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Rhesus macaques naturally exposed to wildfire smoke early in pregnancy had an increased rate of miscarriage, according to new research from the California National Primate Research Center at the University of California, Davis. The work is published online in the journal Reproductive Toxicology.
In November 2018, smoke from the Camp Fire in Paradise, California, about 100 miles away blanketed the Davis area. Air quality exceeded national limits and reached unhealthy levels.
The disaster coincided with the peak of breeding season for the center’s colony of rhesus macaques. Hundreds of animals at the center live in outdoor corrals in large family groups. Breeding typically takes place in the fall, with offspring born in spring after an average gestation of 166 days.
Bryn Willson, an OB/GYN resident at UC Davis Health, began the research project in collaboration with Professor Kent Pinkerton, UC Davis Center for Health and Environment, and Bill Lasley, professor emeritus at the Center for Health and Environment and School of Veterinary Medicine, and colleagues.
The researchers randomly selected 66 female animals of reproductive age from the colony to follow for pregnancy outcomes. They were compared to pregnancies from nine previous years.
Of the 66, 45 became pregnant while levels of smoke pollution were high, based on measurements of small particles (PM2.5). Twenty animals conceived after air quality had returned to normal levels in December. One animal did not become pregnant.

How can government slow the spread of COVID-19 in the U.S.? Look to America’s unique epidemic engines: jails and prisons in America.
Extremely high rates of incarceration in the U.S. undercut national public health and safety. The overcrowded, tight quarters in jails fuel constant risks of outbreaks. Add to that the daily movement of 420,000 guards in and out of the facilities and 30,000 newly released people who are likely to inadvertently carry the virus back to communities.
A new study from Northwestern Medicine, Toulouse School of Economics and the French National Centre for Scientific Research found the best way to address this public safety threat is through decarceration (i.e., reducing the number of people detained in jails).
“If we can immediately stop jailing people for minor alleged offenses and begin building a national decarceration program to end mass incarceration, these changes will protect us from COVID-19 now and will also benefit long-term U.S. public health and pandemic preparedness,” said first author Dr. Eric Reinhart, an anthropologist of public health and resident physician in the department of psychiatry and behavioral sciences at Northwestern University Feinberg School of Medicine.
The study evaluated the association of jail decarceration and government anti-contagion policies with reductions in the spread of SARS-CoV-2 in the U.S. It will be published Sept. 2 in the journal JAMA Network Open.
It is the first study to link mass incarceration systems to pandemic vulnerability and international biosecurity (i.e., systems for protecting against disease or harmful biological agents). In a pandemic, amplification of COVID-19 spread by one country spills over into other nations such that mass incarceration in the U.S. is a threat not only to Americans but also to global public health at large.

In an article published in the journal Patterns, scientists at the Icahn School of Medicine at Mount Sinai described the creation of a new, automated, artificial intelligence-based algorithm that can learn to read patient data from electronic health records. In a side-by-side comparison, they showed that their method, called Phe2vec (FEE-to-vek), accurately identified patients with certain diseases as well as the traditional, “gold-standard” method, which requires much more manual labor to develop and perform.
“There continues to be an explosion in the amount and types of data electronically stored in a patient’s medical record. Disentangling this complex web of data can be highly burdensome, thus slowing advancements in clinical research,” said Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai (HPIMS), and a senior author of the study. “In this study, we created a new method for mining data from electronic health records with machine learning that is faster and less labor intensive than the industry standard. We hope that this will be a valuable tool that will facilitate further, and less biased, research in clinical informatics.”
The study was led by Jessica K. De Freitas, a graduate student in Dr. Glicksberg lab.
Currently, scientists rely on a set of established computer programs, or algorithms, to mine medical records for new information. The development and storage of these algorithms is managed by a system called the Phenotype Knowledgebase (PheKB). Although the system is highly effective at correctly identifying a patient diagnosis, the process of developing an algorithm can be very time-consuming and inflexible. To study a disease, researchers first have to comb through reams of medical records looking for pieces of data, such as certain lab tests or prescriptions, which are uniquely associated with the disease. They then program the algorithm that guides the computer to search for patients who have those disease-specific pieces of data, which constitute a “phenotype.” In turn, the list of patients identified by the computer needs to be manually double-checked by researchers. Each time researchers want to study a new disease, they have to restart the process from scratch.
In this study, the researchers tried a different approach — one in which the computer learns, on its own, how to spot disease phenotypes and thus save researchers time and effort. This new, Phe2vec method was based on studies the team had already conducted.
“Previously, we showed that unsupervised machine learning could be a highly efficient and effective strategy for mining electronic health records,” said Riccardo Miotto, PhD, a former Assistant Professor at the HPIMS and a senior author of the study. “The potential advantage of our approach is that it learns representations of diseases from the data itself. Therefore, the machine does much of the work experts would normally do to define the combination of data elements from health records that best describes a particular disease.”
Essentially, a computer was programmed to scour through millions of electronic health records and learn how to find connections between data and diseases. This programming relied on “embedding” algorithms that had been previously developed by other researchers, such as linguists, to study word networks in various languages. One of the algorithms, called word2vec, was particularly effective. Then, the computer was programmed to use what it learned to identify the diagnoses of nearly 2 million patients whose data was stored in the Mount Sinai Health System.
Finally, the researchers compared the effectiveness between the new and the old systems. For nine out of ten diseases tested, they found that the new Phe2vec system was as effective as, or performed slightly better than, the gold standard phenotyping process at correctly identifying a diagnoses from electronic health records. A few examples of the diseases included dementia, multiple sclerosis, and sickle cell anemia.
“Overall our results are encouraging and suggest that Phe2vec is a promising technique for large-scale phenotyping of diseases in electronic health record data,” Dr. Glicksberg said. “With further testing and refinement, we hope that it could be used to automate many of the initial steps of clinical informatics research, thus allowing scientists to focus their efforts on downstream analyses like predictive modeling.”
This study was supported by the Hasso Plattner Foundation, the Alzheimer’s Drug Discovery Foundation, and a courtesy graphics processing unit donation from the NVIDIA Corporation.

An analysis of more than 20,000 individual medical records suggests that a form of heart valve disease thought to be relatively benign during pregnancy may put women at risk for serious bleeding, high blood pressure, organ damage and other complications during childbirth, according to research from Johns Hopkins Medicine.
In a report on the study, published online Aug. 24 in the American Journal of Cardiology, researchers at Johns Hopkins Medicine found that pregnant women with a history of regurgitant or “leaky” heart valves, as well as those with narrowed or stenotic valves, are up to 100 times more likely to experience cardiac complications such as heart failure at the time of delivery compared with women without heart valve disease.
Although relatively rare among pregnant women in the United States, heart valve disease causes complications such as premature labor and heart failure in up to 10% of women giving birth each year.
The study leaders say their findings suggest that heart and obstetrical experts should increase attention to assessing risk in all women with a history of any type of heart valve disease before and during pregnancy.
The heart’s four valves — mitral, tricuspid, pulmonic and aortic — keep blood flowing in the correct direction. Most risk-assessment guidelines focus on any degree of stenosis, or narrowing and tightening of valves that reduces blood flow and causes extra strain on the heart.
This new analysis found that maternal complications, such as fluid buildup in the lungs, heart rhythm problems or heart failure, occur just as often in women with regurgitant heart valve disease, a type of valve lesion long thought to be low risk and marked by incomplete closure of a valve, leading to leakage and backward blood flow.

A research team at the RIKEN Center for Emergent Matter Science (CEMS) in Japan has developed a diagnostic system that can rapidly and sensitively measure the amount of antibodies in the blood that can protect us from SARS-CoV-2, the virus that causes COVID-19. This achievement was published in the scientific journal Bulletin of the Chemical Society of Japan, and is expected to enable efficient and precise testing of SARS-CoV-2 vaccine efficacy at medical facilities.
At present, several vaccines against SARS-CoV-2 have been developed, and vaccination is being conducted worldwide. In the medical field, antibody tests using a technique called immunochromatography are performed to determine whether antibodies have been produced as a result of viral infection or vaccination. However, because the results of this test are determined by looking with the naked eye at colored stripes on paper, it is not precise and not very sensitive. Compounding the problem, for more precise, quantitative results, blood samples must be sent to an outside testing center, with turnaround taking several days to a week.
The new research was led by Yoshihiro Ito at RIKEN CEMS, who developed a technology several years ago that immobilizes any organic compound, including substances of biological origin. Since then, Ito and his colleagues have continued to improve on the system, focusing on immobilizing various allergens to measure the history of immune infection. They have already succeeded in developing a test kit using a microchip that contains more than 40 different allergens immobilized on it. Now, they have expanded their diagnostic tools for use in a system that immobilizes several key SARS-CoV-2 proteins, allowing the presence of antibodies against SARS-CoV-2 to be detected automatically.
The technique is based on the use of light. A substance that reacts to light is first coated on a plastic microchip, and a sample liquid containing the protein of interest is dropped onto the microchip in the form of a spot. Then the chip is exposed to ultraviolet light, which immobilizes the proteins. Using this method, the researchers developed a chip called a microarray upon which key SARS-CoV-2 are fixed. When antibodies in blood serum bind to the viral proteins on the chip they emit light, and the amount of emitted light can be measured precisely with a CCD camera. This value can therefore be used to quantify the number of antibodies in a way that is not possible with immunochromatography.
“Standard quantitative analysis of antibodies usually requires a half milliliter of blood drawn from one of your arms, which is a lot!,” says Ito. “But in our system, all that is needed is a small drop of blood from the fingertip, and the sensitivity of the system is 500 times higher than that of conventional immunochromatography, meaning that detection is possible even when the number of antibodies is very low.” Furthermore, its operation is quite simple — just drop human blood serum onto the chip, press the start button, and wait. The reaction process, washing, and antibody detection are performed automatically in about 30 minutes.
“In the past, our team has succeeded in developing antibody detection systems for measles, rubella, and chicken pox. Now we can also detect antibodies for the COVID-19 virus. This system is practical to use and will enable precision testing at any medical facility, making it easier to quickly determine on-site whether or not vaccination is necessary. It can also be used to conduct epidemiological surveys in preparation for future pandemics,” says Ito.
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In the United States, as many as 187,000 people are coping with myelodysplastic syndromes (MDS) that damage the bone marrow’s ability to produce healthy blood cells.
In most cases, MDS is caused by mutations that arise in blood stem cells during a person’s life. These patients experience anemia, fatigue, and other complications, with a typical age of MDS diagnosis at 70 years and beyond. However, about 2% to 5% of people with MDS are born with mutations that predispose them to a form of MDS but even they do not experience symptoms until their 60s.
In 2016, other scientists documented that people with this inherited form of adult MDS share mutations in the DDX41 gene, but it had not been clear what role the mutations played. Now, a study led by experts at Cincinnati Children’s Cancer and Blood Diseases Institute, published Sept. 1, 2021, in Cell Stem Cell explains its significance.
Their discovery was based on extensive work to develop a more accurate mouse model of human MDS caused by mutations in DDX41. An important facet of the disease seems to depend on cells that acquire an additional mutation in their other copy of the DDX41 gene, creating blood stem cells with two DDX41 mutations.
The team reasoned that these cells, which are relatively rare in the patient’s bone marrow, could indirectly affect the rest of the bone marrow and trigger abnormal blood production. In this way, this minor cell population could become a driver of MDS. Their findings suggest that targeting this minor cell population could lead to a treatment to prevent some cases of MDS.
“Basically, these rare cells help create a polluted bone marrow environment that in turn allows other stem cells with MDS-related gene mutations to thrive,” says Daniel Starczynowski, PhD, senior author of the study. “Without the presence of these trigger cells, the bone marrow might go on making blood cells normally as it had throughout the patient’s life.”
While this study focuses on MDS, similar types of crosstalk between cells with different combinations of mutations may play a role in other diseases.

One of the major causes of tooth loss is the inflammation and weakening of the supporting structures of the teeth caused by bacterial infection, a condition commonly known as “periodontitis.” The oral cavity is home to a myriad of microorganisms, including bacteria that generally maintain a “symbiotic” (mutually beneficial) or neutral relationship with the host, but are also capable of initiating many diseases.
Aggregation of the bacterial community into “biofilms” is often associated with the development of infections, including periodontitis. With currently available treatment options often proving inadequate, there is a pressing need to understand the beginning and development of the disease better. Now, in a study published in International Journal of Environment and Public Health Research, a group of researchers led by Assistant Professor Naoki Toyama from Okayama University, Japan, reveal insightful findings that could provide new directions to the treatment strategies for periodontitis.
The physiology of an individual directly affects the development of infection. Genetic differences among hosts contribute to differences in susceptibility to specific pathogens and the chance of developing certain diseases. In their study, Dr. Toyama and colleagues focused on understanding the microbes associated with the presence of periodontitis and the host genetic factors that might facilitate the development of the conditions. Dr. Toyama explains the motivation behind their study, “Several studies on periodontitis have shown that the development of the disease is associated with the nature of the oral microbiome as well as with genetic ‘polymorphism,’ the most common type of genetic variation among people. However, there is no study that simultaneously assesses the importance of these two risk factors in developing the disease.”
Accordingly, the team conducted a cross-sectional study in which they genotypically analyzed 14,539 participants and conducted saliva sampling of 385 individuals. They finally retained 22 individuals for statistical analysis, and based on their periodontal status, divided them into “periodontitis” and “control” groups.
The team found that the “β-diversity” of the microbes, which refers to the ratio between regional and local species diversity, was significantly different between the periodontitis and control groups. Furthermore, they attributed the presence of the bacteria species, P. gingivalis and the bacterial families, Lactobacillaceae and Desulfobulbaceae, to periodontitis. In contrast, they found no relation between genetic polymorphism and periodontitis. Taking these inferences into account, the team concluded that our oral microbiome affects the status of periodontitis more than our genes.
So, how do these findings influence current clinical practices? Dr. Toyama surmises, “The fact that the prevalence of periodontitis is associated with the members of the microbiome rather than the genetic identity of the individual would motivate clinicians to pay more attention to microbiome composition than to host factors in the routine work of periodontal examination, and design customized treatment strategy for periodontitis.”
These findings further reinforce the importance of regular tooth cleaning in keeping periodontitis at bay.
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Air pollution and greater distance with green of blue spaces negatively impact the health-related quality of life in patients with chronic obstructive pulmonary disease (COPD), according to a new study led by the Barcelona Institute for Global Health (ISGlobal), an institution supported by “la Caixa” Foundation. The findings highlight the need for implementing urban policies that improve the life quality of a great number of people living with respiratory diseases across the world.
The health-related quality of life has become one of the most relevant parameters to measure the progression of chronic obstructive pulmonary disease (COPD). “We know that several clinical and psychological factors can affect this parameter, but little is known on the effect of environmental factors,” explains Judith Garcia-Aymerich, ISGlobal researcher. Thus, Garcia-Aymerich and her team assessed, for the first time, the association between health-related quality of life and exposure to different environmental factors in over 400 COPD patients with different levels of disease severity, from moderate to high.
The patients, all of them residing in Barcelona, underwent a COPD assessment test and answered a clinical questionnaire. The research team determined the residential exposure of each patient to air pollutants (NO2, PM2.5 and PM10 fine particles, and PM2.5 absorbance), traffic noise, land surface temperatures, and distance to green or blue (water) spaces. They found that exposure to high levels of NO2 and PM2.5 absorbance (an indicator of black carbon emanating from combustion) were associated with worse assessment and mental health questionnaire scores. “This might be explained by the restorative effect of blue and green spaces, although it could also be related to the fact that these spaces encourage greater physical activity,” says Subhabrata Moitra, first author of the study.
The authors acknowledge that, being a cross-sectional study rather than a longitudinal one, they cannot demonstrate causality, and that further studies are needed to better understand the contribution of each pollutant. “However, this study, performed for the first time on a Mediterranean population, provides evidence that air pollutants (particularly NO2 and black carbon) and the distance to green or blue spaces negatively affects the health-related quality of life in COPD patients,” says Garcia-Aymerich.
These results can help clinicians to provide recommendations that improve the quality of life of their COPD patients, for example by avoiding traffic zones or being close to blue and green spaces. They also underline the need to limit air pollution in cities and redefine urban policies that improve the quality of life of the great number of people who live with respiratory diseases across the world.
These findings are published shortly before the celebration of the European Respiratory Society Congress 2021, which will take place virtually September 5-8.
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Brain regulation of feeding behavior traits has remained incompletely understood. In their latest study, researchers at the Turku PET Centre, Finland, discovered a connection between the function of the opioid system and food craving triggered by appetitive external stimuli.
Animal studies have established that the brain’s opioid and endocannabinoid systems are important in regulating eating behavior and mediate the food reward experience. For instance, alterations in these systems’ signaling have been associated with obesity. In general, both internal signals of the body, such as fluctuation in blood sugar levels, and external stimuli, such as food advertisements, can spark an appetite in humans.
In their new study, researchers at the University of Turku, Finland, investigated the connection between the brain’s opioid and endocannabinoid signaling and different types of eating behavior. They discovered that the function of the opioid system is connected to eating triggered by external stimuli.
“The less binding sites there were for the opioids, the greater was the tendency to eat in response to external stimuli, such as seeing appetizing food. Moreover, the number of binding sites for endocannabinoids was connected to several different types of eating behavior, describes first author,” Doctoral Candidate Tatu Kantonen from the University of Turku.
According to Kantonen, the results indicate that especially the opioid system could be a potential target for anti-obesity drugs in humans.
The research data was obtained from the AIVO database hosted by the Turku PET Centre.
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New research has uncovered a precision medicine test using blood proteins to identify a novel patient subgroup of idiopathic multicentric Castleman disease (iMCD), a rare blood disorder, who are more likely to respond to siltuximab, the only FDA approved treatment for the disease. The international study was led by researchers at Penn Medicine and the Castleman Disease Collaborative Network (CDCN).
Prior research suggests that half of patients do not respond to the monoclonal antibody treatment, siltuximab. For those patients, rapid administration of other treatments is needed to prevent deterioration, so understanding who is likely to benefit is critical. This study also revealed that an existing drug approach, Janus kinase (JAK) inhibitors, which are already approved for treating certain cancers and rheumatoid arthritis, are a promising alternative treatment option for patients who do not respond to siltuximab. The study, which is the largest to date for iMCD, is published in Blood Advances.
“This discovery has the potential to improve precision medicine for iMCD — the concept that the right patient is given the right drug at the right time. Knowing which patients are likely to benefit from which drugs is a key piece of this puzzle,” said David Fajgenbaum, MD, MBA, MSc, an assistant professor of Translational Medicine and Human Genetics, Director of the Center for Cytokine Storm Treatment & Laboratory at the Perelman School of Medicine at the University of Pennsylvania, co-founder of the CDCN, and associate director of patient impact at the Penn Orphan Disease Center, and the study’s senior author. Fajgenbaum is also an iMCD patient.
Castleman disease isn’t actually a single disease. The term describes a group of inflammatory disorders and is diagnosed in about 5,000 people of all ages each year in the United States, which makes it roughly as common as Amyotrophic Lateral Sclerosis (ALS). Patients experience a range of symptoms — from a single enlarged lymph node with mild flu-like symptoms (unicentric) to enlarged lymph nodes located throughout their body, abnormal blood cell counts, and life-threatening failure of multiple organ systems (multicentric). The most severe subtype is iMCD, which has similarities to autoimmune conditions and cancer, and involves a cytokine storm. A cytokine storm describes what happens when the immune system goes into overdrive as can be seen in severe COVID-19 and a number of other conditions. About 35 percent of patients with iMCD will die within five years of diagnosis.
Studies have shown that siltuximab can send between one-third and one-half of patients into a remission that generally lasts for years. However, patients who are in the ICU and don’t respond to siltuximab have few options and limited time. They typically receive chemotherapy, but often relapse, meaning many iMCD patients endure months without appropriate treatment. It took more than 11 weeks for Fajgenbaum to be correctly diagnosed, during which time he experienced two life-threatening episodes of the disease, did not respond to siltuximab, and had to be treated with rapid chemotherapy.
For this study, researchers examined blood samples from 88 iMCD patients and measured 1,178 blood proteins in each of those samples, identifying seven blood proteins that were able to effectively predict the subgroup of patients who were most likely to respond to siltuximab. The results were subsequently validated in an independent cohort of 23 iMCD patients.