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Background The improvement efficacy of consolidation chemotherapy after definitive concurrent chemotherapy and radio-therapy (CCRT) on the prognosis of patients with locally advanced esophageal squamous cell carcinoma (ESCC) remains controversial. In addition, there is a lack of nutritional risk screening tools which can consistently and accurately predict the survival of patients with esophageal cancer. Objective To investigate the effect of consolidation chemotherapy on the prognosis of patients with locally advanced ESCC receiving definitive CCRT. Methods A total of 223 patients with ESCC who received definitive CCRT in the department of radiotherapy, the Fourth Hospital of Hebei Medical University from January 2013 to December 2018 were selected as the research objects and divided into the simple CCRT group (n=87) and combined consolidation chemotherapy group (n=136) according to chemoradiotherapy regimen adopted by the patients. General data, ECOG score, tumor site, tumor length, TNM stage, radiotherapy dose, irradiation mode and chemotherapy regimen of the included patients were collected by electronic medical record system. Nutritional Risk Screening 2002 (NRS 2002) was used to score the nutritional status of the patients before chemoradiotherapy. Efficacy evaluation was performed within 1 month after CCRT, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Patients were followed up by telephone (completed by the follow-up center) and outpatient review until 2022-09-30, with overall survival (OS), local relapse-free survival (LRRFS) and distant metastasis-free survival (DMFS) collected. Survival curves of OS, LRRFS and DMFS were plotted by Kaplan-Meier method and compared by Log-rank test. Univariate and multivariate Cox risk regression models were used to explore the influencing factors of patient prognosis. Results There was no significant difference in baseline data between the simple CCRT group and combined consolidation chemotherapy group (P>0.05). There was no significant difference in the rates of OS, LRRFS and DMFS between the two groups (χ2=1.942, 0.743, 1.272; P=0.163, 0.389, 0.259). There were significant differences in the rates of OS, LRRFS and DMFS between patients with NRS 2002 score

Background The incidence of cervical and upper thoracic esophageal cancer is relatively low, with difficulties and controversies of mode in treatment, lack of convenient and accurate prognostic biomarkers, and poor overall prognosis. Objective To investigate the long-term prognosis of patients with cervical and upper thoracic esophageal squamous cell carcinoma under concurrent chemotherapy and radio-therapy (CCRT) . Methods Patients with cervical and thoracic upper esophageal cancer who received CCRT in the Department of Radiotherapy of the Fourth Hospital of Hebei Medical University from January 2013 to December 2017 were selected as the research subjects. General data, Eastern Cooperative Oncology Group (ECOG) score, tumor site, tumor length, TNM stage, radiotherapy dose, irradiation mode, chemotherapy regimen, toxic and side effects were collected by electronic medical record system, and neutrophil to lymphocyte ratio (dNLR) was calculated. The patients were divided into the dNLR

[Purpose/Significance] In 2021, the Ministry of Education of China issued the "Norms for the Construction of Digital Campuses in Higher Education Institutions (Trial)", which mentioned that universities should attempt to form a new model of academic exchange and resource sharing through the construction of institutional repositories (IRs). The construction of knowledge resources in university IRs is gradually developing towards the next generation IRs. Research data resources are an important component of the future resources. From the perspective of the generation, acquisition, and existence of information resources in universities, the institutional resources in universities primarily consist of resources generated by "industry, academia, research and application" activities, including teaching resources, scientific research resources, design resources, scientific activity resources, etc., which are specifically reflected in the activity process such as scholar resources, research activity resources, research process resources, and research achievements resources. Research data resources are studied as the research object in this article. [Method/Process] This paper analyzes the formation mechanism of institutional knowledge resources, and conduct research on the integration, management and reuse of research data resources, including: 1) internal factors of research data, data generation, source, type, structure, and dependency mode; 2) research on external elements of data, such as data standards, unique identifiers, data registration, data protocols, data rights, data reuse, and data sharing Through the study of internal and external factors, the influencing factors, functions and roles, responsibilities and rights, permissions and attribution of data integration management and data reuse were sorted and interpreted and feasible methods were designed for research data management, collection, implementation, and registration. In order to ensure the effectiveness of data management, a series of standards and schemes have been developed, such as data type and format standards, metadata schemes, and data guardianship demand survey templates. The purpose is to achieve data discovery, interoperability, and reuse through continuous monitoring of scientific data. The basic rules of reuse are mainly divided into: 1) reuse: the concept of reuse, reuse, sharing, and incomplete equivalence in reuse; 2) sharing: possible to be used; 3) protocol usage: discussion about how to use it; and 4) rights use: complying with the data copyright agreement. [Results/Conclusions] Through research courses, the development and implementation of favorable data management and reuse strategies have clarified the data objects, management set management services, and data reuse permissions of research data in university IRs. We have clarified the current situation and urgent issues to integrate research data into the long-term preservation, management, and sharing and reuse system of knowledge resources in university IRs. We have defined the responsibilities, management mechanisms, standardized business processes, permission attributes, data exchange platforms, data registration, and data reuse of research data subjects by category, region, and field, and provided some suggestions and guarantee measures for the establishment of a data management center in China.

Objective To establish a fungal image-assisted classification model using deep learning technology. Methods The microscope images of people infected with Aspergillus, Saccharomyces and Cryptococcus neoformans were retrospectively collected from the Eighth Medical Center of PLA General Hospital from September 2020 to April 2021. The images were randomly divided into training set, validation set and test set according to the ratio of 7∶1.5∶1.5. The improved MobileNetV2 network structure was trained using the training set, a convolutional neural network (CNN) fungal image 11 classification model based on multi-scale attention mechanism was constructed and the parameters were debugged based on the validation set. Machine identification results were taken as the gold standard, the performance of the model on 11 fungal image classification tasks was evaluated, and the results were shown by precision, recall and F1 value. In addition, the performance of the proposed model with 5 classic CNN models were compared, and the results were measured in terms of model parameters, memory usage, frames per second (FPS), accuracy, and area under the curve (AUC) of receiver operating characteristic curve. Results A total of 7666 fungal microscope images were collected, including 2781, 4115, and 770 images of Aspergillus, Saccharomyces, and Cryptococcus neoformans, respectively. Among them, there were 5366 training images, 1150 validation images, and 1150 test images. The improved MobileNetV2 model had high performance for the classification of 11 fungal images in the test set. The precision rate was distributed between 96.36% and 100%, the recall rate was distributed between 96.53% and 100%, and the F1 value was distributed between 97.01% and 100%. The parameters, memory usage, FPS, accuracy, and AUC of the improved MobileNetV2 model were 4.22 M, 356.89 M, 573, (99.09±0.18)%, and 0.9944±0.0018, respectively, and the comprehensive performance was better than 5 kinds of classical networks. Conclusion The proposed fungal image classification model based on the improved MobileNetV2 can obtain higher fungal image recognition ability while maintaining low computational cost, with an overall performance better than classical CNN model.