Narcisoidni poremećaj ličnosti kod odraslih osoba

SAŽETAK Narcisoidni poremećaj ličnosti je obrazac grandioznosti, potrebe za divljenjem i nedostatka empatije prema Dijagnostičkom i statističkom priručniku za mentalne poremećaje. Poremećaj je klasificiran u dimenzionalni model "poremećaja ličnosti". Narcisoidni poremećaj ličnosti je visoko komorbidan s drugim poremećajima mentalnog zdravlja. Osobe s narcisoidnim poremećajem ličnosti često mogu imati smetnje u održavanju posla i odnosa. Poremećaj je vrlo raširen u društvu; međutim, bilo je ograničenih istraživanja o istom. S obzirom na ograničena istraživanja i razlike u dijagnozi bolesti, isprva se planiralo ukinuti iz Dijagnostičkog priručnika. Narcisoidni poremećaj ličnosti je pod Skupinom B poremećaja osobnosti, koji uključuje antisocijalni poremećaj osobnosti, histrionični poremećaj osobnosti i granični poremećaj ličnosti. Skupina B obično se manifestira izrazito emocionalnim i nepredvidivim ponašanjem. Kod oboljelih od narcističkog poremećaja ličnosti naglašava se da provodeći zdravstvenu njegu sestra ima mogućnost slušati bolesnika, identificirati se s njim i njegovom obitelji, procijeniti njegove potrebe i graditi osobit odnos koji je izuzetno važan za učinkovit proces zdravstvene njege. Sposobnost osjećaja bliskosti s oboljelim od NPL-i obilježje je kompetentnih sestara jer znaju slušati, osjetljive su na neverbalnu komunikaciju i ohrabruju bolesnike da kažu svoje osjećaje na razne način

Narcisoidni poremećaj ličnosti kod odraslih osoba

SAŽETAK Narcisoidni poremećaj ličnosti je obrazac grandioznosti, potrebe za divljenjem i nedostatka empatije prema Dijagnostičkom i statističkom priručniku za mentalne poremećaje. Poremećaj je klasificiran u dimenzionalni model "poremećaja ličnosti". Narcisoidni poremećaj ličnosti je visoko komorbidan s drugim poremećajima mentalnog zdravlja. Osobe s narcisoidnim poremećajem ličnosti često mogu imati smetnje u održavanju posla i odnosa. Poremećaj je vrlo raširen u društvu; međutim, bilo je ograničenih istraživanja o istom. S obzirom na ograničena istraživanja i razlike u dijagnozi bolesti, isprva se planiralo ukinuti iz Dijagnostičkog priručnika. Narcisoidni poremećaj ličnosti je pod Skupinom B poremećaja osobnosti, koji uključuje antisocijalni poremećaj osobnosti, histrionični poremećaj osobnosti i granični poremećaj ličnosti. Skupina B obično se manifestira izrazito emocionalnim i nepredvidivim ponašanjem. Kod oboljelih od narcističkog poremećaja ličnosti naglašava se da provodeći zdravstvenu njegu sestra ima mogućnost slušati bolesnika, identificirati se s njim i njegovom obitelji, procijeniti njegove potrebe i graditi osobit odnos koji je izuzetno važan za učinkovit proces zdravstvene njege. Sposobnost osjećaja bliskosti s oboljelim od NPL-i obilježje je kompetentnih sestara jer znaju slušati, osjetljive su na neverbalnu komunikaciju i ohrabruju bolesnike da kažu svoje osjećaje na razne način

Toward synthesis and characterization of unconventional C-66 and C-68 fullerenes inside carbon nanotubes

We present compelling evidence pointing to the possible synthesis of unconventional C-66 and C-68 fullerenes in the interior of single-walled carbon nanotubes. The production proceeds from C(60)(-)toluene/benzene clathrates encapsulated inside the nanotubes using heat-driven nanotesttube chemistry. All isomers violate the so-called isolated pentagon rule and are stabilized solely by the proximity of the wall of the host nanotube. We present detailed characterization of the unconventional fullerenes using Raman spectroscopy, C-13 isotope labeling of the benzene molecules, transmission electron microscopy, X-ray diffractometry, and first-principles calculations. Multiple isomers of both C-66 and C-68 are identified in the sample. We argue that our method opens the way to high-yield synthesis of unconventional fullerenes

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd