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Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) represent a spectrum of acute respiratory failure arising from the same underlying pathophysiological processes and are associated with substantial morbidity and mortality worldwide. Although corticosteroids such as Dexamethasone are commonly administered to patients with moderate-to-severe ARDS, their clinical benefit remains controversial, and systemic administration is often associated with significant adverse effects. We developed a targeting ligand-free, sulfonium lipid nanoparticle (sLNP)-based drug delivery system, Dex/DOSEH, for intravenous lung-targeted delivery of dexamethasone. In a lipopolysaccharide (LPS)-induced murine ALI model, treatment with Dex/DOSEH formulation significantly reduced proinflammatory cytokine production, decreased immune cell infiltration, preserved capillary-alveolar barrier integrity, and attenuated histopathological lung injury compared with controls. Our findings demonstrate that Dex/DOSEH drug formulation enables effective lung-targeted delivery of dexamethasone and achieves robust anti-inflammatory therapeutic efficacy in ALI. This platform represents a promising therapeutic strategy for the treatment of ALI/ARDS.

Background HPV-negative (−) head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous cancer characterized by high mutational burden, an immunosuppressive microenvironment, and poor response to standard therapies. These features highlight the urgent need for novel and more effective treatment strategies. Methods Drug sensitivity prediction was performed using integrated datasets from TCGA, GDSC, and CCLE. To assess the therapeutic potential and underlying mechanisms of combining the CDK inhibitor AZD5438 with the MEK1/2 inhibitor PD0325901, we employed a comprehensive panel of HNSCC models, including established cell lines, orthotopic mouse tumor models, and patient-derived organoids (PDOs). Lipid nanoparticles (LNPs) were engineered to co-deliver both agents into the same cancer cell populations. The tumor secretome was profiled using biotinylation coupled with liquid chromatography-mass spectrometry (LC-MS). Molecular alterations were examined by immunofluorescence, immunohistochemistry, ELISA, flow cytometry, and Western blot. Results Our bioinformatics analysis identified AZD5438 and PD0325901 as two of thirteen candidate drugs whose sensitivity is consistently associated with the five most frequently mutated genes in HPV (−) HNSCC. Notably, among these candidates, AZD5438 and PD0325901 exhibited the lowest correlation in their sensitivity profiles, suggesting complementary mechanisms of action. In experimental models, the combination of AZD5438 and PD0325901 not only outperformed either monotherapy in suppressing tumor growth but also augmented CD8⁺ T cell-mediated antitumor immunity by promoting caspase-8/gasdermin E-dependent pyroptosis. Furthermore, in both orthotopic tumor-bearing mice and PDOs, the LNP-encapsulated drug combination produced significantly greater therapeutic efficacy compared with the free drug formulation. Conclusions Our findings indicate that the combination of AZD5438 and PD0325901 holds therapeutic potential for the treatment of HPV (−) HNSCC, particularly in tumors with a high mutational burden. By targeting complementary pathways, this combination may improve treatment outcomes in this aggressive cancer subtype.

FAT atypical cadherin 1 (FAT1) is one of the most frequently mutated genes in head and neck squamous cell carcinoma (HNSCC), exhibiting the highest mutation rate across different tumor types. Although FAT1's role has attracted considerable attention, its impact on cancer metabolism and treatment resistance remains poorly understood. In this study, it is demonstrated that knockout of mutant FAT1 in HNSCC cells attenuates CPT1A‐driven fatty acid oxidation (FAO) through downregulation of the transcription factor ASCL2, leading to marked suppression of tumor growth. Notably, FAT1‐mutant HNSCC cells exhibit resistance to the TCA cycle inhibitor CPI‐613 through activation of CPT1A‐mediated FAO, whereas genetic ablation of mutant FAT1 restores sensitivity to CPI‐613. To achieve in vivo depletion of FAT1, LNP‐sgFAT1 is developed, a novel lipid nanoparticle (LNP) system encapsulating Cas9 mRNA and FAT1‐targeting sgRNA. In murine models bearing FAT1‐mutant head and neck tumors, LNP‐sgFAT1 demonstrated enhanced antitumor activity when combined with CPI‐613. Collectively, these findings establish that mutant FAT1 drives CPT1A‐dependent FAO, facilitating a metabolic bypass that confers resistance to TCA cycle inhibition in HNSCC. This mechanistic insight highlights promising opportunities for combinatorial therapeutic strategies co‐targeting genetic and metabolic vulnerabilities in cancer. This study demonstrates that mutant FAT1 promotes ASCL2‐driven, CPT1A‐dependent fatty acid oxidation, leading to resistance to CPI‐613‐mediated TCA cycle inhibition in head and neck cancer. In vivo gene depletion of mutant FAT1 with LNP‐sgFAT1 suppresses tumor growth and restores CPI‐613 sensitivity, revealing a targetable metabolic bypass with therapeutic potential in FAT1‐mutant cancer. Figure created using Biorender (https://biorender.com/).

Acute respiratory distress syndrome (ARDS) is the most severe manifestation of acute lung injury (ALI), characterized by diffuse pulmonary inflammation, impaired gas exchange, and high morbidity and mortality. Despite its clinical significance, no specific or effective pharmacological therapies are currently available for its treatment. In this study, we developed a lung-targeted mRNA-sulfonium lipid nanoparticle (mRNA/sLNP) delivery system for the treatment of ALI in a mouse model. We first optimized sulfonium lipid structures, and the optimized sLNP was comprehensively characterized and subsequently loaded with interleukin-10 (IL-10) mRNA. In a lipopolysaccharide (LPS)-induced ALI mouse model, IL-10/sLNP demonstrated both prophylactic and therapeutic efficacy, significantly attenuating pulmonary and systemic inflammation, restoring barrier integrity, and reducing tissue injury.

Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCSs) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCSs has been tested in laboratory and field environments, and a growing body of literature on uses of LCSs primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network's ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCSs across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ∼ 35 % average uncertainty (root mean square error) in the calibrated LCSs, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London's air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network's ability to generate robust information about London's air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing, and interpreting data.

Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained, and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOx emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57 %–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67 %–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOx.

Background Fournier’s gangrene is an acute soft tissue necrotizing infection involving the perineum and the external genitalia which can result in a major loss of the scrotal wall with exposure of the testicles. Reconstruction of such major defect is quite challenging; the use of pedicled gracilis muscle flap helps to create an aesthetically acceptable scrotum with minimal donor site morbidity. Case presentation We described the case of a 60-year-old man with a large scrotal loss from Fournier’s gangrene following bladder outlet obstruction and perineal abscess. He had multiple debridement and reconstruction with pedicled left gracilis muscle flap with a good aesthetic and functional post-operative outcome. The major challenge encountered was the loss of the skin graft as a result of the retraction of the muscle flap due to too early ambulation; this can thus be avoided by adequate pre-operative counseling and enforcing bed rest. Conclusions The use of gracilis muscle flap in the reconstruction of large scrotal defect described in this report has the additional advantage of creating a pliable and soft feel like that of the original scrotum with minimal donor site morbidity.

Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural areas with limited bandwidth resources. Despite the potential of federated learning to fine-tune deep learning models using data collected from edge devices in low-resource environments, its application in the field of bird monitoring remains underexplored. This study proposes a federated learning pipeline tailored for bird species classification in wetlands. The proposed approach is based on lightweight convolutional neural networks optimized for use on resource-constrained devices. Since the performance of federated learning is strongly influenced by the models used and the experimental setting, this study conducts a comprehensive comparison of well-known lightweight models such as WideResNet, EfficientNetV2, MNASNet, GoogLeNet and ResNet in different training settings. The results demonstrate the importance of the training setting in federated learning architectures and the suitability of the different models for bird species recognition. This work contributes to the wider application of federated learning in ecological monitoring and highlights its potential to overcome challenges such as bandwidth limitations.

Background: Molar incisor hypomineralisation (MIH) is a common disorder of tooth development, which has recently been found to be associated with a higher prevalence of hypodontia. The aim of this international multicentre study is to determine the association between MIH and other developmental anomalies in different populations. Methods: Investigators were trained and calibrated for the assessment of MIH and dental anomalies and ethical approvals obtained in each participating country. The study aimed to recruit 584 children with MIH and 584 children without MIH. Patients aged 7–16 years who attend specialist clinics will be invited to participate. Children will undergo a clinical examination to determine the presence and severity of MIH, using an established index. The presence of any other anomalies, affecting tooth number, morphology, or position, will be documented. Panoramic radiographs will be assessed for dental anomalies and the presence of third permanent molars. Statistical analysis, using a chi squared test and regression analysis, will be performed to determine any differences in dental anomaly prevalence between the MIH and non-MIH group and to determine any association between dental anomalies and patient characteristics. Conclusion: This large-scale study has the potential to improve understanding about MIH with benefits for patient management.

This research evaluates advanced waste management technologies suitable for long-duration space missions, particularly focusing on artificial intelligence (AI)-driven sorting systems, biotechnological bioreactors, and thermal processing methods, such as plasma gasification. It quantitatively assesses the waste generated per crew member. It analyses energy efficiency, integration capabilities with existing life-support systems, and practical implementation constraints based on experimental ground and ISS data. Challenges are addressed, including energy demands, microbial risks, and integration complexities. The research also discusses methodological approaches, explicitly outlining selection criteria and comparative frameworks used. Key findings indicate that plasma arc technologies significantly reduce waste volume, although high energy consumption remains challenging. Enhanced recycling efficiencies of water and oxygen are also discussed. Future research directions and actionable policy recommendations are outlined to foster sustainable and autonomous waste management solutions for space exploration.