Prediction of treatment response in lupus nephritis using density of tubulointerstitial macrophage infiltration

BackgroundLupus nephritis (LN) is a common disease with diverse clinical and pathological manifestations. A major challenge in the management of LN is the inability to predict its treatment response at an early stage. The objective of this study was to determine whether the density of tubulointerstitial macrophage infiltration can be used to predict treatment response in LN and whether its addition to clinicopathological data at the time of biopsy would improve risk prediction.MethodsIn this retrospective cohort study, 430 patients with LN in our hospital from January 2010 to December 2017 were included. We used immunohistochemistry to show macrophage and lymphocyte infiltration in their biopsy specimens, followed by quantification of the infiltration density. The outcome was the treatment response, defined as complete or partial remission at 12 months of immunosuppression.ResultsThe infiltration of CD68+ macrophages in the interstitium increased in patients with LN. High levels of CD68+ macrophage infiltration in the interstitium were associated with a low probability of treatment response in the adjusted analysis, and verse vice. The density of CD68+ macrophage infiltration in the interstitium alone predicted the response to immunosuppression (area under the curve [AUC], 0.70; 95% CI, 0.63 to 0.76). The addition of CD68+cells/interstitial field to the pathological and clinical data at biopsy in the prediction model resulted in an increased AUC of 0.78 (95% CI, 0.73 to 0.84).ConclusionThe density of tubulointerstitial macrophage infiltration is an independent predictor for treatment response in LN. Adding tubulointerstitial macrophage infiltration density to clinicopathological data at the time of biopsy significantly improves risk prediction of treatment response in LN patients

Soil Nutrient Status and Leaf Nutrient Diagnosis in the Main Apple Producing Regions in China

Soil and leaf nutrient analysis are widely used as effective methods of diagnosing nutrient deficiency in fruit trees, the results of which are used to properly manage fertilizer application. Therefore, a survey was conducted for assessment of the soil nutrient status and leaf nutrient concentration in 2 827 apple orchards in the Bohai Bay and Loess Plateau apple production regions of China. The soil organic matter, alkali hydrolyzable N, available P, and available K were 10.91 g·kg−1, 73.21 mg·kg−1, 70.22 mg·kg−1, and 169.23 mg·kg−1 in the Bohai Bay region, respectively, and 11.72 g·kg−1, 56.46 mg·kg−1, 14.91 mg·kg−1, and 135.78 mg·kg−1 in the Loess Plateau region, respectively. Soil organic matter was at a medium-to-low level in both regions, whereas the soil alkali hydrolyzable N was low. In the Bohai Bay region, soil available P was high, but soil available K was deficient. In contrast, both soil available P and K were insufficient in the Loess Plateau region. The Diagnosis and Recommendation Integrated System (DRIS) diagnostic results indicated that the most deficient elements were Ca and K in low-yielding orchards (<35 t·hm−2) of the Bohai Bay region followed by Fe, N, and Zn; however in the Loess Plateau region, the most deficient elements were P and K followed by N, Zn, and Cu. The findings imply that the application of Ca, K, Fe, N, and Zn fertilizer should be increased in the Bohai Bay region, whereas P, K, N, Zn, and Cu fertilizer should be enhanced in the Loess Plateau region. Meanwhile, use of organic manure is recommended to improve soil quality in the two apple producing regions. Keywords: apple, soil nutrition, leaf nutrient, nutrient deficiency, diagnosi

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data

Inversion of Nitrogen Concentration in Apple Canopy Based on UAV Hyperspectral Images

