Global NMR Discussion Meetings

2025

  • Polyelectrolyte Hydrogels for Water Desalination Examined via Sodium NMR Spectroscopy

    Huijing Zou (New York University, United States)

    LinkedIn: @Huijing Zou

    Abstract: Hydrogel desalination is a recently developed method for removing salt from water. Its mechanism is based on the electrostatic imbalance between the fixed charge groups in the polyelectrolyte hydrogels and the surrounding solution. Combining polyelectrolyte hydrogels with thermoresponsive materials enables a temperature-driven swelling and shrinking cycle, which provides a more energy-efficient way to extract desalinated water from hydrogels by using solar energy. In this study, the interactions between salt ions and sodium polyacrylate hydrogels in NaCl solution were analyzed using NMR spectroscopy. The Na+ ion distribution was characterized by 23Na NMR imaging. The relaxation rates of Na+ in the supernatant and hydrogel phase were measured from 298 K to 318 K. The hydrogels in multi-salt environments were measured under two conditions: one with controlled concentrations of each type of salt, and another with controlled total ionic strength. Furthermore, multiple quantum filtered NMR was applied to analyze quadrupolar interactions between Na+ ions and hydrogels. Current state-of-the-art analyses for studying the ionic flux are based on conductivity measurements. The use of sodium NMR spectroscopy and imaging provides much deeper insights into the salt-rejection mechanisms. The results from this study provide valuable insights for the design of hydrogel structures and the improvement of desalination performance.

    1. Jonas Koppe Avatar
      Jonas Koppe

      Thank you for the presentation. Can the amount of Na+ ions in the supernatant quantified by both imaging and relaxation analysis? If so, do the results agree?

      Reply
      1. Huijing Zou Avatar
        Huijing Zou

        Hi Jonas, thank you for your comment!
        The Na+ ions in the supernatant can be quantified by NMR imaging by using a reference sample (NaCl solution only). We can compare the integrals to estimate the amount of Na+ ions within the detection region. The integral ratio (with half-tube hydrogels: without hydrogels) is 2.51 for 1dzg, and 2.72 for 1d imaging. The relaxation analysis help us understand the Na+ ion mobility. When comparing relaxation rates under different conditions (temperature, multi-salt…), we can get a rough view on how different conditions affect the bound Na+ and free Na+ ions.
        Hope this answers your question.

        Reply
    2. Blake Wilson Avatar
      Blake Wilson

      Hi Huijing, thank you for the presentation. What is the spatial resolution of your imaging measurements, and what is the size of the average piece of hydrogel?

      Reply
      1. Huijing Zou Avatar
        Huijing Zou

        Hi Blake, thank you for your comment!
        The 1D imaging is done on Bruker AVIII 400 MHz, and has TD=512 and swh=39682.5 Hz, spatial resolution is 152.3 μm. In fact, I haven’t done much measurement on the size of hydrogels and I assume you mean the dried hydrogels, most dried hydrogels I added has a length within 3 mm.
        Hope this answers your question!

        Reply

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  • A BOTTOM-UP APPROACH: COMPLIMENTING NMR RELAXOMETRY WITH THEORY AND SIMULATIONS

    Angel Mary Chiramel Tony (University of Rostock, Germany)

    LinkedIn: @Angel Mary C T; X: @AngelMaryCT1; Bluesky: @angelchirameltony.bsky.social

    Abstract: By using Fast Field Cycling (FFC) NMR spectroscopy, dynamical processes can be studied over many orders of magnitude. However, interpreting FFC-NMR data often requires models that are specific to certain systems. Here we propose a novel approach for computing the inter- and intramolecular contribution to the magnetic dipolar relaxation from molecular dynamics (MD) simulations. This method is enabling us to predict NMR relaxation rates, addressing the full FFC frequency range, covering many orders of magnitude, while also avoiding influences due to limitations in system size and the accessible time interval. Our methodology is based on combining the analytical theory of Hwang and Freed (HF) for the long-range intermolecular contribution of the magnetic dipole-dipole correlation function with MD simulations. Here we apply this approach to compute the inter- and intramolecular NMR relaxation of 19F nuclei in the ionic liquid C5Py-NTf2 to study the dynamics of the NTf2 anion. By employing our MD simulation-based approach, we could show that the correlation functions due to the HF theory does asymptotically converge with our MD simulation results at long times. This approach is successful in disentangling the different contributions to the intramolecular 19F-NMR relaxation rate due to the complex intramolecular dynamics of the anion. We successfully described the rotational anisotropy, differentiating between the overall tumbling of the anion and internal rotation of the CF3 group, which is difficult to decipher with the fitting models.

    1. Amit Bhattacharya Avatar
      Amit Bhattacharya

      Hi Angel, nice presentation! I was wondering—on slide 11, why doesn’t the 283 K data fit as well as the 303 K and 323 K data, which show excellent fit?

      Reply
      1. Angel Mary Chiramel Tony Avatar
        Angel Mary Chiramel Tony

        Hello Amit,
        Many thanks for the question.
        The lines are indicated for the Relaxation rate calculated from MD(with correction term) in the bottom approach manner. It’s not a fit for experimental data points obtained from FFC NMR.
        The mismatch we have for 283K can probably be attributed to the quadrupolar nuclei(Deuterium on cation) and dipolar nuclei (Fluorine on anion) interaction. As this effect becomes pronounced at lower temperature, we see the effect for 283K compared to the other two higher temperatures.

        Let me know if it clarifies your question and if you have any other questions/curiosity.

        Best regards
        Angel

        Reply
        1. Amit Bhattacharya Avatar
          Amit Bhattacharya

          Thanks Angel, it clarifies my question.

          Reply
    2. Raj Chaklashiya Avatar
      Raj Chaklashiya

      Hi Angel, nice talk! I was wondering, is it possible to apply this method or a modified version of this method to vitrified samples (e.g. frozen solutions at ultralow temperatures)? I am assuming this would be in a regime of significantly less tumbling, but there would still be processes (e.g. vibrational, rotational) that contribute to and create a relaxation time.

      Reply
    3. Angel Mary Chiramel Tony Avatar
      Angel Mary Chiramel Tony

      Hello Raj,
      Yes, it should be ideally possible to use the framework for different kinds of soft matter systems possibly with slight modifications in the fitting part with KWW functions for intramolecular part(mostly rotational and vibrational in nature).
      However I have personally used it for water, ionic liquids and electrolytes in the temperature range where it remains liquid state. And it works perfectly predicting the broad frequency range (verification done from both from low resolution and high resolution spectrometers).

      I will be happy to answer if you write to me at angel.tony@uni-rostock.de if you need more clarification or have more questions/ curiosity.

      Best regards
      Angel

      Reply

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  • Study of host-guess capacity of N-acylhydrazone-based macrocycles using NMR spectroscopy

    Anca Gabriela Mirea (National Institute of Materials Physics, Romania)

    Abstract: We present herein the synthesis of novel [2 + 2] and [3 + 3] N-acylhydrazone-based macrocycles using a pool of dialdehydes and dihydrazides. The macrocycles were used in various assays to investigate the hosting capacity of various guests using NMR.

    1. Amit Bhattacharya Avatar
      Amit Bhattacharya

      Hi Anca, interesting work! Could you please elaborate a bit more on how you inferred the triangular or tetragonal geometry using 2D NMR?

      Reply
      1. Anca Gabriela Mirea Avatar
        Anca Gabriela Mirea

        Hy Amit! To elucidate the macrocyclic shape, we recorded 2D NMR (NOESY) indicating a triangular shape, according to the space interactions of protons within the molecule. The spectrum revealed NOE connectivity between NH and H (hydrazide moieties) protons, as well as CH=N and H (aldehyde moieties) protons. For more details you can read our article DOI: 10.1039/d4ta09035g.

        Thank you very much for your interest! I appreciate your question!

        Reply
    2. Anca Gabriela Mirea Avatar
      Anca Gabriela Mirea

      Hy Amit! To elucidate the macrocyclic shape, we recorded 2D NMR (NOESY) indicating a triangular shape, according to the space interactions of protons within the molecule. The spectrum revealed NOE connectivity between NH and H (hydrazide moieties) protons, as well as CH=N and H (aldehyde moieties) protons. For more details you can read our article DOI: 10.1039/d4ta09035g.

      Thank you very much for your interest! I appreciate your question!

      Reply
      1. Amit Bhattacharya Avatar
        Amit Bhattacharya

        Thanks Anca.

        Reply
    3. Riley Hooper Avatar
      Riley Hooper

      Nice work Anca – why do you think the rectangular macrocycle readily took up F- and not Br-? What about Cl-? From your work, do you have any insights on what kind of synthetic considerations give rise to different macrocycle shapes?
      Thanks!

      Reply
      1. Anca Gabriela Mirea Avatar
        Anca Gabriela Mirea

        Hy Riley! The interaction of the rectangular macrocycle with TBAF may either lead to deprotonation of the hydroxyl and amide groups or cause
        hydrogen bonding between the fluoride and the two groups.
        The shape of the macrocycle depends on the precursors used for the synthesis.

        Thank you very much for your interest! I appreciate your question!

        Reply

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  • Toward Understanding of Nuclear Spin Relaxation at Zero-to-Ultralow Fields

    Chengtong Zhang (New York University, United States)

    Abstract: NMR experiments can interrogate a broad spectrum of molecular tumbling regimes and can accurately measure interatomic distances in solution with sub-nanometer resolution. Relaxation rates of nuclear spin polarization can unravel many dynamical and structural aspects of biomolecules in their native form. The high field relaxometry/dispersion techniques are widely used to monitor protein folding, molecular size, intermolecular interactions etc. However, little effort was devoted to extracting molecular information from relaxation rates at zero-to-ultralow-field (ZULF) mainly due to poor SNR and intricate spin dynamics. We develop the theoretical framework to understand relaxation rates measured in a ZULF-NMR setup with detection using optical-atomic magnetometers. In regimes where the scalar coupling constants and differences in Larmor frequencies of heteronuclear systems AX(N-1) have similar magnitudes, the spectrum reaches its maximum complexity with 2^N peaks. Populations and coherences’ lifetime measurements will greatly depend on the choice of monitored peaks’ decay. This multifaceted analysis of the same relaxation interaction can lead to a more accurate and robust determination of its strength and correlation time leading to new strategies for simultaneous extraction of structural and dynamical. We highlight how several factors impact the observed rates, such as (i) the shuttling profile from the (pre)polarizing magnet to the detection region, (ii) the measurement field, (iii) the detection method (single- or dual-channel) and even (iv) the nutation angle induced by the detection pulse. Our findings are compared to experimental relaxation rates measured for two [13C]-labelled molecules, showing how structural constraints and rotational tumbling can be inferred from ZULF relaxometry studies.

    1. Blake Wilson Avatar
      Blake Wilson

      Hello Chengtong, great presentation. This is very interesting with all of the different rates contributing. Can you comment on the origin of the two relaxation components (slow and fast)?

      Reply
      1. Chengtong Zhang Avatar
        Chengtong Zhang

        Hi Blake! Thank you for your question! The two relaxation components were derived from a Bi exponential decay model by fitting the amplitude of the signal through storage time(The time we wait for polarization). And the model of the Bi exponential decay was from the population evolution, via the dipole-dipole interaction in the relaxation mechanism.

        Reply

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  • Understanding the differential RNA-binding of HuD isoforms through conformational dynamics using solution NMR spectroscopy

    Nikhil Sunny (IISER Pune, India)

    LinkedIn: @Nikhil Sunny; X: @Nikhil__Sunny__

    Abstract: HuD is an RNA-binding protein (RBP) essential for neuronal development and glucose homeostasis. It has tandemly arranged three RNA recognition motifs (RRMs). Multiple isoforms, namely, HuD A, HuD B, and HuD D—have been reported that are differentially expressed in various tissues. The A and B isoforms both feature an unstructured N-terminal region; however, the A isoform has five additional amino acids when compared to the B isoform. This difference significantly impacts the RNA-binding and translation of insulin 2 mRNA. While the structure of HuD RRM12 is known, it does not include the unstructured N-terminal region, creating a gap in understanding its role in RNA targeting. In this study, we focus on understanding the role of the unstructured N-terminal region of A and B isoforms in the RNA-binding activity of the RRM1 domain by using NMR-based dynamics experiments and other biophysical techniques. FOur preliminary results show that the presence of the N-terminal leads to line-broadening and the disappearance of peaks in the 2D 15N-1H HSQC spectra. The disappeared peaks are mainly from the N-terminal region and the possible site of intra- and/or intermolecular interactions. In the presence of the N-terminal region, CSP is found in or near the RNP motifs of RRM1, which are the sites for RNA binding. We believe that this study will provide insights into how intrinsically disordered regions affect the intrinsic dynamics and RNA-binding activity of the RRM.

    1. Nicolas Bolik-Coulon Avatar
      Nicolas Bolik-Coulon

      Interesting!
      Although I believe you are planning on further experiments to confirm the interaction of the N-ter with the folded domain, can you comment on the effect it could have on the binding to RNA?
      Also, how do the R1 and R2 of the N-ter tail evolve as a function of residue number?

      Reply
      1. Nikhil Sunny Avatar
        Nikhil Sunny

        1) The RNA-binding region can either be masked, reducing overall binding affinity, or it can function as an auxiliary region that enhances binding affinity. Since this RNA recognition motif (RRM) is a weak binder, we are working on optimizing the experimental parameters for the binding studies. I suspect it will act as an auxiliary region facilitating RNA binding, as shown from EMSA studies on Isoforms of HuD (full-length)(https://doi.org/10.1371/journal.pone.0194482)
        2) “R1 and R2 of the N-ter tail evolve as a function of residue number.” I haven’t done those experiments. That’s the next part of my project.

        Reply
    2. Chandan Singh Avatar
      Chandan Singh

      Interesting work:

      What type of experiments you are planning to look for exact functional role of this protein?

      Reply
      1. Nikhil Sunny Avatar
        Nikhil Sunny

        I will be doing in vitro studies only; majorly binding studies with RNA using ITC, NMR, fluorescence, etc.

        Reply

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  • Characterizing Metabolic Dysregulation in Early-Stage Chronic Kidney Disease for Diagnostic Insight

    Upasna Gupta (Centre of Biomedical Research (CBMR) & Lucknow and Academy of Scientific and Innovative Research(AcSIR), India)

    LinkedIn: @Upasna Gupta; X: @Upasnagupta30

    Abstract: The progressive illness known as chronic kidney disease (CKD) can often be challenging to diagnose in its early stages with conventional diagnostic approaches such as serum creatinine and albumin assessment. Identifying possible biomarkers for early detection and personalized treatment, as well as physiological changes linked to early CKD—an area that hasn’t been fully investigated before—is the goal of this study to address this gap.
    We performed a metabolomic analysis using ¹H NMR on 115 human serum samples (24 healthy controls, 91 patients with early-stage CKD). MetaboAnalyst 6.0 was used for data pre-processing and statistical analyses (PCA, PLS-DA, OPLS-DA, ANOVA, and Wilcoxon Mann-Whitney test). Strong differentiation between CKD stages was shown by random forest modelling. The KEGG database was used to perform pathway enrichment, and ROC analysis evaluated the diagnostic value of important metabolites.
    Across CKD stages, significant changes in ten different metabolites: myo-inositol, glycerol, pyruvate, carnitine, phenylalanine, tyrosine, histidine, TMAO, 2-hydroxyisobutyrate, and 3-hydroxyisobutyrate (p 1). AUC values > 0.7 from ROC curves demonstrated its potential for diagnosis. Pathway analysis revealed significant dysregulation in metabolism of inositol phosphate, tyrosine, histidine, pyruvate, and biosynthesis of phenylalanine, tryptophan and tyrosine.
    This comprehensive metabolomics investigation identified potential early-stage CKD biomarkers in addition to significant metabolic abnormalities. These findings could help provide individualized care for CKD early management.

    1. Chandan Singh Avatar
      Chandan Singh

      Thanks for a nice presentation. I have following questions regarding the same:
      1. In the stack plot showing the 1D NMR spectra shown gradual variation of creatinine and format in different groups but these two do not show up in the contributing metabolic factors of group deafferentation. What can be possible explanation?

      2. Similarly, my-inositol does not seem to vary much in the 1D plots but its there in contributing factors of group differentiations. What can be the reason?
      Thanks again.

      Reply
      1. Upasna Gupta Avatar
        Upasna Gupta

        Thank you, sir.
        1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.

        Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.

        2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.

        Reply
    2. Marco Schiavina Avatar
      Marco Schiavina

      Hey, very interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? what kind of NMR pulse sequences did you use? Is there any evidence of these proteins to be a biomarker of the disease?

      Reply
      1. Upasna Gupta Avatar
        Upasna Gupta

        Thank You, Dr. Marco Schiavina
        Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
        While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.

        Reply
    3. Upasna Gupta Avatar
      Upasna Gupta

      Thank you, sir.
      1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.

      Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.

      2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.

      Reply
    4. Upasna Gupta Avatar
      Upasna Gupta

      Thank You, Dr. Marco Schiavina
      Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
      While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.

      Reply

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  • Fibrosis Under the Lens: NMR Metabolomics and Machine Learning Illuminate Hidden Pathways and Offer a Non-Invasive Alternative to Liver Biopsy”

    Shreya Pandey (Centre of Biomedical Research, India)

    LinkedIn: Shreya Pandey; X: @shreyapandey171

    Abstract: The landscape of chronic liver disease has changed significantly, with metabolic dysfunction-associated steatotic liver disease(MASLD) now emerging as the most widespread form worldwide. In Asia, particularly in India, the prevalence of MASLD is increasing, largely driven by poor dietary habits and a sedentary way of life. MASLD spans from fat deposition to inflammation and fibrosis. Fibrosis stands out as the most critical indicator of liver-related complications and overall risk of death in MASLD. Early identification of fibrosis is critical, but current tests are often invasive or unreliable. While studies have explored metabolic changes in MASLD, few have focused on distinguishing early-stage fibrosis from steatosis.
    In this study, we used NMR-based metabolomics to analyse serum samples from n = 103 MASLD patients, divided into fibrosis (n = 44) and non-fibrosis (n = 59) groups based on standard non-invasive scoring systems. We identified seven metabolites—arginine, glycerol, aspartate, glucose, phenylalanine, histidine, and citrate—that significantly differed between the two groups and showed good diagnostic potential (AUROC> 0.70). Pathway analysis revealed disruptions in arginine and nitrogen metabolism, associated with liver scarring processes, and in energy and lipid metabolism, pointing to mitochondrial dysfunction and lipotoxic stress. Reduced aspartate levels also suggested loss of natural protection against fibrosis.
    This is the first study of MASLD cohort to differentiate early-stage fibrosis from steatosis using metabolomics. Our findings highlight the potential of a simple NMR based blood test to aid early diagnosis, guide treatment decisions, and personalize care—offering a non-invasive alternative to improve MASLD management.

    1. Chandan Singh Avatar
      Chandan Singh

      Thanks for the nice presentation. I have following questions related to the presentation:

      1. As per the study enhanced level of arginine leads to increased proline synthesis. Is this enhanced proline level reflected in metabolic profile?
      2. How is the lipid profile? Are there specific lipids which are changing?
      3. How does enhanced level of collagen synthesis lead to fibrosis?
      4. What portion of the result was used in machine learning?

      Thanks again

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you sir.
        1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
        2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
        3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
        4- Only the data from bins of significant metabolites was used. We excluded the water region and the regions that were not significant

        Reply
    2. Chandan Singh Avatar
      Chandan Singh

      Thanks for a nice presentation. I have following questions:

      1. As shown in the presentation the enhanced level of arginine leads to increased collagen production via increased proline level. Is increased proline reflected in the NMR profiling?
      2. How is does lipid profile look? Any specific lipids which are enhanced?
      3. How does increased collagen lead to liver fibrosis?
      4. What exact data was used in machine learning?

      Thanks again

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you sir.
        1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
        2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
        3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
        4- Sir, the data from the binned sheet that we obtained from chenomx was used in machine learning learning.

        Reply
    3. Daniel Vincent Avatar
      Daniel Vincent

      Interesting work! Could you tell the pulse program used? What data did you use for building the model ? Is NMETA available online

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you Daniel, we have used CPMG pulse program which is basically used to suppress large molecules. We have used the binned sheet generated using chenomx for creating model. As far as NMETA is concerned, it is not available online we are still working on it.

        Reply
    4. Ch s karthik Avatar
      Ch s karthik

      The presentation looks very informative but can you answer me the following question: What is NMeta? What all information does it require?

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you, Karthik,
        NMETA is a web-based application we are currently working on as part of our effort to develop a non-invasive alternative to liver biopsy.
        The process is simple: perform a 1D NMR experiment on a serum sample and upload the resulting spectrum to our webpage. The tool will then provide the probability of the individual having liver fibrosis.

        Reply
    5. Marco Schiavina Avatar
      Marco Schiavina

      Hello Shreya!
      Interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? Is there any evidence of these proteins to be a biomarker of the disease?

      Reply
      1. Shreya Pandey Avatar
        Shreya Pandey

        Thank you, Dr. Schiavina,
        We have not filtered the serum as we had to perform diffusion edited experiment on the same serum sample. Instead, we have used the CPMG pulse sequence to suppress signals from large molecules, particularly lipoproteins and lipid fragments, thereby minimizing their interference.
        While previous studies have compared MASLD (formerly NAFLD) with hepatocellular carcinoma (reference: ref:-https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(21)00455-2/fulltext), none have specifically examined non-fibrotic MASH versus early fibrotic MASH within the MASLD cohort. We are currently investigating this comparison and expect to share promising results soon.

        Reply

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