Ruthenium-Cathepsin Inhibitor Conjugates for Green Light-Activated Photodynamic Therapy and Photochemotherapy.

Dysregulated cathepsin activity is linked to various human diseases including metabolic disorders, autoimmune conditions, and cancer. Given the overexpression of cathepsin in the tumor microenvironment, cathepsin inhibitors are promising pharmacological agents and drug delivery vehicles for cancer treatment. In this study, we describe the synthesis and photochemical and biological assessment of a dual-action agent based on ruthenium that is conjugated with a cathepsin inhibitor, designed for both photodynamic therapy (PDT) and photochemotherapy (PCT). The ruthenium-cathepsin inhibitor conjugate was synthesized through an oxime click reaction, combining a pan-cathepsin inhibitor based on E64d with the Ru(II) PCT/PDT fragment [Ru(dqpy)(dppn)], where dqpy = 2,6-di(quinoline-2-yl)pyridine and dppn = benzo[i]dipyrido[3,2-a:2',3'-c]phenazine. Photochemical investigations validated the conjugate's ability to release a triazole-containing cathepsin inhibitor for PCT and to generate singlet oxygen for PDT upon exposure to green light. Inhibition studies demonstrated the conjugate's potent and irreversible inactivation of purified and intracellular cysteine cathepsins. Two Ru(II) PCT/PDT agents based on the [Ru(dqpy)(dppn)] moiety were evaluated for photoinduced cytotoxicity in 4T1 murine triple-negative breast cancer cells, L929 fibroblasts, and M0, M1, and M2 macrophages. The cathepsin inhibitor conjugate displayed notable selectivity for inducing cell death under irradiation compared to dark conditions, mitigating toxicity in the dark observed with the triazole control complex [Ru(dqpy)(dppn)(MeTz)]2+ (MeTz = 1-methyl-1H-1,2,4-triazole). Notably, our lead complex is among a limited number of dual PCT/PDT agents activated with green light.

GTP Signaling Links Metabolism, DNA Repair, and Responses to Genotoxic Stress.

How cell metabolism regulates DNA repair is incompletely understood. Here, we define a GTP-mediated signaling cascade that links metabolism to DNA repair and has significant therapeutic implications. GTP, but not other nucleotides, regulates the activity of Rac1, a guanine nucleotide-binding protein, which promotes the dephosphorylation of serine 323 on Abl-interactor 1 (Abi-1) by protein phosphatase 5 (PP5). Dephosphorylated Abi-1, a protein previously not known to activate DNA repair, promotes nonhomologous end joining. In patients and mouse models of glioblastoma, Rac1 and dephosphorylated Abi-1 mediate DNA repair and resistance to standard-of-care genotoxic treatments. The GTP-Rac1-PP5-Abi-1 signaling axis is not limited to brain cancer, as GTP supplementation promotes DNA repair and Abi-1-S323 dephosphorylation in nonmalignant cells and protects mouse tissues from genotoxic insult. This unexpected ability of GTP to regulate DNA repair independently of deoxynucleotide pools has important implications for normal physiology and cancer treatment. SIGNIFICANCE: A newly described GTP-dependent signaling axis is an unexpected link between nucleotide metabolism and DNA repair. Disrupting this pathway can overcome cancer resistance to genotoxic therapy while augmenting it can mitigate genotoxic injury of normal tissues. This article is featured in Selected Articles from This Issue, p. 5.

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.

Changes in emergency department utilisation in Germany before and during different phases of the COVID-19 pandemic, using data from a national surveillance system up to June 2021.

