Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.
暂无摘要（点击查看详情）
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. It reduces quality of life and increases the risk of complications such as stroke. Although progress has been made in understanding its pathogenesis, the key cellular subtypes and therapeutic targets remain unclear. We applied single-cell transcriptomics to identify critical cellular subtypes in AF. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and machine learning were used to screen AF-related genes. Mendelian randomization (MR) and colocalization analyses were performed to assess causal relationships between these genes and AF. Single-cell analysis showed a significant increase in macrophages in AF, especially SPP1-expressing macrophages, which may drive AF onset and progression. HdWGCNA identified AF-related gene modules. Three genes, LRCH1, RSRC2 and VAMP2, were found to be causally associated with AF. MR analysis confirmed their significant causal effects. The accumulation of SPP1-expressing macrophages may drive the onset and progression of AF. Furthermore, LRCH1, RSRC2, and VAMP2 were identified as key causal genes for AF, providing novel insights into its molecular mechanisms and potential therapeutic targets.
Special Operations Forces (SOF) comprise a segment of military personnel whose tasks and missions often require complex cognition. Sleep is important to maintain peak cognitive performance, as lack of adequate sleep can lead to decrements in reaction time, memory, and other facets of cognition. Little research has assessed the association between sleep quality and cognitive performance in SOF populations. We broadly hypothesized that poorer sleep quality in SOF personnel would be associated with decrements in cognitive performance. A group of 183 male SOF personnel answered questions from the Pittsburgh Sleep Quality Index (PSQI) about their sleep habits during the month prior to their study visit across seven components of sleep quality. Participants also completed ten cognitive tasks using the Senaptec Sensory Station during the same study visit. We compared PSQI and cognitive performance scores using Spearman correlations, exploring any significant correlations (α = .05) further in multivariate analysis. We performed linear regression to assess the potential effect of sleep quality on cognitive performance while controlling for demographics, years of military service, traumatic brain injury history, and mental health symptoms. We used SAS 9.4 for statistical analysis. This study was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill (IRB #14-1364). Poorer sleep was weakly correlated with longer times to accurately capture targets ("Target Capture") and improved inhibitory control ("Go/No-Go" (GNG)). When controlling for confounders, reduced sleep efficiency (β = 1.50, 95% CI: 0.46, 2.54) and more frequent sleep disturbance (β = 2.05, 95% CI: 0.18, 3.91) were both associated with better scores on GNG, which evaluates an individual's ability to quickly distinguish between targets that require a response ("Go") and those that do not ("No-Go"). When adjusting for multiple comparisons using a Bonferroni correction, the association between reduced sleep efficiency and faster/more accurate GNG performance maintained statistical significance (β = 1.50, Bonferroni-corrected 99.2% CI: 0.10, 2.90), but the association between more frequent sleep disturbance and faster/more accurate performance on GNG did not (β = 2.05, Bonferroni-corrected 99.2% CI: -0.46, 4.55). Contrary to our hypothesis, reduced sleep efficiency was associated with better GNG performance in a population of SOF personnel. GNG assesses visual processing speed, reaction time, and decision-making skills under pressure to obtain a measure of inhibitory control, a high-level cognitive process that is a key facet of executive function. Decreased sleep quality impairs these constructs in civilian populations, so the ability to overcome fatigue and execute demanding decisions accurately and with high speed in this population is notable. Future research is needed to determine if the relationship between sleep quality and cognitive performance is possibly related to increased cognitive effort by those with worse sleep or other yet undiscovered determinants of military brain health.
Coarse-grain Lagrangian methods, such as Dissipative Particle Dynamics (P. J. Hoogerbrugge et al., EPL, 1992, 19, 155), are suitable to describe mesoscopic fluid systems with the inclusion of thermal fluctuations. However, the realistic simulation of liquids using these methods represents a longstanding problem. In this work, we develop a local thermodynamic (LTh) model for the description of condensed phases within the framework of the Generalized Dissipative Particle Dynamics with Energy Conservation (GenDPDE) method (J. Bonet Avalos et al., Phys. Chem. Chem. Phys. 2019, 21, 24891). Such a model is appropriate for the analysis of liquids, due to the explicit account of the thermal expansion coefficient and isothermal compressibility at the mesoscale. We demonstrate the accuracy of the LTh model by inspecting the thermodynamic properties of argon at both liquid and supercritical conditions, through equilibrium simulations carried out around two characteristic reference states (T = 125.7 K, P = 85.31 MPa, ϱ = 1419.7 kg/m3 for liquid Ar, and T = 418.8 K, P = 85.31 MPa, ϱ = 695.99 kg/m3 for supercritical Ar). Remarkably, we show that the model is also valid in a range of thermodynamic conditions near the reference states, allowing for a correct description of the physics of systems with spatial density and temperature variations. We furthermore derive analytical expressions for the macroscopic pressure and energy equations of state in terms of the model parameters, discussing their validity and limitations. We show that, even at the mean-field level, a correct account of the local particle arrangements is necessary to obtain accurate predictions of the macroscopic thermodynamic quantities from mesoscopic properties. Thus, we also investigate the applicability of the Hypernetted Chain approximation as a tool to predict the radial distribution function of the GenDPDE system, examining the strengths and deficiencies of this approach. With the proposed LTh model, GenDPDE provides a reliable and flexible tool for the analysis of condensed phases through coarse-grain techniques.
Respiratory frequency (fR) and tidal volume (VT) show distinct responses during high-intensity interval training (HIIT) bouts, yet the underlying mechanisms remain unclear. We investigated the effects of hypoxia and hyperthermia on ventilatory responses to HIIT bouts. Ten recreationally trained males (mean ± SD: peak oxygen uptake: 3.98 ± 0.62 L/min, age: 28 ± 6 years) performed the same HIIT protocol to exhaustion in three randomized conditions: normobaric hypoxia (HYP, 15% O2), hyperthermia (HOT, 35 °C, 40% humidity), and control (CON, 18 °C and 40% humidity). Work-recovery phases (30-30 s) were performed at 109% of peak power output from a prior incremental test and 50 W, respectively. Time-to-exhaustion (TTE) differed (P < 0.05) across CON (18.0 ± 4.4 min), HYP (9.4 ± 2.8 min), and HOT (12.6 ± 3.1 min) conditions. Iso-time fR was associated with changes in TTE and positively correlated with perceived exertion (P < 0.001; r = 0.85) and aural temperature (P < 0.001; r = 0.58). On average, fR increased during the work phase and decreased during recovery, primarily driving pulmonary ventilation ([Formula: see text]) during work-recovery alternations. Conversely, VT showed a smaller and opposite response, with higher values during recovery. VT plateaued in all conditions, but higher values were observed in HYP versus CON and HOT, accompanied by higher capillary blood lactate levels and lower blood oxygen saturation. fR is associated with changes in exercise tolerance irrespective of the environmental conditions tested and is more closely related to perceived exertion than to aural temperature. fR primarily drives the [Formula: see text] response to high-intensity work-recovery alternations, while VT appears fine-tuned on fR levels and the magnitude of metabolic inputs.
