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Frustration-competing interactions that cannot be simultaneously optimized-shapes energy landscapes in proteins and soft matter, but has rarely been exploited as a programmable design principle in nucleic acids. Here we demonstrate that sequence-encoded frustration programmes DNA G-quadruplex folding, yielding stable "abridged" architectures with fewer guanine tetrads than the sequence would nominally permit. Using an integrated structural, spectroscopic, thermodynamic, and computational approach, we map a frustration-biased folding landscape featuring a thermodynamically stabilized G-triplex intermediate, whose identity we assign via TD-DFT computed electronic circular dichroism spectra, and resolve the dominant unfolding pathway at atomic resolution. These results demonstrate programmable frustration as a predictive design principle for controlling nucleic acid topology and dynamics, offering new strategies for engineering functional DNA-based systems and for interpreting genomic G-quadruplex plasticity.

HLA-E's function as an immune checkpoint in cancer depends on its display of the canonical peptide (VL9), yet direct profiling of these complexes has been stymied by lack of specific reagents. We now introduce ABX002, a fully human TCR-mimic antibody capable of recognizing all tested VL9/HLA-E complexes with high affinity and specificity in situ. Using ABX002, we reveal that canonical VL9/HLA-E surface expression is tightly controlled by inflammatory cues, remarkably infrequent on tumors without stimulation, and almost absent from immune cells except myeloid-lineage cells. ABX002 unlocks cell-type and context-specific quantification of HLA-E antigen presentation, providing unprecedented insight into immune evasion and regulation. It additionally disrupts the NKG2A checkpoint, restoring cytotoxic lymphocyte function and enabling mechanistic and therapeutic mapping of HLA-E restricted peptide presentation. Together, these findings position ABX002 as a transformative tool for dissecting the landscape and biology of canonical peptide restriction in cancer immunity.

Circular RNAs (circRNAs) are emerging as key regulators of gene expression, synaptic plasticity, and neuronal function in Alzheimer's disease (AD). Here, we characterize the biological actions of circPDE4B, a highly expressed circRNA markedly reduced in AD. circPDE4B knockdown in neuronal progenitor cells was combined with RNA sequencing to identify regulated pathways. circPDE4B affinity purification identified major protein and micro RNA (miRNA) interactors. Assays of translation and autophagy integrated circPDE4B actions. We found that circPDE4B knockdown inhibited translation through a mechanism mediated by its major interacting protein, gem-associated protein 5. circPDE4B knockdown also decreased mechanistic target of rapamycin and correspondingly enhanced autophagic flux. Consistent with these actions, circPDE4B knockdown strongly attenuated microtubule-associated protein tau pathology in a 3D human assembloid model of tauopathy. Collectively, our findings identify circPDE4B as a regulator of neuronal homeostasis that integrates translation, autophagy, and miRNA pathways, highlighting a potentially important role in the pathophysiology of AD.

Stromal cells (SCs) provide important instructive cues for endothelial cells (ECs) during both normal and neoplastic vascularization. While the tissue-specific origins of ECs are important for function, the impact of SC identity on microvascular function and concurrent changes in tissue mechanical properties remains unclear. We previously showed robust microvasculature forms by codelivery of ECs and supportive SCs, and that SC identity regulates the rate of neovascularization and vessel functionality. Here, we used active microrheology (AMR) and traditional macrorheology to evaluate the dynamics of both local and global ECM mechanics in a 3D EC-SC co-culture model of vascular morphogenesis. Human umbilical vein ECs were co-embedded with either highly contractile lung fibroblasts (LFs) or significantly less contractile bone marrow-derived mesenchymal stromal cells (MSCs) within fibrin gels across various cell-seeding densities. By day 14, interconnected vascular networks developed, with rates of capillary morphogenesis higher in EC-LF than in EC-MSC co-cultures. Vascularization in EC-LF co-cultures was accompanied by ECM stiffening across length scales, in part due to cell contractility. AMR revealed highly heterogeneous local stiffness, with values ranging over 2 orders of magnitude in the same construct. AMR also identified the emergence of local stiffness anisotropy in the direction of capillary growth for EC-LF but not EC-MSC co-cultures by day 14, which was accompanied by significant matrix remodeling and local degradation. Together, these data suggest that different SC populations, through active cell contractility-dependent stiffening and matrix degradation, induce local mechanical cues that differentially influence vascular development. These results highlight the importance of the mechanobiological effects of SCs on the ECM in vascularized engineered tissues.

