Background: Acute ischemic stroke (AIS) is closely related to the level of inflammatory factors. This study aimed to explore the correlation between interleukin-6 (IL-6), interleukin-8 (IL-8), and the modified early warning score (MEWS) of AIS patients and their condition and prognosis.
Methods:The clinical data of 95 AIS patients admitted to our hospital from January 2019 to October 2019 were selected, and 91 cases were finally recruited to the study group according to the inclusion and exclusion criteria. A control group was recruited comprising 70 healthy patients. The differences in IL-6 and IL-8 levels between the 2 groups were compared. Multiple logistic regression analysis was used to analyze the independent risk factors affecting the prognosis of AIS patients. A receiver-operating characteristic (ROC) curve was used to analyze the predictive value of IL-6, IL-8, and MEWS for the poor prognosis of AIS patients.Results: The levels of IL-6 and IL-8 in the study group were higher than those of the control group (P<0.05). After 90 days of treatment, 69 cases in the study group allocated into the good prognosis group, and 22 were allocated into the poor prognosis group. The National Institutes of Health Stroke Scale (NIHSS) scores before thrombolysis, blood glucose before thrombolysis, systolic blood pressure 2 h after thrombolysis, IL-6, IL-8, and MEWS scores within 24 h of admission in the good prognosis group were lower than those of the poor prognosis group (P<0.05). The area under the curve (AUC) of IL-6, IL-8, MEWS, and the 3 combined curves were 0.937, 0.897, 0.839, and 0.976, respectively, and the area under the combined detection curve was the largest.
Conclusions:The inflammatory response and secondary brain damage after AIS are influenced by IL-6 and IL-8. Combined with the MEWS score, IL-6 and IL-8 can be used as important indicators to judge the severity of the early condition of AIS patients. The combination of these 3 indicators has high accuracy in evaluating the prognosis of patients and is worthy of clinical promotion.
Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers’ attention for their excellent generalization performance. They usually select the same class of support features according to the query labels to weight the query features. However, the model cannot possess the ability of active identification only by using the same category support features, and feature selection causes difficulties in the testing process without labels. The single-scale feature of the model also leads to poor performance in small object detection. In addition, the hard samples in the support branch impact the backbone’s representation of the support features, thus impacting the feature weighting process. To overcome these problems, we propose a multi-scale feature fusion and attentive learning (MSFFAL) framework for few-shot object detection. We first design the backbone with multi-scale feature fusion and channel attention mechanism to improve the model’s detection accuracy on small objects and the representation of hard support samples. Based on this, we propose an attention loss to replace the feature weighting module. The loss allows the model to consistently represent the objects of the same category in the two branches and realizes the active recognition of the model. The model no longer depends on query labels to select features when testing, optimizing the model testing process. The experiments show that MSFFAL outperforms the state-of-the-art (SOTA) by 0.7–7.8% on the Pascal VOC and exhibits 1.61 times the result of the baseline model in MS COCO’s small objects detection.
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