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Abstract Background Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds. Methods A structured search designed around three concepts—individual decision-maker, decision threshold, and medical decision—was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision. Results Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles. Conclusions Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

Young men from disadvantaged contexts are the least likely to attend university in Australia; furthermore, when they do attend, they are likely to struggle. This article draws on empirical data documenting the aspirations and resilience of first-in-family young men in Australian higher education, with the aim of nuancing their classed experience of university. Drawing on an exploratory longitudinal study (n = 42) and adopting a mixed method approach, we use the 25-item Connor-Davidson Resilience Scale (CD-RISC) and semi-structured interviews over a three-year period to explore changes in resilience of first-in-family men from the age of 17 to 20. The mixed-method approach employed in this study allows us to draw connections between the participants' subjective experience of resilience and the more objective measures of resilience as captured by the Connor-Davidson Resilience Scale, a psychometrically sound and validated instrument. Quantitative analyses of data enable us to document the trends in resilience over time for different groups of first-in-family men, while qualitative data provide insights structured around three key themes: independence and isolation; managing and adjusting; and using support structures. The article draws on analysis across these data to consider the participants' perceptions of their resilience, and how these perceptions change in reference to their experience, in order to paint a more nuanced picture of first-in-family men's classed experience of higher education.

Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors--including student mental health--remains poorly understood. Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope--for example, they often rely on small and/or non-representative samples or subjective measures, and focus almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3 (adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention.

Driven by the focus on standardised assessment and performance-driven accountability, a considerable body of literature has documented differences in students' academic achievement across school sectors, both internationally and in Australia. However, few studies have to date explored the potential mechanisms underlying such differences, particularly through the lens of school climate and student engagement. And despite extensive literature on school climate and student engagement, including their relationships with achievement, the differences in these patterns across school sectors remain under-studied. In this paper, we leverage nationally representative data from a large-scale longitudinal survey in Australia with linked administrative data on student achievement to reveal different patterns of school climate and student engagement across government, Catholic and independent sectors. Employing multivariable regression analyses, we identify unique school climate and student engagement facets that are associated with improved achievement in each of these sectors, offering important pointers for educational policies.

Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40,000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.

Background Despite tremendous therapeutic advancements, a significant proportion of coronary artery disease patients suffer from refractory angina pectoris, that is, quality-of-life-compromising angina that is non-manageable with established pharmacological and interventional treatment options. Adenoviral vascular endothelial growth factor-DΔNΔC (AdVEGF-D)-encoding gene therapy (GT) holds promise for the treatment of refractory angina.Methods ReGenHeart is an investigator-initiated, multicentre, randomised, placebo-controlled and double-blinded phase 2 clinical trial that aims to study the safety and efficacy of intramyocardially administered angiogenic AdVEGF-D GT for refractory angina. Patients will be randomised in a 2:1 ratio and blocks of six to receive either AdVEGF-D or placebo. Primary endpoints are improvements in functional capacity assessed with the 6 min walking test and angina symptoms with Canadian Cardiovascular Society class after 6 month follow-up. Secondary endpoints are improvements in myocardial perfusion assessed with either positron emission tomography or single-photon emission CT after 6 month follow-up and functional capacity and angina symptoms after 12 months. In addition, changes in the quality of life, the use of angina medication and the incidence of major adverse cardiac and cerebrovascular events will be evaluated.Conclusions The phase 2 ReGenHeart trial will provide knowledge of the safety and efficacy of AdVEGF-D GT to ameliorate symptoms in refractory angina patients, extending and further testing positive results from the preceding phase 1/2a trial.

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using non-task-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications.

