Local influences of geothermal anomalies on permafrost distribution in an active volcanic island (Deception Island, Antarctica)

This study aims at understanding the spatial distribution and characteristics of the frozen and unfrozen terrain in an alluvial fan on Deception Island, which is an active strato-volcano located in the Bransfield Strait (South Shetland Islands) with recent eruptions in 1967, 1969 and 1970. The alluvial fan is dominated by debris-flow, run-off and rock fall processes and permafrost occurs in several parts in the vicinity of anomalous geothermal heat flux. The aim is to assess the ways volcanic activity controls permafrost development and associated geomorphic dynamics using shallow subsurface, surface and air temperature measurements as well as thaw depth and electrical resistivity tomography (ERT) surveys.Results show a temperature increase with depth in the lower part of the fan reaching 13 °C at 0.80 m depth, without the presence of permafrost. The shallow borehole located at this site showed a stable thermal stratification all year-round, with only the upper 0.20 m reacting to meteorological forcing. In the upper part of the alluvial fan and debris cones, c. 100 m from the coast, frozen ground is present at c. 0.70 m depth. There, the shallow borehole shows a good coupling with air temperatures and the thermal regime favours the presence of permafrost. ERT shows the lowest resistivity values in the lower part of the alluvial fan and a highly resistivity zone in the upper sector of the fan and in the debris cones. These large variations in resistivity mark the presence of a saline water wedge from the sea into the fan, reaching frozen ground conditions about 100 m inland. It can be shown that the volcano-hydrothermal activity only inhibits frost development very locally, with frozen ground conditions occurring about 100 m away

Recent shallowing of the thaw depth at Crater Lake, Deception Island, Antarctica (2006-2014)

The Western Antarctic Peninsula region is one of the hot spots of climate change and one of the most ecologically sensitive regions of Antarctica, where permafrost is near its climatic limits. The research was conducted in Deception Island, an active stratovolcano in the South Shetlands archipelago off the northern tip of the Antarctic Peninsula. The climate is polar oceanic, with high precipitation and mean annual air temperatures (MAAT) close to -3°C. The soils are composed by ashes and pyroclasts with high porosity and high water content, with ice-rich permafrost at -0.8°C at the depth of zero annual amplitude, with an active layer of about 30 cm. Results from thaw depth, ground temperature and snow cover monitoring at the Crater Lake CALM-S site over the period 2006 to 2014 are analyzed. Thaw depth (TD) was measured by mechanical probing once per year in the end of January or early February in a 100 × 100 m with a 10 m spacing grid. The results show a trend for decreasing thaw depth from ci. 36 cm in 2006 to 23 cm in 2014, while MAAT, as well as ground temperatures at the base of the active layer, remained stable. However, the duration of the snow cover at the CALM-S site, measured through the Snow Pack Factor (SF) showed an increase from 2006 to 2014, especially with longer lasting snow cover in the spring and early summer. The negative correlation between SF and the thaw depth supports the significance of the influence of the increasing snow cover in thaw depth, even with no trend in the MAAT. The lack of observed ground cooling in the base of the active layer is probably linked to the high ice/water content at the transient layer. The pyroclastic soils of Deception Island, with high porosity, are key to the shallow active layer depths, when compared to other sites in the Western Antarctic Peninsula (WAP).Ministerio de Economía y Competitivida

A customizable 3D printed device for enzymatic removal of drugs in water

The infiltration of drugs into water is a key global issue, with pharmaceuticals being detected in all nearly aqueous systems at often alarming concentrations. Pharmaceutical contamination of environmental water supplies has been shown to negatively impact ecological equilibrium and pose a risk to human health. In this study, we design and develop a novel system for the removal of drugs from water, termed as Printzyme. The device, fabricated with stereolithography (SLA) 3D printing, immobilises laccase sourced from Trametes Versicolor within a poly(ethylene glycol) diacrylate hydrogel. We show that SLA printing is a sustainable method for enzyme entrapment under mild conditions, and measure the stability of the system when exposed to extremes of pH and temperature in comparison to free laccase. When tested for its drug removal capacity, the 3D printed device substantially degraded two dissolved drugs on the European water pollution watch list. When configured in the shape of a torus, the device effectively removed 95% of diclofenac and ethinylestradiol from aqueous solution within 24 and 2 h, respectively, more efficiently than free enzyme. Being customizable and reusable, these 3D printed devices could help to efficiently tackle the world's water pollution crisis, in a flexible, easily scalable, and cost-efficient manner

M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) 3-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/)

Machine learning predicts 3D printing performance of over 900 drug delivery systems

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow

Communication research in Spain: labor temporality, intensive production and competitiveness

In the last years, the academic body seems to have exceeded the saturation point of the employment structure. This situation has led to an increase in professional competitiveness that affects the practices of communication research. Through the longitudinal quantitative analysis of public financing, academic personnel employment, and the scientific production in communication –explained by the development in the number of papers, the methodological approach and its specialization–, we interpret the effects of the current paradigm of this discipline, characterized by the stagnation of the investment in science, labor temporality and the numerical increase of articles and researchers

Predicting pharmaceutical inkjet printing outcomes using machine learning

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings

Emergence of 3D Printed Dosage Forms: Opportunities and Challenges

The recent introduction of the first FDA approved 3D-printed drug has fuelled interest in 3D printing technology, which is set to revolutionize healthcare. Since its initial use, this rapid prototyping (RP) technology has evolved to such as extent that it is currently being used in a wide range of applications including in tissue engineering, dentistry, construction, automotive and aerospace. However, in the pharmaceutical industry this technology is still in its infancy and its potential yet to be fully explored. This paper presents various 3D printing technologies such as stereolithographic, powder based, selective laser sintering, fused deposition modelling and semi-solid extrusion 3D printing. It also provides a comprehensive review of previous attempts at using 3D printing technologies on the manufacturing dosage forms with a particular focus on oral tablets. Their advantages particularly with adaptability in the pharmaceutical field have been highlighted, including design flexibility and control and manufacture which enables the preparation of dosage forms with complex designs and geometries, multiple actives and tailored release profiles. An insight into the technical challenges facing the different 3D printing technologies such as the formulation and processing parameters is provided. Light is also shed on the different regulatory challenges that need to be overcome for 3D printing to fulfil its real potential in the pharmaceutical industry