Resilience and challenges of marine social-ecological systems under complex and interconnected drivers.

In this paper, we summarize the contributions made by an interdisciplinary group of researchers from different disciplines (biology, ecology, economics, and law) that deal with key dimensions of marine social-ecological systems. Particularly, the local and global seafood provision; the feasibility and management of marine protected areas; the use of marine ecosystem services; the institutional dimension in European fisheries, and the affordable models for providing scientific advice to small-scale fisheries. This Special Issue presents key findings from selected case studies around the world available to educators, policy makers, and the technical community. Together, these papers show that a range of diverse ecological, economic, social, and institutional components often mutually interact at spatial and temporal scales, which evidence that managing marine social-ecological systems needs a continuous adaptability to navigate into new governance systems.

All fish for China?

In this paper we examine the effect of Gross Domestic Product (GDP) on the level of fish intake in China in comparison with the rest of the world. We also analyse the origin and destination of China's seafood products in order to understand the main patterns during the last decades. The results show that in the 1961-2011 period the rate of growth of the GDP in China doubled that of other developing regions, while the daily fish intake of China increased fourfold, making China the largest fish consumer in the world. Given the size and scale of China's role in production, consumption, and global transformation of seafood markets, China is shaping a new era of industrialization in the history of the fishing industry.

Why are prices in wild catch and aquaculture industries so different?

Through a comparative analysis of prices in capture fisheries and aquaculture sectors, the objectives of this paper are a) to investigate three the trends in prices of forage catches to feed the aquaculture species, b) to analyze the amount of fish species need to feed aquaculture species in order to assess the level of efficiency in resource use, and c) to examine the degree of economic concentration either in wild-catch industry and aquaculture sectors. The results show that prices of cultivated species are higher than prices of the same species when harvested from the sea. We explain this fact by the interplay of three forces. First, the amount of wild fish to feed aquaculture species continues to improve over time. Second, the pressure of fishing activities has not been reduced since catches of most forage fishes are declining, which induce higher prices of capture species that feed aquaculture production. Third, the level of seafood market concentration is significantly higher in aquaculture than in wild catches, which generates higher prices in aquaculture.

A deep learning model for prognosis prediction after intracranial hemorrhage.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. METHODS: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model). RESULTS: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively. CONCLUSIONS: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.

Pellets and gummies: Seeking a 3D printed gastro-resistant omeprazole dosage for paediatric administration.

The production of 3D printed pharmaceuticals has thrived in recent years, as it allows the generation of customised medications in small batches. This is particularly helpful for patients who need specific doses or formulations, such as children. Compounding pharmacies seek alternatives to conventional solid oral doses, opting for oral liquid formulations. However, ensuring quality and stability, especially for pH-sensitive APIs like omeprazole, remains a challenge. This paper presents the application of semi-solid extrusion 3D printing technology to develop patient-tailored medicinal gummies, with an eye-catching appearances, serving as an innovative omeprazole pharmaceutical form for paediatric use. The study compares 3D printing hydrogels with dissolved omeprazole to hydrogels loaded with gastro-resistant omeprazole pellets, a ground-breaking approach.. Gastro-resistance and dissolution profiles were studied using different methods for better comparison and to emphasize the significance of the assay's methodology. Both developed formulas exhibit proper rheology, good printability, and meet content and mass uniformity standards. However, the high gastro-resistance and suitable release profile of 3D printed chewable semi-solid doses with enteric pellets highlight this as an effective strategy to address the challenge of paediatric medication.

Effect on Rheological Properties and 3D Printability of Biphasic Calcium Phosphate Microporous Particles in Hydrocolloid-Based Hydrogels.

The production of patient-specific bone substitutes with an exact fit through 3D printing is emerging as an alternative to autologous bone grafting. To the success of tissue regeneration, the material characteristics such as porosity, stiffness, and surface topography have a strong influence on the cell-material interaction and require significant attention. Printing a soft hydrocolloid-based hydrogel reinforced with irregularly-shaped microporous biphasic calcium phosphate (BCP) particles (150-500 µm) is an alternative strategy for the acquisition of a complex network with good mechanical properties that could fulfill the needs of cell proliferation and regeneration. Three well-known hydrocolloids (sodium alginate, xanthan gum, and gelatin) have been combined with BCP particles to generate stable, homogenous, and printable solid dispersions. Through rheological assessment, it was determined that the crosslinking time, printing process parameters (infill density percentage and infill pattern), as well as BCP particle size and concentration all influence the stiffness of the printed matrices. Additionally, the swelling behavior on fresh and dehydrated 3D-printed structures was investigated, where it was observed that the BCP particle characteristics influenced the constructs' water absorption, particle diffusion out of the matrix and degradability.

Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland.

Introduction: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. MS prevalence varies geographically and is notably high in Scotland. Disease trajectory varies significantly between individuals and the causes for this are largely unclear. Biomarkers predictive of disease course are urgently needed to allow improved stratification for current disease modifying therapies and future targeted treatments aimed at neuroprotection and remyelination. Magnetic resonance imaging (MRI) can detect disease activity and underlying damage non-invasively in vivo at the micro and macrostructural level. FutureMS is a prospective Scottish longitudinal multi-centre cohort study, which focuses on deeply phenotyping patients with recently diagnosed relapsing-remitting MS (RRMS). Neuroimaging is a central component of the study and provides two main primary endpoints for disease activity and neurodegeneration. This paper provides an overview of MRI data acquisition, management and processing in FutureMS. FutureMS is registered with the Integrated Research Application System (IRAS, UK) under reference number 169955. Methods and analysis: MRI is performed at baseline (N=431) and 1-year follow-up, in Dundee, Glasgow and Edinburgh (3T Siemens) and in Aberdeen (3T Philips), and managed and processed in Edinburgh. The core structural MRI protocol comprises T1-weighted, T2-weighted, FLAIR and proton density images. Primary imaging outcome measures are new/enlarging white matter lesions (WML) and reduction in brain volume over one year. Secondary imaging outcome measures comprise WML volume as an additional quantitative structural MRI measure, rim lesions on susceptibility-weighted imaging, and microstructural MRI measures, including diffusion tensor imaging and neurite orientation dispersion and density imaging metrics, relaxometry, magnetisation transfer (MT) ratio, MT saturation and derived g-ratio measures. Conclusions: FutureMS aims to reduce uncertainty around disease course and allow for targeted treatment in RRMS by exploring the role of conventional and advanced MRI measures as biomarkers of disease severity and progression in a large population of RRMS patients in Scotland.

Three-dimensional bioprinted cancer models: A powerful platform for investigating tunneling nanotube-like cell structures in complex microenvironments.

Bioprinting technology offers layer-by-layer positioning of cells within 3D space with complexity and a defined architecture. Cancer models based in this biofabrication technique are important tools to achieve representative and realistic in vivo conditions of the tumor microenvironment. Here, we show the development of a proof-of-concept three-dimensional bioprinted cancer model that successfully recapitulates the intercellular communication via the assembly of functional tunneling nanotube (TNT)-like cell projections. Different combinations of collagen-containing culture medium, sodium alginate and gelatin were initially prepared and rheologically evaluated. The optimized mixture was used to print two preliminary 3D models for cancer cell seeding. Favourable results in cell viability and proliferation led to the inclusion of 786-O renal cancer cells into the biomaterial mixture to directly bioprint the most suitable 3D model with embedded cells. Bioprinted cells remained viable for at least 15 days of culture and proliferated. More importantly, these cancer cells were able to build TNT-like cellular projections inside the hydrogel that established direct contacts between distant cells. We show that these structures were used as channels for the scrolling and intercellular transfer of mitochondria thus reproducing TNT's function in 2D culture systems. This 3D bioprinted renal cancer model provides a novel alternative tool for studying the functional relevance of TNT-like structures in tumorigenesis and anticancer drug susceptibility in a highly controlled and reproducible tumor microenvironment.

3D printed gummies: Personalized drug dosage in a safe and appealing way.

Obtention of customized dosage forms is one of the main attractions of 3D printing in pharmaceuticals. In this sense, children are one of the groups within the population with a greater need for drug doses adapted to their requirements (age, weight, pathological state…), but most 3D printed oral dosages are solid forms and, therefore, not suitable for them. This work developed patient-tailored medicinal gummies, an alternative oral dosage form with eye-catching appearance and appropriate organoleptic characteristics. Four inks were formulated, characterised and 3D printed by means of syringe-based extrusion mechanism. Different tests were performed to ensure reproducibility of the process and validate work methodology for dosage unit fabrication applying basic manufacturing standards. Rheological test helped in evaluating inks printability. Visual characterization concluded that drugmies, apart from a high fidelity in the 3D model shape reproduction, had a bright and uniformly coloured appearance and a pleasant aroma, which made them highly appetising and attractive. The printed gummy oral dosages complied comfortably with the mass uniformity assay regardless of the formulated ink used or the 3D model selected for printing. Ranitidine hydrochloride individual contents were determined using uv-vis spectrophotometry, showing successful results both in dose accuracy, uniformity of drug content and dissolution.

Lesion area detection using source image correlation coefficient for CT perfusion imaging.

Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as cerebral blood flow, cerebral blood volume, and mean transit time that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilization of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra, and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in magnetic resonance perfusion images.

Computed tomography perfusion imaging denoising using gaussian process regression.

Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.

Satisfacción percibida por los pacientes en el servicio de hemodiálisis del hospital general de La Palma / Patient-perceived satisfaction in the haemodialysis service of La Palma General Hospital

En los tiempos actuales en los que la mayoría de los esfuerzos se centran en las nuevas actualizaciones y novedosos tratamientos para los pacientes con Insufi ciencia Renal Crónica, nuestro interés, sin embargo, se ha dirigido hacia algo mucho más básico: conseguir saber cómo perciben los pacientes nuestros cuidados y cómo se sienten ellos en el servicio. Por tanto, el objetivo de este estudio es determinar el grado de satisfacción del paciente. El servicio de Hemodiálisis en la Isla de La Palma lleva desarrollando su actividad desde mayo de 1984, no sólo para atender a los pacientes residentes en la isla, sino también a los transeúntes que, por diversos motivos, se encuentran aquí. Se ha efectuado un estudio mediante encuestas anónimas y voluntarias efectuadas a pacientes de la Unidad de Hemodiálisis. Hemos utilizado la encuesta SERVQUOS modifi cada, y la escala Likert. Se realiza el tratamiento informático de los datos con el programa SPSS-19 para Windows. Según los resultados obtenidos, el índice neto de satisfacción fue de 86,36% para el ítem referido a la satisfacción general, teniendo una valoración de excelente. La media de edad de los pacientes fue de 64,63 años; el número medio de ingresos en el último año por pacientes fue de 1,08%; el porcentaje de pacientes en lista de trasplante fue del 20,7%. Los 5 ítems más valorados fueron: la confi anza trasmitida, la disposición del personal a prestarle ayuda, el trato personalizado, la amabilidad del personal y la información relacionada con la higiene. Por otro lado, los menos valorados fueron: la señalización para llegar al Servicio, la capacitación del personal, la información médica, la tecnología de los equipos y el tiempo de espera para ser atendido por el médico(AU)

Although most of the current researches are focussed on new actualizations and innovative treatments, the aim of this study is to find out how patients perceive our health care and how they feel at our Hospital Unit. Thus, the main objective is to find out the degree of patient satisfaction. Haemodialysis Service in La Palma Island is working since 1984, not only to give treatment to local patients, but also to support the health assistance of outpatients who are temporally on the island because of different reasons. Data were collected through anonymous surveys of volunteer patients using the modifi ed SERVQHOS and the Likert scale, too. Quantitative data analysis was made through SPSS- 19 program for Windows. According to the results, the Satisfaction Net Index showed that the 86,36% of the participants punctuated “excellent” the item referred to the general satisfaction degree. The average age and admissions to hospital during last year were 64.63 years old and 1.08% respectively. The percentage of patients registered on the kidney transplant waiting list was 20,7%. The most positively considered features were: The confi dence, the staff disposition to help, their kindness and the information related with hygiene. On the other hand, the most negatively viewed items were: the indications to find the dialysis unit, the staff’s qualifi cation, medical information, equipment technology and the waiting time to be seen by the doctor(AU)