SCTIMST DSpace
Digital repository of Sree Chitra Tirunal Institute for Medical Sciences and Technology(SCTIMST), Trivandrum.
This repository is for SCTIMST's research, including project reports, theses, publications and more...
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- Academic Presentations by various Departments and Divisions
Recent Submissions
National Centre for Preclinical Evaluation of Cardiovascular Devices
(SCTIMST, 2025-08-02) Umashankar, PR
The primary objective of the Center is to set up a facility for in vitro and ex vivo / in vivo evaluation of cardiovascular devices for providing design verification / test reports for regulatory submission. Procurement and commissioning of necessary test systems in different laboratories and getting necessary certification for the facilities is ongoing. Modification of the existing laboratories and animal housing rooms to enhance the testing capacity is also envisaged as part of the project. Investigators in the project includes faculty from the departments of Medical Devices Engineering, Applied Biology, Cardiovascular and Thoracic surgery, and Technology & Quality Management
The center will carry out preclinical evaluation of identified devices supported by ICMR schemes from other institutes in addition to seven technologies from SCTIMST. Devices from SCTIMST includes Left Ventricular Assist Device, Automatic contrast injector, Implantable leads, Aortic stent graft, ASD occluder, Automatic External Defibrillator and Implantable Cardioverter Defibrillator. Total cost of the project is close to ₹15 crores. The project was started in May 2025
Centre of Excellence in Cardiovascular Minimally Invasive Devices
(SCTIMST, 2025-08-02) Sujesh, Sreedharan
A Centre of Excellence in Cardiovascular Minimally Invasive Devices with a fund of ₹20 crores has been sanctioned in June 2024 by DBT. Ten cardiovascular devices that were identified along with the clinical team are proposed for development. High risk devices such as Transcatheter Aortic Valve Replacement (TAVR) device, Thoracic Aortic Stent Graft (ASG), Clot Retriever, Flow Modulator, Superficial Femoral Artery (SFA) stent, Patent Ductus Arteriosus (PDA) closure device, are some of the selected devices. These will be developed to the completion of proof-of-concept stage and made ready for technology transfer.
The COE was conceived to further expand and utilize the skills, strengths and IPR obtained during the Technical Research Center (TR) projects. Several equipment for design, in vitro evaluation and precision prototyping are included in the program. It is supported by several faculty of BMT Wing along with clinical faculty from the Interventional Radiology, Interventional Cardiology, as well as Cardiac and Neuro Surgery.
Enhancing chemical signal transformation in lateral flow assays using aptamer-architectured plasmonic nanozymes and para-phenylenediamine
(Nanoscale, 2025-01) Sarathkumar, E; Jibin, K; Sivaselvam, S; Sharma, AS; Alexandar, V; Resmi, AN; Velswamy, P; Jayasree, RS
The widespread adoption and commercialization of lateral flow assays (LFAs) for clinical diagnosis have been hindered by limitations in sensitivity, specificity, and the absence of quantitative data. To address these challenges, we developed aptamer-architectured gold nanoparticles as nanozymes that catalytically convert para-phenylenediamine (PPD) into Bandrowski's base (BB), thereby amplifying signal strength and sensitivity. The physiochemical properties of the nanozymes were characterized and their specific binding efficiency was demonstrated using experimental studies. The nanozymes and PPD-based LFA test strips were evaluated for the detection of the COVID-19 spike protein in both test and clinical samples. Notably, we achieved a significant visual detection limit of 168 pg mL−1, with a signal quality enhancement of over 20-fold within 15-minute timeframe. Moreover, we rigorously tested 25 clinical samples to assess the transformative potential of the product, demonstrating a semi-quantitative analysis efficiency exceeding 90%. This performance outstripped commercially available LFA kits (87.5%). Notably, the colorimetric system exhibited an R2 value of 0.9989, a critical factor for clinical testing and industry integration. The incorporation of nanozymes and PPD in LFAs offers a cost-effective solution with significantly improved sensitivity, enabling the detection of ultra-low concentrations (picograms) of spike protein. By addressing key challenges in LFA-based diagnostics, the current technique underscores the potential of this transformative biomedical sensor for industry integration. It also highlights its suitability for commercialization, positioning it as a universal platform for diagnostic applications.
Ultrasensitive Detection of Blood-Based Alzheimer’s Disease Biomarkers: A Comprehensive SERSImmunoassay Platform Enhanced by Machine Learning
(ACS Chemical Neuroscience, 2024-11) Resmi, AN; Nazeer, SS; Dhushyandhun, ME; Paul, Willi; Chacko, BP; Menon, RN; Jayasree, RS
Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a source for biomarkers is particularly important for managing Alzheimer’s disease (AD). This study introduces a novel approach for the precise and ultrasensitive detection of multiple core AD biomarkers (Aβ40, Aβ42, p-tau, and t-tau) using surface-enhanced Raman spectroscopy (SERS) combined with machine-learning algorithms. Our method employs an antibody-immobilized aluminum SERS substrate, which offers high precision, sensitivity, and accuracy. The platform achieves an impressive detection limit in the attomolar (aM) range and spans a wide dynamic range from aM to micromolar (μM) concentrations. This ultrasensitive and specific SERS immunoassay platform shows promise for identifying mild cognitive impairment (MCI), a potential precursor to AD, from blood plasma. Machine-learning algorithms applied to the spectral data enhance the differentiation of MCI from AD and healthy controls, yielding excellent sensitivity and specificity. Our integrated SERS-machine-learning approach, with its interpretability, advances AD research and underscores the effectiveness of a cost-efficient, easy-to-prepare Al-SERS substrate for clinical AD detection.