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Aims: The application of indices in the context of metabolic dysfunction‐associated steatotic liver disease (MASLD) remains unexplored. We aimed to validate the ability of alanine aminotransferase (ALT), fatty liver index (FLI), and hepatic steatosis index (HSI) to identify MASLD during health checkups. Methods: We recruited 627 participants and utilized their health checkup data and ultrasound to assess the potential of using ALT, FLI, and HSI as indices for MASLD; this was indicated by the area under the curve (AUC) and restricted cubic spline (RCS) model. The optimal, rule‐out (sensitivity ≥90%), and rule‐in (specificity ≥90%) cutoff values of each index for identifying MASLD were reported. Results: Among participants with a median age of 46 years, the prevalence of MASLD was 28% in total (38% in males and 18% in females). RCS models confirmed a linear association between indices and MASLD. ROC analyses indicated that the AUC of ALT in identifying MASLD was 0.79 for the total cohort, 0.81 for males, and 0.69 for females. The optimal, rule‐out, and rule‐in cutoff values for ALT were 21, 13, and 29, respectively. Similarly, the AUC of FLI/HSI in identifying MASLD was 0.90/0.88 for the total cohort, 0.86/0.85 for males, and 0.93/0.90 for females. Considering the reference cutoff values, distinct cutoff values were observed between the sexes for FLI, while HSI had similar cutoff values. Conclusion: This study demonstrated that ALT > 30 IU/L is a reasonable cutoff value to rule‐in MASLD. ALT, FLI, and HSI are reliable indices for identifying MASLD during health checkups. [ABSTRACT FROM AUTHOR]

This study compared the effects of academic year, college department, and undergraduate or graduate status on Japanese students' mental health during the COVID-19 pandemic. From 2021–2023, an online survey was conducted using the Counseling Center Assessment of Psychological Systems-Japanese (CCAPS-Japanese) to evaluate students' mental health; 9395 undergraduate students (4623 female, 4772 male) and 1169 graduate students (380 female, 789 male) responded. Undergraduate students in medicine had lower levels of depression, generalized anxiety, and social anxiety than those in other departments. Engineering students exhibited the highest level of academic distress. First-year students had the highest levels of generalized and social anxiety but the lowest level of academic distress. Second-year students had the lowest level of depression, and third-year students had the highest level of academic distress. Among graduate students, first-year students had higher levels of depression, generalized anxiety, social anxiety, academic distress, and hostility than second-year students. Undergraduates had poorer mental health than graduate students. Females had higher levels of eating concerns than males among undergraduate students. This study revealed that the mental health of university students was affected by various factors. These findings demonstrate the characteristics of university students requiring early support. [ABSTRACT FROM AUTHOR]

We are exploring the use of semantic scene understanding in autonomous navigation for the Tsukuba Challenge. However, manually creating a comprehensive dataset that covers various outdoor scenes with time and weather variations to ensure high accuracy in semantic segmentation is onerous. Therefore, we propose modifications to the model and backbone of semantic segmentation, along with data augmentation techniques. The data augmentation techniques, including the addition of virtual shadows, histogram matching, and style transformations, aim to improve the representation of variations in shadow presence and color tones. In our evaluation using images from the Tsukuba Challenge course, we achieved the highest accuracy by switching the model to PSPNet and changing the backbone to ResNeXt. Furthermore, the adaptation of shadow and histogram proved effective for critical classes in robot navigation, such as road, sidewalk, and terrain. In particular, the combination of histogram matching and shadow application demonstrated effectiveness for data not included in the base training dataset. [ABSTRACT FROM AUTHOR]

This study focuses on visual navigation methods for autonomous mobile robots based on semantic segmentation results. The challenge is to perform the expected actions without being affected by the presence of pedestrians. Therefore, we implemented a semantics-based localization method that is not affected by dynamic obstacles and a direction change method at intersections that functions even with coarse-grain localization results. The proposed method was evaluated through driving experiments in the Tsukuba Challenge 2022, where a 290 m run including 10 intersections was achieved in the confirmation run section. [ABSTRACT FROM AUTHOR]

The most popular external sensor for robots capable of autonomous movement is 3D LiDAR. However, cameras are typically installed on robots that operate in environments where humans live their daily lives to obtain the same information that is presented to humans, even though autonomous movement itself can be performed using only 3D LiDAR. The number of studies on autonomous movement for robots using only visual sensors is relatively small, but this type of approach is effective at reducing the cost of sensing devices per robot. To reduce the number of external sensors required for autonomous movement, this paper proposes a novel visual navigation scheme using only a monocular camera as an external sensor. The key concept of the proposed scheme is to select a target point in an input image toward which a robot can move based on the results of semantic segmentation, where road following and obstacle avoidance are performed simultaneously. Additionally, a novel scheme called virtual LiDAR is proposed based on the results of semantic segmentation to estimate the orientation of a robot relative to the current path in a traversable area. Experiments conducted during the course of the Tsukuba Challenge 2019 demonstrated that a robot can operate in a real environment containing several obstacles, such as humans and other robots, if correct results of semantic segmentation are provided. [ABSTRACT FROM AUTHOR]

Conventional colonoscopy and barium enema are the main examinations for colonic lesions, but each of them has different limitations according to the condition of the patient. It has been reported that preparations for these examinations also caused complications, such as colonic perforation leading to fatality. To avoid these complications, colonic enema with water-soluble contrast medium (Gatrografin) has been performed as a screening method for those suspected to have obstructive colonic diseases, or those in whom it was difficult to be form colonoscopy and barium enema because of their poor condition. There are few reports about water-soluble contrast enema (WSCE). We retrospectively examined 121 cases of WSCE performed from January 2004 to December 2005 and assessed the acceptability of examination and its ability to detect colonic lesions. We divided our patients into five groups according to the reason for performing WSCE. In all cases, we were able to perform WSCE without complications and assess colonic lesions. In 58 cases, we compared the results of WSCE with those of barium enema or colonoscopy, or both In 8 cases we missed small colonic polyps and erosions, but we missed only one large polyp 3 cm in diameter, which we detected retrospectively. In conclusion, WSCE is safe and useful for the diagnosis of colonic disease, and may be one of the choices of colonic examination especially for those who are suspected to have obstructive colonic diseases or hemorrhagic lesions as well as for those in whom it is difficult to perform barium enema or colonoscopy.