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Cardiovascular disease risk assessment through sensing the circulating microbiome with perovskite quantum dots leveraging deep learning models for bacterial species selection

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Abstract

Perovskite quantum dots (PQDs) are novel nanomaterials wherein perovskites are used to formulate quantum dots (QDs). The present study utilizes the excellent fluorescence quantum yields of these nanomaterials to detect 16S rRNA of circulating microbiome for risk assessment of cardiovascular diseases (CVDs). A long short-term memory (LSTM) deep learning model was used to find the association of the circulating bacterial species with CVD risk, which showed the abundance of three different bacterial species (Bauldia litoralis (BL), Hymenobacter properus (HYM), and Virgisporangium myanmarense (VIG)). The observations suggested that the developed nano-sensor provides high sensitivity, selectivity, and applicability. The observed sensitivities for Bauldia litoralis, Hymenobacter properus, and Virgisporangium myanmarense were 0.606, 0.300, and 0.281 fg, respectively. The developed sensor eliminates the need for labelling, amplification, quantification, and biochemical assessments, which are more labour-intensive, time-consuming, and less reliable. Due to the rapid detection time, user-friendly nature, and stability, the proposed method has a significant advantage in facilitating point-of-care testing of CVDs in the future. This may also facilitate easy integration of the approach into various healthcare settings, making it accessible and valuable for resource-constrained environments.

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Data availability

The data that support the findings of this study are available on request from the corresponding author (PKM).

Abbreviations

BL:

Bauldia litoralis

CMB:

Circulatory microbiome

CCF:

Cell-free circulating

CVDs:

Cardiovascular diseases

DAPI:

4′,6-Diamidino-2-phenylindole

EDC:

1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide

ESRD:

End-stage renal disease

HYM:

Hymenobacter properus

IBD:

Intestinal barrier dysfunction

NFW:

Nuclease-free water

PI:

Propidium iodide

PQDs:

Perovskite quantum dots

QDs:

Quantum dots

VIG:

Virgisporangium myanmarense

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Author notes

  1. Nazim Nazeer and Vikas Gurjar contributed equally.

Authors and Affiliations

  1. Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bypass Road, Bhauri, Bhopal, 462 030, MP, India

    Nazim Nazeer, Vikas Gurjar, Pooja Ratre, Rakhi Dewangan, Kaniz Zaidi, Rajnarayan Tiwari, Nikita Soni, Arpit Bhargava & Pradyumna Kumar Mishra

  2. Faculty of Science, Ram Krishna Dharmarth Foundation (RKDF) University, Bhopal, India

    Arpit Bhargava

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  1. Nazim Nazeer
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Contributions

PKM devised the concept, developed the methodology, and supervised the experiments; NN, VG, RD, KZ, and NS performed most of the experiments; VG, PR, and NS collected and characterized the samples; NN and VG designed the figures; VG, PR, AB, and PKM performed the data analysis and interpretation; RT reviewed and edited the manuscript; and NN, VG, AB, and PKM drafted the original manuscript.

Corresponding author

Correspondence to Pradyumna Kumar Mishra.

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Ethical approval

The study was approved by the Institutional Ethics Committee (IEC), and the guidelines of the Indian Council of Medical Research (ICMR), Department of Health Research (DHR), Ministry of Health and Family Welfare (MoHFW), Government of India, were followed.

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The authors declare no competing interests.

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Nazeer, N., Gurjar, V., Ratre, P. et al. Cardiovascular disease risk assessment through sensing the circulating microbiome with perovskite quantum dots leveraging deep learning models for bacterial species selection. Microchim Acta 191, 255 (2024). https://doi.org/10.1007/s00604-024-06343-y

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