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CXCL12-CXCR4 signaling controls multiple physiological processes and its dysregulation is associated with cancers and inflammatory diseases. To discover as-yet-unknown endogenous ligands of CXCR4, we screened a blood-derived peptide library for inhibitors of CXCR4-tropic HIV-1 strains. This approach identified a 16 amino acid fragment of serum albumin as an effective and highly specific CXCR4 antagonist. The endogenous peptide, termed EPI-X4, is evolutionarily conserved and generated from the highly abundant albumin precursor by pH-regulated proteases. EPI-X4 forms an unusual lasso-like structure and antagonizes CXCL12-induced tumor cell migration, mobilizes stem cells, and suppresses inflammatory responses in mice. Furthermore, the peptide is abundant in the urine of patients with inflammatory kidney diseases and may serve as a biomarker. Our results identify EPI-X4 as a key regulator of CXCR4 signaling and introduce proteolysis of an abundant precursor protein as an alternative concept for chemokine receptor regulation.
Low-to-moderate quality meta-analytic evidence shows that motor control stabilisation exercise (MCE) is an effective treatment of non-specific low back pain. A possible approach to overcome the weaknesses of traditional meta-analyses would be that of a prospective meta-analyses. The aim of the present analysis was to generate high-quality evidence to support the view that motor control stabilisation exercises (MCE) lead to a reduction in pain intensity and disability in non-specific low back pain patients when compared to a control group. In this prospective meta-analysis and sensitivity multilevel meta-regression within the MiSpEx-Network, 18 randomized controlled study arms were included. Participants with non-specific low back pain were allocated to an intervention (individualized MCE, 12 weeks) or a control group (no additive exercise intervention). From each study site/arm, outcomes at baseline, 3 weeks, 12 weeks, and 6 months were pooled. The outcomes were current pain (NRS or VAS, 11 points scale), characteristic pain intensity, and subjective disability. A random effects meta-analysis model for continuous outcomes to display standardized mean differences between intervention and control was performed, followed by sensitivity multilevel meta-regressions. Overall, 2391 patients were randomized; 1976 (3 weeks, short-term), 1740 (12 weeks, intermediate), and 1560 (6 months, sustainability) participants were included in the meta-analyses. In the short-term, intermediate and sustainability, moderate-to-high quality evidence indicated that MCE has a larger effect on current pain (SMD = −0.15, −0.15, −0.19), pain intensity (SMD = −0.19, −0.26, −0.26) and disability (SMD = −0.15, −0.27, −0.25) compared with no exercise intervention. Low-quality evidence suggested that those patients with comparably intermediate current pain and older patients may profit the most from MCE. Motor control stabilisation exercise is an effective treatment for non-specific low back pain. Sub-clinical intermediate pain and middle-aged patients may profit the most from this intervention.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Introduction: Bipolar disorder (BD) is characterized by recurrent episodes of depression and mania and affects up to 2% of the population worldwide. Patients suffering from bipolar disorder have a reduced life expectancy of up to 10 years. The increased mortality might be due to a higher rate of somatic diseases, especially cardiovascular diseases. There is however also evidence for an increased rate of diabetes mellitus in BD, but the reported prevalence rates vary by large.
Material and Methods: 85 bipolar disorder patients were recruited in the framework of the BiDi study (Prevalence and clinical features of patients with Bipolar Disorder at High Risk for Type 2 Diabetes (T2D), at prediabetic state and with manifest T2D) in Dresden and Würzburg. T2D and prediabetes were diagnosed measuring HBA1c and an oral glucose tolerance test (oGTT), which at present is the gold standard in diagnosing T2D. The BD sample was compared to an age-, sex- and BMI-matched control population (n = 850) from the Study of Health in Pomerania cohort (SHIP Trend Cohort).
Results: Patients suffering from BD had a T2D prevalence of 7%, which was not significantly different from the control group (6%). Fasting glucose and impaired glucose tolerance were, contrary to our hypothesis, more often pathological in controls than in BD patients. Nondiabetic and diabetic bipolar patients significantly differed in age, BMI, number of depressive episodes, and disease duration.
Discussion: When controlled for BMI, in our study there was no significantly increased rate of T2D in BD. We thus suggest that overweight and obesity might be mediating the association between BD and diabetes. Underlying causes could be shared risk genes, medication effects, and lifestyle factors associated with depressive episodes. As the latter two can be modified, attention should be paid to weight changes in BD by monitoring and taking adequate measures to prevent the alarming loss of life years in BD patients.
Background: Predicted increases in suicide were not generally observed in the early months of the COVID-19 pandemic. However, the picture may be changing and patterns might vary across demographic groups. We aimed to provide a timely, granular picture of the pandemic's impact on suicides globally.
Methods: We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age- and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation.
Findings: We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries’ COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries’ income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well.
Interpretation: Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Background: Glioblastoma (GBM) patients are at particularly high risk for thrombotic complications. In the event of a postoperative pulmonary embolism, therapeutic anticoagulation (tAC) is indispensable. The impact of therapeutic anticoagulation on recurrence pattern in GBM is currently unknown. Methods: We conducted a matched-pair cohort analysis of 57 GBM patients with or without tAC that were matched for age, sex, gross total resection and MGMT methylation status in a ratio of 1:2. Patients’ characteristics and clinical course were evaluated using medical charts. MRI characteristics were evaluated by two independent authors blinded to the AC status. Results: The morphologic MRI appearance in first GBM recurrence showed a significantly higher presence of multifocal, midline crossing and sharp demarcated GBM recurrence patterns in patients with therapeutic tAC compared to the matched control group. Although statistically non-significant, the therapeutic tAC cohort showed increased survival. Conclusion: Therapeutic anticoagulation induced significant morphologic changes in GBM recurrences. The underlying pathophysiology is discussed in this article but remains to be further elucidated.
Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.