Potential genomic biomarkers of obesity and its comorbidities for phthalates and bisphenol A mixture: <i>In silico</i> toxicogenomic approach

This in silico toxicogenomic study aims to explore the relationship between phthalates and bisphenol A (BPA) co-exposure and obesity, as well as its comorbid conditions, in order to construct a possible set of genomic biomarkers. The Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org) was used as the main data mining tool, along with GeneMania (https://genemania.org), ToppGene Suite (https://toppgene.cchmc.org) and DisGeNET (http://www.disgenet.org). Among the phthalates, bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) were chosen as the most frequently curated phthalates in CTD, which also share similar mechanisms of toxicity. DEHP, DBP and BPA interacted with 84, 90 and 194 obesity-related genes/proteins, involved in 67, 65 and 116 pathways, respectively. Among these, 53 genes/proteins and 42 pathways were common to all three substances. 31 genes/proteins had matching interactions for all three investigated substances, while more than half of these genes/proteins (56.49%) were in co-expression. 7 of the common genes/proteins (6 relevant to humans: CCL2, IL6, LPL, PPARG, SERPINE1, and TNF) were identified in all the investigated obesity comorbidities, while PPARG and LPL were most closely linked to obesity. These genes/proteins could serve as a target for further in vitro and in vivo studies of molecular mechanisms of DEHP, DBP and BPA mixture obesogenic properties. Analysis reported here should be applicable to any mixture of environmental chemicals and any disease present in CTD.     

Maternal dexamethasone treatment reduces ovarian follicle number in neonatal rat offspring

Elevated glucocorticoid levels in the gravid female circulation affect a number of endocrine functions in the fetuses and neonates. The aim of this study was to examine the effects of maternal dexamethasone (Dx) administration during late pregnancy on the ovaries of neonatal offspring. On the 16th day of pregnancy, experimental dams received subcutaneously 1.0 mg Dx/kg b.w., followed by 0.5 mg Dx/kg b.w./day on the 17th and 18th days of gestation. The control gravid females received the same volume of saline vehicle. Left ovaries from 5‐day‐old female pups were stereologically analyzed. The ovary volumes were estimated using Cavalieri's principle. The number of healthy and atretic primordial and primary follicles was estimated using a fractionator–physical disector method. The number of secondary follicles was determined by exact counts of every fourth section encompassing whole cross‐sections of the ovary. The ovary volume was significantly decreased (by 44.4%; P < 0.05) in the group of female pups from Dx‐treated mothers comparing to the controls. The numbers of healthy primordial and atretic follicles were 38.8% (P < 0.05) and 50.9% (P < 0.05), respectively, reduced in the ovaries of pups from the Dx‐treated mothers, when compared with the control values. There were 53.4% (P < 0.05) fewer healthy primary and 41.8% (P < 0.05) fewer healthy secondary follicles as well. The numbers of atretic primary and secondary follicles were reduced by 60.0% (P < 0.05) and 61.7% (P < 0.05), respectively. It can be concluded that fetal exposure to glucocorticoids decreased the pool of non‐growing follicles in the neonatal ovary, whereas the processes of folliculogenesis and atresia remained unaffected.     

Decision theoretic approach to real-time robust identification

A decision-theoretic approach to the estimation of unknown parameters from a linear discrete-time dynamic measurement model in the presence of disturbance uncertainty is considered. The unknown disturbance statistics are characterized by a certain class of distributions to which the real disturbance distribution is confined. Using game theory and the asymptotic estimation error covariance matrix as the criteria of how good an estimator is, the stochastic gradient-type algorithm is shown to be optimal in the min-max sense. Since the optimal solution is not tractable in practice, several suboptimal procedures are derived on the basis of suitable approximations. The convergence of the derived algorithms is established theoretically using the ordinary differential equation approach. Monte Carlo simulation results are presented for the quantitative performance evaluation of the algorithms.     

Robust real-time identification for a class of linear time-varying discrete systems

The problem of robust real-time identification of linear single-input-single-output dynamic systems with stochastically time-varying parameters is considered. Two ways of constructing robust algorithms that are able to handle outliers contaminating the gaussian observation disturbance samples are discussed. The first way is based on the general formulation of dynamic stochastic approximation schemes characterized by an adequate non-linear residual transformation, as well as the step-by-step optimization with respect to the weighting matrix of the algorithm. The second way is based on the formulation of one-step optimal estimates. Monte Carlo simulation results illustrate the discussion and show the efficiency of the proposed algorithms in the presence of outliers.     

SARS-CoV-2 induced myocarditis: Current knowledge about its molecular and pathophysiological mechanisms

The existence of an inflammatory process in the heart muscle, related to a progressive worsening of myocardial function, different etiopathogenetic mechanisms concur and often overlap, thus making the diagnosis and the therapeutic approach complex. As the COVID-19 pandemic progresses, the effects of the disease on the organ systems and in particular on the cardiovascular system are becoming more and more profound. Cardiac involvement is a well-known event with a high percentage of findings in the heart’s magnetic field, even in asymptomatic areas. There are numerous uncertainties regarding their evolution, in the long and short term, due not only to a difficult to determine the varied clinical expression and the rarely performed intramyocardial biopsy which additionally presents diagnostic problems but also in part to different clinical prognosis. Today, the new SARS-CoV-2 virus that uses the angiotensin converting enzyme 2 (ACE2) which is present at high levels in myocardial cells as its entrance it can create even severe heart injury. The pathophysiology in all of these cases can involve multiple immune and non-immune mechanisms within organs and vessels and can be occur in the clinical phases. Possible mechanisms of direct and indirect myocardial infarction in patients with COVID-19 include additional lesion and oxygen-rich and generalized inflammation response with myocardial immune hyperactivity (myocarditis). Therefore, these can occur through the excessive release of cytokines, the presence of thrombocytopenia, endocrine damage, heart failure, arrhythmias and more. Patients can show average signs of myocardial damage, and some develop spontaneous cardiac complications, such as heart failure, arrhythmias and, rarely, rare cardiogenic disorders. Pathophysiology in all of these may involve multiple mechanisms within the cytokine cephalic membrane, endocrine damage and thrombogenicity. The diagnosis of this myocardial injuri is mainly based on the myocardial enzyme troponin. This viewpoint paper explains today’s knowledge on viral myocarditis, in particular that from SARS-CoV-2 infection, if there is a connection with other possible biomolecular pathogenetic factors that can influence its natural course. In fact, it is for this reason that the pathogenetic mechanisms are analyzed and described. At the same time, its possible interaction with other parameters that are documented risk factors for cardiovascular disease was examined. Although these biomolecular findings were mainly related to necrotic parts of the myocardium, it is important to recognize that myocardial damage early for a better approach and prognosis.     

Isolation of Low-Molecular-Weight Taste Peptides from Vacherin Mont ďOr Cheese

Vacherin cheese samples weie extracted with water and the extracts fractionated by ultrafiltration excluding compounds with a molecular weight less than 1,000. Extract aliquots were fractionated by ligand exchange chromatography on a Sephadex derivative containing N-(2-pyridylmethyl)glycine-groups in Cu²⁺ form, permitting a specific group separation of peptides. Five peptide sub-groups were then chromato-graphed on Aminex A6 and Durrum DC 4 resin. Nine fractions from these separations were characterized by manual gas-phase isofhiocy-anate degradation and HPLC of the amino acid derivatives. Seven peptides could be identified: H-Leu-Pro-OH, H-Val-Pro-OH, H-Phe-Pro-OH, H-Lys-Pro-OH, H-Gly-Pro-Val-Arg-OH, H-Tyr-Pro-OH, and H-Arg-Pro-OH. A partial elucidation of the structure was possible for peptides containing Asp/Pro/Val/Leu, Glu/Leu, and Ala/Pro.     

Investigation of properties of electrochemically synthesizediron oxide nano‐powders

Nano‐sized powders of iron oxides have been synthesized electrochemically at temperatures in the range of 295–361 K, and current densities in the range of 200–1000 mA dm^?2. The structure and morphology of the powders were investigated by X‐ray diffraction and scanning electron and transmission electron microscopy techniques. Their infrared absorption spectra, specific heat C_p(T) and magnetic susceptibility χ(T) temperature dependences are also determined. The obtained powders consist of two phases, each possessing distinguished characteristics: the one formed of large plates and the other of whiskers. By appropriate adjustment of the synthesis conditions, it is possible to change features and relative abundances of the two phases, and that way to control morphology and other powder properties. Relaxation and transformation of the phases under external influences was also investigated, and the optimal procedure for preparation and stabilization of iron oxide nano‐sized powders with desired characteristics was established.     

Robust real-time algorithms for identification of linear multivariable time-varying systems

The paper presents a discussion on the problem of the robust real-time identification of linear multivariable time-varying dynamic systems working in a noisy environment. Two methodologically different approaches to the design of such algorithms are presented. The first is based on the one-step estimation, optimal in the sense of the minimal conditional mean-square error, combined with convenient approximations of the underlying error covariance matrix. The second is based on the general formulation of robustified stochastic approximation algorithms, characterized by a suitable non-linear transformation of normalized residuals. Particular algorithms are derived on the basis of step-by-step optimization with respect to the weighting matrix of the algorithm. Monte Carlo simulation results illustrate the discussion, and show the efficiency of the proposed robust algorithms in the presence of large disturbance realizations, the so-called outliers.     

Local Aromaticity in Benzo- and Benzocyclobutadieno-Annelated Phenanthrenes

The effects of benzo- and benzocyclobutadieno-(BCBD-)annelation on the local aromaticity of the central ring (X) of phenanthrene were examined. The local aromaticity of the ring X was estimated using a graph-theory-based index and two geometry-based indices. DFT calculations of phenanthrene congeners showed that many of these molecules are nonplanar, with significant steric strain. All three indices indicate that the effect of benzo-annelation is opposite to that of BCBD-annelation. The deviation of some phenanthrene derivatives from planarity does not influence the effects of annelation. This finding is in accord with some previously reported studies on the dependence of the aromaticity of the ring(s) on distortion from planarity.