Hasil untuk "Therapeutics. Psychotherapy"

P. Cuijpers, E. Karyotaki, L. D. de Wit et al.

Abstract Objective: In the past decades, many different types of psychotherapy for adult depression have been developed. Method: In this meta-analysis we examined the effects of 15 different types of psychotherapy using 385 comparisons between a therapy and a control condition: Acceptance and commitment therapy, mindfulness-based cognitive behavior therapy (CBT), guided self-help using a self-help book from David Burns, Beck’s CBT, the “Coping with Depression” course, two subtypes of behavioral activation, extended and brief problem-solving therapy, self-examination therapy, brief psychodynamic therapy, non-directive counseling, full and brief interpersonal psychotherapy, and life review therapy. Results: The effect sizes ranged from g = 0.38 for the “Coping with Depression” course to g = 1.10 for life review therapy. There was significant publication bias for most therapies. In 70% of the trials there was at least some risk of bias. After adjusting studies with low risk of bias for publication bias, only two types of therapy remained significant (the “Coping with Depression” course, and self-examination therapy). Conclusions: We conclude that the 15 types of psychotherapy may be effective in the treatment of depression. However, the evidence is not conclusive because of high levels of heterogeneity, publication bias, and the risk of bias in the majority of studies.

Yasmin Zaraket, Céline Mougenot

Mental healthcare services in the UK lack tools and resources to address the cultural needs of Muslim women, often leaving them feeling as though their values are pathologised and limiting trust and engagement [1]. Despite growing awareness of cultural competency, few interventions integrate Islamic frameworks into therapeutic support. This report investigates the design and evaluation of YAQIN, a co-designed AI-based application supporting culturally and faith-sensitive mental health engagement for Muslim women. With almost 1.9 million Muslim women in England in 2021, YAQIN responds to a gap in care [2]. It leverages AIś anonymity and continuous support through a faith-aware chatbot and guided journaling tool grounded in user-centred design and Islamic psychology. The YAQIN design research methodology comprised three stages: contextual investigation and literature review, user research with N=14 stakeholders including Muslim women and mental health experts, and prototype development informed by deductive thematic analysis, personas, journey maps, and design specifications. Evaluation involved a co-designed user study with five participants: four Muslim women and one mental health expert who reviewed therapeutic alignment and cultural sensitivity after using the chatbot prototype. Feedback focused on tone, faith relevance, emotional resonance, and the Retrieval-Augmented Generation pipeline enabling contextual continuity. Participants highlighted YAQINś ability to bridge cultural gaps in trust and therapeutic confidence. Feedback included suggestions of including linguistic diversity and routine-based guidance. This project demonstrates how culturally sensitive AI can improve mental healthcare accessibility and trust for marginalised communities and highlights the potential of faith-integrated technology in healthcare innovation.

Fang Wu, Zhengyuan Zhou, Shuting Jin et al.

Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

Sana Javaid, Zainab Noor Qazi, Muhammad Ansar et al.

Abstract: Background: Cervicogenic headache is a common condition caused by issue in the cervical spine, leading to chronic head pain. Various treatments exist, including kinesiology Taping and Instrument Assisted Soft Tissue Mobilization, but there is limited evidence comparing their effectiveness. Objective: In this study, the effects of instrument-assisted soft tissue mobilization and kinesiology taping on pain severity, range of motion, and functional status in individuals with cervicogenic headache were compared. Methodology: In the physiotherapy department of Healing Hands Institute, Mega Medical Complex, Rawalpindi, 36 participants with clinically diagnosed cervicogenic headache, headache, and stiffness in the neck, positive flexion rotation test with restriction of 6-10 degrees unilateral headache, aged 30-44 years, were divided into two equal groups for a clinical experiment that was randomized. While Group B received conventional therapy together with Instrument Assisted Soft Tissue Mobilizations (IASTM), Group A received conventional treatment along with Kinesiology Taping. Using a non-probability purposeful sampling approach, data was gathered at baseline and the fourth week to measure the indicator using the Numeric Pain Rating Scale, Neck Disability Index, and Bubble Inclinometer. SPSS version 22 was used for data analysis. (CTR : NCT05474612) Results: At 4 weeks of intervention, both groups A and B saw substantial improvements in disability, range of motion, and discomfort (p < 0.05). Although there were no statistically significant differences in cervical flexion, extension, left lateral flexion, or left rotation (p > 0.05), between-group analysis revealed statistically significant differences in NPRS, NDI, cervical right rotation, and cervical right lateral flexion (p < 0.05). In the case of within-group comparisons, all measures showed statistically significant changes (p < 0.05). Conclusion: The study concludes that the use of both instrument-assisted soft tissue mobilization and kinesiology taping has been successful in improving range of motion and lowering pain and impairment. However, Group B (Instrument Assisted Soft Tissue Mobilization) had a more notable improvement. Keywords: Cervical Atlas, Cervicogenic Headache, Headache Unilateral, Mobilization, Range of Motion.

