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Aims: To investigate the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of BPL-003, a novel intranasal benzoate salt formulation of 5-methoxy- N,N -dimethyltryptamine (5-MeO-DMT), in healthy participants. Methods: In all, 44 psychedelic-naïve participants enrolled in the double-blind, placebo-controlled single ascending dose study (1–12 mg BPL-003). Concentrations of 5-MeO-DMT and its pharmacologically active metabolite, bufotenine, were determined in plasma and urine. PD endpoints included subjective drug intensity (SDI) rating, the Mystical Experience Questionnaire (MEQ-30) and the Ego Dissolution Inventory (EDI). Results: BPL-003 was well tolerated at doses up to 12 mg. There were no serious adverse events (AEs), and most AEs were mild; the most common being nasal discomfort, nausea, headache and vomiting. 5-MeO-DMT was rapidly absorbed and eliminated; the median time to peak plasma concentration was approximately 8–10 min and the mean terminal elimination half-life was <27 min. 5-MeO-DMT systemic exposure increased approximately dose-proportionally, while plasma bufotenine concentrations and urinary excretion of 5-MeO-DMT and bufotenine were negligible. The intensity of the SDI ratings was associated with plasma 5-MeO-DMT concentrations. MEQ-30 and EDI scores generally increased with the BPL-003 dose; 60% of participants had a 'complete mystical experience' at 10 and 12 mg doses. Profound and highly emotional consciousness-altering effects were observed with BPL-003, with a rapid onset and short-lasting duration. Conclusion: The novel intranasal formulation of BPL-003 was well tolerated with dose-proportional increases in PK and PD effects. The short duration of action and induction of mystical experiences suggest clinical potential, warranting further trials. Clinical trial registration: NCT05347849. [ABSTRACT FROM AUTHOR]

A large proportion of animal species enjoy the benefits of being active at night, and have evolved the corresponding optical and neural adaptations to cope with the challenges of low light intensities. However, over the past century electric lighting has introduced direct and indirect light pollution into the full range of terrestrial habitats, changing nocturnal animals’ visual worlds dramatically. To understand how these changes affect nocturnal behavior, we here propose an animal-centered analysis method based on environmental imaging. This approach incorporates the sensitivity and acuity limits of individual species, arriving at predictions of photon catch relative to noise thresholds, contrast distributions, and the orientation cues nocturnal species can extract from visual scenes. This analysis relies on just a limited number of visual system parameters known for each species. By accounting for light-adaptation in our analysis, we are able to make more realistic predictions of the information animals can extract from nocturnal visual scenes under different levels of light pollution. With this analysis method, we aim to provide context for the interpretation of behavioral findings, and to allow researchers to generate specific hypotheses for the behavior of nocturnal animals in observed light-polluted scenes.

BackgroundYouper is a widely used, commercially available mobile app that uses artificial intelligence therapy for the treatment of anxiety and depression. ObjectiveOur study examined the acceptability and effectiveness of Youper. Further, we tested the cumulative regulation hypothesis, which posits that cumulative emotion regulation successes with repeated intervention engagement will predict longer-term anxiety and depression symptom reduction. MethodsWe examined data from paying Youper users (N=4517) who allowed their data to be used for research. To characterize the acceptability of Youper, we asked users to rate the app on a 5-star scale and measured retention statistics for users’ first 4 weeks of subscription. To examine effectiveness, we examined longitudinal measures of anxiety and depression symptoms. To test the cumulative regulation hypothesis, we used the proportion of successful emotion regulation attempts to predict symptom reduction. ResultsYouper users rated the app highly (mean 4.36 stars, SD 0.84), and 42.66% (1927/4517) of users were retained by week 4. Symptoms decreased in the first 2 weeks of app use (anxiety: d=0.57; depression: d=0.46). Anxiety improvements were maintained in the subsequent 2 weeks, but depression symptoms increased slightly with a very small effect size (d=0.05). A higher proportion of successful emotion regulation attempts significantly predicted greater anxiety and depression symptom reduction. ConclusionsYouper is a low-cost, completely self-guided treatment that is accessible to users who may not otherwise access mental health care. Our findings demonstrate the acceptability and effectiveness of Youper as a treatment for anxiety and depression symptoms and support continued study of Youper in a randomized clinical trial.

On March 7, 1962 the Royal College of Physicians published a report entitled Smoking and Health that made the causal link between smoking and lung cancer clear and explicit. Using a historical data set that contains information on smoking from 1958 to 1965, I find a decrease in smoking for those with more schooling after the report's publication. I do not find a difference in the effect for those with greater access to information (measured by TV ownership or newspaper readership). This suggests the education health gradient is more likely due to processing of information rather than access to information. [ABSTRACT FROM AUTHOR]

This paper describes a simple and interesting application of structural equation modeling for a single lecture in an undergraduate econometrics course to introduce students to the concept of using data to recover latent variables. The application centers around using hourly observations on ride wait times at Disney's Magic Kingdom to infer how crowded it is at the theme park. Pedagogically, the material is presented in the context of the linear regression model, so the discussion works to enhance students' understanding of core material, not to introduce new disparate methods. The application provides interesting economic-based insights, like which ride's wait times are categorically most informative about how crowded it is at the park. [ABSTRACT FROM AUTHOR]

From an interdiscoursive and interdisciplinary perspective, this paper explores different representations with which the Mapuche People of the Gulumapu constructed their processes of cultural and territorial differentiation in tension with the Chilean Nation State. Through heterogeneous discourses -historical, literary, media, testimonial and of political organizations-, a process is expressed that moves from an ancestral unitary vision to strategically assumed modern conceptions, typical of new geopolitical and supranational scenarios, such as those of the State and/or nation. It is concluded that this process mainstream their discourses, which as a whole represent a shared collective imaginary that relates the ancestral notion -Wallmapu- with the modern vision -nation and/or Mapuche State.

Aims: The method of diagnosis of ductal carcinoma in situ (DCIS) has changed since the 1980s. The aim of this audit was to assess changes in the preoperative diagnosis of DCIS since the introduction of needle core biopsy, particularly the proportion with a preoperative biopsy diagnosis of DCIS. Methods and results: The preoperative diagnoses of patients with a final diagnosis of DCIS in the surgical specimen were reviewed (i) in 809 patients who presented through breast screening from 1997 to 2021, and (ii) in all patients in 5 individual years at 5‐year intervals from 2000 to 2020 (254 in total). For screening‐detected DCIS the proportion with a preoperative diagnosis of DCIS increased from 75% to 98% over the study period. In a detailed analysis of all cases of DCIS in 5 separate years the proportion with a preoperative diagnosis of DCIS increased from 68% in 2000 to 96% in 2020. For high‐grade DCIS the proportion increased from 87% to 97%, and for low‐ or intermediate‐grade DCIS from 48% to 93%. The proportion of women who had vacuum‐assisted biopsy increased from 7% in 2000 to 58% in 2015. There was a small increase in the number of biopsies that had basal cytokeratin and oestrogen receptor immunohistochemistry to aid diagnosis. Conclusion: There has been an increase in the preoperative diagnosis of DCIS, particularly of low‐ or intermediate‐grade, over the last two decades. The increasing use of vacuum‐assisted biopsy is likely to be a major contributory factor to this increase. [ABSTRACT FROM AUTHOR]

This paper examines the impact of daylight saving time (DST) on fatal road crashes in Australia. I exploit within year transitions to and from DST in a regression discontinuity in time framework. To examine the long run effect of the policy, I use trials of DST implemented in various states, and a DST extension due to the Sydney Olympics. Neither the transition to or out of DST, nor the long run effects of DST have an impact on fatalities on the road. However, there is evidence of reallocation of accidents over the day due to ambient light. [ABSTRACT FROM AUTHOR]

Simple Summary: Current AI algorithms show breast cancer detection rates that are comparable to human readers, but it is not clear whether AI and humans detect cancers with similar characteristics. As these factors will influence survival, we aimed to compare the invasiveness status, histological grade, lymph node stage, and tumour size of cancers. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI indicated 85.9% and 31.7%, respectively, as suspicious. The first human reader detected 90.8% of SDCs and 7.2% of ICs. There were no differences in the detected proportion for any of the investigated prognostic factors. The AI algorithm detected more ICs. These findings imply that using AI has limited or no downstream effects on screening programmes, supporting its potential role in the double-reading workflow. Invasiveness status, histological grade, lymph node stage, and tumour size are important prognostic factors for breast cancer survival. This evaluation aims to compare these features for cancers detected by AI and human readers using digital mammography. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. Differences in prognostic features of cancers detected by AI and the first human reader (R1) were assessed using chi-square tests, with significance at p < 0.05. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI flagged 85.9% and 31.7%, respectively. R1 detected 90.8% of SDCs and 7.2% of ICs. Of the screen-detected cancers detected by the AI, 82.5% had an invasive component, compared to 81.1% for R1 (p-0.374). For the ICs, this was 91.5% and 93.8% for AI and R1, respectively (p = 0.829). For the invasive tumours, no differences were found for histological grade, tumour size, or lymph node stage. The AI detected more ICs. In summary, no differences in prognostic factors were found comparing SDC and ICs identified by AI or human readers. These findings support a potential role for AI in the double-reading workflow. [ABSTRACT FROM AUTHOR]

Reducing preventable readmissions is important to help manage current strains on healthcare systems. The metric of 30‐day readmissions is commonly cited in discussions regarding this topic. While such thresholds have contemporary funding implications, the rationale for individual cut‐off points is partially historical in nature. Through the examination of the basis for the analysis of 30‐day readmissions, greater insight into the possible benefits and limitations of such a metric may be obtained. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. Methods: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. Results: Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). Conclusion: The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree. [ABSTRACT FROM AUTHOR]