Raghunathan Rengaswamy’s research while affiliated with Indian Institute of Technology Madras and other places

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user’s identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

Background A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India.

Background A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India. Methods Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for the second and third trimesters. The gold standard for GA estimation in the first trimester was determined using ultrasonography. The Garbhini-GA2, a polynomial regression model, was developed using the genetic algorithm-based method, showcasing the best performance among the models considered. This model incorporated three of the five routinely measured ultrasonographic parameters during the second and third trimesters. To assess its performance, the Garbhini-GA2 model was compared against the Hadlock and INTERGROWTH-21st models using both the TEST set (N = 1493) from the GARBH-Ini cohort and an independent VALIDATION dataset (N = 948) from the Christian Medical College (CMC), Vellore cohort. Evaluation metrics, including root-mean-squared error, bias, and preterm birth (PTB) rates, were utilised to comprehensively assess the model's accuracy and reliability. Findings With first trimester GA dating as the baseline, Garbhini-GA2 reduced the GA estimation median error by more than three times compared to the Hadlock formula. Further, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to the INTERGROWTH-21st and Hadlock formulae, which overestimated the rate by 22.47% and 58.91%, respectively. Interpretation The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and validated using Indian population data. Its higher accuracy in GA estimation, comparable to GA estimation in the first trimester and PTB classification, underscores the significance of deploying population-specific GA formulae to enhance antenatal care. Funding The GARBH-Ini cohort study was funded by the Department of Biotechnology, Government of India (BT/PR9983/MED/97/194/2013). The ultrasound repository was partly supported by the Grand Challenges India-All Children Thriving Program, 10.13039/501100014825Biotechnology Industry Research Assistance Council, Department of Biotechnology, Government of India (BIRAC/GCI/0115/03/14-ACT). The research reported in this publication was made possible by a grant (BT/kiData0394/06/18) from the Grand Challenges India at 10.13039/501100014825Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC. The external validation study at 10.13039/501100005918CMC Vellore was partly supported by a grant (BT/kiData0394/06/18) from the Grand Challenges India at 10.13039/501100014825Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC and by Exploratory Research Grant (SB/20-21/0602/BT/RBCX/008481) from Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), 10.13039/501100003845IIT Madras. An alum endowment from Prakash Arunachalam (BIO/18-19/304/ALUM/KARH) partly funded this study at the Centre for Integrative Biology and Systems Medicine, 10.13039/501100003845IIT Madras.

The synthesis of drug-loaded microparticles with precise control over size distribution and shape is crucial for achieving desired drug distribution in microparticles and tuning drug release profiles. Common large-scale production techniques produce microparticles with a broad particle size distribution and require challenging operating conditions. Recent methods employing microfluidics have enabled the production of microparticles with a uniform size distribution. Still, these methods are limited to low and moderate production rates and can handle fluids with a limited range of physicochemical properties. In this study, we couple the spinning disk atomization (SDA) technique for microdroplet production with a precipitation method to generate drug-loaded polymeric microparticles with a narrow size distribution. The design criteria and fabrication of equipment with a non-contact seal system that integrates spinning disk atomization and precipitation methods for conducting laboratory experiments involving volatile hydrocarbons while ensuring operational and personnel safety are discussed. The production of itraconazole drug-loaded microparticles using the SDA setup that considers the system's operation, maintenance, and safety aspects are discussed, and the system's efficiency is evaluated through material balance. This laboratory equipment is capable of producing drug-loaded microparticles with a narrow size distribution under moderate operating conditions and can be scaled up suitably to meet high production requirements. The applications of this equipment can be explored in various fields, such as the production of drug particles, conversion of waste polymers into microparticles, and microencapsulation of food ingredients.

With the ever‐increasing volume of scientific literature, there is a strong need to develop methods that allow rigorous information identification. In this contribution, a state‐of‐the‐art natural language processing (NLP) model was used to select perovskite materials for electrocatalytic applications from literature. This was accomplished by obtaining word embeddings for perovskite materials from the NLP model and subsequently designing downstream tasks to discover perovskite‐based electrocatalyst materials. However, embeddings could be obtained only for materials available in the literature. Consequently, a novel methodology was devised to generate embeddings for newly designed materials. Results from the analysis showed that the computed embeddings could be used to rank materials for their suitability for electrocatalytic applications. Further, the word embeddings were also employed as features in predicting the electrocatalytic activity of perovskite‐based electrocatalysts. The analysis demonstrated that the fidelity of regression models increased when the embeddings were used as features.

The onset of colorectal cancer (CRC) is often attributed to gut bacterial dysbiosis, and thus gut microbiota are highly relevant in devising treatment strategies. Certain gut microbes, like Enterococcus spp ., exhibit remarkable anti-neoplastic and probiotic properties, which can aid in silver nanoparticle (AgNPs) induced reactive oxygen species (ROS)-based CRC treatment. However, the effects of AgNPs on gut microbial metabolism have not been reported thus far. In this study, a detailed systems-level understanding of ROS metabolism in Enterococcus durans ( E. durans ), a representative gut microbe, was gained using constraint-based modeling, wherein, the critical association between ROS and folate metabolism was established. Experimental studies involving low AgNP concentration treatment of E. durans cultures confirmed these modeling predictions (an increased extracellular folate concentration by 52%, at the 9 th h of microbial growth, was observed). Besides, the computational studies established various metabolic pathways involving amino acids, energy metabolites, nucleotides, and SCFAs as the key players in elevating folate levels on ROS exposure. The anti-cancer potential of E. durans was also studied through MTT analysis of HCT 116 cells treated with microbial culture (AgNP treated) supernatant. A decrease in cell viability by 19% implicated the role of microbial metabolites (primarily folate) in causing cell death. The genome-scale modeling approach was then extended to extensively model CRC metabolism, as well as CRC– E. durans interactions in the context of CRC treatment, using tissue-specific metabolic models of CRC and healthy colon. These findings on further validation can facilitate the development of robust and effective cancer therapy.