Two naphthalene degrading bacteria belonging to the genera Paenibacillus and Pseudomonas isolated from a highly polluted lagoon perform different sensitivities to the organic and heavy metal contaminants

Two bacterial strains were isolated in the presence of naphthalene as the sole carbon and energy source from sediments of the Orbetello Lagoon, Italy, which is highly contaminated with both organic compounds and metals. 16S rRNA gene sequence analysis of the two isolates assigned the strains to the genera Paenibacillus and Pseudomonas. The effect of different contaminants on the growth behaviors of the two strains was investigated. Pseudomonas sp. ORNaP2 showed a higher tolerance to benzene, toluene, and ethylbenzene than Paenibacillus sp. ORNaP1. In addition, the toxicity of heavy metals potentially present as co-pollutants in the investigated site was tested. Here, strain Paenibacillus sp. ORNaP1 showed a higher tolerance towards arsenic, cadmium, and lead, whereas it was far more sensitive towards mercury than strain Pseudomonas sp. ORNaP2. These differences between the Gram-negative Pseudomonas and the Gram-positive Paenibacillus strain can be explained by different general adaptive response systems present in the two bacteria

Bayesian analysis of a disability model for lung cancer survival

Bayesian reasoning, survival analysis and multi-state models are used to assess survival times for Stage IV non-small-cell lung cancer patients and the evolution of the disease over time. Bayesian estimation is done using minimum informative priors for the Weibull regression survival model, leading to an automatic inferential procedure. Markov chain Monte Carlo methods have been used for approximating posterior distributions and the Bayesian information criterion has been considered for covariate selection. In particular, the posterior distribution of the transition probabilities, resulting from the multi-state model, constitutes a very interesting tool which could be useful to help oncologists and patients make efficient and effective decisions.This study has been partially supported by the Ministerio de Ciencia e Innovación [grant number MTM2010- 19528], Mutua Madrileña [grant AP75942010], Ministero dell'Istruzione, dell'Universitá e della Ricerca of Italy and the visiting professor program of the Regione Autonoma della Sardegna

YAP activation is an early event and a potential therapeutic target in liver cancer development

Background and Aims: Although the growth suppressor Hippo pathway has been implicated in hepatocellular carcinoma (HCC) pathogenesis, it is unknown at which stage of hepatocarcinogenesis its dysregulation occurs. We investigated in early rat and human preneoplastic lesions whether overexpression of the transcriptional co-activator Yes-associated protein (YAP) is an early event. Methods: The experimental model used is the Resistant-Hepatocyte (R-H) rat model. Gene expression was determined by qRT-PCR or immunohistochemistry. Forward genetic experiments were performed in human HCC cells and in murine oval cells. Results All foci of preneoplastic hepatocytes generated in rats 4 weeks after diethylnitrosamine (DENA) treatment, displayed YAP accumulation. This was associated with down-regulation of the β-TRCP ligase, known to mediate YAP degradation, and of microRNA-375, targeting YAP. YAP accumulation was paralleled by up-regulation of its target genes. Increased YAP expression was also observed in early dysplastic nodules and adenomas in humans. Animal treatment with verteporfin (VP), which disrupts the formation of the YAP–TEAD complex, significantly reduced preneoplastic foci and oval cell proliferation. In vitro experiments confirmed that VP-mediated YAP inhibition impaired cell growth in HCC and oval cells; notably, oval cell transduction with wild type or active YAP conferred tumorigenic properties in vitro and in vivo. Conclusions: These results suggest that i) YAP overexpression is an early event in rat and human liver tumorigenesis; ii) it is critical for the clonal expansion of carcinogen-initiated hepatocytes and oval cells, and, iii) VP-induced disruption of YAP-TEAD interaction may provide an important approach for the treatment of YAP-overexpressing cancers

Direct-acting antivirals used in HCV-related liver disease do not affect thyroid function and autoimmunity

Purpose It is well known that interferon-alpha (IFN-alpha), used for long time as the main therapy for HCV-related disease, induces thyroid alterations, but the impact of the new direct-acting antivirals (DAAs) on thyroid is not established. Aim of this prospective study was to evaluate if DAAs therapy may induce thyroid alterations.Methods A total of 113 HCV patients, subdivided at the time of the enrollment in naive group (n = 64) and in IFN-alpha group (n = 49) previously treated with pegylated interferon-alpha and ribavirin, were evaluated for thyroid function and autoimmunity before and after 20-32 weeks of DAAs.Results Before starting DAAs, a total of 8/113 (7.1%) patients showed Hashimoto's thyroiditis (HT) all belonging to IFN-alpha group (8/49, 16.3%), while no HT cases were found in the naive group. Overall, 7/113 (6.2%) patients were hypothyroid: 3/64 (4.7%) belonging to naive group and 4/49 (8.2%) to IFN-alpha group. Furthermore, a total of 8/113 patients (7.1%) showed subclinical hyperthyroidism: 2/64 (3.1%) were from naive group and 6/49 (12.2%) from IFN-alpha group. Interestingly, after DAAs therapy, no new cases of HT, hypothyroidism and hyperthyroidism was found in all series, while 6/11 (54.5%) patients with non-autoimmune subclinical thyroid dysfunction became euthyroid. Finally, the only association between viral genotypes and thyroid alterations was genotype 1 and hypothyroidism.Conclusions This study supports evidence that DAAs have a limited or missing influence on thyroid in patients with HCV-related diseases. Moreover, it provides preliminary evidence that subclinical non-autoimmune thyroid dysfunction may improve after HCV infection resolution obtained by DAAs

