Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal‐striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top‐down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks—and corresponding symptom improvement—following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.
Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev
et al.
Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.
The efficacy of anti-cancer therapies is severely limited by the emergence of drug resistance. While genetic drivers are well-characterized, growing evidence suggests that non-genetic mechanisms, particularly those involving drug-tolerant persisters (DTPs), play a pivotal role in solid tumor relapse. To elucidate the evolutionary dynamics of DTP-induced resistance, we develop a stochastic agent-based model (ABM) of solid tumor evolution that couples macroscopic population dynamics with microscopic epigenetic state inheritance during the cell cycle. Our simulations accurately reproduce the temporal progression of relapse observed in experimental studies, capturing the dynamic transition from sensitive cells to DTPs, and ultimately to stable resistant phenotypes under prolonged therapy. By explicitly modeling the epigenetic plasticity of individual cells, our model bridges the gap between cellular heterogeneity and population-level tumor evolution. Furthermore, we performed \textit{in silico} clinical trials using virtual patient cohorts to evaluate therapeutic outcomes, demonstrating that optimized adaptive treatment strategies can significantly delay tumor relapse compared to standard dosing. This study provides a quantitative framework for dissecting DTP-driven resistance mechanisms and designing more effective, biologically informed therapeutic strategies.
The 2023 film Yannick by Quentin Dupieux, which is set in a theatre-in-real time, contains many interesting provocations about the relationship between stage and audience. In reviewing the film, I tried to comment them from my perspective as a dramatherapist.
Dramatic representation. The theater, Therapeutics. Psychotherapy
Multicultural awareness is important for students in the 21st century, and its level needs to be measured using standardized instruments. This study aimed to examine the acceptability of the multicultural awareness instrument developed by Ali (2011) for Junior High School (SMP) students in Sukoharjo, Indonesia. The instrument adaptation process involved five stages with a sample of 308 participants. Acceptability of the instrument was analyzed using the RASCH Model. The research findings present the instrument's ability to measure what is intended to measure, the relationship between respondents and items, and the difficulty level of the items. The results indicate that the multicultural awareness instrument is valid and reliable for application among students. Future researchers are suggested to examine this instrument at the senior high school level and use it as a measure of students' multicultural awareness for experimental research. Moreover, integrating multicultural awareness into the learning process can enhance educational practices.
Agnieszka Anna Tomaka, Leszek Luchowski, Michał Tarnawski
et al.
Digital technology plays a crucial role in designing customized medical devices, such as occlusal splints, commonly used in the management of disorders of the stomatognathic system. This methodological proof-of-concept study presents a computer-aided approach for designing and evaluating occlusal positioning splints. The primary aim is to demonstrate the feasibility and geometric accuracy of the proposed method at the preclinical stage. In this approach, a three-dimensional splint is generated using a transformation matrix to represent the therapeutic mandibular position. An experienced operator defines this position using a virtual patient model reconstructed from intraoral scans, CBCT, 3D facial scans, and a digitized plaster model. We introduce a novel method for generating splints that reproduces occlusal conditions in the therapeutic position and resolves surface conflicts through virtual embossing. The process for obtaining transformation matrices using dental tools and intraoral devices commonly employed in dental and laboratory workflows is described, and the geometric accuracy of both designed and printed splints is evaluated using profile and surface deviation analysis. The method supports reproducible, patient-specific splint fabrication and provides a transparent foundation for future validation studies, supporting multimodal image registration and quantification of occlusal discrepancies in research settings.
Suhas BN, Andrew M. Sherrill, Rosa I. Arriaga
et al.
The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma treatment. We present Thousand Voices of Trauma, a synthetic benchmark dataset of 3,000 therapy conversations based on Prolonged Exposure therapy protocols for Post-traumatic Stress Disorder (PTSD). The dataset comprises 500 unique cases, each explored through six conversational perspectives that mirror the progression of therapy from initial anxiety to peak distress to emotional processing. We incorporated diverse demographic profiles (ages 18-80, M=49.3, 49.4% male, 44.4% female, 6.2% non-binary), 20 trauma types, and 10 trauma-related behaviors using deterministic and probabilistic generation methods. Analysis reveals realistic distributions of trauma types (witnessing violence 10.6%, bullying 10.2%) and symptoms (nightmares 23.4%, substance abuse 20.8%). Clinical experts validated the dataset's therapeutic fidelity, highlighting its emotional depth while suggesting refinements for greater authenticity. We also developed an emotional trajectory benchmark with standardized metrics for evaluating model responses. This privacy-preserving dataset addresses critical gaps in trauma-focused mental health data, offering a valuable resource for advancing both patient-facing applications and clinician training tools.
Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD), have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and chem-informatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness, particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure-function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.
