In recent years, nanotechnology has been increasingly applied to the area of drug development. Nanoparticle-based therapeutics can confer the ability to overcome biological barriers, effectively deliver hydrophobic drugs and biologics, and preferentially target sites of disease. However, despite these potential advantages, only a relatively small number of nanoparticle-based medicines have been approved for clinical use, with numerous challenges and hurdles at different stages of development. The complexity of nanoparticles as multi-component three dimensional constructs requires careful design and engineering, detailed orthogonal analysis methods, and reproducible scale-up and manufacturing process to achieve a consistent product with the intended physicochemical characteristics, biological behaviors, and pharmacological profiles. The safety and efficacy of nanomedicines can be influenced by minor variations in multiple parameters and need to be carefully examined in preclinical and clinical studies, particularly in context of the biodistribution, targeting to intended sites, and potential immune toxicities. Overall, nanomedicines may present additional development and regulatory considerations compared with conventional medicines, and while there is generally a lack of regulatory standards in the examination of nanoparticle-based medicines as a unique category of therapeutic agents, efforts are being made in this direction. This review summarizes challenges likely to be encountered during the development and approval of nanoparticle-based therapeutics, and discusses potential strategies for drug developers and regulatory agencies to accelerate the growth of this important field.
Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.
Malignant tumors exhibit complex pathogenesis, yet classical oncological theories remain fragmented, failing to provide a unifying framework to address this complexity. This gap limits the utility and translational potential of the prevailing "confront-and-eradicate" therapeutic paradigm, constraining transformative therapeutic breakthroughs and driving the emergence of acquired and recurrent drug resistance. Here, we propose the Tumor Existential Crisis-Driven Survival (ECDS) theory, anchored in the core proposition that impairment of Existential Stability drives the compensatory hyperactivation of Survival Capacity. This framework defines three foundational constructs (Existential Stability, Survival Capacity, and Existence Threshold) and three guiding principles, unifying and integrating canonical core theories of tumorigenesis. It delineates the dynamic coupling between declining Existential Stability and escalating Survival Capacity during tumor evolution, reinterprets the hierarchical activation of the well-established 14 cancer hallmarks, elucidates the redundancy of survival signaling pathways that underpins intratumoral and intertumoral heterogeneity, and unravels the "hierarchical leap" in therapeutic resistance. By reframing tumors as "Existential Stability erosion-driven passive survival systems" rather than "intrinsically aggressive cellular aggregates", ECDS challenges prevailing dogma, uncovers tumors' intrinsic vulnerability, and establishes a robust meta-theoretical foundation for both basic cancer research and translational clinical management.
Nathan M. Stover, Marieke De Bock, Julie Chen
et al.
The in vitro transcription reaction (IVT) is of growing importance for the manufacture of RNA vaccines and therapeutics. While the kinetics of the microscopic steps of this reaction (promoter binding, initiation, and elongation) are well studied, the rate law of overall RNA synthesis that emerges from this system is unclear. In this work, we show that a model that incorporates both initiation and elongation steps is essential for describing trends in IVT kinetics in conditions relevant to RNA manufacturing. In contrast to previous reports, we find that the IVT reaction can be either initiation- or elongation-limited depending on solution conditions. This initiation-elongation model is also essential for describing the effect of salts, which disrupt polymerase-promoter binding, on transcription rates. Polymerase-polymerase interactions during elongation are incorporated into our modeling framework and found to have nonzero but unidentifiable effects on macroscopic transcription rates. Finally, we develop an extension of our modeling approach to quantitatively describe and experimentally evaluate RNA- and DNA-templated mechanisms for the formation of double-stranded RNA (dsRNA) impurities. We show experimental results that indicate that an RNA-templated mechanism is not appropriate for describing macroscopic dsRNA formation in the context of RNA manufacturing.
Fabian Schmidt, Karin Hammerfald, Henrik Haaland Jahren
et al.
Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of therapeutic dialogue. This paper introduces CFiCS, a hierarchical classification framework integrating graph machine learning with pretrained contextual embeddings. We represent common factors, intervention concepts, and microcounseling skills as a heterogeneous graph, where textual information from ClinicalBERT enriches each node. This structure captures both the hierarchical relationships (e.g., skill-level nodes linking to broad factors) and the semantic properties of therapeutic concepts. By leveraging graph neural networks, CFiCS learns inductive node embeddings that generalize to unseen text samples lacking explicit connections. Our results demonstrate that integrating ClinicalBERT node features and graph structure significantly improves classification performance, especially in fine-grained skill prediction. CFiCS achieves substantial gains in both micro and macro F1 scores across all tasks compared to baselines, including random forests, BERT-based multi-task models, and graph-based methods.
Lipid nanoparticles (LNPs) are a leading platform in the delivery of RNA-based therapeutics, playing a pivotal role in the clinical success of mRNA vaccines and other nucleic acid drugs. Their performance in RNA encapsulation and delivery is critically governed by the molecular structure of ionizable lipids and the overall formulation composition. However, mechanistic insight into how these factors govern LNP architecture and function remains limited, primarily owing to the challenges of capturing nanoscale assembly and organization using experimental techniques. Here, we employ coarse-grained molecular dynamics simulations to systematically investigate how ionizable lipid chemistry influences LNP self-assembly, internal organization, and surface properties. We further explore the effects of formulation ratios and pH-dependent deprotonation on both the internal structure and surface morphology of LNPs. Leveraging these insights, we demonstrate how in silico structural characteristics can inform the rational design of novel ionizable lipids and optimization of formulation ratios, supported with experimental validations. Our findings offer a molecular-level understanding of LNP assembly dynamics and architecture, thereby establishing a computational framework linking lipid chemistry and LNP formulation to the structure and performance of LNP, to advance the rational design of novel LNP delivery systems.
Objective: This study proposes and preliminarily validates a novel "Functional-Energetic Topology Model" to uncover neurodynamic mechanisms of Non-Suicidal Self-Injury (NSSI), using Graph Neural Networks (GNNs) to decode brain network patterns from single-channel EEG in real-world settings.Methods: EEG data were collected over ~1 month from three adolescents with NSSI using a smartphone app and a portable Fp1 EEG headband during impulsive and non-impulsive states. A theory-driven GNN with seven functional nodes was built. Performance was evaluated via intra-subject (80/20 split) and leave-one-subject-out cross-validation (LOSOCV). GNNExplainer was used for interpretability.Results: The model achieved high intra-subject accuracy (>85%) and significantly above-chance cross-subject performance (approximately73.7%). Explainability analysis revealed a key finding: during NSSI states, a critical feedback loop regulating somatic sensation exhibits dysfunction and directional reversal. Specifically, the brain loses its ability to self-correct via negative bodily feedback, and the regulatory mechanism enters an "ineffective idling" state.Conclusion: This work demonstrates the feasibility of applying theory-guided GNNs to sparse, single-channel EEG for decoding complex mental states. The identified "feedback loop reversal" offers a novel, dynamic, and computable model of NSSI mechanisms, paving the way for objective biomarkers and next-generation Digital Therapeutics (DTx).
We set up a mathematical model for a DC current in a human tissue that shows an attenuation effect in an extended circuit. We give a positive lower bound on the time duration over which this is guaranteed to happen in terms of the parameters of the model. We also discuss shielding and coupling in the context of electrical aspects of biological processes.
As large language models (LLMs) are embedded into mental health technologies, they are often framed either as tools assisting therapists or autonomous therapeutic systems. Such perspectives overlook their potential to mediate relational complexities in therapy, particularly for systemically marginalized clients. Drawing on in-depth interviews with 12 therapists and 12 marginalized clients in China, including LGBTQ+ individuals or those from other marginalized backgrounds, we identify enduring relational challenges: difficulties building trust amid institutional barriers, the burden clients carry in educating therapists about marginalized identities, and challenges sustaining authentic self-disclosure across therapy and daily life. We argue that addressing these challenges requires AI systems capable of actively mediating underlying knowledge gaps, power asymmetries, and contextual disconnects. To this end, we propose the Dynamic Boundary Mediation Framework, which reconceptualizes LLM-enhanced systems as adaptive boundary objects that shift mediating roles across therapeutic stages. The framework delineates three forms of mediation: Epistemic (reducing knowledge asymmetries), Relational (rebalancing power dynamics), and Contextual (bridging therapy-life discontinuities). This framework offers a pathway toward designing relationally accountable AI systems that center the lived realities of marginalized users and more effectively support therapeutic relationships.
Rongxin Li,1– 3 Yanan Wang,1– 3 Yongfeng Lao,1– 3 Chengyu You,1– 3 Liangliang Qing,1– 3 Xin Guan,1– 3 Jian Wang,1– 3 Xiaolong Li,1– 3 Qingchao Li,1– 3 Shuai Liu,1– 3 Zhilong Dong1– 3 1Department of Urology, The Second Hospital of Lanzhou University, Lanzhou, Gansu, 730000, People’s Republic of China; 2Gansu Province Key Laboratory of Urological Diseases, Lanzhou, Gansu, 730030, People’s Republic of China; 3Gansu Province Clinical Research Center for Urinary System Disease, Lanzhou, Gansu, 730000, People’s Republic of ChinaCorrespondence: Zhilong Dong, Department of Urology, The Second Hospital of Lanzhou University, 82, Cuiyingmen, Chengguan District, Lanzhou, Gansu Province, People’s Republic of China, Email dzl19780829@163.comPurpose: This research aims to investigate the role and potential mechanisms of Aloin in Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) through network pharmacology and experimental approaches.Methods: Using network pharmacology methods, potential targets of Aloin and targets related to CP/CPPS were screened from public databases. The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the core targets and pathways of Aloin against CP/CPPS. The effects of Aloin in ameliorating CP/CPPS were verified in animal experiments.Results: A total of 235 genes interacting with Aloin in CP/CPPS were identified. PPI network analysis revealed five core targets: AKT1, EGFR, ESR1, HSP90AA1, and SRC. GO analysis yielded 2916 enrichment results, with 2562 related to Biological Process (BP), 94 to Cellular Component (CC), and 260 to Molecular Function (MF). KEGG pathway analysis identified 172 pathways. Molecular docking confirmed stable binding between Aloin and core targets. Molecular dynamics simulations further validated binding stability by analyzing Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), hydrogen bonds, Solvent Accessible Surface Area (SASA), and Gibbs free energy of Aloin-target complexes. Experimental validation showed that Aloin alleviated pain, reduced inflammatory factors, and decreased oxidative stress in a rat model of CP/CPPS. The qRT-PCR results showed that Aloin intervention reduced the mRNA expression of AKT1, EGFR, HSP90AA1, and SRC, while increasing ESR1 mRNA expression. These changes may underlie its therapeutic effects in CP/CPPS.Conclusion: Our study revealed that Aloin exerts a beneficial effect on mitigating the pain symptoms associated with CP/CPPS, ameliorating inflammation, and reducing oxidative stress. Through network pharmacology, potential targets and signaling pathways were identified, suggesting the therapeutic promise of Aloin for CP/CPPS. These findings advocate for further exploration into its clinical efficacy and mechanistic underpinnings in the treatment of CP/CPPS.Keywords: Aloin, chronic prostatitis, CP/CPPS, network pharmacology
Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom \textbf{Pep}tide design with \textbf{G}eometric \textbf{LA}tent \textbf{D}iffusion (PepGLAD) given the binding site. We first establish a benchmark consisting of both 1D sequences and 3D structures from Protein Data Bank (PDB) and literature for systematic evaluation. We then identify two major challenges of leveraging current diffusion-based models for peptide design: the full-atom geometry and the variable binding geometry. To tackle the first challenge, PepGLAD derives a variational autoencoder that first encodes full-atom residues of variable size into fixed-dimensional latent representations, and then decodes back to the residue space after conducting the diffusion process in the latent space. For the second issue, PepGLAD explores a receptor-specific affine transformation to convert the 3D coordinates into a shared standard space, enabling better generalization ability across different binding shapes. Experimental Results show that our method not only improves diversity and binding affinity significantly in the task of sequence-structure co-design, but also excels at recovering reference structures for binding conformation generation.
Varvara D. Sanikovich, Marina I. Sekacheva, Ekaterina V. Orlova
et al.
Squamous cell carcinoma of the head and neck is the sixth most common cancer worldwide. However, malignant tumors of the middle ear are extremely rare. Squamous cell carcinoma is the most common histologic type of tumor in this area, accounting for more than 50% of cases. Complex anatomical and topographic features of the inner ear and inapparent clinical symptoms result in delayed diagnosis of this disease. A case of a patient with highly differentiated middle ear squamous cell carcinoma is presented. The patient received complex treatment, including drug antitumor therapy according to the TPEx protocol: docetaxel + cisplatin + cetuximab. Being highly effective, this protocol requires a multidisciplinary approach in order to ensure maximum safety of therapy. The case illustrates the classic “portrait” of a patient with recurrent or metastatic squamous cell carcinoma of the head and neck and also provides a comprehensive description of the spectrum of adverse events faced by such patients and their oncologists. Based on this clinical case, the features of management of such adverse events are analyzed.
The N-methyl-d-aspartate (NMDA) receptors, which belong to the ionotropic Glutamate receptors, constitute a family of ligand-gated ion channels. Within the various subtypes of NMDA receptors, the GluN1/2A subtype plays a significant role in central nervous system (CNS) disorders. The present article aims to provide a comprehensive review of ligands targeting GluN2A-containing NMDA receptors, encompassing negative allosteric modulators (NAMs), positive allosteric modulators (PAMs) and competitive antagonists. Moreover, the ligands’ structure–activity relationships (SARs) and the binding models of representative ligands are also discussed, providing valuable insights for the clinical rational design of effective drugs targeting CNS diseases.
Background: Low levels of living standards amongst street dwellers worldwide limit their access to conventional healthcare services, resulting in self-medication use for the treatment of an illness. Nevertheless, self-medication use has risks, including adverse drug reactions, increased polypharmacy, drug resistance, drug dependence, drug interactions and incorrect diagnosis. Ethiopia has a large street-dwelling community; however, there are no studies conducted in Ethiopia assessing self-medication use amongst street dwellers. This study provides insight into self-medication use and predictors amongst street dwellers in Ethiopia.
Methods: A community-based, multicentre cross-sectional study was conducted amongst street dwellers from 1 September 2022 to 1 February 2023 at community drug-retail outlets in the three major cities in the Amhara region of Ethiopia. The data were obtained using an interviewer-administered questionnaire. Frequencies and percentages of descriptive statistics were calculated. Bivariable and multivariable logistic regression analyses were employed to indicate predictors of self-medication use. To determine statistical significance, a 95% confidence interval with a p value below 0.05 was utilized.
Results: The prevalence of self-medication use was 67.4%. Time and financial savings were reported as the reasons for most self-medication use. The most commonly reported illnesses for which people sought self-medication were gastrointestinal diseases. Low monthly income (adjusted OR 3.72, 95% CI 2.34–5.91) and residing near sewage areas (adjusted OR 3.37, 95% CI 2.03–5.58) were significantly associated with self-medication use.
Conclusion: Street dwellers had a high rate of self-medication use. Residing near sewage areas and having a low level of income were factors in self-medication use. Gastrointestinal diseases, respiratory ailments and dermatological conditions were the most frequently reported complaints, whereas antimicrobials and anthelmintics were the most commonly used medications. We recommend that healthcare services enhance outreach programmes to the most vulnerable people, such as street dwellers, especially those with lower monthly incomes and who live near sewage areas, to reduce self-medication rates.
Colin A. Grambow, Hayley Weir, Christian N. Cunningham
et al.
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.