Background: Central venous catheter is required in multiply injured patients either in the initial resuscitation phase or during an intensive care unit stay. There are potential complications associated with central line access as infection. Nurses play a crucial role in preventing this infection. Aim of the study: To evaluate the effect of nursing guidelines on reducing central line related infection among traumatic patients. Design: A quasi-experimental research design. Sample: A purposive sample of 60 newly admitted adult patients with central venous catheter divided equally into two groups (study and control). Setting: The current study was conducted in traumatic intensive care unit of Qena university hospital, Qena governorate, Egypt. Tools: Two tools structure interview questionnaire and central line related infection assessment sheet. Results: There were highly statistically significant differences between the study and control groups regarding central line related infection with (p<0.001). Conclusion: The application of nursing guidelines was effective in reducing central line related infection among traumatic patients. Recommendations: Nursing guidelines for preventing central line-related infection should be educated for nurses of intensive care units in Qena university hospital.

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Studying the effect of vitamin B12 and folic acid on the levels of brain-derived neurotrophic factor in the blood and the brain of obese male rats.

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Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions’ language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference.