Put simply, empathy refers to understanding what another person is experiencing or trying to express. Therapist empathy has a long history as a hypothesized key change process in psychotherapy. We begin by discussing definitional issues and presenting an integrative definition. We then review measures of therapist empathy, including the conceptual problem of separating empathy from other relationship variables. We follow this with clinical examples illustrating different forms of therapist empathy and empathic response modes. The core of our review is a meta-analysis of research on the relation between therapist empathy and client outcome. Results indicated that empathy is a moderately strong predictor of therapy outcome: mean weighted r = .28 (p < .001; 95% confidence interval [.23, .33]; equivalent of d = .58) for 82 independent samples and 6,138 clients. In general, the empathy–outcome relation held for different theoretical orientations and client presenting problems; however, there was considerable heterogeneity in the effects. Client, observer, and therapist perception measures predicted client outcome better than empathic accuracy measures. We then consider the limitations of the current data. We conclude with diversity considerations and practice recommendations, including endorsing the different forms that empathy may take in therapy.
This systematic review and meta-analysis examines the impact of measuring, monitoring, and feeding back information on client progress to clinicians while they deliver psychotherapy. It considers the effects of the 2 most frequently studied routine outcome monitoring (ROM) practices: The Partners for Change Outcome Management System and the Outcome Questionnaire System. Like other ROM practices, they typify attempts to enhance routine care by assisting psychotherapists in recognizing problematic treatment response and increasing collaboration between therapist and client to overcome poor treatment response. A total of 24 studies were identified and considered suitable for analysis. Two-thirds of the studies found that ROM-assisted psychotherapy was superior to treatment-as-usual offered by the same practitioners. Mean standardized effect sizes indicated that the effects ranged from small to moderate. Feedback practices reduced deterioration rates and nearly doubled clinically significant/reliable change rates in clients who were predicted to have a poor outcome. Clinical examples, diversity considerations, and therapeutic advances are provided.
Santy Berliana Naibaho, Hendy Tannady, Lista Meria
et al.
The COVID-19 pandemic has created rapid changes that led many companies to speed up digital transformation, particularly in the management of human resources. A key development in this process is the adoption of Digital Human Resource Management (DHRM), an approach that brings digital technologies into HR functions. This study aims to systematically review the literature linking DHRM to employee resilience, focusing on the psychological impact of the interaction between technology and individuals. Using a systematic literature review (SLR) method, 50 articles from Scopus and ProQuest were analyzed. The results show that DHRM contributes to employee psychological resilience by increasing self-efficacy, psychological safety, and agility and creating an emotionally supportive digital workspace. The study found that psychological aspects like coping strategies, emotions, and perceived support are essential in linking digital systems with employee resilience. The study suggests using an interdisciplinary perspective that brings together technology and psychology to develop effective DHRM systems while also addressing employees’ psychological well-being.
Gianni HLIBOCIANU , Tudor - Andrei BARBUR, Codrut BULZ
et al.
Introduction: Rectus femoris muscle injuries are relatively uncommon in football compared
to other lower-limb muscle strains, yet they can significantly affect athletic performance and
return-to-play timelines. Aim: The main objective of this study is to analyze the effect of
physiotherapy on the recovery process of a professional football player following a rectus
femoris tear, with particular attention to the restoration of functional capacity and the
facilitation of a safe Return-to-Play. Material and methods: An eight-week, three-phase
physiotherapy program was implemented, focusing sequentially on pain and inflammation
reduction, restoration of joint mobility and muscle strength, and sport-specific functional
recovery. Clinical evaluations - including pain assessment, manual muscle testing,
goniometry, and thigh circumference - along with functional tests such as the Active Knee
Extension Test, Squat Test, Single-Leg Hop Test, Reverse Nordic Curl, and Y-Balance Test,
were conducted at three time points to track progress. Results: Findings showed progressive
improvement across all parameters, with complete pain resolution, normalization of muscle
strength, and restoration of functional performance by the final evaluation. The athlete
successfully returned to full team training and competitive play within 56 days. Conclusion:
This case highlights the effectiveness of a structured, criteria-based rehabilitation approach
in achieving a safe and efficient return to sport following rectus femoris muscle injury.
Serena Jinchen Xie, Shumenghui Zhai, Yanjing Liang
et al.
Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support, but lack cultural responsiveness for diverse populations. This study evaluated the effectiveness of cultural prompting in improving cultural responsiveness and perceived empathy of LLM-generated therapeutic responses for Chinese American family caregivers. Using a randomized controlled experiment, we compared GPT-4o and Deepseek-V3 responses with and without cultural prompting. Thirty-six participants evaluated input-response pairs on cultural responsiveness (competence and relevance) and perceived empathy. Results showed that cultural prompting significantly enhanced GPT-4o's performance across all dimensions, with GPT-4o with cultural prompting being the most preferred, while improvements in DeepSeek-V3 responses were not significant. Mediation analysis revealed that cultural prompting improved empathy through improving cultural responsiveness. This study demonstrated that prompt-based techniques can effectively enhance the cultural responsiveness of LLM-generated therapeutic responses, highlighting the importance of cultural responsiveness in delivering empathetic AI-based therapeutic interventions to culturally and linguistically diverse populations.
Identifying variables associated with clinical endpoints is of much interest in clinical trials. With the rapid growth of cell and gene therapy (CGT) and therapeutics for ultra-rare diseases, there is an urgent need for statistical methods that can detect meaningful associations under severe sample-size constraints. Motivated by data-borrowing strategies for historical controls, we propose the Adaptive Posterior-Informed Shrinkage Prior (APSP), a Bayesian approach that adaptively borrows information from external sources to improve variable-selection efficiency while preserving robustness across plausible scenarios. APSP builds upon existing Bayesian data borrowing frameworks, incorporating data-driven adaptive information selection, structure of mixture shrinkage informative priors and decision making with empirical null to enhance variable selection performances under small sample size. Extensive simulations show that APSP attains better efficiency relative to traditional and popular data-borrowing and Bayesian variable-selection methods while maintaining robustness under linear relationships. We further applied APSP to identify variables associated with peak C-peptide at Day 75 from the Clinical Islet Transplantation (CIT) Consortium study CIT06 by borrowing information from the study CIT07.
The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs ($<$ 100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.
Prostate cancer (PCa) remains a significant global health concern among men, particularly due to the lethality of its more aggressive variants. Despite therapeutic advancements that have enhanced survival for many patients, high grade PCa continues to contribute substantially to cancer related mortality. Emerging evidence points to the MYB proto-oncogene as a critical factor in promoting tumor progression, therapeutic resistance, and disease relapse. Notably, differential expression patterns have been observed, with markedly elevated MYB levels in tumor tissues from Black men relative to their White counterparts potentially offering insight into documented racial disparities in clinical outcomes. This study investigates the association between MYB expression and key oncogenic features, including androgen receptor (AR) signaling, disease progression, and the risk of biochemical recurrence. Employing a multimodal approach that integrates histopathological examination, quantitative digital imaging, and analyses of public transcriptomic datasets, our findings suggest that MYB overexpression is strongly linked to adverse prognosis. These results underscore MYB's potential as a prognostic biomarker and as a candidate for the development of individualized therapeutic strategies.
Maria Francesca Abbate, Pierre Toxe, Nicolas Maestrali
et al.
The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key time points. By mapping the output from STAR to single-cell data taken at the last time point, we successfully identified a pool of antibodies, of which 50% demonstrated binding capabilities. We observed convergent selection, where responding sequences with identical amino acid complementarity determining regions 3 (CDR3) were found in different mice. We provide a catalog of 67 experimentally validated antibodies against GFRAL. The potential of these antibodies as antagonists or agonists against GFRAL suggests therapeutic solutions for conditions like cancer cachexia, anorexia, obesity, and diabetes. This study underscores the utility of integrating computational methods and experimental validation for antibody discovery in therapeutic contexts by reducing time and increasing efficiency.
Shinichi Kumagai, Tomoyo Isoguchi Shiramatsu, Kensuke Kawai
et al.
Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various neurological and psychiatric conditions, including epilepsy, depression, and stroke rehabilitation; however, its mechanisms of action on neural circuits remain incompletely understood. Here, we present a novel theoretical framework based on predictive coding that conceptualizes VNS effects through differential modulation of feedforward and feedback neural circuits. Based on recent evidence, we propose that VNS shifts the balance between feedforward and feedback processing through multiple neuromodulatory systems, resulting in enhanced feedforward signal transmission. This framework integrates anatomical pathways, receptor distributions, and physiological responses to explain the influence of the VNS on neural dynamics across different spatial and temporal scales. VNS may facilitate neural plasticity and adaptive behavior through acetylcholine and noradrenaline (norepinephrine), which differentially modulate feedforward and feedback signaling. This mechanistic understanding serves as a basis for interpreting the cognitive and therapeutic outcomes across different clinical conditions. Our perspective provides a unified theoretical framework for understanding circuit-specific VNS effects and suggests new directions for investigating their therapeutic mechanisms.
