Glioma segmentation based on dense contrastive learning and multimodal features recalibration.

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder. Each encoder is used to extract MR image features of a given modality. To overcome the entangle problems of multimodal semantic features, a dense contrastive learning (DCL) strategy is presented to extract the modality-specific and common features. Following that, feature recalibration block (RFB) based on modality-wise attention is used to recalibrate the semantic features of each modality, enabling the model to focus on the features that are beneficial for glioma segmentation. These recalibrated features are input into the decoder to obtain the segmentation results. To verify the superiority of the proposed method, we compare it with several state-of-the-art (SOTA) methods in terms of Dice, average symmetric surface distance (ASSD), HD95 and volumetric similarity (Vs). The comparison results show that the average Dice, ASSD, HD95 and Vs of DCL-MANet on all tumor regions are improved at least by 0.66%, 3.47%, 8.94% and 1.07% respectively. For small enhance tumor (ET) region, the corresponding improvement can be up to 0.37%, 7.83%, 11.32%, and 1.35%, respectively. In addition, the ablation results demonstrate the effectiveness of the proposed DCL and RFB, and combining them can significantly increase Dice (1.59%) and Vs (1.54%) while decreasing ASSD (40.51%) and HD95 (45.16%) on ET region. The proposed DCL-MANet could disentangle multimodal features and enhance the semantics of modality-dependent features, providing a potential means to accurately segment small lesion regions in gliomas.

Design, Synthesis, Structure-Activity Relationship, and Three-Dimensional Quantitative Structure-Activity Relationship of Fusarium Acid Derivatives and Analogues as Potential Fungicides.

In research related to fungicides, the development of compounds from natural products with high antifungal activity has attracted considerable attention. Fusaric acid (FA), an alkaloid isolated from the metabolites of Fusarium oxysporum, is an important precursor for developing pharmacologically active herbicides. In our previous work, we reported that FA has a wide range of inhibitory activities against 14 plant pathogenic fungi. In particular, it exhibited excellent antifugal effects on Colletotrichum higginsianum (EC50 = 31.7 µg/mL). Herein, to explore the practical application in the agricultural field, the design and synthesis of three series of FA derivatives and their inhibitory activities against plant pathogenic fungi were examined. Results demonstrated that the optimized FA derivatives had excellent inhibitory activities against C. higginsianum, Helminthosporium (Harpophora maydis), and Pyricularia grisea. In particular, the inhibitory activities were considerably improved when the 5-butyl groups of FA were substituted. The EC50 of C. higginsianum and P. grisea was only 1.2 and 12.0 µg/mL when 5-butylalkyl groups were substituted with 5-([1,1'-biphenyl]-4-yl) and 5-phenyl, respectively. Moreover, the safety index of target compounds, which was obtained from the treatment index of medicines, on rice seeds was evaluated. Finally, 16 leading compounds (H4, H22-H24, H27, H29, H30-H34, H37, H45, H50, H52, and H53) were obtained; they had considerable potential for additional modification and optimization as agricultural fungicides. Moreover, three-dimensional quantitative structure-activity relationship models were developed for obtaining a systematic structure-activity relationship profile to explore the possibility of more potent FA derivatives as novel fungicides.

nnUnetFormer: an automatic method based on nnUnet and transformer for brain tumor segmentation with multimodal MR images.

Objective. Both local and global context information is crucial semantic features for brain tumor segmentation, while almost all the CNN-based methods cannot learn global spatial dependencies very well due to the limitation of convolution operations. The purpose of this paper is to build a new framework to make full use of local and global features from multimodal MR images for improving the performance of brain tumor segmentation.Approach. A new automated segmentation method named nnUnetFormer was proposed based on nnUnet and transformer. It fused transformer modules into the deeper layers of the nnUnet framework to efficiently obtain both local and global features of lesion regions from multimodal MR images.Main results.We evaluated our method on BraTS 2021 dataset by 5-fold cross-validation and achieved excellent performance with Dice similarity coefficient (DSC) 0.936, 0.921 and 0.872, and 95th percentile of Hausdorff distance (HD95) 3.96, 4.57 and 10.45 for the regions of whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively, which outperformed recent state-of-the-art methods in terms of both average DSC and average HD95. Besides, ablation experiments showed that fusing transformer into our modified nnUnet framework improves the performance of brain tumor segmentation, especially for the TC region. Moreover, for validating the generalization capacity of our method, we further conducted experiments on FeTS 2021 dataset and achieved satisfactory segmentation performance on 11 unseen institutions with DSC 0.912, 0.872 and 0.759, and HD95 6.16, 8.81 and 38.50 for the regions of WT, TC, and ET, respectively.Significance. Extensive qualitative and quantitative experimental results demonstrated that the proposed method has competitive performance against the state-of-the-art methods, indicating its interest for clinical applications.

Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction.

Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers. First, transformation-invariant features were extracted from multimodal MR images with a WS network, and then the multimodal WS features were used instead of the original images as the inputs of WSOFNet and were fully fused through an adaptive multimodal feature fusion module (AMF2M) and an orthogonal projection module (OPM). Finally, the fused features were input into a fully connected classifier to predict IDH mutation status. In addition, to achieve improved prediction accuracy, four auxiliary losses were also used in the feature extraction modules. The comparison results showed that the prediction area under the curve (AUC) of WSOFNet on a single-center dataset was 0.9966 and that on a multicenter dataset was approximately 0.9655, which was at least 3.9% higher than that of state-of-the-art methods. Moreover, the ablation experimental results also proved that the adaptive multimodal feature fusion strategy based on orthogonal projection could effectively improve the prediction performance of the model, especially for an external validation dataset.

Chemical Corrosion-Water-Confining Pressure Coupling Damage Constitutive Model of Rock Based on the SMP Strength Criterion.

Aiming at the problem of chemical-mechanics-hydro (C-M-H) action encountered by rocks in underground engineering, chemical damage variables, water damage variables, and force damage variables are introduced to define the degree of degradation of rock materials. Stone is selected as the sample for acid corrosion treatment at pH 3, 4, and 7, and a chemical damage factor is defined that coupled the pH value and duration of exposure. Then based on the spatial mobilized plane (SMP) criterion and the Lemaitre strain equivalence hypothesis, this research develops a constitutive model considering rock chemical corrosion-water-confining pressure damage. The proposed damage constitutive model employs the extremum method to ascertain the two Weibull distribution parameters (m and F0) by theoretical derivation and exhibits satisfactory conformity between the theoretical and experimental curves. The damage constitutive model can be consistent in the stress-strain characteristics of the rock triaxial compression process, which verifies the rationality and reliability of the model parameters. The model effectively represents the mechanical properties and damage characteristics of rocks when subjected to the combined influence of water chemistry and confinement. The presented model contributes to a better understanding of tangible rock-engineered structures subjected to chemical corrosion in underwater environments.

Antiviral Activity of Ailanthone from Ailanthus altissima on the Rice Stripe Virus.

Rice stripe disease caused by the rice stripe virus (RSV), which infects many Poaceae species in nature, is one of the most devastating plant viruses in rice that causes enormous losses in production. Ailanthone is one of the typical C20 quassinoids synthesized by the secondary metabolism of Ailanthus altissima, which has been proven to be a biologically active natural product with promising prospects and great potential for use as a lead structure for pesticide development. Based on the achievement of the systemic infection and replication of RSV in Nicotiana benthamiana plants and rice protoplasts, the antiviral properties of Ailanthone were investigated by determining its effects on viral-coding RNA gene expression using reverse transcription polymerase chain reaction, and Western blot analysis. Ailanthone exhibited a dose-dependent inhibitory effect on RSV NSvc3 expression in the assay in both virus-infected tobacco plants and rice protoplasts. Further efforts revealed a potent inhibitory effect of Ailanthone on the expression of seven RSV protein-encoding genes, among which NS3, NSvc3, NS4, and NSvc4 are the most affected genes. These facts promoted an extended and greater depth of understanding of the antiviral nature of Ailanthone against plant viruses, in addition to the limited knowledge of its anti-tobacco mosaic virus properties. Moreover, the leaf disc method introduced and developed in the study for the detection of the antiviral activity of Ailanthone facilitates an available and convenient screening method for anti-RSV natural products or synthetic chemicals.

A Chemical Damage Creep Model of Rock Considering the Influence of Triaxial Stress.

In order to accurately describe the characteristics of each stage of rock creep behavior under the combined action of acid environment and true triaxial stress, based on damage mechanics, chemical damage is connected with elastic modulus; thus, the damage relations considering creep stress damage and chemical damage are obtained. The elastic body, nonlinear Kelvin body, linear Kelvin body, and viscoelastic-plastic body (Mogi-Coulomb) are connected in series, and the actual situation under the action of true triaxial stress is considered at the same time. Therefore, a damage creep constitutive model considering the coupling of rock acid corrosion and true triaxial stress is established. The parameters of the deduced model are identified and verified with the existing experimental research results. The yield surface equation of rock under true triaxial stress is obtained by data fitting, and the influence of intermediate principal stress on the creep model is discussed. The derived constitutive model can accurately describe the characteristics of each stage of true triaxial creep behavior of rock under acid environment.

A Quantitative Study of Micro and Macro Mechanical Parameters Based on the PFC3D Flat-Joint Model.

The flat-joint model, which constructs round particles as polygons, can suppress rotation after breakage between particles and simulate more larger compression and tension ratios than the linear parallel-bond model. The flat-joint contact model was chosen for this study to calibrate the rock for 3D experiments. In the unit experiments, the triaxial unit was loaded with flexible boundaries, and the influence of each microscopic parameter on the significance magnitude of the macroscopic parameters (modulus of elasticity E, Poisson's ratio ν, uniaxial compressive strength UCS, crack initiation strength σci, internal friction angle φ and uniaxial tensile strength TS) was analysed by ANOVA (Analysis of Variance) in an orthogonal experimental design. Among them, Eƒ, kƒ has a significant effect on E; Cƒ and kƒ have a significant effect on ν; Cƒ, σƒ and kƒ have a significant effect on UCS; Cƒ; σƒ and Eƒ have a significant effect on TS; Rsd has a significant effect on σci; and φf, Eƒ, kƒ, µƒ, and σƒ have a significant effect on φ. Regressions were then carried out to establish the equations for calculating the macroscopic parameters of the rock material so that the three-dimensional microscopic parameters of the PFC can be quantitatively analysed and calculated. The correctness of the establishment of the macroscopic equations was verified by comparing the numerical and damage patterns of uniaxial compression, Brazilian splitting, and triaxial experiments with those of numerical simulation units in the chamber.

Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification.

Purpose: Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images. Method: MRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max-min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc. Results: The proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen's Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods. Conclusion: Compared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice.

Diabetes patients with comorbidities had unfavorable outcomes following COVID-19: A retrospective study.

BACKGROUND: Previous studies have shown that diabetes mellitus is a common comorbidity of coronavirus disease 2019 (COVID-19), but the effects of diabetes or anti-diabetic medication on the mortality of COVID-19 have not been well described. AIM: To investigate the outcome of different statuses (with or without comorbidity) and anti-diabetic medication use before admission of diabetic after COVID-19. METHODS: In this multicenter and retrospective study, we enrolled 1422 consecutive hospitalized patients from January 21, 2020, to March 25, 2020, at six hospitals in Hubei Province, China. The primary endpoint was in-hospital mortality. Epidemiological material, demographic information, clinical data, laboratory parameters, radiographic characteristics, treatment and outcome were extracted from electronic medical records using a standardized data collection form. Most of the laboratory data except fasting plasma glucose (FPG) were obtained in first hospitalization, and FPG was collected in the next day morning. Major clinical symptoms, vital signs at admission and comorbidities were collected. The treatment data included not only COVID-19 but also diabetes mellitus. The duration from the onset of symptoms to admission, illness severity, intensive care unit (ICU) admission, and length of hospital stay were also recorded. All data were checked by a team of sophisticated physicians. RESULTS: Patients with diabetes were 10 years older than non-diabetic patients [(39 - 64) vs (56 - 70), P < 0.001] and had a higher prevalence of comorbidities such as hypertension (55.5% vs 21.4%, P < 0.001), coronary heart disease (CHD) (9.9% vs 3.5%, P < 0.001), cerebrovascular disease (CVD) (3% vs 2.2%, P < 0.001), and chronic kidney disease (CKD) (4.7% vs 1.5%, P = 0.007). Mortality (13.6% vs 7.2%, P = 0.003) was more prevalent among the diabetes group. Further analysis revealed that patients with diabetes who took acarbose had a lower mortality rate (2.2% vs 26.1, P < 0.01). Multivariable Cox regression showed that male sex [hazard ratio (HR) 2.59 (1.68 - 3.99), P < 0.001], hypertension [HR 1.75 (1.18 - 2.60), P = 0.006), CKD [HR 4.55 (2.52-8.20), P < 0.001], CVD [HR 2.35 (1.27 - 4.33), P = 0.006], and age were risk factors for the COVID-19 mortality. Higher HRs were noted in those aged ≥ 65 (HR 11.8 [4.6 - 30.2], P < 0.001) vs 50-64 years (HR 5.86 [2.27 - 15.12], P < 0.001). The survival curve revealed that, compared with the diabetes only group, the mortality was increased in the diabetes with comorbidities group (P = 0.009) but was not significantly different from the non-comorbidity group (P = 0.59). CONCLUSION: Patients with diabetes had worse outcomes when suffering from COVID-19; however, the outcome was not associated with diabetes itself but with comorbidities. Furthermore, acarbose could reduce the mortality in diabetic.

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo.

BACKGROUND: Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information. METHODS: Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out. RESULTS: A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. CONCLUSIONS: The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.

Chronic Cardio-Metabolic Disease Increases the Risk of Worse Outcomes Among Hospitalized Patients With COVID-19: A Multicenter, Retrospective, and Real-World Study.

Background Although chronic cardio-metabolic disease is a common comorbidity among patients with COVID-19, its effects on the clinical characteristics and outcome are not well known. Methods and Results This study aimed to explore the association between underlying cardio-metabolic disease and mortality with COVID-19 among hospitalized patients. This multicenter, retrospective, and real-world study was conducted from January 22, 2020 to March 25, 2020 in China. Data between patients with and without 5 main cardio-metabolic diseases including hypertension, diabetes mellitus, coronary heart disease, cerebrovascular disease, and hyperlipidemia were compared. A total of 1303 hospitalized patients were included in the final analysis. Of them, 520 patients (39.9%) had cardio-metabolic disease. Compared with patients without cardio-metabolic disease, more patients with cardio-metabolic disease had COVID-related complications including acute respiratory distress syndrome (9.81% versus 3.32%; P<0.001), acute kidney injury (4.23% versus 1.40%; P=0.001), secondary infection (13.9% versus 9.8%; P=0.026), hypoproteinemia (12.1% versus 5.75%; P<0.001), and coagulopathy (19.4% versus 10.3%; P<0.001), had higher incidences of the severe type of COVID-19 (32.9% versus 16.7%; P<0.001), more were admitted to the intensive care unit (11.7% versus 7.92%; P=0.021), and required mechanical ventilation (9.8% versus 4.3%; P<0.001). When the number of the patients' cardio-metabolic diseases was 0, 1, and >2, the mortality was 4.2%, 11.1%, and 19.8%, respectively. The multivariable-adjusted hazard ratio of mortality among patients with cardio-metabolic disease was 1.80 (95% CI, 1.17-2.77). Conclusions Cardio-metabolic disease was a common condition among hospitalized patients with COVID-19, and it was associated with higher risks of in-hospital mortality.

Laboratory markers associated with COVID-19 progression in patients with or without comorbidity: A retrospective study.

