ProsTAV, a novel blood-based test for biopsy decision management in significant prostate cancer.

BACKGROUND: Current pathways in early diagnosis of prostate cancer (PCa) can lead to unnecessary biopsy procedures. Here, we used telomere analysis to develop and evaluate ProsTAV®, a risk model for significant PCa (Gleason score >6), with the objective of improving the PCa diagnosis pathway. METHODS: This retrospective, multicentric study analyzed telomeres from patients with serum PSA 3-10 ng/mL. High-throughput quantitative fluorescence in-situ hybridization was used to evaluate telomere-associated variables (TAVs) in peripheral blood mononucleated cells. ProsTAV® was developed by multivariate logistics regression based on three clinical variables and six TAVs. The predictive capacity and accuracy of ProsTAV® were summarized by receiver operating characteristic (ROC) curves and its clinical benefit with decision curves analysis. RESULTS: Telomeres from 1043 patients were analyzed. The median age of the patients was 63 years, with a median PSA of 5.2 ng/mL and a percentage of significant PCa of 23.9%. A total of 874 patients were selected for model training and 169 patients for model validation. The area under the ROC curve of ProsTAV® was 0.71 (95% confidence interval [CI], 0.62-0.79), with a sensitivity of 0.90 (95% CI, 0.88-1.0) and specificity of 0.33 (95% CI, 0.24-0.40). The positive predictive value was 0.29 (95% CI, 0.21-0.37) and the negative predictive value was 0.91 (95% CI, 0.83-0.99). ProsTAV® would make it possible to avoid 33% of biopsies. CONCLUSIONS: ProsTAV®, a predictive model based on telomere analysis through TAV, could be used to increase the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/mL.

Early biochemical analysis of COVID-19 patients helps severity prediction.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

Differential Immune Checkpoint and Ig-like V-Type Receptor Profiles in COVID-19: Associations with Severity and Treatment.

Identifying patients' immune system status has become critical to managing SARS-CoV-2 infection and avoiding the appearance of secondary infections during a hospital stay. Despite the high volume of research, robust severity and outcome markers are still lacking in COVID-19. We recruited 87 COVID-19 patients and analyzed, by unbiased automated software, 356 parameters at baseline emergency department admission including: high depth immune phenotyping and immune checkpoint expression by spectral flow cytometry, cytokines and other soluble molecules in plasma as well as routine clinical variables. We identified 69 baseline alterations in the expression of immune checkpoints, Ig-like V type receptors and other immune population markers associated with severity (O2 requirement). Thirty-four changes in these markers/populations were associated with secondary infection appearance. In addition, through a longitudinal sample collection, we described the changes which take place in the immune system of COVID-19 patients during secondary infections and in response to corticosteroid treatment. Our study provides information about immune checkpoint molecules and other less-studied receptors with Ig-like V-type domains such as CD108, CD226, HVEM (CD270), B7H3 (CD276), B7H5 (VISTA) and GITR (CD357), defining these as novel interesting molecules in severe and corticosteroids-treated acute infections.

The relationship between behavior acquisition and persistence abilities: Involvement of adult hippocampal neurogenesis.

The influence of the learning process on the persistence of the newly acquired behavior is relevant both for our knowledge of the learning/memory mechanisms and for the educational policy. However, it is unclear whether during an operant conditioning process with a continuous reinforcement paradigm, individual differences in acquisition are also associated to differences in persistence of the acquired behavior. In parallel, adult neurogenesis has been implicated in spatial learning and memory, but the specific role of the immature neurons born in the adult brain is not well known for this process. We have addressed both questions by analyzing the relationship between water maze task acquisition scores, the persistence of the acquired behavior, and the size of the different subpopulations of immature neurons in the adult murine hippocampus. We have found that task acquisition and persistence rates were negatively correlated: the faster the animals find the water maze platform at the end of acquisition stage, the less they persist in searching for it at the learned position in a subsequent non-reinforced trial; accordingly, the correlation in the number of some new neurons' subpopulations and the acquisition rate is negative while with persistence in acquired behavior is positive. These findings reveal an unexpected relationship between the efficiency to learn a task and the persistence of the new behavior after a non-reinforcement paradigm, and suggest that the immature neurons might be involved in different roles in acquisition and persistence/extinction of a learning task. © 2016 Wiley Periodicals, Inc.

Core gene set as the basis of multilocus sequence analysis of the subclass Actinobacteridae.

Comparative genomic sequencing is shedding new light on bacterial identification, taxonomy and phylogeny. An in silico assessment of a core gene set necessary for cellular functioning was made to determine a consensus set of genes that would be useful for the identification, taxonomy and phylogeny of the species belonging to the subclass Actinobacteridae which contained two orders Actinomycetales and Bifidobacteriales. The subclass Actinobacteridae comprised about 85% of the actinobacteria families. The following recommended criteria were used to establish a comprehensive gene set; the gene should (i) be long enough to contain phylogenetically useful information, (ii) not be subject to horizontal gene transfer, (iii) be a single copy (iv) have at least two regions sufficiently conserved that allow the design of amplification and sequencing primers and (v) predict whole-genome relationships. We applied these constraints to 50 different Actinobacteridae genomes and made 1,224 pairwise comparisons of the genome conserved regions and gene fragments obtained by using Sequence VARiability Analysis Program (SVARAP), which allow designing the primers. Following a comparative statistical modeling phase, 3 gene fragments were selected, ychF, rpoB, and secY with R2>0.85. Selected sets of broad range primers were tested from the 3 gene fragments and were demonstrated to be useful for amplification and sequencing of 25 species belonging to 9 genera of Actinobacteridae. The intraspecies similarities were 96.3-100% for ychF, 97.8-100% for rpoB and 96.9-100% for secY among 73 strains belonging to 15 species of the subclass Actinobacteridae compare to 99.4-100% for 16S rRNA. The phylogenetic topology obtained from the combined datasets ychF+rpoB+secY was globally similar to that inferred from the 16S rRNA but with higher confidence. It was concluded that multi-locus sequence analysis using core gene set might represent the first consensus and valid approach for investigating the bacterial identification, phylogeny and taxonomy.

Mycobacterium shinjukuense sp. nov., a slowly growing, non-chromogenic species isolated from human clinical specimens.

Seven isolates of a slowly growing, non-chromogenic Mycobacterium species were obtained from sputum and bronchial lavage fluid samples from elderly patients in different regions of Japan. These isolates were distinguished from related non-tuberculous species by colony morphology, positive results for Tween hydrolysis, catalase at 68 °C, nitrate reductase and pyrazinamidase and negative results for semi-quantitative catalase, urease and arylsulfatase. The mycolic acid pattern obtained by HPLC revealed a single cluster of late-eluting mycolic acids similar to but different from those of Mycobacterium malmoense ATCC 29571(T). The 16S rRNA gene, 16S-23S internal transcribed spacer (ITS), rpoB and hsp65 sequences were unique in comparison with those of other mycobacteria. Comparison of 16S rRNA gene sequences showed that the isolates were most closely related to Mycobacterium tuberculosis H37Rv(T) (21 base differences in 1508 bp; 98.6 % 16S rRNA gene sequence similarity). A representative strain, GTC 2738(T), showed 91.9 % rpoB sequence similarity with Mycobacterium marinum strain M, 95 % hsp65 sequence similarity with Mycobacterium kansasii CIP 104589(T) and 81.1 % 16S-23S ITS sequence similarity with Mycobacterium gordonae ATCC 14470(T). Phylogenetic analysis of concatenated sequences of the 16S rRNA, rpoB and hsp65 genes showed that strain GTC 2738(T) was located on a distinct clade adjacent to M. tuberculosis, M. ulcerans and M. marinum, with bootstrap values of 81 %. DNA-DNA hybridization demonstrated less than 70 % reassociation with type strains of genetically related species and supported the novel species status of the isolates. On the basis of this evidence, a novel species with the name Mycobacterium shinjukuense sp. nov. is proposed. The type strain, isolated from a sputum sample, is strain GTC 2738(T)( = JCM 14233(T) = CCUG 53584(T)).

Bromate analysis in groundwater and wastewater samples.

Bromate (BrO(3)(-)) is a disinfection by-product formed during ozonation of potable water supplies containing bromide (Br(-)). Bromate has been classed by the World Health Organisation as a 'possible human carcinogen', leading to implementation of 10-25 microg L(-1)(as BrO(3)(-)) drinking water limits in legislative areas including the United States and European Union. Techniques have been developed for bromate analysis at and below regulatory limits, with Ion Chromatography (IC) coupled with conductivity detection (IC-CD), post-column reaction and ultra-violet (UV) detection (IC-PCR), or inductively coupled plasma-mass spectrometry detection (IC-ICPMS) in widespread use. The recent discovery of bromate groundwater contamination in a UK aquifer has led to a requirement for analysis of bromate in a groundwater matrix, for environmental monitoring and development of remediation strategies. The possibility of bromate-contaminated water discharge into sewage treatment processes, whether accidental or as a pump-and-treat strategy, also required bromate analysis of wastewater sources. This paper summarises techniques currently available for trace bromate analysis in potable water systems and details studies to identify a methodology for routine analysis of groundwater and wastewater samples. Strategies compared were high performance liquid chromatography (HPLC) with direct UV or PCR/UV detection, IC-CD, IC-PCR, and a simple spectrophotometric technique. IC-CD was the most cost-effective solution for simultaneous analysis of bromate and bromide within groundwater samples, having a 5 microg L(-1) detection limit of both anions with limited interference from closely-eluting species. Wastewater samples were successfully analysed for bromate only using HPLC with PCR/UV detection, with detection limits below 20 microg L(-1)(as BrO(3)(-)) and low interference. HPLC with direct UV detection was unsuitable for bromate analysis within the concentration range 50-5000 microg L(-1) which was required for this project, but column choice was shown to be a major factor in determining limits of detection. Spectrophotometry could not reproducibly determine bromate concentration, although the technique showed promise as a quick field method for high-level groundwater bromate analysis.