Neurodegenerative and neuropsychiatric disorders lack disease-modifying therapies. The microbiota-gut-brain (MGB) axis, particularly short-chain fatty acid (SCFA)-producing microbiota dysbiosis, has emerged as a conserved driver of neuroinjury pathogenesis. Natural food-derived polysaccharides have been explored as prebiotic substrates, but their clinical translation is hindered by poor target specificity, high interindividual heterogeneity, and low bioavailability. Engineered food-derived polysaccharides, as a next-generation precision prebiotic platform, enable rational tailoring of molecular fine structures via targeted physical, chemical, biological, and combinatorial modification technologies, aiming for strain-specific directional modulation of intestinal SCFA-producing microbiota and multi-pathway neuroprotection through the MGB axis. In this review, we systematically delineate the bidirectional regulatory mechanisms between SCFA-producing microbiota and neural homeostasis, dissect disease-specific pathological cascades driven by SCFA-producing microbiota dysbiosis, and discuss conflicting findings on the dual effects of SCFAs. We further propose a full-chain framework of the structure-activity relationship of engineered polysaccharides, dissecting core modification strategies, strain-specific targeting mechanisms, and a multi-dimensional efficacy evaluation system for these precision prebiotics. Additionally, we assess safety evaluation status, major global regulatory differences, and core clinical translation bottlenecks. Finally, we outline key unresolved challenges and propose a conceptual roadmap for AI-assisted rational design of precision prebiotics, personalized microbiota-adapted intervention strategies, and multicenter clinical translation directions. This review provides a mechanism-driven theoretical framework and practical guidance for developing engineered food-derived polysaccharides as precision nutrition interventions for neuroinjury-related disorders.
Wound healing is a complex process involving various factors and reconstruction of tissue structures. Platelets and their bioactive substances play an indispensable role in various stages of wound healing. Tissue engineering scaffolds are instrumental in maintaining, repairing, and enhancing tissue structure and function. Combining platelet concentrates with tissue engineering scaffolds heralds a novel direction for wound healing research. In this combination, platelet concentrates serve as tissue repair facilitators, while scaffolds function as tissue support and drug release platforms. This synergistic approach produces superior tissue repair outcomes in comparison to using either method independently. As a result, it is crucial to consolidate the application of platelet concentrates with tissue engineering scaffolds in the context of wound healing. This review begins by exploring the role of platelet concentrates and tissue engineering scaffolds in wound healing, while also outlining the essential criteria that scaffolds must meet. It then summarizes the application advances of platelet concentrates combined with tissue engineering scaffolds in wound healing. Finally, we highlighted current limitations in translation, including interpatient variability, formulation reproducibility, and complex regulatory hurdles. Developing innovative strategies and delving deeper into potential molecular mechanisms will drive further advancements in wound healing therapies through biomaterials engineering.
Adenovirus type 7 (AdV-7) frequently causes outbreaks in crowded settings such as military barracks and schools. This study seeks to deepen understanding of individual-level virus transmission mechanisms and examine how intervention timing and stringency shape epidemic trends. Methodological innovations in model construction allow refined description and reconstruction of transmission processes, offering methodological support for epidemic analysis and prediction. Based on the framework of this model, additional simulation models tailored to other scenarios can also be developed. We constructed a time-varying multilevel social contact network (trainees, class monitors, team leaders, company officers) matching field survey data's statistical characteristics, used an individual-based dynamic model to simulate AdV-7 transmission, calibrated parameters via fitting predicted and real incidence data, and verified reliability through sensitivity analysis. Results identified trainees as key transmitters; effective reproduction number (Re) surged above 3 initially and fell below 1 after isolation and contact restrictions took effect on Day 14 following the first index case. New cases declined after a brief surge, consistent with real epidemic trends. Sensitivity analysis revealed significant positive correlations between infection numbers, trainees' susceptibility, and isolation timing. The study confirms the model's validity, showing timely early warning, isolation, and social distancing effectively control the epidemic.
Hematopoietic stem cell transplantation (HSCT) is a curable treatment for refractory/relapse B-cell acute lymphoblastic leukemia (R/R B-ALL).Traditional chemotherapy or Chimeric antigen receptor T-cell (CAR-T) therapy are both important methods to achieve MRD negativity before HSCT. However, which treatment is preferred needs to be clarified. In this study, 269. s with R/R B-ALL who underwent allo-HSCT after CAR-T cell therapy (CAR-T, n = 142) or chemotherapy (CT, n = 127) were enrolled from multicenters. The 3-year overall survival (OS) after transplantation was 66.8% in the CAR-T group and 72.3% in the chemotherapy (CT) group. The 3-year relapse-free survival (RFS) was 65.3% in the CAR-T group and 65.9% in the CT group. The 3-year graft-versus-host disease, relapse-free survival (GRFS) and cumulative incidence of relapse (CIR) were also similar between the two groups. Among patients who achieved first complete remission (CR1) before transplantation, the proportion receiving chemotherapy was significantly higher than that receiving CAR-T therapy (37.5% vs. 13.6%; P < 0.001). In this CR1 subgroup, the CT group demonstrated superior outcomes, including improved OS, RFS, and GRFS. In pediatric R/R B-ALL, CAR-T bridging to HSCT appears to yield post-transplant survival comparable to chemotherapy, though a higher rate of moderate to severe chronic GVHD was observed. CAR-T may be a reasonable option, but closer GVHD monitoring seems warranted.
The trade-off between mechanical robustness and ionic conductivity in gel materials impedes their application in flexible electronics. Herein, a eutectogel is engineered via a synergistic strategy that integrates a ternary deep eutectic solvent (DES) (choline chloride/ethylene glycol/zinc chloride) with dynamic Zn2 + coordination. Through in situ photopolymerization of 1-vinylimidazole in the ternary DES, a dynamically cross-linked organic-inorganic hybrid network is constructed. Crucially, Zn2 + ions play a dual role: they form reversible Zn2 +-imidazole coordination sites, enhancing the mechanical properties with an elongation at break of 1100% and a Young's modulus of 0.23 MPa, while inducing coordination-driven densification of the amorphous network. This compaction effect tightens the polymer network without triggering crystallization, while accessible ion-transport pathways are retained within the amorphous network. Consequently, the eutectogel exhibits a high ionic conductivity of 0.38 mS cm- 1, overcoming the typical conductivity loss in high-strength gels. Using these properties, a flexible strain-sensing system with Bluetooth transmission is developed. It can capture real-time motor signals and convert them into visual commands, highlighting its potential for wireless assistive monitoring, particularly in rehabilitation for hemiplegic patients. This work provides a promising strategy for achieving a balance between mechanical robustness and ionic conductivity in soft materials by regulating the amorphous structure.
This paper addresses the distributed predefined-time optimal adaptive formation control problem in heterogeneous systems consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). To cope with asymmetric state constraints in the planar subsystem, a nonlinear mapping along with a relaxation function is employed to transform the constrained states into an equivalent unconstrained form. Then, the single critic structure is integrated into the command filtered backstepping design framework to derive the distributed predefined-time optimal formation controller, where the adaptive fuzzy approximation is utilized to approximate unknown nonlinearities and the auxiliary performance function. It is demonstrated that the formation errors converge within a predefined time, while the positions of vehicles remain within the prescribed constraints. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.
To systematically review the existing literature on risk prediction models for malnutrition in cancer patients. Malnutrition is highly prevalent among cancer patients, and timely identification and intervention can improve patient outcomes and quality of life. Risk prediction models can forecast the future disease risk of patients. This study aims to conduct a comprehensive scoping review of malnutrition risk prediction models for cancer patients developed domestically and internationally, analyse current gaps in the field, and provide references for clinical practice and future research. This review followed the PRISMA Extension for Scoping Reviews and the Arksey and O'Malley framework, and the study was conducted in accordance with the CHARMS checklist. Systematic searches were conducted in PubMed, Web of Science Core Collection, the Cochrane Library, EMbase, CINAHL, CNKI, Wanfang, and Sinomed, with the search period spanning from database inception to October 23, 2025. Original studies reporting the development or validation of malnutrition risk prediction models in cancer patients were eligible for inclusion. A total of 36 studies were included in this scoping review. Extracted data included study characteristics, model development methods, predictors included, and performance evaluation metrics. Considerable overlap was observed in the development methods across models, with traditional statistical analysis being predominant. Retrospective data collection was applied in the majority of the included studies. These prediction models covered multiple malignancies, including rectal, liver, nasopharyngeal, gastric, and oesophageal cancers. A wide range of predictors was used, with age and BMI being the most frequently included. This review maps cancer patients' malnutrition prediction models. Existing models screen malnutrition but have flaws in construction and validation. Future studies will adopt longitudinal, multimodal data and AI to optimize tools for early screening and intervention.
Gliomas are aggressive brain tumors associated with a poor prognosis. Although SIL1, an endoplasmic reticulum chaperone factor, is known to maintain protein homeostasis, its specific role in glioma pathogenesis remains poorly understood. This study aimed to investigate the clinical significance and biological functions of SIL1 in glioma. We performed a pan-cancer multi-omics analysis using TCGA and GTEx datasets to evaluate SIL1 expression, its prognostic value, and its associations with genomic instability, tumor stemness, and immune infiltration. Gene set enrichment analysis (GSEA) was utilized to identify potentially involved signaling pathways. For in vitro functional validation, human glioma cell lines (U251 and A172) were subjected to siRNA-mediated knockdown and lentiviral rescue assays to assess cellular proliferation, migration, apoptosis, and epithelial-mesenchymal transition (EMT) dynamics. SIL1 was significantly upregulated in gliomas and correlated with poor patient survival. High SIL1 expression was associated with increased tumor heterogeneity (based on MATH scores), DNA methylation-derived stemness indices, and an immunosuppressive microenvironment characterized by the enrichment of M2 macrophages, regulatory T cells (Tregs), and immune checkpoint molecules. In vitro, SIL1 knockdown suppressed glioma cell proliferation and invasion, promoted mitochondrial apoptosis, and mitigated the EMT phenotype, potentially by impairing SNAIL nuclear translocation. Notably, these phenotypic changes were effectively rescued following lentiviral overexpression of SIL1. Our findings suggest that SIL1 is a potential prognostic biomarker in glioma. Its elevated expression correlates with increased malignancy, stemness features, EMT, and an immunosuppressive microenvironment, indicating that SIL1 may serve as a promising therapeutic target for glioma intervention.
This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during Overt Speech (OS) and Covert Speech (CS) in different languages. The dataset comprises repetitions of five different commands pronounced covertly and overtly in English and Spanish from 21 healthy native Spanish speakers (13 male, 8 female, 23 ± 2 years old), while EEG and audio were recorded. In addition, 3 non-native healthy Spanish speakers were recorded under the same circumstances (3 male, 23.3 ± 0.6 years old). A total of 7936 available epochs (i.e., 11.02 hours of data) were recorded with a 32 channel, 256 Hz sampling rate Water based EEG device. The database was designed to maximize the number of different analysis involving EEG signals. The final number of epochs, as well as the statistical analysis (showing significance in Broca's and Wernicke's areas) and machine learning experiments (with subjects exceeding the chance level with basic machine learning models), show that this material is a valuable resource for research on future ways of communication.
Biliary organoids, as an emerging three-dimensional (3D) in vitro modeling technology, have demonstrated significant value in studying biliary development, elucidating disease mechanisms, drug screening, and enabling personalized medicine. This review provides a comprehensive overview of the current strategies for constructing biliary organoids, including their cellular sources, differentiation pathways, and functional characteristics. Particular emphasis is placed on their strengths and limitations in recapitulating biliary physiology, modeling biliary tract cancers (BTCs), and performing pharmacological and toxicological assessments. The article further analyzes major technical challenges, such as low modeling efficiency, limited structural and functional fidelity, absence of microenvironmental simulation, and issues of standardization and ethics. Future directions are proposed in the areas of multicellular co-culture systems, dynamic cultivation technologies, high-throughput platforms, and clinical translation pipelines. As a versatile and evolving tool, biliary organoids are poised to serve as a critical bridge between basic research and clinical applications, offering new insights and methodologies for the study and precision treatment of biliary tract cancers.
Cooperative Vehicle-Infrastructure Systems (CVIS) can significantly enhance tunnel safety, yet most research relies on driving simulators that lack the psychological realism of actual confined environments. This study addresses the gap by evaluating Human-Machine Interaction (HMI) effectiveness in real-world tunnel blind spots, where restricted visibility and confined roadway geometry create high-risk scenarios. A naturalistic driving experiment was conducted in the Guayanling Tunnel, China. Participants drove a vehicle equipped with eye-tracking and onboard data units through a curved blind spot under four conditions: no warning, informative (implicit), instructive, and commanding (explicit) warnings. The study analyzed vehicle data (speed, braking, lateral acceleration) and eye-tracking metrics to assess safety and effectiveness, while also examining the influence of driver gender, experience, and driving style. Across the fixed sequential warning rounds, commanding warnings were associated with the largest changes in speed regulation and earlier braking, but they were also associated with increased lateral-control variability. Instructive warnings also advanced braking, whereas informative warnings showed smaller behavioral effects. Formal interaction analyses provided limited support for subgroup moderation: braking responses varied by driving experience, whereas gender- and driving-style-related differences were weak or exploratory. Explicit, commanding HMIs are superior for immediate hazard response in tunnel blind spots but impose a higher cognitive load. The effectiveness of warnings is not universal; it varies significantly based on individual driver characteristics. Therefore, relying solely on generic warning strategies may be insufficient for diverse driver populations.
The breastmilk microbiome plays a crucial role in gut microbial colonisation and immune development, but little is known about how it is influenced by type 1 diabetes. We conducted a longitudinal 16S rRNA gene sequencing study of milk from women with type 1 diabetes (n=69 pregnancies; 174 samples) and women who did not have type 1 diabetes (n=49 pregnancies; 123 samples), collected at seven timepoints from birth to 15 months postpartum. Alpha diversity (richness, inverse Simpson evenness) was analysed by generalised linear mixed models, beta diversity was analysed by Bray-Curtis dissimilarities and PERMANOVA, and differential abundance was analysed by limma. Additionally, we examined associations with maternal genetic risk score (GRS), maternal HLA type, glycaemic management (HbA1c) and breastmilk secretory IgA (sIgA), and performed a parallel analysis for the infant stool microbiome. A significant interaction between type 1 diabetes status and timepoint was observed for alpha diversity, both richness (p=0.01) and inverse Simpson diversity (p=0.003), indicating distinct temporal trajectories between women with and without type 1 diabetes. In those without type 1 diabetes, richness increased significantly between birth and 1 week postpartum, but this early increase was delayed in women with type 1 diabetes to between 1 week and 3 months postpartum (p=0.002). Beta diversity analysis revealed earlier and more extensive compositional shifts in women without type 1 diabetes compared to those with type 1 diabetes. These differences persisted after adjusting for Caesarean delivery, BMI, parity and infant sex, and were not attributable to a delay in initiating breastfeeding. Taxa with delayed enrichment in women with type 1 diabetes included Streptococcus spp. and Rothia mucilaginosa, which metabolise human milk oligosaccharides to short-chain fatty acids to promote development of the infant's gut barrier and immune system. Maternal GRS, HLA, HbA1c or sIgA were not associated with milk microbiota diversity trajectories. In infant stool samples, alpha diversity did not differ between exposure groups, and showed no evidence of delayed maturation. Beta diversity revealed an early compositional shift between birth and 1 week postpartum only in infants born to women without type 1 diabetes. Similarly, significant taxonomic changes between birth and 1 week postpartum were detected only in infants born to women without type 1 diabetes, but with some taxa differing between exposure groups at 1 week. Maternal type 1 diabetes is associated with delayed early maturation of the breastmilk microbiome. Early compositional differences in microbiota restructuring were also observed in the infant gut, partially mirroring the pattern in the milk microbiome; however, sustained differences in infant gut microbiota diversity were not detected. Further investigation could determine whether these changes affect development of the infant's gut and immune system.
Despite long-standing research, no licensed vaccine exists for shigella, a leading cause of bacterial diarrhoea and dysentery. WRSs2 is a live-attenuated Shigella sonnei vaccine candidate which has previously shown safety and immunogenicity. In this trial, we evaluated its safety and efficacy in a controlled human infection model. In this phase 2, double-blind, randomised, placebo-controlled trial at two sites in the USA, healthy adults aged 18-49 years were assigned using a site-stratified permuted-block schedule. The original three-arm design allocated participants 1:1:1 to two-dose WRSs2 (106 colony-forming units [CFU]), one-dose placebo followed by one-dose WRSs2 (106 CFU), or two-dose placebo; doses were given 28 days apart. After 69 participants were enrolled, a Data and Safety Monitoring Board (DSMB)-triggered safety review and protocol amendment resulted in subsequent participants being assigned 2:1 to two-dose WRSs2 (5 × 105 CFU) or placebo. Participants were challenged orally 28 days after the second vaccination with approximately 1·5 × 103 CFU of S sonnei 53G. The primary endpoint was endpoint review committee-adjudicated shigellosis in challenged participants. Safety was assessed in all vaccinated participants. This trial is registered with ClinicalTrials.gov, NCT04242264. The trial is complete. Between Oct 11, 2022, and Jan 9, 2024, 108 participants were enrolled, with 22 assigned to two-dose 106CFU, 26 to two-dose 5 × 105 CFU, 23 to one-dose 106 CFU, and 37 to placebo. 73 participants underwent challenge (16, 18, 13, and 26 participants in the respective groups). Endpoint review committee-adjudicated shigellosis occurred in three (9%) of 34 participants given pooled two-dose vaccine and 21 (81%) of 26 placebo recipients (vaccine efficacy 89% [95% CI 71-96]; p<0·0001). Six participants had grade 3 post-vaccination adverse events, prompting two DSMB reviews; after the first review, the protocol was amended to reduce the vaccine dose and revise eligibility criteria. There was no change after the second review. No vaccine-related serious adverse events or deaths occurred. In adults in the USA, WRSs2 provided high-level protection against S sonnei shigellosis. Although protection was substantial, the occurrence of a few self-limiting grade 3 adverse events indicates that further optimisation is needed to better define the safety-efficacy balance. These findings support further clinical development of live-attenuated shigella vaccines. US National Institutes of Health with pharmaceutical support from the US Department of Defense.
Studies on the effects of γ-radiation on nonhuman primate (NHP) brains are limited, despite the critical need to understand the impact of radiation exposure on the brain from various sources like radiotherapy equipment, space travel, and potential nuclear events. We investigated molecular and neuropathological changes in rhesus macaque brains after a single 5.8 Gy total-body γ-radiation exposure. We analyzed samples dissected from frontal cortex (FCtx), hippocampus (Hippo), and cerebellum (CRB) of irradiated (RAD) vs. unirradiated/control (CTRL) animals. Western blotting and digital PCR (dPCR) analyses were used to measure different phosphorylated-Tau (pTau) forms and neurodegeneration markers (i.e., amyloid protein precursor [APP], neurofilament-light chain [NFL], glial fibrillary acidic protein [GFAP], ionized calcium-binding adapter molecule 1 [IBA1/AIF1], and myelin basic protein [MBP]). We detected lower levels of different forms of soluble pTau species (pTau181, and pTau217, among others) in RAD vs. CTRL animals across all three examined brain regions. While APP and GFAP levels were unchanged in the FCtx, increased IBA1 and NFL levels were detected alongside decreased MBP levels. Moreover, dPCR data identified decreased expression of GFAP and MBP in the FCtx. Importantly, the molecular changes observed were not accompanied by overt signs of neurodegeneration or cellular abnormalities upon neuropathological assessment. These findings in irradiated NHPs' brains are novel and indicate that a single total-body γ-radiation exposure significantly alters soluble pTau levels after a few weeks from irradiation without causing obvious neurohistological damage. These results open intriguing new possibilities of exploring γ-radiation-based strategies to modulate the progression of tauopathies, including Alzheimer's disease.