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Background: Ascertaining the total body water (V), usually obtained by the Watson formula or bioimpedance spectroscopy (BIS), is crucial for the calculation of Kt/V in patients with peritoneal dialysis (PD). The aim of our study was to compare two different methods of determining V and explore which one is suitable for clinical application.Methods: This was a retrospective observational study. V was determined using the Watson formula (Vwat) and BIS (Vbis). The differences between Vbis and Vwat and between Kt/Vbis and Kt/Vwat were assessed. The patients were allocated to different groups according to the Kt/Vwat and Kt/Vbis values. Clinical parameters were compared between these groups to investigate which method of obtaining the Kt/V value was more suitable.Results: 150 patients on PD were included. Vwat was significantly higher than Vbis, apart from in female patients with volume overload. Consequently, weekly Kt/Vwat was lower than Kt/Vbis in these patients. A significant negative correlation between mean Vwat-Vbis and overhydration values was also found. Moreover, through uniform manifold approximation and projection analysis, a clustering tendency between patients in the adequate group with both Kt/Vwat and Kt/Vbis > =1.7 and patients in the inconsistent group with Kt/Vwat =1.7 was identified, suggesting that their clinical features were similar.Conclusion There were significant differences between Vwat and Vbis and between Kt/Vwat and Kt/Vbis. Kt/Vwat may underestimate small-solute dialysis adequacy in most cases. Kt/Vbis instead of Kt/Vwat could be accounted for in creating individualized dialysis prescriptions if the patient has no obvious clinical symptoms.

Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training. Using blind-spot networks, we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample. Based on the assumption that noise is statistically independent between samples, the network struggles to predict the noise component of the sample due to its randomicity, whilst the signal component is accurately predicted due to its spatio-temporal coherency. Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as post-stack inversion. To conclude our study, the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques: FX-deconvolution and sparsity-promoting inversion by Curvelet transform. By demonstrating that blind-spot networks are an efficient suppressor of random noise, we believe this is just the beginning of utilising self-supervised learning in seismic applications.

Fine particle (PM2.5, less than 2.5 micrometers in diameter) is regarded as a harmful carcinogen. However, the molecular mechanisms of the carcinogenic effects of ambient fine particles have not been fully elucidated, and therapeutic options to address this major public health challenge are lacking. Here, we present global gene-specific DNA methylation and transcriptomic (RNA-Seq) analyses after HBE cells were exposed to fine particles on a portable, small, and all-in-one organ-level lung-mimicking air-liquid interface exposure (MALIE) microfluidic platform. A series of cancer-related signal transduction pathways were activated. ErbB1, ErbB2, and ErbB3 gene expression altered by fine particle exposure was the result of changes in the cellular DNA methylome. The protein expression of ErbB family was inhibited by drugs and could regulate downstream Grb2/Raf pathway and Akt/MDM2 pathway. All of the above results indicated that ErbB family may be promising drug targets for air pollution-related diseases and that inhibitor drugs can be used as therapeutic options to treat these diseases.

Purpose. To evaluate the influence of intraocular pressure (IOP) measurement while wearing bandage contact lens (BCL) and the effect of Pentacam and Corvis ST’s correction systems. Methods. It was a prospective comparative study. Forty healthy subjects (40 eyes) were included in this study. Eyes were measured using noncontact tonometer (NCT), Corvis ST, and Pentacam before and after wearing BCL. Pentacam’s five correction formulas (Ehlers formula, Shah formula, Dresden formula, Kohlhaas formula, Orssengo/Pye formula) and Corvis ST’s correction formulas (Ehlers formula and biomechanical corrected formula) were used to correct the IOP values before and after BCL wearing. The IOP values were compared, and the correction effect of different systems were evaluated. Results. The mean age of the subjects was 24.4 ± 0.60 years. The mean IOP obtained by NCT was 14.8 ± 3.2 mmHg before BCL wearing and was 15.7 ± 3.4 mmHg after BCL wearing. The mean IOP was significantly increased after BCL wearing (0.9 ± 2.9 mmHg, P=0.05). Four of the five Pentacam’s correction formulas (except Kohlhaas formula) showed no significant difference in the mean corrected IOP values before and after BCL wearing (all P>0.05). The mean IOP obtained by Corvis ST was 13.7 ± 2.8 mmHg before BCL wearing and was 15.0 ± 4.0 mmHg after BCL wearing. The mean IOP was significantly increased after BCL wearing (1.3 ± 2.4 mmHg, P0.05). Conclusion. The IOP measurements over BCL by NCT and Corvis ST was found to be increased. The correction systems of Pentacam (Ehlers formula, Shah formula, Dresden formula, and Orssengo/Pye formula) and Corvis ST (Ehlers formula) are useful in correcting the IOP measuring deviation induced by BCL wearing.

Summary: The application of machine learning toward DNA encoded library (DEL) technology is lacking despite obvious synergy between these two advancing technologies. Herein, a machine learning algorithm has been developed that predicts the conversion rate for the DNA-compatible reaction of a building block with a model DNA-conjugate. We exemplify the value of this technique with a challenging reaction, the Pictet-Spengler, where acidic conditions are normally required to achieve the desired cyclization between tryptophan and aldehydes to provide tryptolines. This is the first demonstration of using a machine learning algorithm to cull potential building blocks prior to their purchase and testing for DNA-encoded library synthesis. Importantly, this allows for a challenging reaction, with an otherwise very low building block pass rate in the test reaction, to still be used in DEL synthesis. Furthermore, because our protocol is solution phase it is directly applicable to standard plate-based DEL synthesis.

Background/Aims: Previous studies have shown that homocysteine (Hcy) is an important intestinal-derived uremic toxin. However, whether Hcy is involved in the epithelial barrier dysfunction observed in uremia remains unclear. This study aimed to investigate the effect of Hcy on intestinal permeability and intestinal barrier structure and function in adenine-induced uremic rats. Methods: Sprague-Dawley rats were divided into five groups: normal control (group NC), Hcy (group H), uremia (group U), uremia + Hcy (group UH), and uremia + Hcy + VSL#3 (group UHV). Experimental uremia was induced by intragastric adenine administration, and Hcy was injected subcutaneously. The animal models were assessed for renal function and pathological tissue staining. The pathological changes of intestinal tissue were observed by hematoxylin and eosin staining and electron microscopy. The serum and intestinal tissue levels of Hcy, interleukin (IL)-6, tumor necrosis factor (TNF)-α, superoxide dismutase (SOD), and malondialdehyde (MDA) as well as serum endotoxin and intestinal permeability were assessed. The levels of the tight junction proteins claudin-1, occludin, and zonula occludens-1 (ZO-1) were assessed by western blotting. Results: Blood analyses and renal pathology indicated that experimental uremia was induced successfully. Pathological damage to intestinal structure was most obvious in group UH. Serum and tissue Hcy, serum endotoxin, and intestinal permeability were significantly elevated in group UH. The protein levels of claudin-1, occludin, and ZO-1 were decreased to various degrees in group UH compared with groups NC, H, and U. The serum and tissue levels of IL-6, TNF-α, and MDA were significantly increased, while SOD activity was markedly decreased. Supplementation with the probiotic VSL#3 improved these parameters to various degrees and up-regulated the abundance of tight junction proteins, which indicated a role for Hcy in the increase of intestinal permeability and destruction of the epithelial barrier in uremia. Conclusion: Hcy aggravates the increase of intestinal permeability and destruction of the epithelial barrier by stimulating inflammatory and oxidative damage. Probiotic administration can ameliorate this damage by reducing the levels of Hcy-induced inflammation and oxidation.

Background/Aims: Recently, the colon has been recognized as an important source of various uremic toxins in patients with end stage renal disease. Medicinal charcoal tablets are an oral adsorbent that are widely used in patients with chronic kidney disease in China to remove creatinine and urea from the colon. A parallel fecal and serum metabolomics study was performed to determine comprehensive metabolic profiles of patients receiving hemodialysis (HD). The effects of medicinal charcoal tablets on the fecal and serum metabolomes of HD patients were also investigated. Methods: Ultra-performance liquid chromatography/mass spectrometry was used to investigate the fecal and serum metabolic profiles of 20 healthy controls and 31 HD patients before and after taking medicinal charcoal tablets for 3 months. Results: There were distinct metabolic variations between the HD patients and healthy controls both in the feces and serum according to multivariate data analysis. Metabolic disturbances of alanine, aspartate and glutamate metabolism, arginine and proline metabolism figured prominently in the serum. However, in the feces, alterations of tryptophan metabolism, lysine degradation and beta-alanine metabolism were pronounced, and the levels of several amino acids (leucine, phenylalanine, lysine, histidine, methionine, tyrosine, and tryptophan) were increased dramatically. Nineteen fecal metabolites and 21 serum metabolites were also identified as biomarkers that contributed to the metabolic differences. Additionally, medicinal charcoal treatment generally enabled the serum and fecal metabolomes of the HD patients to draw close to those of the control subjects, especially the serum metabolic profile. Conclusion: Parallel fecal and serum metabolomics uncovered the systematic metabolic variations of HD patients, especially disturbances in amino acid metabolism in the colon. Medicinal charcoal tablets had an impact on the serum and fecal metabolomes of HD patients, but their exact effects still need to be studied further.