Influence of ongoing discharge from multiple wastewater treatment plants on microplastic patterns in small-scale receiving rivers.

As widely acknowledged, wastewater treatment plants (WWTPs) stand as significant contributors to the presence of microplastics in surface water. Nonetheless, there exists a notable research gap regarding the extent of potential pollution resulting from the concurrent and uninterrupted discharges originating from multiple WWTPs into small-scale receiving water bodies. This study endeavors to address this knowledge deficit by conducting a thorough investigation into the prevalence of microplastics in surface water. The research encompasses seven distinct locations within the Changzhou section of the Beijing-Hangzhou Grand Canal and the effluent of three WWTPs situated along the tributary. The results indicate differences in the distribution of microplastics in surface waters of mainstream and tributaries. While the microplastic abundance and composition showed little variation along the main stream, the tributaries displayed an overall increasing trend in microplastic abundance from upstream to downstream. Notably, the major contributors to this increase were fragments, fiber particles, and microplastics with particle sizes ranging from 100 to 300 µm. Considering that the primary distinction between the tributaries and the mainstream is the presence of the three WWTPs along the tributaries, the study conducted a correlation analysis between river surface water and effluents from these plants. The results indicated a stronger correlation between the tributaries and the effluents, suggesting that WWTPs are one of the primary factors contributing to the elevated levels of microplastics in the tributaries. Finally, a comparative analysis of microplastic abundance in the Beijing-Hangzhou Grand Canal's Changzhou section and other regions was conducted. The findings revealed that the microplastic pollution level in the Beijing-Hangzhou Grand Canal's Changzhou section is higher than that in most other rivers. Therefore, the issue of microplastic pollution in the Beijing-Hangzhou Grand Canal's Changzhou section warrants our attention, particularly with regard to the effectiveness of microplastic removal by the WWTPs along its course.

Establishment and validation of a nomogram for subsequent first-cycle live births in patients diagnosed with recurrent implantation failure: a population-based analysis.

Background: The inability of patients with recurrent implantation failure (RIF) to achieve pregnancy and a live birth after multiple high-quality embryo transfer treatments has been recognized as a major obstacle to successful application of artificial reproductive technologies. The objective of this study was to establish and validate a nomogram for prediction of subsequent first-cycle live births to guide clinical practice in patients diagnosed with RIF. Methods: A total of 538 patients who underwent in vitro fertilization/intracytoplasmic sperm injection treatment and were first diagnosed with RIF at the Reproductive Center of the First Affiliated Hospital of Xinjiang Medical University between January 2017 and December 2020 were enrolled. The patients were randomly divided into a training cohort (n=408) and a validation set (n=175) in a ratio of 7:3. A nomogram model was constructed using the training set based on the results of univariate and multivariate logistic regression analyses and validated in the validation set. Results: Age, body mass index, duration of RIF, endometrial thickness, type of embryo transferred, and number of previous biochemical pregnancies were included in the nomogram for prediction of subsequent first-cycle live births in patients diagnosed with RIF. Analysis of the area under the receiver-operating characteristic curve, calibration plots, and decision curve analysis showed that our predictive model for live births had excellent performance. Conclusion: We have developed and validated a novel predictive model that estimates a woman's chances of having a live birth after a diagnosis of RIF and provides clinicians with a personalized clinical decision-making tool.

Identification of MARK2, CCDC71, GATA2, and KLRC3 as candidate diagnostic genes and potential therapeutic targets for repeated implantation failure with antiphospholipid syndrome by integrated bioinformatics analysis and machine learning.

Background: Antiphospholipid syndrome (APS) is a group of clinical syndromes of thrombosis or adverse pregnancy outcomes caused by antiphospholipid antibodies, which increase the incidence of in vitro fertilization failure in patients with infertility. However, the common mechanism of repeated implantation failure (RIF) with APS is unclear. This study aimed to search for potential diagnostic genes and potential therapeutic targets for RIF with APS. Methods: To obtain differentially expressed genes (DEGs), we downloaded the APS and RIF datasets separately from the public Gene Expression Omnibus database and performed differential expression analysis. We then identified the common DEGs of APS and RIF. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and we then generated protein-protein interaction. Furthermore, immune infiltration was investigated by using the CIBERSORT algorithm on the APS and RIF datasets. LASSO regression analysis was used to screen for candidate diagnostic genes. To evaluate the diagnostic value, we developed a nomogram and validated it with receiver operating characteristic curves, then analyzed these genes in the Comparative Toxicogenomics Database. Finally, the Drug Gene Interaction Database was searched for potential therapeutic drugs, and the interactions between drugs, genes, and immune cells were depicted with a Sankey diagram. Results: There were 11 common DEGs identified: four downregulated and seven upregulated. The common DEG analysis suggested that an imbalance of immune system-related cells and molecules may be a common feature in the pathophysiology of APS and RIF. Following validation, MARK2, CCDC71, GATA2, and KLRC3 were identified as candidate diagnostic genes. Finally, Acetaminophen and Fasudil were predicted as two candidate drugs. Conclusion: Four immune-associated candidate diagnostic genes (MARK2, CCDC71, GATA2, and KLRC3) were identified, and a nomogram for RIF with APS diagnosis was developed. Our findings may aid in the investigation of potential biological mechanisms linking APS and RIF, as well as potential targets for diagnosis and treatment.

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI.

Background: A number of live birth predictive model during assisted reproductive technology treatment have been available in recent years, but few targeted evaluating the chances of live birth in poor ovarian response(POR) patients. The aim of this study was to develop a nomogram based on POSEIDON criteria to predict live birth in patients with expected POR. Methods: This retrospective cohort study using clinical data from 657 patients in POSEIDON Groups 3 and 4 (antral follicle count [AFC] ≤5 and AMH <1.2 ng/ml) in the Center for Reproductive Medicine, First Affiliated Hospital of Xinjiang Medical University, and Construction a nomogram model t. Results: Among 657 expected POR patients, 111 (16.89%) had live births, and 546 (83.11%) did not have live births. These were divided into a training set(n=438) and a validation set (n=219). Multivariate logistic regression analysis showed that the age (OR = 0.91, 95% CI: 0.86-0.97), BMI (OR = 1.98, 95% CI: 1.09-3.67), AMH (OR = 3.48, 95% CI: 1.45-8.51), normal fertilized oocytes (OR = 1.40, 95% CI: 1.21-1.63), and the basal FSH (OR = 0.89, 95% CI: 0.80-0.98) of the female were independent factors predicting live birth in patients with expected POR. Then, an individualized nomogram prediction model was built from these five factors. The area under the ROC curve of the live birth prediction model was 0.820 in the training set and 0.879 in the validation set. Conclusion: We have developed a nomogram combining clinical and laboratory factors to predict the probability of live birth in patients with an expected POR during IVF/ICSI, which can helpful for clinician in decision-making. However, the data comes from the same center, needs a prospective multicenter study for further in-depth evaluation and validation of this prediction model.

Relationship of PD-1 (PDCD1) and PD-L1 (CD274) Single Nucleotide Polymorphisms with Polycystic Ovary Syndrome.

This study is to investigate the relationship of programmed cell death 1 (PD-1; also known as PDCD1) and programmed death-1-ligand 1 (PD-L1; also known as CD274) single nucleotide polymorphisms (SNPs) with polycystic ovary syndrome (PCOS). This study enrolled 330 PCOS patients and 350 matched controls. ELISA was used to detect the PD-1 and PD-L1 levels in serum. SnaPshot genotyping was performed to analyze the PD-1 and PD-L1 genotyping. Linkage disequilibrium and haplotype of TagSNP loci of PD-1 and PD-L1 genes were also detected. The relationship of genotypes and alleles with PCOS was analyzed. The levels of PD-1 and PD-L1 in the serum of PCOS patients were significantly lower than those in the control group (P < 0.01). The haplotype TT of PD-1 gene at rs10204525 and rs7421861 loci was significantly lower in the PCOS group than in the control group (P < 0.001, OR = 0.67, and 95%CI = 0.54-0.84). PD-L1 gene SNP loci rs2282055, rs2890658, rs10125854, and rs702275 had linkage disequilibrium. The haplotypes TAAA, GAAC, GAGC, GCAA, and TCGA of PD-L1 gene SNP loci were significantly higher in PCOS patients than in the control group, whereas haplotypes GAAA, TAAC, TCAA, GCGA, GCAC, and TCGC of PD-L1 gene SNP loci were significantly lower in PCOS patients than in the control group. PD-1 and PD-L1 SNPs may be related to the pathogenesis of PCOS. PD-1 gene SNP loci rs10204525 and rs7421861 and PD-L1 gene SNP loci rs2282055, rs2890658, rs10125854, and rs702275 may be new candidate polymorphic loci for PCOS.