Development of an early alert model for pandemic situations in Germany

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

Spatial patterns of white matter hyperintensities: a systematic review

Background

White matter hyperintensities are an important marker of cerebral small vessel disease. This disease burden is commonly described as hyperintense areas in the cerebral white matter, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging data. Studies have demonstrated associations with various cognitive impairments, neurological diseases, and neuropathologies, as well as clinical and risk factors, such as age, sex, and hypertension. Due to their heterogeneous appearance in location and size, studies have started to investigate spatial distributions and patterns, beyond summarizing this cerebrovascular disease burden in a single metric-its volume. Here, we review the evidence of association of white matter hyperintensity spatial patterns with its risk factors and clinical diagnoses.

Design/methods

We performed a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. We used the standards for reporting vascular changes on neuroimaging criteria to construct a search string for literature search on PubMed. Studies written in English from the earliest records available until January 31st, 2023, were eligible for inclusion if they reported on spatial patterns of white matter hyperintensities of presumed vascular origin.

Results

A total of 380 studies were identified by the initial literature search, of which 41 studies satisfied the inclusion criteria. These studies included cohorts based on mild cognitive impairment (15/41), Alzheimer's disease (14/41), Dementia (5/41), Parkinson's disease (3/41), and subjective cognitive decline (2/41). Additionally, 6 of 41 studies investigated cognitively normal, older cohorts, two of which were population-based, or other clinical findings such as acute ischemic stroke or reduced cardiac output. Cohorts ranged from 32 to 882 patients/participants [median cohort size 191.5 and 51.6% female (range: 17.9-81.3%)]. The studies included in this review have identified spatial heterogeneity of WMHs with various impairments, diseases, and pathologies as well as with sex and (cerebro)vascular risk factors.

Conclusion

The results show that studying white matter hyperintensities on a more granular level might give a deeper understanding of the underlying neuropathology and their effects. This motivates further studies examining the spatial patterns of white matter hyperintensities.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Cell cycle regulation of the murine cyclin E gene depends on an E2F binding site in the promoter

Cyclin E controls progression through the G1 phase of the cell cycle in mammalian fibroblasts and potentially in many other cell types. Cyclin E is a rate-limiting activator of cdk2 kinase in late G1. The abundance of cyclin E is controlled by phase-specific fluctuations in the mRNA level; in mammalian fibroblasts, mRNA is not detected under conditions of serum starvation and is accumulated upon serum stimulation, with expression starting in mid-G1. Here, we report the cloning of the murine cyclin E promoter. We isolated a 3.8-kb genomic fragment that contains several transcriptional start sites and confers cell cycle regulation on a luciferase reporter gene. This fragment also supports transcriptional activation by adenovirus E1A, a known upstream regulator of cyclin E gene expression. An E2F binding site which is required for G1-specific activation of the cyclin E promoter in synchronized NIH 3T3 cells was identified in this fragment.

Differential modulation of cyclin gene expression by MYC

We have investigated the effects of deregulated expression of the human c-MYC protooncogene on cyclin gene expression and on the transcription factor E2F. We found that constitutive expression of MYC or activation of conditional MycER chimeras led to higher levels of cyclin A and cyclin E mRNA. Activation of cyclin A expression by MYC led to a growth factor-independent association of cyclin A and cdk2 with the transcription factor E2F and correlated with an increase in E2F transcriptional activity. In contrast, expression of the G1 phase cyclin D1 was strongly reduced in MYC-transformed cells. In synchronized cells, repression of cyclin D1 by MYC occurred very early in the G1 phase of the cell cycle.