OBJECTIVES: To investigate laboratory markers for COVID-19 progression in patients with different medical conditions. METHODS: We performed a multicenter retrospective study of 836 cases in Hubei. To avoid the collinearity among the indicators, principal component analysis (PCA) followed by partial least squares discriminant analysis (PLS-DA) was performed to obtain an overview of laboratory assessments. Multivariable logistic regression analysis and multivariable Cox proportional hazards regression analysis were respectively used to explore risk factors associated with disease severity and mortality. Survival analysis was performed in patients with the most common comorbidities. RESULTS: Lactate dehydrogenase (LDH) and prealbumin were associated with disease severity in patients with or without comorbidities, indicated by both PCA/PLS-DA and multivariable logistic regression analysis. The mortality risk was associated with age, LDH, C-reactive protein (CRP), D-dimer, and lymphopenia in patients with comorbidities. CRP was a risk factor associated with short-term mortality in patients with hypertension, but not liver diseases; additionally, D-dimer was a risk factor for death in patients with liver diseases. CONCLUSIONS: Lactate dehydrogenase was a reliable predictor associated with COVID-19 severity and mortality in patients with different medical conditions. Laboratory biomarkers for mortality risk were not identical in patients with comorbidities, suggesting multiple pathophysiological mechanisms following COVID-19 infection.

Disorders of sodium balance and its clinical implications in COVID-19 patients: a multicenter retrospective study.

BACKGROUND: The worldwide spread of SARS-CoV-2 has infected millions of people leading to over 0.3 million mortalities. The disruption of sodium homeostasis, tends to be a common occurrence in patients with COVID-19. METHODS AND RESULTS: A total of 1,254 COVID-19 patients comprising 124 (9.9%) hyponatremic patients (under 135 mmol/L) and 30 (2.4%) hypernatremic patients (over 145 mmol/L) from three hospitals in Hubei, China, were enrolled in the study. The relationships between sodium balance disorders in COVID-19 patients, its clinical features, implications, and the underlying causes were presented. Hyponatremia patients were observed to be elderly, had more comorbidities, with severe pneumonic chest radiographic findings. They were also more likely to have a fever, nausea, higher leukocyte and neutrophils count, and a high sensitivity C-reactive protein (HS-CRP). Compared to normonatremia patients, renal insufficiency was common in both hyponatremia and hypernatremia patients. In addition, hyponatremia patients required extensive treatment with oxygen, antibiotics, and corticosteroids. The only significant differences between the hypernatremia and normonatremia patients were laboratory findings and clinical complications, and patients with hypernatremia were more likely to use traditional Chinese medicine for treatment compared to normonatremia patients. This study indicates that severity of the disease, the length of stay in the hospital of surviving patients, and mortality were higher among COVID-19 patients with sodium balance disorders. CONCLUSION: Sodium balance disorder, particularly hyponatremia, is a common condition among hospitalized patients with COVID-19 in Hubei, China, and it is associated with a higher risk of severe illness and increased in-hospital mortality.

Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study.

OBJECTIVE: This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm. DESIGN: Retrospective cohort study. SETTING: Five designated tertiary hospitals for COVID-19 in Hubei province, China. PARTICIPANTS: We routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19. METHODS: The model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers' D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted. MAIN OUTCOME MEASURES: The primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19. RESULTS: The full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort. CONCLUSION: The prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification. WEB CALCULATOR: We provided a freely accessible web calculator (https://www.whuyijia.com/).

Two Novel Quassinoid Glycosides with Antiviral Activity from the Samara of Ailanthus altissima.

Phytochemistry investigations on Ailanthus altissima (Mill.) Swingle, a Simaroubaceae plant that is recognized as a traditional herbal medicine, have afforded various natural products, among which C20 quassinoid is the most attractive for their significant and diverse pharmacological and biological activities. Our continuous study has led to the isolation of two novel quassinoid glycosides, named chuglycosides J and K, together with fourteen known lignans from the samara of A. altissima. The new structures were elucidated based on comprehensive spectra data analysis. All of the compounds were evaluated for their anti-tobacco mosaic virus activity, among which chuglycosides J and K exhibited inhibitory effects against the virus multiplication with half maximal inhibitory concentration (IC50) values of 56.21 ± 1.86 and 137.74 ± 3.57 µM, respectively.

Candida in Lower Respiratory Tract Increases the Frequency of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective Case-Control Study.

Objective: To explore impact of Candida on the acute exacerbation of chronic obstructive pulmonary disease (AECOPD) outcome. Methods: A retrospective, multi-center, case-control study was performed. Patients hospitalized for AECOPD in 25 centers during Jan 2011-Dec 2016 were enrolled. Data were collected, including demographic information, conditions during the stable phase of COPD, clinical characteristics of AECOPD, and follow-up information within 1 year after discharge. Univariate analysis and binary logistic regression were applied, and p < 0.05 was regarded as significant. Results: Totally 1,103 patients were analyzed, with 644 lower respiratory airway (LTR) Candida positive cases and 459 Candida negative controls. Long-term prognosis was significantly different between Candida positive and negative group, including the recurrent AECOPD within 180 days (75.5 vs. 6.6%, p < 0.001) and mortality within 1 year (6.9 vs. 0.4%, p < 0.001). Univariate logistic analysis showed that LTR Candida isolation was related to higher recurrence rate of AECOPD within 180 days and mortality within 1 year. Binary logistic regression analysis demonstrated that LTR Candida isolation was independently associated with recurrence of AECOPD within 180 days. Conclusions: LTR Candida isolation was associated with worse long-term prognosis of AECOPD and independently related to higher risks of recurrent AECOPD within 180 days.

Elevated Lactate Dehydrogenase (LDH) level as an independent risk factor for the severity and mortality of COVID-19.

Early identification of severe patients with coronavirus disease 2019 (COVID-19) is very important for individual treatment. We included 203 patients with COVID-19 by propensity score matching in this retrospective, case-control study. The effects of serum lactate dehydrogenase (LDH) at admission on patients with COVID-19 were evaluated. We found that serum LDH levels had a 58.7% sensitivity and 82.0% specificity, based on a best cut-off of 277.00 U/L, for predicting severe COVID-19. And a cut-off of 359.50 U/L of the serum LDH levels resulted in a 93.8% sensitivity, 88.2% specificity for predicting death of COVID-19. Additionally, logistic regression analysis and Cox proportional hazards model respectively indicated that elevated LDH level was an independent risk factor for the severity (HR: 2.73, 95% CI: 1.25-5.97; P=0.012) and mortality (HR: 40.50, 95% CI: 3.65-449.28; P=0.003) of COVID-19. Therefore, elevated LDH level at admission is an independent risk factor for the severity and mortality of COVID-19. LDH can assist in the early evaluating of COVID-19. Clinicians should pay attention to the serum LDH level at admission for patients with COVID-19.

Two new compounds from the fruit of Ailanthus altissima.

A new phenolic derivative, 4-hydroxyphenol-1-O-[6-O-(E)-feruloyl-ß-d-glucopyranosyl]-(1→6)-ß-d-glucopyranoside (1), and a new terpenylated coumarin, named altissimacoumarin H (2) were identified from the fruit of Ailanthus altissima (Mill.) Swingle (Simaroubaceae), together with ten known compounds (3-12), including two coumarins and eight phenylpropanoids. Their structures were determined on the basis of chemical method and spectroscopic data. Antiviral effect against Tobacco mosaic virus (TMV) of all the compounds obtained were evaluated using leaf-disc method.

Anti-Tobacco Mosaic Virus Quassinoids from Ailanthus altissima (Mill.) Swingle.

Quassinoids are bitter constituents characteristic of the family Simaroubaceae. A total of 18 C20 quassinoids, including nine new quassinoid glycosides, named chuglycosides A-I (1-6 and 8-10), were identified from the samara of Ailanthus altissima (Mill.) Swingle. All of the quassinoids showed potent anti-tobacco mosaic virus (TMV) activity. A preliminary structure-anti-TMV activity relationship of quassinoids was discussed. The effects of three quassinoids, including chaparrinone (12), glaucarubinone (15), and ailanthone (16), on the accumulation of TMV coat protein (CP) were studied by western blot analysis. Ailanthone (16) was further investigated for its influence on TMV spread in the Nicotiana benthamiana plant.