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  • This AI-powered lab runs itself—and discovers new materials 10x faster

    This AI-powered lab runs itself—and discovers new materials 10x faster

    Researchers have demonstrated a new technique that allows “self-driving laboratories” to collect at least 10 times more data than previous techniques at record speed. The advance – which is published in Nature Chemical Engineering – dramatically expedites materials discovery research, while slashing costs and environmental impact.

    Self-driving laboratories are robotic platforms that combine machine learning and automation with chemical and materials sciences to discover materials more quickly. The automated process allows machine-learning algorithms to make use of data from each experiment when predicting which experiment to conduct next to achieve whatever goal was programmed into the system.

    “Imagine if scientists could discover breakthrough materials for clean energy, new electronics, or sustainable chemicals in days instead of years, using just a fraction of the materials and generating far less waste than the status quo,” says Milad Abolhasani, corresponding author of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at North Carolina State University. “This work brings that future one step closer.”

    Until now, self-driving labs utilizing continuous flow reactors have relied on steady-state flow experiments. In these experiments, different precursors are mixed together and chemical reactions take place, while continuously flowing in a microchannel. The resulting product is then characterized by a suite of sensors once the reaction is complete.

    “This established approach to self-driving labs has had a dramatic impact on materials discovery,” Abolhasani says. “It allows us to identify promising material candidates for specific applications in a few months or weeks, rather than years, while reducing both costs and the environmental impact of the work. However, there was still room for improvement.”

    Steady-state flow experiments require the self-driving lab to wait for the chemical reaction to take place before characterizing the resulting material. That means the system sits idle while the reactions take place, which can take up to an hour per experiment.

    “We’ve now created a self-driving lab that makes use of dynamic flow experiments, where chemical mixtures are continuously varied through the system and are monitored in real time,” Abolhasani says. “In other words, rather than running separate samples through the system and testing them one at a time after reaching steady-state, we’ve created a system that essentially never stops running. The sample is moving continuously through the system and, because the system never stops characterizing the sample, we can capture data on what is taking place in the sample every half second.

    “For example, instead of having one data point about what the experiment produces after 10 seconds of reaction time, we have 20 data points – one after 0.5 seconds of reaction time, one after 1 second of reaction time, and so on. It’s like switching from a single snapshot to a full movie of the reaction as it happens. Instead of waiting around for each experiment to finish, our system is always running, always learning.”

    Collecting this much additional data has a big impact on the performance of the self-driving lab.

    “The most important part of any self-driving lab is the machine-learning algorithm the system uses to predict which experiment it should conduct next,” Abolhasani says. “This streaming-data approach allows the self-driving lab’s machine-learning brain to make smarter, faster decisions, honing in on optimal materials and processes in a fraction of the time. That’s because the more high-quality experimental data the algorithm receives, the more accurate its predictions become, and the faster it can solve a problem. This has the added benefit of reducing the amount of chemicals needed to arrive at a solution.”

    In this work, the researchers found the self-driving lab that incorporated a dynamic flow system generated at least 10 times more data than self-driving labs that used steady-state flow experiments over the same period of time, and was able to identify the best material candidates on the very first try after training.

    “This breakthrough isn’t just about speed,” Abolhasani says. “By reducing the number of experiments needed, the system dramatically cuts down on chemical use and waste, advancing more sustainable research practices.

    “The future of materials discovery is not just about how fast we can go, it’s also about how responsibly we get there,” Abolhasani says. “Our approach means fewer chemicals, less waste, and faster solutions for society’s toughest challenges.”

    The paper, “Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery,” will be published July 14 in the journal Nature Chemical Engineering. Co-lead authors of the paper are Fernando Delgado-Licona, a Ph.D. student at NC State; Abdulrahman Alsaiari, a master’s student at NC State; and Hannah Dickerson, a former undergraduate at NC State. The paper was co-authored by Philip Klem, an undergraduate at NC State; Arup Ghorai, a former postdoctoral researcher at NC State; Richard Canty and Jeffrey Bennett, current postdoctoral researchers at NC State; Pragyan Jha, Nikolai Mukhin, Junbin Li and Sina Sadeghi, Ph.D. students at NC State; Fazel Bateni, a former Ph.D. student at NC State; and Enrique A. López-Guajardo of Tecnologico de Monterrey.

    This work was done with support from the National Science Foundation under grants 1940959, 2315996 and 2420490; and from the University of North Carolina Research Opportunities Initiative program.

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  • New Nanotech Boosts Solar Cell Efficiency Over 10%

    New Nanotech Boosts Solar Cell Efficiency Over 10%

    A research team led by Prof. Mingtai Wang at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a finely tuned method for growing titanium dioxide nanorod arrays (TiO2-NA) with controllable spacing without changing individual rod size and demonstrated its application in high-performance solar cells.

    Their findings, published in Small Methods, offer a new toolkit for crafting nanostructures across clean energy and optoelectronics.

    Single-crystalline TiO2 nanorods excel at harvesting light and conducting charge, making them ideal for solar cells, photocatalysts, and sensors. However, traditional fabrication methods link rod density, diameter, and length — if one parameter is adjusted, the others shift accordingly, often affecting device efficiency.

    In this study, by carefully extending the hydrolysis stage of a precursor film, the team showed that longer “gel chains” assemble into smaller anatase nanoparticles. When the anatase film is subjected to hydrothermal treatment, those anatase nanoparticles convert in situ into rutile ones, serving as seeds for nanorod growth. The hydrolysis stage provides an effective way to control the rod density without altering the nanorod dimensions.

    Using this strategy, they produced TiO2-NA films with constant rod diameter and height, even as the number of rods per area varied. When incorporated into low-temperature-processed CuInS2 solar cells, these films achieved power conversion efficiencies above ten percent, peaking at 10.44 percent. To explain why spacing matters so profoundly, the team introduced a Volume-Surface-Density model, clarifying how rod density influences light trapping, charge separation, and carrier collection.

    This research overcomes the limitations of traditional methods for regulating nanostructures by establishing a complete system linking “macro-process regulation-microstructure evolution-device performance optimization.”

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  • Woad to the LPGA: Take a Look at How Lottie Woad Earned Her 20 LEAP Points – LPGA

    Woad to the LPGA: Take a Look at How Lottie Woad Earned Her 20 LEAP Points – LPGA

    1. Woad to the LPGA: Take a Look at How Lottie Woad Earned Her 20 LEAP Points  LPGA
    2. In Good Graces: Australia’s Kim Becomes First-Time Major Winner at The Amundi Evian Championship  LPGA
    3. Amateur phenom earns LPGA card in thrilling, historic fashion  GOLF.com
    4. Lottie Woad to turn professional after missing out on £500,000 in a fortnight  The Telegraph
    5. Lottie Woad Wraps Up LPGA Tour Card After Strong Amundi Evian Championship Final Round Showing  Golf Monthly

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  • Pakistani pilgrims will travel to Iraq under registered Zaireen Group Organizers: Naqvi – RADIO PAKISTAN

    1. Pakistani pilgrims will travel to Iraq under registered Zaireen Group Organizers: Naqvi  RADIO PAKISTAN
    2. Trilateral working group in the offing between Pakistan, Iran and Iraq to prevent ‘illegal migration’  Dawn
    3. Pakistan minister to attend tomorrow tri-nation conference in Tehran on pilgrim, border issues  Arab News
    4. Interior Minister Mohsin Naqvi arrives in Iran on official visit to strengthen bilateral and regional ties  Ptv.com.pk
    5. Preparations complete at Iran-Pakistan border to welcome Arbaeen pilgrims  ABNA English

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  • EU to Ease Clozapine Monitoring Frequency After First Year

    EU to Ease Clozapine Monitoring Frequency After First Year

    The European Medicines Agency’s (EMA) Pharmacovigilance Risk Assessment Committee has recommended easing routine blood count monitoring for patients on clozapine, citing new data that show that the risk for severe neutropenia and agranulocytosis declines significantly after the first year of treatment. 

    Under the updated guidelines, monitoring for patients without a history of neutropenia can be reduced to once every 12 weeks after the first year, and to once annually after 2 years. In addition, absolute neutrophil count (ANC) will now be the sole parameter used for hematologic monitoring, replacing the previous requirement to also measure white blood cell count.

    The revised recommendations are supported by a joint expert statement from the European Clozapine Task Force, published this year, which called for changes to the monitoring protocol due to the very low incidence of late-onset agranulocytosis. 

    Additional evidence comes from a large-scale study involving more than 26,000 patients, which found that clozapine-induced severe neutropenia peaked around week 9 (0.128% weekly incidence) and declined sharply after week 18, with rates becoming negligible — 0.001% per week — after 2 years of continuous use.

    Mechanism and Risk Profile

    Clozapine is an atypical antipsychotic indicated for treatment-resistant schizophrenia and for patients who cannot tolerate other antipsychotics due to neurologic side effects. It is also used to manage psychosis associated with Parkinson’s disease when standard treatments fail.

    Clozapine works by antagonizing dopamine D2 and serotonin 5-HT2A receptors, contributing to its unique efficacy in refractory schizophrenia. However, it carries a known risk for drug-induced neutropenia and its most severe form, agranulocytosis. 

    Research suggests that a reactive metabolite of clozapine, the nitrenium ion, may bind to neutrophil proteins. This complex is then thought to act as a hapten, triggering an immune response that leads to the destruction of neutrophils, a process for which certain individuals may have a genetic predisposition.

    All clozapine-containing products in the European Union will be updated to reflect the new ANC-based monitoring schedule and thresholds for treatment initiation and continuation. The direct healthcare professional communication will be distributed by the marketing authorization holders in coordination with national authorities, and published on EMA and national regulatory websites.

    Clinicians are encouraged to review and update monitoring protocols accordingly and continue reporting suspected adverse events through established pharmacovigilance channels.

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  • The Effect of Comorbidities on Asthma Related outcomes over a two-year

    The Effect of Comorbidities on Asthma Related outcomes over a two-year

    Introduction

    Asthma is a chronic inflammatory condition of the airways, marked by increased sensitivity and airway obstruction. The severity and occurrence of symptoms, as well as airflow restrictions, can fluctuate over time, influenced by various external factors, including physical activity, allergens, weather changes, and respiratory infections, or comorbid conditions.1

    Asthma affects over 300 million people globally, making it one of the most prevalent chronic non-communicable diseases (NCDs), driven by factors such as urbanization and lifestyle changes.2,3

    Severe asthma (SA) is a distinct, high-burden asthma phenotype that requires a high level of therapy and is characterized by persistent symptoms and frequent exacerbations despite. It is defined as asthma that requires treatment with high-dose inhaled corticosteroids (ICS) plus a second controller with or without oral corticosteroids (OCS) for 50% of the previous year to prevent the disease to become uncontrolled or the disease remains uncontrolled despite a high level of therapy.1,4 According to estimates 5% to 10% of asthma patients are affected by severe asthma.3 Despite affecting only a small proportion of asthma patients, severe asthma accounts for a disproportionate share of asthma-related healthcare utilization and costs.5 SA impacts not only daily life by its high disease burden, but often leads to long-term use of oral OCS and its associated side effects such as obesity, diabetes, osteoporosis, hypertension, adrenal suppression, or psychological issues like depression and anxiety.6

    Comorbidities play a significant role in both the clinical course but also the management of SA. Comorbid conditions, can both exacerbate respiratory symptoms and reduce the quality of life.7,8 Certain comorbidities are linked to more frequent or SA exacerbations and can affect the severity and course of the disease.7,9 These conditions, which are more common in patients with SA, include allergic rhinitis, chronic sinusitis, nasal polyps, sleep apnea, gastroesophageal reflux disease (GERD), diabetes, dyslipidemia, cardiovascular diseases (CVDs), osteoporosis and depression.9–11 SA may contribute to worsening of these conditions, through factors like corticosteroid use, reduced physical activity, or poor sleep.10,11

    Diseases of the upper airways, including allergic and nonallergic rhinitis or sinusitis are described to be moderately to strongly associated with asthma outcomes.9,12 Chronic Rhinosinusitis with or without nasal polyps is associated with type 2 inflammation pathways, which also plays an important role in severe asthma.6,13 GERD affects around 40 to up to 60% of individuals with SA, which may be facilitated by an abnormal respiratory physiology, and is described to be associated with worse symptom control and poorer quality of life.14,15 The common co-occurrence of depression, diabetes mellitus, obesity, and asthma is particularly notable in clinical practice.16 Obesity is commonly co-occurring with asthma and is associated with a higher use of asthma medication, reduced lung function and worse disease control and quality of life.17

    Previous studies have shown that patients with asthma have a higher risk for CVDs including arterial hypertension compared to individuals without asthma, requiring an integrated management due to overlapping risk factors like obesity or smoking.12,18,19 The co-existence of asthma and chronic obstructive pulmonary disease (COPD) results in worse outcomes such as higher exacerbation rates and a lower quality of life and a faster decline in lung function.20

    The presence of multiple comorbidities (multimorbidity) becomes more common with age and is associated with worse clinical outcomes, increased mortality, and higher medical costs. However, multimorbidity remains inconsistently defined in asthma research, often referring to the coexistence of two or more chronic conditions.14,21,22 Despite its clinical importance, there are limited longitudinal data on the evolution and impact of comorbidities in patients with SA, especially within structured care pathways.

    To our knowledge, longitudinal data on the specific population of patients with severe asthma and other diseases, with and without associated clinical pathways, are scarce. For Switzerland, no such data are available.

    Due to the lack of thorough data, the Swiss Severe Asthma Registry (SSAR) was started to obtain comprehensive, longitudinal information regarding patients with severe asthma in Switzerland to optimize the diagnostic evaluation, treatment of patients with severe asthma and better understanding influencing factors.23 The aim of the current study is to take a closer look at comorbidities and severe asthma, especially their prevalence over a period of three years. Furthermore, we want to investigate if there are associations between comorbidities and asthma control, asthma-related quality of life, and FEV1.

    Materials and Methods

    For this analysis, we used data from the SSAR. The SSAR is an ongoing multicenter, longitudinal, prospective cohort study that includes patients with SA in Switzerland.23 This study is conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by all seven ethics committees in Switzerland under the lead of the ethics committee of northwestern and central Switzerland (EKNZ) with the BASEC (Business Administration System for Ethics Committees) 2018–01553.

    Participants and Data Collection

    Patients with severe asthma aged 6 years or older who fulfill the definition of SA according to ERS/ATS guidelines can be included in the registry after giving their written informed consent, in children under the age of 14 the parents had to give their consent.4 In this study, we analyzed the data of 234 patients who were included in the registry from May 2019 to July 2022 and had completed at least two years of follow-up.

    Socio-demographic information and medical data are collected both at inclusion visit and in the annual follow up visit. In addition, self-reported asthma-related quality of life (Mini-AQLQ) and self-reported asthma control (ACT) are assessed. The data are recorded in a non-public electronic database.

    Outcomes

    Our study aimed to investigate how asthma-related outcomes change over time in the study population and in individuals with certain comorbidities. The comorbidities were chosen according to their prevalence (<10% at time of inclusion) in our cohort as well as the current literature. Asthma-related outcomes were defined as the asthma control test score (ACT), the Mini AQLQ (disease-specific health-related quality of life questionnaire), the presence of acute exacerbations (AE) and pulmonary function parameters (FEV1, FVC, DLCO and FeNO).24–27

    The minimal clinically important differences (MCID) for the outcomes were 3 points for the ACT, and 0.5 points for the MiniAQLQ.28,29

    For the airway parameter, FEV1, FVC and DLCO, no MICD values have been established so far, due to multiple limitations, for FeNO a change of 20% in either direction is considered as MCID.29

    Statistical Analysis

    R version 4.3.1 was used for all statistical analyses. All tests were two-tailed and a p value <0.05 was defined as statistically significant, missing data were noted but not imputed.30 For continuous variables, the mean and standard deviation (SD) were calculated across all included patients per visit. For categorical variables, absolute and relative frequencies were evaluated. A t-test for dependent samples was used to evaluate the mean change of continuous variables between follow-up visits. The Χ2 test was used to compare the change of categorical variables between follow-up visits. To estimate the association of comorbidities with the expected change in the outcome variables over the two years, we used a generalized estimating equation (GEE) model. The GEE accounts for repeated measures, especially the within-subject correlation and non-independence of the variables.31,32

    Results

    Descriptive Analysis

    Baseline characteristics, asthma-related outcomes, and treatment patterns were described and compared over the two-year follow-up period. This section summarizes the temporal changes in patient demographics, clinical parameters, and management approaches observed during the study. At time of inclusion, the mean age was 56.1 years, and 47.9% of patients were female. 22.2% of patients had their asthma diagnosed in childhood. In terms of asthma type, 46.2% of patients were reported to have an allergic form of asthma, 37.6% have a non-allergic type of asthma and 16.2% have a mixed form of asthma (Table 1).

    Table 1 Patient Characteristics

    Longitudinal Assessment of Patient Characteristic and Asthma-Related Outcomes

    Changes of patient characteristics, asthma-related outcomes and treatment over the follow-up period are summarized in Table 2. At time of inclusion, 9.8% of patients were current smokers at time of inclusion in the registry and 7.7% at visit 2, respectively.

    Table 2 Patient Characteristics Over Time

    The mean BMI remained stable in the overweight range at all three visits (27.3 kg/m2 vs 27.1 kg/m2 vs 27.2 kg/m2 respectively, p = 0.916). The proportion of patients with no exacerbation in the previous 12 months increased over the observational period from 47% at time of inclusion to 73.5% after two years (p < 0.001). The mean ACT score increased significantly from 18.9 to 19.9 (p = 0.009), but did not reach the MCID of three points. The Mini-AQLQ increased significantly from a mean per question of 4.9 to 5.5 (p < 0.001), which is above the MCID of 0.5 points. There was no significant change in pulmonary function as measured by FEV1, FVC, DLCO or FeNO.

    Longitudinal Assessment of Treatment

    Most patients received a fix combination of inhaled corticosteroids with a long-acting beta agonist (ICS- LABA) as maintenance therapy. At the end of the follow-up period, significantly more patients received a triple therapy compared to the inclusion visit (7.7% vs 2.6% respectively. p = 0.049). In addition, significantly less patients received maintenance OCS (23.1% vs 13.7%, p = 0.010) at the end of the follow-up period, with no significant decrease in the mean dose of daily OCS (11.7 mg/day vs 9.2 mg/day prednisone equivalent, p = 0.682). However, there was a significant decrease in the mean daily dose of inhaled corticosteroids at the end of the follow-up period (1502 µg/day 1239 µg/day, beclomethasone equivalent; p = 0.003).

    Most of the included patients have been treated with monoclonal antibodies during the observational period (81.6% at inclusion vs 81.6% at visit 2, p = 0.946). A significantly higher number of patients were treated with dupilumab at the second follow-up visit, compared to inclusion (19.2% vs 8.9% respectively, p = 0.002). There was no significant change in the usage frequency of the other biologics.

    Longitudinal Assessment of Comorbidities

    The most prevalent comorbidity at the time of inclusion were allergies (55.1%), followed by CRS (48.3%) and nasal polyps (35%). None of the comorbidities showed statistically significant differences in prevalence during the observational period (Table 3).

    Table 3 Comorbidities Over Time

    Most of the patients had no reported steroid induced side effects or associated long-term complications at all three visits (73.9% vs, 73.9% vs, 71.4%, p = 0.262). Diabetes skin lesions and weight gain were the most frequently reported OCS induced side effects (5.6%, 6.4% and 3.8% respectively).

    Comorbidities and Asthma-Related Outcomes

    To investigate the association between comorbidities and longitudinal changes in clinical and physiological outcomes over the two-year follow-up period, GEE models were conducted. Outcome variables included the ACT, the Mini-AQLQ, FEV₁, FVC, DLCO, and FeNO.

    An overall time effect was found for both the 1-year and 2-year follow-up in the ACT and the Mini-AQLQ and Exacerbations. Our model showed significant positive associations between time and ACT (1 year: b2 = 1.06; 95% CI: 1.02–1.10; p < 0.001; 2 years: b2 = 1.06; 95% CI: 1.06–1.09; p < 0.001) (Figure 1), Mini-AQLQ (1 year: b2 = 1.10; 95% CI: 1.06–1.14; p < 0.001; 2 years: b2 = 1.11; 95% CI: 1.08–1.15; p < 0.001) (Figure 2), as well as exacerbations (1 year: OR = 0.43 95% CI: 0.22–0.83); p = 0.012, 2 years OR = 0.30; 95% CI: 0.16–0.57; p < 0.001) (Figure 3). The time-based changes are also shown in the Supplementary Figures 1–4. For the other outcome variables, no time effect was shown.

    Figure 1 GEE Model Estimates and forest plot for the ACT Score. Model estimate: generalized estimating equations with Gamma Distribution and Log Link function. Dependent Variable: ACT, Controller Variable: Age (b2= 1.00, 95% CI: 0.99–1.00, p = 0.173), Biologic Treatment (b2= 1.12, 95% CI1.05–1.18, p < 0.001). Multimorbidity defined as 2 or more additional chronic diseases.

    Abbreviations: COPD, chronic obstructive pulmonary disease; GERD, gastroesophageal reflux disease; CVDs, cardiovascular disease.

    Figure 2 GEE Model Estimates and forest plot for the Mini AQLQ Score. Model estimate: generalized estimating equations with Gamma Distribution and Log Link function. Dependent Variable: MiniAQLQ, Controller Variable: Age (b2= 1.00, 95% CI: 1.00–1.00, p = 0.088), Biologic Treatment (b2= 1.11, 95% CI1.06–1.17, p < 0.001). Multimorbidity defined as 2 or more additional chronic diseases.

    Abbreviations: COPD, chronic obstructive pulmonary disease; GERD, gastroesophageal reflux disease; CVDs, cardiovascular disease.

    Figure 3 GEE Model Estimates and forest plot for Exacerbations. Model estimate: generalized estimating equations (binominal family). Dependent Variable: Minimum of 1 exacerbation, Controller Variable: Age (OR= 0.96, 95% CI 0.94–0.99, p= 0.018), Biologic Treatment (OR 0.60, 95% CI 0.26–1.41, p= 0.249). Multimorbidity defined as 2 or more additional chronic diseases.

    Abbreviations: COPD, chronic obstructive pulmonary disease; GERD, gastroesophageal reflux disease; CVDs, cardiovascular disease.

    Allergies

    Allergic comorbidities occur in more than 55% of the investigated population, with a non-significant increase over the observational period (Table 3). When looking at the effect of allergies on asthma-related outcomes our models showed that Allergies have a significant negative association on the Mini AQLQ score (b2 = 0.95; 95% CI: 0.90–0.99; p = 0.036) (Figure 2). In the other outcome variables, no significant association was found with the ACT (Figure 1), Exacerbations (Figure 3), FEV1 (Figure 4), FVC, DLCO or FeNO (Supplementary Figures 5–7).

    Figure 4 GEE Model Estimates and forest plot for the FEV 1% predicted. Model estimate: generalized estimating equations with Gamma Distribution and Log Link function. Dependent Variable: FEV1, Controller Variable: Biologic Treatment (b2= 1.05, 95% CI 0.98–1.12, p= 0.163). Multimorbidity defined as 2 or more additional chronic diseases.

    Abbreviations: COPD, chronic obstructive pulmonary disease; GERD, gastroesophageal reflux disease; CVDs, cardiovascular disease.

    CRS and Nasal Polyps

    In our population, around 50% of the patients suffer from CRS, and more than a third of the patients has nasal polyps, without relevant changes in the prevalence over the observational period (Table 3). CRS was not associated with a change in either direction in the outcome variables investigated (Figures 1–4). The same was observed for nasal polyps, our models did not show any significant associations, with either the patient reported outcome variables (ACT and Mini AQLQ and exacerbations; Figures 1–3) or the physiological outcome measures (FEV1, FVC, DLCO and FeNO; Figure 4; Supplementary Figures 5–7).

    COPD

    Around 10% of our population were reported to have a coexisting COPD (Table 3). Over the observational period, no significant change in the prevalence was observed. Our models found a significant negative association with the patient reported outcomes ACT (b2 = 0.83, 95% CI: 0.76–0.92, p < 0.001; Figure 1) and Mini AQLQ (b2 = 0.88; 95% CI: 0.80–0.97; p = 0.012; Figure 2) but not with exacerbations (OR = 1.75; 95% CI: 0.65–4.68; p = 0.266). In the ACT model, patients with COPD showed a persistent lower ACT score despite an overall improvement of the score over time (Figure 1, Supplementary Figure 4). The same effect is observed for the Mini AQLQ, that patients with COPD showed a persistent lower Mini AQLQ compared to patients with no COPD (Figure 2; Supplementary Figure 2). In addition to the patient reported outcomes, we observed negative associations of COPD with physiological parameters. Concomitant COPD was associated with lower FEV1 and persistent lower FEV1 over the observational period (b2 = 0.79; 95% CI: 0.67–0.93; p = 0.006, Figure 4, Supplementary Figure 3) and FVC (b2= 0.90; 95% CI: 0.83–0.99, p = 0.028, Supplementary Figures 4 and 5). Our analysis also showed a negative association of COPD on FeNO, meaning that patients with COPD having a lower FeNO (b2 = 0.51; 95% CI: 0.37–0.70; p < 0.001, Supplementary Figure 7). No association was found for COPD and DLCO (Supplementary Figure 6).

    GERD

    Around 30% of our study population were reported to have GERD, with slightly less in the follow-up period (Table 3). For GERD our analyses revealed significant associations with both ACT score (b2 = 0.91, 95% CI: 0.87–0.97, p = 0.009) and the Mini AQLQ (b2= 0.92; 95% CI: 0.86–0.98; p = 0.006), which is shown in Figures 1 and 3. Further, we observed that patients with GERD are 2.51 times more likely to experience exacerbations compared to patients without GERD (OR = 2.51; 95% CI 1.20–5.23; p = 0.014, Figure 3). The analysis did not reveal a significant association with the physiological outcome variables and GERD (Figure 4 and Supplementary Figures 5–7).

    Depression

    Depression was observed in around 10% of our severe asthma population with a minimal reduction to 8.9% at the one year follow-up (Table 3). Our GEE models did not show any significant association with patient reported outcomes ACT Score (b2= 0.95; 95% CI: 0.88–0.102; p = 0.192), Mini AQLQ (b2= 1.00; 95% CI: 0.94–0.1.09; p = 0.959) or exacerbations (OR = 0.36; 95% CI 0.09–1.40; p = 0.141) (Figures 1–3). When looking at the physiological parameters, we could not detect an association between depression, FEV1 (Figure 4), FVC or FeNo (Supplementary Figures 5 and 6). However, the GEE Model revealed a significant negative association between depression and DLCO (b2= 0.74; 95% CI: 0.64–0.85; p < 0.001) (Supplementary Figure 7).

    Arterial Hypertension and Cardiovascular Diseases

    More than 20% of our population was reported to have arterial hypertension, with no changes in the prevalence (Table 3). Other CVD’s where present in 11.5% of the patients with a non-significant increase up to 15.8% at follow-up two (Table 3).

    We could not observe any significant associations in our analyses of either arterial hypertension or CVD’s with the patient reported outcomes ACT score, Mini AQLQ or exacerbations (Figure 1–3). When looking in the physiological parameters, we detected no significant associations between arterial hypertension and FEV1 (Figure 4), FVC, DLCO or FeNO (Supplementary Figures 5–7). In CVDs, a significant positive association between with FVC % predicted (b2= 1.09; 95% CI: 1.05–1.14, p < 0.001) compared to patients without CVDs was detected (Supplementary Figure 1). For the other outcome variables, we did not detect any significant associations.

    Obesity

    Obesity was defined as having a BMI ≥ 30 kg/m2. In our population, 27.3% of the patients were considered as obese with a minimal decrease over time (Table 3).

    When looking at the patient reported outcomes, we found no association between obesity and the ACT score (Figure 1), or exacerbations (OR = 1.37; 95% CI 0.69–2.73; p = 0.364) (Figure 3), but a significant negative association between obesity and the Mini AQLQ score (b2= 0.93; 95% CI: 0.88–0.99; p = 0.016). For the physiological outcome measures, we only found a significant association between obesity and a higher DLCO % predicted (b2 = 1.12; 95% CI: 1.03–1.21; p = 0.005) (Supplementary Figure 7).

    Multimorbidity

    In our population, more than 60% of patients were affected by multimorbidity, meaning to have more than 2 other conditions (Table 3). In the patient reported outcomes, we could not find an association between multimorbidity and the ACT Score or the Mini AQLQ (Figures 1 and 2). But we observed, that patients with multimorbidity were four times more likely to experience at least one exacerbation (OR = 4.18; 95% CI 1.60–10.91; p = 0.003) (Figure 3). When looking at the physiological parameters, our models did not show any significant associations between multimorbidity and any of the outcome variables (Figure 4, Supplementary Figures 5–7).

    Discussion

    This study presents a comprehensive characterization of patients with SA in Switzerland enrolled in a real-world registry, focusing on demographic features, clinical outcomes, treatment patterns, and comorbidities over a two-year observational period. Our findings showed notable improvements in asthma control and health-related quality of life, as reflected by increased ACT and Mini AQLQ scores, as well as a reduction of exacerbations. These trends suggest enhanced disease management despite the absence of significant changes in pulmonary function test parameters. While our results are broadly consistent with those reported in previous cohorts, careful interpretation is essential. The absence of formal assessment of adherence or diagnostic variability may limit internal consistency of the findings, which should be considered when contextualizing the nuances of SA management in real-world clinical settings.

    Overall, our results are in line with previous observations on SA patients. The demographic profile with a mean age of 56.1, as well as the proportion of asthma types (46.2% allergic, 37.6% non-allergic, and 16.2% mixed) aligns with the characteristics of adults with moderate-to-severe asthma observed in other studies and cohorts. However, our cohort showed a more equal gender distribution, whereas other studies reported a higher number of female patients.33–35

    Univariate analysis revealed significant improvements in asthma control (measured by ACT scores) and quality of life (measured by Mini AQLQ scores) over the observational period. Despite not reaching the MCID of 3 points for the mean ACT score and the number of patients with score ≥20 increased, reflecting better asthma control, fewer exacerbations, and improved quality of life, which is in line with previous findings.36,37 The mean answer per question in the mini AQLQ score, increased by 1.6 points, which is three times more than the MICD of 0.5 points. This finding indicates not only a statistical but also a clinical important difference of the quality of life in our population over the observational period. Reduced quality of life.

    However, no significant changes were observed in pulmonary function test parameters, suggesting that ACT and Mini AQLQ improvements may not directly correlate with pulmonary function.

    Improvement of asthma control and quality of life is often associated with optimized management, such as biologic treatment or lifestyle modifications.36 While there was no increased use of biologics over the observational period, reductions in daily ICS doses and OCS use confirm a positive long-term effect of biologic treatment, already found by previous studies.37–40 Improved treatment adherence, better monitoring or guideline adherence since inclusion in the registry could be other contributing factors, however those factors were not systematically assessed.41

    Prevalence of comorbidities was similar to prior studies.7,15 No significant increase in comorbidities, multimorbidity, or steroid-induced side effects was observed during the 2-years follow-up. The prevalence of allergies, CRS, nasal polyps and GERD was notably higher in SA patients (55.1% for Allergies, 48.3% for CRS, 35% for nasal polyps, 29.1% for GERD) compared to the general population (1–20%, for CRS, 1–4% or nasal polyps, 20–30% respectively).14,15,42 Hypertension and CVD rates (22% and 16% respectively) were lower compared to global averages (30–50%), possibly due to reporting gaps, highlighting the need for better cross-disciplinary data sharing.43 COPD prevalence (10–12%) was slightly lower compared to global prevalence (12–14%), likely due to lower smoking rates in patients with asthma or diagnostic overlap of both diseases.44 The identification of COPD in severe asthma is challenging, due to heterogenous clinical presentations or the lack of established definition criteria.45

    About 27% of patients were obese (BMI > 30 kg/m²), which is nearly double the global prevalence (13%), likely due to OCS use, reduced physical activity, and disability linked to SA.46,47 Furthermore, obesity worsens asthma severity and control and complicates asthma management. Multimorbidity was higher in the study population (63%) compared to the general population over the age of 45 (30%), and multimorbidity increased with age.48

    Prevalence of steroid induced side effects was lower in our study compared to other cohorts. Treatment for SA in our study did not differ from other cohorts. Thus, this finding is less likely to be associated with a better asthma management and could be due to potential underreporting or misclassification.49

    We found several associations between comorbidities and asthma-related outcomes which should be highlighted:

    • 11% of the patients with SA were reported to have a comorbid COPD with a significant negative association with pulmonary function, asthma control and quality of life, as described in previous studies.50 This finding highlights the importance of early detection, diagnosis and treatment of both disease in a personalized manner in order to improve patients outcomes.51

    • Second, GERD was associated with poorer asthma control and quality of life, as described in previous studies.14,15 However, the prevalence of GERD was significantly lower in our study (30%) compared to other cohorts (60%).14 GERD may have been underdiagnosed or underreported in our registry. Thus, special attention should be paid to the assessment of GERD in the management of patients with SA.6

    • Finally, we found a strong association between depression and a reduction of DLCO over time. DLCO was found to be a predictor for sleep onset latency and other sleep disorders, especially insomnia and nighttime awakening, which are also common in patients with depression. Therefore, patients with SA and especially those with reduced DLCO might benefit if regularly assessed for depression and sleep disorders. Further studies are needed to investigate the impact of sleep on asthma-related outcomes.

    Limitations

    Data on socioeconomic status, medication adherence, and incorrect inhalation technique that might also influence asthma-related outcomes are not collected in the SSAR and could not be included in the models used. Due to study design, reporting errors cannot be excluded and therefore the results have to be interpreted with regards to the limitations.

    In addition, we must assume a selection bias of recruited patients, as preferably patients treated with biologics were included due to the regular follow-ups. Last, the reporting on comorbidities has its limitations. Due to the structure of the database only the prevalence of certain comorbidities was assessed in our population. Diabetes was only assessed as secondary to OCS use, and we do not have information about diagnostic procedures. In addition, the registry data does not allow a systematic evaluation (including treatment) of comorbidities.

    Conclusion

    Management of Comorbidities

    Given the strong associations between comorbidities such as, GERD and obesity, as well as COPD and lower quality of life, asthma control, exacerbations or pulmonary function, an integrated and personalized approach to asthma care that addresses these comorbid conditions should be adopted.

    Future Research

    Further studies should investigate the outcomes of multidisciplinary management in the subgroups of patients with SA and GERD or depression as well as the clinical entity of the co-existence of asthma and COPD.

    Abbreviations

    ACT, Asthma Control Test; ATS, American Thoracic Society; BMI, Body Mass Index; CI, Confidence Interval; COPD, Chronic Obstructive Pulmonary Disease; CRS, Chronic Rhinosinusitis; CVDs, Cardiovascular Disease; DLCO, Diffusing Capacity for Carbon Monoxide; EKNZ, Ethical Committee of Northwestern and Central Switzerland; ERS, European Respiratory Society; FeNO, Fractional Exhaled Nitric Oxide; FEV1, Forced Expiratory Volume in one second; FVC, Forced Vital Capacity; GERD, Gastroesophageal Reflux Disease; GEE, Generalized Estimation Equations; ICS, Inhaled Corticosteroids; LABA, Long-Acting Beta-Agonists; LAMA, Long-Acting Muscarinic Antagonist; LTRA, Leukotriene Receptor Antagonist; NCD, Non-communicable diseases; MCID, Minimal clinical important difference; OCS, Oral corticosteroids; SA, Severe Asthma; SSAR, Swiss Severe Asthma Registry.

    Acknowledgments

    The authors would like to thank all our participating centers and pneumologists for their engagement in this research. The inclusion of patients is tremendously important for this research and the Swiss Severe Asthma Registry in General. Namely: Rehaklinik Heiligenschwendi (Dr. med. P. Brun), Bürgerspital Solothurn, GZO Spital Wetzikon (Dr. med. M. Huber), Gesundheitszentrum Unterengadin (Dr. med. M. Nemec), Kantonsspital Frauenfeld (Dr.med. I. Thüer), Kanronsspital Münsterlingen (Dr. med. I. Schlecht), Lungenpraxis Morgenthal (Dr. med. C. Eich), Lungenpraxis Wohlen (Dr. med. L. Schlatter), Medizin Stollturm (Dr. med. J. Rüdiger), Praxis Quartier Bleu (Dr. med. D. Schilter, Dr. med R. Fischer), Pneumologie Nordwest (Dr. med. A. Tschacher), Spital Thun (Dr. med. L. Junker), Spital Uster (Prof. Dr. med. D. Franzen, Dr. med. V. Popov), Stadtspital Triemli (KD Dr. med. I Laube, KD. Dr. med. D. Scholtze). In addition, we highly appreciate the work of all the study nurses working for the registry.

    Funding

    The Swiss Severe Asthma Registry has received or is currently receiving project funding from the following sources: Astra Zeneca, GSK, Sanofi Mundipharma, Novartis, Teva Pharma, Vifor, Lungenliga Schweiz, Lunge Zürich, Lungneliga Bern, Lungenliga St.-Gallen Appenzell, Lungenliga Graubünden, Lungenliga beider Basel.

    Disclosure

    Dr Nikolay Pavlov reports personal fees for lectures and consulting fees from AstraZeneca, CSL Behring, GlaxoSmithKline, OM Pharma, and Sanofi, outside the submitted work. Prof. Dr. Christophe Von Garnier reports grants and/or personal fees from OM Pharma, Astra Zeneca, GSK, Boehringer Ingelheim, Sanofi, Pfizer, MSD, Mundipharma, Novartis, Pulmonx, PneumRx, Swiss Cancer Research, Swiss Lung League, and Vaud Lung League, during the conduct of the study. Prof. Dr. Joerg Leuppi reports grants from AstraZeneca AG Switzerland, GSK AG Switzerland, and OM Pharma AG Switzerland, outside the submitted work. The authors report no other conflicts of interest in this work.

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    28. Juniper EF, Price DB, Stampone PA, Creemers JPHM, Mol SJM, Fireman P. Clinically important improvements in asthma-specific quality of life, but no difference in conventional clinical indexes in patients changed from conventional beclomethasone dipropionate to approximately half the dose of extrafine beclomethasone dipropionate. Chest. 2002;121(6):1824–1832. doi:10.1378/chest.121.6.1824

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    51. Shim JS, Kim H, Kwon JW, et al. A comparison of treatment response to biologics in asthma-COPD overlap and pure asthma: findings from the PRISM study. World Allergy Organ J. 2023;16(12):100848. doi:10.1016/j.waojou.2023.100848

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  • Maya Jama will not return for Love Island Games season 2

    Maya Jama will not return for Love Island Games season 2

    Instead, Ariana Madix – who first found fame on Bravo series Vanderpump Rules – will be taking her place for the next season.

    Ariana has become a Love Island fan favourite after she stepped in to become the host of Love Island USA in 2024, returning for the 2025 season, which aired its finale last night (13th July).

    After the news was announced by Deadline, Ariana shared the post on her Instagram Stories, declaring: “Secret’s Out! See you soon.”

    Ariana Madix. Ben Symons/PEACOCK via Getty Images

    Love Island Games will return to screens on Sunday 14th September on Peacock in the US, and is expected to air on ITVX in the UK.

    Iain Stirling has been confirmed to continue his role as voiceover for the show.

    For the first season of Love Island Games, stars from multiple iterations of the show flew out to Fiji for a new summer of love and competitive games to win an advantage within the competition itself.

    The UK was represented by Love Island season 4’s Jack Fowler, Eyal Booker, Megan Barton-Hanson and Georgia Steel, season 5’s Curtis Pritchard, season 6’s Mike Boeteng, season 7’s Liberty Poole and Toby Aromolaran, and season 10’s Scott van-der-Sluis.

    Jack Fowler eventually won the season alongside Justine Ndiba, who appeared on Love Island USA’s second season.

    Maya Jama in a white see-through floral dress. She has her hand on her hip and is stood in front of a love heart

    Maya Jama. ITV

    However, despite seeming like a strong couple by the end of the show, Jack confirmed that he and Justine had decided to remain friends four months after the finale aired.

    Prior to the break-up, both noted in interviews after winning that the distance between the UK and US was a major obstacle in their relationship.

    While Jama may be stepping down from Love Island Games, the star is still being kept busy by Love Island, with the UK season currently airing.

    She has also hosted Love Island All Stars for the past two seasons, on top of her other commitments as a model, brand ambassador and entrepreneur.

    Add Love Island to your watchlist on the Radio Times: What to Watch app – download now for daily TV recommendations, features and more.

    Check out more of our Entertainment coverage or visit our TV Guide and Streaming Guide to find out what else is on. For more from the biggest stars in TV, listen to The Radio Times Podcast.

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  • How to Distinguish Extraterrestrial Spacecraft from Interstellar Rocks? | by Avi Loeb | Jul, 2025

    How to Distinguish Extraterrestrial Spacecraft from Interstellar Rocks? | by Avi Loeb | Jul, 2025

    (Image credit: Mark Garlick, Science Photo Library)

    The discovery of interstellar objects over the past decade raises an important question that could shape the future of humanity: how to distinguish extraterrestrial spacecraft from interstellar asteroids? Both types of objects reflect sunlight. However, no telescope on Earth can resolve a hundred-meter object — the scale of our largest rocket — Starship, from a distance of about a billion kilometers — the distance where 3I/ATLAS was discovered on July 1, 2025.

    Unfortunately, we cannot rely on sky watchers to alert us to the possibility that a spacecraft just entered the solar system. Even after the first reported interstellar object, 1I/`Oumuamua, showed the anomalies of a flat shape and a non-gravitational acceleration without a cometary tail that distinguished it from any known asteroid or comet, it was nevertheless labeled as a “dark comet”, namely a comet without the unique signature that would flag it as a comet: a visible plume of gas and dust. Given this definition, any object launched by humans to space — which is pushed by rocket fuel or solar radiation pressure, is a dark comet.

    The best we can hope for is courageous astronomers that would admit anomalies exhibited by outliers, namely features that may fit better the description of a technologically manufactured object than a natural rock.

    Recently, I listed the anomalies of the new interstellar object 3I/ATLAS. This object is anomalously bright, implying a diameter of ~20 kilometers for the typical reflectance of asteroids. The implied diameter and detection rate are untenable by the mass budget in interstellar asteroids, as I showed in a new paper — just published in Research Notes of the American Astronomical Society. If 3I/ATLAS ends up being a comet, its nucleus must be an order of magnitude smaller. But if it happens not to possess a large cometary plume of dust or gas, what is the nature of this object?

    Without asking this question, humanity will remain in the “stone age,” regarding interstellar objects. Even if 3I/ATLAS will show up as a genuine comet, like 2I/Borisov, as it gets closer to the Sun and heats up, we should always ask this question about future interstellar objects.

    An interstellar comet is easy to identify by its tail. But what are the markers that would distinguish a technological interstellar object — a spacecraft, from an asteroid? Here is a list of some of them:

    1. Propulsion: a central engine or solar radiation pressure (as I suggested in a paper with Shmuel Bialy for 1I/’Oumuamua) would cause a technological object to deviate from a Keplerian hyperbolic orbit, dictated solely by gravity.

    2. Trajectory: the path of the object could selectively target the inner planets in the Solar system. For example, the orbital plane of 3I/ATLAS was within 5 degrees of the ecliptic plane for the Earth’s orbit around the Sun. The likelihood for these orbital angular momenta to be aligned so well is ~0.001, as I had mentioned in my recent anomalies essay.

    3. Artificial lights: Reflection of sunlight can be distinguished from artificial light by its spectrum and through its faster decline with increasing distance from the Sun, as I discussed in a paper with Ed Turner.

    4. Shape: An artificially-designed shape can be inferred from the light curve of reflected sunlight as the object rotates. This is how 1I/’Oumuamua was inferred in a paper by Sergei Mashchenko to have a disk-like shape.

    5. Image from a flyby: Resolved details of the object’s surface could instantly distinguish a technological object from a rock. Such an image can be taken by a camera on a dedicated intercept mission or in case the object happens to be passing very close to Earth. Landing on a technological object through a rendezvous mission like OSIRIS-REx would offer the benefit of a direct inspection, including the privilege of pressing buttons on it.

    6. Surface composition: remote spectroscopy of the surface might show signatures of bombardment by cosmic-rays, interstellar dust particles and interstellar protons. The energy deposition rate scales as velocity cubed and travel duration. Faster or older objects should be more scarred by interstellar damage.

    7. Signals: A functioning technological device might transmit electromagnetic signs that terrestrial telescopes could search for over a broad range of frequencies from the radio to gamma-rays.

    8. Launch of Mini-Probes from a mothership: An efficient way to seed habitable planets with probes is to pass near them and release small devices at the right time and place with the appropriate velocity kick, so that they will intercept the planets while the mothership continues on its journey to the next star.

    Ironically, 3I/ATLAS was discovered by the small ATLAS telescope with an aperture diameter of half a meter, during the same month that the 8.36-meter aperture of the Rubin Observatory started to search for interstellar objects from nearly the same location in Chile. Over the next decade, the Rubin observatory is expected to find tens of new interstellar objects.

    My advocacy is simple. We should study the Rubin data with an open mind to the possibility that it may discover technological objects from extraterrestrial civilizations. If we insist that all interstellar objects are asteroids and comets with the outliers catalogued as dark comets, then the answer to the question “Are we alone?” would be “Yes, by choice.” Some of the loneliest people in the world are those who stopped searching for a partner. Their status is a self-fulfilling prophecy. In order to find our cosmic partners, we must allow them to exist in our mind as we inspect the Rubin data.

    Surely, interstellar objects were passing overhead in the sky in 1950 when Enrico Fermi asked: “where is everybody?” As an experimental physicist, his oversight was not to build a large telescope to search for them.

    ABOUT THE AUTHOR

    (Image Credit: Chris Michel, National Academy of Sciences, 2023)

    Avi Loeb is the head of the Galileo Project, founding director of Harvard University’s — Black Hole Initiative, director of the Institute for Theory and Computation at the Harvard-Smithsonian Center for Astrophysics, and the former chair of the astronomy department at Harvard University (2011–2020). He is a former member of the President’s Council of Advisors on Science and Technology and a former chair of the Board on Physics and Astronomy of the National Academies. He is the bestselling author of “Extraterrestrial: The First Sign of Intelligent Life Beyond Earth” and a co-author of the textbook “Life in the Cosmos”, both published in 2021. The paperback edition of his new book, titled “Interstellar”, was published in August 2024.

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  • Stellar, meet the explorers of space and the Universe

    This event will bring together renowned scientists and astronauts to talk about their research and expeditions

    At every scale, our Universe is composed of the same particles. This Universe of particles is at the heart of the International Cosmic Ray Conference (ICRC), which kicks off its 39th edition on Monday, 14 July. The astroparticle specialists who meet there, like the physicists working on experiments at CERN, share a desire to understand our Universe by studying its fundamental constituents.

    A special event will take place as part of the conference. The general public and the scientific community are invited to Science Gateway on 19 July to meet Nobel Prize winners and astronauts at the Stellar soirée.

    Samuel Ting, winner of the 1976 Nobel Prize in Physics, will present AMS, the dark matter detector mounted on the International Space Station. Barry Barish, winner of the 2017 Nobel Prize in Physics, will look back on the first detection of gravitational waves in 2015, a real leap forward for contemporary science. Takaaki Kajita, winner of the 2015 Nobel Prize in Physics, will talk about neutrinos, the mysterious and elusive particles that pervade our Universe. Michel Mayor, winner of the 2019 Nobel Prize in Physics, will explain the path that led to the discovery of exoplanets. He will be sharing the stage with Meganne Christian, a reserve astronaut with the European Space Agency (ESA), who was selected at the same time as Slawosz Uznański, who has just returned from space. In this discussion, Meganne Christian and Michel Mayor will describe their respective journeys to the Moon and the Nobel Prize.

    Three other ESA Astronauts will also share their experiences: Paolo Nespoli, who spent more than 300 days on board the International Space Station; Matthias Maurer, who will be talking about exploration of the Moon; and Luca Parmitano, who will be speaking live from the NASA Space Center in Houston and has made four spacewalks to perform maintenance on the AMS experiment.

    Stellar will also be offering a host of fun activities, including an interactive science village with workshops, Apollo simulators where you can step into the shoes of an astronaut, and access to the Science Gateway exhibitions. The evening will be punctuated by live music, and stands will be serving refreshments.

    You can consult the full programme here. For those unable to attend in person, the talks will be streamed on YouTube.

    Speakers

    • Barry C. Barish, experimental physicist, Nobel Prize in Physics 2017
    • Michel Mayor, astrophysicist, Nobel Prize in Physics 2019
    • Takaaki Kajita, particle physicist, Director of the Institute for Cosmic Ray Research at the University of Tokyo, Nobel Prize in Physics 2015
    • Samuel Chao Chung Ting, physicist, Professor at the Massachusetts Institute of Technology, Nobel Prize in Physics 1976
    • Meganne Christian, ESA Reserve Astronaut, Exploration Commercialisation Lead at the UK Space Agency
    • Matthias Maurer, ESA Astronaut
    • Paolo Nespoli, ESA and ASI Astronaut
    • Luca Parmitano, ESA Astronaut and Colonel in the Italian Air Force
    • Patrizia Caraveo, astrophysicist, neutron star specialist
    • Teresa Montaruli, astroparticle physicist
    • Clara Nellist, particle physicist

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  • Stock Market Today: Dow Futures Fall After Trump's EU, Mexico Tariff Threats — Live Updates – The Wall Street Journal

    1. Stock Market Today: Dow Futures Fall After Trump’s EU, Mexico Tariff Threats — Live Updates  The Wall Street Journal
    2. European markets head south after Trump slaps 30% tariff on EU  CNBC
    3. The week ahead: EU tariffs to hit sentiment, as Bitcoin reaches record  FXStreet
    4. Shares in German carmakers fall after Trump tariff announcement By Reuters  Investing.com
    5. FTSE 100 hits record high as investors shrug off trade war concerns  The Guardian

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