Oriented Metal Stripping for Highly Reversible Zinc Anode.

Aqueous zinc metal batteries are a viable candidate for next-generation energy storage systems, but suffer from poor cycling efficiency of the Zn anode. Emerging approaches aim to regulate zinc plating behavior to suppress uncontrolled dendrites, while the stripping process is seldom considered. Herein, an oriented metal stripping strategy is demonstrated to stabilize the Zn anode by removing high-index facets for exposing the (002) plane through the addition of anionic additive sodium citrate (SC). Consequently, high-index facets that coordinate strongly with SC are preferentially stripped out due to a reduced stripping barrier, rendering stable (002) facet preponderant in epitaxial plating. After repeat stripping/plating, the ultra-high proportion of 93% for (002) and large-size grains of ≈100 µm (six times larger than before) can be obtained. Zn anode shows continuous 25 000 cycles with low overpotential at 100 mA cm-2 in symmetric cells and more than 70 h of stable operation even at an ultra-high depth of discharge of 92.3%. Moreover, an extremely long lifespan of 12 000 cycles at 10 A g-1 with a high capacity retention of 89% is achieved by the assembled Zn//I2 battery. This work provides a distinctive approach to improving the stripping process to design highly efficient zinc anodes for promising aqueous zinc metal batteries.

Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.

BACKGROUND: To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC). METHODS: Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values. RESULTS: TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849-0.917) in the training set and 0.730 (95%CI = 0.629-0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823-0.919) in the training set and 0.760 (95%CI = 0.638-0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities. CONCLUSION: PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

Ascorbic acid enhanced the circulation between Fe(II) and Fe(III) in peroxymonosulfate system for fluoranthene degradation.

In this research, ascorbic acid (AA) was used to enhance Fe(II)/Fe(III)-activated permonosulfate (PMS) systems for the degradation of fluoranthene (FLT). AA enhanced the production of ROS in both PMS/Fe(II) and PMS/Fe(III) systems through chelation and reduction and thus improved the degradation performance of FLT. The optimal molar ratio in PMS/Fe(II)/AA/FLT and PMS/Fe(III)/AA/FLT processes were 2/2/4/1 and 5/10/5/1, respectively. In addition, the experimental results on the effect of FLT degradation under different groundwater matrixes indicated that PMS/Fe(III)/AA system was more adaptable to different water quality conditions than the PMS/Fe(II)/AA system. SO4·- was the major reactive oxygen species (ROS) responsible for FLT removal through the probe and scavenging tests in both systems. Furthermore, the degradation intermediates of FLT were analyzed using gas chromatograph-mass spectrometry (GC-MS), and the probable degradation pathways of FLT degradation were proposed. In addition, the removal of FLT was also tested in actual groundwater and the results showed that by increasing the dose and pre-adjusting the solution pH, 88.8 and 100% of the FLT was removed for PMS/Fe(II)/AA and PMS/Fe(III)/AA systems. The above experimental results demonstrated that PMS/Fe(II)/AA and PMS/Fe(III)/AA processes have a great perspective in practice for the rehabilitation of FLT-polluted groundwater.

Association between squamous cell carcinoma antigen level and EGFR mutation status in Chinese lung adenocarcinoma patients.

BACKGROUND: To investigate the association between squamous cell carcinoma antigen (SCCAg) level and epidermal growth factor receptor (EGFR) mutation status in Chinese lung adenocarcinoma patients. METHODS: We retrospectively analyzed 293 patients with lung adenocarcinoma, divided into EGFR mutant group (n = 178) and EGFR wild-type group (n = 115). The general data and laboratory parameters of the two groups were compared. We used univariable and multivariable logistic regression to analyze the association between SCCAg level and EGFR mutation. Generalized additive model was used for curve fitting, and a hierarchical binary logistic regression model was used for interaction analysis. RESULTS: Squamous cell carcinoma antigen level in the EGFR wild-type group was significantly higher than that in the mutant group (p < 0.001). After adjusting for confounding factors, we found that elevated SCCAg was associated with a lower probability of EGFR mutation, with an OR of 0.717 (95% CI: 0.543-0.947, p = 0.019). For the tripartite SCCAg groups, the increasing trend of SCCAg was significantly associated with the decreasing probability of EGFR mutation (p for trend = 0.015), especially for Tertile 3 versus Tertile 1 (OR = 0.505; 95% CI: 0.258-0.986; p = 0.045). Curve fitting showed that there was an approximate linear negative relationship between continuous SCCAg and EGFR mutation probability (p = 0.020), which was first flattened and then decreased (p < 0.001). The association between the two was consistent among different subgroups, suggesting no interaction (all p > 0.05). CONCLUSION: There is a negative association between SCCAg level and EGFR mutation probability in Chinese lung adenocarcinoma patients.

