Social anxiety (SA) is a prevalent mental health challenge that significantly impacts daily social interactions. Imaginal Exposure (IE), a Cognitive Behavioral Therapy (CBT) technique involving imagined anxiety-provoking scenarios, is effective but underutilized, in part because traditional IE homework requires clients to construct and sustain clinically relevant fear narratives. In this work, we explore the feasibility of an LLM-enabled tool that supports IE by generating vivid, personalized exposure scripts. We first co-designed ImaginalExpoBot with mental health professionals, followed by a formative evaluation with five therapists and a user study involving 19 individuals experiencing SA symptoms. Our findings show that LLM-enabled support can facilitate preparation for anxiety-inducing situations while enabling immediate, user-specific adaptation, with scenarios remaining within a therapeutically beneficial "window of tolerance". Our participants and MHPs also identified limitations in continuity and customization, pointing to the need for deeper adaptivity in future designs. These findings offer preliminary design insights for integrating LLMs into structured therapeutic practices in accessible, scalable ways.
Andrew Salij, R. Seaton Ullberg, Megan C. Davis
et al.
The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements.
IntroductionThis study explores the regulatory challenges posed by digital mental health applications in South Africa, particularly regarding the collection and protection of personal data. It aimed to assess whether South Africa’s current legal framework sufficiently protects users’ sensitive mental health data amidst the rise of digital mental health solutions, especially in the context of privacy concerns.MethodsThe research focused on the intersection of digital mental health applications, data protection laws, and user privacy in South Africa. It examined existing legal frameworks, including the Protection of Personal Information Act (POPIA), National Health Act (NHA), and Consumer Protection Act (CPA). The study reviewed relevant literature, legal texts, and case studies, focusing on mental health applications in both urban and rural contexts.ResultsWhile South Africa has laws in place to protect personal information, these laws have significant gaps in addressing the unique risks associated with digital mental health technologies. Key findings include inadequate regulation of AI-driven mental health tools, insufficient guidelines for third-party data sharing, and challenges with cross-border data transfers.DiscussionThe implications of these findings suggest that South Africa needs to modernize its legal framework to better regulate digital mental health tools and ensure user privacy. This includes improving AI regulation, strengthening consent mechanisms, and enhancing protections against third-party data misuse. Future research should focus on developing specific legal guidelines for mental health data and addressing the vulnerabilities faced by rural populations with low digital literacy. The study’s conclusions align with global concerns over the ethical implications of mental health datacommodification and emphasize the need for robust, adaptable regulatory approaches.
Recent advancements in large language models (LLMs) promise to expand mental health interventions by emulating therapeutic techniques, potentially easing barriers to care. Yet there is a lack of real-world empirical evidence evaluating the strengths and limitations of LLM-enabled psychotherapy interventions. In this work, we evaluate an LLM-powered chatbot, designed via prompt engineering to deliver cognitive restructuring (CR), with 19 users. Mental health professionals then examined the resulting conversation logs to uncover potential benefits and pitfalls. Our findings indicate that an LLM-based CR approach has the capability to adhere to core CR protocols, prompt Socratic questioning, and provide empathetic validation. However, issues of power imbalances, advice-giving, misunderstood cues, and excessive positivity reveal deeper challenges, including the potential to erode therapeutic rapport and ethical concerns. We also discuss design implications for leveraging LLMs in psychotherapy and underscore the importance of expert oversight to mitigate these concerns, which are critical steps toward safer, more effective AI-assisted interventions.
Recent technological advances have expanded the availability of high-throughput biological datasets, enabling the reliable design of digital twins of biomedical systems or patients. Such computational tools represent key reaction networks driving perturbation or drug response and can guide drug discovery and personalized therapeutics. Yet, their development still relies on laborious data integration by the human modeler, so that automated approaches are critically needed. The success of data-driven system discovery in Physics, rooted in clean datasets and well-defined governing laws, has fueled interest in applying similar techniques in Biology, which presents unique challenges. Here, we reviewed methodologies for automatically inferring digital twins from biological time series, which mostly involve symbolic or sparse regression. We evaluate algorithms according to eight biological and methodological challenges, associated to noisy/incomplete data, multiple conditions, prior knowledge integration, latent variables, high dimensionality, unobserved variable derivatives, candidate library design, and uncertainty quantification. Upon these criteria, sparse regression generally outperformed symbolic regression, particularly when using Bayesian frameworks. We further highlight the emerging role of deep learning and large language models, which enable innovative prior knowledge integration, though the reliability and consistency of such approaches must be improved. While no single method addresses all challenges, we argue that progress in learning digital twins will come from hybrid and modular frameworks combining chemical reaction network-based mechanistic grounding, Bayesian uncertainty quantification, and the generative and knowledge integration capacities of deep learning. To support their development, we further propose a benchmarking framework to evaluate methods across all challenges.
Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection, yet its mechanisms remain poorly understood. In this study, we applied quantum walk (QW), a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2-induced protein (SIP) networks. Compared to the conventional random walk with restart (RWR) method, QW demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. QW uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight QW as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID.
Individual cognitive stimulation therapy (iCST) is a non-pharmacological intervention for improving the cognition and quality of life of persons with dementia (PwDs); however, its effectiveness is limited by low adherence to delivery by their family members. In this work, we present the user-centered design and evaluation of a novel socially assistive robotic system to provide iCST therapy to PwDs in their homes for long-term use. We consulted with 16 dementia caregivers and professionals. Through these consultations, we gathered design guidelines and developed the prototype. The prototype was validated by testing it with three dementia professionals and five PwDs. The evaluation revealed PwDs enjoyed using the system and are willing to adopt its use over the long term. One shortcoming was the system's speech-to-text capabilities, where it frequently failed to understand the PwDs.
Weronika Bielska, Igor Jaszczyszyn, Pawel Dudzic
et al.
Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the proliferation of these protein design tools, their direct application to antibodies is often limited by the unique structural biology of these molecules. Here, we review the current computational methods for antibody design, highlighting their role in advancing computational drug discovery.
IntroductionDiabetic retinopathy (DR) is a common complication of diabetes. Retinal neuronal degeneration is an early event in DR, indicated by the declined electroretinogram (ERG). Dipeptide alanine-glutamine (Ala-Gln) is widely used as a nutritional supplement in the clinic and has anti-inflammatory effects on the gastrointestinal system. Studies also reported that glutamine has beneficial effects on diabetes. This study aimed to investigate the possible therapeutic effects of Ala-Gln in diabetic retinal neurodegeneration and to delineate its mechanism of action.MethodsThe Streptozotocin (STZ)-induced rat model was used as a DR model. ERG was used to measure the neuronal function of the retina. Western blot analysis was performed to test the expression of proteins. Immunofluorescence staining was used for the detection and localization of proteins.ResultsIn diabetic rats, the amplitudes of ERG were declined, while Ala-Gln restored the declined ERG. Retinal levels of inflammatory factors were significantly decreased in Ala-Gln-treated diabetic rats. Ala-Gln mitigated the declined levels of glutamine synthetase and ameliorated the upregulated levels of glial fibrillary acidic protein (GFAP) in diabetic retinas. Moreover, Ala-Gln upregulated the glycolytic enzymes pyruvate kinase isozymes 2 (PKM2), lactate dehydrogenase A (LDHA) and LDHB and stimulated the mTOR signaling pathway in diabetic retinas. The mitochondrial function was improved after the treatment of Ala-Gln in diabetic retinas.DiscussionAla-Gln ameliorates retinal neurodegeneration by reducing inflammation and enhancing glucose metabolism and mitochondrial function in DR. Therefore, manipulation of metabolism by Ala-Gln may be a novel therapeutic avenue for retinal neurodegeneration in DR.
Amir Raza, Jitendra Chaudhary, Azmat Ali Khan
et al.
Abstract Background Neurodegenerative disorders (NDs), primarily affecting the elderly, are marked by complex pathophysiological processes and are projected to become the second leading cause of death. Parkinson’s disease (PD), one of the most common NDs, is characterized by motor impairments due to reduced dopamine levels in the substantia nigra (SN), a crucial midbrain region involved in motor control and reward mechanisms. PD also impacts cognitive functions, potentially leading to depression and sleep disturbances. Recent research highlights the importance of MAO-B inhibitors in PD management, as these enzymes play a critical role in regulating neurotransmitter levels by catalyzing the oxidative deamination of intracellular amines and monoamine neurotransmitters. Result Computational virtual screening of several quinoline-based ligands against the target protein MAO-B (PDB ID: 1OJA) was performed using molecular docking simulation and ADMET studies to identify promising inhibitors for neurodegenerative disease treatment. The most active hit, Compound PA001, exhibited a MolDock score of − 207.76 kcal/mol. Subsequent investigation of 6-methoxy-2-(4-phenylpiperazin-1-yl)quinoline (Compound PA001) using molecular dynamics (MD) simulations with GROMACS revealed potent inhibition and significant interactions at key active site residues. MD simulations confirmed the stability of the Compound PA001-MAO-B complex under physiological conditions. Additionally, ADMET analysis demonstrated that Compound PA001 possesses favorable drug-like properties, including absorption, distribution, metabolism, excretion, and toxicity profiles. These findings underscore 6-methoxy-2-(4-phenylpiperazin-1-yl)quinoline (Compound PA001) as a promising candidate for developing new MAO-B inhibitors to treat neurodegenerative diseases. Conclusion The research highlighted 6-methoxy-2-(4-phenylpiperazin-1-yl)quinoline (Compound PA001) as a promising MAO-B inhibitor, exhibiting strong binding affinity, stability, and desirable drug-like characteristics for the treatment of neurodegenerative diseases. Among the top ten molecules, Compound PA001 was selected for molecular dynamics (MD) simulation using GROMACS. The compound showed potent inhibition, significant interactions with key active site residues, and stable complex formation under physiological conditions. ADMET analysis further confirmed its favorable pharmacokinetic profile.
Therapeutics. Pharmacology, Pharmacy and materia medica
Daniel Colon Hidalgo, Mariah Jordan, Janelle N. Posey
et al.
Pulmonary hypertension (PH) is a progressive disease marked by pulmonary vascular remodeling and right ventricular failure. Inflammation and oxidative stress are critical in PH pathogenesis, with early pulmonary vascular inflammation preceding vascular remodeling. Extracellular superoxide dismutase (EC-SOD), a key vascular antioxidant enzyme, mitigates oxidative stress and protects against inflammation and fibrosis in diverse lung and vascular disease models. This study utilizes a murine hypobaric hypoxia model to investigate the role of lung EC-SOD on hypoxia-induced platelet activation and platelet lung accumulation, a critical factor in PH-related inflammation. We found that lung EC-SOD overexpression blocked hypoxia-induced platelet activation and platelet accumulation in the lung. Though lung EC-SOD overexpression increased lung EC-SOD content, it did not impact plasma extracellular SOD activity. However, ex vivo, exogenous extracellular SOD treatment specifically blunted convulxin-induced platelet activation but did not blunt platelet activation with thrombin or ADP. Our data identify platelets as a novel target of EC-SOD in response to hypoxia, providing a foundation to advance the understanding of dysregulated redox signaling and platelet activation in PH and other chronic hypoxic lung diseases.
Percutaneous coronary intervention (PCI) is currently the main method for treating coronary artery stenosis, but the incidence of restenosis after PCI is relatively high. Restenosis, the narrowing of blood vessels by more than 50% of the normal diameter after PCI, severely compromises the therapeutic efficacy. Therefore, preventing postinterventional restenosis is important. Vascular restenosis is mainly associated with endothelial injury, the inflammatory response, the proliferation and migration of vascular smooth muscle cells (VSMCs), excessive deposition of extracellular matrix (ECM) and intimal hyperplasia (IH) and is usually prevented by administering antiproliferative or anti-inflammatory drugs through drug-eluting stents (DESs); however, DESs can lead to uncontrolled drug release. In addition, as extracorporeal implants, they can cause inflammation and thrombosis, resulting in suboptimal treatment. Therefore, there is an urgent need for a drug carrier with controlled drug release and high biocompatibility for in vivo drug delivery to prevent restenosis. The development of nanotechnology has enabled the preparation of nanoparticle drug carriers with low toxicity, high drug loading, high biocompatibility, precise targeting, controlled drug release and excellent intracellular delivery ability. This review summarizes the advantages of nanoparticle drug carriers for treating vascular restenosis, as well as how nanoparticles have improved targeting, slowed the release of therapeutic agents, and prolonged circulation in vivo to prevent vascular restenosis more effectively. The overall purpose of this review is to present an overview of nanoparticle therapy for vascular restenosis. We expect these findings to provide insight into nanoparticle-based therapeutic approaches for vascular restenosis.
