bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comfort Terminal Care auf der Intensivstation: Empfehlungen für die Praxis
Authors: Schaden Eva , Dier Helga , Weixler Dietmar , Hasibeder Walter , Lenhart-Orator Andrea , Roden Christian , Fruhwald Sonja , Friesenecker Barbara ,   ARGE Ethik in Anästhesie und Intensivmedizin der ÖGARI
Published: February 2024

Zusammenfassung
Hintergrund und Ziel der Arbeit
Die Arbeitsgemeinschaft Ethik in Anästhesie und Intensivmedizin der Österreichischen Gesellschaft für Anästhesiologie, Reanimation und Intensivmedizin (ÖGARI) hat schon vor 10 Jahren Dokumentationswerkzeuge für die Therapiezieländerung entwickelt. Seither hat insbesondere die praktische Umsetzung der Comfort Terminal Care in der täglichen Routine zahlreiche Fragenstellungen aufgeworfen, die in dieser Folgearbeit diskutiert und, wann immer möglich, evidenzbasiert beantwortet werden.

Ergebnisse
Die praktische Umsetzung der Schmerztherapie sowie der Reduktion von Angst, Stress und Atemnot, die im Rahmen einer Comfort Terminal Care selbstverständlich indiziert sind, werden näher beschrieben. Zu den Maßnahmen, die nicht (mehr) indiziert sind, wie Sauerstoffgabe und Beatmung sowie Gabe von Flüssigkeit und Ernährung, wird Stellung genommen. Weiterhin werden Empfehlungen hinsichtlich Monitoring, (Labor‑)Befunderhebung sowie zu medikamentösen Therapien gegeben und die Bedeutung von Pflegehandlungen im Rahmen der Comfort Terminal Care benannt. Zuletzt werden noch die Begleitung der An- und Zugehörigen und das Vorgehen in der Zeit nach dem Versterben dargestellt.

Diskussion
Eine Therapiezieländerung mit zeitgerechter Umstellung auf Comfort Terminal Care ermöglicht eine gute und menschliche Betreuung schwerst kranker Patient:innen und ihrer An- und Zugehörigen am Lebensende und die Wertschätzung ihres bisherigen Lebens mit der Möglichkeit positiver Erfahrungen bis zuletzt.
Digitale Gesundheitskompetenz der Bevölkerung in Deutschland: Aktueller Stand, Konzepte und Herausforderungen
Authors: Dratva Julia , Schaeffer Doris , Zeeb Hajo 
Published: February 2024

ZusammenfassungEine wesentliche Voraussetzung für eine erfolgreiche digitale Transformation des Gesundheitswesens ist eine gut ausgeprägte digitale Gesundheitskompetenz (DGK) der Bevölkerung. DGK ist die Fähigkeit zum Umgang mit gesundheitsbezogenen digitalen Informationen und Informationsmöglichkeiten mit dem Ziel, Gesundheit und Wohlbefinden für sich selbst und sein Umfeld zu fördern und zu erhalten. Der Artikel beleuchtet die Diskussion über DGK, vorhandene Studien und die darin verwendeten Messinstrumente sowie die Datenlage in Deutschland und erörtert aktuelle Herausforderungen.DGK besteht aus verschiedenen Teilkompetenzen, die aktuelle digitale Informationsverhalten, -möglichkeiten und -risiken widerspiegeln. Die Datenlage ist, aufgrund unterschiedlicher Studiendesigns und -instrumente, sehr heterogen, was die Aussagekraft limitiert. Zwei repräsentative Studien, HLS-GER 2 der Universität Bielefeld sowie die Studie der AOK Rheinland/Hamburg und des Leibniz-WissenschaftsCampus, weisen trotz unterschiedlicher Methoden auf einen hohen Anteil von Menschen mit geringer DGK hin. National wie international zeigt sich, dass die DGK einem sozialen Gradienten unterliegt und mit Bildungsniveau, Sozialstatus, finanzieller Deprivation und Alter assoziiert ist.Die DGK ist in Deutschland den vorliegenden Daten zufolge noch unzureichend; somit besteht ein großer Handlungsbedarf. Erforderliche gesetzliche Rahmenbedingungen sind gegeben, dennoch fehlt es an verlässlichen finanziellen Ressourcen ebenso an einer soliden Datengrundlage auf Bevölkerungsebene zu DGK. Damit ließen sich Vulnerabilitätsfaktoren identifizieren und die Implementation von Maßnahmen vorbereiten und evaluieren. Zudem bedarf es einer vertiefenden konzeptionellen Diskussion zur DGK, die an das etablierte Gesundheitskompetenzkonzept anknüpft und auch die gesundheitsbezogene Infodemie und ihre Folgen für die DGK aufgreift.
More certainty in uncertainty: a special life-cycle approach for management decisions in volatile markets
Authors: Röser Marcel 
Published: February 2024

AbstractRisk managers as corporate stewards are important gatekeepers in enterprises and they are essential to managing risks. Relatedly, a more accurate evaluation of the risk factors allows a well-founded decision-making process and reduces the number of surprising situations that could occur especially in volatile markets. Forward-looking risk management increases entrepreneurial resilience because risk factors are already estimated at an earlier stage in the corresponding risk analysis. Furthermore, the range of potential solutions can be estimated in a successive way. These aspects are relevant especially for products with follow-up effects. Such new product bundles are typical in current businesses. They require a more precise risk analysis, which allows an effective view of the life-cycles of the whole products and the customer relationships. Within well-founded risk decomposition, the existing risk can be managed appropriately. The following study presents a special life-cycle approach for evaluating products and customers in risky situations. Especially for important management decisions, such an approach is necessary, given that only a few products or customers can have a major influence on the success of the enterprise. Therefore, a systematic risk-oriented approach is essential that adequately identifies, assesses and controls the risk factors and enables agile adaptability for fast changes. Considering the risk management perspective, simulation techniques are a useful approach to solve sophisticated decision situations. The applicability of this concept is demonstrated using a computation example reflecting real-world circumstances. The presented approach is broadly defined. That is the reason why the approach can also be transferred to other sectors.
Arabidopsis retromer subunit AtVPS29 is involved in SLY1-mediated gibberellin signaling
Authors: Min Wang Ki , Kwon Dae Hwan , Song Jong Tae , Seo Hak Soo 
Published: February 2024

Abstract
Key message
Retromer protein AtVPS29 upregulates the SLY1 protein and downregulates the RGA protein, positively stimulating the development of the root meristematic zone, which indicates an important role of AtVPS29 in gibberellin signaling.

Abstract
In plants, the large retromer complex is known to play roles in multiple development processes, including cell polarity, programmed cell death, and root hair growth in Arabidopsis. However, many of its roles in plant development remain unknown. Here, we show that Arabidopsis trimeric retromer protein AtVPS29 (vacuolar protein sorting 29) modulates gibberellin signaling. The SLEEPY1 (SLY1) protein, known as a positive regulator of gibberellic acid (GA) signaling, exhibited lower abundance in vps29-3 mutants compared to wild-type (WT) plants. Conversely, the DELLA repressor protein, targeted by the E3 ubiquitin ligase SCF (Skp, Cullin, F-box) complex and acting as a negative regulator of GA signaling, showed increased abundance in vps29-3 mutants compared to WT. The vps29-3 mutants exhibited decreased sensitivity to exogenous GA supply in contrast to WT, despite an upregulation in the expression of GA receptor genes within the vps29-3 mutants. In addition, the expression of the GA synthesis genes was downregulated in vps29-3 mutants, implying that the loss of AtVPS29 causes the downregulation of GA synthesis and signaling. Furthermore, vps29-3 mutants exhibited a reduced meristematic zone accompanied by a decreased cell number. Together, these data indicate that AtVPS29 positively regulates SLY1-mediated GA signaling and plant growth.
Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study
Authors: Lopez-Lopez Victor , Morise Zeniche , Albaladejo-González Mariano , Gavara Concepción Gomez , Goh Brian K. P. , Koh Ye Xin , Paul Sijberden Jasper , Hilal Mohammed Abu , Mishima Kohei , Krürger Jaime Arthur Pirola , Herman Paulo , Cerezuela Alvaro , Brusadin Roberto , Kaizu Takashi , Lujan Juan , Rotellar Fernando , Monden Kazuteru , Dalmau Mar , Gotohda Naoto , Kudo Masashi , Kanazawa Akishige , Kato Yutaro , Nitta Hiroyuki , Amano Satoshi , Valle Raffaele Dalla , Giuffrida Mario , Ueno Masaki , Otsuka Yuichiro , Asano Daisuke , Tanabe Minoru , Itano Osamu , Minagawa Takuya , Eshmuminov Dilmurodjon , Herrero Irene , Ramírez Pablo , Ruipérez-Valiente José A. , Robles-Campos Ricardo , Wakabayashi Go 
Published: February 2024

Abstract
Background
Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.

Methods
We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.

Results
Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables “resection type” and “largest tumor size” for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables “tumor location,” “blood loss,” “complications,” and “operation time.”

Conclusion
We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
Enso influence on water vapor transport and thermodynamics over Northwestern South America
Authors: Ruiz-Vásquez Melissa , Arias Paola A. , Martínez J. Alejandro 
Published: February 2024

AbstractThe interannual variability of hydroclimatic conditions in Northwestern South America, especially precipitation, is mainly influenced by the El Niño–Southern Oscillation (ENSO). We explore potential mechanisms that affect precipitation occurrence in Northwestern South America during El Niño and La Niña events over the period 1980–2019, using data from the ERA5 reanalysis. We look at the atmospheric moisture contribution from different sources using the Dynamic Recycling Model to track water vapor trajectories. Interestingly, conditions with reduced precipitation during El Niño events can take place along with increased precipitable water. To understand this, we analyze thermodynamic conditions in the atmosphere that are necessary for precipitation to occur over the region, such as convective available potential energy, convective inhibition, lifting condensation level, and low-level relative humidity. With this approach, we find more favorable thermodynamic conditions for the occurrence of precipitation during La Niña events, even if the content of water vapor is equal or even less than during El Niño events. We also look at the structure of the regional Hadley and Walker circulation in both types of events and find that a weaker ascending motion during El Niño events also inhibits convection. This study provides an integral picture of how precipitation anomalies over Northwestern South America during ENSO events are related to dynamic and thermodynamic conditions and sources of atmospheric moisture.
Dynamic indentation of viscoelastic orthotropic layer supported by a Winkler–Pasternak foundation
Authors: Çömez İsa 
Published: February 2024

AbstractIn this study, the two-dimensional dynamic contact problem between a rigid flat punch and a viscoelastic orthotropic layer is investigated. The motivation of the study is to provide a better understanding of the vertical vibration of the two-parameter Winkler–Pasternak foundation, which has not yet been investigated. For the contact problem, the mixed boundary conditions on the top and bottom surfaces are transformed into linear equations using the Fourier transform technique and Helmholtz functions. Based on the Gauss–Chebyshev integration formula, the singular integral equation is obtained and solved numerically. As a result of the solutions, the effects of various parameters on the contact stresses are analyzed and examples are given. It was found that the Winkler foundation modulus does not affect the dynamic contact stress, while the Pasternak foundation modulus significantly affects the contact stress.
Investigating metabolic dysregulation in serum of triple transgenic Alzheimer’s disease male mice: implications for pathogenesis and potential biomarkers
Authors: Zhuang Hongbin , Cao Xueshan , Tang Xiaoxiao , Zou Yongdong , Yang Hongbo , Liang Zhiyuan , Yan Xi , Chen Xiaolu , Feng Xingui , Shen Liming 
Published: February 2024

AbstractAlzheimer’s disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-l-homocysteine, S-adenosine-l-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.
Automatic caries detection in bitewing radiographs—Part II: experimental comparison
Authors: Tichý Antonín , Kunt Lukáš , Nagyová Valéria , Kybic Jan 
Published: February 2024

Abstract
Objective
The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth.

Materials and methods
Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method.

Results
The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors.

Conclusions
The automatic method consistently outperformed novices and performed as well as highly experienced dentists.

Clinical significance
The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension
Authors: Park Woo Yeon , Jeon Kyulee , Schmidt Teri Sippel , Kondylakis Haridimos , Alkasab Tarik , Dewey Blake E. , You Seng Chan , Nagy Paul 
Published: February 2024

AbstractThe rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.
Medical students’ motivations for participating in an elective focused on social inequalities and health disparities
Authors: Arps Shahna , Noviski Krista McCarthy , Tucker Lauren , Tutwiler Ameisha 
Published: February 2024

AbstractIn this study, we examine students’ reasons for pursuing elective training focused on medical racism and systemic health inequities at a midwestern medical school. Data collection included semi-structured interviews with students who participated in an optional course focused on these topics. We analyzed their motivations, goals, and interests using reflexive thematic analysis and created three themes based on students’ responses. Theme (1) “pre-existing conditions” focuses on students’ knowledge, beliefs, worldviews and experience prior to the class. Theme (2) “enacting change” examines their desires to become effective physicians and improve medicine overall. Theme (3) “creating community” considers their preferences for a supportive and connected learning and social environment. We discuss the findings within the context of adult learning theory and Self-Determination Theory. The research provides insight about the overt and underlying factors that drive medical students’ participation in training focused on social inequality. We also share recommendations for curriculum development and future research based on the patterns we found in students’ discussions of their needs and expectations.
Moderately supervised learning: definition, framework and generality
Authors: Yang Yongquan 
Published: February 2024

AbstractLearning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by referring to the properties of the labels prepared for the training dataset, learning with supervision is categorized as supervised learning (SL) and weakly supervised learning (WSL). SL concerns the situation where the training dataset is assigned with ideal (complete, exact and accurate) labels, while WSL concerns the situation where the training dataset is assigned with non-ideal (incomplete, inexact or inaccurate) labels. However, various solutions for SL tasks under the era of deep learning have shown that the given labels are not always easy to learn, and the transformation from the given labels to easy-to-learn targets can significantly affect the performance of the final SL solutions. Without considering the properties of the transformation from the given labels to easy-to-learn targets, the definition of SL conceals some details that can be critical to building the appropriate solutions for specific SL tasks. Thus, for practitioners in various AI application fields, it is desirable to reveal these details systematically. This article attempts to achieve this goal by expanding the categorization of SL and investigating the sub-type that plays the central role in SL. More specifically, taking into consideration the properties of the transformation from the given labels to easy-to-learn targets, we firstly categorize SL into three narrower sub-types. Then we focus on the moderately supervised learning (MSL) sub-type that concerns the situation where the given labels are ideal, but due to the simplicity in annotation, careful designs are required to transform the given labels into easy-to-learn targets. From the perspectives of the definition, framework and generality, we conceptualize MSL to present a complete fundamental basis to systematically analyse MSL tasks. At meantime, revealing the relation between the conceptualization of MSL and the mathematicians’ vision, this article as well establishes a tutorial for AI application practitioners to refer to viewing a problem to be solved from the mathematicians’ vision.
Responsible Design Thinking for Sustainable Development: Critical Literature Review, New Conceptual Framework, and Research Agenda
Authors: Baldassarre Brian , Calabretta Giulia , Karpen Ingo Oswald , Bocken Nancy , Hultink Erik Jan 
Published: February 2024

AbstractIn the 1960s, influential thinkers defined design as a rational problem-solving approach to deal with the challenges of sustainable human development. In 2009, a design consultant and a business academic selected some of these ideas and successfully branded them with the term “design thinking.” As a result, design thinking has developed into a stream of innovation management research discussing how to innovate faster and better in competitive markets. This article aims to foster a reconsideration of the purposes of design thinking moving forward, in view of the sustainable development challenges intertwined with accelerating innovation in a perpetual economic growth paradigm. To this end, we use a problematization method to challenge innovation management research on design thinking. As part of this method, we first systematically collect and critically analyze the articles in this research stream. We uncover a prominent focus on economic impact, while social and environmental impacts remain largely neglected. To overcome this critical limitation, we integrate design thinking with responsible innovation theorizing. We develop a framework for responsible design thinking, explaining how to apply this approach beyond a private interest and competitive advantage logic, to address sustainable development challenges, such as climate change, resource depletion, poverty, and injustice. The framework contributes to strengthening the practical relevance of design thinking and its theoretical foundations. To catalyze this effort, we propose an agenda for future research.