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Abstract Background Multiple rib fractures can lead to flail chest with up to 35% mortality rate due to severe pulmonary complications. Current treatments of flail chest remain controversial. Studies have shown that surgical treatments can improve outcomes and reduce mortality, comparing to non-operative treatments. Current surgical fixation methods focus on stabilization of ribs on the outward facing side, and they require division of intercostal muscles. Damages to surrounding nerves and vessels may lead to chronic pain. This study tests a novel interior fixation method that minimizes neurovascular injuries. Methods Twelve healthy canines were divided in two surgical operation groups for exterior and interior fixation using titanium metal plates. Osteotomy with oblique fractures was prepared under general anesthesia. Exterior fixation was performed in open surgery. Interior fixation was minimally invasive using custom made tools including a flexible shaft extension screwdriver, solid plate stand, guiding wire loop and metal plates with threaded holes. Results Respiratory and cardiovascular functions (RR, PO2, PCO2, SpO2, and HR) together with body temperature were measured before anesthesia and within 48 h after surgery. The difference in measurements was not statistically significant between the two groups before surgery with P values greater than 0.05. However, the interior group canines had better RR and PO2 values starting from the 24th hour, and better PCO2, SpO2, and HR values starting from the 48th hour. It took longer operation time to complete the minimally invasive interior fixation surgery (P value less than 0.001), but the total blood loss was less than the exterior fixation group (P value less than 0.001). Results also showed that interior group canines suffered less pain, and they had quicker recovery in gastrointestinal and physical mobility. Conclusions The investigative interior fixation method was safe and effective in rib stabilization on a canine rib fracture model, comparing to the exterior fixation method. The interior fixation was minimally invasive, with less damages to tissues and nerves surrounding the ribs, leading to better postoperative outcomes.

Abstract Background Lung cancer is reported to be the leading cause of death in males and females, globally. Increasing evidence highlights the paramount importance of Lactate dehydrogenase D (LDHD) in different types of cancers, though it’s role in lung adenocarcinoma (LUAD) is still inadequately explored. In this study, we aimed to investigate and determine the relationship between LDHD and LUAD. Methods The collection of the samples was guided by The Cancer Genome Atlas (TCGA) datasets and Gene Expression Omnibus (GEO). To ascertain various aspects around LDHD function, we analyzed different expression genes (DEGs), functional enrichment, and protein–protein interaction (PPI) networks. The predictive values for LDHD were collectively determined using the Kaplan–Meier method, Cox regression analysis, and a nomogram. Evaluation of the immune infiltration analysis was completed using Estimate and ssGSEA. The prediction of the immunotherapy response was based on TIDE and IPS. The LDHD expression levels in LUAD were validated through Western blot, qPCR, and immunohistochemistry methods. Wound healing and transwell assays were also performed to illustrate the aggressive features in LUAD cell lines. Results The results showed that LDHD was generally downregulated in LUAD patients, with the low LDHD group presenting a decline in OS, DSS, and PFI. Enriched pathways, which include pyruvate metabolism, central carbon metabolism, and oxidative phosphorylation were observed through KEGG analysis. It was also noted that the expression of LDHD expression was inversely related to immune cell infiltration and typical checkpoints. The high LDHD group’s response to immunotherapy was remarkable, particularly in CTAL4 + /PD1- therapy. In vitro studies revealed that the overexpression of LDHD caused tumor migration and invasion to be suppressed. Conclusion In conclusion, our study revealed that LDHD might be an effective predictor of prognosis and immune filtration, possibly leading to better choices for immunotherapy.

Abstract The green electrochemical synthesis of ammonia (NH3) through solid electrolysis has been intensively investigated. This research reported an improvement of NH3 production rate using in situ exsolution of ruthenium (Ru) atoms into lanthanum strontium chromium ferrite perovskite (LaxSr1−xCryFe1−yO3−δ) catalyst. The in situ Ru exsolution was achieved by reducing the optimized stoichiometric La0.33Sr0.67Cr0.33Fe0.52Ru0.15O3−δ (LSCrFRu) powders obtained in 10‐vol% H2/Ar at 800°C for 1.0 h. These Ru nanoparticles (NPs) were embedded on the surface of the LSCrFRu matrix, evenly distributed with a size varying from 4.4 ± 0.5 nm. With the exsolution of Ru atoms, greater oxygen vacancies were formed in ex‐LSCrFRu and gadolinium‐doped ceria (GDC) composite than those in LSCrF‐GDC electrocatalyst, beneficial to N2 gas adsorption and triple bond cleavage. These Ru‐modified LSCrFRu‐GDC catalysts showed a synthesis rate of 4.73 × 10−10 mol s−1 cm−2 at 550°C under 1.6 V, doubling the rate using LSCrF‐GDC catalyst. The improved ammonia synthesis kinetic is mainly attributed to embedded Ru NPs and the increased oxygen vacancies formed during the in situ exsolution process. More active sites and higher activity for H2O and N2 adsorption and activation collectively advanced facile transportation of O2− that further promotes the cleavage of covalent bonds in H2O, providing more H+ for the hydrogenation in the nitrogen reduction reaction. This research will open a new paradigm for the electrochemical synthesis of ammonia with mitigating the drawbacks of traditional NH3 production.

BackgroundImmune checkpoint inhibitors have achieved breakthrough efficacy in treating lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFR), leading to the revision of the treatment guidelines. However, most patients with EGFR mutation are resistant to immunotherapy. It is particularly important to study the differences in tumor microenvironment (TME) between patients with and without EGFR mutation. However, relevant research has not been reported. Our previous study showed that secreted phosphoprotein 1 (SPP1) promotes macrophage M2 polarization and PD-L1 expression in LUAD, which may influence response to immunotherapy. Here, we assessed the role of SPP1 in different populations and its effects on the TME.MethodsWe compared the expression of SPP1 in LUAD tumor and normal tissues, and in samples with wild-type and mutant EGFR. We also evaluated the influence of SPP1 on survival. The LUAD data sets were downloaded from TCGA and CPTAC databases. Clinicopathologic characteristics associated with overall survival in TCGA were assessed using Cox regression analysis. GSEA revealed that several fundamental signaling pathways were enriched in the high SPP1 expression group. We applied CIBERSORT and xCell to calculate the proportion and abundance of tumor-infiltrating immune cells (TICs) in LUAD, and compared the differences in patients with high or low SPP1 expression and wild-type or mutant EGFR. In addition, we explored the correlation between SPP1 and CD276 for different groups.ResultsSPP1 expression was higher in LUAD tumor tissues and in people with EGFR mutation. High SPP1 expression was associated with poor prognosis. Univariate and multivariate cox analysis revealed that up-regulated SPP1 expression was independent indicator of poor prognosis. GSEA showed that the SPP1 high expression group was mainly enriched in immunosuppressed pathways. In the SPP1 high expression group, the infiltration of CD8+ T cells was lower and M2-type macrophages was higher. These results were also observed in patients with EGFR mutation. Furthermore, we found that the SPP1 expression was positively correlated with CD276, especially in patients with EGFR mutation.ConclusionSPP1 levels might be a useful marker of immunosuppression in patients with EGFR mutation, and could offer insight for therapeutics.

Non–small cell lung cancer accounts for 85% of all types of lung cancer and is the leading cause of worldwide cancer-associated mortalities. MiR-124 is epigenetically silenced in various types of cancer and plays important roles in tumor development and progression. MiR-124 was also significantly downregulated in non–small cell lung cancer patients. Glycolysis has been considered as a feature of cancer cells; hypoxia-inducible factor 1-alpha/beta and Akt are key enzymes in the regulation of glycolysis and energy metabolism in cancer cells. However, the role of miR-124 in non–small cell lung cancer cell proliferation, glycolysis, and energy metabolism remains unknown. In this research, cell proliferation was investigated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; furthermore, glucose consumption and lactic acid production were assessed; adenosine triphosphate content and NAD + /NADH were also detected. These tests were conducted using the normal non–small cell lung cancer cell line A549, which was transfected variedly with miR-mimics, miR-124 mimics, miR-124 inhibitor, pc-DNA3.1(+)-AKT1, and pc-DNA3.1(+)-AKT2 plasmid. Here, we show that miR-124 overexpression directly decreased cell growth, glucose consumption, lactate production, and energy metabolism. MiR-124 also negatively regulates glycolysis rate–limiting enzymes, glucose transporter 1 and hexokinase II. Our results also showed that miR-124 negatively regulates AKT1 and AKT2 but no regulatory effect on hypoxia-inducible factor 1-alpha/beta. Overexpression of AKT reverses the inhibitory effect of miR-124 on cell proliferation and glycolytic metabolism in non–small cell lung cancer. AKT inhibition blocks miR-124 silencing–induced AKT1/2, glucose transporter 1, hexokinase II activation, cell proliferation, and glycolytic or energy metabolism changes. In summary, this study demonstrated that miR-124 is able to inhibit proliferation, glycolysis, and energy metabolism, potentially by targeting AKT1/2–glucose transporter 1/hexokinase II in non–small cell lung cancer cells.

An on-line least squares algorithm has previously been successfully applied to linear vibration systems in order to identify time varying parameters. In this article the limitations of the approach and the factors affecting the identification are further examined. The existence of the nonlinear term is determined by means of the time varying characteristics of the estimated linear parameters using the linear model and the data from a time invariant nonlinear system. The identification of the time varying linear parameters is also examined in accordance with the linear model by using the data with nonlinear elements.

Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application.

