Public health measures during the COVID-19 pandemic through the lens of community organisations and networks in the Netherlands (2020-2021): five lessons for pandemic decision-making.

BackgroundDuring the coronavirus disease (COVID-19) pandemic, key persons who were formally or informally active in community organisations and networks, such as sports clubs or cultural, educational, day care and healthcare facilities, occupied a key position between governments and citizens. However, their experiences, the dilemmas they faced and the solutions they generated when implementing COVID-19 measures in their respective settings are understudied.AimWe aimed to understand how key persons in different community organisations and networks experienced and responded to the COVID-19 measures in the Netherlands.MethodsBetween October 2020 and December 2021, the Corona Behavioural Unit at the Dutch national public health institute, conducted qualitative research based on narratives derived from 65 in-depth interviews with 95 key persons from 32 organisations and networks in eight different sectors.ResultsFirstly, key persons enhanced adherence and supported the resilience and well-being of people involved in their settings. Secondly, adherence was negatively affected where COVID-19 measures conflicted with important organisational goals and values. Thirdly, small changes and ambiguities in COVID-19 policy had substantial consequences, depending on the context. Fourthly, problem-solving was achieved through trial-and-error, peer support, co-creation and transparent communication. Lastly, the COVID-19 pandemic and measures highlighted inequalities in access to resources.ConclusionPandemic preparedness requires organisational and community preparedness and a multidisciplinary public health approach. Structural engagement of governments with key persons in community organisations and networks is key to enhance public trust and adherence to pandemic measures and contributes to health equity and the well-being of the people involved

Rabbit colony infected with a bovine-like G6P[11] rotavirus strain

Group A rotaviruses (RVAs) are the main etiological agent of infantile diarrhea in both humans and animals worldwide. A limited number of studies have investigated the molecular characteristics of RVA strains in stool specimens of rabbits, with only a few lapine RVA strains isolated and (partially) characterized to date. The most common G/P-genotype combinations found in rabbits are G3P[14] and G3P[22]. In this study a RVA strain was isolated from the small intestine of a 9-week-old rabbit from an infected laboratory rabbit colony. The RVA strain RVA/Rabbit-tc/NLD/K1130027/2011/G6P[11] was shown to possess the typical bovine G6 and P[11] genotypes. The complete genome of this unusual lapine strain was sequenced and characterized. Phylogenetic analyses of all 11 gene segments revealed the following genotype constellation: G6-P[11]-I2-R2-C2-M2-A13-N2-T6-E2-H3. The VP1, VP2, VP3, VP6, NSP2 and NSP4 genes all belonged to DS-1-like genotype 2, but clustered more closely to bovine RVA strains than to lapine RVA strains. The NSP1 genotype A13 is typically associated with bovine RVAs, while the NSP3 genotype T6 and the NSP5 genotype H3 have been found in a wide variety of species. However, the isolated strain clustered within bovine(-like) T6 and H3 subclusters. Overall, the data indicate that the RVA strain is most closely related to bovine-like RVA strains and most likely represents a direct interspecies transmission from a cow to a rabbit. Altogether, these findings indicate that a RVA strain with an entirely bovine genome constellation was able to infect and spread in a laboratory rabbit colony.publisher: Elsevier articletitle: Rabbit colony infected with a bovine-like G6P[11] rotavirus strain journaltitle: Veterinary Microbiology articlelink: http://dx.doi.org/10.1016/j.vetmic.2013.05.028 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

Pitfalls in complement analysis : A systematic literature review of assessing complement activation

Background: The complement system is an essential component of our innate defense and plays a vital role in the pathogenesis of many diseases. Assessment of complement activation is critical in monitoring both disease progression and response to therapy. Complement analysis requires accurate and standardized sampling and assay procedures, which has proven to be challenging. Objective: We performed a systematic analysis of the current methods used to assess complement components and reviewed whether the identified studies performed their complement measurements according to the recommended practice regarding pre-analytical sample handling and assay technique. Results are supplemented with own data regarding the assessment of key complement biomarkers to illustrate the importance of accurate sampling and measuring of complement components. Methods: A literature search using the Pubmed/MEDLINE database was performed focusing on studies measuring the key complement components C3, C5 and/or their split products and/or the soluble variant of the terminal C5b-9 complement complex (sTCC) in human blood samples that were published between February 2017 and February 2022. The identified studies were reviewed whether they had used the correct sample type and techniques for their analyses. Results: A total of 92 out of 376 studies were selected for full-text analysis. Forty-five studies (49%) were identified as using the correct sample type and techniques for their complement analyses, while 25 studies (27%) did not use the correct sample type or technique. For 22 studies (24%), it was not specified which sample type was used. Conclusion: A substantial part of the reviewed studies did not use the appropriate sample type for assessing complement activation or did not mention which sample type was used. This deviation from the standardized procedure can lead to misinterpretation of complement biomarker levels and hampers proper comparison of complement measurements between studies. Therefore, this study underlines the necessity of general guidelines for accurate and standardized complement analysi

Distinction of early complement classical and lectin pathway activation via quantification of C1s/C1-INH and MASP-1/C1-INH complexes using novel ELISAs.

Peer reviewed: TrueThe most commonly used markers to assess complement activation are split products that are produced through activation of all three pathways and are located downstream of C3. In contrast, C4d derives from the cleavage of C4 and indicates either classical (CP) or lectin pathway (LP) activation. Although C4d is perfectly able to distinguish between CP/LP and alternative pathway (AP) activation, no well-established markers are available to differentiate between early CP and LP activation. Active enzymes of both pathways (C1s/C1r for the CP, MASP-1/MASP-2 for the LP) are regulated by C1 esterase inhibitor (C1-INH) through the formation of covalent complexes. Aim of this study was to develop validated immunoassays detecting C1s/C1-INH and MASP-1/C1-INH complex levels. Measurement of the complexes reveals information about the involvement of the respective pathways in complement-mediated diseases. Two sandwich ELISAs detecting C1s/C1-INH and MASP-1/C1-INH complex were developed and tested thoroughly, and it was investigated whether C1s/C1-INH and MASP-1/C1-INH complexes could serve as markers for either early CP or LP activation. In addition, a reference range for these complexes in healthy adults was defined, and the assays were clinically validated utilizing samples of 414 COVID-19 patients and 96 healthy controls. The immunoassays can reliably measure C1s/C1-INH and MASP-1/C1-INH complex concentrations in EDTA plasma from healthy and diseased individuals. Both complex levels are increased in serum when activated with zymosan, making them suitable markers for early classical and early lectin pathway activation. Furthermore, measurements of C1-INH complexes in 96 healthy adults showed normally distributed C1s/C1-INH complex levels with a physiological concentration of 1846 ± 1060 ng/mL (mean ± 2SD) and right-skewed distribution of MASP-1/C1-INH complex levels with a median concentration of 36.9 (13.18 - 87.89) ng/mL (2.5-97.5 percentile range), while levels of both complexes were increased in COVID-19 patients (p<0.0001). The newly developed assays measure C1-INH complex levels in an accurate way. C1s/C1-INH and MASP-1/C1-INH complexes are suitable markers to assess early classical and lectin pathway activation. An initial reference range was set and first studies showed that these markers have added value for investigating and unraveling complement activation in human disease

Distinction of early complement classical and lectin pathway activation via quantification of C1s/C1-INH and MASP-1/C1-INH complexes using novel ELISAs.

The most commonly used markers to assess complement activation are split products that are produced through activation of all three pathways and are located downstream of C3. In contrast, C4d derives from the cleavage of C4 and indicates either classical (CP) or lectin pathway (LP) activation. Although C4d is perfectly able to distinguish between CP/LP and alternative pathway (AP) activation, no well-established markers are available to differentiate between early CP and LP activation. Active enzymes of both pathways (C1s/C1r for the CP, MASP-1/MASP-2 for the LP) are regulated by C1 esterase inhibitor (C1-INH) through the formation of covalent complexes. Aim of this study was to develop validated immunoassays detecting C1s/C1-INH and MASP-1/C1-INH complex levels. Measurement of the complexes reveals information about the involvement of the respective pathways in complement-mediated diseases. Two sandwich ELISAs detecting C1s/C1-INH and MASP-1/C1-INH complex were developed and tested thoroughly, and it was investigated whether C1s/C1-INH and MASP-1/C1-INH complexes could serve as markers for either early CP or LP activation. In addition, a reference range for these complexes in healthy adults was defined, and the assays were clinically validated utilizing samples of 414 COVID-19 patients and 96 healthy controls. The immunoassays can reliably measure C1s/C1-INH and MASP-1/C1-INH complex concentrations in EDTA plasma from healthy and diseased individuals. Both complex levels are increased in serum when activated with zymosan, making them suitable markers for early classical and early lectin pathway activation. Furthermore, measurements of C1-INH complexes in 96 healthy adults showed normally distributed C1s/C1-INH complex levels with a physiological concentration of 1846 ± 1060 ng/mL (mean ± 2SD) and right-skewed distribution of MASP-1/C1-INH complex levels with a median concentration of 36.9 (13.18 - 87.89) ng/mL (2.5-97.5 percentile range), while levels of both complexes were increased in COVID-19 patients (p<0.0001). The newly developed assays measure C1-INH complex levels in an accurate way. C1s/C1-INH and MASP-1/C1-INH complexes are suitable markers to assess early classical and lectin pathway activation. An initial reference range was set and first studies showed that these markers have added value for investigating and unraveling complement activation in human disease

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1,024 OCD patients and 1,028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC=0.702) than unmedicated (AUC=0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level