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Precision Chemistry of Metallofullerenes and Graphene: Recent Advances
Get PDFPrecision chemistry of synthetic carbon allotropes including fullerene and graphene, characterized by a well‐controlled and spatially resolved addends bonding, has received widespread attention owing to its capability to tailor their physicochemical properties for high‐end applications. In the context of fullerene, particularly endohedral metallofullerenes (EMFs), precision chemistry emphasizes the regioselective binding of a specific number of moieties to the fullerene cage. In the case of graphene, precision chemistry focuses on achieving precise patterning and tailored modifications. Inspired by their intriguing advantages, the precision chemistry of these two members has witnessed rapid advancements. While existing reviews have outlined advancements in the precision chemistry of EMFs and graphene, this review uniquely concentrates on the most recent progress. Finally, the prospects in this field, with a special focus on the potential for creating functional materials through strategically patterned binding of fullerene and graphene networks are envisioned.Precision chemistry of synthetic carbon allotropes including fullerene and graphene is at the forefront of nanocarbons chemistry. This minireview provides a concise overview of the latest advancements in the precision chemistry of metallofullerene and graphene, underlining the challenges and prospects for ongoing research. imageNational Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Guangdong Basic, Applied Basic Research FoundationDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659DFG, German Research Foundatio
A smartphone application to reduce problematic drinking: a feasibility trial
Get PDFBackgroundProblematic drinking is common among college students and associated with various somatic and mental health problems. Given significant evidence for the efficacy of smartphone-based interventions and the frequent use of smartphones among college students, it can be assumed that such interventions have great potential to facilitate access to evidence-based interventions for students suffering from problematic drinking. Thus, we developed a brief intervention that combined a counseling session with an app that utilizes approach-avoidance modification training to reduce alcohol consumption.MethodsTo test the feasibility and explore the potential efficacy of the intervention, we conducted a before-after single-arm study with N = 11 participants reportedly engaging in problematic drinking, who were instructed to practice with the app for 14 days. Feasibility was assessed with the System Usability Scale (SUS). Outcomes included the reduction of self-reported problematic drinking behavior, dysfunctional attitudes about alcohol, and craving, as well as implicit associations between alcohol and self during the training period. Additionally, self-reported problematic drinking behavior was assessed at a 4-week follow-up.ResultsOn average, participants rated app usability on the SUS (possible range: 0 to 100) with M = 84.32 (SD = 6.53). With regard to efficacy, participants reported a significant reduction of problematic drinking behavior (dpre vs. post = 0.91) which was sustained at follow-up (dfollow-up vs. baseline = 1.07). Additionally, participants reported a significant reduction of dysfunctional attitudes about alcohol (dpre vs. post = 1.48). Results revealed no significant changes in craving nor in implicit associations regarding alcohol.ConclusionsFindings from this feasibility study provide preliminary evidence that smartphone-based interventions might help reduce problematic drinking in college students. Further research needs to replicate these findings with larger samples in randomized controlled trials.Trial registrationDRKS00014675 (retrospectively registered).Open Access funding enabled and organized by Projekt DEAL.Friedrich-Alexander-Universität Erlangen-Nürnberg (1041
A Dataset of Electrical Components for Mesh Segmentation and Computational Geometry Research
Get PDFData quality is of crucial importance in the field of automated or digitally assisted assembly. This paper presents a comprehensive data set of triangle meshes representing electrical and electronic components obtained by scraping Computer Aided Design (CAD) models from the Internet. Consisting of a total of 234 triangle meshes with labelled vertices, this data set was specifically created for segmentation tasks. Its versatility for multimodal tasks is underscored by the presence of various labels, including vertex labels, categories, and subcategories. This paper presents the data set and provides a thorough statistical analysis, including measures of shape, size, distribution, and inter-rater reliability. In addition, the paper suggests several approaches for using the data set, considering its multimodal characteristics. The data set and related findings presented in this paper are intended to encourage further research and advancement in the field of manufacturing automation, specifically spatial assembly.Friedrich-Alexander-Universität Erlangen-Nürnberg Deutsche Forschungsgemeinschaft RITTAL GmbH & Co KGFriedrich-Alexander-Universität Erlangen-Nürnberg Deutsche Forschungsgemeinschaft RITTAL GmbH & Co KGFriedrich-Alexander-Universität Erlangen-Nürnberg Deutsche Forschungsgemeinschaft RITTAL GmbH & Co KGFriedrich-Alexander-Universität Erlangen-Nürnberg Deutsche Forschungsgemeinschaft RITTAL GmbH & Co KGFriedrich-Alexander-Universität Erlangen-Nürnberg Deutsche Forschungsgemeinschaft RITTAL GmbH & Co K
