Hasil untuk "Therapeutics. Psychotherapy"

Runhua Wang, Rong Ma, Yuanyuan Cai et al.

Abstract Subthreshold depression (SD) in adolescents is a prevalent condition associated with significant functional impairment and an increased risk of developing major depressive disorder. Currently, the lack of reliable objective markers complicates its accurate identification. Investigating the gut microbiome may offer novel insights into its underlying mechanisms. This study aimed to investigate the association between gut microbiome and cognitive function in adolescents with subthreshold Depression (SD). Thirty-eight adolescents with SD and 139 clinically-well (CW) adolescents were recruited. Gut microbiome and cognitive function were assessed by metagenomic sequencing and the MATRICS Consensus Cognitive Battery (MCCB), respectively. Compared with the CW adolescents, the SD group showed higher relative abundance of Spirochaetes, Synergistetes, Spirochaetia, Synergistia, Spirochaetales, Rhizobiales, Synergistales, Thermoanaerobacterales, Rhodospirillales, Synergistaceae, and Oxalobacteraceae at four levels. The Spatial Span scores were higher in the SD group compared to the CW group. Moreover, EggNOG analyses showed a significant negative correlation of the intracellular trafficking secretion, and vesicular transport with the Spatial Span scores. The KEGG pathway of the neurodegenerative diseases and translation was depleted in the microbiome of adolescents with SD. The higher abundance of Spirochaetes, Spirochaetia, and Spirochaetales was the best predictor of SD in adolescents. Our findings suggest that gut microbiome abnormalities, depressive symptoms, and cognitive influences co-occur in adolescents with SD, which may play a crucial role in the pathogenesis of SD and cognitive function in adolescent. Gut microbiome may serve as a potential biomarker for the identification and treatment of adolescents with SD.

António Farinhas, Nuno M. Guerreiro, José Pombal et al.

Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish between therapeutic disclosures and genuine clinical crises, leading to safety failures. To address this gap, we introduce a clinically grounded risk taxonomy, developed in collaboration with PhD-level psychologists, that identifies actionable harm (e.g., self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of real-world multi-turn conversations annotated at the turn level by clinical experts. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates in adversarial multi-turn interactions compared to general-purpose safeguards. We release all models and human evaluation data.

Wenhui Chu, Khang Tran, Nikolaos V. Tsekos

Magnetic Resonance Imaging (MRI) is a well-established modality for pre-operative planning and is also explored for intra-operative guidance of procedures such as intravascular interventions. Among the experimental robot-assisted technologies, the magnetic field gradients of the MRI scanner are used to power and maneuver ferromagnetic applicators for accessing sites in the patient's body via the vascular network. In this work, we propose a computational platform for preoperative planning and modeling of MRI-powered applicators inside blood vessels. This platform was implemented as a two-way data and command pipeline that links the MRI scanner, the computational core, and the operator. The platform first processes multi-slice MR data to extract the vascular bed and then fits a virtual corridor inside the vessel. This corridor serves as a virtual fixture (VF), a forbidden region for the applicators to avoid vessel perforation or collision. The geometric features of the vessel centerline, the VF, and MRI safety compliance (dB/dt, max available gradient) are then used to generate magnetic field gradient waveforms. Different blood flow profiles can be user-selected, and those parameters are used for modeling the applicator's maneuvering. The modeling module further generates cues about whether the selected vascular path can be safely maneuvered. Given future experimental studies that require a real-time operation, the platform was implemented on the Qt framework (C/C++) with software modules performing specific tasks running on dedicated threads: PID controller, generation of VF, generation of MR gradient waveforms.

Stav Yosef, Moreah Zisquit, Ben Cohen et al.

Abstract The use of generative large language models (LLMs) in mental health applications is gaining traction, with some proposals even suggesting LLM-based automated therapists. In this study, we assess the impact of fine-tuning therapist LLMs to improve the quality of therapy sessions, addressing a critical question in LLM-based mental health research. Specifically, we demonstrate that fine-tuning with datasets focused on specific therapeutic techniques significantly enhances the performance of LLM therapists. To facilitate this assessment, we introduce a novel evaluation system based on digital patients, powered by LLMs, which engage in text-based therapy sessions and provide session evaluations through questionnaires designed for human patients. This method addresses the inadequacies of traditional text-similarity metrics, which are insufficient for assessing the quality of therapeutic interactions. This study centers on motivational interviewing (MI), a structured and goal-oriented therapeutic approach. However, our digital therapists and patients can be adapted to work in other forms of therapy. We believe that our digital therapists offer a standardized method for assessing automated therapists and showcasing the potential of LLMs in mental health care.

Matthew W. Cotton, Alain Goriely, David Klenerman et al.

Neurodegenerative diseases are driven by the accumulation of protein aggregates in the brain of affected individuals. The aggregation behaviour in vitro is well understood and driven by the equilibration of a super-saturated protein solution to its aggregated equilibrium state. However, the situation is altered fundamentally in living systems where active processes consume energy to remove aggregates. It remains unclear how and why cells transition from a state with predominantly monomeric protein, which is stable over decades, to one dominated by aggregates. Here, we develop a simple but universal theoretical framework to describe cellular systems that include both aggregate formation and removal. Using a two-dimensional phase-plane representation, we show that the interplay of aggregate formation and removal generates cell-level bistability, with a bifurcation structure that explains both the emergence of disease and the effects of therapeutic interventions. We explore a wide range of aggregate formation and removal mechanisms and show that phenomena such as seeding arise robustly when a minimal set of requirements on the mechanism are satisfied. By connecting in vitro aggregation mechanisms to changes in cell state, our framework provides a general conceptual link between molecular-level therapeutic interventions and their impact on disease progression.

Paolo Madeddu, Styliani Goulopoulou, David Wambeke

Endothelial cells release various vasorelaxing molecules, such as nitric oxide and prostacyclin, along with defined factors that induce hyperpolarization of vascular smooth muscle cells through the opening of calcium-sensitive potassium channels. Potassium channel-dependent vasorelaxation is prevalent in microvessels and can partially compensate for deficiencies in other vasodilatory mechanisms. Enhancing this backup vasorelaxant mechanism may aid the treatment of microvascular disorders, such as cerebral small vessel disease and preeclampsia, a pregnancy-specific hypertensive syndrome, which is characterized by systemic endothelial dysfunction. The development of pharmacological potassium channel openers has encountered significant challenges, including issues of specificity, safety concerns, and off-target effects. This study critically evaluates the advantages and drawbacks of integrating hyperpolarization into a holistic vasorelaxant strategy for managing ischemic disease through single or combination drug therapies.

Asmita Saha, Belaguppa Manjunath Ashwin Desai, Pronama Biswas

Moringa oleifera, known for its medicinal properties, contains bioactive compounds such as polyphenols and flavonoids with diverse therapeutic potentials, including anti-cancer effects. This study investigates the efficacy of M. oleifera leaf phytochemicals in inhibiting BCL-2, a critical protein involved in cancer cell survival. For the first time, multi-ligand simultaneous docking (MLSD) has been employed to understand the anti-cancer properties of M. oleifera leaf extract. Molecular docking techniques, including single-ligand and MLSD, were used to assess binding interactions with BCL-2. Single-ligand docking revealed strong binding affinities for compounds such as niazinin, alpha carotene, hesperetin, apigenin, niaziminin B, and niazimicin A, with some compounds even surpassing Venetoclax, a commercial BCL-2 inhibitor. MLSD highlighted inter-ligand interactions among apigenin, hesperetin, and niazimicin A, exhibiting a binding affinity of -14.96 kcal/mol, indicating a synergistic effect. These results shed light on the potential synergistic effects of phytochemicals when using multi-ligand simultaneous docking, underscoring the importance of considering compound interactions in the development of therapeutic strategies.

Natasha Yamane, Varun Mishra, Matthew S. Goodwin

In the last decade, researchers have increasingly explored using biosensing technologies for music-based affective regulation and stress management interventions in laboratory and real-world settings. These systems -- including interactive music applications, brain-computer interfaces, and biofeedback devices -- aim to provide engaging, personalized experiences that improve therapeutic outcomes. In this scoping and mapping review, we summarize and synthesize systematic reviews and empirical research on biosensing systems with potential applications in music-based affective regulation and stress management, identify gaps in the literature, and highlight promising areas for future research. We identified 28 studies involving 646 participants, with most systems utilizing prerecorded music, wearable cardiorespiratory sensors, or desktop interfaces. We categorize these systems based on their biosensing modalities, music types, computational models for affect or stress detection and music prediction, and biofeedback mechanisms. Our findings highlight the promising potential of these systems and suggest future directions, such as integrating multimodal biosensing, exploring therapeutic mechanisms of music, leveraging generative artificial intelligence for personalized music interventions, and addressing methodological, data privacy, and user control concerns.

Akshay Shendre, Naman Kumar Mehta, Anand Singh Rathore et al.

Several formats, including FASTA, PIR, GenBank, EMBL, and GCG, have been developed for representing protein sequences composed of natural amino acids. Among these, FASTA remains the most widely used due to its simplicity and human readability. However, FASTA lacks the capability to represent chemically modified or non-natural residues, as well as structural annotations and mutations in protein variants. To address some of these limitations, the PEFF format was recently introduced as an extension of FASTA. Additionally, formats such as HELM and BILN have been proposed to represent amino acids and their modifications at the atomic level. Despite their advancements, these formats have not achieved widespread adoption within the bioinformatics community due to their complexity. To complement existing formats and overcome current challenges, we propose a new format called MAP (Modification and Annotation in Proteins), which enables comprehensive annotation of protein sequences. MAP introduces meta tags in the header for protein-level annotations and inline tags within the sequence for residue-level modifications. In this format, standard one-letter amino acid codes are augmented with curly-brace tags to denote various modifications, including phosphorylation, acetylation, non-natural residues, cyclization, and other residue-specific features. The header metadata also captures information such as organism, function, and sequence variants. We describe the structure, objectives, and capabilities of the MAP format and demonstrate its application in bioinformatics, particularly in the domain of protein therapeutics. To facilitate community adoption, we are developing a comprehensive suite of MAP-format resources, including a detailed manual, annotated datasets, and conversion tools, available at http://webs.iiitd.edu.in/raghava/maprepo/.

Yuanrong Tang, Yu Kang, Yifan Wang et al.

Current AI counseling systems struggle with maintaining effective long-term client engagement. Through formative research with counselors and a systematic literature review, we identified five key design considerations for AI counseling interactions. Based on these insights, we propose CA+, a Cognition Augmented counselor framework enhancing contextual understanding through three components: (1) Therapy Strategies Module: Implements hierarchical Goals-Session-Action planning with bidirectional adaptation based on client feedback; (2) Communication Form Module: Orchestrates parallel guidance and empathy pathways for balanced therapeutic progress and emotional resonance; (3) Information Management: Utilizes client profile and therapeutic knowledge databases for dynamic, context-aware interventions. A three-day longitudinal study with 24 clients demonstrates CA+'s significant improvements in client engagement, perceived empathy, and overall satisfaction compared to a baseline system. Besides, two licensed counselors confirm its high professionalism. Our research demonstrates the potential for enhancing LLM engagement in psychological counseling dialogues through cognitive theory, which may inspire further innovations in computational interaction in the future.

E. Heinonen, H. Nissen-Lie

Abstract Objective: Psychotherapists differ notably in the outcomes their patients achieve, and the characteristics that may explain these differences have attracted increasing interest. We systematically review studies on therapist pre-treatment characteristics predicting patient outcomes. Method: Systematic searches on databases for psychotherapy research, clinical psychology, and medical science for the years 2000–2018 identified published research examining therapist characteristics and psychotherapy outcomes. Of 2041 studies, 31 met inclusion criteria. Results: Findings show a few direct effects of therapist intrapersonal variables (e.g., self-relatedness, attachment) and several interaction effects with other constructs (e.g., patient pathology) on outcome. There is little support for the relevance of self-rated social skills. However, more consistent evidence has recently emerged for performance-based measurements of professional interpersonal skills, especially when elicited in challenging situations. Patient outcomes were also predicted by therapists’ self-rated professional characteristics, such as their experienced difficulties in practice, coping mechanisms, and attitudes towards therapeutic work, indicating that therapist self-perception also matters, although not always in the direction expected. Conclusions: More effective therapists seem characterized by professionally cultivated interpersonal capacities, which are likely rooted in their personal lives and attachment history. Research guidelines are proposed for moving this field forward (including larger samples, multilevel modeling, and in-depth qualitative work).

Don E. Davis, C. DeBlaere, Jesse Owen et al.

Habibeh Salehi, Ali Sheykholeslami, Somayyeh Taklavi

A suitable and healthy sexual function is one of the signs of the physical and mental health and it makes common pleasure sense between couples. Regarding the importance of women's sexual and psychological processes, this research was done to investigate the effectiveness of behavioral activation training on sexual satisfaction and sexual assertiveness of women suffering from sexual problems. The method of this study was quasi-experimental with pre-test, post-test design with control group. The population included all the women who were in 25-40 age range who suffered from sexual problems and referred to psychotherapy centers for sexual disorders in Ardabil city. In 2022, 30 people were selected by convenience sampling and they were assigned randomly in the experimental and control groups, every group included 15 people. Data collection was done by using women's sexual disorders questionnaire (Isidori et al., 2010), Hudson's sexual satisfaction questionnaire (Hudson et al., 1981) and Hulbert index of sexual assertiveness (1992). The behavioral activation training was presented to the experimental group in eight sessions and every session lasted 75 minutes (Dimidjian et al., 2014). Data analysis was done by multivariate analysis of covariance test (MANCOVA) with SPSS 20 software. Based on the results, there was a significant difference between two experimental and control groups on sexual satisfaction and sexual assertiveness and behavioral activation increased sexual satisfaction (F=742.88, p < 0.01) and sexual assertiveness (F=2279.08, p < 0.01) among women suffering from sexual problems. It can be concluded that behavioral activation training can increase sexual satisfaction and sexual assertiveness in women who suffering from sexual problems. Therefore, the counselors and therapists can use the mentioned therapy method in order to increase sexual satisfaction and sexual assertiveness in women suffering from sexual problems.

Tehmina Nazir, Ashfaq Ahmad, Arooj Fatima et al.

Background: Literature reveals high prevalence of neck disability in Pakistani population, and limited translated tools are available in Urdu language. Therefore, due to unavailability of screening tools in national language of Pakistan people usually remain unaware regarding the significance of early screening regarding any disease. Objective: To Translate and validate the Copenhagen neck functional disability scale in Urdu language. Methodology: This cross sectional study was conducted at Physical Therapy Department of Haider Hospital in Multan. A total of 75 participants aged between 18 to 40 years of both gender with nonspecific neck pain for minimum 12 weeks were included. Initially, two independent professionals made two forward translation into Urdu language. Then, these translations were synthesized into single translation. The synthesized translated version was translated backward into English language. The expert reviewed the synthesized translated version and backward translated version and approved the translated final synthesized version. The psychometric properties test included internal consistency validity (Cronbach’s alpha), test-retest reliability (ICC 2, 1), and concurrent validity (correlation with Numeric pain rating scale and Neck disability index). Results: The mean The Cronbach’s alpha for Copenhagen neck functional disability scale Urdu version (CNFDS-U) was 0.76 showing good internal consistency. The test-retest reliability determined with intraclass correlation coefficient ICC (2, 1) was 0.84 (95% CI; 0.74 to 0.90, P< 0.001). Test-retest mean scores correlation was r = 0.86 (P < 0.001). The Pearson’s correlation coefficient between CNFDS-U and NDI was r = 0.65 (P<0.001). The Pearson’s correlation coefficient between CNFDS-U and NPRS was r = 0.60 (P<0.001). Conclusion: The Urdu translated version of Copenhagen neck functional disability scale showed high internal consistency, excellent test-retest reliability, and good concurrent validity.

Ian Batanda

Abstract The work environment in most hospitals is characterised by activities that are strenuous both physically and mentally. These can result in physical and mental exhaustion, which can lead to burnout if not adequately addressed. Burnout among healthcare professionals can negatively affect their clinical decision-making, quality of communication with patients and colleagues as well as their ability to cope with work-related pressure, and ultimately affect the quality of care and patient outcomes. The inclusion of burnout in the 11th revision of the International Classification of Diseases (ICD-11) as an occupational phenomenon indicates that it is an issue of concern in the workplace for which people may need professional attention. This descriptive cross-sectional survey aimed to determine the point prevalence of burnout among healthcare professionals at Fort Portal Regional Referral Hospital and the factors contributing to burnout. The study also evaluated the linear relationship between the age of workers, their work duration at the hospital, and their burnout score, in addition to the possible impact on patient care. Participants were selected from the hospital WhatsApp group and invitations to participate were sent to their individual accounts. Burnout was assessed using the Copenhagen Burnout Inventory. Generally, burnout scores ranged from 16% to 86%, with an overall mean burnout score of 57.4%. The notable factors contributing to burnout included imbalances in duty allocation, physically strenuous work, and resource constraints. Burnout of varying levels was found to be prevalent across all carders in the hospital, although the results indicate that most healthcare professionals experience moderate burnout. Most of the factors contributing to burnout are within the scope of hospital leadership to address. The possible impact on staff performance and patients’ clinical outcomes is speculative, and additional studies are required.

Dwi Susi Miftakhul Jannah, Ninik Setiyowati

Adolescents are a group of individuals who are vulnerable to experiencing anxiety due to various factors that influence them. Anxiety that occurs in adolescents has several negative impacts on physique, psychology, and self-development of adolescents. If this anxiety is not overcome, it will evoke other symptoms and disorders that are more serious. Because of many negative impacts that arise when adolescents experience anxiety, an intervention is needed to overcome it, one of which can be done with mindfulness. This research uses a systematic literature review method using Publish or Perish software to collect articles from several sources such as Google Scholar, Research Gate, SINTA, DOAJ and Scopus. The keywords used are “mindfulness”, “anxiety”, “kecemasan”, “remaja”, “adolescents” and “teenage”. Based on these keywords, more than 500 articles published in 2019-2024 were found. Next, the researcher used 16 articles that were closely related to this research with a total of samples of 6.034. The research results show that mindfulness has proven effective in overcoming adolescents’ anxiety and there is a negative relationship between mindfulness and anxiety that adolescents who have high mindfulness will have low anxiety and vice versa. There are several programs of mindfulness intervention that can be carried out by adolescents, ranging from formal to informal practices that can be practiced when doing daily activities according to needs, situations and conditions.

Ziheng Wang, Xinhe Li, Haruki Momma et al.

To enhance therapeutic outcomes from a pharmacological perspective, we propose MiranDa, designed for medication recommendation, which is the first actionable model capable of providing the estimated length of stay in hospitals (ELOS) as counterfactual outcomes that guide clinical practice and model training. In detail, MiranDa emulates the educational trajectory of doctors through two gradient-scaling phases shifted by ELOS: an Evidence-based Training Phase that utilizes supervised learning and a Therapeutic Optimization Phase grounds in reinforcement learning within the gradient space, explores optimal medications by perturbations from ELOS. Evaluation of the Medical Information Mart for Intensive Care III dataset and IV dataset, showcased the superior results of our model across five metrics, particularly in reducing the ELOS. Surprisingly, our model provides structural attributes of medication combinations proved in hyperbolic space and advocated "procedure-specific" medication combinations. These findings posit that MiranDa enhanced medication efficacy. Notably, our paradigm can be applied to nearly all medical tasks and those with information to evaluate predicted outcomes. The source code of the MiranDa model is available at https://github.com/azusakou/MiranDa.

Han Huang, Ziqian Lin, Dongchen He et al.

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the non-unique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of $11\%$ for sequence similarity splits and $16\%$ for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in-silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

Yuanle Mo, Xin Hong, Bowen Gao et al.

Protein-protein interactions are central mediators in many biological processes. Accurately predicting the effects of mutations on interactions is crucial for guiding the modulation of these interactions, thereby playing a significant role in therapeutic development and drug discovery. Mutations generally affect interactions hierarchically across three levels: mutated residues exhibit different sidechain conformations, which lead to changes in the backbone conformation, eventually affecting the binding affinity between proteins. However, existing methods typically focus only on sidechain-level interaction modeling, resulting in suboptimal predictions. In this work, we propose a self-supervised multi-level pre-training framework, ProMIM, to fully capture all three levels of interactions with well-designed pretraining objectives. Experiments show ProMIM outperforms all the baselines on the standard benchmark, especially on mutations where significant changes in backbone conformations may occur. In addition, leading results from zero-shot evaluations for SARS-CoV-2 mutational effect prediction and antibody optimization underscore the potential of ProMIM as a powerful next-generation tool for developing novel therapeutic approaches and new drugs.

Azin Narimani, Mehrdad Mirzarahimi, Mohammad Narimani et al.

Attention deficit/hyperactivity disorder is a very common disorder that is usually diagnosed in childhood. The present study was conducted with the aim of comparing mental indicators in mothers with hyperactive and normal children. The method of this research was causal-comparative. The population of the research included all mothers in Ardabil city in 2022. A sample of 50 mothers with hyperactive children were selected by availability sampling and 50 mothers with normal children were selected by purposive sampling from the relevant population as a comparison group and participated in the study. They answered the questionnaire of mental disorder and Connors parent rating scale in the presence of researchers. The collected data were also analyzed using descriptive statistics of mean, standard deviation, and multivariate analysis of variance through SPSS (25) software. The results of multivariate analysis of variance showed that there is a significant difference in average mental indices of interpersonal sensitivity (F = 11.512), depression (F = 127.956) and anxiety (F = 42.468) in mothers with hyperactive and normal children (p < 0.001). From a practical point of view, it is suggested that prevention strategies be taken to promote parents' mental health so that addressing parents' stress and anxiety can have long-term positive effects on children's health, well-being, and behavioral outcomes.