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Abstract Background Doxorubicin (Dox) is an anti-cancer anthracycline drug that causes double-stranded DNA breaks. It is highly effective against several types of tumours; however, it also has adverse effects on regenerative populations of normal cells, such as human cardiac mesenchymal progenitor cells (hCmPCs), and its clinical use is limited by cardiotoxicity. Another known effect of Dox is nucleolar disruption, which triggers the ubiquitously expressed nucleolar phosphoprotein Nucleophosmin (NPM) to be released from the nucleolus into the cell, where it participates in the orchestration of cellular stress responses. NPM has also been observed in the extracellular space in response to different stress stimuli; however, the mechanism behind this and its functional implications are as yet largely unexplored. The aim of this study was to establish whether Dox could elicit NPM secretion in the extracellular space and to elucidate the mechanism of secretion and the effect of extracellular NPM on hCmPCs. Results We found that following the double-strand break formation in hCmPCs caused by Dox, NPM was rapidly secreted in the extracellular space by an active mechanism, in the absence of either apoptosis or necrosis. Extracellular release of NPM was similarly seen in response to ultraviolet radiation (UV). Furthermore, we observed an increase of NPM levels in the plasma of Dox-treated mice; thus, NPM release also occurred in vivo. The treatment of hCmPCs with extracellular recombinant NPM induced a decrease of cell proliferation and a response mediated through the Toll-like receptor (TLR)4. We demonstrated that NPM binds to TLR4, and via TLR4, and nuclear factor kappa B (NFkB) activation/nuclear translocation, exerts proinflammatory functions by inducing IL-6 and COX-2 gene expression. Finally, we found that in hCmPCs, NPM secretion could be driven by an autophagy-dependent unconventional mechanism that requires TLR4, since TLR4 inhibition dramatically reduced Dox-induced secretion. Conclusions We hypothesise that the extracellular release of NPM could be a general response to DNA damage since it can be elicited by either a chemical agent such as Dox or a physical genotoxic stressor such as UV radiation. Following genotoxic stress, NPM acts similarly to an alarmin in hCmPCs, being rapidly secreted and promoting cell cycle arrest and a TLR4/NFκB-dependent inflammatory response.

We previously showed that genotoxic stress induced an active extracellular release of nucleophosmin (NPM) in human cardiac mesenchymal progenitor cells, and that serum deprivation provokes NPM secretion from human endothelial cells, eliciting inflammation via nuclear factor kappa B (NF-kB) transcriptional activation. In this study, we wanted to determine whether NPM was similarly modulated in the skin and plasma of psoriatic patients (Pso). We found that NPM was induced in 6 skin biopsies compared to 6 normal skin biopsies and was markedly increased in lesional (LS) vs. non-lesional skin (NLS) biopsies. Moreover, NPM was also increased at the transcriptional levels in LS vs. NLS. Both the innate stimuli, such as lipopolysaccharides and Poly inositol–cytosine and adaptive stimuli, that is, cytokine mix, were able to induce the extracellular release of NPM in immortalized keratinocytes and human skin fibroblasts in the absence of cytotoxicity. Interestingly, NPM interacts with Toll-like receptor (TLR)4 in these cells and activates an NF-kB-dependent inflammatory pathway upregulating interleukin IL-6 and COX-2 gene expression. Finally, circulating NPM was increased in the plasma of 29 Pso compared to 29 healthy controls, and positively correlates with psoriasis area severity index (PASI) and with determinants of cardiovascular diseases (CVDs), such as pulse wave velocity, systolic pressure, and left ventricular mass. Furthermore, NPM positively correlates with miR-200c circulating levels, which we previously showed to increase in Pso and correlate with CVD progression. Our data show that circulating miR-200c is physically associated with extracellular NPM, which most probably is responsible for its extracellular release and protection upon cytokine mix via a TLR4-mechanism. In conclusion, NPM is increased in psoriasis both in the skin and plasma and might be considered a novel biologic target to counteract chronic inflammation associated with CVD risk.

Most oropharyngeal squamous cell carcinomas (OPSCCs) are human papillomavirus (HPV)-associated, high-risk (HR) cancers that show a better response to chemoradiotherapy and are associated with improved survival. Nucleophosmin (NPM, also called NPM1/B23) is a nucleolar phosphoprotein that plays different roles within the cell, such as ribosomal synthesis, cell cycle regulation, DNA damage repair and centrosome duplication. NPM is also known as an activator of inflammatory pathways. An increase in NPM expression has been observed in vitro in E6/E7 overexpressing cells and is involved in HPV assembly. In this retrospective study, we investigated the relationship between the immunohistochemical (IHC) expression of NPM and HR-HPV viral load, assayed by RNAScope in situ hybridization (ISH), in ten patients with histologically confirmed p16-positive OPSCC. Our findings show that there is a positive correlation between NPM expression and HR-HPV mRNA (Rs = 0.70, p = 0.03), and a linear regression (r2 = 0.55; p = 0.01). These data support the hypothesis that NPM IHC, together with HPV RNAScope, could be used as a predictor of transcriptionally active HPV presence and tumor progression, which is useful for therapy decisions. This study includes a small cohort of patients and, cannot report conclusive findings. Further studies with large series of patients are needed to support our hypothesis.

Psoriasis is a chronic inflammatory disease involving the skin. Both genetic and environmental factors play a pathogenic role in psoriasis and contribute to the severity of the disease. Psoriasis, in fact, has been associated with different comorbidities such as diabetes, metabolic syndrome, gastrointestinal or kidney diseases, cardiovascular disease (CVD), and cerebrovascular diseases (CeVD). Indeed, life expectancy in severe psoriasis is reduced by up to 5 years due to CVD and CeVD. Moreover, patients with severe psoriasis have a higher prevalence of traditional cardiovascular (CV) risk factors, including dyslipidemia, diabetes, smoking, and hypertension. Further, systemic inflammation is associated with oxidative stress increase and induces endothelial damage and atherosclerosis progression. Different miRNA have been already described in psoriasis, both in the skin tissues and in the blood flow, to play a role in the progression of disease. In this review, we will summarize and discuss the most important miRNAs that play a role in psoriasis and are also linked to CVD.

A prospective, randomized clinical trial was designed to compare four epidural treatments in dogs undergoing total unilateral mastectomy. The epidural volume of injectate was based on the individual occipito-coccygeal length (OCL) aiming to reach the first thoracic vertebra (T1). The first ten dogs were allocated in a control group (C) and did not receive epidural treatment. Subsequently, forty dogs were randomly allocated in four groups of ten: epidural ropivacaine 0.5% (R0.5%); morphine 0.1 mg kg−1 plus ropivacaine 0.5% (MR0.5%); morphine 0.1 mg kg−1 plus ropivacaine 0.35% (MR0.35%); morphine 0.1 mg kg−1 plus ropivacaine 0.25% (MR0.25%). Intraoperatively, isoflurane requirement (1.3% vs. −1 h−1) were significantly higher in C group compared to all epidural groups. Postoperatively, methadone requirement was higher (1.8 mg kg−1 vs. −1) for C group compared to all epidural treatment groups. The ability to walk and to urinate returned 4 h earlier in MR0.35% and MR0.25%. The mean epidural volume of ropivacaine, using a dose regimen based on OCL, to reach T1 was about 0.15 mL cm−1. The addition of morphine further reduced the methadone requirement, without affecting urinary and motor functions.

Recent findings suggest that epithelial to mesenchymal transition (EMT), a key step during heart development, is involved in cardiac tissue repair following myocardial infarction (MI). MicroRNAs (miRNAs) act as key regulators in EMT processes; however, the mechanisms by which miRNAs target epicardial EMT remain largely unknown. Here, by using an in vitro model of epicardial EMT, we investigated the role of miRNAs as regulators of this process and their potential targets. EMT was induced in murine epicardial-mesothelial cells (EMCs) through TGF β1 treatment for 48, 72, and 96 h as indicated by the expression of EMT-related genes by qRT-PCR, WB, and immunofluorescence. Further, enhanced expression of stemness genes was also detected. Among several EMT-related miRNAs, miR-200c-3p expression resulted as the most strongly suppressed. Interestingly, we also found a significant upregulation of Follistatin-related protein 1 (FSTL1), a miR-200c predicted target already identified as a potent cardiogenic factor produced by epicardial cells that promotes regeneration following MI. Dual-luciferase reporter assay demonstrated that miR-200c-3p directly targeted the 3′-untranslated region of FSTL1 in EMCs. Consistently, WB analysis showed that knockdown of miR-200c-3p significantly increased FSTL1 expression, whereas overexpression of miR-200c-3p counteracted TGF β1-mediated FSTL1 upregulation. Importantly, FSTL1 silencing maintained epithelial features in EMCs, despite EMT induction by TGF β1, and attenuated EMT-associated traits, including migration and stemness. In conclusion, epicardial FSTL1, an important cardiogenic factor in its secreted form, induces EMT, stemness, and migration of EMCs in a miR-200c-3p dependent pathway.

Nucleophosmin (NPM), a nucleolar multifunctional phosphoprotein, acts as a stress sensor in different cell types. NPM can be actively secreted by inflammatory cells, however its biology on endothelium remains unexplored. In this study, we show for the first time that NPM is secreted by human vein endothelial cells (HUVEC) in the early response to serum deprivation and that NPM acts as a pro-inflammatory and angiogenic molecule both in vitro and in vivo. Accordingly, 24 h of serum starvation condition induced NPM relocalization from the nucleus to cytoplasm. Interestingly, NPM was increasingly excreted in HUVEC-derived conditioned media in a time dependent fashion upon stress conditions up to 24 h. The secretion of NPM was unrelated to cell necrosis within 24 h. The treatment with exogenous and recombinant NPM (rNPM) enhanced migration as well as the Intercellular Adhesion Molecule 1 (ICAM-1) but not Vascular cell adhesion protein 1 (VCAM-1) expression and it did not affect cell proliferation. Notably, in vitro tube formation by Matrigel assay was significantly increased in HUVEC treated with rNPM compared to controls. This result was confirmed by the in vivo injection of Matrigel plug assay upon stimulation with rNPM, displaying significant enhanced number of functional capillaries in the plugs. The stimulation with rNPM in HUVEC was also associated to the increased expression of master genes regulating angiogenesis and migration, including Vascular Endothelial Growth Factor-A (VEGF-A), Hepatocyte Growth Factor (HGF), Stromal derived factor-1 (SDF-1), Fibroblast growth factor-2 (FGF-2), Platelet Derived Growth Factor-B (PDGF-B), and Matrix metallopeptidase 9 (MMP9). Our study demonstrates for the first time that NPM is physiologically secreted by somatic cells under stress condition and in the absence of cell necrosis. The analysis of the biological effects induced by NPM mainly related to a pro-angiogenic and inflammatory activity might suggest an important autocrine/paracrine role for NPM in the regulation of both phenomena.

An outline of the main distribution patterns of lichens in the ecoregions of Italy, accounting for their climatic, geographic, and environmental features, is still missing. On the basis of a GIS-based analysis, we summarized: (1) the main features (e.g., surface, climate, landscape, topographic heterogeneity, bedrock, eutrophication) of the 9 ecoregions adopted in ITALIC, the information system on Italian lichens, and (2) the patterns of richness, functional traits, and ecological requirements of lichens in the ecoregions. Our GIS-based analysis describes for the first time the main features of the 9 ecoregions adopted in ITALIC, highlighting differences which could explain the main lichen patterns. Overall, the exploration of the Italian lichen biota is still a work in progress, some regions being still underexplored, especially in the South, with new taxa being reported every year. Our research could provide a baseline for further advancements in the understanding of species richness and community composition of Italian lichens, at a regional scale.

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

IntroductionDeveloping efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.MethodsWe describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.Results and discussionOverall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Volatile anesthetics (VAs) are medicinal chemistry compounds commonly used to enable surgical procedures for patients who undergo painful treatments and can be partially or fully sedated, remaining in an unconscious state during the operation. The specific molecular mechanism of anesthesia is still an open issue, but scientific evidence supports the hypothesis of the involvement of both putative hydrophobic cavities in membrane receptors as binding pockets and interactions between anesthetics and cytoplasmic proteins. Previous studies demonstrated the binding of VAs to tubulin. Since actin is the other major component of the cytoskeleton, this study involves an investigation of its interactions with four major anesthetics: halothane, isoflurane, sevoflurane, and desflurane. Molecular docking was implemented using the Molecular Operating Environment (MOE) software (version 2022.02) and applied to a G-actin monomer, extrapolating the relative binding affinities and root-mean-square deviation (RMSD) values. A comparison with the F-actin was also made to assess if the generally accepted idea about the enhanced F-to-G-actin transformation during anesthesia is warranted. Overall, our results confirm the solvent-like behavior of anesthetics, as evidenced by Van der Waals interactions as well as the relevant hydrogen bonds formed in the case of isoflurane and sevoflurane. Also, a comparison of the interactions of anesthetics with tubulin was made. Finally, the short- and long-term effects of anesthetics are discussed for their possible impact on the occurrence of mental disorders.

The capability of amyloid‐like peptide fibers to emit intrinsic‐fluorescence enables the study of their formation, stability and hardening through time‐resolved fluorescence analysis, without the need for additional intercalating dyes. This approach allows the monitoring of amyloid‐like peptides aggregation kinetics using minimal sample volumes, and the simultaneous testing of numerous experimental conditions and analytes, offering rapid and reproducible results. The analytical procedure applied to the aromatic hexapeptide F6, alone or derivatized with PEG (polyethylene glycol) moiety of different lengths, suggests that aggregation into large anisotropic structures negatively correlates with initial monomer concentration and relies on the presence of charged N‐ and C‐termini. PEGylation reduces the extent of aggregates hardening, possibly by retaining water, and overall impacts the final structural properties of the aggregates. [ABSTRACT FROM AUTHOR]

Bitter taste involves the detection of diverse chemical compounds by a family of G protein‐coupled receptors, known as taste receptor type 2 (TAS2R). It is often linked to toxins and harmful compounds and in particular bitter taste receptors participate in the regulation of glucose homeostasis, modulation of immune and inflammatory responses, and may have implications for various diseases. Human TAS2Rs are characterized by their polymorphism and differ in localization and function. Different receptors can activate various signaling pathways depending on the tissue and the ligand. However, in vitro screening of possible TAS2R ligands is costly and time‐consuming. For this reason, in silico methods to predict bitterant‐TAS2R interactions could be powerful tools to help in the selection of ligands and targets for experimental studies and improve our knowledge of bitter receptor roles. Machine learning (ML) is a branch of artificial intelligence that applies algorithms to large datasets to learn from patterns and make predictions. In recent years, there has been a record of numerous taste classifiers in literature, especially on bitter/non‐bitter or bitter/sweet classification. However, only a few of them exploit ML to predict which TAS2R receptors could be targeted by bitter molecules. Indeed, the shortage and incompleteness of data on receptor‐ligand associations in literature make this task non‐trivial. In this work, we provide an overview of the state of the art dealing with this specific investigation, focusing on three ML‐based models, namely BitterX (2016), BitterSweet (2019) and BitterMatch (2022). This review aims to establish the foundation for future research endeavours focused on addressing the limitations and drawbacks of existing models. [ABSTRACT FROM AUTHOR]

Volatile anesthetics (VAs) are medicinal chemistry compounds commonly used to enable surgical procedures for patients who undergo painful treatments and can be partially or fully sedated, remaining in an unconscious state during the operation. The specific molecular mechanism of anesthesia is still an open issue, but scientific evidence supports the hypothesis of the involvement of both putative hydrophobic cavities in membrane receptors as binding pockets and interactions between anesthetics and cytoplasmic proteins. Previous studies demonstrated the binding of VAs to tubulin. Since actin is the other major component of the cytoskeleton, this study involves an investigation of its interactions with four major anesthetics: halothane, isoflurane, sevoflurane, and desflurane. Molecular docking was implemented using the Molecular Operating Environment (MOE) software (version 2022.02) and applied to a G-actin monomer, extrapolating the relative binding affinities and root-mean-square deviation (RMSD) values. A comparison with the F-actin was also made to assess if the generally accepted idea about the enhanced F-to-G-actin transformation during anesthesia is warranted. Overall, our results confirm the solvent-like behavior of anesthetics, as evidenced by Van der Waals interactions as well as the relevant hydrogen bonds formed in the case of isoflurane and sevoflurane. Also, a comparison of the interactions of anesthetics with tubulin was made. Finally, the short- and long-term effects of anesthetics are discussed for their possible impact on the occurrence of mental disorders. [ABSTRACT FROM AUTHOR]