Cloning and Functional Characterization of a Squalene Synthase from Paris polyphylla var. yunnanensis.

Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. is a precious traditional Chinese medicine, and steroidal saponins are its major bioactive constituents possessing extensive biological activities. Squalene synthase (SQS) catalyzes the first dedicated step converting two molecular of farnesyl diphosphate (FDP) into squalene, a key intermediate in the biosynthetic pathway of steroidal saponins. In this study, a squalene synthase gene (PpSQS1) was cloned and functionally characterized from P. polyphylla var. yunnanensis, representing the first identified SQS from the genus Paris. The open reading frame of PpSQS1 is 1239 bp, which encodes a protein of 412 amino acids showing high similarity to those of other plant SQSs. Expression of PpSQS1 in Escherichia coli resulted in production of soluble recombinant proteins. Gas chromatography-mass spectrometry analysis showed that the purified recombinant PpSQS1 protein could produce squalene using FDP as a substrate in the in vitro enzymatic assay. qRT-PCR analysis indicated that PpSQS1 was highly expressed in rhizomes, consistent with the dominant accumulation of steroidal saponins there, suggesting that PpSQS1 is likely involved in the biosynthesis of steroidal saponins in the plant. The findings lay a foundation for further investigation on the biosynthesis and regulation of steroidal saponins, and also provide an alternative gene for manipulation of steroid production using synthetic biology.

Redox control of GPx catalytic activity through mediating self-assembly of Fmoc-phenylalanine selenide into switchable supramolecular architectures.

Artificial enzymes capable of achieving tunable catalytic activity through stimuli control of enzymatic structure transition are of significance in biosensor and biomedicine research. Herein we report a novel smart glutathione peroxidise (GPx) mimic with modulatory catalytic activity based on redox-induced supramolecular self-assembly. First, an amphiphilic Fmoc-phenylalanine-based selenide was designed and synthesized, which can self-assemble into nanospheres (NSs) in aqueous solution. The NSs demonstrate extremely low GPx activity. Upon the oxidation of hydroperoxides (ROOH), the selenide can be quickly transformed into the selenoxide form. The change of the molecular structure induces complete morphology transition of the self-assemblies from NSs to nanotubes (NTs), resulting in great enhancement in the GPx catalytic activity. Under the reduction of GSH, the selenoxide can be further reversibly reduced back into the selenide; therefore the reversible switch between the NSs and NTs can be successfully accomplished. The relationship between the catalytic activity and enzymatic structure was also investigated. The dual response nature makes this mimic play roles of both a sensor and a GPx enzyme at the same time, which can auto-detect the signal of ROOH and then auto-change its activity to achieve quick or slow/no scavenging of ROOH. The dynamic balance of ROOH is vital in organisms, in which an appropriate amount of ROOH does benefit to the metabolism, whereas surplus ROOH can cause oxidative damage of the cell instead and this smart mimic is of remarkable significance. We expect that such a mimic can be developed into an effective antioxidant drug and provide a new platform for the construction of intelligent artificial enzymes with multiple desirable properties.

The beneficial effect of α-lipoic acid on spinal cord injury repair in rats is mediated through inhibition of oxidative stress: A transcriptomic analysis.

BACKGROUND: Oxidative stress is a crucial factor contributing to the occurrence and development of secondary damage in spinal cord injuries (SCI), ultimately impacting the recovery process. α-lipoic acid (ALA) exhibits potent antioxidant properties, effectively reducing secondary damage and providing neuroprotective benefits. However, the precise mechanism by which ALA plays its antioxidant role remains unknown. METHODS: We established a model of moderate spinal cord contusion in rats. Experimental rats were randomly divided into 3 distinct groups: the sham group, the model control group (SCI_Veh), and the ALA treatment group (SCI_ALA). The sham group rats were exposed only to the SC without contusion injury. Rats belonging to SCI_Veh group were not administered any treatment after SCI. Rats of SCI_ALA group were intraperitoneally injected with the corresponding volume of ALA according to body weight for three consecutive days after the surgery. Subsequently, three days after SCI, spinal cord samples were obtained from three groups of rats: the sham group, model control group, and administration group. Thereafter, total RNA was extracted from the samples and the expression of three sets of differential genes was analyzed by transcriptome sequencing technology. Real-time PCR was used to verify the sequencing results. The impact of ALA on oxidative stress in rats following SCI was assessed by measuring their total antioxidant capacity and hydrogen peroxide (H2O2) content. The effects of ALA on rat recovery following SCI was investigated through Beattie and Bresnahan (BBB) score and footprint analysis. RESULTS: The findings from the transcriptome sequencing analysis revealed that the model control group had 2975 genes with altered expression levels when compared to the ALA treatment group. Among these genes, 1583 were found to be upregulated while 1392 were down-regulated. Gene ontology (GO) displayed significant enrichment in terms of functionality, specifically in oxidative phosphorylation, oxidoreductase activity, and signaling receptor activity. The Kyoto encyclopedia of genes and genomes (KEGG) pathway was enriched in oxidative phosphorylation, glutathione metabolism and cell cycle. ALA was found to have multiple benefits for rats after SCI, including increasing their antioxidant capacity and reducing H2O2 levels. Additionally, it was effective in improving motor function (such as 7 days after SCI, the BBB score for SCI_ALA was 8.400 ± 0.937 compared to 7.050 ± 1.141 for SCI_Veh) and promoting histological recovery after SCI (The results of HE demonstrated that the percentage of damage area in was 44.002 ± 6.680 in the SCI_ALA and 57.215 ± 3.964 in the SCI_Veh at the center of injury.). The sequence data from this study has been deposited into Sequence Read Archive (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE242507). CONCLUSION: Overall, the findings of this study confirmed the beneficial effects of ALA on recovery in SCI rats through transcriptome sequencing, behavioral, as well histology analyses.

Exosomes derived from vMIP-II-Lamp2b gene-modified M2 cells provide neuroprotection by targeting the injured spinal cord, inhibiting chemokine signals and modulating microglia/macrophage polarization in mice.

Inflammation is one of the key injury factors for spinal cord injury (SCI). Exosomes (Exos) derived from M2 macrophages have been shown to inhibit inflammation and be beneficial in SCI animal models. However, lacking targetability restricts their application prospects. Considering that chemokine receptors increase dramatically after SCI, viral macrophage inflammatory protein II (vMIP-II) is a broad-spectrum chemokine receptor binding peptide, and lysosomal associated membrane protein 2b (Lamp2b) is the key membrane component of Exos, we speculated that vMIP-II-Lamp2b gene-modified M2 macrophage-derived Exos (vMIP-II-Lamp2b-M2-Exo) not only have anti-inflammatory properties, but also can target the injured area by vMIP-II. In this study, using a murine contusive SCI model, we revealed that vMIP-II-Lamp2b-M2-Exo could target the chemokine receptors which highly expressed in the injured spinal cords, inhibit some key chemokine receptor signaling pathways (such as MAPK and Akt), further inhibit proinflammatory factors (such as IL-1ß, IL-6, IL-17, IL-18, TNF-α, and iNOS), and promote anti-inflammatory factors (such as IL-4 and Arg1) productions, and the transformation of microglia/macrophages from M1 into M2. Moreover, the improved histological and functional recoveries were also found. Collectively, our results suggest that vMIP-II-Lamp2b-M2-Exo may provide neuroprotection by targeting the injured spinal cord, inhibiting some chemokine signals, reducing proinflammatory factor production and modulating microglia/macrophage polarization.

Reliability modelling and evaluating of wind turbine considering imperfect repair.

To model and evaluate the reliability of wind turbine (WT) under imperfect repair, an improved Log-linear Proportional Intensity Model (LPIM)-based method was proposed. Initially, using the three-parameter bounded intensity process (3-BIP) as the benchmark failure intensity function of LPIM, an imperfect repair effect-aware WT reliability description model was developed. Among them, the 3-BIP was used to describe the evolution process of the failure intensity in the stable operation stage with running time, while the LPIM reflected the repair effect. Second, the estimation problem for model parameters was transformed into a minimum solution problem for a nonlinear objective function, which was then solved using the Particle Swarm Optimization algorithm. The confidence interval of model parameters was finally estimated using the inverse Fisher information matrix method. Key reliability indices interval estimation based on the Delta method and point estimation was derived. The proposed method was applied to a wind farm's WT failure truncation time. The proposed method has a higher goodness of fit based on verification and comparison. As a result, it can bring the evaluated reliability closer to engineering practice.

Association Analysis of Maximum Standardized Uptake Values Based on 18F-FDG PET/CT and EGFR Mutation Status in Lung Adenocarcinoma.

(1) Background: To investigate the association between maximum standardized uptake value (SUVmax) based on 18F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups' general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUVmax and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUVmax and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUVmax and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUVmax and EGFR mutation was approximately linear (df = 1.000, c2 = 3.897, p = 0.048); with the increase in SUVmax, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908-0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUVmax and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUVmax in predicting EGFR mutations.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma.

BACKGROUND: This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. METHODS: A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. RESULTS: Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723). CONCLUSIONS: Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice.

Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases.

Importance: The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. Objective: To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. Design, Setting, and Participants: This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. Exposures: Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. Main Outcomes and Measures: The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. Results: In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. Conclusions and Relevance: In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules.

PURPOSE: This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). METHODS: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). RESULTS: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372-1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. CONCLUSION: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

The Immunomodulatory Effects of Phellodendri Cortex Polysaccharides on Cyclophosphamide-Induced Immunosuppression in Mice.

Cyclophosphamide is a commonly used anticancer drug, and immunosuppression is one of the most common side effects. How to recover the immunological function is important for cyclophosphamide-treated patients. In the present study, Phellodendri Cortex polysaccharides (CPP) could enhance the proliferation of mouse spleen lymphocytes in vitro. The immunoregulatory function of CPP was then investigated in cyclophosphamide-induced immunosuppressed mice. In CPP-treated groups, mice were orally treated with CPP at doses of 1, 0.5, and 0.25 g/kg bodyweight from 1 to 11 d, respectively. The cyclophosphamide was administrated in CPP and cyclophosphamide groups from 12 to 14 d. In the cyclophosphamide and normal control groups, the mice received equal volume of saline from 1 to 14 d. The results showed that CPP (1 g/kg) could significantly increase the bodyweight of mice, even during cyclophosphamide treatment. The organ coefficients of the spleen and thymus were recovered by CPP treatment. CPP upregulated the contents of cytokines (IL-2, IL-6, IFN-γ, and TNF-α) in serum, which were downregulated by cyclophosphamide. The mRNA levels of these cytokines were also elevated by CPP treatment in the spleen. Cyclophosphamide upregulated the expressions of NF-κB p65, TLR4, and MyD88, suggesting that the NF-κB signaling pathway was activated by cyclophosphamide. After CPP treatment, it was recovered to normal level. These results indicated that CPP alleviated the cyclophosphamide-induced immunosuppression.

Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.

To investigate whether the maximum standardized uptake value (SUVmax) of 18F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent 18F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them (P = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.

Immunosuppressive and Adipogenesis Inhibitory Sesterterpenoids with a Macrocyclic Ether System from Eurysolen gracilis.

Eurysoloids A (1) and B (2), two novel diastereomeric sesterterpenoids possessing a pentacyclic 5/6/5/10/5 framework with an unusual macrocyclic ether system, were isolated from Eurysolen gracilis Prain. Their structures were unambiguously determined by spectroscopic, single-crystal X-ray diffraction and DP4+ analyses. A plausible biosynthetic pathway for compounds 1 and 2 was proposed. Both compounds exhibited immunosuppressive activity via inhibiting the production of cytokine IFN-γ of T cells, and compound 2 inhibited adipogenesis in 3T3-L1 adipocytes.

Optically Tunable Chiral Plasmonic Guest-Host Cellulose Films Weaved with Long-range Ordered Silver Nanowires.

Plasmonic materials with large chiroptical activity at visible wavelength have attracted considerable attention due to their potential applications in metamaterials. Here we demonstrate a novel guest-host chiral nematic liquid crystal film composed of bulk self-co-assembly of the dispersed plasmonic silver nanowires (AgNWs) and cellulose nanocrystals (CNCs). The AgNWs-CNCs composite films show strong plasmonic optical activities, that are dependent on the chiral photonic properties of the CNCs host medium and orientation of the guest AgNWs. Tunable chiral distribution of the aligned anisotropic AgNWs with long-range order is obtained through the CNCs liquid crystal mediated realignment. The chiral plasmonic optical activity of the AgNWs-CNCs composite films can be tuned by changing the interparticle electrostatic repulsion between the CNCs nanorods and AgNWs. We also observe an electromagnetic energy transfer phenomena among the plasmonic bands of AgNWs, due to the modulation of the photonic band gap of the CNCs host matrix. This facile approach for fabricating chiral macrostructured plasmonic materials with optically tunable property is of interest for a variety of advanced optics applications.

Free-Standing Optically Switchable Chiral Plasmonic Photonic Crystal Based on Self-Assembled Cellulose Nanorods and Gold Nanoparticles.

Photonic crystals incorporating with plasmonic nanoparticles have recently attracted considerable attention due to their novel optical properties and potential applications in future subwavelength optics, biosensing and data storage device. Here we demonstrate a free-standing chiral plasmonic film composed of entropy-driven self-co-assembly of gold nanoparticles (GNPs) and rod-like cellulose nanocrystals (CNCs). The CNCs-GNPs composite films not only preserve the photonic ordering of the CNCs matrix but also retain the plasmonic resonance of GNPs, leading to a distinct plasmon-induced chiroptical activity and a strong resonant plasmonic-photonic coupling that is confirmed by the stationary and ultrafast transient optical response. Switchable optical activity can be obtained by either varying the incidence angle of lights, or by taking advantage of the responsive feature of the CNCs matrix. Notably, an angle-dependent plasmon resonance in chiral nematic hybrid film has been observed for the first time, which differs drastically from that of the GNPs embed in three-dimensional photonic crystals, revealing a close relation with the structure of the host matrix. The current approach for fabricating device-scale, macroscopic chiral plasmonic materials from abundant CNCs with robust chiral nematic matrix may enable the mass production of functional optical metamaterials.