iScience

Improvements in data availability, sharing, and integration, together with the development of explainable artificial intelligence (XAI) techniques, are advancing precision medicine for pediatric cancer by facilitating diagnosis, biomarker discovery, and drug development. Data sharing commons and initiatives like the Childhood Cancer Data Initiative (CCDI) provide access to pediatric-specific genomic and clinical data cohorts and improve data availability for pediatric cancer research. Based on CCDI, a scalable AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), integrates various data modalities from bulk and single-cell omics data to clinical information. Such multi-modal data facilitates the training and development of advanced XAI models for pediatric cancers. We then developed an end-to-end multi-modality framework, PCGS, for pediatric cancer by incorporating omics-specific representation learning via GNN models with cross-attention fusion and multi-objective learning for downstream tasks such as classification, clustering, and survival analysis. This framework outperforms previous supervised multi-omics integration baseline approaches based on glioma and Wilms tumor cohorts and enables GNN model explainability via Shapley value-based feature attribution approaches to explain the contributions of gene-level features across various biomedical tasks, including classification and survival. Given specific background samples (e.g., age groups, sex, grades) as baselines, this explainable GNN model estimates and ranks the importance scores for input features from each omics modality. It identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have shown great promise in treating bladder cancer and are now part of the standard treatment for advanced disease. However, many patients still fail to respond to treatment and at present many biomarkers are assessed but have yet shown only limited results. Therefore, with the advent of combination treatments and the increase of immune related adverse event, the search for reliable predictive biomarkers is paramount. Using a multiplexed enhanced FRET-FLIM based technique (QF-Pro) we quantified the interaction of PD-1/PD-L1, CTLA-4/CD80 and TIGIT/CD155 immune checkpoints in a pre-treatment TMA of 46 patients treated with atezolizumab. The association between higher PD-1/PD-L1 ICP interaction state and treatment efficacy was demonstrated in the male sample cohort, where it identified patients with better PFS. Conversely, patients exhibiting higher CTLA-4/CD80 engagement had a worse response to atezolizumab. Remarkably, the dual assessment of patients with high PD-1/PD-L1 and low CTLA-4/CD80 allowed to identify the best responders. These results indicate that the monitoring of patients immune profile in urothelial carcinoma might be critical in identifying patients who may benefit from combination therapy.

Post-acute infection syndromes (PAIS) follow viral syndromes including post-acute sequelae of COVID19 (PASC) which complicates 10-25% of SARS-CoV-2 infections. These syndromes lack precise explanatory mechanisms. We studied 173 human saliva proteomes during respiratory viral syndromes, seeking associations between 44 clinically-relevant protein expression patterns and subsequent sequelae counts. Exploratory models adjusted by extensive clinical annotations found interactions between 23 acutely-responsive proteins and SARS-CoV-2 infection that inversely predicted subsequent neurocognitive sequelae. An overlapping 19 acutely-responsive proteins during any acute respiratory viral syndrome inversely predicted general fatigue-related sequelae. Altogether, 29 proteins, derived from interferon stimulated genes (ISG), were uniformly beneficial, including 13 predictive of both neurocognitive and general sequelae. The proteins suggested both shared early pathobiology and virus-specific protective responses that shaped resolution of acute disease and different PAIS. Acutely elevated protective ISG proteins associated with reduced post-viral symptoms identify investigational starting points for novel mechanisms, diagnostics and therapeutics for PASC and PAIS.

Background. Patient stratification from multi-omics and clinical data is essential for uncovering disease heterogeneity and moving toward more personalized treatment strategies. However, integrating heterogeneous data layers while identifying robust patient strata remains challenging. Methods. We introduce Reduced Fusion of Multi-Omics Stratification (RedFuMOS), a novel three-step approach for patient stratification based on mixed-type multi-omics data. RedFuMOS extends Similarity Network Fusion to accommodate mixed-type data layers and layer-specific similarity measures for data integration, includes a dimensionality reduction step to mitigate the curse of dimensionality, and performs patient stratification using density-based hierarchical clustering with HDBSCAN. It also implemented an automated optimization procedure to identify the best set of hyperparameters, minimizing the need for manual tuning. Results. RedFuMOS outperformed six state-of-the-art tools for multi-omics patient stratification in a comprehensive simulated benchmarking study, which also confirmed that, although computationally expensive, the dimensionality reduction step is crucial for achieving good stratification performance. Additionally, RedFuMOS identified two clinically relevant patient strata in a small real-world cohort of patients with Philadelphia chromosome-positive chronic myeloid leukaemia. Conclusion. RedFuMOS provides a flexible framework for integrating heterogeneous multi-omics and clinical data. RedFuMOS is available as an R package at http://github.com/delucasara/RedFuMOS.

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets, on The Cancer Genome Atlas breast cancer cohort. The system did not identify clinically meaningful novel findings without human input. However, a seeded warm-start run with minimal text input from an oncologist revealed multiple interesting and surprising hypotheses. Among these was that a robust immune signature was present across all subtypes of invasive lobular carcinoma (ILC) that exceeded invasive ductal carcinoma (IDC). This observation was independently validated in independent cohorts and confirmed by high-sensitivity multi-immunofluorescence tumor tissue analyses. These results suggest immunotherapy approaches should be tested in ILC including early stage ER+HER2- ILC; these patients are currently excluded from large neoadjuvant immunotherapy trials. They further demonstrate that surprisal-based hypothesis generation frameworks can extract previously unappreciated patterns from deeply interrogated cancer datasets and imply that disease domain experts working with LLMs can derive more meaningful insights from complex data than either could achieve alone.

Motivation: Cell-free DNA methylation provides a minimally invasive signal for early cancer detection and tissue-of-origin prediction. Most methods represent methylation measurements as independent fixed-window features and therefore do not explicitly model relationships among genomic regions. Results: We developed PANGEM (Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome), a graph-learning framework that represents genomic bins as nodes and integrates CpG context, genomic proximity, and sample-specific methylation similarity in the graph topology. Across five repeated stratified train-test splits, PANGEM achieved the highest mean performance among evaluated methods, with an AUROC/AUPR of 0.997/1.000 for binary cancer detection and macro-AUROC/AUPR of 0.977/0.870 for multiclass tissue-of-origin prediction. In the independent INSPIRE cohort, 72 of 78 cancer cases (92.3%) exceeded the binary classification threshold, and PANGEM correctly classified 9 of 17 head and neck cancer cases (52.9%), the highest accuracy among evaluated methods. Subnetwork analysis further identified recurrent, graph-connected methylation patterns, including a 111-DMR subnetwork with increased methylation in cancer samples.

The increasing incidence of H5N1 influenza virus transmission from animal species to humans has heightened concerns about an imminent H5N1 pandemic. Prior studies using recombinant hemagglutinin and neuraminidase proteins have reported age-dependent cross-reactivity to H5N1, attributed to immune imprinting from an individual's first influenza virus exposure. However, whether this pattern holds when using whole inactivated virus (WIV), capturing antibodies against diverse viral proteins, and is stable over time remains unknown. We therefore aimed to determine whether H5N1 cross-reactivity of pre-existing antibodies to whole virus follows an age-dependent or imprinting-specific pattern, and whether this pattern is stable over a five-year period. To this end, we measured serum antibody levels in adolescents, adults and seniors by ELISA using whole inactivated H5N1 virus as antigen rather than purified proteins. Detectable, albeit generally low, levels of H5N1-reactive antibodies were present in most individuals, irrespective of age. Comparison of antibody levels against H5N1 with those to five historical influenza virus strains revealed a consistent positive correlation between H5N1-reactive antibodies and responses to the H1N1pdm09 strain A/California/7/2009 (CA), across all age groups. Using unbiased clustering of antibody titers against H5N1, CA, and the H3N2 strain A/Perth/16/2009 (PE), we identified seven distinct age-transcending antibody profiles. These profiles covered individuals with varying titers to all three included viruses but also identified individuals with high anti-CA levels, yet low anti-H5N1 levels and vice versa. Moreover, despite stable antibody levels over a five-year interval in the study population, individual antibody levels and profiles fluctuated considerably over this period. Taken together, our results confirm the presence of H5N1-reactive antibodies in human sera and their association with previously circulating strains. However, they also caution against inferring antibody levels against a new strain based solely on responses to antigenically related strains and highlight the limitations of extrapolating immune status from single timepoint measurements.

