Gastrointestinal and hepatic manifestations of COVID‐19: A systematic review and meta‐analysis

Background and Aim: This review investigates the role of gastrointestinal and hepatic manifestations in COVID-19, particularly with regard to the prevalence of isolated gastrointestinal (GI) symptoms. Methods: We searched PubMed, Embase, and Cochrane library for COVID-19 publications from 1 December 2019 to 18 May 2020. We included any study that reported the presence of GI symptoms in a sample of >5 COVID-19 patients. Data collection and risk of bias assessment were performed independently by two reviewers. Where ≥3 studies reported data sufficiently similar to allow calculation of a pooled prevalence, we performed random effects meta-analysis. Results: This review included 17 776 COVID-19 patients from 108 studies. Isolated GI symptoms only occurred in 1% (95% confidence interval [CI] 0-6%) of patients. GI symptoms were reported in 20% (95% CI 15-24%) of patients. The most common were anorexia (21%, 95% CI 15-27%), diarrhea (13%, 95% CI 11-16%), nausea or vomiting (8%, 95% CI 6-11%), and abdominal pain (4%, 95% CI 2-6%). Transaminase elevations were present in 24% (95% CI 17-31%) of patients. Higher prevalence of GI symptoms were reported in studies published after 1st April, with prevalence of diarrhea 16% (95% CI 13-20), nausea or vomiting 12% (95% CI 8-16%), and any GI symptoms 24% (95% CI 18-34%). GI symptoms were associated with severe COVID-19 disease (odds ratio [OR] 2.1, 95% CI 1.3-3.2), but not mortality (OR 0.90, 95% CI 0.52-1.54). Conclusions: Patients with isolated GI symptoms may represent a small but significant portion of COVID-19 cases. When testing resources are abundant, clinicians should still consider testing patients with isolated GI symptoms or unexplained transaminase elevations for COVID-19. More recent studies estimate higher overall GI involvement in COVID-19 than was previously recognized

Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation

Design of an Enhanced Public Health Surveillance System for Hepatitis C Virus Elimination in King County, Washington

Introduction: With the goal of eliminating hepatitis C virus (HCV) as a public health problem in Washington State, Public Health–Seattle & King County (PHSKC) designed a Hepatitis C Virus Test and Cure (HCV-TAC) data system to integrate surveillance, clinical, and laboratory data into a comprehensive database. The intent of the system was to promote identification, treatment, and cure of HCV-infected persons (ie, HCV care cascade) using a population health approach. Materials and Methods: The data system automatically integrated case reports received via telephone and fax from health care providers and laboratories, hepatitis test results reported via electronic laboratory reporting, and data on laboratory and clinic visits reported by 6 regional health care systems. PHSKC examined patient-level laboratory test results and established HCV case classification using Council of State and Territorial Epidemiologists criteria, classifying patients as confirmed if they had detectable HCV RNA. Results: The data enabled PHSKC to report the number of patients at various stages along the HCV care cascade. Of 7747 HCV RNA-positive patients seen by a partner site, 5377 (69%) were assessed for severity of liver fibrosis, 3932 (51%) were treated, and 2592 (33%) were cured. Practice Implications: Data supported local public heath surveillance and HCV program activities. The data system could serve as a foundation for monitoring future HCV prevention and control programs

Abrolhos Bank Reef Health Evaluated by Means of Water Quality, Microbial Diversity, Benthic Cover, and Fish Biomass Data

The health of the coral reefs of the Abrolhos Bank (southwestern Atlantic) was characterized with a holistic approach using measurements of four ecosystem components: (i) inorganic and organic nutrient concentrations, [1] fish biomass, [1] macroalgal and coral cover and (iv) microbial community composition and abundance. The possible benefits of protection from fishing were particularly evaluated by comparing sites with varying levels of protection. Two reefs within the well-enforced no-take area of the National Marine Park of Abrolhos (Parcel dos Abrolhos and California) were compared with two unprotected coastal reefs (Sebastião Gomes and Pedra de Leste) and one legally protected but poorly enforced coastal reef (the “paper park” of Timbebas Reef). The fish biomass was lower and the fleshy macroalgal cover was higher in the unprotected reefs compared with the protected areas. The unprotected and protected reefs had similar seawater chemistry. Lower vibrio CFU counts were observed in the fully protected area of California Reef. Metagenome analysis showed that the unprotected reefs had a higher abundance of archaeal and viral sequences and more bacterial pathogens, while the protected reefs had a higher abundance of genes related to photosynthesis. Similar to other reef systems in the world, there was evidence that reductions in the biomass of herbivorous fishes and the consequent increase in macroalgal cover in the Abrolhos Bank may be affecting microbial diversity and abundance. Through the integration of different types of ecological data, the present study showed that protection from fishing may lead to greater reef health. The data presented herein suggest that protected coral reefs have higher microbial diversity, with the most degraded reef (Sebastião Gomes) showing a marked reduction in microbial species richness. It is concluded that ecological conditions in unprotected reefs may promote the growth and rapid evolution of opportunistic microbial pathogens

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Funding: H2020 European Research Council (Grant Number(s): GA 101044975, GA 101098020).Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables: Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.Peer reviewe

Punic people were genetically diverse with almost no Levantine ancestors

Please see the document for a full list of funding organisations.The maritime Phoenician civilization from the Levant transformed the entire Mediterranean during the first millennium bce1,2,3. However, the extent of human movement between the Levantine Phoenician homeland and Phoenician–Punic settlements in the central and western Mediterranean has been unclear in the absence of comprehensive ancient DNA studies. Here, we generated genome-wide data for 210 individuals, including 196 from 14 sites traditionally identified as Phoenician and Punic in the Levant, North Africa, Iberia, Sicily, Sardinia and Ibiza, and an early Iron Age individual from Algeria. Levantine Phoenicians made little genetic contribution to Punic settlements in the central and western Mediterranean between the sixth and second centuries bce, despite abundant archaeological evidence of cultural, historical, linguistic and religious links4. Instead, these inheritors of Levantine Phoenician culture derived most of their ancestry from a genetic profile similar to that of Sicily and the Aegean. Much of the remaining ancestry originated from North Africa, reflecting the growing influence of Carthage5. However, this was a minority contributor of ancestry in all of the sampled sites, including in Carthage itself. Different Punic sites across the central and western Mediterranean show similar patterns of high genetic diversity. We also detect genetic relationships across the Mediterranean, reflecting shared demographic processes that shaped the Punic world.Natur

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Motivation. Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included. The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain. Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain. The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement. The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format. csv and. SQL