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Objectives: To characterize the splicing machinery (SM) of leukocytes from primary antiphospholipid syndrome (APS), systemic lupus erythematosus (SLE) and antiphospholipid syndrome with lupus (APS + SLE) patients, and to assess its clinical involvement., Methods: Monocytes, lymphocytes and neutrophils from 80 patients (22 APS, 35 SLE and 23 APS + SLE) and 50 HD were purified, and 45 selected SM components were evaluated by qPCR-microfluidic array. Relationship with clinical features and underlying regulatory mechanisms were assessed., Results: APS, SLE and APS + SLE leukocytes displayed significant and specific alterations in SM-components (SMC), associated with clinical features [autoimmune profiles, disease activity, lupus nephritis (LN), and CV-risk markers]. A remarkable relationship among dysregulated SMC in monocytes and the presence of LN in SLE was highlighted, revealing a novel pathological mechanism, which was further explored. Immunohistology analysis of renal biopsies highlighted the pathological role of the myeloid compartment in LN. Transcriptomic analysis of monocytes from SLE-LN(+) vs SLE-LN(-) identified 271 genes differentially expressed, mainly involved in inflammation and IFN-signaling. Levels of IFN-related genes correlated with those of SMC in SLE-LN(+). These results were validated in two external SLE-LN(+) datasets of whole-blood and kidney biopsies. In vitro, SLE-LN(+)-serum promoted a concomitant dysregulation of both, the IFN signature and several SMC, further reversed by JAKinibs treatment. Interestingly, IFNs, key inflammatory cytokines in SLE pathology, also altered SMC. Lastly, the over/down-expression of selected SMC in SLE-monocytes reduced the release of inflammatory cytokines and their adhesion capacity., Conclusion: Overall, we have identified, for the first time, a specific alteration of SMC in leukocytes from APS, SLE and APS + SLE patients that would be responsible for the development of distinctive clinical profiles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The anterior pituitary gland is a key organ involved in the control of multiple physiological functions including growth, reproduction, metabolism and stress. These functions are controlled by five distinct hormone-producing pituitary cell types that produce growth hormone (somatotropes), prolactin (lactotropes), adrenocorticotropin (corticotropes), thyrotropin (thyrotropes) and follicle stimulating hormone/luteinizing hormone (gonadotropes). Classically, the synthesis and release of pituitary hormones was thought to be primarily regulated by central (neuroendocrine) signals. However, it is now becoming apparent that factors produced by pituitary hormone targets (endocrine and non-endocrine organs) can feedback directly to the pituitary to adjust pituitary hormone synthesis and release. Therefore, pituitary cells serve as sensors to integrate central and peripheral signals in order to fine-tune whole-body homeostasis, although it is clear that pituitary cell regulation is species-, age- and sex-dependent. The purpose of this review is to provide a comprehensive, general overview of our current knowledge of both central and peripheral regulators of pituitary cell function and associated intracellular mechanisms, focusing on human and non-human primates., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Context: There is concern that pegvisomant could be associated with a higher risk of tumor growth. The rate and possible determinants of this tumor growth are unknown., Objective: The objective of the study was to investigate the clinical, immunohistological, and molecular factors conditioning tumor growth in patients taking pegvisomant., Design and Setting: This was a cross-sectional study performed from 2004 to 2010 in four university hospitals in Spain., Patients: Seventy-five acromegalic patients with active disease resistant to somatostatin analogs treated with pegvisomant were followed up for a mean of 29 ± 20 months., Main Outcome Measures: Magnetic resonance images before initiation of pegvisomant, at 6 months, and then yearly were examined in all patients. Immunohistological and molecular studies were performed in tumors that grew., Results: A significant increase in tumor size was observed in five patients (6.7%). Absence of previous irradiation (P = 0.014) and shorter duration of prepegvisomant somatostatin analog therapy (P < 0.001) were associated with an increased risk of tumor growth. A stepwise multivariate linear regression analysis (R(2) = 0.334, P < 0.001) identified the duration of somatostatin analog therapy prior to pegvisomant (beta = -4.509, P = 0.014) as the only significant predictor of tumor growth. In those tumors that grew, GH expression and insulin receptor expression were higher (P = 0.033 in both cases) than in the control group., Conclusions: No previous radiotherapy, shorter duration of prepegvisomant somatostatin analog therapy, and higher tumor expression of GH and insulin receptor could be risk factors for tumor growth during pegvisomant therapy.