Consider two similar drug companies with access to similar chemical libraries and synthesis methods, who each run an R&D program. The programs have the same number of stages, which each take the same amount of time, with the same costs, with the same historic stepwise progression rates, and which aim to address the same therapeutic indication. Now let us suppose one of these companies invests in new scientific tools that make it unusually good at critical progression decisions, while the other company does not. How do we assess the difference in value between the two programs? Surprisingly, standard discounted cash flow valuation methods, such as risk-adjusted net present value (rNPV), ubiquitous in drug industry portfolio management and venture capital, are largely useless in this case. They fail to value the decisions that make drug candidates more or less valuable because rNPV conflates wrong decisions to progress bad candidates with right decisions to progress good ones. The purpose of this paper is to set out a new class of valuation model that logically links the value of therapeutic assets with the value of "decisions tools" that are used to design, optimize, and test those assets. Our model makes clear the interaction between asset value and decision tool value. It also makes clear the downstream consequences of better, or worse, upstream decisions. This new approach may support more effective allocation of R&D capital; helping fund therapeutic assets that are developed using good decision tools, and funding better decision tools to distinguish between good and bad therapeutic assets.
Mahsa Yahag, Ghasem Abdolpour, Arezoo Lashkari
et al.
Sexual shame and dysfunctional sexual beliefs are very important in sexual relationships. Therefore, the present research was conducted with the aim of predicting sexual performance based on sexual shame and ineffective sexual beliefs. This study was of a descriptive-correlational type. Using the availability sampling, 400 women working in the administrative departments of the universities of Tabriz were selected. By means of the sexual function questionnaire (female sexual function inventory-2004), Sexual Shame Inventory-2021 and Sexual Dysfunctional Beliefs Questionnaire-2003, the data was collected and analyzed simultaneously using Pearson correlation and multiple regression. There was a negative and significant relationship between the general index of women's sexual performance and all components of sexual shame and ineffective sexual beliefs (p<0.05). Among the examined components, all sexual shame components and some components of sexual dysfunctional beliefs (body image, emotional priority and maternal priority) had the ability to predict sexual performance and explained 63% of its variance. The results showed that sexual shame and having ineffective sexual beliefs lead to the weakening of sexual performance. Considering dysfunctional sexual beliefs and signs of sexual shame to diagnose and treat sexual problems requires effective conceptualization.
The coronavirus (COVID-19) pandemic has forced the implementation of online psychotherapy (OP) practice. A literature search has revealed the paucity of studies on the psychoanalytic approach to OP and the lack of clinical experiences of analytic psychodrama (AP) via the Internet. The present work aims to discuss the difficulties encountered and the resources offered by a brief account of group psychotherapy through the technique of AP realized via videoconference. With some adjustments, the online roleplaying game was feasible through the two-dimensional space of the screen and allowed the patient to change his/her point of view, even if the AP technique struggled in the absence of the physical body in action. Performing psychodrama without the active use of the physical body, from an adaptation initially forced by the emergency situation, has proven to be an interesting psychoanalytical challenge worthy of deeper investigation and pursuit in the future.
Adolescence, marked by significant changes, often leads to emotional tension and instability associated with alexithymia. This study aimed to validate the Indonesian version of the Adolescent Alexithymia Scale, hypothesizing that its internal structure was unidimensional. Participants included 70 adolescents aged 13 to 18 years (M_age = 16.44; SD = 1.44). Data were collected using the Indonesian version of the Adolescent Alexithymia Scale, consisting of 24 items representing the four alexithymia indicators. The results revealed that the scale has a unidimensional internal structure, with 11 valid items, and sufficient validity and reliability coefficients. These items collectively represent the four main characteristics of alexithymia. The most dominant indicator is the difficulty in identifying and distinguishing between feelings and emotional bodily sensations. This research implies that this scale needs to be tested on different populations and in different contexts.
The article explores the concepts of the sense of rightness and inner compass as critically important elements of personality structure. Based on the theories of prominent psychologists such as Carl Rogers, Abraham Maslow, Viktor Frankl, and George Kelly, as well as Ukrainian authors including Tetiana Bondarenko, Olena Hromova, Anatolii Kochehryhin, Liudmyla Petranovska, and Sonia Lyubomyrska, the article analyzes the functional and structural features of the sense of rightness. The sense of rightness is defined as a combination of moral values and beliefs that shape personality and influence behavior. It is emphasized that the inner compass is the foundation of self-awareness, helping individuals navigate complex ethical and moral dilemmas. Social environment, cultural traditions, and family upbringing play an important role in the formation of this compass, contributing to the development of moral values. The article examines the dynamic nature of the sense of rightness, which may change depending on new experiences and social context. The results of the study indicate the importance of awareness of one's values for achieving psychological well-being and social adaptation. The conclusions may serve as a basis for further research in the fields of psychology, education, and social development, as well as for practical recommendations in working with individuals.
Muhammad Sufiyan Minhas, Sana Khan, Mahrukh Majid
et al.
Background: Idiopathic carpal tunnel syndrome is a carpal tunnel median nerve neuropathy. CTS is the most typical type of peripheral neuropathy
Objective: To determine the effects of carpal bones mobilization and tendon gliding exercise on patients with Carpal Tunnel Syndrome.
Methodology: This was a quasi-experimental study. Data was collected from Faisal Hospital and Civil Hospital Faisalabad from April 2022 to August 2022. The total sample size was 32 and calculated from the open epi tool. Patients were recruited in this study by considering the inclusion and exclusion criteria. Patients were divided into two groups. The patients in group A were treated by carpal bone mobilization, and those in group B were treated by tendon gliding exercise. Throughout the course of four weeks, the intervention was carried out three times per week. The outcome measure tools of this study were the Numeric Pain Rating Scale and the Boston Carpal Tunnel Questionnaire. The data was analyzed using SPSS version 23.
Results: There was a significant difference in reduction of pain intensity, symptom severity and improvement in functional ability between two groups i.e. Carpal Bone Mobilization and Tendon Gliding at end of four weeks (P<0.002) (P<0.000) (P<0.001). There was more reduction in pain (P<0.002), and symptom severity (P<0.000) and improvement in functional ability (P<0.001) in Tendon Gliding group at end of 4 weeks
Conclusion: Tendon gliding exercises were most effective in reducing pain, range of motion, and neck function as compared to nerve mobilization in patients with carpal tunnel syndrome.
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Recently, deep learning has made rapid progress in antibody design, which plays a key role in the advancement of therapeutics. A dominant paradigm is to train a model to jointly generate the antibody sequence and the structure as a candidate. However, the joint generation requires the model to generate both the discrete amino acid categories and the continuous 3D coordinates; this limits the space of possible architectures and may lead to suboptimal performance. In response, we propose an antibody sequence-structure decoupling (ASSD) framework, which separates sequence generation and structure prediction. Although our approach is simple, our idea allows the use of powerful neural architectures and demonstrates notable performance improvements. We also find that the widely used non-autoregressive generators promote sequences with overly repeating tokens. Such sequences are both out-of-distribution and prone to undesirable developability properties that can trigger harmful immune responses in patients. To resolve this, we introduce a composition-based objective that allows an efficient trade-off between high performance and low token repetition. ASSD shows improved performance in various antibody design experiments, while the composition-based objective successfully mitigates token repetition of non-autoregressive models.
Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth
et al.
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded the ability of Cell Painting to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.
Cognitive behavioral therapy (CBT) is a widely used therapeutic method for guiding individuals toward restructuring their thinking patterns as a means of addressing anxiety, depression, and other challenges. We developed a large language model (LLM)-powered prompt-engineered socially assistive robot (SAR) that guides participants through interactive CBT at-home exercises. We evaluated the performance of the SAR through a 15-day study with 38 university students randomly assigned to interact daily with the robot or a chatbot (using the same LLM), or complete traditional CBT worksheets throughout the duration of the study. We measured weekly therapeutic outcomes, changes in pre-/post-session anxiety measures, and adherence to completing CBT exercises. We found that self-reported measures of general psychological distress significantly decreased over the study period in the robot and worksheet conditions but not the chatbot condition. Furthermore, the SAR enabled significant single-session improvements for more sessions than the other two conditions combined. Our findings suggest that SAR-guided LLM-powered CBT may be as effective as traditional worksheet methods in supporting therapeutic progress from the beginning to the end of the study and superior in decreasing user anxiety immediately after completing the CBT exercise.