DATA-DRIVEN IMAGING GEOMETRIC RECOVERY OF ULTRAHIGH RESOLUTION ROBOTIC MICRO-CT FOR IN-VIVO AND OTHER APPLICATIONS.

We introduce an ultrahigh-resolution (50µm) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom scan, both producing sharper images with finer details than that prior correction.

Dynamic memristor for physical reservoir computing.

Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.

A denoising diffusion probabilistic model for metal artifact reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p < 10-26; PSNR: p < 10-21), the CNN (SSIM: p < 10-25; PSNR: p < 10-9) and the GAN (SSIM: p < 10-6; PSNR: p < 0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Unbiasing Fairness Evaluation of Radiology AI Model.

Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model bias, this study suggests that traditional fairness evaluation methods may be biased, highlighting the need for a proper evaluation scheme with multiple evaluation metrics due to varying results under different criteria. Moreover, the limited data size of minority groups introduces significant data uncertainty, which can undermine the judgement of fairness. This paper introduces an innovative evaluation approach that estimates data uncertainty in minority groups through bootstrapping from majority groups for a more objective statistical assessment. Extensive experiments reveal that traditional evaluation methods might have drawn inaccurate conclusions about model fairness. The proposed method delivers an unbiased fairness assessment by adeptly addressing the inherent complications of model evaluation on imbalanced datasets. The results show that such comprehensive evaluation can provide more confidence when adopting those models.

A universal method for surface-based binding assays by preparing immobilized ß2-adrenergic receptor stationary phase using solid binding peptide as a fusion tag.

Protein functionalized surface has the potential to develop new assays for determining the drug-like properties of potential compounds and discovering specific partners of G protein-coupled receptors (GPCRs). However, a universal method for purifying and immobilizing functional GPCRs has remained elusive. To this end, we developed a general and rapid way to purify and immobilize ß2-adrenergic receptor (ß2AR) by silicon-specific peptide. We screened CotB1p as a tag from six silica-binding peptides (minTBP-1, CotB1p, SB7, Car9, and Si4-1) by examining their affinity to macroporous silica gel. We investigated the adsorption and desorption of CotB1p-tagged ß2-adrenoceptor (ß2AR-CotB1p) under diverse conditions to propose a protocol for receptor purification and immobilization. Under optimized conditions, ß2AR immobilization were achieved by directly immersing cell lysates harboring the receptor with silica gel, and the elution of the receptor without demonstratable contaminants was realized by including l-arginine/L-lysine in the elutes. This allows purification of the receptor from Escherichia coli (E.coli) lysates with a purity of 95 %. The immobilized receptor was utilized as a stationary phase to reveal the tag impact on ligand-binding outputs by comparing the CotB1p-strategy with a typical covalent method. The KAs of salbutamol, chlorprenaline, tulobuterol, and terbutaline on ß2AR-CotB1p column were 1.26 × 106, 6.59 × 106, 7.90 × 106, and 8.97 × 105 M-1 respectively, which were two orders of magnitude higher than those on the Halo-ß2AR column. The whole immobilization was accomplished within 30 min without the requirement of any special treatment, resulting in enhanced accuracy for determining receptor-ligand binding parameters. Taken together, CotB1p-mediated strategy is simple, rapid, and universal for purification or immobilization of unstable biomolecules like GPCRs for analytical and biological applications.

Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction.

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes, especially as the disease progresses into liver metastases. Computed tomography (CT) is a frontline tool for this task; however, the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm. We hypothesized that image reconstruction with a high-frequency kernel could result in a better characterization of liver metastases features via deep neural networks. This kernel produces images that appear noisier but preserve more sinogram information. A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases. This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases, and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim. Datasets of 10,000 liver metastases were generated, scanned, and reconstructed using either standard or high-frequency kernels. These data were used to train and validate deep neural networks to recover crafted metastases characteristics, such as internal heterogeneity, edge sharpness, and edge fractal dimension. In the absence of noise, models scored, on average, 12.2% ( α = 0.012 ) and 7.5% ( α = 0.049 ) lower squared error for characterizing edge sharpness and fractal dimension, respectively, when using high-frequency reconstructions compared to standard. However, the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan. Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization, provided that noise is limited. Future work should investigate the information-preserving kernels in datasets with clinical labels.

The circular RNA hsa_circ_0045800 serves as a favorable biomarker in pathogenesis of sjögren's syndrome.

BACKGROUND: Circular RNAs (circRNAs) play various roles in the development of many autoimmune diseases. However, their expression profiles and specific function in Sjögren's Syndrome remains largely unknown. OBJECTIVES: We aimed to investigate circRNAs potential diagnostic value in primary Sjögren's syndrome (pSS) and contribution to the pathogenesis of pSS. METHODS: This study included 102 subjects, 51 pSS patients and 51 healthy controls. The concentration of hsa_circ_0045800 was analyzed in peripheral blood mononuclear cells obtained from 51 pSS patients and 51 healthy controls by qRT-PCR. We established a receiver operating characteristic curve (ROC) to assess the biological diagnostic value of hsa_circ_0045800 for pSS. In addition, we analyzed the correlation between hsa_circ_0045800 and disease activity in Sjogren's syndrome. A differential analysis was also conducted on the concentration of hsa_circ_0045800 in patients in pSS patients before and after treatment. We studied the downstream mechanism of hsa_circ_0045800 through bioinformatics analysis and confirmed it using luciferase reporter gene assay. RESULTS: We confirmed that the concentration of hsa_circ_0045800 was elevated 10.4-fold in peripheral blood mononuclear cells of pSS patients than in healthy controls (p = 0.00). In the pSS active disease group, the concentration of hsa_circ_0045800 is 2.5-fold higher compared to the pSS non-active disease group (p = 0.04). The concentration of hsa_circ_0045800 after treatment was decreased by 80% compared with that before treatment (p = 0.037), suggesting its utility as a potential marker for monitoring treatment efficacy. ROC curve analysis showed that the diagnostic value of hsa_circ_0045800 in pSS patients was significantly higher than that in healthy controls, with an area under the curve of 0.865, a sensitivity of 74%, and a specificity of 92%. The concentration of hsa_circ_0045800 is correlated with various clinical factors: the concentration of hsa_circ_0045800 is positively associated with age (r = 0.328, P = 0.019), oral dryness (r = 0.331, P = 0.017), while it is negatively correlated with HGB (r = -0.435, P = 0.001) and and hypothyroidism (r = -0.318, P = 0.023). Bioinformatics predictions and luciferase assays indicated that hsa_circ_0045800 acts as a molecular sponge for miR-1247-5p, with SMAD2 being a target gene of miR-1247-5p. CONCLUSION: Our study results show that hsa_circ_0045800 potentially contributes to the development and progression of pSS via the miR-1247-5p/SMAD2 pathway. Peripheral blood mononuclear cells are directly involved in the pathogenesis of pSS, and the discovery of hsa_circ_0045800 in peripheral blood mononuclear cells highlights its potential as a novel biomarker for disease activity and diagnosis in patients with pSS. Key Points • The concentration of hsa_circ_0045800 was higher in peripheral blood mononuclear cells of pSS patients. • Hsa_circ_0045800 promoted pSS progression through miR-1247-5p-SMAD2 axis. • Hsa_circ_0045800 is a potential biomarker for pSS.

Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

[Effect of metabolic reprogramming on abdominal aortic aneurysm].

Abdominal aortic aneurysm (AAA) is a life-threatening disease that remains undetected until it acutely ruptures. Due to lack of effective pharmaceutic therapies, it is urgent to explore new prevention and treatment strategies. Metabolic reprogramming is a cellular process through which cells change their metabolic patterns to meet material and energy requirements, including glucose metabolism, lipid metabolism and amino acid metabolism. Recently, the regulatory role of metabolic reprogramming in cardiovascular diseases, especially AAA, has attracted significant attention. This review article focuses on the research progress regarding the effects of metabolic reprogramming of vascular smooth muscle cells (VSMCs) and macrophages on the occurrence and development of AAA, especially their roles in major pathological processes such as VSMCs apoptosis and phenotype transformation, extracellular matrix remodeling, oxidative stress, and inflammatory response. The aim is to provide new clues for the mechanism research and clinical treatment of AAA from the perspective of metabolism.

FvUVI4 inhibits cell division and cell expansion to modulate fruit development in Fragaria vesca.

Fruit development is mainly regulated by cell division and expansion. As a negative regulator of the anaphase-promoting complex/cyclosome, UVI4 plays important roles in plant growth and development via coordinating cell cycle. However, currently there is no report on UVI4's functions in regulating fruit development in strawberry. Here, Fragaria vesca homolog FvUVI4 is identified and localizes in the nucleus. FvUVI4 has high gene expression in roots, leaves, flower, buds and green fruits, and low expression in petiole, stem, white and yellow fruit. Fruit development of F. vesca 'Hawaii4' is regulated by endoreduplication, and the expression of FvUVI4 is negatively correlated with fruit cell size. Overexpression of FvUVI4 inhibits endoreduplication of leaves, flowers and fruits in both Arabidopsis and F. vesca 'Hawaii4', thereby limiting cell expansion and decreasing cell area. Overexpression of FvUVI4 also inhibits mitotic cell cycle leading to decreased cell number, and ultimately affects the growth of leaves, petals and seeds or fruits. Arabidopsis uvi4 mutants obtained via CRISPR-Cas9 technology display opposite growth phenotypes to Arabidopsis and F. vesca 'Hawaii4' overexpression lines, which can be restored by overexpression of FvUVI4 in Arabidopsis uvi4 mutants. In conclusion, our study indicates that FvUVI4 inhibits cell expansion and cell division to modulate receptacle development in woodland strawberry.

Undervalued dry riverbeds: A key factor in equating intermittent river CO2 emissions to perennial rivers.

Intermittent rivers in semiarid and arid regions, constituting over half of the world's rivers, alternate the carbon cycle interactions among the biosphere, hydrosphere, and atmosphere. Inadequate quantification of flow duration and river water surface area, along with overlooked CO2 emissions from dry riverbeds, result in notable inaccuracies in global carbon cycle assessments. High-resolution remote sensing images combined with intensive field measurements and hydrological modelling were used to estimate and extract the flow duration, river water surface area and dry riverbed area of Huangfuchuan, an intermittent river watershed that acts as a major tributary of the Yellow River in semiarid Northwest China. CO2 emission rates and partial pressures in water and air across the watershed were in-situ measured. In 2018, the flow duration of Huangfuchuan increased from less than 5 days in the first-order tributary to 150 days in the sixth-order mainstream. River water surface area estimated by remote sensing extraction plus the hydrodynamic model simulation varied from 3.9 to 88.6 km2 under 5 %-95 % discharge frequencies. CO2 emissions from the water-air interface and dry riverbed in 2018 were estimated at 582.3 × 103 and 355.2 × 103 ton, respectively. The estimated total annual emission (937.5 × 103 ton) aligns closely with the range of emissions (67.3 × 103-1377.2 × 103 ton) calculated for the water-air interface alone, derived using DEM river length and hydraulic geometry method. This similarity can be attributed to the overestimation of flow duration and flow velocity, as well as the over- or under-estimation of river water surface area and slope. The new method proposed in this study has large potential to be applied in estimating CO2 emissions from data-scarce intermittent rivers located in mountainous regions and provides a standardized solution in the estimation of CO2 emission. Results of this research reveal the spatiotemporal distribution of CO2 emissions along an intermittent river system and highlight the substantial role of dry riverbed in carbon cycle.

HDAC inhibitor regulates the tumor immune microenvironment via pyroptosis in triple negative breast cancer.

Pyroptosis, an inflammatory form of cell death, promotes the release of immunogenic substances and stimulates immune cell recruitment, a process, which could turn cold tumors into hot ones. Thus, instigating pyroptosis in triple-negative breast cancer (TNBC) serves as a viable method for restoring antitumor immunity. We analyzed the effects of Histone Deacetylase Inhibitors (HDACi) on TNBC cells using the Cell Counting Kit-8 and colony formation assay. Apoptosis and lactate dehydrogenase (LDH) release assays were utilized to determine the form of cell death. The pyroptotic executor was validated by quantitative real-time polymerase chain reaction and western blot. Transcriptome was analyzed to investigate pyroptosis-inducing mechanisms. A subcutaneously transplanted tumor model was generated in BALB/c mice to evaluate infiltration of immune cells. HDACi significantly diminished cell proliferation, and pyroptotic "balloon"-like cells became apparent. HDACi led to an intra and extracellular material exchange, signified by the release of LDH and the uptake of propidium iodide. Among the gasdermin family, TNBC cells expressed maximum quantities of GSDME, and expression of GSDMA, GSDMB, and GSDME were augmented post HDACi treatment. Pyroptosis was instigated via the activation of the caspase 3-GSDME pathway with the potential mechanisms being cell cycle arrest and altered intracellular REDOX balance due to aberrant glutathione metabolism. In vivo experiments demonstrated that HDACi can activate pyroptosis, limit tumor growth, and escalate CD8+ lymphocyte and CD11b+ cell infiltration along with an increased presence of granzyme B in tumors. HDACi can instigate pyroptosis in TNBC, promoting infiltration of immune cells and consequently intensifying the efficacy of anticancer immunity.

Clinical features and possible pathogenesis of multiple evanescent white dot syndrome with different retinal diseases and events: a narrative review.

Multiple evanescent white dot syndrome (MEWDS) is a rare fundus disease, characterized by acute vision loss and visual field defects. Many previous studies have explained the possible pathogenesis and clinical features of primary MEWDS. However, as the number of reported cases increases, secondary MEWDS occurs in other related retinal diseases and injuries, exhibiting some special characteristics. The associated retinal diseases include multifocal choroiditis/punctate inner choroidopathy (MFC/PIC), acute zonal occult outer retinopathy, best vitelliform macular dystrophy, pseudoxanthoma elasticum, and ocular toxoplasmosis. The related retinal injury is laser photocoagulation, surgery, and trauma. Although primary MEWDS often have a self-limiting course, secondary MEWDS may require treatment in some cases, according to the severity of concomitant diseases and complications. Notably, MEWDS secondary to MFC/PIC that is prone to forming choroidal neovascularization and focal choroidal excavation, needs positive treatment with corticosteroids. The possible underlying pathogenesis of secondary MEWDS is the exposure of choroidal antigen after the disruption of Bruch's membrane. The MEWDS-related features in secondary MEWDS are still evanescent under most circumstances. Its prognosis and treatment depend on the severity of complications. Current studies propose that the etiology is associated with immune factors, including viral infection, inflammation in choroid and Bruch's membrane, and antigen exposure caused by retinal and/or choroidal insults. More pathogenic studies should be conducted in the future. Accurate diagnosis for secondary MEWDS could benefit patients in aspects of management and prognosis.

Photochemical and biological dual-effects enhance the inhibition of photosensitizers for tumour growth.

Photosensitizers typically rely on a singular photochemical reaction to generate reactive oxygen species, which can then inhibit or eradicate lesions. However, photosensitizers often exhibit limited therapeutic efficiency due to their reliance on a single photochemical effect. Herein, we propose a new strategy that integrates the photochemical effect (type-I photochemical effect) with a biological effect (proton sponge effect). To test our strategy, we designed a series of photosensitizers (ZZ-sers) based on the naphthalimide molecule. ZZ-sers incorporate both a p-toluenesulfonyl moiety and weakly basic groups to activate the proton sponge effect while simultaneously strengthening the type-I photochemical effect, resulting in enhanced apoptosis and programmed cell death. Experiments confirmed near-complete eradication of the tumour burden after 14 days (Wlight/Wcontrol ≈ 0.18, W represents the tumour weight). These findings support the notion that the coupling of a type-I photochemical effect with a proton sponge effect can enhance the tumour inhibition by ZZ-sers, even if the basic molecular backbones of the photosensitizers exhibit nearly zero or minimal tumour inhibition ability. We anticipate that this strategy can be generalized to develop additional new photosensitizers with improved therapeutic efficacy while overcoming limitations associated with systems relying solely on single photochemical effects.

Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial.

The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality.

Modulator-assisted solvent-free synthesis of amorphous zirconium terephthalate catalyst for efficient oxidative desulfurization.

Oxidative desulfurization (ODS) emerges as a critical player in enhancing efficient fuel desulfurization and promoting sustainable clean energy. Metal-organic frameworks (MOFs) show great potential as ODS catalysts because of their exceptional porosity and versatility. This study explores the use of amorphous metal-organic frameworks (aMOFs), which combine MOFs' structural advantages with unique properties of amorphous materials, to enhance catalytic efficiency in ODS. Traditional methods for synthesizing MOFs rely on solvent-thermal or solvent-free methods, each with limitations in environmental impact or scalability. To address this, we introduce a novel strategy utilizing a small quantity of benzoic acid (BA) modifier to facilitate the solvent-free, one-pot, mechanical synthesis of amorphous zirconium terephthalate (GU-2BA-3h). The resulting GU-2BA-3h demonstrates exceptional ODS performance, efficiently removing 1000 ppm of dibenzothiophene (DBT) in just 6 min at 60 °C. Amorphous GU-2BA-3h features an expanded external surface area, increased acidic sites, and exceptional stability, resulting in a high turnover frequency (19.6 h-1) and outstanding catalytic activity (53.2 mmol g-1 h-1), establishing it as a highly efficient ODS catalyst. This remarkable performance arises from the formation of dangling carboxyl groups and active metal sites due to the competitive coordination of benzoic acid with the linker. Experimental evidence confirms that these carboxyl groups and exposed Zr-OH sites interact with oxidants, generating hydroxyl radicals that effectively eliminate sulfur-containing compounds. Furthermore, the methodology exhibits universality in constructing amorphous Zr-based MOFs, and provides an eco-friendly, cost-effective route for efficient ODS catalyst production.

Erratum to "PGAM5-Mediated PHB2 Dephosphorylation Contributes to Diabetic Cardiomyopathy by Disrupting Mitochondrial Quality Surveillance".

[This corrects the article DOI: 10.34133/research.0001.].

Establishing superfine nanofibrils for robust polyelectrolyte artificial spider silk and powerful artificial muscles.

Spider silk exhibits an excellent combination of high strength and toughness, which originates from the hierarchical self-assembled structure of spidroin during fiber spinning. In this work, superfine nanofibrils are established in polyelectrolyte artificial spider silk by optimizing the flexibility of polymer chains, which exhibits combination of breaking strength and toughness ranging from 1.83 GPa and 238 MJ m-3 to 0.53 GPa and 700 MJ m-3, respectively. This is achieved by introducing ions to control the dissociation of polymer chains and evaporation-induced self-assembly under external stress. In addition, the artificial spider silk possesses thermally-driven supercontraction ability. This work provides inspiration for the design of high-performance fiber materials.

Sustainable high-strength and dimensionally stable composites through in situ regulation and reconstitution of bamboo-derived lignin and hemicellulose contents.

The development of modern construction and transportation industries demands increasingly high requirements for thin, lightweight, high-strength, and highly tough composite materials, such as metal carbides and concrete. Bamboo is a green, low-carbon, fast-growing, renewable, and biodegradable material with high strength and toughness. However, the density of its inner layer is low due to the functional gradient (the volume fraction of vascular bundles decreases from the outer layer to the inner layer), resulting in low performance, high compressibility, and significant amounts of bamboo waste. We utilized chemical and mechanical treatments of bamboo's low-density, low-strength inner layers to create lightweight, ultra-thin, high-strength, and high-toughness composites. The treatment included the partial removal of lignin and hemicellulose to alter the chemical components, followed by mechanical drying and hot pressing. The treated bamboo had 100.8 % higher tensile strength (150.35 MPa), 47.7 % higher flexural strength (97.67 MPa), and 132.0 % higher water resistance and was approximately 68.9 % thinner than the natural bamboo. The excellent physical and mechanical properties of the treated bamboo are attributed to the contraction of parenchyma cells during delignification, the interlocking due to the collapse of parenchyma cells during mechanical drying, and an increase in the density of hydrogen bonds between cellulose molecular chains during hot pressing. Our research provides a new strategy for obtaining sustainable, ultra-thin, lightweight, high-strength, and high-toughness composite materials from bamboo for construction and transportation applications.