Epigenetic mechanisms of endocrine-disrupting chemicals in obesity

The incidence of obesity has dramatically increased over the last decades. Recently, there has been a growing interest in the possible association between the pandemics of obesity and some endocrine-disrupting chemicals (EDCs), termed “obesogens”. These are a heterogeneous group of exogenous compounds that can interfere in the endocrine regulation of energy metabolism and adipose tissue structure. Oral intake, inhalation, and dermal absorption represent the major sources of human exposure to these EDCs. Recently, epigenetic changes such as the methylation of cytosine residues on DNA, post-translational modification of histones, and microRNA expression have been considered to act as an intermediary between deleterious effects of EDCs and obesity development in susceptible individuals. Specifically, EDCs exposure during early-life development can detrimentally affect individuals via inducing epigenetic modifications that can permanently change the epigenome in the germline, enabling changes to be transmitted to the next generations and predisposing them to a multitude of diseases. The purpose of this review is to analyze the epigenetic alterations putatively induced by chemical exposures and their ability to interfere with the control of energy metabolism and adipose tissue regulation, resulting in imbalances in the control of body weight, which can lead to obesity

Thyroid nodules treated with percutaneous radiofrequency thermal ablation: a comparative study

Percutaneous radiofrequency thermal ablation (RTA) was reported as an effective tool for the management of thyroid nodules (TNs). The aim of this study was to investigate the effects of RTA and to establish whether they were treatment-related by comparison with a matched, untreated control group

Surface plasmon resonance assay for label-free and selective detection of hiv-1 p24 protein

The early detection of the human immunodeficiency virus (HIV) is of paramount importance to achieve efficient therapeutic treatment and limit the disease spreading. In this perspective, the assessment of biosensing assay for the HIV-1 p24 capsid protein plays a pivotal role in the timely and selective detection of HIV infections. In this study, multi-parameter-SPR has been used to develop a reliable and label-free detection method for HIV-1 p24 protein. Remarkably, both physical and chemical immobilization of mouse monoclonal antibodies against HIV-1 p24 on the SPR gold detecting surface have been characterized for the first time. The two immobilization techniques returned a capturing antibody surface coverage as high as (7.5 ± 0.3) × 1011 molecule/cm2 and (2.4 ± 0.6) × 1011 molecule/cm2, respectively. However, the covalent binding of the capturing antibodies through a mixed self-assembled monolayer (SAM) of alkanethiols led to a doubling of the p24 binding signal. Moreover, from the modeling of the dose-response curve, an equilibrium dissociation constant KD of 5.30 × 10−9 M was computed for the assay performed on the SAM modified surface compared to a much larger KD of 7.46 × 10−5 M extracted for the physisorbed antibodies. The chemically modified system was also characterized in terms of sensitivity and selectivity, reaching a limit of detection of (4.1 ± 0.5) nM and an unprecedented selectivity ratio of 0.02

Thyroid disease treatment prediction with machine learning approaches

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists' activities and improve the quality of the patients' life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient's treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%

Performance of the Two Aerogel Cherenkov Detectors of the JLab Hall A Hadron Spectrometer

We report on the design and commissioning of two silica aerogel Cherenkov detectors with different refractive indices. In particular, extraordinary performance in terms of the number of detected photoelectrons was achieved through an appropriate choice of PMT type and reflector, along with some design considerations. After four years of operation, the number of detected photoelectrons was found to be noticeably reduced in both detectors as a result of contamination, yellowing, of the aerogel material. Along with the details of the set-up, we illustrate the characteristics of the detectors during different time periods and the probable causes of the contamination. In particular we show that the replacement of the contaminated aerogel and parts of the reflecting material has almost restored the initial performance of the detectors.Comment: 18 pages, 9 Figures, 4 Tables, 44 Reference

A large-area organic transistor with 3D-printed sensing gate for noninvasive single-molecule detection of pancreatic mucinous cyst markers

Early diagnosis in a premalignant (or pre-invasive) state represents the only chance for cure in neoplastic diseases such as pancreatic-biliary cancer, which are otherwise detected at later stages and can only be treated using palliative approaches, with no hope for a cure. Screening methods for the purpose of secondary prevention are not yet available for these cancers. Current diagnostic methods mostly rely on imaging techniques and conventional cytopathology, but they do not display adequate sensitivity to allow valid early diagnosis. Next-generation sequencing can be used to detect DNA markers down to the physical limit; however, this assay requires labeling and is time-consuming. The additional determination of a protein marker that is a predictor of aggressive behavior is a promising innovative approach, which holds the potential to improve diagnostic accuracy. Moreover, the possibility to detect biomarkers in blood serum offers the advantage of a noninvasive diagnosis. In this study, both the DNA and protein markers of pancreatic mucinous cysts were analyzed in human blood serum down to the single-molecule limit using the SiMoT (single-molecule assay with a large transistor) platform. The SiMoT device proposed herein, which exploits an inkjet-printed organic semiconductor on plastic foil, comprises an innovative 3D-printed sensing gate module, consisting of a truncated cone that protrudes from a plastic substrate and is compatible with standard ELISA wells. This 3D gate concept adds tremendous control over the biosensing system stability, along with minimal consumption of the capturing molecules and body fluid samples. The 3D sensing gate modules were extensively characterized from both a material and electrical perspective, successfully proving their suitability as detection interfaces for biosensing applications. KRAS and MUC1 target molecules were successfully analyzed in diluted human blood serum with the 3D sensing gate functionalized with b-KRAS and anti-MUC1, achieving a limit of detection of 10 zM and 40 zM, respectively. These limits of detection correspond to (1 ± 1) KRAS and (2 ± 1) MUC1 molecules in the 100 μL serum sample volume. This study provides a promising application of the 3D SiMoT platform, potentially facilitating the timely, noninvasive, and reliable identification of pancreatic cancer precursor cysts

XPS characterization of (copper-based) coloured stains formed on limestone surfaces of outdoor Roman monuments

Limestone basements holding bronzes or other copper alloys artefacts such as sculptures, decorations and dedicatory inscriptions are frequently met both in modern and ancient monuments. In outdoor conditions, such a combination implies the corrosion products of the copper based alloy, directly exposed to rainwater, will be drained off and migrate through the porous surfaces, forming stains of different colours and intensities, finally causing the limestone structures to deteriorate

Predictive and prognostic value of inflammatory markers in locally advanced rectal cancer (PILLAR) – A multicentric analysis by the Italian Association of Radiotherapy and Clinical Oncology (AIRO) Gastrointestinal Study Group

Background: Patients (pts) affected with locally advanced rectal cancer (LARC) may respond differently to neoadjuvant chemoradiotherapy (nCRT). The identification of reliable biomarkers able to predict oncological outcomes could help in the development of risk-adapted treatment strategies. It has been suggested that inflammation parameters may have a role in predicting tumor response to nCRT and survival outcomes and in rectal cancer, but no definitive conclusion can be drawn at present. The aim of the current study is to evaluate the role of baseline inflammatory markers as prognostic and predictive factors in a large multicentric Italian cohort of LARC pts. Methods: Patients diagnosed with LARC from January 2002 to December 2019 in 9 Italian centers were retrospectively collected. Patients underwent long-course RT with chemotherapy based on fluoropyrimidine ± oxaliplatin followed by surgery. Inflammatory markers were retrieved based on a pre-treatment blood sample including HEI (hemo-eosinophils inflammation index), SII (systemic index of inflammation), NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio) and MLR (monocyte-to-lymphocyte ratio). Outcomes of interest were pathological complete response (pCR), disease-free survival (DFS), and overall survival (OS). Results: 808 pts were analyzed. pCR rate was 22 %, 5yOS and 5yDFS were 84.0% and 63.1% respectively. Multivariate analysis identified that a NLR cut-off value >1.2 and SII cut-off value >500 could predict pCR (p = 0.05 and 0.009 respectively). In addition to age, extramesorectal nodes and RT dose, MLR >0.18 (p = 0.03) and HEI = 3 (p = 0.05) were independent prognostic factors for DFS. Finally, age, RT dose, MLR with a cut-off >0.35 (p = 0.028) and HEI = 3 (p = 0.045) were independent predictors of OS. Conclusions: Higher values of baseline composite inflammatory markers can serve as predictors of lower pCR rates and worse survival outcomes in LARC patients undergoing nCRT. More reliable data from prospective studies could lead to the integration of these inexpensive and easy-to-derive tools into clinical practice

A multicentric study on prevalence of clinical patterns and clinical phenotypes in adult atopic dermatitis

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A Single-Molecule Bioelectronic Portable Array for Early Diagnosis of Pancreatic Cancer Precursors

A cohort of 47 patients is screened for pancreatic cancer precursors with a portable 96-well bioelectronic sensing-array for single-molecule assay in cysts fluid and blood plasma, deployable at point-of-care (POC). Pancreatic cancer precursors are mucinous cysts diagnosed with a sensitivity of at most 80% by state-of-the-art cytopathological molecular analyses (e.g., KRASmut DNA). Adding the simultaneous assay of proteins related to malignant transformation (e.g., MUC1 and CD55) is deemed essential to enhance diagnostic accuracy. The bioelectronic array proposed here, based on single-molecule-with-a-large-transistor (SiMoT) technology, can assay both nucleic acids and proteins at the single-molecule limit-of-identification (LOI) (1% of false-positives and false-negatives). It comprises an enzyme-linked immunosorbent assay (ELISA)-like 8 × 12-array organic-electronics disposable cartridge with an electrolyte-gated organic transistor sensor array, and a reusable reader, integrating a custom Si-IC chip, operating via software installed on a USB-connected smart device. The cartridge is complemented by a 3D-printed sensing gate cover plate. KRASmut, MUC1, and CD55 biomarkers either in plasma or cysts-fluid from 5 to 6 patients at a time, are multiplexed at single-molecule LOI in 1.5 h. The pancreatic cancer precursors are classified via a machine-learning analysis resulting in at least 96% diagnostic-sensitivity and 100% diagnostic-specificity. This preliminary study opens the way to POC liquid-biopsy-based early diagnosis of pancreatic-cancer precursors in plasma