Hasil untuk "Therapeutics. Pharmacology"

Mingwen Zhang, Minqiang Yang, Changsheng Ma et al.

Proactive questioning, where therapists deliberately initiate structured, cognition-guiding inquiries, is a cornerstone of cognitive behavioral therapy (CBT). Yet, current psychological large language models (LLMs) remain overwhelmingly reactive, defaulting to empathetic but superficial responses that fail to surface latent beliefs or guide behavioral change. To bridge this gap, we propose the \textbf{Socratic Inquiry Framework (SIF)}, a lightweight, plug-and-play therapeutic intent planner that transforms LLMs from passive listeners into active cognitive guides. SIF decouples \textbf{when to ask} (via Strategy Anchoring) from \textbf{what to ask} (via Template Retrieval), enabling context-aware, theory-grounded questioning without end-to-end retraining. Complementing SIF, we introduce \textbf{Socratic-QA}, a high-quality dataset of strategy-aligned Socratic sequences that provides explicit supervision for proactive reasoning. Experiments show that SIF significantly enhances proactive questioning frequency, conversational depth, and therapeutic alignment, marking a clear shift from reactive comfort to proactive exploration. Our work establishes a new paradigm for psychologically informed LLMs: not just to respond, but to guide.

Linhui Xie, Aurelien Pelissier, Yanjun Shao et al.

Artificial intelligence (AI) is accelerating progress in modeling T and B cell receptors by enabling predictive and generative frameworks grounded in sequence data and immune context. This chapter surveys recent advances in the use of protein language models, machine learning, and multimodal integration for immune receptor modeling. We highlight emerging strategies to leverage single-cell and repertoire-scale datasets, and optimize immune receptor candidates for therapeutic design. These developments point toward a new generation of data-efficient, generalizable, and clinically relevant models that better capture the diversity and complexity of adaptive immunity.

N.K. Gupta, Ayesha Ahmad, Uma Gupta

Education is derived 'Educatum' a Latin word, combination of 'e' and 'duco'. 'e' means 'out of' or 'from inside' and 'duco' means 'to lead out' - means to lead out of what is there inside the mind and soul of learner. Medical education has undergone significant changes in the last few decades due to the technological explosion, and medical students need to be exposed in appropriate and calculated manner at that stage of education

Zhonglin Liu

Innate resistance to anti-PD-1 immunotherapy remains a major clinical challenge in metastatic melanoma, with the underlying molecular networks being poorly understood. To address this, we constructed a dynamic Probabilistic Boolean Network model using transcriptomic data from patient tumor biopsies to elucidate the regulatory logic governing therapy response. We then employed a reinforcement learning agent to systematically discover optimal, multi-step therapeutic interventions and used explainable artificial intelligence to mechanistically interpret the agent's control policy. The analysis revealed that a precisely timed, 4-step temporary inhibition of the lysyl oxidase like 2 protein (LOXL2) was the most effective strategy. Our explainable analysis showed that this ''hit-and-run" intervention is sufficient to erase the molecular signature driving resistance, allowing the network to self-correct without requiring sustained intervention. This study presents a novel, time-dependent therapeutic hypothesis for overcoming immunotherapy resistance and provides a powerful computational framework for identifying non-obvious intervention protocols in complex biological systems.

Elisabeth Erbes, Naireeta Biswas, Calvin J. Gavilett et al.

A novel concept of nano-scaled interwoven templates for drug delivery with alternating hydro- and lipophilicity properties is introduced. They are built from cellulose and peptide hydrogel in tandem, and characterized by a nano-stacked interwoven design, thus enabling for tuning the lipophilicity in the mesh nano-domains in which drug candidates of complementary lipophilicities can be embedded. This allows for low-dose-controlled consumption and therapeutic applications. Time-resolved and in-situ grazing incidence X-ray scattering studies confirm the design of the therapeutic nano-paper and create conditions suitable for the drug storage of complementary properties. The molecular design has the potential of a locally controlled, site-specific drug release on a beyond-nanomolar scale. Generalized, the design may contribute to facile developments of personalized medicine.

Sebastian Lotter, Elisabeth Mohr, Andrina Rutsch et al.

