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Polycystic ovary syndrome (PCOS), a common endocrine and metabolic disorder affecting women in their reproductive years. Emerging evidence suggests that the maternal-fetal immune system is crucial for proper pregnancy. However, whether immune function is altered at the end of pregnancy in PCOS women and the underlying molecular mechanisms is currently unexplored. Herein, the basic maternal immune system was investigated (n = 136 in the control group; n = 103 in the PCOS group), and whole-transcriptome sequencing was carried out to quantify the mRNAs, miRNAs, and lncRNAs expression levels in fetal side placental tissue of women with PCOS. GO, KEGG, and GSEA analysis were employed for functional enrichment analysis. The process of identifying hub genes was conducted utilizing the protein-protein interaction network. CIBERSORT and Connectivity Map were deployed to determine immune cell infiltration and predict potential drugs, respectively. A network of mRNA-miRNA-lncRNA was constructed and then validated by qRT-PCR. First, red blood cell count, neutrophil count, lymphocyte count, hypersensitive C-reactive protein, and procalcitonin were significantly elevated, while placental growth factor was hindered in PCOS women. We identified 308 DEmRNAs, 77 DEmiRNAs, and 332 DElncRNAs in PCOS samples. Functional enrichment analysis revealed that there were significant changes observed in terms of the immune system, especially the chemokine pathway. Eight genes, including FOS, JUN, EGR1, CXCL10, CXCR1, CXCR2, CXCL11, and CXCL8, were considered as hub genes. Furthermore, the degree of infiltration of neutrophils was dramatically decreased in PCOS tissues. In total, 57 ceRNA events were finally obtained, and immune-related ceRNA networks were validated. Some potential drug candidates, such as enalapril and RS-100329, could have a function in PCOS therapy. This study represents the inaugural attempt to evaluate the immune system at the end of pregnancy and placental ceRNA networks in PCOS, indicating alterations in the chemokine pathway, which may impact fetal and placental growth, and provides new therapy targets. [ABSTRACT FROM AUTHOR]

Nearly one-fifth of patients with non-small cell Lung Cancer (NSCLC) will develop liver metastases (LMs), and the overall treatment strategy of LMs will directly affect the survival of patients. However, some retrospective studies have found that patients receiving chemotherapy or targeted therapy have a poorer prognosis once LMs develop. In recent years, multiple randomised controlled trials (RCTS) have shown significant improvements in outcomes for patients with advanced lung cancer following the introduction of immune checkpoint inhibitors (ICIs) compared to conventional chemotherapy. ICIs is safe and effective in patients with LMs, although patients with LMs are mostly underrepresented in randomised clinical trials. However, NSCLC patients with LMs have a significantly worse prognosis than those without LMs when treated with ICIs, and the mechanism by which LMs induce systemic anti-tumour immunity reduction is unknown, so the management of LMs in patients with NSCLC is a clinical challenge that requires more optimised therapies to achieve effective disease control. In this review, we summarised the mechanism of ICIs in the treatment of LMs, the clinical research and treatment progress of ICIs and their combination with other therapies in patients with LMs from NSCLC. [ABSTRACT FROM AUTHOR]

Given the difficulties in measuring the effective wind speed of wind turbine during the maximum power point tracking (MPPT) process and the unknown nature of the system, this paper proposes a neural network super-twisted sliding mode control (NNST-SMC) method with echo state network (ESN) wind speed estimation. The rotor speed and electromagnetic power are taken as ESN inputs, and the effective wind speed is estimated through the inverse model of wind turbine dynamics. The super-twisted algorithm (STA) can effectively improve the chattering problem of the traditional sliding mode control (SMC) system. The RBF neural network is introduced to compensate for disturbance and uncertainty characteristics of the wind turbine. Results show that compared with the neural network first-order sliding mode control (NNSMC) and super-twisted sliding mode control (ST-SMC), the proposed method can improve the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]