Medical services for sports injuries and illnesses in the Beijing 2022 Olympic Winter Games.

BACKGROUND: Beijing 2022 Olympic Winter Games was the second Games held amid the COVID-19 pandemic. To a certain extent, it has altered the way sporting activities operate. There is a lack of knowledge on injury risk and illness occurrence in elite winter sport athletes amid the COVID-19 pandemic. This study aimed to describe the incidence of injuries and illnesses sustained during the XXIV Olympic Winter Games in Beijing from February 4 to 20, 2022. METHODS: We recorded the daily number of injuries and illnesses among athletes reported by Beijing 2022 medical staff in the polyclinic, medical venues, and ambulance. We calculated injury and illness incidence as the number of injuries or illnesses occurring during competition or training, respectively, with incidence presented as injuries/illnesses per 100 athlete-days. RESULTS: In total, 2,897 athletes from 91 nations experienced injury or illness. Beijing 2022 medical staff reported 326 injuries and 80 illnesses, equaling 11.3 injuries and 2.8 illnesses per 100 athletes over the 17-day period. Altogether, 11% of the athletes incurred at least one injury and nearly 3% incurred at least one illness. The number of injured athletes was highest in the skating sports (n=104), followed by alpine skiing (n=53), ice track (n=37), freestyle skiing (n=36), and ice hockey (n=35), and was the lowest in the Nordic skiing disciplines (n=20). Of the 326 injuries, 14 (4.3%) led to an estimated absence from training or competition of more than 1 week. A total of 52 injured athletes were transferred to hospitals for further care. The number of athletes with illness (n=80) was the highest for skating (n=33) and Nordic skiing (n=22). A total of 50 illnesses (62.5%) were admitted to the department of dentistry/ophthalmology/otolaryngology, and the most common cause of illness was other causes, including preexisting illness and medicine (n=52, 65%). CONCLUSION: Overall, 11% of athletes incurred at least one injury during the Games, which is similar to the findings during the Olympic Winter Games in 2014 and 2018. Regarding illness, 2% of athletes were affected, which is approximately one-third of the number affected in the 2018 Olympic Winter Games.

Medical services for sports injuries and illnesses in the Beijing 2022 Olympic Winter Games / 世界急诊医学杂志（英文）

BACKGROUND: Beijing 2022 Olympic Winter Games was the second Games held amid the COVID-19 pandemic. To a certain extent, it has altered the way sporting activities operate. There is a lack of knowledge on injury risk and illness occurrence in elite winter sport athletes amid the COVID-19 pandemic. This study aimed to describe the incidence of injuries and illnesses sustained during the XXIV Olympic Winter Games in Beijing from February 4 to 20, 2022. METHODS: We recorded the daily number of injuries and illnesses among athletes reported by Beijing 2022 medical staff in the polyclinic, medical venues, and ambulance. We calculated injury and illness incidence as the number of injuries or illnesses occurring during competition or training, respectively, with incidence presented as injuries/illnesses per 100 athlete-days. RESULTS: In total, 2,897 athletes from 91 nations experienced injury or illness. Beijing 2022 medical staff reported 326 injuries and 80 illnesses, equaling 11.3 injuries and 2.8 illnesses per 100 athletes over the 17-day period. Altogether, 11％ of the athletes incurred at least one injury and nearly 3％ incurred at least one illness. The number of injured athletes was highest in the skating sports (n=104), followed by alpine skiing (n=53), ice track (n=37), freestyle skiing (n=36), and ice hockey (n=35), and was the lowest in the Nordic skiing disciplines (n=20). Of the 326 injuries, 14 (4.3％) led to an estimated absence from training or competition of more than 1 week. A total of 52 injured athletes were transferred to hospitals for further care. The number of athletes with illness (n=80) was the highest for skating (n=33) and Nordic skiing (n=22). A total of 50 illnesses (62.5％) were admitted to the department of dentistry/ophthalmology/otolaryngology, and the most common cause of illness was other causes, including preexisting illness and medicine (n=52, 65％). CONCLUSION: Overall, 11％ of athletes incurred at least one injury during the Games, which is similar to the findings during the Olympic Winter Games in 2014 and 2018. Regarding illness, 2％ of athletes were affected, which is approximately one-third of the number affected in the 2018 Olympic Winter Games.

Non-motor symptoms are associated with midline tremor in essential tremor.

OBJECTIVES: Essential tremor (ET) patients presenting tremor in the midline structures may be a distinct subtype of the syndrome. Therefore, we sought to explore the clinical manifestations, especially non-motor symptoms (NMS) of Chinese ET patients with midline tremor (MT). METHODS: In the cross-sectional study, we grouped 290 definite or probable ET patients based on their MT conditions. The NMS in ET patients were evaluated using the NMS scale (NMSS). NMS and other clinical correlates were then compared among subgroups with, and without MT. RESULTS: We revealed that 39.0%, 27.6%, and 6.9% of the patients respectively had neck, voice, and facial tremors. With the accumulation of tremor in midline structures, NMS became more severe and prevalent. Logistic regression analyses revealed that factors such as: female gender (OR = 2.164, 95% CI: 1.307-3.583), having least or highest action arm tremor (OR = 2.512, 95% CI: 1.520-4.151), having higher score of sleep/fatigue domain (OR = 1.692, 95% CI: 1.004-2.850) and mood/apathy (OR = 1.926, 95% CI: 1.143-3.246) domain, to be independently associated with MT manifestation. CONCLUSIONS: Our study demonstrates the heterogeneity of symptoms in ET patients with MT, especially in prominent NMS. In addition, the discrepancy of NMS between patients with, and without MT provides novel insight into the underlying pathophysiology and therapeutic of ET.

Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer's disease.

The study is aimed to assess whether the addition of contourlet-based hippocampal magnetic resonance imaging (MRI) texture features to multivariant models improves the classification of Alzheimer's disease (AD) and the prediction of mild cognitive impairment (MCI) conversion, and to evaluate whether Gaussian process (GP) and partial least squares (PLS) are feasible in developing multivariant models in this context. Clinical and MRI data of 58 patients with probable AD, 147 with MCI, and 94 normal controls (NCs) were collected. Baseline contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters based on MRI, and regional CMgl measurement based on fluorine-18 fluorodeoxyglucose-positron emission tomography were included to develop GP and PLS models to classify different groups of subjects. GPR1 model, which incorporated MRI texture features and was based on GPG, performed better in classifying different groups of subjects than GPR2 model, which used the same algorithm and had the same data as GPR1 except that MRI texture features were excluded. PLS model, which included the same variables as GPR1 but was based on the PLS algorithm, performed best among the three models. GPR1 accurately predicted 82.2% (51/62) of MCI convertors confirmed during the 2-year follow-up period, while this figure was 53 (85.5%) for PLS model. GPR1 and PLS models accurately predicted 58 (79.5%) vs. 61 (83.6%) of 73 patients with stable MCI, respectively. For seven patients with MCI who converted to NCs, PLS model accurately predicted all cases (100%), while GPR1 predicted six (85.7%) cases. The addition of contourlet-based MRI texture features to multivariant models can effectively improve the classification of AD and the prediction of MCI conversion to AD. Both GPR and LPS models performed well in the classification and predictive process, with the latter having significantly higher classification and predictive accuracies. Advances in knowledge: We combined contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters, and regional CMgl measurement to develop models using GP and PLS algorithms to classify AD patients.