Sujay Ratna

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy

Nature biomedical engineering, Jul 10, 2024

SURG-24. Quantitative Analysis of Tumor Cell Density and Protoporphyrin IX Flourescence in Glioma Patients Using Paired Stimulated Raman Histology and Two-Photon Excitation Fluorescence Microscopy

Neuro-oncology, Nov 1, 2022

METHODS: Data from LAANTERN prospective multicenter registry (NCT02392078) was analyzed to determ... more METHODS: Data from LAANTERN prospective multicenter registry (NCT02392078) was analyzed to determine clinical outcomes for patients with new and recurrent IDH wild-type glioblastoma (N=89). Demographics, intraprocedural data, adverse events, KPS, health-economics, and survival data were prospectively collected then analyzed separately for newly diagnosed GBM (N=29) and recurrent GBM (N=60). RESULTS: Median overall-survival was 9.73 months (95% CI: 5.16, 15.91) for newly diagnosed patients and median post-procedure survival was 8.97 (6.94, 12.36) months for recurrent patients. Median overall-survival for newly diagnosed patients receiving post-LITT chemoradiotherapy was 16.14 months (6.11, not reached). The median length of hospital stay was 50 hours and 80% of patients were discharged to home. CONCLUSIONS: LITT offers an effective cytoreductive approach for patients with newly diagnosed and recurrent IDH wild-type glioblastoma. Importantly, its use in newly diagnosed patients who receive post-LITT chemoradiotherapy leads to a median OS similar to that of patients treated with conventional surgical resection. LITT remains an important alternative for patients with inoperable tumors or those not amenable to resection. Enrollment in LAANTERN is ongoing and these cohorts will be revisited as data continues to mature. Benefits beyond cytoreduction are also being actively explored.

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Localization of protoporphyrin IX in glioma patients with paired stimulated Raman histology and two-photon excitation fluorescence microscopy

Research Square (Research Square), Dec 22, 2022

Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevul... more Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevulinic acid, the most widely used uorophore in glioma surgery, is thought to cause selective accumulation of uorescent protoporphyrin IX (PpIX) in tumor cells. 5-aminolevulinic acid is highly speci c for densely tumor-in ltrated tissue but less effective for visualizing the tumor periphery. To improve clinical detection of PpIX, we developed a microscope to perform paired stimulated Raman histology and two-photon excitation uorescence microscopy (TPEF) and validated it in 175 fresh tumor specimens from 75 highgrade glioma patients across three institutions. Here, we demonstrate that intracellular PpIX accumulation occurs most prominently in histiocytic, rather than neoplastic, appearing cells. Spatially resolved metabolomics, transcriptomics and RNA sequencing revealed that PPIX is most avidly concentrated in tumor associated macrophages. There was no correlation between the degree of tissue cellularity and PpIX concentration across all imaged specimens (R=-0.21). Our ndings encourage reconsideration of the existing theory of 5-ALA-induced glioma cell uorescence and demonstrate how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of human gliomas.

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Classification and Prediction of Antimicrobial Peptides Using N-gram Representation and Machine Learning

Current antibiotic treatments for infectious diseases are drastically losing effectiveness, as th... more Current antibiotic treatments for infectious diseases are drastically losing effectiveness, as the organisms they target have developed resistance to the drugs over time. In the United States, antibiotic-resistant bacterial infections annually result in more than 23,000 deaths, the morbidity rates are much higher. A promising alternative to current antibiotic treatments are antimicrobial peptides (AMPs), short sequences of amino acid residues that have been experimentally identified to inhibit the propagation of pathogens. In this study, we demonstrated that an N-gram representation of AMP sequences using reduced amino acid alphabet combined with machine learning (ML) methods provide a simple and efficient AMP classification with performance comparable to the more complex algorithms. All AMP sequences were retrieved from public data sources. Our AMP set consists of 7760 sequences, regardless of AMP subclass. We also used class-specific AMP sets (antibacterial, antiviral, antifungal, and antiparasitic). We created a raw negative set consisting of 20258 non-antimicrobial peptides (non-AMPs) using sequence fragments from annotated protein sequence databases. Models for all AMP against non-AMP sequences classification achieved a maximum accuracy of 85.0% using frequency N-gram analysis, and the RF model with 10-fold cross-validation. The datasets ranged from 200 to 7760 sequences per class. Classification using more specific classes of AMPs was conducted next. First, classification of ABPs against non-ABP sequences achieved an accuracy of up to 100% depending on a ML algorithm and alphabet reduction used. ABP against AVP sequences classification yielded a maximum accuracy of 81.8% AVP against non-AVP - 80.7% and AVP against AFP - 80.5%. The common trends present across multiple experiment series include the following: Random Forest frequently outperforms other algorithms. The optimal size of the reduced alphabet is either 3 or 4 letters. Reduction to 2 letters leads to a significant drop in accuracy, reduction to 5 or more letters does not provide any noticeable gains in classification accuracy. The results of this study indicate that N-gram based classification of AMPs is a promising approach with a strong potential for providing important insights into understanding AMP mechanisms and computationally designing new AMPs.

Machine Learning Classification of Antimicrobial Peptides Using Reduced Alphabets

Antimicrobial peptides (AMPs) are being considered as a promising replacement for antibiotics. Th... more Antimicrobial peptides (AMPs) are being considered as a promising replacement for antibiotics. They take action in the bodies' adaptive immune system. While its effect inside the body is primarily known, a problem of correctly identifying AMPs based on their sequence features remains a subject of active investigations. Here we optimize the use of the reduced alphabet, simplify 20-letter amino acid alphabet to 2-4 letters, and the use of N-grams, short strings of amino acids, to find a correlation between a profile of N-gram frequencies. The calculations were carried out using java programs written for this study and WEKA machine learning software. Classification using machine learning methods was then conducted for AMP subclasses, including antibacterial, antifungal, and antiviral peptides. The results show that reduced alphabets with N-gram frequency analysis are a promising alternative in the area of AMP classification and prediction. All AMP sequences were retrieved from different sources. AMP set consists of 7984 sequences, not necessarily of any specific class. We also used class-specific AMP sets (antibacterial, antiviral, and antifungal). A raw negative set consisting of 20258 non-AMPs using sequence fragments from annotated protein sequence databases. The classification of AMPs against non-AMPs was successful. Models achieved maximum accuracy of 87.71% using frequency N-gram analysis, alphabet reduction option 47, and the RF model with 10 trees cross-validation. Classification using more specific classes of AMPs was conducted next. First, classification of ABPs against non-ABPs AMPs achieved maximum accuracy of 86.83% using frequency N-gram analysis, alphabet reduction option 47, and RF model, while with bagging algorithm 84.35%. Second, classification of AVPs against non-AVP AMPs achieved an accuracy of 92.75% and 92.30% using frequency N-gram analysis, alphabet reduction option 47 and 29 respectively, and with RF model. This experiment also consisted of many other successful trials. RF significantly outperforms each of the other six learning algorithms. Alphabet reduction 47 most often yielded the highest classification accuracies. This finding implies that 4-cluster alphabet is optimal for N-gram frequency analysis and machine learning. Our results suggest that the classifiers produced possess great predictive power and can be of significant use in various biological and medical applications, potentially saving tens or hundreds of thousands of lives.

Rapid Evaluation of Bronchoscopic Biopsies Using Stimulated Raman Histology

Localization of protoporphyrin IX in glioma patients with paired stimulated Raman histology and two-photon excitation fluorescence microscopy

Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevul... more Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevulinic acid, the most widely used fluorophore in glioma surgery, is thought to cause selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumor cells. 5-aminolevulinic acid is highly specific for densely tumor-infiltrated tissue but less effective for visualizing the tumor periphery. To improve clinical detection of PpIX, we developed a microscope to perform paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF) and validated it in 175 fresh tumor specimens from 75 high-grade glioma patients across three institutions. Here, we demonstrate that intracellular PpIX accumulation occurs most prominently in histiocytic, rather than neoplastic, appearing cells. Spatially resolved metabolomics, transcriptomics and RNA sequencing revealed that PPIX is most avidly concentrated in tumor associated macrophages. There was no correlation between the degre...

SURG-24. Quantitative Analysis of Tumor Cell Density and Protoporphyrin IX Flourescence in Glioma Patients Using Paired Stimulated Raman Histology and Two-Photon Excitation Fluorescence Microscopy

Neuro-Oncology

Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevul... more Fluorescence guidance is widely utilized to improve the precision of cancer surgery. 5-aminolevulinic acid, the most widely used fluorophore in glioma surgery, is thought to cause selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumor cells by exploiting pathologic alterations in heme biosynthetic pathways. PpIX-induced fluorescence is highly specific for densely tumor-infiltrated tissue but less effective for visualizing the tumor periphery. To improve clinical detection of PpIX, we developed a microscope to perform paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF) and validated it in 175 fresh, unprocessed core tumor specimens from 75 high-grade glioma patients and three central nervous system lymphoma patients across three institutions. Surprisingly, intracellular PpIX accumulation was observed primarily in cells with histiocytic, rather than neoplastic morphology, and the number of cells concentrating PpIX within the cyto...

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A nover view on context aware recommender system in mobile environment

Services Proceedings of the 2016 S2, 2016

With the rapid development of wireless communication and mobile internet, it is impossible to obt... more With the rapid development of wireless communication and mobile internet, it is impossible to obtain high-quality recommendation just depending on traditional user-item binary relation. How to use multi-context to generate satisfying recommendation in such a complicated environment has been a hot pot in some fields like e-commerce, tourism, music and video. Context aware recommender system (CARS) import multi-context into recommender to generate recommendation with ubiquitousness and personalization. This paper presents an overview of the development status of CARS in recent years. We detail the definition of context, usage, process of CARS and evaluation. We also analyze some challengingresearch extension.

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Ultrasensitive molecular profiling of EGFR mutations in glioblastoma multiforme using a rapid & high-resolution digital PCR approach

Glioblastoma Multiforme (GBM) is the most common and aggressive adult brain cancer with a 14.6-mo... more Glioblastoma Multiforme (GBM) is the most common and aggressive adult brain cancer with a 14.6-month average survival time, even with current therapies. 57% of GBM has amplified the Epidermal Growth Factor Receptor (EGFR). EGFR variant III, A289V, & R108K are mutations in EGFR that lead to increased tumor aggression and poorer prognosis. Learning more about these mutations will stop gliomas and prevent diminished survival, and shorter life expectancies in these patients. More quantitative methods of detection would allow for elucidation of mutations that occur during cancer development, with emphasis on metastasizing tumors. To inhibit EGFR in certain cancers, it is necessary to identify why increased proliferation occurs, usually because of mutation. After looking at the molecular changes in GBM, current methods of detection will be discussed, and finally, a novel method, digital Polymerase Chain Reaction (dPCR), will be introduced. While the detection of EGFR mutations in GBM tumo...

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Classification and Prediction of Antimicrobial Peptides Using N-gram Representation and Machine Learning

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, 2017

Current antibiotic treatments for infectious diseases are drastically losing effectiveness, as th... more Current antibiotic treatments for infectious diseases are drastically losing effectiveness, as the organisms they target have developed resistance to the drugs over time. In the United States, antibiotic-resistant bacterial infections annually result in more than 23,000 deaths, the morbidity rates are much higher. A promising alternative to current antibiotic treatments are antimicrobial peptides (AMPs), short sequences of amino acid residues that have been experimentally identified to inhibit the propagation of pathogens. In this study, we demonstrated that an N-gram representation of AMP sequences using reduced amino acid alphabet combined with machine learning (ML) methods provide a simple and efficient AMP classification with performance comparable to the more complex algorithms. All AMP sequences were retrieved from public data sources. Our AMP set consists of 7760 sequences, regardless of AMP subclass. We also used class-specific AMP sets (antibacterial, antiviral, antifungal,...

Machine Learning Classification of Antimicrobial Peptides Using Reduced Alphabets

Antimicrobial peptides (AMPs) are being considered as a promising replacement for antibiotics. Th... more Antimicrobial peptides (AMPs) are being considered as a promising replacement for antibiotics. They take action in the bodies' adaptive immune system. While its effect inside the body is primarily known, a problem of correctly identifying AMPs based on their sequence features remains a subject of active investigations. Here we optimize the use of the reduced alphabet, simplify 20-letter amino acid alphabet to 2-4 letters, and the use of N-grams, short strings of amino acids, to find a correlation between a profile of N-gram frequencies. The calculations were carried out using java programs written for this study and WEKA machine learning software. Classification using machine learning methods was then conducted for AMP subclasses, including antibacterial, antifungal, and antiviral peptides. The results show that reduced alphabets with N-gram frequency analysis are a promising alternative in the area of AMP classification and prediction. All AMP sequences were retrieved from diff...

High resolution profiling of EGFR mutations in glioblastoma patients using an ultrasensitive digital PCR approach

ATP Synthase and Mitochondrial Bioenergetics Dysfunction in Alzheimer’s Disease

International Journal of Molecular Sciences

Alzheimer’s Disease (AD) is the most common neurodegenerative disorder in our society, as the pop... more Alzheimer’s Disease (AD) is the most common neurodegenerative disorder in our society, as the population ages, its incidence is expected to increase in the coming decades. The etiopathology of this disease still remains largely unclear, probably because of the highly complex and multifactorial nature of AD. However, the presence of mitochondrial dysfunction has been broadly described in AD neurons and other cellular populations within the brain, in a wide variety of models and organisms, including post-mortem humans. Mitochondria are complex organelles that play a crucial role in a wide range of cellular processes, including bioenergetics. In fact, in mammals, including humans, the main source of cellular ATP is the oxidative phosphorylation (OXPHOS), a process that occurs in the mitochondrial electron transfer chain (ETC). The last enzyme of the ETC, and therefore the ulterior generator of ATP, is the ATP synthase. Interestingly, in mammalian cells, the ATP synthase can also degrad...

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