Roller skiing is one primary form of training method as it is an off-snow equivalent to cross-country (XC) skiing during the dry land preseason training, but the results could only be applied to on-snow skiing with appropriate caution. The aim of this present study was to investigate the similarities and differences in roller skiing and on-snow skiing with the diagonal stride (DS) technique. Six youth (age: 14.3 ± 2.9 years) skiers participated in this study. Two high-definition video camcorders and FastMove 3D Motion 2.23.3.3101 were used to obtain the three-dimensional kinematic data. The cycle characteristics and joint angle ROM of the DS technique while skiing on different surfaces were similar. Almost all joint angle–time curves that were obtained from roller skiing showed a moderate-to-high degree of similarity to the angle–time curves obtained from on-snow skiing, except the hip adduction–abduction angle. The differences between roller skiing and on-snow skiing were mainly found in the body and calf anteversion angles, and the joint angles at critical instants. DS roller skiing can simulate DS on-snow skiing to a large extent in youth athletes. The hip movement, knee flexion, and calf anteversion at ski/roller ski touchdown and take-off, pole inclination at pole touchdown, body anteversion angle, and trunk anteversion angle at pole touchdown were the points that required caution when transferring preseason practice roller skiing to on-snow skiing.

Morphology and cortical thickness of tibia bone influence the strength and strain distribution of bone and also influence fatigue fracture risk. However, current studies have not extensively explored the effect of morphology and cortical thickness on tibial strain distribution during different activities. This study aims to assess the effect of tibial morphology and cortical thickness on tibial strain during six different sports movements. The tibial surfaces were reconstructed from 40 males' CT data, with cortical thickness assessed at the outer surface. A statistical shape model captured main variations in tibial morphology and cortical thickness. Finite Element models were created by scaling the mean shape along the first four principal components. Muscle and joint forces from different activities were calculated using static optimization and joint reaction analysis and applied to the models, assessing strained volume and peak strain at middle and distal tibia. The first four principal components accounted for 87 % of the total cumulative variance. Perturbations in the second principal components resulted in the greatest relative differences in peak mid-tibia tensile (128 %) and distal-tibia compressive (160 %) strain during sidestep cutting, but perturbations in the first principal components resulted in the greatest relative differences during other activities (70 %∼118 %, 107 %∼129 %). Perturbations in the first four principal components resulted in the small relative differences in strained volume during walking (-9%∼5%). For runners, tibial size and cortical thickness are more related to tibial fatigue fracture risk, whereas for athletes with frequent directional changes, like basketball players, the tibial shaft size is more relevant.

Squamous cell carcinoma of the head and neck is currently the eighth most common cancer worldwide. In recent years, medical researchers have proposed a promising immune checkpoint blockade treatment to treat the disease, however, only a small number of patients with advanced head and neck squamous cell carcinoma (about 13%) can benefit from immune checkpoint blockade therapy. Therefore, assessing the patient’s response to immune checkpoint blockade before treatment can help develop a treatment strategy. Tertiary lymphoid structures (TLSs) are complex structures that often appear around tumors, and their presence suggests strong immunoactivity and sensitivity to immunotherapy. Therefore, the presence or absence of TLSs is a valid indicator of the patient’s response to immune checkpoint blockade before surgery. At present, the detection methods for TLSs are all postoperative pathological examinations, but their speed is slow and aggressive. However, few studies have employed non-invasive methods to detect and assess the TLS status of cancer before surgery. In this paper, we propose a 3D convolutional neural network UDNet based on the original U-Net and Dense-Net, which can determine the presence of TLSs by using the patient’s contrast-enhanced CT image. At last, we verify the ability of the model to predict TLSs by comparing the predicting results of the model and the predicting results of three experienced clinicians. The results show that the accuracy of UDNet (72.7%) is much higher than the average accuracy (42.4%) predicted by the three experienced clinicians.