Abstract
Background/Objectives: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. Our aims are (1) to validate OSABayes prospectively, (2) to build a smartphone app based on the proposed model, and (3) to evaluate app usability. Methods: We prospectively included adult patients suspected of OSA, without suspicion of other sleep disorders, who underwent level I or III diagnostic PSG. Apnea-hypopnea index (AHI) and OSABayes probabilities were obtained and compared using the area under the ROC curve (AUC [95%CI]) for OSA diagnosis (AHI >= 5/h) and higher severity levels (AHI >= 15/h) prediction. We built the OSABayes app on 'App Inventor 2', and the usability was assessed with a cognitive walkthrough method and a general evaluation. Results: 216 subjects were included in the validation cohort, performing PSG levels I (34%) and III (66%). OSABayes presented an AUC of 83.6% [77.3-90.0%] for OSA diagnosis and 76.3% [69.9-82.7%] for moderate/severe OSA prediction, showing good response for both types of PSG. The OSABayes smartphone application allows one to calculate the probability of having OSA and consult information about OSA and the tool. In the usability evaluation, 96% of the proposed tasks were carried out. Conclusions: These results show the good discrimination power of OSABayes and validate its applicability in identifying patients with a high pre-test probability of OSA. The tool is available as an online form and as a smartphone app, allowing a quick and accessible calculation of OSA probability.

Abstract

Abstract

As climate change drives increasingly severe heatwaves, the strain on public health systems continues to grow, particularly for vulnerable populations. Our work argues for the integration of digital health technologies into heatwave action plans, drawing lessons from the COVID-19 pandemic's success in deploying such tools. It explores the potential of digital communication strategies, telemedicine, and data-driven simulations to enhance public awareness, maintain healthcare accessibility, and improve real-time crisis responses. Despite their effectiveness, digital solutions remain underutilized in existing European heat-health action plans. We emphasize the need for a proactive, systems-based approach to optimize heatwave management and ensure equitable healthcare access, particularly for at-risk communities. Integrating digital health innovations can transform heatwave response strategies, making them more flexible, efficient, and capable of saving lives.

Abstract
Background: We assessed the pertinence of updating the International Working Group on the Diabetic Foot (IWGDF) risk classification yearly in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot assessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data were collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly, mainly for those at very low risk. The IWGDF classification can accurately identify those not at risk of DFU.

Abstract
Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation. Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems. Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor. Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes.

Abstract
Tight relationships exist in the local Universe between the central stellar properties of galaxies and the mass of their supermassive black hole (SMBH)1-3. These suggest that galaxies and black holes co-evolve, with the main regulation mechanism being energetic feedback from accretion onto the black hole during its quasar phase4-6. A crucial question is how the relationship between black holes and galaxies evolves with time; a key epoch to examine this relationship is at the peaks of star formation and black hole growth 8-12 billion years ago (redshifts 1-3)7. Here we report a dynamical measurement of the mass of the black hole in a luminous quasar at a redshift of 2, with a look back in time of 11 billion years, by spatially resolving the broad-line region (BLR). We detect a 40-mu as (0.31-pc) spatial offset between the red and blue photocentres of the H alpha line that traces the velocity gradient of a rotating BLR. The flux and differential phase spectra are well reproduced by a thick, moderately inclined disk of gas clouds within the sphere of influence of a central black hole with a mass of 3.2 x 108 solar masses. Molecular gas data reveal a dynamical mass for the host galaxy of 6 x 1011 solar masses, which indicates an undermassive black hole accreting at a super-Eddington rate. This suggests a host galaxy that grew faster than the SMBH, indicating a delay between galaxy and black hole formation for some systems. Using the GRAVITY+ instrument, dynamical measurement of the black hole mass in a quasar at a redshift of 2.3 (11 billion years ago) shows how the relationship between galaxies and black holes evolves with time.