Early appropriate antimicrobial therapy is crucial in patients with sepsis and septic shock. Studies often focus on time to first dose of appropriate antibiotics, but subsequent dosing is equally important. Our aim was to investigate the impact of fulfillment of early treatment, with focus on appropriate administration of first and second doses of antibiotics, on 28-day mortality in patients with community-onset severe sepsis and septic shock. A retrospective study on adult patients admitted to the emergency department with community-onset sepsis and septic shock was conducted 2012-2013. The criterion early appropriate antibiotic treatment was defined as administration of the first dose of adequate antibiotics within 1h, and the second dose given with less than 25% delay after the recommended dose interval. A high-risk patient was defined as a septic patient with either shock within 24h after arrival or red triage level on admittance according to the Medical Emergency Triage and Treatment System Adult. Primary endpoint was 28-day mortality. Of 90 patients, less than one in four (20/87) received early appropriate antibiotic treatment, and only one in three (15/44) of the high-risk patients. The univariate analysis showed a more than threefold higher mortality among high-risk patients not receiving early appropriate antibiotic treatment. Multivariable analysis identified early non-appropriate antibiotic treatment as an independent predictor of mortality with an odds ratio for mortality of 10.4. Despite that the importance of early antibiotic treatment has been established for decades, adherence to this principle was very poor.

Objectives The aim of this study was to identify prehospital and early hospital risk factors associated with 30-day mortality in patients with blood culture-confirmed community-acquired bloodstream infection (CA-BSI) in Sweden.

Methods A retrospective case–control study of 1624 patients with CA-BSI (2015–2016), 195 non-survivors satisfying the inclusion criteria were matched 1:1 with 195 survivors for age, gender and microorganism. All forms of contact with a healthcare provider for symptoms of infection within 7 days prior CA-BSI episode were registered. Logistic regression was used to analyse risk factors for 30-day all-cause mortality.

Results Of the 390 patients, 61% (115 non-survivors and 121 survivors) sought prehospital contact. The median time from first prehospital contact till hospital admission was 13 hours (6–52) for non-survivors and 7 hours (3–24) for survivors (p<0.01). Several risk factors for 30-day all-cause mortality were identified: prehospital delay OR=1.26 (95% CI: 1.07 to 1.47), p<0.01; severity of illness (Sequential Organ Failure Assessment score) OR=1.60 (95% CI: 1.40 to 1.83), p<0.01; comorbidity score (updated Charlson Index) OR=1.13 (95% CI: 1.05 to 1.22), p<0.01 and inadequate empirical antimicrobial therapy OR=3.92 (95% CI: 1.64 to 9.33), p<0.01. In a multivariable model, prehospital delay >24 hours from first contact remained an important risk factor for 30-day all-cause mortality due to CA-BSI OR=6.17 (95% CI: 2.19 to 17.38), p<0.01.

Conclusion Prehospital delay and inappropriate empirical antibiotic therapy were found to be important risk factors for 30-day all-cause mortality associated with CA-BSI. Increased awareness and earlier detection of BSI in prehospital and early hospital care is critical for rapid initiation of adequate management and antibiotic treatment.All data relevant to the study are included in the article or uploaded as supplemental information.

Background Rapid, adequate treatment is crucial to reduce mortality in sepsis. Risk stratification scores used at emergency departments (ED) are limited in detecting all septic patients with increased mortality risk. We assessed whether the addition of prehospital lactate analysis to clinical risk stratification tools improves detection of patients with increased risk for rapid deterioration and death in sepsis. Methods A10-month observational study with consecutive, prospective prehospital inclusion of adult patients with suspected sepsis. Prehospital lactate was used as a continuous variable and in intervals. Analyses of patient subgroups with high and lower priorities according to Rapid Emergency Triage and Treatment System (RETTS) and National Early Warning Score 2 (NEWS2) were performed. Primary outcome was 30-day mortality, secondary outcomes were sepsis at the ED and in-hospital mortality. Results In all, 714 patients were included with a 30-day mortality of 10%. Among the 322 cases (45%) fulfilling Sepsis-3 criteria, the 30-day mortality was 14%. Prehospital lactate was higher among non-survivors (2.6 vs 2.0 mmol/L, p < 0.001). Mortality at different lactate intervals were: 6.7%, at 0-2 mmol/l; 10.0% at > 2-3 mmol/l; 19.2% at > 3-4 mmol/l; and 17.0% at levels > 4 mmol/l. The highest RETTS priority (red) group had higher lactate levels than the lower (non-red) priority group (2.5 vs 1.9 mmol/L, p < 0.001). In the non-red group, prehospital lactate was higher among non-survivors (2.4 vs 1.8 mmol/L, p = 0.002). In the multivariable regression analysis, prehospital lactate > 3 mmol/l was a predictor of 30-day mortality (OR 2.20, p = 0.009) This association was even stronger in the lower priority RETTS non-red group (OR 3.02, p = 0.009). Adding prehospital lactate > 3 mmol/l increased identification of non-survivors from 48 to 68% in the RETTS red group and from 77 to 85% for the NEWS2 >= 7 group. Conclusion The addition of a prehospital lactate level > 3 mmol/l improved early recognition of individuals with increased mortality risk in a cohort with suspected sepsis admitted to the ED. This was particularly evident in patients whose risk stratification scores did not indicate severe illness. We suggest that the addition of prehospital lactate analysis could improve recognition of subjects with suspected sepsis and increased mortality risk.

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. We present a state-of-the-art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time-varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

We present a method for detecting patterns in time-varying functional magnetic resonance imaging (fMRI) data based on topological analysis. The oxygenated blood flow measured by fMRI is widely used as an indicator of brain activity. The signal is, however, prone to noise from various sources. Random brain activity, physiological noise, and noise from the scanner can reach a strength comparable to the signal itself. Thus, extracting the underlying signal is a challenging process typically approached by applying statistical methods. The goal of this work is to investigate the possibilities of recovering information from the signal using topological feature vectors directly based on the raw signal without medical domain priors. We utilize merge trees to define a robust feature vector capturing key features within a time step of fMRI data. We demonstrate how such a concise feature vector representation can be utilized for exploring the temporal development of brain activations, connectivity between these activations, and their relation to cognitive tasks.

We present a visual analysis environment based on a multi-scale partitioning of a 2d domain into regions bounded by cycles in weighted planar embedded graphs. The work has been inspired by an application in granular materials research, where the question of scale plays a fundamental role in the analysis of material properties. We propose an efficient algorithm to extract the hierarchical cycle structure using persistent homology. The core of the algorithm is a filtration on a dual graph exploiting Alexander's duality. The resulting partitioning is the basis for the derivation of statistical properties that can be explored in a visual environment. We demonstrate the proposed pipeline on a few synthetic and one real-world dataset.

This paper presents a nested tracking framework for analyzing cycles in 2D force networks within granular materials. These materials are composed of interacting particles, whose interactions are described by a force network. Understanding the cycles within these networks at various scales and their evolution under external loads is crucial, as they significantly contribute to the mechanical and kinematic properties of the system. Our approach involves computing a cycle hierarchy by partitioning the 2D domain into segments bounded by cycles in the force network. We can adapt concepts from nested tracking graphs originally developed for merge trees by leveraging the duality between this partitioning and the cycles. We demonstrate the effectiveness of our method on two force networks derived from experiments with photoelastic disks.

Studies of bicycle traffic characteristics are essential for understanding bicyclist behavior and developing suitable microscopic models for traffic simulation. Based on empirical data on real-world bicycle traffic, obtained through video-based data collection techniques, we describe characteristics of free-riding, following, and overtaking that facilitate the simulation of bidirectional bicycle traffic. In total, we analyze data from 195 605 bicyclists across seven bicycle paths in Sweden, with five located in Stockholm and two in Gothenburg. The analysis reveals multimodal distributions of speed and lateral position due to the distinct preferences of bicyclists that vary according to the type of bicycle used. Moreover, speeds are generally highest during morning rush hours at the locations under investigation. Based on wind measurements, we conclude that there is no statistically significant effect on mean free speed from wind speeds in the range of +/- 3 m/s. The distribution of speed differences between bicyclists at overtakings indicates a broad range of speed disparities among bicyclists, and is significantly influenced by infrastructure design. Furthermore, infrastructure design (path width, horizontal alignment, and presence of fixed objects along the edge) and traffic volumes significantly lateral positioning. Our results demonstrate the inherent heterogeneity in the characteristics of bicyclists, underscoring the need to advance modeling to incorporate these distinct characteristics into microscopic traffic simulation.

Microscopic traffic simulation is a useful tool for the planning of motorized traffic, yet bicycle traffic still lacks this type of modeling support. Nonetheless, certain microscopic traffic simulators, such as Vissim, model bicycle traffic by applying models originally designed for car traffic. The gradient of a bicycle path has a significant impact on the speed of cyclists; therefore, this impact should be captured in microscopic traffic simulation. We investigate two calibration approaches to reproduce the effect of gradient on the speed of cyclists using the default driver behavioral model in Vissim. The first approach is to modify the simulated gradient to represent different values of the gradient-acceleration parameter: a fixed value that represents a decrease in the maximum acceleration that cyclists can apply on an uphill. The second approach is to adjust the maximum-acceleration function. We evaluate both approaches by applying a Vissim model of a bidirectional bicycle path with a 3% gradient in Stockholm. The results show that the current default implementation in the Vissim model underestimates the effect of gradient on speed. Moreover, the gradient-acceleration parameter does not directly reduce the maximum acceleration of all cyclists, but only of those cyclists riding above a certain speed. We conclude that by using a higher gradient-acceleration value than the default, we accurately estimate the observed mean speed on the uphill. However, neither of the investigated calibration approaches provides accurate estimates of the speed distributions. We emphasize the need for developing more accurate behavioral models designed for cyclists.

To simulate bicycle traffic accurately, it is essential to capture how bicyclists react to features of the infrastructure such as the longitudinal gradient of a bicycle path. Bicycling requires human-powered motion, and the power output provided by bicyclists differ significantly among bicyclists due to physical capabilities and preferences. Therefore, the objective of this paper is to investigate the connection between power output and gradient in bicycle traffic, with the purpose of developing a power-based model that predicts accurately the speed of bicyclists. Based on trajectory data of free-riding bicyclists travelling on a non-flat bicycle path segment, we estimate changes in power output as a function of gradient considering the physical forces acting on a bicycle. The results suggest a linear correlation between power output and gradient; while bicyclists increase their power output on the uphill as gradient increases, they decrease their power output on the downhill as gradient increases. By implementing this correlation into a traffic simulation algorithm, we show that the simulation captures well the impact of gradients in a population of bicyclists as it reproduces similar speed profiles. We conclude that bicyclists adapt their power output to compensate for the gradient and its associated change in speed, and that the impact of gradient varies greatly among bicyclists. Furthermore, we conclude that power-based modelling of free-riding bicyclists is an attractive alternative to investigate further.

Understanding free riding behavior-where bicyclists are unconstrained by other road users or traffic control measures-is essential for planning efficient and appealing bicycle traffic systems. Bicyclist behavior is shaped by a combination of environmental conditions and individual preferences. This study examines free riding behavior, and identifies correlations with individual characteristics and contextual features such as infrastructure design (slopes and curves) and wind speed. We introduce a method using instrumented bicycles in a semi-controlled experiment to collect data describing the speed, power output, and heart rate of commuting bicyclists. Participants in two study populations (28 in Sweden and 29 in Germany) ride their bicycles equipped with sensors along designated routes during off-peak demand periods, enabling comparative analysis of different trip features. Results highlight significant inter-and intrapersonal variations in speed and power output along a trip. Approximately 80 percent of the variation in free riding speed and power output over a trip, and over both populations of bicyclists, is explained by gender, individual preferences, topography, curvature, crossing intersections, and wind speeds. Headwinds and uphills generally reduce speeds but bicyclists increase power output to partially offset these effects. Downhills lead to high speed variation and distinct tactical behaviors, such as braking, coasting, and accelerating. These findings underscore the complexity of bicycling behavior and quantify how bicyclists adapt to varying features of the trip.

The starting point for developing teaching abilities is the experiences that you have of the object of learning. To prepare student teachers for teaching technology in a digital society, research related to their experiences of phenomena such as programmed technological artefacts is important. As future teachers, they must be able to prepare teaching about programmed technological artefacts as objects of learning. Therefore, more knowledge is needed about student teachers’ understanding of them in everyday life.

To describe the different ways in which student teachers experience programmed technological artefacts, such as elevators, tumble dryers, traffic lights, and keyboards, we used a phenomenographic approach. Semi-structured interviews with eight student teachers in primary teacher education were conducted to find the variation in their experiences. This resulted in experiences described as: (1) the physical interface, (2) components as parts of a process, (3) connected, controlled, and regulated components, and (4) components as and in a system.

Aspects essential for understanding programmed technological artefacts are related to three dimensions; computational thinking, systems thinking and the dual nature of technology. For student teachers to understand these artefacts, aspects from all three dimensions need to be discerned. A problem is that student teachers in this study discerned only a few aspects of the phenomenon that they themselves will be teaching about.

This study explores the changing landscape of technology teacher education, in relation to the increasing integration of digital content, especially programming, in teacher education for grades 4–6 (pupils 10-12 years old) and how student teachers in Sweden perceive this content. Limited research exists on student teachers in technology, particularly focusing on programming. This study therefore investigates student teachers' perceptions of teaching programming in technology education, after completing their technology course in teacher education. We answer the following research questions: What are the student teachers’ perceptions of teaching programming in technology education? and How is potential subject didactics knowledge for teaching programming manifested in student teachers’ perceptions of technology teaching? Using a phenomenographic approach, 25 student teachers’ perceptions of programming in technology education were investigated through semi-structured individual and group interviews. Different perceptions were revealed and presented in four categories: (1) following instructions in a logical order, (2) learning a programming language, (3) solving technological problems, and (4) understanding and describing a technological environment. The results show that student teachers' perceptions of the subject of technology predominantly focuses on following instructions and the learning of a programming language. The identified potential subject didactics knowledge is constituted of an awareness of three critical aspects: understanding programming language, understanding programming as a way of solving problems, and the relationships of technological problems to everyday life and society. This study offers valuable insight into the development of competencies required to teach programming in technology, informing educational strategies and future research in this emerging field.

As programming has become an integral part of technology education in Sweden, teacher education must prepare future teachers to integrate programming meaningfully into their teaching. However, research on how teacher educators understand programming in technology teacher education remains scarce. The purpose of this study is to explore teacher educators' experience of programming in primary technology teacher education. The phenomenon, programming in technology, is explored by using a phenomenographic approach in order to find the variations of experiences among a group of teacher educators. The empirical material consists of interviews with 12 technology teacher educators from 11 Swedish higher education institutions. The results identify four qualitatively distinct categories of how programming is experienced: (1) as a technological tool, (2) as a technological tool for control, (3) as a technological tool for problem-solving, and (4) as part of technological solutions in society. The study addresses the importance of the relationships between programming, systems thinking, and computational thinking, ensuring that student teachers develop a holistic understanding of programming as part of technological knowledge. The study also provides information that the understanding of the relationship between technological education and educational technology is crucial for the relevance of programming in technology education.

One of the key advantages of perovskite light-emitting diodes (PeLEDs) is their potential to achieve high performance at much higher current densities compared to conventional solution-processed emitters. However, state-of-the-art PeLEDs have not yet reached this potential, often suffering from severe current-efficiency roll-off under intensive electrical excitations. Here, we demonstrate bright PeLEDs, with a peak radiance of 2409 W sr-1 m-2 and negligible current-efficiency roll-off, maintaining high external quantum efficiency over 20% even at current densities as high as 2270 mA cm-2. This significant improvement is achieved through the incorporation of electron-withdrawing trifluoroacetate anions into three-dimensional perovskite emitters, resulting in retarded Auger recombination due to a decoupled electron-hole wavefunction. Trifluoroacetate anions can additionally alter the crystallization dynamics and inhibit halide migration, facilitating charge injection balance and improving the tolerance of perovskites under high voltages. Our findings shed light on a promising future for perovskite emitters in high-power light-emitting applications, including laser diodes.

Despite the enormous developments in perovskite light-emitting diodes (PeLEDs) recently, obtaining efficient blue PeLEDs is still considered a critical challenge due to the non-radiative recombination and unbalanced charge injection caused by the unmatched carrier mobility and the deep hole-injection barrier between the hole-transport layer (HTL) and the emissive layer (EML). Herein, we incorporate tris(4-trifluoromethylphenyl)phosphine oxide (TMFPPO), obtained through a facile oxidation synthesis process, into poly(9-vinylcarbazole) (PVK). TMFPPO incorporation modulated the energy level and hole mobility of the binary-blend HTLs to eliminate the hole-injection barrier and balance the charge injection within the EML. Consequently, the blue PeLEDs with blended HTL presented an external quantum efficiency (EQE) of 7.23% centred at 477 nm, which was much higher than the EQE of a PVK device (4.95%). Our results demonstrate that modulating the energy level and charge injection of the HTL in the device is a promising method for obtaining efficient blue PeLEDs. TMFPPO is developed and incorporated into PVK to modulate the hole mobility and energy level of the hole-transport layer, giving rise to a barrier-free blue perovskite light-emitting diode and an enhancement of the EQE from 4.95 to 7.23% at 477 nm.

Background: Child and young adult refugees are a heterogeneous group comprising both vulnerable and resilient individuals. Person-centered statistical methods could help disentangle this heterogeneity, enabling tailored interventions. This systematic review examined person-centered studies on adversity, mental health, and resilience in children and young adults with refugee backgrounds to identify subgroups and assess their theoretical and practical relevance. Methods: The strategy included three search blocks: 1) refugee, 2) child and/or youth, and 3) person-centered method. Studies were identified through searches of PubMed, Academic Search Complete, Scopus, PsycINFO, CINAHL, ERIC, and Cochrane. The search included all published studies until December 2023. Studies were eligible for review if they used adversity, mental health or resilience variables as indicators in a person-centered analysis. The study population needed to have a refugee background with a mean age of <= 25. The reporting quality of the studies was assessed using the adapted version of the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist. The results were analyzed in a narrative format and using summary tables. Results: A total of 6706 studies were initially identified, of which seven were eligible for review. The studies included 2409 individuals and were conducted in refugee camps, communities, and institutional and clinical settings across Africa, the Middle East, Europe, Asia, and North America. Five of the seven studies included adversity as an indicator, and three articles mental ill-health. Only one article specifically investigated resilience. All studies identified subgroups, but the findings regarding predictors of group membership were inconclusive. Risks for adverse outcomes, such as mental health problems, also varied across subgroups. The studies generally displayed inadequate reporting of important methodological aspects of the data analysis, a lack of theoretical consideration, and an absence of reliability testing. Conclusions: The use of person-centered approaches in research on children and young adults with refugee backgrounds, focusing on adversity, mental health, and resilience, is currently limited. Nevertheless, the reviewed studies provided valuable insights into subgroups within this population, indicating that personcentered approaches can be employed when studying this group. Future research should consider theory and prior knowledge in the selection of the final number of groups, thoroughly report quality criteria, and rigorously test the reliability of classes.

ObjectiveChildren and young adults comprise a significant proportion of the world´s refugee population and are disproportionately negatively affected by the social determinants of health. This heterogeneous group faces high rates of poor mental health, yet research investigating within-group inequalities in mental health remains limited. We performed a latent profile analysis to explore classes of mental health based on posttraumatic stress symptoms (PTSS), general functioning, and well-being. This study aimed to improve the understanding of mental health differences, thereby providing better guidance for assessment and tailored interventions.MethodThis study involved 131 children and 127 young adults with refugee backgrounds (mean age 18.21 years, 44.6% female, 23.6% unaccompanied) recruited nationwide in Sweden (2019-2022). To examine classes and their predictors, latent profile analysis was conducted, followed by multinomial logistic regression analysis.ResultsLatent profile analysis identified four distinct classes: Good Mental Health (58.1%; low PTSS, good functioning and well-being), Severe Mental Distress (13.6%; high PTSS, low functioning and well-being), Moderate Mental Strain (12.4%; low PTSS, moderate functioning, low well-being), and Resilient (15.9%, high PTSS, good functioning, moderate well-being). Social determinants of health, such as being unaccompanied, asylum status, exposure to multiple types of violence, sexual victimization, and child maltreatment, distinguished the classes.ConclusionChildren and young adults with refugee backgrounds can be categorized into classes based on clinically relevant mental health indicators. Focusing solely on those at the highest risk for poor mental health may overlook many who are mentally healthy and those who need more targeted support. Future research should aim to replicate findings and evaluate additional predictive factors at the family and societal levels.

Research on complex posttraumatic stress disorder (CPTSD) among individuals with refugee backgrounds is limited, and its validity within this group remains underexplored. This study aimed to assess the prevalence and discriminant validity of posttraumatic stress disorder (PTSD) and CPTSD, as well as the risk factors for CPTSD, in a community sample of adolescents with refugee backgrounds residing in Sweden. This study involved 296 adolescents. Probable diagnoses were evaluated according to DSM-5 and ICD-11 criteria. Latent class analysis was employed to examine the discriminant validity of PTSD and CPTSD, while logistic regression analysis was used to explore risk factors for CPTSD. The findings indicated that 24.1% had a probable diagnosis of PTSD according to the DSM-5. For ICD-11, the equivalent proportions were 7.1% for PTSD and 10.8% for CPTSD. Latent class analysis identified three distinct classes: Low symptoms (46.9%), PTSD (29.6%), and CPTSD (23.6%). Compared to the PTSD class, membership in the CPTSD class was predicted by exposure to more types of violence and child maltreatment. It was also associated with higher posttraumatic stress symptoms, worse general functioning, poorer mental well-being, increased suicidal thoughts, more treatment-seeking behavior, and greater comorbidity. This study found a high prevalence of PTSD and CPTSD among adolescents with refugee backgrounds living in Sweden. Distinct classes aligned with the ICD-11 formulation of PTSD and CPTSD were identified, with exposure to violence and child maltreatment emerging as key risk factors for CPTSD. Results underscore the importance of identifying and addressing posttraumatic stress in adolescents with refugee backgrounds. Future research should aim to further validate the CPTSD diagnosis in larger samples of adolescents with refugee backgrounds.

Gadolinium chelates are employed worldwide today as clinical contrast agents for magnetic resonance imaging. Until now, the commonly used linear contrast agents based on the rare-earth element gadolinium have been considered safe and well-tolerated. Recently, concerns regarding this type of contrast agent have been reported, which is why there is an urgent need to develop the next generation of stable contrast agents with enhanced spin-lattice relaxation, as measured by improved T-1 relaxivity at lower doses. Here, we show that by the integration of gadolinium ions in cerium oxide nanoparticles, a stable crystalline 5 nm sized nanoparticulate system with a homogeneous gadolinium ion distribution is obtained. These cerium oxide nanoparticles with entrapped gadolinium deliver strong T-1 relaxivity per gadolinium ion (T-1 relaxivity, r(1) = 12.0 mM(-1) s(-1)) with the potential to act as scavengers of reactive oxygen species (ROS). The presence of Ce3+ sites and oxygen vacancies at the surface plays a critical role in providing the antioxidant properties. The characterization of radial distribution of Ce3+ and Ce4+ oxidation states indicated a higher concentration of Ce3+ at the nanoparticle surfaces. Additionally, we investigated the ROS-scavenging capabilities of pure gadolinium-containing cerium oxide nanoparticles by bioluminescent imaging in vivo, where inhibitory effects on ROS activity are shown.

Lysosomes are known as key players in cellular signalling and act as terminal degradation stations involved in a multitude of cellular processes. Being a highly influential physiological factor, pH is essential in the regulation of lysosome-mediated physiological and pathological processes. Aberrant pH fluctuations are highly related to lysosomal dysfunction that correlates to lysosomal storage diseases and neurodegenerative disorders. As such, real-time quantitative monitoring of lysosomal pH (pHL) is crucial for gaining insight into lysosomal dysfunction but challenging by the lack of effective lysosome-specific probes with high signal fidelity. Toward this end, we have proposed a lysosomal fluorogenic nanoprobe (TR-MP) for reliable ratiometric measuring of pHL. It is fabricated by rational manipulation of fluorescence resonance energy transfer (FRET) in a tailorable nanoplatform. The nanoprobe consists of biocompatible silica nanoparticles assembled with a pH-sensitive rhodamine derivative (RDM-TEOS) as an acceptor and aggregation-induced emission (AIE) fluorophore (TPE-OMe) as a donor to ensure high energy transfer efficiency. Further equipped with cell-penetrating facilitator and morpholine to enable effective cell-internalization and high lysosome affinity of TR-MP. Results show that TR-MP can quantitatively measure pH in a range of 3.0 - 7.0 and detect pHL fluctuations in live cells under various stimuli, as well as real-time monitor pHL during apoptosis.

Background Thrombin is a multifunctional regulatory enzyme of the haemostasis and has both pro- and anticoagulant roles. It has, therefore, been a main target for drug discovery over many decades. Thrombin is a serine protease and possesses two positively charged regions called exosites, through which it is known to bind to many substrates. Dabigatran is a thrombin inhibitor and is widely used as an oral anticoagulant for the antithrombotic treatment of atrial fibrillation and venous thromboembolism. The mechanism by which dabigatran inhibits thrombin is the blockage of the active site, however, its effect on thrombin binding to its substrates has not been studied thoroughly and is thus poorly understood. Material and Methods The effect of dabigatran on thrombin binding to platelets was evaluated by flow cytometry using fluorescently labelled thrombin and washed platelets. Further, to confirm the results we utilized modern techniques for biomolecular binding studies, microscale thermophoresis (MST) and surface plasmon resonance (SPR), which validated the results. Results Dabigatran inhibited thrombin binding to platelets as analysed by flow cytometry. The inhibition was dose dependent with IC50 of 118 nM which was slightly lower than for inhibition of platelet activation and is close to the clinically relevant plasma concentration of dabigatran. MST and SPR also confirmed inhibitory effect of dabigatran on thrombin binding to platelets. Conclusion Apart from blocking the active site, dabigatran also inhibits thrombin binding to platelets. Since thrombin has numerous functions beyond the cardiovascular system, this finding may have important implications.

Organizations that deal with humanitarian assistance, disaster response and military activities are often exposed to dynamic environments where chaos rules. Under these circumstances, standard operating procedures may not be always be applicable, forcing the controllers to resort to opportunistic, or even scrambled, control. The lack of tactical or strategic control forces the teams to rely on experience from scenario-based training and prior missions. Acquiring, and retaining, such experience is thus essential to prepare for future events. Based on ideas from the knowledge management community, this article proposes an externalizable control model, supporting methods for retaining mission experience through internalization via hypermedia. Such a knowledge base of experience can be used to simplify knowledge sharing, an important matter since first-hand experience from rare and extreme events is, naturally, rare. The knowledge base synthesizes actual decision making processes, complete with context, history, cues, and interactions and is captured through a combination of heterogeneous multimedia recordings, sensor readings, and documents relating to the mission. The approach can complement regular training and apprenticeships, to help establish and maintain a pool of knowledge and increase tactical commanders' recognition-primed decision-making capability.

In 2010, IT-security experts from northern European governments and organizations gathered to conduct the first of a series of NATO-led cyber-defense exercises in a pilot attempt of training cyber defense. To gain knowledge on how to assess team effectiveness in cyber-defense exercises, this case study investigates the role of behavioral assessment techniques as a complement to task-based performance measurement. The collected data resulted in a massive data set including system logs, observer reports, and surveys. Six different methods were compared for feasibility in assessing the teams’ performance, including automated availability check, exploratory sequential data analysis, and network intrusion detection system attack analysis. In addition, observer reports and surveys were used to collect aspects relating to team structures and processes, aiming to discover whether these aspects can explain differences in effectiveness. The crossdisciplinary approach and multiple metrics create possibilities to study not only the performance-related outcome of the exercise, but also why this result is obtained. The main conclusions found are (1) a combination of technical performance measurements and behavioral assessment techniques are needed to assess team effectiveness, and (2) cyber situation awareness is required not only for the defending teams, but also for the observers and the game control.

Human factors research popularly employs perception-based techniques to investigate team performance and its dependency to cognitive processes. Such studies frequently rely upon either observer-based or self-assessment techniques to collect data. In this study, we examined behavioral observer ratings and self-assessment ratings for measuring team performance in virtual teams, with team performance regarded as a combination of task outcome and team cognition. Juxtaposing self-assessments and observer ratings from a quasi-experiment comparing team performance rating techniques reveals that they indeed produce overall similar results, with both singling out teamwork effectiveness ratings as the strongest contributor to overall team performance. However, the comparisons show remarkably low correlation on individual questionnaire items. The most striking difference is that the team members self-assessments of workload are lower than the corresponding observer ratings. In particular, the self-assessments do not correlate at all with overall team performance, whereas the observers workload ratings are more consistent with contemporary research that suggests a strong correlation between workload and team performance, suggesting that observer-based techniques are more reliable than self-assessments for assessing workload. For other ratings, the results show that the two techniques are fairly equal, suggesting that the choice between methods to employ can be deferred to other considerations such as obtrusiveness, accessibility, and resource availability.

Automation is the holy grail of performance assessment. Cheap and reliable automated systems that produce consistent feedback on performance. Many such systems have been proposed that accurately measure the state of a product or the outcome of a process. Procedural faults can be detected and even mitigated without the need for human interference. In production industry and professional sports, this is a natural part of business. However, in macrocognitive team performance studies, human appraisal is still king. This study investigates the reliability of human observers as assessors of performance among virtual teams, and what they base their assessments on when only able to monitor one of the team members at a time. The results show that expert observers put a lot of emphasis on task outcomes and on communication and are generally reliable raters of team performance, but there are several aspects that they cannot rate reliably under these circumstances, e.g., team workload, stress, and collaborative problem-solving. Through simple algorithms, this study shows that by capturing task scores and different quantitative communication metrics, team performance ratings can be estimated to closely match how the expert observers assess team performance in a virtual team setting. The implication of the study is that numeric team performance estimations can be acquired by automated systems, with reasonable accuracy and reliability compared to observer ratings.

Policy coherence is crucial in the 2030 Agenda's transformative ambitions and heralded as of paramount importance to ensure the successful implementation of the 17 Sustainable Development Goals and climate policy targets. Despite political efforts to achieve policy coherence, apparent trade-offs and goal conflicts have emerged – even in a proclaimed ‘front-runner’ country like Sweden. This paper examines the role of ideas in proposing and legitimising policy options and achieving policy coherence in the light of the Swedish recovery debate in 2020 following the COVID-19 pandemic. Ideas of a green economic recovery put forward in the public debate are examined through thematic text and frame analysis. We show that ideas of a green transition, boosted by economic recovery spending, draw on a synergistic frame in combining social, environmental, and economic policy options, carrying a potential for coherency. However, the absence of a discussion on power, as in who stands to gain what under which circumstances, coupled with an inherent understanding of a temporal hierarchy of policy priorities does not only impact the ability to design coherent policies but may have considerable impacts on the prospects of achieving sustainability transformations.

Achieving policy coherence between conflicting objectives of sustainable development has long been on the agendas of policymakers, practitioners and researchers alike. Turning the ideational analysis of policy studies to the focus of policy coherence, and particularly coherence in implementation of potentially conflicting sustainability goals, this paper addresses a conceptual and an empirical research gap. This study departs from the empirical example of domestic aviation in Sweden, a topic which is characterized by diverging ideas of sustainability. The polarized idea of sustainability provides a particularly fruitful base to address how ideas of policy coherence impact process and outcomes; by applying a simplified framework of analysis of observed ideational and implementation incoherence. The study finds that formulated coherence on a national level does contribute to perceived clarity of responsibility and cooperation, but that this legitimizes business as usual around ideas of growth and development. On the other hand, ideas regarding incoherence are closely linked to perceptions of justice and fairness in policy processes and ultimately an understanding of outcomes as illegitimate and unfit for rural realities; contributing to our understanding that despite a shared idea of the need for sustainable development certain sustainability policies may still face backlash.

Operationalizing the indivisibility and integrated nature of the UN 2030 Agenda poses a complex governance challenge. Although research has advanced our understanding of SDG localization and interlinkages, important gaps remain. In this paper, we depart from these two strands of literature by asking how synergies and trade-offs manifest in localized SDG programs and what governance opportunities, challenges, and recommendations this may bring. We find that the SDGs provide a platform to organize and communicate local sustainability work but that organizational realities will drive prioritization of goals considered connected and synergistic, such as SDG4, SDG9, and SDG11, which stand in contrast with global assessments of interlinkages. In line with emerging literature on the strategic effect of the SDGs on governance, we argue that the ethos of indivisibility serves as an important heuristic for civil servants and policy makers beyond prioritizing or reporting progress on global goals.

Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem of lack of grounding, e.g., addressing issues like models’ insufficient commonsense knowledge. While it is more straightforward to evaluate the effects of such models on multimodal tasks, such as visual question answering or image captioning, it is not as well-understood how these tasks affect the model itself, and its internal linguistic representations. In this work, we experiment with language models grounded in videos and measure the models’ performance on predicting masked words chosen based on their imageability. The results show that the smaller model benefits from video grounding in predicting highly imageable words, while the results for the larger model seem harder to interpret.of lack of grounding, e.g., addressing issues like models’ insufficient commonsense knowledge. While it is more straightforward to evaluate the effects of such models on multimodal tasks, such as visual question answering or image captioning, it is not as well-understood how these tasks affect the model itself, and its internal linguistic representations. In this work, we experiment with language models grounded in videos and measure the models’ performance on predicting masked words chosen based on their imageability. The results show that the smaller model benefits from video grounding in predicting highly imageable words, while the results for the larger model seem harder to interpret.

In recent years, instruction finetuning models have received increased attention due to their remarkable zero-shot and generalization capabilities. However, the widespread implementation of these models has been limited to the English language, largely due to the costs and challenges associated with creating instruction datasets. To overcome this, automatic instruction generation has been proposed as a resourceful alternative. We see this as an opportunity for the adoption of instruction finetuning for other languages. In this paper we explore the viability of instruction finetuning for Swedish. We translate a dataset of generated instructions from English to Swedish, using it to finetune both Swedish and non-Swedish models. Results indicate that the use of translated instructions significantly improves the models’ zero-shot performance, even on unseen data, while staying competitive with strong baselines ten times in size. We see this paper is a first step and a proof of concept that instruction finetuning for Swedish is within reach, through resourceful means, and that there exist several directions for further improvements.

Work on instruction-tuned Large Language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In this paper, we perform a meta-evaluation of such methods and assess their reliability across a broad range of tasks. In evaluating how well automatic methods align with human evaluations, correlation metrics are the most commonly employed method despite their inherent limitations when dealing with ties and different scales. To address these shortcomings, we use Pairwise Accuracy as an alternative to standard correlation measures. We observe that while automatic evaluation methods can approximate human ratings under specific conditions, their validity is highly context-dependent. Specifically, the simple ROUGE-L metric correlates very well with human ratings for short-answer English tasks but is unreliable in free-form generation tasks and cross-lingual scenarios. The effectiveness of the more advanced method of using GPT-4 as a judge diminishes significantly if reference answers are not included in the prompt, which is the scenario where this method has the potential to provide the most value compared to other metrics. Our findings enhance the understanding of how automatic methods should be applied and interpreted when developing and evaluating instruction-tuned LLMs.

Recent work on the Retrieval-Enhanced Transformer (Retro) model has shown that offloading memory from trainable weights to a retrieval database can significantly improve language modeling and match the performance of non-retrieval models that are an order of magnitude larger in size. It has been suggested that at least some of this performance gain is due to non-trivial generalization based on both model weights and retrieval. In this paper, we try to better understand the relative contributions of these two components. We find that the performance gains from retrieval largely originate from over-lapping tokens between the database and the test data, suggesting less non-trivial generalization than previously assumed. More generally, our results point to the challenges of evaluating the generalization of retrieval-augmented language models such as Retro, as even limited token overlap may significantly decrease test-time loss. We release our code and model at https://github.com/TobiasNorlund/retro

Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented models commonly rely on a semantic retrieval mechanism based on the similarity between dense representations of the query chunk and potential neighbors. In this paper, we study the state-of-the-art Retro model and observe that its performance gain is better explained by surface-level similarities, such as token overlap. Inspired by this, we replace the semantic retrieval in Retro with a surface-level method based on BM25, obtaining a significant reduction in perplexity. As full BM25 retrieval can be computationally costly for large datasets, we also apply it in a re-ranking scenario, gaining part of the perplexity reduction with minimal computational overhead.

Retrieval-augmented language models have demonstrated performance comparable to much larger models while requiring fewer computational resources. The effectiveness of these models crucially depends on the overlap between query and retrieved context, but the optimal degree of this overlap remains unexplored. In this paper, we systematically investigate how varying levels of query–context overlap affect model performance during both training and inference. Our experiments reveal that increased overlap initially has minimal effect, but substantially improves test-time perplexity and accelerates model learning above a critical threshold. Building on these findings, we demonstrate that deliberately increasing overlap through synthetic context can enhance data efficiency and reduce training time by approximately 40% without compromising performance. We specifically generate synthetic context through paraphrasing queries. We validate our perplexity-based findings on question-answering tasks, confirming that the benefits of retrieval-augmented language modeling extend to practical applications. Our results provide empirical evidence of significant optimization potential for retrieval mechanisms in language model pretraining.

This study empirically identifies the effects of innovations on firms' survival during the COVID-19 pandemic and the factors moderating the impact of innovations on firms' survival. The dataset of this study uses three consecutive COVID-19 surveys that are the follow-up of the World Bank Enterprise Survey. All the surveys used the same stratified sampling method on the same population. The study's results offer crucial insights into how innovations influence business survival during the pandemic. The univariate Kaplan-Meier survival function is utilized in the study first to depict graphically whether firms' survival probability varies with and without innovation. The Cox proportional hazard models are then employed to assess the impact of breakthroughs on firms' survival. The study reveals that innovations of any kind, such as product, distribution, marketing, and organizational innovations upturn the survival probability of businesses during the pandemic. The study also reveals that, albeit with slight alterations in impact size, the effects of innovations on business survival are robust among enterprises of all sizes and industries. This study offers crucial managerial policy implications and issues regarding innovation and firms' survival relationships during the COVID-19 pandemic.

Water investments play an increasingly important role in sustainable finance, yet their response to climate policy uncertainty (CPU) under different market conditions remains poorly understood. This study examines the regime-dependent influence of CPU on water equity performance using monthly data for the First Trust Water ETF (FIW) and the Invesco Global Water ETF (PIO) from 2007 to 2024. A Markov regime-switching VAR framework is employed to capture nonlinear dynamics that conventional linear models may overlook. The results reveal two distinct volatility regimes with contrasting CPU effects. In low-volatility periods, CPU is associated with higher returns, indicating that climate-policy developments can signal investment opportunities when markets are stable. During high-volatility periods, CPU exerts a negative influence, consistent with rising discount rates applied to long-term water-infrastructure cash flows. Regime persistence differs across ETFs: FIW exhibits frequent, short-lived transitions, whereas PIO displays more persistent states. A complementary DCC-GARCH analysis shows that crude oil provides a relatively cost-effective hedge for water portfolios, while technology ETFs offer substantially weaker hedging performance. Overall, the findings highlight the importance of regime-sensitive portfolio strategies for investors and emphasize that policymakers should consider prevailing market conditions when communicating climate initiatives. The study demonstrates that nonlinear models are essential for uncovering climate-finance linkages that linear approaches fail to detect. 

Readiness, riskiness, and renewables appear to form a "tripartite symbiosis" in the clean energy realm. Previous research underscores the significance of readiness as a prerequisite for a nation's advancement toward sustainable energy, urging careful navigation of uncertainties within this framework. Our research expands upon existing literature by delving into how country-level readiness influences a country's innovation in renewable energy in the face of uncertainty. Employing panel fixed effect threshold regression with four distinct models, we analyze this dynamic across 65 countries, representing both advanced and emerging economies. The results validate the presence of an uncertainty threshold effect across all model regressions, confirming a non-linear relationship among uncertainty, country-level readiness, and renewable energy innovation. Overall country-level readiness, along with its components-economic and social readiness-individually fosters renewable energy innovation under low uncertainty. However, this positive influence weakens as uncertainty exceeds the threshold. Conversely, governance readiness exerts a negative impact on renewable energy innovation under low uncertainty, with its detrimental effects becoming more significant at higher levels of uncertainty. The lagged uncertainty has a significant negative association with renewable energy innovation. Policymakers and investors should prioritize developing country level readiness to successfully manage the potential negative influence of uncertainties on renewable energy innovation.

Background Survival among critically ill COVID-19 patients varies between countries and time periods. Mortality rates up to 60% have been reported in intensive care units (ICUs). Standard-of-care has evolved throughout the pandemic. The purpose of the study was to explore management and mortality of COVID-19 ICU-patients during the first pandemic wave and assess their post-ICU health status. Methods We conducted an exploratory observational ambidirectional population-based study of ICU-patients with COVID-19 in a Swedish county during 1 March-30 June 2020. Primary outcome was 60-day mortality with secondary outcomes including treatments, complications, self-reported general health and dyspnoea post-discharge. Patients were consecutively divided into equal tertiles with cut-offs on April 4 and April 20, 2020, to analyse time trends. Results One hundred patients, median age was 63 years, were included, and 60-day mortality rate was 22%. Ninety-one percent had moderate/severe ARDS and 88% required mechanical ventilation. In the first tertile of patients 60-day mortality was 33%, declining to 15% and 18% in the following two. This reduction paralleled increased use of thromboprophylaxis, less steep rise of treated ICU-patients per day and expanded ICU resources. Four months post-discharge, 63% of survivors reported self-assessed decline in general health retrospectively compared to prior COVID-19. Conclusions In this cohort, the initial 60-day mortality quickly declined, despite continuous admittance of critically ill patients. This was parallel to adaptation to increased workload and more intense thromboembolic prophylaxis. A majority of survivors reported declined general health four months after discharge. Further studies on long-term health status of ICU-survivors are indicated.

Background Few studies have reported the long-term health effects of COVID-19. The regional population-based Linkoping COVID-19 study (LinCoS) included all patients hospitalised due to COVID-19 during the first pandemic wave. Four months post-discharge, over 40% (185/433) experienced persisting symptoms and activity/ participation limitations, indicating post-COVID-19 condition (PCC). The present follow-up study aimed to determine the long-term recovery among these patients 24 months post-admission.Methods This prospective cohort study included all patients from LinCoS with PCC at four months post-discharge. We repeated the same structured interview at a 24-month follow-up to identify persisting symptoms and their impact on daily life. Intercurrent health issues were identified by reviewing medical records.Findings Of 185 patients with PCC at 4 months post-discharge, 181 were alive at the 24-month assessment and 165 agreed to participate. Of those, 21% (35/165) had been readmitted to hospital for various causes in the interim period. The majority of patients (139/165, 84%) reported persisting problems affecting everyday life at 24 months. Significant improvements were seen in the prevalence and magnitude of some symptoms/limitations compared with four months post-discharge. Cognitive, sensorimotor, and fatigue symptoms were the most common persisting symptoms at 24 months. No clear difference was evident between individuals treated in the intensive care unit (ICU) and non-ICU-treated individuals. Approximately half of those who were on sick leave related to PCC at four months after infection were on sick leave at 24 months.Interpretation This is one of the first studies to report 2-year outcomes in patients with PCC following COVID-19 hospitalisation. Despite some improvements over time, we found a high prevalence of persisting symptoms and a need for long-term follow-up and rehabilitation post COVID-19 infection.

Background: Ventilator-associated lower respiratory tract infections (VA-LRTI) increase morbidity and mortality in intensive care unit (ICU) patients. Higher incidences of VA-LRTI have been reported among COVID-19 patients requiring invasive mechanical ventilation (IMV). The primary objectives of this study were to describe clinical characteristics, incidence, and risk factors comparing patients who developed VA-LRTI to patients who did not, in a cohort of Swedish ICU patients with acute hypoxemic respiratory failure due to COVID-19. Secondary objectives were to decipher changes over the three initial pandemic waves, common microbiology and the effect of VA-LTRI on morbidity and mortality.Methods: We conducted a multicenter, retrospective cohort study of all patients admitted to 10 ICUs in southeast Sweden between March 1, 2020 and May 31, 2021 because of acute hypoxemic respiratory failure due to COVID-19 and were mechanically ventilated for at least 48 h. The primary outcome was culture verified VA-LRTI. Patient characteristics, ICU management, clinical course, treatments, microbiological findings, and mortality were registered. Logistic regression analysis was conducted to determine risk factors for first VA-LRTI.Results: Of a total of 536 included patients, 153 (28.5%) developed VA-LRTI. Incidence rate of first VA-LRTI was 20.8 per 1000 days of IMV. Comparing patients with VA-LRTI to those without, no differences in mortality, age, sex, or number of comorbidities were found. Patients with VA-LRTI had fewer ventilator-free days, longer ICU stay, were more frequently ventilated in prone position, received corticosteroids more often and were more frequently on antibiotics at intubation. Regression analysis revealed increased adjusted odds-ratio (aOR) for first VA-LRTI in patients treated with corticosteroids (aOR 2.64 [95% confidence interval [CI]] [1.31-5.74]), antibiotics at intubation (aOR 2.01 95% CI [1.14-3.66]), and days of IMV (aOR 1.05 per day of IMV, 95% CI [1.03-1.07]). Few multidrug-resistant pathogens were identified. Incidence of VA-LRTI increased from 14.5 per 1000 days of IMV during the first wave to 24.8 per 1000 days of IMV during the subsequent waves.Conclusion: We report a high incidence of culture-verified VA-LRTI in a cohort of critically ill COVID-19 patients from the first three pandemic waves. VA-LRTI was associated with increased morbidity but not 30-, 60-, or 90-day mortality. Corticosteroid treatment, antibiotics at intubation and time on IMV were associated with increased aOR of first VA-LRTI.

The purpose of this study was to compare the correlation and agreement between AS derived from either an energy-integrating detector CT (EID-CT) or a photon-counting detector CT (PCD-CT). Reproducibility was also compared. In total, 26 calcified coronary lesions (from five cadaveric hearts) were identified for inclusion. The hearts were positioned in a chest phantom and scanned in both an EID-CT and a prototype PCD-CT. The EID-CT and PCD-CT acquisition and reconstruction parameters were matched. To evaluate the reproducibility, the phantom was manually repositioned, and an additional scan was performed using both methods. The EID-CT reconstructions were performed using the dedicated calcium score kernel Sa36. The PCD-CT reconstructions were performed with a vendor-recommended kernel (Qr36). Several monoenergetic energy levels (50-150 keV) were evaluated to find the closest match with the EID-CT scans. A semi-automatic evaluation of calcium score was performed on a post-processing multimodality workplace. The best match with Sa36 was PCD-CT Qr36 images, at a monoenergetic level of 72 keV. Statistical analyses showed excellent correlation and agreement. The correlation and agreement with regards to the Agatston score (AS) between the two methods, for each position as well as between the two positions for each method, were assessed with the Spearman s rank correlation. The correlation coefficient, rho, was 0.98 and 0.97 respectively 0.99 and 0.98. The corresponding agreements were investigated by means of Bland-Altman plots. High correlation and agreement was observed between the AS derived from the EID-CT and a PCD-CT. Both methods also demonstrated excellent reproducibility.

Background: As guidelines endorse the use of computed tomography (CT) for examining coronary artery disease (CAD), it is important to compare the advantages and disadvantages of the novel photon counting detector CT (PCD-CT) technology with the established energy integrating detector CT (EID-CT). Purpose: To compare the image quality of coronary computed tomography angiography (CCTA) and the Agatston scores (AS) derived from EID-CT and PCD-CT. Material and Methods: In this prospective observational study, 28 patients underwent clinical calcium score and CCTA scans on an EID-CTand a PCD-CT scanner. CCTA images were qualitatively analyzed by five observers using visual grading characteristics. The correlation and agreement of the AS were assessed using Spearmans rank correlation and Bland-Altman plots. Results: This qualitative analyses demonstrated a high fraction of " good" or "excellent" ratings for the image criteria in both CT systems. The sharpness of the distal lumen and image quality regarding motion artifacts were rated significantly higher for EID-CT (P < 0.05). However, the sharpness of coronary calcification was rated significantly higher for PCD-CT (P < 0.05). Spearmans rank correlation and Bland-Altman plots showed good correlation (P = 0.95) and agreement regarding the AS between EID-CT and PCD-CT. Conclusion: Both CT systems exhibited high CCTA image quality. The sharpness of calcifications was rated significantly higher for PCD-CT. A good correlation was observed between the AS derived from the two systems.

Objectives 

Coronary computed tomography angiography is the primary modality for noninvasive assessment of coronary artery disease. Photon-counting computed tomography (PCCT) offers superior spatial resolution and spectral imaging for detailed characterization of atherosclerotic plaques. This study aimed to evaluate the impact of virtual monoenergetic imaging (VMI) energy levels and reconstruction kernels on segmentation-based measurement of plaque volume in individuals with coronary atherosclerosis using PCCT.

Materials and Methods 

Fifty study participants underwent coronary computed tomography angiography with ultra-high-resolution PCCT. Both polyenergetic, 120 kVp (T3D) images and spectral images at varying VMI energy levels were reconstructed using different kernels. Plaque volumes were measured using semiautomated attenuation-based segmentation, adjusting segmentation thresholds for each VMI energy level. In addition, absolute plaque volume measurements were conducted using a coronary phantom simulating different plaque types.

Results 

Using a sharper kernel (Bv64 vs Bv48) significantly increased noncalcified plaque volume measurements (P < 0.005) in study participants, whereas a 0.2-mm slice thickness reduced calcified plaque volumes compared with 0.4 mm (P < 0.005). VMI energy level had no impact on measured volumes. Phantom measurements confirmed significant variability in measured volumes of calcified and noncalcified plaques depending on reconstruction method, as well as a minor effect of VMI level.

Conclusions 

In PCCT, the reconstruction kernel predominantly affects noncalcified coronary plaque quantification, whereas slice thickness mainly impacts calcified plaque volumes. In study participants, measured plaque volumes were not affected by VMI energy level when energy-specific segmentation thresholds were used, although a minor effect of VMI was observed in the phantom model.

Background: Coronary CT angiography (CCTA) is a key non-invasive tool for evaluating coronary artery disease (CAD). While energy-integrating detector CT (EID-CT) offers high negative predictive value (NPV), its positive predictive value (PPV) is limited in heavily calcified vessels. Photon-counting detector CT (PCD-CT), with higher spatial resolution and reduced blooming, may enhance diagnostic performance. Current PCD-CT systems provide both standard-resolution (SR) and ultra-high-resolution (UHR) modes, but the clinical impact of these modes remains under investigation.

Objectives: To compare the diagnostic accuracy and image quality of SR-PCD-CT versus EID-CT in quantifying coronary stenosis, using quantitative coronary angiography (QCA) as reference. Materials and methods: In this prospective, single-centre study, 21 patients (5 women, mean age 71.5 years) with suspected CAD underwent CCTA with both EID-CT and SR-PCD-CT prior to QCA. A total of 301 coronary segments were assessed for stenosis severity, with ≥50 % stenosis deemed significant. Image quality was graded using a 5-point scale. 

Results: No significant differences in percentage diameter stenosis (%DS) were found between imaging techniques (p = 0.20). Both EID-CT and SR-PCD-CT showed good agreement with QCA (AUC: PCD-CT 0.89, EID-CT 0.86). Specificity and NPV were high for both; sensitivity and PPV were moderate. SR-PCD-CT yielded higher image quality compared to EID-CT (p < 0.001).

Conclusions: In standard resolution mode, PCD-CT offers excellent image quality for quantifying coronary stenosis at comparable diagnostic accuracy compared to EID-CT