Stimuli-responsive hydrogels for cancer immunotherapy

Immunotherapy plays an important role in cancer treatment by activating or suppressing the immune system. However, there are still a series of challenges to overcome regarding the delivery vehicles of immunotherapeutic agents and their effective activation at tumor sites. Meanwhile, owing to their well-hydrated environment and capability of immobilizing biological cargos, hydrogels in combination with immunotherapies provide a chance to enhance the antitumor immune response with reduced side effects. In addition, stimuli-responsiveness has been also widely applied to optimize the pharmacokinetics with an improved therapeutic outcome. In this review, we discuss the opportunities for the combination of immunotherapy and stimuli-responsive hydrogels, such as light, temperature, ultrasound and magnetically responsive hydrogels, for effective cancer treatment. Finally, we explore the potential of stimuli-responsive hydrogels as vaccine implants against cancer and Covid-19.

The Value of Pooling in Last-Mile Delivery

Last-mile delivery services have become ubiquitous in the recent past. Delivery services for food (eg., DoorDash, Grubhub, Uber Eats) and groceries (eg., Instacart, Cornershop) earned a combined revenue of $25B in 2020, and are expected to exceed $72B in revenues by 2025. The COVID-19 pandemic accelerated the growth of such services by making their value proposition even more attractive. The lower risk of contact coupled with the convenience of ordering from the comfort of their homes led to widespread customer adoption. Even so, most last-mile delivery services are not profitable. The high cost of delivery is cited as the major cause of losses. Thus, analyzing the factors influencing delivery costs is crucial for understanding the long-term viability of these services. The pooling of orders is a critical source of efficiency in last-mile delivery. We propose a queuing-based spatial model for the delivery process to analyze the value created by pooling. We demonstrate how the trade-off between delivery times and the cost of delivery, mediated by the extent of pooling, dictates which services will be economically viable. Our simulation study of a typical grocery delivery service in Los Angeles, California suggests that delivery times of less than 1 hour are unprofitable for most regions in the US. We find that driver wages account for 90% of the delivery cost. We also discuss the potential impact of technological innovations such as automated delivery and labor regulations on the profitability of last-mile delivery services. © 2022 IEEE.

Robotic and artificial intelligence in health care during the COVID-19 pandemic

The main purpose of this research study is determining the robotic and artificial intelligence in the health care during the COVID-19 pandemic. This research study presents that secondary data analysis related to the artificial intelligence and robotic. This research study depends upon questions for gathering the data used closed ended and open-ended question related to the variables. The robotic and artificial intelligence is main in independent variable and the COVID-19 is considered as dependent variables. For determine the research study used E-views software and generate different results included descriptive statistical analysis, correlation coefficient, the unit root test analysis, the normality test analysis also that explain the ganger causality test between dependent and independent variables. The overall result founded that there are direct effect and relation of robotic and artificial intelligence in health care during COVID-19 pandemic. Copyright © 2022 Authors. All rights reserved.

Parallel Emergency Management of Incidents by Integrating OODA and PREA Loops: The C2 Mechanism and Modes

Given the differences of command and control (C2) activities between the field command center and the emergency operations center (EOC), this article combined the edge C2 theory with the parallel C2 theory, and proposed a parallel incident C2 mode based on the observe–orient–decide–act (OODA) loop and planning–readiness–execution–assessment (PREA) loop. The aim is to build up a PREA loop-based parallel incident C2 mode and its related operating mechanism of edge empowerment and energy release in parallel incident C2 mode. The parallel incident C2 mode based on the PREA loop and OODA loop supports the co-existence and connection of the two roles of the incident C2 agent at the emergency scene. The two roles are the executive role of emergency response and operation and the command and organization role of the edge emergency system. This article initiates a deep integration of two different C2 process mechanisms in the emergency response and operation process, taking into account the local emergency scene and the global emergency system. Taken together, a key issue has been well addressed regarding the contradiction that the traditional emergency response cannot be reconciled in terms of rapidity and thoroughness. Author

A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise

The outbreak of COVID-19 on the Diamond Princess cruise ship has attracted much attention. Motivated by the PCR testing data on the Diamond Princess, we propose a novel cure mixture nonparametric model to investigate the detection pattern. It combines a logistic regression for the probability of susceptible subjects with a nonparametric distribution for the detection of infected individuals. Maximum likelihood estimators are proposed. The resulting estimators are shown to be consistent and asymptotically normal. Simulation studies demonstrate that the proposed approach is appropriate for practical use. Finally, we apply the proposed method to PCR testing data on the Diamond Princess to show its practical utility.

Test-Time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our approach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynamically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks. Code is available at https://github.com/med-air/TTADC. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction of novel coronavirus pneumonia epidemic trend based on ARIMA model

In order to improve the speed and efficiency of the Department of epidemic prevention and control, this paper uses ARIMA model to train and fit the number of confirmed cases on the basis of the historical epidemic diagnosis information of Guangdong Province. By dealing with the stability of time series, determining the parameters of ARIMA model and testing residual white noise, the ARIMA model is established to predict the number of confirmed epidemic cases, and the number of confirmed epidemic cases in March may 2021 in Guangdong Province is accurately predicted, so as to help the epidemic prevention and control departments improve the accuracy and effectiveness of epidemic control. © 2022 SPIE.

Convolutional Neural Network for Computer-aided Identification: Detection of Lung CT Images Using CNN for COVID-19 Identification

The angle of the photo, the size of the lung, and some occlusions make it difficult to control the accuracy when recognizing COVID-19 images, so recognition of related images is always a major problem. In our paper, we built a Convolutional Neural Network (CNN) model based on Keras, and ReLU function was employed in our model for hierarchical computation of image data, which aims to speed up the computation and improve the accuracy. To be more specific, the overall process of our method is to first convert the label set created during image reading into a specific format using one-hot labels and reduce it to a new label set, then apply the proposed CNN model to perform the hierarchical computation, and finally applied Adam optimizer to compile the neural network. The experimental results show that our proposed method has a very good accuracy rate, and it can be used in the field of COVID-19 identification. © 2022 IEEE.

Quality assessment for pediatric COVID-19 guidelines

Objective To assess the methodological quality of pediatric COVID-19 guidelines using the AGREE Ⅱ. Methods Domestic and foreign pediatric COVID-19 guidelines from inception to 1st Oct 2021 were electronically searched in PubMed, CBM, CNKI, VIP, WanFang Data, Medlive, NGC, GIN, and NICE databases and relevant websites. Two researchers independently assessed the methodological quality of the guidelines by using AGREE Ⅱ. Results A total of 21 guidelines were included. The AGREE Ⅱ results revealed that the average scores of included guidelines in 6 domains (scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence) were 62.70%, 36.24%, 20.34%, 50.42%, 22.12% and 53.17%, respectively. Conclusion The methodological quality of pediatric COVID-19 guidelines is poor. Guideline developers should follow the requirements of AGREE Ⅱ in guideline development.

Rethinking Educational Excellence with the Digital Transformation: A New Perspective on Developing Tech-driven Virtual Mentoring Platform for Unfinished Learning amid Covid-19

The world has been upended by the ongoing COVID-19 pandemic which has posed significant impacts and challenges to the learning communities. Educational excellence for the new normal is calling for guiding students to "learn how to learn"and to develop their own individual talents and abilities along their educational journey. Through the preliminary baseline need assessments conducted in six universities, we observed that students are losing learning opportunities to a complete higher educational experience for their all-round development which they should have received in normal study years. Therefore, with the nature of students' educational experience radically changing-the demand for an improved virtual learning experience to attain educational excellence for this vulnerable population is magnified. The objectives of this case study are to 1) identify the learning obstacles that result in unfinished learning amid Covid-19, 2) explore the underlying learning mechanisms with online learning platforms to characterize students' anticipated level of educational excellence through the virtual learning environment, and 3) gain understanding about students' engagement, expectations, and satisfaction through students' feedbacks on the platform and evaluate potential impacts on educational effectiveness. This paper begins by summarizing and identifying limitations in the current higher education mentorship programs from both the mentors' and the mentees' perspectives. Then it introduces the innovative design and implementation of the Virtual Mentoring Platform built upon grounded theory, innovative technologies, evidence-based observations, and participants' feedbacks. Finally, it presented and discussed the preliminary results of the evaluation and implications of the case study. © 2021 IEEE.

Evaluation and Study of Danao Flood Canal and BaiShou Bay Sediment in Huizhou under COVID -19 Epidemic

The COVID-19 epidemic has had a huge impact on human society, providing an opportunity for human beings to reflect on environmental governance. The sediment samples were collected from the Diversion Channel and Baishou Bay in Huizhou to analyze the element speciation distribution and pollution status. By graphite furnace atomic absorption spectrometry, atomic fluorescence spectrophotometry, flame atomic absorption Spectrophotometric methods to determine the content of the bottom sediments. The single factor index method, the Nemero comprehensive index method, the pollution load index method and the coefficient of variation analysis method were used to analyze. This study on the river bottom sediments of Huizhou is significant environmental effects of harmful elements. © 2021 Kriedt Enterprises Ltd. All right reserved.

