Bone Mineral Density Reference Standards for Chinese Children Aged 3-18: Cross-Sectional Results of the 2013-2015 China Child and Adolescent Cardiovascular Health (CCACH) Study

Objectives: No nationwide paediatric reference standards for bone mineral density (BMD) are available in China. We aimed to provide sex-specific BMD reference values for Chinese children and adolescents (3-18 years). Methods: Data (10 818 participants aged 3-18 years) were obtained from cross-sectional surveys of the China Child and Adolescent Cardiovascular Health in 2015, which included four municipality cities and three provinces. BMD was measured using Hologic Discovery Dual Energy X-ray Absorptiometry (DXA) scanner. The DXA measures were modelled against age, with height as an independent variable. The LMS statistical method using a curve fitting procedure was used to construct reference smooth cross-sectional centile curves for dependent versus independent variables. Results: Children residing in Northeast China had the highest total body less head (TBLH) BMD while children residing in Shandong Province had the lowest values. Among children, TBLH BMD was higher for boys as compared with girls; but, it increased with age and height in both sexes. Furthermore, TBLH BMD was higher among US children as compared with Chinese children. There was a large difference in BMD for height among children from these two countries. US children had a much higher BMD at each percentile (P) than Chinese children; the largest observed difference was at P50 and P3 and the smallest difference was at P97. Conclusions: This is the first study to present a sex-specific reference dataset for Chinese children aged 3-18 years. The data can help clinicians improve interpretation, assessment and monitoring of densitometry results

Low-dose aspirin in the prevention of preeclampsia in twin pregnancies: A real-world study

BackgroundThe use of low-dose aspirin for women with twin pregnancies remains controversial. This study was to describe the frequency of preeclampsia and aspirin use in twin pregnancies in real practice.MethodsThis retrospective cohort study based on real-world data was conducted in the Obstetrics and Gynecology Hospital of Fudan University between 2013 and 2020. Women with twin pregnancies who received prenatal care before 20 weeks of gestational age were included. They were divided into those using low-dose aspirin (LDA group) and those not using aspirin group (N-LDA group). The primary outcome was the frequency of preeclampsia, and secondary outcomes included early-onset and preterm mild and severe preeclampsia.ResultsA total of 2,946 women had twin pregnancies, and 241 were excluded due to missing information. Of 2,705 eligible women, 291 (10.75%) were administered aspirin and the other 2,414 (89.25%) did not. The patients in the LDA group were significantly more likely to be older, have a higher rate of use of ART, have a previous history of hypertension, and have gestational diabetes (p < 0.05). In the LDA group, aspirin compliance ≥50% was relatively low (14.43%, 42/291). Preeclampsia occurred in 106 of 291 participants (36.43%) in the LDA group, as compared to 449 of 2,411 (18.62%) in the N-LDA group (OR: 2.15, 95% CI: 1.62–2.82; p < 0.01). The association was confirmed (OR: 1.74, 95% CI: 1.26–2.4; p < 0.01) in the 1:2 case-matched analysis. Higher odds of ratio in the LDA group were demonstrated (aORs > 1, p < 0.01), except for early-onset and preterm mild preeclampsia (p > 0.05). This association was confirmed in a subgroup analysis of methods of conception (aORs ≥ 1, p > 0.05).ConclusionAspirin prescription of 75 to 100 mg in twin pregnancies was associated with no significant reduction of preeclampsia, which may be due to poor compliance with the aspirin used. Further randomized controlled or prospective cohort studies are required

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

Photodynamic Therapy of Up-Conversion Nanomaterial Doped with Gold Nanoparticles

Two key concerns exist in contemporary cancer chemotherapy: limited therapeutic efficiency and substantial side effects in patients. In recent years, researchers have been investigating the revolutionary cancer treatment techniques of photodynamic therapy (PDT) and photothermal therapy (PTT) proposed by many scholars. A photothermal treatment of cancer was synthesized using the hydrothermal method which has high photothermal conversion efficiency and can generate reactive oxygen species (ROS) in cells. Photothermal treatment of tumors has a good short-term effect and photodynamic therapy lasts longer. However, both PTT and PDT have their inevitable shortcomings and it is difficult to completely eradicate a tumor using a single mode of treatment. PTT and PDT synergistic treatment not only inherits the advantages of low toxicity and side effects of phototherapy but also enables the two treatment methods to complement each other. It is an effective strategy to improve curative effects and reduce toxic and side effects. Furthermore, gold doped UCNPs have an exceptionally high target recognition for tumor cells. The gold doped UCNPs, in particular, are non-toxic to normal tissues, endowing the as-prepared medications with outstanding therapeutic efficacy and exceptionally low side effects. These findings may encourage the creation of fresh, effective imaging-guided approaches to meet the goal of photothermal cancer therapy

Synthesis of Rare-Earth Nanomaterials Ag-Doped NaYF4:Yb3+/Er3+@NaYF4:Nd3+@NaGdF4 for In Vivo Imaging

In this study. a novel near-infrared fluorescent-driven contrast agent (Ag-doped NaYF4:Yb3+/Er3+@NaYF4:Nd3+@NaGdF4) was synthesized using a coprecipitation-hydrothermal-solvothermal-solvothermal (CHSS) method. The results shows that hexagonal NaYF4:Yb3+/Er3+ with a diameter of 300 nm was successfully synthesized by the CHSS method. The new contrast agent was characterized using scanning electron microscopy, fluorescence spectrometry, transmission electron microscopy, energy-dispersive spectrometry and ultraviolet-visible light diffuse reflectance absorption spectroscopy. Even at low concentrations (0.2 M), this proposed contrast agent can be excited by near-infrared light with a wavelength of 980 nm and emits a dazzling green light with a wavelength of 540 nm, and the comparison of the luminescence intensity proves that doping with silver increases the luminescence intensity of the upconverted nanomaterial by nearly 13 times based on the calculated quantum yield. TEM images show the successful preparation of silver nanoparticles with a diameter of 30 nm, and the energy spectrum shows the successful doping of silver nanoparticles and the successful preparation of the core-shell structure of NaYF4:Yb3+/Er3+@NaYF4:Nd3+@NaGdF4. Furthermore, the mechanism of the increased luminous intensity has been studied using simulation calculations. Finally, cytotoxicity tests were used to test material which was modified by 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (DSPE-PEG2K), and the biocompatibility was significantly improved, meeting the standard for biological applications