Hasil untuk "Therapeutics. Psychotherapy"

Lucy Newman

Member countries of the European Federation of Dramatherapy hold distinct stories about how dramatherapy evolved within their unique cultural and professional landscapes. Each story reveals a pioneer whose qualities, works and encounters were key in how the plot then unfolded. This article will tell the story of how dramatherapy evolved in Switzerland, giving insight into the characteristics of dramatherapy in Switzerland in terms of recognition, training and practice.

Viet Cuong Nguyen, Nhi Yen Nguyen, Kristin A. Candan et al.

Large Language Models (LLMs) are increasingly used in mental health-related settings, yet they struggle to sustain realistic, goal-directed dialogue over extended interactions. While LLMs generate fluent responses, they optimize locally for the next turn rather than maintaining a coherent model of therapeutic progress, leading to brittleness and long-horizon drift. We introduce CALM-IT, a framework for generating and evaluating long-form Motivational Interviewing (MI) dialogues that explicitly models dual-actor conversational dynamics. CALM-IT represents therapist-client interaction as a bidirectional state-space process, in which both agents continuously update inferred alignment, mental states, and short-term goals to guide strategy selection and utterance generation. Across large-scale evaluations, CALM-IT consistently outperforms strong baselines in Effectiveness and Goal Alignment and remains substantially more stable as conversation length increases. Although CALM-IT initiates fewer therapist redirections, it achieves the highest client acceptance rate (64.3%), indicating more precise and therapeutically aligned intervention timing. Overall, CALM-IT provides evidence for modeling evolving conversational state being essential for generating high-quality long-form synthetic conversations.

Wenhui Chu, Aobo Jin, Hardik A. Gohel

Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance Imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot's precision and stability under strong magnetic fields. This research focuses on enhancing MRI's role in medical imaging, especially in its application to guide intravascular interventions using robot-assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system's capability to create tailored magnetic field gradient patterns for device control, considering the vessel's geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system's modeling aspect assesses the safety and feasibility of navigating pre-set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures.

Leila Hefazi Torghabeh, Mahmoud Najafi

The aim of the present study was to explain a structural model of the tendency toward substance abuse based on childhood trauma in adolescents, mediated by roles of self-compassion and mentalization. The population included all high school students )male and female) in the second stage of secondary education in Mashhad city during 2023-2024 academic year, with 360 students selected as the sample through multi-stage cluster random sampling. To measure the research variables, the Tendency toward Substance Abuse Scale, the Childhood Trauma Questionnaire, the Reflective Functioning Questionnaire, and the Self-Compassion Scale for Adolescents were used. The research method was descriptive-correlational using structural equation modeling (SEM). The data was analyzed using SPSS25 and AMOS24 software. The findings indicated a significant relationship between childhood trauma and the tendency toward substance abuse, which is mediated by self-compassion, certainty mentalization, and uncertainty mentalization. Based on these results, it is recommended that focusing on improving certainty mentalization capacity and self-compassion in adolescents who have experienced childhood trauma can be an effective approach to prevent substance abuse and promote their mental health.

Louisa Niehaus, Maria Papaikonomou

Midlife is often stereotyped as a time of turbulence, angst and chaos, marked by inappropriate behaviours, broken marriages, infidelity and destructive changes. However, this article aims to challenge these stereotypes and present a different perspective. It argues that midlife is not a crisis; instead, it is a transformative journey into epiphany. This transcendent midlife journey is possible through gaining self-awareness, garnering insights and resonating with accumulated inherent (and previously undiscovered) values and desires. This starkly counters dated and prescriptive narratives that, left unchecked, descend into negative midlife tropes. Navigating this necessary period in adult actualisation has the potential to be deeply transformative and equip the adult at midlife to live a more meaningful and aligned life. This article presents a detailed discussion and findings from the journeys of five research participants who transitioned from midlife crisis to epiphany. Each participant’s life story is analysed, drawing on themes from existing literature on midlife crises and emphasising the variables of transcendence and evolution into epiphany. The article challenges conventional tropes and describes the unique epiphanies that resulted from these transcendent journeys, providing a rich and diverse perspective on midlife transitions.

Julien Tempone-Wiltshire, Tyson Yunkaporta

This collaboration between an Aboriginal and non-Aboriginal Australian scholar explores a simple but important contention: that human memory is not stored in the brain alone but shaped through ongoing relationship with land. Aboriginal Australian traditions demonstrate that memory is carried and passed through natural systems, with the aid of story, song, and sacred sites. We explore how such place-based cultural memory practices integrate locatedness, relatedness, embodiment, orality, narrative, and imagery. Through these means, the practices give rise to songlines—narrative pathways that do not merely store information but activate knowledges, forming a living map that connects people, place, and understanding. We also explore parallels and distinctions with the classical method of loci or memory palace, which uses spatial orientation as a mnemonic aid. While long assumed to originate in ancient Greece, we show that place-based memory practices in Aboriginal Australia precede this by at least 50,000 years. This exploration contributes to understanding Indigenous knowledge transmission and offers insight into how human memory is held, embodied, and shared.

Shuwei Xing, Derek W. Cool, David Tessier et al.

3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this work, we propose a novel framework for integrating 3D US into the standard ablation workflow. We present a key component, a clinically viable 2D US-CT/MRI registration approach, leveraging 3D US as an intermediary to reduce registration complexity. To facilitate efficient verification of the registration workflow, we also propose an intuitive multimodal image visualization technique. In our study, 2D US-CT/MRI registration achieved a landmark distance error of approximately 2-4 mm with a runtime of 0.22s per image pair. Additionally, non-rigid registration reduced the mean alignment error by approximately 40% compared to rigid registration. Results demonstrated the efficacy of the proposed 2D US-CT/MRI registration workflow. Our integration framework advanced the capabilities of 3D US imaging in improving percutaneous tumour ablation, demonstrating the potential to expand the therapeutic role of 3D US in clinical interventions.

Yinghui Huang, Yuxuan Jiang, Hui Liu et al.

Large language models (LLMs) like GPT-4 show potential for scaling motivational interviewing (MI) in addiction care, but require systematic evaluation of therapeutic capabilities. We present a computational framework assessing user-perceived quality (UPQ) through expected and unexpected MI behaviors. Analyzing human therapist and GPT-4 MI sessions via human-AI collaboration, we developed predictive models integrating deep learning and explainable AI to identify 17 MI-consistent (MICO) and MI-inconsistent (MIIN) behavioral metrics. A customized chain-of-thought prompt improved GPT-4's MI performance, reducing inappropriate advice while enhancing reflections and empathy. Although GPT-4 remained marginally inferior to therapists overall, it demonstrated superior advice management capabilities. The model achieved measurable quality improvements through prompt engineering, yet showed limitations in addressing complex emotional nuances. This framework establishes a pathway for optimizing LLM-based therapeutic tools through targeted behavioral metric analysis and human-AI co-evaluation. Findings highlight both the scalability potential and current constraints of LLMs in clinical communication applications.

Yaroslav Balytskyi, Inna Hubenko, Alina Balytska et al.

Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for the accurate prediction of binding pockets and seamless integration with docking pipelines. On the PoseBusters benchmark, RAPID-Net-guided AutoDock Vina achieves 54.9% of Top-1 poses with RMSD < 2 A and satisfying the PoseBusters chemical-validity criterion, compared to 49.1% for DiffBindFR. On the most challenging time split of PoseBusters aiming to assess generalization ability (structures submitted after September 30, 2021), RAPID-Net-guided AutoDock Vina achieves 53.1% of Top-1 poses with RMSD < 2 A and PB-valid, versus 59.5% for AlphaFold 3. Notably, in 92.2% of cases, RAPID-Net-guided Vina samples at least one pose with RMSD < 2 A (regardless of its rank), indicating that pose ranking, rather than sampling, is the primary accuracy bottleneck. The lightweight inference, scalability, and competitive accuracy of RAPID-Net position it as a viable option for large-scale virtual screening campaigns. Across diverse benchmark datasets, RAPID-Net outperforms other pocket prediction tools, including PUResNet and Kalasanty, in both docking accuracy and pocket-ligand intersection rates. Furthermore, we demonstrate the potential of RAPID-Net to accelerate the development of novel therapeutics by highlighting its performance on pharmacologically relevant targets. RAPID-Net accurately identifies distal functional sites, offering new opportunities for allosteric inhibitor design. In the case of the RNA-dependent RNA polymerase of SARS-CoV-2, RAPID-Net uncovers a wider array of potential binding pockets than existing predictors, which typically annotate only the orthosteric pocket and overlook secondary cavities.

