A review on cultivating effective learning: synthesizing educational theories and virtual reality for enhanced educational experiences.

Immersive technology, especially virtual reality (VR), transforms education. It offers immersive and interactive learning experiences. This study presents a systematic review focusing on VR's integration with educational theories in higher education. The review evaluates the literature on VR applications combined with pedagogical frameworks. It aims to identify effective strategies for enhancing educational experiences through VR. The process involved analyzing studies about VR and educational theories, focusing on methodologies, outcomes, and effectiveness. Findings show that VR improves learning outcomes when aligned with theories such as constructivism, experiential learning, and collaborative learning. These integrations offer personalized, immersive, and interactive learning experiences. The study highlights the importance of incorporating educational principles into VR application development. It suggests a promising direction for future research and implementation in education. This approach aims to maximize VR's pedagogical value, enhancing learning outcomes across educational settings.

RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis.

Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpretability. To address this issue, we propose a Reliable Hippo-guided Learning model for Alzheimer's Disease diagnosis (RLAD), which employs multi-task learning for AD classification as a main task supplemented by hippocampus segmentation. More specifically, our model consists of 1) a hybrid shared features encoder that encodes local and global information in MRI to enhance the model's ability to learn discriminative features; 2) Task Specific Decoders to accomplish AD classification and hippocampus segmentation; and 3) Task Coordination module to correlate the two tasks and guide the classification task to focus on the hippocampus area. Our proposed RLAD model is evaluated on MRI scans of 1631 subjects from three independent datasets, including ADNI-1, ADNI-2, and HarP. Our extensive experimental results demonstrate that the proposed model significantly improves the performance of AD classification and hippocampus segmentation with strong generalization capabilities. Our implementation and model are available at https://github.com/LeoLjl/Explainable-Alzheimer-s-Disease-Diagnosis.

Novel acyl hydrazide derivatives of polyhydroquinoline as potent anti-diabetic and anti-glycating agents: Synthesis, in vitro α-amylase, α-glucosidase inhibition and anti-glycating activity with molecular docking insights.

In this study, eleven novel acyl hydrazides derivative of polyhydroquinoline were synthesized, characterized and screened for their in vitro anti-diabetic and anti-glycating activities. Seven compounds 2a, 2d, 2i, 2 h, 2j, 2f, and 2 g exhibited notable α-amylase inhibitory activity having IC50 values from 3.51 ± 2.13 to 11.92 ± 2.30 µM. Similarly, six compounds 2d, 2f, 2 h, 2i, 2j, and 2 g displayed potent α-glucosidase inhibitory activity compared to the standard acarbose. Moreover, eight derivatives 2d, 2 g, 2f, 2j, 2a, 2i, 2 g, and 2e showed excellent anti-glycating activity with IC50 values from 6.91 ± 2.66 to 15.80 ± 1.87 µM when compared them with the standard rutin (IC50 = 22.5 ± 0.90 µM). Molecular docking was carried out to predict the binding modes of all the compounds with α-amylase and α-glucosidase. The docking analysis revealed that most of the compounds established strong interactions with α-amylase and α-glucosidase. All compounds fitted well into the binding pockets of α-amylase and α-glucosidase. Among all compounds 2a and 2f were most potent based on docking score -8.2515 and -7.3949 against α-amylase and α-glucosidase respectively. These results hold promise for the development of novel candidates targeted at controlling postprandial glucose levels in individuals with diabetes.

SPE-UHPLC-ESI-MS/MS Analysis of Cocaine and Its Metabolites in Conventional and Alternative Biological Specimens: Application to Real Samples.

An increase in cocaine abuse has been observed globally since the past decade. Cocaine is among the commonly abused stimulants used for recreational purposes. In this study, the SPE-UHPLC-MS/MS method was developed and validated to be applied on real specimens of 20 chronic cocaine abusers to quantify cocaine/metabolites in conventional as well as alternative biological matrices. Cocaine was extracted from biological specimens using solid-phase extraction followed by liquid chromatography tandem mass spectrometry analysis. Chromatographic separation was achieved on a Poroshell120EC-18 column (2.1 mm × 50 mm, 2.7 µm particle size) using water-acetonitrile in 0.1% formic acid as a mobile phase in gradient elution mode. The flow rate of the mobile phase was 0.5 mL/min with a gradient varying the percentage of acetonitrile linearity ranging 15-95% in 6.0 min acquisition time, and the injection volume was set at 5 µL. Positive electrospray ionization with multireaction ion monitoring mode using two ion transitions for cocaine/metabolites and one for cocaine-d3 was employed. The quantification method demonstrated good linear ranges of 0.025-250 ng/mL in blood, urine, and oral fluid (ng/mg for hair and nail) with a ≥0.991% determination coefficient. The detection limit and lower quantification limit were 0.005 and 0.025 ng/mL in all matrices, respectively. The mean extraction recovery and ionization suppression ranged from 89.3 to 99.8% and -4.6 to -14.4% in the studied matrices. Within-run and between-days precisions were 1.8-7.2% and 1.9-6.1%, respectively. This study will not only help in quantifying cocaine/metabolites in alternative specimens (hair, nail, and oral fluid) but also guide clinical and forensic toxicologists in interpretation of exhumation cases. Furthermore, multiple specimens' analyses can be of significance in estimating the time/manner of drug exposure, in confirming the results of laboratories in cases of doubtful clinical histories, or in aiding medico-legal investigations.

Solitonic solutions and stability analysis of Benjamin Bona Mahony Burger equation using two versatile techniques.

This study aims to explore the precise resolution of the nonlinear Benjamin Bona Mahony Burgers (BBMB) equation, which finds application in a variety of nonlinear scientific disciplines including fluid dynamics, shock generation, wave transmission, and soliton theory. Within this paper, we employ two versatile methodologies, specifically the extended exp ( - Ψ ( χ ) ) expansion technique and the novel Kudryashov method, to identify the exact soliton solutions of the nonlinear BBMB equation. The solutions we discovered involve trigonometric functions, hyperbolic functions, and rational functions. The uniqueness of this research lies in uncovering the bright soliton, kink wave solution, and periodic wave solution, and conducting stability analysis. Furthermore, the solutions' graphical characteristics were explored through the utilization of the mathematical software Maple 2022 ( https://maplesoft.com/downloads/selectplatform.aspx?hash=61ab59890f2313b2241fde3423fd975e ). The system's physical interpretation is defined through various types of graphs, including contour graphs, 3D-surface graphs, and line graphs, which use appropriate parameter values. These recommended techniques hold significant importance and are applicable in diverse nonlinear evolutionary equations found in the field of nonlinear sciences for illustrating nonlinear physical models.

Isothermal and kinetic screening of methyl red and methyl orange dyes adsorption from water by Delonix regia biochar-sulfur oxide (DRB-SO).

In this study, Delonix regia seed pods (DRSPs) as a locally available material were refluxed in 90% H2SO4 to yield a novel D. regia seed pods biochar-sulfur oxide (DRB-SO). FTIR, BET, BJH, SEM, EDX, XRD, DSC and TGA were applied to investigate the characterizations of the prepared DRB-SO. Various adsorption parameters like pH effect, dye concentration effect, adsorbent dose, reaction time isotherm and kinetic study were carried out to explain the process of adsorption of methyl orange (MO) and methyl red (MR) onto DRB-SO. Langmuir's adsorption model perfectly explained the adsorption process onto the surface of DRB-SO as a monolayer. The maximum adsorption efficiency of DRB-SO was (98%) and (99.6%) for MO and MR respectively which attained after 150 min with an adsorbent dose of 0.75 g/L. The pseudo-second-order kinetic model best explained the process of adsorption of MO and MR dyes by DRB-SO. The highest observed adsorption amount was as high as 144.9 mg/g for MO dye and 285.7 mg/g for MR dye, comparable with other reported materials based on activated carbon materials. All of the outcomes signposted a prodigious perspective of the fabricated biochar composite material in wastewater treatment. Using the regenerating DRB-SO through an acid-base regeneration process, six cycles of adsorption/desorption were examined. Over the course of the cycles, there was a minor decrease in the adsorption and desorption processes. Also, it was revealed what the most plausible mechanism was for DRB-SO to absorb the ions of the MO and MR dyes.

The UK Soft Drinks Industry Levy and childhood hospital admissions for asthma in England.

Sugar sweetened beverage consumption has been suggested as a risk factor for childhood asthma symptoms. We examined whether the UK Soft Drinks Industry Levy (SDIL), announced in March 2016 and implemented in April 2018, was associated with changes in National Health Service hospital admission rates for asthma in children, 22 months post-implementation of SDIL. We conducted interrupted time series analyses (2012-2020) to measure changes in monthly incidence rates of hospital admissions. Sub-analysis was by age-group (5-9,10-14,15-18 years) and neighbourhood deprivation quintiles. Changes were relative to counterfactual scenarios where the SDIL wasn't announced, or implemented. Overall, incidence rates reduced by 20.9% (95%CI: 29.6-12.2). Reductions were similar across age-groups and deprivation quintiles. These findings give support to the idea that implementation of a UK tax intended to reduce childhood obesity may have contributed to a significant unexpected and additional public health benefit in the form of reduced hospital admissions for childhood asthma.

A Comprehensive Analysis of Conventional Acupuncture and Pharmacological Approaches for Cardiac Arrhythmias: An Umbrella Review.

With a global incidence of approximately 3.4% and an annual mortality rate of 3.7 million, cardiac arrhythmias (CAs) are a pressing global health issue. Their increasing prevalence, especially among older people, is intensifying the challenge for health care systems worldwide. This study aims to compare the safety and effectiveness of acupuncture and pharmacological treatments for CAs, addressing critical gaps in understanding optimal therapeutic approaches. A search of PubMed, EMBASE, and the Cochrane database of systematic reviews was performed to identify data compiled through September 2023 for this umbrella review. Randomized controlled trials (RCTs) as the foundation for meta-analyses and peer-reviewed systematic reviews were the primary focus of the literature search. The Grading of Recommendations Assessment, Development, and Evaluation method was used to assess the overall certainty of the evidence, whereas AMSTAR 2 and the Cochrane Collaboration tool were used to evaluate the quality of the included reviews. Following a comprehensive review, three systematic analyses of 27 RCTs were integrated. Acupuncture led to a slightly greater reduction in the recurrence rate of paroxysmal supraventricular tachycardia (SVT) compared to standard pharmaceutical therapy (risk ratio [RR], 1.06; 95% confidence interval [CI], 0.88-1.27; I2 = 56%; P = .55), although the difference was not statistically significant. In contrast, acupuncture significantly outperformed pharmacological treatment in the context of ventricular premature beats (VPBs) (RR, 1.16; 95 CI, 1.08-1.25; I2 = 0%; P < .0001). The reduction in paroxysmal atrial fibrillation (AF)/atrial flutter was increased with acupuncture, albeit without statistical significance (RR, 1.12; 95% CI, 0.88-1.42; I2 = 0%; P = .36). Acupuncture also led to a greater reduction in heart rate (HR) compared to pharmaceutical treatment despite notable heterogeneity and a lack of statistical significance (mean difference, -1.55; 95% CI, -41.37 to 38.28; I2 = 99%; P = .94). Adverse events were effectively managed, affirming the favorable safety profile of acupuncture. Our study suggests that acupuncture leads to a greater reduction in the recurrence rates of VPBs, AF, and atrial flutter but not significantly so in paroxysmal SVT or post-treatment HR. While promising for specific arrhythmias, the varying effectiveness of acupuncture underscores the need for further research and clinical assessment to determine its precise role and suitability in managing particular cardiac conditions.

The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species' Blood in the Investigation of Crime Scenes.

OBJECTIVES: The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology. METHODS: A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated. RESULTS: Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%. CONCLUSION: These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.

Synthesis of NiMn2O4/PANI nanosized composite with increased specific capacitance for energy storage applications.

Polyaniline (PANI) stands out as a highly promising conducting polymer with potential for advanced utilization in high-performance pseudocapacitors. Therefore, there exists a pressing need to bolster the structural durability of PANI, achievable by developing composite materials that can enhance its viability for supercapacitor applications. In this particular study, a pioneering approach was undertaken to produce a novel NiMn2O4/PANI supercapacitor electrode material. A comprehensive array of analytical techniques was employed to ascertain the structural configuration, morphology, oxidation states of elements, composition, and surface characteristics of the electrode material. The electrochemical evaluation of the NiMn2O4/PANI composite shows a specific capacitance (Cs) of 1530 ± 2 F g-1 at 1 A g-1. Significantly, the composite material displays an outstanding 93.61% retention of its capacity after an extensive 10 000 cycles, signifying remarkable cycling stability, while the 2-electrode configuration reveals a Cs value of 764 F g-1 at 5 mV s-1 and 826 F g-1 at 1 A g-1 with a smaller charge transfer resistance (Rct) value of 0.67 Ω. Chronoamperometric tests showed excellent stability of the fabricated material up to 50 h. This significant advancement bears immense promise for its potential implementation in high-efficiency energy storage systems and heralds a new phase in the development of supercapacitor technology with improved stability and performance metrics.

Valve-in-Valve Transcatheter Aortic Valve Replacement Versus Redo-Surgical Aortic Valve Replacement in Patients With Aortic Stenosis: A Systematic Review and Meta-analysis.

Aortic stenosis is a common and significant valve condition requiring bioprosthetic heart valves with transcatheter aortic valve replacement (TAVR) being strongly recommended for high-risk patients or patients over 75 years. This meta-analysis aimed to pool existing data on postprocedural clinical as well as echocardiographic outcomes comparing valve-in-valve (ViV)-TAVR to redo-surgical aortic valve replacement to assess the short-term and medium-term outcomes for both treatment methods. A systematic literature search on Cochrane Central, Scopus, and Medline (PubMed interface) electronic databases from inception to August 2023. We used odds ratios (OR) for dichotomous outcomes and mean differences (MD) for continuous outcomes. Twenty-four studies (25,216 patients) were pooled with a mean follow-up of 16.4 months. The analysis revealed that ViV-TAVR group showed a significant reduction in 30-day mortality (OR 0.50, 95% confidence interval [CI] 0.43 to 0.58, p <0.00001), new-onset atrial fibrillation (OR 0.34, 95% CI 0.17 to 0.67, p = 0.002), major bleeding event (OR 0.28, 95% CI 0.17 to 0.45, p <0.00001) and lower rate of device success (OR 0.25, 95% CI 0.12 to 0.53, p = 0.0003). There were no significant differences between either group when assessing 1-year mortality, stroke, myocardial infarction, postoperative left ventricular ejection fraction, and effective orifice area. ViV-TAVR cohort showed a significantly increased incidence of paravalvular leaks, aortic regurgitation, and increased mean aortic valve gradient. ViV-TAVR is a viable short-term option for elderly patients with high co-morbidities and operative risks, reducing perioperative complications and improving 30-day mortality with no significant cardiovascular adverse events. However, both treatment methods present similar results on short-term to medium-term complications assessment.

SEM and LM insights: Spore morphology and its taxonomic significance in Sino Himalayan, Malesian, and European elements of Pteridaceae.

The Pteridaceae family, known for its taxonomic complexity, presents challenges in identification due to high variability among its species. This study investigates the spore morphology employing both SEM and LM techniques in 10 Pteridaceae taxa phytogeographicaly Sino-Himalayan, Malesian, and European elements in Pakistan. The taxa include Adiantum capillus-veneris, A. incisum, A. venustum, Aleuritopteris bicolor, Oeosporangium nitidulum, O. pteridioides, Onychium cryptogrammoides, O. vermae, Pteris cretica, and P. vittata. The objective is to assess their taxonomic relevance and develop a spore-based taxonomic key. Findings indicate differences in spore shape, sizes, exospore thickness, and in surface ornamentation highlighting the potential for taxonomic differentiation. Spores are trilete, and notable differences are observed in the dimension of spores in both distal and proximal sides. Equatorial dimensions vary between 35 and 50 µm, while the polar diameter ranges from 29 to 50 µm. SEM revealed different spore ornamentation types that show several useful characteristics establishing valuable taxonomic variations. The studied Adiantum taxa feature a perispore with tubercules and a micro-granulose surface. The spores of examined Oeosporangium and Aleuritopteris taxa shows cristate sculptures with variable ornamentations. Both species of Onychium have tuberculate-pleated tubercles with sinuous folds on both distal and proximal sides. The surface ornamentation among examined Pteris taxa show variability. PCA analysis indicated that spore quantitative data identified distinct groups, underscoring taxonomic significance. Nevertheless, there was variation observed in surface ornamentation and spore shape, indicating the potential for discrimination among taxa. RESEARCH HIGHLIGHTS: Spore morphology of 10 Pteridaceae taxa has been investigated through LM and SEM. Investigated species shows differences in spore shape, sizes, exospore thickness, and in surface ornamentation. Ornamentation on the perispore provides several valuable characteristics, establishing useful taxonomic distinctions. Spore morphological analysis is effective at the generic level, with minor distinctions discernible at the species level.

Highly Selective Electroreduction of Nitrobenzene to Aniline by Co-Doped 1T-MoS2.

The selective electrocatalytic reduction of nitrobenzene (NB) to aniline demands a desirable cathodic catalyst to overcome the challenges of the competing hydrogen evolution reaction (HER), a higher overpotential, and a lower selectivity. Here, we deposit Co-doped 1T MoS2 on Ti mesh by the solvothermal method with different doping percentages of Co as x % Co-MoS2 (where x = 3, 5, 8, 10, and 12%). Because of the lowest overpotential, lower charge-transfer resistance, strong suppression of the competing HER, and higher electrochemical surface area, 8% Co-MoS2 achieves 94% selectivity of aniline with 54% faradaic efficiency. The reduction process follows first-order dynamics with a reaction coefficient of 0.5 h-1. Besides, 8% Co-MoS2 is highly stable and retains 81% selectivity even after 8 cycles. Mechanistic studies showed that the selective and exothermic adsorption of the nitro group at x % Co-MoS2 leads to a higher rate of NB reduction and higher selectivity of aniline. The aniline product is successfully removed from the solution by polymerization at FTO. This study signifies the impact of doping metal atoms in tuning the electronic arrangement of 1T-MoS2 for the facilitation of organic transformations.

Transforming Waste into Wealth: Advanced Carbon-Based Electrodes Derived from Refinery and Coal By-Products for Next-Generation Energy Storage.

This comprehensive review addresses the need for sustainable and efficient energy storage technologies against escalating global energy demand and environmental concerns. It explores the innovative utilization of waste materials from oil refineries and coal processing industries as precursors for carbon-based electrodes in next-generation energy storage systems, including batteries and supercapacitors. These waste-derived carbon materials, such as semi-coke, coal gasification fine ash, coal tar pitch, petroleum coke, and petroleum vacuum residue, offer a promising alternative to conventional electrode materials. They present an optimal balance of high carbon content and enhanced electrochemical properties while promoting environmental sustainability through effectively repurposing waste materials from coal and hydrocarbon industries. This review systematically examines recent advancements in fabricating and applying waste-derived carbon-based electrodes. It delves into the methodologies for converting industrial by-products into high-quality carbon electrodes, with a particular emphasis on carbonization and activation processes tailored to enhance the electrochemical performance of the derived materials. Key findings indicate that while higher carbonization temperatures may impede the development of a porous structure, using KOH as an activating agent has proven effective in developing mesoporous structures conducive to ion transport and storage. Moreover, incorporating heteroatom doping (with elements such as sulfur, potassium, and nitrogen) has shown promise in enhancing surface interactions and facilitating the diffusion process through increased availability of active sites, thereby demonstrating the potential for improved storage capabilities. The electrochemical performance of these waste-derived carbon materials is evaluated across various configurations and electrolytes. Challenges and future directions are identified, highlighting the need for a deeper understanding of the microstructural characteristics that influence electrochemical performance and advocating for interdisciplinary research to achieve precise control over material properties. This review contributes to advancing electrode material technology and promotes environmental sustainability by repurposing industrial waste into valuable resources for energy storage. It underscores the potential of waste-derived carbon materials in sustainably meeting global energy storage demands.

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring.

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

Relationship between nitrate, heavy metal, and sterols contents in Japanese agricultural soils with risk of groundwater pollution.

In Japanese agricultural lands, nitrate-nitrogen contamination of soil and groundwater often occurs due to the application of livestock excrements and compost. Therefore, rural soils in Japan were sampled and analyzed for nitrate-nitrogen leaching, heavy metal content, and sterols associated with livestock excrement and compost to calculate contamination risk indicators. The results were analyzed using self-organizing maps and cluster analysis. Nitrate-nitrogen content using water extraction was detected in most of the sampled soils. In addition, many samples from areas that were already severely contaminated with nitrate-nitrogen showed particularly high concentrations. Coprostanol, an indicator of fecal contamination, was detected in more than half of the samples. The main source of nitrate-nitrogen contamination in these areas is livestock excrement and compost. Self-organization maps showed that areas with high nitrate-nitrogen contamination also corresponded to areas with high copper and zinc soil contents. The self-organization maps and cluster analysis resulted in five clusters: a nitrate-contaminated group mainly originating from livestock excrement and compost, a heavy metal-contaminated group, a general group, a nitrate-contaminated group mainly originating from chemical fertilizers, and a contaminated group with potentially hazardous substances requiring attention. Authorities and decision-makers can use the results to prioritize areas requiring remediation.

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

INTRODUCTION: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS: A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.

Antidiabetic Properties of Caffeoylmalic Acid, a Bioactive Natural Compound Isolated from Urtica dioica.

The uncontrolled hyperglycemia that characterizes diabetes mellitus (DM) causes several complications in the organism. DM is among the major causes of deaths, and the limited efficacy of current treatments push the search for novel drug candidates, also among natural compounds. We focused our attention on caffeoylmalic acid, a phenolic derivative extracted from Urtica dioica, a plant investigated for its potential against type 2 DM. This compound was tested for its antidiabetic activity in vitro through a glucose uptake assay, in vivo in a mouse DM model and through molecular docking towards α-amylase and α-glucosidase. The effects on glucose blood level, liver enzymes, insulin and creatinine levels as well as on lipid and blood parameters, considered biochemical markers of diabetes, were also evaluated. The results showed an antidiabetic activity in vitro and in vivo, as the compound stimulates glucose absorbtion and reduces blood glucose levels. Moreover, it ameliorates lipid profile, liver and blood parameters, with moderate effect on insulin secretion. Taken together, these findings pave the way for the compounds from this class of caffeoylmalic acid as potential antidiabetic compounds.