ABSTRACT
The East Kolkata Wetlands (EKW) in Kolkata, India, span 12,500 ha and are a vital ecological zone providing several benefits, including water purification, flood control, and biodiversity support. This study investigated land use and land cover (LULC) alterations in the EKW from 1991 to 2023, using a random forest (RF) machine learning model. Significant LULC changes were observed over the 32 years, with wetland areas decreasing from 91.2 km2 in 1991 to 33.4 km2 in 2023, reflecting substantial habitat loss and reduced ecosystem services. Conversely, agricultural land expanded from 27.8 km2 to 58.7 km2, driven by economic and food production needs, and built-up areas increased dramatically from 0.2 km2 to 10.5 km2, indicating rapid urbanization. This study evaluated the health, resilience, and ecosystem functionality of EKW by analysing human-induced land use changes and using ecological indicators and landscape metrics. Landscape and class level metrics such as PLAND, largest patch index (LPI), total edge (TE), edge density (ED), number of patches (NP), and patch density (PD) were used to analyse the spatiotemporal dynamics of the wetlands. This study revealed a significant increase in fragmentation, with the number of patches increasing from 2689 in 1991 to 4532 in 2023, despite a consistent decrease in core wetland areas. Ecosystem health indicators, such as the ecosystem structure index (ESI) and landscape deviation degree (LDD), were used to assess landscape metrics and fragmentation changes. The ESI and other metrics revealed significant temporal fluctuations, providing insights into landscape structure, connectivity, and heterogeneity. The ESI improved from 0.87 in 1991 to 1.03 in 2023, indicating enhanced connectivity and diversity. Conversely, the LDD increased from 20.6% to 56.85%, indicating a shift towards impervious surfaces. The vegetation productivity and ecosystem health index (EHI) decreased, indicating biodiversity loss and reduced carbon sequestration. The EHI also dropped from 0.67 to 0.55, signifying ongoing environmental stress. This study emphasizes the need for conservation efforts to maintain the ecological integrity of the EKW amidst urbanization and land use changes and recommends a balanced approach for sustainable urban development and enhanced wetland resilience.
ABSTRACT
INTRODUCTION: The present histologic and immunohistochemical prognostic markers of breast carcinoma do not effectively identify the subset of patients with poor prognosis. Y-Box binding protein 1 (YB1) is a novel biomarker which may identify and aid in targeted personalized therapy for such patients. MATERIALS AND METHODS: The study was conducted on histopathology specimens of 74 patients of breast carcinoma who had undergone modified radical mastectomy. YB1 immunohistochemistry (IHC) was performed on manual tissue microarray blocks (each having 15 cores). The YB1 expression was quantified in terms of "immunoreactive score" which was correlated with clinical parameters, hormone receptor status, and Her2neu overexpression by IHC. The Her2neu status of the equivocal cases was further evaluated by fluorescent in situ hybridization (FISH). RESULTS: YB1 was positive in 36/74 (48.6%) cases. On IHC and analysis by FISH, 25/74 (34%) cases had Her2neu overexpression. Estrogen receptor (ER) and progesterone receptor (PR) positivity was found in 42% and 36.5% cases, respectively. YB1 immunopositivity was negatively correlated with ER and PR expression, but showed a significant positive correlation with Her2neu expression. No correlation was found with other clinical parameters, tumor stage, and grade, except lymph node involvement, which showed a positive association with YB1 expression. Triple-negative breast carcinoma constituted 25.6% of the total cases, out of which 73.6% were YB1 positive. CONCLUSION: This study found that YB1 has an association with Her2neu expression. It may in future provide a therapeutic target in Her2neu overexpressing tumors.
