Aquaculture in Africa: Aquatic Animal Welfare, Impact on the Environment and the Sustainability of the Sector

The African aquaculture sector recorded the fastest growth in the world between 2006-2018, averaging 10% or more, and is expected to partially fill the growing fish supply-demand gap up to 2063. In 2018, there were about 1.2 million aquafarmers across the continent, an increase from 920 thousand in 2014. According to the African Development Bank, expansion of aquaculture in Africa is hampered by the overwhelming predominance of tilapia farming, which relies heavily on the production of fingerlings from a limited number of genetically improved strains that are resistant to the many diseases affecting this species, and on the production of feed that is still largely imported . The Bank cited projections that African aquaculture would grow far more dramatically if it rapidly diversified, rather than remaining dominated by tilapia farming. The review also noted that the sector now generates products not only for direct consumption, but also used in food processing, feed, fuels, cosmetics, nutraceuticals, pharmaceuticals, and a variety of other industrial products

Helicobacter pylori-induced inhibition of vascular endothelial cell functions: a role for VacA-dependent nitric oxide reduction

Epidemiological and clinical studies provide compelling support for a causal relationship between Helicobacter pylori infection and endothelial dysfunction, leading to vascular diseases. However, clear biochemical evidence for this association is limited. In the present study, we have conducted a comprehensive investigation of endothelial injury in bovine aortic endothelial cells (BAECs) induced by H. pylori-conditioned medium (HPCM) prepared from H. pylori 60190 [vacuolating cytotoxin A (Vac(+))]. BAECs were treated with either unconditioned media, HPCM (0-25% vol/vol), or Escherichia coli-conditioned media for 24 h, and cell functions were monitored. Vac(+) HPCM significantly decreased BAEC proliferation, tube formation, and migration (by up to 44%, 65%, and 28%, respectively). Posttreatment, we also observed sporadic zonnula occludens-1 immunolocalization along the cell-cell border, and increased BAEC permeability to FD40 Dextran, indicating barrier reduction. These effects were blocked by 5-nitro-2-(3-phenylpropylamino)benzoic acid (VacA inhibitor) and were not observed with conditioned media prepared from either VacA-deleted H. pylori or E. coli. The cellular mechanism mediating these events was also considered. Vac(+) HPCM (but not Vac(-)) reduced nitric oxide (NO) by \u3e50%, whereas S-nitroso-N-acetylpenicillamine, an NO donor, recovered all Vac(+) HPCM-dependent effects on cell functions. We further demonstrated that laminar shear stress, an endothelial NO synthase/NO stimulus in vivo, could also recover the Vac(+) HPCM-induced decreases in BAEC functions. This study shows, for the first time, a significant proatherogenic effect of H. pylori-secreted factors on a range of vascular endothelial dysfunction markers. Specifically, the VacA-dependent reduction in endothelial NO is indicated in these events. The atheroprotective impact of laminar shear stress in this context is also evident

Common protocol for validation of the QCOVID algorithm across the four UK nations

Introduction The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm. Methods and analysis We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell’s C, Brier Score, R2 and Royston’s D. Ethics and dissemination Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal

An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK

Introduction At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. Objectives To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. Methods We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. Results The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). Conclusion This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks

Go open! Supporting higher education staff engagement in open educational practices

his paper reports on the activities of a team in Dublin City University (DCU), composed of academic staff and library staff, who engaged in a collaborative project, Go Open, in 2020 and 2021 that encourages members of the DCU community to engage in open educational practices. The academic staff team members, from DCU’s Open Education Unit, have experience in providing online, flexible, open education/access programmes that include many examples of open educational practice, while the library staff have experience in advocating for engagement in open educational practice and open scholarship/science. The project goals were: to produce user-friendly resources that would give an introduction to open education generally and more specifically key types of open educational practice; to provide concrete examples of open educational practice to contextualise the educational problems for which such practices can provide a solution; and provide links to other sources of information on open education such as key websites and thought leaders so that those whose interest in open education is sparked by the resources are facilitated in taking their next step. These project goals align with the first action area of the UNESCO OER recommendation, that of building capacity of stakeholders to create, access, re-use, adapt and redistribute OER. The Go Open project developed both a guide, ‘Go Open: A beginners guide to open education’, and a LibGuide (https://dcu.libguides.com/GoOpen), both of which were launched at an event in April 2021 as part of the team’s ongoing project output dissemination and advocacy for open education practice. In the two weeks after the launch event the guide had 387 views and 178 downloads, while the libguide had 781 views. The Go Open Project was funded by the National Forum for the Enhancement of Teaching and Learning in Higher Education and DCU’s Teaching Enhancement Unit through the SATLE 19 fund

Go open: a beginners guide to open education

In this short guide, we aim to give you a beginners guide to the area of open education, so that you can engage with open education practices in your teaching, research and support activities and to Go Open

Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records

<p>Abstract</p> <p>Background</p> <p>Information on ethnicity is commonly used by health services and researchers to plan services, ensure equality of access, and for epidemiological studies. In common with other important demographic and clinical data it is often incompletely recorded. This paper presents a method for imputing missing data on the ethnicity of cancer patients, developed for a regional cancer registry in the UK.</p> <p>Methods</p> <p>Routine records from cancer screening services, name recognition software (Nam Pehchan and Onomap), 2001 national Census data, and multiple imputation were used to predict the ethnicity of the 23% of cases that were still missing following linkage with self-reported ethnicity from inpatient hospital records.</p> <p>Results</p> <p>The name recognition software were good predictors of ethnicity for South Asian cancer cases when compared with data on ethnicity derived from hospital inpatient records, especially when combined (sensitivity 90.5%; specificity 99.9%; PPV 93.3%). Onomap was a poor predictor of ethnicity for other minority ethnic groups (sensitivity 4.4% for Black cases and 0.0% for Chinese/Other ethnic groups). Area-based data derived from the national Census was also a poor predictor non-White ethnicity (sensitivity: South Asian 7.4%; Black 2.3%; Chinese/Other 0.0%; Mixed 0.0%).</p> <p>Conclusions</p> <p>Currently, neither method for assigning individuals to an ethnic group (name recognition and ethnic distribution of area of residence) performs well across all ethnic groups. We recommend further development of name recognition applications and the identification of additional methods for predicting ethnicity to improve their precision and accuracy for comparisons of health outcomes. However, real improvements can only come from better recording of ethnicity by health services.</p

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

Objectives To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.Design Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.Settings Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.Main outcome measures Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.Results Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down’s syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson’s disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.Conclusion This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

In this paper by Hippisley-Cox and colleagues (BMJ 2021;374:n2244, doi:10.1136/bmj.n2244, published 17 September 2021), coauthor Jonathan Valabhji should be linked to affiliation 11 (NHS England and Improvement, London, UK). The article will be updated in due course