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The IL-6 family of proteins has a bad reputation: it can promote inflammation, arthritis, autoimmune disease and even cancer. However, a new USC-led study published in Communications Biology reveals the importance of IL-6 and associated genes for maintaining and regenerating cartilage in both the joints and in the growth plates that enable skeletal growth in children.
“We show, for the first time, that the IL-6 family, previously almost exclusively associated in the musculoskeletal field with arthritis, bone and muscle loss, and other chronic inflammatory diseases, is required for the maintenance of skeletal stem and progenitor cells, and for the healthy growth and function of the joints and spine,” said the study’s corresponding author Denis Evseenko, who is the J. Harold and Edna LaBriola Chair in Genetic Orthopedic Research, and an associate professor of orthopaedic surgery, and stem cell biology and regenerative medicine at the Keck School of Medicine of USC. “Our study establishes a link between inflammation and regeneration, and may explain why stem and progenitors are exhausted in chronic inflammation.”
In the study, first author Nancy Q. Liu from USC and her colleagues took a close look at a key gene activated by IL-6: STAT3. In both lab-grown human cells and in mice, the scientists demonstrated that STAT3 is critical for the proliferation, survival, maturation and regeneration of cartilage-forming cells in the joints and growth plates. When the gene ceased to function, cartilage-forming cells became increasingly dysfunctional over time, resulting in smaller body size, prematurely fused growth plates, underdeveloped skeletons and mildly degenerated joint cartilage.
Mice experienced the same issues when they lacked a protein called glycoprotein 130 (gp130), which all IL-6 proteinsuse to activate Stat3. Deactivating another gene Lifr, which encodes a protein that works with gp130 to recognize one of the IL-6 proteins called Lif, produced similar but milder skeletal and cartilage changes.
In mice lacking gp130, the scientists could restore normal growth plates by over-activating Stat3 — although this also caused an overgrowth of cartilage that led to other skeletal abnormalities.
Interestingly, the researchers noted significant sex-related differences: when Stat3 ceased to function, females experienced more severe cartilage and skeletal changes than males. To understand why, the researchers altered estrogen levels in mice, as well as in lab-grown pig cartilage cells. In both cases, estrogen increased the amount and activity of Stat3, suggesting that females might rely more heavily on this gene.
The study has clinical implications for the use of existing drugs that inhibit STAT3 to curb inflammation in autoimmune diseases: these drugs may also interfere with growth and regeneration.
Conversely, the Evseenko Lab has leveraged their understanding of the nuances of STAT3 and associated genes and proteins to develop a highly targeted drug with the potential to regenerate joint cartilage without triggering inflammation. This drug will soon be tested in human clinical trials.
“Our findings really shift the paradigm and challenge the existing dogmas in the field about how IL-6, STAT3, and associated genes and proteins influence not only inflammation, but also regeneration,” said Evseenko.
About the study
Additional co-authors of the study include: Yucheng Lin from USC, Nanjing Medical University, and Southeast University in Nanjing; Liangliang Li and Dawei Geng from USC and Nanjing Medical University; Jinxiu Lu, Zorica Buser, Jenny Magallanes, Jade Tassey, Ruzanna Shkhyan, Arijita Sarkar, Siyoung Lee, Youngjoo Lee, Frank A. Petrigliano, Ben Van Handel, and Tea Jashashvili from USC; Jiankang Zhang from USC and Sichuan University; Noah Lopez and Karen Lyons from UCLA; and Liming Wang from Nanjing Medical University and Sichuan University.
The work was supported by federal funding from the National Institutes of Health (grants R01AR071734 and R01AG058624) and the Department of Defense (grant W81XWH-13-1-0465), and the California Institute for Regenerative Medicine (grant TRAN1-09288).

Among the most troubling scenes from the COVID-19 era are the images of patients dying in isolation, unable to be with loved ones during their final moments. But even before the pandemic, harrowing deaths were all too common in most parts of the world, a new survey of end-of-life care shows.
The study, detailed in three papers to be published in the Journal of Pain and Symptom Management, ranked 81 countries on how well their health systems provide for the physical and mental wellbeing of patients at the end of life. Only six countries earned grades of A, while 36 earned Ds or Fs.
The survey results appeared in the Journal of Pain and Symptom Management last month, and additional details may be found on a website created by the Lien Centre for Palliative Care, part of the Duke-NUS Medical School in Singapore.
