Nova técnica para contagem do número de células de blastocistos

A simplified, fast, and innovative method was developed to count the total cell number in blastocysts. Murine blastocysts (N = 195) were used in this study. They were obtained after 10h culture of initial blastocysts, compact morulae grades I and II recovered from superovulated mouse. After culture, the blastococysts were selected to test the new proposal of counting. The process was done after embryo fixation in a sodium citrate solution, and adherence in glass slide. Following, the coloration was done using a fast panoptic coloration kit. As a result, it was possible to identify the blastomeres and count them in each blastocyst. This method provided a fast and effective analysis of the total cell number when compared with other techniques. Moreover, this new method shows advantages related to the cell visualization, which can be done in more simple equipment like stereoscopic microscope. Other interesting observed point was the long period of time and quality that the coloration stays on slides, considering other techniques

Desenvolvimento de embriões de camundongas após manutenção em diferentes soluções de manipulação

Avaliou-se a eficácia de duas soluções de manipulação (SM) de embriões de camundongas nos estádios de blastocisto inicial (Bin), mórula compacta grau I (McI) e II (McII), distribuídos aleatoriamente em três tratamentos (T), de acordo com a solução de manutenção. No T1 usou-se PBS modificado (controle); no T2, SME e no T3, SME enriquecida. Os embriões foram mantidos durante quatro horas na solução de manutenção e posteriormente classificados quanto ao estádio de desenvolvimento e à qualidade embrionária. Logo após, foram cultivados em meio TCM 199 e classificados novamente quanto ao estádio de desenvolvimento e à qualidade embrionária. A taxa de desenvolvimento dos embriões após manutenção por quatro horas em solução de manipulação foi menor (P0,05) após o cultivo in vitro. Os embriões McII do T3 tiveram maior desenvolvimento (P0.05) as compared to EMS and EMS supplemented embryos. After in vitro culture, no differences (P>0.05) on embryo development rate among control, EMS, and EMS supplemented were observed. Moreover, McII from EMS supplemented had a higher development (P<0.05) (93%) as compared to control (82.5%) and EMS (83.9%), suggesting a beneficial effect of EMS supplemented. EMS and EMS supplemented embryos had a positive effect on embryo development, showing higher embryo development than those in PBS solution

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

Background and aims: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. Methods: Matched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. Results: 262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. Conclusions: The combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

Background and aims: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. Methods: Matched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. Results: 262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. Conclusions: The combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components.Radiolog

Correction to: Is diet partly responsible for differences in COVID-19 death rates between and within countries? (Clinical and Translational Allergy, (2020), 10, 1, (16), 10.1186/s13601-020-00323-0)

Following publication of the original article [1], the authors identified an error in the affiliation list. The affiliation of author G. Walter Canonica should have been split up into two affiliations: • Personalized Medicine, Asthma and Allergy – Humanitas Clinical and Research Center – IRCCS, Rozzano (MI), Italy • Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (MI), Italy The corrected affiliation list is reflected in this Correction. © 2020, The Author(s)