Search Results (2,394)

Cannabis, derived from Cannabis sativa plants, is a prevalent illicit substance in the United States, containing over 400 chemicals, including 100 cannabinoids, each affecting the body’s organs differently upon ingestion. Cannabis hyperemesis syndrome (CHS) is a gut–brain axis disorder characterized by recurring nausea and vomiting intensified by excessive cannabis consumption. CHS often goes undiagnosed due to inconsistent criteria, subjective symptoms, and similarity to cyclical vomiting syndrome (CVS). Understanding the endocannabinoid system (ECS) and its dual response (pro-emetic at higher doses and anti-emetic at lower doses) is crucial in the pathophysiology of CHS. Recent research noted that type 1 cannabinoid receptors in the intestinal nerve plexus exhibit an inhibitory effect on gastrointestinal motility. At the same time, the thermoregulatory function of endocannabinoids might explain compulsive hot bathing in CHS patients. The prevalence of cannabis CHS is expected to rise as legal restrictions on its recreational use decrease in several states. Education and awareness are vital in diagnosing and treating CHS as its prevalence increases. This comprehensive review explores the ECS’s involvement, CHS management approaches, and knowledge gaps to enhance understanding of this syndrome. Full article

Low water resistance is the main shortcoming of unfired earth materials, requiring chemical stabilisation for some durable applications. Ordinary Portland cement (PC) is an efficient stabiliser, but it goes against the ecological and sustainable nature of earth construction. This study explores the use of low-carbon thermoactivated recycled cement (RC) obtained from old cement waste as a new eco-efficient alternative to PC in the stabilisation of compressed earth blocks (CEBs). The objective is to improve the durability of the CEB masonry even when applied in direct contact with water, without compromising its eco-efficiency. The water resistance of the CEBs with 0% (unstabilised) and 5% and 10% (wt. of earth) stabiliser and partial to total replacement of PC with RC (0, 20, 50, 100% wt.) was evaluated in terms of compressive strength under different moisture contents, immersion and capillary water absorption, low-pressure water absorption, water permeability and water erosion. Low absorption and high resistance to water erosion were achieved in stabilised CEBs, regardless of the type of cement used. The incorporation of RC increased the total porosity and water absorption of the CEBs compared to PC, but significantly improved the water resistance of the unstabilised blocks. The eco-friendlier RC proved to be a promising alternative to PC stabilisation. Full article

This paper examines the essential role of urban green spaces in fostering healthy living from a landscape architecture perspective. Health goes beyond the mere absence of disease to include physical, mental, and social wellbeing, all of which are greatly enhanced by accessible green spaces. By synthesising existing literature, this paper shows that urban green spaces have strong positive associations with health outcomes, especially in urban settings where environmental stressors are pronounced. The paper stresses the importance of designing attractive and accessible green spaces that encourage physical activity, mental wellbeing, and social interaction, addressing public health issues such as obesity and mental health disorders. In addition to physical and mental health benefits, the paper explores the potential of local food production through edible green infrastructure, such as community gardens, which can significantly improve diet and nutrition. Additionally, the study discusses disparities in the access to quality green spaces, particularly between the Global North and South, and advocates for equitable design strategies that serve diverse populations. Integrating evidence-based approaches into landscape architecture, the paper argues for the establishment of urban green spaces as essential elements of public health infrastructure. Finally, the paper calls for future research and policy efforts to maximise the health benefits of urban green spaces and improve the quality of life in urban environments. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

Currently, more studies are focusing on traffic classification in software-defined networks (SDNs). Accurate classification and selecting the appropriate controller have benefited from the application of machine learning (ML) in practice. In this research, we study different classification models to see which one best classifies the generated dataset and goes on to be implemented for real-time classification. In our case, the classification and regression tree (CART) classifier produces the best classification results for the generated dataset, and logistic regression is also considerable. Based on the evaluation of various algorithmic outputs for the training and validation datasets, and also when execution time is taken into account, the CART is found to be the best algorithm. While testing the impact of load balancing in a multi-controller SDN environment, in different load case scenarios, we observe network performance parameters like bit rate, packet rate, and jitter. Here, the use of traffic classification-based load balancing improves the bit rate as well as the packet rate of traffic flow on a network and thus considerably enhances throughput. Finally, the reduction in jitter while increasing the controllers confirms the improvement in QoS in a balanced multi-controller SDN environment. Full article

The mechanism of transcription proceeds through the formation of R-loop structures containing a DNA–RNA heteroduplex and a single-stranded DNA segment that should be placed inside the elongation complex; therefore, these nucleic acid segments are limited in length. The attachment of each nucleotide to the 3′ end of an RNA strand requires a repeating cycle of incoming nucleoside triphosphate binding, catalysis, and enzyme translocation. Within these steps of transcription elongation, RNA polymerase sequentially goes through several states and is post-translocated, catalytic, and pre-translocated. Moreover, the backward movement of the polymerase, which is essential for transcription pausing and proofreading activity, gives rise to a backtracked state. In the present study, to analyze both the efficacy of transcription elongation complex (TEC) formation and the rate of RNA synthesis, we used a set of model R-loops that mimic the pre-translocated state, post-translocated state, backtracked state, and a misincorporation event. It was shown that TEC assembly proceeds as an equilibrium process, including the simultaneous formation of a catalytically competent TEC as well as a catalytically inactive conformation. Our data suggest that the inactive complex of RNA polymerase with an R-loop undergoes slow conformational changes, resulting in a catalytically competent TEC. It was revealed that the structural features of R-loops affect the ratio between active and inactive states of the TEC, the rate of conformational rearrangements required for the induced-fit transition from the inactive state to the catalytically competent TEC, and the rates of accumulation of both the total RNA products and long RNA products. Full article

COVID-19 can range from a mild to severe acute respiratory syndrome and also could result in multisystemic damage. Additionally, many people develop post-acute symptoms associated with immune and metabolic disturbances in response to viral infection, requiring longitudinal and multisystem studies to understand the complexity of COVID-19 pathophysiology. Here, we conducted a ¹H Nuclear Magnetic Resonance metabolomics in saliva of symptomatic subjects presenting mild and moderate respiratory symptoms to investigate prospective changes in the metabolism induced after acute-phase SARS-CoV-2 infection. Saliva from 119 donors presenting non-COVID and COVID-19 respiratory symptoms were evaluated in the acute phase (T1) and the post-acute phase (T2). We found two clusters of metabolite fluctuation in the COVID-19 group. Cluster 1, metabolites such as glucose, (CH₃)₃ choline-related metabolites, 2-hydroxybutyrate, BCAA, and taurine increased in T2 relative to T1, and in cluster 2, acetate, creatine/creatinine, phenylalanine, histidine, and lysine decreased in T2 relative to T1. Metabolic fluctuations in the COVID-19 group were associated with overweight/obesity, vaccination status, higher viral load, and viral clearance of the respiratory tract. Our data unveil metabolic signatures associated with the transition to the post-acute phase of SARS-CoV-2 infection that may reflect tissue damage, inflammatory process, and activation of tissue repair cascade. Thus, they contribute to describing alterations in host metabolism that may be associated with prolonged symptoms of COVID-19. Full article

Fire blight is a highly destructive plant disease that affects the pome fruit value chain, with high economic impacts. Its etiological agent is the Gram-negative bacterium Erwinia amylovora. The origin of fire blight goes back to the late 1700s in North America, and the disease since then has spread to New Zealand, Europe, North Africa, the Middle East, and Asia. Due to its worldwide dissemination, advances have been made to identify and characterize E. amylovora strains from different regions and understand their evolutionary adaptation. Additionally, many efforts have been made in recent decades to stop the occurrence and impacts of fire blight, but in many countries, only preventive measures have been applied, as the application of antibiotics and copper-based compounds has become more restricted. Thus, new sustainable methods to control the pathogen are constantly required. This article presents a comprehensive review of the pathogen, from the phenotypic and molecular characterization methods applied to advances in comparative genomics and the development of new compounds for sustainable control of E. amylovora. Full article

This work reports and discusses the results of a secondary analysis on statistical data regarding the phenomenon of school dropout in Italy starting from primary school. The research was conducted as part of Next GenerationEU funded by the European Union. The aim is to highlight any territorial differences in the phenomenon at the European, national, and local level. The data were collected from reliable sources (Eurostat, National Institute of Statistics, Ministry of Education and Merit, Regional School Office of Campania) and are updated to the latest year available. In line with the goals of PRIN, the aim was to photograph the national situation starting from the results of the INVALSI tests recorded in primary and lower secondary schools. The results of the analysis show that the levels of school dropout in Italy are among the highest in EU countries and, within our country, the well-known gap between the North and South remains, with the latter in a worse position. An econometric model is presented that demonstrates a cause–effect relationship that goes from the results of the primary cycle to those of the secondary cycle. This outcome attests to the importance of strengthening and increasing the skills necessary to prevent the possible conditions of school dropout starting from primary school. Full article

We developed an innovative system that combines Natural Language Understanding (NLU), a curated knowledge base, and the efficient management of a Large Language Model (LLM) to support motivational health coaching. Using Rasa as the core framework, we enhanced it by integrating the GPT-3.5-turbo model. Users opt into reflective dialogues during conversations. When they respond to open-ended questions, their input goes directly to the GPT-3.5-turbo model, allowing for more flexible responses. To provide curated trustworthy content, we integrated a knowledge provision component that searches a PDF-based knowledge base and generates user-friendly responses using Retrieval-Augmented Generation. We tested the system in a real-world scenario by deploying it on a Nao robot in seven older adults’ homes for 1–2 weeks, encouraging positive behavioral changes in some users. Our system serves as a valuable foundation for building an even more integrated, personalized system that can connect with other Application Programing Interfaces (APIs) and integrate with home sensors and edge devices. Full article

Background: While several studies investigate the utility and clinical value of 3D printing in aiding diagnosis, medical education, preoperative planning, and intraoperative guidance of surgical interventions, there is a scarcity of literature regarding concrete applications of mixed reality in the cardiovascular domain due to its nascent stage of study and expansion. This study goes beyond a mere three-dimensional visualization of the cardiac district, aiming to visualize the intracardiac structures within the scope of preoperative planning for cardiac surgery. Methods: The segmentation of the heart was performed through an open-source and a professional software and by applying different procedures. Each anatomical component of the heart, including the aortic valve, was accurately segmented and a 3D model was built to represent the entire heart. Results: Beyond the three-dimensional visualization of the cardiac region, the intracardiac structures were also segmented. A mixed-reality app was implemented with the possibility of exploding the model, interacting with it, and freely sectioning it with a plane. Conclusions: The proposed segmentation methodology allows a segmentation of the valve and the intracardiac structures. Furthermore, the mixed-reality app has confirmed the potential of this technology in diagnostic and preoperative planning, although some limitations should still be overcome. Full article

Omics approaches were extensively applied during the coronavirus disease 2019 (COVID-19) pandemic to understand the disease, identify biomarkers with diagnostic and prognostic value, and discover new molecular targets for medications. COVID-19 continues to challenge the healthcare system as the virus mutates, becoming more transmissible or adept at evading the immune system, causing resurgent epidemic waves over the last few years. In this study, we used saliva from volunteers who were negative and positive for COVID-19 when Omicron and its variants became dominant. We applied a direct solid-phase extraction approach followed by non-target metabolomics analysis to identify potential salivary signatures of hospital-recruited volunteers to establish a model for COVID-19 screening. Our model, which aimed to differentiate COVID-19-positive individuals from controls in a hospital setting, was based on 39 compounds and achieved high sensitivity (85%/100%), specificity (82%/84%), and accuracy (84%/92%) in training and validation sets, respectively. The salivary diagnostic signatures were mainly composed of amino acids and lipids and were related to a heightened innate immune antiviral response and an attenuated inflammatory profile. The higher abundance of thyrotropin-releasing hormone in the COVID-19 positive group highlighted the endocrine imbalance in low-severity disease, as first reported here, underscoring the need for further studies in this area. Full article

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies. Full article

Background/Objectives: Tendon structure is predominantly composed of the extracellular matrix (ECM), and genetic variants in non-collagenous ECM components may influence susceptibility to tendinopathy. We investigated the potential influence of single nucleotide polymorphisms (SNPs) in fibrillin-2 (FBN2), tenascin-C (TNC), and matrix metalloproteinase-3 (MMP3) on the tendon regeneration failure phenotype and impact on the susceptibility to tendinopathy in Brazilian high-performance athletes. Methods: This case–control study was conducted with 397 high-performance athletes from different sports modalities (197 tendinopathy cases and 200 controls), and they were analyzed by validated TaqMan^TM SNP genotyping assays of the SNPs FBN2 (rs331079), TNC (rs2104772), and MMP3 (rs591058). Results: Out of the 197 tendinopathy cases, 63% suffered from chronic tendon pain and 22% experienced more than three episodes of disease manifestation. The TNC-rs2104772-A allele was significantly associated with tendinopathy (OR: 1.4; 95% CI: 1.1–1.8), while athletes carrying the MMP3-rs591058-T allele were linked to an increased risk of more episodes of disease manifestation (OR: 1.7; 95% CI: 1.1–2.8). The TNC-MMP3 tendon regeneration failure phenotype (TNC-A/MMP3-T) was associated with an increased risk of tendinopathy (OR: 1.4; 95% CI: 1.1–2.0) and episodes of disease manifestation (OR: 2.0; 95% CI: 1.2–3.5). Athletes with tendinopathy who had the TNC-A/MMP3-T interaction were more prone to experiencing more than three disease exacerbations (OR: 4.3; 95% CI: 1.8–10.5) compared to TNC-A/TNC-C. Conclusions: This study suggests that rs2104772 and rs591058 SNPs could be involved in the tendon regeneration failure phenotype and may influence the molecular mechanism related to the regulation of the tendon ECM during training workload. Full article