Roles of Osteopontin Gene Polymorphism (rs1126616), Osteopontin Levels in Urine and Serum, and the Risk of Urolithiasis: A Meta-Analysis

Objective. Previous studies have investigated the relationships between osteopontin gene polymorphism rs1126616 and OPN levels and urolithiasis, but the results were controversial. Our study aimed to clarify such relationships. Methods. A meta-analysis was performed by searching the databases Pubmed, Embase, and Web of Science for relevant studies. Crude odds ratios (ORs) or standardised mean differences with 95% confidence intervals (CIs) were calculated to evaluate the strength of association. Publication bias was estimated using Begg's funnel plots and Egger's regression test. Results. Overall, a significantly increased risk of urolithiasis was associated with OPN gene polymorphism rs1126616 for all the genetic models except recessive model. When stratified by ethnicity, the results were significant only in Turkish populations. For OPN level association, a low OPN level was detected in the urine of urolithiasis patients in large sample size subgroup. Results also indicated that urolithiasis patients have lower OPN level in serum than normal controls. Conclusion. This meta-analysis revealed that the T allele of OPN gene polymorphism increased susceptibility to urolithiasis. Moreover, significantly lower OPN levels were detected in urine and serum of urolithiasis patients than normal controls, thereby indicating that OPN has important functions in the progression of urolithiasis

Construction and validation of a glioblastoma prognostic model based on immune-related genes

BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research

A Study on Evaporation Calculations of Agricultural Reservoirs in Hyper-Arid Areas

Free surface evaporation is an important process in regional water cycles and energy balance. The accurate calculation of free surface evaporation is of great significance for evaluating and managing water resources. In order to improve the accuracy of estimating reservoir evaporation in data-scarce arid regions, the applicability of the energy balance method was assessed to calculate water surface evaporation based on the evaporator and reservoir evaporation experiment. A correlation analysis was used to assess the major meteorological factors that affect water surface temperature to obtain the critical parameters of the machine learning models. The water surface temperature was simulated using five machine learning algorithms, and the accuracy of results was evaluated using the root mean square error (RMSE), correlation coefficient (r), mean absolute error (MAE), and Nash efficiency coefficient (NSE) between observed value and calculated value. The results showed that the correlation coefficient between the evaporation capacity of the evaporator, calculated using the energy balance method and the observed evaporation capacity, was 0.946, and the RMSE was 0.279. The r value between the calculated value of the reservoir evaporation capacity and the observed value was 0.889, and the RMSE was 0.241. The meteorological factors related to the change in water surface temperature were air temperature, air pressure, relative humidity, net radiation and wind speed. The correlation coefficients were 0.554, −0.548, −0.315, −0.227, and 0.141, respectively. The RMSE and MAE values of five models were: RF (0.464 and 0.336), LSSVM (0.468 and 0.340), LSTM (1.567 and 1.186), GA-BP (0.709 and 0.558), and CNN (1.113 and 0.962). In summary, the energy balance method could accurately calculate the evaporation of evaporators and reservoirs in hyper-arid areas. As an important calculation parameter, the water surface temperature is most affected by air temperature, and the RF algorithm was superior to the other algorithms in predicting water surface temperature, and it could be used to predict the missing data. The energy balance model and random forest algorithm can be used to accurately calculate and predict the evaporation from reservoirs in hyper-arid areas, so as to make the rational allocation of reservoir water resources

Polyamine-Targeting Gefitinib Prodrug and its Near-Infrared Fluorescent Theranostic Derivative for Monitoring Drug Delivery and Lung Cancer Therapy

The therapy of non-small-cell lung cancer (NSCLC) is challenging because of poor prognosis. There are urgent demands for targeting anti-tumor drugs with reliable efficacy and clear pharmacokinetics

Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Dam deformation monitoring data are generally characterized by non-smooth and no-saturated S-type fluctuation. The grey Verhulst model can get better results only when the data series is non-monotonic swing development and the saturated S-shaped sequence. Due to the limitations of the grey Verhulst model, the prediction accuracy will be limited to a certain extent. Aiming at the shortages in the prediction based on the traditional Verhulst model, the optimized grey Verhulst model is proposed to improve the prediction accuracy of the dam deformation monitoring. Compared with those of the traditional GM (1,1) model, the DGM (2,1) model, and the traditional Verhulst (1,1) model, the experimental results show that the new proposed optimized Verhulst model has higher prediction accuracy than the traditional gray model. This study offers an effective model for dealing with the non-saturated fluctuation sequence to predict dam deformation under uncertain conditions

A mitochondrial-targeting near-infrared fluorescent probe for bioimaging and evaluating endogenous superoxide anion changes during ischemia/reperfusion injury

The outburst of superoxide anion (O-2(center dot-)) in mitochondrial during ischemia/reperfusion (I/R) process will cause a series of oxidative damage including polarity loss of mitochondrial membrane potential, overload of secondary cellular calcium, and cascade apoptosis. To monitor the O-2(center dot-) level fluctuations as well as to evaluate the relationship between O-2(center dot-) concentration and the degree of cell apoptosis during I/R process, we propose a ratiometric near-infrared mitochondrial targeting fluorescent probe Mito-Cy-Tfs for the detection of level changes of O-2(center dot-) in cells and in vivo. The probe Mito-Cy-Tfs is composed of three moieties: near-infrared heptamethine cyanine as fluorescence signal transducer, trifluoromethanesulfonamide as fluorescence modulator, and lipophilic triphenylphosphonium cation as mitochondrial guider. The probe can well locate in mitochondria and respond the concentration changes of endogenous O-2(center dot-) selectively and sensitively. The probe has been successfully utilized to image the endogenous O-2(center dot-) fluctuations in four kinds of cell I/R models (glucose deprivation/reperfusion, serum deprivation/reperfusion, oxygen deprivation/reperfusion and glucose-serum-oxygen deprivation/reperfusion). The probe also exhibits deep tissue penetration for real-time imaging of O-2(center dot-) concentration in liver of I/R mice model. We confirm that the adoption of ischemic preconditioning (IPC) and postconditioning (IPTC) can protect liver from I/R injury. The probe can be employed to accurately indicate and evaluate the mutual relationship between the levels of O-2(center dot-) and the degrees of organ damage during I/R, IPC and IPTC processes. The above applications make our new probe a potential candidate for the clinical surgery assessment. (C) 2017 Elsevier Ltd. All rights reserved

Fluorescent chemical probes for accurate tumor diagnosis and targeting therapy

Surgical resection of solid tumors is currently the gold standard and preferred therapeutic strategy for cancer. Chemotherapy drugs also make a significant contribution by inhibiting the rapid growth of tumor cells and these two approaches are often combined to enhance treatment efficacy. However, surgery and chemotherapy inevitably lead to severe side effects and high systemic toxicity, which in turn results in poor prognosis. Precision medicine has promoted the development of treatment modalities that are developed to specifically target and kill tumor cells. Advances in in vivo medical imaging for visualizing tumor lesions can aid diagnosis, facilitate surgical resection, investigate therapeutic efficacy, and improve prognosis. In particular, the modality of fluorescence imaging has high specificity and sensitivity and has been utilized for medical imaging. Therefore, there are great opportunities for chemists and physicians to conceive, synthesize, and exploit new chemical probes that can image tumors and release chemotherapy drugs in vivo. This review focuses on small molecular ligand-targeted fluorescent imaging probes and fluorescent theranostics, including their design strategies and applications in clinical tumor treatment. The progress in chemical probes described here suggests that fluorescence imaging is a vital and rapidly developing field for interventional surgical imaging, as well as tumor diagnosis and therapy

A near-infrared fluorescent probe for evaluating glutamyl transpeptidase fluctuation in idiopathic pulmonary fibrosis cell and mice models

Idiopathic pulmonary fibrosis (IPF), whose early diagnosis and effective treatment still remain the focus of clinical studies, is a chronic, irreversible and finally fatal pulmonary disease. Glutamyl transpeptidase (GGT) has a potential relationship with the occurrence and development of IPF. Therefore, explore whether GGT can be applied as a biological indicator for the clinical identification and diagnosis of IPF, sensitive and accurate detection of GGT under physiological conditions is necessary. In this research, a new fluorescent probe Cy-GGT was exhibited for detecting GGT concentrations in pulmonary fibrosis cell and mice models. Cy-GGT was capable of rapidly and selectively detecting GGT in vitro. The probe was successfully applied for visualizing GGT in oxidative stress cell models, pulmonary fibrosis cells and mice models. The results revealed that intracellular GGT increased in the cells and mice models of pulmonary fibrosis. GGT plays a significant part in pulmonary fibrosis, and the GGT level abnormally expressed in the lung tissue may be employed as a potential biological indicator to diagnose IPF lesions. Furthermore, the discovery of the close relationship between IPF and GGT will provide a new idea for effective therapy of IPF in the future