Hasil untuk "Therapeutics. Psychotherapy"

Jiying Zhang, Shuhao Zhang, Pierre Vandergheynst et al.

G-protein-coupled receptors (GPCRs), primary targets for over one-third of approved therapeutics, rely on intricate conformational transitions to transduce signals. While Molecular Dynamics (MD) is essential for elucidating this transduction process, particularly within ligand-bound complexes, conventional all-atom MD simulation is computationally prohibitive. In this paper, we introduce GPCRLMD, a deep generative framework for efficient all-atom GPCR-ligand simulation.GPCRLMD employs a Harmonic-Prior Variational Autoencoder (HP-VAE) to first map the complex into a regularized isometric latent space, preserving geometric topology via physics-informed constraints. Within this latent space, a Residual Latent Flow samples evolution trajectories, which are subsequently decoded back to atomic coordinates. By capturing temporal dynamics via relative displacements anchored to the initial structure, this residual mechanism effectively decouples static topology from dynamic fluctuations. Experimental results demonstrate that GPCRLMD achieves state-of-the-art performance in GPCR-ligand dynamics simulation, faithfully reproducing thermodynamic observables and critical ligand-receptor interactions.

Tianmeng Hu, Yongzheng Cui, Biao Luo et al.

The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While recent deep learning approaches have advanced this field, they are typically optimized and evaluated using native sequence recovery, which is a limited surrogate for structural fidelity, since different sequences can fold into similar 3D structures and high recovery does not necessarily indicate correct folding. To address this limitation, we propose RIDER, an RNA Inverse DEsign framework with Reinforcement learning that directly optimizes for 3D structural similarity. First, we develop and pre-train a GNN-based generative diffusion model conditioned on the target 3D structure, achieving a 9% improvement in native sequence recovery over state-of-the-art methods. Then, we fine-tune the model with an improved policy gradient algorithm using four task-specific reward functions based on 3D self-consistency metrics. Experimental results show that RIDER improves structural similarity by over 100% across all metrics and discovers designs that are distinct from native sequences.

Grace Bastian, Sri Aryanti Kristianingsih, Margaretta Erna Setianingrum

Married working women face multiple role demands that can potentially impact their psychological well-being. Work pressures, family responsibilities, and relationship dynamics with their partners make marital quality a crucial factor in maintaining psychological health. This study aims to examine the role of marital commitment and interpersonal communication as predictors of psychological well-being in married working women. The research method used was a quantitative approach with a predictive design through a cross-sectional survey. The study participants were 318 married working women. Data were collected using a psychological well-being scale, a marital commitment scale, and an interpersonal communication scale. Data analysis used multiple linear regression. The results showed that marital commitment and interpersonal communication simultaneously significantly predict the psychological well-being of working women (R² = 0.092). This finding indicates that the higher the marital commitment and the more effective the interpersonal communication in the marital relationship, the better the perceived psychological well-being. Therefore, strengthening the quality of marital relationships through increased commitment and interpersonal communication is crucial in efforts to improve the psychological well-being of working women.

JunJie Wee, Jian Jiang

Topological Data Analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelligence (AI) modeling and topological deep learning (TDL). This review provides a comprehensive overview of the development, methodologies, and applications of TDA in molecular sciences. We trace the evolution of TDA from early qualitative tools to advanced quantitative and predictive models, highlighting innovations such as persistent homology, persistent Laplacians, and topological machine learning. The paper explores TDA's transformative impact across diverse domains, including biomolecular stability, protein-ligand interactions, drug discovery, materials science, and viral evolution. Special attention is given to recent advances in integrating TDA with machine learning and AI, enabling breakthroughs in protein engineering, solubility and toxicity prediction, and the discovery of novel materials and therapeutics. We also discuss the limitations of current TDA approaches and outline future directions, including the integration of TDA with advanced AI models and the development of new topological invariants. This review aims to serve as a foundational reference for researchers seeking to harness the power of topology in molecular science.

Azam Bayat, Aref Khalkhali, Ali Reza Mahjoub

Summary Osteoporosis is a skeletal disorder, characterized by a decrease in bone strength and puts the individual at risk for fracture. On the other hand, rheumatoid arthritis is a systemic disease of unknown etiology that causes inflammation of the joints of the organs. Purpose Due to the destructive effects of these diseases and its increasing prevalence and lack of appropriate medication for treatment, the present study aimed to evaluate the therapeutic effect of a new type of healthy and live food supplement on rheumatoid arthritis and induced osteoporosis in rats. Methods In this research, healthy and live food powder were synthesized by a new and green route. This organic biomaterial was named NBS. The NBS food supplement had various vitamins, macro and micro molecules, and ingredients. The new healthy and nutritious diet showed that the use of this supplement led to the return of the parameters to normal levels. Results The concentration of 12.5 mg/ kg showed the least therapeutic effect and 50 mg/ kg had the highest therapeutic effect for osteoporosis. The results of blood parameters involved in inflammation in both healthy and patient groups showed that the use of complete adjuvant induction causes joint inflammation. In the study of the interaction of the concentrations, it was observed that the concentration of 50 mg/ kg had the highest therapeutic effect against the disease in the studied mice. Conclusion The results showed that the new healthy and viable supplement restores the blood osteoporotic and rheumatoid factors of the mice to normal.

Maedeh Asadi Rajani, Bagher Hasanvand, Zahra ahmadbeygi

Adolescence is a sensitive period characterized by rapid cognitive and emotional development, during which exposure to trauma or severe stressors can have lasting effects on cognitive functions. This study aimed to compare working memory and selective attention in adolescents who experienced parental-loss trauma and those who had recovered from acute COVID-19. A total of 120 adolescents (aged 12–20 years) participated, including 61 in the trauma group and 59 in the COVID-19 recovery group. The participants were assessed using the Wechsler Memory Scale, the Computerized Stroop Color–Word Test, the GHQ-28, and the WAIS. Descriptive statistics, independent t-tests, and multivariate analysis of variance (MANOVA) were employed to analyze group differences. Results indicated no significant differences between the two groups in working memory (t = 1.902, p = 0.062) or selective attention (Pillai’s Trace = 0.0169, p = 0.462). These findings suggest that both trauma and recovery from acute COVID-19 may similarly affect adolescents’ cognitive functions, though no group exhibited superior performance in either domain. The study highlights the importance of monitoring and supporting cognitive functions in adolescents exposed to significant stressors, emphasizing early intervention and preventive strategies. Future longitudinal studies are recommended to explore the long-term impact of trauma and COVID-19 on various cognitive domains.

Primadi Candra Susanto, Jatmiko Murdiono, Dewi Susita

This research aims to see the influences that cause increased performance. The research method used is Quantitative Descriptive. The data used is primary data, obtained from a questionnaire instrument with a Likert scale measurement scale of 1-5 (strongly disagree-strongly agree), filled in by employees at 6 freight forwarding companies in Jakarta, Indonesia. The sampling technique used purposive sampling with a sample size of 108 participants. The analysis software utilized is SmartPLS 4.1.0.0. The results are: 1) Training and self-efficacy have a positive and significant effect on employee engagement; 2) Discipline has no positive and insignificant effect on employee engagement; 3) Training has a positive and significant effect on employee performance; 4) Self-efficacy and discipline have no positive and insignificant effect on employee performance; 5) Employee engagement has a positive and significant effect on employee performance; 6) Training and self-efficacy have a positive and significant effect on employee performance throughout the employee's existence; and 7) Discipline has no positive and insignificant effect on employee performance through the presence of employees. To improve employee performance, it is supported by providing continuous training

Magdalena Krow, Thomas Brooks, Anissa Hernandez et al.

Tarot cards, with their adaptability and capacity to stimulate insight, imagination, and intuition within the realm of spiritual exploration, provide a unique avenue for self-discovery and emotional processing (Semesky, 2011). Over the course of four weeks, our research aimed to investigate the enduring effects of tarot card interpretations on participants' psychological well-being and their perceived sense of control. Participants maintained journals to record their tarot card encounters and reflections, shedding light on the relationship between tarot interpretations and personal growth. The interpretation of tarot cards coupled with the practice of mindfulness journaling appears to support positive changes in well-being and one's perception of control, emphasizing their potential as therapeutic tools. Furthermore, the participants' journals yielded comprehensive insights into various aspects of tarot card readings, including interpretation techniques, the evolving competence of participants, diverse interpretations of tarot, and their influence on psychological well-being.  

