Objective: To investigate the prevalence and influencing factors of dyslipidemia among residents in Chengdu City, so as to provide insights into improving the prevention and control of dyslipidemia. Methods: Based on the baseline survey of the Natural Population Cohort Study in Southwest China, residents aged 30 to 79 years was selected from 34 towns (communities) in 5 counties (districts) of Chengdu City using the multi-stage stratified cluster random sampling method in 2018. Demographic information and lifestyle behaviors were collected through questionnaires. Blood pressure, fasting blood glucose, serum uric acid, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were collected through physical examination and laboratory tests. A multivariable logistic regression model was used to identify the factors affecting dyslipidamia. Results: A total of 21 113 participants were surveyed, including 9 331 males (44.20%) and 11 782 females (55.80%), and had a mean age of (50.80±12.32) years. The prevalence rate of dyslipidemia was 35.64%, and the prevalence rates of high TG, low-HDL-C, high TC and high LDL-C were 17.25%, 11.88%, 10.11% and 7.35%, respectively. Multivariable logistic regression analysis identified gender (male, OR=1.584, 95%CI: 1.463-1.716), age (50 to 79 years old, OR：1.221-1.444, 95%CI: 1.079-1.632), residence (urban, OR=1.123, 95%CI: 1.052-1.198), marital status (not married, OR=1.246, 95%CI: 1.128-1.376), educational level (high school and above, OR=0.914, 95%CI: 0.849-0.983), current smoking (OR=1.220, 95%CI: 1.121-1.327), drinking (1 to 2 d/week, OR=1.525, 95%CI: 1.368-1.700; 3 to 5 d/week, OR=1.857, 95%CI: 1.575-2.191; almost every day, OR=1.512, 95%CI: 1.269-1.801), sedentary time in leisure time (>2 h/d, OR=1.123, 95%CI: 1.046-1.206), central obesity (OR=2.212, 95%CI: 1.986-2.265), hypertension (OR=1.489, 95%CI: 1.388-1.598), diabetes (OR=1.998, 95%CI: 1.833-2.157) and hyperuricemia (OR=2.012, 95%CI: 1.848-2.192) as factors affecting dyslipidemia. Conclusion The prevalence of dyslipidemia among residents in Chengdu City was mainly associated with smoking, drinking, sedentary time, central obesity, hypertension, diabetes and hyperuricemia.

To obtain a primary overview of gene diversity and expression pattern in Lycoris longituba, 4,992 ESTs (Expressed Sequence Tags) from L. longituba bud were sequenced and 4,687 cleaned ESTs were used for gene expression analysis. Clustered by the PHRAP program, 967 contigs and 1,343 singlets were obtained. Blast search showed that 179 contigs and 227 singlets (totally 1,066 ESTs) had homologues in GenBank and 3,621 ESTs were novel.
Base Composition ; Computational Biology ; Expressed Sequence Tags ; Flowering Tops ; genetics ; Gene Expression Regulation, Plant ; Genetic Variation ; Lycoris ; genetics

Objective To explore the effect of mobile internet management in continuing nursing care of premature infants. Methods The convenience sampling method was adopted to divide the premature infants from January to December in the year 2015 into 2 groups according to the time order, the control group (n = 56), and the observation group (n = 57). The control group received continuing nursing care for the whole course from admission to discharge. Based on the nursing care of the control group, the observation group were given an extra continuing nursing care by using the mobile internet. Both group's continuing nursing care were lasted from the birth till 12 month.Comparisons of two groups were made from the following aspects: of preterm infants born at 12 months in the length, weight, head circumference of the preterm infants at 12 months old and 40 weeks corrected gestational age Neonatal Behavioral Neurological Assessment,breastfeeding confidence;parents care knowledge scores at the time of admission, discharge and 1month after discharge; referral rate; parents satisfaction at the time of discharge and the end of extended care). Results In the observation group,the length,weight and head circumference of the preterm infants at birth were(74.10 ± 2.66)cm,(8.70 ± 1.43)kg,(45.40 ± 1.38)cm, Neonatal Behavioral Neurological Assessment at 40 weeks of gestation,self-confidence in breastfeeding,1 post-discharge and 1 post-discharge parents care knowledge score of the month, parents of preterm children satisfaction scores, respectively(37.30 ± 3.22),(120.31 ± 13.65),(82.28 ± 3.99, 96.70 ± 2.28), (93.55±2.91,96.61±2.37),the control group were(73.20±2.80)cm,(8.44±1.02)kg,(44.2±1.40)cm, (36.00±2.87),(114.54±12.21),(80.66±3.51, 95.02±3.87),(92.57±2.41, 95.72±2.02). The difference between the two groups was statistically significant(t=-5.244~-2.014,all P<0.05). Conclusions The mobile internet application of continuing nursing care in premature infants can improve the growth and development of premature infants and maternal breastfeeding confidence,promote parents care knowledge, referral rate and satisfaction,thus guarantee the he living quality of the preterm infants.

Objective:To development a new method for sensitive detection of Porphyromanus gingivalis (P.gingivalis) based on magnetic encoding nanospheres and upconversion fluorescent encoding nanospheres.Methods:Magnetic and upconversion fluorescent encoding nanospheres were prepared by sol-gel method respectively,combined the monoclonal antibodies specific to P.gingivalis after modifying the surface of nanospheres.The system was used to detect P.gingivalis from mixed bacteria solution of P.gingivalis,F.nucleatum and S.mutans.Fluorescent microscopy with an external 980 nm near-infrared hght pulse laser,scanning and transmission microscope were used to evaluate the effectiveness of the detection system.Results:Magnetic and upconversion encoding nanospheres had better dispersion,particle size uniformity and homogeneous morphology.Besides,the magnetic encoding nanospheres had good magnetic properties and strong fluorescence intensity.P.gingivalis was captured by magnetic and upconversion encoding nanospheres in a mixed solution of the 3 bacteria with a detection limit of 10 CFU/ml.Conclusion:The method designed in this study can capture P.gingivalis sensitively in a mixed bacteria liquid.

Objective To systematically evaluate the risk factors for pulmonary infection in patients with lung cancer after chemotherapy.Methods CNKI,Wanfang Data,VIP,PubMed,Embase,and the Cochrane Library were searched from inception to October 2022 to collect case-control studies and cohort studies about risk factors for pulmonary infection in patients with lung cancer after chemotherapy.Two researchers independently conducted literature screening,data extraction,and quality assessment.Rev Man 5.3 software was used for Meta-analysis.Results A total of 15 literatures were included,including 3960 patients with lung cancer after chemotherapy.Meta analysis results showed that age≥60 years,smoking history,drinking history,hypertension,diabetes mellitus,atelectasis,hypoproteinaemia,TNM staging of stage Ⅲ-Ⅳ,central lung cancer,small cell lung cancer,invasive operation,Karnofsky performance status score＜80 points before chemotherapy,combined chemotherapy drugs,duration of chemotherapeutic ?2 weeks,white blood cell count≤3.0×109/L after chemotherapy,albumin＜30g/L after chemotherapy,and hospital stay>20 days were risk factors for pulmonary infection in patients with lung cancer after chemotherapy(P＜0.05).Conclusion There were many risk factors for pulmonary infection in patients with lung cancer after chemotherapy.Prevention and control measures should be taken based on the related risk factors to reduce the incidence rate of pulmonary infection.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.