Site-Specific DC Surface Signatures Influence CD4⁺ T Cell Co-stimulation and Lung-Homing

Dendritic cells (DCs) that drain the gut and skin are known to favor the establishment of T cell populations that home to the original site of DC-antigen (Ag) encounter by providing soluble "imprinting" signals to T cells in the lymph node (LN). To study the induction of lung T cell-trafficking, we used a protein-adjuvant murine intranasal and intramuscular immunization model to compare in vivo-activated Ag+ DCs in the lung and muscle-draining LNs. Higher frequencies of Ag+ CD11b+ DCs were observed in lung-draining mediastinal LNs (MedLN) compared to muscle-draining inguinal LNs (ILN). Ag+ CD11b+ MedLN DCs were qualitatively superior at priming CD4+ T cells, which then expressed CD49a and CXCR3, and preferentially trafficked into the lung parenchyma. CD11b+ DCs from the MedLN expressed higher levels of surface podoplanin, Trem4, GL7, and the known co-stimulatory molecules CD80, CD86, and CD24. Blockade of specific MedLN DC molecules or the use of sorted DC and T cell co-cultures demonstrated that DC surface phenotype influences the ability to prime T cells that then home to the lung. Thus, the density of dLN Ag+ DCs, and DC surface molecule signatures are factors that can influence the output and differentiation of lung-homing CD4+ T cells

Different Wines from Different Yeasts? 'Saccharomyces cerevisiae Intraspecies Differentiation by Metabolomic Signature and Sensory Patterns in Wine'

Alcoholic fermentation is known to be a key stage in the winemaking process that directly impacts the composition and quality of the final product. Twelve wines were obtained from fermentations of Chardonnay must made with twelve different commercial wine yeast strains of Saccharomyces cerevisiae. In our study, FT-ICR-MS, GC-MS, and sensory analysis were combined with multivariate analysis. Ultra-high-resolution mass spectrometry (uHRMS) was able to highlight hundreds of metabolites specific to each strain from the same species, although they are characterized by the same technological performances. Furthermore, the significant involvement of nitrogen metabolism in this differentiation was considered. The modulation of primary metabolism was also noted at the volatilome and sensory levels. Sensory analysis allowed us to classify wines into three groups based on descriptors associated with white wine. Thirty-five of the volatile compounds analyzed, including esters, medium-chain fatty acids, superior alcohols, and terpenes discriminate and give details about differences between wines. Therefore, phenotypic differences within the same species revealed metabolic differences that resulted in the diversity of the volatile fraction that participates in the palette of the sensory pattern. This original combination of metabolomics with the volatilome and sensory approaches provides an integrative vision of the characteristics of a given strain. Metabolomics shine the new light on intraspecific discrimination in the Saccharomyces cerevisiae species Keywords: yeast; Saccharomyces cerevisiae; Chardonnay wine; metabolomic; volatile compounds; sensory analysi

Risk for opioid misuse in chronic pain patients is associated with endogenous opioid system dysregulation

µ-Opioid receptors (MOR) are a major target of endogenous and exogenous opioids, including opioid pain medications. The µ-opioid neurotransmitter system is heavily implicated in the pathophysiology of chronic pain and opioid use disorder and, as such, central measures of µ-opioid system functioning are increasingly being considered as putative biomarkers for risk to misuse opioids. To explore the relationship between MOR system function and risk for opioid misuse, 28 subjects with chronic nonspecific back pain completed a clinically validated measure of opioid misuse risk, the Pain Medication Questionnaire (PMQ), and were subsequently separated into high (PMQ > 21) and low (PMQ ≤ 21) opioid misuse risk groups. Chronic pain patients along with 15 control participants underwent two separate [11C]-carfentanil positron emission tomography scans to explore MOR functional measures: one at baseline and one during a sustained pain-stress challenge, with the difference between the two providing an indirect measure of stress-induced endogenous opioid release. We found that chronic pain participants at high risk for opioid misuse displayed higher baseline MOR availability within the right amygdala relative to those at low risk. By contrast, patients at low risk for opioid misuse showed less pain-induced activation of MOR-mediated, endogenous opioid neurotransmission in the nucleus accumbens. This study links human in vivo MOR system functional measures to the development of addictive disorders and provides novel evidence that MORs and µ-opioid system responsivity may underlie risk to misuse opioids among chronic pain patients.publishedVersionPeer reviewe

Developing the University of Tartu in Estonia into a wellnetworked Patient Safety Research Centre (PATSAFE): A study protocol

Background: Patient safety (PS) is a serious global public health problem affecting all countries. Estimates show that around 10 percent of the patients are harmed during hospital care, resulting in 23 million disability-adjusted life years lost per year. Experts emphasize research advancements as a key precondition for safer care. Aim: The Patient Safety Research Centre (PATSAFE) project enhances the Institute of Clinical Medicine of the University of Tartu’s (ICM-UT) research potential and capacities in PS in order to improve and strengthen knowledge and skills in methods, techniques and experience for PS research. Methods: A strategic partnership with Avedis Donabedian Research Institute in Spain, and IQ Healthcare in the Netherlands, both international leaders in PS research, enables the development of a long-lasting knowledge exchange, allowing the ICM-UT to capitalise on its current achievements and to overcome gaps in scientific excellence in the field of PS research. These twining activities will strengthen and raise the research profile of the ICM-UT academic staff and early-stage researchers (ESRs), by implementing the hands-on training on methods, techniques, and experience in PS research. The project also encourages the active participation of early stage researchers in PS research by increasing their soft skills, to ensure the continuity and sustainability of PS research in ICM-UT. Finally, development of the research strategy on PS contributes to the long-term sustainability of PS research in Estonia. To implement these activities, PATSAFE foresees a comprehensive strategy consisting of knowledge exchange, soft research skills capacity building, strategic planning, and strong dissemination and exploitation efforts. Expected results: As a result of the project, ICM-UT will have the capacity to carry out PS research using the appropriate methodology and the competences to apply state-of-the-art evidence-based strategies for PS research

Physical activity and checkpoint inhibition: association with toxicity and survival

BACKGROUND: Although animal experiments suggest beneficial effects of physical activity (PA) on antitumor immunity, little is known about the effects of PA on immune checkpoint inhibitor (ICI) toxicity and effectiveness in humans. We assessed the association of PA with immune-related adverse events (irAE) and survival in patients undergoing ICI. METHODS: Patients receiving ICI who completed the Dutch short questionnaire to assess health enhancing physical activity (SQUASH) questionnaire at the start of treatment as part of the prospective UNICIT study in an academic hospital were included. PA was quantified by calculating total metabolic equivalent task hours per week (total PA) and hours per week of moderate- to vigorous-intensity PA during sport and leisure time (MVPA-SL). Associations of PA with severe irAE occurrence within 1 year and overall survival (OS) were evaluated using logistic regression and Cox proportional hazard regression, respectively, with adjustment for probable confounders. RESULTS: In total, 251 patients were included, with a median follow-up of 20 months. Moderate and high levels of total PA were associated with lower odds of severe irAE occurrence compared to low levels of total PA (adjusted OR: 0.34 [95% CI = 0.12 to 0.90] and 0.19 [95% CI = 0.05 to 0.55], respectively). Moderate and high levels of total PA were also associated with prolonged survival (adjusted HR: 0.58 [95% CI = 0.32 to 1.04] and 0.48 [95% CI = 0.27 to 0.89], respectively). Similar associations were observed in patients who performed more MVPA-SL. CONCLUSIONS: Higher physical activity levels at the start of ICI treatment are associated with lower risk of severe irAEs and probably prolonged survival. Randomized controlled trials are needed to investigate whether patients indeed benefit from increasing PA levels after diagnosis

Machine learning and big data analytics in bipolar disorder:A position paper from the International Society for Bipolar Disorders Big Data Task Force

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.Peer reviewe

Smartphone-based interventions in bipolar disorder : Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

Background: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. Objectives: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. Methods: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). Results: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. Conclusion: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.Peer reviewe

A predictive model relating daily fluctuations in summer temperatures and mortality rates

<p>Abstract</p> <p>Background</p> <p>In the context of climate change, an efficient alert system to prevent the risk associated with summer heat is necessary. The authors' objective was to describe the temperature-mortality relationship in France over a 29-year period and to define and validate a combination of temperature factors enabling optimum prediction of the daily fluctuations in summer mortality.</p> <p>Methods</p> <p>The study addressed the daily mortality rates of subjects aged over 55 years, in France as a whole, from 1975 to 2003. The daily minimum and maximum temperatures consisted in the average values recorded by 97 meteorological stations. For each day, a cumulative variable for the maximum temperature over the preceding 10 days was defined.</p> <p>The mortality rate was modelled using a Poisson regression with over-dispersion and a first-order autoregressive structure and with control for long-term and within-summer seasonal trends. The lag effects of temperature were accounted for by including the preceding 5 days. A "backward" method was used to select the most significant climatic variables. The predictive performance of the model was assessed by comparing the observed and predicted daily mortality rates on a validation period (summer 2003), which was distinct from the calibration period (1975–2002) used to estimate the model.</p> <p>Results</p> <p>The temperature indicators explained 76% of the total over-dispersion. The greater part of the daily fluctuations in mortality was explained by the interaction between minimum and maximum temperatures, for a day <it>t </it>and the day preceding it. The prediction of mortality during extreme events was greatly improved by including the cumulative variables for maximum temperature, in interaction with the maximum temperatures. The correlation between the observed and estimated mortality ratios was 0.88 in the final model.</p> <p>Conclusion</p> <p>Although France is a large country with geographic heterogeneity in both mortality and temperatures, a strong correlation between the daily fluctuations in mortality and the temperatures in summer on a national scale was observed. The model provided a satisfactory quantitative prediction of the daily mortality both for the days with usual temperatures and for the days during intense heat episodes. The results may contribute to enhancing the alert system for intense heat waves.</p

Green spaces and respiratory, cardiometabolic, and neurodevelopmental outcomes:An individual-participant data meta-analysis of &gt;35.000 European children

Studies evaluating the benefits and risks of green spaces on children's health are scarce. The present study aimed to examine the associations between exposure to green spaces during pregnancy and early childhood with respiratory, cardiometabolic, and neurodevelopmental outcomes in school-age children. We performed an Individual-Participant Data (IPD) meta-analysis involving 35,000 children from ten European birth cohorts across eight countries. For each participant, we calculated residential Normalized Difference Vegetation Index (NDVI) within a 300 m buffer and the linear distance to green spaces (meters) during prenatal life and childhood. Multiple harmonized health outcomes were selected: asthma and wheezing, lung function, body mass index, diastolic and systolic blood pressure, non-verbal intelligence, internalizing and externalizing problems, and ADHD symptoms. We conducted a two-stage IPD meta-analysis and evaluated effect modification by socioeconomic status (SES) and sex. Between-study heterogeneity was assessed via random-effects meta-regression. Residential surrounding green spaces in childhood, not pregnancy, was associated with improved lung function, particularly higher FEV1 (β = 0.06; 95 %CI: 0.03, 0.09 I2 = 4.03 %, p &lt; 0.001) and FVC (β = 0.07; 95 %CI: 0.04, 0.09 I2 = 0 %, p &lt; 0.001) with a stronger association observed in females (p &lt; 0.001). This association remained robust after multiple testing correction and did not change notably after adjusting for ambient air pollution. Increased distance to green spaces showed an association with lower FVC (β = −0.04; 95 %CI: −0.07, −0.02, I2 = 4.8, p = 0.001), with a stronger effect in children from higher SES backgrounds (p &lt; 0.001). No consistent associations were found between green spaces and asthma, wheezing, cardiometabolic, or neurodevelopmental outcomes, with direction of effect varying across cohorts. Wheezing and neurodevelopmental outcomes showed high between-study heterogeneity, and the age at outcome assessment was only associated with heterogeneity in internalizing problems. This large European meta-analysis suggests that childhood exposure to green spaces may lead to better lung function. Associations with other respiratory outcomes and selected cardiometabolic and neurodevelopmental outcomes remain inconclusive.</p

Ensembl’s 10th year

Ensembl (http://www.ensembl.org) integrates genomic information for a comprehensive set of chordate genomes with a particular focus on resources for human, mouse, rat, zebrafish and other high-value sequenced genomes. We provide complete gene annotations for all supported species in addition to specific resources that target genome variation, function and evolution. Ensembl data is accessible in a variety of formats including via our genome browser, API and BioMart. This year marks the tenth anniversary of Ensembl and in that time the project has grown with advances in genome technology. As of release 56 (September 2009), Ensembl supports 51 species including marmoset, pig, zebra finch, lizard, gorilla and wallaby, which were added in the past year. Major additions and improvements to Ensembl since our previous report include the incorporation of the human GRCh37 assembly, enhanced visualisation and data-mining options for the Ensembl regulatory features and continued development of our software infrastructure