Sven Larsen-Ledet, Aleksandra Panfilova, Amelie Stein

Advances in sequencing have revealed that each individual carries about 10,000 missense variants. For the vast majority, we do not know what the functional consequences - if any - will be. Further, mechanistic insight, such as structural details, would be immensely helpful in development of therapeutic approaches. Here we review recent developments in experimental and computational techniques aimed to assess the impact of variants on protein-protein interactions, including limitations and upcoming challenges.

Agnieszka Anna Tomaka, Leszek Luchowski, Michał Tarnawski et al.

Digital technology plays a crucial role in designing customized medical devices, such as occlusal splints, commonly used in the management of disorders of the stomatognathic system. This methodological proof-of-concept study presents a computer-aided approach for designing and evaluating occlusal positioning splints. The primary aim is to demonstrate the feasibility and geometric accuracy of the proposed method at the preclinical stage. In this approach, a three-dimensional splint is generated using a transformation matrix to represent the therapeutic mandibular position. An experienced operator defines this position using a virtual patient model reconstructed from intraoral scans, CBCT, 3D facial scans, and a digitized plaster model. We introduce a novel method for generating splints that reproduces occlusal conditions in the therapeutic position and resolves surface conflicts through virtual embossing. The process for obtaining transformation matrices using dental tools and intraoral devices commonly employed in dental and laboratory workflows is described, and the geometric accuracy of both designed and printed splints is evaluated using profile and surface deviation analysis. The method supports reproducible, patient-specific splint fabrication and provides a transparent foundation for future validation studies, supporting multimodal image registration and quantification of occlusal discrepancies in research settings.

Alina Shaukat, Komal Ahmed, Iram Shafee et al.

Introduction: Accepting the levels of professionalism that are currently in place not only provides a baseline for promoting development through educational and professional growth approaches, but also helps to make improved professionalisation possible by focusing on individual and professional-related factors. The objective of the current study in my thesis was to evaluate the professionalism and guiding principles of the physical therapists at RCRS, Lahore. Objective: to assess the professionalism of candidates pursuing a postgraduate degree in physical therapy. Methodology: A cross-sectional investigation was conducted. Before taking part in the study, each participant signed a written informed consent form. Data were gathered using a closed-ended professionalism rating questionnaire. 50 postgraduate students from Riphah International University's Lahore campus were included in the sample.. Results: The average age of the participants in the study was 24.0600 (SD±.956). Honesty and Integrity 82% of participants (n=41) Reliability and Responsibility 74% of participants (n=37) Respect for Patients 86% of participants (n=43) Respect for Others 72% of participants (n=36)   Attendance and Approach to Learning 60% of participants (n=30) Compassion and Empathy 68% of participants (n=34) Communication and Collaboration 68% of participants (n=34) Self Awareness and Knowledge of Limits 56% of participants (n=28) Altruism and Advocacy 76% of participants (n=38) Health 86% of participants (n=43) Conclusion: It was concluded that an overall levels of professionalism among post graduate students of physical therapists were unsatisfactory.

Niharika Gajawelli, Andrew D. Geoly, Jean-Marie Batail et al.

Abstract SNT is a high-dose accelerated intermittent theta-burst stimulation (iTBS) protocol coupled with functional-connectivity-guided targeting that is an efficacious and rapid-acting therapy for treatment-resistant depression (TRD). We used resting-state functional MRI (fMRI) data from a double-blinded sham-controlled randomized controlled trial1 to reveal the neural correlates of SNT-based symptom improvement. Neurobehavioral data were acquired at baseline, post-treatment, and 1-month follow-up. Our primary analytic objective was to investigate changes in seed-based functional connectivity (FC) following SNT and hypothesized that FC changes between the treatment target and the sgACC, DMN, and CEN would ensue following active SNT but not sham. We also investigated the durability of post-treatment observed FC changes at a 1-month follow-up. Study participants included transcranial magnetic stimulation (TMS)-naive adults with a primary diagnosis of moderate-to-severe TRD. Fifty-four participants were screened, 32 were randomized, and 29 received active or sham SNT. An additional 5 participants were excluded due to imaging artifacts, resulting in 12 participants per group (Sham: 5F; SNT: 5F). Although we did not observe any significant group × time effects on the FC between the individualized stimulation target (L-DLPFC) and the CEN or sgACC, we report an increased magnitude of negative FC between the target site and the DMN post-treatment in the active as compared to sham SNT group. This change in FC was sustained at the 1-month follow-up. Further, the degree of change in FC was correlated with improvements in depressive symptoms. Our results provide initial evidence for the putative changes in the functional organization of the brain post-SNT.

Qian Wan, Xin Feng, Yining Bei et al.

Human emotions are essentially molded by lived experiences, from which we construct personalised meaning. The engagement in such meaning-making process has been practiced as an intervention in various psychotherapies to promote wellness. Nevertheless, to support recollecting and recounting lived experiences in everyday life remains under explored in HCI. It also remains unknown how technologies such as generative AI models can facilitate the meaning making process, and ultimately support affective mindfulness. In this paper we present Metamorpheus, an affective interface that engages users in a creative visual storytelling of emotional experiences during dreams. Metamorpheus arranges the storyline based on a dream's emotional arc, and provokes self-reflection through the creation of metaphorical images and text depictions. The system provides metaphor suggestions, and generates visual metaphors and text depictions using generative AI models, while users can apply generations to recolour and re-arrange the interface to be visually affective. Our experience-centred evaluation manifests that, by interacting with Metamorpheus, users can recall their dreams in vivid detail, through which they relive and reflect upon their experiences in a meaningful way.

Rochelle A. Mann, Md. Emran Hossen, Alexander David McGuire Withrow et al.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for advanced therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to improve the specificity and efficacy of anticancer treatments. This review examines three cutting-edge mesoporous silica nanoparticle (MSN)-based drug delivery to introduce novel CRC therapy, each utilizing unique functionalization strategies for targeted drug release. The first system, hyaluronidase-responsive MSN-HA/DOX, employs biotin-modified hyaluronic acid to facilitate dual-stimulus drug release in the tumor microenvironment, exhibiting enhanced in vivo tumor inhibition. The DOX/SLN-PEG-Biotin utilizes polyethylene glycol and biotin to improve drug stability and target biotin-overexpressing CRC cells, demonstrating superior anti-cancer efficacy in vitro and in vivo. Lastly, galactosylated chitosan-functionalized MSNs enable targeted delivery through asialoglycoprotein receptors, providing controlled drug release and strong therapeutic potential. Collectively, these systems highlight the advancements in nanoparticle functionalization for CRC treatment, offering a pathway to overcome the limitations of conventional chemotherapy. Further research is required to translate these innovations into clinical practice, ensuring safety and scalability.

Kuan Yan, Yue Zeng, Dai Shi et al.

Age-related macular degeneration (AMD) is a major cause of blindness in older adults, severely affecting vision and quality of life. Despite advances in understanding AMD, the molecular factors driving the severity of subretinal scarring (fibrosis) remain elusive, hampering the development of effective therapies. This study introduces a machine learning-based framework to predict key genes that are strongly correlated with lesion severity and to identify potential therapeutic targets to prevent subretinal fibrosis in AMD. Using an original RNA sequencing (RNA-seq) dataset from the diseased retinas of JR5558 mice, we developed a novel and specific feature engineering technique, including pathway-based dimensionality reduction and gene-based feature expansion, to enhance prediction accuracy. Two iterative experiments were conducted by leveraging Ridge and ElasticNet regression models to assess biological relevance and gene impact. The results highlight the biological significance of several key genes and demonstrate the framework's effectiveness in identifying novel therapeutic targets. The key findings provide valuable insights for advancing drug discovery efforts and improving treatment strategies for AMD, with the potential to enhance patient outcomes by targeting the underlying genetic mechanisms of subretinal lesion development.

Brendan Conway-Smith, Robert L. West

This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity.

Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz et al.

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Robert Wasenmüller, Kevin Hilbert, Christoph Benzmüller

Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack the ability to (a) consistently and reliably act by predefined rules to align their conversation with an overarching therapeutic concept and (b) make their decision paths inspectable for risk management and clinical evaluation -- both essential requirements for an "AI Therapist". In this work, we introduce a novel paradigm for dialog policy planning in conversational agents enabling them to (a) act according to an expert-written "script" that outlines the therapeutic approach and (b) explicitly transition through a finite set of states over the course of the conversation. The script acts as a deterministic component, constraining the LLM's behavior in desirable ways and establishing a basic architecture for an AI Therapist. We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients. The results demonstrate the feasibility of this new technology and provide insights into the efficiency and effectiveness of different implementation variants.

J. Guidi, E. Brakemeier, C. Bockting et al.