ABSTRACT
Objectives: Post-operative urinary retention (POUR) is a frequent complication following surgical procedures, characterized by an acute inability to void, leading to additional complications and extended hospitalization. Acupuncture has been shown to be effective in facilitating spontaneous urination and alleviating anxiety in patients experiencing poor urination. The present study aims to evaluate the effectiveness of electroacupuncture in the management of POUR in patients who have undergone lumbar spine surgery. Methods: This retrospective study conducted at the National Hospital of Acupuncture in Vietnam and reviewed the medical records of patients over 18 years old who underwent lumbar spine surgery and were diagnosed with POUR between January to December 2019. Electroacupuncture was administered at five specific acupuncture points Qugu (CV2), Zhongji (CV3), Zhibian (BL54), Pangguanshu (BL28), and Kunlun (BL60). This study monitored key parameters related to the effectiveness of the acupuncture treatment, including the number of acupuncture treatment sessions required until a patient was successfully treated was recorded, with a maximum of three acupuncture treatment sessions per patient, the time elapsed until urination following the treatment (minutes), and urinary bladder volume before and after treatment (mL). Results: The study demonstrated a 93.3% success rate in treating POUR with electroacupuncture. A significant reduction in post-void residual volume was noted, and patients could void within 30 minutes post-treatment. No significant differences in treatment effectiveness were observed across difference genders and age groups. Conclusion: Electroacupuncture proved to be a highly effective treatment for POUR in patients post-lumbar spine surgery, with a rapid response time and substantial reduction in PVR. However, the retrospective nature of the study and single-center focus limit its generalizability. Future research incorporating randomized controlled trials or multi-center observational studies are recommended to validate these findings and explore the potential of acupuncture in POUR management on a broader scale.
ABSTRACT
Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects. These features are extracted from abundant data of base classes, which could be utilized to reasonably describe classes with scarce data. Specifically, we propose a novel superclass approach that automatically creates a hierarchy considering base and novel classes as fine-grained classes for few-shot instance segmentation (FSIS). Based on the hierarchical information, we design a novel framework called Soft Multiple Superclass (SMS) to extract relevant features or characteristics of classes in the same superclass. A new class assigned to the superclass is easier to classify by leveraging these relevant features. Besides, in order to effectively train the hierarchy-based-detector in FSIS, we apply the label refinement to further describe the associations between fine-grained classes. The extensive experiments demonstrate the effectiveness of our method on FSIS benchmarks. The source code is available here: https://github.com/nvakhoa/superclass-FSIS.
ABSTRACT
In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting. These analysis bring monitoring and understanding of the activity of public figures, political strategies, explanation and even prediction of critical media events. Such tools can help public figures in managing their public image, as well as support the work of journalists, social scientists and other media experts. News analysis can also be seen from the lens of complex systems, gathering many types of entities, attributes and interactions over time. As many public figures intervene in different news stories, a first interesting task is to observe the social interactions between these actors. Towards this goal, we propose to use video analysis to automatise the process of constructing social networks directly from news video archives. In this paper we are introducing a system deriving multiple social networks from face detections in news videos. We present preliminary results obtained from analysis of these networks, by monitoring the activity of more than a hundred public figures. We finally use these networks as a support for political studies and we provide an overview of the political landscape presented by the Japanese public broadcaster NHK over a decade of the 7 PM news archives.
