Small amounts of PGE inhibit mitogen-induced [3H]thymidine incorporation in human peripheral lymphocytes. The 50% inhibitory concentration is approximately 10(-7) M, and this is reduced to approximately 10(-8) M when endogenous PGE production is blocked. PGE inhibits PHA- and Con A-stimulated cultures much better than PWM cultures, suggesting a differential effect of PGE on T-cell vs. B-cell function. In vitro blockade of PG synthesis results in approximately 50% increase in [3H]thymidine incorporation in PHA cultures. PGE is produced endogenously in PHA cultures by glass adherent suppressor cells.

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Suppression of human T-cell mitogenesis by prostaglandin. Existence of a prostaglandin-producing suppressor cell.

Authored by Dr. Safiya U. Noble, an assistant professor at the University of Southern California Annenberg School of Communication, this text is one of the preeminent works that explicitly addresse...

Algorithms of Oppression: How Search Engines Reinforce Racism

Regulation of the immune response by prostaglandins.

Of all the arachidonic acid metabolites, only prostaglandin E (PGE) has been shown to have a clear role in the regulation of cellular and humoral immune responses. In cellular immune responses such as T cell proliferation, lymphokine production, and cytotoxicity, PGE usually acts as a feedback inhibor of the response. This is also true of macrophage and natural killer cytotoxicity. In some instances PGE is responsible for cellular activation rather than inhibition. This is clearest in the control of humoral immunity, where PGE production is a necessary component in the generation of some type of T suppressor cells. Disturbances in immune function found in several human conditions and diseases have been linked to changes in PGE mediated immunoregulation. Either increased production of PGE or increased sensitivity to PGE results in depressed cellular immunity. Conversely drugs which inhibit PGE production act as stimulants of cellular immune functionin vitro andin vivo.

Regulation of the immune response by prostaglandins

Summary Background The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and effectiveness of these vaccines in a UK community setting. Methods In this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI Findings Between Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49–68) for ChAdOx1 nCoV-19 and 69% (66–72) for BNT162b2 at 21–44 days and 72% (63–79) for BNT162b2 after 45–59 days. Interpretation Systemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days. Funding ZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association.

Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study.

SEVERAL lines of evidence exist implicating prostaglandins (PGs) as mediators in inflammation1. In type IV hypersensitivity (delayed hypersensitivity, cellular immunity) reactions, although there is evidence of PG involvement2,3 lymphokines, the products of antigen-activated thymus-dependent lymphocytes, have received greater attention as potential mediators4,5. It has been reported that PGE1 is a potent inhibitor of lymphocyte activation in vitro6 and we have observed that guinea pig peritoneal exudate macrophages produce substantial amounts of E-type PG (ref. 7). It is well established that macrophages are capable of modifying lymphocyte activation8,9, and that there is close association between these two cell types in reactions of cellular immunity in vitro10 and in vivo11. We have now shown that production of E-type PGs by macrophages modifies the secretion of lymphokines by lymphocytes in response to antigen and that this may serve to provide a negative feedback mechanism for regulating the extent and duration of reactions of cellular immunity.

Control of lymphokine secretion by prostaglandins

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741-742, 1960. https://doi.org/10.1126/science.132.3429.741 ; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

/pdf/from-what-to-how-an-initial-review-of-publicly-available-ai-6o7v14qudx.pdf

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.

Protect privacy, equality and fairness in digital contact tracing with these key questions. Protect privacy, equality and fairness in digital contact tracing with these key questions.

/pdf/ethical-guidelines-for-covid-19-tracing-apps-1hvmib5e0x.pdf

Ethical guidelines for COVID-19 tracing apps.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s. However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such debate has primarily focused on principles - the what of AI ethics - rather than on practices, the how. Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

https://philpapers.org/archive/MORTEO-92.pdf

Jessica Morley

Papers

Control of lymphokine secretion by prostaglandins

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.

Ethical guidelines for COVID-19 tracing apps.

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

The ethics of AI in health care: A mapping review.