Safety and quality of life with maintenance olaparib plus bevacizumab in older patients with ovarian cancer: subgroup analysis of PAOLA-1/ENGOT-ov25

Background: In PAOLA-1/ENGOT-ov25, the addition of olaparib to bevacizumab maintenance improved overall survival in patients with newly diagnosed advanced ovarian cancer. We describe the safety profile and quality of life (QoL) of this combination in older patients in PAOLA-1. Methods: Safety (CTCAE v4.03) and QoL (EORTC QoL Questionnaires Core 30 and Ovarian 28) data were collected. We compared safety by age (&amp;gt;= 70 vs &amp;lt;70 years) in the olaparib-containing arm. QoL by treatment arm was assessed in older patients. Geriatric features, including Geriatric Vulnerability Score (GVS), were also gathered. Results: Of 806 patients randomized, 142 were &amp;gt;= 70 years old (olaparib-containing arm: n = 104; placebo arm: n = 38). Older patients treated with olaparib exhibited a similar safety profile to younger patients, except for higher rates of all grades of lymphopenia and grade &amp;gt;= 3 hypertension (31.7% vs 21.6%, P =.032 and 26.9% vs 16.7%, P =.019, respectively). No hematological malignancy was reported. Two years after randomization, mean Global Health Status and cognitive functioning seemed better with olaparib than bevacizumab alone (adjusted mean difference: +4.47 points [95% CI, -0.49 to 9.42] and +4.82 [-0.57 to 10.21], respectively), and other QoL items were similar between arms. In the olaparib-containing arm, older patients with baseline GVS &amp;gt;= 1 (n = 48) exhibited increased toxicity and poorer QoL than those with GVS of 0 (n = 34). Conclusion: Among older patients in PAOLA-1, olaparib plus bevacizumab had a manageable safety profile and no adverse impact on QoL. Additional data are required to confirm these results in more vulnerable patients.Funding Agencies|AstraZeneca; AstraZeneca; Merck Sharp &amp; Dohme LLC, a subsidiary of Merck &amp; Co., Inc., Rahway, NJ, USA</p

Patient experiences of narcolepsy and idiopathic hypersomnia in the Nordics: a patient journey map

Central disorders of hypersomnolence (CDH) are chronic diseases that significantly impact the lives of affected individuals. We aimed to explore the perspectives of individuals with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH), and the challenges they encounter in their daily lives and within the healthcare systems in the Nordics. Interviews with patients (N = 41) and healthcare professionals (n = 14) and a patient survey (n = 70) were conducted in 2022 in Denmark, Sweden, Finland, and Norway to develop a patient journey map that visualises the patient with CDH journey and provides insights into the difficulties faced by these individuals. The patient journey mapping approach was chosen to focus on the processes and experiences of patients, highlighting the challenges they confront. Our findings revealed that the process of receiving a CDH diagnosis, as well as subsequent misdiagnoses and treatment, can be protracted and burdensome. CDH diagnoses remain poorly understood by neurologists, general practitioners, and the public, resulting in adverse consequences, with patients reporting a mean (standard deviation [SD]) time from symptom onset to diagnosis of 8.4 (5.11) years and a mean (SD) of 5.5 (4.17) productive hours lost/day. The available non-pharmaceutical support for patients with CDH, encompassing medical, psychological, educational, and professional assistance, was insufficient. The generalisability of the findings to one specific diagnosis is limited due to the collective analysis of the CDH. These findings are invaluable for identifying disruptions in the patient with CDH journeys and for designing improved pathways for those with NT1, NT2, and IH in the future.Funding Agencies|Takeda Pharma AB, a subsidiary of Takeda Pharmaceuticals Inc</p

Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review

Purpose: There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digitised screen film mammography, 2D digital mammography and digital breast tomosynthesis) used in the development of AI technologies and present their characteristics, including their transparency of documentation, content, populations included and accessibility. Materials and methods: MEDLINE and Google Dataset searches identified studies describing AI technology development and referencing breast imaging datasets up to June 2024. The characteristics of each dataset are summarised. In particular, the accompanying documentation was reviewed with a focus on diversity and inclusion of populations represented within each dataset. Results: 254 datasets were referenced in the literature search, 190 were privately held, 36 had barriers which prevented access, and 28 were accessible. Most datasets originated from Europe, East Asia and North America. There was poor reporting of individuals' attributes: 32 (12 %) datasets reported race or ethnicity; 76 (30 %) reported female/male categories with only one dataset explicitly defining whether these categories represented sex or gender attributes. Conclusion: Through this review, we demonstrate gaps in the data landscape for mammography, highlighting poor representation globally. To ensure datasets in breast imaging have maximum utility for researchers, their characteristics should be documented and limitations of datasets, such as their representativeness of populations and settings, should inform scientific efforts to translate data-driven insights into technologies and discoveries

Discontinuation and reinitiation of mineralocorticoid receptor antagonists in patients with heart failure and reduced ejection fraction

Aims Mineralocorticoid receptor antagonists (MRA) improve outcomes in heart failure with reduced ejection fraction (HFrEF) but are underused. Point prevalent use has been described, but the kinetics of discontinuation and the extent of reinitiation have not been studied. Methods and resultsPatients with HFrEF enrolled in the Swedish Heart Failure Registry between 2006 and 2021 were linked to the Prescribed Drug Register. The rate of discontinuation during the first year of treatment and reinitiation the year after discontinuation were estimated using the Kaplan-Meier method. Multivariable Cox proportional hazards models were used to assess the predictors of discontinuation. Of 11 474 MRA new users, 71% remained on therapy at 1 year. Baseline characteristics independently associated with discontinuation were: estimated glomerular filtration rate (eGFR) &amp;lt;30 ml/min/1.73 m2 (hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.34-2.27), hyperkalaemia (HR 1.73, 95% CI 1.25-2.40), eGFR 30-60 ml/min/1.73 m2 (HR 1.51, 95% CI 1.37-1.66), age &amp;gt;= 80 years (HR 1.26, 95% CI 1.10-1.43), enrolment as inpatient (HR 1.25, 95% CI 1.14-1.38), a diagnosis of atrial fibrillation (HR 1.24, 95% CI 1.10-1.39), living alone (HR 1.23, 95% CI 1.13-1.34), ischaemic heart disease (HR 1.20, 95% CI 1.09-1.31), anaemia (HR 1.17, 95% CI 1.07-1.29), diabetes mellitus (HR 1.15, 95% CI 1.04-1.27) and New York Heart Association class III-IV (HR 1.13, 95% CI 1.02-1.24). Reinitiation within a year occurred in 46% of cases, mostly within 3 months after discontinuation. ConclusionAmong patients with HFrEF initiated on MRA, 71% remained on therapy at 1 year. Discontinuation occurred early and was more common in patients with advanced kidney disease, hyperkalaemia, lack of follow-up in specialty care, more severe heart failure, comorbidities, and markers of sociodemographic frailty. Among those who discontinued, almost half reinitiated treatment the year following discontinuation.Funding Agencies|Swedish Research Council</p

Thermoelectric properties of CrN alloy thin films

A thermoelectric material can be used to convert heat to electricity, and vice versa, all without moving parts. Thermoelectric devices can be used for a multitude of applications, such as thermoelectric generators (TEGs) and Peltier heaters/coolers. TEGs can be used to generate electricity from e.g. waste heat, and small and efficient Peltier coolers could have a use in microelectronics. The lack of moving parts means that thermoelectric devices generally are robust. Transition metal nitrides are versatile, durable, and have found use in many different applications. CrN, for instance, is known for its hardness, corrosion resistance, near room-temperature magnetic phase transition as well as its thermoelectric properties. Especially crystalline CrN has interesting thermoelectric properties, including a high power-factor and low thermal conductivity. These properties are essential for the constituent materials of thermoelectric devices. For durability, the stability and mechanical properties of CrN would be a bonus. The thermoelectric properties of CrN have a strong correlation to the stoichiometry, which then becomes crucial to control. The focus of this thesis is on the thermoelectric properties of CrN, with and without alloying transition metals V and Mo. Doping and alloying can help change properties, both electrical and thermal. I have grown thin films of CrN with and without alloying elements, using reactive magnetron sputtering. Film growth using this technique happens far from thermodynamic equilibrium and thus, not all aspects are easy to control, stoichiometry being one. The films were grown on c-plane sapphire (Al2O3 (0001)) substrates. I investigated thin, epitaxial films of CrMoVN, i.e. CrN thin films co-doped with Mo and V. They were grown on c-plane sapphire substrates, which allows the rock-salt cubic structured CrN to be grown epitaxially, albeit with a non-negligible strain. I investigated the effect of co-doping on phase composition and thermoelectric properties. While the effects of singly doped films (CrVN and CrMoN) were similar to other reports, co-doping with V and Mo resulted in the retention of the rock-salt cubic phase at much higher Mo-content than what has previously been reported. Furthermore, I tackled the issue with stoichiometry, motivated by discrepancies in literature correlating thermoelectric properties and stoichiometry of CrN. After growing sets of thin films of CrN, some epitaxial and some mix-phased, the samples were annealed in ammonia environment to approach the thermodynamic equilibrium of Cr:N = 1:1. The films that were closest to stoichiometry before annealing turned insulator – in line with theory and some articles. The films with larger under-stoichiometry got significantly improved thermoelectric properties, one by as much as 900%.   Funding: This work has been supported by the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linköping University (Faculty Grant SFO-Mat-LiU No. 2009 00971), the Knut and Alice Wallenberg foundation through the Wallenberg Academy Fellows program (grant no. KAW-2020.0196), the Swedish Research Council (VR) under project no. 2021-03826, and the Swedish Energy Agency under project number 52740-1. </p

Data-Driven Smart Maintenance of Historic Buildings

Digital transformation in the built environment offers new opportunities to improve building maintenance through data-driven approaches. Smart monitoring, predictive modeling, and artificial intelligence can enhance decision-making and enable proactive strategies. The preservation of historic buildings is an important scenario where preventive maintenance is essential to ensure long-term sustainability while protecting heritage values. This thesis presents a comprehensive solution for data-driven smart maintenance of historic buildings, integrating Internet of Things (IoT), cloud computing, edge computing, ontology-based data modeling, and machine learning to improve indoor climate management, energy efficiency, and conservation practices. To enable long-term environmental monitoring, a scalable digitalization solution is developed in Paper I, integrating an IoT-based sensing system with edge and cloud computing. Field deployments confirm the long-run reliability of the system in supporting real-time and historical data analysis for maintenance decisions. Papers II and III further introduce the concept of parametric digital twins, incorporating ontology-based data models to ensure a consistent representation of building structures, systems, and environmental conditions. Case studies at the City Theatre of Norrköping and Löfstad Castle in Östergötland, Sweden, validate the effectiveness of digital twins in identifying indoor climate risks and guiding conservation strategies. Based on the collected data, Papers IV and VI explore deep learning methods for building energy forecasting. Paper IV evaluates state-of-the-art deep learning architectures for point and probabilistic multi-horizon forecasting, showing that incorporating future exogenous factors improves prediction accuracy. It also highlights how different building operating modes impact forecasting performance. Paper VI integrates deep learning with digital twins to identify energy-saving opportunities and optimize operations. Papers V and VII focus on predictive modeling for indoor climate management. Paper VII proposes an edge-centric approach as an alternative to cloud-centric solutions, ensuring low latency and data privacy. Paper V explores federated deep learning as a privacy-aware solution for decentralized indoor climate forecasting. A comparative study of federated learning algorithms demonstrates that federated models can achieve prediction accuracy comparable to centralized learning while preserving data privacy. These findings offer practical insights for managing heterogeneous, distributed environmental data to support sustainable building operations. This thesis advances data-driven conservation of historic buildings by combining smart monitoring, digital twins, and artificial intelligence. The proposed methods enable preventive maintenance and pave the way for the next generation of heritage conservation strategies.Funding: The Swedish Energy Agency (Energimyndigheten) and the Swedish Innovation Agency (Vinnova). </p

T-type diarylethenes for molecular solar thermal energy storage: aromaticity as a design principle

Molecular photoswitches that absorb sunlight and store it in the form of chemical energy are attractive for applications in molecular solar thermal energy storage (MOST) systems. Typically, these systems utilize the absorbed energy to photoisomerize into a metastable form, which acts as an energy reservoir. Diarylethenes featuring aromatic ethene pi-linkers have garnered research interest in recent years as a promising class of T-type photoswitches, which undergo photocyclization from an aromatic ring-open form into a less aromatic or non-aromatic ring-closed form. Based on several recent computational and experimental studies, this perspective analyzes the potential of these switches for MOST applications. Specifically, we discuss how they can be made to simultaneously achieve high energy-storage densities, long energy-storage times, and high photocyclization quantum yields by tuning the aromatic character of the ethene pi-linker.Funding Agencies|Olle Engkvists Stiftelse [SUR/2022/001766]; Department of Science and Technology (DST), New Delhi, India [2019-03664]; Swedish Research Council [204-0183]; Olle Engkvist Foundation [CTS 20:102, CTS 21:1545, SRMAP/URG/SEED/2023-24/028]; Carl Trygger Foundation; SRM University-AP [2022-06725, 2018-05973]; National Academic Infrastructure for Supercomputing in Sweden - Swedish Research Council</p

Determinants of digital well-being

How can people lead fulfilling lives both thanks to and despite the constant use of digital media and artificial intelligence? While the prevailing narrative often portrays these technologies as generally harmful to well-being, the reality is of course more nuanced—some individuals benefit, while others do not. Existing research has predominantly focused on the general consequences of digital media on well-being, with less attention given to the individual-level antecedents of digital well-being. In the present study, we aimed to identify the traits and characteristics of individuals who use digital tools in ways that promote their well-being. Using a large representative sample from Sweden (N = 1999), we explore how digital self-control, digital literacy (objective and subjective), and digital information ignorance predict digital well-being, life satisfaction, and social anxiety. Digital self-control and subjective digital literacy positively predicted digital well-being. Digital self-control also predicted greater life satisfaction. Finally, digital information ignorance predicted increased life satisfaction and social anxiety. Overall, the current study contributes to a growing literature on digital well-being by exploring its antecedents.Funding Agencies|Handelsrdet</p

Development of 24-hour rhythms in cortisol secretion across infancy : a systematic review and meta-analysis of individual participant data.

BACKGROUND: In adults, cortisol levels show a pronounced 24-hour rhythm with a peak in the early morning. It is unknown at what age this early-morning peak in cortisol emerges during infancy, hampering the establishment of optimal dosing regimens for hydrocortisone replacement therapy in infants with an inborn form of adrenal insufficiency. Therefore, we aimed to characterize daily variation in salivary cortisol concentration across the first year of life. METHODS: We conducted a systematic review followed by an individual participant data meta-analysis of studies reporting on spontaneous (i.e., not stress induced) salivary cortisol concentrations in healthy infants aged 0-1 year. A one-stage approach using linear mixed-effects modelling was used to determine the interaction between age and time of day on cortisol concentrations. FINDINGS: Through the systematic review, 54 eligible publications were identified, reporting on 29,177 cortisol observations. Individual participant data were obtained from 15 study cohorts, combining 17,079 cortisol measurements from 1,904 infants. The morning/evening cortisol ratio increased significantly from 1.7 (95% CI: 1.3-2.1) at birth to 3.7 (95% CI: 3.0-4.5) at 6-9 months (p &lt; 0.0001). Cosinor analysis using all available data revealed the gradual emergence of a 24-hour rhythm during infancy. INTERPRETATION: The early-morning peak in cortisol secretion gradually emerges from birth onwards to form a stable morning/evening ratio from age 6-9 months. This might have implications for hydrocortisone replacement therapy in infants with an inborn form of adrenal insufficiency.Funding Agencies|International Fund Congenital Adrenal Hyperplasia; Netherlands Organization for Health Research and Development [2020-09150161910128]; BioClock Consortium [1292.19.077]; NWA-ORC of the Dutch Research Council (NWO)</p

PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography

ObjectivesTo develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA).Materials and methodsCCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Link &amp; ouml;ping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance.ResultsPlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684).ConclusionWe developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Funding Agencies|VINNOVA; Medis Medical Imaging for software development</p