Prokaryotes play a crucial role in marine ecosystem health and drive biogeochemical processes. The northern Ninety East Ridge (NER) of the Indian Ocean, a pivotal yet understudied area for these cycles, has been the focus of our study. We employed high-throughput 16S rRNA gene sequencing to analyze 35 water samples from five stations along the ridge, categorized into three depth- and dissolved oxygen-level-based groups. Our approach uncovered a clear stratification of microbial communities, with key bioindicators such as Prochlorococcus MIT9313, Sva0996 marine group, and Candidatus Actinomarina in the upper layer; Ketobacter, Pseudophaeobacter, Nitrospina, and SAR324 clade in the middle layer; and Methylobacterium-Methylorubrum, Sphingomonas, Sphingobium, and Erythrobacter in the deep layer. Methylobacterium-Methylorubrum emerged as the most abundant bacterial genus, while Nitrosopumilaceae predominated among archaeal communities. The spatial and depth-wise distribution patterns revealed that Ketobacter was unique to the northern NER, whereas Methylobacterium-Methylorubrum, UBA10353, SAR324 clade, SAR406, Sva0996_marine_group, Candidatus Actinomarina were ubiquitous across various marine regions, exhibiting niche differentiation at the OTU level. Environmental factors, especially dissolved oxygen (DO), silicate, nitrate, and salinity, significantly influence community structure. These findings not only reveal the novelty and adaptability of the microbial ecosystem in the northern NER but also contribute to the broader understanding of marine microbial diversity and its response to environmental heterogeneity.

Abstract Recombinant adeno-associated viruses (rAAVs) have emerged as promising gene therapy vectors due to their proven efficacy and safety in clinical applications. In non-human primates (NHPs), rAAVs are administered via suprachoroidal injection at a higher dose. However, high doses of rAAVs tend to increase additional safety risks. Here, we present a novel AAV capsid (AAVv128), which exhibits significantly enhanced transduction efficiency for photoreceptors and retinal pigment epithelial (RPE) cells, along with a broader distribution across the layers of retinal tissues in different animal models (mice, rabbits, and NHPs) following intraocular injection. Notably, the suprachoroidal delivery of AAVv128-anti-VEGF vector completely suppresses the Grade IV lesions in a laser-induced choroidal neovascularization (CNV) NHP model for neovascular age-related macular degeneration (nAMD). Furthermore, cryo-EM analysis at 2.1 Å resolution reveals that the critical residues of AAVv128 exhibit a more robust advantage in AAV binding, the nuclear uptake and endosome escaping. Collectively, our findings highlight the potential of AAVv128 as a next generation ocular gene therapy vector, particularly using the suprachoroidal delivery route.

With inspiration from natural systems, various soft actuators and robots have been explored in recent years with versatile applications in biomedical and engineering fields. Soft active materials with rich stimulus-responsive characteristics have been an ideal candidate to devise these soft machines by using different manufacturing technologies. Among these technologies, three-dimensional (3D) printing shows advantages in fabricating constructs with multiple materials and sophisticated architectures. In this Review, we aim to provide an overview of recent progress on 3D printing of soft materials, robotics performances, and representative applications. Typical 3D printing techniques are briefly introduced, followed by state-of-the-art advances in 3D printing of hydrogels, shape memory polymers, liquid crystalline elastomers, and their hybrids as soft actuators and robots. From the perspective of material properties, the commonly used printing techniques and action-generation principles for typical printed constructs are discussed. Actuation performances, locomotive behaviors, and representative applications of printed soft materials are summarized. The relationship between printing structures and action performances of soft actuators and robots is also briefly discussed. Finally, the advantages and limitations of each soft material are compared, and the remaining challenges and future directions in this field are prospected.

Tetrazole is widely utilized as a bioisostere for carboxylic acid in the field of medicinal chemistry and drug development, enhancing the drug-like characteristics of various molecules. Typically, tetrazoles are introduced from their nitrile precursors through late-stage functionalization. In this work, we propose a novel strategy involving the use of diversely protected, unprecedented tetrazole aldehydes as building blocks. This approach facilitates the incorporation of the tetrazole group into multicomponent reactions or other chemistries, aiding in the creation of a variety of complex, drug-like molecules. These innovative tetrazole building blocks are efficiently and directly synthesized using a Passerini three-component reaction (PT-3CR), employing cost-effective and readily available materials. We further showcase the versatility of these new tetrazole building blocks by integrating the tetrazole moiety into various multicomponent reactions (MCRs), which are already significantly employed in drug discovery. This technique represents a unique and complementary method to existing tetrazole synthesis processes. It aims to meet the growing demand for tetrazole-based compound libraries and novel scaffolds, which are challenging to synthesize through other methods.

Abstract Background Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. Methods This retrospective study analyzed 337 ESCC resection specimens from 2020–2021 at the Pudong-Branch (Cohort 1) and 114 from 2021–2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. Results The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. Conclusion This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Abstract Deep learning‐based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi‐scale complexity‐aware registration network (MSCAReg‐Net) was proposed by devising a complexity‐aware technique to facilitate DLIR under a single‐resolution framework. Specifically, the complexity‐aware technique devised a multi‐scale complexity‐aware module (MSCA‐Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC‐Module) and a feature aggregation module (FA‐Module) to facilitate the MSCA‐Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg‐Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non‐positive Jacobian determinant (|Jϕ|≤0), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi‐atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, |Jϕ|≤0, and the downstream application on MAHS demonstrated the advances of this method.

Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.