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Sex Differences in Immune Responses that underlie COVID-19 Disease Outcomes

Reporter: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD COVID-19 is a non-discriminatory virus, it can infect anyone from young to old, but it seems that older men are twice more susceptible to it and most likely to become severely sick and die in comparison to women of the same age. Researchers from Yale university, published an article suggesting that men, particularly those over the age of 60 may need to depend more on vaccines to protect themselves from infection. According to their research published in Nature in August 2020, known sex differences between men and women pose challenges to the immune system. Women mount faster and stronger immune responses, possibly because their bodies are equipped to fight pathogens that threaten unborn or newborn children. Over time, an immune system in a constant state of high alert can be harmful. The findings underline the necessity for companies developing coronavirus vaccines to analyze their data by sex and may influence decisions about dosing. Dr. Iwasaki’s team from Yale  analyzed immune responses in 17 men and 22 women who were admitted to the hospital soon after they were infected with the coronavirus. The investigators collected blood, nasopharyngeal swabs, saliva, urine and stool from the patients every three to seven days. The researchers also analyzed data from an additional 59 men and women who did not meet those criteria. Over all, the scientists found, the women’s bodies produced more T cells, which can kill and stop the infection from spreading. Men on the other hand presented  a much weaker activation of T cells and that delay was linked to how sick the men became. The older the men, the weaker their T cell responses. Even though the study provided some more information about why men become sicker when diagnosed with coronavirus than women,  it did not offer a clear reason for the differences between men and women. SOURCE https://www.nature.com/articles/s41586-020-2700-3
Article

This is an unedited manuscript that has been accepted for publication. Nature Research are providing this early version of the manuscript as a service to our authors and readers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

Sex differences in immune responses that underlie COVID-19 disease outcomes

Abstract

A growing body of evidence indicates sex differences in the clinical outcomes of coronavirus disease 2019 (COVID-19)1–5. However, whether immune responses against SARS-CoV-2 differ between sexes, and whether such differences explain male susceptibility to COVID-19, is currently unknown. In this study, we examined sex differences in
  • viral loads,
  • SARS-CoV-2-specific antibody titers,
  • plasma cytokines, as well as
  • blood cell phenotyping in COVID-19 patients.
By focusing our analysis on patients with moderate disease who had not received immunomodulatory medications, our results revealed that
  • male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. In contrast,
  • female patients mounted significantly more robust T cell activation than male patients during SARS-CoV-2 infection, which was sustained in old age.
  • Importantly, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients.
  • Conversely, higher innate immune cytokines in female patients associated with worse disease progression, but not in male patients.
  • These findings reveal a possible explanation underlying observed sex biases in COVID-19, and provide an important basis for the development of
  • a sex-based approach to the treatment and care of men and women with COVID-19.

Author information

Affiliations

Consortia

Corresponding author

Correspondence to Akiko Iwasaki.

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Most significant article published in the Society of Evolution, Medicine and Public Health won Prize: polygenic scores, polygenic adaptation, and human phenotypic differences

Reporter: Aviva Lev-Ari, PhD, RN 

 

UPDATED on 8/30/2020

Analysis of polygenic risk score usage and performance in diverse human populations

Abstract

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

SOURCE

https://www.nature.com/articles/s41467-019-11112-0

The Voice of Prof. Marcus W. Feldman

You might be interested in the paper “interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” by N. Rosenberg, M. Edge, J. Pritchard, and M. Feldman, published in Evolution, Medicine and Public Health  (2019).    Rosenberg and Pritchard are my former PhD students, both full professors at Stanford, and M.Edge is a student of Rosenberg.

 

On Aug 28, 2020, at 4:36 PM, Horowitz, Barbara Natterson <natterson-horowitz@fas.harvard.edu> wrote:

Dear Dr. Rosenberg,

It is my pleasure in my role as President of the International Society for Evolution, Medicine and Public Health to inform you that your 2019 EMPH article, “Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” has won The George C. Williams Prize which is awarded each year to the first author of the most significant article published in the Society’s flagship journal, Evolution, Medicine and Public Health.  

The Prize recognizes the contributions of George C. Williams to evolutionary medicine and aims to encourage and highlight important research in this growing field. It includes $5,000 and an invitation to present at the online lecture series, Club EvMed. The Prize is made possible by donations from Doris Williams, Randolph Nesse, and other supporters of EMPH.

The winning article:

 

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Evolution, Medicine, and Public Health, Volume 2019, Issue 1, 2019, Pages 26–34, https://doi.org/10.1093/emph/eoy036
Published:
27 December 2018

Article history

SOURCE

Abstract

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

INTRODUCTION

We are currently witnessing a surge in public interest in the intersection of evolutionary genetics with such topics as cognitive phenotypes, disease, race and heritability of human traits [1–7]. This attention emerges partly from recent advances in genomics, including the introduction of polygenic scores—the aggregation of estimated effects of genome-wide variants to predict the contribution of a person’s genome to a phenotypic trait [8–10]—and a new focus on polygenic adaptations, namely adaptations that have occurred by natural selection on traits influenced by many genes [11–13].

Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16]. Although new tools for statistical analysis of polygenic variation and polygenic adaptation provide opportunities for studying human evolution and the genetic basis of traits, they also generate potential for misinterpretation. In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17]. Recognizing pitfalls in the interpretation of new research on human variation is therefore important for advancing discussions on associated sensitive and controversial topics.

The contribution of polygenic score distributions to phenotype distributions. Two populations are considered, populations 1 (red) and 2 (blue). Each population has a distribution of genetic propensities, which are treated as accurately estimated in the form of polygenic scores (left). The genetic propensity distribution and an environment distribution sum to produce a phenotype distribution (right). All plots have the same numerical scale. (A) Environmental differences amplify an underlying difference in genetic propensities. (B) Populations differ in their phenotypes despite having no differences in genetic propensity distributions. (C) Environmental differences obscure a difference in genetic propensities opposite in direction to the difference in phenotype means. (D) Similarity in phenotype distributions is achieved despite a difference in genetic propensity distributions by an intervention that reduces the environmental contribution for individuals with polygenic scores above a threshold. (E) Within populations, heritability is high, so that genetic variation explains the majority of phenotypic variation; however, the difference between populations is explained by an environmental difference. Panels (A–C and E) present independent normal distributions for genotype and environment that sum to produce normal distributions for phenotype. In (D), (genotype, environment) pairs are simulated from independent normal distributions and a negative constant—reflecting the effect of a medication or other intervention—is added to environmental contributions associated with simulated genotypic values that exceed a threshold

Summary

These limitations illustrate that much of the complexity embedded in use of polygenic scores—the effects of the environment on phenotype and its relationship to genotype, the proportion of variance explained, and the peculiarities of the underlying GWAS data that have been used to estimate effect sizes—is obscured by the apparent simplicity of the single values computed for each individual for each phenotype. Consequently, in using polygenic scores to describe genomic contributions to traits, particularly traits for which the total contribution of genetic variation to trait variation, as measured by heritability, is low—but even if it is high (Fig. 1E)—a difference in polygenic scores between populations provides little information about potential genetic bases for trait differences between those populations.

Unlike heritability, which ranges from 0 to 1 and therefore makes it obvious that the remaining contribution to phenotypic variation is summarized by its difference from 1, the limited explanatory role of genetics is not embedded in the nature of the polygenic scores themselves. Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences. Attributions of phenotypic differences among populations to genetic differences should therefore be treated with as much caution as similar genetic attributions from heritability in the pre-genomic era.

 

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The Wide Variability in Reported COVID-19 Epidemiologic Data May Suggest That Personalized Omic Testing May Be Needed to Identify At-Risk Populations

Curator: Stephen J. Williams, PhD

I constantly check the Youtube uploads from Dr. John Campbell, who is a wonderful immunologist and gives daily reports on new findings on COVID-19 from the scientific literature.  His reporting is extremely insightful and easily understandable.  This is quite a feat as it seems the scientific field has been inundated with a plethora of papers, mostly reported clinical data from small retrospective studies, and many which are being put on preprint servers, and not peer reviewed.

It has become a challenge for many scientists, already inundated with expanding peer reviewed literature in their own fields, as well as the many requests to review papers, to keep up with all these COVID related literature.  Especially when it is up to the reader to do their own detailed peer review. So many thanks to people like Dr. Campbell who is an expert in his field for doing this.

However the other day he had posted a video which I found a bit disturbing, as a central theme of the video was that many expert committee could not find any reliable epidemiologic study concerning transmission or even incidence of the disease.  In all studies, as Dr. Campell alluded to, there is such a tremendous variability in the reported statistics, whether one is looking at percentage of people testing positive who are symptomatic, the percentage of asymptomatic which may be carriers, the transmission of the disease, and even the percentage of people who recover.

With all the studies being done it would appear that, even if a careful meta analysis were done using all available studies, and assuming their validity before peer review, that there would be a tighter consensus on some of these metrics of disease spread, incidence and prevalence.

Below is the video from Dr. Campbell and the topic is on percentage of asymptomatic carriers of the COVID-19 virus.  This was posted last week but later in this post there will be updated information and views by the WHO on this matter as well as other literature (which still shows to my point that this wide variability in reported data may be adding to the policy confusion with respect to asymptomatic versus symptomatic people and why genetic testing might be needed to further discriminate these cohorts of people.

 

Below is the video: 

From the Oxford Center for Evidence Based Medicine: COVID-19 Portal at https://www.cebm.net/oxford-covid-19-evidence-service/

“There is not a single reliable study to determine the number of asymptomatic infections”

And this is very troubling as this means there is no reliable testing resulting in any meaningful data.

As Dr. Campell says

” This is not good enough.  There needs to be some sort of coordinated research program it seems all ad hoc”

A few other notes from post and Oxford Center for Evidence Based Medicine:

  • Symptom based screening will miss a lot of asymptomatic and presymptomatic cases
  • Some asymptomatic cases will become symptomatic over next week (these people were technically presymptomatic but do we know the %?)
  • We need a population based antibody screening program
  • An Italian study of all 3,000 people in city of Vo’Euganeo revealed that 50-75% of those who tested positive were asymptomatic and authors concluded that asymptomatic represents “a formidable source of infection”; Dr. Campbell feels this was a reliable study
  • Another study from a Washington state nursing facility showed while 56% of positive cases were asymptomatic, 75% of these asymptomatic developed symptoms within a week. Symptom based screening missed half of cases.
  • Other studies do not follow-up on the positive cases to determine in presymptomatic
  • It also appears discrepancies between data from different agencies (like CDC, WHO) on who is shedding virus as different tests used (PCR vs antibody)

 

Recent Studies Conflict Concerning Asymptomatic, Presymtomatic and Viral Transmission

‘We don’t actually have that answer yet’: WHO clarifies comments on asymptomatic spread of Covid-19

From StatNews

A top World Health Organization official clarified on Tuesday that scientists have not determined yet how frequently people with asymptomatic cases of Covid-19 pass the disease on to others, a day after suggesting that such spread is “very rare.”

The clarification comes after the WHO’s original comments incited strong pushback from outside public health experts, who suggested the agency had erred, or at least miscommunicated, when it said people who didn’t show symptoms were unlikely to spread the virus.

Maria Van Kerkhove, the WHO’s technical lead on the Covid-19 pandemic, made it very clear Tuesday that the actual rates of asymptomatic transmission aren’t yet known.

Some of the confusion boiled down to the details of what an asymptomatic infection actually is, and the different ways the term is used. While some cases of Covid-19 are fully asymptomatic, sometimes the word is also used to describe people who haven’t started showing symptoms yet, when they are presymptomatic. Research has shown that people become infectious before they start feeling sick, during that presymptomatic period.

At one of the WHO’s thrice-weekly press briefings Monday, Van Kerkhove noted that when health officials review cases that are initially reported to be asymptomatic, “we find out that many have really mild disease.” There are some infected people who are “truly asymptomatic,” she said, but countries that are doing detailed contact tracing are “not finding secondary transmission onward” from those cases. “It’s very rare,” she said.

Source: https://www.statnews.com/2020/06/09/who-comments-asymptomatic-spread-covid-19/

 

Therefore the problems have been in coordinating the testing results, which types of tests conducted, and the symptomology results.  As Dr. Campbell previously stated it appears more ‘ad hoc’ than coordinated research program.  In addition, defining the presymptomatic and measuring this group have been challenging.

However, an alternative explanation to the wide variability in the data may be we need to redefine the cohorts of patients we are evaluating and the retrospective data we are collecting.  It is feasible that sub groups, potentially defined by genetic background may be identified and data re-evaluated based on personalized omic data, in essence creating new cohorts based on biomarker data.

From a Perspective in The Lancet about a worldwide proteomic effort (COVID-19 MS Coalition) to discover biomarkers related to COVID19 infection risk, by identifying COVID-related antigens.

The COVID-19 MS Coalition is a collective mass spectrometry effort that will provide molecular level information on SARS-CoV-2 in the human host and reveal pathophysiological and structural information to treat and minimise COVID-19 infection. Collaboration with colleagues at pace involves sharing of optimised methods for sample collection and data generation, processing and formatting for maximal information gain. Open datasets will enable ready access to this valuable information by the computational community to help understand antigen response mechanisms, inform vaccine development, and enable antiviral drug design. As countries across the world increase widespread testing to confirm SARS-CoV-2 exposure and assess immunity, mass spectrometry has a significant role in fighting the disease. Through collaborative actions, and the collective efforts of the COVID-19 MS Coalition, a molecular level quantitative understanding of SARS-CoV-2 and its effect will benefit all.

 

In an ACS Perspective below, Morteza Mahmoudi suggests a few possible nanobased technologies (i.e., protein corona sensor array and magnetic levitation) that could discriminate COVID-19-infected people at high risk of death while still in the early stages of infection.

Emerging Biomolecular Testing to Assess the Risk of Mortality from COVID-19 Infection

Morteza Mahmoudi*

Publication Date:May 7, 2020

 

Please see other articles on COVID-19 on our Coronavirus Portal at

An Epidemiological Approach Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN Lead Curators – e–mail Contacts: sjwilliamspa@comcast.net and avivalev-ari@alum.berkeley.edu

https://pharmaceuticalintelligence.com/coronavirus-portal/an-epidemiological-approach-stephen-j-williams-phd-and-aviva-lev-ari-phd-rn-lead-curators-e-mail-contacts-sjwilliamspacomcast-net-and-avivalev-arialum-berkeley-edu/

and

https://pharmaceuticalintelligence.com/coronavirus-portal/

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COVID-19’s seasonal cycle to be estimated at Lawrence Berkeley National Laboratory (Berkeley Lab) by Artificial Intelligence and Machine Learning Algorithms: Will A Fall and Winter resurgence be Likely??

Reporter: Aviva Lev-Ari, PhD, RN

Using machine learning to estimate COVID-19’s seasonal cycle

Woman walks down empty city street wearing a mask

Credit: Ivan Marc/Shutterstock

Berkeley Lab researchers have launched a project to determine if the novel coronavirus might be seasonal, waning in summer months and resurging in fall and winter.

One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu — waning in warm summer months then resurging in the fall and winter.

Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.

“Environmental variables, such as temperature, humidity, and UV [ultraviolet radiation] exposure, can have an effect on the virus directly, in terms of its viability. They can also affect the transmission of the virus and the formation of aerosols,” said Berkeley Lab scientist Eoin Brodie, the project lead. “We will use state-of-the-art machine-learning methods to separate the contributions of social factors from the environmental factors to attempt to identify those environmental variables to which disease dynamics are most sensitive.

The research team will take advantage of an abundance of health data available at the county level — such as the severity, distribution and duration of the COVID-19 outbreak, as well as what public health interventions were implemented when — along with demographics, climate and weather factors, and, thanks to smartphone data, population mobility dynamics. The initial goal of the research is to predict — for each county in the United States — how environmental factors influence the transmission of the SARS-CoV-2 virus, which causes COVID-19.

Multidisciplinary team for a complex problem

Untangling environmental factors from social and health factors is a knotty problem with a large number of variables, all interacting in different ways. On top of that, climate and weather affect not only the virus but also human physiology and behavior. For example, people may spend more or less time indoors, depending on the weather; and their immune systems may also change with the seasons.

It’s a complex data problem similar to others tackled by Berkeley Lab’s researchers studying systems like watersheds and agriculture; the challenge involves integrating data across scales to make predictions at the local level. “Downscaling of climate information is something that we routinely do to understand how climate impacts ecosystem processes,” Brodie said. “It involves the same types of variables — temperature, humidity, solar radiation.”

Brodie, deputy director of Berkeley Lab’s Climate and Ecosystem Sciences Division, is leading a cross-disciplinary team of Lab scientists with expertise in climate modeling, data analytics, machine learning, and geospatial analytics. Ben Brown, a computational biologist in Berkeley Lab’s Biosciences Area, is leading the machine-learning analysis. One of their main aims is to understand how climate and weather interact with societal factors.

“We don’t necessarily expect climate to be a massive or dominant effect in and of itself. It’s not going to trump which city shut down when,” Brown said. “But there may be some really important interactions [between the variables]. Looking at New York and California for example, even accounting for the differences between the timing of state-instituted interventions, the death rate in New York may be four times higher than in California — though additional testing on random samples of the population is needed to know for sure. Understanding the environmental interactions may help explain why these patterns appear to be emerging. This is a quintessential problem for machine learning and AI [artificial intelligence].”

The computing work will be conducted at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility located at Berkeley Lab.

Signs of climatic influences

map of the worldwide incidence rate of COVID-19
The worldwide incidence rate of COVID-19.
Credit: Center for Systems Science and Engineering at Johns Hopkins University

Already, geographical differences in how the disease behaves have been reported, the researchers point out. Temperature, humidity, and the UV Index have all been statistically associated with rates of COVID-19 transmission — although contact rates are still the dominant influence on the spread of disease. In the southern hemisphere, for example, where it’s currently fall, disease spread has been slower than in the northern hemisphere. “There’s potentially other factors associated with that,” Brodie said. “The question is, when the southern hemisphere moves into winter, will there be an increase in transmission rate, or will fall and winter 2020 lead to a resurgence across the U.S. in the absence of interventions?”

India is another place where COVID-19 does not yet appear to be as virulent. “There are cities where it behaves as if it’s the most infectious disease in recorded history. Then there are cities where it behaves more like influenza,” Brown said. “It is really critical to understand why we see those massive differences.”

Brown notes other experiments suggesting the SARS-CoV-2 virus could be seasonal. In particular, the National Biodefense Analysis and Countermeasures Center (NBACC) assessed the longevity of the virus on various surfaces. “Under sunlight and humidity, they found that the virus loses viability in under 60 minutes,” Brown said. “But in darkness and low temperatures it’s stable for eight days. There’s some really serious differences that need investigating.”

The Berkeley Lab team believes that enough data may now be available to determine what environmental factors may influence the virulence of the virus. “Now we should have enough data from around the world to really make an assessment,” Brown said.

The team hopes to have the first phase of their analysis available by late summer or early fall. The next phase will be to make projections under different scenarios, which could aid in public health decisions.

“We would use models to project forward, with different weather scenarios, different health intervention scenarios — such as continued social distancing or whether there are vaccines or some level of herd immunity — in different parts of the country. For example, we hope to be able to say, if you have kids going back to school under this type of environment, the climate and weather in this zone will influence the potential transmission by this amount,” Brodie explained. “That will be a longer-term task for us to accomplish.”

This research is supported by Berkeley Lab’s Laboratory Directed Research and Development (LDRD) program. Other team members include Dan Feldman, Zhao Hao, Chaincy Kuo, Haruko Wainwright, and Nicola Falco. Berkeley Lab mobilized quickly to provide LDRD funding for several research projects to address the COVID-19 pandemic, including one on text mining scientific literature and another on indoor transmission of the virus.

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

 

Curator: Stephen J. Williams, Ph.D.

 

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse.  In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries.  China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

 

 

 

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging.  For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality.  Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data.  This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

 

 

 

 

 

 

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

 

 

 

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data.  Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

 

 

 

 

 

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal.  In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data.  In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020).  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.  The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

  • The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
  • More data was curated from news media, television and includes time-stamped information on COVID-19 cases
  • These reports are publicly available, de-identified patient data
  • No patient consent was needed and no ethics approval was required
  • Data was collected between January 20, 2020 and January 31,2020
  • Sex, age, province of identification, travel history, dates of symptom development was collected
  • Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
  • All patients in database had laboratory confirmation of infection

 

Results

  • 507 patient data was collected with 153 visited and 152 resident of Wuhan
  • Reported cases were skewed toward males however the overall population curve is skewed toward males in China
  • Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
  • Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
  • Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
  • Median reporting delay in the authors crowd-sourcing data was 5 days
  • Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

 

 

 

 

 

 

 

 

 

 

 

 

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

  1. Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
  2. Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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via Special COVID-19 Christopher Magazine

Special COVID-19 Christopher Magazine

Christopher-coverAntonio Giordano, MD, PhD. explains what COVID is and how to contain the infection, pointing also to what will require attention next.

Please see this special release at http://online.fliphtml5.com/qlnw/zgau/#p=1

 

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via Dr. Giordano Featured in Forbes Article on COVID-19 Antibody Tests in Italy and USA

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Decline in Sperm Count – Epigenetics, Well-being and the Significance for Population Evolution and Demography

 

Dr. Marc Feldman, Expert Opinion on the significance of Sperm Count Decline on the Future of Population Evolution and Demography

Dr. Sudipta Saha, Effects of Sperm Quality and Quantity on Human Reproduction

Dr. Aviva Lev-Ari, Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions affecting Sperm Quality and Quantity

 

UPDATED on 2/3/2018

Nobody Really Knows What Is Causing the Overdose Epidemic, But Here Are A Few Theories

https://www.buzzfeed.com/danvergano/whats-causing-the-opioid-crisis?utm_term=.kbJPMgaQo4&utm_source=BrandeisNOW%2BWeekly&utm_campaign=58ada49a84-EMAIL_CAMPAIGN_2018_01_29&utm_medium=email#.uugW6mx1dG

 

Recent studies concluded via rigorous and comprehensive analysis found that Sperm Count (SC) declined 52.4% between 1973 and 2011 among unselected men from western countries, with no evidence of a ‘leveling off’ in recent years. Declining mean SC implies that an increasing proportion of men have sperm counts below any given threshold for sub-fertility or infertility. The high proportion of men from western countries with concentration below 40 million/ml is particularly concerning given the evidence that SC below this threshold is associated with a decreased monthly probability of conception.

1.Temporal trends in sperm count: a systematic review and meta-regression analysis 

Hagai Levine, Niels Jørgensen, Anderson Martino‐Andrade, Jaime Mendiola, Dan Weksler-Derri, Irina Mindlis, Rachel Pinotti, Shanna H SwanHuman Reproduction Update, July 25, 2017, doi:10.1093/humupd/dmx022.

Link: https://academic.oup.com/humupd/article-lookup/doi/10.1093/humupd/dmx022.

2. Sperm Counts Are Declining Among Western Men – Interview with Dr. Hagai Levine

https://news.afhu.org/news/sperm-counts-are-declining-among-western-men?utm_source=Master+List&utm_campaign=dca529d919-EMAIL_CAMPAIGN_2017_07_27&utm_medium=email&utm_term=0_343e19a421-dca529d919-92801633

3. Trends in Sperm Count – Biological Reproduction Observations

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

4. Long, mysterious strips of RNA contribute to low sperm count – Long non-coding RNAs can be added to the group of possible non-structural effects, possibly epigenetic, that might regulate sperm counts.

http://casemed.case.edu/cwrumed360/news-releases/release.cfm?news_id=689

https://scienmag.com/long-mysterious-strips-of-rna-contribute-to-low-sperm-count/

Dynamic expression of long non-coding RNAs reveals their potential roles in spermatogenesis and fertility

Published: 29 July 2017
Thus, we postulated that some lncRNAs may also impact mammalian spermatogenesis and fertility. In this study, we identified a dynamic expression pattern of lncRNAs during murine spermatogenesis. Importantly, we identified a subset of lncRNAs and very few mRNAs that appear to escape meiotic sex chromosome inactivation (MSCI), an epigenetic process that leads to the silencing of the X- and Y-chromosomes at the pachytene stage of meiosis. Further, some of these lncRNAs and mRNAs show strong testis expression pattern suggesting that they may play key roles in spermatogenesis. Lastly, we generated a mouse knock out of one X-linked lncRNA, Tslrn1 (testis-specific long non-coding RNA 1), and found that males carrying a Tslrn1 deletion displayed normal fertility but a significant reduction in spermatozoa. Our findings demonstrate that dysregulation of specific mammalian lncRNAs is a novel mechanism of low sperm count or infertility, thus potentially providing new biomarkers and therapeutic strategies.

This article presents two perspectives on the potential effects of Sperm Count decline.

One Perspective identifies Epigenetics and male well-being conditions

  1. as a potential explanation to the Sperm Count decline, and
  2. as evidence for decline in White male longevity in certain geographies in the US since the mid 80s.

The other Perspective, evaluates if Sperm Count Decline would have or would not have a significant long term effects on Population Evolution and Demography.

The Voice of Prof. Marc Feldman, Stanford University – Long term significance of Sperm Count Decline on Population Evolution and Demography

Poor sperm count appears to be associated with such demographic statistics as life expectancy (1), infertility (2), and morbidity (3,4). The meta-analysis by Levine et al. (5) focuses on the change in sperm count of men from North America, Europe, Australia, and New Zealand, and shows a more than 50% decline between 1973 and 2011. Although there is no analysis of potential environmental or lifestyle factors that could contribute to the estimated decline in sperm count, Levine et al. speculate that this decline could be a signal for other negative changes in men’s health.

Because this study focuses mainly on Western men, this remarkable decline in sperm count is difficult to associate with any change in actual fertility, that is, number of children born per woman. The total fertility rate in Europe, especially Italy, Spain, and Germany, has slowly declined, but age at first marriage has increased at the same time, and this increase may be more due to economic factors than physiological changes.

Included in Levine et al.’s analysis was a set of data from “Other” countries from South America, Asia, and Africa. Sperm count in men from these countries did not show significant trends, which is interesting because there have been strong fertility declines in Asia and Africa over the same period, with corresponding increases in life expectancy (once HIV is accounted for).

What can we say about the evolutionary consequences for humans of this decrease? The answer depends on the minimal number of sperm/ml/year that would be required to maintain fertility (per woman) at replacement level, say 2.1 children, over a woman’s lifetime. Given the smaller number of ova produced per woman, a change in the ovulation statistics of women would be likely to play a larger role in the total fertility rate than the number of sperm/ejaculate/man. In other words, sperm count alone, absent other effects on mortality during male reproductive years, is unlikely to tell us much about human evolution.

Further, the major declines in fertility over the 38-year period covered by Levine et al. occurred in China, India, and Japan. Chinese fertility has declined to less than 1.5 children per woman, and in Japan it has also been well below 1.5 for some time. These declines have been due to national policies and economic changes, and are therefore unlikely to signal genetic changes that would have evolutionary ramifications. It is more likely that cultural changes will continue to be the main drivers of fertility change.

The fastest growing human populations are in the Muslim world, where fertility control is not nearly as widely practiced as in the West or Asia. If this pattern were to continue for a few more generations, the cultural evolutionary impact would swamp any effects of potentially declining sperm count.

On the other hand, if the decline in sperm count were to be discovered to be associated with genetic and/or epigenetic phenotypic effects on fetuses, newborns, or pre-reproductive humans, for example, due to stress or obesity, then there would be cause to worry about long-term evolutionary problems. As Levine et al. remark, “decline in sperm count might be considered as a ‘canary in the coal mine’ for male health across the lifespan”. But to date, there is little evidence that the evolutionary trajectory of humans constitutes such a “coal mine”.

References

  1. Jensen TK, Jacobsen R, Christensen K, Nielsen NC, Bostofte E. 2009. Good semen quality and life expectancy: a cohort study of 43,277 men. Am J Epidemiol 170: 559-565.
  2. Eisenberg ML, Li S, Behr B, Cullen MR, Galusha D, Lamb DJ, Lipshultz LI. 2014. Semen quality, infertility and mortality in the USA. Hum Reprod 29: 1567-1574.
  3. Eisenberg ML, Li S, Cullen MR, Baker LC. 2016. Increased risk of incident chronic medical conditions in infertile men: analysis of United States claims data. Fertil Steril 105: 629-636.
  4. Latif T, Kold Jensen T, Mehlsen J, Holmboe SA, Brinth L, Pors K, Skouby SO, Jorgensen N, Lindahl-Jacobsen R. Semen quality is a predictor of subsequent morbidity. A Danish cohort study of 4,712 men with long-term follow-up. Am J Epidemiol. Doi: 10.1093/aje/kwx067. (Epub ahead of print]
  5. Levine H, Jorgensen N, Martino-Andrade A, Mendiola J, Weksler-Derri D, Mindlis I, Pinotti R, Swan SH. 2017. Temporal trends in sperm count: a systematic review and meta-regression analysis. Hum Reprod Update pp. 1-14. Doi: 10.1093/humupd/dmx022.

SOURCE

From: Marcus W Feldman <mfeldman@stanford.edu>

Date: Monday, July 31, 2017 at 8:10 PM

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Subject: Fwd: text of sperm count essay

Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions as POTENTIAL effects on Sperm Quality and Quantity and Evidence of its effects on Male Longevity

The Voice of Carol GrahamSergio Pinto, and John Juneau II , Monday, July 24, 2017, Report from the Brookings Institute

  1. The IMPACT of Well-being, Stress induced by Worry, Pain, Perception of Hope related to Employment and Lack of employment on deterioration of Physiological Conditions as evidence by Decrease Longevity

  2. Epigenetics and Environmental Factors

The geography of desperation in America

Carol GrahamSergio Pinto, and John Juneau II Monday, July 24, 2017, Report from the Brookings Institute

In recent work based on our well-being metrics in the Gallup polls and on the mortality data from the Centers for Disease Control and Prevention, we find a robust association between lack of hope (and high levels of worry) among poor whites and the premature mortality rates, both at the individual and metropolitan statistical area (MSA) levels. Yet we also find important differences across places. Places come with different economic structures and identities, community traits, physical environments and much more. In the maps below, we provide a visual picture of the differences in in hope for the future, worry, and pain across race-income cohorts across U.S. states. We attempted to isolate the specific role of place, controlling for economic, socio-demographic, and other variables.

One surprise is the low level of optimism and high level of worry in the minority dense and generally “blue” state of California, and high levels of pain and worry in the equally minority dense and “blue” states of New York and Massachusetts. High levels of income inequality in these states may explain these patterns, as may the nature of jobs that poor minorities hold.

We cannot answer many questions at this point. What is it about the state of Washington, for example, that is so bad for minorities across the board? Why is Florida so much better for poor whites than it is for poor minorities? Why is Nevada “good” for poor white optimism but terrible for worry for the same group? One potential issue—which will enter into our future analysis—is racial segregation across places. We hope that the differences that we have found will provoke future exploration. Readers of this piece may have some contributions of their own as they click through the various maps, and we welcome their input. Better understanding the role of place in the “crisis” of despair facing our country is essential to finding viable solutions, as economic explanations, while important, alone are not enough.

https://www.brookings.edu/research/the-geography-of-desperation-in-america/?utm_medium=social&utm_source=facebook&utm_campaign=global

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

During pregnancy, the baby is mostly protected from harmful microorganisms by the amniotic sac, but recent research suggests the baby could be exposed to small quantities of microbes from the placenta, amniotic fluid, umbilical cord blood and fetal membranes. One theory is that any possible prenatal exposure could ‘pre-seed’ the infant microbiome. In other words, to set the right conditions for the ‘main seeding event’ for founding the infant microbiome.

When a mother gives birth vaginally and if she breastfeeds, she passes on colonies of essential microbes to her baby. This continues a chain of maternal heritage that stretches through female ancestry for thousands of generations, if all have been vaginally born and breastfed. This means a child’s microbiome, that is the trillions of microorganisms that live on and in him or her, will resemble the microbiome of his/her mother, the grandmother, the great-grandmother and so on, if all have been vaginally born and breastfed.

As soon as the mother’s waters break, suddenly the baby is exposed to a wave of the mother’s vaginal microbes that wash over the baby in the birth canal. They coat the baby’s skin, and enter the baby’s eyes, ears, nose and some are swallowed to be sent down into the gut. More microbes form of the mother’s gut microbes join the colonization through contact with the mother’s faecal matter. Many more microbes come from every breath, from every touch including skin-to-skin contact with the mother and of course, from breastfeeding.

With formula feeding, the baby won’t receive the 700 species of microbes found in breast milk. Inside breast milk, there are special sugars called human milk oligosaccharides (HMO’s) that are indigestible by the baby. These sugars are designed to feed the mother’s microbes newly arrived in the baby’s gut. By multiplying quickly, the ‘good’ bacteria crowd out any potentially harmful pathogens. These ‘good’ bacteria help train the baby’s naive immune system, teaching it to identify what is to be tolerated and what is pathogen to be attacked. This leads to the optimal training of the infant immune system resulting in a child’s best possible lifelong health.

With C-section birth and formula feeding, the baby is not likely to acquire the full complement of the mother’s vaginal, gut and breast milk microbes. Therefore, the baby’s microbiome is not likely to closely resemble the mother’s microbiome. A baby born by C-section is likely to have a different microbiome from its mother, its grandmother, its great-grandmother and so on. C-section breaks the chain of maternal heritage and this break can never be restored.

The long term effect of an altered microbiome for a child’s lifelong health is still to be proven, but many studies link C-section with a significantly increased risk for developing asthma, Type 1 diabetes, celiac disease and obesity. Scientists might not yet have all the answers, but the picture that is forming is that C-section and formula feeding could be significantly impacting the health of the next generation. Through the transgenerational aspect to birth, it could even be impacting the health of future generations.

References:

https://blogs.scientificamerican.com/guest-blog/shortchanging-a-babys-microbiome/

https://www.ncbi.nlm.nih.gov/pubmed/23926244

https://www.ncbi.nlm.nih.gov/pubmed/26412384

https://www.ncbi.nlm.nih.gov/pubmed/25290507

https://www.ncbi.nlm.nih.gov/pubmed/25974306

https://www.ncbi.nlm.nih.gov/pubmed/24637604

https://www.ncbi.nlm.nih.gov/pubmed/22911969

https://www.ncbi.nlm.nih.gov/pubmed/25650398

https://www.ncbi.nlm.nih.gov/pubmed/27362264

https://www.ncbi.nlm.nih.gov/pubmed/27306663

http://www.mdpi.com/1099-4300/14/11/2036

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464665/

https://www.ncbi.nlm.nih.gov/pubmed/24848255

https://www.ncbi.nlm.nih.gov/pubmed/26412384

https://www.ncbi.nlm.nih.gov/pubmed/28112736

http://ndnr.com/gastrointestinal/the-infant-microbiome-how-environmental-maternal-factors-influence-its-development/

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Finding the Actions That Alter Evolution

The biologist Marcus Feldman creates mathematical models that reveal how cultural traditions can affect the evolution of a species.

By Elizabeth Svoboda

January 5, 2017

In a commentary in Nature, you and your co-authors wrote, “We hold that organisms are constructed in development, not simply ‘programmed’ to develop by genes.” What does “constructed in development” mean?

It means there’s an interaction between the subject and the environment. The idea of a genetic blueprint is not tenable in light of all that is now known about how all sorts of environmental contingencies affect traits. For many animals it’s like that. Even plants — the same plant that is genetically identical, if you put it in this environment, it’s going to look totally different from if you put it in that environment.

We now have a better picture of the regulatory process on genes. Epigenetics changes the landscape in genetics because it’s not only the pure DNA sequence which influences what’s going on at the level of proteins and enzymes. There’s this whole other stuff, the other 95 percent of the genome, that acts like rheostats — you slide this thing up and down, you get more or less of this protein. It’s a critical thing in how much of this protein is going to be made. It’s interesting to think about the way in which cultural phenomena, which we used to think were things by themselves, can have this effect on how much messenger RNA is made, and therefore on many aspects of gene regulation.

Article to review and VIEW VIDEO

https://www.quantamagazine.org/20170105-marcus-feldman-interview-culture-and-evolution/

 

ABOUT QUANTA

Quanta Magazine’s mission is to enhance public understanding of research developments in mathematics and the physical and life sciences. Quanta articles do not necessarily represent the views of the Simons Foundation. Learn more

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