The Journal of Physical Chemistry Letters

Biomolecular condensates formed by intrinsically disordered proteins require molecular models that accurately describe proteins in both dilute solution and condensed phases. Explicit-solvent coarse-grained models offer an attractive balance between chemical resolution and computational efficiency. Yet, it remains unclear whether improving dilute-state properties is sufficient to obtain an accurate description of condensates. Here, we address this question by introducing minimal modifications to the Martini 3 force field that combine recent advances in bonded interactions with refined protein-water interactions and strengthened glycine self-interactions, while preserving the underlying chemical transferability of the model. The resulting model substantially improves the description of single-chain conformations across a diverse benchmark of disordered proteins. We then investigate phase separation of the well-characterized low-complexity domain of heterogeneous nuclear ribonucleoprotein A1 and its sequence variants. The model reproduces several key physicochemical properties of biomolecular condensates, including chain expansion in the dense phase, sequence-dependent intermolecular contacts, protein diffusion and its relation to single-chain dimensions, and hydration, while revealing quantitative limitations in condensate density, phase equilibria, and ion partitioning. Our results show that improving dilute-state behaviour translates into a better description of condensed-phase properties, including condensate density, but is not sufficient to quantitatively reproduce the equilibrium between the dilute and dense phases.

Nanodiscs and bicelles are widely used as membrane mimetics for structural studies of membrane-associated systems. Studies have reported that their magnetic alignment behavior and phase stability are highly sensitive to composition and temperature. In this study, we systematically investigate the effects of cholesterol on bicelle formation and magnetic alignment in DMPC + 0.2 glycyrrhizic acid (GA) systems using a combined 31P and 14N solid-state NMR experimental and simulation-based approach. Temperature dependent 31P NMR spectra reveal a clear transition from vesicle dominant to aligned bicelles/nanodsics phase, while 1N quadrupolar splitting and lineshape analysis provides quantitative insights into heterogeneous lipid bilayer populations, distinguishing large aligned nanodiscs (B(L)), small nanodiscs (B(S)), and isotropic/random components (B(R)). A strong correlation is observed between the 31P derived bicelle fraction and the 14N B(L) population, confirming that macroscopic alignment in the presence of an external magnetic field directly reflects the growth of large, well-ordered nanodiscs. Cholesterol is found to play a critical dual role by modulating membrane order and curvature. At low cholesterol concentration (0 to 5 mole percent), nanodiscs alignment occurs gradually with increasing temperature, while at higher cholesterol concentration (15 to 25 mole percent), the alignment is delayed and accompanied by broader spectral features, indicating structural heterogeneity. Notably, 10 mole percent cholesterol consistently provides the optimal balance, enabling efficient temperature dependent conversion to aligned bicelles while maintaining high B(L) populations (about 70-80 percentage) and minimal isotropic fractions. In contrast, higher cholesterol maintains significant B(S) and B(R) populations, even at elevated temperature. The 14N quadrupolar coupling (Cq is approximately 8.5 to 9.2 kHz for aligned nanodiscs) remains nearly invariant across compositions, showing that cholesterol does not change local headgroup dynamics but instead redistributes lipid populations. These findings establish a combined 31P and 14N solid -state NMR approach provides a valuable platform for quantitatively correlating membrane structure, dynamics, and alignment, offering practical guidelines for optimizing bicelle systems for high resolution solid-state NMR studies of membrane associated biomolecules.

Amyloid fibrils are highly ordered protein aggregates characterized by a conserved cross-{beta}-sheet architecture despite originating from structurally diverse precursor proteins. Growing evidence suggests that interactions between different amyloidogenic proteins can modulate aggregation pathways through heterologous cross-seeding; however, the influence of seed polymorphism on the structure and biological properties of cross-seeded fibrils remains poorly understood. Here, we investigated the cross-seeding of native human insulin by two structurally distinct polymorphs of hen egg-white lysozyme (HEWL): flexible fibrils (FFs) and rigid fibrils (RFs). Native insulin remained stable under physiological conditions and underwent spontaneous fibrillation only under acidic conditions. In contrast, both HEWL polymorphs efficiently induced insulin aggregation at physiological pH, bypassing the nucleation barrier. Thioflavin T fluorescence, circular dichroism spectroscopy, and transmission electron microscopy revealed that lysozyme FFs templated the formation of insulin flexible fibrils (IFFs), whereas lysozyme RFs produced insulin rigid fibrils (IRFs), demonstrating that the structural characteristics of the parental HEWL polymorphs were propagated during heterologous cross-seeding. The toxicity of the resulting insulin fibrils was evaluated in SH-SY5Y neuronal cells and CCF-STTG1 astrocytes. IFFs exhibited minimal cytotoxicity and only subtle morphological alterations, whereas IRFs caused modest reductions in cell viability accompanied by more pronounced cellular damage. These findings demonstrate that the structural polymorphism of HEWL fibrils governs both the architecture and biological activity of cross-seeded insulin fibrils, highlighting amyloid polymorphism as an important determinant of heterologous amyloid propagation and a potential design principle for engineering functional amyloid-based biomaterials and protein delivery platforms.

Contact inhibition of proliferation (CIP) enables epithelial tissues to self-regulate growth and maintain tissue homeostasis. However, how cell-level mechanical contact, tissue-scale structural order, and proliferation kinetics interplay remains a fundamental open question in living matter physics. Here, we present a particle-based model of a confluent epithelial monolayer governed by overdamped dynamics, where individual cells interact via a two-dimensional hard core- soft shoulder potential. By comparing structural evolution during quasistatic densification with previously reported experimental division kinetics, we find that the dynamics of proliferation arrest mimics the onset of direct steric contacts between the hard cores of the shell. Identifying hard core contacts as the physical driver of CIP, we couple our mechanical model with a stochastic Monte Carlo division scheme in which the instantaneous division rate decreases to zero from an intrinsic value as the number of hard core contact increases to six from zero. We demonstrate that for high intrinsic division rates, the cellular densification outpaces mechanical relaxation. This kinetic mismatch drives premature hard-core contact formation, shifts the onset of jamming and contact inhibition to lower packing fractions, and induces increasingly disordered transient configurations before the tissue universally converges to a hexagonal close-packed limit. Our model's predicted division kinetics and structural order evolution are consistent with epithelial monolayer experiments, both reported and our own. This minimal physical framework links single-cell steric contact mechanics directly to tissue-scale growth regulation and structural evolution.

Investigating the conformational dynamics of intrinsically disordered proteins (IDPs) is essential to understanding how their structural heterogeneity underlies function and how their dysregulation contributes to diseases. Here, we utilized an MspA nanopore-based approach for studying the conformational dynamics and interactions of IDPs at the single-molecule level. The platform was demonstrated using the intrinsically disordered transactivation domain of tumor suppressor p53 (p53-TAD), one of the important proteins in cancer biology. We showed that MspA can stably capture p53-TAD and resolve up to six distinct current states with frequent interconversions, revealing a rich conformational landscape. The nanopore also detected the effect of a cancer-associated double mutational variant, N29K/N30D. Combining experiments with steered molecular dynamics simulations, we showed that the mutant sampled compact conformational states more frequently than wild type, consistent with previous NMR studies. Importantly, the MspA platform enabled direct monitoring of E3 ligase MDM2 binding to p53-TAD and resolved how this interaction is inhibited by anti-cancer compound epigallocatechin gallate (EGCG). Notably, EGCG stabilizes one of the six states sampled by p53-TAD, providing a mechanistic explanation for its inhibitory effect. Together, these findings demonstrate the promise of the nanopore platform for label-free monitoring of IDP conformational dynamics, modulation, binding and inhibition at single-molecule resolution.

Obtaining sufficient amounts of pure protein for downstream applications such as native mass spectrometry (nMS) is often challenging, especially when expression yields are low or proteins are unstable. In these cases, the commonly required buffer-exchange step is a major bottleneck, as it often leads to substantial protein loss and compromises biophysical characterization. These challenges are exacerbated in insect or eukaryotic expression systems, where protein yields are typically lower than in bacteria, making protein loss during purification particularly detrimental. Standard lysis and purification buffers contain non-volatile components such as Tris, phosphate, HEPES and sodium chloride, which form adducts during electrospray ionization (ESI) interfering with the signal and therefore must be re-moved prior to nMS. To address protein loss associated with this mandatory buffer-exchange, we evaluated an affinity-purification workflow, in which non-volatile salts are excluded throughout purification and proteins are directly eluted into nMS-compatible ammonium acetate-based buffers. This approach eliminates the need for a separate buffer exchange step and enables rapid nMS analysis immediately after affinity purification. We show that common eluents used in His- and Strep- based affinity purification, such as imidazole, biotin, and desthiobiotin, are well tolerated at relevant concentrations, allowing acquisition of high-quality spectra suitable for determining protein stoichiometry and for monitoring enzymatic or assembly processes. Together, this fast-track affinity workflow increases protein recovery, shortens sample preparation and complements online exchange protocols, which are less suited for monitoring processes. It hence expands the applicability of nMS to proteins and protein complexes that are difficult to obtain in sufficient quantity using conventional purification and buffer exchange strategies.

Alzheimer's disease is characterized by the accumulation and aggregation of amyloid-{beta}(A{beta}), but the molecular mechanisms linking environmental and infectious factors to A$\beta$ conformational changes remain incompletely understood. Herpes simplex virus type 1 (HSV-1) has been proposed as a potential contributor to AD pathology, and interactions between the viral glycoprotein B (gB) and A$\beta$ may influence the conformational behaviour of the peptide. Molecular dynamics (MD) simulations provide atomic-scale information on such interactions, but conventional structural descriptors may not fully capture changes in the organization of residue interaction networks. Here, we introduce a graph-geometric framework based on Forman-Ricci curvature to characterize the evolution of residue interaction networks during MD simulations. Each simulation frame is represented as a residue interaction graph based on C--C contacts, and residue-wise curvature profiles are analysed across time. We apply the framework to A{beta}1-42 in isolation and in complex with HSV-1 gB. Conventional MD analyses indicate stable association of the simulated complex, favourable interaction energetics, and conformational changes in A{beta}, including a transition from -helical structure toward {beta}-turn-rich conformations over the simulated timescale. Forman-Ricci curvature reveals pronounced and spatially localized remodelling of the A{beta} residue interaction network in the complex, with the strongest changes concentrated in the C-terminal region. These regions also exhibit reduced temporal curvature fluctuations and progressively distinct geometric behaviour throughout the simulation. Hierarchical clustering further identifies cooperative groups of residues with coordinated curvature dynamics, including a prominent C-terminal domain. Together, these results demonstrate that Forman-Ricci curvature provides a complementary description of biomolecular dynamics by capturing changes in the geometric organization of residue interaction networks that are not directly represented by conventional structural descriptors. The framework provides a general computational approach for studying network-level structural remodelling in protein molecular dynamics and offers a quantitative perspective on the conformational consequences of HSV-1 gB--A{beta} association.

The folding thermodynamics of proteins are dominated by two opposing forces, the loss in backbone entropy and the packing of hydrophobic groups. The same forces are major contributors to the extension thermodynamics of elastic proteins with the distinction that both processes act in concert, favoring the higher chain and solvent entropy of a relaxed conformation. The relative entropic contributions specify the recoil mechanism; human elastin recoil is primarily driven by hydrophobic forces, whereas fly resilin has a rubber-like mechanism driven by backbone entropy. Despite the importance of elastic proteins to tissue biomechanics, few have been identified, let alone characterized to the same extent as elastin and resilin. We develop a thermodynamic framework that maps proteins by sequence-derived estimates of extension-induced backbone and solvent entropy changes. Putative elastic proteins are proposed and classified by recoil mechanism based on estimated thermodynamic features. Proteins that map to elastic regions are overrepresented by the skin proteome. The set of predicted elastic domains is further extended by incorporating sequence context embedded in protein language models. Protein domains with distinct thermodynamic recoil mechanisms cluster on the latent space manifold. Some of these domains are anticipated to have roles within molecular machines, expanding the scope of elastic protein function beyond mechanical materials like elastin and resilin.

The binding affinities of a chimeric analog of a riboflavin derivative linked to biotin, (6- (7,8-dimethyl-2,4-dioxo-3,4-dihydrobenzo[g]pteridin-10(2H)-yl)hexyl 5-((3aS,4S,6aR)-2- oxohexahydro-1H-thieno[3,4-d]imidazol-4-yl)pentanoate), referred to as C6-Rf-biotin-tag, to the riboflavin binding retain or streptavidin are in the M range, 1.29 {+/-} 0.277 and 3.00 {+/-} 0.459, respectively. These values suggest that C6-Rf-biotin-tag has potential applications in diagnostic assay and labelling target flavin binding proteins. The C6-Rf-biotin-tag which was characterized with respect to physical and biochemical properties retains UV/Vis spectroscopic and fluorescence behavior similar to riboflavin.

Reflectin proteins drive dynamic structural coloration in cephalopods by organizing into dense intracellular lamellar structures that dictate local refractive index. While reconstituted reflectins readily undergo liquid-liquid phase separation in vitro, these assemblies frequently undergo dynamic arrest, vitrifying into non-dynamic condensates. Here, we investigate the primary sequence features, post-translational modifications, and heterotypic interactions that regulate the material properties of reflectin condensates within the crowded cellular environment of mammalian HeLa cells. Using confocal microscopy and fluorescence recovery after photobleaching (FRAP), we demonstrate that canonical block copolymeric A-type reflectins readily form dynamically arrested condensates, with the linker blocks primarily responsible for the observed arrest. In contrast, non-canonical B/C reflectin variants exhibit significantly greater fluidity and rapid recovery kinetics. We show that phosphomimetic substitutions progressively fluidize some reflectin condensates. Lastly, we find that heterotypic condensates composed of canonical and non-canonical reflectins in combinations associated with reversible iridescence in squid substantially enhance canonical mobility. Our findings establish a biophysical framework in which phosphorylation and heterotypic mixing cooperatively suppress dynamic arrest, enabling the reversible material transitions required for active cephalopod camouflage and communication.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

Linker chemistry and conformation are central determinants of PROTAC activity, shaping ternary-complex geometry, cooperativity, target-lysine presentation and cellular permeability. Existing linker generators often lack explicit control over linker flexibility, require predefined attachment sites and linker lengths, or produce structures that demand substantial geometric correction, limiting their utility in practical PROTAC design. Here we introduce FlexiTAC, a Bayesian flow network that jointly generates linker atom types and coordinates from the warhead and E3-ligase-ligand contexts. We also assemble PROTAC-3D, a quality-controlled collection of 63,554 component-resolved PROTAC structures for model training, and PROTAC-Bench, which covers molecular quality, fragment preservation, geometric fidelity, conformational stability, fragment awareness, rediscovery and sampling efficiency. Compared to the best 3D baseline models, FlexiTAC improves validity by 12.0-12.7% and achieves the highest PoseBusters pass rate of 79.5%-80.0%. A differentiable guidance module shifted generated linkers along a conformational ensemble-derived rigidity axis without retraining the generator. In silico case studies further show that the model can accept crystal-derived, redocked or predicted structural inputs. Together, FlexiTAC, PROTAC-3D and PROTAC-Bench establish an integrated and reproducible framework for data-driven PROTAC linker design, combining controllable structure-conditioned generation with standardized training data and evaluation protocols. This framework expands the linker chemical and conformational space accessible to computational exploration, provides a foundation for future method development and enables the systematic generation of structure-conditioned linker designs with tunable conformational flexibility.

Accurate determination of kinase inhibitor binding modes could provide essential information for understanding resistance mechanisms and accelerating drug discovery. While conventional structural methods such as X-ray crystallography, cryo-EM and NMR provide high-resolution information but are low-throughput and capture largely static snapshots of dynamic protein-ligand interactions Here, we introduce a single-molecule nanopore tweezer platform that functionally subtypes ATP-competitive Abl kinase inhibitors by resolving distinct ionic current signatures of Abl-inhibitor complexes. This approach distinguishes Type I, Type IIA, and Type IIB inhibitors without structural determination. We further show how clinically relevant Abl variants (T315I and E255V) reshape inhibitor engagement and binding modes. By combining baseline probability features with wavelet-based time-frequency descriptors, ensemble machine-learning models achieved 97.5% classification accuracy across seven kinase inhibitor binding modes at sub-angstrom resolution and enabled deconvolution of mixed-inhibitor samples at nanomolar concentrations. These results establish nanopore tweezers as a label-free, super-resolution platform for profiling kinase conformational states and inhibitor binding modes, complementing structural approaches and supporting precision oncology.

Human cytochrome P450 2C9 (CYP2C9) is a hepatic microsomal enzyme involved in the oxidative metabolism of clinically important drugs, but the structural organization of its oligomeric assemblies outside crystallographic packing environments remains poorly understood. Here, we report the cryo-EM structure of human CYP2C9 determined under aqueous, membrane-free conditions at 3.31 Angstrom resolution. The structure reveals a C2-symmetric hexameric assembly organized as a dimer of trimers. Individual protomers retain the conserved P450 fold and heme-binding architecture observed in previously reported crystal structures, indicating that assembly formation does not substantially perturb the catalytic core. The hexamer is stabilized by defined intra-trimer interfaces involving the N-terminal region and residues around Trp212 and Phe482, together with inter-trimer interfaces involving Leu71 and the 220-227 loop. These interfaces are distinct from the crystal packing contacts observed in CYP2C9 crystal structures, demonstrating that the assembly is not a simple recapitulation of crystallographic packing. Notably, the inter-trimer interface is located near the FG-loop-containing surface previously implicated in membrane association. This suggests that the observed hexamer may represent a membrane-free association of two trimers through membrane-related surfaces, whereas the trimeric arrangement itself may be compatible with membrane-associated organization. The structure therefore provides a framework for investigating how trimer formation, membrane interaction and local conformational changes in the FG-loop region may influence CYP2C9 function.

Deep learning has accelerated drug discovery, yet most existing models are trained using in vitro affinity datasets and consequently remain disconnected from the cellular context in which functional ligand-protein interactions occur. This limitation hinders the ability to reflect the complexity of native interactomes and characterize biological responses to molecular perturbation. Here we introduce C-PLANK (Chemi-Proteome Language Attention NetworK), a deep learning framework trained on fragment-protein interactions profiled directly in living cells using fully functionalized fragment (FFF) chemoproteomics. C-PLANK combines physicochemical embeddings with a bilinear attention network (BAN) to model both global cellular context and local residue-atom interactions, generating interpretable interaction fingerprints. Particularly, C-PLANK incorporates Cellular Interaction State Index (CISI), a systems-level evidential metric that contextualizes the biological plausibility of each predicted interaction against the global cellular interaction landscape. Across 431 ligand interactomes curated from eight independent chemoproteomic studies, C-PLANK consistently outperformed current state-of-the-art interaction prediction frameworks under both random and cold-protein evaluation settings. The inferred interaction fingerprints aligned with orthogonal evidence from structure-based pocket predictions, co-crystal structures, and cellular binding-site annotations. C-PLANK further generalized to unseen ligands. In a cellular target-focused discovery campaign, C-PLANK identified a previously unrecognized ligand that was subsequently advanced into an active chemical probe acting as a SIRT3 agonist in cellular assays. By learning directly from cellular chemoproteomics, C-PLANK moves beyond isolated interaction prediction toward cellular interaction-state modelling, establishing a computational foundation for future digital-twin frameworks in drug discovery.

Scoring biomolecular complexes is central to structure assessment and drug discovery, yet the complexes themselves vary widely in pose, size, and molecular composition. A scoring function tuned for one interaction type rarely carries over to another, and most existing methods compound the problem by leaning heavily on task-specific labels. We introduce OmniScore, a universal structure-based framework that learns a shared geometry-aware representation of complexes once and then adapts it to downstream scoring through lightweight task-specific heads. OmniScore couples a graph view and a sequence view of each structure, encodes its three-dimensional geometry, and compresses representations into a compact latent space that a reconstruction module and prediction heads can reuse. We pretrain this backbone on diverse datasets including complexes, monomers, and small molecules with complementary objectives: coordinate recovery, correcting corrupted input tokens, predicting molecular identity, and grounding the representation in structure-level physical quantities. Across the evaluated benchmarks, OmniScore gave the best antibody-antigen and nanobody-antigen quality assessment on all reported metrics compared to state-of-the-art baselines. Its frozen residue embeddings matched the state-of-the-art protein-tokenization method with an average functional-site accuracy of 71.8% on a standard residue-level benchmark. On protein-ligand scoring and ranking benchmarks, it performed on par with methods built specifically for that single task. These results suggest that geometry-aware pretraining can provide a reusable scoring backbone for tasks that depend on interfacial and residue-level structure, within the evaluated settings.

Next-generation sequencing (NGS) for the detection of somatic variants has become the method of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT (Profiling of Actionable Cancer Targets, LG-PACT) assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a targeted panel covering 606 genes and the TERT promoter. Indications for testing are cancer (solid tumors and hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, small insertions/deletions (indels), and copy number aberrations for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) cleared diagnostic test (510K: K202304). The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. This final mostly manual review of data step is intensive, involving highly trained scientists, encompassing literature review, interpretation and clinical tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. We provide analysis of 1339 clinical genomic profiles from 31 different cancers and their subtypes, comprising of central nervous system (CNS) 792 (59%) cases (incl. meningioma, glioma and glioblastoma), with 267 (20%) cases predominantly of lung, pancreatic and colorectal and 280 of others (21%). Here, we present the technical challenges of validating an NGS oncological diagnostic targeted assay for clinical grade accuracy and sensitivity for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the utility of targeted panel sequencing based on certified pathologist selection of reportable variants for our current patient cohort. Where analysis of variant detection has led to 49.4% (661/1339) of our clinical tumor samples containing mutations in known therapy targeted genes, 35.6% (477/1339) with mutation detected in other genes, and 15% (201/1339) cases being negative.

Importance: Patients are increasingly learning Alzheimers disease (AD) genetic and biomarker results through electronic health portals. Evaluation of alternative scalable delivery models for return of AD risk information is needed to best support patient understanding and psychological well-being. Objective: To determine whether a patient-centered digital platform is comparable to clinician-mediated telehealth sessions for returning APOE and plasma pTau-217 results on outcomes of knowledge and psychological well-being. Design: The Evaluation of Self-Mediated Alternatives for Risk Testing Education and Return of Results (eSMARTER) study was a noninferiority trial of a patient-centered digital platform compared to clinician-mediated disclosure of APOE genotype and optional pTau-217 disclosure. Setting: Decentralized, fully remote trial enrolled participants in the contiguous United States (U.S.) between October 2024 and February 2025, with follow-up completed in November 2025. Participants: Eligible participants were aged 60-80 and had previously undergone APOE genotyping (without disclosure) via the GeneMatch program, passed psychological screening, had internet access, and were English-speaking. Interventions: Participants were randomized, 2:1, to the eSMARTER digital platform or clinician-mediated disclosure of APOE genotype. Following the 6-month post-APOE assessment, participants were offered optional pTau-217 disclosure via the same randomized modality. Main Outcomes and Measures: Primary outcomes at 1-7 days following APOE disclosure included changes in anxiety, disease-specific distress, and AD-related knowledge within a priori non-inferiority margins. Results: 674 persons (mean [SD] age 68 [4.7] years; 451 [67%] female; mean [SD] telephone MoCA=19 [2]) were eligible and provided demographic information. 651 participants were randomized to clinician-mediated (n=216) or digital disclosure (n=435) and completed APOE disclosure (66 [10%] APOE4 homozygotes, 377 [58%] heterozygotes, 208 [32%] non-carriers). 604 participants completed the study; 500 completed optional pTau-217 disclosure. Baseline characteristics were balanced across groups. At 1-7 days following APOE disclosure, scores on AD-related knowledge, PROMIS Anxiety, and disease-specific distress measures met non-inferiority. Conclusions and Relevance: Disclosure of APOE genotype by the eSMARTER digital platform is non-inferior to clinician-mediated telehealth disclosure. No significant between group differences were found following disclosure of pTau-217 results. Together, these results suggest that this digital platform may provide an evidence-based scalable approach for returning AD genetic and biomarker results.

Abstract: Objective: Post-traumatic epilepsy (PTE) is a common sequela of traumatic brain injury (TBI). Research indicates that individuals with PTE tend to experience greater cognitive difficulties compared to those with TBI alone. However, it is plausible that a distinct cognitive profile exists that distinguishes between TBI cases with and without PTE. We aimed to identify longitudinal changes in cognitive measures among TBI patients to better assess the changes associated with developing PTE. Setting: Outpatient. Participants: Prospective subjects who had suffered TBI within 6 months post-injury (TBI-6M, n=32), retrospective subjects with pre-existing PTE diagnoses (PTE, n=20), and healthy control subjects (HC, n=41). Design: We examined cognitive performance for TBI patients within 6 months post-injury, then again within 12 months (TBI-12M, n=26), and within 18-months (TBI-18M, n=25), and compared this with cognitive performance among HC and PTE. Main Measures: Cognitive tests administered yielded 15 test components for analysis. We utilized linear mixed effects modeling to examine cohort-level differences cognitive function. Results: 11/15 tests showed a significant performance deficit in the PTE subjects compared to HC. TBI-6M was not significantly different from the PTE subjects; with time, 9/15 tests showed some degree of recovery in TBI subjects. Tests for information processing speed/working memory and executive function showed strong recovery (TBI-6M vs. TBI-18M, SDMT written: p<0.0001, SDMT oral and COWAT: p<0.001). Tests for visual attention/working memory also showed a smaller but significant recovery (TBI-18M vs. PTE, p<0.05). By contrast, tests for verbal memory [HVLT-R Delayed Recall] showed chronic impairment in TBI (TBI-18M vs HC, p<0.0001). TBI subjects generally trend towards recovery in cognitive performance post-TBI. Conclusions: Information processing speed/working memory are strong indicators for TBI recovery, while auditory learning/memory shows chronic impairment. The stagnation of recovery in cognitive domains typically characterized by robust recovery may correlate with an elevated risk of developing PTE.

The distribution of fat-soluble compounds (including antioxidants) in follicular fluid (FF) remains sparsely documented in relation to in vitro fertilization (IVF) outcomes and existing studies have reported diverging associations. This study aimed to describe plasma and FF concentrations of fat-soluble micronutrients in women undergoing IVF and to analyze their adjusted associations with ovarian function, embryo development and pregnancy outcomes. In 2021-2022, plasma and FF samples were collected from 82 women (first IVF cycle) at oocyte puncture, along with lifestyle data covering the three preceding months. Eleven compounds (two tocopherols, three xanthophylls, five carotenes and retinol) were quantified. All compounds were detected in both compartments (lowest in FF) except phytoene, undetectable in FF. Plasma and FF -tocopherol concentrations were positively associated with plasma estradiol levels before oocyte puncture (both p<0.01) while FF -carotene and lycopene were inversely associated with plasma progesterone concentrations (p=0.01 and 0.02, respectively). Plasma phytofluene and phytoene were positively associated with mature oocyte rate (p=0.03 and p=0.01, respectively), while FF retinol was negatively associated (p=0.03). Carotenes, tocopherols and retinol were inversely associated with later IVF outcomes: fertilization rate (p<0.001 for plasma g-tocopherol, 0.02 for FF retinol), top-quality embryo (p=0.02 for plasma phytofluene), biochemical pregnancy at day 7 post-embryo transfer (p=0.05 for plasma -tocopherol, 0.02 for plasma -carotene), clinical pregnancy (p=0.03 for plasma -tocopherol, 0.01 for plasma phytoene) and live birth (p=0.04 for plasma -tocopherol, 0.02 for plasma phytoene). Plasma and FF g-tocopherol were positively associated with embryo fragmentation (both p<0.05). Finally, among xanthophylls, only plasma {beta}-cryptoxanthin was positively associated with plasma progesterone concentrations (p=0.02). Our findings of heterogeneous associations between tocopherols, carotenes, retinol and IVF outcomes across the stages of IVF suggest a beneficial effect limited to early outcomes and support a complex and context-dependent role of these compounds in female reproduction. This manuscript has been submitted to PlosOne on August 19, 2026.