The QxxR motif is evolutionarily conserved within DEAD-box RNA helicases, including Drosophila Me31B and human DDX6, which post-transcriptionally regulate gene expression during animal development. A pathogenic H372R substitution (QxHR to QxRR) in the QxxR motif of human DDX6 has been associated with various developmental defects, but how this motif contributes to DDX6-family protein function remains unclear. Here, we used Drosophila Me31B as an in vivo model to investigate the QxxR motifs developmental role. We generated a Drosophila strain carrying the corresponding H333R missense mutation in Me31B and characterized its effects on female fertility, embryonic viability, germline development, and Me31B-associated molecular pathways. The me31BH333R mutation reduced female fertility in a gene dose-dependent manner, with homozygous mutant females being sterile. Embryos from the mutant females also exhibited primordial germ cell defects. Despite these developmental phenotypes, the me31BH333R mutation did not significantly alter Me31B protein abundance, global ovarian transcriptome or proteome profiles, or representative germ plasm mRNA and protein localization. In contrast, bait-normalized IP-MS analysis revealed altered enrichment of selected Me31B-associated proteins, including increased association of known Me31B interactors Trailer hitch (Tral) and Ypsilon Schachtel (Yps). These findings establish Me31BH333R as an in vivo model for investigating the conserved QxxR motif and suggest that disruption of this motif compromises development not through broad changes in gene expression, but potentially through altered composition or regulation of Me31B-containing ribonucleoprotein complexes.

Human deep-space missions face bone-kidney risks that cannot be extrapolated from six-month ISS data. We built a 12-state Ca-bone-urine-stone mechanistic ODE model and jointly calibrated its 11 physiological parameters on eight ISS targets by Bayesian identification (M0 base = 19-D; M1 extension adds a GCR-bone coupling term for parsimony testing only), then propagated the M0 posterior to four environments (ISS, Lunar subsurface, Lunar surface, Mars). Lumbar-lower BMD loss increases with mission duration and partial-gravity unloading (ISS 180 d -4.83% -> Mars 730 d -12.15%; 2^3 factorial: duration 82.9%, gravity 12.5%, GCR main effect ~ 0), whereas stone rate follows the opposite gradient (ISS 16.1 vs Mars 13.1 per 1000 person-years), reflecting weakened partial-gravity bone resorption alongside residual urinary chemistry changes. The dominant pathway thus shifts from bone-centric on the ISS to kidney-centric on Mars, where residual urinary-chemistry changes-not bone resorption-drive stone risk. The direct GCR-bone coupling term is unidentifiable at current ISS doses (DeltaWAIC = +0.0076 +/- 0.126 SE), so M0 is retained as the main inference model. Bisphosphonates provide >=84% BMD protection but leave a urinary-chemistry residual, so bisphosphonate monotherapy would underestimate Mars stone risk; potassium-magnesium-citrate combinations (RRR_RSS 51%) should therefore be added to deep-space countermeasures. A Lunar-surface 365-day mission is the earliest environment on the NASA roadmap to cross a composite RED threshold. That profile differs from the regolith-shielded 180-day case in both cumulative GCR (~69x) and duration (2x), so a shielding-specific effect cannot be isolated here; forcing the GCR coupling terms to zero leaves all four composite tiers unchanged (0/4, Supp S24), and the shielded 180-day profile is YELLOW rather than GREEN. Independent hold-out validation (Culliton 2025 60-day HDT-bedrest RCT, n=8 control arm of n=24 total) supports the M0 posterior predictive distribution on the lumbar-BMD sub-scope.

