Author: scizentox scizentox
Protected: Abinaya Rajendiran, Medicine, Best Innovator Award
Protected: Muthukumar Murugesan, Engineering, Best Innovator Award
Pavan Sugoor, Medicine, Innovative Researcher Award
Dr. Pavan Sugoor: Associate Professor at Kidwai Memorial Institute of Oncology, India
Article Details
The study titled “Flare Study – Fluorescence-Guided Lymphadenectomy for Augmented Retrieval and Evaluation in Rectal Cancer Surgery” was published in the Indian Journal of Surgical Oncology on 10 February 2026. It is a single-centre, investigator-initiated, quasi-experimental study conducted on patients with mid-to-lower rectal cancer who had undergone neoadjuvant chemoradiotherapy (NACRT). The study compares fluorescence-guided lymphadenectomy using peri-tumoral indocyanine green (ICG) with standard lymphadenectomy in propensity score–matched cohorts, focusing on lymph node retrieval, aberrant node detection, and modifications to surgical extent.
Novelty
The study introduces fluorescence-guided surgery (FGS) with ICG specifically for post-NACRT rectal cancer, where fibrotic changes often hinder lymph node retrieval. The novelty lies in combining peri-tumoral ICG tattooing with systematic fluorescence-guided dissection, enabling the detection of small aberrant nodes outside the conventional total mesorectal excision (TME) field—a concept not widely applied in rectal cancer surgery to date.
Impact
The findings demonstrate that fluorescence guidance doubles lymph node yield (14 vs 7 nodes) and identifies small malignant nodes (mean 4 mm) that standard TME could miss. Clinically, this could enhance staging accuracy, influence adjuvant therapy decisions, and potentially improve oncologic outcomes, highlighting the method’s direct impact on surgical precision and patient prognosis.
Originality
This research is original in its approach to using FGS as an adjunctive tool in a post-radiation fibrotic surgical field, rather than as a general lymphatic mapping technique. Its focus on aberrant nodes outside standard TME boundaries addresses a gap in current rectal cancer surgical protocols, positioning it as a pioneer study in fluorescence-guided rectal lymphadenectomy.
Experimental Rigor
The study demonstrates reasonable rigor for a quasi-experimental design. It uses a prospectively maintained database, 1:1 propensity score matching, and clearly defines primary and secondary outcomes. Limitations include single-centre design, moderate sample size (46 patients per group), and short-term endpoints, with long-term oncologic outcomes not yet evaluated.
Sustainability Impact
From a sustainability perspective, FGS may contribute indirectly by reducing the need for repeat surgeries due to missed nodes or understaging. It is minimally resource-intensive, requiring only ICG and standard laparoscopic equipment, and does not introduce significant environmental or material waste. Widespread adoption could optimize resource use in rectal cancer surgery by improving surgical efficiency.
Applicability
The technique is highly applicable in tertiary care and oncology centers familiar with laparoscopic surgery and ICG fluorescence imaging. It could be integrated into standard rectal cancer surgical protocols after NACRT to enhance lymph node retrieval, guide individualized surgery, and refine staging. Broader applicability requires multicenter validation and assessment of long-term survival outcomes.
Research Portfolio
Dr. Pavan Sugoor is a leading surgical oncologist with over 15 years of experience in gastrointestinal and colorectal cancers. Renowned for his expertise in minimally invasive, laparoscopic, and robotic surgeries, he combines cutting-edge clinical practice with a commitment to research and medical education. He currently serves as Associate Professor at Kidwai Memorial Institute of Oncology, Bengaluru, India, and has trained extensively in India, Japan, and China. Dr. Sugoor is passionate about improving patient outcomes through innovation in surgical techniques and enhanced recovery protocols.
Online Profile
Dr. Sugoor maintains a robust online presence through academic and professional platforms, sharing insights on robotic and laparoscopic colorectal surgery, multidisciplinary cancer care, and surgical innovations. He actively contributes to international conferences, webinars, and workshops, and serves on editorial boards of journals such as Indian Journal of Surgical Oncology and Colorectal Disease. Additionally, he collaborates with medical technology companies like Stryker, Medtronic, and Johnson & Johnson, promoting research-driven innovation in surgical devices.
Education
Dr. Sugoor earned his MBBS from Sree Siddhartha Medical College (2004), followed by an MS in General Surgery at M.S. Ramaiah Medical College (2009). He completed his MCh in Surgical Oncology at Tata Memorial Hospital, Mumbai (2016). Further, he pursued specialized fellowships in gastrointestinal and colorectal surgery and advanced surgical oncology training in esophageal, gastric, and thoracic cancers in Japan and China. His comprehensive educational background reflects a blend of foundational surgical skills and advanced oncological expertise.
Research Focus
His primary research focus includes minimally invasive and robotic colorectal surgery, pelvic exenteration, fluorescence-guided oncologic surgery, and molecular profiling of gastrointestinal cancers. He investigates patient outcomes in enhanced recovery after surgery (ERAS) programs, neoadjuvant versus adjuvant therapy strategies, and surgical approaches for complex colorectal and gastric cancers. He is also involved in multicentric clinical trials aimed at advancing precision surgery and improving postoperative quality of life.
Experience
Dr. Sugoor has extensive clinical experience across various oncology disciplines, including colorectal, gastric, hepatopancreatobiliary, thoracic, and breast-oncoplastic surgeries. He has held positions as Assistant Professor and Associate Professor at Kidwai Memorial Institute of Oncology and completed senior residency and fellowship roles at Tata Memorial Hospital. He has performed numerous complex oncologic procedures and is recognized for his skill in robotic and laparoscopic surgeries, teaching, and mentoring young surgeons.
Research Timeline & Activities
Over the years, Dr. Sugoor has led multiple research projects, including studies on drug-resistant colorectal cancer, timing of stoma closure, Bakri balloon applications in pelvic surgery, and patient-reported outcomes in enhanced recovery programs. He actively collaborates on multicentric trials, supervises postgraduate research, and explores innovations in surgical oncology techniques. His work bridges clinical practice and research, ensuring evidence-based improvements in cancer care.
Awards & Honors
Dr. Sugoor has received numerous awards recognizing both his academic excellence and research contributions. Highlights include ranking 3rd in his MS final exams, first prizes for oral presentations at the Association of Surgeons of India conferences, and recognition for surgical teaching and mentorship. He is also invited as a guest speaker and panelist at national and international surgical oncology conferences.
strengths of Dr. Pavan Sugoor for the Best Innovator Award:
1. Pioneering Surgical Techniques
Dr. Sugoor has consistently introduced and implemented innovative surgical approaches, such as fluorescence-guided lymphadenectomy and advanced robotic colorectal surgeries. His work in post-NACRT rectal cancer demonstrates a commitment to enhancing surgical precision, improving lymph node retrieval, and detecting aberrant nodes outside standard dissection fields—a novel application with significant clinical impact.
2. Integration of Research and Clinical Practice
He bridges cutting-edge research with practical patient care, conducting quasi-experimental and multicentric studies while directly applying findings in the operating theatre. This ensures that innovations are evidence-based, clinically relevant, and immediately translatable, increasing patient safety, outcomes, and the efficiency of cancer care.
3. International Training and Collaboration
With fellowships and training in Japan and China, along with collaborations with global medical technology companies (Stryker, Medtronic, Johnson & Johnson), Dr. Sugoor leverages international expertise and technological innovations to pioneer new surgical techniques and adapt them to the Indian healthcare context.
4. Educational and Mentorship Contributions
Dr. Sugoor has a strong focus on training the next generation of surgeons through mentorship, workshops, webinars, and conference presentations. His ability to teach advanced surgical methods and disseminate innovative practices contributes to long-term impact and the propagation of surgical innovation across institutions.
5. Multidisciplinary and Patient-Centered Innovation
His research spans fluorescence-guided surgery, minimally invasive and robotic oncology, ERAS protocols, and molecular profiling, emphasizing precision, personalized care, and patient outcomes. By combining technology, research, and patient-focused strategies, he consistently develops sustainable and high-impact innovations that improve both short- and long-term quality of life for cancer patients.
Protected: Kanwarpreet Singh, Engineering, Best Innovator Award
Protected: M. L. N. Deepika, Medicine, Best Innovator Award
Ana Carpio, Mathematics, Innovative Researcher Award
Prof. Dr. Ana Carpio: Professor of Applied Mathematics at Universidad Complutense de Madrid, Spain
Article Details
The article “Hierarchical topological clustering and meaningful outliers” by Ana Carpio & Gema Duro was published in Soft Computing on 07 February 2026. It presents a mathematical and computational framework that integrates topological data analysis with hierarchical clustering to detect clusters of any shape and identify meaningful outliers in datasets. The approach is demonstrated using MATLAB on datasets from diverse domains, including biological, physical, and economic data.
Novelty
The work introduces a hierarchical topological clustering (HTC) algorithm that combines persistence-based topological analysis with clustering, a method not found in traditional algorithms like K-means, K-medoids, or DBSCAN. Its novelty lies in enabling multiscale clustering of arbitrary shapes while providing a mathematically grounded method to quantify cluster stability and identify significant outliers.
Impact
The algorithm has the potential to transform how researchers analyze complex, high-dimensional data where conventional clustering fails. By accurately identifying clusters and meaningful outliers, it can impact fields such as genomics, image analysis, swarm robotics, and economic modeling. Its robustness to noise also enhances the reliability of insights drawn from real-world datasets.
Originality
The approach is original because it integrates persistent homology from topological data analysis into a hierarchical clustering framework. Unlike other methods, it does not rely on pre-set parameters or assume convexity of clusters. The identification of “meaningful outliers” adds a layer of interpretability uncommon in standard clustering techniques.
Experimental Rigor
The authors validate the algorithm across multiple datasets of moderate size, employing different distance measures (Euclidean, Wasserstein, Fermat). MATLAB simulations demonstrate its ability to detect both complex cluster structures and significant outliers. However, large-scale computational benchmarking and comparisons with state-of-the-art topological clustering methods could further strengthen rigor.
Sustainability Impact
By efficiently identifying key data patterns and reducing misclassification, the method may indirectly support sustainable decision-making, for example, in resource allocation, disease diagnosis, and trade analysis, where misinterpretation of data can lead to resource waste or inefficiency. Its computational efficiency also reduces unnecessary computational load compared to repeated parameter tuning in traditional clustering methods.
Applicability
The HTC algorithm is highly applicable across domains requiring nuanced data analysis. Its ability to handle non-convex clusters, hierarchical structures, and meaningful outliers makes it suitable for bioinformatics, materials science, social network analysis, economic modeling, robotics, and image compression. The flexibility in distance metrics further broadens its applicability across varied datasets.
Research Portfolio
Ana Carpio is a Professor of Applied Mathematics at the Universidad Complutense de Madrid, specializing in numerical analysis and its applications to physical and biological systems. She has built an international reputation for bridging rigorous mathematical theory with real-world problems in fluid dynamics, biomedicine, and complex systems modeling. Throughout her career, she has supervised numerous Ph.D. students, led interdisciplinary research projects, and collaborated with top institutions worldwide.
Online Profile
Carpio maintains a verified academic profile at UCM and a personal homepage detailing her research, publications, collaborations, and ongoing projects. Her work is widely cited, with over 3,600 total citations, an h-index of 35, and an i10-index of 91, reflecting her substantial impact in applied mathematics. Her profile highlights both theoretical contributions and practical applications, emphasizing the relevance of mathematics to engineering, biology, and computational science.
Education
Carpio earned her B.S. and M.S. in Mathematics (Numerical Analysis) from Universidad Politécnica de Valencia in 1988. She completed her DEA d’Analyse Numérique at Université Pierre et Marie Curie (Paris VI) in 1989 and obtained her Ph.D. in Mathematics in 1993 under the supervision of A. Haraux, focusing on numerical methods for partial differential equations. In 2004, she earned her Habilitation as Full Professor of Applied Mathematics from the MEC, Spain, marking her qualification for senior academic leadership.
Research Focus
Her research centers on the development and application of mathematical models and numerical methods to complex systems, with a focus on physical and biological phenomena. She works on partial differential equations, fluid dynamics, and microbiome interactions, integrating computational tools to solve problems in biomedicine and industry. Her interdisciplinary approach emphasizes both theoretical rigor and the translation of mathematics into actionable scientific and technological solutions.
Experience
Carpio’s professional experience spans multiple institutions and countries. She began as a predoctoral researcher at Université Pierre et Marie Curie, followed by assistant and associate professorships at UCM, and postdoctoral research at Oxford. She has held visiting scholar and researcher positions at Stanford, Harvard, NYU Courant Institute, Institut Henri Poincaré, the University of Iceland, and the Fields Institute. Currently, she is Professor of Applied Mathematics at UCM and directs the Research Group on Mathematics Applied to Physical and Biological Systems.
Research Timeline & Activities
Her research timeline reflects a continuous evolution from foundational numerical analysis in the late 1980s and 1990s to interdisciplinary applications in fluid dynamics, biomedicine, and computational modeling from 2005 onward. She has led projects that connect mathematics with industrial, environmental, and health-related applications, and has collaborated with international research centers and institutes. Her activities include supervising graduate students, organizing workshops, and contributing to collaborative scientific networks.
Awards & Honors
Carpio has received multiple awards recognizing both her research excellence and innovative contributions. She won the Best Ph.D. Thesis Award at UCM in 1993, the SEMA Prize for Young Researchers in 1998, a mention from the Madrid-MIT Vision Program for innovative ideas in biomedicine in 2013, and a winning project in the Repsol Inspire Program in 2013. These honors reflect her sustained impact in applied mathematics and her leadership in interdisciplinary research initiatives.
Prof. Dr. Ana Carpio the Innovative Researcher Award based:
1. Pioneering Research in Applied Mathematics
Prof. Carpio has consistently developed novel mathematical frameworks that bridge theory and practice. Her work on hierarchical topological clustering exemplifies her ability to translate advanced mathematical concepts, such as persistent homology, into actionable tools for data analysis across biology, economics, and engineering. This demonstrates a forward-thinking, innovative approach in applied mathematics.
2. Interdisciplinary Impact
Her research seamlessly integrates mathematics with biomedicine, fluid dynamics, microbiome studies, and complex systems modeling. By tackling real-world problems with rigorous mathematical methods, she has created solutions that cross traditional disciplinary boundaries, exemplifying innovation through interdisciplinary application.
3. Methodological Originality and Novelty
The hierarchical topological clustering algorithm she co-developed introduces new ways to detect non-convex clusters and meaningful outliers. This method addresses limitations of conventional clustering techniques, showing her strength in creating original, high-impact methodologies that advance both theoretical and practical aspects of data science.
4. Global Academic Leadership and Collaboration
Carpio’s career demonstrates international leadership and collaboration, with research positions and partnerships at top institutions like Stanford, Harvard, NYU, Institut Henri Poincaré, and the Fields Institute. Her ability to lead interdisciplinary projects and mentor future scientists reflects her innovative influence on global research networks.
5. Sustained Excellence and Recognition
Her numerous awards—Best Ph.D. Thesis, SEMA Prize, Madrid-MIT Vision Program recognition, and Repsol Inspire Program winner—highlight her sustained excellence and innovation. Coupled with an h-index of 35 and over 3,600 citations, this underscores her impact as a researcher whose innovations have both academic and societal relevance.