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Understanding the variables that promote pro-environmental behaviors (PEBs) can be useful for sustainable development. Research has shown that several factors, including emotions, can influence PEBs. Eco-empathy and helplessness beliefs toward the environment (HBTE) can facilitate or discourage PEBs. To understand the role of these factors, it may be interesting to study their connection with different emotion regulation strategies suggested by Self-Determination Theory. The current study sought to test a model that could explain the associations of dysregulation, suppressive regulation, and integrative regulation with eco-empathy, HBTE, and PEBs. In a sample of 305 emerging adults, we found an association between integrative regulation and eco-empathy, dysregulation, and HBTE. The analyses revealed an indirect pathway from integrative regulation to PEBs through eco-empathy, suggesting the need to implement educational and informational programmes that consider the impact of emotional constraints on PEB implementation in the younger generation.

Abstract Background Multidrug-resistant (MDR) bacteria are a global health threat, notably in low- and middle-income countries. The aim of this review was to estimate the prevalence of multidrug-resistant bacteria in healthcare and community settings in West Africa. Methods In accordance with PRISMA guidelines, we searched PubMed, CINAHL, African Index Medicus, and other databases for studies published from 2010 onward. Data on MDR bacterial prevalence, study characteristics, and infection types were extracted and analyzed via R software. Subgroup analyses were performed to explore differences in prevalence across infection settings and sample types. Results Out of the 5,320 articles identified, 50 studies from 13 West African countries met the inclusion criteria, with the majority from Nigeria (34%) and Ghana (22%). Among the 35,820 bacteria isolated in these studies, gram-negative bacteria (GNB), particularly Escherichia coli and Klebsiella sp., were the most frequently isolated species, accounting for 63.3% of the bacteria. The overall prevalence of MDR bacteria was 59% (95% CI: 48-69%), with significant heterogeneity between studies (I² = 98%, p

Abstract Sedimentary processes cause large changes in riverscapes and alter river banks and margins, leading to major hazards for riverine communities. However, regional mapping of the resulting risk remains scarce, especially in remote Amazon regions. Here, we combine environmental observations with regional socioeconomic information to map erosion and sedimentation risk for Central Amazon river-floodplain communities. We combine long-term trends of open water changes (sedimentation/erosion hazard) with exposure (population size) and social vulnerability (socio-economic indices) to estimate risk at each community. Half of the region’s population, located either in uplands or floodplains, is subject to an unstable riverscape, with 18.5% of the communities affected by sedimentation processes and 26% by erosion. We identified four communities (out of 51 assessed) at very high risk, and seven at high-to-moderate risk. We highlight the need to include sedimentary processes in disaster management strategies in the changing river landscapes of the Amazon.

Abstract Background Current guidelines for osteoporosis diagnosis do not recommend forearm dual-energy X-ray absorptiometry (DXA) as a standard tool, except in specific cases. This systematic review and meta-analysis investigates the potential correlation between forearm BMD and BMD at the lumbar and/or hip sites. Methods The protocol was registered in PROSPERO (CRD42024568756), and the study adhered to the PRISMA guidelines. Major databases were systematically searched from their inception until August 2024 to identify studies evaluating the ability of forearm DXA scans to detect osteoporosis, particularly in comparison to central sites like the femoral neck and lumbar spine. A meta-analysis was conducted on studies that reported correlation coefficients between these measurements. Quality assessment was conducted independently by 3 reviewers following Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria. Additionally, a narrative synthesis of the main findings across different patient groups was performed. Results Thirteen studies were included. Published between 1992 and 2023, these studies involved 5941 participants. Forearm scans exhibited strong correlations with lumbar and femoral sites (pooled effect size 0.603, 95%CI 0.579–0.627 and 0.641, 95%IC 0.600–0.680, respectively) demonstrating good predictive value for central osteoporosis. Despite some result variations, forearm DXA scanning emerged as a valid method, especially when lumbar and femoral measures are challenging. Conclusions A DXA scan of the distal forearm proves to be a valuable supplementary tool for identifying osteoporotic conditions. This could be particularly relevant in older patients, where conducting lumbar or hip scans is often challenging or not feasible.

Abstract The dataset comprises a 45-year-long directional wave time series recorded at the Acqua Alta Oceanographic research Tower (AAOT) since 1979. The AAOT is located in the Northern Adriatic Sea and it is managed by the Institute of Marine Sciences of the National Research Council of Italy (CNR-ISMAR). The extent of the time series enables the description of the wave climate in the North Adriatic region and the identification of trends and links with large-scale climate patterns from a single and permanent observational source. Different wave gauges have been used since the start of the measurements, progressively upgraded and repositioned during maintenance operations. The recent addition of more precise instruments and the availability of the related raw dataset allowed for a re-evaluation of the previously published data, while extending the timeseries in time. This has enabled the creation of a substantially improved, yet homogeneous, measured dataset, thereby enhancing the reliability of the related long-term scenario analysis.

While global hydrological models (GHMs) are affected by large uncertainties regarding model structure, forcing and calibration data, and parameters, observations of model output variables are rarely used to calibrate the model. Pareto-dominance-based multi-objective calibration, often referred to as Pareto-optimal calibration (POC), may serve to estimate model parameter sets and analyse trade-offs among different objectives during calibration. Within a POC framework, we determined optimal parameter sets for the WaterGAP global hydrological model (WGHM) in the two largest basins of the Indian subcontinent – the Ganges and the Brahmaputra, collectively supporting nearly 580 million inhabitants. The selected model parameters, determined through a multi-variable, multi-signature sensitivity analysis, were estimated using up to four types of observations: in situ streamflow (Q), GRACE and GRACE Follow-On terrestrial water storage anomaly (TWSA), LandFlux evapotranspiration (ET), and surface water storage anomaly (SWSA) derived from multi-satellite observations. While our sensitivity analysis ensured that the model parameters that are most influential for the four variables were identified in a transparent and comprehensive way, the rather large number of calibration parameters, 10 for the Ganges and 16 for the Brahmaputra, had a negative impact on parameter identifiability during the calibration process. Calibration against observed Q was crucial for reasonable streamflow simulations, while additional calibration against TWSA was crucial for the Ganges basin and helpful for the Brahmaputra basin to obtain a reasonable simulation of both Q and TWSA. Additionally calibrating against ET and SWSA enhanced the overall model performance slightly. We identified several trade-offs among the calibration objectives, with the nature of these trade-offs closely tied to the physiographic and hydrologic characteristics of the study basins. The trade-offs were particularly pronounced in the Ganges basin, in particular between Q and SWSA, as well as between Q and ET. When considering the observational uncertainty of the calibration data, model performance decreases in most cases. This indicates an overfitting to the singular observation time series by the calibration algorithm. We therefore propose a transparent algorithm to identify high-performing Pareto solutions under consideration of observational uncertainties of the calibration data.

Abstract Duchenne Muscular Dystrophy (DMD) progressively leads to loss of limb function due to muscle weakness. The incurable nature of the disease shifts the focus to improving quality of life, including assistive supports to improve arm function. Over time, the passive joint impedance (Jimp) of people with DMD increases. Force-based controlled motorised arm supports require a clear distinction between the user’s movement intention and passive forces, such as passive Jimp. Therefore, Jimp identification is essential. This review aims to define Jimp, identify factors influencing it, and outline experimental methods used for quantification, with a focus on the upper extremities in DMD. A literature review was performed in May 2021 and updated in March 2024 using SCOPUS, PubMed, IEEEXplore, and WebOfScience. The results reveal confusion in definitions and show various Jimp measuring practices for both DMD and individuals without muscle weakness. This study presents an overview and lists important parameters affecting passive Jimp, such as the joint’s position, velocity and the multi-articular nature of the upper arm muscles. For personalised passive Jimp compensation in arm supports, ramp-type perturbations with constant velocity across the full joint range appear most optimal for identifying the elevated and non-linear nature of the passive Jimp in DMD.

Abstract Background The PalliPed project is a nationwide, observational, cross-sectional study designed with the aim of providing a constantly updated national database for the census and monitoring of specialized pediatric palliative care (PPC) activities in Italy. This paper presents the results of the first monitoring phase of the PalliPed project, which was developed through the PalliPed 2022–2023 study, to update current knowledge on the provision of specialized PPC services in Italy. Methods Italian specialized PPC centers/facilities were invited to participate and asked to complete a self-reporting, ad-hoc, online survey regarding their clinical activity in 2022–2023, in the revision of the data initially collected in the first PalliPed study of 2021. Results 18 specialized PPC centers/facilities from 14 Italian regions and two autonomous provinces participated; 13 were identified as regional referral centers (72.2%), with the acquisition of three new centers in comparison to 2021. Full coverage of the regional territory was reported by 54% of them, compared with 45% in 2021, while a 24/7 service was offered by 23%, compared with 27% in 2021. Eight of 13 referral centers (61%) had a dedicated team, compared with 91% in 2021. Also, an overall increase in the number of followed patients was observed, rising from 1,209 (2019) to 2,734 (2023). In line with previous data, most PPC healthcare providers were nurses (n = 181) and physicians (n = 89), with an overall increased number of PPC providers from 2021. Conclusions The nationwide PalliPed project established the first comprehensive overview and monitoring of the state of specialized PPC in Italy. Data reported within the PalliPed 2022–2023 monitoring study suggest a general improving trend in the availability of the specialized PPC service in Italy, compared with data collected in 2021. At the same time, the need for a greater effort to provide better care models and resources for specialized PPC remains, especially considering that the number of children needing PPC is constantly increasing.

Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesting using brain age prediction models for early diagnosis of neurodegenerative disorders, such as Parkinson’s, Schizophrenia, and Alzheimer’s disease. However, data availability, acquisition protocols diversity and models’ computational complexity remain limiting factors for clinical adoption. This study proposes a low-complexity convolutional neural network (CNN) model that tackles these challenges, focusing on three main aspects: performance accuracy, computational complexity, and adaptability to new, external datasets. We developed a brain-age prediction system using a minimally preprocessed T1-weighted MRI images with a multi-site dataset of healthy individuals covering the whole human lifespan (2251 subjects, age range 6-90 years). We proposed a lighter version of the Simple Fully Convolutional Network (SFCN) that contain only 1.2 million parameters. Computational load was further reduced by cropping the brain images. Finally, we employed transfer learning approach to achieve domain adaptation to external, unseen sites. We demonstrated that leveraging the cropped brain images reduced the computational time for training by 50%, maintaining a comparable accuracy to using the entire brain. The model achieved a Mean Absolute Error (MAE) of 3.557 for the full brain and 4.139 for the cropped images with a Pearson correlation $ r = 0.988 $ between the full and cropped brain predictions when evaluated on the same test set. Domain adaptation of our model to new external data showed a significant improvement in the prediction performance, reducing MAE from 7.219 to 4.750 for full brain images and from 12.107 to 5.770 for the cropped images. This study is the first to demonstrate comparable prediction accuracy using only a small segment of a 3D full brain MRI scan. Our results show that it is feasible to build lightweight CNN models trained on small-scale, heterogeneous datasets and fine-tuned to new external clinical data, making significant steps toward practical clinical application.

In this paper, we have modified the workflow of the traditional light transmission aggregometry (LTA) protocol to characterize tumor cell clusters in vitro in a quantifiable and multifaceted manner. Circulating tumor cell (CTC) clusters have high metastatic potential compared to single tumor cells traveling in the bloodstream. Thus, engineering new therapeutic strategies that specifically target this CTC population is essential. To accomplish this, quantifiable methods to characterize their therapeutic effect on tumor cell clusters is a prerequisite. The method presented here enables the user to precisely quantify the dissociation of cancer cell clusters in the presence of clinically relevant fibrinolytic agents, such as alteplase and tenecteplase. The efficacy of the fibrinolytic agents can be quantified using this in vitro assay, prior to conducting preclinical studies. Here, we have obtained the fibrinolytic activity data in terms of lag time to the initiation of tumor cell dissociation, time to 25% dissociation, and trend of dissociation over time. To validate the assay, cell counts and phase‐contrast microscopy images were recorded over time. Further, we explored an LTA‐assisted preparation of platelet–tumor‐cell clusters of calibrated size for potential downstream testing/applications. To assess whether the assay is applicable to characterize the dissociation of cancer cell clusters in the presence of platelets, we added low (50 000 platelets·μL−1), normal (200 000 platelets·μL−1) and high (450 000 platelets·μL−1) concentrations of platelets to the tumor cell clusters. In addition to dissociation parameters, microcopy images were recorded over time to validate the assay and enabled the enumeration of clusters and single cells. The correlative light electron microscopy (CLEM) technique was utilized to visualize the morphology and composition of platelet–tumor cell clusters.