Olfactory swab sampling optimization for α-synuclein aggregate detection in patients with Parkinson’s disease

Background: In patients with Parkinson’s disease (PD), real-time quaking-induced conversion (RT-QuIC) detection of pathological α-synuclein (α-syn) in olfactory mucosa (OM) is not as accurate as in other α-synucleinopathies. It is unknown whether these variable results might be related to a different distribution of pathological α-syn in OM. Thus, we investigated whether nasal swab (NS) performed in areas with a different coverage by olfactory neuroepithelium, such as agger nasi (AN) and middle turbinate (MT), might affect the detection of pathological α-syn. Methods: NS was performed in 66 patients with PD and 29 non-PD between September 2018 and April 2021. In 43 patients, cerebrospinal fluid (CSF) was also obtained and all samples were analyzed by RT-QuIC for α-syn. Results: In the first round, 72 OM samples were collected by NS, from AN (NSAN) or from MT (NSMT), and 35 resulted positive for α-syn RT-QuIC, including 27/32 (84%) from AN, 5/11 (45%) from MT, and 3/29 (10%) belonging to the non-PD patients. Furthermore, 23 additional PD patients underwent NS at both AN and MT, and RT-QuIC revealed α-syn positive in 18/23 (78%) NSAN samples and in 10/23 (44%) NSMT samples. Immunocytochemistry of NS preparations showed a higher representation of olfactory neural cells in NSAN compared to NSMT. We also observed α-syn and phospho-α-syn deposits in NS from PD patients but not in controls. Finally, RT-QuIC was positive in 22/24 CSF samples from PD patients (92%) and in 1/19 non-PD. Conclusion: In PD patients, RT-QuIC sensitivity is significantly increased (from 45% to 84%) when NS is performed at AN, indicating that α-syn aggregates are preferentially detected in olfactory areas with higher concentration of olfactory neurons. Although RT-QuIC analysis of CSF showed a higher diagnostic accuracy compared to NS, due to the non-invasiveness, NS might be considered as an ancillary procedure for PD diagnosis

Clinical global impression-severity score as a reliable measure for routine evaluation of remission in schizophrenia and schizoaffective disorders

Aims: This study aimed to compare the performance of Positive and Negative Syndrome Scale (PANSS) symptom severity criteria established by the Remission in Schizophrenia Working Group (RSWG) with criteria based on Clinical Global Impression (CGI) severity score. The 6-month duration criterion was not taken into consideration. Methods: A convenience sample of 112 chronic psychotic outpatients was examined. Symptomatic remission was evaluated according to RSWG severity criterion and to a severity criterion indicated by the overall score obtained at CGI-Schizophrenia (CGI-SCH) rating scale (≤3) (CGI-S). Results: Clinical remission rates of 50% and 49.1%, respectively, were given by RSWG and CGI-S, with a significant level of agreement between the two criteria in identifying remitted and non-remitted cases. Mean scores at CGI-SCH and PANSS scales were significantly higher among remitters, independent of the remission criteria adopted. Measures of cognitive functioning were largely independent of clinical remission evaluated according to both RSWG and CGI-S. When applying RSWG and CGI-S criteria, the rates of overall good functioning yielded by Personal and Social Performance scale (PSP) were 32.1% and 32.7%, respectively, while the mean scores at PSP scale differed significantly between remitted and non-remitted patients, independent of criteria adopted. The proportion of patients judged to be in a state of well-being on Social Well-Being Under Neuroleptics-Short Version scale (SWN-K) were, respectively, 66.1% and 74.5% among remitters according to RSWG and CGI-S; the mean scores at the SWN scale were significantly higher only among remitters according to CGI-S criteria. Conclusions: CGI severity criteria may represent a valid and user-friendly alternative for use in identifying patients in remission, particularly in routine clinical practic

Damage detection via shortest-path network sampling

Large networked systems are constantly exposed to local damages and failures that can alter their functionality. The knowledge of the structure of these systems is, however, often derived through sampling strategies whose effectiveness at damage detection has not been thoroughly investigated so far. Here, we study the performance of shortest-path sampling for damage detection in large-scale networks. We define appropriate metrics to characterize the sampling process before and after the damage, providing statistical estimates for the status of nodes (damaged, not damaged). The proposed methodology is flexible and allows tuning the trade-off between the accuracy of the damage detection and the number of probes used to sample the network. We test and measure the efficiency of our approach considering both synthetic and real networks data. Remarkably, in all of the systems studied, the number of correctly identified damaged nodes exceeds the number of false positives, allowing us to uncover the damage precisely

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Epidemic spreading on time-varying multiplex networks

Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently, mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study SIS epidemic models unfolding at comparable time-scale respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the multiplexity and the features of each layer. We found that, higher values of multiplexity significantly reduce the epidemic threshold especially when the temporal activation patterns of nodes present on multiple layers are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics are driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the multiplexity. However, this is not the case in the other layers where the spreading dynamics are sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena