Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity.

The "death cap", Amanita phalloides, is the world's most poisonous mushroom, responsible for 90% of mushroom-related fatalities. The most fatal component of the death cap is α-amanitin. Despite its lethal effect, the exact mechanisms of how α-amanitin poisons humans remain unclear, leading to no specific antidote available for treatment. Here we show that STT3B is required for α-amanitin toxicity and its inhibitor, indocyanine green (ICG), can be used as a specific antidote. By combining a genome-wide CRISPR screen with an in silico drug screening and in vivo functional validation, we discover that N-glycan biosynthesis pathway and its key component, STT3B, play a crucial role in α-amanitin toxicity and that ICG is a STT3B inhibitor. Furthermore, we demonstrate that ICG is effective in blocking the toxic effect of α-amanitin in cells, liver organoids, and male mice, resulting in an overall increase in animal survival. Together, by combining a genome-wide CRISPR screen for α-amanitin toxicity with an in silico drug screen and functional validation in vivo, our study highlights ICG as a STT3B inhibitor against the mushroom toxin.

Study and Microanalysis on the Effect of the Addition of Polypropylene Fibres on the Bending Strength and Carbonization Resistance of Manufactured Sand Concrete.

To popularize the complete replacement of natural sand with manufactured sand, a study was performed to determine the effect of adding polypropylene fibres (PPFs) to increase the bending strength and carbonization resistance of manufactured sand concrete (MSC). A 2 × 3 factorial design with the content and length of PPF as variables was used to establish a carbonization depth prediction model and a response surface model (RSM). The phase composition and microstructure of polypropylene-fibre-reinforced manufactured sand concrete (PPF-MSC) were analysed using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results show the addition of PPF with different contents and lengths increases the bending strength of PPF-MSC to varying degrees, while reducing the carbonization depth and increasing the dynamic elastic modulus after 28 days of carbonization. The highest bending strength (6.12 MPa) and carbonization resistance of PPF-MSC are obtained by the addition of 1 kg/m3 of 12 mm PPF, while the carbonization depth and an increase in the dynamic elastic modulus after 28 days of carbonization are maintained at a minimum of 2.26% and 1.94 mm, respectively. A prediction model was established to obtain a formula for the PPF-MSC carbonization depth in terms of the content and length of PPF and the carbonization time. The following results were obtained from the RSM: compared to the PPF length, the PPF content has a larger impact on the PPF-MSC bending strength and a smaller impact on the PPF-MSC carbonization resistance; there is no significant interaction between the content and length of PPF; and the predicted and measured values are close, indicating that the model is highly reliable. A comparison of the XRD patterns and SEM micrographs of PPF-MSC and MSC after 28 days of carbonization show a lower peak intensity of CaCO3 in the pattern for the carbonized area for PPF-MSC than for MSC and considerably fewer surface pores and cracks in PPF-MSC than in MSC. These results indicate that the addition of PPF increases the compactness of MSC and creates an effective resistance to the erosion by water molecules and carbon dioxide (CO2), thus enhancing the bending strength and carbonization resistance of MSC.

In-season assessment of agronomic nitrogen use efficiency and its components in winter wheat using critical nitrogen dilution curve.

Accurate and timely nitrogen (N) scheduling requires knowledge of in-season crop N deficit. Therefore, understanding the association between crop growth and crop N demand during its growth period is imperative for fine-tuning N scheduling decisions to actual crop N demand and to enhance N use efficiency. The concept of the critical N dilution curve has been employed to assess and quantify the intensity and time of crop N deficit. However, research regarding the association between crop N deficit and N use efficiency in wheat is limited. The present study was carried out to determine whether there are relationships between the accumulated nitrogen deficit (Nand) and agronomic N use efficiency (AEN) as well as with its components (N fertilizer recovery efficiency (REN) and N fertilizer physiological efficiency (PEN)) of winter wheat and to explore the potential capacity of Nand for predicting AEN and its components. Data acquired from five variable N rates (0, 75, 150, 225, and 300 kg ha-1) field experiments using six winter wheat cultivars were used to establish and validate the relationships between Nand and AEN, REN, and PEN. The results indicated that plant N concentration in winter wheat was significantly affected by N application rates. Nand varied from -65.73 to 104.37 kg ha-1 after Feekes stage 6 under different N application rates. The AEN and its components were also affected by cultivars, N levels, seasons, and growth stages. A positive correlation was observed between Nand, AEN, and its components. Validation using an independent data set showed the robustness of the newly developed empirical models to accurately predict AEN, REN, and PEN with an RMSE of 3.43 kg kg-1, 4.22%, and 3.67 kg kg-1 and RRMSE of 17.53%, 12.46%, and 13.17%, respectively. This indicates that Nand has the potential to predict AEN and its components during the growth period of winter wheat. The findings will assist in improving in-season N use efficiency by fine-tuning N scheduling decisions in winter wheat cultivation.

The combined effect of metformin and mirabegron on diet-induced obesity.

Anti-obesity medications act by suppressing energy intake (EI), promoting energy expenditure (EE), or both. Metformin (Met) and mirabegron (Mir) cause weight loss by targeting EI and EE, respectively. However, anti-obesity effects during concurrent use of both have yet to be explored. In this study, we investigated the anti-obesity effects, metabolic benefits, and underlying mechanisms of Met/Mir combination therapy in two clinically relevant contexts: the prevention model and the treatment model. In the prevention model, Met/Mir caused further 12% and 14% reductions in body weight (BW) gain induced by a high-fat diet compared to Met or Mir alone, respectively. In the treatment model, Met/Mir additively promoted 17% BW loss in diet-induced obese mice, which was 13% and 6% greater than Met and Mir alone, respectively. Additionally, Met/Mir improved glucose tolerance and insulin sensitivity. These benefits of Met/Mir were associated with increased EE, activated brown adipose tissue thermogenesis, and white adipose tissue browning. Significantly, Met/Mir did not cause cardiovascular dysfunction in either model. Together, the combination of Met and Mir could be a promising approach for the prevention and treatment of obesity by targeting both EI and EE simultaneously.

Effects of water deficit at different stages on growth and ear quality of waxy maize.

Introduction: Extreme weather has occurred more frequently in recent decades, which results in more frequent drought disasters in the maize growing season. Severe drought often decreases remarkably plant growth and yield of maize, and even reduces significantly the quality of maize production, especially for waxy maize. Results: To study the changes in plant growth, fresh ear yield, and fresh grain quality of waxy maize under water deficits occurring at different growth stages, and further strengthen the field water management of waxy maize, water deficit experiments were carried out under a rain shelter in 2019 and 2020. Water deficit treatments were imposed respectively at the V6-VT (DV6-VT), VT-R2 (DVT-R2), and R2-R3 (DR2-R3) stages of waxy maize, and treatment with non-water deficit in the whole growing season was taken as the control (CK). The lower limit of soil water content was 50% of field capacity for a water deficit period and 65% of field capacity for a non-water deficit period. Results: In this study, water deficits imposed at V6-VT and VT-R2 stages decreased plant growth rate and leaf gas exchange parameters, accelerated leaf senescence, and limited ear growth of waxy maize, which resulted in 11.6% and 23.1% decreases in grains per ear, 19.4% and 7.3% declines in 100-grain weight, 20.3% and 14.2% losses in fresh ear yield in 2019 and 2020 growing seasons, respectively, while water deficit at R2-R3 stage had no significant effect on ear traits and fresh ear yield, but the fresh ear yield with husk of DR2-R3 decreased by 9.1% (P<0.05). The obvious water deficit imposed at the V6-VT and VT-R2 stages also lowered grain quality. Water deficits at the V6-VT and VT-R2 stages led to accelerated maturity, resulting in increased total protein, starch, and lysine content in grains at the R3 stage and decreased soluble sugar content. Principal component analysis revealed that when water deficits occurred in the waxy maize growing season, they firstly altered maize physiological processes, then affected ear characteristics and yield, and finally resulted in significant grain quality changes. In conclusion, a water deficit during V6-VT and VT-R2 not only reduced fresh ear yield but also adversely affected grain quality. However, water deficit during R2-R3 had little effect on total protein, starch, and soluble sugar content,but increased obviously lysine content. Discussion: The above results suggested that avoiding serious water deficits at the V6-VT and VT-R2 stages of waxy maize while imposing a slight water deficit at the R2-R3 stage has not only little effects on fresh ear yield but also a remarkable improvement in grain quality.

Bibliometric study of neuroinflammation in autism spectrum disorder.

Background: Neuroinflammation is closely associated with the occurrence and development of autism spectrum disorder (ASD). This study aims to describe the global development history and current status of neuroinflammation in ASD from 2004 to 2021 and reveal the research hotspots and frontiers to provide a reference for scholars in related fields to carry out further research. Methods: Journal articles on ASD and neuroinflammation-related research were obtained from the Web of Science Core Collection (WOSCC) database from its inception to 2021. Literature was analyzed visually by VOSviewer, CiteSpace, and R language, including publication analysis, author, institution, national/regional cooperative network analysis, and keyword analysis. We screened the most accumulatively cited 10 experimental papers in the field and the most cited 10 experimental papers in the last 2 years (2020 and 2021) for combing. Results: A total of 620 publications were included in this study, and the number of publications has increased in recent years. The United States (256, 41.29%) was the country with the largest number of publications. King Saud University (40, 6.45%) was the most published institution; Laila Al-Ayadhi Yousef was the most published researcher; the Brain Behavior and Immunity was the main journal for the study of neuroinflammation in autism, having published 22 related articles. Keyword co-occurrence analysis showed that short chain fatty acid, mast cells, and glial cells have been the focus of recent attention. Burst keywords show that gut microbiota and immune system are the future research trends. Conclusion: This bibliometric study describes the basic framework for the development in the field of neuroinflammation and ASD through an exploration of key indicators (countries, institutions, journals, authors, and keywords). We found that the key role of neuroinflammation in the development of ASD is attracting more and more researchers' attention. Future studies can investigate the changes in cytokines and glial cells and their related pathways in ASD neuroinflammation. Immunotherapy to inhibit neuroinflammation may be intensively studied as a direction for ASD treatment or intervention.

Effect of Cementitious Capillary Crystalline Waterproofing Materials on the Mechanical and Impermeability Properties of Engineered Cementitious Composites with Microscopic Analysis.

Building structures are prone to cracking, leakage, and corrosion under complex loads and harsh marine environments, which seriously affect their durability performance. To design cementitious composites with excellent mechanical and impermeability properties, Engineered Cementitious Composites (ECCs) doped with ultrahigh molecular weight polyethylene short-cut fibers (PE-ECCs) were used as the reference group. Different types (XYPEX-type from Canada, SY1000-type from China) and doses (0%, 0.5%, 1.0%, 1.5%, 2.0%) of Cementitious Capillary Crystalline Waterproofing materials (CCCWs) were incorporated. The effect of CCCWs on the mechanical and impermeability properties of PE-ECCs, and the microscopic changes, were investigated to determine the best type of CCCW to use and the best amount of doping. The results showed that with increasing the CCCW dosage, the effects of both CCCWs on the mechanical and impermeability properties of PE-ECC increased and then decreased, and that the best mechanical and impermeability properties of PE-ECC were achieved when the CCCW dosing was 1.0%. The mechanical properties of the PE-ECC were more obviously improved by XYPEX-type CCCW, with a compressive strength of 53.8 MPa, flexural strength of 11.8 MPa, an ultimate tensile stress of 5.56 MPa, and an ultimate tensile strain of 7.53 MPa, which were 37.95%, 53.25%, 14.17%, and 21.65% higher than those of the reference group, respectively. The effects of the two CCCWs on impermeability were comparable. CCCW-PE-ECC(X1.0%) and CCCW-PE-ECC(S1.0%) showed the smallest permeation heights, 2.6 mm and 2.8 mm, respectively. The chloride ion diffusion coefficients of CCCW-PE-ECC(X1.0%) and CCCW-PE-ECC(S1.0%) exhibited the smallest values, 0.15 × 10-12 m2/s and 0.10 × 10-12 m2/s, respectively. Micromorphological tests showed that the particle size of the XYPEX-type CCCW was finer, and the intensity of the diffraction peaks of C-S-H and CaCO3 of PE-ECC increased after doping with two suitable doping amounts of CCCW. The pore structure was improved, the surface of the matrix was smoother, and the degree of erosion of hydration products on the fiber surface was reduced after chloride ion penetration. XYPEX-type CCCW demonstrated a more obvious improvement in the PE-ECC pore structure.

Interactive effects of irrigation system and level on grain yield, crop water use, and greenhouse gas emissions of summer maize in North China Plain.

Irrigation management is one of most critical factors influencing soil N2O and CO2 emissions in dryland agriculture. To explore the effects of irrigation systems and levels on the mitigation of N2O and CO2 emissions from maize fields and to determine the balance among greenhouse gases (GHG) emission, water-saving and grain yield, a two-year field experiment was conducted in the North China Plain (NCP) during the growing seasons of 2018 and 2019. Two irrigation systems (i.e., flood irrigation, FI, and drip irrigation, DI) were adopted with four irrigation levels in each system, including 65 mm/event (sufficient irrigation, CK), 50 mm/event (decreased by 23 %), 35 mm/event (by 46 %) and 20 mm/event (by 69 %), respectively. The results showed that both irrigation systems and levels had significant effects on soil N2O and CO2 emissions (P < 0.05). Nitrous oxide (N2O) and CO2 emissions peaked following irrigation or irrigation + fertilization events during sowing to early filling stage (R1), with the peak values increasing with irrigation levels. Meanwhile, peak values from FI were higher than those from DI at 50 mm and 65 mm irrigation levels. The average cumulative N2O and CO2 emissions of DI treatments were 14.9 % and 6.23 % lower than those of FI treatments (P < 0.05), respectively. Soil moisture was identified as one of the most crucial factors influencing N2O and CO2 fluxes. Deficit irrigation efficiently deceased cumulative N2O and CO2 emissions, but moderate to severe deficit irrigation brought significant reduction in grain yield. Drip irrigation with a slight deficit irrigation level (decreased by 23 %) obtained the best economic and environmental benefits, which achieved the dual goal of lower GHG emissions but higher WUE without sacrificing grain yield.

Effects of Polypropylene Fibers on the Frost Resistance of Natural Sand Concrete and Machine-Made Sand Concrete.

In order to study the effect of polypropylene fibers on the frost resistance of natural sand and machine-made sand concrete, polypropylene fibers (PPF) of different volumes and lengths were mixed into natural sand and machine-made sand concrete, respectively. The freeze-thaw cycle test was carried out on polypropylene-fiber-impregnated natural sand concrete (PFNSC) and polypropylene-fiber-impregnated manufactured sand concrete (PFMSC), respectively, and the apparent structural changes before and after freezing and thawing were observed. Its strength damage was analyzed. A freeze-thaw damage model and a response surface model (RSM) were established used to analyze the antifreeze performance of PFMSC, and the effects of the fiber content, fiber length, and freeze-thaw times on the antifreeze performance of PFMSC were studied. The results show that with the increase in the number of freeze-thaw cycles, the apparent structures of the PFMSC gradually deteriorated, the strength decreased, and the degree of freeze-thaw damage increased. According to the strength damage model, the optimum volume of PPF for the PFNSC specimens is 1.2%, and the optimum volume of PPF for the PFMSC specimens is 1.0%. According to the prediction of RSM, PFNSC can maintain good antifreeze performance within 105 freeze-thaw cycles, and when the PPF length is 11.8 mm, the antifreeze performance of PFNSC reaches the maximum, its maximum compressive strength value is 33.8 MPa, and the split tensile strength value is 3.1 MPa; PFMSC can maintain a good antifreeze performance within 96 freeze-thaw cycles. When the length of PPF is 9.1 mm, the antifreeze performance of PFMSC reaches the maximum, its maximum compressive strength value is 45.8 MPa, and its split tensile strength value is 3.2 MPa. The predicted values are in good agreement with the measured values, and the model has high reliability.

A Genetic Variant in CD274 Is Associated With Prognosis in Metastatic Colorectal Cancer Patients Treated With Bevacizumab-Based Chemotherapy.

Bevacizumab plus chemotherapy is a well-established first-line treatment for metastatic colorectal cancer (mCRC). We investigated whether polymorphisms of genes involved in immune regulation signaling are related to the clinical outcome of mCRC patients treated with bevacizumab-based chemotherapy. In this study, we genotyped 14 single-nucleotide polymorphisms (SNP) in IFN-γ/IFNGRs/JAKs/STATs/PD-L1 pathway by using DNA from blood samples of 141 mCRC patients treated with first-line bevacizumab-based chemotherapy. In the univariate and multivariate analysis, patients with AA genotype of CD274:rs2297136 had a significantly better PFS and OS than patients with AG or GG genotype (10.8 versus 9.8, log-rank P=0.0031; 31.4 versus 20.9, log-rank P=0.0233). Patients with AG/GG genotype of IFNGR1:rs2234711, CT/TT genotype of IFNGR1:rs9376267 also showed longer OS than patients with AA or CC genotype, however, the statistic did not reach significant after adjusted by clinical factors in the multivariate analysis. A nomogram based on the genetic variants and clinic characteristics was developed with a good accuracy to predict patients' survival. Our result indicates that CD274:rs2297136 is one of the most important predictors for the prognosis of mCRC patients treated with bevacizumab-based chemotherapy, if further validated in larger population.

Merging the Non-Natural Catalytic Activity of Lipase and Electrosynthesis: Asymmetric Oxidative Cross-Coupling of Secondary Amines with Ketones.

We describe the enantioselective oxidative cross-coupling of secondary amines with ketones by combining the non-natural catalytic activity of lipase with electrosynthesis. Various 2,2-disubstituted 3-carbonyl indoles with a stereogenic quaternary carbon center were synthesized from 2-substituted indoles in yields up to 78 % with good enantio- and diastereoselectivities (up to 96 : 4 e.r. and >20 : 1 d.r.). This unprecedented protocol demonstrated that hydrolase catalysis is compatible with electrosynthesis, and the reaction can be carried out in organic solvents with a broad substrate scope and good stereoselectivity. This work provides insights into enzymatic electrosynthesis.

Estimating the Impacts of Plant Internal Nitrogen Deficit at Key Top Dressing Stages on Corn Productivity and Intercepted Photosynthetic Active Radiation.

Accurate and timely appraisal of plant nitrogen (N) demand is imperative to regulate the canopy structure and corn production. The strength and time of plant N deficit can be quantified by critical N concentration. The study was aimed to analyze nitrogen nutrition index (NNI), nitrogen deficit content (NDC), plant nitrogen productivity (PNP), and a fraction of intercepted photosynthetic active radiation (FIPAR) across different N treatments and to develop NNI-NDC, NNI-PNP, NNI-FIPAR, NDC-PNP, and NDC-FIPAR relationships from V6 to V12 stages of corn to quantify the suitable PNP and FIPAR values under the optimal plant N condition. Four multi-N rates (0, 75, 90, 150, 180, 225, 270, and 300 kg N ha-1) field experiments were conducted with two cultivars of corn in Henan province of China. Results indicated that N fertilization affected yield, plant biomass, plant N content, and leaf area index. The values of NNI and NDC were from 0.54 to 1.28 kg ha-1 and from -28.13 to 21.99 kg ha-1 under the different treatments of N rate, respectively. The NDC and NNI showed significantly negative relationships from V6 to V12 stages. The values of PNP and FIPAR increased gradually with the crop growth process. The PNP values gradually declined while the FIPAR values of every leaf layer increased with the increase of N supply. The NDC-PNP and NNI-FIPAR relationships were significantly positive; however, the relationships between NNI-PNP and NDC-FIPAR were significantly negative during the vegetative period of corn. The coefficient of determination (R 2) based on NNI was better than that on NDC. The FIPAR values were ~0.35, 0.67, and 0.76% at the upper, middle, and bottom of leaf layers, respectively, and PNP values were ~39, 44, and 51 kg kg-1 at V6, V9, and V12 stages, respectively, when NNI and NDC values were equal to 1 and 0 kg ha-1, respectively. This study described the quantitative information about the effect of a plant's internal N deficit on plant N productivity and canopy light intercept. The projected results would assist in predicting the appropriate plant growth status during key N top-dressing stages of corn, which can optimize N application and improve N use efficiency.

Experimental Study on Mechanical Properties of Basalt Fiber Concrete after Cryogenic Freeze-Thaw Cycles.

Basalt fiber (BF) has received much attention in recent years for engineering practice and scientific research related to basalt fiber reinforced concrete (BFRC) due to its advantageous mechanical properties and cost-effectiveness. By researching its performance characteristics after cryogenic freeze-thaw cycles, the advantages of BFRC's mechanical properties can be further exploited in order to expand its application scope. The effects of the fiber volume fraction, temperature gradient, and number of freeze-thaw cycles on the compressive strength, toughness index, splitting tensile strength, flexural strength, etc., of BFRC were investigated. Additionally, the damage mechanism of BFRC after freeze-thaw cycles was analyzed via scanning electron microscopy (SEM). The results show that the compressive strength of BFRC reaches its peak value when the fraction reaches 0.1% under the conditions of freezing and thawing cycles from room temperature to -80 °C. When the fraction of BFRC is 0.1%, and the maximum reduction is 17.1%, the splitting tensile strength decreased most sharply when the fraction was 0.1%, and the decrease amplitude was 40.9%, and the flexural strength decreased most acutely when the fraction was 0.3%, and the maximum decrease was 44.62%. The addition of basalt fibers can reduce the damage to the microstructure of concrete and improve its plastic degradation characteristics to a certain extent. With a decrease in the minimum temperature of the cryogenic freeze-thaw cycle, the optimal fiber content for compressive strength increases. Nevertheless, the splitting tensile strength and flexural strength of BFRC is improved as the fiber content increases under the cryogenic freeze-thaw environment.

The optimal vaccination strategy to control COVID-19: a modeling study in Wuhan City, China.

BACKGROUND: Reaching optimal vaccination rates is an essential public health strategy to control the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to simulate the optimal vaccination strategy to control the disease by developing an age-specific model based on the current transmission patterns of COVID-19 in Wuhan City, China. METHODS: We collected two indicators of COVID-19, including illness onset data and age of confirmed case in Wuhan City, from December 2, 2019, to March 16, 2020. The reported cases were divided into four age groups: group 1, ≤ 14 years old; group 2, 15 to 44 years old; group 3, 44 to 64 years old; and group 4, ≥ 65 years old. An age-specific susceptible-exposed-symptomatic-asymptomatic-recovered/removed model was developed to estimate the transmissibility and simulate the optimal vaccination strategy. The effective reproduction number (Reff) was used to estimate the transmission interaction in different age groups. RESULTS: A total of 47 722 new cases were reported in Wuhan City from December 2, 2019, to March 16, 2020. Before the travel ban of Wuhan City, the highest transmissibility was observed among age group 2 (Reff = 4.28), followed by group 2 to 3 (Reff = 2.61), and group 2 to 4 (Reff = 1.69). China should vaccinate at least 85% of the total population to interrupt transmission. The priority for controlling transmission should be to vaccinate 5% to 8% of individuals in age group 2 per day (ultimately vaccinated 90% of age group 2), followed by 10% of age group 3 per day (ultimately vaccinated 90% age group 3). However, the optimal vaccination strategy for reducing the disease severity identified individuals ≥ 65 years old as a priority group, followed by those 45-64 years old. CONCLUSIONS: Approximately 85% of the total population (nearly 1.2 billion people) should be vaccinated to build an immune barrier in China to safely consider removing border restrictions. Based on these results, we concluded that 90% of adults aged 15-64 years should first be vaccinated to prevent transmission in China.

Antitumor immune response is associated with favorable survival in GEP-NEN G3.

The tumor immune microenvironment (TME) represents a key determinant for responses to cancer treatment. However, the immune phenotype of highly proliferative gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) is still largely elusive. In this retrospective study, we characterized the TME of high-grade (G3, Ki-67 > 20%) GEP-NEN. We analyzed formalin-fixed paraffin-embedded samples from 37 patients with GEP-NEN G3 by immunohistochemistry and multiplex immunofluorescence to address the abundance and spatial interaction of relevant immune subsets. We focused on the expression of immune checkpoint molecules PD-1 and PD-L1, the cytotoxic T-cell marker CD8, and the tumor-associated macrophage marker CD206. Findings were correlated with overall survival (OS) from the date of a cancer diagnosis. Patients with PD-L1-positive tumors (CPS ≥ 1) and intense PD-1+CD8+ immune cell infiltration showed the most favorable median OS. Multiplex immunofluorescence staining of ten representative tissue samples illustrated intratumoral heterogeneity of PD-L1 expression. Dense PD-1+CD8+ immune cell infiltrates were observed in PD-L1-positive tumor regions but not in PD-L1-negative regions. Proximity analysis revealed a spatial interaction between PD-1+CD8+ cells and PD-L1-positive cells. Our data suggest a pre-existing antitumor immune response in the TME in a subgroup of GEP-NEN G3. This supports a targeted clinical exploration of immunotherapeutic approaches.

Feasibility of controlling hepatitis E in Jiangsu Province, China: a modelling study.

BACKGROUND: Hepatitis E, an acute zoonotic disease caused by the hepatitis E virus (HEV), has a relatively high burden in developing countries. The current research model on hepatitis E mainly uses experimental animal models (such as pigs, chickens, and rabbits) to explain the transmission of HEV. Few studies have developed a multi-host and multi-route transmission dynamic model (MHMRTDM) to explore the transmission feature of HEV. Hence, this study aimed to explore its transmission and evaluate the effectiveness of intervention using the dataset of Jiangsu Province. METHODS: We developed a dataset comprising all reported HEV cases in Jiangsu Province from 2005 to 2018. The MHMRTDM was developed according to the natural history of HEV cases among humans and pigs and the multi-transmission routes such as person-to-person, pig-to-person, and environment-to-person. We estimated the key parameter of the transmission using the principle of least root mean square to fit the curve of the MHMRTDM to the reported data. We developed models with single or combined countermeasures to assess the effectiveness of interventions, which include vaccination, shortening the infectious period, and cutting transmission routes. The indicator, total attack rate (TAR), was adopted to assess the effectiveness. RESULTS: From 2005 to 2018, 44 923 hepatitis E cases were reported in Jiangsu Province. The model fits the data well (R2 = 0.655, P < 0.001). The incidence of the disease in Jiangsu Province and its cities peaks are around March; however, transmissibility of the disease peaks in December and January. The model showed that the most effective intervention was interrupting the pig-to-person route during the incidence trough of September, thereby reducing the TAR by 98.11%, followed by vaccination (reducing the TAR by 76.25% when the vaccination coefficient is 100%) and shortening the infectious period (reducing the TAR by 50.05% when the infectious period is shortened to 15 days). CONCLUSIONS: HEV could be controlled by interrupting the pig-to-person route, shortening the infectious period, and vaccination. Among these interventions, the most effective was interrupting the pig-to-person route.