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Benefit of urban greenness on patients after an ischaemic stroke: mortality or recurrence? A registry-based cohort study
Background Whether living environment may influence outcome of stroke survivors remains to be elucidated. Aim This registry-based cohort study aimed to assess the relationship between urban greenness around the residence and one-year death or recurrence after a first-ever ischaemic stroke. Methods Patients with a first-ever ischaemic stroke who directly returned home were identified from the population-based registry of Dijon, France. For each patient, after geolocation of residential building, two greenness indices were calculated: the distance by road and pedestrian networks to the nearest public green space, and the area of green spaces within radii of 100 and 400 metres. Atmospheric NO 2 and PM 10 outdoor concentrations around the residence and deprivation index were assessed. Results During the 2005–2008 study period, 360 patients were identified and included (median age: 75 years-old (IQR: 63–83), 56% women). Fifteen died and 17 had recurrent stroke during the one year of follow-up. In adjusted models, the distance between public green spaces and patients’ residence was associated with stroke recurrence or death (HR = 1.26, 95% CI: 1.08–1.48, P < 0.01, for each 100 metre section of city network). In age-stratified analysis, this association remained significant only in patients aged 65–79 years (HR: 1.37, 95% CI: 1.10–1.71, P < 0.01). When considering separately stroke recurrence and death, this association remained significant for recurrence (HR = 1.30, 95% CI: 1.07–1.58, P < 0.01) but not for death (HR = 1.17, 95% CI: 0.89–1.52). Conclusion This study highlighted a beneficial influence of greenness on post-stroke recurrence in an urban area. These results indicate that urban planning policy could impact secondary prevention.
Correction to: Neurocognitive Dysfunction and Smaller Brain Volumes in Adolescents and Adults With a Fontan Circulation
Genetic diversity and core collection construction of glutinous rice landraces in 'He' cultivation zone of Guizhou using SSR sequencing
Kam Sweet Rice (KSR), a distinctive group of glutinous rice landraces, has evolved over millennia through agro-ecological adaptation by the Dong ethnic group in the 'He' cultivation zone of Southeast Guizhou, China. This study examined the genetic diversity of 388 glutinous rice landraces from the region, comprising 325 KSR and 63 non-KSR varieties, using Simple Sequence Repeat (SSR) sequencing. Results revealed that non-KSR germplasm exhibited significantly higher genetic diversity than KSR germplasm. Collectively, diversity patterns were strongly shaped by the numerical predominance of genetically similar KSR germplasms, resulting in an uneven distribution of genetic diversity between KSR and non-KSR groups. Five strategies were applied to construct and evaluate core collections (see Methods for full details). Among them, the simulated annealing algorithm (SA)-based Allelic Richness Maximization Strategy (SANA) (20% sampling intensity) demonstrated superior performance in preserving genetic diversity, except for the number of alleles ( Na) and observed heterozygosity (Ho) , where the Modified Heuristic Sampling (M-HS) strategy (13.66% sampling intensity) performed better at lower sampling intensities. By optimizing both approaches, a core collection of 65 germplasms was established, capturing 90.86% of alleles and retaining key genetic parameters. This core set effectively represents the genetic diversity of the entire collection, providing a strong foundation for future germplasm innovation and utilization.
Prospective Associations of Obesity and Obesity Severity With 9 Cardiovascular Outcomes: The Cross-Cohort Collaboration
BACKGROUND: Obesity is an established risk factor for cardiovascular disease (CVD); however, the overall and sex-specific relationships across the full spectrum of body mass index (BMI), particularly severe obesity defined as class 2 (BMI 35 to <40.0 kg/m 2 ) and class 3 (BMI ≥40 kg/m 2 ), and long-term CVD outcomes remain incompletely described. METHODS: We included 289 875 participants (mean age, 60.3 years; 79.2% women) from 21 cohorts of the Cross-Cohort Collaboration enrolled between 1948 and 2015 with harmonized BMI data, a BMI ≥18.5 kg/m 2 , and at least one of nine adjudicated outcomes: time to first fatal and non-fatal myocardial infarction (MI), fatal and nonfatal stroke, heart failure (HF), atrial fibrillation (AF), total coronary heart disease (CHD), total CVD, CHD mortality, CVD mortality, and all-cause mortality. Multivariable Cox proportional hazard models with restricted cubic splines were used to estimate the hazard ratio (HR) of BMI for each outcome, adjusting for demographic and clinical risk factors. RESULTS: Over 19.2 median years (25th, 11.6; 75th, 23.5 percentile) of follow-up, there were a total of 15 542 incident MI events, 15 467 incident stroke events, 14 172 incident HF events, 9066 diagnosed AF, and 113 918 total deaths, of which 14 647 were CHD-related and 28 879 were CVD-related. Overall, compared with individuals with normal weight, those with class 2 and class 3 obesity had the highest risk of HF (HR, 2.1, 95% CI, 2.0–2.3, and HR, 3.0, 95% CI, 2.7–3.2, respectively) and AF (HR, 1.8, 95% CI 1.7–2.0, and HR, 2.8, 95% CI, 2.5–3.1, respectively). Compared with men, women experienced higher relative increases in the risk associated with severe obesity for outcomes stroke, CVD, and all-cause mortality (P for interaction: <0.001, 0.003, and <0.001, respectively). Furthermore, in men, no increase in stroke risk was observed with higher BMI categories (P trend=0.49), whereas in women, the risk of stroke plateaued across obesity subclasses (compared with normal weight) yet remained significantly increased. CONCLUSIONS: Higher BMI was robustly associated with increased risk for most outcomes, particularly HF and AF, even after adjusting for traditional risk factors. Observed sex differences in the relative hazard of higher BMI categories and stroke, total CVD, and all-cause mortality suggest potential differences in underlying biological mechanisms.
GRSF1 Protects Against Heart Failure by Maintaining BCAA Homeostasis
BACKGROUND: Imbalances in cardiac branched-chain amino acid (BCAA) metabolism and mitochondrial homeostasis are implicated in the onset and development of heart failure. However, the mechanisms triggering the downregulation of cardiac BCAA metabolism in heart failure remain unclear. Here, we identify a novel role of the RNA-binding protein GRSF1 (guanine-rich RNA sequence binding factor 1) in post-transcriptionally regulating cell-intrinsic BCAA metabolic pathways, ultimately contributing to the pathogenesis of heart failure. METHODS: We examined GRSF1 expression in the heart tissues of patients with dilated cardiomyopathy and generated mice with cardiomyocyte-specific deletion or overexpression of GRSF1 in vivo to investigate its role in heart failure. The effect of GRSF1 on BCAA homeostasis was assessed through untargeted and targeted metabolomics and mitochondrial function analysis. To elucidate the mechanisms underlying GRSF1-mediated metabolic regulation, we employed mice with cardiomyocyte-specific deletion of BCKDHB (branched-chain keto acid dehydrogenase E1 subunit β) and mice with cardiomyocyte-specific expression of GRSF1 lacking a quasi-RNA recognition motif. RESULTS: GRSF1 expression was significantly decreased in the hearts of patients with heart failure and failing murine hearts. Cardiomyocyte-specific GRSF1 deletion resulted in cardiac dysfunction, spontaneous progression to dilated cardiomyopathy, and heart failure, accompanied by increased cardiac hypertrophy and fibrosis. Conversely, GRSF1 overexpression attenuated cardiac remodeling and heart failure induced by transverse aortic constriction. Mechanistically, GRSF1 maintained BCAA homeostasis and mitochondrial function by directly interacting with the G-tracts in the coding region of BCKDHB mRNA through a quasi-RNA recognition motif to promote the stability of BCKDHB mRNA at the post-transcriptional level, thereby increasing its protein expression. Functional recovery mediated by GRSF1 overexpression in cardiomyocytes was partially blocked upon cardiac-specific deletion of BCKDHB. CONCLUSIONS: Our study identified GRSF1 as a cell-intrinsic metabolic checkpoint that maintains cardiac BCAA homeostasis by regulating BCKDHB mRNA turnover. Targeting GRSF1 may offer therapeutic benefits for heart failure and other cardiometabolic diseases requiring BCAA manipulation.
Daily briefing: A daily multivitamin slows the signs of biological ageing
Detectable global temperature responses to wildfires and volcanic eruptions
Large volcanic eruptions and intense wildfires perturb Earth’s atmospheric temperature. Understanding the climate response to such natural forcings is essential for obtaining reliable estimates of its response to anthropogenic greenhouse gas emissions. While the climate impacts of volcanic sulfate aerosols are well documented, other natural forcings—including wildfire smoke reaching the stratosphere and water vapor injections from a submarine eruption—pose new challenges for detecting and attributing their atmospheric temperature impacts. Here, we demonstrate robust detection of statistically significant temperature anomalies in the troposphere and stratosphere using multidecadal satellite observations and internal variability estimates from a climate model ensemble and from observations. We analyze three landmark events: the 1991 Pinatubo eruption, the 2019-2020 Australian wildfires, and the 2022 Hunga Tonga eruption. Each leaves a fingerprint with distinct altitudinal, geographical, and temporal structure. The global-mean stratospheric signal from Australian wildfires is detectable even in time averages extending beyond 10 mo, despite injecting only ~5% of Pinatubo’s aerosol mass. For Hunga Tonga, we detect significant and prolonged stratospheric cooling, but no robust tropospheric signal in the first 2 y. These findings show that both sulfate and nonsulfate stratospheric perturbations produce distinct, statistically identifiable global temperature signals. Accounting for such forcings in climate model simulations is therefore essential for improving comparisons of simulated and observed variability.
Keep calm and be transparent: advice from scientists who retracted their papers
Polyphenol mediated zinc-oxygen synergistic hydrogel remodels senescent microenvironment for periodontal tissue regeneration
High-speed Si-Ge avalanche photodiode with a gain-bandwidth product of 7564 GHz
Fermi-liquid transport beyond the upper critical field in superconducting La2PrNi2O7 thin films
Reversible H2 storage at moderate temperature by a trilayered lithium borohydride nanocomposite
Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila
Abstract Parkinson’s disease (PD) and related familial Parkinsonism are defined by motor dysfunction, but the specific upstream molecular causes of these clinical symptoms can vary widely. We hypothesize that these causes converge onto a limited number of core cellular pathways. To investigate this, we created a collection of 24 genetically well-controlled Drosophila models of familial forms of PD and related mono-genic forms of Parkinsonism. Using unbiased behavioral screening and machine learning we identify clusters of mutants that converge on (1) mitochondrial function; (2) retromer/vesicle trafficking and proteostasis/autophagy. Genes within each cluster have a similar genetic interaction profile and compounds that target specific molecular pathways ameliorate dopaminergic neuron dysfunction in a cluster-specific manner. Together, our data indicate that familial PD and related forms of Parkinsonism may fall into two broad functional groups, and may inform further work toward targeted biomarker discovery and therapeutic development.
Transcranial focused ultrasound induces source localizable cortical activation in resting state humans when applied concurrently with transcranial electric stimulation
Magnetic-driven multifunctional optoelectronic catheter for in vivo chemical mapping and precisely guided-tumor therapy
CF2H: a cell-free two-hybrid platform for rapid protein binder screening
Combined physical and pharmacological anabolic osteoporosis therapies increase bone response and mechanoregulation in female mice
Abstract Bone’s ability to adapt to mechanical demands is governed by mechanoregulation, the process by which cells sense and respond to mechanical stimuli to maintain skeletal integrity. In osteoporosis, increased bone resorption activity leads to structural deterioration and elevated fracture risk. While existing pharmacological therapies aim to restore bone mass to reduce fracture risk, it is unclear how they modulate mechanoregulation, especially when combined with physical interventions. Here, we investigate the joint effects of load-bearing physical and pharmacological treatment in a female mouse model of osteoporosis using longitudinal in vivo micro-computed tomography and computational mechanics. We demonstrate that mechanical loading additively and synergistically enhanced predicted strength, bone volume, and mechanoregulation parameters when combined with anabolic therapies (parathyroid hormone and sclerostin antibody) but not with anti-catabolic treatments (bisphosphonates). Increases in predicted strength are associated with reductions in bone resorption rates, shifts in the (re)modeling thresholds as anticipated by Frost in the mechanostat theory, and the modeling capacity of anabolic pharmacological treatments. These findings underscore the therapeutic potential of combining anabolic pharmacological therapies with load-bearing physical activity, particularly in early treatment phases, to optimize bone adaptation and fracture prevention in osteoporosis management.
A meta learning and task adaptive approach for drug target affinity prediction
Abstract Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose AdaMBind, a novel DTA prediction model based on meta-learning framework with an adaptive task module designed for low-data scenarios. It employs a dynamic “easy-to-hard” task scheduling mechanism to enhance training efficiency and robustness. Experimental results on three benchmark datasets demonstrate that AdaMBind outperforms 8 baseline models in predicting affinity for unseen targets, particularly under few-shot conditions. Under stringent data constraints, the model successfully identifies high-affinity compounds for ESR and TP53, achieving outstanding virtual screening performance. Furthermore, when applied to inhibitor discovery against FLT3 for acute myeloid leukemia, AdaMBind successfully identified candidate compounds with potent inhibitory activity, as verified by preliminary experimental assays. In summary, AdaMBind provides a robust framework for few-shot DTA prediction.