“Society should also be judged on how well people die,” says Eric Finkelstein, a palliative care expert and professor with Duke-NUS and the Duke Global Health Institute in Durham, North Carolina, who led the study. “Many individuals in both the developed and developing world die very badly — not at their place of choice, without dignity, or compassion, with a limited understanding about their illness, after spending down much of their savings, and often with regret about their course of treatment. These things are very common.”
To compile the rankings, Finkelstein and colleagues surveyed more than 1,200 caregivers from several countries to identify what is most important to patients at the end of life. They then asked 181 palliative care experts across the globe to grade their countries’ health systems on 13 weighted factors that people most often listed, including proper management of pain and comfort, having a clean and safe space, being treated kindly, and treatments that address quality of life, rather than merely extending life.
The United Kingdom earned the highest ranking in the study, followed by Ireland, Taiwan, Australia, South Korea and Costa Rica, which all earned A grades. The United States earned a C, ranking 43rd of the 81 countries.

New therapies for autoimmune rheumatic diseases (AIRDs) that are designed to better regulate lipid (fat) metabolism, could significantly reduce the harmful side-effects caused by conventional treatments, finds a new large-scale review led by UCL researchers.
AIRDs affect millions globally and include rheumatoid arthritis, lupus and Sjögren’s syndrome — all with high rates of morbidity. They occur when the immune system mistakenly attacks and damages its own tissues, though the pathogenesis (the mechanism which triggers this) is still ill-defined and delivering targeted therapeutic strategies is challenging.
As a result, current treatments for AIRDs are primarily designed to supress the symptoms (inflammation), but are ‘low target’ meaning the drugs may also have unintended side-effects. In this regard, AIRDs drugs often cause changes to cell metabolism (such as lipid metabolism) and function, putting patients at greater risk of co-morbidities such as cardiovascular disease (CVD).
Lead author Dr George Robinson (Centre for Rheumatology Research, UCL Division of Medicine) said: “While the mechanisms that cause rheumatic diseases are ill-defined, some recent research indicates cell metabolism may play an important role in triggering or worsening their onset or affect.
“In this review we therefore sought to understand the effect of both conventional and emerging therapies on lipid metabolism in patients with AIRDs.”
For the study, published in the Journal of Clinical Investigation, researchers carried out a literature review of more than 200 studies, to assess and interpret what is known regarding the on-target/off-target (adverse) effects and mechanisms of action of current AIRD therapies on lipid metabolism, immune cell function and CVD risk.
Explaining the findings, Dr Robinson said: “Our review found that current AIRD therapies can both improve or worsen lipid metabolism, and either of these changes could cause inflammation and increased CVD risk.
“Many conventional drugs also require cell metabolism for their conversion into therapeutically beneficial products; however drug metabolism often involves the additional formation of toxic by-products, and rates of drug metabolism can be different between patients.”
The review noted that better control of inflammation using optimal combinations of immunosuppressive treatments, could lead to an improved metabolic/lipid profile in AIRDs.
However, it also revealed many studies have shown that lipid lowering drugs (such as statins) are not sufficient to reduce CVD risk in some AIRDs, potentially because they cannot completely restore the anti-inflammatory properties
Dr Robinson added: “The unfavourable off-target adverse effects of current therapies used to treat AIRDs provides an opportunity for optimal combination co-therapies targeting lipid metabolism that could reduce immune complications and potential increased CVD risk in patients.
“New therapeutic technologies and research have also highlighted alternative metabolic pathways that can be more specifically targeted to reduce inflammation but also to prevent undesirable off-target metabolic consequences of conventional anti-inflammatory therapies.”
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A set of parameters including sleep and biological rhythm variables are closely associated with the severity of depressive and anxiety symptoms, starting in the third trimester of pregnancy to the third postpartum month, according to a new study.
The three-month period before and after giving birth is a vulnerable time for women’s mental health. It is estimated that 15 to 18 per cent of women experience anxiety and seven to 13 per cent experience depression during this peripartum period. In addition, nearly 10 per cent of women experience clinical levels of comorbid anxiety and depression during this time.
In the largest observational study to date investigating changes in sleep and biological rhythms during the peripartum period, researchers identified several variables that are linked to depression and anxiety. Most notably, changes in the circadian quotient (the strength of the circadian rhythms), the average amount of activity during nighttime rest, and the amount of fragmentation of nighttime rest were strongly linked to higher depressive and anxiety symptoms.
“Our findings highlight the importance of stabilizing the internal biological clock during the peripartum period to maintain healthy mood and minimize anxiety,” said Benicio Frey, senior author of the study and professor in the department of psychiatry and behavioural neurosciences at McMaster University.
“Given the findings, future efforts should be made to standardize evidence-based interventions targeting these biological rhythms variables identified by our team, either as treatment or prevention strategies.”
Frey and his research team conducted the study from the Women’s Health Concerns Clinic at St. Joseph’s Healthcare Hamilton. This clinic specializes in psychiatric disorders during the peripartum, premenstrual, and perimenopausal periods.
Researchers recruited 100 women, 73 of whom they followed from the start of the third trimester to three months postpartum. They analyzed subjective and objective measures of sleep, biological rhythms, melatonin levels, and light exposure using a variety of tools, including questionnaires, actigraphs (wearable sleep monitors), laboratory assays, and other methods.
Interestingly, the findings indicate that certain biological rhythms variables may be important to depressive symptoms at specific points along the peripartum timeline. For instance, higher fragmentation of nighttime rest was linked to a decrease in depressive symptoms at six to 12 weeks postpartum — a period that tends to coincide with a higher risk of developing postpartum depression.
Support for the study was provided in part by The Research Institute of St. Joe’s Hamilton and the Teresa Cascioli Charitable Foundation Research Award in Women’s Health.
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Materials provided by McMaster University. Original written by Fram Dinshaw. Note: Content may be edited for style and length.

A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité — Universitätsmedizin Berlin, Germany, and colleagues.
Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19.
In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria. A machine learning approach was used to find associations between the measured proteins and patient survival.
Fifteen of the patients in the cohort died; the average time from admission to death was 28 days. For patients who survived, the median time of hospitalization was 63 days. The researchers pinpointed 14 proteins which, over time, changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. The team then developed a machine learning model to predict survival based on a single time-point measurement of relevant proteins and tested the model on an independent validation cohort of 24 critically ill COVID-10 patients. The model demonstrated high predictive power on this cohort, correctly predicting the outcome for 18 of 19 patients who survived and 5 out of 5 patients who died (AUROC = 1.0, P = 0.000047).
The researchers conclude that blood protein tests, if validated in larger cohorts, may be useful in both identifying patients with the highest mortality risk, as well as for testing whether a given treatment changes the projected trajectory of an individual patient.
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Health care professionals often encourage women with obesity to lose weight prior to trying to conceive or start infertility treatments. But a new nationwide study led by Penn State College of Medicine found that women with obesity and unexplained infertility who lost weight prior to starting infertility treatments did not have a greater chance of having a healthy baby than those who did not lose weight prior to starting therapy.
Forty percent of U.S. women between the ages of 20 and 40 are estimated to have obesity, which has been associated with trouble getting pregnant, pregnancy complications and loss. As a result, it is common for those women to receive guidance to lose weight prior to conception to increase their chances of having a healthy baby.
Dr. Richard Legro, professor and chair of the Department of Obstetrics and Gynecology at Penn State Health Milton S. Hershey Medical Center, led a multi-center National Institutes of Health?sponsored study of more than 300 women with obesity and unexplained infertility to evaluate whether targeted weight loss prior to fertility treatments could increase their likelihood of delivering a healthy baby. Participants had to have a body mass index greater than or equal to 30 kg/m2 with regular ovulation and at least one year of unexplained infertility. Women with anovulation, where an egg doesn’t release from the ovary during the menstrual cycle, and polycystic ovary syndrome, where women often experience infrequent or abnormal menstrual cycles as a result of hormone imbalance in the ovaries, were not eligible to participate in the study.
Participants were divided into two groups. One group followed a protocol of increased physical activity and targeted weight loss through meal replacements and medication, while members of the other group increased their physical activity without guided weight loss. The women completed these programs for a period of 16 weeks before beginning three cycles of infertility therapy that consisted of ovarian stimulation and intrauterine insemination.
At the end of the study period, the researchers noticed no significant differences in the amount of pregnancies and healthy births between the two groups. Members of the guided weight loss group lost an average of 7% of their body weight, while the other participants experienced no significant weight loss. The results were published in PLOS Medicine on Jan. 18.
According to Legro, the results add to a growing body of evidence that healthy births are not more likely to occur in women with obesity who lose weight prior to starting infertility treatment than those who have not lost weight prior to conception.
“Although it differs from current clinical standards of care, there’s just not enough evidence to recommend preconception weight loss in women with obesity and unexplained infertility,” Legro said.
While it may not increase a woman with obesity’s chances of delivering a healthy baby, the researchers noted there may be other health benefits for these women if they lose weight. Some of the women in the weight loss group had decreased blood pressure and a reduction in waist circumference.
Karl Hansen and Robert Wild of University of Oklahoma Health Sciences Center; Michael Diamond of Augusta University; Anne Steiner and Jennifer Mersereau of University of North Caroline, Chapel Hill; Christos Coutifaris and Kurt Barnhart of University of Pennsylvania; Marcelle Cedars of University of California at San Francisco; Kathleen Hoeger of University of Rochester; Rebecca Usadi of Atrium Health; Erica Johnstone of University of Utah; Daniel Haisenleder of University of Virginia Center for Research in Reproduction; J.C. Trussell of SUNY Upstate University Hospital; Stephen Krawetz of Wayne State University; Penny Kris-Etherton of Penn State College of Health and Human Development; David Sarwer of Temple University; Nanette Santoro of University of Colorado School of Medicine; Esther Eisenberg of Eunice Kennedy Shriver National Institute of Child Health and Human Development; and Hao Huang and Heping Zhang of Yale University also contributed to this research. Competing interests from authors can be viewed in the manuscript.
This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant numbers U10HD38992, U10HD077680, U10HD39005, U10HD077844, U10HD055925, U10HD27049, U54-HD29834 and R24-HD102061). This project was also supported by Penn State Clinical and Translational Science Institute and the Yale Center for Clinical Investigation through the National Center for Advancing Translational Sciences of the National Institutes of Health (grant numbers UL1 TR002014 and UL1 TR001863). Nutrisystem and Fitbit also provided discounts for study materials.
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Researchers use advanced technology and mice to study dopamine neuron structure, addiction and the brain’s ability to recover.
A late 1980s commercial meant to combat drug addiction used a pair of frying eggs as a metaphor for the effects of drugs on the human brain. While researchers have long understood that there is a connection between drug abuse and adverse changes in the brain, it is only now that they can study, in fine detail, alterations that actually occur.
Using state-of-the-art technology, researchers from the University of Chicago and the U.S. Department of Energy’s (DOE) Argonne National Laboratory detailed, for the first time, specific changes that occur in the brains of mice exposed to cocaine.
The research provides new insights into the function of key dopamine neuron structures, which are Involved in multiple functions, from voluntary movement to behavior. The results turned the page on older questions regarding how dopamine is transmitted, while opening a new chapter on others. Through continued work, the researchers hope to understand how certain types of addictions work and, perhaps, develop targeted treatments.
In a recent paper published in the journal eLife, the researchers describe how they are building on the burgeoning field of connectomics, the development of highly detailed and accurate 3D maps of every neuron in the brain and their connections.
For their part, the team set out to more clearly identify the process by which dopamine is transmitted across neurons, as they don’t make conventional physical connections, where signals are transferred across synapses.

Gymnasts Becky and Ellie Downie have joined a campaign calling for a new national database of defibrillators after their brother died from a cardiac arrest last year.Josh, aged 24, died suddenly while playing cricket from an undiagnosed heart condition.

Scientists have developed a new machine learning model for the discovery of genetic risk factors for diseases such as Motor Neurone Disease (MND).
Designed by researchers from the University of Sheffield and the Stanford University School of Medicine in the US, the machine learning tool, named RefMap, has already been utilised by the team to discover 690 risk genes for motor neurone disease, many of which are new discoveries.
One of the genes highlighted as a new MND gene, called KANK1, has been shown by the team to produce neurotoxicity in human neurons very similar to that observed in the brains of patients. Although at an early stage, this is potentially a new target for the design of new drugs.
Dr Johnathan Cooper-Knock, from the University of Sheffield’s Neuroscience Institute, said: “This new tool will help us to understand and profile the genetic basis of MND. Using this model we have already seen a dramatic increase in the number of risk genes for MND, from approximately 15 to 690.
“Each new risk gene discovered is a potential target for the development of new treatments for MND and could also pave the way for genetic testing for families to work out their risk of disease.”
The 690 new genes identified by RefMap lead to a five-fold increase in discovered heritability, a measure which describes how much of the disease is due to a variation in genetic factors.
“RefMap identifies risk genes by integrating genetic and epigenetic data. It is a generic tool and we are applying it to more diseases in the lab,” Sai Zhang, PhD, instructor of genetics at the Stanford University School of Medicine said.
Michael Snyder, PhD, professor and chair of the department of genetics at the Stanford School of Medicine and also the corresponding author of this work added: “By doing machine learning for genome analysis, we are discovering more hidden genes for human complex diseases such as MND, which will eventually power personalised treatment and intervention.”
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Expanding rapid testing stands out as an affordable way to help mitigate risks associated with COVID-19 and emerging variants. Infectious disease researchers at The University of Texas at Austin have developed a new model that tailors testing recommendations to new variants and likely immunity levels in a community, offering a new strategy as public health leaders seek a way out of a pandemic that has so far thwarted the best efforts to end its spread. It is the first study to identify optimal levels of testing in a partially immunized population.
Analysis from the UT Covid-19 Modeling Consortium, published in The Lancet Regional Health — Americas, describes cost-effective testing for people without symptoms and recommends isolation strategies to help policymakers safeguard against COVID-19 resurgences linked to new variants. A prior study from the team published in The Lancet Public Health provided optimal testing strategies for a fully unvaccinated population.
“As COVID-19 continues to evolve and cause waves of infections worldwide, rapid testing is an economic strategy for slowing spread and saving lives. Our study helps decision makers determine whether and how often to test,” said Lauren Ancel Meyers, director of the consortium and a professor of integrative biology and statistics and data sciences at UT Austin. “Frequent testing is recommended when the virus is spreading rapidly in a population with low levels of immunity.”
The consortium developed a multiscale model that uses how much the virus is circulating in a local population, how much of the population is immunized against COVID-19, and other factors to determine how often people without symptoms should be tested in order to help reduce the spread of the virus.
The study recommends a staged strategy that tracks the changing risks as new variants emerge and subside. If a rapidly spreading variant emerges in a partially immunized population, the researchers recommend testing everyone at least once per week combined with a 10-day isolation of people who test positive and their households. As the level of immunity increases in a population, testing can be rolled back to once per month and eventually suspended. For example, for a variant as infectious and immune-evasive as omicron, daily testing is advised until 70% of the population is immunized against the variant, followed by monthly testing until 80% are immunized.
The U.S. may face future waves of transmission caused by vaccine-evasive variants. The study suggests that proactive testing will remain a cost-effective strategy for reducing risks and avoiding burdensome restrictions as new threats arise. The recommended testing strategies balance the costs associated with administering tests and missing school or work during isolation with the benefits of preventing COVID-19 hospitalizations and deaths.
“As COVID-19 continues to evolve, so does our arsenal of effective countermeasures. Our research shows that mass use of rapid tests coupled with voluntary isolation and household quarantine can be both life saving and cost saving, if tailored to local risks,” Meyers said. “Now is the time to prepare for yet unknown COVID-19 variants and future pandemics. Proactive testing and isolation can be key to keeping schools and businesses open while preventing overwhelming surges in our hospitals.”
Co-corresponding authors are Zhanwei Du, previously of Meyer’s lab, Yan Bai of The University of Hong Kong and Lin Wang of the University of Cambridge. Other authors are Xutong Wang of The University of Texas at Austin; Abhishek Pandey, Meagan Fitzpatrick and Alison P. Galvani of Yale School of Public Health; Matteo Chinazzi, Ana Pastore y Piontti and Alessandro Vespignani of Northeastern University; Nathaniel Hupert of Weill Cornell Medicine and Cornell Institute for Disease and Disaster Preparedness; Michael Lachmann of Santa Fe Institute; and Benjamin J. Cowling of Hong Kong University. Meyers is the Cooley Centennial Professor of Integrative Biology and Statistics & Data Sciences at The University of Texas at Austin.
The research was supported by the National Institutes of Health, Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council and EPSRC Impact Acceleration Grant.
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