Objective This study aimed to compare the results of the pectoralis major myocutaneous (PMM) flap in primary and salvage head and neck cancer surgery. Methods A total of 160 patients were enrolled in this study. The salvage group consisted of 30 patients who received immediate PMM flap surgery following free flap failure. In the primary group, the PMM flap was primarily chosen for 130 patients. Related information was collected and analysed. The University of Washington (UW)-Quality of Life questionnaire, version 4, was mailed to every patient. Results Partial necrosis was significantly lower in the primary group (n = 13, 10.0%) than in the salvage group (n = 7, 23.3%). Surgical site infection was found in 10 (7.8%) patients in the primary group and in six (20.0%) patients in the salvage group. The mean composite quality of life scores were 66.8 ± 20.5 and 66.2 ± 22.1 in the two groups, respectively. Differences in scores for domains of activity, mood, and anxiety were significant. Disease-specific survival and recurrence-free survival rates were not different between the two groups. Conclusion PMM flap salvage reconstruction has a higher complication rate and poorer functional results, but similar survival prognosis, compared with primary surgery.
Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments.
Background. Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Successful treatment of CRC relies on accurate early diagnosis, which is currently a challenge due to its complexity and personalized pathologies. Thus, novel molecular biomarkers are needed for early CRC detection. Methods. Gene and microRNA microarray profiling of CRC tissues and miRNA-seq data were analyzed. Candidate microRNA biomarkers were predicted using both CRC-specific network and miRNA-BD tool. Validation analyses were carried out to interrogate the identified candidate CRC biomarkers. Results. We identified miR-451a as a potential early CRC biomarker circulating in patient’s serum. The dysregulation of miR-451a was revealed both in primary tumors and in patients’ sera. Downstream analysis validated the tumor suppressor role of miR-451a and high sensitivity of miR-451a in CRC patients, further confirming its potential role as CRC circulation biomarker. Conclusion. The miR-451a is a potential circulating biomarker for early CRC diagnosis.
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