Extending from Adaptation to Resilience Pathways: Perspectives from the Conceptual Framework to Key Insights.

The extent and timescale of climate change impacts remain uncertain, including global temperature increase, sea level rise, and more frequent and intense extreme events. Uncertainties are compounded by cascading effects. Nevertheless, decision-makers must take action.

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Purpose: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Methods: We reviewed EHRs of patients aged 1 month to 21 years from Boston Children's Hospital (BCH). We included all patients with convulsive ESE or RSE during admission.

Individual application patterns of Cannabis-based Medicines in Germany - Descriptive evaluation of a patient survey and discussion from a forensic perspective.

Background: An increasing number of countries have legalised cannabis for medicinal purposes in recent years leading to tensions with other regulatory frameworks. Some countries grant a medical defence according to their drug driving legislations. This may lead to specific medico-legal assessments relating to the participation of cannabis patients in road traffic.

Mandibular Advancement Devices in OSA Patients: Impact on Occlusal Dynamics and Tooth Alignment Modifications-A Pilot Prospective and Retrospective Study.

: The widespread prevalence of obstructive sleep apnea (OSA) underscores the necessity for effective therapies. Mandibular advancement devices (MADs) have emerged as valid treatment for mild to moderate cases, despite the associated dental side effects. : This study evaluates the nature, onset, and long-term manifestation of these side effects.

Sharing for Caring? A Patients' and Clinicians' View on Handling Personal Medical Data in the Context of Digitization: An Exploratory Study.

Background: The healthcare sector is currently undergoing a significant transformation, driven by an increased utilization of data. In this evolving landscape, surveys are of pivotal importance to the comprehension of patient needs and preferences. Moreover, the digital affinity of patients and physicians within the healthcare system is reforming the manner in which healthcare services are accessed and delivered.

Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.

Objectives: To investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS).

Methods: The study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels").

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units.

Background: The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing.

Paving the Way for Climate Neutral and Resilient Historic Districts.

Climate change is a major global threat to our society's urban areas, with the majority of Europe's population living in cities and their cultural heritage. Historic districts of significant cultural value and the communities connected to these places have an important role to play in fostering location-based identity and economy, social cohesion, innovation, urban regeneration, and climate change adaptation. Thus, it is important to make historic districts climate resilient, by jointly considering climate change adaptation, disaster risk management, heritage management, and sustainable urban development.

Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis.

Achieving a good outcome for a person with Psoriatic Arthritis (PsA) is made difficult by late diagnosis, heterogenous clinical disease expression and in many cases, failure to adequately suppress inflammatory disease features. Single-centre studies have certainly contributed to our understanding of disease pathogenesis, but to adequately address the major areas of unmet need, multi-partner, collaborative research programmes are now required. HIPPOCRATES is a 5-year, Innovative Medicines Initiative (IMI) programme which includes 17 European academic centres experienced in PsA research, 5 pharmaceutical industry partners, 3 small-/medium-sized industry partners and 2 patient-representative organizations.

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks.

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far.

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals.

Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting.

Transformer-based structuring of free-text radiology report databases.

Objectives: To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies.

Methods: A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist.

Symptom diaries as a digital tool to detect SARS-CoV-2 infections and differentiate between prevalent variants.

The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary.

Yes we care!-Certification for machine learning methods through the care label framework.

Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g.

Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision.

Background: Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously.

A hybrid approach for identifying drug repurposing candidates and their mechanisms: An interview with Vanessa Lage-Rupprecht and two co-authors.

Senior researcher Vanessa Lage-Rupprecht and two collaborators talk about what data science means to them and illustrate how they managed to create a data and lab coexistence in their drug-repurposing project, which was recently published in . In this article, they have developed a drug-target-mechanism-oriented data model, Human Brain PHARMACOME, and have presented it as a resource to the community.

A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism.

The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau).

Propensity-score-matched comparison of safety, efficacy, and outcome of intravascular lithotripsy versus high-pressure PTCA in coronary calcified lesions.

Background: Data regarding safety, efficacy, and outcome of intravascular lithotripsy (IVL) in comparison to standard techniques are lacking. This study sought to compare IVL with non-compliant high-pressure balloon percutaneous coronary angioplasty (PTCA).

Methods And Results: We performed a retrospective propensity-score-matched study to compare procedural success in 57 consecutive patients who received IVL-guided PCI in calcified coronary lesions with 171 matched patients who were treated with high-pressure PTCA with a non-compliant (NC)-balloon.

Benchmarking table recognition performance on biomedical literature on neurological disorders.

Motivation: Table recognition systems are widely used to extract and structure quantitative information from the vast amount of documents that are increasingly available from different open sources. While many systems already perform well on tables with a simple layout, tables in the biomedical domain are often much more complex. Benchmark and training data for such tables are however very limited.

CLEP: a hybrid data- and knowledge-driven framework for generating patient representations.

Summary: As machine learning and artificial intelligence increasingly attain a larger number of applications in the biomedical domain, at their core, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLinical Embedding of Patients (CLEP), a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data.

Microplastics in lakeshore and lakebed sediments - External influences and temporal and spatial variabilities of concentrations.

Microplastics have been predominantly studied in marine environments compared to freshwater systems. However, the number of studies analyzing microplastic concentrations in water and sediment within lakes and rivers are increasing and are of utmost importance as freshwaters are major pathways for plastics to the oceans. To allow for an adequate risk assessment, detailed knowledge concerning plastic concentrations in different environmental compartments of freshwaters are necessary.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.

Summary: Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface.

Prediction of memory formation based on absolute electroencephalographic phases in rhinal cortex and hippocampus outperforms prediction based on stimulus-related phase shifts.

Absolute (i.e. measured) rhinal and hippocampal phase values are predictive for memory formation.