Hasil untuk "Therapeutics. Psychotherapy"

Ryan LaRose, Antonios M. Alvertis, Alan Bidart et al.

We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.

M. Amin Rahimian, Benjamin Panny, James Joshi

Digitized, networked healthcare promises earlier detection, precision therapeutics, and continuous care; yet, it also expands the surface for privacy loss and compliance risk. We argue for a shift from siloed, application-specific protections to privacy-by-design at scale, centered on decision-theoretic differential privacy (DP) across the full healthcare data lifecycle; network-aware privacy accounting for interdependence in people, sensors, and organizations; and compliance-as-code tooling that lets health systems share evidence while demonstrating regulatory due care. We synthesize the privacy-enhancing technology (PET) landscape in health (federated analytics, DP, cryptographic computation), identify practice gaps, and outline a deployable agenda involving privacy-budget ledgers, a control plane to coordinate PET components across sites, shared testbeds, and PET literacy, to make lawful, trustworthy sharing the default. We illustrate with use cases (multi-site trials, genomics, disease surveillance, mHealth) and highlight distributed inference as a workhorse for multi-institution learning under explicit privacy budgets.

Magdalena Krow, Thomas Brooks, Anissa Hernandez et al.

Tarot cards, with their adaptability and capacity to stimulate insight, imagination, and intuition within the realm of spiritual exploration, provide a unique avenue for self-discovery and emotional processing (Semesky, 2011). Over the course of four weeks, our research aimed to investigate the enduring effects of tarot card interpretations on participants' psychological well-being and their perceived sense of control. Participants maintained journals to record their tarot card encounters and reflections, shedding light on the relationship between tarot interpretations and personal growth. The interpretation of tarot cards coupled with the practice of mindfulness journaling appears to support positive changes in well-being and one's perception of control, emphasizing their potential as therapeutic tools. Furthermore, the participants' journals yielded comprehensive insights into various aspects of tarot card readings, including interpretation techniques, the evolving competence of participants, diverse interpretations of tarot, and their influence on psychological well-being.  

Sapna Ali Khan, Uzma Amin, Hoor Bakht et al.

Abstract: Background: Effective teaching is crucial in medical education, where instructor’s performance and personality attributes significantly impact student learning. Objective: The objective of the study was to identify the perception of students of Khyber Medical University (KMU) regarding teachers' most and least effective attributes. Methodology: A cross-sectional survey of 288 undergraduate students was conducted in constituent institutes of KMU, Peshawar. Undergraduate students of any gender from the 2nd year to the final year were allowed in this survey after taking their written informed consent. One-way ANOVA and Tukey test were used to identify the differences among institutes. Results: Performance attributes (M=4.39 ± 0.48) were more important than personality attributes (M=4.29 ± 0.53) to the students. The top 3 performance items were: Expert on the subject, facilitation of students’ learning and desire to promote students’ learning. The top 3 personality items were: Helpful, punctual and good communication skills. Uniform marking and not being strict/showing leniency were the least valued. Conclusion:  Performance attributes were found to be more important than personality. However, there were some highly endorsed personality attributes, which show that students want their learning needs to be met and desire some personality attributes which make a teacher effective. Keywords: Perception, Clinical Competence, Students, Faculty, Teaching, Personality

Khan MUSLIM, Iqra MUSLIM, Ifra MUSLIM

Introduction: Foot dystonia, a frequent complication in stroke patients with foot drop, is traditionally managed with ankle-foot orthosis (AFO). Functional electrical stimulation (FES) offers a dynamic alternative to improve gait. This case report evaluates the effectiveness of FES in a post-stroke patient with foot dystonia. Methods and Materials: A 25-year-old man with left-sided foot dystonia and partial foot drop after a right middle cerebral artery infarct received treatment using the ODFS Pace FES device. Participants in the intervention were asked to stimulate their left dorsiflexors and evertors during walking for 40 minutes each day, 5 days a week, for 6 weeks, along with traditional physiotherapy. Measurements of outcome were made with ROM for the ankle, Berg Balance Scale scores, 10-Meter Walk Test results and the degree to which individuals felt sure or strong while walking. Results: The patient still had difficulties with foot twisting following intervention, but said they felt their stamin There was not much change in ankle range of motion with dorsiflexion at 19° rising to 20° and eversion rising from 40° to 41°. The average speed at which participants walked on the 10MWT improved very slightly, from 0.678 m/s to 0.692 m/s. Conclusions: FES may serve as an adjunctive therapy for post-stroke foot dystonia, enhancing endurance and confidence but with limited impact on dystonic symptoms. Larger, controlled studies are needed to establish its efficacy and optimal protocols.

Abd. Hafid, Nur Hidayah, M. Ramli et al.

This article aims to analyze the effectiveness of group counseling role playing techniques charged with the value of tapa ngrame in Panji stories to increase the altruism of elementary school students, which focuses on what the condition of elementary school students ' altruism looks like before and after treatment, analyzing the relationship between Panji stories and altruistic behavior. The design used in this study is an experimental design in the form of pretest and postetst control group design, the number of subjects in this study is 30 elementary school students in Bojonegoro Regency, where the groups set are not randomly selected (nonrandomly assigned group). The technique of data collection is done by using the scale of altruistic attitude insrtrumen amounting to 35 items. And data analysis techniques using independent sample t test and Pired samples test. The results of statistical tests using independent sample t test showed that there is a significant difference between the average value of pretest and posttes in the experimental group and the control group. This is indicated by a significance value of 0.000 or below 0.05. This means that there is a difference in the altrusitic attitude scores of students before being treated and after being treated, the results of this test are reinforced by the comparison of the results of the average posttest score in both groups. With the acquisition of scores for the experimental group of 104.46 and the control group of 89.93. And the results of the paired samples test showed an increase in the average score for the experimental group of 36.73 and for the control group of 22.4. This shows that the Counseling Group role playing techniques charged with the value of tapa ngrame in Panji's story are effective to improve the altruistic attitude of elementary school students.

Nicola Mackay

Helen Davies

NHS England (2024) estimates that perinatal mental illness affects up to 27% of all new mums, yet little explains what is typically experienced mentally when an individual becomes a mother. The void in research infers that matrescence is of little import to the maternal experience and may subsequently contribute to maternal distress as a mother’s expectations do not meet their lived reality. This article aims to better understand the lived experience of matrescence so consideration may be given to its impact on mothers. A hermeneutic phenomenological approach was selected to support an in-depth exploration of matrescence phenomena. Semi-structured qualitative interviews centred around a genogram and creation of clay self-symbols, were conducted with six mothers 8-10 months after their first child’s arrival. Participants were considered low-risk, and included birth mothers and one adoptive mother, from single and dual parent families, in England. The study resulted in four themes: (1) A change of state and a state of change; expresses multi-dimensional and ongoing adjustment (2) Mother matters; explores a paradox of existential mattering (3) M/other merger; reflects relational shifts (4) Prepare to be unprepared; considers the impact of unpredictability, and real and ideal notions. The study's findings contribute toward an emerging conceptualisation of matrescence. Greater understanding may help enable perinatal health-care providers to develop preventative policies and interventions which better support mothers.

Anna Katharina Holl-Etten, Nina Schnaderbeck, Elizaveta Kosareva et al.

The rapid development of language-based artificial intelligence (AI) offers new possibilities for psychotherapy and assistive systems, particularly benefitting autistic individuals who often respond well to technology. Parents of autistic persons emphasize the importance of appropriate and context-specific communication behavior. This study investigated whether GPT-3.5 Turbo and GPT-4, as language-based AI applications, are fundamentally capable of replicating this type of adequate communication behavior in the form of applied Theory of Mind (ToM). GPT-3.5 Turbo and GPT-4 were evaluated on three established higher-order ToM tasks: the Faux Pas Test, the Social Stories Questionnaire, and the Story Comprehension Test in English and German. Two independent raters scored response accuracy based on standardized manuals. In addition, responses were rated for epistemic markers as indicators of uncertainty. GPT's results were compared to human neurotypical and neurodivergent samples from previous own and others' research. GPT-4 achieved near human accuracy on the Faux Pas Test and outperformed GPT-3.5 Turbo and individuals with autistic traits. On the Social Stories Questionnaire, GPT-4 scored comparable to neurotypical adults, while GPT-3.5 Turbo remained well below. In the Story Comprehension Test, GPT-4 reached scores that exceeded neurotypical adult and adolescent benchmarks. However, GPT-4 used epistemic markers in up to 42% of responses. GPT-4 shows encouraging performance in complex higher-order ToM tasks and may offer future potential as an assistive tool for individuals with (and without) social communication difficulties. Its ability to interpret complex social situations is promising; however, the frequent use of uncertainty markers highlights the need for further study for assistive use and possibly further refinement to ensure consistent and reliable support in real-world use.

