Application of Improved Packing Method in the Repair of Infectious Wounds in Special Body Parts

Objective: To investigate the effect of improved packing method in the repair of infectious wounds in special parts. Methods: From December 2017 to December 2020, 68cases of infectious wounds in special body parts were treated with improved packing and dressing method (including 28 cases of hip abscess, 16 cases of sacrococcygeal pressure ulcer, 12 cases of buttock pressure ulcer, 8 cases of perineal necrotizing fasciitis and 4 cases of hip pressure ulcer). After active anti infection, abscess incision and drainage, and debridement of necrotic tissue, the wound inflammation subsided, necrotic tissue was removed, and granulation tissue grew. The wound edge was fully dissociated, and the wound was directly closed and sutured or transferred to the adjacent skin flap to repair the wound. The drainage tube was prevented according to the condition of the wound. Meilan marked the area of the basal cavity of the wound, and the packing suture was placed outside the edge of the cavity to fix the wound. Result: Of the 68 patients, 58 had primary wound healing; 8 cases of partial wound dehiscence after removal of packing and bandage were treated with secondary suture combined with improved packing and bandage method; Two patient's wound was uncooperative due to the poor consciousness of the patient. The bandage was completely loose and the wound split again. Conclusion It has the advantages of simple operation, easy nursing and less hospitalization cost

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user

Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement. In this work, we propose a quality-aware memory network for interactive segmentation of 3D medical images. Provided by user guidance on an arbitrary slice, an interaction network is firstly employed to obtain an initial 2D segmentation. The quality-aware memory network subsequently propagates the initial segmentation estimation bidirectionally over the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To further facilitate interactive segmentation, a quality assessment module is introduced to suggest the next slice to segment based on the current segmentation quality of each slice. The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribbles, boxes). Experimental results on various medical datasets demonstrate the superiority of our approach in comparison with existing techniques.Comment: MICCAI 2021. Code: https://github.com/0liliulei/Mem3