Modeling The Bioconcentration Factors and Bioaccumulation Factors of Polychlorinated Biphenyls with Posetic Quantitative Super Structure/Activity Relationship

Summary During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment thereby producing widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSAR for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Due to their widespread use in bactericides, insecticides, herbicides, andfungicides, chlorophenols represent an important source of soil contaminants. Theenvironmental fate of these chemicals depends on their physico-chemical properties. In theabsence of experimental values for these physico-chemical properties, one can use predictedvalues computed with quantitative structure-property relationships (QSPR). As analternative to correlations to molecular structure we have studied the super-structure of areaction network, thereby developing three new QSSPR models (poset-average, cluster-expansion, and splinoid poset) that can be applied to chemical compounds which can behierarchically ordered into a reaction network. In the present work we illustrate these posetQSSPR models for the correlation of the octanol/water partition coefficient (log Kow) and thesoil sorption coefficient (log KOC) of chlorophenols. Excellent results are obtained for allQSSPR poset models to yield: log Kow, r = 0.991, s = 0.107, with the cluster-expansionQSSPR; and log KOC, r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPRmodels predict environmentally important properties of chlorophenols

SDAP: database and computational tools for allergenic proteins

SDAP (Structural Database of Allergenic Proteins) is a web server that provides rapid, cross-referenced access to the sequences, structures and IgE epitopes of allergenic proteins. The SDAP core is a series of CGI scripts that process the user queries, interrogate the database, perform various computations related to protein allergenic determinants and prepare the output HTML pages. The database component of SDAP contains information about the allergen name, source, sequence, structure, IgE epitopes and literature references and easy links to th

Actualizări în diagnosticul de laborator al imunodeficiențelor primare

State University of Medicine and Pharmacy “Nicolae Testemitanu”, Chisinau, Republic of Moldova, Congresul consacrat aniversării a 75-a de la fondarea Universității de Stat de Medicină și Farmacie „Nicolae Testemițanu” din Republica Moldova, Ziua internațională a științei pentru pace și dezvoltareIntroducere: Imunodeficiențele primare (PID) cuprind peste 150 de afecțiuni, care afectează dezvoltarea și/sau funcționarea sistemului imun. PID prezintă cea mai mare povară diagnostică datorită complexității și numărului tot mai mare de entități clinice, care cuprind acest grup de afecțiuni. Scopul lucrării: Lucrarea de fаţă se doreşte а fi o trecere în revistă a dignosticului de laborator al PID, pentru un management mai eficace al acestui grup de afecțiuni. Materiale și metode: Studierea literaturii, privind diagnosticul de laborator al PID, a fost efectuată utilizându-se căutarea în bazele de date electronice. Rezultate: PID sunt divizate în defecte ale imunității înnăscute și adaptative. Diagnosticul PID este unul complex bazat pe o anamneză minuțioasă pentru a determina dacă este prezentă o adevărată susceptibilitate la infecții recurente, identificarea factorilor care predispun la o vulnerabilitate la infecție, determinarea dacă susceptibilitatea crescută la infecție se datorează factorilor gazdei (cauze imunologice primare și secundare). Astfel, este necesar de studiat istoricul bolii, semnele fizice și istoricul familial al pacientului, deoarece sunt niște verigi importante în diagnosticul PID. Putem doar suspecta că un pacient are PID dacă a avut >8 ori/an infectarea urechii, >2ori/an infectarea sinusurilor, necesitatea terapiei intravenoase pentru a trata infecția sau a avut o infecție cu patogeni oportuniști, însă investigațiile de laborator sunt necesare pentru a confirma sau exclude patologia. Deoarece sunt mai multe subgrupe ale PID, investigațiile de laborator se fac în mai multe etape. Inițial se realizează o hemoleucograma completă și testarea nivelul imunoglobulinelor IgG, IgM, IgA. Dacă rezultate sunt în limitele normei, se poate exclude PID. Atunci cînd la analize depistăm rezultate ce depășesc limitele normei, se va alege următoarele etape de analize de laborator, mai detaliate pentru a depista o patologie PID. Concluzie: În acest review s-a discutat despre progresele în medicina de laborator, aplicate studiului PID. Laboratorul joacă un rol esențial în diagnosticul PID, având în vedere semnele și simptomele, care frecvent se suprapun

The Connectivity and the Harary Index of a Graph

The Harary index of a graph is defined as the sum of reciprocals of distances between all pairs of vertices of the graph. In this paper we provide an upper bound of the Harary index in terms of the vertex or edge connectivity of a graph. We characterize the unique graph with maximum Harary index among all graphs with given number of cut vertices or vertex connectivity or edge connectivity. In addition we also characterize the extremal graphs with the second maximum Harary index among the graphs with given vertex connectivity

Building-block Computation of Wiener-type Indices for the Virtual Screening of Combinatorial Libraries

Screening Virtual and synthetic combinatorial libraries may facilitate rapid drug lead discovery by selecting subsets of molecules ac-cording to their similarity or dissimilarity toward specific compound collections. Topological indices computed from atomic connectivities or graph distances are increasingly used as structural descriptors in order to maximize the molecular diversity of libraries or to quantify the drug-like character of compounds. In this paper we present efficient equations for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. These equations offer an effective way to compute for non-weighted molecular graphs the Wiener index, even/odd Wiener index, resistance distance index, Wiener polynomial, and even/odd Wiener polynomial. Using a simple and fast algorithm one can compute these topological indices for very large virtual combinatorial libraries without computing the indices from the atomic scale up for each individual compound - rather only distance-based indices of the building blocks are needed to generate the topological indices of the compound assembled from the building blocks

Design of Topological Indices. Part 6.1,2 A New Topological Parameter for the Steric Effect of Alkyl Substituents

The steric effect of alkyl groups is characterized by the Steric Vertex Topological Index (SVTI). SVTI is equal to the sum of the distances not greater than three between the attachment site and the carbon atoms of the alkly group. The correlations presented, either with reaction rates of acid-catalyzed esterification of carboxylic acids or with various steric parameters, i.e. Es, E\u27s, v, and E, indicated that SVTI can serve as a useful steric parameter in reactivity studies