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Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN.Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN.Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples.Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue.

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan.

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527–0.667] compared to using a single multispectral sensor (R2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha–1, T2 = 180 kg ha–1, and T3 = 240 kg ha–1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha–1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.