Trends in cardiovascular risk factor prevalence, treatment, and control among US adolescents aged 12 to 19 years, 2001 to March 2020.

BACKGROUND: Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades. METHODS: This is a serial cross-sectional study using data from nine National Health and Nutrition Examination Survey cycles (January 2001-March 2020). US adolescents (aged 12 to 19 years) with information regarding CVRFs (including hypertension, elevated blood pressure [BP], diabetes, prediabetes, hyperlipidemia, obesity, overweight, cigarette use, inactive physical activity, and poor diet quality) were included. Age-adjusted trends in CVRF prevalence, treatment, and control were examined. Joinpoint regression analysis was performed to estimate changes in the prevalence, treatment, and control over time. The variation by sociodemographic characteristics were also described. RESULTS: A total of 15,155 US adolescents aged 12 to 19 years (representing ≈ 32.4 million people) were included. From 2001 to March 2020, there was an increase in the prevalence of prediabetes (from 12.5% [95% confidence interval (CI), 10.2%-14.9%] to 37.6% [95% CI, 29.1%-46.2%]) and overweight/obesity (from 21.1% [95% CI, 19.3%-22.8%] to 24.8% [95% CI, 21.4%-28.2%]; from 16.0% [95% CI, 14.1%-17.9%] to 20.3% [95% CI, 17.9%-22.7%]; respectively), no improvement in the prevalence of elevated BP (from 10.4% [95% CI, 8.9%-11.8%] to 11.0% [95% CI, 8.7%-13.4%]), diabetes (from 0.7% [95% CI, 0.2%-1.2%] to 1.2% [95% CI, 0.3%-2.2%]), and poor diet quality (from 76.1% [95% CI, 74.0%-78.2%] to 71.7% [95% CI, 68.5%-74.9%]), and a decrease in the prevalence of hypertension (from 8.1% [95% CI, 6.9%-9.4%] to 5.5% [95% CI, 3.7%-7.3%]), hyperlipidemia (from 34.2% [95% CI, 30.9%-37.5%] to 22.8% [95% CI, 18.7%-26.8%]), cigarette use (from 18.0% [95% CI, 15.7%-20.3%] to 3.5% [95% CI, 2.0%-5.0%]), and inactive physical activity (from 83.0% [95% CI, 80.7%-85.3%] to 9.5% [95% CI, 4.2%-14.8%]). Sex and race/ethnicity affected the evolution of CVRF prevalence differently. Whilst treatment rates for hypertension and diabetes did not improve significantly (from 9.6% [95% CI, 3.5%-15.8%] to 6.0% [95% CI, 1.4%-10.6%]; from 51.0% [95% CI, 23.3%-78.7%] to 26.5% [95% CI, 0.0%-54.7%]; respectively), BP control was relatively stable (from 75.7% [95% CI, 56.8%-94.7%] to 73.5% [95% CI, 40.3%-100.0%]), while glycemic control improved to a certain extent, although it remained suboptimal (from 11.8% [95% CI, 0.0%-31.5%] to 62.7% [95% CI, 62.7%-62.7%]). CONCLUSIONS: From 2001 to March 2020, although prediabetes and overweight/obesity increased, hypertension, hyperlipidemia, cigarette use, and inactive physical activity decreased among US adolescents aged 12 to 19 years, whereas elevated BP, diabetes, and poor diet quality remained unchanged. There were disparities in CVRF prevalence and trends across sociodemographic subpopulations. While treatment and control rates for hypertension and diabetes plateaued, BP control were stable, and improved glycemic control was observed.

Pretrained Quantum-Inspired Deep Neural Network for Natural Language Processing.

Natural language processing (NLP) may face the inexplicable "black-box" problem of parameters and unreasonable modeling for lack of embedding of some characteristics of natural language, while the quantum-inspired models based on quantum theory may provide a potential solution. However, the essential prior knowledge and pretrained text features are often ignored at the early stage of the development of quantum-inspired models. To attacking the above challenges, a pretrained quantum-inspired deep neural network is proposed in this work, which is constructed based on quantum theory for carrying out strong performance and great interpretability in related NLP fields. Concretely, a quantum-inspired pretrained feature embedding (QPFE) method is first developed to model superposition states for words to embed more textual features. Then, a QPFE-ERNIE model is designed by merging the semantic features learned from the prevalent pretrained model ERNIE, which is verified with two NLP downstream tasks: 1) sentiment classification and 2) word sense disambiguation (WSD). In addition, schematic quantum circuit diagrams are provided, which has potential impetus for the future realization of quantum NLP with quantum device. Finally, the experiment results demonstrate QPFE-ERNIE is significantly better for sentiment classification than gated recurrent unit (GRU), BiLSTM, and TextCNN on five datasets in all metrics and achieves better results than ERNIE in accuracy, F1-score, and precision on two datasets (CR and SST), and it also has advantage for WSD over the classical models, including BERT (improves F1-score by 5.2 on average) and ERNIE (improves F1-score by 4.2 on average) and improves the F1-score by 8.7 on average compared with a previous quantum-inspired model QWSD. QPFE-ERNIE provides a novel pretrained quantum-inspired model for solving NLP problems, and it lays a foundation for exploring more quantum-inspired models in the future.

Endogenous NO-release multi-responsive hollow mesoporous silica nanoparticles for drug encapsulation and delivery.

Novel multi-responsive drug delivery vehicles (CDs/PNVCL@HMSNs) were prepared by grafting amino-terminated poly (N-vinyl caprolactam) (PNVCL-NH2) and amino-rich carbon dots (CDs) on the surface of aldehyde-functionalized HMSNs (HMSNs-CHO) via Schiff base reaction. The CDs were prepared from L-arginine and their surfaces were rich in guanidine. Doxorubicin (DOX) was loaded into the nanoparticles to form drug loaded vehicles (CDs/PNVCL@HMSNs-DOX) and the drug loading efficiency was 58.38%. The drug release behaviors of CDs/PNVCL@HMSNs-DOX showed temperature and pH responsiveness due to the poly (N-vinyl caprolactam) (PNVCL) and Schiff base bond. The high concentration of NO released in high concentration H2O2 of tumor site could induce tumor cells apoptosis. The multi-responsive CDs/PNVCL@HMSNs are intriguing drug carriers, which combine drug delivery and NO release in one.

Parameterized Hamiltonian Learning With Quantum Circuit.

Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution. Then, a PHL algorithm is developed to prepare a specific Hamiltonian system by iteratively updating the gradient of the loss function about circuit parameters. Finally, the experiments are conducted on Origin Pilot, and it demonstrates that the PHL algorithm can deal with the image segmentation problem and provide a segmentation solution accurately. Compared with the classical Grabcut algorithm, the PHL algorithm eliminates the requirement of early manual intervention. It provides a new possibility for solving practical application problems with quantum devices, which also assists in solving increasingly complicated problems and supports a much wider range of application possibilities in the future.

Willingness of parents of 9-to-18-year-old females in China to vaccinate their daughters with HPV vaccine.

OBJECTIVE: To assess knowledge about HPV and HPV vaccine, willingness to have their daughters receive HPV vaccine, and factors associated with knowledge and willingness among parents of females 9 to 18 years of age in China. METHODS: We conducted a cross-sectional survey of parents with daughters 9 to 18 years of age in four provinces of China using a self-administered questionnaire. We used multivariable regression analyses to determine factors associated with willingness vaccinate. RESULTS: We received completed surveys from 5,215 parents. Among respondents, 846 (16.24 %) had a high level of knowledge about HPV (total score ≥ 5). High level of HPV knowledge was associated with having daughters who were <15 years old; having experience paying out-of-pocket for other vaccines; being a mother; having higher educational achievement; being a medical staff member; being in a higher-income category; and residing in an urban area. There were 2,419 (46.39 %) parents who were willing to vaccinate their daughter against HPV and 2,757 (52.87 %) who were willing to receive vaccine themselves (for female respondents) or indicated willingness of their spouse to be vaccinated (for male respondents). Willingness of mothers to be vaccinated was associated with daughters' age being greater than 14 years; having previously purchased self-paid vaccines; having higher educational achievement; being in a higher-income category; having a higher level of knowledge; and residing in an urban area. Parental willingness to vaccinate daughters was associated with daughters' age being greater than 14 years, age of parents younger 40, a history of purchasing self-paid vaccines; higher educational achievement; having a higher level of knowledge; being willing to receive HPV vaccine (themselves or their spouse); and residing in an urban area. CONCLUSIONS: Level of knowledge and willingness to have daughters vaccinated with HPV vaccine are low among Chinese parents. For universal HPV vaccination of females 9-18 years of age, it is necessary to educate parents about HPV and HPV vaccine, and to reduce the price of HPV vaccine.

Effect of PM2.5 exposure on Vitamin D status among pregnant women: A distributed lag analysis.

BACKGROUND: Serum vitamin D levels are associated with exposure to air pollution, however, the lagged effect of exposure to air pollution remains unknown in pregnant women. METHODS: Pregnant women who delivered at a maternity center in Shanghai, China, from 2015 to 2019 were included in the present study. The concentration of particulate matter 2.5 (PM2.5) before 25-Hydroxyvitamin D [25(OH)D] detection was estimated using the satellite-based grid models. The distributed lag non-linear models were performed to examine the lagged association between weekly-specific PM2.5 exposure and vitamin D deficiency (VDD) or serum 25(OH)D levels. RESULTS: Among the 58,025 pregnant women included in the study (mean age at conception, 30.77 ± 3.75 years; mean prepregnancy BMI, 21.09 ± 2.55 kg/m2), 61.32% were diagnosed with VDD. Weekly-specific PM2.5 exposure at weeks 1-10 before the detection of 25(OH)D was significantly associated with an increased incidence of VDD (p < 0.05). For every 10 µg/m3 increase in PM2.5 exposure, the serum 25(OH)D level decreased by 1.346 nmol/L (95%CI: 1.183-1.508 nmol/L). The association between average PM2.5 exposure and VDD at 1-10 weeks was more significant in weather conditions with low mean sunshine hours (OR: 1.246, 95%CI: 1.221-1.271). CONCLUSION: Our study provided suggestive evidence that PM2.5 exposure at 1-10 weeks before the 25(OH)D detection may decrease the circulating 25(OH)D levels in pregnant women and increase VDD risk in pregnant women. More attention should be paid to the long-term impact of PM2.5, in particular, during weather conditions with a relatively short duration of sunshine.

An Approach to Cryptography Based on Continuous-Variable Quantum Neural Network.

An efficient cryptography scheme is proposed based on continuous-variable quantum neural network (CV-QNN), in which a specified CV-QNN model is introduced for designing the quantum cryptography algorithm. It indicates an approach to design a quantum neural cryptosystem which contains the processes of key generation, encryption and decryption. Security analysis demonstrates that our scheme is security. Several simulation experiments are performed on the Strawberry Fields platform for processing the classical data "Quantum Cryptography" with CV-QNN to describe the feasibility of our method. Three sets of representative experiments are presented and the second experimental results confirm that our scheme can correctly and effectively encrypt and decrypt data with the optimal learning rate 8e - 2 regardless of classical or quantum data, and better performance can be achieved with the method of learning rate adaption (where increase factor R1 = 2, decrease factor R2 = 0.8). Indeed, the scheme with learning rate adaption can shorten the encryption and decryption time according to the simulation results presented in Figure 12. It can be considered as a valid quantum cryptography scheme and has a potential application on quantum devices.