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This paper is in response, from an international perspective, to the manuscript entitled “Improving teacher professional development for online and blended learning a systematic meta-aggregative review” (Philipsen, B., Tondeur, J., Pareja Roblin, N. et al. 2019). The impact of the manuscript has been reinforced by the claims of international organisations like UNESCO and ILO, as far as they highlight that Teacher Professional Development (TPD) for Online and Blended Learning are a priority in the present scenario. The findings can be clearly applied to guide appropriated TPD for the recovery as well as for a resilient education system. Nevertheless, the research was conducted in a West-European context where most learners use computers on an everyday basis, while half the world’s students do not have access to a household computer, and this has determined the emergency response to the pandemic. Dreesen et al. (2020) reported that most of the countries have adopted a remote education based on some combination of digital platforms, television, radio, take-home packages, home visits, text messaging and phone calls. It would be very interesting to incorporate these recent discoveries in the use of frugal technologies and elucidate if new components should be aggregated for TPD strategies from an international perspective. As far as the authors adopted the approach of a systematic meta-aggregative review, new data supported by unequivocal or credible evidence can be conveniently incorporated without reinterpreting the original findings.

Some patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rapidly develop to critical condition. Tuvusertib ic50 Here, we investigated the clinical features of critically ill SARS-CoV-2 patients with and without diabetes and identified risk factors for death of these patients.

The medical records including epidemiological, demographic, clinical, and laboratory data from 49 critically ill SARS-CoV-2 patients were collected and analyzed in Huanggang City and Xiaogan City, Hubei Province, outside Wuhan.

Sixty-seven percent (33) of patients survived and 33% (16) of patients died in 49 critically ill patients (32 men, 17 women), with a median age of 63years (IQR 53-73). Univariate analyses indicated that the deceased patients were more often associated with two or more comorbidities, one or more gastrointestinal symptoms, high neutrophil percentage, low lymphocytes and lymphocyte percentage, high C-reactive protein, high procalcitonin, high fasting blood glucose (FBG), and high lactate dehydrogenase (LDH) compared with the survivors; moreover, the patients with T2DM had the higher neutrophil percentage, the lower lymphocyte percentage, and the higher levels of FBG and LDH compared with the patients without T2DM. Multivariable logistic regression analyses indicated that gastrointestinal symptoms (≥ 1 symptoms), decreased lymphocytes (< 1.1 × 10

/L), and increased FBG (≥ 7.0mmol/L) were the independent risk factors for death of critically ill patients.

Critically ill COVID patients with T2DM had more severe damages of the lymphocytes, islet cells, and heart function, and gastrointestinal symptoms, lymphopenia, and increased FBG may be early predictors for poor prognosis.

The online version contains supplementary material available at 10.1007/s13410-020-00888-3.

The online version contains supplementary material available at 10.1007/s13410-020-00888-3.In this contribution, the problem of multistability control in a simple model of 3D HNNs as well as its application to biomedical image encryption is addressed. The space magnetization is justified by the coexistence of up to six disconnected attractors including both chaotic and periodic. The linear augmentation method is successfully applied to control the multistable HNNs into a monostable network. The control of the coexisting four attractors including a pair of chaotic attractors and a pair of periodic attractors is made through three crises that enable the chaotic attractors to be metamorphosed in a monostable periodic attractor. Also, the control of six coexisting attractors (with two pairs of chaotic attractors and a pair of periodic one) is made through five crises enabling all the chaotic attractors to be metamorphosed in a monostable periodic attractor. Note that this controlled HNN is obtained for higher values of the coupling strength. These interesting results are obtained using nonlinear analysis tools such as the phase portraits, bifurcations diagrams, graph of maximum Lyapunov exponent, and basins of attraction. The obtained results have been perfectly supported using the PSPICE simulation environment. Finally, a simple encryption scheme is designed jointly using the sequences of the proposed HNNs and the sequences of real/imaginary values of the Julia fractals set. The obtained cryptosystem is validated using some well-known metrics. The proposed method achieved entropy of 7.9992, NPCR of 99.6299, and encryption time of 0.21 for the 256*256 sample 1 image.Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images.