Overexpression of RRM2 decreases thrombspondin-1 and increases VEGF production in human cancer cells in vitro and in vivo: implication of RRM2 in angiogenesis

<p>Abstract</p> <p>Background</p> <p>In addition to its essential role in ribonucleotide reduction, ribonucleotide reductase (RNR) small subunit, RRM2, has been known to play a critical role in determining tumor malignancy. Overexpression of RRM2 significantly enhances the invasive and metastatic potential of tumor. Angiogenesis is critical to tumor malignancy; it plays an essential role in tumor growth and metastasis. It is important to investigate whether the angiogenic potential of tumor is affected by RRM2.</p> <p>Results</p> <p>We examined the expression of antiangiogenic thrombospondin-1 (TSP-1) and proangiogenic vascular endothelial growth factor (VEGF) in two RRM2-overexpressing KB cells: KB-M2-D and KB-HURs. We found that TSP-1 was significantly decreased in both KB-M2-D and KB-HURs cells compared to the parental KB and mock transfected KB-V. Simultaneously, RRM2-overexpressing KB cells showed increased production of VEGF mRNA and protein. In contrast, attenuating RRM2 expression via siRNA resulted in a significant increased TSP-1 expression in both KB and LNCaP cells; while the expression of VEGF by the two cells was significantly decreased under both normoxia and hypoxia. In comparison with KB-V, overexpression of RRM2 had no significant effect on proliferation in vitro, but it dramatically accelerated in vivo subcutaneous growth of KB-M2-D. KB-M2-D possessed more angiogenic potential than KB-V, as shown in vitro by its increased chemotaxis for endothelial cells and in vivo by the generation of more vascularized tumor xenografts.</p> <p>Conclusion</p> <p>These findings suggest a positive role of RRM2 in tumor angiogenesis and growth through regulation of the expression of TSP-1 and VEGF.</p

An approach to urban system spatial planning in Chengdu Chongqing economic circle using geospatial big data

Evidence suggests city grouping is an important way to implement urbanization in China. However, the Chengdu-Chongqing Economic Circle (CCEC) is a typical dual-core structure, and the development level of each city is different. If we do not focus on the key directions for urban development, it will not be conducive to the new-type urbanization process. Therefore, we use spatial analysis techniques and geographic big data sets to construct an approach for urban system layout optimization from a global perspective. It mainly includes urban extended trend analysis based on night light, multi-modal traffic network analysis, and spatial economic density analysis using Open Street Map (OSM) and Point of Interest (POI) data. The research results show the following interesting findings. Firstly, the historical relationship of cities has a significant impact on city grouping, and efficient transportation connections and prosperous enterprise distribution are key conditions for urban grouping during the acceleration period of urbanization. Secondly, the development of urban grouping should break through administrative restrictions and achieve a moderate separation of administrative divisions and economic divisions. It is beneficial to the rapid growth of the city group and the improvement of the internal structure. Thirdly, the urban group of Southern Sichuan and Western Chongqing (SSWC) is the region with the most potential for growth in CCEC. The urban expansion index (UEI) of the Yibin-Luzhou area is 2.16, and the spatial economic density has increased by 130/km2 in the past decade. Providing flexible development authority and focus on the construction of the Yibin-Luzhou Urban Belt along the Yangtze River is an important way to integrate southern Sichuan. Moreover, we found the UEI has a good universality and it can be used for studying urban expansion trends and city relationships in rapidly developing regions, especially in metropolitan areas or urban agglomeration

Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease

Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients

Arginine starvation impairs mitochondrial respiratory function in ASS1-deficient breast cancer cells.

Autophagy is the principal catabolic response to nutrient starvation and is necessary to clear dysfunctional or damaged organelles, but excessive autophagy can be cytotoxic or cytostatic and contributes to cell death. Depending on the abundance of enzymes involved in molecule biosynthesis, cells can be dependent on uptake of exogenous nutrients to provide these molecules. Argininosuccinate synthetase 1 (ASS1) is a key enzyme in arginine biosynthesis, and its abundance is reduced in many solid tumors, making them sensitive to external arginine depletion. We demonstrated that prolonged arginine starvation by exposure to ADI-PEG20 (pegylated arginine deiminase) induced autophagy-dependent death of ASS1-deficient breast cancer cells, because these cells are arginine auxotrophs (dependent on uptake of extracellular arginine). Indeed, these breast cancer cells died in culture when exposed to ADI-PEG20 or cultured in the absence of arginine. Arginine starvation induced mitochondrial oxidative stress, which impaired mitochondrial bioenergetics and integrity. Furthermore, arginine starvation killed breast cancer cells in vivo and in vitro only if they were autophagy-competent. Thus, a key mechanism underlying the lethality induced by prolonged arginine starvation was the cytotoxic autophagy that occurred in response to mitochondrial damage. Last, ASS1 was either low in abundance or absent in more than 60% of 149 random breast cancer biosamples, suggesting that patients with such tumors could be candidates for arginine starvation therapy

Identification of Drugs that Interact with Herbs in Drug Development

To date, several clinically important drugs have been identified that interact with commonly used herbs. These drugs include (among others) warfarin, midazolam, digoxin, amitriptyline, indinavir, cyclosporine, tacrolimus and irinotecan. Importantly, many of these drugs have very narrow therapeutic indices. Most of them are substrates for cytochrome P450s (CYPs) and/or P-glycoprotein (P-gp). Because drug–herb interactions can significantly affect circulating levels of drug and, hence, alter the clinical outcome, the identification of drugs that interact with commonly used herbal medicines has important implications in drug development. In silico, in vitro, animal and human studies are often used to identify drug interactions with herbs. We propose that drug–herb and herb–CYP interaction studies should be incorporated into drug development. Because of the clinical significance of drug interactions with herbs, there is a strong necessity to identify drugs that may interact with herbal medicines in drug development

A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria

Lymph node dissection effectively shortens the course of anti-tuberculosis treatment

Objective: To evaluate the clinical efficacy of postoperative ultra-short-course chemotherapy in treating cervical lymph node tuberculosis in the Wuhan region. Methods: Follow-up of patients in the surgery and non-surgery group after discharge, evaluating the number of cervical lymph nodes during the administration of antituberculosis drugs. Results: The age of the patients in the surgical therapy group ranged from 6 to 83 years old with an average age of 45 and a standard deviation of 20. The number of cervical lymph nodes in the patients ranged from 1.61 to 8.15. The average antituberculosis treatment duration before surgery for patients in the surgical group was 98.02 days, while for patients in the non-surgical group it was 96.13 days. The average length of hospital stay for patients receiving surgical treatment was 12.76 days, while for patients receiving non-surgical treatment it was 8.74 days. The average antituberculosis treatment duration after discharge for patients in the surgical group was 205 days, with a standard deviation of 42.39, while for patients in the non-surgical group it was 372 days, with a standard deviation of 71.54. The T-test results for antituberculosis treatment during hospitalization and after discharge were 98.3x10-10 and 5.02x10-67, respectively. Conclusion: After surgical treatment of cervical lymph node tuberculosis, the effectiveness of a 4–6 month short-course chemotherapy in Wuhan region is not weaker than the effectiveness of a conventional 6–9 month drug treatment

Influencing factors of care dependence in patients with coronary heart disease after percutaneous coronary intervention—A cross‐sectional study

Abstract Aim Care dependence has been scarcely investigated in coronary heart disease patients after percutaneous coronary intervention. This study aimed to investigate the association between frailty, self‐efficacy, combined effects of frailty and self‐efficacy, mental health, and care dependence in coronary heart disease patients after percutaneous coronary intervention. Design Cross‐sectional study. Methods Data from 400 patients after percutaneous coronary intervention were collected from 2017–2020. Logistic regression model and mediating analysis were used to identify the association between frailty, self‐efficacy, combined effects of frailty and self‐efficacy, and care dependence. Results Patients with frailty and self‐efficacy tended to have severe care dependence symptoms. There was no correlation between frailty symptoms, self‐efficacy, and care dependence in patients without symptoms of anxiety or depression. But in patients with anxiety or depression symptoms, there is a strong correlation between frailty symptoms, lower self‐efficacy, and care dependence. Mental health played an inhibitory effect on frailty and care dependence

A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria