Evaluation of non-cancer risk owing to groundwater fluoride and iron in a semi-arid region near the Indo-Bangladesh international frontier.

Groundwater quality in Hili, a semi-arid border region at Indo-Bangladesh border, was investigated in the post-monsoon season of 2021, succeeded by assessment of probabilistic health risk arising from fluoride (F-) and iron (Fe) intake, with the hypothesis that groundwater quality of the region was not satisfactory for human consumption and health, considering earlier reports on high groundwater F- and Fe in few of the neighboring districts. All water samples were found to be potable in terms of Ca2+, Mg2+, Cl-, SO42- and NO3-, , but F- and Fe exceeded prescribed safe limits for drinking water in about 48% and 7% samples. Almost all water samples were found to be good for irrigation in terms of sodium adsorption ratio (SAR), soluble sodium percentage (SSP), Kelly's index (KI), %Na and magnesium ratio (MR). The principal component analysis (PCA) identified three major factors influencing groundwater quality, explaining about 71.8% of total variance and indicated that groundwater quality was primarily influenced by geochemical factors. Carbonate and silicate weathering were mainly responsible for dissolution of minerals in groundwater. Non-carcinogenic risk due to cumulative impact of F-and Fe intake was in the order of THIChildren > THIInfant > THIAdult. As per Monte Carlo simulation run with 5000 trials to ascertain the order of probabilistic health risk, the most dominant governing factors behind non-carcinogenic risk caused by F-and Fe intake were their concentration (Ci) followed by ingestion rate (IR), and exposure duration (ED).

Adjuvant therapy may improve overall survival in high-risk periampullary adenocarcinomas patients - A match-pair analysis from a multi-institutional cohort study (The MIPPAP study).

BACKGROUND: The role of adjuvant therapy in resected periampullary adenocarcinomas is equivocal due to contrasting data and limited prospective trials. METHODS: The Multicentre Indian Pancreatic & Periampullary Adenocarcinoma Project (MIPPAP), included data from 8 institutions across India. Of the 1679 pancreatic resections, 736 patients with T3/T4 and/or Node positive adenocarcinomas (considered as high risk for recurrence) were included for analysis. Three (adjuvant): one (observation) matching, using T3/T4 T staging, nodal positivity and ampullary subtype was performed by using the nearest neighbour matching method. RESULTS: Of 736 patients eligible for inclusion, 621 patients were matched of which 458 patients received adjuvant therapy (AT) (predominantly gemcitabine-based) and 163 patients were observed (O). With a median follow-up of 42 months, there was a statistical difference in overall survival in favour of patients receiving AT as compared to those on observation [68.7 months vs. 61.1 months, Hazard ratio: 0.73 (95% CI: 0.54-0.97); p = 0.03]. Besides AT, presence of nodal involvement (median OS: 65.4 months vs not reached; p = 0.04) predicted for inferior OS. CONCLUSIONS: The results of the match-pair analysis suggest that adjuvant therapy improves overall survival in periampullary adenocarcinomas at high risk of recurrence with a greater benefit in T3/T4, node-positive and ampullary subtypes.

Cellular components of the hematopoietic niche and their regulation of hematopoietic stem cell function.

PURPOSE OF REVIEW: Development and functions of hematopoietic stem cells (HSC) are regulated by multiple cellular components of the hematopoietic niche. Here we review the recent advances in studying the role of three such components -- osteoblasts, osteomacs, and megakaryocytes and how they interact with each other in the hematopoietic niche to regulate HSC. RECENT FINDINGS: Recent advances in transgenic mice models, scRNA-seq, transcriptome profile, proteomics, and live animal imaging have revealed the location of HSC within the bone and signaling molecules required for the maintenance of the niche. Interaction between megakaryocytes, osteoblasts and osteomacs enhances hematopoietic stem and progenitor cells (HSPC) function. Studies also revealed the niche as a dynamic entity that undergoes cellular and molecular changes in response to stress. Aging, which results in reduced HSC function, is associated with a decrease in endosteal niches and osteomacs as well as reduced HSC--megakaryocyte interactions. SUMMARY: Novel approaches to study the cellular components of the niche and their interactions to regulate HSC development and functions provided key insights about molecules involved in the maintenance of the hematopoietic system. Furthermore, these studies began to build a more comprehensive model of cellular interactions and dynamics in the hematopoietic niche.

Clinicopathological characteristics, prognostic factors and treatment outcomes of seminomatous germ cell tumours from a tertiary cancer centre in eastern India.

Background: . Seminomatous germ cell tumour (SGCT) is a rare but curable malignancy of young adults. The literature on management and outcome of SGCT is scarce from India. We report the demography and treatment outcome of SGCT at our centre. Methods: . We did a retrospective analysis of patients with SGCT treated from March 2011 to December 2018. Patients were staged appropriately with imaging, and pre- and postoperative tumour markers. High inguinal orchiectomy was performed in all with a testicular primary and received subsequent stage-adjusted adjuvant treatment. Patients were monitored for metabolic syndrome during follow-up after completion of treatment. Results: . We treated 85 patients with a median age of 37 (range 20-68) years. The primary site of the tumour was the testis in 80 (94%) and mediastinum in 5 (6%) patients. Cryptorchidism was present in 20 (25%) patients and testicular violation was present in 11 (14%) patients. Stage of the disease was I in 61, II in 13 and III in 6 patients. Adjuvant treatment in stage I disease was single-agent carbo-platin (area under the curve ×7) in 38 (62%), surveillance in 20 (33%) and radiotherapy in 3 (5%) patients. Five patients in the surveillance group relapsed. The 7-year mean (SD) relapse-free survival and overall survival were 83.1% (8%) and 98.7% (1.3%), respectively. Thirty-one patients (n = 52, 60%) had features of metabolic syndrome. Conclusions: . SGCTs have a high cure rate. Long-term follow-up is essential for monitoring toxic effects. Early diagnosis, avoidance of testicular violation and multidisciplinary management are the key features for better long-term outcome in SGCT.

COVID-19 and the Paris Agreement target: A CGE analysis of alternative economic recovery scenarios for India.

In this paper the Covid-19 pandemic has been analysed from sustainability and climate change perspectives with the help of a recursive dynamic CGE model for India. The Covid-19 could have major long term impacts on GDP, household income, inequality, CO2 emissions, and carbon prices. Significant slowdown in labour intensive informal sectors such as construction and services, as well as in energy intensive and capital goods sectors, leads to adverse impacts on household income and inequality. Our analysis further suggests that climate policy consistent with the Paris Agreement target can complement the economic recovery process. Specifically, recycling of carbon tax revenues to investments could stimulate growth and employment, reduce inequality, and reduce carbon emissions, compared to a scenario without climate policy. Therefore, the need of the hour is to formulate and implement climate friendly recovery strategies.

CD166 Engagement Augments Mouse and Human Hematopoietic Progenitor Function via Activation of Stemness and Cell Cycle Pathways.

Hematopoietic stem (HSC) and progenitor (HPC) cells are regulated by interacting signals and cellular and noncellular elements of the hematopoietic niche. We previously showed that CD166 is a functional marker of murine and human HSC and of cellular components of the murine niche. Selection of murine CD166+ engrafting HSC enriched for marrow repopulating cells. Here, we demonstrate that CD166-CD166 homophilic interactions enhance generation of murine and human HPC in vitro and augment hematopoietic function of these cells. Interactions between cultured CD166+ Lineage- Sca-1+ c-Kit+ (LSK) cells and CD166+ osteoblasts (OBs) significantly enhanced the expansion of colony-forming units (CFUs). Interactions between CD166+ LSK cells and immobilized CD166 protein generated more CFU in short-term cultures than between these cells and bovine serum albumin (BSA) or in cultures initiated with CD166- LSK cells. Similar results were obtained when LSK cells from wildtype (WT) or CD166 knockout (KO) (CD166-/- ) mice were used with immobilized CD166. Human cord blood CD34+ cells expressing CD166 produced significantly higher numbers of CFUs following interaction with immobilized CD166 than their CD166- counterparts. These data demonstrate the positive effects of CD166 homophilic interactions involving CD166 on the surface of murine and human HPCs. Single-cell RNA-seq analysis of CD150+ CD48- (signaling lymphocyte activation molecule (SLAM)) LSK cells from WT and CD166-/- mice incubated with immobilized CD166 protein revealed that engagement of CD166 on these cells activates cytokine, growth factor and hormone signaling, epigenetic pathways, and other genes implicated in maintenance of stem cell pluripotency-related and mitochondria-related signaling pathways. These studies provide tangible evidence implicating CD166 engagement in the maintenance of stem/progenitor cell function. Stem Cells 2019;37:1319-1330.

Tamm-Horsfall Protein Regulates Mononuclear Phagocytes in the Kidney.

Tamm-Horsfall protein (THP), also known as uromodulin, is a kidney-specific protein produced by cells of the thick ascending limb of the loop of Henle. Although predominantly secreted apically into the urine, where it becomes highly polymerized, THP is also released basolaterally, toward the interstitium and circulation, to inhibit tubular inflammatory signaling. Whether, through this latter route, THP can also regulate the function of renal interstitial mononuclear phagocytes (MPCs) remains unclear, however. Here, we show that THP is primarily in a monomeric form in human serum. Compared with wild-type mice, THP-/- mice had markedly fewer MPCs in the kidney. A nonpolymerizing, truncated form of THP stimulated the proliferation of human macrophage cells in culture and partially restored the number of kidney MPCs when administered to THP-/- mice. Furthermore, resident renal MPCs had impaired phagocytic activity in the absence of THP. After ischemia-reperfusion injury, THP-/- mice, compared with wild-type mice, exhibited aggravated injury and an impaired transition of renal macrophages toward an M2 healing phenotype. However, treatment of THP-/- mice with truncated THP after ischemia-reperfusion injury mitigated the worsening of AKI. Taken together, our data suggest that interstitial THP positively regulates mononuclear phagocyte number, plasticity, and phagocytic activity. In addition to the effect of THP on the epithelium and granulopoiesis, this new immunomodulatory role could explain the protection conferred by THP during AKI.

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.

BACKGROUND: Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. OBJECTIVE: The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. METHODS: PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. RESULTS: PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET's analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes. CONCLUSIONS: Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

Evolving Gaussian Mixture Models with Splitting and Merging Mutation Operators.

This paper describes the evolutionary split and merge for expectation maximization (ESM-EM) algorithm and eight of its variants, which are based on the use of split and merge operations to evolve Gaussian mixture models. Asymptotic time complexity analysis shows that the proposed algorithms are competitive with the state-of-the-art genetic-based expectation maximization (GA-EM) algorithm. Experiments performed in 35 data sets showed that ESM-EM can be computationally more efficient than the widely used multiple runs of EM (for different numbers of components and initializations). Moreover, a variant of ESM-EM free from critical parameters was shown to be able to provide competitive results with GA-EM, even when GA-EM parameters were fine-tuned a priori.

Building bridges across electronic health record systems through inferred phenotypic topics.

OBJECTIVE: Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems. METHODS: We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC). RESULTS: The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems. CONCLUSIONS: Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.

Multi-model comparison of the economic and energy implications for China and India in an international climate regime.

This paper presents a modeling comparison on how stabilization of global climate change at about 2 °C above the pre-industrial level could affect economic and energy systems development in China and India. Seven General Equilibrium (CGE) and energy system models on either the global or national scale are soft-linked and harmonized with respect to population and economic assumptions. We simulate a climate regime, based on long-term convergence of per capita carbon dioxide (CO2) emissions, starting from the emission pledges presented in the Copenhagen Accord to the United Nations Framework Convention on Climate Change and allowing full emissions trading between countries. Under the climate regime, Indian emission allowances are allowed to grow more than the Chinese allowances, due to the per capita convergence rule and the higher population growth in India. Economic and energy implications not only differ among the two countries, but also across model types. Decreased energy intensity is the most important abatement approach in the CGE models, while decreased carbon intensity is most important in the energy system models. The reduction in carbon intensity is mostly achieved through deployment of carbon capture and storage, renewable energy sources and nuclear energy. The economic impacts are generally higher in China than in India, due to higher 2010-2050 cumulative abatement in China and the fact that India can offset more of its abatement cost though international emission trading.

Analysis of polymorphic residues reveals distinct enzymatic and cytotoxic activities of the Streptococcus pyogenes NAD+ glycohydrolase.

The Streptococcus pyogenes NAD(+) glycohydrolase (SPN) is secreted from the bacterial cell and translocated into the host cell cytosol where it contributes to cell death. Recent studies suggest that SPN is evolving and has diverged into NAD(+) glycohydrolase-inactive variants that correlate with tissue tropism. However, the role of SPN in both cytotoxicity and niche selection are unknown. To gain insight into the forces driving the adaptation of SPN, a detailed comparison of representative glycohydrolase activity-proficient and -deficient variants was conducted. Of a total 454 amino acids, the activity-deficient variants differed at only nine highly conserved positions. Exchanging residues between variants revealed that no one single residue could account for the inability of the deficient variants to cleave the glycosidic bond of ß-NAD(+) into nicotinamide and ADP-ribose; rather, reciprocal changes at 3 specific residues were required to both abolish activity of the proficient version and restore full activity to the deficient variant. Changing any combination of 1 or 2 residues resulted in intermediate activity. However, a change to any 1 residue resulted in a significant decrease in enzyme efficiency. A similar pattern involving multiple residues was observed for comparison with a second highly conserved activity-deficient variant class. Remarkably, despite differences in glycohydrolase activity, all versions of SPN were equally cytotoxic to cultured epithelial cells. These data indicate that the glycohydrolase activity of SPN may not be the only contribution the toxin has to the pathogenesis of S. pyogenes and that both versions of SPN play an important role during infection.

p85ß regulatory subunit of class IA PI3 kinase negatively regulates mast cell growth, maturation, and leukemogenesis.

We show that loss of p85α inhibits the growth and maturation of mast cells, whereas loss of p85ß enhances this process. Whereas restoring the expression of p85α in P85α(-/-) cells restores these functions, overexpression of p85ß has the opposite effect. Consistently, overexpression of p85ß in WT mast cells represses KIT-induced proliferation and IL-3-mediated maturation by inhibiting the expression of Microphthalmia transcription factor. Because p85α and p85ß differ in their N-terminal sequences, chimeric proteins consisting of amino or carboxy-terminal of p85α and/or p85ß do not rescue the growth defects of p85α(-/-) cells, suggesting cooperation between these domains for normal mast cell function. Loss of p85ß impaired ligand induced KIT receptor internalization and its overexpression enhanced this process, partly because of increased binding of c-Cbl to p85ß relative to p85α. In vivo, loss of p85ß resulted in increased mast cells, and bone marrow transplantation of cells overexpressing p85ß resulted in significant reduction in some tissue mast cells. Overexpression of p85ß suppressed the growth of oncogenic KIT-expressing cells in vitro and prolonged the survival of leukemic mice in vivo. Thus, p85α and p85ß differentially regulate SCF and oncogenic KIT-induced signals in myeloid lineage-derived mast cells.

Role of SHP2 phosphatase in KIT-induced transformation: identification of SHP2 as a druggable target in diseases involving oncogenic KIT.

Intracellular mechanism(s) that contribute to promiscuous signaling via oncogenic KIT in systemic mastocytosis and acute myelogenous leukemia are poorly understood. We show that SHP2 phosphatase is essential for oncogenic KIT-induced growth and survival in vitro and myeloproliferative disease (MPD) in vivo. Genetic disruption of SHP2 or treatment of oncogene-bearing cells with a novel SHP2 inhibitor alone or in combination with the PI3K inhibitor corrects MPD by disrupting a protein complex involving p85α, SHP2, and Gab2. Importantly, a single tyrosine at position 719 in oncogenic KIT is sufficient to develop MPD by recruiting p85α, SHP2, and Gab2 complex to oncogenic KIT. Our results demonstrate that SHP2 phosphatase is a druggable target that cooperates with lipid kinases in inducing MPD.

Limestone: high-throughput candidate phenotype generation via tensor factorization.

The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to medical concepts that clinical researchers need or use. Some recent studies have focused on EHR-derived phenotyping, which aims at mapping the EHR data to specific medical concepts; however, most of these approaches require labor intensive supervision from experienced clinical professionals. Furthermore, existing approaches are often disease-centric and specialized to the idiosyncrasies of the information technology and/or business practices of a single healthcare organization. In this paper, we propose Limestone, a nonnegative tensor factorization method to derive phenotype candidates with virtually no human supervision. Limestone represents the data source interactions naturally using tensors (a generalization of matrices). In particular, we investigate the interaction of diagnoses and medications among patients. The resulting tensor factors are reported as phenotype candidates that automatically reveal patient clusters on specific diagnoses and medications. Using the proposed method, multiple phenotypes can be identified simultaneously from data. We demonstrate the capability of Limestone on a cohort of 31,815 patient records from the Geisinger Health System. The dataset spans 7years of longitudinal patient records and was initially constructed for a heart failure onset prediction study. Our experiments demonstrate the robustness, stability, and the conciseness of Limestone-derived phenotypes. Our results show that using only 40 phenotypes, we can outperform the original 640 features (169 diagnosis categories and 471 medication types) to achieve an area under the receiver operator characteristic curve (AUC) of 0.720 (95% CI 0.715 to 0.725). Moreover, in consultation with a medical expert, we confirmed 82% of the top 50 candidates automatically extracted by Limestone are clinically meaningful.

A natural language processing approach to detect inconsistencies in death investigation notes attributing suicide circumstances.

BACKGROUND: Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causing factors of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-circumstance attributions. METHODS: We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify possible label errors. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. We measured annotation inconsistency by the degree of changes in the F-1 score. RESULTS: Our results show that incorporating the target state's data into training the suicide-circumstance classifier brings an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. CONCLUSIONS: To conclude, we present an NLP framework to detect the annotation inconsistencies, show the effectiveness of identifying and rectifying possible label errors, and eventually propose an improvement solution to improve the coding consistency of human annotators.

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) contains the recording of individual suicide incidents taking place in the United States, and the contributing suicide circumstances. We used a computational method to check the accuracy of NVDRS records. Our method identified and rectified possible errors in labeling within the database. This method could be used to improve the label accuracy in the NVDRS database, enabling more accurate recording and study of suicide circumstances. Improved data recording of suicide circumstances could potentially be used to develop improved approaches to prevent suicide in the future.

An advanced double-phase stacking ensemble technique with active learning classifier: Toward reliable disruption prediction in Aditya tokamak.

Disruptions in tokamak nuclear reactors, where plasma confinement is suddenly lost, pose a serious threat to the reactor and its components. Classifying discharges as disruptive or non-disruptive is crucial for effective plasma operation and advanced prediction. Traditional disruption identification systems often struggle with noise, variability, and limited adaptability. To address these challenges, we propose an enhanced stacking generalization model called the "Double-Phase Stacking Technique" integrated with Pool-based Active Learning (DPST-PAL) for designing a robust classifier with minimal labor cost. This innovative approach improves classification accuracy and reliability using advanced data analysis techniques. We trained the DPST-PAL model on 162 diagnostic shots from the Aditya dataset, achieving a high accuracy of 98% and an F1-score of 0.99, surpassing conventional methods. Subsequently, the deep 1D convolutional predictor model is implemented and trained using the classified shots obtained from the DPST-PAL model to validate the reliability of the dataset, which is tested on 47 distinct shots. This model accurately predicts the disruptions 7-13 ms in advance with 93.6% accuracy and exhibited no premature alarms or misclassifications for our experimental shots.

Pattern of care and treatment outcomes of metastatic non-clear cell kidney cancer: a single centre experience from India.

Background: Non-clear-cell renal cell carcinoma (nccRCC) refers to a rare diverse heterogeneous group of tumours; usually treated with immune check point inhibitors and or tyrosine kinase inhibitors (TKIs). Prospective large-scale data from Asian countries is limited. Methods: This is a retrospective study of patients with metastatic nccRCC treated at Tata Medical Centre, Kolkata, India, from 2012 to 2022. Demographic profiles, histologic subtypes, treatment details, response to therapy (by response evaluation criteria in solid tumours (RECIST v1.1)) and survival status were captured from electronic medical records (EMRs) of hospitals up till May 2023. Kaplan Meier methods were estimated to assess progression-free survival (PFS) and overall survival (OS). Results: A total of 89 consecutive patients were screened for this study, 24 were excluded due to inadequate data in EMR. 65 patients were included in the final analysis, with a median age at diagnosis of 59 years (range 20-84) of which 81% were male. Histologic subtypes comprised of 43% papillary, 31% clear cell with mixed histology, 3% sarcomatoid and 23% others including chromophobe, mucinous-tubular, spindle cell, oncocytic, medullary, poorly differentiated and rhabdoid). The most common site of metastasis was the lung 62% (n = 40) followed by non-regional nodes 32%, bone 26% and liver 14%. 15% patients presented with haematuria and 62% underwent nephrectomy prior to systemic therapy. The majority received pazopanib 46% (n = 30), chemotherapy 20% (n = 13) including bevacizumab plus erlotinib, sunitinib 15% (n = 10) or cabozantinib 14% (n = 9). Only 3(5%) patients received nivolumab plus cabozantinib combination. Response to treatment showed complete response in 1.5%, partial response in 20%, stable disease in 51% and progressive disease in 23% as per RECIST v1.1. 17 patients required dose reduction and interruption due to adverse effects and 33% (n = 22) received second-line therapy with nivolumab 18% (n = 4), axitinib and everolimus among others. After a median follow up of 44 months, the median PFS was 13 months (95%CI 7.2-18.9) and the median OS was 17 months (95%CI 12.1-22.1) for the entire cohort. Conclusion: The overall response and survival for metastatic nccRCC was relatively better in comparison with published data, despite the limited number of patients treated with ICIs due to cost and access barriers.

Outcomes of Resectable Gallbladder Cancer: A Retrospective Analysis From a Tertiary Care Centre in India.

Background Gallbladder carcinoma (GBC) has deleterious outcomes, but due to its reduced incidence in Western countries, there is a paucity of data on this disease. Here we report the outcomes of a retrospective analysis of resectable gallbladder cancer from a tertiary cancer centre in eastern India. The primary objective of this study is to evaluate the overall survival (OS) and relapse-free survival (RFS) rates among patients with resectable GBC. Methods A retrospective analysis was carried out on patients who underwent radical surgery between 2007 and 2022 and received various neoadjuvant and adjuvant chemotherapy methods. Patients who had adjuvant chemoradiotherapy concurrently or who did not receive adjuvant therapy were excluded. All the baseline clinicopathological characteristics were retrieved from electronic medical records. The survival data were collected from records of follow-up visits as well as telephonic calls to the patients who were lost to follow-up. Simple proportions were used for baseline characteristics, and the Kaplan-Meier method was used for survival analysis. Results A total of 161 patients were identified, and data were captured from electronic medical records. The included patients' ages ranged between 26 and 80 years, with a median age of 56 years. Among the participants, 103 were female (64%) and 58 (36%) were male. Among the 161 patients, the median number of lymph nodes harvested was nine (ranging from one to 43), and only three patients were margin-positive. The tumour, nodes, and metastasis (TNM) distributions were as follows: pT2 in 111 patients (70.25%), pT3 in 44 patients (27.85%), and pT4 in three patients (1.90%). The nodal statuses were pN0 in 91 patients (61.9%), pN1 in 51 patients (34.69%), and pN2 in five patients (3.4%). The majority (64%) received single-agent capecitabine, 27% received gemcitabine-based platinum doublet therapy, and 4.3% received neoadjuvant therapy. Of the full sample, 2.4% received concurrent adjuvant chemo plus radiation therapy, and three patients did not receive any adjuvant therapy. Additionally, among the 161 patients, 34.16% had a relapse, with 47% being local and 52% being distant relapses. The median follow-up was 49 months (interquartile range (IQR) 23-71 months). The 24-month RFS rate was 67.1% (SD+/- 4.3%), and the 24-month OS rate was 78.1% (SD+/- 4.1%). Conclusion Our data, which is from one of the largest samples from India, show that resectable gallbladder cancer has very good outcomes after radical surgery and adjuvant chemotherapy. There was a higher proportion of T2 and node-negative disease, which could have led to better survival compared to published literature.