BACKGROUND: During the COVID-19 pandemic and associated public health and social measures, decreasing patient numbers have been described in various healthcare settings in Germany, including emergency care. This could be explained by changes in disease burden, e.g. due to contact restrictions, but could also be a result of changes in utilisation behaviour of the population. To better understand those dynamics, we analysed routine data from emergency departments to quantify changes in consultation numbers, age distribution, disease acuity and day and hour of the day during different phases of the COVID-19 pandemic. METHODS: We used interrupted time series analyses to estimate relative changes for consultation numbers of 20 emergency departments spread throughout Germany. For the pandemic period (16-03-2020 - 13-06-2021) four different phases of the COVID-19 pandemic were defined as interruption points, the pre-pandemic period (06-03-2017 - 09-03-2020) was used as the reference. RESULTS: The most pronounced decreases were visible in the first and second wave of the pandemic, with changes of - 30.0% (95%CI: - 32.2%; - 27.7%) and - 25.7% (95%CI: - 27.4%; - 23.9%) for overall consultations, respectively. The decrease was even stronger for the age group of 0-19 years, with - 39.4% in the first and - 35.0% in the second wave. Regarding acuity levels, consultations assessed as urgent, standard, and non-urgent showed the largest decrease, while the most severe cases showed the smallest decrease. CONCLUSIONS: The number of emergency department consultations decreased rapidly during the COVID-19 pandemic, without extensive variation in the distribution of patient characteristics. Smallest changes were observed for the most severe consultations and older age groups, which is especially reassuring regarding concerns of possible long-term complications due to patients avoiding urgent emergency care during the pandemic.

Host genetic background regulates the capacity for anti-tumor antibody-dependent phagocytosis.

Background: Antitumor antibody, or targeted immunotherapy, has revolutionized cancer treatment and markedly improved patient outcomes. A prime example is the monoclonal antibody (mAb) trastuzumab, which targets human epidermal growth factor receptor 2 (HER2). However, like many targeted immunotherapies, only a subset of patients benefit from trastuzumab long-term. In addition to tumor-intrinsic factors, we hypothesize that host genetics may influence subsequent immune activation. Methods: To model the human population, we produced F1 crosses of genetically heterogeneous Diversity Outbred (DO) mice with BALB/c mice (DOCF1). Distinct DOCF1 mice were orthotopically implanted with the BALB/c-syngeneic TUBO mammary tumor line, which expresses the HER2 ortholog rat neu. Treatment with anti-neu mAb clone 7.16.4 began once tumors reached ∼200 mm 3 . Genetic linkage and quantitative trait locus (QTL) effects analyses in R/qtl2 identified loci associated with tumor growth rates. Locus validation was performed with BALB/c F1 crosses with recombinant-inbred Collaborative Cross (CC) strains selected for therapy-associated driver genetics (CCxCF1). The respective roles of natural killer (NK) cells and macrophages were investigated by selective depletion in vivo. Ex vivo macrophage antibody-dependent phagocytosis (ADCP) assays were evaluated by confocal microscopy using 7.16.4-opsonized E2Crimson-expressing TUBO tumor cells. Results: We observed a divergent response to anti-tumor antibody therapy in DOCF1 mice. Genetic linkage analysis detected a locus on chromosome 10 that correlates to a robust response to therapy, which was validated in CCxCF1 models. Single-cell RNA sequencing of tumors from responder and non-responder models identified key differences in tumor immune infiltrate composition, particularly within macrophage (Mφ) subsets. This is further supported by ex vivo analysis showing Mφ ADCP capacity correlates to in vivo treatment outcomes in both DOCF1 and CCxCF1 models. Conclusions: Host genetics play a key regulatory role in targeted immunotherapy outcomes, and putative causal genes are identified in murine chromosome 10 which may govern Mφ function during ADCP.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Understanding COVID-19 reporting behaviour to support political decision-making: a retrospective cross-sectional study of COVID-19 data reported to WHO.

OBJECTIVE: Daily COVID-19 data reported by WHO may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, are heterogeneous and metrics to evaluate its quality are scarce. In this work, we analysed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviours. METHODS: In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3 January 2020 until 14 June 2021 were analysed. We proposed the concepts of binary reporting rate and relative reporting behaviour and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behaviour to identify salient patterns in these metrics. RESULTS: Our final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55-0.87). Descriptive analyses of the binary reporting rate and relative reporting behaviour showed constant reporting with a slight 'weekend effect' for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns. CONCLUSION: The majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight 'weekend effect' suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policy makers to consider country-specific reporting behaviours in political ad hoc decisions.

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021.

BACKGROUND: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. METHODS: We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. RESULTS: We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. CONCLUSIONS: Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

We compare forecasts of weekly case and death numbers for COVID-19 in Germany and Poland based on 15 different modelling approaches. These cover the period from January to April 2021 and address numbers of cases and deaths one and two weeks into the future, along with the respective uncertainties. We find that combining different forecasts into one forecast can enable better predictions. However, case numbers over longer periods were challenging to predict. Additional data sources, such as information about different versions of the SARS-CoV-2 virus present in the population, might improve forecasts in the future.

Using routine emergency department data for syndromic surveillance of acute respiratory illness, Germany, week 10 2017 until week 10 2021.

BackgroundThe COVID-19 pandemic expanded the need for timely information on acute respiratory illness at population level.AimWe explored the potential of routine emergency department data for syndromic surveillance of acute respiratory illness in Germany.MethodsWe used routine attendance data from emergency departments, which continuously transferred data between week 10 2017 and 10 2021, with ICD-10 codes available for > 75% of attendances. Case definitions for acute respiratory infection (ARI), severe acute respiratory infection (SARI), influenza-like illness (ILI), respiratory syncytial virus infection (RSV) and COVID-19 were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age.ResultsWe included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020 during the first COVID-19 pandemic wave, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/21. A pattern of seasonality of respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017-2020 and the dynamics of the COVID-19 pandemic in 2020/21 were apparent. The absence of the 2020/21 influenza season was visible, parallel to the resurge of COVID-19 cases. SARI among ARI cases peaked in April-May 2020 (17%) and November 2020-January 2021 (14%).ConclusionSyndromic surveillance using routine emergency department data can potentially be used to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza viruses and SARS-CoV-2.

SARS-CoV-2 Omicron variants BA.1 and BA.2 both show similarly reduced disease severity of COVID-19 compared to Delta, Germany, 2021 to 2022.

German national surveillance data analysis shows that hospitalisation odds associated with Omicron lineage BA.1 or BA.2 infections are up to 80% lower than with Delta infection, primarily in ≥ 35-year-olds. Hospitalised vaccinated Omicron cases' proportions (2.3% for both lineages) seemed lower than those of the unvaccinated (4.4% for both lineages). Independent of vaccination status, the hospitalisation frequency among cases with Delta seemed nearly threefold higher (8.3%) than with Omicron (3.0% for both lineages), suggesting that Omicron inherently causes less severe disease.

Impact of COVID-19 immunisation on COVID-19 incidence, hospitalisations, and deaths by age group in Germany from December 2020 to October 2021.

BACKGROUND: Utilising national surveillance data, we investigated the impact of the COVID-19 immunisation campaign on COVID-19 morbidity and mortality between December/2020 and October/2021 in Germany. METHODS: We compared patterns in immunisation coverage, incidence, hospitalisations, and deaths among 12-17, 18-59, and 60+ year-olds and examined these patterns within the context of anti-pandemic measures. RESULTS: COVID-19 incidence increased in all age groups following the end of lockdown restrictions in March/2021, but as Germany experienced successive peaks in incidence, age groups with higher immunisation coverage experienced successively smaller peaks. Notwithstanding corresponding increases during periods of higher incidence, among those aged 60+ years, COVID-19 related hospitalisations and deaths declined considerably as immunisation coverage increased, despite circulation of virus variants known to cause more severe illness. CONCLUSION: Although ecological in nature, this study allows us to demonstrate clear patterns of decline in COVID-19 morbidity and mortality in Germany during the course of the immunisation campaign.

[Using emergency department routine data for the surveillance of suicide attempts and psychiatric emergencies]. / Nutzung von Routinedaten aus Notaufnahmen zur Surveillance von Suizidversuchen und psychiatrischen Notfällen.

BACKGROUND: The occurrence of suicide attempts is a key indicator of the population's mental health and therefore belongs in the domain of Mental Health Surveillance at the Robert Koch Institute. No data source is currently being used systematically for the continuous observation of psychiatric emergencies (including suicide attempts) in Germany. Therefore, the use of routine data from emergency departments will be explored in this work. METHODS: We included routine data from 12 emergency departments between 1 January 2018 and 28 March 2021. We developed syndrome definitions for suicide attempts, psychiatric emergencies based on combinations of chief complaints, and diagnoses from patients presenting with psychopathological symptoms. A descriptive analysis over time was presented and stratified by age and sex. RESULTS: In total 1,516,883 emergency department attendances were included, among which we identified 5,133 cases (0.3%) as suicide attempts, 31,085 (2.1%) as psychiatric emergencies, and 34,230 (2.3%) as cases with psychiatric symptoms. Among psychiatric emergencies, 16.5% presented because of a suicide attempt. Of cases presenting with a suicide attempt, 53.4% were male and 20.2% were aged between 25 and 34 years. Cases identified by all 3 syndrome definitions and their temporal variations could be displayed over the entire observation period. CONCLUSION: Syndromic surveillance using emergency department data indicates a potential for continuous surveillance of suicide attempts and psychiatric emergencies and provides a basis for further validation and analysis. The display of changes in real time extends the current research opportunities for psychiatric emergencies in Germany. Systematic surveillance of suicide attempts can contribute to evidence-based suicide prevention.

Impact of the COVID-19 pandemic and associated non-pharmaceutical interventions on other notifiable infectious diseases in Germany: An analysis of national surveillance data during week 1-2016 - week 32-2020.

BACKGROUND: The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) affect healthcare seeking behaviour, access to healthcare, test strategies, disease notification and workload at public health authorities, but may also lead to a true change in transmission dynamics. We aimed to assess the impact of the pandemic and NPIs on other notifiable infectious diseases under surveillance in Germany. METHODS: We included 32 nationally notifiable disease categories with case numbers >100/year in 2016-2019. We used quasi-Poisson regression analysis on a weekly aggregated time-series incorporating trend and seasonality, to compute the relative change in case numbers during week 2020-10 to 2020-32 (pandemic/NPIs), in comparison to week 2016-01 to 2020-09. FINDINGS: During week 2020-10 to 2020-32, 216,825 COVID-19 cases, and 162,942 (-35%) cases of other diseases, were notified. Case numbers decreased across all ages and notification categories (all p<0•05), except for tick-borne encephalitis, which increased (+58%). The number of cases decreased most for respiratory diseases (from -86% for measles, to -12% for tuberculosis), gastro-intestinal diseases (from -83% for rotavirus gastroenteritis, to -7% for yersiniosis) and imported vector-borne diseases (-75% dengue fever, -73% malaria). The less affected infections were healthcare associated pathogens (from -43% infection/colonisation with carbapenem-non-susceptible Acinetobacter, to -28% for Methicillin-resistant Staphylococcus aureus invasive infection) and sexually transmitted and blood-borne diseases (from -28% for hepatitis B, to -12% for syphilis). INTERPRETATION: During the COVID-19 pandemic a drastic decrease of notifications for most infectious diseases and pathogens was observed. Our findings suggest effects of NPIs on overall disease transmission that require further investigation. FUNDING: The Robert Koch Institute is the National Public Health Institute of Germany, and is an institute within the portfolio of the Federal Ministry of Health.

Disease severity of the first COVID-19 wave in Germany using reporting data from the national notification system.

As of December 31, 2019, initial reports circulated internationally of an unusual cluster of pneumonia of unknown cause in China. By the end of January 2020, the virus affected Germany with the first case confirmed on January 27, 2020. Intensive contact tracing and infection control measures contained the first two clusters in the country. However, the dynamic of the first wave gained momentum as of March, and by mid-June 2020 over 190,000 laboratory-confirmed cases had been reported to the Robert Koch Institute. This article examines these cases as part of a retrospective descriptive analysis focused on disease severity. Most cases (80%) were mild and two thirds of the cases were younger than 60 years (median age: 50 years). Severe cases were primarily reported among men aged 60 or over who had at least one risk factor (particularly cardiovascular disease, diabetes, neurological disorders and/or lung diseases). Cases between the ages of 40 and 59 years had the longest interval between symptom onset and hospitalisation (median: six days) and - if admitted to an intensive care unit (ICU) - also the longest ICU stay (median: eleven days). This analysis provides valuable information about disease severity of COVID-19 and particularly affected groups.

EventEpi-A natural language processing framework for event-based surveillance.

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles' key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI's EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.

Surveillance on speed: Being aware of infectious diseases in migrants mass accommodations - an easy and flexible toolkit for field application of syndromic surveillance, Germany, 2016 to 2017.

Europe received an increased number of migrants in 2015. Housing in inadequate mass accommodations (MA) made migrants prone to infectious disease outbreaks. In order to enhance awareness for infectious diseases (ID) and to detect clusters early, we developed and evaluated a syndromic surveillance system in three MA with medical centres in Berlin, Germany. Healthcare workers transferred daily data on 14 syndromes to the German public health institute (Robert Koch-Institute). Clusters of ID syndromes and single cases of outbreak-prone diseases produced a signal according to a simple aberration-detection algorithm that computes a statistical threshold above which a case count is considered unusually high. Between May 2016-April 2017, 9,364 syndromes were reported; 2,717 (29%) were ID, of those 2,017 (74%) were respiratory infections, 262 (10%) skin parasites, 181 (7%) gastrointestinal infections. The system produced 204 signals, no major outbreak was detected. The surveillance reinforced awareness for public health aspects of ID. It provided real-time data on migrants' health and stressed the burden of non-communicable diseases. The tool is available online and was evaluated as being feasible and flexible. It complements traditional notification systems. We recommend its usage especially when laboratory testing is not available and real-time data are needed.

The influence of polymer excipients on the dissolution and recrystallization behavior of ketoconazole: Application, variation and practical aspects of a pH shift method.

The formulation of amorphous solid dispersions (ASDs) is an effective way to improve the bioavailability of poorly water-soluble active pharmaceutical ingredients (APIs). The combination of an amorphous state of the drug and the presence of crystallization-inhibiting polymers retains a high amount of dissolved API over time. ASDs with ketoconazole and different polymers were manufactured by spray drying and their characteristics as well as performance were analyzed. Dissolution tests with a change of the dissolution medium from 0.1 M HCl to phosphate buffer at pH 6.8 to simulate pH conditions for instant release formulations, and a direct dissolution of the ASDs in phosphate buffer pH 6.8 to simulate conditions for an enteric formulation, were performed. All ASDs with API contents between 25 and 50% by weight were completely X-ray amorphous. Varying dissolution behaviors between medium change and direct dissolution occurred. It was possible to identify the superior ASD-compositions for both types of tests. The acidic polymers methacrylic acid-ethyl acrylate copolymer, hypromellose acetate succinate and the solubilizer macrogolglycerol hydroxystearate showed the best performances. The combination of the acidic polymers with macrogolglycerol hydroxystearate showed an improved dissolution behavior at higher API contents. The optimization of such formulations with different release-patterns plays an important role for the enhancement of the oral bioavailability of poorly water-soluble drugs.

Lipid-mediated PX-BAR domain recruitment couples local membrane constriction to endocytic vesicle fission.

Clathrin-mediated endocytosis (CME) involves membrane-associated scaffolds of the bin-amphiphysin-rvs (BAR) domain protein family as well as the GTPase dynamin, and is accompanied and perhaps triggered by changes in local lipid composition. How protein recruitment, scaffold assembly and membrane deformation is spatiotemporally controlled and coupled to fission is poorly understood. We show by computational modelling and super-resolution imaging that phosphatidylinositol 3,4-bisphosphate [PI(3,4)P2] synthesis within the clathrin-coated area of endocytic intermediates triggers selective recruitment of the PX-BAR domain protein SNX9, as a result of complex interactions of endocytic proteins competing for phospholipids. The specific architecture induces positioning of SNX9 at the invagination neck where its self-assembly regulates membrane constriction, thereby providing a template for dynamin fission. These data explain how lipid conversion at endocytic pits couples local membrane constriction to fission. Our work demonstrates how computational modelling and super-resolution imaging can be combined to unravel function and mechanisms of complex cellular processes.

Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone.

Synaptic vesicle fusion is mediated by SNARE proteins forming in between synaptic vesicle (v-SNARE) and plasma membrane (t-SNARE), one of which is Syntaxin-1A. Although exocytosis mainly occurs at active zones, Syntaxin-1A appears to cover the entire neuronal membrane. By using STED super-resolution light microscopy and image analysis of Drosophila neuro-muscular junctions, we show that Syntaxin-1A clusters are more abundant and have an increased size at active zones. A computational particle-based model of syntaxin cluster formation and dynamics is developed. The model is parametrized to reproduce Syntaxin cluster-size distributions found by STED analysis, and successfully reproduces existing FRAP results. The model shows that the neuronal membrane is adjusted in a way to strike a balance between having most syntaxins stored in large clusters, while still keeping a mobile fraction of syntaxins free or in small clusters that can efficiently search the membrane or be traded between clusters. This balance is subtle and can be shifted toward almost no clustering and almost complete clustering by modifying the syntaxin interaction energy on the order of only 1 kBT. This capability appears to be exploited at active zones. The larger active-zone syntaxin clusters are more stable and provide regions of high docking and fusion capability, whereas the smaller clusters outside may serve as flexible reserve pool or sites of spontaneous ectopic release.