Synthetic molecular communication (SMC) is a key enabler for future healthcare systems in which Internet of Bio-Nano-Things (IoBNT) devices facilitate the continuous monitoring of a patient's biochemical signals. To close the loop between sensing and actuation, both the detection and the generation of in-body molecular communication (MC) signals is key. However, generating signals inside the human body, e.g., via synthetic nanodevices, poses a challenge in SMC, due to technological obstacles as well as legal, safety, and ethical issues. Hence, this paper considers an SMC system in which signals are generated indirectly via the modulation of a natural in-body MC system, namely the gut-brain axis (GBA). Therapeutic GBA modulation is already established as treatment for neurological diseases, e.g., drug refractory epilepsy (DRE), and performed via the administration of nutritional supplements or specific diets. However, the molecular signaling pathways that mediate the effect of such treatments are mostly unknown. Consequently, existing treatments are standardized or designed heuristically and able to help only some patients while failing to help others. In this paper, we propose to leverage personal health data, e.g., gathered by in-body IoBNT devices, to design more versatile and robust GBA modulation-based treatments as compared to the existing ones. To show the feasibility of our approach, we define a catalog of theoretical requirements for therapeutic GBA modulation. Then, we propose a machine learning model to verify these requirements for practical scenarios when only limited data on the GBA modulation exists. By evaluating the proposed model on several datasets, we confirm its excellent accuracy in identifying different modulators of the GBA. Finally, we utilize the proposed model to identify specific modulatory pathways that play an important role for therapeutic GBA modulation.

Hao Qian, Pu You, Lin Zeng et al.

Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A, a potential peptide-binding protein for GBM. Our framework introduces the first lead-conditioned generative model, which focuses exploration on geometrically relevant regions around lead peptides and mitigates the combinatorial complexity of de novo methods. Specifically, we propose POTFlow, a \underline{P}rior and \underline{O}ptimal \underline{T}ransport-based \underline{Flow}-matching model for peptide optimization. POTFlow employs secondary structure information (e.g., helix, sheet, loop) as geometric constraints, which are further refined by optimal transport to produce shorter flow paths. With this design, our method achieves state-of-the-art performance compared with five popular approaches. When applied to GBM, our method generates peptides that selectively inhibit cell viability and significantly prolong survival in a patient-derived xenograft (PDX) model. As the first lead peptide-conditioned flow matching model, POTFlow holds strong potential as a generalizable framework for therapeutic peptide design.

Eric Hua Qing Zhang, Julia Ive

Mental health disorders impose a substantial global socioeconomic burden. While large language models (LLMs) offer 24/7, non-judgmental interactions to address this gap, pretrained models lack contextual coherence and emotional alignment for appropriate therapeutic dialogue. Existing methods suffer from three critical methodological gaps: 1) Supervised Fine-Tuning (SFT) produces repetitive, context-insensitive outputs that fail to balance clinical accuracy with genuine empathy; 2) Reinforcement Learning (RL)-based therapeutic systems rely on generic reward functions (e.g., BLEU, ROUGE) that prioritise lexical similarity over clinical-specific emotional appropriateness and contextual relevance; 3) LLMs are resource-intensive and pose data privacy risks, making local deployment in clinical settings infeasible. To address these gaps, this study investigates the application of SFT and RL techniques to enhance GPT-2's capacity for therapeutic dialogue generation. The methodology restructured input formats to enable simultaneous processing of contextual information and emotional states alongside user input, employing a novel multi-component reward function that explicitly aligns model outputs with professional therapeutic logic (not just lexical overlap) and annotated emotions. Results demonstrated substantial improvements through RLs over baseline GPT-2 across multiple evaluation metrics: BLEU (0.0111), ROUGE-1 (0.1397), ROUGE-2 (0.0213), ROUGE-L (0.1317), and METEOR (0.0581). LLM evaluation confirmed high contextual relevance and professionalism, while RL achieved 99.34% emotion accuracy compared to 66.96% for baseline GPT-2. These findings demonstrate RL's effectiveness in developing therapeutic dialogue systems that can serve as valuable assistive tools for therapists, while maintaining essential human clinical oversight.

Muhamad F. Zainudin, Natiara M. Hashim, Wan N.W.M. Zohdi et al.

Purpose: To explore healthcare utilization patterns and healthcare services satisfaction among individuals with spinal cord injury in Malaysia. Methods: This cross-sectional study utilized the International Spinal Cord Injury (InSCI) Community Survey and involved 8 hospitals and 1 spinal cord injury organization. A total of 285 participants met the inclusion criteria. Subsequently, 6/11 sections of the InSCI questionnaire were analysed through a path analysis. Results: The 3 most utilized healthcare providers reported were physical and rehabilitation medicine specialists (76.5%), physiotherapists (36.8%), and primary care physicians (27.4%). The top 3 most severe health problems reported were sexual dysfunction, muscle spasm and spasticity, and contractures. Healthcare services satisfaction was high. Health problems predicted healthcare utilization (β = 0.443), while activity limitation and participation restriction predicted healthcare services satisfaction (β = –0.202). The activity limitation and participation restriction in male participants was moderated by the spinal cord injury severity (B = 2.330, p  <  0.001) and health problems (B = 0.550, p < 0.001). Conclusions: Individuals with spinal cord injury in Malaysia rely heavily on physical and rehabilitation medicine specialists, highlighting accessibility challenges due to the centralized specialized rehabilitation services. Sexual dysfunction remains a significant yet under-addressed health concern. Despite these issues, satisfaction with healthcare services is high.

David Núñez-Jurado, Alejandro Fernández-Vega, Carmen del Río et al.

Abstract Background Timely differentiation between ischemic stroke (IS) and intracerebral hemorrhage (ICH) is critical for guiding appropriate acute management strategies. While neuroimaging is the diagnostic gold standard, its accessibility is often limited in urgent clinical settings. Blood biomarkers offer a promising, scalable diagnostic alternative; however, no validated panel is yet available for distinguishing stroke subtypes during the hyperacute phase. Methods In a multicenter study, plasma samples were collected within 6 h of symptom onset. A total of 3,072 proteins were measured using Olink® proximity extension assays. We applied differential expression analysis, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and receiver operating characteristic (ROC) curve evaluation. To interpret the biological relevance of the findings, we conducted functional enrichment and protein–protein interaction (PPI) analyses. Results Among the 388 patients (344 IS, 44 ICH), 2,531 proteins were retained; 878 reached nominal significance (p < 0.05), and 67 remained significant after multiple-testing correction (FDR-adjusted p < 0.05). Of these, 844 were overexpressed in ICH and 34 in IS. GFAP, a glial marker, emerged as the most discriminative biomarker for ICH versus IS (AUC = 0.887; sensitivity: 80%, specificity: 90%), followed by BCAN (AUC = 0.820), SNAP25 (AUC = 0.797), and SPOCK1 (AUC = 0.786). For IS, S100A12 (AUC = 0.677) and MNDA (AUC = 0.657) showed the best performance. Multivariate analyses confirmed the presence of distinct proteomic patterns, with enrichment revealing a significant overrepresentation of neurodevelopmental and synaptic pathways. In PPI networks, GFAP and LYN emerged as central hubs. Conclusion This study reveals a robust plasma proteomic signature distinguishing IS from ICH within hours of onset. These results lay the groundwork for scalable, blood-based diagnostics to guide early stroke management when imaging is delayed or unavailable.

Wenkai Zhang, Wenkai Zhang, Songbo Zhao et al.

PurposeTo guide the drug selection for treatment of moderate to severe ulcerative colitis (UC) by evaluating the efficacy and safety of various drugs.MethodsThis systematic review searched the Embase, PubMed, The Cochrane Library, and Web of Science databases and included randomized controlled trials (RCTs) based on the drugs used alone or in combination for treating UC. Moreover, the Stata17.0 software was employed for statistical analysis and results were reported as relative risk (RR) and 95% confidence interval (CI).ResultsFor the efficacy of induction, upadacitinib ranked first in clinical response, clinical remission, and endoscopic improvement rates, with cumulative probabilities of 96.0%, 99.3%, and 99.0%, respectively. Moreover, for the efficacy of maintenance, upadacitinib ranked first in both clinical remission and endoscopic improvement with a cumulative probability of 93.2% and 93.3%, respectively. For safety, vedolizumab showed the best incidence of adverse events (AE) with 16.8% cumulative probability, while upadacitinib showed the best incidence of serious adverse events (SAE) with 13.8% cumulative probability.ConclusionIn a systematic review and network meta-analysis, we found upadacitinib showed the best efficacy and safety in to be ranked highest in patients with moderate to severe ulcerative colitis. More trials of direct comparisons are needed to inform clinical decision making with greater confidence.

Sankar V, Anand Babu K, Deepak M et al.