The emergence of generative AI chatbots such as ChatGPT has prompted growing public and academic interest in their role as informal mental health support tools. While early rule-based systems have been around for several years, large language models (LLMs) offer new capabilities in conversational fluency, empathy simulation, and availability. This study explores how users engage with LLMs as mental health tools by analyzing over 10,000 TikTok comments from videos referencing LLMs as mental health tools. Using a self-developed tiered coding schema and supervised classification models, we identify user experiences, attitudes, and recurring themes. Results show that nearly 20% of comments reflect personal use, with these users expressing overwhelmingly positive attitudes. Commonly cited benefits include accessibility, emotional support, and perceived therapeutic value. However, concerns around privacy, generic responses, and the lack of professional oversight remain prominent. It is important to note that the user feedback does not indicate which therapeutic framework, if any, the LLM-generated output aligns with. While the findings underscore the growing relevance of AI in everyday practices, they also highlight the urgent need for clinical and ethical scrutiny in the use of AI for mental health support.
Designing RNA sequences that reliably adopt specified three-dimensional structures while maintaining thermodynamic stability remains challenging for synthetic biology and therapeutics. Current inverse folding approaches optimize for sequence recovery or single structural metrics, failing to simultaneously ensure global geometry, local accuracy, and ensemble stability-three interdependent requirements for functional RNA design. This gap becomes critical when designed sequences encounter dynamic biological environments. We introduce RiboPO, a Ribonucleic acid Preference Optimization framework that addresses this multi-objective challenge through reinforcement learning from physical feedback (RLPF). RiboPO fine-tunes gRNAde by constructing preference pairs from composite physical criteria that couple global 3D fidelity and thermodynamic stability. Preferences are formed using structural gates, PLDDT geometry assessments, and thermostability proxies with variability-aware margins, and the policy is updated with Direct Preference Optimization (DPO). On RNA inverse folding benchmarks, RiboPO demonstrates a superior balance of structural accuracy and stability. Compared to the best non-overlap baselines, our multi-round model improves Minimum Free Energy (MFE) by 12.3% and increases secondary-structure self-consistency (EternaFold scMCC) by 20%, while maintaining competitive 3D quality and high sequence diversity. In sampling efficiency, RiboPO achieves 11% higher pass@64 than the gRNAde base under the conjunction of multiple requirements. A multi-round variant with preference-pair reconstruction delivers additional gains on unseen RNA structures. These results establish RLPF as an effective paradigm for structure-accurate and ensemble-robust RNA design, providing a foundation for extending to complex biological objectives.
Background: Amputation is the surgical or traumatic removal of extremity or part of body. Limb amputation is the last option to save the patient’s life by removing the dead or the dying part of the limb with trans-tibial amputation commonly known as below knee amputation.
Objectives: To determine correlation of temporo-spatial and kinemetic trends with stump length in Trans-tibial amputees.
Methodology: This correlational cross-sectional study was conducted in the Gait Lab of PIPOS Peshawar after Approval from the Institutional Research Board, Isra University Islamabad. Study recruited N=180 unilateral, male, K3 and K4 level trans tibial amputees, aged 15-30 years of age using purposive sampling. The data was collected under the “Simi Motion Analysis camera system” to measure the gait parameters related to tempura-spatial variables and kinematics. The stump length and stride length was measured using the measuring tape. Correlations of patients’ stump length with temporo-spatial and kinematics were determined using Pearson’s correlation matrix. P-value of 0.05 was considered significant.
Results: Study sample with mean age of 25.69±4.17 years, revealed that stump length had no significant correlation with stride length (r=0.032, p=0.665), cadence (r-0.079,p=0.29), velocity (r=-0.039, p=0.6), stance phase (r=-0.068, p=0.363), swing phase (r=0.06, p=0.423), hip joint kinematics of amputated side (r=-0.06, p=0.426), knee joint kinematics ie., flexion at terminal stance (r=-0.129, p=0.085), flexion at mid swing (r=0.004, p=0.954) of amputated side , pelvic tilt (r=0.049, p=0.517) and trunk bending both lateral trunk flexion (r=0.041, p=0.588) and forward lean (r=-0.036, p=0.634) at mid stance.
Conclusion: In conclusion, the stump length had no substantial influence on the temporo-spatial and kinematic gait parameters in subjects with Trans tibial amputation
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Gentrification has led to the eviction and displacement of many people from working-class areas around the world. However, the relationship between gentrification and displacement has sparked much debate in the literature, with some researchers downplaying displacement, while others have argued that gentrification can occur without the displacement of people. These studies have tended to be quantitative in nature. However, there are few qualitative accounts of the experience of displacement and there is little consideration of the affective or phenomenological dimensions of displacement in current debates about gentrification. This is in part because researchers have tended not to engage directly with displaced people as it is often difficult to locate them. The purpose of this article is to describe the essence of displacement from the perspective of a group of individuals who were evicted from their homes in a gentrifying inner-city area of Johannesburg, South Africa. Through the methodology of transcendental phenomenology, five interrelated themes were derived from in-depth interviews with the participants. The findings show that the essence of displacement is one of great pain, loss and trauma, which disrupts the lifeworld of those displaced and impacts their overall health and well-being.
Touraj Hashemi Nosratabad, Zeynab Khanjani, Majid Mahmood alilou
et al.
Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties in sustaining attention, impulsivity, and hyperactivity According to the DSM-5, three presentations of ADHD are described: combined, predominantly inattentive, and predominantly hyperactive/impulsive. The theta-beta ratio (TBR), also referred to as the inattention index, is defined by increased theta band power (typically 4–7 Hz) and, specifically, increased theta relative to beta band power (typically 13–30 Hz). It has been reported as the most reproducible psychophysiological finding in ADHD. The present study aims to review the literature on QEEG parameters related to ADHD. The design of the study was a systematic review article. Due to increased theta, TBR is reported by many investigators as a consistent characteristic of ADHD. However, it is not a diagnostic measure for all individuals with ADHD. TBR is unnecessary in making the diagnosis for all ADHD presentations. In other words, a review of studies suggests that TBR cannot serve as a comprehensive diagnostic measure for all ADHD subtypes. It should not be generalized to all presentations. Rather, each presentation may have its specific QEEG measure. Therefore, a QEEG spectrum classification of ADHD population is a significant consideration.
This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity.
Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz
et al.
The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.
Robert Wasenmüller, Kevin Hilbert, Christoph Benzmüller
Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack the ability to (a) consistently and reliably act by predefined rules to align their conversation with an overarching therapeutic concept and (b) make their decision paths inspectable for risk management and clinical evaluation -- both essential requirements for an "AI Therapist". In this work, we introduce a novel paradigm for dialog policy planning in conversational agents enabling them to (a) act according to an expert-written "script" that outlines the therapeutic approach and (b) explicitly transition through a finite set of states over the course of the conversation. The script acts as a deterministic component, constraining the LLM's behavior in desirable ways and establishing a basic architecture for an AI Therapist. We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients. The results demonstrate the feasibility of this new technology and provide insights into the efficiency and effectiveness of different implementation variants.
The therapeutic alliance is considered an important mechanism of change in youth psychotherapy. Accordingly, it has become one of the most investigated psychotherapy variables. Yet, the theoretical and empirical literature on the alliance with young people is complex and has received criticism. This article aims to (a) critically review the existing knowledge on the alliance in youth psychotherapy from its definition to the existing research and (b) discuss some of the implications of this knowledge for clinical practice ad future research. This review highlights that the alliance in youth psychotherapy, as commonly measured, has a significant, although small, impact on outcomes; and that the alliance-outcome association may be influenced by the young person and the therapist's characteristics, as well as therapy types. This points to the importance of finding tailored ways of fostering a strong alliance when working with young people and questions the assumption of the alliance as a generic aspect of all types of youth psychological treatments. Attention to repairing alliance ruptures also emerged as key, especially to preventing early dropout in adolescent therapy. It is argued that despite its limitations, alliance research in youth psychotherapy can have important clinical implications to improve youth psychotherapy. A resumption of a conversation between the clinical and research field on the alliance is needed to better understand the nature and role of this important variable when working with young people and to use this knowledge to inform and improve clinical practice and therapeutic training. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Abstract Objective: Prior research has established that common therapeutic relationship factors are potent predictors of change in psychotherapy, but such factors are typically studied one at a time and their underlying structure when studied simultaneously is not clear. We assembled empirically validated relationship factors (e.g., therapist empathy; patient expectations; agreement about goals) into a single instrument and subjected it to factor analysis. Method: The instrument was applied to patients (N = 332) undergoing intensive psychotherapy of different types for depressive disorders, anxiety disorders, eating disorders, and childhood trauma in an inpatient specialized mental health setting. In order to examine the psychometric properties of the scale, we used half the sample (N=164) to conduct exploratory factor analysis (EFA) and parallel analysis before we tested the solution using exploratory structural equation modeling (ESEM) on the second half of the sample (N=168). Measurement invariance analysis was conducted to examine the stability of the factor structure. Results: The analysis yielded two factors, which were termed 1. “Confidence in the therapist” and 2. “Confidence in the treatment.” Discussion: When assessed simultaneously, patients differentiate between their evaluation of the therapist and of the treatment. The results indicate that there is substantial overlap among previously established relationship factors. Trial registration: ClinicalTrials.gov identifier: NCT03503981.