OBJECTIVES:. Anemia has been associated with an increased risk of both cardiac arrest and stroke, frequent complications of COVID-19. The effect of hemoglobin level at ICU admission on a composite outcome of cardiac arrest or stroke in an international cohort of COVID-19 patients was investigated. DESIGN:. Retrospective analysis of prospectively collected database. SETTING:. A registry of COVID-19 patients admitted to ICUs at over 370 international sites was reviewed for patients diagnosed with cardiac arrest or stroke up to 30 days after ICU admission. Anemia was defined as: normal (hemoglobin ≥ 12.0 g/dL for women, ≥ 13.5 g/dL for men), mild (hemoglobin 10.0–11.9 g/dL for women, 10.0–13.4 g/dL for men), moderate (hemoglobin ≥ 8.0 and < 10.0 g/dL for women and men), and severe (hemoglobin < 8.0 g/dL for women and men). PATIENTS:. Patients older than 18 years with acute COVID-19 infection in the ICU. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Of 6926 patients (median age = 59 yr, male = 65%), 760 patients (11.0%) experienced stroke (2.0%) and/or cardiac arrest (9.4%). Cardiac arrest or stroke was more common in patients with low hemoglobin, occurring in 12.8% of patients with normal hemoglobin, 13.3% of patients with mild anemia, and 16.7% of patients with moderate/severe anemia. Time to stroke or cardiac arrest by anemia status was analyzed using Cox proportional hazards regression with death as a competing risk. Covariates selected through clinical knowledge were age, sex, comorbidities (diabetes, hypertension, obesity, and cardiac or neurologic conditions), pandemic era, country income, mechanical ventilation, and extracorporeal membrane oxygenation. Moderate/severe anemia was associated with a higher risk of cardiac arrest or stroke (hazard ratio, 1.32; 95% CI, 1.05–1.67). CONCLUSIONS:. In an international registry of ICU patients with COVID-19, moderate/severe anemia was associated with increased hazard of cardiac arrest or stroke.

Australian children from regional, rural and remote (RRR) areas exhibit lower educational outcomes than their peers in metropolitan areas. The mechanisms driving the comparatively poorer educational outcomes of children in RRR areas, however, are not well understood. This paper proposes and tests two sets of factors that may be responsible for these disparities: population socio-demographic composition and school climate. Using rich survey and linked administrative data from the "Longitudinal Study of Australian Children" (n = 9,248 observations), we estimate the relative contribution of these sets of factors to RRR children's disadvantage in NAPLAN numeracy test scores. Our results indicate that both socio-demographic and school climate factors account for part of the educational disparities between children in RRR and metropolitan areas. These findings suggest that hybrid policy approaches that tackle both the social determinants of educational success and use schools as an intervention site are required to close the achievement gap.

BackgroundRight anterior mini thoracotomy (RAMT) for aortic valve replacement (AVR) is a minimally invasive procedure that avoids sternotomy. Herein, we report the outcomes of patients who underwent redo-cardiac via a RAMT approach for AVR.MethodsThis case series reports the clinical outcomes of 14 consecutive redo operations, done in Calgary (Canada) and Gdansk (Poland) between 2020 and 2023. Primary outcomes were 30-day mortality and disabling stroke. Secondary outcomes included surgical times, hemodynamics, permanent pacemaker implantation (PPM), length of ICU and hospital stay, new post-operative atrial fibrillation (POAF), post-operative blood transfusion, incidence of acute respiratory distress syndrome (ARDS), rate of continuous renal replacement therapy (CRRT) and/or dialysis, and chest tube output in the first 12-hours after surgery.ResultsNine patients were male, and the mean age was 64.36 years. There were no deaths, while one patient had a disabling stroke postoperatively. Mean cardiopulmonary bypass and cross clamp-times were 136 min and 90 min, respectively. Three patients needed a PPM, 3 patients needed blood transfusions, and 2 developed new onset POAF. Median lengths of ICU and hospital stays were 2 and 12 days, respectively. There was no incidence of paravalvular leak greater than trace and the average transvalvular mean gradient was 12.23 mmHg.ConclusionThe number of patients requiring redo-AVR is increasing. Redo-sternotomy may not be feasible for many patients. This study suggests that the RAMT approach is a safe alternative to redo-sternotomy for patients that require an AVR.

This paper presents a new approach for protein generation based on one-shot learning and hybrid quantum neural networks. Given a single protein complex, the system learns how to predict the remaining unknown properties, without resorting to autoregression, from the physicochemical properties of the receptor and a prior on the physicochemical properties of the ligand. In contrast with other approaches, QuantumBound learns from a single instance, not from a large dataset, as is common in deep learning. By knowing half of the properties of the ligand, the system can predict the remaining half with an average relative error of 1.43% for a dataset consisting of one hundred and twenty Covid-19 spikes complexes. To the best of our knowledge, this is the first time that one-shot learning and hybrid quantum computing have been applied to protein generation.

Purpose: Gain of chromosome 8q has been associated with poor prognosis in uveal melanoma (UM), and an increase in the absolute number of 8q-copies correlated with an even shorter survival. Splicing factor 3b subunit 1 (SF3B1)-mutated (SF3B1MUT) tumors display structural chromosomal anomalies and frequently show a partial gain of chromosome 8qter. A recent subset of SF3B1MUT UM with early-onset metastases has been identified, prompting the investigation of the relationship between survival, 8q gain, and SF3B1MUT UM. Design: Retrospective cohort study. Subjects: Patients diagnosed with UM who underwent enucleation or received a biopsy at the Erasmus MC Cancer Institute or the Rotterdam Eye Hospital, The Netherlands were included. Methods: Fifty-nine patients with SF3B1MUT tumors and 211 patients with BRCA1 associated protein 1 (BAP1)-mutated (BAP1MUT) tumors were included in this study. Copy number status and gene expression were assessed using either a single nucleotide polymorphism array, fluorescence in situ hybridization, and karyotyping, or a combination of these techniques. Disease-free survival was determined and a cut-off of 60 months was used to define early-onset metastatic disease. Main Outcome Measures: Disease-free survival. Results: Forty-eight patients with SF3B1MUT UM (81%) had chromosome 8q gain (3 copies, 78%; 4 copies, 22%). Kaplan–Meier analysis of SF3B1MUT UM did not indicate a difference in survival in patients with or without gain of 8q (P = 0.99). Furthermore, the number of 8q copies was not associated with survival when comparing early (P = 0.97) versus late (P = 0.23) metastases group. In contrast, the presence of 8q gain (86%) was correlated with a decreased survival in BAP1MUT UM (P = 0.013). Conclusions: We did not find a correlation between 8q gain and early-onset metastasis in SF3B1MUT tumors. Gain of 8q has no additional predictive value in SF3B1MUT tumors. In contrast, 8q gain is predictive of a worse prognosis in patients with BAP1MUT tumors. Thus, gain of chromosome 8q has additional predictive value for BAP1MUT tumors, but not for SF3B1MUT tumors. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Lifelong machine learning concerns the development of systems that continuously learn from diverse tasks, incorporating new knowledge without forgetting the knowledge they have previously acquired. Multi-label classification is a supervised learning process in which each instance is assigned multiple non-exclusive labels, with each label denoted as a binary value. One of the main challenges within the lifelong learning paradigm is the stability-plasticity dilemma, which entails balancing a model’s adaptability in terms of incorporating new knowledge with its stability in terms of retaining previously acquired knowledge. When faced with multi-label data, the lifelong learning challenge becomes even more pronounced, as it becomes essential to preserve relations between multiple labels across sequential tasks. This scoping review explores the intersection of lifelong learning and multi-label classification, an emerging domain that integrates continual adaptation with intricate multi-label datasets. By analyzing the existing literature, we establish connections, identify gaps in the existing research, and propose new directions for research to improve the efficacy of multi-label lifelong learning algorithms. Our review unearths a growing number of algorithms and underscores the need for specialized evaluation metrics and methodologies for the accurate assessment of their performance. We also highlight the need for strategies that incorporate real-world data from varying contexts into the learning process to fully capture the nuances of real-world environments.

Introduction Cardiovascular diseases are the most numerous group of diseases prevalent in the world. They are a challenge for many health systems, in terms of keeping life comfortable and also economics. The cause of selected disease entities is too much cholesterol in the blood. The most popular treatment for hypercholesterolemia is based on statins. Many patients are affected by intolerance to these drugs, so an important issue is the discovery and improvement of alternatives to statins. Purpose of work The purpose of this review is to collect literature data on the latest treatments for hypercholesterolemia with drugs other than statins and ezetimibe. Materials and methods Materials are from a review of recent literature available in PubMed. To search for articles, we used keywords such as: bempedoic acid, non-statin therapy, cardiovascular risk, inclisiran, alirocumab, cardiovascular disease, dyslipidemia, evolocumab. Summary Treatment of hypercholesterolemia with statins remains the most popular management strategy. Intolerance to treatment with these drugs creates serious clinical problems for patients. Recently, we could see the emergence of new drugs as alternatives to statins. As the results show, the new drugs can effectively replace statins in the hypolipemic treatment especially of patients who cannot be treated with them.

Background Dialysis is a rare but serious complication after transcatheter aortic valve replacement. We analyzed the large multicenter TRITAVI (transfusion requirements in transcatheter aortic valve implantation) registry in order to develop and validate a clinical score assessing this risk. Methods and Results A total of 10 071 consecutive patients were enrolled in 19 European centers. Patients were randomly assigned (2:1) to a derivation and validation cohort. Two scores were developed, 1 including only preprocedural variables (TRITAVIpre) and 1 also including procedural variables (TRITAVIpost). In the 6714 patients of the derivation cohort (age 82±6 years, 48% men), preprocedural factors independently associated with dialysis and included in the TRITAVIpre score were male sex, diabetes, prior coronary artery bypass graft, anemia, nonfemoral access, and creatinine clearance

Background. The aim of this open-label, multicenter, randomized controlled study was to investigate whether the life cycle pharma (LCP)-tacrolimus compared with the extended-release (ER)-tacrolimus formulation results in a difference in the prevalence of posttransplant diabetes, hypertension and chronic kidney disease (CKD) at 12 mo after liver transplantation. Methods. Patients were 1:1 randomized to either of the 2 tacrolimus formulations. The primary endpoint was defined as a composite endpoint of any of 3 events: sustained (>3 mo postrandomization) posttransplant diabetes, new-onset hypertension, and/or CKD, defined as estimated glomerular filtration rate 3 m during the follow-up. Results. In total, 105 patients were included. In the intention-to-treat analysis, a statistically significant lower proportion of liver transplant recipients in the LCP-tacrolimus group reached the composite primary endpoint at 12 mo compared with the ER-tacrolimus group (50.9% [27/53], 95% confidence interval [CI], 37.9%-63.9% versus 71.2% [37/52], 95% CI, 57.7%-81.7%; risk difference: 0.202; 95% CI, 0.002-0.382; P = 0.046). No significant difference was found in the per protocol analysis. In the intention-to-treat and per protocol population, fewer liver transplant recipients in the LCP-tacrolimus group developed CKD and new-onset hypertension compared with the ER-tacrolimus group. No differences in rejection rate, graft and patient survival were found. Conclusions. A statistically significant and clinically relevant reduction in the prevalence of the composite primary endpoint was found in the LCP-tacrolimus group compared with the ER-tacrolimus group in the first year after liver transplantation with comparable efficacy.

Abstract The design of binder proteins for specific target proteins using deep learning is a challenging task that has a wide range of applications in both designing therapeutic antibodies and creating new drugs. Machine learning-based solutions, as opposed to laboratory design, streamline the design process and enable the design of new proteins that may be required to address new and orphan diseases. Most techniques proposed in the literature necessitate either domain knowledge or some appraisal of the target protein’s 3-D structure. This paper proposes an approach for designing binder proteins based solely on the amino acid sequence of the target protein and without recourse to domain knowledge or structural information. The sequences of the binders are generated with two new transformers, namely the AppendFormer and MergeFormer architectures. Because, in general, there is more than one binder for a given target protein, these transformers employ a binding score and a prior on the sequence of the binder to obtain a unique targeted solution. Our experimental evaluation confirms the strengths of this novel approach. The performance of the models was determined with 5-fold cross-validation and clearly indicates that our architectures lead to highly accurate results. In addition, scores of up to 0.98 were achieved in terms of Needleman-Wunsch and Smith-Waterman similarity metrics, which indicates that our solutions significantly outperform a seq2seq baseline model.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.