Basavatarakam Indo American Cancer Hospital and Research Institute

Biliary tract cancers (BTCs), including gallbladder cancer (GBC) and cholangiocarcinoma (CCA), are rare but aggressive malignancies with distinct molecular landscapes and poor prognoses. Genomic profiling has revealed significant molecular alterations, but the genomic landscape of BTC in the Indian population remains underexplored. This study aims to comprehensively characterize the mutation landscape of BTC in the Indian population. A total of 154 BTC cases, including 69 CCA and 85 GBC, were retrospectively analyzed using data collected from various targeted sequencing panels. Somatic mutations, copy number variations (CNVs), and gene fusions in key oncogenic and tumor suppressor genes were identified from these panel reports. Downstream analyses were performed to derive key biological insights, including pathway enrichment and mutual exclusivity and co-occurrence analyses of genomic alterations. TP53 was the most frequently mutated gene (53%), followed by KRAS (18%), ARID1A (9%), IDH1 (7%), and PIK3CA (7%). Recurrent amplifications were observed in MYC (12%) and ERBB2 (9%). Pathway enrichment analysis revealed significant dysregulation in the PI3K-AKT-mTOR, Notch, and Wnt/β-catenin signaling pathways. Notably, IDH1 mutations were primarily observed in CCA, while STK11 mutations were exclusive to GBC, highlighting distinct molecular characteristics between the two subtypes. PD-L1-negative tumors exhibited distinct genomic alterations, notably SMAD4 mutations, which were associated with reduced PD-L1 expression. This loss of SMAD4, involved in TGF-β signaling, could impair immune response regulation and facilitate immune evasion. This study provides a comprehensive molecular profiling of BTCs in the Indian population, revealing key genomic alterations, subtype-specific differences, and associations with immune features. The findings underscore the importance of molecular profiling in guiding personalized treatment strategies.

Radiotherapy (RT) is essential in pediatric cancer treatment and often requires complete immobility. In younger or noncompliant children, this is typically achieved through sedation or general anesthesia (GA), which raises concerns about acute complications and potential long-term neurodevelopmental effects. Despite widespread use, standardized anesthesia protocols for pediatric RT are lacking. To support the development of practice recommendations, the SIOPE Working Group on Pediatric Anesthesia in Radiation Therapy conducted a systematic literature review. Studies from Medline and Embase were reviewed (March-September 2024) according to PRISMA guidelines, focusing on sedation and GA in pediatric RT. Thirty-nine studies were included, mostly retrospective and of low to moderate quality. Considerable heterogeneity was observed in anesthetic techniques, staffing, and monitoring. Propofol-based sedation was most frequently reported, with favorable safety when delivered by experienced pediatric anesthetists. Complication rates varied widely and were often poorly defined. Additional concerns included long-term neurocognitive impact, vascular access, and procedural burden, especially in resource-limited settings. Evidence supports the safe use of sedation/GA in pediatric RT, but current literature is limited and inconsistent. Standardized protocols and prospective studies are urgently needed to better define safety, long-term outcomes, and staffing requirements.

Abstract Introduction/Objective Systemic inflammation plays a key role in cancer progression and recovery. Inflammatory markers such as neutrophil lymphocyte ratio (NLR), lymphocyte monocyte ratio (LMR) and platelet lymphocyte ratio (PLR), derived from routine tests like complete blood count have shown prognostic value. However, their potential to predict the length of stay in postoperative surgical intensive care unit (SICU) among cancer patients remains underexplored. This study aims to evaluate the correlation between postoperative NLR, LMR and PLR, and SICU stay duration in post surgical oncological patients. Methods/Case Report A retrospective observational study was conducted on 92 oncological patients admitted to the surgical ICU between July & October 2024 at Basavatarakam Indo-American Cancer Hospital and Research Institute, Hyderabad, India. All data was extracted from electronic medical records system named Basavatarakam Hospital Information Management (BHIM). Exclusion criteria included patients younger than 18 years, those with hematological malignancies, those who died within 48 hours of admission and those with insufficient laboratory data. Paired t-test was used to calculate the difference between preoperative and postoperative means of inflammatory markers. Correlation and linear regression analysis was performed to evaluate the relationship between hematological parameters and length of stay in the SICU. To assess the potential clinical utility of the whole blood cell indices, receiver operating characteristic (ROC) curve analysis was performed to determine optimal cut off values for prolonged SICU stay. Results We found that there were statistically significant changes in the mean difference postoperatively in all the three parameters with p < 0.001. Additionally, significant, although modest correlation was seen between NLR (r = 0.2600, p = 0.016), PLR ( r = 0.257, p = 0.016) and the duration of SICU stay in post surgical oncological patients. Conclusion Postoperative inflammatory markers such as NLR, PLR represent simple cost effective biomarkers that may help predict SICU length of stay in adult postsurgical oncological patients. While their prognostic value appears modest, their integration into clinical practice could contribute to improved risk stratification and perioperative management. However, prospective studies are required to confirm the findings and fully explore their potential in clinical practice.

Accurate prediction of reaction barriers and transition states is central to understanding electrolyte degradation pathways in battery systems, yet existing approaches face significant computational trade-offs. Classical quantum-chemistry methods like density functional theory (DFT) scale favorably but often miss crucial multi-reference character at transition states, while high-accuracy methods such as CCSD(T) become prohibitively expensive for realistic systems. Recent quantum algorithmic approaches, including Variational Quantum Eigensolver (VQE) and Quantum Subspace Diagonalization (QSD), offer pathways toward near-chemical accuracy but suffer from manual active-space selection, excessive measurement overhead, and lack of systematic refinement protocols. We introduce Adaptive Quantum Subspace Diagonalization (A-QSD), a comprehensive pipeline that addresses these limitations through automated chemically-aware active-space selection, compact matrix-element estimation via Hadamard test and classical shadows, and iterative quantum imaginary-time evolution (QITE) preconditioning. Targeting ethylene glycol oxidation pathways relevant to battery electrolyte stability, A-QSD achieves transition-state energy accuracy within 0.1 eV of high-level references while maintaining resource requirements of <=18 qubits and <=700 seconds runtime per reaction point. This work demonstrates the first systematic quantum approach for mapping realistic electrolyte reaction coordinates, establishing a practical framework for quantum-enhanced materials discovery in energy storage applications.