Hasil untuk "Therapeutics. Psychotherapy"

Kezya Jenifer Aring, Enjang Wahyuningrum, Wahyuni Kristinawati

Keguguran berulang merupakan peristiwa traumatis yang sering memicu krisis psikologis yang mendalam, ditandai dengan rasa bersalah terhadap diri sendiri, kesedihan yang mendalam, dan isolasi sosial. Penelitian ini bertujuan untuk menganalisis secara mendalam proses self compassion terhadap diri sendiri pada wanita yang mengalami keguguran berulang dan faktor-faktor yang mempengaruhi proses tersebut. Penelitian ini menggunakan desain studi kasus kualitatif untuk mengeksplorasi pengalaman empat peserta perempuan yang mengalami keguguran berulang (dua hingga empat kali), berusia 20-40 tahun, menikah, dan belum memiliki anak. Data dikumpulkan melalui wawancara mendalam semi-struktural, observasi, dan triangulasi sumber (suami dan ibu). Analisis data menggunakan model interaktif Miles & Huberman. Hasil penelitian menunjukkan bahwa self compassion terjadi secara non-linear. Proses self compassion dimulai dengan krisis, kemudian menemukan titik balik, dan secara bertahap mengaktifkan self compassion. Proses ini dipengaruhi oleh faktor internal dan eksternal. Temuan signifikan lainnya adalah munculnya pertumbuhan pascatrauma. Temuan ini memberikan wawasan teoretis tentang dinamika self compassion dalam keguguran berulang dan menawarkan implikasi praktis untuk intervensi klinis dan dukungan sosial bagi wanita setelah keguguran.

Fei Wang, Jiangnan Yang, Junjie Chen et al.

Web-based platforms are becoming a primary channel for psychological support, yet most LLM-driven chatbots remain opaque, single-stage, and weakly grounded in established therapeutic practice, limiting their usefulness for web applications that promote digital well-being. To address this gap, we present \textbf{XInsight}, a counseling-inspired multi-agent framework that models psychological support as a stage-consistent workflow aligned with the classical \textit{Exploration-Insight-Action} paradigm. Building on structured client representations, XInsight orchestrates specialized agents under a unified \textit{Reason-Intervene-Reflect} cycle: an Exploration agent organizes background and concerns into a structured Case Conceptualization Form, a Routing agent performs Adaptive Therapeutic Routing (ATR) across SFBT, CBT, and MBCT, a unified Therapeutic agent executes school-consistent submodules, and a Consolidation agent guides review, skill integration, and relapse-prevention planning. A Recording agent continuously transforms open-ended web dialogues into standardized psychological artifacts, including case formulations, therapeutic records, and relapse-prevention plans, enhancing interpretability, continuity, and accountability. To support rigorous and transparent assessment, we introduce \textbf{XInsight-Bench} with a Scale-Guided LLM Evaluation (SGLE) protocol that combines therapy-specific clinical scales with general counseling criteria. Experiments show improved paradigm alignment, multi-therapy integration, interaction depth, and interpretability over existing multi-agent counseling systems, indicating that XInsight provides a practical blueprint for integrating counseling-inspired support agents into web applications for digital well-being.

Aarón Argudo Palacios, Omneia Sadek El Shahat, Xose Antón Gómez-Fraguela

La psicosis en sus etapas iniciales puede manifestarse con síntomas ansioso-depresivos que afectan al funcionamiento y a la calidad de vida. Este estudio de caso presenta a un joven de 27 años con un primer episodio psicótico, cuyos síntomas atenuados pero persistentes generaban ansiedad y afectaban a su estado emocional. A lo largo de 12 sesiones utilizando la terapia de aceptación y compromiso, se trabajó en la mejora de la flexibilidad cognitiva, el manejo de la ansiedad y la depresión, así como en el compromiso con acciones alineadas con los valores personales. Se observó mejora clínica, evaluada mediante el índice de cambio fiable y los puntos de corte establecidos, que sugiere la efectividad de ACT en el manejo de la sintomatología ansioso-depresiva asociada a un trastorno psicótico incipiente. Los resultados apuntan a que la terapia de aceptación y Compromiso puede ser una intervención efectiva para mejorar la calidad de vida y el bienestar emocional en individuos que enfrentan este tipo de trastornos.

Haydeh Faraji, Buse Duran, Songül Oğur

In individuals with borderline personality traits, there is a profound sense of worthlessness and negative perceptions of body image. Additionally, there is an intense need for others and a constant fear of rejection. These conditions can lead to uncertainties and conflicts in relationships. Individuals who exhibit borderline traits in intimate relationships may overreact, expecting their spouse to reject them and misinterpreting their partner's actions. They may exhibit clingy behavior or change themselves to avoid ending the relationship due to fear of abandonment. Body image encompasses thoughts, perceptions, attitudes, and behaviors related to physical appearance. When individuals develop a negative body image, they may hide their bodies or exert excessive control. Self-silencing is often a coping mechanism to avoid conflicts, maintain relationships, or feel secure, and it can significantly impact romantic relationships. This study aims to understand the relationship between borderline personality traits, self-silencing, and body image coping strategies and to examine the mediating role of rejection sensitivity in this relationship. This research was applied based on the relational screening model. Convenience sampling was preferred in the sample selection. The study sample consists of 400 individuals over 18 living in Istanbul. Borderline Personality Questionnaire (BPQ), Silencing the Self Scale (STSS), Body Image Coping Strategies Inventory (BICSI), and Adult Rejection Sensitivity Questionnaire (A-RSS) were applied to the participants with a Socio-Demographic Information Form prepared by the researcher. The data obtained were analyzed using the SPSS 25 program, and Pearson correlation analysis, independent sample t-test, and ANOVA were used. As a result of the findings, rejection sensitivity has a partial mediator role in the relationship between borderline personality traits and self-silencing, rejection sensitivity has a full mediator role between borderline personality traits and appearance fixing, and rejection sensitivity has a partial mediator role between borderline personality traits and avoidance of body image coping strategies has been determined. The study results reveal that the increase in rejection sensitivity may cause the individuals to avoid expressing themselves and showing their perceived physical flaws and to try to correct the perceived physical flaws, thus showing various forms of avoidance. Considering the reinforcing effect of avoidance on behavior, the importance of psychotherapy studies targeting rejection sensitivity to prevent the deepening of existing problems is understood.

Zihao Jing, Yan Sun, Yan Yi Li et al.

Multimodal molecular models often suffer from 3D conformer unreliability and modality collapse, limiting their robustness and generalization. We propose MuMo, a structured multimodal fusion framework that addresses these challenges in molecular representation through two key strategies. To reduce the instability of conformer-dependent fusion, we design a Structured Fusion Pipeline (SFP) that combines 2D topology and 3D geometry into a unified and stable structural prior. To mitigate modality collapse caused by naive fusion, we introduce a Progressive Injection (PI) mechanism that asymmetrically integrates this prior into the sequence stream, preserving modality-specific modeling while enabling cross-modal enrichment. Built on a state space backbone, MuMo supports long-range dependency modeling and robust information propagation. Across 29 benchmark tasks from Therapeutics Data Commons (TDC) and MoleculeNet, MuMo achieves an average improvement of 2.7% over the best-performing baseline on each task, ranking first on 22 of them, including a 27% improvement on the LD50 task. These results validate its robustness to 3D conformer noise and the effectiveness of multimodal fusion in molecular representation. The code is available at: github.com/selmiss/MuMo.

Mason Minot, Gisbert Schneider

Simultaneously optimizing multiple, frequently conflicting, molecular properties is a key bottleneck in the development of novel therapeutics. Although a promising approach, the efficacy of multi-task learning is often compromised by destructive gradient interference, especially in the data-scarce regimes common to drug discovery. To address this, we propose AIM, an optimization framework that learns a dynamic policy to mediate gradient conflicts. The policy is trained jointly with the main network using a novel augmented objective composed of dense, differentiable regularizers. This objective guides the policy to produce updates that are geometrically stable and dynamically efficient, prioritizing progress on the most challenging tasks. We demonstrate that AIM achieves statistically significant improvements over multi-task baselines on subsets of the QM9 and targeted protein degraders benchmarks, with its advantage being most pronounced in data-scarce regimes. Beyond performance, AIM's key contribution is its interpretability; the learned policy matrix serves as a diagnostic tool for analyzing inter-task relationships. This combination of data-efficient performance and diagnostic insight highlights the potential of adaptive optimizers to accelerate scientific discovery by creating more robust and insightful models for multi-property molecular design.

