Hasil untuk "Therapeutics. Psychotherapy"

T. Berger

Huixin Xue, Guangjun Xu, Shihong Ren et al.

Home-based music therapy devices require accessible and cost-effective solutions for users to understand and track their therapeutic progress. Traditional physiological signal analysis, particularly EEG interpretation, relies heavily on domain experts, creating barriers to scalability and home adoption. Meanwhile, few experts are capable of interpreting physiological signal data while also making targeted music recommendations. While large language models (LLMs) have shown promise in various domains, their application to automated physiological report generation for music therapy represents an unexplored task. We present a prototype system that leverages LLMs to bridge this gap -- transforming raw EEG and cardiovascular data into human-readable therapeutic reports and personalized music recommendations. Unlike prior work focusing on real-time physiological adaptation during listening, our approach emphasizes post-session analysis and interpretable reporting, enabling non-expert users to comprehend their psychophysiological states and track therapeutic outcomes over time. By integrating signal processing modules with LLM-based reasoning agents, the system provides a practical and low-cost solution for short-term progress monitoring in home music therapy contexts. This work demonstrates the feasibility of applying LLMs to a novel task -- democratizing access to physiology-driven music therapy through automated, interpretable reporting.

Gaëlle Audéoud, Louis Moine, Laura Bonnecaze et al.

The aggregation of amyloid-forming peptides is a dynamic, complex process that underlies their diverse biological activities, from physiological functions to disease-associated dysfunctions. While the structure of fibrillar end-products is well-characterized for most amyloids, the heterogeneous and often transient oligomers, likely key in cytotoxicity, remain poorly investigated, especially for peptides with low-yield aromatic residues. Here, by exploiting and developing flow induced dispersion analysis in both peak and front modes, we demonstrate that intrinsic phenylalanine fluorescence can be harnessed to quantify the conversion of diffusing monomers into non-diffusing oligomers and fibrils. We further characterize low-molecular-weight oligomers, and their size evolution from 2 to 10 nm over time. Importantly, we validate the robustness of our approach using two tryptophan-free and fast-fibrillating amyloid peptides, PSM$α$3 and hIAPP, known for their key roles in S. aureus virulence and type 2 diabetes respectively. Our results overcome the limitations of traditional biochemical and biophysical amyloid assays by extending analysis from large oligomers and fibrils to small heterogeneous oligomers, under near-physiological conditions. This study thus offers a new analytical framework, thereby filling a critical gap in amyloid research, to probe the early stages of aggregation, key in the design of alternative therapeutics for amyloid-diseases.

Xiaoqiong Xia, Cesar de la Fuente-Nunez

Peptide-based drugs can bind to protein interaction sites that small molecules often cannot, and are easier to produce than large protein drugs. However, designing effective peptide binders is difficult. A typical peptide has an enormous number of possible sequences, and only a few of these will fold into the right 3D shape to match a given protein target. Existing computational methods either generate many candidate sequences without considering how they will fold, or build peptide backbones and then find suitable sequences afterward. Here we introduce ApexGen, a new AI-based framework that simultaneously designs a peptide's amino-acid sequence and its three-dimensional structure to fit a given protein target. For each target, ApexGen produces a full all-atom peptide model in a small number of deterministic integration steps. In tests on hundreds of protein targets, the peptides designed by ApexGen fit tightly onto their target surfaces and cover nearly the entire binding site. These peptides have shapes similar to those found in natural protein-peptide complexes, and they show strong predicted binding affinity in computational experiments. Because ApexGen couples sequence and structure design at every step of Euler integration within a flow-matching sampler, it is much faster and more efficient than prior approaches. This unified method could greatly accelerate the discovery of new peptide-based therapeutics.

Pronama Biswas, Asmita Saha, Bhoomika Sridhar et al.

Quantum dots (QDs) have emerged as promising nanomaterials with unique optical and physical properties, making them highly attractive for various applications in biomedicine. This review provides a comprehensive overview of the types, modes of synthesis, characterization, applications, and recent advances of QDs in the field of biomedicine, with a primary focus on bioimaging, drug delivery, and biosensors. The unique properties of QDs, such as tunable emission spectra, long-term photostability, high quantum yield, and targeted drug delivery, hold tremendous promise for advancing diagnostics, therapeutics, and imaging techniques in biomedical research. However, several significant hurdles remain before their full potential in the biomedical field, like bioaccumulation, toxicity, and short-term stability. Addressing these hurdles is essential to effectively incorporate QDs into clinical use and enhance their influence on healthcare outcomes. Furthermore, the review conducts a critical analysis of potential QD toxicity and explores recent progress in strategies and methods to mitigate these adverse effects, such as surface modification, surface coatings, and encapsulation. By thoroughly examining current research and recent advancements, this comprehensive review offers invaluable insights into both the future possibilities and the challenges that lie ahead in fully harnessing the potential of QDs in the field of biomedicine, promising a revolution in the landscape of medical diagnostics, therapies, and imaging technologies.

Yi He, Ailun Wang, Zhi Wang et al.

Recent advances in generative models, particularly diffusion and auto-regressive models, have revolutionized fields like computer vision and natural language processing. However, their application to structure-based drug design (SBDD) remains limited due to critical data constraints. To address the limitation of training data for models targeting SBDD tasks, we propose an evolutionary framework named MEVO, which bridges the gap between billion-scale small molecule dataset and the scarce protein-ligand complex dataset, and effectively increase the abundance of training data for generative SBDD models. MEVO is composed of three key components: a high-fidelity VQ-VAE for molecule representation in latent space, a diffusion model for pharmacophore-guided molecule generation, and a pocket-aware evolutionary strategy for molecule optimization with physics-based scoring function. This framework efficiently generate high-affinity binders for various protein targets, validated with predicted binding affinities using free energy perturbation (FEP) methods. In addition, we showcase the capability of MEVO in designing potent inhibitors to KRAS$^{\textrm{G12D}}$, a challenging target in cancer therapeutics, with similar affinity to the known highly active inhibitor evaluated by FEP calculations. With high versatility and generalizability, MEVO offers an effective and data-efficient model for various tasks in structure-based ligand design.

Mithat Can Ozgun, Jiahuan Pei, Koen Hindriks et al.

LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding and addressing user needs. Yet, their effectiveness remains limited in specialized domains such as mental health diagnosis, where they underperform compared to general applications. Current approaches to integrating diagnostic capabilities into LLMs rely on scarce, highly sensitive mental health datasets, which are challenging to acquire. These methods also fail to emulate clinicians' proactive inquiry skills, lack multi-turn conversational comprehension, and struggle to align outputs with expert clinical reasoning. To address these gaps, we propose DSM5AgentFlow, the first LLM-based agent workflow designed to autonomously generate DSM-5 Level-1 diagnostic questionnaires. By simulating therapist-client dialogues with specific client profiles, the framework delivers transparent, step-by-step disorder predictions, producing explainable and trustworthy results. This workflow serves as a complementary tool for mental health diagnosis, ensuring adherence to ethical and legal standards. Through comprehensive experiments, we evaluate leading LLMs across three critical dimensions: conversational realism, diagnostic accuracy, and explainability. Our datasets and implementations are fully open-sourced.

Jiawei Wu, Jun Wen, Mingyuan Yan et al.

Medicinal synergy prediction is a powerful tool in drug discovery and development that harnesses the principles of combination therapy to enhance therapeutic outcomes by improving efficacy, reducing toxicity, and preventing drug resistance. While a myriad of computational methods has emerged for predicting synergistic drug combinations, a large portion of them may overlook the intricate, yet critical relationships between various entities in drug interaction networks, such as drugs, cell lines, and diseases. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. We introduce a salient deep hypergraph learning method, namely, Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES), to predict anti-cancer drug synergy. HERMES integrates heterogeneous data sources, encompassing drug, cell line, and disease information, to provide a comprehensive understanding of the interactions involved. By leveraging advanced hypergraph neural networks with gated residual mechanisms, HERMES can effectively learn complex relationships/interactions within the data. Our results show HERMES demonstrates state-of-the-art performance, particularly in forecasting new drug combinations, significantly surpassing previous methods. This advancement underscores the potential of HERMES to facilitate more effective and precise drug combination predictions, thereby enhancing the development of novel therapeutic strategies.

Shengjie Xu, Lingxi Xie

Antibody-drug conjugates (ADCs) have emerged as a promising class of targeted cancer therapeutics, but the design and optimization of their cytotoxic payloads remain challenging. This study introduces DumplingGNN, a novel hybrid Graph Neural Network architecture specifically designed for predicting ADC payload activity based on chemical structure. By integrating Message Passing Neural Networks (MPNN), Graph Attention Networks (GAT), and GraphSAGE layers, DumplingGNN effectively captures multi-scale molecular features and leverages both 2D topological and 3D structural information. We evaluate DumplingGNN on a comprehensive ADC payload dataset focusing on DNA Topoisomerase I inhibitors, as well as on multiple public benchmarks from MoleculeNet. DumplingGNN achieves state-of-the-art performance across several datasets, including BBBP (96.4\% ROC-AUC), ToxCast (78.2\% ROC-AUC), and PCBA (88.87\% ROC-AUC). On our specialized ADC payload dataset, it demonstrates exceptional accuracy (91.48\%), sensitivity (95.08\%), and specificity (97.54\%). Ablation studies confirm the synergistic effects of the hybrid architecture and the critical role of 3D structural information in enhancing predictive accuracy. The model's strong interpretability, enabled by attention mechanisms, provides valuable insights into structure-activity relationships. DumplingGNN represents a significant advancement in molecular property prediction, with particular promise for accelerating the design and optimization of ADC payloads in targeted cancer therapy development.

Linsen Pei, Yujia Xian, Xiaodan Yan et al.

Membraneless droplets formed through liquid-liquid phase separation (LLPS) play a crucial role in mRNA storage, enabling organisms to swiftly respond to environmental changes. However, the mechanisms underlying mRNA integration and protection within droplets remain unclear. Here, we unravel the role of bacterial aggresomes as stress granules (SGs) in safeguarding mRNA during stress. We discovered that upon stress onset, mobile mRNA molecules selectively incorporate into individual proteinaceous SGs based on length-dependent enthalpic gain over entropic loss. As stress prolongs, SGs undergo compaction facilitated by stronger non-specific RNA-protein interactions, thereby promoting recruitment of shorter RNA chains. Remarkably, mRNA ribonucleases are repelled from bacterial SGs, due to the influence of protein surface charge. This exclusion mechanism ensures the integrity and preservation of mRNA within SGs during stress conditions, explaining how mRNA can be stored and protected from degradation. Following stress removal, SGs facilitate mRNA translation, thereby enhancing cell fitness in changing environments. These droplets maintain mRNA physiological activity during storage, making them an intriguing new candidate for mRNA therapeutics manufacturing.

Binglin Zeng, Jialin Lai, Jingyuan Chen et al.

Self-propelled micromotors can efficiently convert ambient energy into mechanical motion, which is of great interest for its potential biomedical applications in delivering therapeutics noninvasively. However, navigating these micromotors through biological barriers remains a significant challenge as most micromotors do not provide sufficient disruption forces in in-vivo conditions. In this study, we employed focused scanning laser from conventional confocal microscope to manipulate carbon microbottle based microswimmers. With the increasing of the laser power, the microswimmers' motions translates from autonomous to directional, and finally the high power laser induced the microswimmer explosions, which effectively deliveres microbottle fragments through the cell membrane. It is revealed that photothermally-induced cavitation bubbles enable the propulsion of microbottles in liquids, where the motion direction can be precisely regulated by the scanning orientation of the laser. Furthermore, the membrane penetration ability of the microbottles promised potential applications in drug delivery and cellular injections. As microbottles navigate toward cells, we strategically increase the laser power to trigger their explosion. By loading microswimmers with transfection genes, cytoplasmic transfection can be realized, which is demonstrated by successful gene transfection of GPF in cells. Our findings open new possibilities for cell injection and gene transfection using micromotors.

Monica Whyte

 To me, as a systemic family therapist working in the substance misuse field, it seems that family therapy has always had somewhat of a dilemma when it comes to working with family systems where there is a substance misuse disorder present. In this paper, I will be describing current thinking in family therapy practice within the substance misuse  field and how systemically informed substance misuse services are beginning to pay attention to issues of neurodiversity. Particularly concentrating on how adolescent (under 18) services are adapting to become more neuro-inclusive and neuro-affirmative. For substance misuse services, this is the opportune time to discuss and highlight the use of substances by the neurodivergent population. In this article, which is an elaboration on the presentation at the 4th Systemic Autism conference I outline changes that could be made to substance misuse treatment services to remove barriers to accessing substance misuse treatment that neurodivergent individuals may encounter when trying to change their relationship with substances.

Filippo Corponi, Bryan M. Li, Gerard Anmella et al.

Abstract Bipolar disorder (BD) involves autonomic nervous system dysfunction, detectable through heart rate variability (HRV). HRV is a promising biomarker, but its dynamics during acute mania or depression episodes are poorly understood. Using a Bayesian approach, we developed a probabilistic model of HRV changes in BD, measured by the natural logarithm of the Root Mean Square of Successive RR interval Differences (lnRMSSD). Patients were assessed three to four times from episode onset to euthymia. Unlike previous studies, which used only two assessments, our model allowed for more accurate tracking of changes. Results showed strong evidence for a positive lnRMSSD change during symptom resolution (95.175% probability of positive direction), though the sample size limited the precision of this effect (95% Highest Density Interval [−0.0366, 0.4706], with a Region of Practical Equivalence: [-0.05; 0.05]). Episode polarity did not significantly influence lnRMSSD changes.

Felix Angulo, Pauline Goger, David A. Brent et al.

Abstract By adolescence, two-thirds of youth report exposure to at least one traumatic event, yet the impact of trauma history is not routinely considered when evaluating the effect of psychotherapeutic interventions. Trauma may be a particularly important moderator of the effects of transdiagnostic therapies for emotional disorders, as trauma exposure is associated with risk for the development of comorbid depression and anxiety. The current study examined the history of trauma exposure and the presence of clinically significant depression as moderators of treatment outcomes in the Brief Behavioral Therapy (BBT) trial, the largest study of transdiagnostic psychotherapy for youth. Youths (age 8–16 years) were randomized to BBT (n = 89) based in pediatric primary care or assisted referral to outpatient community care (ARC; n = 86). Clinical response, functioning, anxiety symptoms, and depression symptoms were assessed at post-treatment (Week 16) and at follow-up (Week 32). A significant three-way interaction emerged between the treatment group, comorbid depression, and trauma exposure. BBT was broadly effective for 3/4 of the sample, but, for anxious-depressed youth with trauma exposure, BBT never significantly separated from ARC. Differences in outcome were not accounted for by other participant characteristics or by therapist-rated measures of alliance, youth engagement, or homework completion. Implications for models of learning and for intervention theory and development are discussed.

Serly Elis Hermanoes, Sri Aryanti Kristianingsih, Christiana Hari Soetjiningsih

This research is a quantitative study which aims to determine self-compassion and social support simultaneously as predictors of psychological well-being in female pastors at the Evangelical Christian Church in Timor (GMIT). Data collection used Ryff's Psychological Well-Being Scale (RPWB), Self-Compassion Scale (SCS), and Revised Multidimensional Scale of Perceived Social Support (RMSPSS) which were distributed by sending questionnaires via Google form to the WhatsApp group and print outs were delivered directly to respondents. The respondents involved in this research were 105 female GMIT priests around Kupang City. The data obtained were analyzed using multiple linear regression techniques with the help of the SPSS 29 program. The results showed that the calculated F value (simultaneous) was 211.423 with p < 0.05, which means self-compassion and social support simultaneously as predictor of psychological well-being of GMIT female priests around Kupang City with a coefficient of determination (R²) of 0.806, meaning the contribution of the influence of self-compassion and social support to psychological well-being is 80.6%.

Leonard Wossnig, Norbert Furtmann, Andrew Buchanan et al.

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost. Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D). However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall progress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups. To address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly define a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody D&D. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage.

Samuel Desmarais, Gerardo Ramos-Palacios, Jonathan Poree et al.

Linking cavitation and anatomy was found to be important for predictable outcomes in Focused-Ultrasound Blood-Brain-Barrier-Opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either 1) do not leverage the full resolution capabilities of ultrasound imaging or 2) place strong constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Herein, we develop BP-ETACI, derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood-brain barrier opening with long therapeutic bursts using a 1.5-MHz focused transducer in the brain of C57BL/6 mice. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for 1) monitoring FUS-BBBO, but also for 2) therapy planning and target verification. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

Yeqing Lin, Mohammed AlQuraishi

Proteins power a vast array of functional processes in living cells. The capability to create new proteins with designed structures and functions would thus enable the engineering of cellular behavior and development of protein-based therapeutics and materials. Structure-based protein design aims to find structures that are designable (can be realized by a protein sequence), novel (have dissimilar geometry from natural proteins), and diverse (span a wide range of geometries). While advances in protein structure prediction have made it possible to predict structures of novel protein sequences, the combinatorially large space of sequences and structures limits the practicality of search-based methods. Generative models provide a compelling alternative, by implicitly learning the low-dimensional structure of complex data distributions. Here, we leverage recent advances in denoising diffusion probabilistic models and equivariant neural networks to develop Genie, a generative model of protein structures that performs discrete-time diffusion using a cloud of oriented reference frames in 3D space. Through in silico evaluations, we demonstrate that Genie generates protein backbones that are more designable, novel, and diverse than existing models. This indicates that Genie is capturing key aspects of the distribution of protein structure space and facilitates protein design with high success rates. Code for generating new proteins and training new versions of Genie is available at https://github.com/aqlaboratory/genie.

Yifei Wang, Yunrui Li, Lin Liu et al.

The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, opening new frontiers in chemical understanding and drug discovery. Hence, we introduce Asymmetric Contrastive Multimodal Learning (ACML), a specifically designed approach to enhance molecular understanding and accelerate advancements in drug discovery. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pre-trained chemical unimodal encoders and a shallow-designed graph encoder with 5 layers, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. We demonstrate the effectiveness of this framework through large-scale cross-modality retrieval and isomer discrimination tasks. Additionally, ACML enhances interpretability by revealing chemical semantics in graph presentations and bolsters the expressive power of graph neural networks, as evidenced by improved performance in molecular property prediction tasks from MoleculeNet and Therapeutics Data Commons (TDC). Ultimately, ACML exemplifies its potential to revolutionize molecular representational learning, offering deeper insights into the chemical semantics of diverse modalities and paving the way for groundbreaking advancements in chemical research and drug discovery.

Gail Simon

Writings from 2007 In 2007, I had “one of those years”. Many dreadful things happened. I hear myself think, “It could have been worse”. Nevertheless, it was a challenging year for me. And doing work which is all about supporting others with their struggles, their work, their learning was sometimes tough, frequently moving. I was aware of how I was drawing on the experiences of clients and trainees and other people I had met who had faced serious illness or death. Despite my commitment to limit imbalance of power in therapy, the experience of loss and illness created a sense of levelling that I had not anticipated. I chose to “come out” from behind that generic version of me as therapist, trainer, researcher and reveal more to people than I was used to. I’m not entirely sure how much I made a choice to do that but I did try to manage things in a professional manner. Whatever that means. Four particular conversational clusters stood out for me that took place during that year. I wrote them at the time but I see that I have written them in the past tense. I think I needed to. Put them in the past. I was, in some ways, still in shock so writing in the past tense helped me create a timeline to locate me in another time zone known as The Present. It was a better place to be and one from which I could create another perspective.