Hasil untuk "Therapeutics. Psychotherapy"

Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober et al.

A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that choice of sampling method is at least as impactful as the model used, motivating future research into this under-explored area.

Satoshi Inagaki, Alireza Alikhani, Nassir Navab et al.

Several robotic frameworks have been recently developed to assist ophthalmic surgeons in performing complex vitreoretinal procedures such as subretinal injection of advanced therapeutics. These surgical robots show promising capabilities; however, most of them have to limit their working volume to achieve maximum accuracy. Moreover, the visible area seen through the surgical microscope is limited and solely depends on the eye posture. If the eye posture, trocar position, and robot configuration are not correctly arranged, the instrument may not reach the target position, and the preparation will have to be redone. Therefore, this paper proposes the optimization framework of the eye tilting and the robot positioning to reach various target areas for different patients. Our method was validated with an adjustable phantom eye model, and the error of this workflow was 0.13 +/- 1.65 deg (rotational joint around Y axis), -1.40 +/- 1.13 deg (around X axis), and 1.80 +/- 1.51 mm (depth, Z). The potential error sources are also analyzed in the discussion section.

Ruiwen Ding, Lin Li, Rajath Soans et al.

Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Identifying biomarkers for personalized treatment is crucial. While immune cells play a key role in IBD, accurately identifying ulcer regions in whole slide images (WSIs) is essential for characterizing these cells and exploring potential therapeutics. Multiple instance learning (MIL) approaches have advanced WSI analysis but they lack spatial context awareness. In this work, we propose a weakly-supervised model called DomainGCN that employs a graph convolution neural network (GCN) and incorporates domain-specific knowledge of ulcer features, specifically, the presence of epithelium, lymphocytes, and debris for WSI-level ulcer prediction in IBD. We demonstrate that DomainGCN outperforms various state-of-the-art (SOTA) MIL methods and show the added value of domain knowledge.

Clarissa W. Ong, Hiba Arnaout, Kate Sheehan et al.

Recent advances in psychotherapy have focused on treatment personalization, such as by selecting treatment modules based on personalized networks. However, estimating personalized networks typically requires intensive longitudinal data, which is not always feasible. A solution to facilitate scalability of network-driven treatment personalization is leveraging LLMs. In this study, we present an end-to-end pipeline for automatically generating client networks from 77 therapy transcripts to support case conceptualization and treatment planning. We annotated 3364 psychological processes and their corresponding dimensions in therapy transcripts. Using these data, we applied in-context learning to jointly identify psychological processes and their dimensions. The method achieved high performance even with a few training examples. To organize the processes into networks, we introduced a two-step method that grouped them into clinically meaningful clusters. We then generated explanation-augmented relationships between clusters. Experts found that networks produced by our multi-step approach outperformed those built with direct prompting for clinical utility and interpretability, with up to 90% preferring our approach. In addition, the networks were rated favorably by experts, with scores for clinical relevance, novelty, and usefulness ranging from 72-75%. Our findings provide a proof of concept for using LLMs to create clinically relevant networks from therapy transcripts. Advantages of our approach include bottom-up case conceptualization from client utterances in therapy sessions and identification of latent themes. Networks generated from our pipeline may be used in clinical settings and supervision and training. Future research should examine whether these networks improve treatment outcomes relative to other methods of treatment personalization, including statistically estimated networks.

Peng Qiu, Hanqi Feng, Meng-Chun Zhang et al.

Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "non-toxic." AmpLyze closes this gap by predicting the actual HC50 value from sequence alone and explaining the residues that drive toxicity. The model couples residue-level ProtT5/ESM2 embeddings with sequence-level descriptors in dual local and global branches, aligned by a cross-attention module and trained with log-cosh loss for robustness to assay noise. The optimal AmpLyze model reaches a PCC of 0.756 and an MSE of 0.987, outperforming classical regressors and the state-of-the-art. Ablations confirm that both branches are essential, and cross-attention adds a further 1% PCC and 3% MSE improvement. Expected-Gradients attributions reveal known toxicity hotspots and suggest safer substitutions. By turning hemolysis assessment into a quantitative, sequence-based, and interpretable prediction, AmpLyze facilitates AMP design and offers a practical tool for early-stage toxicity screening.

Jie Gao, Jing Hu, Shanzhuo Zhang et al.

Antibody engineering is essential for developing therapeutics and advancing biomedical research. Traditional discovery methods often rely on time-consuming and resource-intensive experimental screening. To enhance and streamline this process, we introduce a production-grade, high-throughput platform built on HelixFold3, HelixDesign-Antibody, which utilizes the high-accuracy structure prediction model, HelixFold3. The platform facilitates the large-scale generation of antibody candidate sequences and evaluates their interaction with antigens. Integrated high-performance computing (HPC) support enables high-throughput screening, addressing challenges such as fragmented toolchains and high computational demands. Validation on multiple antigens showcases the platform's ability to generate diverse and high-quality antibodies, confirming a scaling law where exploring larger sequence spaces increases the likelihood of identifying optimal binders. This platform provides a seamless, accessible solution for large-scale antibody design and is available via the antibody design page of PaddleHelix platform.

Praveen Muralikrishnan, Jonathan W. P. Zajac, Caryn L. Heldt et al.

The stabilization of macromolecules is fundamental to developing biological formulations, such as vaccines and protein therapeutics. In this study, we employ coarse grained polymer models to investigate the impact of four sugars: $α$-glucose, $β$-fructose, trehalose, and sucrose on macromolecule stability. Free energy decomposition and preferential interaction analysis indicate that polymer-sugar interactions favor folding at low concentrations while driving unfolding at higher concentrations. In contrast, the polymer-solvent soft interaction entropy consistently favors unfolding across all sugar concentrations under study. At low sugar concentrations, polymer-solvent interactions predominantly govern stabilization, whereas at higher concentrations, entropic penalties dictate polymer stability. Local mixing entropy demonstrates that binary sugar mixtures introduce entropic contributions that preferentially stabilize the folded state. These findings contribute to a more nuanced understanding of sugar-based excipient stabilization mechanisms, offering guidance for the rational design of stable biological formulations.

Paskalis Edwin I Nyoman Paska, Dominikus I Gusti Bagus Kusumawanta

Indonesia's plurality is a priceless gift. Unfortunately, in terms of religion, conflict has colored the history of this nation. This study aims to analyze the effect of Emotional Intelligence on Religious Moderation among students in Sekolah Tinggi Pastoral (STP) – IPI Malang. With probability sampling technique, 175 samples were obtained. This research uses a quantitative approach, and data collection using a questionnaire. The research findings show, the correlation coefficient between the criterion variable and the predictor is proven to be very strong (r = 0.980), and the coefficient of determination of 0.960 shows that the predictor variables simultaneously affect the criterion variable (Religious Moderation) by 96%, the rest (4%) is influenced by other variables outside this study. In terms of causality, the predictor variables simultaneously affect the Religious Moderation variable, with a value of F = 0.000 < α (α = 0.05). While, partially the variables of perceiving emotions, using emotions to facilitate thought, understanding emotions, and managing emotions are also proven to have a significant effect on Religious Moderation (t-statistics value of perceiving emotions by 19.230 > 1.96; using emotions to facilitate thought by 3.526 > 1.96; understanding emotions by 6.163 > 1.96; and managing emotions by 2.126 > 1.96). The variable perceiving emotions contributes the most, and the variable managing emotions contributes the least. It can be concluded, both simultaneously and partially, all predictor variables have a significant effect on Religious Moderation.

Zümrüt Gedik, Emine Sevinç Tok

Olumsuz duygularla uyumsuz bir başa çıkma yöntemi olarak değerlendirilen duygusal yeme, birtakım yeme bozuklukları ile ilişkilendirilmiştir. Beden imgesi ise duygusal yemenin ilişkili olduğu yeme bozuklukları açısından önemli bir kavramdır. Beliren yetişkinlik dönemindeki bireylerin, duygusal yeme ve olumsuz bir beden imgesine sahip olma açısından riskli bir grup olduğu bilinmektedir. Bazı yeme bozukluklarının altında yattığı düşünülen duygusal yemeyi ve olumsuz beden imgesini hedef alan müdahalelerin planlanmasında, bu değişkenlerin öncülü olabilecek yapılarla ilişkilerinin incelenmesi önemlidir. Bu çalışmanın amacı, üniversite öğrencilerinde (N = 388) duygusal yemenin beden imgesi, algılanan stres ve tanılar üstü risk etmenleri olan mükemmeliyetçilik, bilinçli farkındalık ve bilişsel esneklik tarafından açıklandığı bir yol modelini sınamaktır. Çalışma kesitsel ve ilişkisel niteliktedir. Veriler Demografik Bilgi Formu, Hollanda Yeme Davranışı Anketi, Algılanan Stres Ölçeği, Vücut Algısı Ölçeği, Frost Çok Boyutlu Mükemmeliyetçilik Ölçeği, Bilinçli Farkındalık Ölçeği ve Bilişsel Esneklik Ölçeği kullanılarak toplanmıştır. Veriler bağımsız gruplar için t-testi, Pearson korelasyon analizi ve yol analizi ile çözümlenmiştir. Bulgulara göre tanılar üstü etmenlerde cinsiyet farkı gözlenmezken; kadınların beden imgelerinin, algılanan stres düzeylerinin ve duygusal yeme puan ortalamalarının erkeklerden istatistiksel açıdan anlamlı düzeyde daha olumsuz olduğu görülmüştür. Tüm değişkenler arasında farklı büyüklüklerde korelasyonlar olduğu bulunmuştur. Yol analizi sonucunda revize edilen açıklayıcı modelde, bilinçli farkındalığın hem doğrudan hem de sırasıyla algılanan stres ve beden imgesi aracılığıyla duygusal yemeyi etkilediği; mükemmeliyetçiliğin ve bilişsel esnekliğin ise duygusal yeme üzerinde doğrudan etkilere sahip olmayıp yine algılanan stres ve beden imgesi üzerinden duygusal yemeye bağlandığı tespit edilmiştir. Bulguların klinik doğurguları, duygusal yeme sergileyen genç yetişkinlere sunulabilecek müdahalelerin tasarlanması bağlamında ele alınmıştır.

Emily Salja, Evren Salja

As practitioners, we often share community memberships with those who consult us. We practice from a place of familiarity with how Western psychotherapy has failed, oppressed, and blamed racialised, neurodiverse, 2SLGBTQIA+, disabled, and otherwise marginalized people. Some is lived experience, the rest we draw from community knowledges. We hope to contribute a response to the question, “What can it mean to intentionally build a therapeutic practice outside the expectations of clinical professionalism as a person, practitioner, and community member?” Or, from another angle: "What does it mean to be a part of the ecosystem we support?" Our experiences and observations are framed by privileges (whiteness, stable housing, access to academic spaces, healthcare, and transport) and marginalisations that inform and contextualize this work. In perpetuity, we acknowledge and honour the brilliance and labour of Kimberly Crenshaw, Leah Lakshmi Piepzna-Samarasinha, Robin Wall Kimmerer, and countless other BIPOC scholars, activists, writers and beyond who have defined and created intersectional (Crenshaw, 1989) systems of resistance and survival. Any errors in the interpretation of these bodies of work are our own.

Sarah Haggenmueller, Michael Matthies, Matthew Sample et al.

DNA origami consists of a long scaffold strand and short staple strands that self-assemble into a target 2D or 3D shape. It is a widely used construct in nucleic acid nanotechnology, offering a cost-effective way to design and create diverse nanoscale shapes. With promising applications in areas such as nanofabrication, diagnostics, and therapeutics, DNA origami has become a key tool in the bionanotechnology field. Simulations of these structures can offer insight into their shape and function, thus speeding up and simplifying the design process. However, simulating these structures, often comprising thousands of base pairs, poses challenges due to their large size. OxDNA, a coarse-grained model specifically designed for DNA nanotechnology, offers powerful simulation capabilities. Its associated ecosystem of visualization and analysis tools can complement experimental work with in silico characterization. This tutorial provides a general approach to simulating DNA origami structures using the oxDNA ecosystem, tailored for experimentalists looking to integrate computational analysis into their design workflow.

Leo Klarner, Tim G. J. Rudner, Garrett M. Morris et al.

Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful approach, excelling at unconditional sample generation and, with data-driven guidance, conditional generation within their training domain. Reliably sampling from high-value regions beyond the training data, however, remains an open challenge -- with current methods predominantly focusing on modifying the diffusion process itself. In this paper, we develop context-guided diffusion (CGD), a simple plug-and-play method that leverages unlabeled data and smoothness constraints to improve the out-of-distribution generalization of guided diffusion models. We demonstrate that this approach leads to substantial performance gains across various settings, including continuous, discrete, and graph-structured diffusion processes with applications across drug discovery, materials science, and protein design.

Patricia Lamirande, Eamonn A. Gaffney, Michael Gertz et al.

Purpose: Standard of care for various retinal diseases involves recurrent intravitreal injections. This motivates mathematical modelling efforts to identify influential factors for drug residence time, aiming to minimise administration frequency. We sought to describe the vitreal diffusion of therapeutics in nonclinical species used during drug development assessments. In human eyes, we investigated the impact of variability in vitreous cavity size and eccentricity, and in injection location, on drug elimination. Methods: Using a first passage time approach, we modelled the transport-controlled distribution of two standard therapeutic protein formats (Fab and IgG) and elimination through anterior and posterior pathways. Detailed anatomical 3D geometries of mouse, rat, rabbit, cynomolgus monkey, and human eyes were constructed using ocular images and biometry datasets. A scaling relationship was derived for comparison with experimental ocular half-lives. Results: Model simulations revealed a dependence of residence time on ocular size and injection location. Delivery to the posterior vitreous resulted in increased vitreal half-life and retinal permeation. Interindividual variability in human eyes had a significant influence on residence time (half-life range of 5-7 days), showing a strong correlation to axial length and vitreal volume. Anterior exit was the predominant route of drug elimination. Contribution of the posterior pathway displayed a small (3%) difference between protein formats, but varied between species (10-30%). Conclusions: The modelling results suggest that experimental variability in ocular half-life is partially attributed to anatomical differences and injection site location. Simulations further suggest a potential role of the posterior pathway permeability in determining species differences in ocular pharmacokinetics.

Jie Gao, Jing Hu, Lihang Liu et al.

The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate molecular interactions that underlie immune responses. Despite recent progress with deep learning models like AlphaFold and RoseTTAFold, accurately modeling antigen-antibody complexes remains a challenge due to their unique evolutionary characteristics. HelixFold-Multimer, a specialized model developed for this purpose, builds on the framework of AlphaFold-Multimer and demonstrates improved precision for antigen-antibody structures. HelixFold-Multimer not only surpasses other models in accuracy but also provides essential insights into antibody development, enabling more precise identification of binding sites, improved interaction prediction, and enhanced design of therapeutic antibodies. These advances underscore HelixFold-Multimer's potential in supporting antibody research and therapeutic innovation.

Yujing Liao, Haitao Shen, Wenjie Duan et al.

M. Barrett, Wee-jhong Chua, P. Crits-Christoph et al.

Maria Francesca Abbate, Thomas Dupic, Emmanuelle Vigne et al.

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and Sars-CoV-2 infections. We show that different lineages converge to the same responding CDR3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

Matías E. Salgado

Método de Niveles (MOL) es un abordaje transdiagnóstico de Terapia Cognitivo-Conductual. Se basa en los aportes de los procesos transdiagnósticos, y en los principios de la Teoría del Control Perceptual (PCT): “control”, “conflicto”, y “reorganización”. En el contexto clínico, la aplicación directa de estos principios deriva en dos objetivos terapéuticos: (1) facilitarle al consultante el proceso de exploración focalizada y detallada de un problema, (2) notar y explorar las “disrupciones”. En la implementación de MOL en una serie de consultantes hispanoparlantes, estos encontraron a MOL como un abordaje eficaz y eficiente, que facilitó una abierta exploración de problemas y experiencias, convirtiéndose en un medio para el autodescubrimiento y autocomprensión. Se presenta un breve ejemplo clínico. MOL es un abordaje parsimonioso que facilita el cambio terapéutico gracias a su sólido modelo psicopatológico y a su sencilla implementación. En la actualidad, MOL continúa acumulando pruebas que respaldan su efectividad y eficiencia psicoterapéutica.

Haim Weinberg

Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris et al.

The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., with or without control inputs; attractor state or a subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is linear on the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.