ing and empowering staff, frequent and regular RHIS supervision, and frequent and regular feedback, for an efficient RHIS strengthening in Yaoundé.Training of health staff in the RHIS favors RHIS good performance. Hence, emphasis should be laid on training and empowering staff, frequent and regular RHIS supervision, and frequent and regular feedback, for an efficient RHIS strengthening in Yaoundé. Because of fast leucocyte degeneration in cerebrospinal fluid (CSF) laboratory examinations of CSF samples should be performed approximately within 30 min after withdrawal. This study examines the storage of canine and feline CSF samples in "TransFix®/EDTA CSF Sample Storage Tubes" (Cytomark, Buckingham, UK) for preventing leucocytes from degeneration, so that routine and flow cytometry examinations are feasible up to 3 days after sampling. After storage in TransFix® tubes, leukocytes could not be adequately stained with Türk's solution and differentiating between erythrocytes and leukocytes was cumbersome. In addition, the cell morphology could not be sufficiently assessed on cytospin preparations because of shrunken leukocytes and indistinct cell nuclei. In contrast, by flow cytometry, a significantly higher cell count was measured over the entire study period in the samples stored in TransFix® tubes compared to the untreated samples. The antibodies(AB) against CD3, CD4 and CD21, against CD11b and against CD45 showed a good binding strength and thus enabled a good differentiation of cell populations. However, after storage in the TransFix® tubes, monocytes were no longer detectable using an AB against CD14. Based on these results, "TransFix®/EDTA CSF Sample Storage Tubes" can be used for extended storage prior to flow cytometric analysis of lymphocytes and granulocytes in CSF samples but not for detecting monocytes. However, standard examinations, such as microscopic cell counting and morphological cell assessment should be performed on fresh CSF samples.Based on these results, "TransFix®/EDTA CSF Sample Storage Tubes" can be used for extended storage prior to flow cytometric analysis of lymphocytes and granulocytes in CSF samples but not for detecting monocytes. However, standard examinations, such as microscopic cell counting and morphological cell assessment should be performed on fresh CSF samples. The increasing use of preprints to disseminate evidence on the effect of interventions for the coronavirus disease 2019 (COVID-19) can lead to multiple evidence sources for a single study, which may differ in the reported evidence. We aim to describe the proportion of evidence on the effect of interventions for COVID-19 from preprints and journal articles and map changes in evidence between and within different sources reporting on the same study. Meta-research study. We screened the Cochrane living systematic review and network meta-analysis (COVID-NMA) database to identify all preprints and journal articles on all studies assessing interventions for COVID-19 published up to 15 August 2020. We compared all evidence sources (i.e., preprint and associated journal article) and the first and latest versions of preprints for each study to identify changes in two evidence components study results (e.g., numeric change in hazard ratio, odds ratio, event rate, or change in p value > or < 0.05 in any outcom article, a median of 29% (IQR 14-50) of total citations were attributed to the preprint instead of the article. Results on the effect of interventions for COVID-19 are often reported in multiple evidence sources or source versions for a single study. Evidence is not stable between and within evidence sources. Real-time linkage of all sources per study could help to keep systematic reviews up-to-date.Results on the effect of interventions for COVID-19 are often reported in multiple evidence sources or source versions for a single study. Evidence is not stable between and within evidence sources. Real-time linkage of all sources per study could help to keep systematic reviews up-to-date. Few studies have shown how the move toward institutional delivery in low and middle-income countries (LMIC) impacts stillbirth and newborn mortality. The study evaluated trends in institutional delivery in research sites in Belagavi and Nagpur India, Guatemala, Kenya, Pakistan, and Zambia from 2010 to 2018 and compared them to changes in the rates of neonatal mortality and stillbirth. We analyzed data from a nine-year interval captured in the Global Network (GN) Maternal Newborn Health Registry (MNHR). Mortality rates were estimated from generalized estimating equations controlling for within-cluster correlation. Cluster-level analyses were performed to assess the association between institutional delivery and mortality rates. From 2010 to 2018, a total of 413,377 deliveries in 80 clusters across 6 sites in 5 countries were included in these analyses. An increase in the proportion of institutional deliveries occurred in all sites, with a range in 2018 from 57.7 to 99.8%. In 2010, the stillbirth rates ality rates remain at high levels. Understanding the relationship between institutional delivery and stillbirth and neonatal deaths in resource-limited environments will enable development of targeted interventions for reducing the mortality burden. The study is registered at clinicaltrials.gov . ClinicalTrial.gov Trial Registration NCT01073475 .The study is registered at clinicaltrials.gov . ClinicalTrial.gov Trial Registration NCT01073475 . Edge effects can influence species composition and community structure as a result of changes in microenvironment and edaphic variables. We investigated effects of habitat edges on vegetation structure, abundance and body mass of one vulnerable Microcebus species in northwestern Madagascar. We trapped mouse lemurs along four 1000-m transects (total of 2424 trap nights) that ran perpendicular to the forest edge. We installed 16 pairs of 20m vegetation plots along each transect and measured nine vegetation parameters. To determine the responses of the vegetation and animals to an increasing distance to the edge, we tested the fit of four alternative mathematical functions (linear, power, logistic and unimodal) to the data and derived the depth of edge influence (DEI) for all parameters. Logistic and unimodal functions best explained edge responses of vegetation parameters, and the logistic function performed best for abundance and body mass of M. ravelobensis. The DEI varied between 50m (no. learn more of seedlings, no.