La mémoire émotionnelle chez les patients schizophrènes consommateurs de cannabis : une étude en imagerie par résonance magnétique fonctionnelle

Contexte Malgré les conséquences néfastes bien connues de la consommation de cannabis chez les patients souffrant de schizophrénie (SCZ), ces derniers en font usage dans une proportion atteignant 40%. Plusieurs études ont tenté d’éclaircir la nature du lien qui existe entre ces deux problématiques, mais cela demeure à ce jour un sujet d’incertitude. Des études précédentes, dont certaines de notre groupe, ont montré que les patients souffrant de schizophrénie et d’un trouble d’abus de cannabis (SCZCAN) présentaient moins de symptômes négatifs, de meilleures capacités cognitives et un processus de traitement des émotions se rapprochant davantage de la normale. Objectif La présente étude vise à évaluer la mémoire émotionnelle ainsi que ses corrélats neurobiologiques par imagerie par résonance magnétique fonctionnelle (IRMf) chez les SCZCAN en comparaison avec les SCZ. Notre hypothèse est que les SCZCAN vont mieux performer lors d’une tâche de mémoire émotionnelle en comparaison avec les SCZ et qu’ils démontreront des activités dans des régions cérébrales plus étendues et impliquées dans la mémoire et le traitement émotionnel. Méthode Trois groupes de sujets, soit un groupe de SCZ (N=14), un groupe de SCZCAN (N=14) et un groupe contrôle (N=21) ont participé à une tâche de mémoire émotionnelle lors d’une imagerie par IRMf. Résultats Les résultats de notre étude ont montré que les SCZCAN performaient mieux que les SCZ dans une tâche de mémoire émotionnelle et montraient des activations dans des régions cérébrales plus étendues à l’IRMf, en particulier celles impliquées dans mémoire et dans le traitement des émotions (cortex orbitofrontal et temporal, hippocampe). Conclusion Les SCZCAN ont une préservation relative de leur mémoire émotionnelle en comparaison aux SCZ, ce qui est corrélé avec de plus grandes activations cérébrales dans les régions qui jouent un rôle dans la mémoire et le traitement émotionnel.Context In spite of well-known adverse consequences of cannabis consumption in patients with schizophrenia (SCZ), it is recognized that cannabis use reaches levels as high as 40 % in this population. Recently, a number of studies have attempted to get a better grasp of the relationship between these two conditions, which remains unclear to this day. Previous studies, including some from our group, have shown that cannabis abusing patients (SCZCAN) tend to have less prominent negative symptoms, better cognitive abilities, and that their emotion processing is closer to normal. Aim The present study aims to compare emotional memory in patients with schizophrenia with or without cannabis abuse, our hypothesis being that SCZCAN will perform better on an emotional memory task and will activate larger brain regions during functional imaging, as compared to SCZ. Method Three groups of subjects, namely SCZ (N=14), SCZCAN (N=14) and control subjects (N=21) underwent an emotional memory task during a functional magnetic resonance imaging session. Results Our study showed that the group of SCZCAN performed better than SCZ in a functional memory task and activated larger brain regions during functional imaging, in particular those linked to memory and emotional processing (orbitofrontal and temporal cortex, hippocampus). Conclusion The group of SCZCAN demonstrated a relative preservation of their emotional memory as compared to SCZ and activated larger brain regions during functional imaging

Anatomical distribution of Toxoplasma gondii in naturally and experimentally infected lambs

Consumption of raw or undercooked meat containing Toxoplasma gondii tissue cysts is one of the main sources of infection for humans worldwide. Among the various species intended for human consumption, sheep appear to be a high risk for human infection. The present study focused on the detailed anatomical distribution of Toxoplasma gondii in naturally and experimentally infected lambs using fresh and frozen samples of various pieces of meat, from a public health perspective. The first objective was to rank the edible parts intended for human consumption according to the detectable parasite burden by real-time PCR targeting the 529-bp repeated element. The second objective was to evaluate the impact of freezing by comparing the detection efficiency of the quantitative PCR between fresh and frozen tissues, as imports of lamb carcasses/cuts may arrive frozen or chilled. The highest estimated parasite loads were observed in skeletal muscles, and more particularly in edible portions such as quadriceps femoris muscle, intercostal muscles, deltoid muscle and diaphragm, with a significant difference in detectable parasite burden between fresh and frozen samples (p < 0.0001) or natural and experimental infection (p < 0.0001). Thoracic and pelvic limbs (3278–1048 parasites/g muscle) were ranked at the top of the list. Toxoplasma gondii DNA was detected in all the edible parts of lamb studied. These results suggest that lamb meat represents a risk for consumers. Further investigations are needed in order to confirm these differences in larger numbers of animals and in different breeds

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Calidad en la prestación de servicios de salud

Cuando habla de calidad, creemos que se trata de algo costoso, lujoso, novedoso y sofisticado. Se piensa en magníficas instalaciones, con equipos de diagnóstico y tratamiento de última generación; realmente, la calidad excelente se logra cuando se tienen en cuenta normas, procedimientos, manual de funciones, guías de manejo y técnicas que satisfagan al cliente. Por lo tanto, podemos decir que la percepción del cliente es la que determina la calidad y la excelencia del servicio, el estímulo para el trabajador y el buen prestigio de la Entidad prestadora de servicios de Salud. Es importante recordar que la legislación Colombiana es muy clara y muy exigente en cuanto a la calidad de servicios en salud, tal como lo expresa la ley 100 / 93 con sus principios de eficiencia, universalidad, solidaridad, integralidad y paricipación