ClinVar: improvements to accessing data.

ClinVar is a freely available, public archive of human genetic variants and interpretations of their relationships to diseases and other conditions, maintained at the National Institutes of Health (NIH). Submitted interpretations of variants are aggregated and made available on the ClinVar website (https://www.ncbi.nlm.nih.gov/clinvar/), and as downloadable files via FTP and through programmatic tools such as NCBI's E-utilities. The default view on the ClinVar website, the Variation page, was recently redesigned. The new layout includes several new sections that make it easier to find submitted data as well as summary data such as all diseases and citations reported for the variant. The new design also better represents more complex data such as haplotypes and genotypes, as well as variants that are in ClinVar as part of a haplotype or genotype but have no interpretation for the single variant. ClinVar's variant-centric XML had its production release in April 2019. The ClinVar website and E-utilities both have been updated to support the VCV (variation in ClinVar) accession numbers found in the variant-centric XML file. ClinVar's search engine has been fine-tuned for improved retrieval of search results.

ClinVar: improving access to variant interpretations and supporting evidence.

ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) is a freely available, public archive of human genetic variants and interpretations of their significance to disease, maintained at the National Institutes of Health. Interpretations of the clinical significance of variants are submitted by clinical testing laboratories, research laboratories, expert panels and other groups. ClinVar aggregates data by variant-disease pairs, and by variant (or set of variants). Data aggregated by variant are accessible on the website, in an improved set of variant call format files and as a new comprehensive XML report. ClinVar recently started accepting submissions that are focused primarily on providing phenotypic information for individuals who have had genetic testing. Submissions may come from clinical providers providing their own interpretation of the variant ('provider interpretation') or from groups such as patient registries that primarily provide phenotypic information from patients ('phenotyping only'). ClinVar continues to make improvements to its search and retrieval functions. Several new fields are now indexed for more precise searching, and filters allow the user to narrow down a large set of search results.

ClinVar: public archive of interpretations of clinically relevant variants.

ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) at the National Center for Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical significance of variants for reported conditions. The database includes germline and somatic variants of any size, type or genomic location. Interpretations are submitted by clinical testing laboratories, research laboratories, locus-specific databases, OMIM®, GeneReviews™, UniProt, expert panels and practice guidelines. In NCBI's Variation submission portal, submitters upload batch submissions or use the Submission Wizard for single submissions. Each submitted interpretation is assigned an accession number prefixed with SCV. ClinVar staff review validation reports with data types such as HGVS (Human Genome Variation Society) expressions; however, clinical significance is reported directly from submitters. Interpretations are aggregated by variant-condition combination and assigned an accession number prefixed with RCV. Clinical significance is calculated for the aggregate record, indicating consensus or conflict in the submitted interpretations. ClinVar uses data standards, such as HGVS nomenclature for variants and MedGen identifiers for conditions. The data are available on the web as variant-specific views; the entire data set can be downloaded via ftp. Programmatic access for ClinVar records is available through NCBI's E-utilities. Future development includes providing a variant-centric XML archive and a web page for details of SCV submissions.

ClinVar: public archive of relationships among sequence variation and human phenotype.

ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) provides a freely available archive of reports of relationships among medically important variants and phenotypes. ClinVar accessions submissions reporting human variation, interpretations of the relationship of that variation to human health and the evidence supporting each interpretation. The database is tightly coupled with dbSNP and dbVar, which maintain information about the location of variation on human assemblies. ClinVar is also based on the phenotypic descriptions maintained in MedGen (http://www.ncbi.nlm.nih.gov/medgen). Each ClinVar record represents the submitter, the variation and the phenotype, i.e. the unit that is assigned an accession of the format SCV000000000.0. The submitter can update the submission at any time, in which case a new version is assigned. To facilitate evaluation of the medical importance of each variant, ClinVar aggregates submissions with the same variation/phenotype combination, adds value from other NCBI databases, assigns a distinct accession of the format RCV000000000.0 and reports if there are conflicting clinical interpretations. Data in ClinVar are available in multiple formats, including html, download as XML, VCF or tab-delimited subsets. Data from ClinVar are provided as annotation tracks on genomic RefSeqs and are used in tools such as Variation Reporter (http://www.ncbi.nlm.nih.gov/variation/tools/reporter), which reports what is known about variation based on user-supplied locations.

Prognostic indicators in pituitary adenoma surgery: a comprehensive analysis of surgical outcomes and complications.

