Les pathogènes émergents, la veille internationale et le Règlement sanitaire international (2005)

Formenty, Pierre; Roth, Cathy; Gonzalez-Martin, Fernando; Grein, Thomas; Ryan, Michael J.; Drury, Patrick; Kindhauser, Mary Kay; Rodier, G

doi:10.1016/j.medmal.2005.06.009

Cited by 23 publications

(7 citation statements)

References 1 publication

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“…It provides an approach that is complementary to countries' national surveillance strategies. Moreover epidemic intelligence was included by the World Health Organization (WHO) in the health threat detection mechanisms integrated into the International Health Regulations [4], [5]. While epidemiological indicator-based surveillance relies on regular reporting of a number of well-defined indicators provided mainly by health care facilities, epidemic intelligence focuses on event detection, prior to official health care reporting, laboratory confirmation and eventual official notification.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

et al. 2013

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The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

et al. 2013

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“…Gabon is part of the Global Outbreak Alert and Response Network (GOARN), which contributes towards global health security by, 65 (1) fighting the international spread of Emerging …”

Section: International Organizationsmentioning

confidence: 99%

Emerging viral threats in Gabon: health capacities and response to the risk of emerging zoonotic diseases in Central Africa

Bourgarel

Wauquier

Gonzalez

2010

Emerging Health Threats Journal

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“…Head and neck squamous cell carcinoma (HNSCC), the sixth most common malignant tumor in the world (Kim et al, 2011a), is an important public health issue worldwide. Among the total HNSCC cases, 30% are oral squamous cell carcinoma (OSCC) cases (World Health, 2003;Petersen, 2003a;Petersen, 2003b). In 2012, about 145,000 patients with OSCC died worldwide, with a mortality rate of 1.8% (Petersen, 2005;Kim et al, 2011a;Ong et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Differentially Expressed Genes Induced by Cisplatin Resistance in Oral Squamous Cell Carcinomas and Their Interaction

Chen

et al. 2020

Front. Genet.

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Background: Oral squamous cell carcinoma (OSCC) is a solid tumor, which originates from squamous epithelium, with about 400,000 new-cases/year worldwidely. Presently, chemoradiotherapy is the most important adjuvant treatment for OSCC, mostly in advanced tumors. However, clinical resistance to chemotherapy still leads to poor prognosis of OSCC patients. Via high-throughput analysis of gene expression database of OSCC, we investigated the molecular mechanisms underlying cisplatin resistance in OSCC, analyzing the differentially expressed genes (DEGs) and their regulatory relationship, to clarify the molecular basis of OSCC chemotherapy resistance and provide a theoretical foundation for the treatment of patients with OSCC and individualized therapeutic targets accurately. Methods: Datasets related to "OSCC" and "cisplatin resistance" (GSE111585 and GSE115119) were downloaded from the GEO database and analyzed by GEO2R. Venn diagram was used to obtain drug-resistance-related DEGs. Functional enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed on DEGs using The Database for Annotation, Visualization and Integrated Discovery (DAVID) software. Protein-protein interaction (PPI) network was constructed by STRING (search tool for recurring instances of neighbouring genes) database. Potential target genes of miRNA were predicted via miRDB, and cBioportal was used to analyze the function and survival of the potential functional genes. Results: Forty-eight upregulated DEGs and 49 downregulated DEGs were obtained from the datasets, with cutoff as p < 0.01 and |log FC| > 1. The DEGs in OSCC mainly enriched in cell proliferation regulation, and chemokine activity. In PPI network with hub score > 300, the hub genes were identified as NOTCH1, JUN, CTNNB1, CEBPA, and ETS1.

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Les pathogènes émergents, la veille internationale et le Règlement sanitaire international (2005)

Cited by 23 publications

References 1 publication

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

Emerging viral threats in Gabon: health capacities and response to the risk of emerging zoonotic diseases in Central Africa

Analysis of the Differentially Expressed Genes Induced by Cisplatin Resistance in Oral Squamous Cell Carcinomas and Their Interaction

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