Objective To investigate the CT and MRI findings of different types of small round cell tumor(SRCT)in the nasal cavity and sinuses.Methods A retrospective analysis was conducted on the imaging data and clinical data of 35 SRCT patients confirmed by pathology.Thirty-one SRCT patients underwent CT examination,and 19 SRCT patients underwent MRI examination.Results There were 20 cases of tumors that invaded the nasal cavity and 19 cases involved the sinuses,including 11 cases of the maxillary sinus,7 cases of the ethmoid sinus,2 cases of the sphenoid sinus,and 1 case of the frontal sinus.CT findings of SRCT were all soft tissue masses.Lymphoma was relatively homogeneous with mild bone destruction,and usually involved nasal vestibular skin.Rhabdomyosarcoma(embryonic type)happened at an early age and easily caused bone destruction and metastasis.Melanoma generally occurred in the nasal septum and nasal cavity,which was prone to bleeding.Small cell neuroendocrine carcinoma was heterogeneous,with moderate to significant enhancement,bone absorption and destruction were often noticed.The MRI manifestations of SRCT were equal or long signal on T1WI,high signal on T2WI,and significant diffusion limitation on diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)except for melanoma.On contrast-enhanced images,lymphoma showed mild to moderate enhancement,rhabdomyosarcoma showed typical"grape sign",and small cell neuroendocrine carcinoma showed"sieve"and"map-like"obvious enhancement.Typical melanoma showed a high signal on T1WI and a low signal on T2WI and usually caused bleeding.The MRI findings were related to the presence of melanoma and hemorrhage within the lesion.Conclusion SRCT of the nasal cavity and sinuses have a high degree of malignancy and poor prognosis,CT and MRI have many similar manifestations.Combining clinical data,bone destruction,MRI enhancement,and DWI sequence can effectively distinguish different types of SRCT,as well as squamous cell carcinoma of the nasal cavity and sinuses and adenoid cystic carcinoma.

BACKGROUND:The sports medicine community has widely called for the use of machine learning technology to efficiently process the huge and complicated sports data resources,and construct intelligent sports injury prediction models,enabling accurate early warning of sports injuries.It is of great significance to comprehensively summarize and review such research results so as to grasp the direction of early warning model improvement and to guide the construction of sports injury prediction models in China. OBJECTIVE:To systematically review and analyze relevant research on sports injury prediction models based on machine learning technology,thereby providing references for the development of sports injury prediction models in China. METHODS:Literature search was conducted on CNKI,Web of Science and EBSCO databases,which mainly searched for literature related to machine learning techniques and sports injuries.Finally,61 articles related to sports injury prediction models were included for analysis. RESULTS AND CONCLUSION:(1)In terms of external risk feature indicators,there is a lack of competition scenario indicators,and the inclusion of related feature indicators needs to be further improved to further enrich the dimensions of the dataset for model training.In addition,the inclusion feature weighting methods of the sports injury prediction model are mainly based on filtering methods and the use of embedding and wrapping weighting methods needs to be strengthened in order to enhance the analysis of the interaction effects of multiple risk factors.(2)In terms of model body training,supervised learning algorithms become the mainstream choice.Such algorithms have higher requirements for the completeness of sample labeling information,and the application scenarios are easily limited.Therefore,the application of unsupervised and semi-supervised algorithms can be increased in the later stage.(3)In terms of model performance evaluation and optimization,the current studies mainly adopt two verification methods:HoldOut crossover and k-crossover.The range of AUC values is(0.76±0.12),the range of sensitivity is(75.92±11.03)%,the range of specificity is(0.03±4.54)%,the range of F1 score is(80.60±10.63)%,the range of accuracy is(69.96±13.10)%,and the range of precision is(70±14.71)%.Data augmentation and feature optimization are the most common model optimization operations.The accuracy and precision of the current sports injury prediction model are about 70%,and the early warning effect is good.However,the model optimization operation is relatively single,and data augmentation methods are often used to improve model performance.Further adjustments to the model algorithm and hyperparameters are needed to further improve model performance.(4)In terms of model feature extraction,most of the internal risk profile indicators included are mainly based on anthropometrics,training load,years of training,and injury history,but there is a lack of sports recovery and physical function indicators.

ObjectiveTo investigate the difference in bilateral lower limb muscle synergy mode during gait in patients after unilateral anterior cruciate ligament reconstruction. MethodsElectromyography from bilateral lower limb muscles during gait were collected from twelve male and eight female patients after unilateral anterior cruciate ligament reconstruction in Affiliated Hospital of Wuhan Sports University, from April to June, 2023. The data were analyzed using non-negative matrix decomposition algorithm to extract the number of muscle synergies in the affected and unaffected legs, the time to peak activation of muscle synergies and the relative weights of the muscles. ResultsSix types of muscle synergy were identified in the unaffected leg of males during gait, while five types were identified in the affected leg, lacking synergy 2 that mainly from the tibialis anterior muscle. Six types of muscle synergy were identified in both legs in females during gait. There was no significant difference in the time to peak activation of muscle synergies between both legs in males (P > 0.05). However, the time to peak activation of muscle synergies increased in females in the affected leg for synergy 3 and synergy 5 (P < 0.05). The relative weight of the rectus femoris was lower in synergy 1 in the affected leg in males (P < 0.05). For female, the relative weight of the vastus lateralis was higher and the relative weight of the biceps femoris was lower in synergy 2 in the affected leg in females (P < 0.05); while the relative weight of the rectus femoris was lower in synergy 3 (P < 0.05), and the relative weight of the biceps femoris was lower in synergy 6 (P < 0.05). ConclusionMales would freeze the muscle synergy dominating ankle dorsiflexion in affected leg to enhance ankle stability, and reduce the relative weight of rectus femoris during the loading response phase to weaken the knee landing cushioning. However, females would delay the activation of synergies dominating in loading response phase and the mid-stance phase, enhance the relative weight of vastus lateralis during the loading response phase, and reduce the relative weights of rectus femoris in the loading response phase and the relative weight of biceps femoris in the mid-stance phase, to limit knee flexion.