Gas flow-directed growth of aligned carbon nanotubes from nonmetallic seeds

Kite growth is a process that utilizes laminar gas flow in chemical vapor deposition to grow long, well-aligned carbon nanotubes (CNTs) for electronic application. This process uses metal nanoparticles (NPs) as catalytic seeds for CNT growth. However, these NPs remain as impurities in the grown CNT. In this study, nanodiamonds (NDs) with negligible catalytic activity were utilized as nonmetallic seeds instead of metal catalysts because they are stable at high temperatures and facilitate the growth of low-defect CNTs without residual metal impurities. Results demonstrate the successful growth of over 100-$\mu$m-long CNTs by carefully controlling the growth conditions. Importantly, we developed an analysis method that utilizes secondary electron (SE) yield to distinguish whether or not CNTs grown from metal impurities. The absence of metallic NPs at the CNT tips was revealed by the SE yield mapping, whereas the presence of some kind of NPs at the same locations was confirmed by atomic force microscopy (AFM). These results suggest that most of the aligned CNTs were grown from nonmetallic seeds, most likely ND-derived NPs, via the tip-growth mode. Structural characterizations revealed the high crystallinity of CNTs, with relatively small diameters. This study presents the first successful use of nonmetallic seeds for kite growth and provides a convincing alternative for starting materials to prepare long, aligned CNTs without metal impurities. The findings of this study pave the way for more convenient fabrication of aligned CNT-based devices, potentially simplifying the production process by avoiding the need for the removal of metal impurities.Comment: Accepted version. Main manuscript: 26 pages, 6 figures. Supporting information: 8 pages, 9 figure

Sequential multiple assignment randomization trials with enrichment design: Sequential Multiple Assignment Randomization Trials with Enrichment Design

Sequential multiple assignment randomization trial (SMART) is a powerful design to study Dynamic Treatment Regimes (DTRs) and allows causal comparisons of DTRs. To handle practical challenges of SMART, we propose a SMART with Enrichment (SMARTer) design, which performs stage-wise enrichment for SMART. SMARTer can improve design efficiency, shorten the recruitment period, and partially reduce trial duration to make SMART more practical with limited time and resource. Specifically, at each subsequent stage of a SMART, we enrich the study sample with new patients who have received previous stages’ treatments in a naturalistic fashion without randomization, and only randomize them among the current stage treatment options. One extreme case of the SMARTer is to synthesize separate independent single-stage randomized trials with patients who have received previous stage treatments. We show data from SMARTer allows for unbiased estimation of DTRs as SMART does under certain assumptions. Furthermore, we show analytically that the efficiency gain of the new design over SMART can be significant especially when the dropout rate is high. Lastly, extensive simulation studies are performed to demonstrate performance of SMARTer design, and sample size estimation in a scenario informed by real data from a SMART study is presented

Thermal defect healing of single-walled carbon nanotubes assisted by supplying carbon-containing reactants

We experimentally investigated the effect of carbon-containing reactants (C2H2) on healing the defects in single-walled carbon nanotubes (SWCNTs) by thermal processes at high temperatures (∼1100 °C). Introducing C2H2 notably improved the crystallinity of healed SWCNTs compared with the thermal process in Ar ambient without C2H2. The defect healing rate increased with increasing C2H2 partial pressure, and the healing effect of C2H2 was more remarkable for relatively thinner SWCNTs (<1.1 nm). Combined with the relevant theoretical work reported previously, we propose a healing model in which C2H2 helps to heal the vacancy defects and increases the healing rate at high temperatures.This is the version of the article before peer review or editing, as submitted by an author to Applied Physics Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.35848/1882-0786/acaaec

The clinicopathological factors associated with disease progression in Luminal a breast cancer and characteristics of metastasis: A retrospective study from a single center in China

Background/Aim: This study investigated the clinicopathological factors associated with outcomes in patients with Luminal A breast cancer. Patients and Methods: Retrospective analysis of the association of clinicopathological factors and breast cancer outcome in 421 patients with newly diagnosed Luminal-A breast cancer that were enrolled from January 2008 to December 2014. Clinicopathological data were analyzed to validate the relationship with disease free survival (DFS) and overall survival (OS). Kaplan-Meier curves and log-rank tests were used to analyze the value of clinicopathological factors (tumor size, node status and lymphovascular invasion), and subsequent Cox regression analysis revealed significant prognostic factors. Results: With a median of 61 months follow up, the 5-year DFS and 5-year OS rate were 98.3% and 99.3%. Cox multivariate regression analysis showed that clinical anatomic stage, tumor size, status of lymph nodes, lymphovascular invasion and systemic treatment are strong prognostic factors for clinical outcome in patients with Luminal-A breast cancer. Of all 413 patients with stage I-III breast cancer, 14 presented with metastasis (3.4%) during the follow up. Bone (6/14, 42.9%) was the most common site of metastasis followed by liver (5/14, 35.7%) and lung (4/14, 28.6%). The median survival time after metastasis was 20.4 months. Of all the sites of distant metastasis, liver metastasis was the only factor that affected survival time after metastasis (χ2=6.263, p=0.012). Conclusion: Patients with Luminal A breast cancer have excellent outcomes. Liver metastasis is an important factor compressing the survival time after distant metastasis presents

Comparison of adverse effects of anti-tumor therapy for breast cancer shortly after COVID-19 diagnosis vs. the control period

BackgroundCOVID-19 is an acute infectious disease caused by SARS-CoV-2. The best time to restart antitumor therapy in breast cancer patients after SARS-CoV-2 infection is unknown. This study aimed to evaluate treatment-related adverse events in breast cancer patients who received antitumor therapies within a short time after SARS-CoV-2 infection (observation) as well as before (control) and to provide safety data.MethodsWe conducted a self-controlled cohort study using the data from the Breast Disease Center of Peking University First Hospital. We identified patients who received antitumor therapy within 28 days after COVID-19 infection between December 20, 2022, and January 20, 2023. The primary outcome was treatment-related adverse events. McNemar’s test was used to compare the incidence rate of adverse reactions between periods.ResultsWe identified 183 patients with breast cancer, of whom 109 were infected with SARS-CoV-2 within 28 days before antitumor treatment and were included. In total, 28 patients (25.7%) received neoadjuvant therapy, 60 (55.0%) received adjuvant therapy, and 21 (19.3%) received advanced rescue therapy. None of patients required hospitalization for severe or critical COVID-19, but 15 patients (13.8%) still had sequelae of COVID-19 while receiving antitumor treatment. The most common adverse events were peripheral neuropathy (n = 32 [29.4%]), pain (n = 29 [26.6%]), fatigue (n = 28 [25.7%]), nausea (n = 23 [21.1%]), and neutropenia (n = 19 [17.4%]). There was no increased risk of overall treatment-related adverse events (n = 87 [79.8%] vs. n = 91 [83.5%]; p = 0.42) or serious adverse events (n = 13 [11.9%] vs. n = 12 [11.0%]; p = 1.00) from receiving antitumor therapy shortly after the diagnosis of COVID-19. We also found no increased risk in subgroup analyses, and no patients discontinued antitumor therapy due to adverse events.ConclusionRestarting antitumor therapy 2-4 weeks after having mild or moderate COVID-19 is a relatively safe strategy for breast cancer patients that does not increase the risk of treatment-related adverse events

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Effect of depressants in the selective flotation of smithsonite and calcite using cationic collector

This work studied the effects of sodium hexametaphosphate (SHMP), tanning extract, water glass (WG) and calcium lignosulfonate (CLS) as depressants for the separation of smithsonite from calcite by using a cationic collector called octadecylamine acetate (ODA). Micro-flotation experimental tests showed that CLS can greatly and selectively depress calcite. When the dosages of CLS used were 20 and 40 mg/L, a concentrate with Zn grades of 42.54% and 49.32% and Zn recoveries of 81.66% and 68.00% was achieved in the flotation separation of mixed mineral (1:1 smithsonite:calcite). Zeta potential and adsorption measurements revealed that the adsorption of CLS on calcite’s surface was greater than that on smithsonite’s surface. When CLS was added, the adsorption of ODA was hindered greatly on the calcite’s surface but slightly on the smithsonite’s surface

Association between the Composite Dietary Antioxidant Index and Atherosclerotic Cardiovascular Disease in Postmenopausal Women: A Cross-Sectional Study of NHANES Data, 2013–2018

The relationship between composite dietary antioxidant index (CDAI) levels and the risk of atherosclerotic cardiovascular disease (ASCVD) in postmenopausal women is unknown. In total, 3109 women from the National Health and Nutrition Examination Survey 2013–2018 were included in this cross-sectional study. We evaluated the association between CDAI levels and the risk of ASCVD by using three logistic regression models and restricted cubic splines. A stratified analysis and sensitivity analysis were also conducted. The restricted cubic splines exhibited an L-shaped dose-response association between CDAI levels and the ASCVD risk. Logistic regression analysis found that CDAI levels were negatively associated with the occurrence of ASCVD. The ORs associated with a per-SD increase in CDAI were 0.67 (95% CI: 0.51–0.88) for ASCVD risk. Similarly, women in the group with high CDAI levels were less likely to have ASCVD (OR = 0.71, 95% CI: 0.50–0.98) compared to those in the group with low CDAI levels. When the CDAI levels were divided into quartiles, it was found that the ORs for ASCVD with CDAI levels in Q2 (−1.04–1.11), Q3 (1.11–3.72), and Q4 (3.72–43.87) were 0.63 (0.44, 0.90), 0.64 (0.42, 0.94), and 0.51 (0.27, 0.97), respectively, compared to those with CDAI levels in Q1 (−6.83–−1.04). In addition, age, high-density lipoprotein cholesterol levels, and smoking behaviors acted as potential modifiers, and ORs were more significant in women aged 40–69 years, in individuals with low high-density lipoprotein cholesterol levels, and in smokers (p for interaction <0.05). These findings may offer valuable insights into the role of CDAI levels in the development of ASCVD among postmenopausal women