Malignant peripheral nerve sheath tumor (MPNST) is an aggressive soft tissue sarcoma with limited therapeutic options and a poor prognosis. Although neurofibromatosis type 1 (NF1) and radiation exposure have been identified as risk factors for MPNST, the genetic and molecular mechanisms underlying MPNST pathogenesis have only lately been roughly elucidated. Plexiform neurofibroma (PN) and atypical neurofibromatous neoplasm of unknown biological potential (ANNUBP) are novel concepts of MPNST precancerous lesions, which revealed sequential mutations in MPNST development. This review summarized the current understanding of MPNST and the latest consensus from its diagnosis to treatment, with highlights on molecular biomarkers and targeted therapies. Additionally, we discussed the current challenges and prospects for MPNST management.
Traumatic neuromas are infrequent in clinical settings but are prevalent following trauma or surgery. A traumatic neuroma is not a true malignancy, rather, it is a hyperplastic, reparative nerve reaction after injury and typically manifests as a nodular mass. The most common clinical manifestations include painful hypersensitivity and the presence of a trigger point that causes neuralgic pain, which could seriously decrease the living standards of patients. While various studies are conducted aiming to improve current diagnosis and management strategies via the induction of emerging imaging tools and surgical or conservative treatment. However, researchers and clinicians have yet to reach a consensus regarding traumatic neuromas. In this review, we aim to start with the possible underlying mechanisms of traumatic neuromas, elaborate on the diagnosis, treatment, and prevention schemes, and discuss the current experiment models and advances in research for the future management of traumatic neuromas.
As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.
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