[Spatio-temporal distribution of COVID-19 in Cologne and associated socio-economic factors in the period from February 2020 to October 2021]. / Die zeitlich-räumliche Verteilung von COVID-19 in Köln und beeinflussende soziale Faktoren im Zeitraum Februar 2020 bis Oktober 2021.

BACKGROUND AND GOALS: Even in the early phase of the COVID-19 pandemic, which took a very different course globally, there were indications that socio-economic factors influenced the dynamics of disease spread, which from the second phase (September 2020) onwards particularly affected people with a lower socio-economic status. Such effects can also be seen within a large city. The present study visualizes and examines the spatio-temporal spread of all COVID-19 cases reported in Cologne, Germany (February 2020-October 2021) at district level and their possible association with socio-economic factors. METHODS: Pseudonymized data of all COVID-19 cases reported in Cologne were geo-coded and their distribution was mapped in an age-standardized way at district level over four periods and compared with the distribution of social factors. The possible influence of the selected factors was also examined in a regression analysis in a model with case growth rates. RESULTS: The small-scale local infection process changed during the pandemic. Neighborhoods with weaker socio-economic indices showed higher incidence over a large part of the pandemic course, with a positive correlation between poverty risk factors and age-standardized incidence. The strength of this correlation changed over time. CONCLUSION: The timely observation and analysis of the local spread dynamics reveals the positive correlation of disadvantaging socio-economic factors on the incidence rate of COVID-19 at the level of a large city and can help steer local containment measures in a targeted manner.

Light-diffractive patterning of Porphyridium purpureum.

Light guidance is a convenient and versatile way to control the positions of phototactic microorganisms. However, the illumination strategies require adaption to the respective organism. We report on the generation of structures composed of the gliding and exopolysaccharide-secreting algae Porphyridium purpureum via their photomovement. Light patterns from a two-dimensional computer-generated hologram were projected onto inoculated agar plates. The obtained pixelated algae patterns were evaluated with regard to the illuminated intensity, contrast and pixel size. Upper and lower thresholds for algae accumulation were determined, allowing to enhance future manipulation of phototactic microorganisms.

Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews.

BACKGROUND: PRISMA-based literature reviews require meticulous scrutiny of extensive textual data by multiple reviewers, which is associated with considerable human effort. OBJECTIVE: To evaluate feasibility and reliability of using GPT-4 API as a complementary reviewer in systematic literature reviews based on the PRISMA framework. METHODOLOGY: A systematic literature review on the role of natural language processing and Large Language Models (LLMs) in automatic patient-trial matching was conducted using human reviewers and an AI-based reviewer (GPT-4 API). A RAG methodology with LangChain integration was used to process full-text articles. Agreement levels between two human reviewers and GPT-4 API for abstract screening and between a single reviewer and GPT-4 API for full-text parameter extraction were evaluated. RESULTS: An almost perfect GPT-human reviewer agreement in the abstract screening process (Cohen's kappa > 0.9) and a lower agreement in the full-text parameter extraction were observed. CONCLUSION: As GPT-4 has performed on a par with human reviewers in abstract screening, we conclude that GPT-4 has an exciting potential of being used as a main screening tool for systematic literature reviews, replacing at least one of the human reviewers.

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. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis.

The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.

A Perspective on Bio-Mediated Material Structuring.

Bioinspiration, biomorphy, biomimicry, biomimetics, bionics, and biotemplating are terms used to describe the fabrication of materials or, more generally, systems to solve technological problems by abstracting, emulating, using, or transferring structures from biological paradigms. Herein, a brief overview of how the different terminologies are being typically applied is provided. It is proposed that there is a rich field of research that can be expanded by utilizing various novel approaches for the guidance of living organisms for "bio-mediated" material structuring purposes. As examples of contact-based or contact-free guidance, such as substrate patterning, the application of light, magnetic fields, or chemical gradients, potentially interesting methods of creating hierarchically structured monolithic engineering materials, using live patterned biomass, biofilms, or extracellular substances as scaffolds, are presented. The potential advantages of such materials are discussed, and examples of live self-patterning of materials are given.

Odor cueing during slow-wave sleep benefits memory independently of low cholinergic tone.

RATIONALE: Sleep-dependent memory consolidation depends on the concerted reactivation of memories in the hippocampo-neocortical system. The communication of reactivated information from the hippocampus to the neocortex is assumed to be enabled by low levels of acetylcholine, particularly during slow-wave sleep (SWS). Recent studies suggest that the reactivation of memories does not only occur spontaneously but can also be externally triggered by re-presenting learning-associated cues during sleep. OBJECTIVES: Here we investigated whether the beneficial effect of cued memory reactivation during sleep depends on similar mechanisms as spontaneous reactivation, and specifically on low cholinergic tone. METHODS: In two experimental nights, healthy volunteers learned a visuo-spatial memory task in the presence of an odor before going to sleep for 40 min. In one night, subjects were presented with the odor again during SWS, whereas in the other night they received an odorless vehicle. In half of the subjects, the availability of acetylcholine during sleep was increased by administering the acetylcholine-esterase inhibitor physostigmine. RESULTS: Contrary to our hypothesis, increased cholinergic tone during sleep did not abolish the beneficial effect of odor cueing: memory performance was better after odor cueing compared to odorless vehicle, independent of physostigmine or placebo administration. CONCLUSIONS: This finding challenges the assumption that odor-cued and spontaneous memory reactivation rely on the same neuropharmacological mechanisms.

Integrated biorefinery concept for grass silage using a combination of adapted pulping methods for advanced saccharification and extraction of lignin.

An integrated refining and pulping process for ensiled biomass from permanent grassland was established on laboratory scale. The liquid phase, containing the majority of water-soluble components, including 24% of the initial dry matter (DM), was first separated by mechanical pressing. The fiber fraction was subjected to high solid load saccharification (25% DM) to enhance the lignin content in the feed for subsequent organosolvation. The saccharification enzymes were pre-selected applying experimental design approaches. Cellulose convertibility was improved by a secondary pressing step during liquefaction. Combined saccharification and organosolvation showed high degree of saccharide solubilization with recovery of 98% of the glucan and 73% of the xylan from the fiber fraction in the hydrolysates, and enabled the recovery of 41% of the grass silage lignin. The effects of the treatment were confirmed by XRD and SEM tracking of cellulose crystallinity and fiber morphology throughout the pulping procedure.