Ultrasmall and Highly Dispersed Pt Entities Deposited on Mesoporous N-doped Carbon Nanospheres by Pulsed CVD for Improved HER.

Vapor-based deposition techniques are emerging approaches for the design of carbon-supported metal powder electrocatalysts with tailored catalyst entities, sizes, and dispersions. Herein, a pulsed CVD (Pt-pCVD) approach is employed to deposit different Pt entities on mesoporous N-doped carbon (MPNC) nanospheres to design high-performance hydrogen evolution reaction (HER) electrocatalysts. The influence of consecutive precursor pulse number (50-250) and deposition temperature (225-300 °C) are investigated. The Pt-pCVD process results in highly dispersed ultrasmall Pt clusters (≈1 nm in size) and Pt single atoms, while under certain conditions few larger Pt nanoparticles are formed. The best MPNC-Pt-pCVD electrocatalyst prepared in this work (250 pulses, 250 °C) reveals a Pt HER mass activity of 22.2 ± 1.2 A mg-1 Pt at -50 mV versus the reversible hydrogen electrode (RHE), thereby outperforming a commercially available Pt/C electrocatalyst by 40% as a result of the increased Pt utilization. Remarkably, after optimization of the Pt electrode loading, an ultrahigh Pt mass activity of 56 ± 2 A mg-1 Pt at -50 mV versus RHE is found, which is among the highest Pt mass activities of Pt single atom and cluster-based electrocatalysts reported so far.

Safety and effectivity of Kono-S anastomosis in Crohn's patients: a systematic review and Meta-analysis.

PURPOSE: Kono-S anastomosis, an antimesenteric, functional, end-to-end handsewn anastomosis, was introduced in 2011. The aim of this meta-analysis is to evaluate the safety and effectivity of the Kono-S technique. METHODS: A comprehensive search of MEDLINE (PubMed), Embase (Elsevier), Scopus (Elsevier), and Cochrane Central (Ovid) from inception to August 24th, 2023, was conducted. Studies reporting outcomes of adults with Crohn's disease undergoing ileocolic resection with subsequent Kono-S anastomosis were included. PRISMA and Cochrane guidelines were used to screen, extract and synthesize data. Primary outcomes assessed were endoscopic, surgical and clinical recurrence rates, as well as complication rates. Data were pooled using random-effects models, and heterogeneity was assessed with I² statistics. ROBINS-I and ROB2 tools were used for quality assessment. RESULTS: 12 studies involving 820 patients met the eligibility criteria. A pooled mean follow-up time of 22.8 months (95% CI: 15.8, 29.9; I2 = 99.8%) was completed in 98.3% of patients. Pooled endoscopic recurrence was reported in 24.1% of patients (95% CI: 9.4, 49.3; I2 = 93.43%), pooled surgical recurrence in 3.9% of patients (95% CI: 2.2, 6.9; I2 = 25.97%), and pooled clinical recurrence in 26.8% of patients (95% CI: 14, 45.1; I2 = 84.87%). The pooled complication rate was 33.7%. The most common complications were infection (11.5%) and ileus (10.9%). Pooled anastomosis leakage rate was 2.9%. CONCLUSIONS: Despite limited and heterogenous data, patients undergoing Kono-S anastomosis had low rates of surgical recurrence and anastomotic leakage with moderate rates of endoscopic recurrence, clinical recurrence and complications rate.

Ultrahigh Mass Activity Pt Entities Consisting of Pt Single atoms, Clusters, and Nanoparticles for Improved Hydrogen Evolution Reaction.

Platinum is one of the best-performing catalysts for the hydrogen evolution reaction (HER). However, high cost and scarcity severely hinder the large-scale application of Pt electrocatalysts. Constructing highly dispersed ultrasmall Platinum entities is thereby a very effective strategy to increase Pt utilization and mass activities, and reduce costs. Herein, highly dispersed Pt entities composed of a mixture of Pt single atoms, clusters, and nanoparticles are synthesized on mesoporous N-doped carbon nanospheres. The presence of Pt single atoms, clusters, and nanoparticles is demonstrated by combining among others aberration-corrected annular dark-field scanning transmission electron microscopy, X-ray absorption spectroscopy, and electrochemical CO stripping. The best catalyst exhibits excellent geometric and Pt HER mass activity, respectively ≈4 and 26 times higher than that of a commercial Pt/C reference and a Pt catalyst supported on nonporous N-doped carbon nanofibers with similar Pt loadings. Noteworthily, after optimization of the geometrical Pt electrode loading, the best catalyst exhibits ultrahigh Pt and catalyst mass activities (56 ± 3 A mg-1 Pt and 11.7 ± 0.6 A mg-1 Cat at -50 mV vs. reversible hydrogen electrode), which are respectively ≈1.5 and 58 times higher than the highest Pt and catalyst mass activities for Pt single-atom and cluster-based catalysts reported so far.

Risk factors for asymptomatic kidney stone passage in adults with recurrent kidney stones.

BACKGROUND: Kidney stones are a common health problem and are characterized by a high risk of recurrence. A certain number of kidney stones passed asymptomatically. Data regarding the frequency of asymptomatic spontaneous stone passages are limited. METHODS: To assess the frequency of asymptomatic spontaneous stone passage and its covariates, we conducted a post-hoc analysis of the prospective randomized NOSTONE trial. All asymptomatic spontaneous stone passages were identified by comparing the total number of kidney stones on low-dose non-contrast CT imaging at the beginning and end of the study, considering symptomatic stone passages and surgical stone removal. The statistical analysis focused on the association of independent variables and the number of asymptomatic spontaneous stone passages using linear regression analyses. RESULTS: Of the 416 randomized patients, 383 with both baseline and end-of-study CT were included in this analysis. The median follow-up period was 35 months, the median patient age was 49 years (IQR:40 - 55), and 20% of the patients were female. A total of 442 stone events occurred in 209 out of 383 (55%) patients: 217 out of 442 (49%) were symptomatic spontaneous stone passages, 67 out of 442 (15%) were surgically removed stones, and 158 out of 442 (36%) were asymptomatic spontaneous stone passages. The median size of asymptomatic stones (2.4 mm; IQR:1.95-3.4) and the size of symptomatic stones (2.15 mm; IQR:1.68-2.79) that passed spontaneously were not significantly different (p=0.37). The number of asymptomatic spontaneous stone passages was significantly associated with a higher number of stones on CT at randomization (P=0.001). Limitations include the lack of data on stone size at the time of passage and overrepresentation of White men. CONCLUSIONS: Asymptomatic stone passage was common in patients with recurrent calcium-containing kidney stones. The higher the number of stones at presentation, the more likely it was that a kidney stone would spontaneously pass over time without causing any symptoms.

Machine learning in pancreas surgery, what is new? literature review.

Background: Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery. Methods: We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included. Results: A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022. Conclusion: The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.