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Risk factor of postoperative pulmonary complications after colorectal cancer surgery: an analysis of nationwide inpatient sample
Alterations of triple network dynamic connectivity and repetitive behaviors after mini-basketball training program in children with autism spectrum disorder
Research on credit risk of listed companies: a hybrid model based on TCN and DilateFormer
AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization
Numerical investigation of coal pillar damage mechanisms for various width-to-height ratios
A novel approach for managing the incisions of tibial plateau fractures with soft tissue swelling
Abstract To investigate the feasibility and clinical efficacy of a novel approach to managing the incisions used to treat tibial plateau fractures (TPFs) with soft tissue swelling. We retrospectively enrolled 64 patients with TPFs who underwent surgery at the Second Hospital of Shandong University. Patients were divided into two groups: Group A (n = 32) underwent early surgery with the novel incision management technique, and Group B (n = 32) underwent conventional surgery after swelling reduction. The perioperative data of the two groups were compared, including the time from injury to surgery, one-stage operation time, intraoperative blood loss, number of dressing changes, wound healing time, and hospitalization time. Preoperative and postoperative complications were assessed in both groups, and pain condition, degree of arthritis, limb function, imaging results, and quality of life were evaluated using validated scales. The time from injury to surgery, number of dressing changes, and hospitalization time in Group A were significantly lower than those in Group B (P < 0.05). There were no significant differences in one-stage operation time, intraoperative blood loss, or wound healing time between the two groups (P > 0.05). There were fewer preoperative and postoperative complications in Group A than in Group B (P < 0.05). The VAS and WOMAC scores were reduced in both groups (P < 0.05); Group A had lower VAS scores two weeks after surgery. There was no statistically significant difference in the WOMAC score between the groups. The modified Rasmussen functional and radiological scores were elevated in both groups (P < 0.05). There was no statistically significant difference between the two groups for the modified Rasmussen functional or radiological score at any of the time points (P > 0.05). In addition, the two groups did not differ in quality of life (P > 0.05). For patients with tibial plateau fractures without congestive blisters, open reduction and internal fixation at the early stage of swelling and wide-spacing interrupted suture and negative pressure wound therapy (NPWT) closure of the wound could help obtain excellent to good functional outcomes with fewer complications. This novel incision management approach and concept expands the surgical indications for these fractures.
Enhancing concealed object detection in active THz security images with adaptation-YOLO
Abstract The terahertz (THz) security scanner offers advantages such as non-contact inspection and the ability to detect various types of dangerous goods, playing an important role in preventing terrorist attacks. We aim to accurately and quickly detect concealed objects in THz security images. However, current object detection algorithms face many challenges when applied to THz images. The main reasons for the detection difficulty are that the concealed objects are small, the image resolution is low, and there is back-ground noise. Many methods often ignore the contextual dependency of the objects, hindering the effective capture of the object’s features. To address this task, this paper first proposes an adaptive context-aware attention network (ACAN), which models global contextual association features in both spatial and channel dimensions. By dynamically combining local features and their global relationships, contextual association information can be obtained from the input features, and enhanced attention features can be achieved through feature fusion to enable precise detection of concealed objects. Secondly, we improved the adaptive convolution and developed the dynamic adaptive convolution block (DACB). DACB can adaptively adjust convolution filter parameters and allocate the filters to the corresponding spatial regions, then filter the feature maps to suppress interference information. Finally, we integrated these two components to YOLOv8, resulting in Adaptation-YOLO. Through wide-ranging experiments on the active THz image dataset, the results demonstrate that the suggested method effectively improves the accuracy and efficiency of object detectors.
Sleep and cardiorespiratory function assessed by a smart bed over 10 weeks post COVID-19 infection
CuTCP: Custom Text Generation-based Class-aware Prompt Tuning for visual-language models
A biomechanical study of locking spongious screws and failure rates are higher than expected in plate fixation
PLK3 weakens antioxidant defense and inhibits proliferation of porcine Leydig cells under oxidative stress
Sustainable application of waste gangue mortar in coal mine tunnel support
Community science as a potential tool to monitor animal demography and human-animal interactions
Renal anemia and hyporesponsiveness to ESA for preservation of residual kidney function in patients undergoing peritoneal dialysis
Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing
Abstract Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback (and potentially additional data on drugs and diseases) has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. In particular, an uncontrolled bias on unknown associations might lead to severe data leakage and a misestimation of the model’s true performance. Moreover, in drug repurposing, the ability of a model to extrapolate beyond its training distribution is crucial and should also be assessed. Finally, we identified a subcategory of collaborative filtering that seems efficient and robust to distribution shifts. Benchmarks constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods.