Inflammatory markers are associated with infertility prevalence: a cross-sectional analysis of the NHANES 2013-2020.

BACKGROUND: Inflammation exerts a critical role in the pathogenesis of infertility. The relationship between inflammatory parameters from peripheral blood and infertility remains unclear. Aim of this study was to investigate the association between inflammatory markers and infertility among women of reproductive age in the United States. METHODS: Women aged 20-45 were included from the National Health and Nutrition Examination Survey (NHANES) 2013-2020 for the present cross-sectional study. Data of reproductive status was collected from the Reproductive Health Questionnaire. Six inflammatory markers, systemic immune inflammation index (SII), lymphocyte count (LC), product of platelet and neutrophil count (PPN), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR) and lymphocyte-monocyte ratio (LMR) were calculated from complete blood counts in mobile examination center. Survey-weighted multivariable logistic regression was employed to assess the association between inflammatory markers and infertility in four different models, then restricted cubic spline (RCS) plot was used to explore non-linearity association between inflammatory markers and infertility. Subgroup analyses were performed to further clarify effects of other covariates on association between inflammatory markers and infertility. RESULTS: A total of 3,105 women aged 20-45 was included in the final analysis, with 431 (13.88%) self-reported infertility. A negative association was found between log2-SII, log2-PLR and infertility, with an OR of 0.95 (95% CI: 0.78,1.15; p = 0.60), 0.80 (95% CI:0.60,1.05; p = 0.10), respectively. The results were similar in model 1, model 2, and model 3. Compared with the lowest quartile (Q1), the third quartile (Q3) of log2-SII was negatively correlation with infertility, with an OR (95% CI) of 0.56 (95% CI: 0.37,0.85; p = 0.01) in model 3. Similarly, the third quartile (Q3) of log2-PLR was negatively correlation with infertility, with an OR (95% CI) of 0.61 (95% CI: 0.43,0.88; p = 0.01) in model 3. No significant association was observed between log2-LC, log2-PPN, log2-NLR, log2-LMR and infertility in model 3. A similar U-shaped relationship between log2-SII and infertility was found (p for non-linear < 0.05). The results of subgroup analyses revealed that associations between the third quartile (Q3) of log2-SII, log2-PLR and infertility were nearly consistent. CONCLUSION: The findings showed that SII and PLR were negatively associated with infertility. Further studies are needed to explore their association better and the underlying mechanisms.

An open dataset for intelligent recognition and classification of abnormal condition in longwall mining.

The underground coal mine production of the fully mechanized mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expect to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners' behaviour and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining.