Intracellular delivery of biomolecules remains a critical challenge in both basic cell biology and translational therapeutics. We introduce Programmable Acoustic Standing-wave Transfection (PAST), a microfluidic tool that leverages dynamically programmable ultrasonic fields to transiently permeabilize cell membranes and enhance biomolecular transport within cell clusters. By generating programmable acoustic potential landscapes, PAST drives cells through cycles of hydrodynamic and acoustic stresses that induce reversible pore formation, enabling diffusion-based delivery without chemical carriers or contrast agents. Experimental studies demonstrate controlled influx and efflux dynamics across multiple biomolecular species, with transport rates tunable via acoustic power, frequency modulation, and duty cycles. Theoretical scaling and numerical simulations reveal that membrane tension, pore energetics, and acoustic field distributions collectively govern transmembrane transport of biomolecules. Post-treatment assays confirm high cellular viability and sustained proliferation, underscoring the biocompatibility of the method. Remarkably, effective diffusivity estimates derived from model predictions closely match experimental transport timescales. Together, these findings establish PAST as a programmable, high-throughput, and non-invasive intracellular delivery platform, offering new opportunities for precision drug screening, gene editing, and mechanistic exploration of cellular membrane biophysics.
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.
Aseem Srivastava, Zuhair Hasan Shaik, Tanmoy Chakraborty
et al.
In mental health counseling, a variety of earlier studies have focused on dialogue modeling. However, most of these studies give limited to no emphasis on the quality of interaction between a patient and a therapist. The therapeutic bond between a patient and a therapist directly correlates with effective mental health counseling. It involves developing the patient's trust on the therapist over the course of counseling. To assess the therapeutic bond in counseling, we introduce trust as a therapist-assistive metric. Our definition of trust involves patients' willingness and openness to express themselves and, consequently, receive better care. We conceptualize it as a dynamic trajectory observable through textual interactions during the counseling. To facilitate trust modeling, we present MENTAL-TRUST, a novel counseling dataset comprising manual annotation of 212 counseling sessions with first-of-its-kind seven expert-verified ordinal trust levels. We project our problem statement as an ordinal classification task for trust quantification and propose a new benchmark, TrustBench, comprising a suite of classical and state-of-the-art language models on MENTAL-TRUST. We evaluate the performance across a suite of metrics and lay out an exhaustive set of findings. Our study aims to unfold how trust evolves in therapeutic interactions.
Mohammad Amin Abbasi, Farnaz Sadat Mirnezami, Ali Neshati
et al.
We present HamRaz, a culturally adapted Persian-language dataset for AI-assisted mental health support, grounded in Person-Centered Therapy (PCT). To reflect real-world therapeutic challenges, we combine script-based dialogue with adaptive large language models (LLM) role-playing, capturing the ambiguity and emotional nuance of Persian-speaking clients. We introduce HamRazEval, a dual-framework for assessing conversational and therapeutic quality using General Metrics and specialized psychological relationship measures. Human evaluations show HamRaz outperforms existing baselines in empathy, coherence, and realism. This resource contributes to the Digital Humanities by bridging language, culture, and mental health in underrepresented communities.
At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichloro- 1,3,5-triazine to form P=N bonds. Fucoidan, a sulfated polysaccharide with intrinsic antiviral activity, and hydrophobic chains are then incorporated to achieve irreversible viral deactivation. The material exhibits strong antiviral inhibition and complete virucidal activity against multiple viruses, including recent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants. It maintains high biocompatibility, remains effective against viral mutations, and is shelf stable for at least five month. The combination of biodegradability, scalable synthesis, and synergistic antiviral and virucidal mechanisms establishes BP-conjugates as a new class of highly efficient antivirals. They offer a broad spectrum antiviral solutions that could bridge the gap between antiviral medicines and general antiseptics.
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.
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).
Zohar Elyoseph, Inbar Levkovich, Eyal Rabin
et al.
Abstract The responsible reporting of suicide in media is crucial for public health, as irresponsible coverage can potentially promote suicidal behaviors. This study examined the capability of generative artificial intelligence, specifically large language models, to evaluate news articles on suicide according to World Health Organization (WHO) guidelines, potentially offering a scalable solution to this critical issue. The research compared assessments of 40 suicide-related articles by two human reviewers and two large language models (ChatGPT-4 and Claude Opus). Results showed strong agreement between ChatGPT-4 and human reviewers (ICC = 0.81–0.87), with no significant differences in overall evaluations. Claude Opus demonstrated good agreement with human reviewers (ICC = 0.73–0.78) but tended to estimate lower compliance. These findings suggest large language models’ potential in promoting responsible suicide reporting, with significant implications for public health. The technology could provide immediate feedback to journalists, encouraging adherence to best practices and potentially transforming public narratives around suicide.
Charlotte Entwistle, Katie Hoemann, Sophie J. Nightingale
et al.
Abstract Self-harm—encompassing suicidality and nonsuicidal self-injury (NSSI)—presents a critical public health concern, particularly as it is a major risk factor of death by suicide. Understanding the psychosocial dynamics of self-harm is imperative. Accordingly, in a large-scale, naturalistic study, we leveraged modern language analysis methods to provide a comprehensive perspective on suicidality and NSSI, specifically in the context of borderline personality disorder (BPD), where self-harm is particularly prevalent. We utilised natural language processing techniques to analyse Reddit data (i.e., BPD forum posts) of 992 users with self-identified BPD (combined N posts = 66,786). The present findings generated further insight into the psychosocial dynamics of suicidality and NSSI, while also uncovering meaningful interactions between the online BPD community and these behaviours. By integrating advanced computational methods with psychological theory, our findings provide a nuanced understanding of self-harm, with implications for clinical practice, clinical and personality theory, and computational social science.
Arthur Huwae, Joanne Marrijda Rugebregt, Mernon Yerlinda Carlista Mage
et al.
Living life as a former drug addict poses significant challenges. Holistically, the impact felt will be very heavy to live. However, life goes on and as a former drug addict is required to improve bad life into a healthy and better quality. The purpose of this study is to explore the description of quality of life in former drug addicts. The method used was qualitative with a descriptive phenomenological design. The participants involved were 3 former drug addicts using purposive sampling technique. The results showed that quality of life physically, psychologically, socially, and environmentally varied in meaning by the three participants. Although, the three participants continue to learn to improve their lives, there is no denying that there are still internal judgments that are expressed about their existence as former drug addicts. Furthermore, the process of improving themselves and their lives continues to be carried out by the three participants by continuing to try positive things to realize a life that has a positive impact on themselves and the environment, in order to achieve quality of life in totality. Social support is important from family, friends, and close people to former addicts, so that they can see opportunities for self-esteem. Future studies should explore social interventions to educate communities on providing holistic support to former drug addicts to overcome the challenges of reintegration.
Stephen Zhewen Lu, Ziqing Lu, Ehsan Hajiramezanali
et al.
High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to characterize novel therapeutics without prior knowledge of the protein target. When combined with deep learning techniques to predict and represent molecular-phenotype interactions, these advancements hold the potential to significantly accelerate and enhance drug discovery applications. This work focuses on the novel task of HCI-guided molecular design. Generative models for molecule design could be guided by HCI data, for example with a supervised model that links molecules to phenotypes of interest as a reward function. However, limited labeled data, combined with the high-dimensional readouts, can make training these methods challenging and impractical. We consider an alternative approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets. The proposed model learns to generate new molecules that could produce phenotypic effects similar to those of the given image target, without relying on pre-annotated phenotypic labels. We demonstrate that the proposed method generates molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity, as confirmed by an independent oracle model.
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.
The research aims to identify and analyse human resource competency variables in order to improve performance in Sragen Regency's batik industry centre. The research methods used were quantitative and qualitative data collection techniques through questionnaires, observation, interviews, and documentation. Employees from the Sragen batik industry centre participated in research activities. Data analysis procedures included validity testing, reliability tests, confirmatory factor analysis, and connection analysis of human resource competencies in order to improve Batik's business performance. According to the findings of the analysis, both intrinsic and extrinsic elements have an impact on increasing batik business performance.
As the population of older adults increases, a growing number of older people will seek psychotherapy and counselling in the coming years. Therefore, therapists should deepen their understanding of the unique developmental challenges that accompany the later years. While varying approaches and theoretical frameworks exist for how to understand these developmental challenges and work in a way that promotes wellbeing, therapy with older adults remains an under-explored area. This paper outlines my clinical observations while working with a single man in his mid-70s who started seeing me for long-term psychotherapy. It explores the intersection of memory, autobiography, self, and ego integration and contributes to the discussion about how to work meaningfully with older adults. I hypothesise that working with our clients to compose an autobiographical consciousness that can be narrated and witnessed by an empathic other could be an important aspect of ego integration. This is because narrative is a meaning-making tool that facilitates one’s sense of self and promotes the process of reconciliation with the past and direction towards the future. This exploration is situated within the framework of the Conversational Model of psychotherapy developed by Robert Hobson and Russell Meares.
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger
et al.
Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift$\unicode{x2013}\unicode{x2013}$a setting that poses a challenge to standard deep learning methods. In this paper, we present Q-SAVI, a probabilistic model able to address these challenges by encoding explicit prior knowledge of the data-generating process into a prior distribution over functions, presenting researchers with a transparent and probabilistically principled way to encode data-driven modeling preferences. Building on a novel, gold-standard bioactivity dataset that facilitates a meaningful comparison of models in an extrapolative regime, we explore different approaches to induce data shift and construct a challenging evaluation setup. We then demonstrate that using Q-SAVI to integrate contextualized prior knowledge of drug-like chemical space into the modeling process affords substantial gains in predictive accuracy and calibration, outperforming a broad range of state-of-the-art self-supervised pre-training and domain adaptation techniques.
Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra
et al.
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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.