Safety and effectiveness of high-dose vitamin C in patients with COVID-19: a randomized open-label clinical trial

Background: Vitamin C is an essential water-soluble nutrient that functions as a key antioxidant and has been proven to be effective for boosting immunity. In this study, we aimed to assess the efficacy of adding high-dose intravenous vitamin C (HDIVC) to the regimens for patients with severe COVID-19 disease. Methods: An open-label, randomized, and controlled trial was conducted on patients with severe COVID-19 infection. The case and control treatment groups each consisted of 30 patients. The control group received lopinavir/ritonavir and hydroxychloroquine and the case group received HDIVC (6 g daily) added to the same regimen. Results: There were no statistically significant differences between two groups with respect to age and gender, laboratory results, and underlying diseases. The mean body temperature was significantly lower in the case group on the 3rd day of hospitalization (p = 0.001). Peripheral capillary oxygen saturations (SpO2) measured at the 3rd day of hospitalization was also higher in the case group receiving HDIVC (p = 0.014). The median length of hospitalization in the case group was significantly longer than the control group (8.5 days vs. 6.5 days) (p = 0.028). There was no significant difference in SpO2 levels at discharge time, the length of intensive care unit (ICU) stay, and mortality between the two groups. Conclusions: We did not find significantly better outcomes in the group who were treated with HDIVC in addition to the main treatment regimen at discharge. Trial registration irct.ir (IRCT20200411047025N1), April 14, 2020 © 2021, The Author(s)

The Visual Object Tracking VOT2013 challenge results

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge. net

The Visual Object Tracking VOT2013 challenge results

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge. net