The pituitary is a master endocrine gland that developed early in vertebrate evolution and therefore exists in all modern vertebrate classes. The last decade has transformed our view of this key organ. Traditionally, the pituitary has been viewed as a randomly organized collection of cells that respond to hypothalamic stimuli by secreting their content. However, recent studies have established that pituitary cells are organized in tightly wired large-scale networks that communicate with each other in both homo and heterotypic manners, allowing the gland to quickly adapt to changing physiological demands. These networks functionally decode and integrate the hypothalamic and systemic stimuli and serve to optimize the pituitary output into the generation of physiologically meaningful hormone pulses. The development of 3D imaging methods and transgenic models have allowed us to expand the research of functional pituitary networks into several vertebrate classes. Here we review the establishment of pituitary cell networks throughout vertebrate evolution and highlight the main perspectives and future directions needed to decipher the way by which pituitary networks serve to generate hormone pulses in vertebrates.
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http://dx.doi.org/10.3389/fendo.2020.619352 | DOI Listing |
BMC Genomics
January 2025
Laboratory for Marine Ecology and Environmental Science and Technology, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China.
Background: Tris (2-chloroethyl) phosphate (TCEP), a widely used flame retardant, is widespread in the environment and potentially harmful to organisms. However, the specific mechanisms of TCEP-induced neurological and reproductive toxicity in fish are largely unknown. Turbot (Scophthalmus maximus) is cultivated on a large scale, and the emergence of pollutants with endocrine disrupting effects seriously affects its economic benefits.
View Article and Find Full Text PDFJ Clin Med
December 2024
Departments of Radiology, Eulji University Hospital, Eulji University College of Medicine, 95 Dunsanseo-ro, Seo-gu, Daejeon 35233, Republic of Korea.
It is known that the pituitary gland volume (PV) in idiopathic central precocious puberty (IPP) is significantly higher than in healthy children. However, most PV measurements rely on manual quantitative methods, which are time-consuming and labor-intensive. This study aimed to automatically measure the PV of patients with IPP using artificial intelligence to accurately quantify the correlation between IPP and PV, and to improve the efficiency of diagnosing IPP.
View Article and Find Full Text PDFMolecules
December 2024
Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159C, 02-776 Warsaw, Poland.
The gut-brain axis (GBA) is a complex communication network connecting the gastrointestinal tract (GIT) and the central nervous system (CNS) through neuronal, endocrine, metabolic, and immune pathways. Omega-3 (n-3) fatty acids, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are crucial food components that may modulate the function of this axis through molecular mechanisms. Derived mainly from marine sources, these long-chain polyunsaturated fatty acids are integral to cell membrane structure, enhancing fluidity and influencing neurotransmitter function and signal transduction.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, 00-908, Poland.
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
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