Tiny pH-Resolved DNA Nanospheres for Cellular Pyroptosis or Apoptosis Regulation.

The design and synthesis of nanomedicines capable of regulating programmed cell death patterns to enhance antitumor efficacy remain significant challenges in cancer therapy. In this study, we developed intelligent DNA nanospheres (NS) capable of distinguishing tiny pH changes between different endosomal compartments to regulate pyroptosis or apoptosis. These NS are self-assembled from two multifunctional DNA modules, enabling tumor targeting, acid-responsive disassembly, and photodynamic therapy (PDT) activation. By modifying the embedded i-motif sequence, the NS can be activated in early endosomes (EE) or lysosomes (Ly), producing singlet oxygen (1O2) at specific locations under laser irradiation. Our results demonstrate that EE-activated PDT induces gasdermin-E-mediated pyroptosis in tumor cells, enhancing antitumor efficacy and reducing systemic toxicity compared to Ly-activated apoptosis. This study offers new insights into the design of endosome-activated nanomedicines, advancing the biomedical applications of targeted cancer therapy.

Modular Assembled Localized Hybridization Chain Reaction for In Situ mRNA Amplified Imaging.

As a nonenzymatic DNA signal amplification technique, localized hybridization chain reaction (LHCR) was designed to improve the limitations in response speed and low sensitivity of conventional free diffusional HCR (hybridization chain reaction). However, it is still confronted with the challenges of complicated DNA scaffolds with low loading capacity and a time-consuming process of diffusion. Herein, we introduced modular assembly of a DNA minimal scaffold for coassembly of DNA hairpins for amplified fluorescence imaging of mRNA in situ. DNA hairpins were spatially bound to two Y-shaped modules to form H-shaped DNA modules, and then multiple H-shaped DNA modules can further assemble into an H-module-based hairpin scaffold (HHS). Benefiting from highly spatial localization and high loading capacity, the HHS system showed higher sensitivity and faster speed. It has also been proven to work perfectly in vitro and in vivo, which could provide a promising bioanalysis system for low abundance biomolecule detection.

CT-based whole lung radiomics nomogram for identification of PRISm from non-COPD subjects.

BACKGROUND: Preserved Ratio Impaired Spirometry (PRISm) is considered to be a precursor of chronic obstructive pulmonary disease. Radiomics nomogram can effectively identify the PRISm subjects from non-COPD subjects, especially when during large-scale CT lung cancer screening. METHODS: Totally 1481 participants (864, 370 and 247 in training, internal validation, and external validation cohorts, respectively) were included. Whole lung on thin-section computed tomography (CT) was segmented with a fully automated segmentation algorithm. PyRadiomics was adopted for extracting radiomics features. Clinical features were also obtained. Moreover, Spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking and least absolute shrinkage and selection operator (LASSO) classifier were adopted to analyze whether radiomics features could be used to build radiomics signatures. A nomogram that incorporated clinical features and radiomics signature was constructed through multivariable logistic regression. Last, calibration, discrimination and clinical usefulness were analyzed using validation cohorts. RESULTS: The radiomics signature, which included 14 stable features, was related to PRISm of training and validation cohorts (p < 0.001). The radiomics nomogram incorporating independent predicting factors (radiomics signature, age, BMI, and gender) well discriminated PRISm from non-COPD subjects compared with clinical model or radiomics signature alone for training cohort (AUC 0.787 vs. 0.675 vs. 0.778), internal (AUC 0.773 vs. 0.682 vs. 0.767) and external validation cohorts (AUC 0.702 vs. 0.610 vs. 0.699). Decision curve analysis suggested that our constructed radiomics nomogram outperformed clinical model. CONCLUSIONS: The CT-based whole lung radiomics nomogram could identify PRISm to help decision-making in clinic.

Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio ≤50.

Background: Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%. Methods: A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Results: Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. Conclusions: A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.

Differentiating calcifications from cerebral microbleeds using quantitative susceptibility mapping.

OBJECTIVES: To differentiate cerebral microbleeds (CMBs) and calcifications using quantitative susceptibility mapping (QSM). METHODS: CMBs were visualized and located using QSM from susceptibility-weighted imaging data collected on a 3-T MR scanner. Calcifications of the pineal gland and the choroid plexus were localized first using CT. All calcifications and CMBs were assessed using QSM to evaluate their magnetic susceptibility. The distribution of the magnetic susceptibility for the CMBs was determined and the CT attenuation was correlated with the mean magnetic susceptibility for the calcifications. RESULTS: A total of 232 hypointense foci were selected from the QSM data: 121 were CMBs and 111 were calcifications. The mean magnetic susceptibility was -214 ± 112 ppb for the calcifications and 392 ± 204 ppb for the CMBs. The minimum value of magnetic susceptibility was 75 ppb for all the CMBs and the maximum value was -52 ppb for all the calcifications. The calcifications were clearly differentiable from the CMBs from the sign alone (p < 0.001). The magnetic susceptibility for the CMBs was 299 ± 133 ppb in the lobar subcortical white matter and 499 ± 220 ppb for deep CMBs in the basal ganglia, thalamus, and brainstem. There was a significant difference in the susceptibility between these two regions (p < 0.001). CONCLUSION: The sign of the magnetic susceptibility was sufficient to differentiate calcifications and CMBs. The concentration of calcium or iron can be determined from the susceptibility value itself. The deep CMBs had higher susceptibility on average than lobar bleeds. CLINICAL RELEVANCE STATEMENT: This study's ability to differentiate between CMBs and calcifications using QSM could enhance diagnostic accuracy, guiding more precise treatment decisions for stroke or tumor patients. KEY POINTS: The sign of magnetic susceptibility is sufficient to differentiate calcifications and CMBs. QSM can successfully differentiate calcifications from microbleeds. The concentration of calcium or iron can be determined from the susceptibility value itself.

Comparison of single-shot, FOCUS single-shot, MUSE, and FOCUS MUSE diffusion weighted imaging for pulmonary lesions: A pilot study.

Rationale and objectives: To compare the performance of SS, FOCUS SS, MUSE, and FOCUS MUSE DWI for pulmonary lesions to obtain a better technique for pulmonary DWI imaging. Materials and methods: 44 patients with pulmonary lesions were recruited to perform pulmonary DWI using SS, FOCUS SS, MUSE, and FOCUS MUSE sequences. Then, two radiologists with 12 and 10 years of chest MRI experiences assessed the overall image quality while another two radiologists both with 3 years of experiences evaluated the SNR, DR, and ADC of pulmonary lesions. Using interclass correlation coefficient (ICC) and kappa statistics to assess consistency of readers, Friedman test and Dunn-Bonferroni post hoc were used to calculate the difference between sequences. Mann-Whitney test and ROC curve were used to distinguish malignant from benign lesions. Results: All the assessed variables of the four sequences presented good to excellent intra-/inter-observer consistency. Compared with SS, FOCUS SS and MUSE, FOCUS MUSE demonstrated better image quality, including significantly higher 5-point Likert scale score (P < 0.001) and smaller DR (P < 0.001). SNR was comparable among SS, FOCUS SS, and FOCUS MUSE (P > 0.05) while MUSE presented with significantly higher SNR over them (P < 0.01). ADC of malignant was significantly smaller than that of benign for all the four sequences (P < 0.05). ROC analysis showed relatively better diagnostic performance of FOCUS MUSE (AUC = 0.820) over SS (AUC = 0.748), FOCUS SS (AUC = 0.778), and MUSE (AUC = 0.729) in distinguishing malignant from benign lesions. Conclusion: FOCUS MUSE possessed sufficient SNR and was better over SS, FOUCS SS, and MUSE for characterizing pulmonary lesions.

The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.

PURPOSE: To establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT). METHODS: 294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram. RESULTS: The combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models. CONCLUSION: An analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.

Ultra-broadband diffractive imaging with unknown probe spectrum.

