Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells

Predictive modeling of ALS progression: an XGBoost approach using clinical features

This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies.

Machine learning and brain-computer interface approaches in prognosis and individualized care strategies for individuals with amyotrophic lateral sclerosis: A systematic review

Amyotrophic lateral sclerosis (ALS) characterized by progressive degeneration of motor neurons is a debilitating disease, posing substantial challenges in both prognosis and daily life assistance. However, with the advancement of machine learning (ML) which is renowned for tackling many real-world settings, it can offer unprecedented opportunities in prognostic studies and facilitate individuals with ALS in motor-imagery tasks. ML models, such as random forests (RF), have emerged as the most common and effective algorithms for predicting disease progression and survival time in ALS. The findings revealed that RF models had an excellent predictive performance for ALS, with a testing R2 of 0.524 and minimal treatment effects of 0.0717 for patient survival time. Despite significant limitations in sample size, with a maximum of 18 participants, which may not adequately reflect the population diversity being studied, ML approaches have been effectively applied to ALS datasets, and numerous prognostic models have been tested using neuroimaging data, longitudinal datasets, and core clinical variables. In many literatures, the constraints of ML models are seldom explicitly enunciated. Therefore, the main objective of this research is to provide a review of the most significant studies on the usage of ML models for analyzing ALS. This review covers a variation of ML algorithms involved in applications in ALS prognosis besides, leveraging ML to improve the efficacy of brain-computer interfaces (BCIs) for ALS individuals in later stages with restricted voluntary muscular control. The key future advances in individualized care and ALS prognosis may include the advancement of more personalized care aids that enable real-time input and ongoing validation of ML in diverse healthcare contexts.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS.

METHODS

We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.

RESULTS

In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.

CONCLUSIONS

Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.

A literature review: machine learning-based stem cell investigation

Background and Objective

Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement.

Methods

In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions.

Key Content and Findings

The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions.

Conclusions

The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Phototoxicity avoidance is a potential therapeutic approach for retinal dystrophy caused by EYS dysfunction

Inherited retinal dystrophies (IRDs) are progressive diseases leading to vision loss. Mutation in the eyes shut homolog (EYS) gene is one of the most frequent causes of IRD. However, the mechanism of photoreceptor cell degeneration by mutant EYS has not been fully elucidated. Here, we generated retinal organoids from induced pluripotent stem cells (iPSCs) derived from patients with EYS-associated retinal dystrophy (EYS-RD). In photoreceptor cells of RD organoids, both EYS and G protein-coupled receptor kinase 7 (GRK7), one of the proteins handling phototoxicity, were not in the outer segment, where they are physiologically present. Furthermore, photoreceptor cells in RD organoids were vulnerable to light stimuli, and especially to blue light. Mislocalization of GRK7, which was also observed in eys-knockout zebrafish, was reversed by delivering control EYS into photoreceptor cells of RD organoids. These findings suggest that avoiding phototoxicity would be a potential therapeutic approach for EYS-RD.

Genetic factors affecting survival in Japanese patients with sporadic amyotrophic lateral sclerosis: a genome-wide association study and verification in iPSC-derived motor neurons from patients

BACKGROUND

Several genetic factors are associated with the pathogenesis of sporadic amyotrophic lateral sclerosis (ALS) and its phenotypes, such as disease progression. Here, in this study, we aimed to identify the genes that affect the survival of patients with sporadic ALS.

METHODS

We enrolled 1076 Japanese patients with sporadic ALS with imputed genotype data of 7 908 526 variants. We used Cox proportional hazards regression analysis with an additive model adjusted for sex, age at onset and the first two principal components calculated from genotyped data to conduct a genome-wide association study. We further analysed messenger RNA (mRNA) and phenotype expression in motor neurons derived from induced pluripotent stem cells (iPSC-MNs) of patients with ALS.

RESULTS

Three novel loci were significantly associated with the survival of patients with sporadic ALS-FGF1 at 5q31.3 (rs11738209, HR=2.36 (95% CI, 1.77 to 3.15), p=4.85×10-9), THSD7A at 7p21.3 (rs2354952, 1.38 (95% CI, 1.24 to 1.55), p=1.61×10-8) and LRP1 at 12q13.3 (rs60565245, 2.18 (95% CI, 1.66 to 2.86), p=2.35×10-8). FGF1 and THSD7A variants were associated with decreased mRNA expression of each gene in iPSC-MNs and reduced in vitro survival of iPSC-MNs obtained from patients with ALS. The iPSC-MN in vitro survival was reduced when the expression of FGF1 and THSD7A was partially disrupted. The rs60565245 was not associated with LRP1 mRNA expression.

CONCLUSIONS

We identified three loci associated with the survival of patients with sporadic ALS, decreased mRNA expression of FGF1 and THSD7A and the viability of iPSC-MNs from patients. The iPSC-MN model reflects the association between patient prognosis and genotype and can contribute to target screening and validation for therapeutic intervention.

Recent Advances in High-Content Imaging and Analysis in iPSC-Based Modelling of Neurodegenerative Diseases

In the field of neurodegenerative pathologies, the platforms for disease modelling based on patient-derived induced pluripotent stem cells (iPSCs) represent a valuable molecular diagnostic/prognostic tool. Indeed, they paved the way for the in vitro recapitulation of the pathological mechanisms underlying neurodegeneration and for characterizing the molecular heterogeneity of disease manifestations, also enabling drug screening approaches for new therapeutic candidates. A major challenge is related to the choice and optimization of the morpho-functional study designs in human iPSC-derived neurons to deeply detail the cell phenotypes as markers of neurodegeneration. In recent years, the specific combination of high-throughput screening with subcellular resolution microscopy for cell-based high-content imaging (HCI) screening allowed in-depth analyses of cell morphology and neurite trafficking in iPSC-derived neuronal cells by using specific cutting-edge microscopes and automated computational assays. The present work aims to describe the main recent protocols and advances achieved with the HCI analysis in iPSC-based modelling of neurodegenerative diseases, highlighting technical and bioinformatics tips and tricks for further uses and research. To this end, microscopy requirements and the latest computational pipelines to analyze imaging data will be explored, while also providing an overview of the available open-source high-throughput automated platforms.

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to, that is, distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation toward hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review

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.

Induced Pluripotent Stem Cells and Their Applications in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in the loss of motor function in the central nervous system (CNS) and ultimately death. The mechanisms underlying ALS pathogenesis have not yet been fully elucidated, and ALS cannot be treated effectively. Most studies have applied animal or single-gene intervention cell lines as ALS disease models, but they cannot accurately reflect the pathological characteristics of ALS. Induced pluripotent stem cells (iPSCs) can be reprogrammed from somatic cells, possessing the ability to self-renew and differentiate into a variety of cells. iPSCs can be obtained from ALS patients with different genotypes and phenotypes, and the genetic background of the donor cells remains unchanged during reprogramming. iPSCs can differentiate into neurons and glial cells related to ALS. Therefore, iPSCs provide an excellent method to evaluate the impact of diseases on ALS patients. Moreover, patient-derived iPSCs are obtained from their own somatic cells, avoiding ethical concerns and posing only a low risk of immune rejection. The iPSC technology creates new hope for ALS treatment. Here, we review recent studies on iPSCs and their applications in disease modeling, drug screening and cell therapy in ALS, with a particular focus on the potential for ALS treatment.

Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis

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.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Safety and tolerability of bosutinib in patients with amyotrophic lateral sclerosis (iDReAM study): A multicentre, open-label, dose-escalation phase 1 trial

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease caused by the loss of motor neurons, and development of effective medicines is urgently required. Induced pluripotent stem cell (iPSC)-based drug repurposing identified the Src/c-Abl inhibitor bosutinib, which is approved for the treatment of chronic myelogenous leukemia (CML), as a candidate for the molecular targeted therapy of ALS.

METHODS

An open-label, multicentre, dose-escalation phase 1 study using a 3 + 3 design was conducted in 4 hospitals in Japan to evaluate the safety and tolerability of bosutinib in patients with ALS. Furthermore, the exploratory efficacy was evaluated using Revised ALS Functional Rating Scale (ALSFRS-R), predictive biomarkers including plasma neurofilament light chain (NFL) were explored, and single-cell RNA sequencing of iPSC-derived motor neurons was conducted. Patients, whose total ALSFRS-R scores decreased by 1-3 points during the 12-week, received escalating doses starting from 100 mg quaque die (QD) up to 400 mg QD based on dose-limiting toxicity (DLT) occurrence, and all participants who received one dose of the study drug were included in the primary analysis. This trial is registered with ClinicalTrials.gov, NCT04744532, as Induced pluripotent stem cell-based Drug Repurposing for Amyotrophic Lateral Sclerosis Medicine (iDReAM) study.

FINDINGS

Between March 29, 2019 and May 7, 2021, 20 patients were enrolled, 13 of whom received bosutinib treatment and 12 were included in the safety and efficacy analyses. No DLTs were observed up to 300 mg QD, but DLTs were observed in 3/3 patients of the 400 mg QD cohort. In all patients receiving 100 mg-400 mg, the prevalent adverse events (AEs) were gastrointestinal AEs in 12 patients (92.3%), liver function related AEs in 7 patients (53.8%), and rash in 3 patients (23.1%). The safety profile was consistent with that known for CML treatment, and ALS-specific AEs were not observed. A subset of patients (5/9 patients) was found to respond well to bosutinib treatment over the 12-week treatment period. It was found that the treatment-responsive patients could be distinguished by their lower levels of plasma NFL. Furthermore, single-cell RNA sequencing of iPSC-derived motor neurons revealed the pathogenesis related molecular signature in patients with ALS showing responsiveness to bosutinib.

INTERPRETATION

This is the first trial of a Src/c-Abl inhibitor, bosutinib, for patients with ALS. The safety and tolerability of bosutinib up to 300 mg, not 400 mg, in ALS were described, and responsiveness of patients on motor function was observed. Since this was an open-label trial within a short period with a limited number of patients, further clinical trials will be required.

FUNDING

AMED and iPS Cell Research Fund.

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence

Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that differentiate into various cell types. iPSCs are expected to be used for disease modeling and for developing novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein-protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.

Exploring Motor Neuron Diseases Using iPSC Platforms

The degeneration of motor neurons is a pathological hallmark of motor neuron diseases (MNDs), but emerging evidence suggests that neuronal vulnerability extends well beyond this cell subtype. The ability to assess motor function in the clinic is limited to physical examination, electrophysiological measures, and tissue-based or neuroimaging techniques which lack the resolution to accurately assess neuronal dysfunction as the disease progresses. Spinal muscular atrophy (SMA), spinal and bulbar muscular atrophy (SBMA), hereditary spastic paraplegia (HSP), and amyotrophic lateral sclerosis (ALS) are all MNDs with devastating clinical outcomes that contribute significantly to disease burden as patients are no longer able to carry out normal activities of daily living. The critical need to accurately assess the cause and progression of motor neuron dysfunction, especially in the early stages of those diseases, has motivated the use of human iPSC-derived motor neurons (hiPSC-MN) to study the neurobiological mechanisms underlying disease pathogenesis and to generate platforms for therapeutic discovery and testing. As our understanding of MNDs has grown, so too has our need to develop more complex in vitro models which include hiPSC-MN co-cultured with relevant non-neuronal cells in 2D as well as in 3D organoid and spheroid systems. These more complex hiPSC-derived culture systems have led to the implementation of new technologies, including microfluidics, multielectrode array, and machine learning which offer novel insights into the functional correlates of these emerging model systems.

iPSC-based disease modeling and drug discovery in cardinal neurodegenerative disorders

It has been 15 years since the birth of human induced pluripotent stem cell (iPSC) technology in 2007, and the scope of its application has been expanding. In addition to the development of cell therapies using iPSC-derived cells, pathological analyses using disease-specific iPSCs and clinical trials to confirm the safety and efficacy of drugs developed using iPSCs are progressing. With the innovation of related technologies, iPSC applications are about to enter a new stage. This review outlines advances in iPSC modeling and therapeutic development for cardinal neurodegenerative diseases, such as amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease.

Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach

Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.

The future of biological dosimetry in mass casualty radiation emergency response, personalized radiation risk estimation and space radiation protection

PURPOSE

The aim of this brief personal, high level review is to consider the state of the art for biological dosimetry for radiation routine and emergency response, and the potential future progress in this fascinating and active field. Four areas in which biomarkers may contribute to scientific advancement through improved dose and exposure characterization, as well as potential contributions to personalized risk estimation, are considered: emergency dosimetry, molecular epidemiology, personalized medical dosimetry, and space travel.

CONCLUSION

Ionizing radiation biodosimetry is an exciting field which will continue to benefit from active networking and collaboration with the wider fields of radiation research and radiation emergency response to ensure effective, joined up approaches to triage; radiation epidemiology to assess long term, low dose, radiation risk; radiation protection of workers, optimization and justification of radiation for diagnosis or treatment of patients in clinical uses, and protection of individuals traveling to space.