Genetic association between obstructive bronchitis and enzymes of oxidative stress

ObjectiveObstructive respiratory diseases, mainly the chronic obstructive pulmonary disease (COPD) and asthma, are associated with functional polymorphisms of xenobiotic-metabolizing enzymes (XMEs). To date, association for obstructive bronchitis has not been described.Material/MethodsIn this study, we investigated the genotypes from 26 functional polymorphisms of 20 XMEs in children (n, 1028) at the age of 6 years from the German prospective birth cohort study (LISAplus) and analyzed the associations between genotypes and obstructive bronchitis.ResultsFor the first time, we found noteworthy gene–disease associations for the functional PON1 M55L and EPHX1 H139R polymorphisms and gene–environment associations for the functional COMT V158M and NQO1 P187S polymorphisms after stratification for maternal active smoking behaviour during pregnancy. The noteworthy associations were substantiated by the biological findings that all the risk genotypes belong to genes involved in oxidative stress and code for proteins with a fast enzymatic activity or concomitantly appear in common estrogene-metabolizing pathway (COMT, NQO1).ConclusionThe oxidative stress has to be taken into account in mechanism of the obstructive bronchitis in early childhood. The risk genotypes may serve as risk factors for respiratory obstruction rather than for signs of COPD or asthma.     

Quantity and quality of gait and turning in people with multiple sclerosis,Parkinson’s disease and matched controls during daily living

Journal of Neurology - Clinical trials need to specify which specific gait characteristics to monitor as mobility measures for each neurological disorder. As a first step, this study aimed to...     

Basic Mechanism of Surface Topography Evolution in Electron Beam Based Additive Manufacturing

This study introduces and verifies a basic mechanism of surface topography evolution in electron beam additive manufacturing (E-PBF). A semi-analytical heat conduction model is used to examine the spatio-temporal evolution of the meltpool and segment the build surface according to the emerging persistent meltpool domains. Each persistent domain is directly compared with the corresponding melt surface, and exhibits a characteristic surface morphology and topography. The proposed underlying mechanism of topography evolution is based on different forms of material transport in each distinct persistent domain, driven by evaporation and thermocapillary convection along the temperature gradient of the emerging meltpool. This effect is shown to be responsible for the upper bound of the standard process window in E-PBF, where surface bulges form. Based on this mechanism, process strategies to prevent the formation of surface bulges for complex geometries are proposed.     

B-cell antigen receptor expression and phosphatidylinositol 3-kinase signaling regulate genesis and maintenance of mouse chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a frequent lymphoproliferative disorder of B cells. Although inhibitors targeting signal proteins involved in B-cell antigen receptor (BCR) signaling constitute an important part of the current therapeutic protocols for CLL patients, the exact role of BCR signaling, as compared to genetic aberration, in the development and progression of CLL is controversial. In order to investigate whether BCR expression per se is pivotal for the development and maintenance of CLL B cells, we used the TCL1 mouse model. By ablating the BCR in CLL cells from TCL1 transgenic mice, we show that CLL cells cannot survive without BCR signaling and are lost within 8 weeks in diseased mice. Furthermore, we tested whether mutations augmenting B-cell signaling influence the course of CLL development and its severity. The phosphatidylinositol-3-kinase (PI3K) signaling pathway is an integral part of the BCR signaling machinery and its activity is indispensable for B-cell survival. It is negatively regulated by the lipid phosphatase PTEN, whose loss mimics PI3K pathway activation. Herein, we show that PTEN has a key regulatory function in the development of CLL, as deletion of the Pten gene resulted in greatly accelerated onset of the disease. By contrast, deletion of the gene TP53, which encodes the tumor suppressor p53 and is highly mutated in CLL, did not accelerate disease development, confirming that development of CLL was specifically triggered by augmented PI3K activity through loss of PTEN and suggesting that CLL driver consequences most likely affect BCR signaling. Moreover, we could show that in human CLL patient samples, 64% and 81% of CLL patients with a mutated and unmutated IgH V_H, respectively, show downregulated PTEN protein expression in CLL B cells if compared to healthy donor B cells. Importantly, we found that B cells derived from CLL patients had higher expression levels of the miRNA-21 and miRNA-29, which suppresses PTEN translation, compared to healthy donors. The high levels of miRNA-29 might be induced by increased PAX5 expression of the B-CLL cells. We hypothesize that downregulation of PTEN by increased expression levels of miR-21, PAX5 and miR-29 could be a novel mechanism of CLL tumorigenesis that is not established yet. Together, our study demonstrates the pivotal role for BCR signaling in CLL development and deepens our understanding of the molecular mechanisms underlying the genesis of CLL and for the development of new treatment strategies.     

A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

BackgroundThe Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application.ObjectiveThis study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak.MethodsBased on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data.ResultsUsing COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61.ConclusionsWe provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.     

Diagnosis and management of an unilateral giant fibroadenoma of the breast in pregnancy

Background  

Fibroadenomas represent the vast majority of breast pathologies in young women. 2–4% of the fibroadenomas exceed 5 cm in size or 500 g in weight and are called “giant fibroadenomas”. Due to their excessive growth they are usually enucleated to clarify a malignant origin, to differentiate from phyllodes tumor and to prevent persisting deformities of the breast.     

In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness

The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person’s infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person’s infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and RT-PCR tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.

SARS-CoV-2 is a new human pathogen that causes COVID-19 (1). It is highly contagious, spread rapidly across the globe and has caused 5 million deaths worldwide as of the end of October 2021. At the molecular level, SARS-CoV-2 enters host cells via the angiotensin converting enzyme 2 (ACE-2) receptor. It infects cells in the upper respiratory tract (URT), can rapidly reach a high viral load (VL) and be effectively transmitted (2–4). However, it is not clear how VL, symptom onset, and infectiousness are quantitatively related.Previously, both VL and log₁₀ VL have been used as surrogates for infectiousness of influenza (5) and SARS-CoV-2 (6, 7). A quantitative understanding of the relationship is critical for both nonpharmaceutical and pharmaceutical interventions. First, it would allow for more precise prediction of the infectiousness of infected individuals, including children and pre- or asymptomatic individuals, based on their VL measurements (8, 9). This could in turn lead to quantification of their contribution to the overall transmission in a community and help to better inform public health policy decisions. Second, as administration of vaccines may lead to lowered VLs in breakthrough infections (10–12), a quantitative understanding will inform how these reductions in VL impact infectiousness and thus allow better predictions of how much transmission vaccinated individuals with breakthrough infection cause. Third, it would provide better insight into a person’s infectiousness throughout the course of infection and thus inform testing strategies for work/school reopening, travel, etc. The effectiveness of test, trace, and quarantine as control strategies heavily depends on the sensitivity and specificity of the tests and rate of testing being implemented (13). It was recently proposed that antigen tests with low sensitivity are preferred over highly sensitive RT-PCR tests because of their potential for wide coverage and short turnaround time (6). However, the effectiveness of this strategy has not been evaluated based on VL and infectiousness dynamics inferred from data.Here, we construct viral dynamic models of SARS-CoV-2 URT infection and a model linking VL to infectiousness. Mathematical modeling has been applied, by us and others, to understand SARS-CoV-2 infection and the potential impact of therapy (14–18). However, there were large uncertainties in model parameter estimates because in almost all studies, viral dynamic models were fit to data that were taken after symptom onset without knowledge of the patients’ infection dates and early VL dynamics. We resolve this issue by using two unique datasets and by using clinical and epidemiological data to inform the quantitative relationship between VL and infectiousness. Using this relationship, we further evaluate the effectiveness of testing strategies using either antigen or RT-PCR tests at different testing frequencies.