Plasma GPI and PGD are associated with vascular normalization and may serve as novel prognostic biomarkers for lung adenocarcinoma: Multi-omics and multi-dimensional analysis.

The aim of this study was to explore potential novel plasma protein biomarkers for lung adenocarcinoma (LUAD). A plasma proteomics analysis was carried out and candidate protein biomarkers were validated in 102 LUAD cases and 102 matched healthy controls. The same LUAD tumor tissues were detected to explore the correlation between the expression of candidate proteins in tissues and plasma and vascular normalization. A LUAD active metastasis mice model was constructed to explore the role of candidate proteins for lung metastasis. GPI and PGD were verified to be upregulated in plasma from LUAD patients, and the expression of GPI in tumor tissue was positively correlated with the expression of GPI in plasma and negatively correlated with the normalization of tumor blood vessels. Meanwhile, a negative correlation between the expression of GPI and PGD in plasma and tumor vascular normalization was discovered. In the LUAD active metastasis model, the lowest levels of vascular normalization and the highest expression of GPI and PGD were found in mice with lung metastases. This study found that GPI and PGD may be potential plasma biomarkers for LUAD, and monitoring those may infer the risk of metastasis and malignancy of the tumor. SIGNIFICANT: We identified GPI and PGD as potential novel diagnostic and prognostic biomarkers for LUAD. PGD and GPI can be used as diagnostic biomarkers in combination with other available strategies to assist in the screening and diagnosis of LUAD, and as prognostic biomarkers aid in predict the risk of tumor metastasis and malignancy in patients with LUAD.

The value of the SUV ratio between lymph node and bone marrow in predicting pelvic lymphatic metastasis of patients with locally advanced cervical cancer: an integrated PET/CT study.

PURPOSE: This study aimed to evaluate the value of the standardized uptake value (SUV) ratio between lymph nodes and bone marrow (BM) measured by Fluorine-18-fluorodeoxyglucose PET and computed tomography ( 18 F-FDG PET/CT) for predicting pelvic lymph node (PLN) metastasis in patients with locally advanced cervical cancer (LACC). MATERIALS AND METHODS: A total of 62 patients with pathological stage Ib-IVa cervical cancer who underwent 18 F-FDG PET/CT before treatment were reviewed retrospectively. We measured the metabolic and morphological parameters of lymph nodes and primary tumors, bone marrow SUV (SUVBM) and calculated the ratio of lymph nodes maximum SUV (SUVmax) to bone marrow SUV (SUVLN/BM) and the ratio of short-axis diameter to long-axis diameter (Ds/l) of lymph nodes. A receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic efficacy of each parameter. RESULTS: There were 180 lymph nodes with pathological evidence included in the study. Our results indicated that Ds/l, SUVmax of lymph nodes (SUVLN) and SUVLN/BM were independent risk factors for PLN metastasis in LACC ( P < 0.05), and SUVLN/BM showed the best diagnostic performance by ROC curve analysis. The SUVBM in the anemia group was significantly higher than that in the nonanemia group (3.05 vs. 2.40, P < 0.05); furthermore, false-positive cases decreased when the SUVLN/BM was used as the diagnostic criterion instead of SUVLN, especially in the anemia group. ROC curve analysis showed that the area under the curve value of the combination of SUVLN/BM and Ds/l was 0.884 ( P < 0.05), which was higher than Ds/l or SUVLN/BM alone. CONCLUSIONS: SUVLN/BM could improve the ability to predicting PLN metastasis in patients with LACC, and the diagnostic efficacy of the combination of SUVLN/BM and Ds/l might be better than that of a single parameter.

Fault diagnosis method of submersible screw pump based on random forest.

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.