Jeremy Goldwasser, Addison J. Hu, Alyssa Bilinski et al.

Several key metrics in public health convey the probability that a primary event will lead to a more serious secondary event in the future. These "severity rates" can change over the course of an epidemic in response to shifting conditions like new therapeutics, variants, or public health interventions. In practice, time-varying parameters such as the case-fatality rate are typically estimated from aggregate count data. Prior work has demonstrated that commonly-used ratio-based estimators can be highly biased, motivating the development of new methods. In this paper, we develop an adaptive deconvolution approach based on approximating a Poisson-binomial model for secondary events, and we regularize the maximum likelihood solution in this model with a trend filtering penalty to produce smooth but locally adaptive estimates of severity rates over time. This enables us to compute severity rates both retrospectively and in real time. Experiments based on COVID-19 death and hospitalization data, both real and simulated, demonstrate that our deconvolution estimator is generally more accurate than the standard ratio-based methods, and displays reasonable robustness to model misspecification.

C. Flückiger, T. Munder, RE A.C.DEL et al.

ABSTRACT Objective In psychotherapy, strength-based methods (SBM) represent efforts to build on patients’ strengths while addressing the deficits and challenges that led them to come to therapy. SBM are incorporated to some extent in all major psychotherapy approaches, but data on their unique contribution to psychotherapy efficacy is scarce. Methods First, we conducted a systematic review and narrative synthesis of eight process-outcome psychotherapy studies that investigated in-session SBM and their relation to immediate outcomes. Second, we conducted a systematic review and multilevel comparative meta-analysis contrasting strength-based bona fide psychotherapy vs. other bona fide psychotherapy at post-treatment (57 effect sizes nested in 9 trials). Results Despite their methodological variability, the pattern of results in the process-outcome studies was generally positive, such that SBM were linked with more favorable immediate, session-level patient outcomes. The comparative meta-analysis found an overall weighted average effect size of g = 0.17 (95% CIs [0.03, 0.31], p < .01) indicating a small but significant effect in favor of strength-based bona fide psychotherapies. There was non-significant heterogeneity among the effect sizes (Q(56) = 69.1, p = .11; I2  = 19%, CI [16%, 22%]). Conclusion Our findings suggest that SBMs may not be a trivial by-product of treatment progress and may provide a unique contribution to psychotherapy outcomes. Thus, we recommend integration of SBM to clinical training and practice across treatment models.

Lecina Fernández

Juan Carlos Baldermann, Jan Niklas Petry-Schmelzer, Thomas Schüller et al.

Abstract Gilles de la Tourette Syndrome (GTS) is a chronic tic disorder, characterized by unwanted motor actions and vocalizations. While brain stimulation techniques show promise in reducing tic severity, optimal target networks are not well-defined. Here, we leverage datasets from two independent deep brain stimulation (DBS) cohorts and a cohort of tic-inducing lesions to infer critical networks for treatment and occurrence of tics by mapping stimulation sites and lesions to a functional connectome derived from 1,000 healthy participants. We find that greater tic reduction is linked to higher connectivity of DBS sites (N = 37) with action-related functional resting-state networks, i.e., the cingulo-opercular (r = 0.62; p < 0.001) and somato-cognitive action networks (r = 0.47; p = 0.002). Regions of the cingulo-opercular network best match the optimal connectivity profiles of thalamic DBS. We replicate the significance of targeting cingulo-opercular and somato-cognitive action network connectivity in an independent DBS cohort (N = 10). Finally, we demonstrate that tic-inducing brain lesions (N = 22) exhibit similar connectivity to these networks. Collectively, these results suggest a critical role for these action-related networks in the pathophysiology and treatment of GTS.

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.

Xu-Wen Wang, Tong Wang, Yang-Yu Liu

Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, and offer guidance on choosing between traditional machine learning and sophisticated deep learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas, from taxonomic profiling, functional annotation \& prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention \& therapeutics. Finally, we discuss challenges in this field and highlight some recent breakthroughs. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies and applications that enhance our understanding of microbial life and its impact on our planet and our health.

Sishen Yuan, Guang Li, Baijia Liang et al.

Capsule endoscopes, predominantly serving diagnostic functions, provide lucid internal imagery but are devoid of surgical or therapeutic capabilities. Consequently, despite lesion detection, physicians frequently resort to traditional endoscopic or open surgical procedures for treatment, resulting in more complex, potentially risky interventions. To surmount these limitations, this study introduces a chained flexible capsule endoscope (FCE) design concept, specifically conceived to navigate the inherent volume constraints of capsule endoscopes whilst augmenting their therapeutic functionalities. The FCE's distinctive flexibility originates from a conventional rotating joint design and the incision pattern in the flexible material. In vitro experiments validated the passive navigation ability of the FCE in rugged intestinal tracts. Further, the FCE demonstrates consistent reptile-like peristalsis under the influence of an external magnetic field, and possesses the capability for film expansion and disintegration under high-frequency electromagnetic stimulation. These findings illuminate a promising path toward amplifying the therapeutic capacities of capsule endoscopes without necessitating a size compromise.

Julieana Moon, Naimul Khan

Within the evolving field of digital intervention, serious games emerge as promising tools for evidence-based interventions. Research indicates that gamified therapy, whether employed independently or in conjunction with online psychoeducation or traditional programs, proves more efficacious in delivering care to patients. As we navigate the intricate realm of serious game design, bridging the gap between therapeutic approaches and creative design proves complex. Professionals in clinical and research roles demonstrate innovative thinking yet face challenges in executing engaging therapeutic serious games due to the lack of specialized design skills and knowledge. Thus, a larger question remains: How might we aid and educate professionals in clinical and research roles the importance of game design to support their innovative therapeutic approaches? This study examines potential solutions aimed at facilitating the integration of gamification design principles into clinical study protocols, a pivotal aspect for aligning therapeutic practices with captivating narratives in the pursuit of innovative interventions. We propose two solutions, a flow chart framework for serious games or a comprehensive reference document encompassing gamification design principles and guidelines for best design practices. Through an examination of literature reviews, it was observed that selected design decisions varied across studies. Thus, we propose that the second solution, a comprehensive reference design guide, is more versatile and adaptable.

Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber

The success of drug discovery and development relies on the precise prediction of molecular activities and properties. While in silico molecular property prediction has shown remarkable potential, its use has been limited so far to assays for which large amounts of data are available. In this study, we use a fine-tuned large language model to integrate biological assays based on their textual information, coupled with Barlow Twins, a Siamese neural network using a novel self-supervised learning approach. This architecture uses both assay information and molecular fingerprints to extract the true molecular information. TwinBooster enables the prediction of properties of unseen bioassays and molecules by providing state-of-the-art zero-shot learning tasks. Remarkably, our artificial intelligence pipeline shows excellent performance on the FS-Mol benchmark. This breakthrough demonstrates the application of deep learning to critical property prediction tasks where data is typically scarce. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to help streamline the identification of novel therapeutics.

Xiangxin Zhou, Dongyu Xue, Ruizhe Chen et al.

Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality. Leveraging a pre-trained conditional diffusion model that jointly models sequences and structures of antibodies with equivariant neural networks, we propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens. Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference. Additionally, we employ gradient surgery to address conflicts between various types of energy, such as attraction and repulsion. Experiments on RAbD benchmark show that our approach effectively optimizes the energy of generated antibodies and achieves state-of-the-art performance in designing high-quality antibodies with low total energy and high binding affinity simultaneously, demonstrating the superiority of our approach.

Yujin Cho, Mingeon Kim, Seojin Kim et al.

This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. With the rapid advancement of artificial intelligence, particularly in natural language processing, LLMs present a novel opportunity to augment traditional psychological counseling methods. This research primarily focuses on evaluating the LLM's ability to engage in empathetic, adaptable, and contextually appropriate interactions within a therapeutic setting. A comprehensive evaluation was conducted by a panel of clinical psychologists and psychiatrists using a specially developed scorecard. The assessment covered various aspects of the LLM's performance, including empathy, communication skills, adaptability, engagement, and the ability to establish a therapeutic alliance. The study avoided direct testing with patients, prioritizing privacy and ethical considerations, and instead relied on simulated scenarios to gauge the LLM's effectiveness. The results indicate that LLMs hold significant promise as supportive tools in therapy, demonstrating strengths in empathetic engagement and adaptability in conversation. However, challenges in achieving the depth of personalization and emotional understanding characteristic of human therapists were noted. The study also highlights the importance of ethical considerations in the application of AI in therapeutic contexts. This research provides valuable insights into the potential and limitations of using LLMs in psychological counseling for autistic adolescents. It lays the groundwork for future explorations into AI's role in mental health care, emphasizing the need for ongoing development to enhance the capabilities of these models in therapeutic settings.