Hasil untuk "Therapeutics. Pharmacology"

Leonie Dyck, Aiko Galetzka, Maximilian Noller et al.

Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved throughout the co-development process, which focused on early integration into real-world paediatric surgical and psychiatric settings. The robot, based on the NAO platform, features a simple setup, adaptable exercise routines with interactive guidance, motivational dialogue, and a graphical user interface (GUI) for monitoring and no-code system feedback. Results: Deployment in hospital environments enabled the identification of key design requirements and usability constraints. Stakeholder feedback led to refinements in interaction design, movement capabilities, and technical configuration. A feasibility study is currently underway to assess acceptance, usability, and perceived therapeutic benefit, with data collection including questionnaires, behavioural observations, and staff-patient interviews. Discussion: Mobirobot demonstrates how multiprofessional, stakeholder-led development can yield a socially assistive system suited for dynamic inpatient settings. Early-stage findings underscore the importance of contextual integration, robustness, and minimal-intrusion design. While challenges such as sensor limitations and patient recruitment remain, the platform offers a promising foundation for further research and clinical application.

Junda Wang, Zonghai Yao, Lingxi Li et al.

Substance use disorders (SUDs) affect millions of people, and relapses are common, requiring multi-session treatments. Access to care is limited, which contributes to the challenge of recovery support. We present \textbf{ChatThero}, an innovative low-cost, multi-session, stressor-aware, and memory-persistent autonomous \emph{language agent} designed to facilitate long-term behavior change and therapeutic support in addiction recovery. Unlike existing work that mostly finetuned large language models (LLMs) on patient-therapist conversation data, ChatThero was trained in a multi-agent simulated environment that mirrors real therapy. We created anonymized patient profiles from recovery communities (e.g., Reddit). We classify patients as \texttt{easy}, \texttt{medium}, and \texttt{difficult}, three scales representing their resistance to recovery. We created an external environment by introducing stressors (e.g., social determinants of health) to simulate real-world situations. We dynamically inject clinically-grounded therapeutic strategies (motivational interview and cognitive behavioral therapy). Our evaluation, conducted by both human (blinded clinicians) and LLM-as-Judge, shows that ChatThero is superior in empathy and clinical relevance. We show that stressor simulation improves robustness of ChatThero. Explicit stressors increase relapse-like setbacks, matching real-world patterns. We evaluate ChatThero with behavioral change metrics. On a 1--5 scale, ChatThero raises \texttt{motivation} by $+1.71$ points (from $2.39$ to $4.10$) and \texttt{confidence} by $+1.67$ points (from $1.52$ to $3.19$), substantially outperforming GPT-5. On \texttt{difficult} patients, ChatThero reaches the success milestone with $26\%$ fewer turns than GPT-5.

Mahya Aghaee, Victoria Cicchirillo, Rowan Milner et al.

Patients with rare types of melanoma such as acral, mucosal, or uveal melanoma, have lower survival rates than patients with cutaneous melanoma; these lower survival rates reflect the lower objective response rates to immunotherapy compared to cutaneous melanoma. Understanding tumor-immune dynamics in rare melanomas is critical for the development of new therapies and for improving response rates to current cancer therapies. Progress has been hindered by the lack of clinical data and the need for better preclinical models of rare melanomas. Canine melanoma provides a valuable comparative oncology model for rare types of human melanomas. We analyzed RNA sequencing data from canine melanoma patients and combined this with literature information to create a novel mechanistic mathematical model of melanoma-immune dynamics. Sensitivity analysis of the mathematical model indicated influential pathways in the dynamics, providing support for potential new therapeutic targets and future combinations of therapies. We share our learnings from this work, to help enable the application of this proof-of-concept workflow to other rare disease settings with sparse available data.

Alice Rueda, Argyrios Perivolaris, Niloy Roy et al.

Engagement between client and therapist is a critical determinant of therapeutic success. We propose a multi-dimensional natural language processing (NLP) framework that objectively classifies engagement quality in counseling sessions based on textual transcripts. Using 253 motivational interviewing transcripts (150 high-quality, 103 low-quality), we extracted 42 features across four domains: conversational dynamics, semantic similarity as topic alignment, sentiment classification, and question detection. Classifiers, including Random Forest (RF), Cat-Boost, and Support Vector Machines (SVM), were hyperparameter tuned and trained using a stratified 5-fold cross-validation and evaluated on a holdout test set. On balanced (non-augmented) data, RF achieved the highest classification accuracy (76.7%), and SVM achieved the highest AUC (85.4%). After SMOTE-Tomek augmentation, performance improved significantly: RF achieved up to 88.9% accuracy, 90.0% F1-score, and 94.6% AUC, while SVM reached 81.1% accuracy, 83.1% F1-score, and 93.6% AUC. The augmented data results reflect the potential of the framework in future larger-scale applications. Feature contribution revealed conversational dynamics and semantic similarity between clients and therapists were among the top contributors, led by words uttered by the client (mean and standard deviation). The framework was robust across the original and augmented datasets and demonstrated consistent improvements in F1 scores and recall. While currently text-based, the framework supports future multimodal extensions (e.g., vocal tone, facial affect) for more holistic assessments. This work introduces a scalable, data-driven method for evaluating engagement quality of the therapy session, offering clinicians real-time feedback to enhance the quality of both virtual and in-person therapeutic interactions.

Ilkka Laakso, Margarethus Marius Paulides, Sachiko Kodera et al.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

Iqra Yousaf, Aneela Anwar, Atika Umer

Chronic wounds present a major challenge in elderly individuals due to diminished regenerative capacity and impaired tissue repair mechanisms associated with aging. In this study, we formulated a topical gel composed of zinc oxide (ZnO), silver (Ag), and cerium oxide (CeO2) nanoparticles, each chosen for their respective antimicrobial, antioxidant, and tissue-regenerative properties. The nanoparticles were synthesized through precise precipitation or reduction techniques and thoroughly characterized using UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FTIR), and electron microscopy to confirm nanoscale structure and purity. An in vivo wound healing model utilizing aged Sprague-Dawley rats was employed, with animals divided into three groups: untreated, placebo-treated, and those receiving the nanoparticle-enriched gel. Wound dimensions were tracked for 14 days, revealing significantly improved healing in the nanoparticle-treated group, with nearly complete closure observed by day 14 (ANOVA, p less than 0.0001). Cytocompatibility was assessed via MTT assay on L929 fibroblasts, confirming greater than 80 percent viability at therapeutically relevant concentrations. These findings underscore the potential of multifunctional nanoparticle-based formulations to enhance wound healing in aged or compromised skin environments, offering a promising therapeutic avenue.

Jiayi Xin, Aniruddh Raghu, Nick Bhattacharya et al.

Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or engineered independently. While these complex formats can expand disease applicability and improve safety, they present a significant engineering challenge: the function and stability of individual domains are not guaranteed in the novel format, and the entire molecule may no longer be synthesizable. To address these challenges, we develop a machine learning framework to predict "reformatting success" -- whether converting an antibody from one format to another will succeed or not. Our framework incorporates both antibody sequence and structural context, incorporating an evaluation protocol that reflects realistic deployment scenarios. In experiments on a real-world antibody reformatting dataset, we find the surprising result that large pretrained protein language models (PLMs) fail to outperform simple, domain-tailored, multimodal representations. This is particularly evident in the most difficult evaluation setting, where we test model generalization to a new starting antibody. In this challenging "new antibody, no data" scenario, our best multimodal model achieves high predictive accuracy, enabling prioritization of promising candidates and reducing wasted experimental effort.

N. G. Olenina

INTRODUCTION. Herbal medicinal products are widely used in medical practice. Special considerations apply to the extent of safety and efficacy studies required for herbal medicinal products in different countries, as documented in their marketing authorisation frameworks. Currently, the Eurasian Economic Union (EAEU) lacks guidelines on the extent of preclinical and clinical studies required for herbal medicinal products.AIM. This study aimed to analyse the possibility of using international standards and approaches in the development of the EAEU guidelines for preclinical and clinical studies of the safety and efficacy of herbal medicinal products.DISCUSSION. First of all, marketing authorisation of herbal medicinal products involves special considerations because these medicinal products contain complex mixtures of bioactive substances. According to the analysis of the regulatory approaches of the European Union (EU), the United States of America (USA), and the EAEU, the safety and efficacy testing requirements for herbal medicinal products are harmonised to a certain degree. The terms used for herbal substances and herbal medicinal products have almost identical definitions in all the studied documents. Despite the differences in their typological classifications of herbal medicinal products, the EU and USA documents provide similar principles for determining the required extent of published data and original studies on the safety and efficacy of herbal medicinal products. Mainly, the extent depends on the herbal medicinal product’s history of previous human use and completed preclinical and clinical studies (if any), type (original/generic), intended administration route (traditional/new), and indications (established/new). Some of the approaches presented in the article are only partially included in the current EAEU regulatory documents.CONCLUSIONS. The discussed approaches can be considered in the development of the EAEU guidelines for preclinical and clinical studies of the safety and efficacy of herbal medicinal products. Such guidelines will contribute to providing the population with broad-spectrum herbal medicinal products that meet current safety and efficacy standards.

Caroline W. Grant, Jerry Li, Swan Lin et al.

ABSTRACT Patient outcomes in advanced renal cell carcinoma (RCC) remain poor, with five‐year survival rates ranging from ~10% to 30%. Early projections of therapeutic outcomes could optimize precision medicine and accelerate drug development. While machine learning (ML) models integrating tumor growth inhibition (TGI) metrics have improved survival predictions over traditional models, their application in RCC remains unexplored. Herein, we used TGI metrics and baseline data to evaluate parametric (PM) and semi‐parametric (SPM) survival models alongside ML approaches for predicting progression‐free (PFS) and overall survival (OS) in 1839 RCC patients from four trials (evaluating sunitinib, axitinib, sorafenib, interferon‐alpha, and avelumab + axitinib). Data were split into training (70%) and testing (30%), and feature selection was used to determine parsimonious and robust models. Bootstrap resampling (n = 100) was employed for models' validation, and performance was assessed using C‐index and Integrated Brier Score. In brief, training data results demonstrated that tree‐based ML models (random survival forest (RSF) and XGBoost) outperformed PM and SPM models in predicting PFS (C‐index: 0.783–0.785 vs. 0.725–0.738 for PM and SPM; p < 0.05) and OS (C‐index: 0.77–0.867 vs. 0.750–0.758 for PM and SPM; p < 0.05), with RSF achieving better prediction of PFS and OS using only 3–5 covariates, compared to 9–35 with other tested methods. Tree‐based methods were also superior in the testing data. SHapley Additive exPlanations revealed nonlinear relationships among top predictors, including TGI metrics, underscoring the ability of tree‐based methods to capture complex prognostic interactions. Further validation is required to confirm models' generalizability to additional therapies and patients with differing tumor severity.

Maximilien Lora, H. A. Ménard, Anastasia Nijnik et al.

ABSTRACT Low dose methotrexate (LD‐MTX) remains the gold standard in rheumatoid arthritis (RA) therapy. Multiple mechanisms on a variety of immune cells contribute to the anti‐inflammatory effects of LD‐MTX. Inflammatory signaling is deeply implicated in hematopoiesis by regulating hematopoietic stem and progenitor cell (HSPC) fate decisions; raising the question of whether HSPC are also modulated by LD‐MTX. This is the first study to characterize the effects of LD‐MTX on HSPC. CD34+ HSPC were isolated from healthy donors' non‐mobilized peripheral blood. Resting and/or cycling HSPCs were treated with LD‐MTX [dose equivalent to that used in RA patients]. Flow cytometry was performed to assess HSPC viability, cell cycle, surface abundance of reduced folate carrier 1 (RFC1), proliferation, reactive oxygen species (ROS) levels, DNA double‐strand breaks, p38 activation, and CD34+ subpopulations. HSPC clonogenicity was tested in colony‐forming cell assays. Our results indicate that in cycling HSPC, membrane RFC1 is upregulated and, following LD‐MTX treatment, they accumulate more intracellular MTX than resting HSPC. In cycling HSPC, LD‐MTX inhibits HSPC expansion by promoting S‐phase cell‐cycle arrest, increases intracellular HSPC ROS levels and DNA damage, and reduces HSPC viability. Those effects involve the activation of the p38 MAPK pathway and are rescued by folinic acid. The effects of LD‐MTX are more evident in CD34+ CD38High progenitors. In non‐cycling HSPC, LD‐MTX also reduces the proliferative response while preserving their clonogenicity. In summary, HSPC uptake LD‐MTX differentially according to their cycling state. In turn, LD‐MTX results in reduced proliferation and the preservation of HSPC clonogenicity.

Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin et al.

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.

Marc Fiammante, Anne-Isabelle Vermersch, Marie Vidailhet et al.

Objective Accurate identification of hypoxic-ischemic brain injury in the early neonatal period is essential for initiating therapeutic hypothermia (TH) within 6 hours of birth to optimize neurodevelopmental outcomes. We aimed to develop a simple decision-making tool for identifying term neonates with hypoxic-ischemic encephalopathy (HIE) based on features of conventional electroencephalograms (EEG) recorded within 6 hours of birth. Methods EEG recordings from 100 full-term neonates with HIE were graded by pediatric neurologists for severity. Amplitude in slow frequency bands was analyzed, focusing on delta (0.5-4 Hz) spectral power. Temporal fluctuations of delta power characterized each HIE grade, with joint level and duration probability densities estimated for delta oscillation power. This study is registered on clinicaltrials.gouv (NCT05114070). Results These 2D EEG representations effectively distinguish mild HIE cases from those requiring hypothermia, achieving 98% accuracy, 99% sensitivity, 99% positive predictive value, 94% negative predictive value, an F1 score of 99%, and a false alarm rate of only 6%. This system accurately discriminates mild from moderate or severe HIE, with only one mild case mistakenly identified as requiring hypothermia and one moderate case erroneously flagged for treatment. Conclusions Quantized probability densities of delta spectral features from early EEG (within 6 hours of birth) revealed significant differences between mild and moderate/severe HIE, enabling accurate discrimination of candidates for TH. Significance Simple, interpretable biomarkers from early EEG can provide an efficient visual clinical decision support tool to identify full-term neonates with HIE eligible for therapeutic hypothermia.

Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer et al.

Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model predicting the target property of interest. The model predictions are used to determine which designs to evaluate in the lab, and the model is updated on the new measurements to inform the next cycle of decisions. A key challenge is that the experimental feedback from each cycle inspires changes in the candidate proposal or experimental protocol for the next cycle, which lead to distribution shifts. To promote robustness to these shifts, we must account for them explicitly in the model training. We apply domain generalization (DG) methods to classify the stability of interactions between an antibody and antigen across five domains defined by design cycles. Our results suggest that foundational models and ensembling improve predictive performance on out-of-distribution domains. We publicly release our codebase extending the DG benchmark ``DomainBed,'' and the associated dataset of antibody sequences and structures emulating distribution shifts across design cycles.

Diwei Shi, Sisi Li, Fan Liu et al.

Early assessment of tumor therapeutic response is an important topic in precision medicine to optimize personalized treatment regimens and reduce unnecessary toxicity, cost, and delay. Although diffusion MRI (dMRI) has shown potential to address this need, its predictive accuracy is limited, likely due to its unspecific sensitivity to overall pathological changes. In this work, we propose a new quantitative dMRI-based method dubbed EXCHANGE (MRI of water Exchange, Confined and Hindered diffusion under Arbitrary Gradient waveform Encodings) for simultaneous mapping of cell size, cell density, and transcytolemmal water exchange. Such rich microstructural information comprehensively evaluates tumor pathologies at the cellular level. Validations using numerical simulations and in vitro cell experiments confirmed that the EXCHANGE method can accurately estimate mean cell size, density, and water exchange rate constants. The results from in vivo animal experiments show the potential of EXCHANGE for monitoring tumor treatment response. Finally, the EXCHANGE method was implemented in breast cancer patients with neoadjuvant chemotherapy, demonstrating its feasibility in assessing tumor therapeutic response in clinics. In summary, a new, quantitative dMRI-based EXCHANGE method was proposed to comprehensively characterize tumor microstructural properties at the cellular level, suggesting a unique means to monitor tumor treatment response in clinical practice.

Himeshwari Verma, Devpriya Lakra, Vyom Agarwal

Introduction: HIV (Human Immunodeficiency Virus) continues to be a major global public health issue with no cure. Vitamin D is a fat-soluble hormone that is majorly involved in the classical function of calcium and phosphorus hemostasis and bone mineralization as well as non-classical functions of immune modulation in various viral and autoimmune diseases. A combination of both traditional risk factors, HIV- specific and antiretroviral therapy (ART)-specific contributors leave HIV-infected persons (PLHIV) at a greater risk for low 25-OH-Vitamin D levels and frank vitamin D deficiency. Aims and Setting: The current study was conducted to assess and characterize the prevalence of Vitamin D deficiency in PLHIV-on-ART attending a tertiary care hospital and assess the factors that may be affecting it. Methods: 95 PLHIV registered at an ART center were selected over a period of 6 months based on Inclusion and Exclusion criteria. Flow cytometry estimation of CD4 count and ELISA based quantitative assessment of serum 25-OH Vitamin D3 were done along with detailed clinical examination. P<0.05 was considered to be statistically significant. Results: About half of the PLHIV assessed were deficient in vitamin D. Severe vitamin D deficiency was noted in one-fourth of subjects. Serum vitamin D levels were significantly less in subjects on ZLN regime compared to TLE regime. No significant difference was found between vitamin D deficiency and duration of treatment, different treatment regimens or differing CD4 counts. No significant association of serum levels of Vitamin D with duration of treatment or varying CD4 count was found. Conclusion: There is greater prevalence of subnormal levels of Vitamin D in PLHIV on ART. ZLN regime appears to have a negative impact on Vitamin D levels in comparison to TLE regimen. More research needs to be done to further evaluate the physiology of Vitamin D in PLHIV on ART.

Veronica Notarbartolo, Bintu Ayla Badiane, Vincenzo Insinga et al.

The discovery of antimicrobial drugs has led to a significant increase in survival from infections; however, they are very often prescribed and administered, even when their use is not necessary and appropriate. Newborns are particularly exposed to infections due to the poor effectiveness and the immaturity of their immune systems. For this reason, in Neonatal Intensive Care Units (NICUs), the use of antimicrobial drugs is often decisive and life-saving, and it must be started promptly to ensure its effectiveness in consideration of the possible rapid evolution of the infection towards sepsis. Nevertheless, the misuse of antibiotics in the neonatal period leads not only to an increase in the development and wide spreading of antimicrobial resistance (AMR) but it is also associated with various short-term (e.g., alterations of the microbiota) and long-term (e.g., increased risk of allergic disease and obesity) effects. It appears fundamental to use antibiotics only when strictly necessary; specific decision-making algorithms and electronic calculators can help limit the use of unnecessary antibiotic drugs. The aim of this narrative review is to summarize the right balance between the risks and benefits of antimicrobial therapy in NICUs; for this purpose, specific Antimicrobial Stewardship Programs (ASPs) in neonatal care and the creation of a specific antimicrobial stewardship team are requested.