The identification of the actual capillary network in the liver is significant for advancing medical science because it can help elucidate how the liver grows and develops its internal complexity. Although the exact mechanism behind the formation of the liver’s unique 3D capillary network is not fully understood, it is crucial to accurately identify the actual capillary network in the liver to better understand this process. Previous research has involved the manual observation of a 3D skeletonized capillary network in very localized areas. However, wide areas have not been visualized and surveyed quantitatively due to the enormous time and effort required for the manual method. In this paper, we design and implement an algorithm to visualize a 3D skeletonized capillary network over a wide area of the liver.

The 3-D projective space is represented as a 3-D Euclidean space extended to infinity. From a projective geometric perspective, it is expected that all 3-D geometric elements derived from 4-D homogeneous processing will provide clearer understanding infinity within a 3-D projective space. In this paper, I present a unified representation of 3-D geometric element definitions and interferences using 4-D homogeneous processing, with a focus on understanding the concept of infinity in 3-D geometric elements. By investigating the geometric relations between geometric element interferences via planes in 3-D space, I explore the nature of geometric elements and infinity within 3-D projective space. Consequently, it was confirmed that 3-D geometric elements can be represented as projective lines and projective planes using 4-D homogeneous processing, allowing a consistent expression of generality, including infinity.

In our 2022 article, “Artificial Intelligence Analysis to Classify Electrogastrograms Before/After Meal Load” (Forma, Vol. 37, No. 2, pp. S23–S29, 2022), we would like to add the following relevant reference to the discussion of EGG classification before and after meal load. This addition clarifies the background of the approach discussed on p. S26 and should be added to the references on p. S29. This addendum does not alter the results or conclusions of the original article.

p. S29, References:

[18] Nakane, K., Ichikawa, K., Ono, R., Matsuura, Y., Takada, H. (2021). A Study of Classification for Electrogastrograms Before/After Caloric Intake Using Autoencoder. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Design Methods and User Experience. HCII 2021. Lecture Notes in Computer Science, vol. 12768. Springer, Cham. https://doi.org/10.1007/978-3-030-78092-0_41

We thank our readers for their continued interest in our work.

Understanding the spatial progression of liver fibrosis is crucial for evaluating the disease severity and therapeutic response. In this study, we developed a method for reconstructing three-dimensional (3D) histological images of fibrotic liver tissue from serial Azan-stained sections in a nonalcoholic steatohepatitis model rat. Quantitative analysis of the reconstructed volumes revealed a time-dependent increase in fibrotic areas. We applied mathematical growth models to describe the progression of fibrosis over time. Among several candidates, the Gompertz model was favored based on the Akaike Information Criterion, its corrected version, and the Bayesian Information Criterion, whereas the Root Mean Square Error suggested that the Generalized Logistic model had the best fit. Compared with random two dimensional sampling, 3D reconstructions showed reduced variability in fibrosis estimates, thereby supporting their robustness. These results demonstrate the utility of 3D reconstruction combined with statistical modeling for capturing fibrotic dynamics and offer a foundation for future diagnostic and therapeutic approaches.

This study evaluated stereopsis during stereoscopic video viewing and examined the effects of repeated visuospatial training on visual and neural functions. In Experiment 1, latency to stereopsis ranged from instantaneous to 3 min: about half of the participants achieved stereopsis within 15 s, while the remaining participants required longer, with 44.8% unable to achieve stereopsis within the 3-min window (defined as non-visible). In Experiment 2, short-term improvements in visual acuity and peripheral recognition were observed following daily 3-min training sessions, whereas sustained improvements required 6-min sessions. Training also increased β–γ band activity in the brain wave during arithmetic tasks, suggesting enhanced attentional resources. These findings underscore the importance of optimizing exercise intensity for the ciliary muscle group and support the potential applications of such training in educational and geriatric care contexts.