Bhubaneswar Borooah Cancer Institute

ObjectiveTo describe the clinical and socio-demographic profile, as well as the induction outcomes, of paediatric patients with acute myeloid leukaemia (AML).MethodsA retrospective analysis was conducted of patients up to 18 years of age who presented between 1 January 2017 and 31 December 2021. Patients with a diagnosis of de novo AML confirmed by flow cytometry, or with extra-medullary soft tissue masses proven to be AML by immunohistochemistry, were included. All records were obtained from the Paediatric Oncology database and supplemented by treatment files.ResultsAmong 56 patients analyzed, the male-to-female ratio was 1.15. The median age was 7 years (range: 0.2–18 years). The white blood cell (WBC) count at presentation ranged from 1,250/μL to 296,000/μL (median: 29,000/μL). Sixty percent of patients were malnourished, and the majority belonged to lower socioeconomic strata. Fever was the most common presenting symptom (93%), followed by bleeding (61%), easy fatigability (53%), bone pain (46%), hepatosplenomegaly (39%) and lymphadenopathy (14%). According to European LeukaemiaNet (ELN) 2017 risk stratification, 52% of patients were categorized as favourable risk, 18% as intermediate risk and 28% as high risk; one patient could not be risk-stratified. Remission was achieved in 47% of patients, 23% did not achieve remission, 16% died during induction and 14% abandoned treatment.ConclusionDemographic variables, nutritional status, WBC count and clinical presentations were not associated with induction outcomes, possibly due to the availability of extensive supportive care services such as accommodation near the hospital, free food and treatment and psychosocial support in the paediatric oncology unit. In contrast, ELN risk categories were strongly associated with outcomes.

Esophageal carcinoma is an aggressive malignancy with multifactorial etiology. Even with radical surgery, a signicant percentage of patients with esophageal cancer can experience residual disease the impact of which can be determined by pathological evaluation of resected specimen. Aims: 1. To observe the pathological changes both macroscopically and microscopically in oesophagectomy specimen receiving NACT/NACRT. 2. To determine the proportion of PCR (Pathological Complete Response) in post NACT/NACRT (Neoadjuvant chemotherapy/Neoadjuvant chemoradiotherapy) oesophagectomy cases. Materials and Methods: A prospective study of 60 patients with esophageal carcinoma, operated between January 2024 and December 2024 is included. Clinical parameters such as tumour length, thickness, tumour site and histopathological parameters such as tumour grade, Lymphovascular invasion (LVI), Perineural invasion(PNI) is studied in relationship to the post NACT/NACRT oesophagectomy cases. Evaluation of tumour regression is done using Modied Ryan Score (Tumour Response Grade, TRG). The study has s Results: howed a male to female ratio of 1:5:1, with age ranging from 40 yrs to 74 yrs (median age is 57 yrs). The most common site involved is mid thoracic oesophagus with squamous cell carcinoma as the most common histology type. Also, it has been observed that PCR is not associated with tumour size, since in this study the largest tumour size is 6.5X2X0.1 cm but it is associated with PCR. LVI, PNI, was also noted in the study. Association of LVI doesnot any show any correlation with PCR, whereas PNI has seen to be associated with incomplete pathological response with a p value of 0.001. Also, patient having squamous cell carcinoma histological type has good prognosis than adenocarcinoma with a p-value of 0.0002. Preoperative Conclusions: CT/CTRT followed by surgery currently represents the standard approach for esophageal carcinoma. Assessment of tumor response to neoadjuvant treatment followed by surgery is done by using parameters like tumour site, length, thickness, LVI,PNI, ENE, grade. In our present study 40% cases showed complete pathological response.

Genotoxin exposure leads to DNA adduct formation, potentially causing mutations if unrepaired. Current DNA adductomics platforms or analytical workflows are limited by incomplete spectral libraries, reliance on experimentally validated adducts, limited cellular contexts, and inefficient computational methodologies. We introduce MutAIverse, an advanced AI-driven DNA adductomics analysis platform that overcomes these limitations by leveraging intracellular mechanistic modeling of genotoxin bioactivation to construct a comprehensive DNA adduct library and offers advanced spectral mapping. MutAIverse integrates experimentally validated and chemically valid putative DNA adducts, enabling interpretable retrograde tracking to parental genotoxins. Validation of MutAIverse against experimental MS/MS datasets demonstrated the detection of both known and novel adducts. Furthermore, application to in-house generated DNA adductomics data from tissue biopsies of smokeless tobacco-induced head and neck cancer patients revealed selective enrichment of both novel and known DNA adducts; a subset of them was validated using MS/MS analysis. Collectively, MutAIverse provides a robust, end-to-end, and interpretable platform for advanced DNA adductomics analysis.

Respiratory diseases, including COVID-19 and related pulmonary conditions, often manifest with complex lung abnormalities such as ground-glass opacities, consolidations, and crazypaving patterns, which pose significant diagnostic challenges. This study presents a novel automated detection framework that integrates the Convolutional Block Attention Module (CBAM) into YOLOv8s, a state-of-the-art object detection algorithm, to enhance the analysis of high-resolution computed tomography images. The incorporation of CBAM enables YOLOv8s to refine feature extraction and classification processes, ensuring precise localization of subtle and overlapping lesions that are typically difficult to discern. Comparative analysis against YOLOv5 and YOLOv7 demonstrates the superiority of this approach. The CBAM-augmented YOLOv8s achieves a mean average precision (mAP) of 65.02% and mAP@0.5:0.95 of 48%, significantly outperforming the standalone YOLOv8s baseline, which achieved an mAP of 41%. The research highlights the transformative potential of attention mechanisms in medical imaging, offering an innovative and robust solution for improving diagnostic accuracy and efficiency in respiratory disease management. By addressing key challenges in lesion detection, this approach has the potential to advance clinical workflows and improve patient outcomes.