Survival and Disease Burden Analysis of Occupational Pneumoconiosis From 1956 to 2021 in Jiangsu Province.

OBJECTIVES: The aims of the study are to investigate the survival status and its influencing factors of pneumoconiosis patients and to analyze the disease burden and its trend in Jiangsu Province. METHODS: We used the life table and Cox model to analyze the survival status. The disease burden and its temporal trend were examined using the disability-adjusted life years (DALY). RESULTS: The average survival time was 16.22 ± 10.11 years. First diagnosis age, first diagnosis stage, and upgrade of stages were crucial risk factors for the survival. The cumulative DALY was 154,500.83 person-years. The DALY attributed to silicosis, coal worker pneumoconiosis, and welder's pneumoconiosis were 99,806.72 (64.60%), 35,483.21 (22.97%), and 37,85.83 (2.45%) person-years, respectively. CONCLUSIONS: Although the disease burden of pneumoconiosis is decreasing because of the implementation of relevant health policies and improved medical standards, the prevention and control of pneumoconiosis still need greater attention.

The association between neurodevelopmental and behavioral problems and tobacco smoke exposure among 3-17 years old children.

Children being exposed to tobacco smoke can lead to poor developmental and behavioral problems. We aimed to explore the correlation between neurodevelopmental and behavioral problems (NBPs) and tobacco smoke exposure (TSE) among children aged 3-17 years. In this study, data were obtained from the 2018-2019 U.S. National Survey of Children's Health (NSCH). Children in the range of 3-17 years old were taken as the research subjects, and their parents were surveyed through questionnaires. TSE status was defined as one of three groups: no tobacco smoke exposure (no TSE), someone smoking but not inside the house (no home TSE), and someone smoking inside the house (home TSE). NBPs mainly included behavioral or conduct problems, intellectual disability, learning disability, speech or other language disorders, and developmental delay. We used the sampling weights provided by the NSCH to weight the data in order to obtain an unbiased population estimate. One-way ANOVA and Chi-square tests were performed to examine the difference of each variable. Logistic regression analysis and stratified analysis were carried out to investigate the association between NBPs and TSE. A total of 48,783 children were included in this study, with an average age of 10.1 years. In total 17.9% of all the participants were preschool children, 35.1% were school-age children, and 47.0% were school-age adolescents. More than 85.0% of children lived with no TSE. Over 90.0% of children were healthy in each NBP. Children living with home TSE and no home TSE showed significant adjusted odds ratios (aORs) compared with no TSE in four NBPs besides intellectual disability. The stratified analysis found aORs were higher for NBPs in preschool children compared to the school-age children and school-age adolescents. Male children living with home TSE showed higher aORs in moderate/severe NBP conditions. Our study indicated it is necessary to protect the health of young children from TSE by intervention measures.

Phenotypic and molecular marker analysis uncovers the genetic diversity of the grass Stenotaphrum secundatum.

BACKGROUND: Stenotaphrum secundatum is an important grass with a rich variety of accessions and great potential for development as an economically valuable crop. However, little is known about the genetic diversity of S. secundatum, limiting its application and development as a crop. Here, to provide a theoretical basis for further conservation, utilization, and classification of S. secundatum germplasm resources, we used phenotypic and molecular markers (single-nucleotide polymorphisms, SNPs; sequence-related amplified polymorphism, SRAP; inter-simple sequence repeat, ISSR) to analyze the genetic diversity of 49 S. secundatum accessions. RESULTS: Based on seven types of phenotypic data, the 49 S. secundatum accessions could be divided into three classes with great variation. We identified 1,280,873 SNPs in the 49 accessions, among which 66.22% were transition SNPs and 33.78% were transversion SNPs. Among these, C/T was the most common (19.12%) and G/C the least common (3.68%). Using 28 SRAP primers, 267 polymorphic bands were detected from the 273 bands amplified. In addition, 27 ISSR markers generated 527 amplification bands, all of which were polymorphic. Both marker types revealed a high level of genetic diversity, with ISSR markers showing a higher percentage of polymorphic loci (100%) than SRAP markers (97.8%). The genetic diversity of the accessions based on SRAP markers (h = 0.47, I = 0.66) and ISSR markers (h = 0.45, I = 0.64) supports the notion that the S. secundatum accessions are highly diverse. S. secundatum could be divided into three classes based on the evaluated molecular markers. CONCLUSIONS: Phenotypic and molecular marker analysis using SNP, SRAP, and ISSR markers revealed great genetic variation among S. secundatum accessions, which were consistently divided into three classes. Our findings provide a theoretical basis for the genetic diversity and classification of S. secundatum. Our results indicate that SNP, SRAP and ISSR markers are reliable and effective for analyzing genetic diversity in S. secundatum. The SNPs identified in this study could be used to distinguish S. secundatum accessions.

A Novel Pre-Processing Algorithm Based on the Wavelet Transform for Raman Spectrum.

Noise and fluorescent background are two major problems for acquiring Raman spectra from samples, which blur Raman spectra and make Raman detection or imaging difficult. In this paper, a novel algorithm based on wavelet transform that contains denoising and baseline correction is presented to automatically extract Raman signals. For the denoising section, the improved conventional-scale correlation denoising method is proposed. The baseline correction section, which is performed after denoising, basically consists of five aspects: (1) detection of the peak position; (2) approximate second derivative calculation based on continuous wavelet transform is performed using the Haar wavelet function to find peaks and background areas; (3) the threshold is estimated from the peak intensive area for identification of peaks; (4) correction of endpoints, spectral peaks, and peak position; and (5) determine the endpoints of the peak after subtracting the background. We tested this algorithm for simulated and experimental Raman spectra, and a satisfactory denoising effect and a good capability to correct background are observed. It is noteworthy that this algorithm requires few human interventions, which enables automatic denoising and background removal.