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Abstract Background Medical staff are regularly confronted with workplace violence (WPV), which poses a threat to the safety of both staff and patients. Structured de-escalation training (DET) for Emergency Department (ED) staff has been shown to positively affect the reporting of WPV incidents and possibly reduce its impact. This study aimed to describe the development of incidence rates, causes, means, targets, locations, responses, and the time of WPV events. Additionally, it explored the effect of the staff trained in DET on the objective and subjective severity of the respective WPV events. Methods In a retrospective, single-center cohort study, we analyzed ten years of WPV events using the data of Staff Observation Aggression Scale-Revised (SOAS-R) score (ranging from 0 to 22) in a tertiary ED from 2014 to 2023. The events were documented by ED staff and stored in the electronic health record (EHR). Results Between 2014 and 2023, 160 staff members recorded 859 incidents, noting an average perceived severity of 5.78 (SD = 2.65) and SOAS-R score of 11.18 (SD = 4.21). Trends showed a non-significant rise in incident rates per 10,000 patients over time. The WPV events were most frequently reported by nursing staff, and the cause of the aggression was most often not discernible (n = 353, 54.56%). In total, n = 273 (31.78%) of the WPV events were categorized as severe, and the most frequent target of the aggressive behavior was the staff. WPV events occurred most frequently in the traumatology section and the detoxification rooms. While the majority of events could be addressed with verbal interventions, more forceful interventions were performed significantly more often for higher severity WPV events. More WPV events occurred during off-hours and were of a significantly higher objective and subjective severity. Overall, the presence of staff with completed DET led to significantly higher SOAS-R scores and higher perceived severity. Conclusion The findings underline the relevance of WPV events in the high-risk environment of an ED. The analyzed data suggest that DET significantly fostered the awareness of WPV. While most events can be addressed with verbal interventions, WPV remains a concern that needs to be addressed through organizational measures and further research.

While there are a number of available reference and testing materials for micro- and nanoplastic (MNP) studies in toxicology, they are not well-characterized and do not cover all major polymer types that may potentially pollute the environment. This review article will address the question of why we need properly designed synthesized micro- and nanoplastics with a core–shell structure (with organic–inorganic units) and provide researchers with a scientific basis for the design of synthesized MNP particles. It will include a list of commercially available MNPs, an overview of the theoretical background to polymer particle synthesis, and an analysis of the advantages and disadvantages of MNP preparation methods, namely, fragmentation and synthesis, along with examples of synthesized MNP particles. The current study will demonstrate that polystyrene is one of the most prevalent MNP particle types among reference materials from certification bodies and among testing particles synthesized by chemical scientists. Nevertheless, the global industrial production of polystyrene represents approximately 5% of the total, and it is not a dominant plastic type in the textile or packaging industries. In contrast to mechanically fragmented MNP particles, the synthesis approach offers the potential to control the physico-chemical properties, enabling the more selective detection and quantification, as well as a greater comparability of the results amongst toxicological studies.

Abstract Intestinal preservation for transplantation is accompanied by hypoperfusion with long periods of ischemia with total blood cessation and absolute withdrawal of oxygen leading to structural damage. The application of intraluminal oxygen has been successfully tested in small-animal series during storage and transport of the organ but have been so far clinically unrelatable. In this study, we tested whether a simple and clinically approachable method of intraluminal oxygen application could prevent ischemic damage in a large animal model, during warm ischemia time. We utilised a local no-flow ischemia model of the small intestine in pigs. A low-flow and high-pressure intraluminal oxygen deliverance system was applied in 6 pigs and 6 pigs served as a control group. Mucosal histopathology, hypoxia and barrier markers were evaluated after two hours of no-flow conditions, in both treatment and sham groups, and in healthy tissue. Macro- and microscopically, the luminal oxygen delivered treatment group showed preserved small bowel’s appearance, viability, and mucosal integrity. A gradual deterioration of histopathology and barrier markers and increase in hypoxia-inducible factor 1-α expression towards the sites most distant from the oxygen application was observed. Intraluminal low-flow, high oxygen delivery can preserve the intestinal mucosa during total ischemia of the small intestine. This finding can be incorporated in methods to overcome small bowel ischemia and improve intestinal preservation for transplantation.

Background Germany has one of the oldest social security systems in the world. Population coverage has subsequently increased, reaching coverage of approximately 90% of the population in the statutory health insurance (SHI) system today. Before this background, Germany has been pioneering the integration of digital therapeutics (DTx) into its SHI system by the introduction of the Digital Healthcare Law (Digitale-Versorgung-Gesetz, DVG) in 2019. Thereby, patients became eligible for digital health applications (Digitale Gesundheitsanwendungen, DiGA), which are available upon prescription by qualified healthcare professionals. Challenge As conventional healthcare delivery often lacks direct outcome measures as and is mostly still reimbursed on a fee-for-service basis, DiGA offer the opportunity to continuously provide individual outcome and performance data. They are, therefore, well-suited for a performance-based payment framework. While the DVG introduced the option for performance-based reimbursement components in 2019 already, the ongoing debate about the value of DiGA and to what extent they can contribute to the healthcare system has now been reflected in a 2023 health policy bill by the German Federal Ministry of Health, which aims to introduce a mandatory performance-based reimbursement component for DiGA. Proposal In this light, we propose a framework for performance-based reimbursement of DiGA, involving an intervention-specific, performance-linked reimbursement framework with shared accountability between manufacturers and payers. The approach aims to align the often contradicting interests of the involved stakeholders to incentivize the delivery of high-value digital health care. Yet, the proposal also acknowledges the need for further research to establish a robust foundation for implementing such a framework.

Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Abstract Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Abstract In the northern forelands of the Alps, farmers report an increase of Jacobaea aquatica in production grasslands. Due to its toxicity, the species affects grassland productivity and calls for costly control measures. We are investigating the extent to which management practices or climatic factors are responsible for the increase of the species and how the situation will change due to climate change. We tested for effects of management intensity, fertilization, agri-environmental measures, and soil disturbance, and modeled the occurrence of the species under rcp4.5 and rcp8.5 scenarios. The main determinants of the occurrence of the species are soil type and summer rainfall. A high risk is associated with wet soils and > 400 mm of rain between June and August; an influence of the management-related factors could not be detected. Under the climate-change scenarios, the overall distribution decreases and shifts to the wetter alpine regions. Thus, the current increase is rather a shift in the occurrence of the species due to the altered precipitation situation. Under future climatic conditions, the species will decline and retreat to higher regions in the Alps. This will decrease the risk of forage contamination for production grassland in the lowlands.

Abstract Background Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).

Abstract INTRODUCTION Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off‐targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors’ off‐target interactions in the context of AD remains unclear. METHODS Putative off‐targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off‐targets were filtered based on their relevance to AD. Targets that had not been previously identified as off‐targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell‐based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling. RESULTS With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2‐α2) and dual leucine zipper kinase (MAP3K12), both with binding constant (Kd) values of 5.8 μM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant. CONCLUSION In this study, we extended the list of baricitinib off‐targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off‐target interaction on AD, the combined approach of ML‐based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD. Highlights This study explored JAK inhibitors' off‐targets in AD using a multidisciplinary approach. We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off‐target interactions of tofacitinib and baricitinib in AD. Previously unknown inhibition of two enzymes (CK2‐a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments. Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing. The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.

This study used bibliometric analysis to investigate global research trends regarding the effect of COVID-19 risks in sustainable facility management fields. Between 2019 and 2021, the Scopus database published 208 studies regarding the effect of COVID-19 risks on sustainable facility control fields. VOSviewer software was used to analyse the co-occurrence of all keywords, and Biblioshiny software allowed getting the most relevant affiliation using the three-field plot. The results show the contribution by authors from 51 countries, and 73 keywords were identified and organised into six clusters, such as the effect of COVID-19 risks on human health, supply chain in construction projects and industry, disaster risk management in a changing climate, sustainable supply chain benchmarking, facility management and quality control, and, finally, sensitivity analysis & decision-making.

Generative Deep Learning holds promise for mining the unexplored “chemical universe” for new drugs. Here, the authors demonstrate the de novo design of phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors for the PI3K/Akt pathway in human tumor cells.

Measurement(s) vegetation-plot resurvey data of vascular plant species Technology Type(s) vegetation-plot records Factor Type(s) Cover of species in plots Sample Characteristic - Organism Vascular plant species Sample Characteristic - Environment Terrestrial habitats Sample Characteristic - Location Germany

Idiomatic verb phrases (e.g., kick the bucket, fig. 'to die') vary in their syntactic flexibility: they can undergo operations like, e.g., passivization (“The bucket was kicked”) to varying degrees. We (re-)consider potential sources of this variability. It has been proposed that compositionality influences syntactic flexibility of idioms. In the first part of the paper, we reassess this finding from a methodological perspective by replicating earlier experiments on German and English, in which we change the previously used – and potentially biased – methods of measuring compositionality. Our results for German are compatible with the view that higher compositionality makes some of the tested structures more acceptable (most consistently: scrambling, prefield fronting, and which-questions), while we do not find a connection between compositionality and flexibility for English. In the second part of the paper, we present an additional experiment following up on the German findings. We extend the empirical domain and explore factors which – in contrast to compositionality – have the potential of explaining the syntactic flexibility of both idioms and non-idioms. We find that definiteness influences the flexibility of idioms and non-idioms in similar ways, supporting the view that both types of expressions are subject to the same grammatical rules. We discuss referentiality as a potential underlying semantic source for the behavior of both idioms and non-idioms.

