Correlation of GDFT combined with rehabilitation therapy in DNA damage repair of esophageal cancer cells

Esophageal cancer is a common malignant tumor with a high incidence and a serious threat to human health. The treatment of esophageal cancer is a complex process, which requires the comprehensive use of a variety of treatment methods. At present, the treatment of esophageal cancer mainly includes surgery, radiotherapy, chemotherapy and immunotherapy. The research on the treatment of cancer cells based on Goal directed fluid therapy (GDFT) combined with rehabilitation therapy is the focus of the current society. This paper proposed a study on DNA damage repair of cancer cells based on goal directed fluid therapy combined with rehabilitation therapy, aiming to optimize the traditional treatment of esophageal cancer by using goal directed fluid therapy technology. The algorithm proposed in this paper was an electroencephalogram (EEG) signal optimization algorithm based on combined rehabilitation therapy. Through this algorithm, the electroencephalogram signal could be optimized. The algorithm could speed up signal processing, and improve signal reliability and stability by reducing the influence of interference signals and improving the signal to noise ratio. These optimization measures could better help researchers analyze and understand electroencephalogram signals, so as to help better study brain functions and diseases. Through the test and investigation on the treatment of cancer cells based on goal directed fluid therapy combined with rehabilitation therapy, the results showed that the blood transfusion volume of goal directed fluid therapy treatment and conventional treatment was 251.5 mL and 288.3 mL respectively. This showed that after goal directed fluid therapy treatment, the input amount of various medical fluids was relatively reduced, and the use of medical fluids was more economical. In addition, their bleeding volumes were 295.2 mL and 324.4 mL, respectively. Urine volume was 382.3 mL and 418.1 mL respectively. This showed that after goal directed fluid therapy treatment, the patient's blood loss and urine volume were relatively reduced, which has improved the patient's health. This experiment has proved the excellent ability of goal directed fluid therapy combined with rehabilitation therapy in the treatment of esophageal cancer, and this research result has also proved the excellent medical effect of goal directed fluid therapy technology. Similarly, this paper also provided valuable reference information for the treatment of esophageal cancer.

Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning

Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical data analysis have emerged. However, the analysis of medical data remains challenging due to the mixed nature of the data, the incompleteness of many records, and the high level of noise. This paper proposes an improved neural network DBN-LSTM that combines a deep belief network (DBN) with a long short-term memory (LSTM) network. The subset of feature attributes processed by CFS-EGA is used for training, and the optimal selection test of the number of hidden layers is performed on the upper DBN in the process of training DBN-LSTM. At the same time, the validation set is combined to determine the hyperparameters of the LSTM. Construct the DNN, CNN, and long short-term memory (LSTM) network for comparative analysis with DBN-LSTM. Use the classification method to compare the average of the final results of the two experiments. The results show that the prediction accuracy of DBN-LSTM for cardiovascular and cerebrovascular diseases reaches 95.61%, which is higher than the three traditional neural networks.

Nursing Teaching Curriculum Setting by Introducing Postcompetency Model under the Vision of Internet Informatization

The nursing curriculum is to be modernized to improve the student's skills in meeting the recent trends in medical and healthcare fields. The curriculum improvements are based on expert recommendations, authors, and informative data from different web sources. The challenging task is to improve the readability and understandability of the curriculum to real-time standards. Considering the above facts, this article introduces a refined curriculum with Internet information analysis (RC-IIA) method. The proposed method incorporates the distributed Internet, journal, and previous curriculum information within the active nursing syllabus. This prevents repetitions and less-informative content within the active curriculum. Besides, classification learning for knowledge-based representations is used within the curriculum to improve competency. Based on the refined information, a recommendation-based curriculum is preferred for varying information across different standards. The proposed analysis method relies on existing and distributed information across multiple curriculum providers for leveraging the visibility and prolonging the stealth of the nursing curriculum.

Construction of Sports Nutrition Dynamic Intervention Mechanism Based on the Improvement of College Students’ Physical Health

Exercise can improve people's physical health and strengthen their body bones. But the physique of college students is generally too poor. The enhancement of College Students' physique cannot be completed in a day. This study aims to explore the best sports nutrition intervention scheme for strengthening college students' physique from the construction of sports nutrition dynamic intervention mechanism, so as to provide theoretical reference for public physical education teaching and students' independent exercise. In this study, 100 full-time associate students in our university were selected as the research objects. Before the experiment, the subjects' body shape, body function, and physical quality were tested. Based on the random mathematical method, the full-time associate students were randomly divided into four groups: control group, exercise intervention group, nutrition intervention group, and sports nutrition intervention group, with 25 people in each group. After the experiment, the body shape, body function, and physical quality of the four groups were tested. The results show that exercise and nutrition intervention have little effect on college students' height, but have great influence on body mass index, vital capacity and 800 meter running. Especially after the experiment, the vital capacity of the exercise group and the sports nutrition group increased by nearly 250 ml and 500 ml, respectively, and the 800 m running time increased by nearly 10 s and 18 s, respectively. Strengthening exercise and increasing nutritional intake can effectively improve the institutional health of college students.

Vehicle Detection Algorithm Based on Embedded Video Image Processing in the Background of Information Technology

As the main means of transportation for urban residents, the number of motor vehicles is increasing year by year. With the continuous development of society and the gradual improvement of people’s quality of life, automobiles have gradually become an indispensable means of transportation in people’s lives, resulting in increased traffic flow. However, the old traffic system is still unable to cope with the rapid growth of traffic pressure, and traffic congestion and various accidents occur frequently, which is a huge test for the contemporary intelligent traffic system. With the gradual development of society, more and more researchers are devoted to intelligent transportation systems, which make the development of target detection technology based on video image processing more and more rapid. The primary problem in embedding digital video in applications is that the complexity of video encoding and decoding far exceeds that of simple image and audio compression and decompression. Digital video can take various forms and formats. Developers need to support complex configurations and various aspects, such as different resolutions/display sizes, different bit rates, real-time issues, and even the reliability of the video source. Intelligent transportation achieved a lot of results. However, there are still some deficiencies in precision and robustness. At the same time, the improvement of video image processing technology gives us a new idea. To further improve the intelligent traffic system, provide accurate data information for all departments, and improve the traffic situation, this study, based on video image processing technology, combined with the three-frame difference algorithm, calculates and studies the data of illegal parking at a certain intersection. The calculated false detection rates for Y2 are 1.1%, 0.9%, and 2.4%, and the leakage rates for Y1 are 2.4%, 1.9%, and 4.7%, respectively. This shows that the algorithm has high accuracy for vehicle parking detection data and can collect information quickly and effectively. Applying the algorithm to the detection of other vehicles can provide efficient services for relevant traffic departments and public security departments and relieve traffic pressure. The image processing technology is a process of analyzing and processing images through certain computer technology to achieve the desired results. The scheme in the article realizes background extraction, image filtering, image binarization, morphological transformation, vehicle detection and segmentation, shadow detection, etc.