Hasil untuk "Therapeutics. Psychotherapy"

C. Ryff, B. Singer

Kevin M. Laska, A. Gurman, B. Wampold

Joshua Southern, Changpeng Lu, Santrupti Nerli et al.

Multispecific antibodies offer transformative therapeutic potential by engaging multiple epitopes simultaneously, yet their efficacy is an emergent property governed by complex molecular architectures. Rational design is often bottlenecked by the inability to predict how subtle changes in domain topology influence functional outcomes, a challenge exacerbated by the scarcity of comprehensive experimental data. Here, we introduce a computational framework to address part of this gap. First, we present a generative method for creating large-scale, realistic synthetic functional landscapes that capture non-linear interactions where biological activity depends on domain connectivity. Second, we propose a graph neural network architecture that explicitly encodes these topological constraints, distinguishing between format configurations that appear identical to sequence-only models. We demonstrate that this model, trained on synthetic landscapes, recapitulates complex functional properties and, via transfer learning, has the potential to achieve high predictive accuracy on limited biological datasets. We showcase the model's utility by optimizing trade-offs between efficacy and toxicity in trispecific T-cell engagers and retrieving optimal common light chains. This work provides a robust benchmarking environment for disentangling the combinatorial complexity of multispecifics, accelerating the design of next-generation therapeutics.

Fangrui Huang, Souhad Chbeir, Arpandeep Khatua et al.

Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks--fail to capture the clinically critical dimensions of psychotherapy. We introduce THERAPYGYM, a framework that evaluates and improves therapy chatbots along two clinical pillars: fidelity and safety. Fidelity is measured using the Cognitive Therapy Rating Scale (CTRS), implemented as an automated pipeline that scores adherence to CBT techniques over multi-turn sessions. Safety is assessed using a multi-label annotation scheme, covering therapy-specific risks (e.g., failing to address harm or abuse). To mitigate bias and unreliability in LLM-based judges, we further release THERAPYJUDGEBENCH, a validation set of 116 dialogues with 1,270 expert ratings for auditing and calibration against licensed clinicians. THERAPYGYM also serves as a training harness: CTRS and safety-based rewards drive RL with configurable patient simulations spanning diverse symptom profiles. Models trained in THERAPYGYM improve on expert ratings, with average CTRS rising from 0.10 to 0.60 (and 0.16 to 0.59 under LLM judges). Our work enables scalable development of therapy chatbots that are faithful to evidence-based practice and safer in high-stakes use.

Banu Aslan, Özgür Önal

This study assesses the validity and reliability of the Multidimensional Individual and Interpersonal Resilience Measure (MIIRM) for older adults in Türkiye, exploring its adaptability to Turkish culture and its association with psychological resilience, perceived social support, and quality of life. A total of 1251 individuals aged 60 and above participated, and data was collected online through snowball sampling. Self-report measures were evaluated using original scoring systems. Exploratory factor analysis revealed a seven-factor structure, explaining 62.971% of the variance. Internal consistency was confirmed by Cronbach’s alpha (.794) and Spearman-Brown Split-Half coefficient (.628). A high positive correlation (r = .620) between Connor-Davidson Resilience Scale and MIIRM suggests their interchangeability. Confirmatory factor analysis affirmed the scale’s consistency, with a good fit. Multivariate analysis revealed factors enhancing resilience: Economic status, positive relationships, social support, and quality of life. This study provides a culturally adapted tool for assessing resilience among older adults in Türkiye, contributing to a deeper understanding of strategies to enhance their psychological resilience and filling a significant gap in the literature.

Junli Wang, Wanyue Cao, Jinyan Huang et al.

Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enriched in many types of cancer, particularly in hepatocellular carcinoma (HCC). The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions. Targeting CD19$^+$ macrophages with anti-CD19 chimeric antigen receptor T (CAR-T) cells inhibited HCC tumor growth. We identify PAX5 as a primary driver of up-regulated mitochondrial biogenesis in CD19$^+$ macrophages, which depletes cytoplasmic Ca$^{2+}$, leading to lysosomal deficiency and consequent accumulation of CD73 and PD-L1. Inhibiting CD73 or mitochondrial oxidation enhanced the efficacy of immune checkpoint blockade therapy in treating HCC, suggesting great promise for CD19$^+$ macrophage-targeting therapeutics.

Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad et al.

Designing mRNA sequences is a major challenge in developing next-generation therapeutics, since it involves exploring a vast space of possible nucleotide combinations while optimizing sequence properties like stability, translation efficiency, and protein expression. While Generative Flow Networks are promising for this task, their training is hindered by sparse, long-horizon rewards and multi-objective trade-offs. We propose Curriculum-Augmented GFlowNets (CAGFN), which integrate curriculum learning with multi-objective GFlowNets to generate de novo mRNA sequences. CAGFN integrates a length-based curriculum that progressively adapts the maximum sequence length guiding exploration from easier to harder subproblems. We also provide a new mRNA design environment for GFlowNets which, given a target protein sequence and a combination of biological objectives, allows for the training of models that generate plausible mRNA candidates. This provides a biologically motivated setting for applying and advancing GFlowNets in therapeutic sequence design. On different mRNA design tasks, CAGFN improves Pareto performance and biological plausibility, while maintaining diversity. Moreover, CAGFN reaches higher-quality solutions faster than a GFlowNet trained with random sequence sampling (no curriculum), and enables generalization to out-of-distribution sequences.

Sierra Haile, Benjamin C. Balzer, Emily Egan et al.

This article focuses on current and emerging therapeutics for CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). CADASIL is an inherited vascular disease that impairs blood flow in the small cerebral vessels of the brain, leading to strokes and other neurological deficits. The disease is caused by a mutation in the NOTCH3 gene located on chromosome 19. NOTCH3 encodes a transmembrane receptor expressed on vascular smooth muscle cells. In CADASIL, mutations in the NOTCH3 gene lead to the accumulation and deposition of the receptor, affecting the number of cysteine residues in its extracellular domain. These mutations result in the loss or gain of a cysteine residue within the epidermal growth factor-like repeat (EGFr) domains of the NOTCH protein. Beyond traditional symptomatic treatments for stroke, this work highlights advances in disease modifying approaches including gene editing, cell therapies, and immune-based interventions aimed at altering the course of CADASIL. It also examines ongoing clinical trials and recent patents related to these novel strategies. In addition to summarizing diagnostic methods and molecular mechanisms, the article emphasizes the translational potential of current research and the experimental models driving therapeutic development. The goal is to offer a comprehensive overview of CADASIL and emerging interventions that hold promise for improving long-term outcomes.

Michaela Cohrs, Shiwoo Koak, Yejin Lee et al.

Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible particles (SvPs), which can compromise their efficacy and trigger immunological responses, emphasizing the critical need for robust monitoring techniques. Flow Imaging Microscopy (FIM) has been a significant advancement in detecting SvPs, evolving from monochrome to more recently incorporating color imaging. Complementing SvP images obtained via FIM, deep learning techniques have recently been employed successfully for stress source identification of monochrome SvPs. In this study, we explore the potential of color FIM to enhance the characterization of stress sources in SvPs. To achieve this, we curate a new dataset comprising 16,000 SvPs from eight commercial monoclonal antibodies subjected to heat and mechanical stress. Using both supervised and self-supervised convolutional neural networks, as well as vision transformers in large-scale experiments, we demonstrate that deep learning with color FIM images consistently outperforms monochrome images, thus highlighting the potential of color FIM in stress source classification compared to its monochrome counterparts.

Yael Kapon, Dror Merhav, Gal Finkelstein-Zuta et al.

Protein aggregation into insoluble amyloid-like fibrils is implicated in a wide range of diseases and understanding its nucleation process is a key for mechanistic insights and advancing therapeutics. The electronic charge of the amyloidogenic monomers significantly influences their self-assembly process. However, the impact of electron spin interactions between monomers on amyloid nucleation has not been considered yet. Here, we studied amyloid formation on magnetic substrates using Scanning Electron Microscopy (SEM), fluorescence microscopy, and Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) Spectroscopy. We observed a preferred magnetization orientation of the ferromagnetic layer for fibril formation, leading to twice as many and significantly longer fibrils (up to 20 times) compared to the opposite magnetization orientation. This preference is related to monomer chirality. Additionally, fibril structure varied with substrate magnetization orientation. Our findings suggest a transient spin polarization in monomers during self-assembly, driven by the Chiral Induced Spin Selectivity (CISS) effect. These effects are consistent for various molecule length scales, from A-beta polypeptide to dipeptides and single amino acids, indicating a fundamental spin-based dependence on biomolecular aggregation that could be applied in novel therapeutic interventions targeted for amyloid-related diseases.

Michael R. Doane

This work presents the development and evaluation of an NLP-enabled probabilistic classifier designed to estimate the probability of technical and regulatory success (pTRS) for clinical trials in the field of neuroscience. While pharmaceutical R&D is plagued by high attrition rates and enormous costs, particularly within neuroscience, where success rates are below 10%, timely identification of promising programs can streamline resource allocation and reduce financial risk. Leveraging data from the ClinicalTrials.gov database and success labels from the recently developed Clinical Trial Outcome dataset, the classifier extracts text-based clinical trial features using statistical NLP techniques. These features were integrated into several non-LLM frameworks (logistic regression, gradient boosting, and random forest) to generate calibrated probability scores. Model performance was assessed on a retrospective dataset of 101,145 completed clinical trials spanning 1976-2024, achieving an overall ROC-AUC of 0.64. An LLM-based predictive model was then built using BioBERT, a domain-specific language representation encoder. The BioBERT-based model achieved an overall ROC-AUC of 0.74 and a Brier Score of 0.185, indicating its predictions had, on average, 40% less squared error than would be observed using industry benchmarks. The BioBERT-based model also made trial outcome predictions that were superior to benchmark values 70% of the time overall. By integrating NLP-driven insights into drug development decision-making, this work aims to enhance strategic planning and optimize investment allocation in neuroscience programs.

Tiffany Tan, Elizabeth Kristi Poerwandari

Pedophiles, individuals who are sexually attracted to children, are regarded as child-molesters. This stigma incites hate and discrimination towards them, even those who have not and committed themselves to not harm children. Thus, understanding and challenging this stigma is important to help them. In this study, we explored the experience of members of Virtuous Pedophiles, an online support group and the preferred identity for pedophiles who are against sexual activity between adults and children. Narrative literature review and thematic analyses were conducted on Gary Gibson’s book titled Virtuous Pedophiles. Virtuous Pedophiles, unfortunately, encounter problems in their daily life, yet they choose to acknowledge and share their sexual orientation with loved ones. They also develop various strategies to protect themselves and children from any form of molestation. They build boundaries in interacting with children, avoid situations involving children, and use fantasy in the form of pornography and masturbation to satisfy their sexual needs without harming children. They hope the stigma on pedophiles is reduced and one day, they can live comfortably by receiving help and support from others and mental health practitioners. Despite acknowledging their sexual orientation, they are still experiencing difficulties in fully accepting themselves due to the stigmatization experienced from others and concerns about their own future. This also hinders them in reaching out for help from professional services to manage their overall wellbeing. As mental health practitioners, we are encouraged to focus more on their psychological distress and then devise strategies that might help improve their daily functioning.

Penisa Sampe Asang, Enjang Wahyuningrum, Sri Aryanti Kristianingsih

This study aims to examine the resilience of parents who have children with cerebral palsy and the factors influencing it. A qualitative approach with a case study method was used to explore the subjective experiences of three couples in raising children with special needs. Data were collected through in-depth interviews, participatory observations, and documentation, then analyzed using the Miles & Huberman model, which includes data reduction, data display, and conclusion drawing. The findings show that parental resilience is reflected in their ability to manage emotions, control impulses, maintain optimism, demonstrate empathy, and have self-efficacy. They are also able to perform causal analysis and build meaning in life through the reaching out attitude. Factors influencing resilience are divided into internal and external factors. Internal factors include proactive coping styles, emotional coping, and problem-focused coping, as well as emotional adaptation skills and intelligence in facing challenges. External factors include support from immediate and extended family, social environment, healthcare workers, and spiritual support from religious leaders. This study is expected to provide theoretical contributions in the field of psychology, as well as practical benefits for parents and society in creating adaptive and constructive support for children with cerebral palsy.

Henry Kenlay, Frédéric A. Dreyer, Aleksandr Kovaltsuk et al.

Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development.

Neil Zhao

The mixing problem is classically encountered in the study of differential equations applied to fluid dynamics. An understanding of fluid movement under constraints is particularly important in the field of medicine as many therapeutics and biologic molecules are dissolved in bodily fluids. Many areas of biomedical research and diagnostics also rely on fluid sampling to obtain accurate measurements of biologic markers. We present in this manuscript the general solution to the mixing problem in the context of studying physiological phenomena based on the movement of fluid acting as a carrier for medically relevant molecules/solutes. We also expanded the general solution to become more compatible with areas of biomedical research and diagnostics that seek to characterize bodily fluids located in areas that are difficult to sample.

Eldar Knar

This study proposes an approach to describing personality dynamics through mathematical modelling of introversion, extroversion, and ambiversion processes. Introversion is interpreted as a recursive process characterized by deep self-awareness and inner reflection; extroversion is presented as an iterative process of accumulating and processing external stimuli; and ambiversion is considered transitivity, integrating the interaction of these two opposing processes. The developed model is based on principles of complex systems theory, nonlinear dynamics, and synergetics, enabling the perception of personality as an adaptive and multilayered system. The results include analytical equations describing the interaction of internal and external factors. Parameters regulating personality dynamics, such as sensitivity coefficients, weighting factors of stimuli, and synergy parameters, are also explored. The practical significance of the proposed model lies in its application in psychotherapy, education, personnel management, and other areas where it is necessary to consider personality traits and their dynamics. The model provides new opportunities for diagnosing, forecasting, and correcting personality changes, enhancing methods of working with individuals. The findings open some prospects for quantitative personality analysis, providing a theoretical understanding of its structure and dynamics. They also create a foundation for further interdisciplinary research in psychology, the cognitive sciences, and social systems. Specifically, the formalization considered in the context of program algorithms allows for the creation of functional procedures for AI and robots with differentiated psychological archetypes (introvert robots, extrovert robots, and ambivert robots).

Quentin Allan

Given the residual homonegativity in evidence throughout our diverse communities, and given the large numbers of gay people who remain “in the closet”, it is critical that we seek to understand in greater depth the complexities of the coming-out process with a view to dispelling some of the confusion relating to sexual identity. Internalised homophobia is more widespread than generally acknowledged, and it manifests in a variety of ways, including the sociological phenomenon of gay men remaining closeted until well into middle age. This article applies a hermeneutic phenomenological lens to examine the process of realisation, where an individual gradually becomes aware of his sexual orientation, and eventually acknowledges to himself that he is gay. This process can take decades. For this research project, twelve participants (gay men who have come out after the age of 40) from Aotearoa New Zealand willingly shared intensely personal accounts of their lived experiences. The findings indicate that individuals experience clarity about same-sex attraction in strikingly different ways. This study helps us to understand the difficulties faced by men who have lived the majority of their lives as “straight”, then in middle age find themselves having to negotiate the tortuous terrain between heterosexuality and a new gay identity.

Carla Floricel, Andrew Wentzel, Abdallah Mohamed et al.

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.

James H. Notwell, Michael W. Wood

While investigating methods to predict small molecule potencies, we found random forests or support vector machines paired with extended-connectivity fingerprints (ECFP) consistently outperformed recently developed methods. A detailed investigation into regression algorithms and molecular fingerprints revealed gradient-boosted decision trees, particularly CatBoost, in conjunction with a combination of ECFP, Avalon, and ErG fingerprints, as well as 200 molecular properties, to be most effective. Incorporating a graph neural network fingerprint further enhanced performance. We successfully validated our model across 22 Therapeutics Data Commons ADMET benchmarks. Our findings underscore the significance of richer molecular representations for accurate property prediction.

Annemarike de Beer, Luzelle Naudé, Lindi Nel

The aim of this study was to conduct an interpretative phenomenological analysis exploring the experiences of differently abled first-year students from a psychofortological perspective. Ryff’s psychological well-being model was used as a theoretical underpinning. Through the course of an academic year, three male participants completed semi-structured interviews and reflective writing exercises. Data were analysed using interpretative phenomenological analysis. A cross-case analysis yielded themes related to participants’ dynamic processes of finding purpose, direction and independence, as well as belonging, positive relations, self-acceptance and mastery. Collectively, the findings demonstrated how the participants moved from viewing themselves as disabled to differently abled, and that, despite numerous challenges, psychological well-being can be facilitated through the first-year higher education experience.