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As one of the most commonly read online sources of medical information, Wikipedia is an influential public health platform. Its medical content, community, collaborations and challenges have been evolving since its creation in 2001, and engagement by the medical community is vital for ensuring its accuracy and completeness. Both the encyclopaedia’s internal metrics as well as external assessments of its quality indicate that its articles are highly variable, but improving. Although content can be edited by anyone, medical articles are primarily written by a core group of medical professionals. Diverse collaborative ventures have enhanced medical article quality and reach, and opportunities for partnerships are more available than ever. Nevertheless, Wikipedia’s medical content and community still face significant challenges, and a socioecological model is used to structure specific recommendations. We propose that the medical community should prioritise the accuracy of biomedical information in the world’s most consulted encyclopaedia.

Background Help-seeking for mental health problems is a complex process that involves handling both personal challenges and dealing with the organizational structure of the healthcare system. The healthcare system is siloed and fragmented, but it is unclear how the challenges are experienced by the young adults and what their healthcare journeys look like. Therefore, the aim of this study was to explore experiences of young adults’ healthcare journeys in the context of help-seeking for common mental health problems. Methods In total, 25 young adults (16 women and 9 men) from a student healthcare centre at a Swedish university seeking help for common mental health problems, such as anxiety and depression, were interviewed. A qualitative thematic analysis with an inductive approach was done, and results were abstracted and presented in terms of journey-related metaphors. Results The healthcare journeys of young adults were described as Taxi Riding, Commuting, Sightseeing, and Backpacking. Taxi riding and Commuting are defined by going in a straightforward and smooth way in the healthcare system, without major obstacles to care. In contrast, Sightseeing and Backpacking are characterized by more diffuse and negative experiences, where the young adults are not satisfied with the help received from healthcare providers. Help-seeking is not conformant with the design of the healthcare system but steered by a range of factors, including individual experiences and young adults’ agency, the available resources at the various healthcare providers, and interaction with healthcare professionals. Conclusions Young adults’ healthcare journeys in the context of help-seeking for common mental health problems are related to individual, relational, and organizational factors. Some journeys run smoothly, epitomizing a functioning healthcare system that accommodates a rational help-seeker. Other journeys depict a rigid healthcare system, where the success and nature of the journey primarily depend on individual agency and on not becoming discouraged by obstacles. There is a need for more knowledge on how to support young adults’ mental health help-seeking. However, we also need more insights into how the healthcare system can become more receptive and accommodating toward the needs of young adults with common mental health problems.

Globally, young adults with mental health problems struggle to access appropriate and timely care, which may lead to a poorer future prognosis. Artificial intelligence (AI) is suggested to improve the quality of mental health care through increased capacities in diagnostics, monitoring, access, advanced decision-making, and digital consultations. Within mental health care, the design and application of AI solutions should elucidate the patient perspective on AI. The aim was to explore the perceptions of AI in mental health care from the viewpoint of young adults with experience of seeking help for common mental health problems. This was an interview study with 25 young adults aged between 18 and 30 years that applied a qualitative inductive design, with content analysis, to explore how AI-based technology can be used in mental health care. Three categories were derived from the analysis, representing the participants' perceptions of how AI-based technology can be used in care for mental health problems. The first category entailed perceptions of AI-based technology as a digital companion, supporting individuals at difficult times, reminding and suggesting self-care activities, suggesting sources of information, and generally being receptive to changes in behavior or mood. The second category revolved around AI enabling more effective care and functioning as a tool, both for the patient and health care professionals (HCPs). Young adults expressed confidence in AI to improve triage, screening, identification, and diagnosis. The third category concerned risks and skepticism toward AI as a product developed by humans with limitations. Young adults voiced concerns about security and integrity, and about AI being autonomous, incapable of human empathy but with strong predictive capabilities. Young adults recognize the potential of AI to serve as personalized support and its function as a digital guide and companion between mental health care consultations. It was believed that AI would function as a support in navigating the help-seeking process, ensuring that they avoid the \"missing middle\" service gap. They also voiced that AI will improve efficiency in health care, through monitoring, diagnostic accuracy, and reduction of the workload of HCPs, while simultaneously reducing the need for young adults to repeatedly tell their stories. Young adults express an ambivalence toward the use of AI in health care and voice risks of data integrity and bias. They consider AI to be more rational and objective than HCPs but do not want to forsake personal interaction with humans. Based on the results of this study and young adults' perceptions of the monitoring capabilities of AI, future studies should define the boundaries regarding information collection responsibilities of the health care system versus the individuals' responsibility for self-care.

Forestry has many dangers, and much effort has been put into supporting accident prevention by developing legislation and certification systems. In Sweden, forestry contractors have an important role in developing and maintaining a good working environment for the employed forestry workers. The objectives of this study were to describe forestry contractors’ current practices regarding occupational safety and health (OSH) management and identify variations in relation to the contractors’ characteristics and finances, as well as to highlight areas that need improvement. In 2023, data were collected through a survey distributed to 1200 limited liability companies registered to perform logging or silvicultural services, with a response rate of 22%. Multi- and univariate analyses were performed to compare contractor groups, and the results show that larger companies had more well-developed and formalized routines and guidelines for their operations. Geographical differences were also identified; for example, employees in the north were more likely to have access to staff facilities when working in the forest. However, no clear connection between OSH management practices and the contractors’ finances could be demonstrated. The study concludes that there is a need for improvements in OSH work within the industry because many companies do not meet all of the formal requirements that exist today.

Drill cores from the Mittersill tungsten mine, also called Felbertal, were scanned with a drill core scanner using X-ray computed tomography (XCT) and X-ray fluorescence (XRF). Matrix amphiboles and quartz stand out as peaks in the XCT attenuation histogram, whereas the main ore mineral scheelite has distinctly high attenuation. Volumetric concentrations of the XCT high attenuation phase show an excellent correlation with tungsten lab assay values for 56 samples. One outlier, which can be attributed to a high bismuth concentration, as observed with the drill core scanner, needs a correction term to fall close to the linear relation formed by all other samples. The results suggest the technique could be used for rapid tungsten analysis in this, or similar, geological settings. It is evident that scheelite shows up as high attenuation values in the XCT data, which enables further ore characterization studies in 3D.

We evaluated the long‐term effect of early angiotensin‐converting enzyme (ACE) inhibition (enalapril maleate) as monotherapy to postpone or prevent congestive heart failure (CHF) in asymptomatic dogs with mitral regurgitation (MR) attributable to myxomatous valvular disease (MVD) in a prospective, randomized, double‐blinded, placebo‐controlled multicenter trial involving 14 centers in Scandinavia. Two hundred twenty‐nine Cavalier King Charles (CKC) Spaniels with MR attributable to MVD but no signs of CHF were randomly allocated to treatment with enalapril 0.25–0.5 mg daily (n = 116) or to placebo groups (n = 113). Each dog was evaluated by physical examination, electrocardiography, and thoracic radiography at entry and every 12 months (±30 days). The number of dogs developing heart failure was similar in the treatment and placebo groups (n = 50 [43%] and n = 48 [42%], respectively; P= .99). The estimated means, adjusted for censored observations, for the period from initiation of therapy to heart failure were 1,150 ± 50 days for dogs in the treatment group and 1,130 ± 50 days for dogs in the placebo group (P= .85). When absence or presence of cardiomegaly at the entrance of the trial was considered, there were still no differences between the treatment and placebo groups (P= .98 and .51, respectively). Multivariate analysis showed that enalapril had no significant effect on the time from initiation of therapy to heart failure (P= .86). Long‐term treatment with enalapril in asymptomatic dogs with MVD and MR did not delay the onset of heart failure regardless of whether or not cardiomegaly was present at initiation of the study.

Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings. We assess the foundation model CT-CLIP as a feature extractor for pretreatment computed tomography images and clinical variables from 242 diagnosed lung cancer patients. The evaluation includes adaptation strategies based on frozen encoders, full fine-tuning, and low-rank adaptation, together with modality ablations and comparisons with clinical and multimodal baselines. The results show that a frozen CT-CLIP model combined with a trainable lightweight survival head outperforms the clinical baseline and achieves comparable or improved performance relative to other multimodal approaches, and separates patients into clinically meaningful high- and low-risk groups.

Accurate prognostication and risk estimation are essential for guiding clinical decision-making and optimizing patient management. While radiologist-assessed features from CT scans provide valuable indicators of disease severity and outcomes, interpreting such images requires expert knowledge, and translating rich visual information into textual summaries inevitably leads to information loss. In this work, we propose a vision-language framework for 3D CT image understanding that leverages large-scale open-sourced CT images paired with radiology reports through visual instruction tuning. This pre-training enables the model to learn clinically meaningful visual-textual representations, which can then be adapted to downstream survival prediction tasks. By incorporating a survival prediction head on top of the pre-trained model, our approach improves survival prediction from CT images and clinical data while generating clinically meaningful language responses to predefined questions. Experimental results demonstrate that our method outperforms baseline methods in survival prediction, particularly, when clinical data alone is less predictive. The code will be released upon acceptance.

We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The forwarder was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, machine position with centimeter accuracy, and crane use while the forwarder operates in forest areas, aerially laser-scanned with a resolution of around 1500 points per square meter. Production log files (Stanford standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weights, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding or handling obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field.

We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The vehicle was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, vehicle position with centimeter accuracy, and crane use while the vehicle operates in forest areas, aerially laser-scanned with a resolution of around 1500 points per square meter. Production log files (StanForD standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weights, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding or handling obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field.