Secondary prevention of coronary heart disease in elderly population of Turkey: A subgroup analysis of ELDERTURK study

Background: Secondary prevention plays an important role after acute coronary event due to high risk of adverse events in elderly. In present study we aimed to evaluate the lifestyle, management of risk factors and medical treatment for secondary protection in elderly patients with known coronary heart disease (CHD). Methods: ELDERTURK is a non-interventional, multi-centered, observational study, which included total of 5694 elderly patients ( > 65 years) from 50 centers in Turkey. In this study elderly patients from the ELDERTURK population with known CHD were evaluated for cardiovascular risk factors, comor- bidities and medication usage. Results: A total of 2976 (52.3% of study) out of 5694 patients included in the ELDERTURK study were evaluated. All had known CHD with a mean age of 73.4 ± 6.2 years and 60.3% were male. 13.0% of patients were smokers, 42.4% were overweight and 21.1% were obese. Only 23.6% of patients reported to do regular exercise, 73.4% had history of hypertension, 47.4% had dyslipidemia and 33.9% had diabetes mellitus. The rate of patients with systolic blood pressure > 140 mmHg were 31.1% and only 13.9% of patients had a recommended ≤ 70 mg/dL level of low-density lipoprotein cholesterol. Anti- platelet, statin, beta-blocker and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker usage was limited to 27.3%. Conclusions: The ELDERTURK study shows that many patients with CHD have a high prevalence of modifiable risk factors and unhealthy lifestyle. Apart from this, many patients are not receiving thera- peutic intervention and as a consequence most were not achieving the recommended goals.   

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Evaluation of nutritional status in pediatric intensive care unit patients: the results of a multicenter, prospective study in Turkey

IntroductionMalnutrition is defined as a pathological condition arising from deficient or imbalanced intake of nutritional elements. Factors such as increasing metabolic demands during the disease course in the hospitalized patients and inadequate calorie intake increase the risk of malnutrition. The aim of the present study is to evaluate nutritional status of patients admitted to pediatric intensive care units (PICU) in Turkey, examine the effect of nutrition on the treatment process and draw attention to the need for regulating nutritional support of patients while continuing existing therapies.Material and MethodIn this prospective multicenter study, the data was collected over a period of one month from PICUs participating in the PICU Nutrition Study Group in Turkey. Anthropometric data of the patients, calorie intake, 90-day mortality, need for mechanical ventilation, length of hospital stay and length of stay in intensive care unit were recorded and the relationship between these parameters was examined.ResultsOf the 614 patients included in the study, malnutrition was detected in 45.4% of the patients. Enteral feeding was initiated in 40.6% (n = 249) of the patients at day one upon admission to the intensive care unit. In the first 48 h, 86.82% (n = 533) of the patients achieved the target calorie intake, and 81.65% (n = 307) of the 376 patients remaining in the intensive care unit achieved the target calorie intake at the end of one week. The risk of mortality decreased with increasing upper mid-arm circumference and triceps skin fold thickness Z-score (OR = 0.871/0.894; p = 0.027/0.024). The risk of mortality was 2.723 times higher in patients who did not achieve the target calorie intake at first 48 h (p = 0.006) and the risk was 3.829 times higher in patients who did not achieve the target calorie intake at the end of one week (p = 0.001). The risk of mortality decreased with increasing triceps skin fold thickness Z-score (OR = 0.894; p = 0.024).ConclusionTimely and appropriate nutritional support in critically ill patients favorably affects the clinical course. The results of the present study suggest that mortality rate is higher in patients who fail to achieve the target calorie intake at first 48 h and day seven of admission to the intensive care unit. The risk of mortality decreases with increasing triceps skin fold thickness Z-score

White mineral trioxide aggregate pulpotomies: Two case reports with long-term follow-up

This case report describes the partial pulpotomy treatment of complicated crown fractures of two cases by using white mineral trioxide aggregate (WMTA) with long-term follow-up. In the cases presented here, to injured incisor teeth were open apices and the pulp exposure site was large, so it was decided to perform vital pulpotomy with WMTA. Long-term follow-up examinations revealed that the treatment preserved pulpal vitality with continued root development and apex formation. WMTA may be considered as an alternative option for the treatment of traumatized immature permanent teeth