Xinyan Zhao, Yi-Ching Tang, Rivaaj Monsia et al.

Motivation: The clinical efficacy of antibody therapeutics critically depends on high-affinity target engagement, yet laboratory affinity-maturation campaigns are slow and costly. In computational settings, most protein language models (PLMs) are not trained to favor high-affinity antibodies, and existing preference optimization approaches introduce substantial computational overhead without clear affinity gains. Therefore, this work proposes SimBinder-IF, which converts the inverse folding model ESM-IF into an antibody sequence generator by freezing its structure encoder and training only its decoder to prefer experimentally stronger binders through preference optimization. Results: On the 11-assay AbBiBench benchmark, SimBinder-IF achieves a 55 percent relative improvement in mean Spearman correlation between log-likelihood scores and experimentally measured binding affinity compared to vanilla ESM-IF (from 0.264 to 0.410). In zero-shot generalization across four unseen antigen-antibody complexes, the correlation improves by 156 percent (from 0.115 to 0.294). SimBinder-IF also outperforms baselines in top-10 precision for ten-fold or greater affinity improvements. A case study redesigning antibody F045-092 for A/California/04/2009 (pdmH1N1) shows that SimBinder-IF proposes variants with substantially lower predicted binding free energy changes than ESM-IF (mean Delta Delta G -75.16 vs -46.57). Notably, SimBinder-IF trains only about 18 percent of the parameters of the full ESM-IF model, highlighting its parameter efficiency for high-affinity antibody generation.

Kate Brockman, Brian Colburn, Joseph Garza et al.

A critical need exists for optimal therapeutic strategies for neuroblastoma, a prevalent and often fatal pediatric solid malignancy. To address the demand for quantitative models that can guide clinical decision-making, a novel mathematical framework was developed. Combination therapies involving immunotherapy, such as Interleukin-2 (IL-2), and chemotherapy, exemplified by Cyclophosphamide, have shown significant clinical potential by enhancing anti-tumor immune responses. In this study, a nonlinear system of coupled ordinary differential equations was formulated to mechanistically describe the interactions among tumor cells, natural killer (NK) cells, and cytotoxic T lymphocytes (CTLs). The pharmacodynamic effects of both IL-2 and Cyclophosphamide on these key immune populations were explicitly incorporated, allowing for the simulation of tumor dynamics across distinct patient risk profiles. The resulting computational framework provides a robust platform for the \textit{\textbf{in silico}} \textbf{optimization} of therapeutic regimens, presenting a quantitative pathway toward the improvement of clinical outcomes for patients with neuroblastoma.

Marvellous Eyube, Courage Enuesueke, Marvellous Alimikhena

Biocompatibility is a critical factor in the application of nanomaterials in medical fields, as these materials must interact safely and effectively with biological systems to be viable for therapeutic and diagnostic use. This article investigates the biocompatibility of nanomaterials, focusing on their interactions with biological cells, tissues, and the immune system. Key properties such as surface chemistry, size, shape, and material composition are examined, as they significantly influence the biological response. The article explores the role of nanomaterials in medical applications, including drug delivery, diagnostic imaging, and tissue engineering, while discussing the challenges involved in enhancing their biocompatibility. A case study on the CaO-CaP binary system is presented, showcasing the use of calcium oxide (CaO) and calcium phosphate (CaP) nanoparticles in bone tissue engineering. This system is widely investigated for its ability to mimic the mineral content of bone and promote osteogenesis, highlighting both its therapeutic potential and challenges in ensuring safe biocompatibility in clinical settings. The article concludes by reviewing strategies to optimize the biocompatibility of nanomaterials and discussing future directions for research in advancing their applications in medical treatments.

Hang Yuan, Chen Li, Wenjun Ma et al.

Hit-like molecular generation with therapeutic potential is essential for target-specific drug discovery. However, the field lacks heterogeneous data and unified frameworks for integrating diverse molecular representations. To bridge this gap, we introduce TextOmics, a pioneering benchmark that establishes one-to-one correspondences between omics expressions and molecular textual descriptions. TextOmics provides a heterogeneous dataset that facilitates molecular generation through representations alignment. Built upon this foundation, we propose ToDi, a generative framework that jointly conditions on omics expressions and molecular textual descriptions to produce biologically relevant, chemically valid, hit-like molecules. ToDi leverages two encoders (OmicsEn and TextEn) to capture multi-level biological and semantic associations, and develops conditional diffusion (DiffGen) for controllable generation. Extensive experiments confirm the effectiveness of TextOmics and demonstrate ToDi outperforms existing state-of-the-art approaches, while also showcasing remarkable potential in zero-shot therapeutic molecular generation. Sources are available at: https://github.com/hala-ToDi.

Jyler Menard, R. A. Mansbach

Generative deep learning techniques have demonstrated an impressive capacity for tackling biomolecular design problems in recent years. Despite their high performance, however, they still suffer from a lack of interpretability and rigorous quantification of associated search spaces, which are necessary to unlock their full potential for scientific inquiry beyond efficient design. An area in which they are of particular interest is in the design of antimicrobial peptides, which are a promising class of therapeutics to treat bacterial infections. Discovering and designing such peptides is difficult because of the vast number of possible sequences and comparatively small amount of experimental information. In this work, we perform a theoretical investigation of latent Bayesian optimization for searching through peptide sequence spaces, with a focus on antimicrobial peptides. We investigate (1) whether searching through a dimensionally-reduced variant of the latent design space may facilitate optimization, (2) how organizing latent spaces by differing amounts of more and less relevant information may improve the efficiency of arriving at an optimal peptide design, and (3) the interpretability of the spaces. We find that employing a dimensionally-reduced version of the latent space is more interpretable and can be advantageous, while the use of less-relevant but more easily-computable physicochemical properties is advantageous to latent space organization in certain contexts and the use of more-relevant but sparser properties associated with the latent Bayesian objective function is advantageous in others. This work lays crucial groundwork for biophysically-motivated peptide design procedures, with an especial focus on antimicrobial peptides.

Prithvi Anickode, Fabio Milner

Acidosis in tumors arises from reprogrammed metabolism and compromised vasculature, creating a harsh, acidic microenvironment that drives the evolutionary selection of acid-resistant cell phenotypes. A mathematical model is proposed to integrate phenotypic evolution, microenvironmental acidification, and tumor density dynamics. Three key mechanisms are incorporated in it: frequency-dependent selection favoring acid-resistant cells below a critical pH, stress-induced phenotypic switching, and a positive feedback loop where resistant cells produce excess acid that intensifies selection pressure. Well-posedness is established. Numerical simulations across biologically relevant parameter regimes lead to identifying two therapeutically targetable parameters as critical bifurcation parameters for resistance evolution: baseline acid clearance rate and a protection factor representing acid-resistance machinery effectiveness. In low-plasticity tumors, both parameters lead to sharp bifurcations with strong parameter interactions: clearance and protection effects are context-dependent, with therapeutic interventions effective only within specific parameter ranges. In high-plasticity tumors, both parameters produce continuous, monotonic responses with independent, additive effects. These regime-dependent dynamics suggest that treatment strategies should adapt to tumor plasticity: in the former, targeting perfusion alone is typically sufficient, though sequential therapy may be required if the perfusion rate approaches or exceeds the bifurcation threshold, whereas in the latter treatment might benefit from combination therapies addressing both parameters simultaneously. These findings suggest that a low-dimensional model can identify therapeutically targetable parameters governing resistance evolution, suggesting interventions to prevent or reverse the harmful effect of acid-resistant phenotypes.

Helen Noble, Beverly Cole

This paper is in two parts comprising a literature review and a creative, non-fiction, lyric essay format to explore interplay between ambivalent emotions, creativity, and resilience. The context is my own state affect between client sessions, as a psychotherapist and researcher. The lyric essay, “Falling into Glăveanu’s Gap,” that comprises the second part of this paper covers a period of great disquiet in my personal and professional life, when adverse life experiences impacted my research, and, as such, formed an integral part of the research itself. My self-search heuristic exploration forms part of a larger, doctoral enquiry into the interplay between ambivalence, creativity, and resilience amongst therapists, examining whether those engaged in creativity experience a greater sense of resilience. Resilience is the antithesis of burnout, a condition which disproportionately affects practicing therapists. This research argues that therapist education, training and continuing professional development provision, would benefit from a stronger focus on therapist emotion and affect, outside of the therapy room. Opportunities for engagement with creativity are recommended to aid the development of therapist resilience and to combat therapist burn-out.

Sadaf Noveen, Ghulam Saqulain, Shaista HabibUllah et al.

Objectives: To explore the impact of bilingualism on speech sound disorders in Pakistani 4-8 years old children. Methodology: This cross sectional exploratory study using convenient sampling recruited N=140 children suspected or having speech sound disorders. Sample included 4-8 years old children of both genders speaking Urdu and their native language from the speech clinics of four provinces of Pakistan from 1st March to 31st October 2016. Basic demographic sheet and Test for Assessment of articulation and Phonology in Urdu was used for data collection. Analysis done using SPSS Version-21. Chi-square & Pearson correlation was utilized and p<0.05 was considered significant. Results: The number of errors and mother tongue did not show correlation (r=.006, p=.499), while error type and mother tongue revealed weak negative correlation (r=-.091), concluding their non-relation with language. However, there was predominance of substitution 93(66.4%) followed by omission 27(19.3%) and distortion 20(14.3%) errors, with substitution being commonest in Pushto, omission in Urdu and distortion in Punjabi speaking. Also phonological process of liquid gliding was absent ( /r/ is substituted with /l/) in Urdu language and children were intelligible despite articulatory or phonological errors and intelligibility continues developing after 4 years of age. Conclusion: The speech sound errors are independent of languages learned by the child because the phonetic repertoire and articulatory movements for a sound in every language is similar. A child growing up in a native language environment will make similar articulatory errors in Urdu and the native language.

Junichi Suzuki

I am very honored and pleased to be asked as one of a keynote speaker in the 21st IAGP International Congress, titled Groups for the World, Strength, Inspiration, and Transformation What I would like to talk about today is (1) personal experiences working into therapeutic communities in the U.K. (2) Trying to establish therapeutic communities in two Japanese mental hospitals, problems and difficulties encountered in practicing therapeutic communities’ concept, (3) How it relates to Japanese group culture and mentality, and I’d like to add (4) Influence of the Covid-19 experience, and online group.

Vibol Yem, Mattia Quartana, Zi Xin et al.

Relaxation is a critical counterbalance to the demands of modern business life. Footbaths, a simple yet highly effective therapeutic practice, have been used for centuries across various cultures to promote relaxation and overall well-being. This study presents a novel approach to simulating the experience of a public footbath through the use of tactile and thermal stimulation of airflow to the calf and those on the foot soles. Our system aims to offer a realistic and immersive virtual footbath experience without the need for actual water, by controlling the temperature and airflow to mimic the sensation of soaking feet in water or a water wave. Without using actual water, our system can be more compact, highly responsive, and more reproducible. The layer of airflow is made as thin as possible by adjusting air outlet, and the Coanda effect is also considered to generate a water surface more realistic. The system can provide a multi-sensory experience, including visual and audio feedback of water flow, enhancing the relaxation and therapeutic benefits of a footbath.

Dong Chen, Gengzhuo Liu, Hongyan Du et al.

The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance prEdictions) to analyze the effects of Mpro mutations on nirmatrelvir-Mpro binding affinities and identify potential drug-resistant mutations. CAPTURE combines a comprehensive mutation analysis with a resistance prediction module based on DFFormer-seq, which is a novel ensemble model that leverages a new Directed Flag Transformer and sequence embeddings from the protein and small-molecule-large-language models. Our analysis of the evolution of Mpro mutations revealed a progressive increase in mutation frequencies for residues near the binding site between May and December 2022, suggesting that the widespread use of PAXLOVID created a selective pressure that accelerated the evolution of drug-resistant variants. Applied to mutations at the nirmatrelvir-Mpro binding site, CAPTURE identified several potential resistance mutations, including H172Y and F140L, which have been experimentally confirmed, as well as five other mutations that await experimental verification. CAPTURE evaluation in a limited experimental data set on Mpro mutants gives a recall of 57\% and a precision of 71\% for predicting potential drug-resistant mutations. Our work establishes a powerful new framework for predicting drug-resistant mutations and real-time viral surveillance. The insights also guide the rational design of more resilient next-generation therapeutics.

Elisabeth Dina Laksmiwati, Marselius Sampe Tondok

Students, especially in the final year, often encounter various sources of stress and increasingly high academic demands, which have the potential to cause academic anxiety. This study aimed to determine the effect of perceived social support on academic anxiety through self-efficacy as a mediator. Employing a cross-sectional quantitative research design, this study involved a sample of 80 final-year undergraduate students, comprising 49 females and 31 males. Data were collected via an online questionnaire encompassing three scales: General Anxiety Disorder-7 (GAD-7), Academic Self-Efficacy Scale (ASES), and The Multidimensional Scale of Perceived Social Support (MSPSS). The mediation analysis results revealed that academic self-efficacy acts as a full mediator in the relationship between perceptions of social support and student anxiety. These findings highlight the potential effectiveness of interventions to bolster students' self-efficacy to mitigate the adverse effects of academic-related stressors, ultimately enhancing their overall well-being and academic performance.   

Margarita Chacin, Alistair McBeath

This qualitative study explored fathers’ lived experience and meanings of being a first-time father in the United Kingdom. Seven fathers were interviewed, and the resulting data were analysed using Interpretative Phenomenological Analysis (IPA). Two main categories emerged from the analysis: The first captures how fathers’ experiences developed over time concerning: i. how becoming a father was a “natural way of being”; ii. how they were confronted with the differences between reality and their expectations; iii. how they described traditional fatherhood and the compulsory equality in parenting; and iv. about their willingness to be involved as a modern father. The second master theme describes the challenges faced by fathers including how fathers: i. cope with tiredness; ii. have undesirable feelings, including fear, worry and uncertainty; iii. lack acknowledgement and feel some resultant shame; and iv. experience guilt when they were not physically present for their children. This research discusses the impact of both their understandings (including representations) of fatherhood and how fatherhood is subjectively experienced. It is recommended that therapists attend to these changing experiences in order to offer appropriate psychotherapeutic approaches and tools to fathers.

Evaristus Silitubun

This research explores the relationship between student self-confidence, artificial intelligence (AI) support, and academic achievement in the psychology of motivation and learning. In this digital era, AI technology is increasingly used in education to facilitate teaching-learning. This research examines How AI support can affect students' self-confidence and how it affects their academic performance. Data are collected from 80 students at STPK ST. Yohanes Rasul Jayapura, Thomas Aquinas College of Agriculture 100 people, and Papua International University 20 people, 120 via questionnaire. The research results show that students' self-confidence and AI support are essential in improving academic performance. Implementing AI technology in education not only helps deliver lesson material but also contributes to improving students' self-confidence. Therefore, schools and institutions​ of education recommend using AI technology as a learning strategy to maximize potential student academics.

Suzanne M. Dufault, Angela M. Crook, Katie Rolfe et al.

We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials. Multi-arm multi-stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow-up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for 1) ranking of arms and 2) comparison of each arm against an internal control. Using a large public-private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi-metric framework provides sufficient confidence for decision-making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision-making procedure has been well-received by research partners and is a practical approach to more efficient assessment of novel therapeutics.

Favour Nerrise, Qingyu Zhao, Kathleen L. Poston et al.

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT .