Hasil untuk "Therapeutics. Psychotherapy"

A. Kazdin

Qi Si, Xuyang Liu, Penglei Wang et al.

RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

Mike Slade, Olamide Todowede, Doreen Boyd et al.

Abstract People living with mental health issues use a range of self-management strategies. Most strategy recommendations have been developed by clinicians and researchers, so they may not reflect the full range of approaches used in practice. A citizen mental health science methodology can address this bias in strategy identification. We co-created a list of 77 pre-defined self-management strategies, and 1116 public contributors (n = 468 mental health service users, n = 497 lived experience not using services, n = 151 no lived experience) living in the United Kingdom completed an online survey identifying their use of each strategy, and identifying extra strategies. A wide range of pre-defined strategies were used by contributors, with differences in usage patterns identified between the three groups. 401 distinct extra strategies were identified. The active use of avoidance as a self-management strategy was more common than anticipated, including avoiding alcohol, social media, thinking about problems, other people, and mental health services.

Khaiwal Ravindra, Abhishek Kumar, Nitasha Vig et al.

Abstract Depression, anxiety, and stress are major mental health concerns globally, especially in India. This study examines the prevalence of mental health symptoms in overweight and normal BMI individuals living near thermal power plants and develops a depression prediction model using binary logistic regression using the DASS-21 score. A community-based cross-sectional study was conducted from October 2018 to March 2019, with data collected through face-to-face interviews. Socio-demographic factors like age, gender, cooking fuel type, and income were analyzed. Significant associations were found between stress and household air pollution (p = 0.011, OR = 17.408, 95% CI) and between anxiety and income below 1 lakh in normal BMI individuals (p = 0.045, OR = 0.303, 95% CI). Depression, anxiety, and stress were more prevalent in females. The depression prediction model demonstrated high performance with an ROC–AUC of 0.8754. These findings highlight the need to address environmental and socio-demographic factors to protect mental health in populations living near thermal power plants.

Mostafa Toobaei, Mehdireza Sarafraz, Mohammad Aminaee

The Beliefs About Losing Control Inventory (BALCI) is a self-report measure of negative beliefs about losing control, including three-factor dimensions. In this study, we assessed the factor structure, reliability, and validity of the Persian version of the BALCI (P-BALCI) among the Iranian population. A total of 336 individuals completed the Persian version of Beliefs about Losing Control (P-BALCI), the Obsessive Beliefs Questionnaire-44 (OBQ-44), the Obsessive-Compulsive Inventory-Revised (OCI-R), the Anxiety Sensitivity Index (ASI), Anxiety Control Scale-Revised (ACS-R), and the Desirability of Control Scale (DCS). Similar to the original version of BALCI, the results showed that the P-BALCI had a three-factor structure. The P-BALCI showed good reliability through Cronbach’s alpha coefficients (α=.91) and test-retest coefficients. Also, the P-BALCI had good convergent and divergent validity. The P-BALCI was also associated with elevated OCD symptoms above and beyond identified obsessive beliefs by the OBQ-44. The findings indicated that the P-BALCI is a reliable measurement scale for assessing beliefs about losing control in an Iranian sample.

ali mohammadzadeh, faezeh sharifi, zahra heyran sangestani et al.

Childhood traumatic and dissociative experiences play a crucial role in psychological disorders, with schizophrenia and major depressive disorder being two serious conditions that require examination of various etiological and maintaining factors. In Iran, the lack of sufficient research evidence comparing childhood traumatic and dissociative experiences between these two disorders is a significant research gap, which this study aims to address. The population consisted of patients with major depressive disorders and schizophrenia who visited Razi Hospital and psychiatric clinics in Tabriz from 2020 to 2023. Among them, 68 patients with major depressive disorders and 68 patients with schizophrenia were selected and tested on a voluntary basis. A non-patient group of 68 individuals was selected based on matched characteristics from students and other citizens of Tabriz. Participants responded to scales measuring dissociative symptoms and childhood trauma. The data was analyzed using multivariate analysis of variance. The results showed that patients with major depressive disorder and schizophrenia scored higher on childhood traumatic and dissociative experiences compared to the control group. Additionally, when comparing the two patient groups, patients with major depressive disorder scored higher on childhood traumatic experiences, while patients with schizophrenia scored higher on dissociative experiences. This study revealed that patients with major depressive disorder scored higher on childhood traumatic experiences compared to patients with schizophrenia, while schizophrenia patients exhibited higher scores on dissociative symptom.

Mary Lemmer

A growing body of research suggests that improvisational theatre can have a positive impact on individuals’ well-being by promoting emotional expression, social connection and personal insight. Similarly, exposure to humour has been shown to confer some benefits to mental and physical health. Most studies have focused on passive exposure to humour or the induction of laughter. Improvisational comedy, which involves active group participation in humour creation, offers a unique opportunity to combine some of the therapeutic elements of both improvisational theatre and humour. Twenty-eight adults participated in a weekly improv comedy course delivered via Zoom and had their physiology and emotional states monitored. Participants wore Oura rings designed to measure physiological states continuously starting two weeks before the onset of improv classes to establish a baseline and throughout the six-week duration of the course. Emotional states were measured using self-report questionnaires. The study utilized a single-group design, so comparisons were within-subject. Participants self-reported increased feelings of creativity, connectedness, energy and empowerment after each class and a decrease in feeling tired and lonely. Oura rings’ data showed improvement in sleep quality and physical activity levels. Improvements in sleep quality were greater for older adults.

Lise Juul, Morten Frydenberg, Emilie Hasager Bonde et al.

Abstract The aim was to assess the effectiveness of an intervention comprising a teacher-training program and the implementation of a ten-session mindfulness-based intervention (MBI) in regular classroom teaching for 16- to 24-year-old students in Denmark. In a cluster-randomized trial (2019-2021), upper secondary schools and vocational schools for social and health care were randomly assigned 1:1 to receive the intervention (21 schools, 35 teachers, 438 students) or teaching-as-usual (22 schools, 38 teachers, 551 students). Eighteen self-report measures of mental health were collected at baseline, three, and six months, with the Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) as the primary outcome. Intention-to-treat analyses were conducted using mixed-effects linear regression and bootstrapping. Fourteen intervention schools (n = 277) and 17 teaching-as-usual schools (n = 419) provided follow-up data. No statistically significant effect on the primary outcome (SWEMWBS) was found in the total population. However, a small positive effect on SWEMWBS was observed among females in upper secondary schools at three months, but not sustained at six months. Trial Registration: ClinicalTrials.gov Identifier: NCT04610333, registered October 26, 2020, https://clinicaltrials.gov/ct2/results?cond=&term=NCT04610333&cntry=&state=&city=&dist= .

Haonan Zhu, Mary Silva, Jose Cadena et al.

Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions between host gene pairs during infection remains relatively slow and labor intensive. Identification of multiple gene knockdowns that effectively inhibit viral replication requires a search over the combinatorial space of all possible target gene pairs and is infeasible via brute-force experiments. Although active learning methods for sequential experimental design have shown promise, existing approaches have generally been restricted to single-gene knockdowns or small-scale double knockdown datasets. In this study, we present an integrated Deep Active Learning (DeepAL) framework that incorporates information from a biological knowledge graph (SPOKE, the Scalable Precision Medicine Open Knowledge Engine) to efficiently search the configuration space of a large dataset of all pairwise knockdowns of 356 human genes in HIV infection. Through graph representation learning, the framework is able to generate task-specific representations of genes while also balancing the exploration-exploitation trade-off to pinpoint highly effective double-knockdown pairs. We additionally present an ensemble method for uncertainty quantification and an interpretation of the gene pairs selected by our algorithm via pathway analysis. To our knowledge, this is the first work to show promising results on double-gene knockdown experimental data of appreciable scale (356 by 356 matrix).

James King, Lewis Cornwall, Andrei Cristian Nica et al.

Accurate prediction of protein-protein binding affinity is vital for understanding molecular interactions and designing therapeutics. We adapt Boltz-2, a state-of-the-art structure-based protein-ligand affinity predictor, for protein-protein affinity regression and evaluate it on two datasets, TCR3d and PPB-affinity. Despite high structural accuracy, Boltz-2-PPI underperforms relative to sequence-based alternatives in both small- and larger-scale data regimes. Combining embeddings from Boltz-2-PPI with sequence-based embeddings yields complementary improvements, particularly for weaker sequence models, suggesting different signals are learned by sequence- and structure-based models. Our results echo known biases associated with training with structural data and suggest that current structure-based representations are not primed for performant affinity prediction.

María Antonia Soto-Baño, Vicente Javier Clemente-Suárez, Jesús Linares Martín et al.

Con el objetivo de dar respuesta a las necesidades de índole psicológica de la población en el inicio de la situación de crisis ocasionada por la COVID-19 denominada “primera ola”, el Colegio Oficial de la Psicología de Madrid puso en marcha un teléfono de asistencia psicológica gratuito con una cobertura única en España de 24 horas, 7 días de la semana. Este estudio analiza las variables más relevantes de las personas que utilizaron el servicio, identificando los principales motivos de consulta, sintomatología y estrategias de intervención aplicadas por los profesionales. Los datos analizados revelan un uso intensivo del servicio con 10.452 llamadas gestionadas y la sintomatología ansiosa (44,2%) y depresiva (23%) como predominante. Los resultados subrayan la alta demanda y necesidad del servicio de atención psicológica telefónica durante la primera ola de la COVID-19. La derivación a otros servicios en el 30% de los casos resalta la importancia de una red coordinada de apoyo, destacando la necesidad de una intervención psicológica proactiva y sostenida en situaciones de crisis.

David Benrimoh, Azeezat Azeez, Jean-Marie Batail et al.

Abstract Stanford Neuromodulation Therapy (SNT), has recently shown rapid efficacy in difficult to treat (DTT) depression. We conducted an exploratory analysis of individual symptom improvements during treatment, correlated with fMRI, to investigate this rapid improvement in 23 DTT participants from an SNT RCT (12 active, 11 sham). Montgomery–Åsberg Depression Rating Scale item 7 (Lassitude) was the earliest to show improvements between active and sham, as early as treatment day 2. Lassitude score at treatment day 3 was predictive of response at 4 weeks post-treatment and response immediately after treatment. Participants with lower lassitude scores at treatment day 3 had different patterns of sgACC functional connectivity compared to participants with higher scores in both baseline and post-treatment minus baseline analyses. Further work will aim to first replicate these preliminary findings, and then to extend these findings and examine how SNT may affect lassitude and behavioral activation early in treatment.

Yoshie Ohashi

In schools, where teachers tend to seek immediate solutions, the question of how to provide meaningful interventions has drawn considerable attention from school psychologists all over the world. The purpose of the present study is to analyze case data, including the phenomenon named “reverie of groups” from a previous study, which refers to the retention of discomfort in a group of teachers, resulting in an indirect change in problematic students. In this case study, it was initially difficult to adopt a clear policy that created immediate solutions; the psychologist and teachers experienced feelings of uncertainty. After several consultations between the psychologist and teachers however, the troubled student indirectly changed. The teachers’ attitudes became responsive and pro-active in the aftermath. Thus, the significance of reverie in the school consultation context was demonstrated, as was the power of groups to maintain a sense of security by sustaining the capacity of reverie.

Lars Tauvon, Ray Haddock Haddock, Teresa von Sommaruga Howard

Guanghao Zhu, Lin Liu, Yuhao Hu et al.

Micro-expressions are subtle facial movements that occur spontaneously when people try to conceal real emotions. Micro-expression recognition is crucial in many fields, including criminal analysis and psychotherapy. However, micro-expression recognition is challenging since micro-expressions have low intensity and public datasets are small in size. To this end, a three-stream temporal-shift attention network based on self-knowledge distillation is proposed in this paper. Firstly, to address the low intensity of muscle movements, we utilize learning-based motion magnification modules to enhance the intensity of muscle movements. Secondly, we employ efficient channel attention modules in the local-spatial stream to make the network focus on facial regions that are highly relevant to micro-expressions. In addition, temporal shift modules are used in the dynamic-temporal stream, which enables temporal modeling with no additional parameters by mixing motion information from two different temporal domains. Furthermore, we introduce self-knowledge distillation into the micro-expression recognition task by introducing auxiliary classifiers and using the deepest section of the network for supervision, encouraging all blocks to fully explore the features of the training set. Finally, extensive experiments are conducted on five publicly available micro-expression datasets. The experimental results demonstrate that our network outperforms other existing methods and achieves new state-of-the-art performance. Our code is available at https://github.com/GuanghaoZhu663/SKD-TSTSAN.

Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda et al.

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases. To accelerate the discovery of antibody therapeutics, there is growing interest in constructing language models using antibody sequences. However, the applicability of pre-trained language models for antibody discovery has not been thoroughly evaluated due to the scarcity of labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2, a dataset featuring the antigen-variable domain of heavy chain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset for antibody language models, containing over two million VHH sequences. We report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT pre-trained on VHHCorpus-2M and existing general protein and antibody-specific pre-trained language models. These results confirm that AVIDa-SARS-CoV-2 provides valuable benchmarks for evaluating the representation capabilities of antibody language models for binding prediction, thereby facilitating the development of AI-driven antibody discovery. The datasets are available at https://datasets.cognanous.com.

Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei et al.

Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.

Krishna Manoj, Theodore W. Grunberg, Domitilla Del Vecchio

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications.

Swagatam Mukhopadhyay

We develop a set of algorithms to solve a broad class of Design of Experiment (DoE) problems efficiently. Specifically, we consider problems in which one must choose a subset of polymers to test in experiments such that the learning of the polymeric design rules is optimal. This subset must be selected from a larger set of polymers permissible under arbitrary experimental design constraints. We demonstrate the performance of our algorithms by solving several pragmatic nucleic acid therapeutics engineering scenarios, where limitations in synthesis of chemically diverse nucleic acids or feasibility of measurements in experimental setups appear as constraints. Our approach focuses on identifying optimal experimental designs from a given set of experiments, which is in contrast to traditional, generative DoE methods like BIBD. Finally, we discuss how these algorithms are broadly applicable to well-established optimal DoE criteria like D-optimality.

Shixian Geng, Ginshi Shimojima, Chi-Lan Yang et al.

Psychotherapy, such as cognitive-behavioral therapy (CBT), is effective in treating various mental disorders. Technology-facilitated mental health therapy improves client engagement through methods like digitization or gamification. However, these innovations largely cater to individual therapy, ignoring the potential of group therapy-a treatment for multiple clients concurrently, which enables individual clients to receive various perspectives in the treatment process and also addresses the scarcity of healthcare practitioners to reduce costs. Notwithstanding its cost-effectiveness and unique social dynamics that foster peer learning and community support, group therapy, such as group CBT, faces the issue of attrition. While existing medical work has developed guidelines for therapists, such as establishing leadership and empathy to facilitate group therapy, understanding about the interactions between each stakeholder is still missing. To bridge this gap, this study examined a group CBT program called the Serigaya Methamphetamine Relapse Prevention Program (SMARPP) as a case study to understand stakeholder coordination and communication, along with factors promoting and hindering continuous engagement in group therapy. In-depth interviews with eight facilitators and six former clients from SMARPP revealed the motivators and demotivators for facilitator-facilitator, client-client, and facilitator-client communications. Our investigation uncovers the presence of discernible conflicts between clients' intrapersonal motivation as well as interpersonal motivation in the context of group therapy through the lens of self-determination theory. We discuss insights and research opportunities for the HCI community to mediate such tension and enhance stakeholder communication in future technology-assisted group therapy settings.