Intracellular calcium (Ca²⁺) serves as a ubiquitous second messenger, governing diverse cellular functions such as muscle contraction, signal transduction, secretion, gene expression, and cell death. The spatiotemporal profiles of Ca²⁺ signals vary widely with cell type, mode of stimulation, and subcellular domain. Ca²⁺ imaging permits direct visualization of these dynamics and is an indispensable approach for monitoring cellular physiology. This article outlines fundamental approaches for Ca²⁺ imaging in cultured cells. Two principal classes of fluorescent Ca²⁺ indicators—chemical dyes and genetically encoded sensors—are introduced, with brief explanation of their operating principles. Mechanisms underlying Ca²⁺ signal generation in response to extracellular cues are described, together with their manifestation as organized patterns such as Ca²⁺ waves and oscillations. Key aspects of image analysis and signal quantification are addressed, including definition of regions of interest, normalization of fluorescence signals, and distinction between local and global responses. Particular attention is given to the interpretation of Ca²⁺ dynamics in the context of cellular function. Ca²⁺ imaging reveals cellular activity beyond membrane excitability, providing valuable insights in both basic research and biomedical applications. This concise guide is intended for researchers, students, and clinicians seeking to implement in vitro Ca²⁺ imaging for the study of cellular physiology.

Background: Advances in machine learning have facilitated the development of automated diagnostic systems in medicine. Periodontal disease, a common condition in adults, requires early diagnosis and treatment. This study investigates the use of convolutional neural networks (CNNs) for automated image analysis to support periodontal disease diagnosis.

Methods: A dataset of 300 standardized frontal intraoral grayscale images (128 × 128 pixels) was used, including 150 from healthy individuals and 150 from patients with periodontal disease. The CNN model utilized ReLU activation functions, followed by normalization and pooling layers, and Softmax for output. Training employed Softmax Cross Entropy as the loss function and the Adam optimizer. Model performance was assessed using accuracy, with progress monitored by evaluating main loss reduction and validation data accuracy over 10 epochs. A 5-fold cross-validation further evaluated generalization.

Results: The model achieved generalization performance within 10 epochs without overfitting. The 5-fold cross-validation showed a mean accuracy of 93.67% (standard deviation: 4.31%). Using the trained model, test data evaluation yielded an accuracy of 95.55%, precision of 91.66%, recall of 1.0, and an F1 score of 95.65%. The high recall rate suggests a strong potential for reducing false negatives, which is critical in the early detection and timely intervention of periodontal disease.

Conclusion: These findings demonstrate the potential utility of CNN-based image analysis as a supportive diagnostic tool for the early detection of periodontal disease in clinical settings.

Purpose: While craniosynostosis has been linked to strabismus, the association between deformational plagiocephaly (DP) and strabismus in young children remains unclear. This study aimed to investigate the potential relationship between DP and strabismus in infants and toddlers, with a focus on the impact of cranial asymmetry.

Methods: We conducted a prospective cohort study involving 134 children aged 12-31 months, including 12 with strabismus and 122 controls. Clinical characteristics including gestational age, birth weight, and mode of delivery were recorded. Cranial morphology was assessed using three-dimensional scanning, and strabismus was evaluated using the Spot Vision Screener, with ophthalmological examination as the gold standard for diagnosis. Cranial asymmetry was quantified using the Cranial Vault Asymmetry Index (CVAI), Anterior Symmetry Ratio (ASR), and Posterior Symmetry Ratio (PSR). Statistical analyses were performed to compare demographic and cranial parameters between the two groups.

Results: There were no significant differences in age, sex, gestational age, birth weight, or cranial asymmetry parameters (CVAI, ASR, control) between the groups. However, the control group exhibited significantly higher DP severity than the strabismus group (p < 0.001). Among the children with strabismus, 5 had DP, of which 4 were classified as mild and 1 as moderate. No cases severe ASR were observed in either group.

Conclusions: This finding suggests that DP without severe ASR is not associated with an increased risk of strabismus in infants and toddlers. Future studies are warranted to explore the association between strabismus and DP in children with severe ASR.