München Klinik Schwabing

Hypothesis-driven academic research identifies interventions with likely disease-specific effects. Yet, many candidate drugs fail upon further development, emphasizing the need for acquisition of more robust preclinical data. We demonstrate that planning and executing multicentre confirmatory preclinical studies in an academic setting by applying the quality standards of early phase clinical trials is feasible. Randomization, blinding, stratification by sex of and quality control measures were carried out successfully. The primary objective of our specific study - to confirm synergistic effects of BET bromodomain protein 4 (BRD4) and cyclin-dependent kinase 9 (CDK9) inhibitors against PAX3::FOXO1 (P3F)-positive rhabdomyosarcoma (RMS) - was not met. Post-hoc analyses support that single-agent BRD4 inhibition by JQ1 effectively reduced the growth and viability of P3F+ RMS cells ex vivo with adequate on-target activity as evidenced by reduced expression of P3F, MYCN, MYOG, and MYOD. The antiproliferative effects of JQ1 and vincristine were comparable, and there was trend towards reduced and delayed xenograft growth in JQ1-treated mice. Yet, in vivo assays were flawed by lower xenograft penetrance, variable xenograft latency, gastrointestinal toxicity, and inadequate on-target activity of drugs. We conclude that confirmatory preclinical trials allow for robust assessment of the efficacy of candidate interventions and reduce bias in academic research. The study platform established here provides a framework that may be of particular benefit for the development of new drugs for rare cancers.

An AI-enhanced threat intelligence tool can detect intrusions before they happen in complex multi-cloud setups. Traditional detection methods don't function as effectively because they can't adapt to changing scenarios or understand cloud activity. The proposed three-step methodology enhances data preparation, identifies multimodal anomalies, and generates semantic insights. It finds key danger patterns utilizing entropy-based filtering, temporal behavior modeling, and unsupervised threat categorization. These patterns are then interpreted using knowledge graphs and adaptive learning. The system's design evolves and employs feedback loops to improve detection logic and reaction protocols. Combining deep learning models with contextual risk information ensures high classification accuracy. Real-time choices with few false positives are possible. The tests demonstrate it's superior at discovering and running stuff. The system finds items 97.8% of the time, delivers real positives 96.9 %, and phony positives 1.5 %. It has the best practical features. The system achieves an AUC-ROC of 98.4 %, a threat response time of 105 ms, and exhibits strong scalability, robustness, and computational efficiency scores. These results demonstrate that the proposed approach is a reliable, adaptable, and extendable cloud security solution. This study establishes a solid foundation for artificial intelligence and contextual semantics to improve cyber resilience. This makes it suitable for massive, multi-cloud company infrastructures.

This paper introduces a blockchain-based method for IoT programs to transfer data securely. As the number of Internet of Things (IoT) devices rises and the demand for safe and immutable data management grows, traditional centralized systems have single points of failure and flaws that allow unauthorized access. The suggested solution protects data during transmission and storage using cryptographic encryption, hashing, consensus mechanisms, and smart contracts. Blockchain-based solutions are decentralized, so data cannot be changed, and encryption and hashing safeguard it. The consensus mechanism verifies transactions and ensures nodes are in sync, making the network secure. Smart contracts allow fine-grained access control, limiting sensitive data to authorized devices and persons. The proposed solution outperforms current systems in privacy, data integrity, security, scalability, and energy economy. The suggested method works well with big IoT networks, requires little energy, and has minimal latency, making it useful for many IoT uses. This study shows how blockchain technology may provide a safe, effective, and scalable IoT data management system. This approach would prepare future IoT ecosystems to securely communicate data.

Sleep disturbances are both a primary symptom of and risk factor for major depressive disorder (MDD). Sleep alterations in MDD include the presence of insomnia or hypersomnia and aberrations in sleep macro- and microstructure, including a reduced latency until and prolonged duration of the first rapid eye movement (REM) episode, decreased slow-wave sleep (SWS), disturbed sleep continuity, and decreased steepening of aperiodic neural activity. MDD sleep is also characterized by dysfunctional autonomic cardiac activity as reflected by decreased heart rate variability. Sedating and novel antidepressants may improve sleep continuity and SWS while activating antidepressants tend to suppress REM sleep. Cognitive processing during sleep may contribute to MDD symptomatology; however, empirical research in this direction is still inconclusive. The rapid effects of antidepressants on sleep structure and of acute sleep deprivation on MDD symptoms suggest an intimate association between sleep and MDD neuropathology; however, the underlying mechanisms still need to be elucidated. This narrative review aims to provide a comprehensive overview of our current understanding of the physiology of sleep alterations in MDD and to discuss how sleep may play a role in current therapeutic approaches while also identifying novel strategies for modulating sleep in the context of depression. It seeks to offer a well-rounded foundation of the current knowledge on sleep and depression to guide basic and clinical scientists in future investigations related to improving existing and developing novel sleep-based therapeutic interventions.

A cloud computing offers numerous advantages in terms of cost strictly compared to the other models, i.e., it makes available to a broad audience the opportunity of taking advantage from provided infrastructure and services. However, it introduces new security and reliability concerns. Financial Institutions had to spend a lot of efforts in keeping data safe and protected. These sensitive data – if affected by breach – are able to damage customers to the same level of institution, with a risk for consumers of loss or damaging of capital and for banks or financial institutions of losing market share, damage to brand or revealing of confidential information.

Big Data is emerging as a transformational tool in finance. Throughout the world, the financial sector has turned to Big Data as a source of new revenue generation, operational cost saving, and risk management for existing products. In this chapter, we present advances in the use of Big Data across different subsectors in finance, namely, asset management, banking, and insurance. Big Data is typically defined by the so-called three Vs – vast amount of data, ranging from terabytes to zettabytes; diverse data sources, including structured transactional data and unstructured data generated from sensors and web activities; and velocity of data flow, which makes real-time processing of the data more desirable yet extremely challenging. Coupled with cheap data storage and processing tools enabled by cloud computing, organizations are increasingly leveraging Big Data to gain valuable business insights and further their objectives. Examples include online e-commerce companies recommending products based on users’ activities; hospitals improving healthcare quality by analyzing drug prescription data; and manufacturers forecasting product demand, based on supply chain and sales data.

Abstract Objective To assess whether neonates with critical congenital heart defects (cCHD) can be safely delivered and stabilized at a Level-III perinatal center lacking a dedicated paediatric cardiology unit. Methods Retrospective single-center cohort study (2018–2022) at a tertiary perinatal center with paediatric cardiologists on staff but no dedicated cardiology unit. Inclusion of all neonates with prenatally or postnatally diagnosed cCHD delivered at the center and followed until transfer or discharge. Main outcome measures Integrated prenatal and neonatal care prior to transfer for cardiac intervention. Neonatal mortality, morbidity, diagnostic confirmation, functional cCHD classification, delivery mode, postnatal bonding, and admission site. Results Among 115 neonates, 94.0% had prenatal cCHD diagnosis. Vaginal delivery occurred in 50.4%; 52.7% achieved postnatal bonding. Initial care was provided at NICU (87.0%), intermediate care (5.2%), and maternity ward (7.8%). Functional cCHD classification was: Univentricular diseases (29%), Systemic Outflow Tract Obstruction (17%), Transposition of the Great Arteries (11%), Left-to-right Shunt Lesion (12%), Tetralogy of Fallot (8%), Ebstein Anomaly (5%), Heterotaxy Syndrome (1%), Arrhythmia (1%) and Other Defects (9%) with a significant difference in the postnatal confirmation of the prenatal diagnosis. (OR 2.1; CI 1.3–3.4). Neonatal thriving was significant for head circumference (p < 0.01), but not for weight gain (p = 0.22). Mortality was 9.5%; notably no acute deaths occurred in-house. Conclusions Contrary to current practice emphasizing delivery at specified centers, our findings show that neonates with cCHD can be safely managed in non-cardiac centers with expert neonatal and cardiologic collaboration. This model optimizes maternal bonding and resource allocation without compromising early survival.

This paper describes a novel shared learning-based cloud computing architecture for smart services that prioritizes privacy and resource efficiency. Flexible resource distribution and differential privacy solutions protect data and speed up numerous energy suppliers. Dynamically altering learning rates and resource utilization, depending on provider computer power, achieves scaling and decreases connection costs. Private data is protected via secure gradient-sharing mechanisms when the model is modified. The recommended strategy improves model accuracy, processing speed, and energy consumption. It speeds up the process while maintaining anonymity. This ensures the system operates properly in diverse settings. The strategy increases utility seller fairness by considering resource supply inequalities. This makes it ideal for large-scale applications. After solving the fundamental concerns of scalability, security, and efficiency, this shared learning system makes it simple for models to function in a variety of data circumstances. Thus, the provided solution protects machine learning privacy for smart utility applications.