Accurate quantification of wound closure in cell migration assays is crucial yet challenging. Still, existing methods often underperform due to omitting cell detection within the wound area, resulting in biased outcomes. We developed the CSMA standalone and ImageJ-compatible tool, which utilizes advanced image processing techniques, including contrast enhancement, edge detection, and morphological operations, to precisely identify and quantify cells in the wound region. CSMA offers user-friendly features and adjustable parameters to accommodate different imaging conditions, ensuring robust performance across diverse experimental setups. Validation against conventional tools confirms CSMA’s superior ability to delineate wound boundaries and provide accurate estimations of area and width at every time point. As applied to SW480-ADH colon cancer cells treated with various compounds, CSMA proves valuable in biomedical research. CSMA represents a significant advancement in wound healing assay analysis, providing researchers with a simple and reliable tool for studying cell migration dynamics with enhanced precision and reproducibility. CSMA is available as a standalone and ImageJ-compatible tool with its source code at https://github.com/AminaSagymbayeva/CSMA_WoundHealing.

Accurate quantification of wound closure in cell migration assays is crucial yet challenging. Still, existing methods often underperform due to omitting cell detection within the wound area, resulting in biased outcomes. To overcome this limitation, we developed the CSMA plugin for ImageJ. CSMA utilizes advanced image processing techniques, including contrast enhancement, edge detection, and morphological operations, to precisely identify and quantify cells in the wound region. The plugin offers user-friendly features and adjustable parameters to accommodate different imaging conditions, ensuring robust performance across diverse experimental setups. Validation against conventional tools confirms CSMA’s superior ability to delineate wound boundaries and provide accurate estimations of area and width at every time point. As applied to SW480-ADH colon cancer cells treated with various compounds, CSMA proves valuable in biomedical research. It represents a significant advancement in wound healing assay analysis, providing researchers with a simple and reliable tool for studying cell migration dynamics with enhanced precision and reproducibility. CSMA is available as an ImageJ plugin and source code at https://github.com/AminaSagymbayeva/CSMA_WoundHealing .

Environmental, Social, and Governance (ESG) metrics are increasingly used to assess corporate sustainability performance, yet their reliance on public disclosure raises questions about whether they capture actual organizational implementation. This study examines the alignment between externally reported ESG–Sustainable Development Goal (SDG) performance and internal perceptions in Kazakhstan, an emerging market characterized by uneven disclosure norms, institutional enforcement, and sectoral exposure. The analysis combines disclosure-based ESGQ scores with original survey data; matched comparisons between the two resulted in 52 responses from 25 firms. Using linear mixed models and principal component analysis, the study identifies a systematic disclosure–perception gap, in which internal perceptions exceed external disclosure-based scores in the governance and SDG dimensions, where standards are least codified and hardest to verify. Additionally, industry membership is more consistently associated with this disclosure–perception gap than ownership structure or firm size. The findings suggest that ESG measurement gaps in emerging markets are not reducible to greenwashing; instead, disclosure-based evaluation systems may under-represent practices that organizational insiders perceive as embedded, although positive response bias among respondents cannot be ruled out. The results support the idea that future research should complement disclosure-based ESG assessment with internal organizational evidence.