Strategi Pembelajaran Anak Usia Dini Berbasis Multiple Intelligence

Gardner explains that intelligence is some of the abilities that a person possesses, which will not all be equal to the abilities others have, because they are of many types, Gardner calls them multiple intelligences. The development of learning strategy is intended to provide an alternative paradigm in order to prepare PAUD/TK/RA teachers who have special skills in early childhood education. Therefore, further research on the effectiveness of early childhood learning strategies based on multiple intelligences was developed to improve the competence of RA teachers. The research method used experiments, involving 116 RA teachers in Pemalang district. Data analysis used statistical analysis of Paired Sample T-Test, which aims to find out the effectiveness of AUD based learning strategy based on multiple intelligence in improving the competence of PAUD/TK/RA teachers. The results showed the significance of paired sample t-test of 0.000 (<0.05) with a t value of 9.555. Thus, the results of the analysis show that statistically, the effectiveness of early childhood learning strategies based on mulitple intelligence in improving the competence of RA teachers is tested

Subretinal fluid morphology in chronic central serous chorioretinopathy and its relationship to treatment: a retrospective analysis on PLACE trial data

Purpose To explore subretinal fluid (SRF) morphology in chronic central serous chorioretinopathy (cCSC) after one session of either high-density subthreshold micropulse laser (HSML) treatment or half-dose photodynamic therapy (PDT).Methods We retrospectively obtained optical coherence tomography (OCT) scans from a subset of patients from a randomized controlled trial on treatment-naive eyes with cCSC allocated to either HSML treatment or half-dose PDT. OCT scans were evaluated prior to treatment and 6-8 weeks post-treatment, where we measured maximum SRF height and width, calculated the maximum height-to-maximum width-ratio (maxHWR) and calculated the total SRF volume.Results Forty-one eyes of 39 cCSC patients were included. SRF morphology ranged from flat to dome-shaped, quantified as maxHWR ranging between 0.02 and 0.12. SRF volume was median 0.373 mu l (range: 0.010-4.425 mu l) and did not correlate to maxHWR (rho = -0.004, p = 0.982). Half-dose PDT was superior to HSML treatment in complete SRF resolution (RR = 3.28, p = 0.003) and in morphological changes of SRF (Delta(maximum height), p = 0.001; Delta(maximum width), p < 0.001; Delta(volume), p = 0.025). SRF resolved completely in 19/22 PDT-treated eyes (86%) and 5/19 HSML-treated eyes (26%). SRF volume increased in five eyes (26%) after HSML treatment, and in none of the eyes after half-dose PDT. SRF morphology at baseline did not predict treatment outcomes.Conclusion SRF morphology changed after both HSML treatment and half-dose PDT in cCSC, with SRF disappearing in most PDT-treated patients, whereas SRF volume increased in a sizeable proportion of HSML-treated patients. Baseline SRF characteristics measured in this study were unable to predict outcomes after either HSML treatment or half-dose PDT

Perspectives and Update on the Global Shortage of Verteporfin (Visudyne).

An ongoing global shortage of verteporfin (Visudyne &lt;sup&gt;®&lt;/sup&gt; ) limits the treatment possibilities for several chorioretinal diseases, including central serous chorioretinopathy, choroidal hemangioma, and polypoidal choroidal vasculopathy. Verteporfin is required to perform photodynamic therapy in these ocular diseases. Therefore, the current situation has a substantial impact on eye care worldwide. The worldwide supply of verteporfin appears to be manufactured by a single factory, which is situated in the United States. The distribution of verteporfin is done by different companies for different regions of the world. Official communication on the shortage by the responsible companies has been scarce and over the past years several promises with regards to resolution of the shortage have not been fulfilled. The delivery of new batches of verteporfin is at irregular intervals, unpredictable, and may not be fairly balanced between different regions or countries in the world. To ensure a fair distribution of available verteporfin within a country, several measures can be taken. In the Netherlands, a national committee, consisting of ophthalmologists, is in place to arrange this. On the European level, the European Union and European Medicine Agency have plans to monitor medicine shortages more closely and to intervene if necessary. With a more intensified monitoring and regulation of medicine supplies, future impending shortages may be prevented. Remarkably, the amount of medicine shortages is increasing, having a significant and sometimes irreversible impact on patient care. Thus, efforts should be undertaken to minimize the consequences and, whenever possible, to prevent future medicine shortages

A New Neutrosophic Cognitive Map with Neutrosophic Sets on Connections

Neutrosophic sets and their application to decision support have become a very important topic. In real situations, there are different sources of indeterminacy. This paper suggests a new decision-making model based on Neutrosophic Cognitive Maps (NCMs) for making comprehensive decisions from a multi-objective approach (diagnosis, decisions, and prediction) during the execution of many projects simultaneously. A Soft Computing technique like Fuzzy Cognitive Maps (FCMs) has been widely used for decision-making process in project management, but this technique has the limitation of not considering the indeterminacy between concepts. This limitation is overcome by the proposed model since NCMs can represent the indeterminacy or neutrality. The new model includes neutrosophic sets in the map’s connections. Finally, the suggested model has been compared with traditional FCM-based model considering efficiency and efficacy

Twelve tips for integrating massive open online course content into classroom teaching

Massive open online courses (MOOCs) are a novel and emerging mode of online learning. They offer the advantages of online learning and provide content including short video lectures, digital readings, interactive assignments, discussion fora, and quizzes. Besides stand-alone use, universities are also trying to integrate MOOC content into the regular curriculum creating blended learning programs. In this 12 tips article, we aim to provide guidelines for readers to integrate MOOC content from their own or from other institutions into regular classroom teaching based on the literature and our own experiences. We provide advice on how to select the right content, how to assess its quality and usefulness, and how to actually create a blend within your existing course

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry.

AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS &gt;5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both