Assessing well-being in clinical research and treatment
Related Articles
Emotions and individual differences shape human foraging under threat
A common behavior in natural environments is foraging for rewards. However, this is often in the presence of predators. Therefore, one of the most fundamental decisions for humans, as for other animals, is how to apportion time between reward-motivated pursuit behavior and threat-motivated checking behavior. To understand what affects how people strike this balance, we developed an ecologically inspired task and looked at both within-participant dynamics (moods) and between-participant individual differences (questionnaires about real-life behaviors) in two large internet samples (n = 374 and n = 702) in a cross-sectional design. For the within-participant dynamics, we found that people regulate task-evoked stress homeostatically by changing behavior (increasing foraging and hiding). Individual differences, even in superficially related traits (apathy–anhedonia and anxiety–compulsive checking) reliably mapped onto unique behaviors. Worse task performance, due to maladaptive checking, was linked to gender (women checked excessively) and specific anxiety-related traits: somatic anxiety (reduced self-reported checking due to worry) and compulsivity (self-reported disorganized checking). While anhedonia decreased self-reported task engagement, apathy, strikingly, improved overall task performance by reducing excessive checking. In summary, we provide a multifaceted paradigm for assessment of checking for threat in a naturalistic task that is sensitive to both moods as they change throughout the task and clinical dimensions. Thus, it could serve as an objective measurement tool for future clinical studies interested in threat, vigilance or behavior–emotion interactions in contexts requiring both reward seeking and threat avoidance.
The dual role of motivation on goals and well-being in higher vocational education students: a self-determination theory perspective
Students’ well-being has received increasing international attention. However, research on well-being among higher vocational education (HVE) students, particularly in non-WEIRD contexts, remains limited. This study addresses this gap by investigating the relationships between goals, motivation, and well-being for HVE students in China through the lens of self-determination theory. A survey was administered to 1106 HVE students at a vocational college in China to collect data on their goal content, motivation, and well-being. Quantitative analyses revealed that motivation plays a dual role, acting as both a mediator and a moderator in the relationship between goals and well-being. This dual role is crucial for understanding not only how goals influence well-being but also under what conditions different types of goals promote or hinder well-being. Specifically, intrinsic goals, when paired with autonomous motivation, were found to significantly predict increased well-being. While extrinsic goals combined with controlled motivation also reliably predicted well-being, this relationship should be interpreted cautiously within the specific cultural context of the study. Furthermore, positive relationships between extrinsic goals and well-being, as well as between amotivation and well-being, were observed, contrasting findings from ‘WEIRD’ contexts. This study provides novel insights into how motivation functions as both a moderator and mediator in the goal-well-being relationship within a ‘non-WEIRD,’ specifically Chinese, HVE context. These findings underscore the importance of supporting students in pursuing goals to enhance their well-being. Further research is needed to explore these relationships in diverse cultural settings.
Artificial intelligence in clinical genetics
Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI. After introductory explanations and examples, the review discusses AI in clinical genetics in three main categories: clinical diagnostics; management and therapeutics; clinical support. The review concludes with short, medium, and long-term predictions about the ways that AI may affect the field of clinical genetics. Overall, while the precise speed at which AI will continue to change clinical genetics is unclear, as are the overall ramifications for patients, families, clinicians, researchers, and others, it is likely that AI will result in dramatic evolution in clinical genetics. It will be important for all those involved in clinical genetics to prepare accordingly in order to minimize the risks and maximize benefits related to the use of AI in the field.
Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Exploring metabolic reprogramming in esophageal cancer: the role of key enzymes in glucose, amino acid, and nucleotide pathways and targeted therapies
Esophageal cancer (EC) is one of the most common malignancies worldwide with the character of poor prognosis and high mortality. Despite significant advancements have been achieved in elucidating the molecular mechanisms of EC, for example, in the discovery of new biomarkers and metabolic pathways, effective treatment options for patients with advanced EC are still limited. Metabolic heterogeneity in EC is a critical factor contributing to poor clinical outcomes. This heterogeneity arises from the complex interplay between the tumor microenvironment and genetic factors of tumor cells, which drives significant metabolic alterations in EC, a process known as metabolic reprogramming. Understanding the mechanisms of metabolic reprogramming is essential for developing new antitumor therapies and improving treatment outcomes. Targeting the distinct metabolic alterations in EC could enable more precise and effective therapies. In this review, we explore the complex metabolic changes in glucose, amino acid, and nucleotide metabolism during the progression of EC, and how these changes drive unique nutritional demands in cancer cells. We also evaluate potential therapies targeting key metabolic enzymes and their clinical applicability. Our work will contribute to enhancing knowledge of metabolic reprogramming in EC and provide new insights and approaches for the clinical treatment of EC.
Responses