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38. Carcinosarcoma of breast – A chimera among breast neoplasms

Author

Vandana Tomey, Raj Kumar Ashwini Kumar, Pravin Nikhade, and Anil Akulwar

Subjects
breast cancer, carcinosarcoma, ductal carcinoma in situ, modified radical mastectomy, phylloides tumor, Medicine
Abstract

Malignant phyllodes, along with ductal carcinoma, is known as metaplastic cancer of the breast. This tumor is additionally known as breast carcinosarcoma. Malignant phyllodes in conjunction with ductal carcinoma is a rare finding in routine clinical practice. We describe the case report of a 47-year-old female patient who arrived with a large right breast mass. A core biopsy was performed, and histopathological examination indicated that the tumor was a malignant phyllodes tumor. A positron emission tomography (PET) scan revealed hyper-metabolic and hypo-metabolic tumors with perilesional stranding and satellite nodularity. There were a few metastatic right axillary nodes visible. There was no indication of distant metastases. Due to the presence of both components, a modified radical mastectomy surgery with axillary dissection was undertaken for this patient. Histopathological analysis of paraffin sections revealed ductal carcinoma in situ (DCIS) comedo-epithelial component and spindle-shaped cells with hyper-chromatic oval nuclei and scanty cytoplasm.

Published
2024
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39. Pseudorapidity Window Size Dependence of Multiplicity Fluctuations in High-Energy Collisions with System Size and Beam Energies

Author

Kumar, Ashwini, Devi, Gauri, Singh, B. K., Ali, Bushra, and Ahmad, Shakeel

Subjects
Nuclear Experiment, High Energy Physics - Phenomenology
Abstract

An investigation of the critical behavior of strongly interacting QCD matter has been performed by analyzing fluctuation observables on event-by-event (ebe) basis measured in high-energy collision experiments. The fluctuation analysis is performed using nuclear interactions at different target sizes and at different colliding beam energies as a function of varying width of pseudorapidity interval. For the sake of comparison, event-by-event multiplicity fluctuations in hadronic and heavy-ion collisions (p-H, p-A and A-B) are studied within the framework of the Lund Monte Carlo based FRITIOF model. Charged particle multiplicity and the variance of the multiplicity distribution are estimated for the interactions involving different target sizes and beam momenta i.e., p-H, p-CNO, p-AgBr at 200A GeV/c and $^{16}$O-AgBr collisions at 14.6, 60 and 200A GeV/c. Further, multiplicity fluctuations are quantified in terms of intensive quantity, the scaled variances $\omega$ and the strongly intensive quantity $\Sigma_{FB}$ derived from the charged particle multiplicity and the width of the multiplicity distribution. Strongly intensive quantity $\Sigma_{FB}$ is a quantity of great significance to extract information about short and long-range multiplicity correlations. Furthermore, the collision centrality and centrality bin width dependent behavior of the multiplicity fluctuation have been examined in the framework of Lund Monte Carlo based FRITIOF model. Results based on the fluctuation analysis carried out in the present study are interpreted in terms of dynamics of collision process and the possibility of related QCD phase transition., Comment: 33 pages, 14 figures, published in Int. J. Mod. Phy. E Vol. 31, 2250056 (2022)

Published
2022
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48. Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

Author

Kumar Ashwini, Briti Sundar Sil, Abdulla Al Kafy, Hamad Ahmed Altuwaijri, Hrithik Nath, and Zullyadini A. Rahaman

Subjects
machine learning, urban microclimate, heatwave, thermal comfort, artificial neural network, environmental management, Agriculture
Abstract

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Published
2024
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