Advance in integrating platinum-based chemotherapy with radiotherapy for locally advanced nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by the malignant transformation of nasopharyngeal epithelial cells. It is highly sensitive to radiation therapy, making radiotherapy the primary treatment modality. However, 60-80% of patients are initially diagnosed with locally advanced NPC (LA-NPC), where radiotherapy alone often fails to achieve desirable outcomes. Therefore, combining radiotherapy with chemotherapy has emerged as an effective strategy to optimize treatment for LA-NPC patients. Among the various chemotherapy regimens, concurrent chemoradiotherapy (CCRT) using platinum-based drugs has been established as the most commonly utilized approach for LA-NPC patients. The extensive utilization of platinum drugs in clinical settings underscores their therapeutic potential and emphasizes ongoing efforts in the development of novel platinum-based complexes for anticancer therapy. The aim of this review is to elucidate the remarkable advances made in the field of platinum-based therapies for nasopharyngeal carcinoma, emphasizing their transformative impact on patient prognosis.

Advancements of radiotherapy for recurrent head and neck cancer in modern era.

Head and neck cancer is a kind of cancer which can be eradicated from radical radiation therapy. However, with best efforts, nearly 40% patients will experience locoregional recurrence. Locoregional recurrence is the main cause of cancer-related death in head and neck cancers, so local treatments play a key role in improving progression free survival. In the last decades, radiation techniques have been tremendously developed, highly conformal radiation techniques such as intensity-modulated radiotherapy, stereotactic body radiation therapy, brachytherapy and proton or heavy ion radiation therapy have their unique radiobiological advances. Although reirradiation is widely used in clinical practice, but little is known when comparing the different techniques. In this review, we will provide a comprehensive overview of the role of reirradiation in recurrent head and neck cancers including radiation techniques, patient selection, overall clinical benefits, and toxicities.

Construction of cuproptosis-related gene signature to predict the prognosis and immunotherapy efficacy of patients with bladder cancer through bioinformatics analysis and experimental validation.

Background: A new form of cell death, copper-dependent cell death (termed cuproptosis), was illustrated in a recent scientific study. However, the biological function or prognostic value of cuproptosis regulators in bladder cancer (BLCA) remains unknown. Materials and Methods: Sequencing data obtained from BLCA samples in TCGA and GEO databases were preprocessed for analysis. Biological function and immune cell infiltration levels evaluated by gene set variation analysis (GSVA) were employed to calculate enrichment scores. Iteration least absolute shrinkage and selection operator (LASSO) and COX regression model were employed to select feature genes and construct a novel cuproptosis-related (CR) score signature. The genomics of drug sensitivity in cancer (GDSC) and tumor immune dysfunction and exclusion (TIDE) analysis were used to predict the chemotherapy and immunotherapy efficacy for BLCA patients. The relative expression of the genes involved in the signature was also verified by real-time quantitative PCR (qRT-PCR) in cell lines and tissues. Results: Expression abundance and the prognostic value of cuproptosis regulators proved that cuproptosis might play a vital part in the carcinogenesis of BLCA. GSVA revealed that cuproptosis regulators might be associated with metabolism and metastasis-related pathways such as TGF-ß, protein secretion, oxidative Phosphorylation, MYC targets, MTORC1, and adipogenesis pathways. CR scores could predict the prognosis and evaluate the chemotherapy and immunotherapy efficacies of BLCA. CR scores were positively correlated with EMT, MYC, MTORC1, HEDGEHOG, and E2F signaling pathways; meanwhile, they were negatively correlated with several immune cell infiltration levels such as CD8+ T cells, γδT cells, and activated dendritic cells. Several GEO datasets were used to validate the power of prognostic prediction, and a nomogram was also established for clinical use. The expressions of DDX10, RBM34, and RPL17 were significantly higher in BLCA cell lines and tissues in comparison with those in the corresponding normal controls. Conclusion: Cuproptosis might play an essential role in the progression of BLCA. CR scores could be helpful in the investigation of prognostic prediction and therapeutic efficacy and could make contributions to further studies in BLCA.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Response to COVID-19 in the Central African Republic: Coping Strategies Combined With China's Experience.

Objectives: The weak health system, domestic political unrest, poverty, and many other factors in the Central African Republic (CAR) have left the country underprepared for the COVID-19 pandemic, resulting in a greater health threat to the entire country. Rapid measures must therefore be taken to prevent the further spread of COVID-19. Methods: This work encompassed a review of relevant literature. We aim to analyze how far Chinese COVID measures can be transferred to the context of the CAR. Results: We argue that the measure that the CAR can learn from China's success is the involvement of community workers and that greater investment in this model may be the optimal solution. Help from the international community is urgently needed. Conclusion: The CAR can benefit from China's successful experience in fighting the epidemic, but the disparity in the combined power of the two countries does not allow for simple replication of China's strategy.

Deletion of the miR-25/93/106b cluster induces glomerular deposition of immune complexes and renal fibrosis in mice.

IgA nephropathy (IgAN), the most common form of primary glomerulonephritis, is caused by immune system dysfunction and affects only the kidneys. miRNA was involved in IgAN, in which their roles are still unknown. Herein, we found increased glomerular medulla size, proteinuria, kidney artery resistance, kidney fibrosis and immune complex deposition in 5-month miR-25/93/106b cluster knockout (miR-TKO) mice. In vitro, the inhibition of miR-25 cluster could promote cell proliferation and increase fibrosis-related protein and transferrin receptor (TFRC) expression in human renal glomerular mesangial cell (HRMC). Luciferase assay revealed that inhibition of miR-93/106b cluster could upregulate Ccnd1 expression through direct binding with the 3'UTR of Ccnd1. Conversely, inhibition of Ccnd1 expression prevented miR-93/106b-induced effect in HRMC. These findings suggested that miR-25 cluster played an important role in the progression of IgAN, which provided new insights into the pathogenesis and treatment of IgAN.