A Hybrid Approach to Enhanced Signal Denoising Using Data-Driven Multiresolution Analysis with Detrended-Fluctuation-Analysis-Based Thresholding and Stationary Wavelet Transform
Get PDFIn this work, a new method for denoising signals is developed that is based on variational mode decomposition (VMD) and a novel metric using detrended fluctuation analysis (DFA). The proposed method first decomposes the signal into band-limited intrinsic mode functions (BLIMFs) using VMD. Then, a DFA-based developed metric is employed to identify the ‘noisy’ BLIMFs (based on their DFA-based scaling exponent and frequency content). The existing DFA-based methods use a single-slope threshold to detect noise, assuming all signals have the same noise pattern and ignoring their unique characteristics. In contrast, the proposed DFA-based metric sets adaptive thresholds for each mode based on their specific frequency and correlation properties, making it more effective for diverse signals and noise types. These predominantly noisy BLIMFs are then denoised using shrinkage techniques in the framework of stationary wavelet transform (SWT). This step allows efficient denoising of components, mainly the noisy BLIMFs identified by the adaptive threshold, without losing important signal details. Extensive computer simulations have been carried out for both synthetic and real electrocardiogram (ECG) signals. It is demonstrated that the proposed method outperforms the state-of-the-art denoising methods and with a comparable computational complexity.This research received no external funding
Percentage Comparison of Fuzzy Numbers Using a Newly Presented Method in the Context of Surrogate Modeling
No full textThe O-index presented here allows a statement about the percentage deviation between two fuzzy numbers. For this purpose, one fuzzy number is defined s the reference. This fuzzy number is described with the help of its core value and its support. The deviation of the other fuzzy number, which is defined as the comparison number, is then quantified via the area differences of the left and right limits of the two numbers. In addition, the case when decomposed fuzzy numbers are involved, which are given as α-cuts is taken into account. Thus, the O-index- αcan be used to calculate a separate percentage deviation for each α-cut and thus generate additional knowledge. The O-index then allows a very detailed description of the deviation between two fuzzy numbers. One application of the O-index is the estimation of the accuracy of a surrogate model in relation to a reference model in the context of uncertainty quantification. This is illustrated by a mechanical example, a bending beam.Open Access funding enabled and organized by Projekt DEAL.Deutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/501100001659Friedrich-Alexander-Universität Erlangen-Nürnberg (1041
Morbus Castleman in der rheumatologischen Praxis
No full textOpen Access funding enabled and organized by Projekt DEAL.Universitätsklinikum Würzburg (8913
Towards Trustworthy Artificial Intelligence - Insights from Selected Studies
No full textThe development of AI has experienced a significant acceleration over the past decade.
In recent years, this trend has been particularly reinforced by the rise of generative AI. As a result, there has been a notable improvement in the performance of AI systems, which have been deployed in a variety of application areas.
For instance, in the field of marketing, generative AI is utilized to create personalized content and advertisements. In industrial settings, predictive maintenance employs AI to assist companies in anticipating equipment failures.
Nevertheless, numerous instances have illustrated that AI systems can potentially pose significant risks.
For example, commercial facial recognition systems have been demonstrated to exhibit substantial racial bias, with individuals with darker skin tones being misidentified at significantly higher rates than those with lighter skin tones.
Furthermore, AI systems have exhibited a lack of interpretability, which presents a significant risk in applications where users require an understanding of the AI's decision-making process.
For instance, in healthcare, a doctor must be able to comprehend why an AI-based diagnostic tool might provide a specific treatment recommendation, as the decision can have a profound impact on a patient's life.
In academia, several research fields aim to better understand the risks of AI and develop strategies to mitigate them.
Notable fields include explainable AI, which explores how to make AI systems more understandable; privacy-preserving machine learning, which concentrates on protecting privacy within AI systems; and fair machine learning, which seeks to prevent AI from exhibiting biased behavior towards certain user groups.
Significant progress has been made in these fields over the past years.
Recently, additional research efforts have emerged that adopt more comprehensive approaches, extending the scope of existing research with new perspectives.
Many of these activities have been addressed within the context of Trustworthy AI.
This emerging field of research is characterized by a holistic perspective, examining a wide range of aspects related to the development and operation of trustworthy AI systems.
For example, it investigates the concept of trust in the context of AI, considers the criteria by which an AI system can be deemed trustworthy, and examines the challenges in developing trustworthy real-world applications.
This dissertation aims to contribute to the field of Trustworthy AI by presenting an overview of its current state of research and providing insights into selected issues.
It describes the results of five largely independent studies that address specific issues related to Trustworthy AI.
Study 1 aims to provide an overview of the current state of research within the field of Trustworthy AI.
First, a meta-review is conducted to give an overview of the research landscape and systematically highlight the similarities and differences among existing reviews.
The results demonstrate that while several areas of Trustworthy AI have been commonly addressed, there is a limited understanding of the feasibility of combining multiple aspects of trustworthiness, potential synergies or trade-offs between them, and the technical challenges involved.
To gain a deeper understanding of this issue, an additional review is conducted to explore how multiple aspects of trustworthiness are currently integrated.
The results reveal a growing interest in comprehensive approaches to Trustworthy AI; however, their implementation remains a significant challenge.
Study 2 investigates the evaluation of Trustworthy AI, which can help to get an impression of the trustworthiness of AI systems before they are deployed.
Initial approaches for evaluating Trustworthy AI are characterized by the fact that, in addition to model performance, they assess various aspects of trustworthiness in AI systems.
A perspective that has received limited attention is the potential for quantitative approaches that could allow better operationalization of trustworthiness.
While previous quantitative approaches focused on individual aspects of trustworthiness, initial studies have started to investigate how to quantify multiple aspects simultaneously.
This study aims to investigate the feasibility of quantitatively evaluating multiple aspects of trustworthiness together and whether it can provide an overall assessment of an AI system's trustworthiness.
In an experimental approach, several image classifiers are trained in a practical context.
To evaluate the models' trustworthiness, a novel framework is developed that quantitatively assesses the aspects of explainability, fairness, privacy, and robustness in a balanced way and aggregates them into a comprehensive score.
The findings indicate the feasibility of the developed framework to provide an overall assessment of trustworthiness and its likely transferability within image classification.
Study 3 builds upon Study 2 and explores the interdependencies between the four evaluated aspects of trustworthiness.
In practice, the evaluation framework of Study 2 might show that one particular aspect of trustworthiness does not meet the desired standards.
In order to counteract this, several modifications to the data set, model, or training process have been examined.
While existing literature has largely focused on the effectiveness of these interventions, one perspective that has been explored less is the extent to which improving one aspect of trustworthiness might affect others.
Prior research has acknowledged that aspects of trustworthiness are not independent; however, comprehensive studies examining their interdependencies are scarce.
This study investigates how modifying training processes to enhance one aspect of trustworthiness impacts others.
In an extensive set of experiments, several image classifiers are trained while enhancing one trustworthiness aspect at a time.
The models are quantitatively evaluated using the framework developed in Study 2.
The study uncovers several interdependencies and trade-offs between explainability, fairness, privacy, and robustness.
The results suggest that the chosen approach is suitable for quantifying these effects and that such investigations could be valuable in application scenarios where multiple trustworthiness aspects are essential.
Study 4 addresses the issue of distribution shifts, where model performance degrades as operational data diverges from training data.
One approach to handle distribution shifts is to make models more robust, thereby prolonging their operational lifetime.
Recent research has used model explanations not only to understand models better but also to leverage them for adjustments that enhance performance.
Study 4 aims to investigate the feasibility of using explainability to improve robustness against distribution shifts.
Hyperparameter tuning is known to affect robustness due to its impact on model generalization.
In this study, explanations are used to guide the tuning process by providing new insights into model behavior to improve robustness.
The effectiveness of this approach is demonstrated on three datasets from different contexts.
Study 5 examines the challenges of Trustworthy AI in the rapidly growing domain of AI-as-a-Service (AIaaS), which represents an AI-specific counterpart to the prominent software-as-a-service model.
While AIaaS offers companies easy access to advanced AI capabilities without relying on large amounts of skill or capital, there could be challenges to trustworthiness due to how these systems are operated.
For instance, developers lack control over the circumstances under which their models will be used.
This study aims to shed light on these considerations and explore the unique challenges associated with Trustworthy AI in the AIaaS domain.
The study begins with a systematic literature review, identifying that while the concept of Trustworthy AI has been sparsely studied in the context of AIaaS, several challenges related to trustworthiness are already emerging in the literature.
The review is followed by an experimental investigation of an AIaaS solution for mobile device identification, finding several implications related to Trustworthy AI, for example, with regard to accountability, privacy, or the need for measures against potential misuse.Die Entwicklung von künstlicher Intelligenz (KI, engl. AI) hat sich in den letzten zehn Jahren erheblich beschleunigt.
In den letzten Jahren wurde dieser Trend insbesondere durch den Aufstieg der generativen KI verstärkt. Infolgedessen hat sich die Leistung von KI-Systemen deutlich verbessert und sie werden in einer Vielzahl von Bereichen eingesetzt.
Im Bereich des Marketings wird generative KI beispielsweise zur Erstellung personalisierter Inhalte und Werbung eingesetzt. In der Industrie wird KI bei der vorausschauenden Wartung eingesetzt, um Unternehmen bei der Vorhersage von Anlagenausfällen zu unterstützen.
Zahlreiche Fälle haben jedoch gezeigt, dass KI-Systeme potenziell erhebliche Risiken bergen können.
So wurde beispielsweise nachgewiesen, dass kommerzielle Gesichtserkennungssysteme eine erhebliche rassistische Voreingenommenheit aufweisen, wobei Personen mit dunklerer Hautfarbe deutlich häufiger falsch identifiziert werden als Personen mit hellerer Hautfarbe.
Darüber hinaus haben KI-Systeme einen Mangel an Interpretierbarkeit gezeigt, was ein erhebliches Risiko bei Anwendungen darstellt, bei denen die Benutzer den Entscheidungsprozess der KI verstehen müssen.
Im Gesundheitswesen muss ein Arzt beispielsweise nachvollziehen können, warum ein KI-basiertes Diagnosewerkzeug eine bestimmte Behandlungsempfehlung ausspricht, da diese Entscheidung das Leben eines Patienten nachhaltig beeinflussen kann.
Im akademischen Bereich zielen mehrere Forschungsbereiche darauf ab, die Risiken von KI besser zu verstehen und Strategien zu ihrer Minderung zu entwickeln.
Dazu gehören erklärbare KI, das sich mit der Frage beschäftigt, wie KI-Systeme verständlicher gemacht werden können, privacy-preserving maschinelles Lernen, das sich auf den Schutz der Privatsphäre in KI-Systemen konzentriert, und fair maschinelles Lernen, die verhindern sollen, dass sich KI gegenüber bestimmten Nutzergruppen voreingenommen verhält.
In diesen Bereichen wurden in den letzten Jahren erhebliche Fortschritte erzielt.
In jüngster Zeit sind zusätzliche Forschungsbemühungen entstanden, die umfassendere Ansätze verfolgen und den Umfang der bestehenden Forschung um neue Perspektiven erweitern.
Viele dieser Aktivitäten wurden im Kontext der vertrauenswürdigen KI behandelt.
Dieses aufstrebende Forschungsgebiet zeichnet sich durch eine ganzheitliche Perspektive aus, bei der ein breites Spektrum von Aspekten im Zusammenhang mit der Entwicklung und dem Betrieb vertrauenswürdiger KI-Systeme untersucht wird.
So wird beispielsweise das Konzept der Vertrauenswürdigkeit im Kontext der KI untersucht, es werden die Kriterien betrachtet, nach denen ein KI-System als vertrauenswürdig eingestuft werden kann, und es werden die technischen Herausforderungen untersucht, die mit der Entwicklung vertrauenswürdiger Anwendungen in der Praxis verbunden sind.
Diese Dissertation möchte einen Beitrag zum Gebiet der vertrauenswürdigen KI leisten, indem sie einen Überblick über den aktuellen Stand der Forschung gibt und Einblicke in ausgewählte Fragestellungen gewährt.
Sie beschreibt die Ergebnisse von fünf weitgehend unabhängigen Studien, die sich mit spezifischen Fragestellungen im Zusammenhang mit vertrauenswürdiger KI befassen.
Studie 1 gibt einen Überblick über den aktuellen Stand der Forschung auf dem Gebiet der vertrauenswürdigen KI.
Zunächst wird ein Meta-Review durchgeführt, um einen Überblick über die Forschungslandschaft zu geben und systematisch die Gemeinsamkeiten und Unterschiede zwischen den vorhandenen Reviews herauszustellen.
