Monitoring Tool Research Articles

Enhancing patient care: pharmacotherapeutic monitoring in community pharmacies - a literature review

Background: The role of community pharmacies has been transitioning from traditional drug dispensing methods to include more patient-centred services such as pharmacotherapeutic monitoring. With the increasing complexity of medication regimens and the rising prevalence of chronic diseases, pharmacotherapeutic monitoring plays a crucial role in preventing medication-related problems. This study aims to review patient outcomes of community pharmacies’ involvement in pharmacotherapeutic monitoring services. It also highlights advances in the monitoring process, the challenges involved, and relevant solutions. Methods: A narrative review was conducted from 8 April 2024 to 9 May 2024, where data was extracted from multiple research studies indexed in PubMed, EBSCOHost, and Google Scholar databases. This review included only previous articles written in English and had information relevant to community pharmacy, patient care, innovations in pharmacotherapeutic monitoring, and challenges associated with the monitoring process in community pharmacies. Results: Implementation of pharmacotherapeutic monitoring in community pharmacies improves patient outcomes and satisfaction. It minimizes adverse events by ensuring optimal dosing and patient adherence, which in turn reduces medical expenses and duration of hospitalization. However, factors such as inefficient analytical resources, lack of skilled personnel, and huge financial costs complicate implementation of pharmacotherapeutic monitoring in community pharmacies. Conclusion: Community pharmacies’ participation in therapeutic drug monitoring is a promising development that prioritizes tailored patient care, eliminating the need for hospital visits. We recommend a multidisciplinary collaboration, robust electronic medical record systems, and large-scale research studies to provide sufficient evidence on the application of therapeutic drug monitoring in community pharmacies, to leverage this service. Also, policy and decision-makers should facilitate this practice by investing more in pharmacotherapeutic monitoring tools and training for community pharmacists.

Continuous monitoring of somatosensory evoked potentials following spinothalamic tract stimulation in brainstem and spinal cord surgery: case report and literature review

The primary objective of neurosurgical intervention for central nervous system tumors is a balance between optimal tumor resection and minimal postoperative neurological impairment. Intraoperative neurophysiological monitoring is essential in safeguarding somatosensory tracts during surgery. Virtually the only tool for continuous monitoring of sensory pathways is registration of peripheral somatosensory evoked potentials (SSEPs). However, this method has some limitations. To increase the quality of intraoperative SSEP monitoring in surgical treatment of brainstem and spinal cord tumors. To ensure continuous monitoring of sensory pathway function in brainstem and spinal cord surgery, we modified the method for monitoring of SSEPs. Unlike standard technique, we placed two-contact electrode for stimulation either in the area of the bottom of the fourth ventricle or on posterior surface of the spinal cord along the midline. This allows us to stimulate pontine tegmentum or posterior columns of spinal cord. We demonstrated the feasibility of this method in 2 cases. The advantage of this method is possible brainstem or spinal cord stimulation reducing the chain of somatosensory tract that is valuable to neutralize the effect of anesthesia on significance of neurophysiological data.

Dynamic, Single‐cell Monitoring of RNA Modifications Response to Viral Infection Using a Genetically Encoded Live‐cell RNA Methylation Sensor

AbstractRNA modifications, such as N6‐methylation of adenosine (m6A), serve as key regulators of cellular behaviors, and are highly dynamic; however, tools for dynamic monitoring of RNA modifications in live cells are lacking. Here, we develop a genetically encoded live‐cell RNA methylation sensor that can dynamically monitor RNA m6A level at single‐cell resolution. The sensor senses RNA m6A in cells via affinity‐induced cytoplasmic retention using a nuclear location sequence‐fused m6A reader. It allows for simultaneously measure RNA m6A dynamics and viral invasion at single‐cell level. Based on the single‐cell analytical tool, we found that SARS‐CoV‐2 infection enhances host‐cell RNA m6A level, and high‐level RNA m6A modification in host cells, in turn, facilitates viral infection. Particularly, Omicron, a variant of SARS‐CoV‐2, that features as high infection capacity, however, exhibits a reduced facilitation of m6A modification in host cells. In addition, the sensor can estimate viral inhibition via measuring cellular m6A level, that was explored for screening potential antiviral drugs. The methylation sensor can serve for elucidating the interplay between pathogens and host‐cell epigenetics at single‐cell level.

A Monitoring Method for Agricultural Soil Moisture Using Wireless Sensors and the Biswas Model

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R2) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture.

Using citizen science Raspberry Shake seismometers to enhance earthquake location and characterization: a case study from Wellington, New Zealand

The recent development of low-cost citizen seismometers has opened new avenues for earthquake analysis. We explore the integration of Raspberry Shake citizen seismometers with the national GeoNet seismic network to improve the precision of earthquake locations in Wellington, New Zealand. We use a dataset of 19 earthquakes between magnitudes 1.1 and 3.5 and between hypocentral distances of 22 km and 102 km. Our findings demonstrate that using Raspberry Shake seismometers in conjunction with the GeoNet network is effective for both the locating and characterisation of earthquakes. Notably, we find that precise station locations are less critical for precise earthquake location, a significant factor given that the publicly available Raspberry Shake locations are obfuscated to protect user privacy. These results suggest that, dependent on network geometry, citizen seismometer data can be a valuable tool in seismic monitoring and improve earthquake location capability, whilst remaining cost-effective.

Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models

Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring.

Enhancing Health Equity and Patient Engagement in Diabetes Care: Technology-Aided Continuous Glucose Monitoring Pilot Implementation Project.

Federally Qualified Health Centers (FQHCs) provide service to medically underserved areas and communities, providing care to over 32 million patients annually. The burden of diabetes is increasing, but often, the vulnerable communities served by FQHCs lag in the management of the disease due to limited resources and related social determinants of health. With the increasing adoption of technologies in health care delivery, digital tools for continuous glucose monitoring (CGM) are being used to improve disease management and increase patient engagement. In this viewpoint, we share insights on the implementation of a CGM program at an FQHC, the Community-University Health Care Center (CUHCC) in Minneapolis, Minnesota. Our intent is to improve diabetes management through better monitoring of glucose and to ensure that the CGM program enables our organization's overarching digital strategy. Given the resource limitations of our population, we provided Libre Pro devices to uninsured patients through grants to improve health care equity. We used an interdisciplinary approach involving pharmacists, nurses, and clinicians and used hemoglobin A1c (HbA1c) levels as a measure of diabetes management. We assessed the CGM program and noted key aspects to guide future implementation and scalability. We recruited 148 participants with a mean age of 54 years; 39.8% (59/148) self-identified their race as non-White, 9.5% (14/148) self-identified their ethnicity as Hispanic or Latino, and one-third (53/148, 35.8%) were uninsured. Participants had diverse language preferences, with Spanish (54/148, 36.5%), English (52/148, 35.1%), Somali (21/148, 14.2%), and other languages (21/148, 14.2%). Their clinical characteristics included an average BMI of 29.91 kg/m2 and a mean baseline HbA1c level of 9.73%. Results indicate that the CGM program reduced HbA1c levels significantly from baseline to first follow-up (P<.001) and second follow-up (P<.001), but no significant difference between the first and second follow-up (P=.94). We share key lessons learned on cultural and language barriers, the digital divide, technical issues, and interoperability needs. These key lessons are generalizable for improving implementation at FQHCs and refining digital strategies for future scalability.

Bycatch of common pollinators in pheromone baited traps for monitoring corn earworm (Lepidoptera: Noctuidae) in Missouri Industrial Hemp

BackgroundSeveral traps are recommended for monitoring corn earworm, Helicoverpa zea (Boddie), an important pest of field, vegetable and fruit crops in the U.S. These traps, which are meant to capture H. zea in many cases end up capturing other insects. Helicoverpa zea has recently been found feeding on different seeds and dual-type (seed and fiber) hemp, Cannabis sativa L. (Rosales: Cannabaceae) causing serious damage. Limited work has been done on developing integrated pest management (IPM) for H. zea industrial hemp in Missouri.MethodsWe evaluated the attractiveness of different traps with the aim of developing a monitoring system for the adult male H. zea in industrial hemp fields in two Missouri locations. In addition, we recorded other non-target insects in the traps to determine trap selectivity. Commercially available green, clear, tricolor bucket traps Heliothis Scentry and Scentry Delta 1X traps baited with H. zea sex pheromones were evaluated in 2021 and 2022.ResultsTricolor traps captured significantly more adult male H. zea than the other traps in both years. Non-target insects, mainly Xylocopa virginica (L.) (Hymenoptera: Apidae), Bombus spp. (Hymenoptera: Apidae), and Apis mellifera L. (Hymenoptera: Apidae) were also captured. The tricolor trap captured the most bees.ConclusionsThe presence of non-target species highlights the risk of using tricolor traps as a monitoring tool. This data provides information for planning the monitoring of corn earworm in industrial hemp farms in Missouri.

A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [18F]FDG-PET Images During Follow-Up

Objective: To investigate the feasibility of a machine learning (ML) model based on radiomic features to identify active giant cell arteritis (GCA) in the aorta and differentiate it from atherosclerosis in follow-up [18F]FDG-PET/CT images for therapy monitoring. Methods: To train the ML model, 64 [18F]FDG-PET scans of 34 patients with proven GCA and 34 control subjects with type 2 diabetes mellitus were retrospectively included. The aorta was delineated into the ascending, arch, descending, and abdominal aorta. From each segment, 95 features were extracted. All segments were randomly split into a training/validation (n = 192; 80%) and test set (n = 46; 20%). In total, 441 ML models were trained, using combinations of seven feature selection methods, seven classifiers, and nine different numbers of features. The performance was assessed by area under the curve (AUC). The best performing ML model was compared to the clinical report of nuclear medicine physicians in 19 follow-up scans (7 active GCA, 12 inactive GCA). For explainability, an occlusion map was created to illustrate the important regions of the aorta for the decision of the ML model. Results: The ten-feature model with ANOVA as the feature selector and random forest classifier demonstrated the highest performance (AUC = 0.92 ± 0.01). Compared with the clinical report, this model showed a higher PPV (0.83 vs. 0.80), NPV (0.85 vs. 0.79), and accuracy (0.84 vs. 0.79) in the detection of active GCA in follow-up scans. Conclusions: The current radiomics ML model was able to identify active GCA and differentiate GCA from atherosclerosis in follow-up [18F]FDG-PET/CT scans. This demonstrates the potential of the ML model as a monitoring tool in challenging [18F]FDG-PET scans of GCA patients.

Universal First-Trimester Screening Biomarkers for Diagnosis of Preeclampsia and Placenta Accreta Spectrum

Background: Disruptions in epigenetic mechanisms regulating placentation, particularly imbalances in the levels of small non-coding RNAs, contribute to various pregnancy complications, including preeclampsia (PE) and placenta accreta spectrum (PAS). Given that abnormal trophoblast differentiation, invasiveness, and angiogenesis—reduced in PE and excessive in PAS—are central to the pathogenesis of these conditions, this study aimed to identify universal circulating piRNAs and their targets. Methods: Small RNA deep sequencing, quantitative reverse transcription combined with real-time polymerase chain reaction, magnetic bead-based multiplex immunoassay, ELISA, and Western blotting were employed to quantify circulating piRNAs and proteins in the blood serum of pregnant women during the 11th–14th weeks of gestation. Results: Statistically significant negative correlations were identified between PE- and PAS-associated piRNAs (hsa_piR_019122, hsa_piR_020497, hsa_piR_019949, and piR_019675) and several molecules, including Endoglin, IL-18, VEGF-A, VEGF-C, Angiopoietin-2, sFASL, HB-EGF, TGFα, and Clusterin. These molecules are involved in processes such as angiogenesis, inflammation, the epithelial–mesenchymal transition, cell proliferation, adhesion, and apoptosis. A first-trimester pregnancy screening algorithm was developed using logistic regression models based on Clusterin concentration and the levels of hsa_piR_020497, hsa_piR_019949, piR_019675, and hsa_piR_019122. Conclusions: The proposed screening tool for early pregnancy monitoring may enable the prediction of PE or PAS in the first trimester, allowing timely interventions to reduce maternal and perinatal morbidity and mortality.

ANTIBIOTIC RESISTANCE: A GLOBAL THREAT AND PATHWAYS TO SOLUTIONS

Introduction. Antimicrobial resistance (AMR) represents a critical global health challenge that poses significant threats to public health, food security, and economic stability. AMR occurs when microorganisms such as bacteria, viruses, fungi, and parasites develop resistance to antimicrobial agents, rendering treatments ineffective and leading to persistent infections, higher medical costs, and increased mortality rates. This crisis is exacerbated by factors such as the overuse and misuse of antibiotics in healthcare and agriculture, insufficient regulation, inadequate infection control measures, and limited access to diagnostic tools. As AMR continues to evolve, its impact is felt worldwide, particularly in low- and middle-income countries where resources for combating this issue are often scarce. The growing burden of AMR underscores the urgency for global and regional collaborations, integrated surveillance systems, and the implementation of effective antimicrobial stewardship programs to mitigate its impact. Purpose of the review. This study aims to analyze AMR as a global threat, identify key factors driving its emergence and spread, evaluate existing mitigation strategies, and propose practical recommendations to enhance the effectiveness of AMR management at regional and global levels. Materials and methods: A systematic analysis was conducted using open-access articles published within the last five years from databases such as PubMed, Medline, Google Scholar, eLibrary, and CyberLeninka. Included studies met evidence levels A and B, comprising meta-analyses, systematic reviews, and cohort studies. Key areas of focus included epidemiological data, resistance patterns, and intervention effectiveness. Results: Between 1990 and 2021, AMR was responsible for over one million deaths globally each year, with significant increases in fatalities among individuals aged 70 and older. In 2021, bacterial AMR contributed to an estimated 4.71 million deaths globally, including 1.14 million directly attributable to AMR. Projections suggest AMR-related deaths could reach 10 million annually by 2050 if current trends continue. Factors such as unregulated antibiotic use, limited diagnostic resources, and the spread of multidrug resistance (MDR), extensive drug resistance (XDR), and pan-resistance (PDR) exacerbate the crisis. Regional disparities, particularly in Africa and Asia, emphasize the need for strengthened surveillance and regulatory measures. Conclusion. AMR is a growing global crisis that demands urgent action. Implementing genomic tools for AMR monitoring and adopting the «One Health» approach, which integrates human, animal, and environmental health, are essential to addressing this challenge. Strengthening regional monitoring, increasing public awareness, and fostering global collaborations are pivotal for sustainable AMR control. These measures are crucial to reducing mortality rates, optimizing healthcare outcomes, and mitigating the socioeconomic impacts of AMR.

Research on Architecture, Applications, and Performance Evaluation using Wireless Sensor Networks for Environmental Surveillance

Wireless Sensor Networks (WSNs) have emerged as a powerful tool for environmental monitoring, offering scalable and cost-effective solutions for tracking environmental parameters such as air quality, soil moisture, and temperature. This paper presents a comprehensive overview of WSNs, highlighting their architecture, key components, applications, and performance metrics. A case study is included, presenting experimental results that evaluate the effectiveness of WSNs in various environmental settings. The paper discusses energy efficiency, scalability, and data accuracy, while also addressing challenges and proposing future directions for WSN technology in environmental monitoring

The best of two worlds: toward large‐scale monitoring of biodiversity combining COI metabarcoding and optimized parataxonomic validation

In a context of unprecedented insect decline, it is critical to have reliable monitoring tools to measure species diversity and their dynamic at large‐scales. High‐throughput DNA‐based identification methods, and particularly metabarcoding, were proposed as an effective way to reach this aim. However, these identification methods are subject to multiple technical limitations, resulting in unavoidable false‐positive and false‐negative species detection. Moreover, metabarcoding does not allow a reliable estimation of species abundance in a given sample, which is key to document and detect population declines or range shifts at large scales. To overcome these obstacles, we propose here a human‐assisted molecular identification (HAMI) approach, a framework based on a combination of metabarcoding and image‐based parataxonomic validation of outputs and recording of abundance. We assessed the advantages of using HAMI over the exclusive use of a metabarcoding approach by examining 492 mixed beetle samples from a biodiversity monitoring initiative conducted throughout France. On average, 23% of the species are missed when relying exclusively on metabarcoding, this percent being consistently higher in species‐rich samples. Importantly, on average, 20% of the species identified by molecular‐only approaches correspond to false positives linked to cross‐sample contaminations or mis‐identified barcode sequences in databases. The combination of molecular methodologies and parataxonomic validation in HAMI significantly reduces the intrinsic biases of metabarcoding and recovers reliable abundance data. This approach also enables users to engage in a virtuous circle of database improvement through the identification of specimens associated with missing or incorrectly assigned barcodes. As such, HAMI fills an important gap in the toolbox available for fast and reliable biodiversity monitoring at large scales.

Ultrasensitive and Selective Nitrogen-Doped Fluorescent Carbon Dots Probe for Quantification Analysis of Trace Cu2+ in the Aqueous Environment.

As a typical non-ferrous metal, copper is heavily used in the manufacturing and chemical industries. Copper pollution has been demonstrated to have a significant detrimental impact on the natural environment, as well as causing irreparable damage to the human body, such as elevated Cu2+ levels have been identified as a factor in the pathogenesis of AD (Alzheimer's disease). In this study, novel nanoscale carbon dots Blue-CDs (B-CDs) were obtained by the solvothermal approach in formamide solution utilizing citric acid as the carbon source and ethylenediamine as the nitrogen dopant. The particle size of B-CD was assessed to be 2.17nm, with a quantum yield (QY) of 10.28%. The B-CDs were found to be extinguished upon exposure to Cu2+, which exhibited a good fluorescence detection linear relationship within the concentration range of 0.25-10.0 µM Cu2+, showing a limit of detection (LOD) is 0.18 µM. B-CDs have been effectively used for the measurement of Cu2+ in actual aqueous systems. It is due to the chemical reactions that take place among the B-CDs and the Cu²⁺ that make the sensor highly sensitivities and highly selectivities. The results of the experiment demonstrate that the fluorescence quenching process is a consequence of Cu2+ binding to the amino groups of carbon dots, forming complexes via a non-radiative photoinduced electron transfer process. In conclusion, the described simple sensing techniques could be effectively utilized as monitoring tools for Cu2+ in environmental waters.

The spectrum of giant cell arteritis and polymyalgia rheumatica: a longitudinal prospective study to assess for subclinical giant cell arteritis in new onset polymyalgia rheumatica

BackgroundPolymyalgia rheumatica (PMR) and giant cell arteritis (GCA) frequently overlap, with each disease potentially presenting as a manifestation of the other. The strong relationship between PMR and GCA raises the question of whether it is a “co-occurrence of different diseases” or a “common spectrum of inflammatory diseases.” The objectives of the current study were to assess the applicability of using ultrasound (US) and clinical data as a monitoring tool to identify PMR patients with subclinical GCA and determine possible predictors of GCA among PMR patients.ResultsOne hundred eight PMR patients and 62 GCA patients were included in this study. The study found that 20.4% of the PMR cohort had subclinical GCA, which later progressed to classical GCA, while 31.5% experienced a PMR relapse. The presence of intimal media thickening of the temporal/axillary artery at baseline was a very early predictor of GCA development; the OGUS score was significantly higher in the PMR group who developed GCA than the standard GCA cohort (p < 0.01).ConclusionThe key finding of this study is the incorporation of the concept that vascular inflammation mainly precedes the classical clinical manifestations. Routine screening for subclinical GCA in PMR patients should be considered as part of standard care for PMR patients. Physicians should be attentive to the presence of mural inflammatory changes at the time of diagnosis and monitoring of the ultrasound composite scores in the follow-up course.

Toward Personalized Immunotherapeutic Drug Monitoring with Multiplexed Extended‐Gate Field‐Effect‐Transistor Biosensors

The selection and optimization of therapies for cancer patients urgently need personalization. Portable point‐of‐care electronic biosensors emerge as a groundbreaking solution contributing to better decision‐making in precision oncology. In this study, the innovative use of extended‐gate field‐effect‐transistor (EG‐FET) biosensors is showcased for monitoring the concentration and pharmacokinetics of immunotherapeutic drugs in vivo. Complementary positron emission tomography and radioactivity biodistribution studies in mice validate the EG‐FET measurements. Herein, a novel indirect assay format is also introduced for detecting target modules (TMs) in an adapter chimeric antigen receptor T‐cell therapy model, effectively addressing the current limitations of potentiometric measurements. In pharmacokinetic evaluations, the EG‐FET biosensor performance aligns with standard radioactivity measurements, revealing the distinct lifespans of small‐sized single‐chain‐fragment‐variable‐derived TMs (15 min) and larger IgG4‐derived TMs (14 h). Advantageously, the EG‐FET sensors exhibit exceptional sensitivity and fulfill the requirements for immunotherapeutic drug monitoring without complex radioactive labeling, which is indispensable. In these promising findings, the exploration of next‐generation electronic biosensors as therapeutic monitoring tools is advocated for. With their cost, size, and response time advantages, these biosensors hold immense potential for advancing personalized oncology, transcending the conventional diagnostic roles typically highlighted in the literature.

The Diversity Index (IDIVERSA) has a new Environmental Social and Governance (ESG) strategy: Perspectives and challenges

This research aims to analyze whether IDIVERSA influences the disclosure of ESG information by regulated and non-regulated companies in [B]3. Specifically, it addresses the discussion of Agency Theory as one of the main monitoring tools for regulating conflicts of interest and presents some inferences about IDIVERSA's contribution to ESG and corporate governance of companies. The research is descriptive, quantitative, and documentary, and the population is made up of companies listed on [B]3 SA, consisting of 80 companies. The results showed that, unlike other existing indexes, this index works with a grouping thesis by considering the two socially marginalized groups, and this should help to induce ESG practices in the market and attract more investments. The research offers important contributions to the discussion related to gender and racial diversity that promote significant social, environmental and governance (ESG) disclosures by providing new evidence from companies listed on [B]3, which from the perspective of Agency Theory, are concerned with reputational and image risks and meeting the demand of investors concerned with sustainability, inclusion and diversity issues.

Towed Video-Diver: A Useful Low-Cost Tool for Rapid Benthic Mapping and Biodiversity Monitoring

Marine Protected Areas (MPAs) require efficient monitoring tools to assess habitats and biodiversity, particularly in remote or understudied regions. This study demonstrates the utility of the towed video-diver technique combined with high-resolution video for rapidly surveying benthic habitats and associated taxa. Applied in Arredondo, a shallow bay within an MPA in Atlantic Patagonia, the method covered 14,000 m2 through eight transects, utilizing just 180 min of dive time and ~300 min of video analysis. Substrate types and their associated taxa were classified using the CATAMI framework, yielding a list of 28 taxa and density estimates of mobile organisms. Additionally, the percentage cover of Gracilaria sp.—a commercially valuable macroalga historically overexploited in the region—was estimated for the bay. The invasive crab Carcinus maenas was found across all substrate types on the bay, underscoring its ecological tolerance and the need for ongoing monitoring. This cost-effective, rapid methodology is highly effective for detecting and describing areas of ecological or conservation interest, providing critical baseline data for targeted, detailed studies. Its simplicity and efficiency make it ideal for initial surveys in remote regions, supporting the conservation and management of MPAs.

Warning Signs, Monitoring Tools, and Actions Taken by World-Class Endurance Coaches in Cases of Underperformance.

To describe warning signs, monitoring tools, and training- and non-training-related actions taken by world-class endurance coaches in cases of underperformance. Twelve highly acclaimed male Norwegian coaches known for coaching world-class endurance athletes with a remarkable collection of over 350 Olympic, World, and European Championship medals-primarily with Norwegian athletes-participated in the study. Data collection and analyses followed a 3-step pragmatic qualitative study design, including an initial questionnaire, in-depth interviews, and structured negotiation between researchers and coaches. Reduced performance levels and discrepancies between external (speed or power output) and internal (heart rate, lactate concentrations, and rating of perceived exertion) training-intensity measures in competitions, key training sessions, and testing scenarios, as well as observed changes in emotional state and coach-athlete communication, were highlighted as early warning signs of underperformance. Consistently, daily follow-up, systematic use of training diaries, training-intensity measures, and information from testing scenarios were rated as the most important monitoring tools in both detection and management of underperformance. In cases of underperformance, ruling out medical conditions as underlying causes was followed by a decreased training load (intensity and volume) and actions to reduce life stress to restore athletes' performance levels. The presented data collectively propose applying both objectively and subjectively measured monitoring tools and systems to the early detection and management of underperformance in endurance athletes. In addition, this should be complemented by the holistic and observational role of the coach.

An interpretable machine learning tool for in-home monitoring of agitation episodes in people living with dementia: a proof-of-concept study.

Agitation affects around 30% of people living with dementia (PLwD), increasing carer burden and straining care services. Agitation identification typically relies on subjective clinical scales and direct patient observation, which are resource-intensive and challenging to incorporate into routine care. Clinical applicability of data-driven methods for agitation monitoring is limited by constraints such as short observational periods, data granularity, and lack of interpretability and generalisation. Current interventions for agitation are primarily medication-based, which may lead to severe side effects and lack personalisation. Understanding how real-world factors interact with agitation within home settings offers a promising avenue towards identifying potential personalised non-pharmacological interventions. We used longitudinal data (32,896 person-days from n=63 PLwD) collected using in-home monitoring devices between December 2020 and March 2023. Employing machine learning techniques, we developed a monitoring tool to identify the presence of agitation during the week. We incorporated a traffic-light system to stratify agitation probability estimates supporting clinical decision-making, and employed the SHapley Additive exPlanations (SHAP) framework to enhance interpretability. We designed an interactive tool that enables the exploration of personalised non-pharmacological interventions, such as modifying ambient light and temperature. Light Gradient-boosting Machine (LightGBM) achieved the highest performance in identifying agitation over an 8-day period with a sensitivity of 71.32%±7.38 and specificity of 75.28%±7.38. Implementing the traffic-light system for stratification increased specificity to 90.3%±7.55 and improved all metrics. Key features for identifying agitation included low nocturnal respiratory rate, heightened alertness during sleep, and increased indoor illuminance, as revealed by statistical and feature importance analysis. Using our interactive tool, we identified indoor lighting and temperature adjustments as the most promising and feasible intervention options within our cohort. Our interpretable framework for agitation monitoring, developed using data from a dementia care study, showcases significant clinical value. The accompanying interactive interface allows for the in-silico simulation of non-pharmacological interventions, facilitating the design of personalised interventions that can improve in-home dementia care. This study is funded by the UK Dementia Research Institute [award number UK DRI-7002] through UK DRI Ltd, principally funded by the Medical Research Council (MRC), and the UKRI Engineering and Physical Sciences Research Council (EPSRC) PROTECT Project (grant number: EP/W031892/1). Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC) and the UKRI Medical Research Council (MRC). P.B. is also funded by the Great Ormond Street Hospital and the Royal Academy of Engineering. C.S. is supported by the UK Dementia Research Institute [award number UK DRI-5209], a UKRI Future Leaders Fellowship [MR/MR/X032892/1] and the Edmond J. Safra Foundation. R.N. is funded by UK Dementia Research Institute [award number UK DRI-7002] and the UKRI Engineering and Physical Sciences Research Council (EPSRC) PROTECT Project (grant number: EP/W031892/1). M.B. and A.K.S. are funded by the UK Dementia Research Institute [award number UKDRI-7002 and UKDRI-5209]. N.F.L., A.C., C.W. and S.K.arefunded bythe UK Dementia Research Institute[award number UK DRI-7002].