Thoughts on the naming of "cOVID-19"

Based on the naming of diseases in the history and the nomenclature of diseases, especially that of novel human infectious diseases, in traditional Chinese medicine (TCM) and modern medicine, we put forward the following suggestions for the naming and severity classification of "coronavirus disease 2019 (COVID-19)": (1) Patients with only nucleic acids positive or nucleic acid positive as well as some of the symptoms but without any evidence of pneumonia should be diagnosed more generally such as "Novel coronavirus respiratory infection (NCRI)"or "Novel coronavirus infection (NCI)". (2) The manifestations concerning pneumonia can be used as the main basis for the classification of the severity of the disease. For instance, those with only nucleic acids positive or nucleic acid positive as well as mild symptoms are mild, those with nucleic acid positive as well as symptoms like fever and cough are moderate, those with nucleic acid positive as well as pneumonia are severe, those with severe symptoms as well as respiratory failure and multiple organ damage are critical. (3) Also, those with infections and clinical manifestations but no pneumonia can be called simple type, and those with pneumonia can be called pneumonia type. (4) Under the current background of integrative medicine, the naming of newly emerging infectious diseases by TCM should be changed from an ambiguous concept to a clearly defined one. It may define the disease according to the etiology, pathogenesis, clinical manifestation or prognosis of the disease, redefine the original concept in TCM and discard the excessively broad part, or carry out the common naming between TCM and modern medicine based on the specific pathogen. (5) According to the nomenclature of diseases in TCM plus that in modern medicine, the NCI may be named "Jihai (2019) - Pestilence"(Ji Hài (2019) - Yì Lì) in TCM.

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students' learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a clustering algorithm based on brain storm optimization (CBSO) to categorize students according to their learning behaviors and determine their characteristics. This enables teaching to be tailored to taken into account those results, thereby, improving the education quality over time. Specifically, we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence. The experiments are performed on the 104 students' online learning data, and the results show that CBSO is feasible and efficient.

Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

During the COVID-19 pandemic, the treatment of aortic dissection has faced additional challenges. The necessary medical resources are in serious shortage, and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection. In this work, we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic. A general scheme of medical data processing is proposed, which includes five modules, namely problem definition, data preprocessing, data mining, result analysis, and knowledge application. Based on effective data preprocessing, feature analysis and boosting trees, our proposed fusion decision model can obtain 100% accuracy for early postoperative mortality prediction, which outperforms machine learning methods based on a singlemodel such as LightGBM,XGBoost, andCatBoost. The results reveal the critical factors related to the postoperative mortality of aortic dissection, which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance. © 2021 Tech Science Press. All rights reserved.

Reaserch and implementation of social distancing monitoring technology based on SSD

In the fight against the Covid-19, social distancing has proven to be a very effective measure to mitigate the spread of the disease. As resumption of work, production and classes accelerates, it is necessary to limit people's social distance to reduce the rate of the virus spread. To solve this problem, a method for monitoring social distancing based on SSD object detection technology is proposed in this study. This method utilizes SSD300 model to detect people in a video or picture, and labels a Red Line as warning on the people whose distances are less than the default one, implementing real-time social distancing monitoring, and the mAP reaches 88.44%. © 2021 The Authors. Published by Elsevier B.V.

Developing Strategies for Primary Healthcare in Times of Epidemic of COVID-19

Primary healthcare settings are the control and prevention network basis of COVID-19 epidemic.So improving COVID-19 control and prevention,service delivery and response levels of these institutions is crucial to the national epidemic control and prevention.Based on the analysis of related field survey results as well as information from national and local official websites,we summed up the important role of primary healthcare in dealing with the epidemic.Moreover,we proposed the following priorities for primary healthcare settings in combating the complex epidemic and delivering daily healthcare services:strengthening community-based control and prevention of COVID-19,providing assistance for other institutions in combating COVID-19,implementing daily healthcare and essential public health services,ensuring medical safety and strengthening the control and prevention of nosocomial infections,and adequately playing the role in county-based healthcare network.Furthermore,developing strategies targeting the weaknesses in combating the epidemic and inadequacies in delivering daily healthcare services of primary healthcare were also put forward:strengthening the development of general practitioner system and hierarchical medical system;improving early warning sensitivity,awareness of timely report of major epidemic,and emergency response level in primary healthcare workers;enhancing the informatization construction and application in primary care using artificial intelligence and cutting-edge technologies;promoting the development of regional medical consortiums and local healthcare networks,and exploring patterns for efficiently integrating medical and prevention services;vigorously carrying out patriotic public health campaigns,strengthening the mechanism of group-based control and prevention of communicable diseases,and facilitating the construction of healthy communities and villages. Copyright © 2020 by the Chinese General Practice.

[Surgical treatment for esophageal cancer during the outbreak of COVID-19].

Since December 2019, unexplained pneumonia has appeared in Wuhan City, Hubei Province, and a new type of coronavirus infection was confirmed as COVID-19. COVID-19 spread rapidly nationwide and abroad. The COVID-19 has brought huge impacts to all the people and walks of life, especially to the medical and health systems. It has also brought great challenges to the treatment of patients with cancer. Esophageal cancer is a common malignant tumor in China and most of the patients are in the middle and advanced stage when diagnosed, with immunosuppressive and poor prognosis. The selection of surgical procedures and perioperative managements of esophageal cancer require all thoracic surgeons work together to figure out a reasonable system of surgical treatment and emergency response.