Die Ergebnisse zeigen, dass mehrere Bereiche der vertrauenswürdigen KI zwar allgemein behandelt wurden, dass aber nur ein begrenztes Verständnis für die Machbarkeit der Kombination mehrerer Aspekte der Vertrauenswürdigkeit, für potenzielle Synergien oder Kompromisse zwischen ihnen und für die damit verbundenen technischen Herausforderungen vorhanden ist.
Um ein tieferes Verständnis dieses Themas zu erlangen, wird ein zusätzliches Review durchgeführt, um zu untersuchen, wie mehrere Aspekte der Vertrauenswürdigkeit derzeit integriert werden.
Die Ergebnisse zeigen ein wachsendes Interesse an umfassenden Ansätzen für vertrauenswürdige KI, deren Umsetzung jedoch weiterhin eine große Herausforderung darstellt.
Studie 2 untersucht die Evaluierung vertrauenswürdiger KI, die ein hilfreicher Weg sein kann, um einen Eindruck von der Vertrauenswürdigkeit von KI-Systemen zu bekommen, bevor sie eingesetzt werden.
Erste Ansätze zur Evaluierung vertrauenswürdiger KI zeichnen sich dadurch aus, dass sie neben der Modellleistung verschiedene Aspekte der Vertrauenswürdigkeit von KI-Systemen bewerten.
Eine bisher wenig beachtete Perspektive ist das Potenzial quantitativer Ansätze, die eine bessere Operationalisierung von Vertrauenswürdigkeit ermöglichen könnten.
Während sich frühere quantitative Ansätze auf einzelne Aspekte der Vertrauenswürdigkeit konzentrierten, haben erste Studien damit begonnen zu untersuchen, ob mehrere Aspekte gleichzeitig quantifiziert werden können.
In dieser Studie soll untersucht werden, ob es möglich ist, mehrere Aspekte der Vertrauenswürdigkeit gleichzeitig quantitativ zu bewerten, und ob dies eine Gesamtbeurteilung der Vertrauenswürdigkeit eines KI-Systems bieten kann.
In einem experimentellen Ansatz werden mehrere Bildklassifikatoren in einem praktischen Kontext trainiert.
Um die Vertrauenswürdigkeit der Modelle zu bewerten, wird ein neues Framework entwickelt, das die Vertrauenswürdigkeitsaspekte Erklärbarkeit, Fairness, Datenschutz und Robustheit quantitativ bewertet.
Die Ergebnisse zeigen die Machbarkeit des entwickelten Frameworks für eine Gesamtbewertung der Vertrauenswürdigkeit und seine wahrscheinliche Übertragbarkeit auf andere Bildklassifizierungsprobleme.
Studie 3 baut auf Studie 2 auf und untersucht die gegenseitigen Abhängigkeiten zwischen den vier bewerteten Aspekten der Vertrauenswürdigkeit.
In realen Anwendungsszenarien könnte der Bewertungsrahmen von Studie 2 zeigen, dass ein bestimmter Aspekt der Vertrauenswürdigkeit nicht den gewünschten Standards entspricht.
Um dem entgegenzuwirken, wurden verschiedene Änderungen am Datensatz, am Modell oder am Trainingsprozess bereits untersucht.
Während sich die vorhandene Literatur weitgehend auf die Wirksamkeit dieser Maßnahmen konzentriert hat, wurde die Frage, inwieweit sich die Verbesserung eines Aspekts der Vertrauenswürdigkeit auf andere Aspekte auswirken könnte, bislang kaum untersucht.
Frühere Forschungsarbeiten haben erkannt, dass die Aspekte der Vertrauenswürdigkeit nicht unabhängig voneinander sind, jedoch gibt es kaum umfassende Studien, die ihre Wechselwirkungen untersuchen.
In dieser Studie wird untersucht, wie sich die Änderung von Trainingsprozessen zur Verbesserung eines Vertrauenswürdigkeitsaspekts auf die anderen auswirkt.
In einer umfangreichen Reihe von Experimenten werden mehrere Bildklassifikatoren trainiert, wobei jeweils ein Aspekt der Vertrauenswürdigkeit verbessert wird.
Die Modelle werden mit Hilfe des in Studie 2 entwickelten Frameworks quantitativ ausgewertet.
Die Studie deckt mehrere gegenseitige Abhängigkeiten und Kompromisse zwischen Erklärbarkeit, Fairness, Privatsphäre und Robustheit auf.
Diese Ergebnisse deuten darauf hin, dass der gewählte Ansatz geeignet ist, diese Effekte zu quantifizieren, und dass solche Untersuchungen in Anwendungsszenarien, in denen mehrere Aspekte der Vertrauenswürdigkeit wesentlich sind, wertvoll sein könnten.
Studie 4 befasst sich mit dem Problem der Verteilungsverschiebungen, bei denen die Modellleistung abnimmt, wenn die Betriebsdaten von den Trainingsdaten abweichen.
Ein Ansatz zur Bewältigung von Verteilungsverschiebungen besteht darin, Modelle robuster zu machen und damit ihre operative Lebensdauer zu verlängern.
In der jüngsten Forschung wurden Modellerklärungen nicht nur dazu verwendet, Modelle besser zu verstehen, sondern auch, um sie für Anpassungen zur Leistungssteigerung zu nutzen.
Studie 4 zielt darauf ab, die Durchführbarkeit der Nutzung von Erklärbarkeit zur Verbesserung der Robustheit gegenüber Verteilungsverschiebungen zu untersuchen.
Es ist bekannt, dass das Tuning von Hyperparametern die Robustheit beeinflusst, da es sich auf die Modellgeneralisierung auswirkt.
In dieser Studie werden Erklärungen verwendet, um den Tuning-Prozess zu steuern, indem neue Einblicke über das Modellverhalten gewonnen werden, um die Modellrobustheit zu verbessern.
Die Wirksamkeit dieses Ansatzes wird an drei Datensätzen aus unterschiedlichen Kontexten demonstriert.
Studie 5 untersucht die Herausforderungen der vertrauenswürdigen KI im schnell wachsenden Bereich des AI-as-a-Service (AIaaS), der ein KI-spezifisches Gegenstück zu dem bekannten Software-as-a-Service-Modell darstellt.
AIaaS bietet Unternehmen zwar einen einfachen Zugang zu fortschrittlichen KI-Fähigkeiten, ohne dass dafür große Mengen an Fachwissen oder Kapital erforderlich sind, doch die Art und Weise, wie diese Systeme betrieben werden, kann Herausforderungen für die Vertrauenswürdigkeit mit sich bringen.
So haben die Entwickler beispielsweise keine Kontrolle über die Umstände, unter denen ihre Modelle verwendet werden.
Diese Studie soll diese Überlegungen beleuchten und die spezifischen Herausforderungen im Zusammenhang mit vertrauenswürdiger KI im AIaaS-Bereich untersuchen.
Die Studie beginnt mit einer systematischen Literaturrecherche, die zeigt, dass das Konzept der vertrauenswürdigen KI im Zusammenhang mit AIaaS bisher nur wenig untersucht wurde, dass aber in der Literatur bereits mehrere Herausforderungen im Zusammenhang mit der Vertrauenswürdigkeit erkennbar sind.
Anschließend wird eine experimentelle Untersuchung einer AIaaS-Lösung für die Identifizierung mobiler Geräte durchgeführt, die mehrere Implikationen im Zusammenhang mit vertrauenswürdiger KI aufzeigt, z. B. in Bezug auf Verantwortlichkeit, Datenschutz oder die Notwendigkeit von Maßnahmen gegen potenziellen Missbrauc
The Influence of Gastric Microbiota and Probiotics in Helicobacter pylori Infection and Associated Diseases
Get PDFThe role of microbiota in human health and disease is becoming increasingly clear as a result of modern microbiome studies in recent decades. The gastrointestinal tract is the major habitat for microbiota in the human body. This microbiota comprises several trillion microorganisms, which is equivalent to almost ten times the total number of cells of the human host. Helicobacter pylori is a known pathogen that colonizes the gastric mucosa of almost half of the world population. H. pylori is associated with several gastric diseases, including gastric cancer (GC) development. However, the impact of the gastric microbiota in the colonization, chronic infection, and pathogenesis is still not fully understood. Several studies have documented qualitative and quantitative changes in the microbiota’s composition in the presence or absence of this pathogen. Among the diverse microflora in the stomach, the Firmicutes represent the most notable. Bacteria such as Prevotella sp., Clostridium sp., Lactobacillus sp., and Veillonella sp. were frequently found in the healthy human stomach. In contrast, H . pylori is very dominant during chronic gastritis, increasing the proportion of Proteobacteria in the total microbiota to almost 80%, with decreasing relative proportions of Firmicutes. Likewise, H. pylori and Streptococcus are the most abundant bacteria during peptic ulcer disease. While the development of H. pylori -associated intestinal metaplasia is accompanied by an increase in Bacteroides, the stomachs of GC patients are dominated by Firmicutes such as Lactobacillus and Veillonella , constituting up to 40% of the total microbiota, and by Bacteroidetes such as Prevotella , whereas the numbers of H. pylori are decreasing. This review focuses on some of the consequences of changes in the gastric microbiota and the function of probiotics to modulate H. pylori infection and dysbiosis in general.The work of S.K.P. was supported by Anusandhan National Research Foundation (ANRF) grant CRG/2022/003093 and BBAU-grant IQAC/2022/800-09; S.B. was supported by German Science Foundation (DFG) grant BA1671/16-1.Anusandhan National Research Foundation (ANRF)BBAU-grantGerman Science Foundation (DFG
A Half-Century of Heterotopic Heart Transplantation in Mice: The Spearhead of Immunology Research
Get PDFSince the success of solid organ transplants, such as human kidneys, livers and hearts, from the 50s to the 60s in the last century, the field of organ transplantation has progressed rapidly. Mainly due to modifications in surgical operation techniques and improvements in immunosuppressive therapy regimes, organ survival time can now be greatly prolonged. This progress has also been dependent upon the availability of appropriate animal models for organ transplantation. Therefore, the mouse heart transplantation model has developed into an irreplaceable research model for solid organ transplantation, providing indelible contributions to the field. In this review, we will provide an overview of the technical developments in murine heart transplantation, as well as its historical and current role for alloimmune research. Further, we will describe its current fields of application and its scientific achievements before we discuss potential future applications.This work was supported, in part, by the German Research Foundation (DFG) to promote international collaborations (HO2581/4-1 to A.H.), the National Science Foundation of China (NSFC; #81760291 to J.F.), the German Research Foundation DFG (RTG2504-401821119 (B2) to C.H.K.L. and D.D.; TRR374-509149993 (B7) to D.D. and A.H., and DU548/9-1-515982377 to D.D.) and the Manfred Roth Stiftung (to A.H.) and by intramural funding from the IZKF Erlangen (A87 to C.H.K.L.).German Research Foundation (DFG) to promote international collaborationsNational Science Foundation of ChinaGerman Research Foundation DFGManfred Roth Stiftungintramural funding from the IZKF Erlange
Exploring CAR T-Cell Dynamics: Balancing Potent Cytotoxicity and Controlled Inflammation in CAR T-Cells Derived from Systemic Sclerosis and Myositis Patients
Get PDFSystemic lupus erythematosus (SLE), systemic sclerosis (SSc), and idiopathic inflammatory myositis (IIM) are autoimmune diseases managed with long-term immunosuppressive therapies. Hu19-CD828Z, a fully human anti-CD19 chimeric antigen receptor (CAR) with a CD28 costimulatory domain, is engineered to potently deplete B-cells. In this study, we manufactured Hu19-CD828Z CAR T-cells from peripheral blood of SLE, IIM, and SSc patients and healthy donors (HDs). CAR-mediated, CD19-specific activity of these cells was evaluated in vitro by assessing cytotoxicity, cytokine release, and proliferation assays in response to autologous CD19 + B-cells, the CD19 + NALM-6 B-cell line, or a CD19 − U937 non-B-cell line as targets. The results demonstrated an increased proliferation of Hu19-CD828Z CAR T-cells and dose-dependent cytotoxicity against primary autologous and NALM-6 B-cells compared to non-transduced controls or co-cultures with non-B-cells. Notably, autoimmune-patient-derived CAR T-cells produced lower levels of inflammatory cytokines than healthy-donor-derived CAR T-cells in response to CD19 + B-cell targets. These data support the potential of Hu19-CD828Z and its therapeutic cell product KYV-101 as a therapeutic strategy to achieve deep B-cell depletion in SLE, IIM, and SSc patients, and highlights its promise for broader application in B-cell-driven autoimmune disorders.This study was supported by Kyverna Therapeutics (M5-AF-WB_KYV-101). The work of G.S. is supported by the Leibniz Award, and the Lupus Insight Prize from the Lupus Research Alliance.Kyverna TherapeuticsLupus Research Allianc