Hasil untuk "Therapeutics. Psychotherapy"

Rebecca EA Walwyn, Rosemary A Bailey, Arpan Singh et al.

It is recognised that treatment-related clustering should be allowed for in the sample size and analyses of individually-randomised parallel-group trials that evaluate therapist-delivered interventions such as psychotherapy. Here, interventions are a treatment factor, but therapists are not. If the aim of a trial is to separate effects of therapists from those of interventions, we propose that interventions and therapists should be regarded as two potentially interacting treatment factors (one fixed, one random) with a factorial structure. We consider the specific design where each therapist delivers each intervention (crossed therapist-intervention design), and the resulting therapist-intervention combinations are randomised to patients. We adopt a classical Design of Experiments (DoE) approach to propose a family of orthogonal factorial designs and their associated data analyses, which allow for therapist learning and centre too. We set out the associated data analyses using ANOVA and regression and report the results of a small simulation study conducted to explore the performance of the proposed randomisation methods in estimating the intervention effect and its standard error, the between-therapist variance and the between-therapist variance in the intervention effect. We conclude that more purposeful trial design has the potential to lead to better evidence on a range of complex interventions and outline areas for further methodological research.

Dini Rakhmawati, Heri Saptadi Ismanto, Jovita Julienjatiningsih et al.

This study analyzed the influence of different dimensions of gender-based violence perceptions on the prevalence of digital violence (DV) among university students. The research background rests on the growing threat of online violence, which may be shaped by individual awareness levels. The research instrument specifically accommodated four forms of online gender-based violence: digital sexual harassment, violence based on physical appearance, violence based on gender roles, and anti-feminist violence. The study employs a cross-sectional design and involves 414 students who actively use social media as respondents. Multiple regression analysis (F-test) shows that the four perception dimensions—perceptions of sexual harassment, gender-based violence, physical appearance violence, and anti-feminist violence—simultaneously exert a significant effect on digital violence (Sig. = 0.001). These results confirm the validity of the predictive model. However, partial testing (t-test) reveals that only perceptions of sexual harassment significantly and positively influence digital violence (B = +0.304; Sig. = 0.002). The positive coefficient reflects a reporting bias: respondents with higher sensitivity to sexual harassment tend to define and report a broader range of online incidents as violence. Meanwhile, perceptions of gender-based violence, physical appearance violence, and anti-feminist violence do not provide unique predictive contributions. The study concludes that, in the context of digital violence, sexual harassment awareness is the most dominant factor. It recommends that online violence prevention programs and policies focus specifically on strengthening understanding and coping strategies related to digital sexual harassment.

Boyu Ren, Federico Ferrari, Sandra Fortini et al.

In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effects has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation but the study design did not account for potential heterogeneous treatment effects. The use of external data from completed clinical studies and electronic health records has the potential to improve decision-making throughout the development of new therapeutics, from early-stage trials to registration. Here we discuss the use of external data to evaluate experimental treatments with potential heterogeneous treatment effects. We introduce a permutation procedure to test, at the completion of a randomized clinical trial, the null hypothesis that the experimental therapy does not improve the primary outcomes in any subpopulation. The permutation test leverages the available external data to increase power. Also, the procedure controls the false positive rate at the desired $α$-level without restrictive assumptions on the external data, for example, in scenarios with unmeasured confounders, different pre-treatment patient profiles in the trial population compared to the external data, and other discrepancies between the trial and the external data. We illustrate that the permutation test is optimal according to an interpretable criteria and discuss examples based on asymptotic results and simulations, followed by a retrospective analysis of individual patient-level data from a collection of glioblastoma clinical trials.

Thomas Jack Samuels, Franco Rugolon, Stephan Hau et al.

This study investigates the efficacy of using multimodal machine learning techniques to detect deception in dyadic interactions, focusing on the integration of data from both the deceiver and the deceived. We compare early and late fusion approaches, utilizing audio and video data - specifically, Action Units and gaze information - across all possible combinations of modalities and participants. Our dataset, newly collected from Swedish native speakers engaged in truth or lie scenarios on emotionally relevant topics, serves as the basis for our analysis. The results demonstrate that incorporating both speech and facial information yields superior performance compared to single-modality approaches. Moreover, including data from both participants significantly enhances deception detection accuracy, with the best performance (71%) achieved using a late fusion strategy applied to both modalities and participants. These findings align with psychological theories suggesting differential control of facial and vocal expressions during initial interactions. As the first study of its kind on a Scandinavian cohort, this research lays the groundwork for future investigations into dyadic interactions, particularly within psychotherapy settings.

Siqi Zhao, Joshua Moller, Porfi Quintero-Cadena et al.

Therapeutic antibodies require not only high-affinity target engagement, but also favorable manufacturability, stability, and safety profiles for clinical effectiveness. These properties are collectively called `developability'. To enable a computational framework for optimizing antibody sequences for favorable developability, we introduce a guided discrete diffusion model trained on natural paired heavy- and light-chain sequences from the Observed Antibody Space (OAS) and quantitative developability measurements for 246 clinical-stage antibodies. To steer generation toward biophysically viable candidates, we integrate a Soft Value-based Decoding in Diffusion (SVDD) Module that biases sampling without compromising naturalness. In unconstrained sampling, our model reproduces global features of both the natural repertoire and approved therapeutics, and under SVDD guidance we achieve significant enrichment in predicted developability scores over unguided baselines. When combined with high-throughput developability assays, this framework enables an iterative, ML-driven pipeline for designing antibodies that satisfy binding and biophysical criteria in tandem.

Xiang Ma, Supantha Dey, Vaishnavey SR et al.

Decoding protein-protein interactions (PPIs) at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind Webserver, a computational framework that leverages fine-tuned protein language models (PLMs) to determine binding affinity between proteins and identify critical binding residues directly from sequences, eliminating the structural requirements that limit most affinity prediction tools. We fine-tuned four architectures including ProtBERT, ProtT5, ESM2, and BiLSTM on the SKEMPI 2.0 dataset containing 5,387 protein pairs with experimental binding affinities. Through systematic alanine mutagenesis on each residue for 14 therapeutically relevant protein complexes, we evaluated each model's ability to identify interface residues. Performance was assessed using N-factor metrics, where N-factor=3 evaluates whether true residues appear within 3n top predictions for n interface residues. ESM2 achieved 49.5% accuracy at N-factor=3, with both ESM2 (37.2%) and ProtBERT (35.1%) outperforming structural docking method HADDOCK3 (32.1%) at N-factor=2. Our sequence-based approach enables rapid screening (minutes versus hours for docking), handles disordered proteins, and provides comparable accuracy, making Seq2Bind a valuable prior to steer blind docking protocols to identify putative binding residues from each protein for therapeutic targets. Seq2Bind Webserver is accessible at https://agrivax.onrender.com under StructF suite.

Jinfeng Zhou, Yuxuan Chen, Jianing Yin et al.

Cognitive Restructuring (CR) is a psychotherapeutic process aimed at identifying and restructuring an individual's negative thoughts, arising from mental health challenges, into more helpful and positive ones via multi-turn dialogues. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, existing efforts implement CR via simple text rewriting, fixed-pattern dialogues, or a one-shot CR workflow, failing to align with the psychotherapeutic process for effective CR. To address this gap, we propose CRDial, a novel framework for CR, which creates multi-turn dialogues with specifically designed identification and restructuring stages of negative thoughts, integrates sentence-level supportive conversation strategies, and adopts a multi-channel loop mechanism to enable iterative CR. With CRDial, we distill Crisp, a large-scale and high-quality bilingual dialogue dataset, from LLM. We then train Crispers, Crisp-based conversational LLMs for CR, at 7B and 14B scales. Extensive human studies show the superiority of Crispers in pointwise, pairwise, and intervention evaluations.

Jacob E. Aronoff, Benjamin C. Trumble

The rise in chronic diseases over the last century presents a significant health and economic burden globally. Here we apply evolutionary medicine and life history theory to better understand their development. We highlight an imbalanced metabolic axis of growth and proliferation (anabolic) versus maintenance and dormancy (catabolic), focusing on major mechanisms including IGF-1, mTOR, AMPK, and Klotho. We also relate this axis to the hyperfunction theory of aging, which similarly implicates anabolic mechanisms like mTOR in aging and disease. Next, we highlight the Brain-Body Energy Conservation model, which connects the hyperfunction theory with energetic trade-offs that induce hypofunction and catabolic health risks like impaired immunity. Finally, we discuss how modern environmental mismatches exacerbate this process. Following our review, we discuss future research directions to better understand health risk. This includes studying IGF-1, mTOR, AMPK, and Klotho and how they relate to health and aging in human subsistence populations, including with lifestyle shifts. It also includes understanding their role in the developmental origins of health and disease as well as the social determinants of health disparities. Further, we discuss the need for future studies on exceptionally long-lived species to understand potentially underappreciated trade-offs and costs that come with their longevity. We close with considering possible implications for therapeutics, including (1) compensatory pathways counteracting treatments, (2) a Goldilocks zone, in which suppressing anabolic metabolism too far introduces catabolic health risks, and (3) species constraints, in which therapeutics tested in shorter lived species with greater anabolic imbalance will be less effective in humans.

Mouhacine Benosman

Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years, several LLMs have been introduced, including ChatGPT by OpenAI, Claude by Anthropic, and Llama by Meta, among others. These models have the potential to be employed across a wide range of human-machine interaction applications, such as chatbots for information retrieval, assistance in corporate hiring decisions, college admissions, financial loan approvals, parole determinations, and even in medical fields like psychotherapy delivered through chatbots. The key question is whether these chatbots will interact with humans in a bias-free manner or if they will further reinforce the existing pathological biases present in human-to-human interactions. If the latter is true, then how can we rigorously measure these biases? We address this challenge by introducing STAMP-LLM (Standardized Test and Assessment Measurement Protocol for LLMs), a psychometric-based principled two-phase framework for designing psychometric measures to evaluate chatbot biases: (i) a Definitional phase for construct mapping, item development, and expert review; and (ii) a Data/Analysis phase for protocol control (prompts/decoding), automated sampling, pre-specified scoring, and basic reliability/validity checks. We illustrate STAMP-LLM on racial bias using one explicit and two implicit measures.

Yamna Mazher, Marwa Muslim, Hassan Shahid Dar et al.

Abstract: Background: Proprioception is the body's ability of sensing the position, movement, and alignment of its joints and limbs. The ACL is rich in proprioceptive fibers, an injury to this ligament, impairs knee function and neuromuscular control, leading to poor coordination. Incorporating proprioception assessment and training in ACL rehabilitation is essential for restoring function and preventing future injuries. Objective: To determine knee joint proprioception in loaded and unloaded position among patients after arthroscopically assisted anterior cruciate ligament (ACL) reconstruction. Methodology: A descriptive cross-sectional study was conducted at Ghurki Trust and Teaching Hospital (GTTH), targeting patients who had undergone surgical reconstruction of anterior cruciate ligament. The total study duration was six months, covering the period from June 2023 to December 2023. Using a non-probability purposive sampling technique, a total of 74 participants meeting the inclusion criteria were selected. A goniometer was utilized to evaluate joint position sense in both loaded and unloaded positions. The data was entered and analyzed using SPSS version 22. Results: The result showed that the mean age of participants was 27.79 ± 5.03. Knee flexion in weight bearing position was 30 degrees and maximum range was 44 degree with mean of 34.89 ± 4.21 whereas minimum knee flexion in non-weight bearing was 30 degree and maximum was 45 degree with mean of 33.68 ± 4.35. Conclusion: Statistically significant difference was found in knee joint proprioception between loaded and unloaded position among patient of ACL reconstruction. Keywords: Anterior Cruciate Ligament, Knee Joint, Proprioception, Weight-Bearing

Filippo Queirazza, Marios G. Philiastides

Abstract Computerised Cognitive Behavioural therapy (CBT) is an effective psychological intervention for mild to moderate depression. While CBT aims to correct maladaptive cognitive biases and ensuing disadvantageous decision-making, our current understanding of decision-making signatures linked to CBT response remains limited. Preliminary behavioural evidence has shown that the process of evidence accumulation (EA), indexing the efficiency of decision dynamics, is impaired in depression. However, little is known about the role of EA in the context of CBT for depression. In this study we recruited 37 (18 females) unmedicated depressed subjects. Participants attended two task-based functional resonance imaging sessions before and two months after completing an online self-help CBT-based intervention. We fitted a hybrid reinforcement learning drift diffusion model to the probabilistic reversal learning task data and investigated accumulator-like brain activity as a function of response to computerised CBT. We found that at baseline, compared to nonresponders, responders exhibited weaker left prefrontal and parieto-occipital EA neural signatures, which subsequently increased in proportion to the sustained symptomatic improvement observed following computerised CBT. We thus provide preliminary evidence that attenuated EA neural signatures in the left prefrontal and parieto-occipital cortical areas are associated with response to computerised CBT in depression. Crucially, the observed increase of accumulator-like brain activity following computerised CBT warrants further replication in future experimental work probing neurocomputational mechanisms of change in CBT.

Danna Abraham

This poem emerged from the raw data material of the author’s most recent teaching evaluations in higher education, recasting the very phrases and opinions that often remain, decontextualized and invisible in the educator’s official records. Rather than accepting the felt judgments that often do harm, the poem reworks reviewers’ bolded sentences into a counter-narrative that centres the context of classroom dynamics and relational learning - transforming deficits into narrative coherence.    Additionally, this poem illustrates how reviewers’ feedback, when clipped from its classroom context, can be situated into surveillance practices of women’s tone as well as feminist critique that often flattens relational learning. By repurposing those words as another act of rebellion, the poem reframes criticism as a site of meaning-making. It moves from accusation to invitation, from rating to reflection, and from surveillance to shared responsibility. The inspiration for this writing is situated in the lived realities of the author - a woman of colour - who has written about embracing poetry as a transformative practice in educational environments (Abraham, 2024). The author invites readers through the journey of reconsideration - from receiving student feedback in the form of teaching evaluation that is centred in anonymity to building dialogic, context-rich response to the felt damages of a consumer-style feedback system.

Caterina Yanet Rae

A lo largo de la historia de la humanidad se han elaborado explicaciones que intentan delinear los bordes del conflictivo campo de lo humano, a partir de aquello que nos diferencia y nos acerca a los animales. En el presente escrito se desarrollan distintas concepciones que cuestionan la idea del ser humano dócil y racional, al tiempo que se ubica su tendencia constitutiva a la agresividad como un intento de dominar aquello que le resulta radicalmente otro. El animal, representante de esa alteridad y objeto histórico de nuestra observación y manipulación en las distintas esferas de nuestras vidas, conscientes e inconscientes, nos confronta con lo real de la vida, que inquieta e interpela por dejar relucir que se puede existir en el mundo sin cargar las marcas de una pérdida.

Alicja Chaszczewicz, Raj Sanjay Shah, Ryan Louie et al.

Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with mental health issues who use peer counseling. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors, especially novices, at scale. To achieve this, we co-design with a group of senior psychotherapy supervisors to develop a multi-level feedback taxonomy, and then construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback. Via qualitative and quantitative evaluation with domain experts, we demonstrate that our method minimizes the risk of potentially harmful and low-quality feedback generation which is desirable in such high-stakes scenarios.

Yanwen Huang, Bowen Gao, Yinjun Jia et al.

Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics. The term "bioactivity" encompasses various biological effects resulting from these interactions, including both binding and functional responses. The magnitude of bioactivity dictates the therapeutic or toxic pharmacological outcomes of small molecules, rendering accurate bioactivity prediction crucial for the development of safe and effective drugs. However, existing structural datasets of small molecule-protein interactions are often limited in scale and lack systematically organized bioactivity labels, thereby impeding our understanding of these interactions and precise bioactivity prediction. In this study, we introduce a comprehensive dataset of small molecule-protein interactions, consisting of over a million binding structures, each annotated with real biological activity labels. This dataset is designed to facilitate unbiased bioactivity prediction. We evaluated several classical models on this dataset, and the results demonstrate that the task of unbiased bioactivity prediction is challenging yet essential.

Guangzeng Han, Weisi Liu, Xiaolei Huang et al.

Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted machine learning to predict patient states during the MI sessions, lacking of domain-specific knowledge and overlooking patient-therapist interactions are major challenges in developing and deploying those models in real practice. To encounter those challenges, we introduce the Chain-of-Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions. The CoI prompting approach systematically breaks down the coding task into three key reasoning steps, extract patient engagement, learn therapist question strategies, and integrates dyadic interactions between patients and therapists. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding. Experiments on real-world datasets can prove the effectiveness and flexibility of our prompting method with multiple state-of-the-art LLMs over existing prompting baselines. We have conducted extensive ablation analysis and demonstrate the critical role of dyadic interactions in applying LLMs for psychotherapy behavior understanding.

Aleksandr Bondarenko, Anastasia Radetska

The article presents the results of a study of the latest trends characteristic of modern processes of providing psychological care in Ukraine, both in terms of paradigms and methods preferred by Ukrainian psychologists. This was facilitated by the study of specialized groups on the Facebook platform, such as "Psychologists of Ukraine", "Psychologists and Psychotherapists", "Clinical Psychologists and Psychotherapists of Ukraine", "Psychology. Ukraine / Event Calendar", other professional communities that unite specialists in psychological care, as well as specialized popular sites such as: "Rozmova" (https://www.rozmova.me/), "Hedepy" (https://app.hedepy.com.ua/), "Mysense" (https://mysense.care/psychologists), and "Pleso" (https://pleso.me/), which present over 1,000 counseling offers in various approaches and methods. The study goes beyond simple statistical analysis, offering a deeper understanding of the evolution of psychological practice in the complex socio-political conditions of modern Ukraine, revealing a complex picture of professional adaptation and transformation of psychological care. The study reveals a unique picture of the professional evolution of Ukrainian psychologists. 25% consciously change their professional trajectory, choosing psychology as a vocation. The predominance of specialists with one to five years of experience indicates an active young generation of psychologists. The increasing use of the online format (65%) is not just a technological innovation, but an existentially important format of consultative communication in conditions when it is necessary to ensure the accessibility of psychological care.Martial law and hostilities increased the volume of crisis assistance by 40%, 30% of psychologists joined volunteer activities. Along with this, new challenges and barriers appeared. This is the high cost of training and certification, which 65% of respondents complained about. High level of professional stress (50% of respondents). Policy in the field of higher education, which prioritizes the financing of higher education institutions at the expense of the impoverished population, and not professional criteria and requirements for the selection of future specialists of such a variety of helping professions as practical psychologists.

Georgina Cardo, Keith Tudor

This article offers a comparison between concepts of heuristic enquiry and tazkiyat-un-nafs, the Islamic concept of self/soul purification and/or reformation of the spiritual heart (qalb). The article outlines key concepts of heuristic research methodology as identified by Moustakas (1990) and Sela-Smith (2002) and elaborated by McCann and Tudor (2024). We reflect on the first author’s experience while undertaking some original research under the supervision of the second author, both of whom share an interest in cultural and religious identity and how this is or is not considered in psychotherapy (Florence et al., 2019; Tudor, 2019). From that original research, in this article, we discuss the concepts of heuristic research as they exemplify heuristic methodology compared with the Islamic practice of tazkiyat-un-nafs, identifying the noticeable differences between them. The article aims to support researchers who consider integrating Islam into their psychotherapeutic research – and, in parallel, their psychotherapy practice; as such, we view this as a contribution to the Islamic psychology and psychotherapy movement.

Cristian Palma

Preguntarse por el valor de la vida en la sociedad de libre mercado implica situarla dentro de las redes biopolíticas de coordinación, producción y regulación de los procesos de la especie, en el triángulo de gobernabilidad en el cual interactúan la soberanía, las redes de expertos y el capital. Es necesario situar el desarrollo histórico de estos elementos en la configuración de formas de gobierno a través de la biopolítica, sin olvidar el lugar de la nuda vida como efecto y resistencia a esos sistemas de poder. El presente artículo se propone analizar, con un enfoque biopolítico y con conceptos también psicoanalíticos, ese desarrollo histórico de las relaciones entre soberanía y vida para vislumbrar posibilidades alternativas de afirmación de la vida ante la crisis del régimen actual de gobernabilidad.

Adrian Hofstede

This case study explores the application of Mentimeter as a sociometric tool in the context of modern leadership and mental healthcare. During the 7th International Association for Group Psychotherapy and Group Processes African regional conference in Monastir, Tunisia, an interactive online app, Mentimeter, was utilized to engage participants in workshops and round-table discussions. The study highlights the effectiveness of Mentimeter in enhancing audience engagement, visualizing real-time data, and promoting inclusive participation. It demonstrates how this cloud-based tool can address cultural biases, foster dynamic interactions, and provide immediate feedback. The findings suggest that Mentimeter’s capabilities are beneficial as a sociometric tool in different settings, making it versatile for understanding and improving group social dynamics.