Enhancement on migration and biodegradation of Diaphorobacter sp. LW2 mediated by Pythium ultimum in soil with different particle sizes.

Introduction: The composition and structure of natural soil are very complex, leading to the difficult contact between hydrophobic organic compounds and degrading-bacteria in contaminated soil, making pollutants hard to be removed from the soil. Several researches have reported the bacterial migration in unsaturated soil mediated by fungal hyphae, but bacterial movement in soil of different particle sizes or in heterogeneous soil was unclear. The remediation of contaminated soil enhanced by hyphae still needs further research. Methods: In this case, the migration and biodegradation of Diaphorobacter sp. LW2 in soil was investigated in presence of Pythium ultimum. Results: Hyphae could promote the growth and migration of LW2 in culture medium. It was also confirmed that LW2 was able to migrate in the growth direction and against the growth direction along hyphae. Mediated by hyphae, motile strain LW2 translocated over 3 cm in soil with different particle size (CS1, 1.0-2.0 mm; CS2, 0.5-1.0mm; MS, 0.25-0.5 mm and FS, <0.25 mm), and it need shorter time in bigger particle soils. In inhomogeneous soil, hyphae participated in the distribution of introduced bacteria, and the total number of bacteria increased. Pythium ultimum enhanced the migration and survival of LW2 in soil, improving the bioremediation of polluted soil. Discussion: The results of this study indicate that the mobilization of degrading bacteria mediated by Pythium ultimum in soil has great potential for application in bioremediation of contaminated soil.

Measurement and Analysis of the Vibration Responses of Piano Soundboards with Different Structures.

The effect of structure on the vibration response was explored for four piano soundboards with different but commonly adopted structures. The vibration response was obtained using the free-vibration method, and the values of the dynamic modulus of elasticity and dynamic shear modulus obtained using the free-vibration frequency method (EF and GF) were compared with the dynamic modulus of elasticity obtained using the Euler beam method (EE) and dynamic shear modulus obtained using the free-plate torsional vibration method (GT), respectively. It was found that the soundboards with different structures had different vibration modes and that excitation at different locations highlighted different vibration modes. For all the soundboards analyzed, the EE and GT were higher than EF and GF by 2.2% and 24.3%, respectively. However, the trends of the results of these methods were the same. The four piano soundboards with different structures possessed varying dynamic moduli of elasticity and dynamic shear moduli. These rules are consistent with the grain directions of the soundboards and the anisotropy of the wood (the direction of the units of the soundboards). The results show that the vibration mode of the piano soundboard is complex. The dynamic elastic modulus of the soundboard can be calculated using the Euler beam method. The results provide a reference for studies on the vibration response, material selection, production technology, and testing of piano soundboards.

Long-term seizure and developmental outcomes of epilepsy surgery in children under 3 years old: A single-center study of 113 patients.

AIMS: To investigate the clinical characteristics, surgical strategy, developmental and seizure outcomes, and predictors of surgical outcome in children with drug-resistant epilepsy (DRE) under 3 years old. METHODS: One hundred thirteen consecutive children younger than 3 years of age with DRE underwent curative surgical treatment after multidisciplinary preoperative evaluation using the strategy developed in the pediatric epilepsy center of Peking University First Hospital (PKFHPEC) between 2014 and 2018. These patients were selected for retrospective study. The relevant clinical data were collected and analyzed. The surgical prognoses were classified using the Engel classification, and the developmental assessment results were collected. Statistical analysis of the clinical data was performed to analyze the predictors of seizure outcomes and their correlation with developmental outcomes. RESULTS: All the patients were followed up for more than 3 years, and 98 (86.7%) patients had no seizure recurrence. One year after surgery, the seizure-free rate was 86.7%, which was as high as that at the last follow-up. Cortical dysplasia was the most frequent etiology of DRE in this cohort, accounting for 77.0%. According to the Engel classification, acute postoperative seizure (APOS; p < 0.001) was a predictor of seizure recurrence. No deaths occurred. No unpredicted long-term severe complications occurred except for one ventricular peritoneal shunt. The patients' neurodevelopmental statuses were improved after successful surgery, while the scores of the pre- and postoperative developmental assessments were closely correlated. CONCLUSIONS: For children who are younger than 3 years old and have DRE and structural abnormalities, early curative treatment can lead to long-term good seizure outcomes and a low complication rate. The development of appropriate strategies for both presurgical evaluation and resection is crucial for the success of surgery.

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human diseases. To obtain high-quality images, the photoacoustic imaging system needs a high-element-density detector array. However, in practical applications, due to the cost limitation, manufacturing technology, and the system requirement in miniaturization and robustness, it is challenging to achieve sufficient elements and high-quality reconstructed images, which may even suffer from artifacts. Different from the latest machine learning methods based on removing distortions and artifacts to recover high-quality images, this paper proposes an adaptive machine learning method to firstly predict and complement the photoacoustic sensor channel data from sparse array sampling and then reconstruct images through conventional reconstruction algorithms. METHODS: We develop an adaptive machine learning method to predict and complement the photoacoustic sensor channel data. The model consists of XGBoost and a neural network named SS-net. To handle data sets of different sizes and improve the generalization, a tunable parameter is used to control the weights of XGBoost and SS-net outputs. RESULTS: The proposed method achieved superior performance as demonstrated by simulation, phantom experiments, and in vivo experiment results. Compared with linear interpolation, XGBoost, CAE, and U-net, the simulation results show that the SSIM value is increased by 12.83%, 6.78%, 21.46%, and 12.33%. Moreover, the median R2 is increased by 34.4%, 8.1%, 28.6%, and 84.1% with the in vivo data. CONCLUSIONS: This model provides a framework to predict the missed photoacoustic sensor data on a sparse ring-shaped array for PACT imaging and has achieved considerable improvements in reconstructing the objects. Compared with linear interpolation and other deep learning methods qualitatively and quantitatively, our proposed methods can effectively suppress artifacts and improve image quality. The advantage of our methods is that there is no need for preparing a large number of images as the training dataset, and the data for training is directly from the sensors. It has the potential to be applied to a wide range of photoacoustic imaging detector arrays for low-cost and user-friendly clinical applications.

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.

Background: The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective: The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods: First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results: Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network.

Background: Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients. Objective: This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition. Methods: First, considering the network interactions in different frequency bands (δ, θ, α, ß, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective. Results: Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

Disconnection Surgery in Pediatric Epilepsy: A Single Center's Experience With 185 Cases.

BACKGROUND AND OBJECTIVES: Lobar and multilobar disconnections have gradually become common surgical methods in pediatric epilepsy surgery in recent years. However, the surgical procedures, postoperative epilepsy outcomes, and complications reported by each center are quite different. To review and analyze the clinical data from lobar disconnection in treating intractable pediatric epilepsy and study the characteristics, surgical outcomes, and safety of different disconnection surgeries. METHODS: This was a retrospective analysis of 185 children with intractable epilepsy who underwent various lobar disconnections at the Pediatric Epilepsy Center, Peking University First Hospital. Clinical information was grouped according to their characteristics. The differences in the abovementioned characteristics among the different lobar disconnections were summarized, and risk factors affecting the surgical outcome and postsurgical complications were explored. RESULTS: Among the 185 patients, 149 patients (80.5%) achieved seizure freedom with a follow-up of 2.1 years. There were 145 patients (78.4%) with malformations of cortical development (MCD). The seizure onset time (median 6 months, P = .001) and surgery time (median 34 months, P = .000) of the MCD group were smaller. Differences were found in etiology, resection of the insular lobe and epilepsy outcome among different disconnection approaches. Both parieto-occipital disconnection ( P = .038, odds ratio = 8.126) and MRI abnormalities larger than the disconnection extent ( P = .030, odds ratio = 2.670) affected the epilepsy outcome. Early postoperative complications were observed in 43 patients (23.3%), and long-term postoperative complications were observed in 5 patients (2.7%). CONCLUSION: The most common etiology of epilepsy in children undergoing lobar disconnection is MCD, whose onset and operative ages are the youngest. Disconnection surgery obtained good seizure outcomes in the treatment of pediatric epilepsy with a low incidence of long-term complications. With advances in presurgical evaluation, disconnection surgery will play a more important role in young children with intractable epilepsy.

[Advances in organoids of the digestive system].

Organoid is a newly developed cellular there-dimensional culture system in recent years. Organoids have a three-dimensional structure, which is similar to that of the real organs. Together with the characteristics of self-renewal and reproduction of tissue origin, organoids can better simulate the function of real organs. Organoids provide a new platform for the study of organogenesis, regeneration, disease pathogenesis, and drug screening. The digestive system is an essential part of the human body and performs important functions. To date, organoid models of various digestive organs have been successfully established. This review summarizes the latest research progress of organoids of taste buds, esophagi, stomachs, livers and intestines, and prospects future application of organoids.

Clinical characteristics and surgical outcomes in children with mild malformation of cortical development and oligodendroglial hyperplasia in epilepsy.

OBJECTIVE: Mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE) is a new and rare histopathological entity of cortical developmental malformations. The clinical characteristics of MOGHE remain challenging. METHODS: Children with histologically confirmed MOGHE were retrospectively studied. The clinical findings, electroclinical and imaging features, and postoperative outcomes were analyzed, and previously published studies were reviewed up to June 2022. RESULTS: Thirty-seven children were included in our cohort. Clinical characteristics included early onset in infancy (94.6% before 3 years), multiple seizure types, and moderate or severe delay. Epileptic spasm is the most common seizure type and initial manifestation. The lesions were mainly multilobar (59.5% multiple lobes and 8.1% hemispheres), and predominance in the frontal lobe was observed. The interictal EEG pattern was circumscribed or widespread. The prominent MRI characteristics were cortical thickening, cortical/subcortical hyperintense T2/FLAIR signal, and blurring at the GM and WM transition. Among the 21 children followed up for more than 1 year after surgery, 76.2% were seizure-free. Preoperative interictal circumscribed discharges and larger resections were significantly associated with a good postoperative outcome. The clinical features of 113 patients in the reviewed studies were similar to those we reported, but the lesions were mainly unilobar (73.5%) and Engel I was achieved in only 54.2% after surgery. SIGNIFICANCE: Distinct clinical characteristics in MOGHE, especially age at onset, epileptic spasm, and age-related MRI characteristics, can help in early diagnosis. Preoperative interictal discharge and surgical strategy may be predictors of postoperative outcomes.

Recent advancement in finite element analysis of spinal interbody cages: A review.

Finite element analysis (FEA) is a widely used tool in a variety of industries and research endeavors. With its application to spine biomechanics, FEA has contributed to a better understanding of the spine, its components, and its behavior in physiological and pathological conditions, as well as assisting in the design and application of spinal instrumentation, particularly spinal interbody cages (ICs). IC is a highly effective instrumentation for achieving spinal fusion that has been used to treat a variety of spinal disorders, including degenerative disc disease, trauma, tumor reconstruction, and scoliosis. The application of FEA lets new designs be thoroughly "tested" before a cage is even manufactured, allowing bio-mechanical responses and spinal fusion processes that cannot easily be experimented upon in vivo to be examined and "diagnosis" to be performed, which is an important addition to clinical and in vitro experimental studies. This paper reviews the recent progress of FEA in spinal ICs over the last six years. It demonstrates how modeling can aid in evaluating the biomechanical response of cage materials, cage design, and fixation devices, understanding bone formation mechanisms, comparing the benefits of various fusion techniques, and investigating the impact of pathological structures. It also summarizes the various limitations brought about by modeling simplification and looks forward to the significant advancement of spine FEA research as computing efficiency and software capabilities increase. In conclusion, in such a fast-paced field, the FEA is critical for spinal IC studies. It helps in quantitatively and visually demonstrating the cage characteristics after implanting, lowering surgeons' learning costs for new cage products, and probably assisting them in determining the best IC for patients.

Photoacoustic imaging with limited sampling: a review of machine learning approaches.

Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing.

Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and ß), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

Design of 3D polycaprolactone/ε-polylysine-modified chitosan fibrous scaffolds with incorporation of bioactive factors for accelerating wound healing.

Electrospun nanofibrous scaffolds show great application potentials for wound healing owing to their effective simulation of extracellular matrix (ECM). Three-dimensional (3D) nanofibrous dressings exhibit relatively high specific surface areas, better mimicry of native ECM, adjustable hydrophilicity and breathability, good histocompatibility, enhanced wound healing, and reduced inflammation. In the present work, we designed the 3D polycaprolactone/ε-polylysine modified chitosan (PCL/PCS) nanofibrous scaffolds by an electrospinning and gas foaming process. Then, gelatin and heparin (Gel/Hep) were assembled onto the surface of PCL/PCS nanofibers by electrostatic adsorption, and vascular endothelial growth factors (VEGFs) were also synchronously incorporated into Gel/Hep layer to form a multifunctional 3D nanofibrous scaffold (PCL/PCS@Gel/Hep+VEGF) for accelerating wound healing. The as-fabricated 3D PCL/PCS@GEL/Hep+VEGF nanofibrous scaffold showed excellent antibacterial ability, hemocompatibility and biocompatibility in vitro and wound healing ability in vivo. Immunological analysis showed that the as-fabricated nanofibrous scaffold inhibited inflammation at the wound sites while promoting angiogenesis during the wound healing process. STATEMENT OF SIGNIFICANCE: The electrospun 3D fibrous scaffolds using polycaprolactone/ε-polylysine modified chitosan (PCL/PCS) have been fabricated as backbone for mimicking the extracellular matrix (ECM). Gelatin and heparin (Gel/Hep) were wrapped onto the surface of PCL/PCS fibers by electrostatic adsorption and vascular endothelial growth factors (VEGFs) were also synchronously incorporated into surface Gel/Hep layer to form multifunctional 3D fibrous scaffolds. The as-fabricated multifunctional 3D fibrous scaffolds with good antibacterial ability and biocompatibility have been used as dressings for accelerating wound healing by inhibiting inflammation at the wound sites while promoting angiogenesis during the wound healing process.

Fabrication of Curcumin-Loaded Silk Fibroin and Polyvinyl Alcohol Composite Hydrogel Films for Skin Wound Healing.

Skin regeneration of full-thickness wounds remains a challenge, requiring a well-regulated interplay of cell-cell and cell-matrix signaling. Herein, the composite hydrogel films composed of silk fibroin (SF) and polyvinyl alcohol (PVA) as scaffolds loaded with curcumin nanoparticles (Cur NPs) were developed for skin wound healing. The structure and physicochemical properties of hydrogel films were first evaluated by scanning electron microscopy (SEM), water contact angle, and chemical and mechanical measurements. In addition, the as-fabricated composite hydrogel films have a unique 3D structure and excellent biocompatibility that facilitates the adhesion and growth of cells. Antimicrobial tests in vitro showed that they could inhibit the growth of bacteria due to the incorporation of Cur NPs into composite hydrogel films. The efficacy of the curcumin-loaded SF/PVA composite hydrogel films for skin wound healing was investigated on the skin defect model in vivo. Immunological analysis showed that the as-fabricated Cur NP-loaded SF/PVA composite hydrogel films inhibited inflammation at the wound sites, while promoting angiogenesis during the wound healing process.

Modification of polyacrylonitrile-derived carbon nanofibers and bacteriophages on screen-printed electrodes: A portable electrochemical biosensor for rapid detection of Escherichia coli.

A facile method was developed for fabricating a disposable phage-based electrochemical biosensor for the detection of Escherichia coli. Bare screen-printed electrodes (SPEs) were modified using a two-step drop-casting method, in which polyacrylonitrile-derived electrospun carbon nanofibers (CNFs) were deposited, followed by E. coli bacteriophage immobilization. The deposition of CNFs increased the surface area for bacteriophage immobilization while maintaining a conductive link for ferro/ferricyanide redox transitions. Cyclic voltammetry and electrochemical impedance spectroscopy confirmed that the CNF modification increased the electron-transfer rate, whereas bacteriophages and E. coli blocked electron transfer at the electrode. The biosensor achieved a response within 10 min and a linear response in the E. coli concentration range of 102-106 CFU/mL. A limit of detection (LOD) of 36 CFU/mL in phosphate-buffered saline was achieved, which is the lowest LOD reported thus far for phage-based disposable SPE sensors. The biosensor exhibited recovery rates between 106 % and 119 % for E. coli detection in apple juice. The proposed fabrication method allowed electrodes to be obtained from different production batches with remarkable consistency and reproducibility, and they remained stable at room temperature for one month. Thus, a phage-based disposable SPE that can be used for bacterial detection was developed for the first time.

Mechanism of biochar functional groups in the catalytic reduction of tetrachloroethylene by sulfides.

In recent years, biochar has become of considerable interest for environmental applications, it can be used as a catalyst for sulfides reduction of perchloroethylene, but the crucial role of biochar properties played in catalyzing dechlorination remained ambiguous investigation. To pinpoint the critical functional groups, the modified biochars were respectively produced by HNO3, KOH and H2O2 with similar dimensional structures but different functional groups. Combined with the adsorption and catalytic results of different biochars, the acid-modified biochar had the best catalytic performance (99.9% removal) due to the outstanding specific surface area and ample functional groups. According to characterization and DFT results, carboxyl and pyridine nitrogen exhibited a positive correlation with the catalytic rate, indicating that their contribution to catalytic performance. Customizing biochar with specific functional groups removed depth demonstrated that the carboxyl was essential component. Further, alkaline condition was conducive to catalytic reduction, while tetrachloroethylene cannot be reduced under acidic conditions, because HS- and S2- mainly existed in alkaline environment and the sulfur-containing nucleophilic structure formed with biochar was more stable under this condition. Overall, this study opens new perspectives for in situ remediation by biochar in chlorinated olefin polluted anoxic environment and promotes our insight of modifying for biochar catalyst design.

Ningmitai capsule promotes calculi expulsion after RIRS for 10-20-mm upper urinary stones: a multicenter, prospective, randomized controlled trial.

To evaluate the efficacy and safety of the use of Ningmitai capsule as an adjunctive stone expulsion therapy after RIRS. All patients were diagnosed with upper urinary tract calculi measuring 10-20 mm. The patients who successfully underwent RIRS were randomly assigned to the NMT capsule group (Ningmitai capsule, 1.52 g, three times daily) or the control group for 4 weeks based on the random number table method. The primary endpoints were the stone expulsion rate (SER) and stone-free rate (SFR). The average stone expulsion time (SET), average stone-free time (SFT) and complications were recorded. Between July 2, 2019, and December 17, 2020, 220 participants successfully underwent RIRS across 6 centers; 123 of them were randomized according to the exclusion criteria, and 102 (83%) were included in the primary analysis. The SERs on the 3rd, 7th, 14th and 28th days were significantly increased in the NMT capsule group compared with the control group (78.95% vs. 31.11%, 92.98% vs. 55.56%, 94.74% vs. 64.44%, 100% vs. 82.22%, respectively, p < 0.05). The SFRs on the 3rd and 7th days were not different (p > 0.05), while those on the 14th and 28th days were higher in the NMT capsule group (63.16% vs. 24.44% and 92.98% vs. 68.89%, p < 0.05). The average SET and average SFT of the NMT capsule group were remarkably shorter than those of the control group (p < 0.001). During the follow-up period, there were no significant differences in urine RBC counts between the two groups (p > 0.05). The urine WBC counts of the NMT capsule group were significantly lower than those of the control group on the 14th day (p = 0.011), but there was no difference on the 3rd, 7th or 28th day (p > 0.05). The analgesic aggregate of the NMT capsule group was also much lower (p = 0.037). There were no significant differences in adverse events (p > 0.05), and they improved significantly without sequelae. This study indicated that NMT capsules can significantly promote stone clearance and are more effective and safer for upper urinary calculi after RIRS.Trial registration Chinese Clinical Trial Registration No. ChiCTR1900024151.Date of registration June 28, 2019.

Nitrogen-doped mesoporous carbon material (NCMK-3) as a catalyst for the removal of 4-chlorophenol during persulfate oxidation and its efficiency after reuse.

Persulfate (PS) oxidation of 4-chlorophenol (4CP) is mostly catalysed by relatively expensive metal substrates. In this study, we investigated the influence of nitrogen-doped and non-doped mesoporous carbon materials (NCMK-3 and CMK-3) during persulfate (PS) oxidation of 4CP in water. Batch experiments were conducted such that PS was added to simulated contaminant mixture after 1 h agitation with NCMK-3 and CMK-3. Further, the experiment was carried out at different temperatures, pH ranges, concentrations of persulfate (PS), and different doses of NCMK-3, since it recorded better removal rates compared to CMK-3. The results revealed that NCMK-3 and CMK-3 aided the removal of 4CP from water during persulfate oxidation. When persulfate was added after an hour of equilibration with CMK-3 and NCMK-3, 83% and 92% of 4CP were removed within 20 min, respectively, whereas lower removal rates (≤40) were recorded in the absence of persulfate (PS). The removal rates of 4CP increased with an increase in temperature but reduced in the alkaline medium in the NCMK-3/PS system. The efficiency of the NCMK-3 reduced significantly after it was reused three times. Based on the results, NCMK-3 influences the activity of PS oxidation of 4-chlorophenol (4CP) and exhibited a synergistic effect in the removal of the organic contaminant from water.

Pulsatility damping in the microcirculation: Basic pattern and modulating factors.

Blood flow pulsatility is an important determinant of macro- and microvascular physiology. Pulsatility is damped largely in the microcirculation, but the characteristics of this damping and the factors that regulate it have not been fully elucidated yet. Applying computational approaches to real microvascular network geometry, we examined the pattern of pulsatility damping and the role of potential damping factors, including pulse frequency, vascular viscous resistance, vascular compliance, viscoelastic behavior of the vessel wall, and wave propagation and reflection. To this end, three full rat mesenteric vascular networks were reconstructed from intravital microscopic recordings, a one-dimensional (1D) model was used to reproduce pulsatile properties within the network, and potential damping factors were examined by sensitivity analysis. Results demonstrate that blood flow pulsatility is predominantly damped at the arteriolar side and remains at a low level at the venular side. Damping was sensitive to pulse frequency, vascular viscous resistance and vascular compliance, whereas viscoelasticity of the vessel wall or wave propagation and reflection contributed little to pulsatility damping. The present results contribute to our understanding of mechanical forces and their regulation in the microcirculation.