As the major nutrient affecting crop growth, accurate assessing of nitrogen (N) is crucial to precise agricultural management. Although improvements based on ground and satellite data nitrogen in monitoring crops have been made, the application of these technologies is limited by expensive costs, covering small spatial scales and low spatiotemporal resolution. This study strived to explore an effective approach for inversing and mapping the distributions of the canopy nitrogen concentration (CNC) based on Unmanned Aerial Vehicle (UAV) hyperspectral image data in a typical apple orchard area of China. A Cubert UHD185 imaging spectrometer mounted on a UAV was used to obtain the hyperspectral images of the apple canopy. The range of the apple canopy was determined by the threshold method to eliminate the effect of the background spectrum from bare soil and shadow. We analyzed and screened out the spectral parameters sensitive to CNC, including vegetation indices (VIs), random two-band spectral indices, and red-edge parameters. The partial least squares regression (PLSR) and backpropagation neural network (BPNN) were constructed to inverse CNC based on a single spectral parameter or a combination of multiple spectral parameters. The results show that when the thresholds of normalized difference vegetation index (NDVI) and normalized difference canopy shadow index (NDCSI) were set to 0.65 and 0.45, respectively, the canopy’s CNC range could be effectively identified and extracted, which was more refined than random forest classifier (RFC); the correlation between random two-band spectral indices and nitrogen concentration was stronger than that of other spectral parameters; and the BPNN model based on the combination of random two-band spectral indices and red-edge parameters was the optimal model for accurately retrieving CNC. Its modeling determination coefficient (R2) and root mean square error (RMSE) were 0.77 and 0.16, respectively; and the validation R2 and residual predictive deviation (RPD) were 0.75 and 1.92. The findings of this study can provide a theoretical basis and technical support for the large-scale, rapid, and non-destructive monitoring of apple nutritional status

Magnesium alleviates aluminum-induced growth inhibition by enhancing antioxidant enzyme activity and carbon–nitrogen metabolism in apple seedlings

Previous studies have determined that magnesium (Mg) in appropriate concentrations prevents plants from suffering from abiotic stress. To better understand the mechanism of Mg alleviation of aluminum (Al) stress in apple, we investigated the effect of Mg on plant growth, photosynthetic fluorescence, antioxidant system, and carbon (C) and nitrogen (N) metabolism of apple seedlings under Al toxicity (1.5 mmol/L) via a hydroponic experiment. Al stress induced the production of reactive oxygen in the leaves and roots and reduced the total dry weight (DW) by 52.37 % after 20 days of treatment relative to plants grown without Al, due to hindered photosynthesis and alterations in C and N metabolism. By contrast, total DW decreased by only 11.07 % in the Mg-treated plants under Al stress. Supplementation with 3.0 mmol/L Mg in the Al treatment decreased Al accumulation in the apple plants and reduced Al-induced oxidative damage by enhancing the activity of antioxidant enzymes (superoxide dismutase, catalase, and peroxidase) and reducing the production of H2O2 and malondialdehyde (MDA). Under Al stress, the Mg-treated plants showed a 46.17 % higher photosynthetic rate than the non-treated plants. Supplementation with Mg significantly increased the sucrose content by increasing sucrose synthase (SS) and sucrose-phosphate synthase (SPS) activities. Moreover, Mg facilitated the transport of 13C-carbohydrates from the leaves to roots. Regarding N metabolism, the nitrate reductase (NR), glutamine synthase (GS), and glutamate synthase (GOGAT) activities in the roots and leaves of the Mg-treated plants were significantly higher than those of the non-treated plants under Al stress. Compared with the non-treated plants under Al stress, the Mg-treated plants exhibited a significantly high level of NO3- and soluble protein content in the leaves, roots, and stems, but a low level of free amino acids. Furthermore, Mg significantly improved nitrogen accumulation and enhanced the transport of 15N from the roots to leaves. Overall, our results revealed that Mg alleviates Al-induced growth inhibition by enhancing antioxidant capacity and C-N metabolism in apple seedlings

The attenuated African swine fever vaccine HLJ/18-7GD provides protection against emerging prevalent genotype II variants in China

ABSTRACTGenetic changes have occurred in the genomes of prevalent African swine fever viruses (ASFVs) in the field in China, which may change their antigenic properties and result in immune escape. There is usually poor cross-protection between heterogonous isolates, and, therefore, it is important to test the cross-protection of the live attenuated ASFV vaccines against current prevalent heterogonous isolates. In this study, we evaluated the protective efficacy of the ASFV vaccine candidate HLJ/18-7GD against emerging isolates. HLJ/18-7GD provided protection against a highly virulent variant and a lower lethal isolate, both derived from genotype II Georgia07-like ASFV and isolated in 2020. HLJ/18-7GD vaccination prevented pigs from developing ASF-specific clinical signs and death, decreased viral shedding via the oral and rectal routes, and suppressed viral replication after challenges. However, HLJ/18-7GD vaccination did not provide solid cross-protection against genotype I NH/P68-like ASFV challenge in pigs. HLJ/18-7GD vaccination thus shows great promise as an alternative strategy for preventing and controlling genotype II ASFVs, but vaccines providing cross-protection against different ASFV genotypes may be needed in China

Development of an ELISA Method to Differentiate Animals Infected with Wild-Type African Swine Fever Viruses and Attenuated HLJ/18-7GD Vaccine Candidate

African swine fever (ASF) is a highly contagious hemorrhagic disease of pigs, posing a significant threat to the world pig industry. Several researchers are investigating the possibilities for developing a safe and efficient vaccine against ASF. In this regard, significant progress has been made and some gene-deleted ASFVs are reported as potential live attenuated vaccines. A seven-gene-deleted live attenuated vaccine candidate HLJ/18-7GD (among which CD2v is included) has been developed in our laboratory and reported to be safe and protective, and it is expected to be commercialized in the near future. There is an urgent need for developing a diagnostic method that can clearly discriminate between wild-type-ASFV-infected and vaccinated animals (DIVA). In the present study, a dual indirect ELISA based on p54 and CD2v proteins was successfully established to specifically distinguish serum antibodies from pigs infected with wild-type ASFV or possessing vaccine immunization. To evaluate the performance of the assay, a total of 433 serum samples from four groups of pigs experimentally infected with the wild-type HLJ/18 ASFV, immunized with the HLJ/18-7GD vaccine candidate, infected with the new lower virulent variant, and specific-pathogen-free pigs were used. Our results showed that the positive rate of immunized serum was 96.54% (p54) and 2.83% (CD2v), and the positive rate of the infection by wild-type virus was 100% (p54) and 97.8% (CD2v). Similarly, the positive rate to infection by the new low-virulent ASFV variant in China was 100% (p54) and 0% (CD2v), indicating the technique was also able to distinguish antibodies from wild-type and the new low-virulent ASFV variant in China. Moreover, no cross-reaction was observed in immune sera from other swine pathogens, such as CSFV, PEDV, PRRSV, HP-PRRSV, PCV2, and PrV. Overall, the developed dual indirect ELISA exhibited high diagnostic sensitivity, specificity, and repeatability and will provide a new approach to differentiate serum antibodies between wild virulent and CD2v-unexpressed ASFV infection, which will play a great role in serological diagnosis and epidemiological monitoring of ASF in the future

Highly lethal genotype I and II recombinant African swine fever viruses detected in pigs

Abstract African swine fever virus (ASFV) poses a great threat to the global pig industry and food security. Currently, 24 ASFV genotypes have been reported but it is unclear whether recombination of different genotype viruses occurs in nature. In this study, we detect three recombinants of genotype I and II ASFVs in pigs in China. These recombinants are genetically similar and classified as genotype I according to their B646L gene, yet 10 discrete fragments accounting for over 56% of their genomes are derived from genotype II virus. Animal studies with one of the recombinant viruses indicate high lethality and transmissibility in pigs, and deletion of the virulence-related genes MGF_505/360 and EP402R derived from virulent genotype II virus highly attenuates its virulence. The live attenuated vaccine derived from genotype II ASFV is not protective against challenge of the recombinant virus. These naturally occurring recombinants of genotype I and II ASFVs have the potential to pose a challenge to the global pig industry