Hasil untuk "Therapeutics. Psychotherapy"

Erik Hartman, Di Tang, Johan Malmström

Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of \textit{Streptococcus pneumoniae}. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \href{https://github.com/ErikHartman/bopep}{GitHub}.

Alana Deng, Sugitha Janarthanan, Yan Sun et al.

Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Abeer Badawi, Md Tahmid Rahman Laskar, Elahe Rahimi et al.

The escalating global mental health crisis, marked by persistent treatment gaps, availability, and a shortage of qualified therapists, positions Large Language Models (LLMs) as a promising avenue for scalable support. While LLMs offer potential for accessible emotional assistance, their reliability, therapeutic relevance, and alignment with human standards remain challenging to address. This paper introduces a human-grounded evaluation methodology designed to assess LLM generated responses in therapeutic dialogue. Our approach involved curating a dataset of 500 mental health conversations from datasets with real-world scenario questions and evaluating the responses generated by nine diverse LLMs, including closed source and open source models. More specifically, these responses were evaluated by two psychiatric trained experts, who independently rated each on a 5 point Likert scale across a comprehensive 6 attribute rubric. This rubric captures Cognitive Support and Affective Resonance, providing a multidimensional perspective on therapeutic quality. Our analysis reveals that LLMs provide strong cognitive reliability by producing safe, coherent, and clinically appropriate information, but they demonstrate unstable affective alignment. Although closed source models (e.g., GPT-4o) offer balanced therapeutic responses, open source models show greater variability and emotional flatness. We reveal a persistent cognitive-affective gap and highlight the need for failure aware, clinically grounded evaluation frameworks that prioritize relational sensitivity alongside informational accuracy in mental health oriented LLMs. We advocate for balanced evaluation protocols with human in the loop that center on therapeutic sensitivity and provide a framework to guide the responsible design and clinical oversight of mental health oriented conversational AI.

Jared Moore, Declan Grabb, William Agnew et al.

Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy.

Shaoqing Chen, Zhou Fang, Zheng Hu et al.

Investigating the dynamics of growing cell populations is crucial for unraveling key biological mechanisms in living organisms, with many important applications in therapeutics and biochemical engineering. Classical agent-based simulation algorithms are often inefficient for these systems because they track each individual cell, making them impractical for fast (or even exponentially) growing cell populations. To address this challenge, we introduce a novel stochastic simulation approach based on a Feynman-Kac-like representation of the population dynamics. This method, named the Feynman-Kac-inspired Gillespie's Stochastic Simulation Algorithm (FKG-SSA), always employs a fixed number of independently simulated cells for Monte Carlo computation of the system, resulting in a constant computational complexity regardless of the population size. Furthermore, we theoretically show the statistical consistency of the proposed method, indicating its accuracy and reliability. Finally, a couple of biologically relevant numerical examples are presented to illustrate the approach. Overall, the proposed FKG-SSA effectively addresses the challenge of simulating growing cell populations, providing a solid foundation for better analysis of these systems.

Ekaterina Podplutova, Anastasia Vepreva, Olga A. Konovalova et al.

The integration of artificial intelligence (AI) in early-stage drug discovery offers unprecedented opportunities for exploring chemical space and accelerating hit-to-lead optimization. However, docking optimization in generative approaches is computationally expensive and may lead to inaccurate results. Here, we present a novel generative framework that balances pharmacophore similarity to reference compounds with structural diversity from active molecules. The framework allows users to provide custom reference sets, including FDA-approved drugs or clinical candidates, and guides the \textit{de novo} generation of potential therapeutics. We demonstrate its applicability through a case study targeting estrogen receptor modulators and antagonists for breast cancer. The generated compounds maintain high pharmacophoric fidelity to known active molecules while introducing substantial structural novelty, suggesting strong potential for functional innovation and patentability. Comprehensive evaluation of the generated molecules against common drug-like properties confirms the robustness and pharmaceutical relevance of the approach.

Latent Labs Team, Henry Kenlay, Daniella Pretorius et al.

Drug discovery has long sought computational systems capable of designing drug-like molecules directly: developable and non-immunogenic from the start. Here we introduce Latent-X2, a frontier generative model that achieves this goal through zero-shot design of antibodies with strong binding affinities, drug-like properties, and, for the first time for any de novo generated antibody, confirmed low immunogenicity in human donor panels. Latent-X2 is an all-atom model conditioned on target structure, epitope specification, and optional antibody framework, jointly generating sequences and structures while modelling the bound complex. Testing only 4 to 24 designs per target in each modality, we successfully generated VHH and scFv antibodies against 9 of 18 evaluated targets, achieving a 50% target-level success rate with picomolar to nanomolar binding affinities. Designed molecules exhibit developability profiles that match or exceed those of approved antibody therapeutics, including expression yield, aggregation propensity, polyreactivity, hydrophobicity, and thermal stability, without optimization, filtering, or selection. In the first immunogenicity assessment of any AI-generated antibody, representative de novo VHH binders targeting TNFL9 exhibit both potent target engagement and low immunogenicity across T-cell proliferation and cytokine release assays. The model generalizes beyond antibodies: against K-Ras, long considered undruggable, we generated macrocyclic peptide binders competitive with trillion-scale mRNA display screens. These properties emerge directly from the model, demonstrating the therapeutic viability of zero-shot molecular design, now available without AI infrastructure or coding expertise at https://platform.latentlabs.com.

Mohammadsaleh Refahi, Bahrad A. Sokhansanj, James R. Brown et al.

Accurate prediction of drug-target binding affinity can accelerate drug discovery by prioritizing promising compounds before costly wet-lab screening. While deep learning has advanced this task, most models fuse ligand and protein representations via simple concatenation and lack explicit geometric regularization, resulting in poor generalization across chemical space and time. We introduce FIRM-DTI, a lightweight framework that conditions molecular embeddings on protein embeddings through a feature-wise linear modulation (FiLM) layer and enforces metric structure with a triplet loss. An RBF regression head operating on embedding distances yields smooth, interpretable affinity predictions. Despite its modest size, FIRM-DTI achieves state-of-the-art performance on the Therapeutics Data Commons DTI-DG benchmark, as demonstrated by an extensive ablation study and out-of-domain evaluation. Our results underscore the value of conditioning and metric learning for robust drug-target affinity prediction.

Diego Enrique Londoño Paredes

El modelo animal para conocer las supuestas bases de aprendizaje o neurobiológicas del trastorno mental ha tomado relevancia en los últimos 50 años. Se compartiría con algunos animales, como los perros o las ratas, ciertas organizaciones neurológicas de base y conductas sensorimotoras. Estas fallarían y las conductas producidas se asemejarían en animales a las del humano cuando enferma, caso de las obsesiones-compulsiones. Sin embargo, surge una serie de problemas conceptuales de esas extrapolaciones, ya que la descripción en cuanto acciones con sentido que les atribuye el psicoanálisis a estos síntomas no corresponde a la conceptualización del modelo animal donde el contenido semántico desaparece. Este modelo presenta una cierta cantidad de inconvenientes que desdibujan la manera en que los pacientes referidos por Freud con neurosis obsesiva relataban su experiencia sintomática.

Tania Roelens

Los indígenas sikuanis son conocidos por la eficacia de sus chamanes rezanderos que los colonos consultan todavía por anemias, enfermedades de la piel y momentos de vulnerabilidad en los estados de paso: nacimiento, caída del ombligo, destete, pubertad femenina. El canto de pescado se dirige a los espíritus de los peces, los ainawi, representación del “otro” u “Otro” en juego en esos momentos. Aquí exploramos los elementos de su “eficacia simbólica”, por un lado, a partir del ritual chamánico, de mitos, relatos y otros textos del pensamiento indígena; y, por otro lado, a partir de significantes como crudo, sangre, carne de caza, palabra, soplo, flujos, orificios, deseo sexual, vida breve o muerte. En fin, interrogaremos la pertinencia de planteamientos transculturales en relación con las transiciones adolescentes de hoy.

Avijit Mitra, Kun Chen, Weisong Liu et al.

Abstract Despite the established association between social and behavioral determinants of health (SBDH) and suicide risk, SBDHs from unstructured electronic health record notes for suicide prediction remain underutilized. This study investigates the impact of SBDH identified from both structured and unstructured data utilizing a natural language processing (NLP) system on suicide prediction at 7, 30, 90, and 180 days post-discharge. Using data from 2,987,006 US Veterans between 1 October 2009, and 30 September 2015, we designed a case-control study demonstrating that structured and NLP-extracted SBDH significantly enhance distinct prediction models’ performance. For example, the random forest model improved its 180-day post-discharge prediction with an area under the receiver operating characteristic curve increase from 83.57% to 84.25% (95% CI = 0.63%–0.98%, p val < 0.001) and area under the precision-recall curve increase from 57.38% to 59.87% (95% CI = 3.86%–4.82%, p val < 0.001) after integrating NLP-extracted SBDH. These findings underscore the potential of NLP-extracted SBDH in advancing suicide prediction.

B. Blanco, R. Palma, M. Hurtado et al.

Targeted therapeutic interventions utilizing low-inten\-sity ultrasound (LIUS) exhibit substantial potential for hindering the proliferation of cancer stem cells. This investigation introduces a multiscale model and computational framework to comprehensively explore the therapeutic LIUS on poroelastic tumor dynamics, thereby unraveling the intricacies of mechanotransduction mechanisms at play. Our model includes both macroscopic timescales encompassing days and rapid timescales spanning from microseconds to seconds, facilitating an in-depth comprehension of tumor behavior. We unveil the discerning suppression or reorientation of cancer cell proliferation and migration, enhancing a notable redistribution of cellular phases and stresses within the tumor microenvironment. Our findings defy existing paradigms by elucidating the impact of LIUS on cancer stem cell behavior. This endeavor advances our fundamental understanding of mechanotransduction phenomena in the context of LIUS therapy, thus underscoring its promising as a targeted therapeutic modality for cancer treatment. Furthermore, our results make a substantial contribution to the broader scientific community by shedding light on the intricate interplay between mechanical forces, cellular responses, and the spatiotemporal evolution of tumors. These insights hold the promising to promote a new perspective for the future development of pioneering and highly efficacious therapeutic strategies for combating cancer in a personalized manner.

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.

Yunlong Yang, Ying Yuan

The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. We considered the model-based design and model-assisted design that allow real-time decision-making in the presence of pending data due to long event assessment windows. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.

Jinyeong Park, Jaegyoon Ahn, Jonghwan Choi et al.

Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence(AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates superior performance over existing approaches in generating molecules with desired properties without any prior knowledge, including penalized LogP, QED, and celecoxib similarity. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.

Justine van Lawick, Hans Bom

We share the love of walking with many. To move proves beneficial for body and mind which are connected and interdependent. This article has three parts. We start with a brief introduction about movement with a focus on walking. The second part contains a dialogue about our own experiences with walking. Several themes are addressed: getting stuck and getting unstuck; the art of doing nothing useful; getting back to basics; the body thinks; time and duration; alone and together; silence. In the third part, we try to highlight the relevance for systemic practices distinguishing between first, second and third order changes.

Wanting Su, Dongwei Liu, Feng Tan et al.

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.

Leila Karimifarshi, Naeimeh Moheb, Reza Abdi et al.

Depression is one of the most common psychological symptoms in the elderly. The present study aimed to evaluate the effectiveness of psychological care program in reducing depression in the elderly. The present study is fundamental-applied research in terms of aim and is considered as quasi-experimental research. The population of the study consisted of all elderly with depression symptoms living in nursing homes in Tehran. The sample was purposefully selected to be 30 people. They were divided into two groups of experimental and control (15 people in each group). The instruments used in this study were the Beck Depression Inventory II (BDI-II) and the Elderly Psychological Care Program. First, all the participants in the experimental and control groups completed the BDI-II, and then the educational intervention was implemented by the intervener on the experimental group during seven sessions based on the elderly psychological care program, and the control group did not receive any intervention. In the posttest phase, both experimental and control groups were tested again through BDI-II. The results of one-way analysis of covariance showed that seven-session training of psychological care program was significantly effective in reducing depression in the elderly. Psychological care program creates new behaviors by activating all cognitive, physical and emotional components of neural pathways and by teaching the skills, it provides a framework for understanding emotional experiences. Also, it reduces depression in the elderly and increases their quality of life by increasing motivation to participate and change.

June Atxa Estalayo, Mercedes Jiménez Benítez

La experiencia de contacto implica la atención de lo que se vive a nivel emocional, corporal y de pensamiento. Este concepto de la psicoterapia Gestalt fundamenta hoy diversos modelos y técnicas psicoterapéuticas; y se requiere investigar si la experiencia de contacto supone la base del cambio y de la eficacia en psicoterapia. Se desarrolló un estudio de caso con diseño mixto y metodología de investigación de proceso-resultado, aplicando técnicas experienciales a una consultante de 19 años durante siete sesiones videograbadas. Para la categorización de los procesos de cambio se seleccionaron segmentos con altos niveles de experiencia, según EXP Scale. Los efectos observados se analizaron comparando los puntajes pre y post del Inventario de Depresión Estado/Rasgo, (IDER), el Cuestionario de Ansiedad Estado-Rasgo (STAI) y las Escalas de Bienestar Psicológico de Ryff, y los efectos percibidos mediante una Entrevista Final de Cambio. Los resultados mostraron que la psicoterapia fue efectiva y que los efectos terapéuticos se relacionaron con los procesos de cambio.

Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz et al.

Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.