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Severcan F, Ozyurt I, Dogan A, Severcan M, Gurbanov R, Kucukcankurt F, Elibol B, Tiftikcioglu I, Gursoy E, Yangin MN, Zorlu Y. Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning. Sci Rep 2024; 14:19316. [PMID: 39164310 PMCID: PMC11336246 DOI: 10.1038/s41598-024-66501-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 07/02/2024] [Indexed: 08/22/2024] Open
Abstract
Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.
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Affiliation(s)
- Feride Severcan
- Department of Biophysics, Faculty of Medicine, Altinbas University, Istanbul, Türkiye.
| | - Ipek Ozyurt
- Department of Biophysics, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Ayca Dogan
- Department of Physiology, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Mete Severcan
- Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Türkiye
| | - Rafig Gurbanov
- Department of Bioengineering, Faculty of Engineering, Bilecik Seyh Edebali University, Bilecik, Türkiye
| | - Fulya Kucukcankurt
- Department of Medical Biology, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Birsen Elibol
- Department of Medical Biology, Faculty of Medicine, Istanbul Medeniyet University, Istanbul, Türkiye
- Department of Medical Biology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Türkiye
| | - Irem Tiftikcioglu
- Cigli Training and Research Hospital, Neurology Clinic, Bakircay University, İzmir, Türkiye
| | - Esra Gursoy
- Department of Neurology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Türkiye
- Basaksehir Cam and Sakura City Hospital, Neurology Clinics, Istanbul, Türkiye
| | - Melike Nur Yangin
- Biomedical Sciences Graduate Program, Institute of Graduate Studies, Altinbas University, Istanbul, Türkiye
| | - Yasar Zorlu
- Tepecik Educational and Training Hospital, Neurology Department, University of Health Sciences, Izmir, Türkiye
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Yadav SK, Yadav RD, Tabassum H, Arya M. Recent Developments in Nanotechnology-Based Biosensors for the Diagnosis of Coronavirus. PLASMONICS (NORWELL, MASS.) 2023; 18:955-969. [PMID: 37229148 PMCID: PMC10040920 DOI: 10.1007/s11468-023-01822-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 05/27/2023]
Abstract
The major challenge in today's world is that medical research is facing the existence of a vast number of viruses and their mutations, which from time to time cause outbreaks. Also, the continuous and spontaneous mutations occurring in the viruses and the emergence of resistant virus strains have become serious medical hazards. So, in view of the growing number of diseases, like the recent COVID-19 pandemic that has caused the deaths of millions of people, there is a need to improve rapid and sensitive diagnostic strategies to initiate timely treatment for such conditions. In the cases like COVID-19, where a real cure due to erratic and ambiguous signs is not available, early intervention can be life-saving. In the biomedical and pharmaceutical industries, nanotechnology has evolved exponentially and can overcome multiple obstacles in the treatment and diagnosis of diseases. Nanotechnology has developed exponentially in the biomedical and pharmaceutical fields and can overcome numerous challenges in the treatment and diagnosis of diseases. At the nano stage, the molecular properties of materials such as gold, silver, carbon, silica, and polymers get altered and can be used for the creation of reliable and accurate diagnostic techniques. This review provides insight into numerous diagnostic approaches focused on nanoparticles that could have been established for quick and early detection of such diseases.
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Affiliation(s)
- Sarita K. Yadav
- Department of Pharmacy, MLN Medical College, Prayagraj, Uttar Pradesh India
| | - Rahul Deo Yadav
- Department of Pharmacy, MLN Medical College, Prayagraj, Uttar Pradesh India
| | - Heena Tabassum
- Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra India
| | - Malti Arya
- Department of Pharmaceutics, Chandra Shekhar Singh College of Pharmacy, Uttar Pradesh Kaushambi, India
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San-Cristobal R, Martín-Hernández R, Ramos-Lopez O, Martinez-Urbistondo D, Micó V, Colmenarejo G, Villares Fernandez P, Daimiel L, Martínez JA. Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort. J Clin Med 2022; 11:jcm11123327. [PMID: 35743398 PMCID: PMC9224935 DOI: 10.3390/jcm11123327] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023] Open
Abstract
The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.
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Affiliation(s)
- Rodrigo San-Cristobal
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- Correspondence:
| | - Roberto Martín-Hernández
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico;
| | - Diego Martinez-Urbistondo
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Víctor Micó
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
| | - Gonzalo Colmenarejo
- Biostatistics & Bioinformatics Unit, Madrid Institute for Advanced Studies (IMDEA) Food, CEI UAM + CSIS, 28049 Madrid, Spain; (R.M.-H.); (G.C.)
| | - Paula Villares Fernandez
- Internal Medicine Department, Hospital Universitario HM Sanchinarro, 28050 Madrid, Spain; (D.M.-U.); (P.V.F.)
| | - Lidia Daimiel
- Nutritional Control of the Epigenome Group, IMDEA Food Institute, CEI UAM + CSIC, 28049 Madrid, Spain;
| | - Jose Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health Researh Program, Institute on Food and Health Sciences (Institute IMDEA Food), 28049 Madrid, Spain; (V.M.); (J.A.M.)
- CIBERobn Physiopathology of Obesity and Nutrition, Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
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