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Abstract The Cherenkov Telescope Array and the KM3NeT neutrino telescopes are major upcoming facilities in the fields of $$\gamma $$ γ -ray and neutrino astronomy, respectively. Possible simultaneous production of $$\gamma $$ γ rays and neutrinos in astrophysical accelerators of cosmic-ray nuclei motivates a combination of their data. We assess the potential of a combined analysis of CTA and KM3NeT data to determine the contribution of hadronic emission processes in known Galactic $$\gamma $$ γ -ray emitters, comparing this result to the cases of two separate analyses. In doing so, we demonstrate the capability of Gammapy, an open-source software package for the analysis of $$\gamma $$ γ -ray data, to also process data from neutrino telescopes. For a selection of prototypical $$\gamma $$ γ -ray sources within our Galaxy, we obtain models for primary proton and electron spectra in the hadronic and leptonic emission scenario, respectively, by fitting published $$\gamma $$ γ -ray spectra. Using these models and instrument response functions for both detectors, we employ the Gammapy package to generate pseudo data sets, where we assume 200 h of CTA observations and 10 years of KM3NeT detector operation. We then apply a three-dimensional binned likelihood analysis to these data sets, separately for each instrument and jointly for both. We find that the largest benefit of the combined analysis lies in the possibility of a consistent modelling of the $$\gamma $$ γ -ray and neutrino emission. Assuming a purely leptonic scenario as input, we obtain, for the most favourable source, an average expected 68% credible interval that constrains the contribution of hadronic processes to the observed $$\gamma $$ γ -ray emission to below 15%.

Identification of disease and therapeutic biomarkers remains a significant challenge in the early diagnosis and effective treatment of juvenile idiopathic arthritis (JIA). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with JIA. Plasma samples from treatment-naïve JIA patients and non-JIA reference patients underwent global metabolomic profiling across discovery (60 JIA, 60 non-JIA) and replication (49 JIA, 38 non-JIA) cohorts. Univariate analysis identified significant metabolites (q-value ≤ 0.05), followed by enrichment analysis using ChemRICH and metabolic network mapping with MetaMapp and Cytoscape. Receiver operating characteristic (ROC) analysis determined the top discriminating biomarkers based on area under the curve (AUC) values. A total of over 800 metabolites were measured, consisting of 714 known and 155 unknown compounds. In the discovery cohort, 587 metabolites were significantly altered in JIA patients compared with the reference population (q < 0.05). In the replication cohort, 288 metabolites were significantly altered, with 78 overlapping metabolites demonstrating the same directional change in both cohorts. JIA was associated with a notable increase in plasma levels of sphingosine metabolites and fatty acid ethanolamides and decreased plasma levels of sarcosine, iminodiacetate, and the unknown metabolite X-12462. Chemical enrichment analysis identified cycloparaffins in the form of naproxen and its metabolites, unsaturated lysophospholipids, saturated phosphatidylcholines, sphingomyelins, ethanolamines, and saturated ceramides as the top discriminating biochemical clusters. ROC curve analysis identified 11 metabolites classified as highly discriminatory based on an AUC > 0.90, with the top discriminating metabolite being sphinganine-1-phosphate (AUC = 0.98). This study identifies specific metabolic changes in JIA, particularly within sphingosine metabolism, through both discovery and replication cohorts. Plasma metabolomic profiling shows promise in pinpointing JIA-specific biomarkers, differentiating them from those in healthy controls and Crohn’s disease, which may improve diagnosis and treatment.

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce very high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy γ -ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the γ -ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The γ -ray luminosity above 0.5 TeV of both sources is (2–3) × 10 ^35 erg s ^−1 . This exceeds by more than a factor of 2 the luminosity of HESS J1646−458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the γ -ray emission from each source is extended with a significance of >3 σ and a Gaussian width of about 30 pc. For 30 Dor C, a connection between the γ -ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the γ -ray signal from R136 are discussed.

We report the discovery of GRB 221009A, the highest flux gamma-ray burst (GRB) ever observed by the Fermi Gamma-ray Burst Monitor (Fermi-GBM). This GRB has continuous prompt emission lasting more than 600 s, which smoothly transitions to afterglow emission visible in the Fermi-GBM energy range (8 keV–40 MeV), and total energetics higher than any other burst in the Fermi-GBM sample. By using a variety of new and existing analysis techniques we probe the spectral and temporal evolution of GRB 221009A. We find no emission prior to the Fermi-GBM trigger time ( t _0 ; 2022 October 9 at 13:16:59.99 UTC), indicating that this is the time of prompt emission onset. The triggering pulse exhibits distinct spectral and temporal properties suggestive of the thermal, photospheric emission of shock breakout, with significant emission up to ∼15 MeV. We characterize the onset of external shock at t _0 + 600 s and find evidence of a plateau region in the early-afterglow phase, which transitions to a slope consistent with Swift-XRT afterglow measurements. We place the total energetics of GRB 221009A in context with the rest of the Fermi-GBM sample and find that this GRB has the highest total isotropic-equivalent energy ( E _γ _,iso = 1.0 × 10 ^55 erg) and second highest isotropic-equivalent luminosity ( L _γ _,iso = 9.9 × 10 ^53 erg s ^–1 ) based on its redshift of z = 0.151. These extreme energetics are what allowed us to observe the continuously emitting central engine of Fermi-GBM from the beginning of the prompt emission phase through the onset of early afterglow.

In 2021 July, PKS 1510−089 exhibited a significant flux drop in the high-energy γ -ray (by a factor 10) and optical (by a factor 5) bands and remained in this low state throughout 2022. Similarly, the optical polarization in the source vanished, resulting in the optical spectrum being fully explained through the steady flux of the accretion disk and the broad-line region. Unlike the aforementioned bands, the very-high-energy γ -ray and X-ray fluxes did not exhibit a significant flux drop from year to year. This suggests that the steady-state very-high-energy γ -ray and X-ray fluxes originate from a different emission region than the vanished parts of the high-energy γ -ray and optical jet fluxes. The latter component has disappeared through either a swing of the jet away from the line of sight or a significant drop in the photon production efficiency of the jet close to the black hole. Either change could become visible in high-resolution radio images.

Magnetic fields in galaxies and galaxy clusters are believed to be the result of the amplification of intergalactic seed fields during the formation of large-scale structures in the universe. However, the origin, strength, and morphology of this intergalactic magnetic field (IGMF) remain unknown. Lower limits on (or indirect detection of) the IGMF can be obtained from observations of high-energy gamma rays from distant blazars. Gamma rays interact with the extragalactic background light to produce electron−positron pairs, which can subsequently initiate electromagnetic cascades. The gamma-ray signature of the cascade depends on the IGMF since it deflects the pairs. Here we report on a new search for this cascade emission using a combined data set from the Fermi Large Area Telescope and the High Energy Stereoscopic System. Using state-of-the-art Monte Carlo predictions for the cascade signal, our results place a lower limit on the IGMF of B > 7.1 × 10 ^−16 G for a coherence length of 1 Mpc even when blazar duty cycles as short as 10 yr are assumed. This improves on previous lower limits by a factor of 2. For longer duty cycles of 10 ^4 (10 ^7 ) yr, IGMF strengths below 1.8 × 10 ^−14 G (3.9 × 10 ^−14 G) are excluded, which rules out specific models for IGMF generation in the early universe.

GRB 221009A is the brightest gamma-ray burst (GRB) ever detected. To probe the very-high-energy (VHE; >100 GeV) emission, the High Energy Stereoscopic System (H.E.S.S.) began observations 53 hr after the triggering event, when the brightness of the moonlight no longer precluded observations. We derive differential and integral upper limits using H.E.S.S. data from the third, fourth, and ninth nights after the initial GRB detection, after applying atmospheric corrections. The combined observations yield an integral energy flux upper limit of ${{\rm{\Phi }}}_{\mathrm{UL}}^{95 \% }=9.7\times {10}^{-12}\,\mathrm{erg}\,{\mathrm{cm}}^{-2}\,{{\rm{s}}}^{-1}$ above E _thr = 650 GeV. The constraints derived from the H.E.S.S. observations complement the available multiwavelength data. The radio to X-ray data are consistent with synchrotron emission from a single electron population, with the peak in the spectral energy distribution occurring above the X-ray band. Compared to the VHE-bright GRB 190829A, the upper limits for GRB 221009A imply a smaller gamma-ray to X-ray flux ratio in the afterglow. Even in the absence of a detection, the H.E.S.S. upper limits thus contribute to the multiwavelength picture of GRB 221009A, effectively ruling out an IC-dominated scenario.

Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.

Abstract Background Inadequate systemic exposure to infliximab (IFX) is associated with treatment failure. This work evaluated factors associated with reduced IFX exposure in children with autoimmune disorders requiring IFX therapy. Methods In this single-center cross-sectional prospective study IFX trough concentrations and anti-drug antibodies (ADAs) were measured in serum from children diagnosed with inflammatory bowel disease (IBD) (n = 73), juvenile idiopathic arthritis (JIA) (n = 16), or uveitis (n = 8) receiving maintenance IFX infusions at an outpatient infusion clinic in a tertiary academic pediatric hospital. IFX concentrations in combination with population pharmacokinetic modeling were used to estimate IFX clearance. Patient demographic and clinical data were collected by chart review and evaluated for their relationship with IFX clearance. Results IFX trough concentrations ranged from 0 to > 40 μg/mL and were 3-fold lower in children with IBD compared to children with JIA (p = 0.0002) or uveitis (p = 0.001). Children with IBD were found to receive lower IFX doses with longer dosing intervals, resulting in dose intensities (mg/kg/day) that were 2-fold lower compared to children with JIA (p = 0.0002) or uveitis (p = 0.02). Use of population pharmacokinetic analysis to normalize for variation in dosing practices demonstrated that increased IFX clearance was associated with ADA positivity (p = 0.004), male gender (p = 0.02), elevated erythrocyte sedimentation rate (ESR) (p = 0.02), elevated c-reactive protein (CRP) (p = 0.001), reduced serum albumin concentrations (p = 0.0005), and increased disease activity in JIA (p = 0.009) and IBD (p ≤ 0.08). No significant relationship between diagnosis and underlying differences in IFX clearance was observed. Multivariable analysis by covariate population pharmacokinetic modeling confirmed increased IFX clearance to be associated with anti-IFX antibody positivity, increased ESR, and reduced serum albumin concentrations. Conclusions Enhanced IFX clearance is associated with immunogenicity and inflammatory burden across autoimmune disorders. Higher systemic IFX exposures observed in children with rheumatologic disorders are driven primarily by provider drug dose and interval selection, rather than differences in IFX pharmacokinetics across diagnoses. Despite maintenance IFX dosing at or above the standard recommended range for IBD (i.e., 5 mg/kg every 8 weeks), the dosing intensity used in the treatment of IBD is notably lower than dosing intensities used to treat JIA and uveitis, and may place some children with IBD at risk for suboptimal maintenance IFX exposures necessary for treatment response.

Abstract Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), the discovery of biomarkers that inform optimal dose selection (precision dosing) continues to be a challenge in many therapeutic areas. Larger and more diverse study populations are necessary to discover additional biomarkers that provide the resolution needed for a more tailored dose. To generate and accommodate large datasets of drug response phenotypes, time‐ and cost‐efficient strategies are necessary. In particular, a multitude of technological advances that originated for purposes outside of biomedical research (electronic health records, direct‐to‐consumer genetic testing, social media, mobile devices, and machine learning) have made it easier to communicate, connect, and gather information from consumers. Although these technologies have been used with success in the health sciences for an array of purposes, these resources have not been fully capitalized on for precision dosing. This perspective will touch on how these innovations can be used as data sources, data collection tools, and data processing tools for drug‐response phenotypes with a unique focus on advancing biomarker‐driven precision dosing.

Abstract Background The COVID-19 pandemic has disrupted routine measles immunisation and supplementary immunisation activities (SIAs) in most countries including Kenya. We assessed the risk of measles outbreaks during the pandemic in Kenya as a case study for the African Region. Methods Combining measles serological data, local contact patterns, and vaccination coverage into a cohort model, we predicted the age-adjusted population immunity in Kenya and estimated the probability of outbreaks when contact-reducing COVID-19 interventions are lifted. We considered various scenarios for reduced measles vaccination coverage from April 2020. Results In February 2020, when a scheduled SIA was postponed, population immunity was close to the herd immunity threshold and the probability of a large outbreak was 34% (8–54). As the COVID-19 contact restrictions are nearly fully eased, from December 2020, the probability of a large measles outbreak will increase to 38% (19–54), 46% (30–59), and 54% (43–64) assuming a 15%, 50%, and 100% reduction in measles vaccination coverage. By December 2021, this risk increases further to 43% (25–56), 54% (43–63), and 67% (59–72) for the same coverage scenarios respectively. However, the increased risk of a measles outbreak following the lifting of all restrictions can be overcome by conducting a SIA with ≥ 95% coverage in under-fives. Conclusion While contact restrictions sufficient for SAR-CoV-2 control temporarily reduce measles transmissibility and the risk of an outbreak from a measles immunity gap, this risk rises rapidly once these restrictions are lifted. Implementing delayed SIAs will be critical for prevention of measles outbreaks given the roll-back of contact restrictions in Kenya.

Variation in methotrexate (MTX) efficacy represents a significant barrier to early and effective disease control in the treatment of autoimmune arthritis. We hypothesize that the utilization of metabolomic techniques will allow for an improved understanding of the biochemical basis for the pharmacological activity of MTX, and can promote the identification and evaluation of novel molecular biomarkers of MTX response. In this work, erythroblastoid cells were exposed to MTX at the physiologic concentration of 1,000 nM and analyzed using three metabolomic platforms to give a broad spectrum of cellular metabolites. MTX pharmacological activity, defined as cellular growth inhibition, was associated with an altered cellular metabolomic profile based on the analysis of 724 identified metabolites. By discriminant analysis, MTX treatment was associated with increases in ketoisovaleric acid, fructose, galactose, and 2‐deoxycytidine, and corresponding reductions in 2‐deoxyuridine, phosphatidylinositol 32:0, orotic acid, and inosine monophosphate. Inclusion of data from analysis of folate metabolism in combination with chemometric and metabolic network analysis demonstrated that MTX treatment is associated with dysregulated folate metabolism and nucleotide biosynthesis, which is in line with its known mechanism of action. However, MTX treatment was also associated with alterations in a diversity of metabolites, including intermediates of amino acid, carbohydrate, and lipid metabolism. Collectively, these findings support a robust metabolic response following exposure to physiologic concentrations of MTX. They also identify various metabolic intermediates that are associated with the pharmacological activity of MTX, and are, therefore, potential molecular biomarker candidates in future preclinical and clinical studies of MTX efficacy in autoimmune arthritis.

The Cherenkov Telescope Array and the KM3NeT neutrino telescopes are major upcoming facilities in the fields of γ -ray and neutrino astronomy, respectively. Possible simultaneous production of γ rays and neutrinos in astrophysical accelerators of cosmic-ray nuclei motivates a combination of their data. We assess the potential of a combined analysis of CTA and KM3NeT data to determine the contribution of hadronic emission processes in known Galactic γ -ray emitters, comparing this result to the cases of two separate analyses. In doing so, we demonstrate the capability of Gammapy, an open-source software package for the analysis of γ -ray data, to also process data from neutrino telescopes. For a selection of prototypical γ -ray sources within our Galaxy, we obtain models for primary proton and electron spectra in the hadronic and leptonic emission scenario, respectively, by fitting published γ -ray spectra. Using these models and instrument response functions for both detectors, we employ the Gammapy package to generate pseudo data sets, where we assume 200 h of CTA observations and 10 years of KM3NeT detector operation. We then apply a three-dimensional binned likelihood analysis to these data sets, separately for each instrument and jointly for both. We find that the largest benefit of the combined analysis lies in the possibility of a consistent modelling of the γ -ray and neutrino emission. Assuming a purely leptonic scenario as input, we obtain, for the most favourable source, an average expected 68% credible interval that constrains the contribution of hadronic processes to the observed γ -ray emission to below 15%. [ABSTRACT FROM AUTHOR]

Methotrexate (MTX) is widely used in the treatment of autoimmune arthritis but is limited by its unpredictable and variable response profile. Currently, no biomarkers exist to predict or monitor early therapeutic responses to MTX. Using a collagen-induced arthritis (CIA) mouse model, this study aimed to identify biochemical pathways and biomarkers associated with MTX efficacy in autoimmune arthritis. Following arthritis disease induction, DBA/1J mice were treated with subcutaneous MTX (20 mg/kg/week) and disease activity was assessed based on disease activity scores (DAS) and paw volume (PV) measurements. Red blood cell (RBC) and plasma samples were collected at the end of the study and were assessed for folate and MTX content. Plasma samples were analyzed by semitargeted global metabolomic profiling and analyzed by univariate and multivariate analysis. Treatment with MTX was associated with significant reductions in disease activity based on both DAS (p = 0.0006) and PV (p = 0.0006). MTX therapy resulted in significant reductions in 5-methyltetrahydrofolate (5mTHF) levels in plasma (p = 0.02) and RBCs (p = 0.001). Reductions in both RBC and plasma 5mTHF were associated with lower DAS (p = 0.0007, p = 0.01, respectively) and PV (p = 0.001, p = 0.005, respectively). Increases in RBC MTX were associated with lower DAS (p = 0.003) but not PV (p = 0.23). Metabolomic analysis identified N-methylisoleucine (NMI) and quinolone as metabolites significantly altered in disease mice, which were corrected towards healthy control levels in mice treated with MTX. Reductions in plasma NMI were associated with lower DAS (p = 0.0002) and PV (p = 9.5 × 10−6). Increases in plasma quinolone were associated with lower DAS (p = 0.02) and PV (p = 0.01). Receiver-operating characteristic curve analysis identified plasma NMI (AUC = 1.00, p = 2.4 × 10−8), RBC 5mTHF (AUC = 0.99, p = 2.4 × 10−5), and plasma quinolone (AUC = 0.89, p = 0.01) as top discriminating metabolites of MTX treatment. Our data support a relationship between MTX efficacy and its effect on circulating folates and identified 5mTHF, NMI, and quinolone as potential therapeutic biomarkers of disease activity and MTX response in the CIA mouse model of autoimmune arthritis.

Methotrexate (MTX) efficacy in the treatment of rheumatoid arthritis (RA) is variable and unpredictable, resulting in a need to identify biomarkers to guide drug therapy. This study evaluates changes in the plasma metabolome associated with response to MTX in RA with the goal of understanding the metabolic basis for MTX efficacy towards the identification of potential metabolic biomarkers of MTX response. Plasma samples were collected from healthy control subjects (n = 20), and RA patients initiating MTX therapy (n = 20, 15 mg/week) before and after 16 weeks of treatment. The samples were analyzed by a semi-targeted metabolomic analysis, and then analyzed by univariate and multivariate methods, as well as an enrichment analysis. An MTX response was defined as a clinically significant reduction in the disease activity score in 28 joints (DAS-28) of greater than 1.2; achievement of clinical remission, defined as a DAS-28 < 2.6, was also utilized as an additional measure of response. In this study, RA is associated with an altered plasma metabolome that is normalized following initiation of MTX therapy. Metabolite classes found to be altered in RA and corrected by MTX therapy were diverse and included triglycerides (p = 1.1 × 10−16), fatty acids (p = 8.0 × 10−12), and ceramides (p = 9.8 × 10−13). Stratification based on responses to MTX identified various metabolites differentially impacted in responders and non-responders including glucosylceramides (GlcCer), phosphatidylcholines (PC), sphingomyelins (SM), phosphatidylethanolamines (PE), choline, inosine, hypoxanthine, guanosine, nicotinamide, and itaconic acid (p < 0.05). In conclusion, RA is associated with significant alterations to the plasma metabolome displaying at least partial normalization following 16 weeks of MTX therapy. Changes in multiple metabolites were found to be associated with MTX efficacy, including metabolites involved in fatty acid/lipid, nucleotide, and energy metabolism.

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases.

Evolution is a key aspect of the biology of many pathogens, driving processes ranging from immune escape to changes in virulence. Because evolution is inherently subject to feedbacks, and because pathogen evolution plays out at scales ranging from within-host to between-host and beyond, evolutionary questions provide special challenges to the modelling community. In this article, we provide an overview of five challenges in modelling the evolution of pathogens and their hosts, and point to areas for development, focussing in particular on the issue of linking theory and data.

Outbreaks of Ebola virus can cause substantial morbidity and mortality in affected regions. The largest outbreak of Ebola to date is currently underway in West Africa, with 3944 cases reported as of 5th September 2014. To develop a better understanding of Ebola transmission dynamics, we revisited data from the first known Ebola outbreak, which occurred in 1976 in Zaire (now Democratic Republic of Congo). By fitting a mathematical model to time series stratified by disease onset, outcome and source of infection, we were able to estimate several epidemiological quantities that have previously proved challenging to measure, including the contribution of hospital and community infection to transmission. We found evidence that transmission decreased considerably before the closure of the hospital, suggesting that the decline of the outbreak was most likely the result of changes in host behaviour. Our analysis suggests that the person-to-person reproduction number was 1.34 (95% CI: 0.92–2.11) in the early part of the outbreak. Using stochastic simulations we demonstrate that the same epidemiological conditions that were present in 1976 could have generated a large outbreak purely by chance. At the same time, the relatively high person-to-person basic reproduction number suggests that Ebola would have been difficult to control through hospital-based infection control measures alone.