Jorge Echeverri,1 Rui Martins,2,3 Kai Harenski,4 J Patrick Kampf,5 Paul McPherson,5 Julien Textoris,6,7 Jay L Koyner8 1Global Medical Affairs, Baxter Healthcare Corporation, Deerfield, IL, USA; 2University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; 3Health Economics; Global Market Access Solutions, Saint Prex, Switzerland; 4Global Medical Affairs, Baxter Deutschland GmbH, Unterschleissheim, Germany; 5Biomarker Research, Astute Medical Inc. (a bioMerieux Company), San Diego, CA, USA; 6Medical Affairs; bioMerieux, SA, Lyon, France; 7Service d´Anesthésie et de Réanimation; Hospices Civils de Lyon, Lyon, France; 8Section of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USACorrespondence: Rui Martins, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, the Netherlands, Email r.m.lopes.martins@umcg.nlBackground: Approximately 24% of hospitalized stage 2– 3 acute kidney injury (AKI) patients will develop persistent severe AKI (PS-AKI), defined as KDIGO stage 3 AKI lasting ≥ 3 days or with death in ≤ 3 days or stage 2 or 3 AKI with dialysis in ≤ 3 days, leading to worse outcomes and higher costs. There is currently no consensus on an intervention that effectively reverts the course of AKI and prevents PS-AKI in the population with stage 2– 3 AKI. This study explores the cost-utility of biomarkers predicting PS-AKI, under the assumption that such intervention exists by comparing C-C motif chemokine ligand 14 (CCL14) to hospital standard of care (SOC) alone.Methods: The analysis combined a 90-day decision tree using CCL14 operating characteristics to predict PS-AKI and clinical outcomes in 66-year-old patients, and a Markov cohort estimating lifetime costs and quality-adjusted life years (QALYs). Cost and QALYs from admission, 30-day readmission, intensive care, dialysis, and death were compared. Clinical and cost inputs were informed by a large retrospective cohort of US hospitals in the PINC AI Healthcare Database. Inputs and assumptions were challenged in deterministic and probabilistic sensitivity analyses. Two-way analyses were used to explore the efficacy and costs of an intervention preventing PS-AKI.Results: Depending on selected costs and early intervention efficacy, CCL14-directed care led to lower costs and more QALYs (dominating) or was cost-effective at the $50,000/QALY threshold. Assuming the intervention would avoid 10% of PS-AKI complications in AKI stage 2– 3 patients identified as true positive resulted in 0.066 additional QALYs and $486 reduced costs. Results were robust to substantial parameter variation.Conclusion: The analysis suggests that in the presence of an efficacious intervention preventing PS-AKI, identifying people at risk using CCL14 in addition to SOC is likely to represent a cost-effective use of resources.Keywords: acute kidney injury, dialysis, biomarkers, nephrology, cost effectiveness
TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports data in time series, static, and eventmodalities and provides an interface for prediction, causal inference, and time-to-event analysis, as well as common preprocessing utilities and model interpretability methods. The library aims to facilitate innovation in the medical ML space by offering a standardized temporal setting toolkit for model development, prototyping and benchmarking, bridging the gaps in the ML research, healthcare professional, medical/pharmacological industry, and data science communities. TemporAI is available on GitHub (https://github.com/vanderschaarlab/temporai) and we welcome community engagement through use, feedback, and code contributions.
Sarita Garg, Tarun K. Garg, Isabelle R. Miousse
et al.
Exposure to high doses of radiation, accidental or therapeutic, often results in gastrointestinal (GI) injury. To date, there are no therapies available to mitigate GI injury after radiation exposure. Gamma-tocotrienol (GT3) is a promising radioprotector under investigation in nonhuman primates (NHP). We have shown that GT3 has radioprotective function in intestinal epithelial and crypt cells in NHPs exposed to 12 Gy total-body irradiation (TBI). Here, we determined GT3 potential in accelerating the GI recovery in partial-body irradiated (PBI) NHPs using X-rays, sparing 5% bone marrow. Sixteen rhesus macaques were treated with either vehicle or GT3 24 h prior to 12 Gy PBI. Structural injuries and crypt survival were examined in proximal jejunum on days 4 and 7. Plasma citrulline was assessed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Crypt cell proliferation and apoptotic cell death were evaluated using Ki-67 and TUNEL staining. PBI significantly decreased mucosal surface area and reduced villous height. Interestingly, GT3 increased crypt survival and enhanced stem cell proliferation at day 4; however, the effects seemed to be minimized by day 7. GT3 did not ameliorate a radiation-induced decrease in citrulline levels. These data suggest that X-rays induce severe intestinal injury post-PBI and that GT3 has minimal radioprotective effect in this novel model.
Abstract Background Hydroquinone (HQ) is a phenolic metabolite of benzene with a potential risk for hematological disorders and hematotoxicity in humans. In the present study, an integrative analysis of microRNA (miRNA) and mRNA expressions was performed to identify potential pathways and miRNA-mRNA network associated with benzene metabolite hydroquinone-induced hematotoxicity. Methods K562 cells were treated with 40 μM HQ for 72 h, mRNA and miRNA expression changes were examined using transcriptomic profiles and miRNA microarray, and then bioinformatics analysis was performed. Results Out of all the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) induced by HQ, 1482 DEGs and 10 DEMs were up-regulated, and 1594 DEGs and 42 DEMs were down-regulated. HQ-induced DEGs were involved in oxidative stress, apoptosis, DNA methylation, histone acetylation and cellular response to leukemia inhibitory factor GO terms, as well as metabolic, Wnt/β-catenin, NF-κB, and leukemia-related pathways. The regulatory network of mRNAs and miRNAs includes 23 miRNAs, 1108 target genes, and 2304 potential miRNAs-mRNAs pairs. MiR-1246 and miR-224 had the potential to be major regulators in HQ-exposed K562 cells based on the miRNAs-mRNAs network. Conclusions This study reinforces the use of in vitro model of HQ exposure and bioinformatic approaches to advance our knowledge on molecular mechanisms of benzene hematotoxicity at the RNA level.