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Chen Z, Tian X, Chen C, Chen C. Research on disease diagnosis based on teacher-student network and Raman spectroscopy. Lasers Med Sci 2024; 39:129. [PMID: 38735976 DOI: 10.1007/s10103-024-04078-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 05/06/2024] [Indexed: 05/14/2024]
Abstract
Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.
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Affiliation(s)
- Zishuo Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Xuecong Tian
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, 830046, China.
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Robertson JL, Dervisis N, Rossmeisl J, Nightengale M, Fields D, Dedrick C, Ngo L, Issa AS, Guruli G, Orlando G, Senger RS. Cancer detection in dogs using rapid Raman molecular urinalysis. Front Vet Sci 2024; 11:1328058. [PMID: 38384948 PMCID: PMC10879274 DOI: 10.3389/fvets.2024.1328058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor. Methods Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software. Results and discussion The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.
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Affiliation(s)
- John L. Robertson
- Department of Biomedical Engineering and Mechanics, College of Engineering, Virginia Tech, Blacksburg, VA, United States
- Rametrix Technologies Inc., Blacksburg, VA, United States
| | - Nikolas Dervisis
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - John Rossmeisl
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Marlie Nightengale
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Daniel Fields
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Cameron Dedrick
- Virginia Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| | - Lacey Ngo
- Department of Biomedical Engineering and Mechanics, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | - Amr Sayed Issa
- Rametrix Technologies Inc., Blacksburg, VA, United States
| | - Georgi Guruli
- Department of Surgery, VCU Health, Richmond, VA, United States
| | - Giuseppe Orlando
- Department of General Surgery, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Ryan S. Senger
- Rametrix Technologies Inc., Blacksburg, VA, United States
- Department of Biological Systems Engineering, College of Agriculture & Life Sciences and College of Engineering, Virginia Tech, Blacksburg, VA, United States
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Harris G, Stickland CA, Lim M, Goldberg Oppenheimer P. Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury. Cells 2023; 12:2589. [PMID: 37998324 PMCID: PMC10670390 DOI: 10.3390/cells12222589] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.
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Affiliation(s)
- Georgia Harris
- Advanced Nanomaterials Structures and Applications Laboratories, School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Clarissa A. Stickland
- Advanced Nanomaterials Structures and Applications Laboratories, School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Matthias Lim
- Advanced Nanomaterials Structures and Applications Laboratories, School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Pola Goldberg Oppenheimer
- Advanced Nanomaterials Structures and Applications Laboratories, School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Institute of Healthcare Technologies, Mindelsohn Way, Birmingham B15 2TH, UK
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Esposito P, Picciotto D, Cappadona F, Costigliolo F, Russo E, Macciò L, Viazzi F. Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes. World J Diabetes 2023; 14:1450-1462. [PMID: 37970131 PMCID: PMC10642421 DOI: 10.4239/wjd.v14.i10.1450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/18/2023] [Accepted: 08/28/2023] [Indexed: 10/09/2023] Open
Abstract
Diabetes mellitus is one of the most common causes of chronic kidney disease. Kidney involvement in patients with diabetes has a wide spectrum of clinical presentations ranging from asymptomatic to overt proteinuria and kidney failure. The development of kidney disease in diabetes is associated with structural changes in multiple kidney compartments, such as the vascular system and glomeruli. Glomerular alterations include thickening of the glomerular basement membrane, loss of podocytes, and segmental mesangiolysis, which may lead to microaneurysms and the development of pathognomonic Kimmelstiel-Wilson nodules. Beyond lesions directly related to diabetes, awareness of the possible coexistence of nondiabetic kidney disease in patients with diabetes is increasing. These nondiabetic lesions include focal segmental glomerulosclerosis, IgA nephropathy, and other primary or secondary renal disorders. Differential diagnosis of these conditions is crucial in guiding clinical management and therapeutic approaches. However, the relationship between diabetes and the kidney is bidirectional; thus, new-onset diabetes may also occur as a complication of the treatment in patients with renal diseases. Here, we review the complex and multifaceted correlation between diabetes and kidney diseases and discuss clinical presentation and course, differential diagnosis, and therapeutic oppor-tunities offered by novel drugs.
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Affiliation(s)
- Pasquale Esposito
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa 16132, Italy
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
| | - Daniela Picciotto
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
| | - Francesca Cappadona
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
| | - Francesca Costigliolo
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
| | - Elisa Russo
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa 16132, Italy
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
| | - Lucia Macciò
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa 16132, Italy
| | - Francesca Viazzi
- Department of Internal Medicine and Medical Specialties (DiMI), University of Genoa, Genoa 16132, Italy
- Unit of Nephrology, Dialysis and Transplantation, IRCCS Ospedale Policlinico San Martino, Genoa 16132, Italy
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