The binding of an activator (target nucleic acid) to a crRNA-Cas ribonucleoprotein (RNP) in CRISPR systems is critical to the activation, kinetics, and specificity of the CRISPR technology. Key to this activation process is the interaction between the protospacer region of the activator and the spacer region of the crRNA in the RNP complex. However, how the nucleotides in the spacer region of the crRNA contribute to the kinetics of RNP binding is not well characterized. We report here profiling of the kinetically critical regions in the process of RNP binding to activators (RNA targets). We introduced the concept and strategy of kinetic manipulators, which enabled mapping of the seed regions (6-9 nucleotides within the spacer that is sensitive to mismatches) of the CRISPR-Cas13a system, including the LbuCas13a and LwaCas13a homologs. The characterization of the binding kinetics and the introduction of kinetic manipulators provided the foundation for a new kinetic approach to improve the specificity of CRISPR techniques without sacrificing the activity. Profiling the kinetically critical regions in the CRISPR system and designing corresponding manipulators maximized the kinetic differences, between the on-target and off-target, and increased discrimination of single-nucleotide mismatches.

BackgroundSleep is an essential component of memory consolidation and waste clearance, including pathology associated with Alzheimer's disease (AD). Facilitation of sleep decreases amyloid-β (Aβ) and tau accumulation and is important for memory consolidation.ObjectiveWe previously found that 6-month female 3xTg-AD mice were impaired at spatial reorientation learning and memory. Given the association between sleep and AD, we assessed the impact of added rest on impaired spatial reorientation that we previously observed.MethodsWe randomly assigned 3xTg-AD mice to a sleep (n = 7; 50-60 min pre- & post-task induced rest) or a non-sleep group (n = 7; remained in home cage pre- & post- task). Mice in both groups were compared to non-Tg, age-matched, non-sleep controls (n = 6). To confirm that our rest condition induced sleep, we performed the same experiment with rest sessions for both 3xTg-AD and non-Tg mice (n = 5/group) implanted with recording electrodes to capture local field potentials, which were used to classify sleep states. Markers of pathology (AT8, 6E10, M78, and M22) were also assessed in the parietal-hippocampal network, where we previously showed pTau (AT8) positive cell density predicted spatial reorientation ability.ResultsWe found that 3xTg-AD sleep mice were unimpaired at spatial reorientation compared to non-Tg mice and performed better than 3xTg-AD non-sleep mice (replicating our previous work). This recovered behavior was apparent despite no change in the density of pathology-positive cells. Further, theta-gamma coupling during sleep may explain the facilitated cognition in 3xTg-AD sleep mice, suggesting brain activity patterns during sleep may mediate the restored cognition.ConclusionsImproving sleep in early stages of AD pathology offers a promising approach for facilitating memory consolidation and improving cognition.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer (BC) and constitutes approximately 15-20% of all BC cases. This subtype has the most aggressive behavior and the worst prognosis. Numerous studies have been conducted over the past several decades to address the lack of clinically available treatment options. In particular, potential markers targeting effective treatment options have been actively studied. However, these efforts were hindered by the complex mechanisms of TNBC, and no study has demonstrated a model with a predictive performance exceeding 0.85. This study developed TNBC prognosis predictive models with a predictive performance exceeding 0.94. Applying the nine selected markers to five independent datasets demonstrated their potential as TNBC-specific prognostic markers. Most of these genes (including GPR61, PZP, IGFL1, and AHCTF1) are associated with overall survival (OS) in patients with TNBC. Based on these results, these nine selected genes may serve as prognostic markers for OS in patients with TNBC.

Rheumatic diseases are chronic, immune-mediated conditions characterized by significant heterogeneity in presentation and disease course. However, current clinical approaches often rely on snapshot-based assessments that fail to capture the complex longitudinal evolution of these conditions. To address these limitations and support the implementation of precision medicine, we present the design for the Rheumatic Digital Twin, a novel, modular conceptual framework intended to integrate heterogeneous multimodal data, ranging from electronic health records and clinical notes to imaging and omics, into a dynamic, computational representation of the patient journey. Our theoretical architecture addresses challenges related to data silos and variable availability of data modalities through a multistage approach that envisions the use of domain-specific foundation models to independently process distinct data modalities. To effectively model the temporal progression inherent in chronic diseases, the proposed design utilizes Transformer architectures, leveraging self-attention mechanisms to treat patient events, such as lab results or medication changes, as sequential data tokens. We describe how these unimodal representations would subsequently be fused via joint embedding techniques to construct a shared, multimodal representational space. Envisioned to function analogously to a recommender system, the Rheumatic Digital Twin framework is modeled to map patients into a latent space where proximity reflects clinical and biological similarity. By identifying "nearest neighbors," historical patients with comparable trajectories, the system aims to enable in silico cohorting, theoretically allowing clinicians to forecast key clinical events, predict treatment responses, and identify likely disease courses based on the outcomes of similar peers.

Biodegradable implantable and wearable biomedical sensors have attracted growing attention as a promising alternative to conventional permanent electronic devices, offering transient functionality that eliminates the need for secondary surgical removal and mitigates electronic waste accumulation. Among various sensing modalities, capacitive sensors have emerged as a particularly attractive platform for monitoring mechanically derived physiological signals owing to their structural simplicity, low power consumption, and compatibility with soft materials. Despite extensive academic progress, however, the clinical translation and commercialization of biodegradable capacitive sensors remain limited. A central challenge arises from the inherent trade-off between sensing sensitivity and operational lifetime. Structural and material modifications that enhance sensitivity often accelerate degradation, whereas strategies designed to prolong functional lifetime can compromise mechanical compliance and signal fidelity. Achieving a precise balance between these competing requirements is therefore critical for practical deployment in biomedical applications. In this review, we systematically examine biodegradable capacitive sensors with a focus on sensitivity enhancement and lifetime modulation as the two key determinants of device performance. We summarize design strategies for improving sensitivity through sensor architecture optimization and dielectric layer engineering, and we review encapsulation approaches for controlling degradation behavior and functional lifetime. By critically analyzing how these complementary strategies are selectively implemented to meet the distinct demands of wearable and implantable biomedical applications, this review provides practical design guidelines and highlights future research directions aimed at advancing biodegradable capacitive sensors toward clinical implementation and scalable manufacturing.

The global aging population has raised concerns regarding age-related health issues like osteoporosis and bone fractures. To address these conditions, bone-like scaffolds containing bioactive molecules and biomaterials have been widely studied. However, uncontrolled burst release and delivery of drugs can incur negative side effects. To overcome this issue, a collagen-hydroxyapatite scaffold (COHAS) that can sequentially deliver Bone morphogenetic protein-2 (BMP-2) and Osteoprotegerin fused to the Fc region of immunoglobulin (OPG-Fc) is synthesized. The COHAS comprises a collagen-hydroxyapatite matrix containing BMP-2 and numerous poly-lactic glycolic acid (PLGA) microspheres with OPG-Fc, dispersed in the matrix. The dispersion of PLGA microspheres enables the retardation of OPG-Fc release compared to BMP-2 release. The controlled sequential delivery of BMP-2 and OPG-Fc exhibits synergistic potential in promoting new bone formation by simultaneously activating osteoblasts and deactivating osteoclasts. This investigation revealed that the COHAS co-loaded with BMP-2 and OPG-Fc possesses excellent cell viability and enhanced osteogenic properties in vitro. In vivo assessment via implantation of the drug-loaded COHAS using an 8 mm-calvarial defect rat model demonstrated high efficacy of new bone formation with good biocompatibility. Hence, these findings provide valuable insights for developing therapeutic scaffolds capable of sequential release of multiple drugs, with the potential to extend a cell-free treatment system for bone regeneration.

The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.

Cancer remains a leading cause of global mortality, where early detection and continuous monitoring are critical for improving therapeutic outcomes. However, conventional diagnostic techniques suffer from high cost, long assay times, invasiveness, and limited sensitivity at early disease stages. In this context, electrochemical biosensors have emerged as promising alternatives due to their rapid response, cost effectiveness, low sample volume requirements, high sensitivity, and compatibility with point-of-care testing. Recent advances highlight the pivotal role of electrode materials in translating biomolecular recognition into reliable electrical signals, particularly for detecting cancer-associated biomarkers near clinically relevant threshold levels. Among emerging semiconducting materials, molybdenum disulfide (MoS2) has attracted significant attention owing to its layered two-dimensional structure, tunable bandgap, abundant electroactive edge sites, and versatile surface chemistry. This review critically examines recent progress in MoS2 nanohybrid based electrochemical biosensors for cancer detection, focusing on material design strategies, fabrication approaches, and biomarker-specific sensing architectures. Emphasis is placed on MoS2 hybrid systems incorporating carbon nanostructures, metal nanomaterials, metal oxides, polymers, MOFs, and other nanostructures, as well as their roles in enhancing signal transduction and real-time applicability in clinical samples. Finally, current challenges are outlined towards developing clinically translatable electrochemical sensing systems for early cancer detection and disease diagnosis. Furthermore, this review highlights the potential of integrating semiconductor-based electrochemical sensors with threshold-based diagnostic strategies as a promising future direction for point-of-care cancer detection.

Monovision is a common correction for presbyopia that focuses one eye at far distances and the other at near distances, resulting in a difference in blur between the eyes. Because blur increases the speed of visual processing by a few milliseconds, these optical conditions can induce dramatic misperceptions of the distances and three-dimensional directions of moving objects. To date, the illusion has been demonstrated only in individuals without presbyopia. We analyze both the induced processing speed differences and the visibility of the resulting illusions in presbyopic (n = 17, 22.2 ± 5.0 years) and non-presbyopic (n = 36, 54.4 ± 5.9 years) populations, with proportions approximately matching those in the general population. Participants viewed two horizontally moving strips of bars on an autostereoscopic display with interocular blur and light-level differences, and reported which strip appeared closer in depth. Interocular delay and an illusion visibility index-the ratio of interocular delay and the detection threshold-were computed for each participant in each condition. Delays were highly consistent in both the presbyopic and general populations. Interocular differences in optical blur and light-level caused highly consistent interocular differences in processing speed in all participants, although they created visible (suprathreshold) illusions in only a subset of participants. This subset, however, included individuals with processing delays that were many times larger than the detection threshold. Such individuals are likely to be afflicted by large, highly visible illusions in real-world conditions. Methods for identifying high-risk individuals and for reducing or eliminating the illusions are discussed.

Biocompatibility is the defining determinant for the clinical translation of implantable biomedical devices. As bioelectronics evolve toward softer, electroactive, and bioresorbable systems, traditional definitions of biocompatibility-largely focused on cytotoxicity and gross inflammation-are no longer sufficient. Instead, emerging bioresorbable devices demand multidimensional biocompatibility, encompassing immune modulation, mechanical and electrical matching, controlled degradation, and functional stability over clinically relevant time windows. This review offers a biocompatibility-focused overview of recent advances in bioresorbable materials and electronics, known as transient devices. Emphasis is placed on how material selection, device architecture, and degradation pathways jointly govern immune responses and tissue integration. A comparative framework is introduced to relate material classes to immune outcomes and degradation behaviors, and current biocompatibility evaluation metrics and international standards (ISO 10993) are critically discussed. Finally, we propose design guidelines and future research directions to accelerate the translation of next-generation bioresorbable electronics.

Although zero-phase lag between cortical regions has been generally regarded as the optimal state, it has also been suggested that a non-zero phase delay of electroencephalography (EEG) signals in the gamma frequency band between bilateral parietal areas may have a significant meaning. Indeed, the phase delays of the gamma band between the cortical regions are reportedly associated with the direction of communication between the regions. In this study, we aimed to demonstrate synchrony with phase lag between cortical regions involved in visuospatial working memory (VWM) performance. We used EEG to compute the weighted phase lag index (wPLI) from the EEG signals concurrently recorded during the VWM task. An increase in wPLI value between the electrodes positioned over the bilateral parietal areas was observed during the VWM task. The wPLI values positively correlated with the lateralization index (LI) between the left and right visual hemifields. Furthermore, event-related desynchronization of gamma band activity is observed when wPLI peaked. Our findings suggest that phase lagged synchronization of high gamma band over bilateral parietal areas may reflect which information to prioritize during processing of VWM.

ObjectiveTo systematically review literature on the use of artificial intelligence (AI) and machine learning (ML) models for detecting velopharyngeal dysfunction (VPD) in patients with cleft palate.DesignSystematic review conducted in accordance with PRISMA guidelines (PROSPERO CRD420251034524).SettingStudies published were identified through EMBASE, ProQuest, Google Scholar, and PubMed.ParticipantsA total of 3967 participants contributed 92,323 training samples. Internal validation included 2331 controls and 2449 VPD cases, generating 81,143 validation samples. Ages ranged from 1 to 93 years.InterventionsML models were trained on speech features such as mel frequency cepstral coefficients (MFCCs) and constant Q cepstral coefficients (CQCCs) to classify or validate VPD-related speech outcomes.Main Outcome Measure(s)Reported performance metrics included accuracy, precision, recall, F1-score, sensitivity, specificity, and Pearson correlation coefficient (PCC). External validation was assessed when reported.ResultsOf 455 screened articles, 34 met the inclusion criteria. Support vector machines were the most commonly used models (16/34, 47.1%), followed by convolutional neural networks (6/34, 17.6%) and deep neural networks (2/34, 5.9%). Across studies reporting performance metrics, midpoint estimates yielded a mean accuracy of 82.9%, precision of 86.7%, F1-score of 0.88, sensitivity of 80.5%, specificity of 82.2%, and PCC of 0.58. Only 3 studies (3/34, 8.8%) performed external validation.ConclusionsAI/ML models demonstrate promise for VPD detection with encouraging performance. Inconsistent reporting, reliance on engineered features, and limited external validation restrict generalizability. No clinically deployable model has yet been achieved.

In this essay, I consider the "social life" of digital twins in translational medicine, exploring how the United States is culturally unprepared for the arrival of digital twins at whole person scales. By looking more closely at our anticipated individual and collective interactions with digital twins for biomedical research and health care purposes, I attempt to highlight how our current approach to biomedical innovation could impede the realization of precision medicine rather than enable it. Extensive translational bioethics research-including specifically more deliberate anthroengineering research-is urgently needed so that we can better understand how these dynamic, data intensive, artificial intelligence-enabled technologies can be responsibly developed, organized, and engaged, and so that we can co-create the necessary cultural conditions for us all to thrive.

In lithium-sulfur batteries, separator modification is a promising approach to suppress the migration of polysulfides and accelerate reaction kinetics. Herein, we propose a supramolecular gel pyrolysis-derived strategy to synthesize a NiCo-N-doped porous carbon/carbon nanotube hybrid (NiCo-NPC/CNT) for separator modification. The supramolecular gel-derived synthesis produces a three-dimensional (3D) porous carbon architecture that effectively anchors the NiCo alloy nanoparticles. Subsequently, the NiCo alloy nanoparticles act as catalysts to induce the in situ growth of carbon nanotubes during pyrolysis, thereby enhancing electrical conductivity and catalytic activity. These structural features synergistically promote physical adsorption and chemical catalytic ability, thereby accelerating the redox reactions of polysulphides. The NiCo-NPC/CNT-modified separator (NiCo-NPC/CNT@PP) cell exhibited remarkable rate capability (890.4 mAh g-1 at 3C) and cycling stability (649.0 mAh g-1 after 500 cycles at 1C). Furthermore, NiCo-NPC/CNT@PP shows excellent cycling stability under high sulfur loading (11 mg cm-2) and lean-electrolyte condition (6 µL mg-1), retaining 269.6 mAh g-1 after 120 cycles at 0.2C.

To identify structural alterations of Descemet's membrane (DM) in bullous keratopathy (BK) and to explore their association with intracellular dark endothelial spots (IDESs) observed by specular microscopy. This multicenter, retrospective, observational study included 75 eyes that underwent endothelial keratoplasty for corneal endothelial dysfunction. Based on preoperative clinical diagnosis, eyes were classified as having Fuchs endothelial corneal dystrophy (FECD)-related or non-FECD BK. DM specimens were collected during endothelial keratoplasty and analyzed as flat-mounted preparations using phase-contrast microscopy. IDESs were evaluated preoperatively by masked assessment using specular microscopy. Among the 75 eyes, 25 were clinically diagnosed with FECD. Of the remaining 50 eyes with BK, 15 showed no characteristic histological abnormalities, 11 exhibited guttae-like changes, and 24 demonstrated a distinct and previously unrecognized DM alteration, termed dome-shaped protrusions (DSPs). Specular microscopy images suitable for IDES evaluation were available in a subset of cases. IDESs were detected in 10 of 18 DSP-positive eyes and in 1 of 10 DSP-negative eyes, indicating a significant association between DSPs and IDESs (odds ratio 11.25; P < 0.05). DSPs represent a distinct structural alteration of the DM in non-FECD BK and are significantly associated with IDESs observed by specular microscopy. These findings provide insight into the heterogeneity of endothelial failure in BK and suggest a link between clinical imaging findings and underlying DM morphology. Dome-shaped protrusions provide a histopathological correlate for intracellular dark endothelial spots observed by specular microscopy and may support improved phenotyping of endothelial dysfunction in non-Fuchs endothelial corneal dystrophy bullous keratopathy.