Objective: The primary aim of this study was to identify predictive factors associated with onset of de-novo clinically significant pituitary insufficiencies following endoscopic endonasal surgery (EES) for pituitary adenomas. The secondary objective explored the predictive factors of surgical success. Methods: A retrospective analysis was conducted on 211 patients who underwent EES. Logistic regression models were employed for the primary and secondary objectives. Patients were stratified into specific groups based on surgical indications and prolactin levels for nuanced analysis. Results: Significant predictors for de-novo pituitary insufficiencies included male sex (OR 3.3, CI95% 1.3-8.1, p=0.01), immediate postoperative insufficiencies (OR 5.6, CI95% 2.8-11.1, p<0.001), and HYPRONOS criteria (OR 5.7, CI95% 1.6-20.9, p=0.008). For surgical success, preoperative insufficiencies (OR 0.7, CI95% 0.5-0.9, p=0.008), repeat surgeries (OR 0.1, CI95% 0-0.4, p=0.001), and gonadotroph or somatotroph adenomas were significant. Age and adenoma size were not predictive in multivariate analysis. Furthermore, we observed a "dip and recover" effect of prolactin after surgery and lower prolactin levels at follow-up (< 3 ng/ml) are correlated with more anterior pituitary insufficiencies than normoprolactinemic patients (p = 0.004). Conclusion: This study identifies key predictors for outcomes in pituitary surgery. Our research is the first to employ individualized success criteria for EES, challenging existing perceptions about the role of age and adenoma size. These findings open avenues for nuanced, individualized preoperative risk assessment and postoperative management.

COVID-19 severity and in-hospital mortality in an area with high HIV prevalence.

Background: HIV infection causes immune dysregulation affecting T-cell and monocyte function, which may alter coronavirus disease 2019 (COVID-19) pathophysiology. Objectives: We investigated the associations among clinical phenotypes, laboratory biomarkers, and hospitalisation outcomes in a cohort of people hospitalised with COVID-19 in a high HIV prevalence area. Method: We conducted a prospective observational cohort study in Tshwane, South Africa. Respiratory disease severity was quantified using the respiratory oxygenation score. Analysed biomarkers included inflammatory and coagulation biomarkers, CD4 T-cell counts, and HIV-1 viral loads (HIVVL). Results: The analysis included 558 patients, of whom 21.7% died during admission. The mean age was 54 years. A total of 82 participants were HIV-positive. People living with HIV (PLWH) were younger (mean age 46 years) than HIV-negative people; most were on antiretroviral treatment with a suppressed HIVVL (72%) and the median CD4 count was 159 (interquartile range: 66-397) cells/µL. After adjusting for age, HIV was not associated with increased risk of mortality during hospitalisation (age-adjusted hazard ratio = 1.1, 95% confidence interval: 0.6-2.0). Inflammatory biomarker levels were similar in PLWH and HIV-negative patients. Detectable HIVVL was associated with less severe respiratory disease. In PLWH, mortality was associated with higher levels of inflammatory biomarkers. Opportunistic infections, and other risk factors for severe COVID-19, were common in PLWH who died. Conclusion: PLWH were not at increased risk of mortality and those with detectable HIVVL had less severe respiratory disease than those with suppressed HIVVL. What this study adds: This study advances our understanding of severe COVID-19 in PLWH.

Novel Method for Accurately Assessing Pull-up Artifacts in STR Analysis.

OSIRIS is a mathematically-based software tool for Short Tandem Repeat (STR) and DNA fragment analysis (https://www.ncbi.nlm.nih.gov/osiris/). As part of its routine sample analyses, OSIRIS computes unique quality metrics that can be used for sample quality assessment. A common artifact of STR analysis is cross-channel pull-up or pull-down (negative pull-up). This occurs because of the spectral overlap between the dyes used with the marker set, and the failure of the color deconvolution matrix to isolate the colors in the dye set adequately. This paper describes a mathematical method for analyzing and quantifying the pull-up patterns across sample channels and effectively identifying and correcting the pull-up artifacts, as implemented in OSIRIS. Unlike approaches to pull-up that require a training set composed of previous samples, the algorithm described here uses a mathematical model of the underlying causes of pull-up. It is based solely on the information intrinsic to the sample it is analyzing and therefore incorporates the effects of the ambient conditions and the specific procedures used in creating the sample. These conditions are the physical determinants of the level of pull-up in the sample and are not likely to be represented in a training set consisting of past samples.

Comparison of inhaled isopropyl alcohol and intravenous ondansetron for treatment of postoperative nausea.

Postoperative nausea, a common complication in patients receiving general anesthesia, was studied in this randomized investigation to compare the efficacy of 70% inhaled isopropyl alcohol and intravenous ondansetron. For the study, 100 healthy women, ASA physical status I or II, scheduled for outpatient gynecologic laparoscopic procedures randomly received 4 mg of intravenous ondansetron or isopropyl alcohol for the treatment of postoperative nausea. Nausea was measured on arrival to the postanesthesia care unit, at first complaint of nausea, every 5 minutes after initiation of therapy until nausea resolution, and every 15 minutes thereafter using a 0 to 10 verbal numerical rating scale. At 5, 10, and 15 minutes, the median verbal numerical rating scores between the ondansetron and alcohol groups were 6.00 and 3.00, 5.00 and 3.00, and 5.00 and 2.00, respectively (P = .002, .015, and .036, respectively). No statistically significant differences were found at any other time interval. Mean times from initiation of therapy to a 50% reduction in nausea between the ondansetron and alcohol groups were 6.3 minutes and 27.7 minutes, respectively (P = 0.022). Based on this study, it seems postoperative nausea can be resolved quicker using 70% inhaled isopropyl alcohol compared with intravenous ondansetron in women undergoing outpatient gynecologic laparoscopic procedures.