Homocysteine levels and cardiovascular disease in migraine with aura

Clinical studies suggest that hyperhomocysteinemia could be considered an independent risk factor for premature cerebral, peripheral and vascular diseases. A number of authors found an epidemiological correlation between increased risk of cerebrovascular disease and migraine with aura. In this study, 34 patients suffering from migraine with aura and 36 healthy controls were evaluated with respect to total plasma homocysteine levels, measured with FPIA immunoassay in the fasting state and after methionine load. Moreover, vitamin B12, folate and other classic biochemical indicators of atherosclerosis disease were evaluated. In this study, homocysteine levels, both at basal and after load, and other cardiovascular risk factors such as vitamin B12 and apo-LpA were within the normal range. Other multicentric randomised trials are needed to carry on and confirm these data

Joint statement on the role of respiratory rehabilitation in the COVID-19 crisis: the Italian position paper.

Due to an exponential growth of the number of subjects affected by coronavirus disease 2019 (COVID-19), the entire Italian healthcare system had to respond promptly and in a very short time with the need of semi-intensive and intensive care units. Moreover, trained dedicated COVID-19 teams consisting of physicians coming from different specialties (intensivists or pneumologists and infectivologists), while respiratory therapists and nurses have been recruited to work on and on with rest. However, due to still limited and evolving knowledge of COVID-19 disease, there are little recommendations for need in respiratory rehabilitation and physiotherapy interventions. The presentation of this manuscript is the result of a consensus promoted by the Italian societies of respiratory health care professionals who contacted pulmonologists directly involved in the treatment and rehabilitation of COVID-19. The aim was to formulate the more proper and common suggestions to be applied in different hospital settings in offering rehabilitative programs and physiotherapy workforce planning in COVID-19 patients. Two main areas of intervention were identified: organization and treatment, which were described in this paper to face with the emergency

The association between dysphagia and OSA Disfagia e OSA

Objective. The aim of our study was to investigate the presence of dysphagia in patients with Obstructive Sleep Apnoea (OSA) and to correlate swallowing impairment with hypnologic and anatomic parameters. Methods. The study population includes 36 patients suffering from OSA. Patients were divided into two groups using the presence of dysphagia as a distinctive parameter. Group 1 included 27 OSA patients without signs of dysphagia and Group 2 included 9 OSA patients with signs of dysphagia. Results. The age of patients in Group 2 was higher compared with the age of patients in Group 1. Analysis of Continuous Positive Airway Pressure (CPAP), obtained in the titration phase, showed that OSA patients with signs of dysphagia required a higher level of CPAP pressure than those who were not affected by swallowing abnormalities (12.6 ± 1 vs 10.5 ± 1.9 p = 0.003). No other differences in anthropometric, hypnologic, or arterial blood gas values were found between the two groups. Conclusions. In clinical practice, all OSA patients should undergo a complete ENT exam, including assessment of swallowing, before CPAP therapy is started. This may predict the need for higher CPAP pressure settings to resolve apnoea episodes in the presence of dysphagia as well as guide the choice of CPAP interfaces (orofacial vs. nasal) in these patients

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

[Image: see text] Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives

Comparative Omics-Driven Genome Annotation Refinement: Application across Yersiniae

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. The annotation process is now performed almost exclusively in an automated fashion to balance the large number of sequences generated. One possible way of reducing errors inherent to automated computational annotations is to apply data from omics measurements (i.e. transcriptional and proteomic) to the un-annotated genome with a proteogenomic-based approach. Here, the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species. Transcriptomic and proteomic data derived from highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis Pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 incorrect (i.e., observed frameshifts, extended start sites, and translated pseudogenes) protein-coding sequences within the three current genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes, including the insertion-ablated argD, underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, a transcriptional regulator, and many hypothetical proteins that were missed during annotation

The prognostic role of KRAS and BRAF in patients undergoing surgical resection of colorectal cancer liver metastasis: a systematic review and meta-analysis

Background: Clinical trials investigated the potential role of both KRAS and BRAF mutations, as prognostic biomarkers, in colorectal cancer (CRC) patients who underwent surgical treatment of liver metastasis (CLM), showing conflicting results. This meta-analysis aims to review all the studies reporting survival outcomes (recurrence free survival (RFS), and/or overall survival (OS)) of patients undergoing resection of CLM, stratified according to KRAS and/or BRAF mutation status. Materials and Methods: Data from all published studies reporting survival outcomes (RFS and/or OS) of CRC patients who received resection of CLM, stratified by KRAS and/or BRAF mutation status were collected by searching in PubMed, Cochrane Library, American Society of Clinical Oncology and European Society of Medical Oncology meeting proceedings. Pooled hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated for both the OS and/or RFS. Results: Seven eligible trials (1403 patients) were included. Pooled analysis showed that KRAS mutations predicted a significant worse both RFS (HR: 1.65; 95% CI: 1.23 \u2013 2.21) and OS (HR: 1.86; 95% CI: 1.51 \u2013 2.30) in patients who underwent surgical resection of CLM. BRAF mutations were also associated with a significant worse OS (HR: 3.90; 95% CI: 1.96 \u2013 7.73) in this subgroup of patients. Conclusion: This meta-analysis suggests both KRAS and BRAF mutations as negative prognostic biomarkers associated with worse survival outcomes in patients undergoing hepatic resection of CLM. Such evidences support the introduction of new treatment decision models, taking into account the tumor molecular profile in order to individualize both systemic and loco-regional treatment strategies

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship

The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation

<p>Abstract</p> <p>Background</p> <p>Crucial foundations of any quantitative systems biology experiment are correct genome and proteome annotations. Protein databases compiled from high quality empirical protein identifications that are in turn based on correct gene models increase the correctness, sensitivity, and quantitative accuracy of systems biology genome-scale experiments.</p> <p>Results</p> <p>In this manuscript, we present the <it>Drosophila melanogaster </it>PeptideAtlas, a fly proteomics and genomics resource of unsurpassed depth. Based on peptide mass spectrometry data collected in our laboratory the portal <url>http://www.drosophila-peptideatlas.org</url> allows querying fly protein data observed with respect to gene model confirmation and splice site verification as well as for the identification of proteotypic peptides suited for targeted proteomics studies. Additionally, the database provides consensus mass spectra for observed peptides along with qualitative and quantitative information about the number of observations of a particular peptide and the sample(s) in which it was observed.</p> <p>Conclusion</p> <p>PeptideAtlas is an open access database for the <it>Drosophila </it>community that has several features and applications that support (1) reduction of the complexity inherently associated with performing targeted proteomic studies, (2) designing and accelerating shotgun proteomics experiments, (3) confirming or questioning gene models, and (4) adjusting gene models such that they are in line with observed <it>Drosophila </it>peptides. While the database consists of proteomic data it is not required that the user is a proteomics expert.</p