Strict requirement of a coherent spectrum in coherent diffractive imaging (CDI) architectures poses a significant obstacle to achieving efficient photon utilization across the full spectrum. To date, nearly all broadband computational imaging experiments have relied on accurate spectroscopic measurements, as broad spectra are incompatible with conventional CDI systems. This paper presents an advanced approach to broaden the scope of CDI to ultra-broadband illumination with unknown probe spectrum, effectively addresses the key challenges encountered by existing state-of-the-art broadband diffractive imaging frameworks. This advancement eliminates the necessity for prior knowledge of probe spectrum and relaxes constraints on non-dispersive samples, resulting in a significant extension in spectral bandwidth, achieving a nearly fourfold improvement in bandlimit compared to the existing benchmark. Our method not only monochromatizes a broadband diffraction pattern from unknown illumination spectrum, but also determines the compressive sampled profile of spectrum of the diffracted radiation. This superiority is experimentally validated using both CDI and ptychography techniques on an ultra-broadband supercontinuum with relative bandwidth exceeding 40%, revealing a significantly enhanced coherence and improved reconstruction with high fidelity under ultra-broadband illumination.

Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci.

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Image Quality Assessment of a Deep Learning-Based Automatic Bone Removal Algorithm for Cervical CTA.

BACKGROUND: The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 100 patients (31 females, 61.4 ± 12.4 years old) who had performed cervical CTA from January 2022 to July 2022 were included retrospectively. Three different types of scanners were used. Ipsilateral cervical artery was divided into 10 segments. The performance of the DL algorithm and conventional algorithm in terms of bone removal and vascular integrity was independently evaluated by two radiologists for each segment. The difference in the performance between the two algorithms was compared. Inter- and intrarater consistency were assessed, and the correlation between the degree of carotid artery stenosis and the rank of bone removal and vascular integrity was analyzed. RESULTS: Significant differences were observed in the rankings of bone removal and vascular integrity between the two algorithms on most segments on both sides. Compared to DL algorithm, the conventional algorithm showed a higher correlation between the degree of carotid artery stenosis and vascular integrity (r = -0.264 vs r = -0.180). The inter- and intrarater consistency of DL algorithm were found to be higher than or equal to those of conventional algorithm. CONCLUSIONS: The DL algorithm for bone removal in cervical CTA demonstrated significantly better performance than conventional postprocessing method, particularly in the segments with complex anatomical structures and adjacent to bone.

Deep-learning-assisted sidewall profiling white light interferometry system for accurately measuring 3D profiles and surface roughness on the groove sidewalls of precision components.

The accurate measurement of surface three-dimensional (3D) profile and roughness on the groove sidewalls of components is of great significance to diverse fields such as precision manufacturing, machining processes, energy transportation, medical equipment, and semiconductor industry. However, conventional optical measurement methods struggle to measure surface profiles on the sidewall of a small groove. Here, we present a deep-learning-assisted sidewall profiling white light interferometry system, which consists of a microprism-based interferometer, an optical path compensation device, and a convolutional neural network (CNN), for the accurate measurement of surface 3D profile and roughness on the sidewall of a small groove. We have demonstrated that the sidewall profiling white light interferometry system can achieve a measurement accuracy of 2.64 nm for the 3D profile on a groove sidewall. Moreover, we have demonstrated that the CNN-based single-image super-resolution (SISR) technique could improve the measurement accuracy of surface roughness by over 30%. Our system can be utilized in cases where the width of the groove is only 1 mm and beyond, limited only by the size of the microprism and the working distance of the objective used in our system.

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients.

Purpose: It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups. Methods: Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results: The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility. Conclusions: The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Novel envelope protein time-resolved fluoroimmunoassay as an alternative in vitro potency assay for quality control of inactivated Japanese encephalitis virus vaccine.

Japanese encephalitis (JE) vaccination is the most effective way to prevent JE. Plaque reduction neutralization test (PRNT) as the standard method for potency testing for inactivated JE vaccine could not provide the exact potency value. Envelope (E) protein of JE virus induces the body to create neutralizing antibodies. There is a potential for using the determination of E protein to assess the immunogenicity and efficacy of JE vaccine. In this study, an automatic time-resolved fluoroimmunoassay for detection of E protein in JE vaccine was established as a simple and rapid in vitro potency assay to complement PRNT, including the expression and paired screening of monoclonal antibodies, the establishment of assay method and performance verification. A pair of anti-E protein neutralizing antibodies (L022 and L034) were screened to construct the sandwich detection pattern. After pre-treating the vaccine sample, the entire analysis was performed using a fully automated machine, which had a little detection time and eliminated manual error. The results of the validation experiment met the requirements for quality control. The linear range was from 0.78125 U/mL to 25 U/mL, the sensitivity was 0.01 U/mL, the intra-assay coefficient of variation was less than 5 %, and the inter-assay coefficient of variation was less than 10 %. The recovery from the dilution was between 90 % and 110 %. This present TRFIA shown good stability and effectiveness in quality control for samples related to JE vaccine production. The outcomes demonstrated that the present TRFIA could be an alternative in vitro potency assay in quality control for inactivated JE vaccine.

Allogeneic CD19-targeted CAR-T therapy in patients with severe myositis and systemic sclerosis.

Allogeneic chimeric antigen receptor (CAR)-T cells hold great promise for expanding the accessibility of CAR-T therapy, whereas the risks of allograft rejection have hampered its application. Here, we genetically engineered healthy-donor-derived, CD19-targeting CAR-T cells using CRISPR-Cas9 to address the issue of immune rejection and treated one patient with refractory immune-mediated necrotizing myopathy and two patients with diffuse cutaneous systemic sclerosis with these cells. This study was registered at ClinicalTrials.gov (NCT05859997). The infused cells persisted for over 3 months, achieving complete B cell depletion within 2 weeks of treatment. During the 6-month follow-up, we observed deep remission without cytokine release syndrome or other serious adverse events in all three patients, primarily shown by the significant improvement in the clinical response index scores for the two diseases, respectively, and supported by the observations of reversal of inflammation and fibrosis. Our results demonstrate the high safety and promising immune modulatory effect of the off-the-shelf CAR-T cells in treating severe refractory autoimmune diseases.

Symmetry-preserving modeling for lithographic imaging.

In computational imaging and lithography, it has been a challenge for a numerical model to faithfully preserve symmetries in the physical imaging system. In this Letter, we present a project-to-symmetry-subspace (PTSS) method to prevent symmetry loss during the iterative generation of optical kernels. Essentially, PTSS is to project iterative vectors onto a predefined symmetric subspace when decomposing the transmission cross coefficient (TCC). Simulation results demonstrate the PTSS-generation of a truncated set of optical kernels that are substantially free of symmetry error, regardless of the order of truncation.

Assessment of Global and Regional Lung Compliance in Pulmonary Fibrosis With Hyperpolarized Gas MRI.

BACKGROUND: Lung compliance, a biomarker of pulmonary fibrosis, is generally measured globally. Hyperpolarized 129Xe gas MRI offers the potential to evaluate lung compliance regionally, allowing for visualization of changes in lung compliance associated with fibrosis. PURPOSE: To assess global and regional lung compliance in a rat model of pulmonary fibrosis using hyperpolarized 129Xe gas MRI. STUDY TYPE: Prospective. ANIMAL MODEL: Twenty Sprague-Dawley male rats with bleomycin-induced fibrosis model (N = 10) and saline-treated controls (N = 10). FIELD STRENGTH/SEQUENCE: 7-T, fast low-angle shot (FLASH) sequence. ASSESSMENT: Lung compliance was determined by fitting lung volumes derived from segmented 129Xe MRI with an iterative selection method, to corresponding airway pressures. Similarly, lung compliance was obtained with computed tomography for cross-validation. Direction-dependencies of lung compliance were characterized by regional lung compliance ratios (R) in different directions. Pulmonary function tests (PFTs) and histological analysis were used to validate the pulmonary fibrosis model and assess its correlation with 129Xe lung compliance. STATISTICAL TESTS: Shapiro-Wilk tests, unpaired and paired t-tests, Mann-Whitney U and Wilcoxon signed-rank tests, and Pearson correlation coefficients. P < 0.05 was considered statistically significant. RESULTS: For the entire lung, the global and regional lung compliance measured with 129Xe gas MRI showed significant differences between the groups, and correlated with the global lung compliance measured using PFTs (global: r = 0.891; regional: r = 0.873). Additionally, for the control group, significant difference was found in mean regional compliance between areas, eg, 0.37 (0.32, 0.39) × 10-4 mL/cm H2O and 0.47 (0.41, 0.56) × 10-4 mL/cm H2O for apical and basal lung, respectively. The apical-basal direction R was 1.12 ± 0.09 and 1.35 ± 0.13 for fibrosis and control groups, respectively, indicating a significant difference. DATA CONCLUSION: Our findings demonstrate the feasibility of using hyperpolarized gas MRI to assess regional lung compliance. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

Aerodynamic Simulation of Small Airway Resistance: A New Imaging Biomarker for Chronic Obstructive Pulmonary Disease.

Purpose: To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD. Patients and Methods: 24 subjects who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively. Subjects were divided into three groups: normal (10), high-risk (6), and COPD (8). The airway from the trachea down to the sixth generation of bronchioles was reconstructed by a 3D slicer. The small airway resistance (RSA) and RSA as a percentage of total airway resistance (RSA%) were calculated by CFD combined with airway resistance and FEV1 measured by pulmonary function test. A correlation analysis was conducted between RSA and pulmonary function parameters, including FEV1/FVC, FEV1% predicted, MEF50% predicted, MEF75% predicted and MMEF75/25% predicted. Results: The RSA and RSA% were significantly different among the three groups (p<0.05) and related to FEV1/FVC (r = -0.70, p < 0.001; r = -0.67, p < 0.001), FEV1% predicted (r = -0.60, p = 0.002; r = -0.57, p = 0.004), MEF50% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001), MEF75% predicted (r = -0.71, p < 0.001; r = -0.60, p = 0.002) and MMEF 75/25% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001). Conclusion: Airway CFD is a valuable method for estimating the small airway resistance, where the derived RSA will aid in the early diagnosis of COPD.

Inverse optical scatterometry using sketch-guided deep learning.

Optical scatterometry, also referred to as optical critical dimension (OCD) metrology, is a widely used technique for characterizing nanostructures in semiconductor industry. As a model-based optical metrology, the measurement in optical scatterometry is not straightforward but involves solving a complicated inverse problem. So far, the methods for solving the inverse scattering problem, whether traditional or deep-learning-based, necessitate a predefined geometric model, but they are also constrained by this model with poor applicability. Here, we demonstrate a sketch-guided neural network (SGNN) for nanostructure reconstruction in optical scatterometry. By learning from training data based on the designed generic profile model, the neural network acquires not only scattering knowledge but also sketching techniques, that allows it to draw the profiles corresponding to the input optical signature, regardless of whether the sample structure is the same as the generic profile model or not. The accuracy and strong generalizability of proposed approach is validated by using a series of one-dimensional gratings. Experiments have also demonstrated that it is comparable to nonlinear regression methods and outperforms traditional deep learning methods. To our best knowledge, this is the first time that the concept of sketching has been introduced into deep learning for solving the inverse scattering problem. We believe that our method will provide a novel solution for semiconductor metrology, enabling fast and accurate reconstruction of nanostructures.

A bioinspired surface tension-driven route toward programmed cellular ceramics.

The intriguing biomineralization process in nature endows the mineralized biological materials with intricate microarchitected structures in a facile and orderly way, which provides an inspiration for processing ceramics. Here, we propose a simple and efficient manufacturing process to fabricate cellular ceramics in programmed cell-based 3D configurations, inspired by the biomineralization process of the diatom frustule. Our approach separates the ingredient synthesis from architecture building, enabling the programmable manufacturing of cellular ceramics with various cell sizes, geometries, densities, metastructures, and constituent elements. Our approach exploits surface tension to capture precursor solutions in the architected cellular lattices, allowing us to control the liquid geometry and manufacture cellular ceramics with high precision. We investigate the geometry parameters for the architected lattices assembled by unit cells and unit columns, both theoretically and experimentally, to guide the 3D fluid interface creation in arranged configurations. We manufacture a series of globally cellular and locally compact piezoceramics, obtaining an enhanced piezoelectric constant and a designed piezoelectric anisotropy. This bioinspired, surface tension-assisted approach has the potential to revolutionize the design and processing of multifarious ceramic materials for structural and functional applications in energy, electronics and biomedicine.