Eunna Huh, Hyeonsu Lee, Hyunjin Shin

With the growing prominence of antibody-based therapeutics, antibody engineering has gained increasing attention as a critical area of research and development. Recent progress in transformer-based protein large language models (LLMs) has demonstrated promising applications in protein sequence design and structural prediction. Moreover, the availability of large-scale antibody datasets such as the Observed Antibody Space (OAS) database has opened new avenues for the development of LLMs specialized for processing antibody sequences. Among these, RoBERTa has demonstrated improved performance relative to BERT, while maintaining a smaller parameter count (125M) compared to the BERT-based protein model, ProtBERT (420M). This reduced model size enables more efficient deployment in antibody-related applications. However, despite the numerous advantages of the RoBERTa architecture, antibody-specific foundational models built upon it have remained inaccessible to the research community. In this study, we introduce Ab-RoBERTa, a RoBERTa-based antibody-specific LLM, which is publicly available at https://huggingface.co/mogam-ai/Ab-RoBERTa. This resource is intended to support a wide range of antibody-related research applications including paratope prediction or humanness assessment.

Aditya Malusare, Vineet Punyamoorty, Vaneet Aggarwal

Recent breakthroughs in generative modeling have demonstrated remarkable capabilities in molecular generation, yet the integration of comprehensive biomedical knowledge into these models has remained an untapped frontier. In this study, we introduce K-DREAM (Knowledge-Driven Embedding-Augmented Model), a novel framework that leverages knowledge graphs to augment diffusion-based generative models for drug discovery. By embedding structured information from large-scale knowledge graphs, K-DREAM directs molecular generation toward candidates with higher biological relevance and therapeutic suitability. This integration ensures that the generated molecules are aligned with specific therapeutic targets, moving beyond traditional heuristic-driven approaches. In targeted drug design tasks, K-DREAM generates drug candidates with improved binding affinities and predicted efficacy, surpassing current state-of-the-art generative models. It also demonstrates flexibility by producing molecules designed for multiple targets, enabling applications to complex disease mechanisms. These results highlight the utility of knowledge-enhanced generative models in rational drug design and their relevance to practical therapeutic development.

Yizhen Luo, Jiashuo Wang, Siqi Fan et al.

Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While recent advances in deep learning have enabled the development of all-atom foundation models for molecular modeling and generation, existing approaches face challenges in generalization due to the multi-modal nature of atomic data and the lack of comprehensive analysis of training and sampling strategies. To address these limitations, we propose PharMolixFM, a unified framework for constructing all-atom foundation models based on multi-modal generative techniques. Our framework includes three variants using state-of-the-art multi-modal generative models. By formulating molecular tasks as a generalized denoising process with task-specific priors, PharMolixFM achieves robust performance across various structural biology applications. Experimental results demonstrate that PharMolixFM-Diff achieves competitive prediction accuracy in protein-small-molecule docking (83.9% vs. 90.2% RMSD < 2Å, given pocket) with significantly improved inference speed. Moreover, we explore the empirical inference scaling law by introducing more sampling repeats or steps. Our code and model are available at https://github.com/PharMolix/OpenBioMed.

Alejandro Velez-Arce, Jesus Caraballo, Marinka Zitnik

Existing biomedical benchmarks do not provide end-to-end infrastructure for training, evaluation, and inference of models that integrate multimodal biological data and a broad range of machine learning tasks in therapeutics. We present PyTDC, an open-source machine-learning platform providing streamlined training, evaluation, and inference software for multimodal biological AI models. PyTDC unifies distributed, heterogeneous, continuously updated data sources and model weights and standardizes benchmarking and inference endpoints. This paper discusses the components of PyTDC's architecture and, to our knowledge, the first-of-its-kind case study on the introduced single-cell drug-target nomination ML task. We find state-of-the-art methods in graph representation learning and domain-specific methods from graph theory perform poorly on this task. Though we find a context-aware geometric deep learning method that outperforms the evaluated SoTA and domain-specific baseline methods, the model is unable to generalize to unseen cell types or incorporate additional modalities, highlighting PyTDC's capacity to facilitate an exciting avenue of research developing multimodal, context-aware, foundation models for open problems in biomedical AI.

Latent Labs Team, Alex Bridgland, Jonathan Crabbé et al.

Traditional drug discovery relies on rounds of screening millions of candidate molecules with low success rates, making drug discovery time and resource intensive. To overcome this screening bottleneck, we introduce Latent-X, an all-atom protein design model that enables a new paradigm of precision AI design. Given a target protein epitope, Latent-X jointly generates the all atom structure and sequence of the protein binder and target, directly modelling the non-covalent interactions essential for specific binding. We demonstrate its efficacy across two therapeutically relevant modalities through extensive wet lab experiments, testing as few as 30-100 designs per target. For macrocyclic peptides, Latent-X achieves experimental hit rates exceeding 90% on all evaluated benchmark targets. For mini-binders, it consistently produces potent candidates against all evaluated benchmark targets, with binding affinities reaching the low nanomolar and picomolar range - comparable to those of approved therapeutics - whilst also being highly specific in mammalian display. In direct comparisons with the state-of-the-art models AlphaProteo, RFdiffusion and RFpeptides under identical conditions demonstrates, Latent-X generates binders with higher hit rates and better binding affinities, and uniquely creates structurally diverse binders, including complex beta-sheet folds. Its end-to-end process is an order of magnitude faster than existing multi-step computational pipelines. By drastically improving the efficiency and success rate of de novo design, Latent-X represents a significant advance towards push-button biologics discovery and a valuable tool for protein engineers. Latent-X is available at https://platform.latentlabs.com, enabling users to reliably generate de novo binders without AI infrastructure or coding.

Yoon Jung Hwang, Junhee Lee, Jihyun Hwang et al.

Objective Infertility, the inability to achieve pregnancy within a year despite normal attempts to conceive without contraception, causes psychosocial burden for individuals and couples. This review summarized the interrelationship between infertility and psychological stress and suggested various forms of psychological intervention for infertility. Methods The PubMed, Google Scholar, and Korean Studies Information Service System databases were searched for English- and Korean-language articles published from 1990 to 2024. Results Infertility leads to emotional distress from diagnosis to treatment. Also, psychological stress affects the trajectory of infertility. This distress may cause psychiatric illnesses, negatively affecting pregnancy. Psychotherapies, psychopharmacotherapies, and biological treatments can be used for the management of psychiatric illnesses in infertile patients. Digital therapeutics also have the potential to be a competitive treatment option. Conclusion Regular assessment and management of psychological stress in infertile couples are essential during the course of infertility treatment. Psychological intervention of infertile patients should be implemented according to a personalized plan that completely reflects the individual clinical characteristics.

Tuğba Çapar Taşkesen, Mujgan Inozu

Due to the limited number of studies in the literature conducted to examine the relationship be-tween emotion regulation strategies and the obsessive-compulsive disorder symptoms, the current study aimed at examining the role of emotion regulation strategies in obsessive compulsive dis-order via an experimental design. The study consisted of two stages. In the first stage, the aim was to determine the participants’ symptom severity of obsessive-compulsive disorder. In the second stage, the participants were presented with a scenario that triggered the feeling of disgust, and the “Emotion Regulation Scale” was given to the participants before and after the scenario. The sample consisted of 328 university students. Findings indicated that the group with severe symptoms used significantly more “the suppression strategies” compared to the group with less severe symptoms. The use of “the cognitive reappraisal strategy” was significantly low in the group with severe symptoms before the scenario was given. However, the difference between these groups after the scenario was not found to be significant. Furthermore, it was found that the participants with severe obsessive-compulsive symptoms experienced more difficulty in emo-tion regulation than the group with less severe symptoms in all the sub-dimensions of the “Diffi-culties in Emotion Regulation Scale.” On the other hand, according to the regression analysis, only the sub-dimensions concerning the impulse control and the ability to choose appropriate emotion regulation strategy predicted obsessive-compulsive disorder. As the symptom severity in obsessive-compulsive disorder intensified, more difficulties in emotion regulation were observed. Findings regarding the current study are presented in the discussion section.

Maryam Golafshani, Daniel Z. Buchman, M. Ishrat Husain

Given the renewed interest in the use of psychedelics for the treatment of mental and substance use disorders in recent decades, there has also been renewed discussion and debate about whether it is necessary or beneficial for those who study and deliver psychedelic-assisted psychotherapy (PAP) to have had personal experience of using psychedelics. This paper provides a brief history of this debate and brings a disability-rights perspective to the discussion, given increasing efforts to dismantle ableism in medical training, practice, and research. Many psychiatric conditions and psychotropic medications, including ones as commonly prescribed as antidepressants, may preclude one from being able to safely and/or effectively use psychedelics. As such, we argue explicitly mandating or even implying the necessity of experiential training for psychedelic researchers and clinicians can perpetuate ableism in medicine by excluding those who cannot safely use psychedelics because of their personal medical histories. As PAP research and practice rapidly grow, we must ensure the field grows with disability inclusion amongst researchers and clinicians.

Ryan P. Fitzgerald, Bradley Alpert, Denis E. Bergeron et al.

We demonstrate a method for radionuclide assay that is spectroscopic with 100 % counting efficiency for alpha decay. Advancing both cryogenic decay energy spectrometry (DES) and drop-on-demand inkjet metrology, a solution of Am-241 was assayed for massic activity (of order 100 kBq/g) with a relative combined standard uncertainty less than 1 %. We implement live-timed counting, spectroscopic analysis, validation by liquid scintillation (LS) counting, and confirmation of quantitative solution transfer. Experimental DES spectra are well modeled with a Monte Carlo simulation. The model was further used to simulate Pu-238 and Pu-240 impurities, calculate detection limits, and demonstrate the potential for tracer-free multi-nuclide analysis, which will be valuable for new cancer therapeutics based on decay chains, Standard Reference Materials (SRMs) containing impurities, and more widely in nuclear energy, environmental monitoring, security, and forensics.

Nikhil Mehta, Sithira Ambepitiya, Thanveer Ahamad et al.

Introduction: Global burden of sexually transmitted infections (STIs) is rising out of proportion to specialists. Current chatbots like ChatGPT are not tailored for handling STI-related concerns out of the box. We developed Otiz, an Artificial Intelligence-based (AI-based) chatbot platform designed specifically for STI detection and counseling, and assessed its performance. Methods: Otiz employs a multi-agent system architecture based on GPT4-0613, leveraging large language model (LLM) and Deterministic Finite Automaton principles to provide contextually relevant, medically accurate, and empathetic responses. Its components include modules for general STI information, emotional recognition, Acute Stress Disorder detection, and psychotherapy. A question suggestion agent operates in parallel. Four STIs (anogenital warts, herpes, syphilis, urethritis/cervicitis) and 2 non-STIs (candidiasis, penile cancer) were evaluated using prompts mimicking patient language. Each prompt was independently graded by two venereologists conversing with Otiz as patient actors on 6 criteria using Numerical Rating Scale ranging from 0 (poor) to 5 (excellent). Results: Twenty-three venereologists did 60 evaluations of 30 prompts. Across STIs, Otiz scored highly on diagnostic accuracy (4.1-4.7), overall accuracy (4.3-4.6), correctness of information (5.0), comprehensibility (4.2-4.4), and empathy (4.5-4.8). However, relevance scores were lower (2.9-3.6), suggesting some redundancy. Diagnostic scores for non-STIs were lower (p=0.038). Inter-observer agreement was strong, with differences greater than 1 point occurring in only 12.7% of paired evaluations. Conclusions: AI conversational agents like Otiz can provide accurate, correct, discrete, non-judgmental, readily accessible and easily understandable STI-related information in an empathetic manner, and can alleviate the burden on healthcare systems.

John Arevalo, Ellen Su, Anne E Carpenter et al.

Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein target, cell-based assays such as Cell Painting can better capture complex DTI interactions. This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds, along with their relationships extracted from seven publicly available databases. We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics. MOTIVE accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTIVE resources are available at https://github.com/carpenter-singh-lab/motive.

Shafiq ur Rehman, Aqeel Ahmed Khan, Muhammad Kamran et al.

Background: Trans-femoral amputation (TFA) is an undesirable transaction of lower limb at the femoral level necessitating the use of a prosthetic device essentially requiring prolonged rehabilitation to ensure ambulation of the patient and integration into daily routine Objectives: The objective of the study was to find the association of quality of life with body mass index in patients using trans femoral prosthesis after amputation Methodology: Current Cross-Sectional study using convenience sampling recruited N=400 trans-femoral prosthesis users from PIPOS. Sample included both genders with age range of 12-60 years, using prosthesis > 1 year while orthosis users were excluded. SF-36 Health Survey tool & Lower Extremity Functional Scale (LEFS) were utilized for collection of data & analyzed utilizing SPSS Version 21. Results: LEFS items revealed association (p<0.05) with BMI including get in & out of bath, walk from rooms to room, squatting, heavy activities, sit for 1 hour & hopping. There was association (p<0.05) between BMI and SF-36 items of emotional wellbeing, social function and pain.  General Health of the participants revealed association (p=0.000) with highest total LEFS score for those with good and very good health. While as regards individual items most items revealed (p<0.05) association with LEFS. General Health also revealed association (p<0.05) with all subsclaes of SF-36 with higher scores for those having very good health except domain of limitation because of emotional & social health. Etiology of amputation also revealed association (p<.001) with total mean LEFS score with highest score when etiology was fracture. Etiology also had association (p<0.05) with most items of LEFS & SF-36. Conclusion: BMI, General Health and Etiology have significant association with status of ambulation and social performance of Trans-femoral amputees using prosthetic devices. Key Words: Amputee, ambulation, quality of life, trans-femoral prosthesis

Philippe Courtet, Odile Amiot, Enrique Baca-Garcia et al.

Depressive disorders represent the largest proportion of mental illnesses, and by 2030, they are expected to be the first cause of disability-adjusted life years [1]. The COVID-19 pandemic exacerbated prevalence and burden of depression and increased the occurrence of depressive symptoms in general population [2]. The urgency of implementing mental health services to address new barriers to care persuaded clinicians to use telemedicine to follow patients and stay in touch with them, and to explore digital therapeutics (DTx) as potential tools for clinical intervention [2]. The combination of antidepressants and psychotherapy is widely recommended for depression by international guidelines [3] but is less frequently applied in real-world practice. Commonly used treatments are pharmacological, but while being effective, some aspects such as adherence to the drug regimen, residual symptoms, resistance, lack of information, and stigma may hinder successful treatment. In case of less severe depression, standalone psychological therapies should be the first-line treatment option [3], but access to trained psychotherapists remains inequitable. DTx are evidence-based therapies driven by software programs to treat or complement treatment of a specific disease. DTx are classified as Medical Devices, and given their therapeutic purpose, they need to be validated through randomized controlled clinical trials, as for drug-based therapies. In the last 10 years, studies of digital interventions have proliferated; these studies demonstrate that digital interventions increase remission rates and lower the severity of depressive symptoms compared with waitlist, treatment as usual, and attention control conditions [4]. Despite the efficacy demonstrated in clinical trials, many of these tools never reach real-life patients; thus, it might be necessary to implement DTx in the public health system to expand access to valid treatment options. In this framework, DTx represent a good opportunity to help people with depression receive optimal psychotherapeutic care [5].

Sana Asgher, Shoaib Waqas, Muhammad Tariq et al.

Objective: The aim of study was to determine functional limitations in congenital talipes equinus after Achilles tendon release post 3months on Functional rating scale by Laavage and Ponesti. Methods: A cross sectional descriptive study was conducted for a period of 6 months from August 2018 till January 2019 at Ghurki Trust and Teaching Hospital, Lahore in which 102 subjects were included according to inclusion criteria by convenient sampling technique. Permission from the Ethical Committee of the Lahore College of Physical Therapy and the concerned patients was obtained. Functional limitations of each patient were determined through Functional Rating Scale (FRS) by Laavage and Ponesti. FRS scale has six domains including satisfaction level, function, and pain, position of heel when standing, passive motion and gait. Results: Results have cleared that out of 51 subjects, 2 subjects have extreme functional limitations, 4 with severe functional limitations, 15 with moderate functional limitations and 30 subjects had mild functional limitations. Conclusion: Patients, who underwent bilateral circumferential Achilles tendon release, had mild functional limitations when assessed through FRS post 3 month.