Diagnosis, treatment and recurrence of a mandibular Langerhans cell histiocytosis: a three-year follow-up case report

Introduction: Langerhans cell histiocytosis (LCH) is an abnormal clonal proliferation of Langerhans cells secondary to immune process, mutation of oncogene or genetic predispositions. It preferentially affects bone, lung and skin. The incidence is 2–6 cases per million per year. Prognosis is variable and depends on number and location of lesions, and impact of the initial treatment. Oral lesions may be the first sign of LCH as illustrated by the present case. Observation: A 24-year-old male consulted first for severe gingival inflammation, teeth mobilities and alveolar bone loss with a suspicion of LCH. A pulmonary involvement was secondarily revealed by tomodensitometry. Histological examination, from gingival biopsy, confirmed the diagnostic of LCH, showing cells positive for the anti-CD1A antibody. The patient was managed by oral surgery and chemotherapy approaches. Alveolar bone loss significantly reduced. But 2 years and a half after the diagnosis, a recurrence was noted and managed by surgical approach. After a three-year follow-up, no recurrence was noted. Conclusion: Oral lesions can be inaugural manifestations of LCH. The dentist has an essential role in the early detection of these lesions

Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

BACKGROUND: During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. METHODS: The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. RESULTS: The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72). CONCLUSION: Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities

Amlodipine induced gingival overgrowth: a case report

International audienc

Diagnosis, treatment and recurrence of a mandibular Langerhans cell histiocytosis: a three-year follow-up case report

Introduction: Langerhans cell histiocytosis (LCH) is an abnormal clonal proliferation of Langerhans cells secondary to immune process, mutation of oncogene or genetic predispositions. It preferentially affects bone, lung and skin. The incidence is 2–6 cases per million per year. Prognosis is variable and depends on number and location of lesions, and impact of the initial treatment. Oral lesions may be the first sign of LCH as illustrated by the present case. Observation: A 24-year-old male consulted first for severe gingival inflammation, teeth mobilities and alveolar bone loss with a suspicion of LCH. A pulmonary involvement was secondarily revealed by tomodensitometry. Histological examination, from gingival biopsy, confirmed the diagnostic of LCH, showing cells positive for the anti-CD1A antibody. The patient was managed by oral surgery and chemotherapy approaches. Alveolar bone loss significantly reduced. But 2 years and a half after the diagnosis, a recurrence was noted and managed by surgical approach. After a three-year follow-up, no recurrence was noted. Conclusion: Oral lesions can be inaugural manifestations of LCH. The dentist has an essential role in the early detection of these lesions

Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

Abstract Background During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. Methods The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. Results The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72). Conclusion Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities.</p

A Data Mining Approach to Identify Climatic Determinants of Dengue Fever Patterns in French Guiana

We applied sequential pattern extraction to identify the most important climatic factors related to dengue fever in French Guiana.  Our findings suggest that the local climate has major effects on the occurrence of dengue epidemics in French Guiana and highlight the utility of the data mining approach to analyze disease surveillance data on a temporal and a spatial scale in relation to climatic, social and environmental variables.  This study is a first step of a data mining project which will help to better understand and accurately predict temporal dynamics of dengue fever in French Guiana.

A Data Mining Approach to Identify Climatic Determinants of Dengue Fever Patterns in French Guiana

We applied sequential pattern extraction to identify the most important climatic factors related to dengue fever in French Guiana.  Our findings suggest that the local climate has major effects on the occurrence of dengue epidemics in French Guiana and highlight the utility of the data mining approach to analyze disease surveillance data on a temporal and a spatial scale in relation to climatic, social and environmental variables.  This study is a first step of a data mining project which will help to better understand and accurately predict temporal dynamics of dengue fever in French Guiana.

Avis de l'Anses relatif à la priorisation des lieux fréquentés par les cas importés d’arbovirose pour la réalisation des prospections entomologiques et des actions de lutte anti-vectorielle

Citation suggérée : Anses. (2023). Priorisation des lieux fréquentés par les cas importés d’arbovirose pour la réalisation des prospections entomologiques et des actions de lutte anti-vectorielle. (saisine n°2022-AST-0103). Maisons-Alfort : Anses, 39 p.Présents sur tous les continents, les moustiques du genre Aedes (Aedes aegypti et Aedes albopictus notamment) sont responsables de la transmission d’agents pathogènes affectant l’être humain et responsables d’arboviroses* telles que la dengue, le chikungunya, le Zika ou encore la fièvre jaune. Ces maladies sévissent principalement dans les régions tropicales mais sont désormais parfois observées en régions tempérées comme en Europe, notamment en raison de l’expansion géographique d'Ae. albopictus en lien avec le développement des activités humaines (transports de biens et de personnes, aménagements du territoire, etc.) (Paupy et al. 2009; Akhoundi 2018). Au cours des dernières décennies, l’incidence de la dengue a augmenté de manière significative au niveau mondial et des épidémies dechikungunya et de Zika ont émergé en dehors de leur aire de distribution d’origine. Dans tous les territoires concernés, la lutte contre ces arboviroses est une priorité de santé publique (Diagne et al. 2021; Mayer et al. 2007).Dans l’attente de vaccins opérationnels (à l’exception de celui de la fièvre jaune) et en l’absence de traitement curatif spécifique, le principal moyen de réduire les risques sanitaires résultant de la transmission vectorielle de ces arboviroses reste la lutte anti-vectorielle (LAV). Celle-ci doit viser la diminution des populations de vecteurs* à un niveau en-dessous des seuils de densités permettant une transmission virale active et/ou la réduction du contact être humain - vecteur pour empêcher la transmission de l’agent pathogène. Pour y parvenir, les moyens de lutte sont variés : lutte mécanique, aménagements de l’environnement, lutte biologique, génétique ou chimique (insecticides et répulsifs) pouvant être utilisés de manière alternée ou combinée. La LAV repose en effet sur l’utilisation d’outils et le recours à des techniques différentes, selon le couple vecteur/agent pathogène ciblé, mais aussi selon les objectifs poursuivis.Pour que la LAV soit efficace, les responsables doivent élaborer une stratégie intégrée tenant compte du contexte local et en particulier de la situation entomo-épidémiologique. La stratégie de LAV, cadrée par la réglementation, doit reposer sur une combinaison optimale d’outils et de techniques adaptés au contexte du territoire et aux ressources.En France, le décret n° 2019-258 du 29 mars 2019 relatif à la prévention des maladies vectorielles a confié aux Agences Régionales de Santé (ARS) les missions de surveillance entomologique et d’intervention autour des nouvelles implantations de moustiques vecteurs, ainsi qu’autour des cas humains d’arboviroses. Ainsi, depuis le 1er janvier 2020, les ARS sont responsables de la LAV et peuvent désigner des opérateurs de démoustication (OpD) chargés de réaliser les interventions autour du domicile et des lieux fréquentés par les cas confirmés* de dengue et autres arboviroses (chikungunya et Zika) transmises par les moustiques du genre Aedes, et plus particulièrement, Ae. albopictus. Ces interventions comprennent notamment la sensibilisation des populations à la prévention des maladies vectorielles et aux moyens pour s’en protéger, la suppression ou la vidange des gîtes larvaires, le traitement larvicide, ainsi que le traitement adulticide contre les vecteurs visant à diminuer la longévité et la densité de femelles potentiellement infectées et susceptibles de transmettre à de nouvellespersonnes le virus considéré dont elles sont porteuses. Ces interventions doivent être réalisées conformément aux dispositions de l’arrêté du 23 juillet 2019