Background: Endometriosis is a chronic pain condition in which hormonal therapies form the cornerstone of long-term management. Treatment tolerability is critical for adherence and therapeutic success, but most comparative studies and reviews have focused on their ability to reduce menstrual pain, while their impact on non-menstrual pelvic pain (NMPP), bleeding patterns, adverse events (AEs), treatment discontinuation and quality of life (QoL) remain poorly characterised. This systematic review and meta-analysis evaluate these outcomes across currently available hormonal therapies, providing practical evidence for clinical decision-making. Methods: PubMed/MEDLINE, Scopus, and Embase were searched up to November 2025 for randomised controlled trials comparing at least two active first- or second-line hormonal treatments for endometriosis. Studies without confirmed endometriosis, treatment duration less than three months and comparing therapies to placebo only were excluded. Data were extracted by two reviewers from reports. Pain outcomes were pooled as mean differences (MD, 95% CI), with bleeding patterns, AEs, and discontinuations as proportions. Analyses were performed in R. PROSPERO: CRD420251137785. Findings: Of 1892 records screened, 48 trials (5583 women) met our inclusion criteria. Overall pelvic pain (0-10 scale) was significantly reduced across all treatment categories (p<0.001): combined oral contraceptives (COCs) (MD 3.17), oral and long-acting progestogens (MD 3.83; MD 4.29), and GnRH-analogues (MD 3.81). Sensitivity analyses restricted to studies reporting NMPP yielded comparable results. GnRH-agonists showed the most favourable bleeding profile, followed by continuous COCs. However, all regimens reported class-specific AEs, including mood changes, nausea, headache, weight gain, and decreased libido (pooled proportions >10%). Overall discontinuation due to AEs was 7.7%, and vaginal bleeding was the leading cause. Heterogeneity across meta-analyses was high. Risk of bias (RoB2) was moderate to high. Interpretation: Given similar reductions in overall pelvic pain across hormonal therapies, treatment decisions should prioritise differences in bleeding profiles, therapy-specific AEs, and QoL. Funding: None.

Large Language Models (LLMs) are increasingly used by clinicians and patients for medical queries, yet their accuracy and safety at the specialist level in hematology remain insufficiently characterised. We benchmarked ten frontier proprietary and open-weight LLMs across two generations on 1,477 board-style hematology multiple-choice questions (MCQs) derived from five educational datasets spanning nine disease areas and six clinical skill domains, including text-only and multimodal case vignettes. Claude Opus 5 had the highest mean accuracy (92.7% text, 76.9% multimodal), followed closely by Gemini-3.1 Pro (91.4% and 78.7%), Gemini-3.6 Flash (91.0% and 74.8%) and GPT-5.6 Sol (89.9% and 76.7%). Accuracy significantly correlated with model size both for text-only and multimodal MCQs. Between model generations, the largest improvements in accuracy were seen for open-weight models whereas proprietary models showed only marginal gains. In error analysis, top-performing models exhibited highly concordant failure patterns, suggesting shared limitations on challenging cases. Frontier LLMs exhibit substantial specialist hematology knowledge across diverse subspecialist domains and clinical skill sets. Yet, despite high accuracy on board-style questions in hematology, continuous expert-on-the-loop output monitoring is paramount.

The availability of hearing aids that can connect wirelessly to smartphone technologies via Bluetooth has grown exponentially in recent years. However, there is limited evidence assessing the benefits of user-adjustability afforded by these devices. This study aimed to assess the benefits of smartphone-connected hearing aids and an accompanying application (or app) in new and existing hearing aid users. In this single-centre, prospective, observational study, 44 adult hearing aid users (14 new and 30 existing) were recruited. Participants were fitted bilaterally with smartphone-connected hearing aids that could be adjusted by the user via an app. Self-reported outcome measures were collected at fitting and after seven-weeks of using the device in everyday life. For both new and existing hearing aid users, significant improvements in social participation, hearing-related fatigue, and hearing aid benefit and satisfaction were found. For existing hearing aid users, all outcomes were significantly better for the smartphone-connected hearing aids plus app in comparison to their existing hearing aids that did not connect to a smartphone, all with moderate-to-large clinical effect sizes (d> .6). User-controllability via the app was identified as the key benefit, and most participants (68%) reported that the app met their needs 'extremely' or 'very well'. These results suggest that, when used in conjunction with an app, smartphone-connected hearing aids can improve hearing outcomes due to greater user-controllability to improve listening. Thus, smartphone-connected hearing aids have the potential to facilitate patient-centred care, empowering the individual to successfully manage their hearing loss.

Wildfire smoke is an escalating global public health threat exposing millions of people, including children, to hazardous air pollution each year. Although wildfire smoke toxicants have been linked to a range of adverse health outcomes, including immune dysregulation, the long-term consequences of real-world pediatric wildfire smoke exposure on health and development remain largely unknown. To investigate the persistent effects of early-life exposure on immune health, here we leveraged a cohort of rhesus macaques that experienced nine consecutive days of hazardous wildfire smoke exposure in infancy during the 2020 Oregon Labor Day wildfires. By integrating ex vivo immune stimulations, multiplex cytokine profiling, single-cell transcriptomics, and genome-wide DNA methylation profiling, we identified persistent immunological consequences across molecular and functional levels. We found that a single severe postnatal exposure, in the first three months of life, was associated with persistent change in the innate immune response, including reduced pro-inflammatory cytokine response to a bacterial endotoxin, with subtle but consistent transcriptional changes in myeloid cells, particularly among males. Wildfire smoke exposure was also associated with changes in proportion of B and T/NK cells, and within the T/NK cell compartment, exposed animals exhibited an expansion of cytotoxic cells. Consistent with this, CD8+ T cells displayed extensive transcriptional remodeling and shifted toward more differentiated effector states, with the greatest differentiation observed in animals exposed at the youngest ages. Genome-wide DNA methylation profiling identified smoke-associated methylation changes consistent with acceleration of epigenetic aging, as well as persistent epigenetic alterations impacting genes involved in oxidative stress responses, innate immunity, T cell differentiation, and hematopoiesis. These findings demonstrate that a single severe wildfire smoke exposure during a critical developmental window is associated with extensive immune and epigenetic remodeling that persist years after exposure, providing new insight into the long-term biological consequences of early-life wildfire smoke exposure.

High-grade serous ovarian carcinoma (HGSC) responds poorly to immune checkpoint blockade, partly due to a macrophage-dominated immunosuppressive microenvironment. We integrated single-cell spatial proteomics and spatial transcriptomics across 50 HGSC tumors and applied SPACEstat to resolve higher-order immune communities and their transcriptional programs. We identified six immune community types, with macrophage-dominated Myelonets representing the predominant spatial pattern of immune organisation. In chemotherapy-exposed tumors, Myelonets showed coordinated lipid metabolism-immunosuppression and inflammation-MHC-II macrophage transcriptional programs, with SPP1, C1Q, VEGF, MMPs, and CCL18 linked to immunosuppressive states and fibroblasts emerging as key mediators of macrophage communication. Chemotherapy contracted large Myelonets while increasing CD8+ T-cell organization into Lymphonets. Persistent macrophage dominance within Myelonets was associated with adverse outcomes among patients who achieved a complete response to treatment. Together, we identify Myelonets as clinically relevant, multicellular immunoregulatory niches sustained by spatiotemporally coordinated macrophage programs and stromal crosstalk.

Animals rely on color to navigate complex environments, yet how eyeless organisms use chromatic information to guide food choice remains poorly understood. Here, we show that Caenorhabditis elegans exhibits robust color dependent foraging driven by microbial chromophores, preferentially consuming red while avoiding blue chromoprotein expressing bacteria across bacterial backgrounds and wild isolates. This discrimination persists in darkness and independently of photoreceptor, revealing a mechanism beyond canonical photoreception. Purified chromoproteins and bacterial metabolite fractions independently reproduce preference, demonstrating complementary chromatic and post ingestive metabolic cues. Mechanistically, blue chromoproteins generate singlet oxygen, producing oxidative stress and remodeling bacterial tryptophan and pterin metabolism, whereas red food promotes serotonin production and feeding-associated neuropeptide signaling. Disrupting serotonin biosynthesis or neuropeptide processing abolishes color preference. Together, our findings reveal a previously unrecognized, novel sensory strategy in which wavelength-selective pigment photochemistry transforms microbial color into metabolic information that is integrated through gut brain neuroendocrine signaling to guide foraging behavior in an eyeless animal.

Large language models perform well on medical examinations, but users routinely challenge their answers and invoke professional roles, and it is unclear what a system does when a medical credential and a stated task-specific accuracy point in opposite directions. In a factorial experiment on 480 items from four Polish specialty examination sets and three consumer large language model systems (ChatGPT, Claude, Gemini), each item and system received eleven independent conversations. Conditions crossed attributed source role (medical student, experienced specialist), stated prior accuracy on similar questions (2/10, 8/10) and suggestion correctness. The primary outcome was adoption of a prespecified incorrect option when the baseline answer matched the official key, comparing a specialist described as 2/10 with a student described as 8/10. Baseline agreement with the key was 87.2% across 15,683 analyzable conversations. The incorrect option was adopted more often from the specialist described as 2/10 than from the student described as 8/10 (10.2% vs. 7.6%; adjusted risk difference +2.82 percentage points, 95% CI +0.65 to +4.99). Estimates varied across the three systems and only one system-specific interval excluded zero. In a prespecified exploratory analysis with a shared eligibility rule, correct suggestions were adopted far more often than incorrect ones (risk difference +35.7 percentage points, 95% CI +30.8 to +40.7), indicating selective rather than indiscriminate compliance. An incorrect suggestion from a specialist with low stated accuracy was therefore slightly more influential than the same suggestion from a student with high stated accuracy, although the difference was modest and varied across systems. Agreement reached only after a user has disclosed a preferred answer should not automatically be treated as an independent second opinion, and medical large language model systems should be evaluated on how they revise answers after such disclosure, not solely on initial accuracy.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

Background: The condition of a patient with acute stroke often changes within hours of ICU admission. Prognostic work here targets fixed endpoints predicted from admission data, and trajectory phenotyping assigns one label per patient. We used longitudinal ICU data to identify interpretable dynamic clinical states, characterize transitions between them, and relate the current state to later events. Methods: Retrospective cohort study of 6368 adults with acute stroke in MIMIC IV v3.1. The first 72 h were divided into twelve 6-hour windows, and a hidden Markov model was fitted to 21 neurological, physiological and organ support variables. State number was chosen against criteria fixed before fitting: statistical fit, restart stability, state occupancy and clinical interpretability. Generalized estimating equations related the current state to new mechanical ventilation and vasopressor use within 12 h, and to ICU death within 72 h. Eleven sensitivity analyses assessed the robustness of the state solution. Results: Four states were selected: neurologically preserved-low support, neurological impairment low support, impairment renal dysfunction and impairment-respiratory support (63.3%, 7.8%, 11.8% and 17.1% of windows). Within 72 h, 40.3% of patients changed state at least once, and transitions ran in both directions rather than along a single severity gradient. States were identified without outcome data, yet ICU mortality by last state ranged from 2.9% to 43.9%. Adjusted for age, sex, subtype and Charlson index, the current state remained associated with organ-support escalation and death. State prevalence differed by at most 1.1 percentage points between training and test sets, and 10 of 11 sensitivity analyses gave a stable four-state solution (ARI 0.754 0.955). Conclusions: The early ICU course of acute stroke can be represented as movement among a small number of clinically interpretable states. The representation was reproducible in a held out set and across admission eras, but requires validation in an independent database before any clinical use.

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.

Integrating multisensory and behavioral information is essential for sensory perception. In the auditory system, multisensory and behavioral influences emerge early in subcortical structures. Descending projections from non-auditory cortical areas are well positioned to convey such signals, yet how they shape subcortical auditory processing remains poorly understood. Here, we investigated corticocollicular projections from the primary somatosensory (S1) and motor (M1) cortices to the inferior colliculus (IC), a principal integration center in the auditory midbrain. We found that trunk- and limb-related regions of S1 and M1 form prominent monosynaptic projections to the IC, and that optogenetic activation of these projections robustly drives IC activity. Notably, a substantial population of cortical-responsive neurons did not respond to sound. In sound-responsive neurons, concurrent cortical stimulation enhanced sound-evoked responses, whereas cortical activation preceding sound onset suppressed them. Furthermore, both cortical-responsive IC neurons and deep-layer S1 and M1 neurons exhibited locomotion-related modulation and anticipatory activity prior to movement onset, suggesting that these descending pathways convey movement-related signals to the IC. Together, our findings identify a descending sensorimotor circuit that integrates body- and movement-related information with auditory processing in the auditory midbrain.