Melanin Pigmentation and Inflammation in Human Gingiva

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141745/1/jper0701.pd

Epidemiology of methicillin-resistant Staphylococcus aureus: results of a nation-wide survey in Switzerland.

To assess the epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) in Switzerland. One-year national survey of all MRSA cases detected in a large sample of Swiss healthcare institutions (HCI). Analysis of epidemiological and molecular typing data (PFGE) of MRSA strains. During 1997, 385 cases of MRSA were recorded in the 5 university hospitals, in 33 acute care community hospitals, and 14 rehabilitation or long-term care institutions. Half of the cases were found at the University of Geneva Hospitals where MRSA was already known to be endemic (41.1 cases/10,000 admissions). The remaining cases (200) were distributed throughout Switzerland. The highest rates (>100 cases/10,000 admissions) were reported from non-acute care institutions. Rates ranged from 3.3 to 41.1 cases/10,000 admissions for university hospitals (mean 15.5); 0.67 to 90.4 for community hospitals (mean 4.8), and 28.2 to 315 for non-acute care institutions reporting MRSA (mean 85.7). Forty percent of MRSA patients were infected, while 60% were only colonised. The leading infection sites were skin and soft tissue (21%), surgical site (15%), and the urinary tract (26%). Whereas in Eastern Swiss HCI most MRSA cases occurred in acute care hospitals (n = 47, 98%), rehabilitation and long-term care institutions accounted for an important number of the identified cases (n = 107, 38%) in Western Switzerland. Low rates of MRSA were still observed in Swiss HCI, despite one outlying acute care centre with endemic MRSA and some nonacute care institutions with epidemic MRSA. Rehabilitation and long-term care institutions contributed to a substantial proportion of cases in Western Switzerland and may constitute a significant reservoir. Overall, a national approach to surveillance and control of MRSA is mandatory in order to preserve a still favourable situation, and to decrease the risk of epidemic MRSA dissemination

Global phylogenomic analysis of nonencapsulated Streptococcus pneumoniae reveals a deep-branching classic lineage that is distinct from multiple sporadic lineages.

The surrounding capsule of Streptococcus pneumoniae has been identified as a major virulence factor and is targeted by pneumococcal conjugate vaccines (PCV). However, nonencapsulated S. pneumoniae (non-Ec-Sp) have also been isolated globally, mainly in carriage studies. It is unknown if non-Ec-Sp evolve sporadically, if they have high antibiotic nonsusceptiblity rates and a unique, specific gene content. Here, whole-genome sequencing of 131 non-Ec-Sp isolates sourced from 17 different locations around the world was performed. Results revealed a deep-branching classic lineage that is distinct from multiple sporadic lineages. The sporadic lineages clustered with a previously sequenced, global collection of encapsulated S. pneumoniae (Ec-Sp) isolates while the classic lineage is comprised mainly of the frequently identified multilocus sequences types (STs) ST344 (n = 39) and ST448 (n = 40). All ST344 and nine ST448 isolates had high nonsusceptiblity rates to β-lactams and other antimicrobials. Analysis of the accessory genome reveals that the classic non-Ec-Sp contained an increased number of mobile elements, than Ec-Sp and sporadic non-Ec-Sp. Performing adherence assays to human epithelial cells for selected classic and sporadic non-Ec-Sp revealed that the presence of a integrative conjugative element (ICE) results in increased adherence to human epithelial cells (P = 0.005). In contrast, sporadic non-Ec-Sp lacking the ICE had greater growth in vitro possibly resulting in improved fitness. In conclusion, non-Ec-Sp isolates from the classic lineage have evolved separately. They have spread globally, are well adapted to nasopharyngeal carriage and are able to coexist with Ec-Sp. Due to continued use of PCV, non-Ec-Sp may become more prevalent

Two Cellular Protein Kinases, DNA-PK and PKA, Phosphorylate the Adenoviral L4-33K Protein and Have Opposite Effects on L1 Alternative RNA Splicing

Accumulation of the complex set of alternatively processed mRNA from the adenovirus major late transcription unit (MLTU) is subjected to a temporal regulation involving both changes in poly (A) site choice and alternative 3′ splice site usage. We have previously shown that the adenovirus L4-33K protein functions as an alternative splicing factor involved in activating the shift from L1-52,55K to L1-IIIa mRNA. Here we show that L4-33K specifically associates with the catalytic subunit of the DNA-dependent protein kinase (DNA-PK) in uninfected and adenovirus-infected nuclear extracts. Further, we show that L4-33K is highly phosphorylated by DNA-PK in vitro in a double stranded DNA-independent manner. Importantly, DNA-PK deficient cells show an enhanced production of the L1-IIIa mRNA suggesting an inhibitory role of DNA-PK on the temporal switch in L1 alternative RNA splicing. Moreover, we show that L4-33K also is phosphorylated by protein kinase A (PKA), and that PKA has an enhancer effect on L4-33K-stimulated L1-IIIa splicing. Hence, we demonstrate that these kinases have opposite effects on L4-33K function; DNA-PK as an inhibitor and PKA as an activator of L1-IIIa mRNA splicing. Taken together, this is the first report identifying protein kinases that phosphorylate L4-33K and to suggest novel regulatory roles for DNA-PK and PKA in adenovirus alternative RNA splicing

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. Methods: We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany (n = 135), Spain (n = 133), Switzerland (n = 20) and the United States (n = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). Findings: We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted p-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. Interpretation: HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2. Funding: Funded by Roche Sequencing Solutions, Inc

A point mutation in cpsE renders Streptococcus pneumoniae nonencapsulated and enhances its growth, adherence and competence.

BACKGROUND: The polysaccharide capsule is a major virulence factor of the important human pathogen Streptococcus pneumoniae. However, S. pneumoniae strains lacking capsule do occur. RESULTS: Here, we report a nasopharyngeal isolate of Streptococcus pneumoniae composed of a mixture of two phenotypes; one encapsulated (serotype 18C) and the other nonencapsulated, determined by serotyping, electron microscopy and fluorescence isothiocyanate dextran exclusion assay.By whole genome sequencing, we demonstrated that the phenotypes differ by a single nucleotide base pair in capsular gene cpsE (C to G change at gene position 1135) predicted to result in amino acid change from arginine to glycine at position 379, located in the cytoplasmic, enzymatically active, region of this transmembrane protein. This SNP is responsible for loss of capsule production as the phenotype is transferred with the capsule operon. The nonencapsulated variant is superior in growth in vitro and is also 117-fold more adherent to and more invasive into Detroit 562 human epithelial cells than the encapsulated variant.Expression of six competence pathway genes and one competence-associated gene was 11 to 34-fold higher in the nonencapsulated variant than the encapsulated and transformation frequency was 3.7-fold greater. CONCLUSIONS: We identified a new single point mutation in capsule gene cpsE of a clinical S. pneumoniae serotype 18C isolate sufficient to cause loss of capsule expression resulting in the co-existence of the encapsulated and nonencapsulated phenotype. The mutation caused phenotypic changes in growth, adherence to epithelial cells and transformability. Mutation in capsule gene cpsE may be a way for S. pneumoniae to lose its capsule and increase its colonization potential

Challenges of COVID-19 Case Forecasting in the US, 2020–2021

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making

Non-surgical instrumentation associated with povidone-iodine in the treatment of interproximal furcation involvements

OBJECTIVE: The aim of this controlled clinical trial was to evaluate the effect of topically applied povidone-iodine (PVP-I) used as an adjunct to non-surgical treatment of interproximal class II furcation involvements. MATERIAL AND METHODS: Thirty-two patients presenting at least one interproximal class II furcation involvement that bled on probing with probing pocket depth (PPD) ≥5 mm were recruited. Patients were randomly chosen to receive either subgingival instrumentation with an ultrasonic device using PVP-I (10%) as the cooling liquid (test group) or identical treatment using distilled water as the cooling liquid (control group). The following clinical outcomes were evaluated: visible plaque index, bleeding on probing (BOP), position of the gingival margin, relative attachment level (RAL), PPD and relative horizontal attachment level (RHAL). BAPNA (N-benzoyl-Larginine-p-nitroanilide) testing was used to analyze trypsin-like activity in dental biofilm. All parameters were evaluated at baseline and 1, 3 and 6 months after non-surgical subgingival instrumentation. RESULTS: Six months after treatment, both groups had similar means of PPD reduction, RAL and RHAL gain (p>0.05). These variables were, respectively, 2.20±1.10 mm, 1.27±1.02 mm and 1.33±0.85 mm in the control group and 2.67±1.21 mm, 1.50±1.09 mm and 1.56±0.93 mm in the test group. No difference was observed between groups at none of the posttreatment periods, regarding the number of sites showing clinical attachment gain ≥2 mm. However, at 6 months posttreatment, the test group presented fewer sites with PPD ≥5 mm than the control group. Also at 6 months the test group had lower BAPNA values than control group. CONCLUSION: The use of PVP-I as an adjunct in the non-surgical treatment of interproximal class II furcation involvements provided limited additional clinical benefits

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages