Optimized detection of circulating anti-nuclear envelope autoantibodies by immunofluorescence

BACKGROUND: Antinuclear antibodies are useful diagnostic tools in several autoimmune diseases. However, the routine detection of nuclear envelope autoantibodies using immunofluorescence (IF) is not always easy to perform in patients' sera because of the presence of autoantibodies to other nuclear and cytoplasmic components which could mask the characteristic rim-like pattern of nuclear envelope autoantibodies. This is particularly common in sera from patients with primary biliary cirrhosis (PBC), which generaly have high titres of anti-mitochondrial antibodies. Therefore, we have assayed a number of commercial slides and alternative fixation conditions to optimize the detection of anti-nuclear envelope antibodies (ANEA) in PBC sera. METHODS: We have explored the presence of ANEA in 33 sera from patients with established PBC using three different Hep2 commercial slides and home-made slides with HeLa and Hep2 cells fixed with methanol, ethanol, 1% or 4% formaldehyde. RESULTS: We observed that the IF pattern was related to the cell type used (Hep2 or HeLa), the manufacturer and the cell fixation scheme. When both cell lines were fixed with 1% formaldehyde, the intensity of the cytoplasmic staining was considerably decreased regardless to the serum sample, whereas the prevalence of cytoplasmic autoantibodies was significantly lowered, as compared to any of the Hep2 commercial slide and fixation used. In addition, the prevalence of ANEA was importantly increased in formaldehyde-fixed cells. CONCLUSION: Immunofluorescence using appropriately fixed cells represent an easy, no time-consuming and low cost technique for the routine screening of sera for ANEA. Detection of ANEA is shown to be more efficient using formaldehyde-fixed cells instead of commercially available Hep2 cells

Corrigendum to "Recognition motifs for importin 4 [(L)PPRS(G/P)P] and importin 5 [KP(K/Y)LV] binding, identified by bio-informatic simulation and experimental in vitro validation" [Comput Struct Biotechnol J 20 (2022) 5952-5961]

Nuclear translocation of large proteins is mediated through karyopherins, carrier proteins recognizing specific motifs of cargo proteins, known as nuclear localization signals (NLS). However, only few NLS signals have been reported until now. In the present work, NLS signals for Importins 4 and 5 were identified through an unsupervised in silico approach, followed by experimental in vitro validation. The sequences LPPRS(G/P)P and KP(K/Y)LV were identified and are proposed as recognition motifs for Importins 4 and 5 binding, respectively. They are involved in the trafficking of important proteins into the nucleus. These sequences were validated in the breast cancer cell line T47D, which expresses both Importins 4 and 5. Elucidating the complex relationships of the nuclear transporters and their cargo proteins is very important in better understanding the mechanism of nuclear transport of proteins and laying the foundation for the development of novel therapeutics, targeting specific importins

Membrane testosterone binding sites in prostate carcinoma as a potential new marker and therapeutic target: Study in paraffin tissue sections

BACKGROUND: Steroid action is mediated, in addition to classical intracellular receptors, by recently identified membrane sites, that generate rapid non-genomic effects. We have recently identified a membrane androgen receptor site on prostate carcinoma cells, mediating testosterone rapid effects on the cytoskeleton and secretion within minutes. METHODS: The aim of this study was to investigate whether membrane androgen receptors are differentially expressed in prostate carcinomas, and their relationship to the tumor grade. We examined the expression of membrane androgen receptors in archival material of 109 prostate carcinomas and 103 benign prostate hyperplasias, using fluorescein-labeled BSA-coupled testosterone. RESULTS: We report that membrane androgen receptors are preferentially expressed in prostate carcinomas, and they correlate to their grade using the Gleason's microscopic grading score system. CONCLUSION: We conclude that membrane androgen receptors may represent an index of tumor aggressiveness and possibly specific targets for new therapeutic regimens

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural network, were also evaluated

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution