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Wang LY, Zhang L, Bai XY, Qiang RR, Zhang N, Hu QQ, Cheng JZ, Yang YL, Xiang Y. The Role of Ferroptosis in Amyotrophic Lateral Sclerosis Treatment. Neurochem Res 2024:10.1007/s11064-024-04194-w. [PMID: 38864944 DOI: 10.1007/s11064-024-04194-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/13/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease with a challenging treatment landscape, due to its complex pathogenesis and limited availability of clinical drugs. Ferroptosis, an iron-dependent form of programmed cell death (PCD), stands distinct from apoptosis, necrosis, autophagy, and other cell death mechanisms. Recent studies have increasingly highlighted the role of iron deposition, reactive oxygen species (ROS) accumulation, oxidative stress, as well as systemic Xc- and glutamate accumulation in the antioxidant system in the pathogenesis of amyotrophic lateral sclerosis. Therefore, targeting ferroptosis emerges as a promising strategy for amyotrophic lateral sclerosis treatment. This review introduces the regulatory mechanism of ferroptosis, the relationship between amyotrophic lateral sclerosis and ferroptosis, and the drugs used in the clinic, then discusses the current status of amyotrophic lateral sclerosis treatment, hoping to provide new directions and targets for its treatment.
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Affiliation(s)
- Le Yi Wang
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Lei Zhang
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Xin Yue Bai
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Rong Rong Qiang
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Ning Zhang
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Qian Qian Hu
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Jun Zhi Cheng
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Yan Ling Yang
- Yan 'an Small Molecule Innovative Drug R&D Engineering Research Center, School of Medicine, Yan'an University, Yan'an, China
| | - Yang Xiang
- College of Physical Education, Yan'an University, Shaanxi, 716000, China.
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Din Abdul Jabbar MA, Guo L, Nag S, Guo Y, Simmons Z, Pioro EP, Ramasamy S, Yeo CJJ. Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning. Amyotroph Lateral Scler Frontotemporal Degener 2024; 25:242-255. [PMID: 38052485 DOI: 10.1080/21678421.2023.2285443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023]
Abstract
OBJECTIVE To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML). METHODS We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction. RESULTS ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%. CONCLUSION Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.
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Affiliation(s)
- Muzammil Arif Din Abdul Jabbar
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
- Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Ling Guo
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Sonakshi Nag
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Yang Guo
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Zachary Simmons
- Department of Neurology, Pennsylvania State University College of Medicine, State College, PA, USA
| | - Erik P Pioro
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Savitha Ramasamy
- Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Crystal Jing Jing Yeo
- Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Lee Kong Chien School of Medicine, Imperial College London and Nanyang Technological University Singapore, Singapore, Singapore
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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Jin W, Boss J, Bakulski KM, Goutman SA, Feldman EL, Fritsche LG, Mukherjee B. Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305037. [PMID: 38585910 PMCID: PMC10996827 DOI: 10.1101/2024.03.28.24305037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background and Objectives Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function and a cure for this devastating disease remains elusive. Early detection and risk stratification are crucial for timely intervention and improving patient outcomes. This study aimed to identify predisposing genetic, phenotypic, and exposure-related factors for Amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential. Methods Utilizing data from the UK Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates. Results Both PRSs modestly predicted ALS diagnosis, but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a 4-fold higher ALS risk (95% CI: [2.04, 7.73]) versus those in the 40%-60% range. Discussions By leveraging UK Biobank data, our study uncovers predisposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.
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Affiliation(s)
- Weijia Jin
- Department of Biostatistics, University of Florida, Gainesville, Florida 32603, United States of America
| | - Jonathan Boss
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, Michigan 48109, United States of America
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan 48109, United States of America
| | - Kelly M. Bakulski
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan 48109, United States of America
| | - Stephen A. Goutman
- Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109, United States of America
| | - Eva L. Feldman
- Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109, United States of America
| | - Lars G. Fritsche
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, Michigan 48109, United States of America
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan 48109, United States of America
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, University of Michigan, Ann Arbor, Michigan 48109, United States of America
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan 48109, United States of America
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan 48109, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan 48109, United States of America
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Turabieh H, Afshar AS, Statland J, Song X. Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:718-725. [PMID: 38222431 PMCID: PMC10785857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a rare and devastating neurodegenerative disorder that is highly heterogeneous and invariably fatal. Due to the unpredictable nature of its progression, accurate tools and algorithms are needed to predict disease progression and improve patient care. To address this need, we developed and compared an extensive set of screener-learner machine learning models to accurately predict the ALS Function-Rating-Scale (ALSFRS) score reduction between 3 and 12 months, by paring 5 state-of-arts feature selection algorithms with 17 predictive models and 4 ensemble models using the publicly available Pooled Open Access Clinical Trials Database (PRO-ACT). Our experiment showed promising results with the blender-type ensemble model achieving the best prediction accuracy and highest prognostic potential.
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Affiliation(s)
- Hamza Turabieh
- Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia
| | - Askar S Afshar
- Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia
| | - Jeffery Statland
- Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Xing Song
- Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia
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Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I, Lourida I, Madan CR, Marzi SJ, Proitsi P, Rajkumar AP, Rittman T, Silajdžić E, Tamburin S, Ranson JM, Llewellyn DJ. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimers Dement 2023; 19:5860-5871. [PMID: 37654029 PMCID: PMC10840606 DOI: 10.1002/alz.13390] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/02/2023]
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
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Affiliation(s)
| | - Eric L Harshfield
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Headington, UK
| | - AmanPreet Badhwar
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Natasha Clarke
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Amir Dehsarvi
- School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Imre Lengyel
- Wellcome-Wolfson Institute of Experimental Medicine, Queen's University, Belfast, UK
| | - Ilianna Lourida
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute at Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anto P Rajkumar
- Institute of Mental Health, Mental Health and Clinical Neurosciences academic unit, University of Nottingham, Nottingham, UK, Mental health services of older people, Nottinghamshire healthcare NHS foundation trust, Nottingham, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Edina Silajdžić
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Janice M Ranson
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - David J Llewellyn
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Huang B, Geng X, Yu Z, Zhang C, Chen Z. Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease. Ann Clin Transl Neurol 2023; 10:892-903. [PMID: 37014017 PMCID: PMC10270250 DOI: 10.1002/acn3.51771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/05/2023] Open
Abstract
OBJECTIVE Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time. METHODS A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score. RESULTS Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients. INTERPRETATION We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.
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Affiliation(s)
- Baoyi Huang
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Xiang Geng
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Zhiyin Yu
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Chengfeng Zhang
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Zheng Chen
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
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Yang S, Kim SH, Kang M, Joo JY. Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges. Arch Pharm Res 2023:10.1007/s12272-023-01450-5. [PMID: 37261600 DOI: 10.1007/s12272-023-01450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/26/2023] [Indexed: 06/02/2023]
Abstract
The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea.
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Lopez-Bernal D, Balderas D, Ponce P, Rojas M, Molina A. Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review. Life (Basel) 2023; 13:life13041031. [PMID: 37109560 PMCID: PMC10146231 DOI: 10.3390/life13041031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/17/2023] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.
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Affiliation(s)
- Diego Lopez-Bernal
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - David Balderas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Pedro Ponce
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Mario Rojas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Arturo Molina
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
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Soares DF, Henriques R, Gromicho M, de Carvalho M, Madeira SC. Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis. Sci Rep 2023; 13:6182. [PMID: 37061549 PMCID: PMC10105751 DOI: 10.1038/s41598-023-33223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/10/2023] [Indexed: 04/17/2023] Open
Abstract
This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.
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Affiliation(s)
- Diogo F Soares
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
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Vidovic M, Müschen LH, Brakemeier S, Machetanz G, Naumann M, Castro-Gomez S. Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis. Cells 2023; 12:cells12050736. [PMID: 36899872 PMCID: PMC10000757 DOI: 10.3390/cells12050736] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/02/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of upper and lower motor neurons, resulting in progressive weakness of all voluntary muscles and eventual respiratory failure. Non-motor symptoms, such as cognitive and behavioral changes, frequently occur over the course of the disease. Considering its poor prognosis with a median survival time of 2 to 4 years and limited causal treatment options, an early diagnosis of ALS plays an essential role. In the past, diagnosis has primarily been determined by clinical findings supported by electrophysiological and laboratory measurements. To increase diagnostic accuracy, reduce diagnostic delay, optimize stratification in clinical trials and provide quantitative monitoring of disease progression and treatment responsivity, research on disease-specific and feasible fluid biomarkers, such as neurofilaments, has been intensely pursued. Advances in imaging techniques have additionally yielded diagnostic benefits. Growing perception and greater availability of genetic testing facilitate early identification of pathogenic ALS-related gene mutations, predictive testing and access to novel therapeutic agents in clinical trials addressing disease-modified therapies before the advent of the first clinical symptoms. Lately, personalized survival prediction models have been proposed to offer a more detailed disclosure of the prognosis for the patient. In this review, the established procedures and future directions in the diagnostics of ALS are summarized to serve as a practical guideline and to improve the diagnostic pathway of this burdensome disease.
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Affiliation(s)
- Maximilian Vidovic
- Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
- Correspondence: (M.V.); (S.C.-G.)
| | | | - Svenja Brakemeier
- Department of Neurology and Center for Translational Neuro and Behavioral Sciences (C-TNBS), University Hospital Essen, 45147 Essen, Germany
| | - Gerrit Machetanz
- Department of Neurology, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Marcel Naumann
- Translational Neurodegeneration Section “Albrecht Kossel”, Department of Neurology, University Medical Center, University of Rostock, 18147 Rostock, Germany
| | - Sergio Castro-Gomez
- Department of Neurodegenerative Disease and Geriatric Psychiatry/Neurology, University Hospital Bonn, 53127 Bonn, Germany
- Institute of Physiology II, University Hospital Bonn, 53115 Bonn, Germany
- Department of Neuroimmunology, Institute of Innate Immunity, University Hospital Bonn, 53127 Bonn, Germany
- Correspondence: (M.V.); (S.C.-G.)
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