Hasil untuk "Therapeutics. Psychotherapy"

Leonie Dyck, Aiko Galetzka, Maximilian Noller et al.

Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved throughout the co-development process, which focused on early integration into real-world paediatric surgical and psychiatric settings. The robot, based on the NAO platform, features a simple setup, adaptable exercise routines with interactive guidance, motivational dialogue, and a graphical user interface (GUI) for monitoring and no-code system feedback. Results: Deployment in hospital environments enabled the identification of key design requirements and usability constraints. Stakeholder feedback led to refinements in interaction design, movement capabilities, and technical configuration. A feasibility study is currently underway to assess acceptance, usability, and perceived therapeutic benefit, with data collection including questionnaires, behavioural observations, and staff-patient interviews. Discussion: Mobirobot demonstrates how multiprofessional, stakeholder-led development can yield a socially assistive system suited for dynamic inpatient settings. Early-stage findings underscore the importance of contextual integration, robustness, and minimal-intrusion design. While challenges such as sensor limitations and patient recruitment remain, the platform offers a promising foundation for further research and clinical application.

Takahide Suzuki, Kazuki Nakanishi, Takashi Fujiwara et al.

Therapeutic discovery remains a formidable challenge, impeded by the fragmentation of specialized domains and the execution gap between computational design and physiological validation. Although generative AI offers promise, current models often function as passive assistants rather than as autonomous executors. Here, we introduce OrchestRA, a human-in-the-loop multi-agent platform that unifies biology, chemistry, and pharmacology into an autonomous discovery engine. Unlike static code generators, our agents actively execute simulations and reason the results to drive iterative optimization. Governed by an Orchestrator, a Biologist Agent leverages deep reasoning over a massive knowledge graph (>10 million associations) to pinpoint high-confidence targets; a Chemist Agent autonomously detects structural pockets for de novo design or drug repositioning; and a Pharmacologist Agent evaluates candidates via rigorous physiologically based pharmacokinetic (PBPK) simulations. This architecture establishes a dynamic feedback loop where pharmacokinetic and toxicity profiles directly trigger structural reoptimization. By seamlessly integrating autonomous execution with human guidance, OrchestRA democratizes therapeutic design, transforming drug discovery from a stochastic search to a programmable evidence-based engineering discipline.

Junda Wang, Zonghai Yao, Lingxi Li et al.

Substance use disorders (SUDs) affect millions of people, and relapses are common, requiring multi-session treatments. Access to care is limited, which contributes to the challenge of recovery support. We present \textbf{ChatThero}, an innovative low-cost, multi-session, stressor-aware, and memory-persistent autonomous \emph{language agent} designed to facilitate long-term behavior change and therapeutic support in addiction recovery. Unlike existing work that mostly finetuned large language models (LLMs) on patient-therapist conversation data, ChatThero was trained in a multi-agent simulated environment that mirrors real therapy. We created anonymized patient profiles from recovery communities (e.g., Reddit). We classify patients as \texttt{easy}, \texttt{medium}, and \texttt{difficult}, three scales representing their resistance to recovery. We created an external environment by introducing stressors (e.g., social determinants of health) to simulate real-world situations. We dynamically inject clinically-grounded therapeutic strategies (motivational interview and cognitive behavioral therapy). Our evaluation, conducted by both human (blinded clinicians) and LLM-as-Judge, shows that ChatThero is superior in empathy and clinical relevance. We show that stressor simulation improves robustness of ChatThero. Explicit stressors increase relapse-like setbacks, matching real-world patterns. We evaluate ChatThero with behavioral change metrics. On a 1--5 scale, ChatThero raises \texttt{motivation} by $+1.71$ points (from $2.39$ to $4.10$) and \texttt{confidence} by $+1.67$ points (from $1.52$ to $3.19$), substantially outperforming GPT-5. On \texttt{difficult} patients, ChatThero reaches the success milestone with $26\%$ fewer turns than GPT-5.

Mahya Aghaee, Victoria Cicchirillo, Rowan Milner et al.

Patients with rare types of melanoma such as acral, mucosal, or uveal melanoma, have lower survival rates than patients with cutaneous melanoma; these lower survival rates reflect the lower objective response rates to immunotherapy compared to cutaneous melanoma. Understanding tumor-immune dynamics in rare melanomas is critical for the development of new therapies and for improving response rates to current cancer therapies. Progress has been hindered by the lack of clinical data and the need for better preclinical models of rare melanomas. Canine melanoma provides a valuable comparative oncology model for rare types of human melanomas. We analyzed RNA sequencing data from canine melanoma patients and combined this with literature information to create a novel mechanistic mathematical model of melanoma-immune dynamics. Sensitivity analysis of the mathematical model indicated influential pathways in the dynamics, providing support for potential new therapeutic targets and future combinations of therapies. We share our learnings from this work, to help enable the application of this proof-of-concept workflow to other rare disease settings with sparse available data.

Alice Rueda, Argyrios Perivolaris, Niloy Roy et al.

Engagement between client and therapist is a critical determinant of therapeutic success. We propose a multi-dimensional natural language processing (NLP) framework that objectively classifies engagement quality in counseling sessions based on textual transcripts. Using 253 motivational interviewing transcripts (150 high-quality, 103 low-quality), we extracted 42 features across four domains: conversational dynamics, semantic similarity as topic alignment, sentiment classification, and question detection. Classifiers, including Random Forest (RF), Cat-Boost, and Support Vector Machines (SVM), were hyperparameter tuned and trained using a stratified 5-fold cross-validation and evaluated on a holdout test set. On balanced (non-augmented) data, RF achieved the highest classification accuracy (76.7%), and SVM achieved the highest AUC (85.4%). After SMOTE-Tomek augmentation, performance improved significantly: RF achieved up to 88.9% accuracy, 90.0% F1-score, and 94.6% AUC, while SVM reached 81.1% accuracy, 83.1% F1-score, and 93.6% AUC. The augmented data results reflect the potential of the framework in future larger-scale applications. Feature contribution revealed conversational dynamics and semantic similarity between clients and therapists were among the top contributors, led by words uttered by the client (mean and standard deviation). The framework was robust across the original and augmented datasets and demonstrated consistent improvements in F1 scores and recall. While currently text-based, the framework supports future multimodal extensions (e.g., vocal tone, facial affect) for more holistic assessments. This work introduces a scalable, data-driven method for evaluating engagement quality of the therapy session, offering clinicians real-time feedback to enhance the quality of both virtual and in-person therapeutic interactions.

Ilkka Laakso, Margarethus Marius Paulides, Sachiko Kodera et al.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

Iqra Yousaf, Aneela Anwar, Atika Umer

Chronic wounds present a major challenge in elderly individuals due to diminished regenerative capacity and impaired tissue repair mechanisms associated with aging. In this study, we formulated a topical gel composed of zinc oxide (ZnO), silver (Ag), and cerium oxide (CeO2) nanoparticles, each chosen for their respective antimicrobial, antioxidant, and tissue-regenerative properties. The nanoparticles were synthesized through precise precipitation or reduction techniques and thoroughly characterized using UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FTIR), and electron microscopy to confirm nanoscale structure and purity. An in vivo wound healing model utilizing aged Sprague-Dawley rats was employed, with animals divided into three groups: untreated, placebo-treated, and those receiving the nanoparticle-enriched gel. Wound dimensions were tracked for 14 days, revealing significantly improved healing in the nanoparticle-treated group, with nearly complete closure observed by day 14 (ANOVA, p less than 0.0001). Cytocompatibility was assessed via MTT assay on L929 fibroblasts, confirming greater than 80 percent viability at therapeutically relevant concentrations. These findings underscore the potential of multifunctional nanoparticle-based formulations to enhance wound healing in aged or compromised skin environments, offering a promising therapeutic avenue.

Jiayi Xin, Aniruddh Raghu, Nick Bhattacharya et al.

Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or engineered independently. While these complex formats can expand disease applicability and improve safety, they present a significant engineering challenge: the function and stability of individual domains are not guaranteed in the novel format, and the entire molecule may no longer be synthesizable. To address these challenges, we develop a machine learning framework to predict "reformatting success" -- whether converting an antibody from one format to another will succeed or not. Our framework incorporates both antibody sequence and structural context, incorporating an evaluation protocol that reflects realistic deployment scenarios. In experiments on a real-world antibody reformatting dataset, we find the surprising result that large pretrained protein language models (PLMs) fail to outperform simple, domain-tailored, multimodal representations. This is particularly evident in the most difficult evaluation setting, where we test model generalization to a new starting antibody. In this challenging "new antibody, no data" scenario, our best multimodal model achieves high predictive accuracy, enabling prioritization of promising candidates and reducing wasted experimental effort.

Shani SELA, Elisha VERED, Leonid KALICHMA

Introduction: Post-stroke spasticity is a prevalent motor complication leading to extremity weakness and impaired coordination, significantly affecting daily activities. This study evaluated the efficacy of combining Kinesiotape with occupational therapy in reducing spasticity and enhancing upper extremity functionality in post-stroke patients. Methods: A prospective randomized controlled trial at Shmuel Harofeh Medical Center involved 16 patients randomly assigned (1:1) to an intervention group (Kinesiotape plus occupational therapy) or a control group (occupational therapy alone). Treatment lasted four weeks, five sessions weekly. Assessments occurred pre-intervention, post-intervention, and one-week follow-up using the Modified Ashworth Scale (MAS) for spasticity, Fugl-Meyer Assessment (FMA) for motor function, handgrip dynamometer for grip strength, and Chedoke Arm and Hand Activity Inventory (CAHAI) for hand function. Results: Kinesiotape significantly reduced spasticity versus control post-intervention (u=6.00, p=0.007) and at follow-up (u=1.00, p=0.003). In the Kinesiotape group, spasticity decreased significantly (z=-2.37, p=0.018) from pre-intervention (7.89±3.79) to one hour post-application (5.33±4.06), with significant increases in upper extremity motor function (t=-4.151, p=0.003) from pre- (9.89±16.56) to post-intervention (21.67±20.70) and grip strength (z=-2.023, p=0.043). Conclusion: Combining Kinesiotape with occupational therapy limits spasticity development, improves upper extremity motor function and grip strength, and enhances quality of life in acute stroke patients.

Katharina M. Rischer, Linda T. Betz, Antje Riepenhausen et al.

Abstract This pragmatic randomized controlled trial examined the effectiveness of reviga, a self-guided digital intervention based on cognitive behavioral therapy, in reducing work-related stress symptoms. A total of 290 adults experiencing significant stress and burnout were assigned to the intervention group (reviga + treatment as usual [TAU]; n = 147) or the control group (TAU only; n = 143). Intent-to-treat analyses showed that 3 months post-randomization, participants in the intervention group experienced significant positive effects on the primary outcome, perceived stress (Cohen’s d = 0.36), as well as on the secondary outcomes anxiety (d = 0.28), burnout (d = 0.31), occupational and social functioning (d = 0.31) and health-related quality of life (d = 0.35) compared to TAU. No effect was found for absenteeism quantified as the number of sick days. Effect sizes increased at 6 month follow-up. This study demonstrates that reviga represents a promising and scalable tool for workplace mental health support.

Hana Budi Prastiwi, Arthur Huwae

Being a student in higher education requires each individual to have the sensitivity to adapt and recognize the scope of lectures independently. The presence of several inclusive universities in Indonesia creates a sense of acceptance, especially for individuals with limitations in carrying out activities, especially lecture activities. It is undeniable that students with disabilities experience difficulties and obstacles and a different dynamic process from non-disabled students. The challenges and obstacles experienced by students with disabilities can affect the decrease in learning motivation so that it leads to learning fatigue both due to the lack of equitable distribution of supporting facilities for students with disabilities and the responsibilities and roles that must be carried out by students with disabilities. The purpose of this research is to find out the portrait of academic well-being of students with disabilities in inclusive universities. This research uses a qualitative approach with a descriptive narrative design. The participants of this study were 3 students with disabilities using snowball sampling technique. Data collection conducted by researchers is using semi-structured interview techniques. The results found that the lack of equitable facilities in inclusive universities is one of the reasons students with disabilities still experience obstacles in undergoing lectures. However, this does not prevent the three participants from continuing to undergo lectures because each individual has positive academic well-being and has social support from the surrounding environment.

Maria Elena ABRUDEANU, Constantin CIUCUREL, Luminita GEORGESCU et al.

Introduction: The COVID-19 pandemic heightened vulnerabilities for individuals with disabilities, limiting access to care and essential services. This study examines its impact on institutionalized individuals, caregivers, and care centers, emphasizing the challenges posed by reduced physical activity and the resulting negative effects on health. It identifies key issues and provides insights to improve future crisis responses. Material and method: This cross-sectional study employed a mixed-methods approach, integrating quantitative and qualitative analyses to assess the impact of COVID-19 on institutionalized individuals with disabilities, caregivers, and residential centers, using structured questionnaires and interviews at both institutional and resident levels. Results: The research, conducted across three residential centers in Arges County with 231 beneficiaries (96 males, 135 females, ages 19-97 years), found that the pandemic significantly affected the mental health and daily routines of residents, especially those with severe psychiatric disorders. The reduced physical activity further contributed to muscle deconditioning and frailty among beneficiaries, exacerbating their health risks. Meanwhile, staff managed stress and adapted to care requirements. Discussion: This study reveals the COVID-19 pandemic's negative impact on residents with disabilities in residential centers, emphasizing the effects of isolation and reduced physical activity. Key insights highlight the importance of maintaining physical engagement, communication, and adaptable care strategies to mitigate the impact of these limitations. Conclusions: The pandemic impacted individuals with mental disabilities differently, with isolation affecting psychological health, especially for those with schizophrenia. Family support and communication helped maintain stability. Caregiver stress was high, but support and collaboration alleviated some challenges. Reduced physical activity and increased sedentary behavior among both residents and caregivers worsened health, highlighting the need for better crisis preparedness.

Steffen T. Eberhardt, Jana Schaffrath, Danilo Moggia et al.

Abstract Objective Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. Method We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. Results The results provide indications in support of the sentiments’ validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. Discussion Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin et al.

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.

Marc Fiammante, Anne-Isabelle Vermersch, Marie Vidailhet et al.

Objective Accurate identification of hypoxic-ischemic brain injury in the early neonatal period is essential for initiating therapeutic hypothermia (TH) within 6 hours of birth to optimize neurodevelopmental outcomes. We aimed to develop a simple decision-making tool for identifying term neonates with hypoxic-ischemic encephalopathy (HIE) based on features of conventional electroencephalograms (EEG) recorded within 6 hours of birth. Methods EEG recordings from 100 full-term neonates with HIE were graded by pediatric neurologists for severity. Amplitude in slow frequency bands was analyzed, focusing on delta (0.5-4 Hz) spectral power. Temporal fluctuations of delta power characterized each HIE grade, with joint level and duration probability densities estimated for delta oscillation power. This study is registered on clinicaltrials.gouv (NCT05114070). Results These 2D EEG representations effectively distinguish mild HIE cases from those requiring hypothermia, achieving 98% accuracy, 99% sensitivity, 99% positive predictive value, 94% negative predictive value, an F1 score of 99%, and a false alarm rate of only 6%. This system accurately discriminates mild from moderate or severe HIE, with only one mild case mistakenly identified as requiring hypothermia and one moderate case erroneously flagged for treatment. Conclusions Quantized probability densities of delta spectral features from early EEG (within 6 hours of birth) revealed significant differences between mild and moderate/severe HIE, enabling accurate discrimination of candidates for TH. Significance Simple, interpretable biomarkers from early EEG can provide an efficient visual clinical decision support tool to identify full-term neonates with HIE eligible for therapeutic hypothermia.

Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer et al.

Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model predicting the target property of interest. The model predictions are used to determine which designs to evaluate in the lab, and the model is updated on the new measurements to inform the next cycle of decisions. A key challenge is that the experimental feedback from each cycle inspires changes in the candidate proposal or experimental protocol for the next cycle, which lead to distribution shifts. To promote robustness to these shifts, we must account for them explicitly in the model training. We apply domain generalization (DG) methods to classify the stability of interactions between an antibody and antigen across five domains defined by design cycles. Our results suggest that foundational models and ensembling improve predictive performance on out-of-distribution domains. We publicly release our codebase extending the DG benchmark ``DomainBed,'' and the associated dataset of antibody sequences and structures emulating distribution shifts across design cycles.

Diwei Shi, Sisi Li, Fan Liu et al.

Early assessment of tumor therapeutic response is an important topic in precision medicine to optimize personalized treatment regimens and reduce unnecessary toxicity, cost, and delay. Although diffusion MRI (dMRI) has shown potential to address this need, its predictive accuracy is limited, likely due to its unspecific sensitivity to overall pathological changes. In this work, we propose a new quantitative dMRI-based method dubbed EXCHANGE (MRI of water Exchange, Confined and Hindered diffusion under Arbitrary Gradient waveform Encodings) for simultaneous mapping of cell size, cell density, and transcytolemmal water exchange. Such rich microstructural information comprehensively evaluates tumor pathologies at the cellular level. Validations using numerical simulations and in vitro cell experiments confirmed that the EXCHANGE method can accurately estimate mean cell size, density, and water exchange rate constants. The results from in vivo animal experiments show the potential of EXCHANGE for monitoring tumor treatment response. Finally, the EXCHANGE method was implemented in breast cancer patients with neoadjuvant chemotherapy, demonstrating its feasibility in assessing tumor therapeutic response in clinics. In summary, a new, quantitative dMRI-based EXCHANGE method was proposed to comprehensively characterize tumor microstructural properties at the cellular level, suggesting a unique means to monitor tumor treatment response in clinical practice.

Catherine Stevens

In recent years there has been a growing body of literature on nature based art therapies. Within this relatively new specialism there are a number of emerging models of environmental art therapy practiced by art therapists in the UK. Alongside this interest in the natural world, there is a growing awareness that for therapeutic practices to remain relevant in today’s world they need to recognise that we exist as a part of, and not apart from, nature and that humans are having a significant detrimental impact on the natural world as seen in the climate emergency, Bird (2023) and Deco (2021). The British Association of Art Therapist’s (BAAT) focused on this concern in their 2023 annual conference on the theme ‘Art therapy and the climate crisis.’ In writing this paper I seek to explore an area where I think art therapy would benefit from developing deeper reflections on the systemic nature of the ecological crises we face. The relationship between ourselves and the natural world necessarily involves ecological thinking such as Deep ecology, [a concept developed by the Norwegian philosopher Arne Naess (1990)], and the systemic thinking of Gregory Bateson (1972). Here I explore how, in taking my art therapy practice outdoors, (rather than the traditional art therapy studio) my therapeutic practice comes into immediate contact with the natural world, its dynamic rhythms, and its ecosystems. This makes it possible to develop a practice which is open to engaging in a dialogue between humans (therapist and clients) and the many other life forms, plants, animals, insects, etc. found in a garden.

Shimrit Fisher, P. Fonagy, H. Wiseman et al.

Epistemic trust (ET) is one's ability to trust others and relies on the information they convey as being relevant and generalizable. This concept has received considerable theoretical and clinical attention, suggesting it is a promising factor in effective psychotherapy, possibly consisting of three elements: sharing, we-mode, and learning. However, for it to be used in clinical practice and research, it is imperative to (a) enhance our clinical understanding of how ET may manifest in the context of treatment and (b) understand how the process of change may occur in the course of treatment. The present study aims to identify patients' trait-like ET characteristics upon initiating treatment and explore the possible state-like changes in ET characteristics throughout treatment. Taking a discovery-oriented approach, we examined how therapists can identify a patient's level of ET at the beginning of treatment. We also examined how, within a treatment for individuals with poor pretreatment ET, the therapist and patient work interactively to bring about a positive change in ET. Identifying the process in which the therapist implements techniques in response to the patient's reactions may enable the active mechanism to be isolated and promote the first formulation of the way changes in ET occur in sequence. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Oluseun A. FAPOJUWO, Sunday R. AKINBO, David O. OREKOYA

Background and Objective: Health locus of control (HLC) beliefs may be valuable predictors of treatment outcomes in patients with knee osteoarthritis (OA). The study aimed to investigate the relationship between HLC with knee pain intensity and duration, knee stiffness severity and treatment satisfaction in patients with knee OA. Materials and Methods: This cross sectional study involved 60 patients with knee OA. Multidimensional HLC, Short Assessment of Patient Satisfaction and Western Ontario and McMaster Osteoarthritis Index questionnaires were used to assess participants' self perceived HLC factors, treatment satisfaction, pain, stiffness, and physical function, respectively. Spearman’s correlation was used to find relationships among variables. Data were analysed using SPSS version 22, α was set at 5%. Result: Mean values of 24.30, 23.68 and 18.76 were reported for External HLC, Internal HLC and Chance HLC respectively. Treatment satisfaction was positively correlated with Internal HLC (r=0.33, p<0.01) Chance HLC was positively correlated with pain severity (r=0.37; p=.0.004), and physical function (r=.39; p=0.002). There was also a positive correlation between External HLC and pain duration (r=0.42; p=0.001). Discussion and Conclusion: Most patients with knee OA have high External HLC which means they ascribe their health status prominently to health care professionals, family members and friends. This could negatively influence their recovery outcome and lead to chronicity. HLC beliefs should be considered when managing patients with knee OA to positively impact adherence to instructions, treatment outcomes and overall quality of life.