A preoperative predictive model for stage IV endometriosis.

This was a retrospective study that evaluated a total of 280 patients who underwent surgery for complete removal of endometriosis to develop and validate the predictive model for stage IV endometriosis. The differences between stage I-III and stage IV endometriosis were performed by logistic regression. A model for the prediction of stage IV endometriosis was constructed, which was subsequently validated. The independent variables were visual analogue scale (VAS)≥4 [3.855, 95% confidence interval (CI): 1.675-8.871, p = 0.002], painful nodularity on uterosacral ligaments (13.954, 95% CI: 1.658-117.423, p = 0.015), and bilateral endometriosis (5.933, 95% CI: 1.931-18.225, p = 0.002). The AUC of the model was 0.777, with a sensitivity of 71.9% and specificity of 76.3% for stage IV endometriosis. Therefore, a complete collection of patient information prior to surgery, asking about pain and VAS scores, careful completion of pelvic examinations, and application of imaging techniques are conducive to better diagnosis and prediction of advanced endometriosis.IMPACT STATEMENTWhat is already known on this subject? Endometriosis, a chronic disease causing pain and infertility, is characterised by endometrial-like tissue outside the uterine cavity, which is often treated via surgery at present. Considering the risks of surgery, it is necessary to identify patients with stage IV endometriosis through non-invasive predictive models for adequate preparation for surgery. However, there is no reliable non-invasive predictive model now, despite utilisation of patient medical history, symptoms especially pain-related ones, pelvic examinations, laboratory examinations, and images in the preoperative diagnosis of endometriosis in the clinic.What do the results of this study add? A model developed based on three simple, accessible and non-invasive indicators displays good performance in predicting stage IV endometriosis.What are the implications of these findings for clinical practice and/or further research? It is conducive to diagnosing and predicting advanced endometriosis before surgery, so as to reduce the difficulty and improve the safety of surgery.

A high-performance SNP panel developed by machine-learning approaches for characterizing genetic differences of Southern and Northern Han Chinese, Korean, and Japanese individuals.

Population stratification analyses targeting genetically closely related East Asians have revealed that distinguishable differentiation exists between Han Chinese, Korean, and Japanese individuals, as well as between southern (S-) and northern (N-) Han Chinese. Previous studies offer a number of choices for ancestry informative single nucleotide polymorphisms (AISNPs) to discriminate East-Asian populations. In this study, we collected and examined the efficiency of 1185 AISNPs using frequency and genotype data from various publicly available databases. With the aim to perform fine-scale classification of S-Han, N-Han, Korean, and Japanese subjects, machine-learning methods (Softmax and Random Forest) were used to screen a panel of highly informative AISNPs and to develop a superior classification model. Stepwise classification was implemented to increase and balance the discrimination in the process of AISNP selection, first discriminating Han, Korean, and Japanese individuals, and then characterizing stratification between S-Han and N-Han. The final 272-AISNP panel is an alternative optimization of various previous works, which promises reliable and >90% accuracy in classification of the four East-Asian groups. This AISNP panel and the machine-learning model could be a useful and superior choice in medical genome-wide association studies and in forensic investigations for unknown suspect identity.

Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic classification of southern (S-) and northern (N-) Han Chinese, Korean and Japanese individuals in this study, we collected and analyzed 1185 ancestry informative SNPs (AISNPs) from previous literature reports and our laboratory findings. First, two machine learning algorithms, softmax and randomForest, were used to build genetic classification models. Then, phylogenetic tree, STRUCTURE and principal component analysis were used to evaluate the performance of classification for different AISNP panels. The 234-AISNP panel achieved a fine-scale differentiation among the target populations in four classification schemes. The accuracy of the softmax model was 92%, which realized the accurate classification of the S-Han, N-Han, Korean and Japanese individuals. The two machine learning models tested in this study provided important references for the high-resolution discrimination of close-range populations and will be useful tools to optimize marker panels for developing forensic DNA ancestry inference systems.

Lipoxin A4 activates alveolar epithelial sodium channel gamma via the microRNA-21/PTEN/AKT pathway in lipopolysaccharide-induced inflammatory lung injury.

Lipoxin A4 (LXA4), as an endogenously produced lipid mediator, promotes the resolution of inflammation. Previously, we demonstrated that LXA4 stimulated alveolar fluid clearance through alveolar epithelial sodium channel gamma (ENaC-γ). In this study, we sought to investigate the mechanisms of LXA4 in modulation of ENaC-γ in lipopolysaccharide (LPS)-induced inflammatory lung injury. miR-21 was upregulated during an LPS challenge and downregulated by LXA4 administration in vivo and in vitro. Serum miR-21 concentration was also elevated in acute respiratory distress syndrome patients as compared with healthy volunteers. LPS increased miR-21 expression by activation of activator protein 1 (AP-1). In A549 cells, miR-21 upregulated phosphorylation of AKT activation via inhibition of phosphatase and tensin homolog (PTEN), and therefore reduced the expression of ENaC-γ. In contrast, LXA4 reversed LPS-inhibited ENaC-γ expression through inhibition of AP-1 and activation of PTEN. In addition, an miR-21 inhibitor mimicked the effects of LXA4; overexpression of miR-21 abolished the protective effects of LXA4. Finally, both AKT and ERK inhibitors (LY294002 and UO126) blocked effects of LPS on the depression of ENaC-γ. However, LXA4 only inhibited LPS-induced phosphorylation of AKT. In summary, LXA4 activates ENaC-γ in part via the miR-21/PTEN/AKT signaling pathway.

Volatile anesthetic sevoflurane suppresses lung cancer cells and miRNA interference in lung cancer cells.

PURPOSE: Sevoflurane is widely used in lung cancer surgery. It is well known that volatile anesthetics have a lung-protective effect in lung cancer surgery. However, the association between the inhibition of cancer cells and miRNAs interference remains unknown. Whether sevoflurane can affect some miRNAs in A549 cells has not been reported. The main aim of the present study was to investigate the effect of 3% sevoflurane on A549 cells and assess whether it regulates A549 cells by interfering with miRNA. METHODS: In three percent sevoflurane-pretreated A549 cells, treated for a duration of 30 minutes, the apoptosis rate of A549 cells was evaluated using a flow cytometer. The expression of 6 types of miRNAs associated with non-small cell lung cancer was analyzed by real-time quantitative polymerase chain reaction. RESULTS: An obvious apoptosis-promoting effect was found in A549 cells, which had been treated with 3% sevoflurane. The expression of several miRNAs that regulate apoptosis was significantly changed compared with the control group. CONCLUSION: Three percent sevoflurane can significantly increase the apoptosis rate of A549 cells, which may reduce the spread of cancer cells caused by operation. Sevoflurane disturbed the expression of the miRNAs that regulate apoptosis.