Effect of combined vitamin D and microwave ablation of parathyroid glands on blood pressure and cardiac function in maintenance-hemodialysis patients with uremic secondary hyperparathyroidism

Purpose: To investigate the effect of microwave ablation of parathyroid glands in combination with active vitamin D on blood pressure and cardiac function in maintenance-hemodialysis patients with uremic secondary hyperparathyroidism. Methods: One hundred and twenty maintenance-hemodialysis patients with uremic secondary hyperparathyroidism admitted to Meizhou People’s Hospital were assigned to 2 groups (A and B) in the order of their admission. Each group had 60 patients. Both groups were treated with active vitamin D, while patients in group A were, in addition, subjected to microwave ablation of parathyroid glands. Blood pressure, and indices for cardiac function, thyroid function s and anemia were determined. Results: After treatment, the blood pressure of group A was significantly lower than that of group B (p < 0.05). Moreover, after treatment, there were significant improvements in indices of cardiac function, thyroid function and anemia in group A patients, relative to group B patients. Conclusion: Microwave ablation of parathyroid glands, when combined with active vitamin D, improves blood pressure, cardiac function and anemia status. Furthermore, the combined therapy enhances recovery of thyroid function in maintenance-hemodialysis patients with uremic secondary hyperparathyroidism. However, the combined therapy should be subjected to further clinical trials prior to application in clinical practice. Keywords: Microwave ablation; Parathyroid glands; Active vitamin D; Hyperparathyroidis

Secondary Production of Gaseous Nitrated Phenols in Polluted Urban Environments

Nitrated phenols (NPs) are important atmospheric pollutants that affect air quality, radiation, and health. The recent development of the time-of-flight chemical ionization mass spectrometer (ToF-CIMS) allows quantitative online measurements of NPs for a better understanding of their sources and environmental impacts. Herein, we deployed nitrate ions as reagent ions in the ToF-CIMS and quantified six classes of gaseous NPs in Beijing. The concentrations of NPs are in the range of 1 to 520 ng m(-3). Nitrophenol (NPh) has the greatest mean concentration. Dinitrophenol (DNP) shows the greatest haze-to-clean concentration ratio, which may be associated with aqueous production. The high concentrations and distinct diurnal profiles of NPs indicate a strong secondary formation to overweigh losses, driven by high emissions of precursors, strong oxidative capacity, and high NOx levels. The budget analysis on the basis of our measurements and box-model calculations suggest a minor role of the photolysis of NPs (Peer reviewe

Precursors and Pathways Leading to Enhanced Secondary Organic Aerosol Formation during Severe Haze Episodes

Publisher Copyright: © 2021 American Chemical SocietyMolecular analyses help to investigate the key precursors and chemical processes of secondary organic aerosol (SOA) formation. We obtained the sources and molecular compositions of organic aerosol in PM2.5in winter in Beijing by online and offline mass spectrometer measurements. Photochemical and aqueous processing were both involved in producing SOA during the haze events. Aromatics, isoprene, long-chain alkanes or alkenes, and carbonyls such as glyoxal and methylglyoxal were all important precursors. The enhanced SOA formation during the severe haze event was predominantly contributed by aqueous processing that was promoted by elevated amounts of aerosol water for which multifunctional organic nitrates contributed the most followed by organic compounds having four oxygen atoms in their formulae. The latter included dicarboxylic acids and various oxidation products from isoprene and aromatics as well as products or oligomers from methylglyoxal aqueous uptake. Nitrated phenols, organosulfates, and methanesulfonic acid were also important SOA products but their contributions to the elevated SOA mass during the severe haze event were minor. Our results highlight the importance of reducing nitrogen oxides and nitrate for future SOA control. Additionally, the formation of highly oxygenated long-chain molecules with a low degree of unsaturation in polluted urban environments requires further research.Peer reviewe

An Autoencoder-Based Deep Learning Method For Genotype Imputation

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems