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.

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.

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.

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.

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.

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.

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.

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.

Optical, Structural, and Synchrotron X-ray Absorption Studies for GaN Thin Films Grown on Si by Molecular Beam Epitaxy.

GaN on Si plays an important role in the integration and promotion of GaN-based wide-gap materials with Si-based integrated circuits (IC) technology. A series of GaN film materials were grown on Si (111) substrate using a unique plasma assistant molecular beam epitaxy (PA-MBE) technology and investigated using multiple characterization techniques of Nomarski microscopy (NM), high-resolution X-ray diffraction (HR-XRD), variable angular spectroscopic ellipsometry (VASE), Raman scattering, photoluminescence (PL), and synchrotron radiation (SR) near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. NM confirmed crack-free wurtzite (w-) GaN thin films in a large range of 180-1500 nm. XRD identified the w- single crystalline structure for these GaN films with the orientation along the c-axis in the normal growth direction. An optimized 700 °C growth temperature, plus other corresponding parameters, was obtained for the PA-MBE growth of GaN on Si, exhibiting strong PL emission, narrow/strong Raman phonon modes, XRD w-GaN peaks, and high crystalline perfection. VASE studies identified this set of MBE-grown GaN/Si as having very low Urbach energy of about 18 meV. UV (325 nm)-excited Raman spectra of GaN/Si samples exhibited the GaN E2(low) and E2(high) phonon modes clearly without Raman features from the Si substrate, overcoming the difficulties from visible (532 nm) Raman measurements with strong Si Raman features overwhelming the GaN signals. The combined UV excitation Raman-PL spectra revealed multiple LO phonons spread over the GaN fundamental band edge emission PL band due to the outgoing resonance effect. Calculation of the UV Raman spectra determined the carrier concentrations with excellent values. Angular-dependent NEXAFS on Ga K-edge revealed the significant anisotropy of the conduction band of w-GaN and identified the NEXAFS resonances corresponding to different final states in the hexagonal GaN films on Si. Comparative GaN material properties are investigated in depth.

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.

Supramolecular prodrug of SN38 based on endogenous albumin and SN38 prodrug modified with semaglutide side chain to improve the tumor distribution.

To improve the biodistribution of the drug in the tumor, a supramolecular prodrug of SN38 was fabricated in situ between endogenous albumin and SN38 prodrug modified with semaglutide side chain. Firstly, SN38 was conjugated with semaglutide side chain and octadecanedioic acid via glycine linkers to obtain SI-Gly-SN38 and OA-Gly-SN38 prodrugs, respectively. Both SI-Gly-SN38 and OA-Gly-SN38 exhibited excellent stability in PBS for over 24 h. Due to the strong binding affinity of the semaglutide side chain with albumin, the plasma half-life of SI-Gly-SN38 was 2.7 times higher than that of OA-Gly-SN38. Furthermore, with addition of HSA, the fluorescence intensity of SI-Gly-SN38 was 4 times higher than that of OA-Gly-SN38, confirming its strong binding capability with HSA. MTT assay showed that the cytotoxicity of SI-Gly-SN38 and OA-Gly-SN38 was higher than that of Irinotecan. Even incubated with HSA, the SI-Gly-SN38 and OA-Gly-SN38 still maintained high cytotoxicity, indicating minimal influence of HSA on their cytotoxicity. In vivo pharmacokinetic studies demonstrated that the circulation half-life of SI-Gly-SN38 was twice that of OA-Gly-SN38. SI-Gly-SN38 exhibited significantly reduced accumulation in the lungs, being only 0.23 times that of OA-Gly-SN38. The release of free SN38 in the lungs from SI-Gly-SN38 was only 0.4 times that from OA-Gly-SN38 and Irinotecan. The SI-Gly-SN38 showed the highest accumulation in tumors. The tumor inhibition rate of SI-Gly-SN38 was 6.42% higher than that of OA-Gly-SN38, and 8.67% higher than that of Irinotecan, respectively. These results indicate that the supramolecular prodrug delivery system can be constructed between SI-Gly-SN38 and endogenous albumin, which improves drug biodistribution in vivo, enhances tumor accumulation, and plays a crucial role in tumor growth inhibition.

Assessment of pulmonary physiological changes caused by aging, cigarette smoking, and COPD with hyperpolarized 129Xe magnetic resonance.

OBJECTIVE: To comprehensively assess the impact of aging, cigarette smoking, and chronic obstructive pulmonary disease (COPD) on pulmonary physiology using 129Xe MR. METHODS: A total of 90 subjects were categorized into four groups, including healthy young (HY, n = 20), age-matched control (AMC, n = 20), asymptomatic smokers (AS, n = 28), and COPD patients (n = 22). 129Xe MR was utilized to obtain pulmonary physiological parameters, including ventilation defect percent (VDP), alveolar sleeve depth (h), apparent diffusion coefficient (ADC), total septal wall thickness (d), and ratio of xenon signal from red blood cells and interstitial tissue/plasma (RBC/TP). RESULTS: Significant differences were found in the measured VDP (p = 0.035), h (p = 0.003), and RBC/TP (p = 0.003) between the HY and AMC groups. Compared with the AMC group, higher VDP (p = 0.020) and d (p = 0.048) were found in the AS group; higher VDP (p < 0.001), d (p < 0.001) and ADC (p < 0.001), and lower h (p < 0.001) and RBC/TP (p < 0.001) were found in the COPD group. Moreover, significant differences were also found in the measured VDP (p < 0.001), h (p < 0.001), ADC (p < 0.001), d (p = 0.008), and RBC/TP (p = 0.032) between the AS and COPD groups. CONCLUSION: Our findings indicate that pulmonary structure and functional changes caused by aging, cigarette smoking, and COPD are various, and show a progressive deterioration with the accumulation of these risk factors, including cigarette smoking and COPD. CLINICAL RELEVANCE STATEMENT: Pathophysiological changes can be difficult to comprehensively understand due to limitations in common techniques and multifactorial etiologies. 129Xe MRI can demonstrate structural and functional changes caused by several common factors and can be used to better understand patients' underlying pathology. KEY POINTS: Standard techniques for assessing pathophysiological lung function changes, spirometry, and chest CT come with limitations. 129Xe MR demonstrated progressive deterioration with accumulation of the investigated risk factors, without these limitations. 129Xe MR can assess lung changes related to these risk factors to stage and evaluate the etiology of the disease.

Efficacy of digital zoning design for the resection of benign parotid gland tumor.

The objective of this study is to verify the role of digital modified parotid tumor zoning method in modified parotid incision. The data of patients with parotid benign tumors from November 2021 to December 2023 were collected. Through the use of digital technology for soft tissue reconstruction, the parotid tumor was divided into four areas according to the digital image marker points. We designed the surgical incision according to the parotid gland division, found that it was feasible to guide the incision selection by division, and summarized the common incision and division corresponding, zone I was I and V-shaped incision, zone II was V incision, zone III was V and C- shaped incision, and zone IV was C- shaped incision. We conclude that the digital modified parotid gland zoning method can provide a better distinction in the surgical incision, and provide a better cosmetic incision and prognosis.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography.

PURPOSE: This study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC). METHODS: A retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set. RESULTS: SUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC. CONCLUSION: Our nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

Carborane-FAPI conjugate: A potential FAP-targeted boron agent with improved boron content.

Boron neutron capture therapy (BNCT) has received extensive attention as an advanced binary radiotherapy method. However, BNCT still faces poor selectivity of boron agent and is insufficient boron content in tumor tissues. To improve the tumor-targeted ability and boron content, this research aims to design, synthesize and preliminary evaluate a new borane agent Carborane-FAPI, which coupling the o-carborane to the compound skeleton of a mature fibroblast activating protein (FAP) inhibitor (FAPI). FAP is a tumor-associated antigen. FAP expressed lowly in normal organs and highly expressed in tumors, so it is a potential target for diagnosis and treatment. Boronophenylalanine (BPA) is the most widely investigated BNCT drug in present. Compared with BPA, the boron content of a single molecule is increased and drug targeting is enhanced. The results show that Carboaren-FAPI has low toxicity to normal cells, and selective enrichment in tumor tissues. It is a promising boron drug that has the potential to be used in BNCT.

Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study.

Purpose: Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Methods: Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Results: Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. Conclusions: The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.

"Microflower-Templated" Janus Sheets: Synthesis and Application in Stabilizing Foams.

In this study, we proposed a method for fabricating Janus sheets using biological "microflowers" as a sacrificial template. The microflower-templated Janus sheets (MF-JNSs) were employed as a foam stabilizer in foam separation of the whey soybean protein (WSP). The MF-JNSs took inorganic hybrid microflowers (BSA@Cu3 (PO4)2-MF) as template, followed by the sequential attachment of protamine and silica to the surface of the BSA@Cu3(PO4)2-MF. Subsequently, the template was removed using ethylenediaminetetraacetic acid after the silicon dioxide was modified by 3-(methacryloyloxy) propyl trimethoxysilane. Upon template dissolution, the modified silica layer, lacking support from the core, fractured to form the MF-JNSs. This method omitted the step of treating the hollow ball by external force and obtained Janus sheets in one step, indicating that it was simple and feasible. The morphology, structure, and composition of the MF-JNSs were analyzed by SEM, TEM, AFM, XRD, and FT-IR. The MF-JNSs were found to delay the breakage time of the Pickering emulsion, demonstrating their emulsion stabilizing capability. Importantly, they significantly enhanced the foam half-life and foam height of soybean whey wastewater (SWW). Moreover, the recovery percentage and enrichment ratio of WSP, separated from SWW by foam separation, were improved to 81 ± 0.28 and 1.20 ± 0.05%, respectively.

Denitrification Characteristics of the Low-Temperature Tolerant Denitrification Strain Achromobacter spiritinus HS2 and Its Application.

The low-temperature environment significantly inhibits the growth and metabolism of denitrifying bacteria, leading to an excessive concentration of ammonia nitrogen and total nitrogen in sewage treatment plants during the cold season. In this study, an efficient denitrifying strain of heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria named HS2 was isolated and screened from industrial sewage of a chemical factory in Inner Mongolia at 8 °C. The strain was confirmed to be Achromobacter spiritinus, a colorless rod-shaped bacterium. When cultured with sodium succinate as the carbon source, a carbon-to-nitrogen ratio of 20-30, a shaking rate of 150-180 r/min, and an initial pH of 6-10, the strain HS2 exhibited excellent nitrogen removal at 8 °C. Through the results of whole-genome sequencing, gene amplification, and gas product detection, the strain HS2 was determined to possess key enzyme genes in both nitrification and denitrification pathways, suggesting a HN-AD pathway of NH4+-N → NH2OH → NO2-N → NO → N2O → N2. At 8 °C, the strain HS2 could completely remove ammonia nitrogen from industrial sewage with an initial concentration of 127.23 mg/L. Microbial species diversity analysis of the final sewage confirmed Achromobacter sp. as the dominant genus, which indicated that the low-temperature denitrifying strain HS2 plays an important role in nitrogen removal in actual low-temperature sewage.

Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence.

Background: Efficiently and accurately detecting cerebral microbleeds (CMBs) is crucial for diagnosing dementia, stroke, and traumatic brain injury. Manual CMB detection, however, is time-consuming and error-prone. This study evaluates a novel artificial intelligence (AI) software designed for the automated detection of CMBs using susceptibility weighted imaging (SWI). Methods: The SWI data from 265 patients, 206 of whom had a history of stroke and others of whom presented a variety of other medical histories, including hypertension, diabetes, hyperlipidemia, cerebral hemorrhage, intracerebral vascular malformations, tumors, and inflammation, collected between January 2015 and December 2018, were analyzed. Two independent radiologists initially reviewed the images to identify and count the number of CMBs. Subsequently, the images were processed using an automatic CMB detection software. The generated reports were then reviewed by the radiologists. A final consensus between the two radiologists, obtained after a second review of the images, was used to compare results obtained from the initial manual detection and those of the automatic CMB detection software. The differences of detection sensitivity and precision for patients with or without CMBs and for individual CMBs between the radiologist and the automatic CMB detection software were compared using Pearson chi-squared tests. Results: A total of 1,738 CMBs were detected among 148 patients (71.4±10.7 years, 100 males) from the analyzed SWI data. While the radiologists identified 139 cases with CMBs, the automatic CMB detection software detected 145 cases. Nevertheless, there was no statistical difference in the sensitivity and specificity of the automatic CMB detection software compared to manual detection in determining patients with CMBs (P=0.656 and P=0.212, chi-square test). However, the radiologist identified 93 patients without CMBs, while the automatic CMB detection software detected 121 patients without CMBs, exhibiting a statistically significant difference (P=0.016, chi-square test). In terms of individual CMBs, the radiologists found 1,284, whereas the automatic CMB detection software detected 1,677 CMBs. The detection sensitivity for human versus the automatic CMB detection software were 75.5% and 96.5% respectively (P<0.001, chi-square test), while the precision rates were 92.2% and 86.0% (P<0.001, chi-square test), respectively. Notably, the radiologists were more likely to overlook CMBs when the number of CMBs was high (above 30). Conclusions: The automatic CMB detection software proved to be an effective tool for the detection and quantification of CMBs. It demonstrated higher sensitivity than the radiologists, especially in detecting minuscule CMBs and in cases with high CMB prevalence.

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.

Background: Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. Methods: This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. Results: In total, 107 radiomics features were extracted from each subject's CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64-0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55-0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). Conclusions: The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.