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In this study, the general processability of cannabidiol (CBD) in colloidal lipid carriers was investigated. Due to its many pharmacological effects, the pharmaceutical use of this poorly water-soluble drug is currently under intensive research and colloidal lipid emulsions are a well-established formulation option for such lipophilic substances. To obtain a better understanding of the formulability of CBD in lipid emulsions, different aspects of CBD loading and its interaction with the emulsion droplets were investigated. Very high drug loads (&gt, 40% related to lipid content) could be achieved in emulsions of medium chain triglycerides, rapeseed oil, soybean oil and trimyristin. The maximum CBD load depended on the type of lipid matrix. CBD loading increased the particle size and the density of the lipid matrix. The loading capacity of a trimyristin emulsion for CBD was superior to that of a suspension of solid lipid nanoparticles based on trimyristin (69% vs. 30% related to the lipid matrix). In addition to its localization within the lipid core of the emulsion droplets, cannabidiol was associated with the droplet interface to a remarkable extent. According to a stress test, CBD destabilized the emulsions, with phospholipid-stabilized emulsions being more stable than poloxamer-stabilized ones. Furthermore, it was possible to produce emulsions with pure CBD as the dispersed phase, since CBD demonstrated such a pronounced supercooling tendency that it did not recrystallize, even if cooled to −60 °C.

Iron(III) carboxylate photochemistry plays an important role in aerosol aging, especially in the lower troposphere. These complexes can absorb light over a broad wavelength range, inducing the reduction of iron(III) and the oxidation of carboxylate ligands. In the presence of O2, ensuing radical chemistry leads to further decarboxylation, and the production of ·OH, HO2·, peroxides, and oxygenated volatile organic compounds, contributing to particle mass loss. The ·OH, HO2·, and peroxides in turn re-oxidize iron(II) back to iron(III), closing a photocatalytic cycle. This cycle is repeated resulting in continual mass loss due to the release of CO2 and other volatile compounds. In a cold and/or dry atmosphere, organic aerosol particles tend to attain highly viscous states. While the impact of reduced mobility of aerosol constituents on dark chemical reactions has received substantial attention, studies on the effect of high viscosity on photochemical processes are scarce. Here, we choose iron(III)-citrate (FeIII(Cit)) as a model light absorbing iron carboxylate complex that induces citric acid (CA) degradation to investigate how transport limitations influence photochemical processes. Three complementary experimental approaches were used to investigate kinetic transport limitations. The mass loss of single, levitated particles was measured with an electrodynamic balance, the oxidation state of deposited particles was measured with X-ray spectromicroscopy, and HO2· radical production and release into the gas phase was observed in coated wall flow tube experiments. To quantitatively compare these experiments and determine important physical and chemical parameters, a numerical multi-layered photochemical reaction and diffusion (PRAD) model that treats chemical reactions and transport of various species was developed. We observed significant photochemical degradation, with up to 80 % mass loss within 24 hours of light exposure. Interestingly, we also observed that mass loss always accelerated during irradiation, resulting in an increase of the mass loss rate by about a factor of 10. When we increased relative humidity, the observed particle mass loss rate also increased. This is consistent with strong kinetic transport limitations for highly viscous particles. The PRAD model was tuned to reproduce all experimental results and captured the essential chemistry and transport during irradiation. In particular, the photolysis rate of FeIII, the re-oxidation rate of FeII, HO2· production, and the diffusivity of O2 in aqueous FeIII(Cit)/CA system as function of relative humidity and FeIII(Cit) / CA molar ratio could be constrained. Photochemical degradation under atmospheric conditions predicted by the PRAD model shows that release of CO2 and re-partitioning of organic compounds to the gas phase may be very significant to accurately predict organic aerosol aging processes.

Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.

Low aqueous solubility of active pharmaceutical ingredients presents a serious challenge in the development process of new drug products. This article provides an overview on some of the current approaches for the formulation of poorly water-soluble drugs with a special focus on strategies pursued at the Center of Pharmaceutical Engineering of the TU Braunschweig. These comprise formulation in lipid-based colloidal drug delivery systems and experimental as well as computational approaches towards the efficient identification of the most suitable carrier systems. For less lipophilic substances the preparation of drug nanoparticles by milling and precipitation is investigated for instance by means of microsystem-based manufacturing techniques and with special regard to the preparation of individualized dosage forms. Another option to overcome issues with poor drug solubility is the incorporation into nanospun fibers.

Machine learning is rapidly becoming a mainstay in research and industry. Particularly for clinical predictive modeling, these approaches are being increasingly applied, as evidenced by the growth in the number of related publications. While different computer tools exist that support rapid prototyping, we observe that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. This leads to an increase in the time needed for development and validation of such models. In this paper, we outline the requirements and challenges inherent to this domain and present a platform for rapid prototyping tailored to the specific needs of clinical modeling for outcome and risk prediction. We argue that a move towards hybrid solutions, i.e., a mix of cloud and on-premise infrastructure, constitutes a viable way to reduce the time needed to develop and validate clinical predictive models in a standardized, reproducible fashion.

This study compared different acquisition protocols performance to detect small liver metastases (

To describe the safety, complications, and liver regeneration associated with the left liver after embolization of the right portal vein (PV) in patients with hepatocellular carcinoma (HCC) developed in the setting of advanced liver fibrosis and cirrhosis.Forty patients (31 men, nine women; mean age, 62 years) with HCC underwent PV embolization over a 4-year period. Embolization was performed from a left PV percutaneous access with use of n-butyl cyanoacrylate (NBCA) mixed with iodized oil. Computed tomography (CT) volumetry was performed before and 1 month after PV embolization to measure the left lobe volume as well as the functional liver ratio defined by the ratio between the left lobe and the total liver volume minus tumoral volume. PV pressure and liver enzyme levels were compared before and 1 month after the procedure and complications were registered. Factors potentially affecting regeneration (age, sex, diabetes, chemoembolization, functional liver ratio before PV embolization, and Knodell histologic score) were evaluated by one-way and stepwise regression analysis.PV embolization could be achieved successfully in all cases. Two patients had partial PV thrombosis on the 1-month follow-up CT and two patients developed transient ascites after PV embolization. The left lobe volume increase was 41% +/- 32% after PV embolization and the functional liver ratio increased from 28% +/- 10% to 36% +/- 10% (P.0001). Hypertrophy of the left lobe was greater in patients with a low functional liver ratio before PV embolization and those with an F3 fibrosis score. Other factors had no influence on left lobe regeneration.PV embolization with use of NBCA is feasible in patients with advanced fibrosis and cirrhosis. Hypertrophy of the left lobe of the liver after PV embolization has a statistically significant correlation with lower functional liver ratio and lower degrees of fibrosis.

Numerical basin modeling provides a strategy for optimizing exploration in frontier areas and evaluating new plays within well-explored basins. Ideally, a basin simulator should span the entire process of source-rock burial, hydrocarbon migration into a potential trap and assessment of trap integrity throughout the evolution of a basin. This paper deals with the migration of hydrocarbons. It is generally accepted that hydrocarbons migrate in separate phases from water, even if part of the light hydrocarbons that may dissolve and diffuse in water. The main driving forces for hydrocarbon migration are buoyancy, capillary forces and pressure gradient. The experience gained through case studies shows that one of the main challenges for the next years will be the simulation of migration in 31) complex geometry, including displacements of blocks along faults.

The compaction of quartzose sandstones is described by a macroscopical visco–elasto–plastic model, derived from microscopical considerations. This model considers that the reduction of total porosity (the relative pore volume) is related to the changes in the relative cemented volume and the relative intergrain volume. Furthermore, we consider that the variations of these two petrophysical parameters result from two physical mechanisms, mechanical compaction and closed system chemical compaction. The model has been calibrated for the Middle Jurassic Garn Formation from the Haltenbanken area of the Mid-Norwegian Continental Shelf. Around 300 data points from 17 wells have been used in this study. The viscous parameters have been optimised against the present day data. With total porosity data the best fit is obtained for an activation energy between 14 and 16 kJ/mole and a macroscopical viscosity at 15°C between 39 and 46 Gpa Ma (1.210 24 –1.410 24 Pa.s). If quartz cement data are used, the best fit is obtained for an activation energy between 16 and 18 kJ/mole and a macroscopical viscosity at 15°C between 47 and 56 Gpa Ma (1.510 24 –17.10 24 Pa.s). The visco–elasto–plastic compaction model has been tested in the Haltenbanken area of Mid Norway, with the result that observed porosity trends and pore pressures can be modelled. Using multiple 2-D basin modelling simulations the results of this model and its implications for fluid flow and petroleum systems analysis can be seen. The compaction method has significant impact on the simulated fluid flow and petroleum migration pattern.

3D basin modelling is now used by some oil companies for exploration purposes. The potentiality of this tool is not fully expressed, but it seems that it should become the core of the basin evaluation process in the near future. In this paper the methodology of 3D basin modelling in a relatively mature area is presented. This methodology is based on a four step process which includes: (1) 3D block building, (2) gridding, (3) backward modelling, and (4) forward modelling. These processes are illustrated through a case study performed on the Franklin structure (Central Graben, North Sea, UK). The main results of this study show that petroleum migration is a 3D process that cannot (in this specific case) be properly addressed by 2D basin modelling alone. Furthermore, the global hydrocarbon mass balance, only accessible by 3D modelling, shows that only 2% of the total hydrocarbons generated by the source rocks are present in the main reservoir.

We report the case of a young break-dancer presenting with hammer syndrome. This syndrome has been correlated with many professional and recreational activities but this is, to our knowledge, the first description of hammer syndrome caused by break-dancing. The etiology, diagnosis and treatment modalities of this rare syndrome are considered.

The number of machine learning clinical prediction models being published is rising, especially as new fields of application are being explored in medicine. Notwithstanding these advances, only few of such models are actually deployed in clinical contexts for a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients when applied to an external cohort of a German research hospital. To help account for the performance differences observed, we utilized interpretability methods which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. We argue that such methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.