scholarly journals MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation

2015 ◽  
Vol 112 (32) ◽  
pp. 9978-9983 ◽  
Author(s):  
David Calligaris ◽  
Daniel R. Feldman ◽  
Isaiah Norton ◽  
Olutayo Olubiyi ◽  
Armen N. Changelian ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Real Time ◽  
Pituitary Adenomas ◽  
Mass Spectrometry Imaging ◽  
Pituitary Tumors ◽  
Pituitary Hormones ◽  
Support Vector ◽  
Proof Of Concept ◽  
Human Pituitary ◽  
Concept Study

We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.

scholarly journals Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication

2020 ◽  
Author(s):  
Eleonora De Filippi ◽  
Mara Wolter ◽  
Bruno Melo ◽  
Carlos J. Tierra-Criollo ◽  
Tiago Bortolini ◽  
...  
Keyword(s):  
Real Time ◽  
Cross Validation ◽  
Support Vector ◽  
Spectral Features ◽  
Emotional States ◽  
Proof Of Concept ◽  
Neurofeedback Training ◽  
Concept Study ◽  
Complex Emotions

AbstractDuring the last decades, neurofeedback training for emotional self-regulation has received significant attention from both the scientific and clinical communities. However, most studies have focused on broader emotional states such as “negative vs. positive”, primarily due to our poor understanding of the functional anatomy of more complex emotions at the electrophysiological level. Our proof-of-concept study aims at investigating the feasibility of classifying two complex emotions that have been implicated in mental health, namely tenderness and anguish, using features extracted from the electroencephalogram (EEG) signal in healthy participants. Electrophysiological data were recorded from fourteen participants during a block-designed experiment consisting of emotional self-induction trials combined with a multimodal virtual scenario. For the within-subject classification, the linear Support Vector Machine was trained with two sets of samples: random cross-validation of the sliding windows of all trials; and 2) strategic cross-validation, assigning all the windows of one trial to the same fold. Spectral features, together with the frontal-alpha asymmetry, were extracted using Complex Morlet Wavelet analysis. Classification results with these features showed an accuracy of 79.3% on average when doing random cross-validation, and 73.3% when applying strategic cross-validation. We extracted a second set of features from the amplitude time-series correlation analysis, which significantly enhanced random cross-validation accuracy while showing similar performance to spectral features when doing strategic cross-validation. These results suggest that complex emotions show distinct electrophysiological correlates, which paves the way for future EEG-based, real-time neurofeedback training of complex emotional states.Significance statementThere is still little understanding about the correlates of high-order emotions (i.e., anguish and tenderness) in the physiological signals recorded with the EEG. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, concerning the therapeutic application, EEG is a more suitable tool with regards to costs and practicability. Therefore, our proof-of-concept study aims at establishing a method for classifying complex emotions that can be later used for EEG-based neurofeedback on emotion regulation. We recorded EEG signals during a multimodal, near-immersive emotion-elicitation experiment. Results demonstrate that intraindividual classification of discrete emotions with features extracted from the EEG is feasible and may be implemented in real-time to enable neurofeedback.

scholarly journals Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1136
Author(s):  
Duc Long Duong ◽  
Quoc Duy Nam Nguyen ◽  
Minh Son Tong ◽  
Manh Tuan Vu ◽  
Joseph Dy Lim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dental Caries ◽  
Sensitivity And Specificity ◽  
Color Images ◽  
Caries Detection ◽  
Support Vector ◽  
Proof Of Concept ◽  
Computational System ◽  
Occlusal Caries ◽  
Concept Study

Dental caries has been considered the heaviest worldwide oral health burden affecting a significant proportion of the population. To prevent dental caries, an appropriate and accurate early detection method is demanded. This proof-of-concept study aims to develop a two-stage computational system that can detect early occlusal caries from smartphone color images of unrestored extracted teeth according to modified International Caries Detection and Assessment System (ICDAS) criteria (3 classes: Code 0; Code 1-2; Code 3-6): in the first stage, carious lesion areas were identified and extracted from sound tooth regions. Then, five characteristic features of these areas were intendedly selected and calculated to be inputted into the classification stage, where five classifiers (Support Vector Machine, Random Forests, K-Nearest Neighbors, Gradient Boosted Tree, Logistic Regression) were evaluated to determine the best one among them. On a set of 587 smartphone images of extracted teeth, our system achieved accuracy, sensitivity, and specificity that were 87.39%, 89.88%, and 68.86% in the detection stage when compared to modified visual and image-based ICDAS criteria. For the classification stage, the Support Vector Machine model was recorded as the best model with accuracy, sensitivity, and specificity at 88.76%, 92.31%, and 85.21%. As the first step in developing the technology, our present findings confirm the feasibility of using smartphone color images to employ Artificial Intelligence algorithms in caries detection. To improve the performance of the proposed system, there is a need for further development in both in vitro and in vivo modeling. Besides that, an applicable system for accurately taking intra-oral images that can capture entire dental arches including the occlusal surfaces of premolars and molars also needs to be developed.

scholarly journals Real-time Neurofeedback Using Functional MRI Could Improve Down-Regulation of Amygdala Activity During Emotional Stimulation: A Proof-of-Concept Study

2013 ◽  
Vol 27 (1) ◽  
pp. 138-148 ◽  
Author(s):  
Annette Beatrix Brühl ◽  
Sigrid Scherpiet ◽  
James Sulzer ◽  
Philipp Stämpfli ◽  
Erich Seifritz ◽  
...  
Keyword(s):  
Real Time ◽  
Functional Mri ◽  
Proof Of Concept ◽  
Down Regulation ◽  
Concept Study ◽  
Amygdala Activity

Advances in Our Understanding of Pituitary Adenoma

2008 ◽  
Vol 04 (01) ◽  
pp. 88
Author(s):  
Sandra Pekic ◽  
Vera Popovic-Brkic
Keyword(s):  
Pituitary Adenoma ◽  
Pituitary Adenomas ◽  
Pituitary Tumors ◽  
Pituitary Hormones ◽  
Current Treatment ◽  
Thyroid Stimulating Hormone ◽  
Treatment Modalities ◽  
High Resolution Computed Tomography ◽  
New Therapeutic Approaches ◽  
Asymptomatic Patients

Pituitary adenomas are common benign monoclonal neoplasms— accounting for 15% of intracranial neoplasms—that may be clinically silent or secrete anterior pituitary hormones such as prolactin, growth hormone (GH), adrenocorticotrophic hormone (ACTH), or, rarely, thyroid-stimulating hormone (TSH) or gonadotrophins. Radiological studies for other reasons using high-resolution computed tomography (CT) or magnetic resonance imaging (MRI) detect incidental pituitary adenomas in approximately 20% of asymptomatic patients.1The incidence of the various types of adenoma varies;2prolactinomas are the most common pituitary adenomas. Clinically non-functioning pituitary adenomas (NFPAs), which do not secrete hormones, often cause local mass symptoms and represent one-third of pituitary adenomas. GH- and ACTH-producing adenomas each account for 10–15% of pituitary adenomas, while TSH-producing adenomas are rare. Pituitary adenomas are infrequent in childhood: fewer than 10% of pituitary adenomas are diagnosed before 20 years of age.3These adenomas can be either microor macroadenomas. The natural course of microadenomas is that a few tumors enlarge over a period of more than eight years.Although several genes and signaling pathways have been identified as important factors in the development of pituitary tumors, our understanding of pituitary tumorigenesis remains incomplete and is the focus of current research. The reason for this is that current treatment modalities fail to completely control this disorder and prevent the associated morbidity and mortality. This article reviews the advances in our understanding of pituitary adenoma, especially in the field of pathogenesis of pituitary tumors, and the possibility of new therapeutic approaches.

Expression of chorionic gonadotrophin in human pituitary adenomas

1994 ◽  
Vol 131 (6) ◽  
pp. 615-622 ◽  
Author(s):  
A Levy ◽  
S Biswas ◽  
PA Burton ◽  
SL Lightman
Keyword(s):  
Pituitary Adenomas ◽  
Pituitary Hormones ◽  
Chorionic Gonadotrophin ◽  
Royal Infirmary ◽  
Glycoprotein Hormone ◽  
Human Pituitary ◽  
Α Subunit ◽  
Human Pituitary Adenomas ◽  
University Of Bristol

Levy A, Biswas S, Burton PA, Lightman SL. Expression of chorionic gonadotrophin in human pituitary adenomas. Eur J Endocrinol 1994;131:615–22. ISSN 0804–4643 In addition to the classical anterior pituitary hormones, many human pituitary adenomas express hormone genes ectopically. Expression of glycoprotein hormone α-subunit and, less commonly, free luteinizing hormone β (LH-β) or follicle-stimulating hormone β is characteristic of endocrinologically inactive adenomas, a subgroup thought to be derived predominantly from the gonadotroph line. Thyrotrophin β expression in these tumours is unusual, and specific chorionic gonadotrophin β (CG-β) expression, identified using oligonucleotide probes that reliably distinguish LH-β and CG-β in situ, is not well described. Using in situ hybridization histochemistry with a CG-β transcript-specific oligonucleotide probe and a non-specific LH/CG-β oligonucleotide and riboprobe, we have demonstrated specific CG-β expression in two of 35 random pituitary adenomas; LH-β expression occurred in a further eight adenomas. Unlike prolactin, growth hormone and proopiomelanocortin, which are transcribed in the vast majority of cells in their respective hormone-positive tumour subtypes, the pattern of LH/CG-β was typically a scattering of single or small clusters of hormone-positive cells in a predominantly LH/CG-β-negative tumour. Finally, although bona fide CG-β expression in human pituitary adenomas does occur, it is only at an incidence similar to that described for carcinomas of other tissues. A Levy, Department of Medicine, University of Bristol, Bristol Royal Infirmary, Marlborough Street, Bristol BS2 8HW, UK

scholarly journals The Upregulation of Molecules Related to Tumor Immune Escape in Human Pituitary Adenomas

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhiyu Xi ◽  
Pamela S. Jones ◽  
Masaaki Mikamoto ◽  
Xiaobin Jiang ◽  
Alexander T. Faje ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Pituitary Adenomas ◽  
Immune Escape ◽  
Pituitary Tumors ◽  
Human Pituitary ◽  
Tumor Immune Escape ◽  
Human Pituitary Adenomas ◽  
Pituitary Glands ◽  
Normal Human ◽  
Checkpoint Receptors

Human pituitary adenomas are one of the most common intracranial neoplasms. Although most of these tumors are benign and can be treated medically or by transsphenoidal surgery, a subset of these tumors are fast-growing, aggressive, recur, and remain a therapeutic dilemma. Because antibodies against immune checkpoint receptors PD-1 and CLTA-4 are now routinely used for cancer treatment, we quantified the expression of mRNA coding for PD-1, CLTA-4, and their ligands, PD-L1, PD-L2, CD80, and CD86 in human pituitary adenomas and normal pituitary glands, with the ultimate goal of exploiting immune checkpoint therapy in aggressive pituitary adenomas. Aggressive pituitary adenomas demonstrated an increased expression of PD-L2, CD80, and CD86 in compared to that of normal human pituitary glands. Furthermore, aggressive pituitary tumors demonstrated significantly higher levels of CD80 and CD86 compared to non-aggressive tumors. Our results establish a rationale for studying a potential role for immune checkpoint inhibition therapy in the treatment of pituitary adenomas.

Endocrine Silent Pituitary Tumors

2018 ◽  
Author(s):  
Harminder Singh ◽  
Smeer Salam ◽  
Theodore H. Schwartz
Keyword(s):  
Pituitary Adenomas ◽  
Case Series ◽  
Pituitary Tumors ◽  
Visual Outcome ◽  
Mass Effect ◽  
Optic Chiasm ◽  
Pituitary Hormones ◽  
Pituitary Dysfunction ◽  
Normal Pituitary Gland ◽  
Field Disturbance

Pituitary adenomas are the most common intracranial neoplasms in adults, with a prevalence of 7% to 17%. Clinically, they can be divided into 2 categories based on whether they secrete pituitary hormones: functional (secretory) and nonfunctional (nonsecretory or endocrine silent) adenomas. The biologic latency of nonfunctional (endocrine silent) adenomas makes them usually diagnosed at the stage of macro (>1 cm) and giant (>4 cm) adenomas. Because these tumors are nonfunctioning, their primary symptoms are due to mass effect, particularly on the optic chiasm and normal pituitary gland and stalk superiorly, and the cavernous sinus laterally. Visual field disturbance is the most common presenting complaint, followed by pituitary dysfunction and headaches. Surgical outcomes, therefore, are aimed at determining visual outcome in addition to rates of gross total resection, recurrence, and postoperative pituitary dysfunction. Several recent case series have documented the increased success of the endonasal endoscopic transsphenoidal approach for resecting nonfunctioning pituitary adenomas, particularly in relation to the classic open cranial and microsurgical transsphenoidal techniques.

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