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Introduction: Pediatric pituitary adenomas are rare, resulting in a limited amount of literature on these patients. The incidence of calcification in adult patients with pituitary adenomas is only 0.2% to 8%, while the incidence of calcification in children has not been reported. Case Report: In this paper, we report a 10-year-old girl with a calcified pituitary adenoma. Laboratory examination showed the growth hormone (GH) to be more than 50ug/L, and pituitary prolactin (PRL) was 27.42 ng/mL. Non-contrast-enhanced computed tomography (CT) scans indicated a large sellar occupation with multiple nodules of calcification. Subsequently, the patient underwent a transcranial resection. Histopathologic evaluation confirmed the diagnosis of pituitary adenoma with multiple calcifications. Conclusion: Since the management and prognosis of adenomas with calcification from other occupations of the pituitary gland differ significantly, it is important to differentiate between the two.

Background: Diabetic neuropathic pain seriously affects the quality of a patient’s life. To predict molecular mechanism based on network pharmacology and verify the interaction between the active ingredient of Astragalus membranaceus and Panax notoginseng coupled-herbs (AP) and target genes related to Diabetic neuropathic pain (DNP) molecular docking assay was performed. AP and their target genes related to DNP were analyzed based on network pharmacology followed by experimental validation. Methods: TCMSP, PubMed and CNKI websites were used to acquire active components in AP. OMIM, DrugBank database and DisGeNET database were used to collect and analyze target genes related to DNP. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) analysis were conducted in the DAVID database. The protein-protein interaction (PPI) network model was constructed by introducing the selected components-disease common target into the string database. Auto- Dock Vina 1.1.2 was used to dock receptor proteins with small ligand molecules. VonFrey’s statement was used to detect mechanical allodynia of DNP rats. Potential targets were detected by Western blot assay. Results: We decided that 22 and 9 chemical compositions possessed the fair ability of absorption, distribution, metabolism and excretion in Astragalus membranaceus and Panax notoginseng, respectively. These active compositions act on 70 target genes related to DNP. The core gene in the protein-protein interaction network are CAT, ESR1, HMOX1, IL1β, IL6, NFE2L2, NOS2, PPARG, PTGS2 and TNF, etc. Furthermore, GO, and KEGG pathway enrichment analyses indicated that DNP related target genes regulated by AP exist in multiple signaling pathways, including insulin resistance, PI3K-Akt signaling, HIF-1 signaling pathway, Fluid shear stress and atherosclerosis, and AGE-RAGE signaling pathway etc. AP inhibited mechanical hyperalgesia and reduced SERPINE1, FN1, IL1β, and IL6 expression of diabetic neuropathic rats in a dose-dependent manner. Conclusion: We first confirm that AP possess an anti-DNP effect through multiple signaling pathways based on network pharmacology. These results provide a theoretical basis for us to further research on the molecular mechanism of AP in the treatment of DNP.

This study aimed to propose a method to predict yarn unevenness grounded on the generalized regression neural network and traditional neural network model to further improve the prediction accuracy. The yarn unevenness model was constructed. Under this model, a three-layer neural network, a four-layer neural network, a five-layer neural network, and a generalized regression neural network were designed. Finally, Python was used for training and simulation. The training parameters and the three network models data were made consistent to ensure the comparability of the results. The results showed that using the yarn unevenness model, the average relative error of the four-layer neural network to cut down 0.87% compared with that of the three-layer neural network. Compared with the five-layer neural network, the four-layer neural network performance was not much different, but the running speed was increased by 46.05%. Compared with the four-layer neural network, the average relative error of the generalized regression neural network was reduced by 0.57%, the mean square error was reduced by 0.98%, he root mean square error was reduced by 4.76%, and the running speed was increased by 74.70%. &nbsp

Strontium doped lanthanum cobaltite (LSC, La1−xSrxCoO3−δ) is a promising oxygen electrode material for intermediate temperature solid oxide cells (SOCs) and oxygen membranes. Its application in service is however impeded by its thermodynamic instability. The present work is aiming to identify the phase stability of LSC by thermodynamic modeling of the La-Sr-Co-O system using the CALculation of PHAse Diagrams (CALPHAD) method coupled with experiments. The calculated results based on the developed database are validated using experimental data both from literature and the present work. General agreement is achieved between model predictions and experimental results. Other properties of the LSC perovskite, such as oxygen non-stoichiometry and cation distribution, are also calculated and predicted based on the developed La-Sr-Co-O database. These calculations can provide important guidance in designing LSC-based materials for various applications and the established thermodynamic functions can serve as input for further thermodynamic calculations on altered and even more complex perovskites.