Interaction of silver atomic quantum clusters with living organisms: bactericidal effect of Ag3 clusters mediated by disruption of topoisomerase-DNA complexes.

Essential processes for living cells such as transcription and replication depend on the formation of specific protein-DNA recognition complexes. Proper formation of such complexes requires suitable fitting between the protein surface and the DNA surface. By adopting doxorubicin (DOX) as a model probe, we report here that Ag3 atomic quantum clusters (Ag-AQCs) inhibit the intercalation of DOX into DNA and have considerable influence on the interaction of DNA-binding proteins such as topoisomerase IV, Escherichia coli DNA gyrase and the restriction enzyme HindIII. Ag-AQCs at nanomolar concentrations inhibit enzyme activity. The inhibitory effect of Ag-AQCs is dose-dependent and occurs by intercalation into DNA. All these effects, not observed in the presence of Ag+ ions, can explain the powerful bactericidal activity of Ag-AQCs, extending the knowledge of silver bactericidal properties. Lastly, we highlight the interest of the interaction of Ag clusters with living organisms, an area that should be further explored due to the potential consequences that it might have, both beneficial and harmful.

Effect of antimicrobial agents incorporated into resilient denture relines on the Candida albicans biofilm.

OBJECTIVE: The antimicrobial action of five drugs incorporated in temporary denture relines on the fungal biofilm was evaluated. MATERIALS AND METHODS: A Candida albicans biofilm (SC5314) was formed on specimens (10 × 1 mm) of materials (Trusoft and Softone) modified or not (control) by the drugs (nystatin, miconazole, ketoconazole, itraconazole, and chlorhexidine diacetate). Cell viability was determined spectrophotometrically by the tetrazolium salt reduction assay (XTT) after 24 h, 48 h, and 7 and 14 days of incubation. The minimum inhibitory concentrations (MICs) were those which inhibited 90% or more of fungal growth. Fungal susceptibility was confirmed by confocal laser scanning microscopy analysis. RESULTS: The MICs of drugs incorporated in the materials were 0.032, 0.256, 0.128, 0.256, and 0.064 g ml(-1) for nystatin, miconazole, ketoconazole, itraconazole, and chlorhexidine, respectively. Images from nystatin, chlorhexidine, and ketoconazole demonstrated no viable cells. CONCLUSIONS: The antimicrobials incorporated in the resilient materials inhibited fungal growth during 14 days, with lower MICs for nystatin and chlorhexidine.

Activation pattern of neutrophils from blood of elderly individuals with Candida-related denture stomatitis.

We have identified impaired neutrophils in elderly individuals which could be involved with Candida-related denture stomatitis (DS), an oral infection predominantly caused by Candida albicans, affecting especially elderly individuals using dental prosthesis. However, specific mechanisms performed by neutrophil contributing to the susceptibility of the elderly to DS are not fully understood. This study evaluated activation features of blood neutrophils from elderly and young individuals with DS. Blood neutrophils cultured with C. albicans from elderly subjects secreted decreased levels of CXCL8. However, C. albicans challenged-neutrophils from DS patients produced high IL-4 and IL-10, and low GM-CSF levels, regardless of age. Additional elastase activity of neutrophils from both elderly groups was detected after incubation with C. albicans, but only neutrophils from elderly DS demonstrated high myeloperoxidase activity. Therefore, DS patients have affected neutrophils, and the advance of age intensifies these damages. In summary, individuals with Candida-related denture stomatitis presented variation in the neutrophil phenotype and activation. Such alterations were more intense in neutrophils from infected elderly individuals.

Differences between salivary and blood neutrophils from elderly and young denture wearers.

We previously showed that neutrophils from patients with Candida-related denture stomatitis exhibited damaged function, and the advance in age intensified this condition. Because such alterations had been determined in elderly people that were not denture wearers, the purpose of this study was to clarify functional and phenotypic characteristics of neutrophils from elderly denture wearers (EDW) and young denture wearers (YDW) without oral lesion. We enrolled 20 denture wearers (12 EDW and 8 YDW) and determined the positivity of Candida species on maxillary prosthesis and palate. Additionally, blood and salivary neutrophils were evaluated. Furthermore, cytokines and chemokines salivary levels were detected. YDW presented higher positivity of Candida albicans than elderly ones. However, blood neutrophils from EDW expressed less CXCR1, CD62L and CD11b and had lower C. albicans phagocytosis than YDW. Although myeloperoxidase and elastase activity was significantly higher in C. albicans-stimulated blood neutrophils from elderly, they produced high levels of IL-10 and low levels of Granulocyte macrophage-colony stimulating factor (GM-CSF). Despite apoptosis rate of salivary neutrophils was enhanced, these cells were at a high number in YDW. GM-CSF and IL10 were lower in saliva from elderly group. These data confirmed that ageing affects blood and salivary neutrophils and could predispose elderly to persistent oral infections.

Evolutionary computation for discovery of composite transcription factor binding sites.

Previous research demonstrated the use of evolutionary computation for the discovery of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes. However, it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where two or more TFBSs form functional complexes, could also be identified by using this approach. Here, we present an important refinement of our previous algorithm and test the identification of composite elements using NFAT/AP-1 as an example. We demonstrate that by using appropriate existing parameters such as window size, novel-scoring methods such as central bonusing and methods of self-adaptation to automatically adjust the variation operators during the evolutionary search, TFBSs of different sizes and complexity can be identified as top solutions. Some of these solutions have known experimental relationships with NFAT/AP-1. We also indicate that even after properly tuning the model parameters, the choice of the appropriate window size has a significant effect on algorithm performance. We believe that this improved algorithm will greatly augment TFBS discovery.

Discovery of RNA structural elements using evolutionary computation.

RNA molecules fold into characteristic secondary and tertiary structures that account for their diverse functional activities. Many of these RNA structures, or certain structural motifs within them, are thought to recur in multiple genes within a single organism or across the same gene in several organisms and provide a common regulatory mechanism. Search algorithms, such as RNAMotif, can be used to mine nucleotide sequence databases for these repeating motifs. RNAMotif allows users to capture essential features of known structures in detailed descriptors and can be used to identify, with high specificity, other similar motifs within the nucleotide database. However, when the descriptor constraints are relaxed to provide more flexibility, or when there is very little a priori information about hypothesized RNA structures, the number of motif 'hits' may become very large. Exhaustive methods to search for similar RNA structures over these large search spaces are likely to be computationally intractable. Here we describe a powerful new algorithm based on evolutionary computation to solve this problem. A series of experiments using ferritin IRE and SRP RNA stem-loop motifs were used to verify the method. We demonstrate that even when searching extremely large search spaces, of the order of 10(23) potential solutions, we could find the correct solution in a fraction of the time it would have taken for exhaustive comparisons.

Evolving artificial neural networks for screening features from mammograms.

Disagreement or inconsistencies in mammographic interpretation motivates utilizing computerized pattern recognition algorithms to aid the assessment of radiographic features. We have studied the potential for using artificial neural networks (ANNs) to analyze interpreted radiographic features from film screen mammograms. Attention was given to 216 cases (mammogram series) that presented suspicious characteristics. The domain expert (Wasson) quantified up to 12 radiographic features for each case based on guidelines from previous literature. Patient age was also included. The existence or absence of malignancy was confirmed in each case via open surgical biopsy (111 malignant, 105 benign). ANNs of various complexity were trained via evolutionary programming to indicate whether or not a malignancy was present given a vector of scored input features in a statistical cross validation procedure. For suspicious masses, the best evolved ANNs generated a mean area under the receiver operating characteristic curve (AZ) of 0.9196 +/- 0.0040 (1 S.E.), with a mean specificity of 0.6269 +/- 0.0272 at 0.95 sensitivity. Results when microcalcifications were included were not quite as good (AZ = 0.8464), however, ANNs with only two hidden nodes performed as well as more complex ANNs and better than ANNs with only one hidden node. The performance of the evolved ANNs was comparable to prior literature, but with an order of magnitude less complexity. The success of small ANNs in diagnosing breast cancer offers the promise that suitable explanations for the ANN's behavior can be induced, leading to a greater acceptance by physicians.

Linear and neural models for classifying breast masses.

Computational methods can be used to provide an initial screening or a second opinion in medical settings and may improve the sensitivity and specificity of diagnoses. In the current study, linear discriminant models and artificial neural networks are trained to detect breast cancer in suspicious masses using radiographic features and patient age. Results on 139 suspicious breast masses (79 malignant, 60 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved at the risk of a small percentage of false positives. Receiver operating characteristic (ROC) analysis favors the use of linear models, however, a new measure related to the area under the ROC curve (AZ) suggests a possible benefit from hybridizing linear and nonlinear classifiers.

A step toward computer-assisted mammography using evolutionary programming and neural networks.

Artificial intelligence techniques can be used to provide a second opinion in medical settings. This may improve the sensitivity and specificity of diagnoses, as well as the cost effectiveness of the physician's effort. In the current study, evolutionary programming is used to train artificial neural networks to detect breast cancer using radiographic features and patient age. Results from 112 suspicious breast masses (63 malignant, 49 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved using simple neural architectures at the risk of a small percentage of false positives.