Talip Ucar, Aubin Ramon, Dino Oglic et al.

We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the immunogenic response to antibody therapeutics, one of the major causes of attrition in drug discovery and a challenging obstacle for their use in clinical settings. We pose the initial learning stage as a weakly-supervised contrastive-learning problem, where each antibody sequence is associated with possibly multiple identifiers of function and the objective is to learn an encoder that groups them according to their patented properties. We then freeze a part of the contrastive encoder and continue training it on the patent data using the cross-entropy loss to predict the humanness score of a given antibody sequence. We illustrate the utility of the patent data and our approach by performing inference on three different immunogenicity datasets, unseen during training. Our empirical results demonstrate that the learned model consistently outperforms the alternative baselines and establishes new state-of-the-art on five out of six inference tasks, irrespective of the used metric.

Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam et al.

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 μM$. Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.

Seungheun Baek, Soyon Park, Yan Ting Chok et al.

Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in this endeavor. Single-cell datasets contain technical artifacts that may hinder the predictability of such models, which poses quality control issues highly regarded in this area. To address this, we propose CRADLE-VAE, a causal generative framework tailored for single-cell gene perturbation modeling, enhanced with counterfactual reasoning-based artifact disentanglement. Throughout training, CRADLE-VAE models the underlying latent distribution of technical artifacts and perturbation effects present in single-cell datasets. It employs counterfactual reasoning to effectively disentangle such artifacts by modulating the latent basal spaces and learns robust features for generating cellular response data with improved quality. Experimental results demonstrate that this approach improves not only treatment effect estimation performance but also generative quality as well. The CRADLE-VAE codebase is publicly available at https://github.com/dmis-lab/CRADLE-VAE.

Bohao Xu, Yingzhou Lu, Yoshitaka Inoue et al.

Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In this work, we introduce Protein-Mamba, a novel two-stage model that leverages both self-supervised learning and fine-tuning to improve protein function prediction. The pre-training stage allows the model to capture general chemical structures and relationships from large, unlabeled datasets, while the fine-tuning stage refines these insights using specific labeled datasets, resulting in superior prediction performance. Our extensive experiments demonstrate that Protein-Mamba achieves competitive performance, compared with a couple of state-of-the-art methods across a range of protein function datasets. This model's ability to effectively utilize both unlabeled and labeled data highlights the potential of self-supervised learning in advancing protein function prediction and offers a promising direction for future research in drug discovery.

Blanca Inés Zamudio Leguizamón

En la búsqueda de comprender las aflicciones de la voluntad en la época del capitalismo se aborda el encuentro fallido de dos comunidades con los imperativos del progreso y la superproducción. Esta falla se traduce en sentimientos de impotencia, desamparo y falta de sentido de la existencia, que no pocas veces se precipita en el suicidio. Este recorrido condujo a las preguntas por los mecanismos que hacen posible la constitución del sentimiento colectivo de desamparo; por la creación de obra colectiva desde la aflicción; y, finalmente, por los efectos del sentimiento de desamparo en la comunidad.

Devashru Patel, Steven A. Sumner, Daniel Bowen et al.

Abstract Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of −2.768% for Utah, −2.823% for Louisiana, −3.449% for New York, and −5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Qi-Ming Ding, Yi-Ming Huang, Xiao Yuan

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.

Giulia Chiari, Giada Fiandaca, Marcello Edoardo Delitala

In the study of therapeutic strategies for the treatment of cancer, eco-evolutionary dynamics are of particular interest, since characteristics of the tumour population, interaction with the environment and effects of the treatment, influence the geometric and epigenetic characterization of the tumour with direct consequences on the efficacy of the therapy and possible relapses. In particular, when considering radiotherapy, oxygen concentration plays a central role both in determining the effectiveness of the treatment and the selective pressure due to hypoxia. We propose a mathematical model, settled in the framework of epigenetically-structured population dynamics and formulated in terms of systems of coupled non-linear integro-differential equations, that aims to catch these phenomena and to provide a predictive tool for the tumour mass evolution and therapeutic effects. The outcomes of the simulations show how the model is able to explain the impact of environmental selection and therapies on the evolution of the mass, motivating observed dynamics such as relapses and therapeutic failures. Furthermore it offers a first hint for the development of therapies which can be adapted to overcome problems of resistance and relapses.

Ning Dai, Wei Yu Tang, Tianshuo Zhou et al.

The tasks of designing RNAs are discrete optimization problems, and several versions of these problems are NP-hard. As an alternative to commonly used local search methods, we formulate these problems as continuous optimization and develop a general framework for this optimization based on a generalization of classical partition function which we call "expected partition function". The basic idea is to start with a distribution over all possible candidate sequences, and extend the objective function from a sequence to a distribution. We then use gradient descent-based optimization methods to improve the extended objective function, and the distribution will gradually shrink towards a one-hot sequence (i.e., a single sequence). As a case study, we consider the important problem of mRNA design with wide applications in vaccines and therapeutics. While the recent work of LinearDesign can efficiently optimize mRNAs for minimum free energy (MFE), optimizing for ensemble free energy is much harder and likely intractable. Our approach can consistently improve over the LinearDesign solution in terms of ensemble free energy, with bigger improvements on longer sequences.

Xiao Yuan

The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development, therapeutics, and diagnostics. Computational epitope prediction is challenging yet rewarding as it significantly reduces the time and cost of laboratory work. Most of the existing tools do not have satisfactory performance and only discriminate epitopes from non-epitopes. This paper presents a new deep learning-based multi-task framework for linear B-cell epitope prediction as well as antibody type-specific epitope classification. Specifically, a sequenced-based neural network model using recurrent layers and Transformer blocks is developed. We propose an amino acid encoding method based on eigen decomposition to help the model learn the representations of epitopes. We introduce modifications to standard cross-entropy loss functions by extending a logit adjustment technique to cope with the class imbalance. Experimental results on data curated from the largest public epitope database demonstrate the validity of the proposed methods and the superior performance compared to competing ones.

Sebastian Fox

This short report shares my experiences as a doctoral student when engaging reflexivity in my research and, in particular, when grappling with constructivist grounded theory methodology and carrying out data generation.  It sets out my philosophical paradigm, the context for the research, my understanding of the term “reflexivity” and the different lenses through which I am being reflexive in my thesis.

Susan Esmailzadeh, Ezzatollah Ahmadi

Corona anxiety has had a penetrating effect on various aspects of people's lives and has led to psychological confusion and lack of regulation of emotions. The aim of the present study was to investigate the role of cognitive regulation of emotion and mental disturbances in predicting anxiety of corona in students. The research method was qualitative-quantitative. First, the cognitive regulation factors of emotion were investigated using the qualitative method (phenomenology). 317 people were selected from the statistical population of Payam Noor Urmia students by available sampling method. In coding, biological factors with 110 open codes, strategic factors with 83 open codes, intrapersonal factors with 71 open codes, and environmental factors with 63 open codes, couple factors with 46 open codes, respectively, are the priority of the cognitive emotion regulation factors in students. they came . And there was a direct relationship between the cognitive regulation of emotion identified and the cognitive regulation of emotion among Payam Noor in students. To predict corona anxiety, students responded to Alipour, et al.'s (2019), cognitive emotion regulation, and Garnoski, et al. (2001) psychological disorder scales (DASS-42). There was a Significant negative and positive correlation between the emotional regulation of adaptive and non-adaptive strategy with Corona anxiety, respectively. There was a Significant negative and positive correlation between emotional regulation of adaptive and non-adaptive strategies with psychological disturbance, respectively.. There was a Significant positive Correlation between mental disorder and corona anxiety. Regression analysis showed that these three components together explain 54% of Corona anxiety in students.