visualizing endogenous PIEZO1 activity and localization to understand its functional roles. To enable physiologically and clinically relevant studies on human PIEZO1, we genetically engineered human induced pluripotent stem cells (hiPSCs) to express a HaloTag fused to endogenous PIEZO1. Combined with advanced imaging, our chemogenetic platform allows precise visualization of PIEZO1 localization dynamics in various cell types. Furthermore, the PIEZO1-HaloTag hiPSC technology facilitates the non-invasive monitoring of channel activity across diverse cell types using Ca 2+ -sensitive HaloTag ligands, achieving temporal resolution approaching that of patch clamp electrophysiology. Finally, we used lightsheet imaging of hiPSC-derived neural organoids to achieve molecular scale imaging of PIEZO1 in three-dimensional tissue organoids. Our advances offer a novel platform for studying PIEZO1 mechanotransduction in human cells and tissues, with potential for elucidating disease mechanisms and targeted therapeutic development.

Employing automated electrical resistivity tomography for detecting short- and long-term changes in permafrost and active-layer dynamics in the maritime Antarctic

Abstract. Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze–thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable time-lapse ERT surveys in Antarctica, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it at the Crater Lake CALM-S site on Deception Island, Antarctica, to collect quasi-continuous ERT measurements. We developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active-layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at the Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted results shows that the A-ERT system can detect the active-layer freezing and thawing events with high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost 1 decade at this site. The evolution of thaw depth exhibits a similar pattern in both years, with the active-layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT datasets, combined with the new processing workflow that we developed, are an effective tool for studying permafrost and active-layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the spatiotemporal progression of thaw depth in two dimensions, and potentially in three dimensions, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. An A-ERT monitoring setup with a longer profile length can investigate greater depths, offering effective monitoring at sites where boreholes are costly and invasive techniques are unsuitable. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and to work towards establishing an early warning system for the consequences of climate change.

Using cerebral regional oxygen saturation and amplitude-integrated electroencephalography in neonates on extracorporeal membrane oxygenation: preliminary experience from a single center

ObjectiveThis study aims to evaluate the application value in neurological outcome of cerebral regional oxygen saturation (CrSO2) and amplitude-integrated electroencephalography (aEEG) monitoring during neonatal extracorporeal membrane oxygenation (ECMO) courses.Methods We retrospectively analyzed 18 neonates receiving veno-arterial ECMO (V-A ECMO) support at our hospital from July 2021 to December 2022. Continuous monitoring of CrSO2 and brain electrical activity was conducted using near-infrared spectroscopy (NIRS) and aEEG throughout the ECMO treatment. We collected and analyzed related clinical data.ResultsAmong the 11 survivors, 5 were categorized as the normal group (N group) and 6 as the abnormal group (AN group) based on post-ECMO brain MRI outcomes. The N group exhibited shorter time percentage of significant CrSO2 reduction (> 25% from baseline or absolute value < 40%), better fractional tissue oxygen extraction (FTOE) rates, and more stable mean percentage changes in CrSO2 compared to the AN group. Neonates in the N group predominantly showed mildly abnormal aEEG readings, with one patient displaying disrupted sleep-wake cycles. This particular patient also had more significant CrSO2 reduction and poorer FTOE compared to others in the N group. Additionally, the Test of Infant Motor Performance (TIMP) scores indicated hypoevolutism in this patient before discharge, while others in the N group had normal TIMP scores. In the AN group, 4 exhibited moderate and 2 severe aEEG abnormalities; 5 had hypoevolutism TIMP scores, and 1 with moderate aEEG abnormalities maintained a normal TIMP score, exhibiting lesser CrSO2 reduction and improved FTOE.ConclusionCrSO2 and aEEG monitoring show potential as routine assessments for neurological outcomes during neonatal ECMO. In our cohort, a tendency was observed where neonates with greater reductions in CrSO2 and more severe aEEG abnormalities experienced poorer neurological outcomes.

Visual Pollution of the Banks of the Tigris River in Baghdad City Capital of Iraq

The study titled "Visual Pollution of the Tigris Riverbanks in Baghdad City" consists of two main sections: the first section is dedicated to the theoretical framework, while the second section covers the practical aspect (field study). Specific areas of Baghdad were selected for this study due to the widespread presence of solid waste along the riverbanks. The importance of the study lies in the fact that visual pollution is one of the dangerous types of pollution that affects both human health and the environment. The study concluded that visual pollution along the Tigris River banks results from the impact of human activities spread linearly along the river, and the lack of environmental awareness among residents has negatively affected the river environment through the disposal of household and construction waste and the discharge of untreated wastewater from sewage treatment plants. The research recommends the importance of strict monitoring of human activities near the river, imposing financial penalties on violators of environmental standards, and working on rehabilitating and developing the riverbanks to make them attractive environments for residents, turning them into tourist and recreational areas that serve the city.

Clustering of Child Stunting Data in Tangerang Regency Using Comparison of K-Means, Hierarchical Clustering and DBSCAN Methods

This study aims to analyze stunting in children in Tangerang Regency using clustering methods such as k-means, Hierarchical Clustering with Agglomerative Nesting, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Stunting is a significant health issue affecting child growth due to chronic malnutrition and recurrent infections. The research revealed that k-means produced the best clustering results with a Silhouette Score of 0.52, indicating its effectiveness in categorizing children based on age, nutritional status, and stunting risk. The k-means method identified three clusters: Cluster 0 (ages 46-55 months, good nutrition, no stunting), Cluster 1 (ages 9-18 months, varied nutritional status, high stunting risk), and Cluster 2 (ages 27-36 months, good nutrition, no stunting). The study suggests preventive actions such as balanced nutrition education, regular health monitoring, complete immunizations, and physical activity, alongside curative measures like nutritional consultations and supplements. The findings provide a framework for targeted preventive and curative interventions, enabling Tangerang Regency's health department to effectively address and reduce stunting rates.

Transparent RFID tag wall enabled by artificial intelligence for assisted living

Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the TransparentRFIDTag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of 95.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$95.6\\%$$\\end{document} under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

Recent Advances in Wearable Textile-Based Triboelectric Nanogenerators.

We review recent results on textile triboelectric nanogenerators (T-TENGs), which function both as harvesters of mechanical energy and self-powered motion sensors. T-TENGs can be flexible, breathable, and lightweight. With a combination of traditional and novel manufacturing methods, including nanofibers, T-TENGs can deliver promising power output. We review the evolution of T-TENG device structures based on various textile material configurations and fabrication methods, along with demonstrations of self-powered systems. We also provide a detailed analysis of different textile materials and approaches used to enhance output. Additionally, we discuss integration capabilities with supercapacitors and potential applications across various fields such as health monitoring, human activity monitoring, human-machine interaction applications, etc. This review concludes by addressing the challenges and key research questions that remain for developing viable T-TENG technology.

Towards maximization of parameters identifiability: Development of the CalOpt tool and its application to the anaerobic digestion model

This work addresses the need for robust approaches to estimate kinetic parameters in continuous anaerobic co-digestion plants. Specifically, it outlines a procedure for including data from batch activity tests on digestate samples, in addition to the poorly informative dataset from conventional monitoring of biogas facilities. To profitably make use of activity tests, the estimation of the biomass composition in digestate samples is required to improve parameter identifiability. To this purpose, an open-source tool (CalOpt) was developed and tested on a pilot-scale co-digester, simultaneously using data from both conventional monitoring and lab-scale activity tests. Targeting the prediction of volatile fatty acids (VFAs) concentrations in the digestate of the continuous co-digester, the kinetic parameters of different microbial groups were selected for calibration based on parameters importance ranking and collinearity index. The results demonstrate that the CalOpt tool significantly improved the fitting of acetic, propionic, and butyric acid concentrations in digestate as well as the methane flowrate from the digester. Furthermore, the study assessed the necessity of modifying the Monod kinetics to account for substrate inhibition by incorporating the Haldane term in the VFAs uptake kinetic model, which proved to be essential for better interpreting batch activity tests.

A wearable EMG sensor for continuous wrist neuromuscular activity for monitoring

Carpal Tunnel Syndrome (CTS) is an entrapment neuropathy that affects 3-6% of the adult population globally. About 90% of nerve-damaging diseases are labeled CTS when diagnosed, making it one of the most prominent nerve dysfunctions in the current population metrics. CTS cases have soared in a world where digital gadgets are progressively gaining traction. There is also a high onset of CTS in laborers specializing in fine motor skills. A patient with CTS typically entails symptoms of pain, numbness, and tingling in the wrist. However, accurate diagnosis and consistent checkups for CTS are becoming a bigger issue. In this research paper, we demonstrate the utilization of various diagnostic tests in supporting CTS diagnosis and the necessity for a monitoring system to track wrist neuromuscular activity. By analyzing traditional assessment techniques and identifying their limitations, we can enhance diagnosis, making it easier for patients to deal with the condition. This approach has culminated in the development of our prototype, BracEMG. Through a wearable EMG sensor, our prototype aims to track possible signs of CTS or other nerve dysfunctions at the wrist. The findings gained through an EMG test support our research by outlining the prevalence of CTS across different age groups and illustrating the levels of muscular activity in a graphical format. Our results demonstrate overall muscle activity at the wrist. Since no universally accepted standard for diagnosing CTS exists, we envision our work as the starting point for more extensive research. The limitations of our prototype can become the foundation for future developments in the industry revolving around CTS.

Patient engagement as a collaborative process in a large Dutch COVID-19 vaccination study (RECOVAC) – insight into the contribution of patient engagement and learnings for the future

BackgroundThe need for patient engagement in health research has been increasingly acknowledged and accepted in recent years. However, implementation is still limited due to lack of evidence on its value and lack of guidance on how to implement patient engagement. This study aims to provide insight into the contribution of patient engagement in the RECOVAC project, which studied COVID-19 vaccination in kidney patients, and formulate concrete practice-based action perspectives for patient engagement.MethodsWe used a qualitative participatory mixed methods approach, based on the Patient Engagement Monitoring and Evaluation (PEME) framework. Patient engagement and data collection were based on the Reflexive Monitoring in Action (RMA) approach. Data collection included participant observations, open ended questionnaires and interactive reflection sessions. Qualitative analysis was done via a thematic approach.ResultsWe have described the process of patient engagement systematically, provided insight in its value and found that there is a need for clear aims, expectations and preparations from the start of the engagement process. We have shown that reflection throughout the process is of utmost importance and the same applies to clear communication between researchers and patient representatives. By being part of the consortium patient representatives had direct access to information, straight from the source, on for example the vaccination schedule and medication availability and had indirect influence on decisions made by the National Institute for Public Health and the Environment (RIVM) on preventive measures and treatment against COVID-19. Having experienced patient representatives is important, otherwise training needs to be provided. We also found that patient engagement had impact on conduct and outcomes of research activities itself and may have impact on future research and patient engagement activities in general.ConclusionPatient engagement has changed the course of the project. Concrete practice-based action perspectives have been formulated, which are already being implemented by the Dutch Kidney Patients Association (NVN). Studying patient engagement in a high pace project with high public interest has resulted in lessons learned and will help prepare and implement patient involvement in future research projects.Clinical trial registrationThe RECOVAC studies in which the patient engagement took place are registered at clinicialtrial.gov (NCT04741386 registration date 2021-02-04, NCT04841785 registration date 2021-03-22 and NCT05030974 registration date 2021-08-20).

A geospatial approach to identifying and mapping areas of relative environmental pressure on ecosystem integrity

The influence of increasing anthropogenic pressure on ecosystem integrity, such as land use change, is resulting in many ecosystems experiencing a decline in their ability to maintain balanced functions and services. Identifying and quantifying these pressures over different scales is challenging and thus impacting the achievement or maintenance of key environmental outcomes. In this study, a GIS-based and scalable tool was developed, the Relative Environmental Pressure (REP) Tool, to address these challenges. The REP tool combines an ecosystem integrity conceptual framework with a weighted linear combination analysis to quantify and rank relative environmental pressure across the scale of interest. The REP Tool was developed as an automated Python-based model in a PyCharm working environment using ArcGIS Pro Arcpy scripting. The REP Tool was applied to spatially contiguous geospatial data for the Province of Alberta, Canada and dynamically scaled relative to Hydrologic Unit Codes at level 8 (HUC8) along with regional and sub-regional scale sub-watersheds. Both cumulative and individual relative pressure levels were calculated and mapped for specific ecosystem integrity framework-derived Environmental Pressure Groups (EPGs) including Atmospheric Alteration, Sedimentation, Habitat Alteration, Hydrologic Alteration, and Social Pressure. Data driven Jenks natural breaks were then applied to classify the relative environmental pressures into a nine-level ranking system. The resulting visualization and data outputs from the REP Tool clearly show that the highest cumulative relative environmental pressure values align with the distribution of major population centres, zones of intense agriculture and major industrial activity. These regions reflect the physiography of Alberta with the Rocky Mountain and Boreal natural regions dominated by low relative environmental pressure. As scales become smaller and more refined, the location of the higher relative environmental pressure levels typically become more subdivided with greater spatial precision where higher pressured areas are located. These patterns are repeated when looking at individual EPGs but with enhanced differentiation of pressure as scales are refined. The framework and geospatial science driven approach behind the REP Tool can be universally applied to support enhanced understanding of relative environmental pressures in, or between regions, as well as informing adaptive environmental resource management and monitoring activities.

Geodetic monitoring of the recent activity and the dome forming eruption at Nevado del Ruiz (Colombia), 2010–2023

Nevado del Ruiz (Colombia) is infamous for the catastrophic eruption of 1985 that destroyed the villages of Armero and Chinchiná. However, this was not the volcano’s first destructive event; similar eruptions also occurred in 1595 and 1845. In 1985, the limited geodetic data available failed to provide a clear warning of the impending eruption. Since then, advancement in geodetic monitoring, now incorporating tilt and satellite geodesy, along with improvements in seismic, geochemical, geological and remote sensing monitoring, have enhanced hazards assessment and mitigated the risk during subsequent eruptions in 1989, 2012, and 2015–2019, as well as during periods of unrest over the last 13 years. Modeling of deformation data over the past 13 years reveals complex interactions between the local, shallow magmatic system beneath Nevado del Ruiz and a deep, regional magmatic system beneath Nevado de Santa Isabel, 9 km southwest of Nevado del Ruiz. Before February 2012, the volcano deflated because of the depressurization of the local shallow reservoir. This same reservoir later fueled ash emissions and gas release (2012–2023), and a dome-forming eruption (2015–2019). In contrast, the inflation observed from 2012 to 2023 is linked to the pressurization of the deep reservoir beneath the Nevado de Santa Isabel.

Imaging in vasculitis.

Systemic vasculitides are characterized by inflammation of blood vessels. Their classification is based on the size of the blood vessels involved - large, medium, or small. Vasculitis early diagnosis and reliable monitoring are crucial to establish a treatment plan and prevent serious complications. Based on these considerations and depending on the location of the affected vessels, the importance of imaging modalities including ultrasonography (US), magnetic resonance Imaging (MRI), magnetic resonance angiography (MRA), computed tomography (CT), computed tomography angiography (CTA), and [18F]-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) has progressively increased. In addition to physical exam and laboratory data, these imaging tools offer complementary information about vascular changes occurring in vasculitis.This review summarizes the different imaging modalities being utilized to diagnose and monitor vasculitis. The most recent update for the use of imaging in vasculitis is referenced in the 2023 European Alliance of Associations for Rheumatology (EULAR) recommendations and the American College of Rheumatology (ACR) guidelines in 2021. Recent advances in PET imaging in large vessel vasculitis include improved technological imaging acquisition and the use of novel radiotracers for cellular and immune targets. FDG-PET has now been demonstrated to have high sensitivity and specificity to detect temporal arteritis. Imaging plays a significant role in the evaluation of vasculitis and continues to gain importance in the diagnosis and monitoring of disease activity. Differences exist between the ACR guidelines, which advocates for temporal artery biopsy, and the EULAR guidelines, which favors imaging modalities for the initial evaluation and diagnosis of large vessel vasculitis (LVV). Prerequisites for appropriate clinical management utilizing imaging in patients with vasculitis are the availability and access to skilled clinicians to interpret the images and the cost of these techniques not being prohibitive.

Alkylated MXene-Carbon Nanotube/Microfiber Composite Material with Flexible, Superhydrophobic, and Sensing Properties.

Superhydrophobic strain sensors are highly promising for human motion and health monitoring in wet environments. However, the introduction of superhydrophobicity inevitably alters the mechanical and conductive properties of these sensors, affecting sensing performance and limiting behavior monitoring. Here, we developed an alkylated MXene-carbon nanotube/microfiber composite material (AMNCM) that is simultaneously flexible, superhydrophobic, and senses properties. Comprising a commercially available fabric substrate that is coated with a functional network of alkylated MXene/multi-walled carbon nanotubes and epoxy-silicone oligomers, the AMNCM offers high mechanical and chemical robustness, maintaining high conductivity and strain sensing properties. Furthermore, the AMNCM strain sensor achieves a gauge factor of up to 51.68 within a strain range of 80-100%, and exhibits rapid response times (125 ms) and long-term stability under cyclic stretching, while also displaying superior direct/indirect anti-fouling capabilities. These properties position the AMNCM as a promising candidate for next-generation wearable devices designed for advanced environmental interactions and human activity monitoring.

Sequence analysis and process mining perspectives to goal setting: What distinguishes business students with high and low self-efficacy beliefs?

This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.

Optimal antioxidant enzyme activity estimation in melon plant leaves based on microhyperspectral imaging technique

BackgroundIn order to select the most suitable type of light for plant growth, we have developed a quantitative model for the detection of antioxidant enzyme activities on melon leaves, which allows us to monitor plant growth in a timely and accurate manner. Leaf of melon ‘M5147’ and ‘M5346’ were used as test material. Six treatments have been established with different illuminance ratios. By the measurement of leaf superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities, the varying response of melons leaf to different grades of light have been studied using microscopic hyperspectral imaging at the plant factory. ResultThe original spectrum has been pre-processed and optimized. And then the characteristic wavelengths were extracted by ant colony optimization (ACO), iterative retained information variable (IRIV), un-information variable elimination (UVE) and genetic partial least squares (GAPLS). Partial Least Squares Regression (PLSR), least squares support vector machine (LSSVM) and convolutional neural network (CNN) are built based on the optimal feature wavelengths. T4 treatment (Light ratio: 7R/3B/5W/1UVa, Flux: μmol (m2-s), photoperi 12 h) was found to be optimum. In the pre-processing of the raw spectral data, orthogonal signal correction (OSC) methods were selected for SOD, POD and CAT. The best performance of SOD prediction model was constructed based on GAPLS-CNN (Rc = 0.751, Rp = 0.638); The best performance of POD prediction model was constructed based on UVE-CNN (Rc = 0.753, Rp = 0.530); The best performance of CAT prediction model constructed based on ACO-CNN (Rc = 0.742, Rp = 0.504). SignificanceThe aim of this study was to provide technical support for the dynamic monitoring of antioxidant enzyme activity in the leaves of other plants by establishing a quantitative detection model for antioxidant enzyme activity in melon leaves by combining chemical methods with microscopic hyperspectral imaging technology. This work provides data and a theoretical basis for screening light ratios in other plants and offers technical guidance for dynamically monitoring antioxidant enzyme activities in their leaves.

In–situ self–reduction preparation of Ti3C2Tx/Ag on flexible PMMA chip for quantitative detection of SARS–CoV–2

Surface enhanced Raman scattering (SERS) has proven to be of great superiority in the assay and prevention of infectious disease attributed to its rapid and specific fingerprint recognition ability toward trace molecules. However, it is still crucial to develop portable and precise chip for the reliable realization of on–site and large–scale screening. Here, a robust in–situ reduction strategy was employed to prepare Ti3C2Tx@Ag nanocomposites, which was installed on a polymethyl methacrylate (PMMA) matrix to construct a novel flexible SERS chip with self–rectification capability. The resulting Ti3C2Tx@Ag nanocomposites exhibited both electromagnetic and chemical enhancement effects, enabling high SERS activity. In particular, the as–developed SERS chip demonstrated fascinating signal reproducibility and quantitative detection capability by conducting the intrinsic Raman signal of PMMA as an internal standard. Furthermore, the PMMA–Ti3C2Tx@Ag chip facilitated the visualization of severe acute respiratory syndrome coronavirus 2 (SARS–CoV–2) nucleocapsid (N) with ultra–low limit of detection, stressing the potential application of this smart self–rectification SERS chip with high activity in real time and rapid monitoring of sudden infectious diseases.

Moisture-wicking Janus-structure electronic knitted fabric for multimodal wearable mechanical sensing

Multimodal mechanical sensors that can recognize bending direction and exhibit excellent wearable performance are crucial for the fields of physical exercise and healthcare. This study fabricated a knitted multimodal sensor with porous and wetting gradients using hydrophobic core-spun yarns and hydrophilic CNT/WPU conductive yarns in plain stitch and partial tuck stitch. The electronic fabric demonstrated reliable strain sensing unaffected by compression disturbances and displayed a selective response to directional bending. The sensor exhibits a 50% sensing range with a GFmax of −4.41 during strain deformation detection. In terms of bending detection, one side achieves a 10% sensing range (−51.68°) with a GFmax of −6.89, while the other side achieves a 20% sensing range (73.72°) with a GFmax of 8.64. Encouragingly, deep learning incorporated with the sensor achieves an impressive recognition rate of 98.16% for mixed signals with different force levels. Furthermore, it possesses excellent wearable properties such as breathability, moisture permeability, perspiration remove, quick-drying and washability, suitable for applications like activity monitoring, medical rehabilitation evaluation, and gesture recognition. This work presents a promising pathway for the advancement of next-generation multifunctional electronic textiles.

Analyzing innovative policies and practices for palliative care in Portugal: a qualitative study

BackgroundCountries with formal policies for palliative care, and advanced and integrated practices in this field, such as Portugal, face challenges in achieving excellence in care, particularly in home-based assistance. Issues include care coordination among providers, confusion regarding the roles of each health care professional in the network, and a lack of monitoring and evaluation of actions. Our objective was to analyze the implementation of palliative care in primary health care in Portugal.MethodsWe conducted a qualitative, descriptive, and exploratory study in Portugal involving health care professionals with experience in palliative care. The data were collected through semistructured interviews and focus groups between March and October 2023. Eighteen health care professionals participated. We used the Alceste software for lexicographic analysis. The research was authorized by an Ethics Committee.ResultsFour classes were identified; classes 1 and 2, comprising 77% of the corpus, addressed the study objectives. Participants highlighted inequitable access, strategic development plans with unattainable short-term goals; and low literacy. They emphasized the importance of legislation, professional training initiatives for generalist palliative care at home, and early referral. Home-based challenges included professionals’ lack of exclusive dedication, absence of 24/7 coverage, and unavailability of capable family caregivers. The networks’ response to hospital admissions and patient transitions from hospital to home, with access to the specialized team, was also inadequate.ConclusionsHealth care professionals aim to increase patients’ time spent at home, reduce emergency department visits, and minimize hospitalizations by leveraging the resources of the national palliative care network. In addition to investments to sustain network implementation and legally guaranteed palliative care rights, the country must focus on measurable indicators for evaluating and monitoring actions, providing better guidance in the short, medium, and long term.