External validation of the R.I.R.S. scoring system to predict stone-free rate after retrograde intrarenal surgery.

BACKGROUND: The R.I.R.S. scoring system is defined as a novel and straightforward scoring system that uses the main parameters (kidney stone density, inferior pole stones, stone burden, and renal infundibular length) to identify most appropriate patients for retrograde intrarenal surgery (RIRS). We strived to evaluate the accuracy of the R.I.R.S. scoring system in predicting the stone-free rate (SFR) after RIRS. METHODS: In our medical center, we retrospectively analyzed charts of patients who had, between September 2018 and December 2019, been treated by RIRS for kidney stones. A total of 147 patients were enrolled in the study. Parameters were measured for each of the four specified variables. RESULTS: Stone-free status was achieved in 105 patients (71.43%), and 42 patients had one or more residual fragments (28.57%). Differences in stone characteristics, including renal infundibulopelvic angle, renal infundibular length, lower pole stone, kidney stone density, and stone burden were statistically significant in patients whether RIRS achieved stone-free status or not (P < 0.001, P: 0.005, P < 0.001, P < 0.001, P: 0.003, respectively). R.I.R.S. scores were significantly lower in patients treated successfully with RIRS than patients in which RIRS failed (P < 0.001). Binary logistic regression analyses revealed that R.I.R.S. scores were independent factors affecting RIRS success (P = 0.033). The area under the curve of the R.I.R.S. scoring system was 0.737. CONCLUSIONS: Our study retrospectively validates that the R.I.R.S. scoring system is associated with SFR after RIRS in the treatment of renal stones, and can predict accurately.

Specular highlight removal based on an iterative light field method.

In this paper, a novel, to the best of our knowledge, iterative approach for highlight removal is proposed using lenselet-based plenoptic cameras without multiple exposures. An unsupervised k-means clustering approach that relates unsaturated pixels to chromatic dispersion based on the intrinsic decomposition and dichromatic reflection model is proposed to recover unsaturated highlights. Meanwhile, an adaptive direction method along with a Gaussian probability distribution model is designed to recover the saturated highlights. Finally, a method that combines the specular residual ratio with information entropy is built to quantitatively evaluate the quality of highlight removal. Generally, our method not only fully removes specular highlights, but also has low spatial complexity of image acquisition, more stability, and outstanding restoration for complex scenes.

Predicting narrow ureters before ureteroscopic lithotripsy with a neural network: a retrospective bicenter study.

In some patients, the passage of semi-rigid ureteroscopes up the ureter is impossible due to narrow ureteral lumen. We established a neural network to predict the inability of the ureter to accommodate the semi-rigid ureteroscope and the need for active or passive dilatation using non-contrast computed tomography (CT) images. Data were collected retrospectively from two centers of 1989 eligible patients who underwent ureteroscopic lithotripsy with ureteral stones. Patients were categorized into two groups: control and narrow ureter. The network was designed and trained for predicting a narrow ureter during initial ureteroscopic lithotripsy, which integrated multi-scale features of the ureter. The predictive efficacy of neural networks DenseNet3D, ResNet3D, ResNet3D MC, and TimeSformer was compared. Furthermore, a previous ureteroscopy or a history of double-J stent placement, ureteral wall thickness and Hounsfield unit (HU) density of the ureter under the stone were compared. Model performance was assessed based on the accuracy, area under the receiver operating characteristic curve (AUC ROC), etc. The DenseNet3D-based network achieved an AUC ROC score of 0.884 and an accuracy of 85.29%, followed by the ResNet3D-based network, the ResNet3D MC-based network, and the TimeSformer-based network. The DenseNet3D-based network significantly outperformed other candidate predictors. Furthermore, the networks were validated in an external test set. Decision curve analysis showed the clinical utility of the neural network. The neural network provides an individualized preoperative prediction of narrow ureter based on non-contrast CT images, which could be employed as part of a surgical decision-making support system.

Determination of Phosphorus in Soil by ICP-OES Using an Improved Standard Addition Method.

In this study, an improved standard addition method (ISAM) was developed for the determination of phosphorus in soil by ICP-OES based on the conventional standard addition method (CSAM) and calibration curve method (CCM). Certified standard soils were analyzed by the proposed ISAM method. The values obtained by ISAM method agreed with the certified values. Additionally, the results obtained by ISAM method were compared with those determined by the other two methods (CSAM and CCM). All the values obtained by the ISAM agreed with those from the other two methods. The detection limit, quantification limit, and recovery rate of each method were calculated, and the recovery rates of soil samples and the blank were all within the range of 90%-110%. Finally, the proposed method was applied to determine phosphorous in soil samples from Guangnan County, Yunnan Province, China, and the meadow soil from Qinghai-Tibet Plateau, China. The relative errors between the results from ISAM and CCM were all within 10%, and t-test showed that the results between ISAM and CCM had no significant difference (P > 0.05). Therefore, the proposed method overcame the matrix effect in some extent and was an acceptable method for the rapid and accurate batch analysis of P content in soil sample, especially batch samples with obvious matrix effect.

Effects of Pseudomonas chenduensis and biochar on cadmium availability and microbial community in the paddy soil.

The cadmium contamination in the paddy soil results in serious environmental pollutions. In situ soil remediation based on the applications of additives such as functional microorganisms and biochars has gradually attained more attentions. However, how these exogenous additives affect the local microbial communities is less discussed. In this study, a heavy metal resistant bacterium (Pseudomonas chenduensis, strain MBR) and biochar derived from oil palm fibers were separately added into the simulated Cd-contaminated paddy soil to investigate the roles of these additives in the soil remediation and regulating local microbial community. The results showed that compared with control, the addition of the strain MBR and biochar reduced the exchangeable/acid soluble cadmium fraction by 30% and 18%, respectively. Moreover, higher microbial diversity, more deterministic effects and less variation in microbial community were observed in the treatments supplemented with the strain MBR and biochar, and the increase of the deterministic effects on microbial interactions was demonstrated by network analysis further. Additionally, the abundance of the strain MBR in the paddy soil decreased as time passed, which maximally decreased the disturbance for the local micro-ecological niche and ensured ecological security. These results showed that two additives supplementation, in particular Pseudomonas chenduensis, can significantly decrease cadmium availability, contributing to the reduction of the disturbance on soil microbial community and maintaining microbial stability under cadmium pressure. It highlights a new criterion referred to micro-ecology for the evaluation of the roles of additives in local soil remediation.