Nrf2 in neoplastic and non-neoplastic liver diseases

Activation of the Keap1/Nrf2 pathway, the most important cell defense signal, triggered to neutralize the harmful effects of electrophilic and oxidative stress, plays a crucial role in cell survival. Therefore, its ability to attenuate acute and chronic liver damage, where oxidative stress represents the key player, is not surprising. On the other hand, while Nrf2 promotes proliferation in cancer cells, its role in non-neoplastic hepatocytes is a matter of debate. Another topic of uncertainty concerns the nature of the mechanisms of Nrf2 activation in hepatocarcinogenesis. Indeed, it remains unclear what is the main mechanism behind the sustained activation of the Keap1/Nrf2 pathway in hepatocarcinogenesis. This raises doubts about the best strategies to therapeutically target this pathway. In this review, we will analyze and discuss our present knowledge concerning the role of Nrf2 in hepatic physiology and pathology, including hepatocellular carcinoma. In particular, we will critically examine and discuss some findings originating from animal models that raise questions that still need to be adequately answered

Achieving macro- and micro-roughness on Ti alloy by etching without prior sandblasting: a surface characterization

INTRODUCTION: Etching is currently the most popular method used to texture the surface of dental implants. Sandblasting prior to etching (SLA) is the only method to achieve a macro- and micro-surface texture with a Sa in the 1-2 μm range, a ‘moderately rough’ surface considered to be an optimized surface. However, SLA surfaces harbor remnant particles from the sandblasting process [l]. Some manufacturers consider the residual alumina particles as a foreign material worth getting rid of. Subsequently, they forgo an optimized moderately rough surface and stick to a ‘minimally rough’ micro-roughened surface displaying a Sa < 1 μm [l]. It has been recently claimed [2] that acid etching is typically not an appropriate treatment for α-β alloys because its biphasic nature leads to an enrichment of the Vanadium-rich β-phase on the surface. The aim of the present paper is to show that it is feasible to achieve an optimized ‘moderately rough’ macro- and micro-textured surface on titanium alloy (Ti6Al4V) through etching only, without any prior sandblasting and to characterize the resulting surface

Epigenetic mechanisms in oral cancer: new diagnostic and therapeutic strategies

Complexity and heterogeneity are frequently present during the development and progression of carcinogenesis and, in the last 15 years, significant progress made in clinical research underlines the role of some epigenetic mechanisms. The most important characteristics of the epigenetic concept are that these events are reversible, not related to modifications in the structure of DNA and may drive fundamental cell signaling alterations1. Among these systems of communication in normal and pathological conditions, also microbiome and staminal cells2 seem to be important. These new profiles of pathological communication develop novel diagnostic, prognostic and therapeutic tool

Fingerprint Adversarial Presentation Attack in the Physical Domain

With the advent of the deep learning era, Fingerprint-based Authentication Systems (FAS) equipped with Fingerprint Presentation Attack Detection (FPAD) modules managed to avoid attacks on the sensor through artificial replicas of fingerprints. Previous works highlighted the vulnerability of FPADs to digital adversarial attacks. However, in a realistic scenario, the attackers may not have the possibility to directly feed a digitally perturbed image to the deep learning based FPAD, since the channel between the sensor and the FPAD is usually protected. In this paper we thus investigate the threat level associated with adversarial attacks against FPADs in the physical domain. By materially realising fakes from the adversarial images we were able to insert them into the system directly from the “exposed” part, the sensor. To the best of our knowledge, this represents the first proof-of-concept of a fingerprint adversarial presentation attack. We evaluated how much liveness score changed by feeding the system with the attacks using digital and printed adversarial images. To measure what portion of this increase is due to the printing itself, we also re-printed the original spoof images, without injecting any perturbation. Experiments conducted on the LivDet 2015 dataset demonstrate that the printed adversarial images achieve ∼ 100% attack success rate against an FPAD if the attacker has the ability to make multiple attacks on the sensor (10) and a fairly good result (∼ 28%) in a one-shot scenario. Despite this work must be considered as a proof-of-concept, it constitutes a promising pioneering attempt confirming that an adversarial presentation attack is feasible and dangerous

Partial purification and MALDI-TOF MS analysis of UN1, a tumor antigen membrane glycoprotein.

UN1 is a membrane glycoprotein that is expressed in immature human thymocytes, a subpopulation of peripheral T lymphocytes, the HPB acute lymphoblastic leukemia (ALL) T-cell line and fetal thymus. We previously reported the isolation of a monoclonal antibody (UN1 mAb) recognizing the UN1 protein that was classified as "unclustered" at the 5th and 6th International Workshop and Conference on Human Leukocyte Differentiation Antigens. UN1 was highly expressed in breast cancer tissues and was undetected in non-proliferative lesions and in normal breast tissues, indicating a role for UN1 in the development of a tumorigenic phenotype of breast cancer cells. In this study, we report a partial purification of the UN1 protein from HPB-ALL T cells by anion-exchange chromatography followed by immunoprecipitation with the UN1 mAb and MALDI-TOF MS analysis. This analysis should assist in identifying the amino acid sequence of UN

Characterization of single-nucleotide polymorphisms in 20 genes affecting milk quality in cattle, sheep, goat and buffalo

AbstractMilk products are important dietary sources of nutrients, providing energy, high quality proteins, and a variety of vitamins and minerals. Recent researches have focused on altering fat and protein contents of milk, in order to improve its nutrient content to more suitably reflect current dietary recommendations and trends. We characterized single nucleotide polymorphisms (SNPs) in 20 candidate genes expected to have an influence on fat composition of milk in four ruminant species (cattle, sheep, goat and buffalo). Genes belonged to different families, including transporters, fatty acid biosynthesis, receptors and enzymes for saturation/desaturation. For each gene, PCR primers were designed using bovine sequence to amplify 3 gene fragments, that covered coding and non coding regions. For each gene, we found polymorphisms in at least one species, but none that was present in homologous fragments of all four species. As expected, different SNPs were found across species, but for a very few genes. We..

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives

Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms-the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)-are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures

Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers’ responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual’s response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers’ estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a “yes” or “no” response are predicted with 70–80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7–0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing