Urothelial carcinoma is a common malignant neoplasm that has a poor prognosis and a high frequency of recurrence and metastasis. Constant disease surveillance with periodic and long term cystoscopy examination is necessary for management of the disease. However, the monitoring and therapy regimen is expensive, incurring a massive burden to patients and the government. Therefore, the development of specific biomarkers for urothelial carcinoma at an early stage and recurrence detection becomes a priority. Homeobox genes are a family of genes that are involved in tumourigenesis. They might be potential prognostic markers for urothelial carcinoma. The study investigated the expression pattern of NANOG which is one of a homeobox gene in different stages and grades of urothelial carcinoma. NANOG expressions were also correlated with patient demographic factors and clinicopathological parameters. The expression of NANOG in 100 formalin-fixed paraffin-embedded urothelial carcinoma tissues was determined by immunohistochemistry. Immunohistochemistry showed positive expression of NANOG in all specimens with detection in the cytoplasm, nuclei and the nuclear membrane of the cancer cells. The immunohistochemical expression of NANOG increased across stages and grades of the tumour. The expression of NANOG was not significantly associated with demographic factors; gender (p = 0.376), race (p = 0.718) and age (p = 0.058) as well as with most of the clinicopathological parameters; pathological stage (p = 0.144), grade (p = 0.625), lymph node involvement (p = 0.174) and distant metastasis (p = 0.228). However, NANOG expression showed significant correlation with tumour invasion (p = 0.019). We concluded that NANOG might be a potential biomarker for early diagnosis of urothelial carcinoma of the bladder.
This study investigates a fractional-order time derivative model of non-Newtonian magnetic blood flow in the presence of thermal radiation and body acceleration through an inclined artery. The blood flow is formulated using the Casson fluid model under the control of a uniformly distributed magnetic field and an oscillating pressure gradient. Caputo-Fabrizio's fractional derivative mathematical model was used, along with Laplace transform and the finite Hankel transform technique. Analytical expressions were obtained for the velocity of blood flow, magnetic particle distribution, and temperature profile. These distributions are presented graphically using Mathcad software. The results show that the velocity increases with the time, Reynolds number and Casson fluid parameters, and diminishes when Hartmann number increases. Moreover, fractional parameters, radiation values, and metabolic heat source play an essential role in controlling the blood temperature. More precisely, these results are beneficial for the diagnosis and treatment of certain medical issues.
Red tide caused severe impacts on marine fisheries, ecology, economy and human life safety. The formation mechanism of the red tide is rather complicated; thus, red tide prediction and forecasting have long been a research hotspot around the globe. This study collected ocean monitoring data before and after the occurrence of red tides in Xiamen sea area from 2009 to 2017. The Pearson correlation coefficient method was used to obtain the associated factors of red tide occurrence, including water temperature, saturated dissolved oxygen, dissolved oxygen, chlorophyll-aand potential of hydrogen. Then, we built a short-time red tide prediction model based on the combination of multiple feature factors. chlorophyll-a, dissolved oxygen, saturated dissolved oxygen, potential of hydrogen, water temperature, salinity, turbidity, wind speed, wind direction and Air pressure were used as the input variables, building a short-time prediction model based on the combination of multiple feature factors to forecast red tide in the next 6 h by using the monitoring data. The accuracy of different forecast models with different feature combinations was compared. Results show that the distinguishing factors which have the most significant influence on red tide prediction in Xiamen are chlorophyll-a, dissolved oxygen, saturated dissolved oxygen, potential of hydrogen, and water temperature. the convergence speed of the Gated Recurrence Unit (GRU) prediction model based on the main feature factor proposed in this paper was faster and obtained the expected result, and the accuracy rates of the buoys are above 92%. The research shows the feasibility to use GRU network model to predict the occurrence of red tide with multi-feature factors as input parameters. the paper provides an effective method for the red tide early warning in Xiamen sea area.