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Aim A testing method for early diagnosis of Mild cognitive dementia (MCI) that can be easily applied in clinical practice was investigated in this study. We examined whether MCI risk can be determined through finger movements. Methods Between 2013 and 2020, 1097 individuals were screened. After applying propensity-score matching to adjust for variability between the groups, 173 individuals each in the mild cognitive impairment and control groups were selected. Thereafter, differences between groups in mean values of parameters extracted from finger tap movements were determined using unpaired t-test and effect size. Furthermore, area under the curve, sensitivity, and specificity were calculated from the receiver operating characteristic curve for parameters with significant difference. Results A significant difference was observed, especially in the number of taps in the MCI group compared with that in the control group ( p < .001; 95% CI, −12.7 to −8.8; r = 0.51). A cut-off value of 30 taps was applied (sensitivity, 0.77; specificity, 0.67; AUC, 0.79). Significant differences were also observed in rhythm-related parameters. Conclusions These parameters might be useful for capturing MCI risk. Finger taps are easily measured and may be suitable for screening large populations. This tool might be used as a supplemental method to increase the sensitivity of traditional cognitive tests.

Background Objective Methods Results Conclusion Oral and social problems can exacerbate long‐term care. Understanding the relationship between social aspects and oral hypofunction can help identify high‐risk factors for long‐term care.This study aimed to investigate the social aspects of oral hypofunction among medical outpatients.This retrospective cross‐sectional study included patients who visited an outpatient clinic for frailty. The oral function was assessed using seven items: oral hygiene, occlusal force, masticatory function, tongue‐lip motor function, tongue pressure, oral dryness and swallowing function. Participants with three or more functional declines were classified as having ‘oral hypofunction’. Social aspects were assessed using 21 items in four categories: general resources, basic social needs, social resources and social behaviours/activities. We analysed the relationship between oral hypofunction and each social aspect.A total of 316 participants (age 78.5 ± 6.4 years) were included, and 128 (41%) had oral hypofunction. Participants with oral hypofunction were significantly more likely to have limited education, require long‐term care, not use transportation, depend on others for shopping, not participate in events, lack association membership and not engage in charity or volunteer work. After logistic regression analysis, ‘long‐term care’ and ‘no association membership’ remained significantly associated with oral hypofunction (odds ratios 2.3, 2.3; 95% CI 1.1–5.0, 1.1–4.7, respectively).Participants with oral hypofunction faced challenges in ‘general resources’ and ‘social behaviour/activities’, which were linked to oral function issues. Future longitudinal studies are necessary to further investigate this relationship. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to clarify (1) the differences in cortical bone thickness (CBT) of the tibial diaphysis between healthy and osteoarthritic knees and (2) the influences of the femorotibial angle (FTA) and inclination of the medial compartment of the proximal tibia (MCT) on tibial CBT. Methods: The study assessed 60 subjects with varus knee osteoarthritis (OA) (22 males and 38 females; mean age, 74 ± 7 years) and 53 healthy elderly subjects (28 males and 25 females; mean age, 70 ± 6 years). Three-dimensional estimated CBT of the tibial diaphysis was automatically calculated for 2752–11,296 points using high-resolution measurements from CT. The standardized CBT was assessed in 24 regions by combining six heights and four areas. Additionally, the association between the CBT, each FTA, and MCT inclination was investigated. Results: The OA group showed a thicker CBT in the medial areas than in the lateral areas of the proximal tibia, while the healthy group had a thicker lateral CBT. The medial-to-lateral ratio of the proximal tibia was significantly higher in the OA group than in the healthy group. The proximal-medial CBT correlated with FTA and MCT inclinations in the OA group. Conclusions: This study demonstrated that varus osteoarthritic knees showed a different trend of proximal-medial CBT with associations in FTA and MCT inclination from healthy knees, possibly due to medial load concentration. [ABSTRACT FROM AUTHOR]

Traffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: The association of the combination of body mass index (BMI) and weight change at admission with prognoses in patients with heart failure (HF) is unclear. Therefore, we investigated whether BMI and weight changes at admission affect mortality in patients with HF. Methods: This retrospective cohort study lasted 99 months, starting in April 2014, and included 4,862 patients with HF from a Japanese real-world database. Cubic and thin-plate smoothing spline analyses were performed to investigate the association of BMI and weight changes with mortality. The percentage weight change was calculated every 6 months. The study outcome was the presence or absence of death. Results: The patients' mean age was 81.5±9.6 years, and 1,239 (25.5%) patients died. Cubic spline analysis revealed a negative correlation of BMI with mortality hazard ratio (HR) (BMI of 18.5 kg/m2 and 25 kg/m2; HR=1.3 [1.2-1.4] and 0.8 [0.7-0.9], respectively). Cubic spline analysis of weight change showed that weight loss tended to increase the mortality HR (each 6% decrease in weight change rate was associated with a 1.1 times higher mortality risk (95% CI [1.0-1.2]). Thin-plate smoothing spline analysis showed that the odds ratio (OR) negatively correlated with BMI (1-year mortality: BMI of 18.5 kg/m², 22 kg/m², and 25 kg/m²; OR at 0% weight change=1.5, 1.0, and 0.7, respectively; 2-year mortality: BMI=18.5 kg/m², 22 kg/ m², and 25 kg/m²; OR at 0% weight change=1.4, 0.9, and 0.7, respectively). Conclusion: A low BMI in patients with HF was associated with a higher risk of mortality. Weight loss in patients, regardless of BMI, was associated with a higher OR for mortality. [ABSTRACT FROM AUTHOR]

This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens. [ABSTRACT FROM AUTHOR]

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper. [ABSTRACT FROM AUTHOR]

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players' importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods. [ABSTRACT FROM AUTHOR]