Measurement(s) Quantum Mechanics Technology Type(s) density functional theory

Abstract As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.

BackgroundMusculoskeletal conditions are the main drivers of global disease burden and cause significant direct and indirect health care costs. Digital health applications improve the availability of and access to adequate care. The German health care system established a pathway for the approval of “Digitale Gesundheitsanwendungen” (DiGAs; Digital Health Applications) as collectively funded medical services through the “Digitale-Versorgung-Gesetz” (Digital Health Care Act) in 2019. ObjectiveThis article presents real-world prescription data collected through the smartphone-based home exercise program “Vivira,” a fully approved DiGA, regarding its effect on self-reported pain intensity and physical inability in patients with unspecific and degenerative pain in the back, hip, and knee. MethodsThis study included 3629 patients (71.8% [2607/3629] female; mean age 47 years, SD 14.2 years). The primary outcome was the self-reported pain score, which was assessed with a verbal numerical rating scale. The secondary outcomes were self-reported function scores. To analyze the primary outcome, we used a 2-sided Skillings-Mack test. For function scores, a time analysis was not feasible; therefore, we calculated matched pairs using the Wilcoxon signed-rank test. ResultsOur results showed significant reductions in self-reported pain intensity after 2, 4, 8, and 12 weeks in the Skillings-Mack test (T3628=5308; P

The prevalence of anxiety disorders and depression are rising worldwide. Studies investigating risk factors on a societal level leading to these rises are so far limited to social-economic status, social capital, and unemployment, while most such studies rely on self-reports to investigate these factors. Therefore, our study aims to evaluate the impact of an additional factor on a societal level, namely digitalization, by using a linguistic big data approach. We extend related work by using the Google Books Ngram Viewer (Google Ngram) to retrieve and adjust word frequencies from a large corpus of books (8 million books or 6 percent of all books ever published) and to subsequently investigate word changes in terms of anxiety disorders, depression, and digitalization. Our analyses comprise and compare data from six languages, British English, German, Spanish, Russian, French, and Italian. We also retrieved word frequencies for the control construct "religion". Our results show an increase in word frequency for anxiety, depression, and digitalization over the last 50 years (r = .79 to .89, p < .001), a significant correlation between the frequency of anxiety and depression words (r = .98, p < .001), a significant correlation between the frequency of anxiety and digitalization words (r = .81, p < .001), and a significant correlation between the frequency of depression and anxiety words (r = .81, p < .001). For the control construct religion, we found no significant correlations for word frequency over the last 50 years and no significant correlation between the frequency of anxiety and depression words. Our results showed a negative correlation between the frequency of depression and religion words (r = -.25, p < .05). We also improved the method by excluding terms with double meanings detected by 73 independent native speakers. Implications for future research and professional and clinical implications of these findings are discussed.

Abstract Mineralogy is the key to understanding the origin of Phobos and its position in the evolution of the Solar System. In situ Raman spectroscopy on Phobos is an important tool to achieve the scientific objectives of the Martian Moons eXploration (MMX) mission, and maximize the scientific merit of the sample return by characterizing the mineral composition and heterogeneity of the surface of Phobos. Conducting in situ Raman spectroscopy in the harsh environment of Phobos requires a very sensitive, compact, lightweight, and robust instrument that can be carried by the compact MMX rover. In this context, the Raman spectrometer for MMX (i.e., RAX) is currently under development via international collaboration between teams from Japan, Germany, and Spain. To demonstrate the capability of a compact Raman system such as RAX, we built an instrument that reproduces the optical performance of the flight model using commercial off-the-shelf parts. Using this performance model, we measured mineral samples relevant to Phobos and Mars, such as anhydrous silicates, carbonates, and hydrous minerals. Our measurements indicate that such minerals can be accurately identified using a RAX-like Raman spectrometer. We demonstrated a spectral resolution of approximately 10 cm−1, high enough to resolve the strongest olivine Raman bands at ~ 820 and ~ 850 cm−1, with highly sensitive Raman peak measurements (e.g., signal-to-noise ratios up to 100). These results strongly suggest that the RAX instrument will be capable of determining the minerals expected on the surface of Phobos, adding valuable information to address the question of the moon’s origin, heterogeneity, and circum-Mars material transport. Graphical Abstract

BackgroundMusculoskeletal conditions are among the main contributors to the global burden of disease. International guidelines consider patient education and movement exercises as the preferred therapeutic option for unspecific and degenerative musculoskeletal conditions. Innovative and decentralized therapeutic means are required to provide access to and availability of such care to meet the increasing therapeutic demand for this spectrum of conditions. ObjectiveThis retrospective observational study of preliminary use and outcome data explores the clinical outcomes of Vivira (hereafter referred to as “program”), a smartphone-based program for unspecific and degenerative pain in the back, hip, and knee before it received regulatory approval for use in the German statutory health insurance system. MethodsAn incomplete matched block design was employed to assess pain score changes over the intended 12-week duration of the program. Post hoc analyses were performed. In addition, a matched comparison of self-reported functional scores and adherence rates is presented. ResultsA total of 2517 participants met the inclusion criteria and provided sufficient data to be included in the analyses. Overall, initial self-reported pain scores decreased significantly from an average of 5.19 out of 10 (SD 1.96) to an average of 3.35 out of 10 (SD 2.38) after 12 weeks. Post hoc analyses indicate a particularly emphasized pain score reduction over the early use phases. Additionally, participants with back pain showed significant improvements in strength and mobility scores, whereas participants with hip or knee pain demonstrated significant improvements in their coordination scores. Across all pain areas and pain durations, a high yet expected attrition rate could be observed. ConclusionsThis observational study provides the first insights into the clinical outcomes of an exercise program for unspecific and degenerative back, hip, and knee pain. Furthermore, it demonstrates a potential secondary benefit of improved functionality (ie, strength, mobility, coordination). However, as this study lacks confirmatory power, further research is required to substantiate the clinical outcomes of the program assessed. Trial RegistrationGerman Clinical Trials Register DRKS00021785; https://drks.de/search/en/trial/DRKS00021785

Abstract Background Monitoring medically certified causes of death is essential to shape national health policies, track progress to Sustainable Development Goals, and gauge responses to epidemic and pandemic disease. The combination of electronic health information systems with new methods for data quality monitoring can facilitate quality assessments and help target quality improvement. Since 2015, Tanzania has been upgrading its Civil Registration and Vital Statistics system including efforts to improve the availability and quality of mortality data. Methods We used a computer application (ANACONDA v4.01) to assess the quality of medical certification of cause of death (MCCD) and ICD-10 coding for the underlying cause of death for 155,461 deaths from health facilities from 2014 to 2018. From 2018 to 2019, we continued quality analysis for 2690 deaths in one large administrative region 9 months before, and 9 months following MCCD quality improvement interventions. Interventions addressed governance, training, process, and practice. We assessed changes in the levels, distributions, and nature of unusable and insufficiently specified codes, and how these influenced estimates of the leading causes of death. Results 9.7% of expected annual deaths in Tanzania obtained a medically certified cause of death. Of these, 52% of MCCD ICD-10 codes were usable for health policy and planning, with no significant improvement over 5 years. Of certified deaths, 25% had unusable codes, 17% had insufficiently specified codes, and 6% were undetermined causes. Comparing the before and after intervention periods in one Region, codes usable for public health policy purposes improved from 48 to 65% within 1 year and the resulting distortions in the top twenty cause-specific mortality fractions due to unusable causes reduced from 27.4 to 13.5%. Conclusion Data from less than 5% of annual deaths in Tanzania are usable for informing policy. For deaths with medical certification, errors were prevalent in almost half. This constrains capacity to monitor the 15 SDG indicators that require cause-specific mortality. Sustainable quality assurance mechanisms and interventions can result in rapid improvements in the quality of medically certified causes of death. ANACONDA provides an effective means for evaluation of such changes and helps target interventions to remaining weaknesses.

BackgroundSlow-paced breathing training can have positive effects on physiological and psychological well-being. Unfortunately, use statistics indicate that adherence to breathing training apps is low. Recent work suggests that gameful breathing training may help overcome this challenge. ObjectiveThis study aimed to introduce and evaluate the gameful breathing training app Breeze 2 and its novel real-time breathing detection algorithm that enables the interactive components of the app. MethodsWe developed the breathing detection algorithm by using deep transfer learning to detect inhalation, exhalation, and nonbreathing sounds (including silence). An additional heuristic prolongs detected exhalations to stabilize the algorithm’s predictions. We evaluated Breeze 2 with 30 participants (women: n=14, 47%; age: mean 29.77, SD 7.33 years). Participants performed breathing training with Breeze 2 in 2 sessions with and without headphones. They answered questions regarding user engagement (User Engagement Scale Short Form [UES-SF]), perceived effectiveness (PE), perceived relaxation effectiveness, and perceived breathing detection accuracy. We used Wilcoxon signed-rank tests to compare the UES-SF, PE, and perceived relaxation effectiveness scores with neutral scores. Furthermore, we correlated perceived breathing detection accuracy with actual multi-class balanced accuracy to determine whether participants could perceive the actual breathing detection performance. We also conducted a repeated-measure ANOVA to investigate breathing detection differences in balanced accuracy with and without the heuristic and when classifying data captured from headphones and smartphone microphones. The analysis controlled for potential between-subject effects of the participants’ sex. ResultsOur results show scores that were significantly higher than neutral scores for the UES-SF (W=459; P

Naturally sourced elements represent a sustainable alternative solution for the textile finishing industry, in the context of intense use of contamined, fossil-based raw materials. This study focuses on the exploration of an alternative naturally sourced blue colorant matter solution, by validating the possibility of the employment of Spirulina platensis sourced phycocyanin in natural fabrics coloration through the pigment printing technique. The experiments involved the laboratory-scale exploratory studies of the application of this blue colorant matter on cotton and wool textile substrates, using commercial synthetic printing mother paste. The influence of five different mordants(Cream of tartar, Alum, Tartaric acid, Tanic acid and Aluminum triformate), though pre-mordanting treatment and the printing mother paste were analyzed by measuring the chromatic coordinates and calculating the color strength (K/S), together with the measurement of VIS absorbance spectrum. The color characterization revealed good compatibility of wool -phycocyanin – synthetic paste. Results on cotton were not as good as expected. They conclusions evidence reduced affinity between the fibres and the phycocyanin. In terms of color improvements, Tannic acid revealed the most promising results, for both cotton and wool experiments. The validation of the finishing process was obtained through a fair behavior in laundering fastness, showing low response when analyzed against light degrading external factors.

Approval of B-cell-depleting therapies signifies an important advance in the treatment of multiple sclerosis (MS). However, it is unclear whether the administration route of anti-CD20 monoclonal antibodies (mAbs) alters tissue distribution patterns and subsequent downstream effects. This study aimed to investigate the distribution and efficacy of radiolabeled ofatumumab and ocrelizumab in humanized-CD20 (huCD20) transgenic mice following subcutaneous (SC) and intravenous (IV) administration. For distribution analysis, huCD20 and wildtype mice (n = 5 per group) were imaged by single-photon emission computed tomography (SPECT)/CT 72 h after SC/IV administration of ofatumumab or SC/IV administration of ocrelizumab, radiolabeled with Indium-111 (111In-ofatumumab or 111In-ocrelizumab; 5 µg, 5 MBq). For efficacy analysis, huCD20 mice with focal delayed-type hypersensitivity lesions and associated tertiary lymphoid structures (DTH-TLS) were administered SC/IV ofatumumab or SC/IV ocrelizumab (7.5 mg/kg, n = 10 per group) on Days 63, 70 and 75 post lesion induction. Treatment impact on the number of CD19+ cells in select tissues and the evolution of DTH-TLS lesions in the brain were assessed. Uptake of an 111In-labelled anti-CD19 antibody in cervical and axillary lymph nodes was also assessed before and 18 days after treatment initiation as a measure of B-cell depletion. SPECT/CT image quantification revealed similar tissue distribution, albeit with large differences in blood signal, of 111In-ofatumumab and 111In-ocrelizumab following SC and IV administration; however, an increase in both mAbs was observed in the axillary and inguinal lymph nodes following SC versus IV administration. In the DTH-TLS model of MS, both treatments significantly reduced the 111In-anti-CD19 signal and number of CD19+ cells in select tissues, where no differences between the route of administration or mAb were observed. Both treatments significantly decreased the extent of glial activation, as well as the number of B- and T-cells in the lesion following SC and IV administration, although this was mostly achieved to a greater extent with ofatumumab versus ocrelizumab. These findings suggest that there may be more direct access to the lymph nodes through the lymphatic system with SC versus IV administration. Furthermore, preliminary findings suggest that ofatumumab may be more effective than ocrelizumab at controlling MS-like pathology in the brain.

This paper presents the results of a novel experimental approach to relative quantifier scope in German that elicits data in an indirect manner. Applying the covered-box method (Huang et al. 2013) to scope phenomena, we show that inverse scope is available to some extent in the free constituent order language German, thereby validating earlier findings on other syntactic configurations in German (Radó & Bott 2018) and empirical claims on other free constituent order languages (Japanese, Russian, Hindi), as well as recent corpus findings in Webelhuth (2020). Moreover, the results of the indirect covered-box experiment replicate findings from an earlier direct-query experiment with comparable target items, in which participants were asked directly about the availability of surface scope and inverse scope readings. The configuration of interest consisted of canonical transitive clauses with deaccented existential subject and universal object QPs, in which the restriction of the universal QP was controlled for by the context.

The computer-assisted design of new chemical entities has made a leap forward with the development of machine learning models for automated molecule generation. The overarching goal of this conceptual approach is to augment the creativity of medicinal chemists with a machine intelligence. In this Perspective we highlight prospective applications of “de novo” drug design and target prediction, aiming to generate natural product-inspired bioactive compounds from scratch. A virtual chemist transforms pharmacologically active natural products into new, easily synthesizable small molecules with desired properties and activity. Computational activity prediction and automated compound generation offer the possibility to systematically transfer the wealth of pharmaceutically active natural products to synthetic small molecule drug discovery. We present selected prospective examples and dare a forecast into the future of natural product-inspired drug discovery.

Health care delivery is undergoing a rapid change from traditional processes toward the use of digital health interventions and personalized medicine. This movement has been accelerated by the COVID-19 crisis as a response to the need to guarantee access to health care services while reducing the risk of contagion. Digital health scale-up is now also vital to achieve population-wide impact: it will only accomplish sustainable effects if and when deployed into regular health care delivery services. The question of how sustainable digital health scale-up can be successfully achieved has, however, not yet been sufficiently resolved. This paper identifies and discusses enablers and barriers for scaling up digital health innovations. The results discussed in this paper were gathered by scientists and representatives of public bodies as well as patient organizations at an international workshop on scaling up digital health innovations. Results are explored in the context of prior research and implications for future work in achieving large-scale implementations that will benefit the population as a whole.

Textile manufacturers are demonstrating an increasing interest in the application of different types of micro or nanoparticles in order to add new properties to the fabric. For example, it is demostrated that fabrics treated with some type of silicate particles, like montmorillonite improve the wrinkle resistance of cotton fabric or improve the flame retardant effect, among other interesting properties. Usually these particles, are not a reactive agent that can react with the fibers. Because of that, the resin product are necessary to make them adhere to the fiber, otherwise they will be lost during the actions of treated fabric use and maintenance such as washing, ironing, drying, etc. The resin plays an important role in treating the fuctionalization of the textile. Instead, this compound can modify its properties. In this work, flexural rigidity of treated cotton fabrics with binder and with and without montmorillonite particles were evaluated. Two different resins were studied, acrilyc and poliurethane, and 1, 2.5 and 5 g/L of this product were applied. It was appreciated that fabric treated with particles modify the flexural rigidity in less difference than when treating it only with resin, regardless of the type of binder used.

In the process of making a print on a fabric, several steps that require many materials and substances are involved. When the purpose is to create a pattern through the use of natural products, it is necessary to pay close attention, not only to the origin and method of extraction of the pigments that you intend to use, but also, for example, to the thickeners to be added to the paste for increasing the viscosity of the pastes and molding, to the stains to be applied for better color rendering on the fabric and to the final fixatives, designed to make the product durable. Thickeners are a fundamental step in the success of printing on fabrics. These agents are generally compounds with a high molecular weight whose task is to transfer to the fabrics the dyes and chemical compounds necessary to create the printing pattern, thanks to the plastic action that they give to the compound used. As far as the environmental consequences of the use of thickeners are concerned, starting from the analysis of the wastewater of the processes, it has emerged that the use of biodegradable additives and guar rubber on the fabrics is preferable, since they are less harmful to the ecosystem. In this work different natural compounds, chitosan and alginate, are used in order to increase the viscosity of the paste prepared for printing. Color measurement of each printed cotton simple was analized and it could be appreciated that alginate paste printed on pretreated fabric with chitosan reached good results.

There are some scientific papers that verify that the generation of negative ions in the air have positive consequences on people’s mood and health. In this work is intended to increase the generation of negative ions from polyester fabrics to achieve an increase in well-being and comfort through the realize of negative ions. To improve the negative ion release ability on different substrates there have been many studies which have considered silicates as a good particle with these characteristics. Polyester fabrics were treated using padding system with different tourmaline microparticles in order to modify the surface behavior when rubbed and demonstrating that it is possible to modify the number of negative ions generated. Two types of tourmaline microparticles with different size particles, 3 and 30 μm, were studied and applied on the samples with and without an acrylic resin to get a better fixation. To determine the quantity of negative ions released, an ion counter was used, testing each sample. Finally, the results were studied, analyzing the different parameters that influence them, as the concentration, the addition of resin or the kind of the particle. The best results were obtained with the Tourmaline with 30 μm at 50 g/L concentration and without the addition of the acrylic resin.

Nowadays, envoronmental concern is increasing and textile industry is aware of it. Circular economy are two words which are linked together and frequently used nowadays. In this report, we focus the reseach on the rice husk in order to dimish the problem rice waste generates to farmers. Rice husk was treated with some chemicals (HNO3). The extract was dried and conveniently dissolved to apply it on textile fabrics made of 100 % cotton and 100 % polyester.. The extract was dissolved on NaOH solution and applied by padding on a cotton and polyester fabric at different concentrations. The dried extract was analysed by FTIR and both the extract and fabrics were observed by FESEM-EDX to detect the presence of some particles Results evidence the presence of some particles on the fibres, despite having applied low concentrations FTIR is sensitive enough to detect its presence on fibers. It was expected to observe Si from the extract but FESEM-EDX could not appreciate it. Once the treatment was applied on the fabric yellowing was observed but also water repellence thus with the FTIR spectrum gives the idea that the extract was mainly comprised of lignin. Further studies will be conducted in order to spcecify the new properties conferred to the textiles.

BackgroundAdvancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. ObjectiveOur objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. MethodsA systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs’ websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs’ publications, websites, and mobile apps were reviewed with regard to the intervention characteristics. ResultsThe 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. ConclusionsOur findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions.

Abstract Objectives Anti‐CD20 monoclonal antibody therapy rapidly depletes > 95% of CD20+ B cells from the circulation. B‐cell depletion is an effective treatment for autoimmune disease and B‐cell malignancies but also increases the risk of respiratory tract infections. This effect on adaptive immunity could be countered by vaccination. We have used mouse models to investigate the effects of B‐cell depletion on pneumococcal vaccination, including protection against infection and timing of vaccination in relation to B‐cell depletion. Methods C57BL/6 female mice were B‐cell depleted using anti‐CD20 antibody and immunized with two doses of Prevnar‐13 vaccine either before or after anti‐CD20 treatment. B‐cell repertoire and Streptococcus pneumoniae–specific IgG levels were measured using whole‐cell ELISA and flow cytometry antibody‐binding assay. Protection induced by vaccination was assessed by challenging the mice using a S. pneumoniae pneumonia model. Results Antibody responses to S. pneumoniae were largely preserved in mice B‐cell depleted after vaccination resulting in full protection against pneumococcal infections. In contrast, mice vaccinated with Prevnar‐13 while B cells were depleted (with > 90% reduction in B‐cell numbers) had decreased circulating anti–S. pneumoniae IgG and IgM levels (measured using ELISA and flow cytometry antibody binding assays). However, some antibody responses were maintained, and, although vaccine‐induced protection against S. pneumoniae infection was impaired, septicaemia was still prevented in 50% of challenged mice. Conclusions This study showed that although vaccine efficacy during periods of profound B‐cell depletion was impaired some protective efficacy was preserved, suggesting that vaccination remains beneficial.

BackgroundThe number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively. ObjectiveThe aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. MethodsWe systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed. ResultsNone of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms. ConclusionsPromising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.

Abstract The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product‐inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX‐1 inhibitors with nanomolar potency. X‐ray structure analysis reveals the binding of the most selective compound to COX‐1. This molecular design approach provides a blueprint for natural product‐inspired hit and lead identification for drug discovery with machine intelligence.

Globally, reducing CO2 emissions is an urgent priority. The hydrogen economy is a system that offers long-term solutions for a secure energy future and the CO2 crisis. From hydrogen production to consumption, storing systems are the foundation of a viable hydrogen economy. Each step has been the topic of intense research for decades; however, the development of a viable, safe, and efficient strategy for the storage of hydrogen remains the most challenging one. Storing hydrogen in polymer-based carriers can realize a more compact and much safer approach that does not require high pressure and cryogenic temperature, with the potential to reach the targets determined by the United States Department of Energy. This review highlights an outline of the major polymeric material groups that are capable of storing and releasing hydrogen reversibly. According to the hydrogen storage results, there is no optimal hydrogen storage system for all stationary and automotive applications so far. Additionally, a comparison is made between different polymeric carriers and relevant solid-state hydrogen carriers to better understand the amount of hydrogen that can be stored and released realistically.

The good interaction between the ceramic powder and the binder system is vital for ceramic injection molding and prevents the phase separation during processing. Due to the non-polar structure of polyolefins such as high-density polyethylene (HDPE) and the polar surface of ceramics such as zirconia, there is not appropriate adhesion between them. In this study, the effect of adding high-density polyethylene grafted with acrylic acid (AAHDPE), with high polarity and strong adhesion to the powder, on the rheological, thermal and chemical properties of polymer composites highly filled with zirconia and feedstocks was evaluated. To gain a deeper understanding of the effect of each component, formulations containing different amounts of HDPE and or AAHDPE, zirconia and paraffin wax (PW) were prepared. Attenuated total reflection spectroscopy (ATR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and rotational and capillary rheology were used for the characterization of the different formulations. The ATR analysis revealed the formation of hydrogen bonds between the hydroxyl groups on the zirconia surface and AAHDPE. The improved powder-binder adhesion in the formulations with more AAHDPE resulted in a better powder dispersion and homogeneous mixtures, as observed by SEM. DSC results revealed that the addition of AAHDPE, PW and zirconia effect the melting and crystallization temperature and crystallinity of the binder, the polymer-filled system and feedstocks. The better powder--binder adhesion and powder dispersion effectively decreased the viscosity of the highly filled polymer composites and feedstocks with AAHDPE; this showed the potential of grafted polymers as binders for ceramic injection molding.

Anti-CD20 antibody therapy is used for both cancer and autoimmune disease but has been shown to be associated with PneumocystisPneumocystis

Abstract We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acceptor and donor databases are the largest on record with 4426 and 1036 data points, respectively. After scanning over radial atomic descriptors and ML methods, our final trained HBA and HBD ML models achieve RMSEs of 3.8 kJ mol−1 (acceptors), and 2.3 kJ mol−1 (donors) on experimental test sets, respectively. This performance is comparable with previous models that are trained on experimental hydrogen bonding free energies, indicating that molecular QC data can serve as substitute for experiment. The potential ramifications thereof could lead to a full replacement of wetlab chemistry for HBA/HBD strength determination by QC. As a possible chemical application of our ML models, we highlight our predicted HBA and HBD strengths as possible descriptors in two case studies on trends in intramolecular hydrogen bonding.

Abstract Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

Over the few past decades, preparation of uniform microparticles has received a great interest in the textile area to add new functionalities to the textile fabric. Amongst them, thermal insulation textile and more specially microencapsulation of phase change materials (PCMs) have attracted more and more attention in recent years. In an latent heat thermal energy storage system, energy is stored during the melting, and recovered during the freezing, of phase change material (PCM). It plays an important role in solving energy imbalance, by improving thermal efficiency and protecting the environment. The aim of this study is obtaining bio-degradable PCM which shell is Ethly cellulose and active agent is coconut oil by using solvent evaporation as a microencansulation method. In order to microencapsulation with solvent evaporation method: we have prepared 4 different combinations of emulsions by changing the amount of ethyl cellulose and coconut oil. We examined the samples obtained by scanning electron microscope (SEM) to see how the ratio of materials used as active agent and shell play a role in microcapsule formation. SEM analysis showed that we obtained significative results when we use ethyl cellulose and coconut oil in a ratio of 1: 1, we obtain the spherical microcapsules.

Electrospinning was patented by Formhals in 1934. Prior to the year 2000 electrospinning was the domain of only a few specialists; This situation has changed dramatically in recent years. Many research groups in research and industry work currently on electrospinning topic, the number of conferences or sessions at conferences devoted to electrospinning is continuously increasing. Electrospinning is a fabrication technique, which can be used to create nanofibrous nonwovens from a variety of starting materials. The structure, chemical and mechanical stability, functionality, and other properties of the mats can be modified to match end applications. On the other hand cellulosic polymers have gathered great interest in recent years in the field of nanoproducts useful for high-value applications. The electrospun cellulosic composite fibers with reliable thermal properties are suitable and promising in serving as thermo-regulating materials in many fields. Based on this information, in this study we applied cellulose acetate by electrospinning into a raw cotton fabric in order to examine electrospinning effect in thermal properties. In order to analize thermal behaviour samples were tested with a thermochromic camera. It was found that coating woven cotton fabric by electrospinning with cellulose acetate, reduced the rates of cooling and heating.

Graphene is a recently discovered material that has attracted a great deal of attention in numerous fields, from electronics to textiles, including medicine and energy harvesting. Its use confers interesting properties to conventional textile substrates, providing them with great versatility and the opportunity to explore new areas and applications beyond their common uses. Despite the many properties it shows, it also faces some challenges regarding critical issues such as its stability throughout the procedures the textile is subjected to, such as rubbing, ironing or washing. In this study, the washing fastness of a cellulosic textile substrate to which a printing paste containing graphene had previously been applied was evaluated. After evaluating the behaviour of the samples after the washings by means of chromatic coordinates and the ISO 105:C06 Color fastness to domestic and commercial laundering, it came to our attention the deficient behaviour of the graphene regarding washing fastness. In order to improve it, a heat treatment was applied, which led to a significant increase of washing fastness behaviour of the samples with graphene printing, compared to non-treated samples. In this research, an efficient and affordable method to improve graphene’s stability onto cellulosic textile substrates that can be carried out with common laboratory equipment is provided.

α-tocopherol transfer protein (TTP) was previously reported to self-aggregate into 24-meric spheres (α-TTPS) and to possess transcytotic potency across mono-layers of human umbilical vein endothelial cells (HUVECs). In this work, we describe the characterisation of a functional TTP variant with its vitamer selectivity shifted towards γ-tocopherol. The shift was obtained by introducing an alanine to leucine substitution into the substrate-binding pocket at position 156 through site directed mutagenesis. We report here the X-ray crystal structure of the γ-tocopherol specific particle (γ-TTPS) at 2.24 Å resolution. γ-TTPS features full functionality compared to its α-tocopherol specific parent including self-aggregation potency and transcytotic activity in trans-well experiments using primary HUVEC cells. The impact of the A156L mutation on TTP function is quantified in vitro by measuring the affinity towards γ-tocopherol through micro-differential scanning calorimetry and by determining its ligand-transfer activity. Finally, cell culture experiments using adherently grown HUVEC cells indicate that the protomers of γ-TTP, in contrast to α-TTP, do not counteract cytokine-mediated inflammation at a transcriptional level. Our results suggest that the A156L substitution in TTP is fully functional and has the potential to pave the way for further experiments towards the understanding of α-tocopherol homeostasis in humans.