Objective: To assess the antioxidant, wound healing, anti-ulcer, and anti-inflammatory properties of Jatropha maheshwari. Methods: Jatropha maheshwari was collected from Kanyakumari district and authenticated. Ethanol was used for continuous hot percolation extraction of the plant. Antioxidant activity was evaluated using DPPH and ABTS assays. The wound healing potential was assessed through a wound scratch assay using 3T3-L1 murine fibroblast cell lines. Anti-ulcer activity was measured using the acid-neutralizing capacity (ANC) and H+/K+-ATPase inhibitory activity methods. Anti-inflammatory effects were determined through dose-dependent studies of the ethanol extract. Results: Antioxidant Activity: The crude extract exhibited strong antioxidant capacity with percentage inhibition values of 88% (DPPH) and 75.7% (ABTS). Wound Healing Activity: The wound closure rate in treated 3T3-L1 cell lines reached 97.88%, indicating potent wound healing properties. Anti-Ulcer Activity: The extract demonstrated an 80.1% inhibition compared to the control when tested alongside omeprazole. ANC per gram of antacid was recorded at 20 and 26 for oral doses of 10 and 500 μg/ml, respectively. Anti-Inflammatory Activity: Dose-dependent inhibition percentages of 30.1% (10 μg/ml) and 96.5% (500 μg/ml) were observed (p<0.001) relative to inflammation control. Additional percentages of 26.3% (10 μg/ml) and 42.6% (500 μg/ml) further confirmed the anti-inflammatory activity. Conclusion: Jatropha maheshwari demonstrates significant antioxidant, wound healing, anti-ulcer, and anti-inflammatory properties. Further research is warranted to elucidate the mechanisms underlying these pharmacological effects.

Xin Sun, Jan de Wit, Zhuying Li et al.

Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although advances in large language models (LLMs) offer potential for more flexible interactions, their lack of controllability and explanability poses challenges in high-stakes contexts like psychotherapy. To address this, we conducted two studies in this work to explore how aligning LLMs with expert-crafted scripts can enhance psychotherapeutic chatbot performance. In Study 1 (N=43), an online experiment with a within-subjects design, we compared rule-based, pure LLM, and LLMs aligned with expert-crafted scripts via fine-tuning and prompting. Results showed that aligned LLMs significantly outperformed the other types of chatbots in empathy, dialogue relevance, and adherence to therapeutic principles. Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a more flexible alignment approach that reduces reliance on fully scripted content while maintaining LLMs' therapeutic adherence and controllability. In a 10-day field Study 2 (N=21), SSAG achieved comparable therapeutic effectiveness to full-scripted LLMs while requiring less than 40\% of expert-crafted dialogue content. Beyond these results, this work advances LLM applications in psychotherapy by providing a controllable and scalable solution, reducing reliance on expert effort. By enabling domain experts to align LLMs through high-level strategies rather than full scripts, SSAG supports more efficient co-development and expands access to a broader context of psychotherapy.

Sophia Tang, Yinuo Zhang, Pranam Chatterjee

We present PepTune, a multi-objective discrete diffusion model for simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with a novel bond-dependent masking schedule and invalid loss function. To guide the diffusion process, we introduce Monte Carlo Tree Guidance (MCTG), an inference-time multi-objective guidance algorithm that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTG integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically-modified peptides simultaneously optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTG for masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.

Jan Alexander, Jan Olav Aaseth, Lutz Schomburg et al.

Low-selenium status was associated with impaired renal function, which improved after selenium and coenzyme Q₁₀ supplementation in an RCT. Here, we evaluated serum glutathione peroxidase-3 (GPx3) and its relation to serum selenium, selenoprotein P (SELENOP), renal function, mortality, and the impact of supplementation, which are all important, especially in elderly individuals. In total, 383 study participants (197 receiving selenium yeast and coenzyme Q₁₀ and 186 on a placebo) were evaluated. We applied benchmark dose modelling to determine GPx3 saturation, ANCOVA, Kaplan–Meier, and multivariate Cox proportional regression analyses for mortality evaluations. Selenium and GPx3 activity were modestly correlated. In comparison with SELENOP, GPx3 levelled off at a much lower value, 100 vs. 150 µg Se/L. GPx3 was associated with renal function, but not SELENOP. Supplementation increased glomerular function by ≈23% with an increase in GPx3. Being low in GPx3 displayed twice the risks of mortality in both placebos and active treatments. At serum selenium <100 µg/L, GPx3 activity was dependent on both selenium status and renal function. As renal function is reduced in the elderly, GPx3 is not an appropriate marker of selenium status. Low GPx3 was associated with an increased risk of mortality dependent of selenium status and independent of renal function.