Last data update: Jun 24, 2024. (Total: 47078 publications since 2009)
Records 1-4 (of 4 Records) |
Query Trace: Prachi M [original query] |
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CATMoS: Collaborative Acute Toxicity Modeling Suite.
Mansouri K , Karmaus AL , Fitzpatrick J , Patlewicz G , Pradeep P , Alberga D , Alepee N , Allen TEH , Allen D , Alves VM , Andrade CH , Auernhammer TR , Ballabio D , Bell S , Benfenati E , Bhattacharya S , Bastos JV , Boyd S , Brown JB , Capuzzi SJ , Chushak Y , Ciallella H , Clark AM , Consonni V , Daga PR , Ekins S , Farag S , Fedorov M , Fourches D , Gadaleta D , Gao F , Gearhart JM , Goh G , Goodman JM , Grisoni F , Grulke CM , Hartung T , Hirn M , Karpov P , Korotcov A , Lavado GJ , Lawless M , Li X , Luechtefeld T , Lunghini F , Mangiatordi GF , Marcou G , Marsh D , Martin T , Mauri A , Muratov EN , Myatt GJ , Nguyen DT , Nicolotti O , Note R , Pande P , Parks AK , Peryea T , Polash AH , Rallo R , Roncaglioni A , Rowlands C , Ruiz P , Russo DP , Sayed A , Sayre R , Sheils T , Siegel C , Silva AC , Simeonov A , Sosnin S , Southall N , Strickland J , Tang Y , Teppen B , Tetko IV , Thomas D , Tkachenko V , Todeschini R , Toma C , Tripodi I , Trisciuzzi D , Tropsha A , Varnek A , Vukovic K , Wang Z , Wang L , Waters KM , Wedlake AJ , Wijeyesakere SJ , Wilson D , Xiao Z , Yang H , Zahoranszky-Kohalmi G , Zakharov AV , Zhang FF , Zhang Z , Zhao T , Zhu H , Zorn KM , Casey W , Kleinstreuer NC . Environ Health Perspect 2021 129 (4) 47013 ![]() BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495. |
Impact of COVID-19 Pandemic on Central Line-Associated Bloodstream Infections During the Early Months of 2020, National Healthcare Safety Network.
Patel PR , Weiner-Lastinger LM , Dudeck MA , Fike LV , Kuhar DT , Edwards JR , Pollock D , Benin A . Infect Control Hosp Epidemiol 2021 43 (6) 1-8 Data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central line-associated bloodstream infections (CLABSIs) in acute care hospitals. Descriptive analysis of the Standardized Infection Ratio (SIR) was conducted by locations, location type, geographic area, and bed size. |
Characterization of Novel Reoviruses [Wad Medani virus (Orbivirus) and Kundal (Coltivirus)] collected from Hyalomma antolicum ticks in India during CCHF surveillance.
Yadav PD , Whitmer SLM , Sarkale P , Ng TFF , Goldsmith CS , Nyayanit DA , Esona MD , Shrivastava-Ranjan P , Lakra R , Pardeshi P , Majumdar TD , Francis A , Klena JD , Nichol ST , Stroher U , Mourya D . J Virol 2019 93 (13) ![]() In 2011, ticks were collected from livestock following an outbreak of Crimean Congo Hemorrhagic fever (CCHF) in Gujarat state, India. CCHF-negative Hyalomma anatolicum tick pools were passaged for virus isolation, and two virus isolates were obtained, designated Karyana virus (KARYV) and Kundal virus (KUNDV) respectively. Traditional RT-PCR identification of known viruses was unsuccessful, but a next-generation sequencing approach identified KARYV and KUNDV as viruses in the Reoviridae family, Orbivirus, and Coltivirus genera, respectively. Viral genomes were de novo assembled, yielding 10 complete segments of KARYV and 12 nearly complete segments of KUNDV. The VP1 gene of KARYV shared a most recent common ancestor with Wad Medani virus (WMV), strain Ar495, and based on nucleotide identity we demonstrate that it is a novel WMV strain. The VP1 segment of KUNDV shares a common ancestor with Colorado tick fever virus, Eyach virus, Tai Forest reovirus and Tarumizu tick virus from the Coltivirus genus. Based on VP1, VP6, VP7, and VP12 nucleotide and amino acid identity, KUNDV is proposed to be a new species of Coltivirus Electron microscopy supported the classification of KARYV and KUNDV as reoviruses and identified replication morphology consistent with other Orbi- and Colti- viruses. The identification of novel tick-borne viruses carried by the CCHF vector is an important step in the characterization of their potential role in human and animal pathogenesis.Importance Ticks, mosquitoes, as well Culicoides, can transmit viruses in the Reoviridae family. With the help of next-generation sequencing (NGS), previously unreported reoviruses such as equine encephalosis virus, Wad Medani virus (WMV), Kammanvanpettai virus (KVPTV) and with this report, KARYV and KUNDV have been discovered and characterized in India. The isolation of KUNDV and KARYV from Hyalomma anatolicum, which is a known vector for zoonotic pathogens, such as Crimean Congo Hemorrhagic Fever virus, Babesia, Theileria and Anaplasma species, identifies arboviruses with the potential to transmit to humans. Characterization of these KUNDV and KARYV isolated from Hyalomma ticks is critical for the development of specific serological and molecular assays that can be used to determine the association of these viruses with disease in humans and livestock. |
The impact of routine data quality assessments on electronic medical record data quality in Kenya
Muthee V , Bochner AF , Osterman A , Liku N , Akhwale W , Kwach J , Prachi M , Wamicwe J , Odhiambo J , Onyango F , Puttkammer N . PLoS One 2018 13 (4) e0195362 BACKGROUND: Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. METHODS: RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. RESULTS: 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32-0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25-3.33). CONCLUSIONS: This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings. |
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