Hasil untuk "Therapeutics. Psychotherapy"

Ecem Güntutmaz, Sude Tavukcu, Emin Özbayrak et al.

Duygusal süreçler üzerine yürütülen çalışmalar insanların hissettikleri duygularla tutarlı duygusal uyaranlara karşı dikkat ve bellek yanlılığı gösterdiğini ortaya koymaktadır. Ancak duygudurum tutarlılık etkisinin, aleksitimi düzeyi yüksek bireylerde de sürdürülüp sürdürülmediği test edilmemiştir. Bu çalışmada, empati ve aleksitimi düzeyleri farklılaşan bireylerde yüz ifadesinden duygu tanıma üzerinde tutarlılık temelli bir yanlılığın olup olmadığının incelenmesi amaçlanmıştır. Çalışmanın ilk aşamasında, 18-55 yaş aralığındaki 247 katılımcı Demografik Bilgi Formu, Empati Ölçeği ve Yirmi Maddelik Toronto Aleksitimi Ölçeği’ni doldurmuştur. Çalışmanın deneysel kısmını oluşturan ikinci aşamasının dışlama kriterleri sonucundaki örneklemi, çalışmanın ilk aşamasına da katılan 18-30 yaş aralığındaki 141 katılımcıdan oluşmuştur. Nihai analizler 105 katılımcı üzerinden yürütülmüştür. İkinci aşamada katılımcılar öncelikle Yirmi Maddelik Toronto Aleksitimi Ölçeği ile ölçülen aleksitimi açısından “düşük” ve “yüksek” olmak üzere iki gruba ayrılmıştır. Ardından bu grupların her biri için Empati Ölçeğinden hareketle empatisi “düşük” ve “yüksek” gruplar elde edilmiştir. Oluşturulan bu dört gruptaki katılımcılar; üzüntü, mutluluk ve kontrol koşullarından birine seçkisiz olarak atanmış ve duygu sevki gerçekleştirilmiştir. Pozitif ve Negatif Duygu Ölçeği ile duygu sevkinin kontrolü gerçekleştirilmiştir. Son olarak farklı duygudurum koşulları altındaki katılımcılar, yüz ifadesinden duygu tanıma görevine alınmışlardır. Görevde katılımcılardan gösterilen yüzlerdeki duygusal ifadeleri saptamaları istenmiştir. Duyguların doğru saptanma oranları mutluluk, üzüntü ve diğer duygular için ayrıştırılmıştır. Yapılan analizler farklı duygu sevki altında yer alan ve/veya empati ve aleksitimi düzeyleri farklılaşan bireylerin duygu sevkiyle tutarlı uyarıcılara yönelik yanlılık göstermediklerini, yüz ifadesinden olumlu, olumsuz ve genel duygu tanıma performansları açısından farklılaşmadıklarını göstermiştir. Mevcut çalışmanın aleksitimi düzeyi yüksek bireylerle duygular üzerine yürütülen klinik araştırma ve uygulamalarda yol gösterici nitelik taşıyabileceği düşünülmektedir.

Edward Wijaya

Agentic systems for drug discovery have demonstrated autonomous synthesis planning, literature mining, and molecular design. We ask how well they generalize. Evaluating six frameworks against 15 task classes drawn from peptide therapeutics, in vivo pharmacology, and resource-constrained settings, we find five capability gaps: no support for protein language models or peptide-specific prediction, no bridges between in vivo and in silico data, reliance on LLM inference with no pathway to ML training or reinforcement learning, assumptions tied to large-pharma resources, and single-objective optimization that ignores safety-efficacy-stability trade-offs. A paired knowledge-probing experiment suggests the bottleneck is architectural rather than epistemic: four frontier LLMs reason about peptides at levels comparable to small molecules, yet no framework exposes this capability. We propose design requirements and a capability matrix for next-generation frameworks that function as computational partners under realistic constraints.

Maria-Theodora Pandi, Μaria Skokou, M. Koufaki et al.

Bridianne O’Dea, Sally Rooke, Eliza-Rose Gordon et al.

Abstract Clinical trials in digital mental health have grown rapidly, yet little research has examined their informativeness. This study assessed the proportions of recent trials that met indicators of informativeness and explored related factors. Using stratified sampling from five trial registries, we randomly selected 25% (N = 152) of recent trials for depression, anxiety, and psychosis in high-income and low- and middle-income countries. Each trial was evaluated against 17 established indicators. On average, trials met only half of these (M = 8.9, SD = 4.57, range 2–17). Just 5.3% (n = 8) met all indicators, with methodological criteria more often satisfied than those related to ethical, equitable, or open research practices. Informativeness did not differ by disorder or region but was higher where trial documentation and reporting were more accessible, with notable variation across registries. Findings highlight that many digital mental health trials may lack value for stakeholders, underscoring the need to prioritise informativeness and improve registry reporting.

Kainat Shahbaz, Esha Ali, Maham Shahbaz et al.

Abstract: Background: Menstrual pain often caused by prostaglandins, triggering uterine contractions is named as Primary Dysmenorrhea. Perceived stress is stated as an individual’s subjective assessment of the degree to which they feel overwhelmed or unable to cope with the demands of life. Objective: To determine the level of perceived stress among undergraduate physiotherapy female students with primary dysmenorrhea in Sialkot, Pakistan. Methodology:  This Cross-sectional study included 232 females with primary dysmenorrhea, selected using Simple non-random sampling technique. Inclusion criteria was age (18-25 years), women with normal menstrual cycle lasting between 21 to 35 days, low back pain that begins one day before the menstrual cycle and lasts for 6-12 hours after the start of menstrual cycle, and leads to 3 days of bleeding in last 3 menstrual cycles whereas polycystic ovarian syndrome (PCOS), amenorrhea, use of oral contraceptives, use of intrauterine devices, pregnancy and secondary dysmenorrhea were excluded from the study. The outcome measuring tool was Perceived Stress Scale (PSS-10). The data was analyzed using SPSS software 22 and interpreted as frequencies and percentages. Results: Out of 232 participants the mean age was (21.34± S.D 1.49 years).  The majority of the participants were unmarried (N=206, 88.8%). Most of the participants (N=138, 59.5%) had normal Body Mass Index, (N=131, 56.5%) had healthy diet with maximum sleep duration of 12 hours. Most of the participants had 3-5 days of menstrual bleeding (N=188, 81.0%), moderate menstrual flow (N=182, 78.4%), 21-35 days of menstrual cycle length (N=169, 72.8%) and menarche at the age of 13 years (N=57, 24.6%). The Participant’s mean score of perceived stress scale was (22.10± S.D 5.92). Conclusion: The study concluded that students with primary dysmenorrhea had moderate level of perceived stress. Keywords: Menarche, Primary Dysmenorrhea, Perceived Stress

Allen M. Osman, Kevin P. Madore, Paul I. Jaffe et al.

Abstract We examined real-world evidence on whether Lumosity, a remote digital health technology designed to deliver cognitive training to healthy adults, can improve cognition and reduce inattention in adults who reported having received a prior (lifetime) diagnosis of ADHD. Over the course of Lumosity training, this cohort of commercial users was assessed repeatedly online with a neuropsychological test battery (NCPT) and a scale of attention and mood in real-world contexts (BAMS-7). More Lumosity training between successive assessments led to greater improvements on the NCPT composite measure and the attentional subscale of the BAMS-7. This positive dose-response relation was found for six of eight NCPT subtests and three of four BAMS-7 attentional items. Additional findings support the participants’ clinical status and sensitivity of the assessments to ADHD symptoms. These findings provide evidence of cognitive and attentional benefits in a real-world cohort of adults reporting a lifetime diagnosis of ADHD from training with Lumosity under real-world conditions.

Ursy Makanga, Akhil Varma, Panayiota Katsamba

Over the past decade, autophoretic colloids have emerged as a prototypical system for studying self-propelled motion at microscopic scales, with promising applications in microfluidics, micro-machinery, and therapeutics. Their motion in a viscous fluid hinges on their ability to induce surface slip flows that are spatially asymmetric, from self-generated solute gradients. Here, we demonstrate theoretically that a straight elastic filament with homogeneous surface chemical properties -- which is otherwise immotile -- can spontaneously achieve self-propulsion by experiencing a buckling instability that serves as the symmetry-breaking mechanism. Using efficient numerical simulations, we characterize the nonlinear dynamics of the elastic filament and show that, over time, it attains distinct swimming modes such as a steadily translating "U" shape and a metastable rotating "S" shape when semi-flexible, and an oscillatory state when highly flexible. Our findings provide physical insight into future experiments and the design of reconfigurable synthetic active colloids.

Kevin Michalewicz, Mauricio Barahona, Barbara Bravi

Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine learning (ML) models mostly based on the application of large language models to sequence information have been developed to predict antibody properties. Yet there are open directions to incorporate structural information, not only to enhance prediction but also to offer insights into the underlying molecular mechanisms. This chapter provides an overview of these approaches and describes two ML frameworks that integrate structural data (via graph representations) with neural networks to predict properties of antibodies: ANTIPASTI predicts binding affinity (a global property) whereas INFUSSE predicts residue flexibility (a local property). We survey the principles underpinning these models; the ways in which they encode structural knowledge; and the strategies that can be used to extract biologically relevant statistical signals that can help discover and disentangle molecular determinants of the properties of interest.

Jane Marsden

Whether psychotherapy should be recognised as a specialist identity within the counselling profession (Beel, 2024) or as a separate profession in its own right (Gale, 2024) are questions raised in this issue's two Viewpoints articles. Another article, "The Movements of Grief" (Cox & Fenwick, 2024), draws on contemporary grief models to posit three phases of grief: transience, transition, and transformation, with a liminal space opening up during transition. Liminality and grief are also themes in reviews of the books _Leaning Into the Liminal: A Guide for Counselors and Companions_ (Thompson & Harris, 2024) and _Collaborative Writing and Psychotherapy: Flattening the Hierarchy Between Therapist and Client_ (Carson, 2024). Led by a migrant from South Korea, a qualitative study on the understanding of mental health amongst South Korean migrants to Australia (Klingenberg et al., 2024) identifies themes centred around shifting cultural norms such as increasing individualism and issues of belonging. Levels of therapeutic relationship when working with men (Ellwood, 2024), the role of embodiment and mindfulness in group-based trauma treatment (Tempone-Wiltshire, 2024), and a systematic review of the ethical considerations around delivering video-based therapy (du Preez et al, 2024) are also featured.

Huma Riaz

Umara Iftikhar Rana, Amina Irfan, Sumbal Saleem et al.

BACKGROUND: Mothers are advised to use the cradle hold and cross-cradle hold positions for breastfeeding (BF) for the benefit of both mother and child. OBJECTIVE: This study aimed to compare the breastfeeding positions (cross-cradle hold and cradle hold) related to musculoskeletal neck pain among lactating mothers METHODOLOGY: This comparative cross-sectional study was carried out at Imran Idress Institute of Rehabilitation Sciences, Sialkot from January 2023. A convenient sampling technique was used. The data was collected from 204 lactating mothers of aged 18-40 years. Self-structured section of the questionnaire comprised of demographic and breastfeeding-related features from lactating mothers and Numeric pain rating scale were used to compare the breastfeeding position and musculoskeletal neck pain among lactating mothers. SPSS version 22 was used for data analysis. RESULTS: The results of this study indicated that the mean age of lactating mothers among participants of group A was 29.83yr± 3.78 and in group B was 28.61yr± 4.76 Independent t-test showed a significant difference of p-value= 0.00 at significance level 0.05 respectively Comparing the means of both groups, lactating mothers with cradle hold Breast feeding position-related MSK neck pain (group A) were more affected than cross cradle hold BF position related MSK neck pain (group B). CONCLUSION: This study concluded that MSK neck pain was more affected in cross cradle hold breastfeeding position than the cradle holds breastfeeding position KEYWORDS: Breast feeding, Cross Cradle, Neck Pain, Lactating Mother

Ali Mohammadzadeh, Tina Daneshyar Asl, Hoorieh Farsad Khatibi

The Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE) is a 104-item instrument that has been designed based on a factor analysis of 15 existing scales for the measurement of schizotypy. The present study was an attempt to validate the short form of the O-LIFE (SO-LIFE). This study was a correlational research wherein a sample of four hundred and sixty-eight participants was selected among Tabriz Payame Noor university students via a stratified random sampling in 2022. The data was analyzed using factor analysis. Factor analysis using principal component analysis (PCA) with Promax rotation extracted four factors including cognitive disorganization, impulsive nonconformity, unusual perceptual experiences, and introversive anhedonia. Concurrent validity coefficient of the scale was equal to 0.85 and the correlation coefficient between the total scale and the subscales ranged from 0.46 to .75. Differential validity was tested by comparing SO-LIFE scores between schizophrenic patients, their first degree relatives and normal people which was acceptable. In the same way, test-retest reliability and internal consistency reliability of the scale were equal to 0.83 and 0.75 respectively. The findings of this study revealed some information about the psychometric properties of the O-LIFE short form in an Iranian sample. It was also found that this questionnaire, as a valid instrument, had applications in research on schizophrenia spectrum disorders in Iran. The employment of this questionnaire can help develop a comprehensive body of research wherein accurate measurement of schizotypy would be of particular importance.

Samet Baş, Orçun Yorulmaz

Interest in internet-based interventions has increased considerably. The effectiveness of these applications continues to be investigated for the treatment of Prolonged Grief Disorder. This study includes preliminary findings of the internet-based and therapist-supported prolonged grief intervention program developed in Turkish. The internet-based program consists of 10 written sessions, and after each session the participants receive written feedback from the therapist. The program takes approximately 6-8 weeks. The preliminary findings of the program were handled in a proof-of-concept study style based on a case series design. Self-report measures were taken from the first eight participants who completed the program at four different times (pre-test, post-test, 1st and 3rd month follow-ups). In addition, the written contents of the first and last sessions were analyzed by content analysis. As a result of the descriptive findings, remarkable decreases were observed in traumatic grief, global meaning violation, depressive symptoms, and stress levels in a significant part of the participants between pre-post and follow-up measurements. Also, five of the participants had considerable increases in meaning reconstruction scores. In addition, the results of the content analysis indicated that following the intervention, the be-reaved individuals expressed less negative and more positive content, as expected. These two data show that the intervention program is promising in reducing the symptoms of Prolonged Grief Disorder in bereaved individuals and may yield good results with controlled designs for a broader range of participants.

Ruolin Li, Lu Wang, Tingting Yang et al.

Micro-expressions (MEs) are spontaneous, unconscious facial expressions that have promising applications in various fields such as psychotherapy and national security. Thus, micro-expression recognition (MER) has attracted more and more attention from researchers. Although various MER methods have emerged especially with the development of deep learning techniques, the task still faces several challenges, e.g. subtle motion and limited training data. To address these problems, we propose a novel motion extraction strategy (MoExt) for the MER task and use additional macro-expression data in the pre-training process. We primarily pretrain the feature separator and motion extractor using the contrastive loss, thus enabling them to extract representative motion features. In MoExt, shape features and texture features are first extracted separately from onset and apex frames, and then motion features related to MEs are extracted based on the shape features of both frames. To enable the model to more effectively separate features, we utilize the extracted motion features and the texture features from the onset frame to reconstruct the apex frame. Through pre-training, the module is enabled to extract inter-frame motion features of facial expressions while excluding irrelevant information. The feature separator and motion extractor are ultimately integrated into the MER network, which is then fine-tuned using the target ME data. The effectiveness of proposed method is validated on three commonly used datasets, i.e., CASME II, SMIC, SAMM, and CAS(ME)3 dataset. The results show that our method performs favorably against state-of-the-art methods.

Jose Arjona-Medina, Ramil Nugmanov

Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular representations that effectively generalize in real-world scenarios to novel compounds remains an elusive and unresolved task. This study examines how atom-level pretraining with quantum mechanics (QM) data can mitigate violations of assumptions regarding the distributional similarity between training and test data and therefore improve performance and generalization in downstream tasks. In the public dataset Therapeutics Data Commons (TDC), we show how pretraining on atom-level QM improves performance overall and makes the activation of the features distributes more Gaussian-like which results in a representation that is more robust to distribution shifts. To the best of our knowledge, this is the first time that hidden state molecular representations are analyzed to compare the effects of molecule-level and atom-level pretraining on QM data.

Zhuoran Qiao, Feizhi Ding, Thomas Dresselhaus et al.

Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes.

Abdur Rasool, Muhammad Irfan Shahzad, Hafsa Aslam et al.

Empathetic and coherent responses are critical in auto-mated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2,000 samples are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Atten-tion mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling temporal modeling of emotion-al shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and con-textually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data.

Ahmad Prayudi, Henny Pratiwi, Muhammad Reza Aulia et al.

This study aims to assess the impact of participatory training on enhancing the psychological well-being and technical skills of Micro, Small, And Medium Enterprise (MSME) artisans in Tangkahan’s ecotourism sector. Fifty MSME participants, who had previously attended training sessions organized by government agencies and universities, were selected as respondents. The study employed observation and statistical analysis, specifically paired samples t-tests, to evaluate changes in knowledge and skills before and after the training. The results revealed a significant improvement in both the well-being and performance of participants, with t-values exceeding the t-table and significance values (p) below 0.05. Additionally, Structural Equation Modeling with Partial Least Squares (SEM-PLS) was utilized to analyze the interrelationships between variables and test the hypotheses. This approach provided a robust framework for evaluating the direct and indirect effects among the factors, enhancing the validity and depth of the findings. These findings highlight that participatory training improves the psychological well-being and technical skills of handicraft MSME actors in managing their businesses. This study also reveals that psychological well-being has a significant direct impact on technical skills and competitiveness. Additionally, technical skills directly influence competitiveness, while psychological well-being indirectly affects competitiveness through the mediation of technical skills. This study advocates for a holistic training approach that not only focuses on technical skill enhancement but also prioritizes psychological well-being to enhancing competitiveness. The success of MSMEs depend not only on technical skills but also on the psychological well-being such as job satisfaction, stress management, mental health, motivation and positive emotion of the workforce. By adopting this comprehensive strategy, future training programs can drive long-term improvements in both individual well-being and business performance, ensuring the resilience and competitiveness of MSMEs in Tangkahan’s ecotourism sector.

Sina Elahimanesh, Shayan Salehi, Sara Zahedi Movahed et al.

In the wake of the post-pandemic era, marked by social isolation and surging rates of depression and anxiety, conversational agents based on digital psychotherapy can play an influential role compared to traditional therapy sessions. In this work, we develop a voice-capable chatbot in Farsi to guide users through Self-Attachment (SAT), a novel, self-administered, holistic psychological technique based on attachment theory. Our chatbot uses a dynamic array of rule-based and classification-based modules to comprehend user input throughout the conversation and navigates a dialogue flowchart accordingly, recommending appropriate SAT exercises that depend on the user's emotional and mental state. In particular, we collect a dataset of over 6,000 utterances and develop a novel sentiment-analysis module that classifies user sentiment into 12 classes, with accuracy above 92%. To keep the conversation novel and engaging, the chatbot's responses are retrieved from a large dataset of utterances created with the aid of Farsi GPT-2 and a reinforcement learning approach, thus requiring minimal human annotation. Our chatbot also offers a question-answering module, called SAT Teacher, to answer users' questions about the principles of Self-Attachment. Finally, we design a cross-platform application as the bot's user interface. We evaluate our platform in a ten-day human study with N=52 volunteers from the non-clinical population, who have had over 2,000 dialogues in total with the chatbot. The results indicate that the platform was engaging to most users (75%), 72% felt better after the interactions, and 74% were satisfied with the SAT Teacher's performance.

Clare Lyle, Arash Mehrjou, Pascal Notin et al.

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the billions of potential interventions to maximize the expected influence on the target phenotype. However, to reduce the risk of failure in future stages of trials, practical experiment design aims to find a set of interventions that maximally change a target phenotype via diverse mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the rate of significant discoveries per experiment while simultaneously probing for a wide range of diverse mechanisms during a genomic experiment campaign. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation covering both synthetic as well as real-world experimental design tasks. DiscoBAX outperforms existing state-of-the-art methods for experimental design, selecting effective and diverse perturbations in biological systems.