Cancer remains a great challenge for mankind. The complex and diverse phenotypes, mechanisms, as well as therapeutic responsiveness of cancer have gradually been understood since the second half of the 20th century, which was a representative of entering the molecular era of oncology research. In 2000, the groundbreaking review “Hallmarks of Cancer” came out and it had been updated to the third version with 14 characteristics of cancer in 2022. These series of reviews helped to distil the increasingly complicated concepts of oncology into a logical science, which contributed to a more comprehensive understanding of the mechanisms for cancer genesis and development and applying the knowledges to the diagnosis and treatment of tumors. Mastering, applying, and exploring the hallmarks of cancer will help us face the challenges of cancer more confidently in the future.
This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers. A novel gesture recognition approach was proposed, which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers. The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach. Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input. Subsequently, a multi-view convolutional neural network model was constructed, utilizing asymmetric convolutional kernels of sizes 1 × n and 3 × n within the same layer to enhance the representation capability of surface electromyography signals. Experimental results showed that the proposed method achieved recognition accuracies of 98.11% for 13 gestures and 98.75% for 12 multi-finger movements, significantly outperforming existing machine learning approaches. The proposed gesture recognition method, based on surface electromyography grayscale images and multi-view convolutional neural networks, demonstrates simplicity and efficiency, substantially improving recognition accuracy and exhibiting strong potential for practical applications.
Postoperative gastrointestinal disorder (POGD) is a common complication after surgery under anesthesia. Strategies in combination with traditional Chinese medicine and Western medicine have shown some distinct effects but standardized clinical practice guidelines are not available. Thus, a multidisciplinary expert team from various professional bodies including the Perioperative and Anesthesia Professional Committees of the Chinese Association of Integrative Medicine (CAIM), jointly with Gansu Province Clinical Research Center of Integrative Anesthesiology/Anesthesia and Pain Medical Center of Gansu Provincial Hospital of Traditional Chinese Medicine and WHO Collaborating Center for Guideline Implementation and Knowledge Translation/Chinese Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) Center /Gansu Provincial Center for Medical Guideline Industry Technology/Evidence-based Medicine Center of Lanzhou University was established to develop evidence-based guidelines. Clinical questions (7 background and 12 clinical questions) were identified through literature reviews and expert consensus meetings. Based on systematic reviews/meta-analyses, evidence quality was analyzed and the advantages and disadvantages of interventional measures were weighed with input from patients’ preferences. Finally, 20 recommendations were developed through the Delphi-based consensus meetings. These recommendations include disease definitions, etiologies, pathogenesis, syndrome differentiation, diagnosis, and perioperative prevention and treatment.