Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis

Phase Separation of SARS-CoV-2 Nucleocapsid Protein with TDP-43 Is Dependent on C-Terminus Domains

The SARS-CoV-2 nucleocapsid protein (N protein) is critical in viral replication by undergoing liquid-liquid phase separation to seed the formation of a ribonucleoprotein (RNP) complex to drive viral genomic RNA (gRNA) translation and in suppressing both stress granules and processing bodies, which is postulated to increase uncoated gRNA availability. The N protein can also form biomolecular condensates with a broad range of host endogenous proteins including RNA binding proteins (RBPs). Amongst these RBPs are proteins that are associated with pathological, neuronal, and glial cytoplasmic inclusions across several adult-onset neurodegenerative disorders, including TAR DNA binding protein 43 kDa (TDP-43) which forms pathological inclusions in over 95% of amyotrophic lateral sclerosis cases. In this study, we demonstrate that the N protein can form biomolecular condensates with TDP-43 and that this is dependent on the N protein C-terminus domain (N-CTD) and the intrinsically disordered C-terminus domain of TDP-43. This process is markedly accelerated in the presence of RNA. In silico modeling suggests that the biomolecular condensate that forms in the presence of RNA is composed of an N protein quadriplex in which the intrinsically disordered TDP-43 C terminus domain is incorporated.

Friend or foe: The role of stress granule in neurodegenerative disease

Stress granules (SGs) are dynamic membraneless organelles that form in response to cellular stress. SGs are predominantly composed of RNA and RNA-binding proteins that assemble through liquid-liquid phase separation. Although the formation of SGs is considered a transient and protective response to cellular stress, their dysregulation or persistence may contribute to various neurodegenerative diseases. This review aims to provide a comprehensive overview of SG physiology and pathology. It covers the formation, composition, regulation, and functions of SGs, along with their crosstalk with other membrane-bound and membraneless organelles. Furthermore, this review discusses the dual roles of SGs as both friends and foes in neurodegenerative diseases and explores potential therapeutic approaches targeting SGs. The challenges and future perspectives in this field are also highlighted. A more profound comprehension of the intricate relationship between SGs and neurodegenerative diseases could inspire the development of innovative therapeutic interventions against these devastating diseases.

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.

Using artificial intelligence to identify drugs for repurposing to treat l-DOPA-induced dyskinesia

Repurposing regulatory agency-approved molecules, with proven safety in humans, is an attractive option for developing new treatments for disease. We identified and assessed the efficacy of 3 drugs predicted by an in silico screen as having the potential to treat l-DOPA-induced dyskinesia (LID) in Parkinson's disease. We analysed ∼1.3 million Medline abstracts using natural language processing and ranked 3539 existing drugs based on predicted ability to reduce LID. 3 drugs from the top 5% of the 3539 candidates; lorcaserin, acamprosate and ganaxolone, were prioritized for preclinical testing based on i) having a novel mechanism of action, ii) having not been previously validated for the treatment of LID, iii) being blood-brain-barrier penetrant and orally bioavailable and iv) being clinical trial ready. We assessed the efficacy of acamprosate, ganaxolone and lorcaserin in a rodent model of l-DOPA-induced hyperactivity, with lorcaserin affording a 58% reduction in rotational asymmetry (P < 0.05) compared to vehicle. Acamprosate and ganaxolone failed to demonstrate efficacy. Lorcaserin, a 5HT2C agonist, was then further tested in MPTP lesioned dyskinetic macaques where it afforded an 82% reduction in LID (P < 0.05), unfortunately accompanied by a significant increase in parkinsonian disability. In conclusion, although our data do not support the repurposing of lorcaserin, acamprosate or ganaxolone per se for LID, we demonstrate value of an in silico approach to identify candidate molecules which, in combination with an in vivo screen, can facilitate clinical development decisions. The present study adds to a growing literature in support of this paradigm shifting approach in the repurposing pipeline.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

Embracing the future-is artificial intelligence already better? A comparative study of artificial intelligence performance in diagnostic accuracy and decision-making

BACKGROUND AND PURPOSE

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize patient care and clinical decision-making. This study aimed to explore the reliability of large language models in neurology by comparing the performance of an AI chatbot with neurologists in diagnostic accuracy and decision-making.

METHODS

A cross-sectional observational study was conducted. A pool of clinical cases from the American Academy of Neurology's Question of the Day application was used as the basis for the study. The AI chatbot used was ChatGPT, based on GPT-3.5. The results were then compared to neurology peers who also answered the questions-a mean of 1500 neurologists/neurology residents.

RESULTS

The study included 188 questions across 22 different categories. The AI chatbot demonstrated a mean success rate of 71.3% in providing correct answers, with varying levels of proficiency across different neurology categories. Compared to neurology peers, the AI chatbot performed at a similar level, with a mean success rate of 69.2% amongst peers. Additionally, the AI chatbot achieved a correct diagnosis in 85.0% of cases and it provided an adequate justification for its correct responses in 96.1%.

CONCLUSIONS

The study highlights the potential of AI, particularly large language models, in assisting with clinical reasoning and decision-making in neurology and emphasizes the importance of AI as a complementary tool to human expertise. Future advancements and refinements are needed to enhance the AI chatbot's performance and broaden its application across various medical specialties.

SERBP1 interacts with PARP1 and is present in PARylation-dependent protein complexes regulating splicing, cell division, and ribosome biogenesis

RNA binding proteins (RBPs) containing intrinsically disordered regions (IDRs) are present in diverse molecular complexes where they function as dynamic regulators. Their characteristics promote liquid-liquid phase separation (LLPS) and the formation of membraneless organelles such as stress granules and nucleoli. IDR-RBPs are particularly relevant in the nervous system and their dysfunction is associated with neurodegenerative diseases and brain tumor development. SERBP1 is a unique member of this group, being mostly disordered and lacking canonical RNA-binding domains. Using a proteomics approach followed by functional analysis, we defined SERBP1's interactome. We uncovered novel SERBP1 roles in splicing, cell division, and ribosomal biogenesis and showed its participation in pathological stress granules and Tau aggregates in Alzheimer's disease brains. SERBP1 preferentially interacts with other G-quadruplex (G4) binders, implicated in different stages of gene expression, suggesting that G4 binding is a critical component of SERBP1 function in different settings. Similarly, we identified important associations between SERBP1 and PARP1/polyADP-ribosylation (PARylation). SERBP1 interacts with PARP1 and its associated factors and influences PARylation. Moreover, protein complexes in which SERBP1 participates contain mostly PARylated proteins and PAR binders. Based on these results, we propose a feedback regulatory model in which SERBP1 influences PARP1 function and PARylation, while PARylation modulates SERBP1 functions and participation in regulatory complexes.

Empowering Radiographers: A Call for Integrated AI Training in University Curricula

Background

Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence.

Methods

An online survey employing Google Forms consisting of 20 questions regarding the radiographers' perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers' perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire.

Results

Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR = 1.89) and 1.87 (COR = 1.87).

Conclusions

This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.

Parkin regulates IGF2BP3 through ubiquitination in the tumourigenesis of cervical cancer

BACKGROUND

Insulin-like growth Factor 2 mRNA-binding protein 3 (IGF2BP3) is a highly conserved RNA-binding protein and plays a critical role in regulating posttranscriptional modifications.

METHODS

Immunoprecipitation was used to examine the interaction of Parkin and IGF2BP3. Mass spectrometry was performed to identify the ubiquitination sites of IGF2BP3. RNA-immunoprecipitation was conducted to examine the target genes of IGF2BP3. Xenograft mouse model was constructed to determine the tumorigenesis of IGF2BP3.

RESULTS

IGF2BP3 expression is negatively correlated with Parkin expression in human cervical cancer cells and tissues. Parkin directly interacts with IGF2BP3, and overexpression of Parkin causes the proteasomal degradation of IGF2BP3, while knockdown of PARK2 increases the protein levels of IGF2BP3. Mechanistically, in vivo and in vitro ubiquitination assays demonstrated that Parkin is able to ubiquitinate IGF2BP3. Moreover, the ubiquitination site of IGF2BP3 was identified at K213 in the first KH domain of IGF2BP3. IGF2BP3 mutation results in the loss of its oncogenic function as an m6A reader, resulting in the inactivation of the phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) signalling pathways. In addition, IGF2BP3 mutation results in the attenuation of Parkin-mediated mitophagy, indicating its inverse role in regulating Parkin. Consequently, the tumourigenesis of cervical cancer is also inhibited by IGF2BP3 mutation.

CONCLUSION

IGF2BP3 is ubiquitinated and regulated by the E3 ubiquitin ligase Parkin in human cervical cancer and ubiquitination modification plays an important role in modulating IGF2BP3 function. Thus, understanding the role of IGF2BP3 in tumourigenesis could provide new insights into cervical cancer therapy.

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.

A candidate protective factor in amyotrophic lateral sclerosis: heterogenous nuclear ribonucleoprotein G

Heterogenous nuclear ribonucleoprotein G is down-regulated in the spinal cord of the Tg(SOD1*G93A)1Gur (TG) amyotrophic lateral sclerosis mouse model. However, most studies have only examined heterogenous nuclear ribonucleoprotein G expression in the amyotrophic lateral sclerosis model and heterogenous nuclear ribonucleoprotein G effects in amyotrophic lateral sclerosis pathogenesis such as in apoptosis are unknown. In this study, we studied the potential mechanism of heterogenous nuclear ribonucleoprotein G in neuronal death in the spinal cord of TG and wild-type mice and examined the mechanism by which heterogenous nuclear ribonucleoprotein G induces apoptosis. Heterogenous nuclear ribonucleoprotein G in spinal cord was analyzed using immunohistochemistry and western blotting, and cell proliferation and proteins (TAR DNA binding protein 43, superoxide dismutase 1, and Bax) were detected by the Cell Counting Kit-8 and western blot analysis in heterogenous nuclear ribonucleoprotein G siRNA-transfected PC12 cells. We analyzed heterogenous nuclear ribonucleoprotein G distribution in spinal cord in the amyotrophic lateral sclerosis model at various time points and the expressions of apoptosis and proliferation-related proteins. Heterogenous nuclear ribonucleoprotein G was mainly localized in neurons. Amyotrophic lateral sclerosis mice were examined at three stages: preonset (60-70 days), onset (90-100 days) and progression (120-130 days). The number of heterogenous nuclear ribonucleoprotein G-positive cells was significantly higher in the anterior horn of the lumbar spinal cord segment of TG mice at the preonset stage than that of control group but lower than that of the control group at the onset stage. The number of heterogenous nuclear ribonucleoprotein G-positive cells in both central canal and surrounding gray matter of the whole spinal cord of TG mice at the onset stage was significantly lower than that in the control group, whereas that of the lumbar spinal cord segment of TG mice was significantly higher than that in the control group at preonset stage and significantly lower than that in the control group at the progression stage. The numbers of heterogenous nuclear ribonucleoprotein G-positive cells in the posterior horn of cervical and thoracic segments of TG mice at preonset and progression stages were significantly lower than those in the control group. The expression of heterogenous nuclear ribonucleoprotein G in the cervical spinal cord segment of TG mice was significantly higher than that in the control group at the preonset stage but significantly lower at the progression stage. The expression of heterogenous nuclear ribonucleoprotein G in the thoracic spinal cord segment of TG mice was significantly increased at the preonset stage, significantly decreased at the onset stage, and significantly increased at the progression stage compared with the control group. heterogenous nuclear ribonucleoprotein G expression in the lumbar spinal cord segment of TG mice was significantly lower than that of the control group at the progression stage. After heterogenous nuclear ribonucleoprotein G gene silencing, PC12 cell survival was lower than that of control cells. Both TAR DNA binding protein 43 and Bax expressions were significantly increased in heterogenous nuclear ribonucleoprotein G-silenced cells compared with control cells. Our study suggests that abnormal distribution and expression of heterogenous nuclear ribonucleoprotein G might play a protective effect in amyotrophic lateral sclerosis development via preventing neuronal death by reducing abnormal TAR DNA binding protein 43 generation in the spinal cord.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Artificial Intelligence for Neurosurgery : Current State and Future Directions

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Stress granule assembly in vivo is deficient in the CNS of mutant TDP-43 ALS mice

Responding effectively to external stress is crucial for neurons. Defective stress granule dynamics has been hypothesized as one of the pathways that renders motor neurons in amyotrophic lateral sclerosis (ALS) more prone to early death. Specifically, it is thought that stress granules seed the cytoplasmic TDP-43 inclusions that are observed in the neurons of most ALS patients, as well as ~50% of all frontotemporal dementia (FTD) patients. In this study, we tested this hypothesis in an intact mammalian nervous system. We established an in vivo heat stress paradigm in mice that effectively triggers the eIF2α pathway and the formation of stress granules in the CNS. In non-transgenic mice, we report an age-dependent decline in the formation of heat-induced stress granules, with 18-month-old animals showing a significant impairment. Furthermore, although neuronal stress granules were robustly observed in non-transgenic mice and SOD1G93A mice, they were largely absent in age-matched TDP-43M337V animals. The observed defect in stress granule formation in TDP-43M337V mice correlated with deficits in expression of key protein components typically required for phase separation. Lastly, while TDP-43 was not localized to stress granules, we observed complete nuclear depletion of TDP-43 in a subset of neurons, with the highest proportion being in the TDP-43M337V mice. Overall, our results indicate that mutant TDP-43 expression is associated with defective stress granule assembly and increased TDP-43 nuclear depletion in the mammalian nervous system, which could be relevant to ALS/FTD pathogenesis.

Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues

Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.

CAPRIN1P512L causes aberrant protein aggregation and associates with early-onset ataxia

CAPRIN1 is a ubiquitously expressed protein, abundant in the brain, where it regulates the transport and translation of mRNAs of genes involved in synaptic plasticity. Here we describe two unrelated children, who developed early-onset ataxia, dysarthria, cognitive decline and muscle weakness. Trio exome sequencing unraveled the identical de novo c.1535C > T (p.Pro512Leu) missense variant in CAPRIN1, affecting a highly conserved residue. In silico analyses predict an increased aggregation propensity of the mutated protein. Indeed, overexpressed CAPRIN1^P512L forms insoluble ubiquitinated aggregates, sequestrating proteins associated with neurodegenerative disorders (ATXN2, GEMIN5, SNRNP200 and SNCA). Moreover, the CAPRIN1^P512L mutation in isogenic iPSC-derived cortical neurons causes reduced neuronal activity and altered stress granule dynamics. Furthermore, nano-differential scanning fluorimetry reveals that CAPRIN1^P512L aggregation is strongly enhanced by RNA in vitro. These findings associate the gain-of-function Pro512Leu mutation to early-onset ataxia and neurodegeneration, unveiling a critical residue of CAPRIN1 and a key role of RNA–protein interactions.

Role of RBMS3 Novel Potential Regulator of the EMT Phenomenon in Physiological and Pathological Processes

RNA-binding protein 3 (RBMS3) plays a significant role in embryonic development and the pathogenesis of many diseases, especially cancer initiation and progression. The multiple roles of RBMS3 are conditioned by its numerous alternative expression products. It has been proven that the main form of RBMS3 influences the regulation of microRNA expression or stabilization. The absence of RBMS3 activates the Wnt/β-catenin pathway. The expression of c-Myc, another target of the Wnt/β-catenin pathway, is correlated with the RBMS3 expression. Numerous studies have focused solely on the interaction of RBMS3 with the epithelial-mesenchymal transition (EMT) protein machinery. EMT plays a vital role in cancer progression, in which RBMS3 is a new potential regulator. It is also significant that RBMS3 may act as a prognostic factor of overall survival (OS) in different types of cancer. This review presents the current state of knowledge about the role of RBMS3 in physiological and pathological processes, with particular emphasis on carcinogenesis. The molecular mechanisms underlying the role of RBMS3 are not fully understood; hence, a broader explanation and understanding is still needed.

Use of Artificial Intelligence in Clinical Neurology

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.

Alpha-Synuclein Targeting Therapeutics for Parkinson's Disease and Related Synucleinopathies

α-Synuclein (asyn) is a key pathogenetic factor in a group of neurodegenerative diseases generically known as synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Although the initial triggers of pathology and progression are unclear, multiple lines of evidence support therapeutic targeting of asyn in order to limit its prion-like misfolding. Here, we review recent pre-clinical and clinical work that offers promising treatment strategies to sequester, degrade, or silence asyn expression as a means to reduce the levels of seed or substrate. These diverse approaches include removal of aggregated asyn with passive or active immunization or by expression of vectorized antibodies, modulating kinetics of misfolding with small molecule anti-aggregants, lowering asyn gene expression by antisense oligonucleotides or inhibitory RNA, and pharmacological activation of asyn degradation pathways. We also discuss recent technological advances in combining low intensity focused ultrasound with intravenous microbubbles to transiently increase blood-brain barrier permeability for improved brain delivery and target engagement of these large molecule anti-asyn biologics.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

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.

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

The role of SAF-A/hnRNP U in regulating chromatin structure

Scaffold attachment factor A (SAF-A) or hnRNP U is a nuclear RNA-binding protein with a well-documented role in processing newly transcribed RNA. Recent studies also indicate that SAF-A can oligomerise in an ATP-dependent manner and interact with RNA to form a dynamic nuclear mesh. This mesh is thought to regulate nuclear and chromatin architecture, yet a mechanistic understanding is lacking. Here, we review developments in the field to understand how the SAF-A/RNA mesh affects chromatin organisation in interphase and mitosis. As SAF-A has an intrinsically disordered domain we discuss how the chromatin mesh is related to nuclear phase-separated condensates, which in other situations have been shown to regulate transcription and cell functions. Finally, we infer possible links between diseases emerging from SAF-A mutations and its role in chromatin organisation and regulation.

Small molecule inhibitors of α-synuclein oligomers identified by targeting early dopamine-mediated motor impairment in C. elegans

BACKGROUND

Parkinson's disease is a disabling neurodegenerative movement disorder characterized by dopaminergic neuron loss induced by α-synuclein oligomers. There is an urgent need for disease-modifying therapies for Parkinson's disease, but drug discovery is challenged by lack of in vivo models that recapitulate early stages of neurodegeneration. Invertebrate organisms, such as the nematode worm Caenorhabditis elegans, provide in vivo models of human disease processes that can be instrumental for initial pharmacological studies.

METHODS

To identify early motor impairment of animals expressing α-synuclein in dopaminergic neurons, we first used a custom-built tracking microscope that captures locomotion of single C. elegans with high spatial and temporal resolution. Next, we devised a method for semi-automated and blinded quantification of motor impairment for a population of simultaneously recorded animals with multi-worm tracking and custom image processing. We then used genetic and pharmacological methods to define the features of early motor dysfunction of α-synuclein-expressing C. elegans. Finally, we applied the C. elegans model to a drug repurposing screen by combining it with an artificial intelligence platform and cell culture system to identify small molecules that inhibit α-synuclein oligomers. Screen hits were validated using in vitro and in vivo mammalian models.

RESULTS

We found a previously undescribed motor phenotype in transgenic α-synuclein C. elegans that correlates with mutant or wild-type α-synuclein protein levels and results from dopaminergic neuron dysfunction, but precedes neuronal loss. Together with artificial intelligence-driven in silico and in vitro screening, this C. elegans model identified five compounds that reduced motor dysfunction induced by α-synuclein. Three of these compounds also decreased α-synuclein oligomers in mammalian neurons, including rifabutin which has not been previously investigated for Parkinson's disease. We found that treatment with rifabutin reduced nigrostriatal dopaminergic neurodegeneration due to α-synuclein in a rat model.

CONCLUSIONS

We identified a C. elegans locomotor abnormality due to dopaminergic neuron dysfunction that models early α-synuclein-mediated neurodegeneration. Our innovative approach applying this in vivo model to a multi-step drug repurposing screen, with artificial intelligence-driven in silico and in vitro methods, resulted in the discovery of at least one drug that may be repurposed as a disease-modifying therapy for Parkinson's disease.

A system-wide mislocalization of RNA-binding proteins in motor neurons is a new feature of ALS

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease characterized by progressive degeneration of motor neurons. Mislocalization of TAR DNA-binding protein 43 (TDP-43) is an early event in the formation of cytoplasmic TDP-43-positive inclusions in motor neurons and a hallmark of ALS. However, the underlying mechanism and the pathogenic impact of this mislocalization are relatively unexplored. We previously reported that abnormal AMPK activation mediates TDP-43 mislocalization in motor neurons of humans and mice with ALS. In the present study, we hypothesized that other nuclear proteins are mislocalized in the cytoplasm of motor neurons due to the AMPK-mediated phosphorylation of importin-α1 and subsequently contribute to neuronal degeneration in ALS. To test this hypothesis, we analyzed motor neurons of sporadic ALS patients and found that when AMPK is activated, importin-α1 is abnormally located in the nucleus. Multiple integrative molecular and cellular approaches (including proteomics, immunoprecipitation/western blot analysis, immunohistological evaluations and gradient analysis of preribosomal complexes) were employed to demonstrate that numerous RNA binding proteins are mislocalized in a rodent motor neuron cell line (NSC34) and human motor neurons derived from iPSCs during AMPK activation. We used comparative proteomic analysis of importin-α1 complexes that were immunoprecipitated with a phosphorylation-deficient mutant of importin-α1 (importin-α1-S105A) and a phosphomimetic mutant of importin-α1 (importin-α1-S105D) to identify 194 proteins that have stronger affinity for the unphosphorylated form than the phosphorylated form of importin-α1. Furthermore, GO and STRING analyses suggested that RNA processing and protein translation is the major machinery affected by abnormalities in the AMPK-importin-α1 axis. Consistently, the expression of importin-α1-S105D alters the assembly of preribosomal complexes and increases cell apoptosis. Collectively, we propose that by impairing importin-α1-mediated nuclear import, abnormal AMPK activation in motor neurons alters the cellular distribution of many RNA-binding proteins, which pathogenically affect multiple cellular machineries in motor neurons and contribute to ALS pathogenesis.

Determination of the informational content of symptoms in the dynamic processes of assessing the patient’s condition in e-health

The study is devoted to substantiating the tactics of choosing the signs of the patient's condition for diagnostic decision-making on corrective medical intervention in mobile medicine. The aim of the research: to study a creation of a methodology for determining the integral informativeness of the patient's symptoms during remote monitoring of his condition. Materials and methods: this article is based on search results in PubMed, Scopus, MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA articles published between January 1991 and January 2021 and containing the search terms “information technology”, “Mobile medicine”, “digital pathology” and “deep learning”, as well as the results of the authors' own research. The authors independently extracted data on concealment of distribution, consistency of distribution, blindness, completeness of follow-up, and interventions. Results: concluded that to determine the Informativeness of symptoms in mobile monitoring of patients, it is possible to use risk indicators of predicted conditions as a universal method. Given that the Informativeness of the patient's condition changes constantly, for online diagnosis of conditions during remote monitoring of the patient it is recommended to use the function of informative symptoms from time to time and use a set of approaches to assess the Informativeness of patient symptoms. It is proposed to use the strategy of diagnosis and treatment using probabilistic algorithms based on the values of the risk of complications of the pathological process, as well as the formulas of Kulbach and Shannon to determine individual trends in the pathological patient process. Conclusion: there was proposed to use risk indicators of predicted conditions as a universal method for determining the informational content of symptoms in mobile monitoring of patients.

MicroRNA-132-3p alleviates neuron apoptosis and impairments of learning and memory abilities in Alzheimer's disease by downregulation of HNRNPU stabilized BACE1

Alzheimer's disease (AD) is a progressive neuro-degenerative disease characterized by dementia. MicroRNAs (miRNAs) are involved in many diseases, including AD. MiR-132-3p has been identified to be downregulated in AD. In this study, we explored the effects of miR-132-3p on neuron apoptosis and impairments of learning and memory abilities. Aβ1-42-stimulated SH-SY5Y cells were used as in vitro models of AD. An AD-like homocysteine (Hcy) rat model was established to evaluate the effects of miR-132-3p on AD pathogenesis in vivo. RIP, RNA pull down and luciferase reporter assays were conducted to investigate the relationship between miR-132-3p and its downstream target genes. The viability and apoptosis of SH-SY5Y cells were measured by CCK-8 and TUNEL assays. The rat spatial learning and memory abilities were accessed using Morris water maze test. Results indicated that miR-132-3p was downregulated in SH-SY5Y cells after Aβ1-42 treatment and promoted cell apoptosis. Mechanistically, miR-132-3p targeted heterogeneous nuclear ribonucleoprotein U (HNRNPU). HNRNPU acted as an RNA binding protein (RBP) to regulate the mRNA stability of β-site amyloid precursor protein cleaving enzyme 1 (BACE1). Overexpression of HNRNPU or BACE1 reversed the effects of miR-132-3p overexpression on the viability and apoptosis of Aβ1-42-treated SH-SY5Y cells. In vivo experiments revealed the downregulation of miR-132-3p in the hippocampus of Hcy-treated rats. MiR-132-3p suppressed levels of apoptotic genes in hippocampus and reduced impairments of learning and memory abilities in Hcy-treated rats. In conclusion, miR-132-3p reduces apoptosis of SH-SY5Y cells and alleviates impairments of learning and memory abilities in AD rats by modulating the HNRNPU/BACE1 axis.

Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons

Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.

The biological function of IGF2BPs and their role in tumorigenesis

The insulin-like growth factor-2 mRNA-binding proteins (IGF2BPs) pertain to a highly conservative RNA-binding family that works as a post-transcriptional fine-tuner for target transcripts. Emerging evidence suggests that IGF2BPs regulate RNA processing and metabolism, including stability, translation, and localization, and are involved in various cellular functions and pathophysiologies. In this review, we summarize the roles and molecular mechanisms of IGF2BPs in cancer development and progression. We mainly discuss the functional relevance of IGF2BPs in embryo development, neurogenesis, metabolism, RNA processing, and tumorigenesis. Understanding IGF2BPs role in tumor progression will provide new insight into cancer pathophysiology.

CBAG: Conditional biomedical abstract generation

Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in the MEDLINE database. While substantial recent work has addressed the problem of text generation in a more general context, applications, such as scientific writing assistants, or hypothesis generation systems, could benefit from the capacity to select the specific set of concepts that underpin a generated biomedical text. We propose a conditional language model following the transformer architecture. This model uses the "encoder stack" to encode concepts that a user wishes to discuss in the generated text. The "decoder stack" then follows the masked self-attention pattern to perform text generation, using both prior tokens as well as the encoded condition. We demonstrate that this approach provides significant control, while still producing reasonable biomedical text.

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

Defining the Caprin-1 Interactome in Unstressed and Stressed Conditions

Cytoplasmic stress granules (SGs) are dynamic foci containing translationally arrested mRNA and RNA-binding proteins (RBPs) that form in response to a variety of cellular stressors. It has been debated that SGs may evolve into cytoplasmic inclusions observed in many neurodegenerative diseases. Recent studies have examined the SG proteome by interrogating the interactome of G3BP1. However, it is widely accepted that multiple baits are required to capture the full SG proteome. To gain further insight into the SG proteome, we employed immunoprecipitation coupled with mass spectrometry of endogenous Caprin-1, an RBP implicated in mRNP granules. Overall, we identified 1543 proteins that interact with Caprin-1. Interactors under stressed conditions were primarily annotated to the ribosome, spliceosome, and RNA transport pathways. We validated four Caprin-1 interactors that localized to arsenite-induced SGs: ANKHD1, TALIN-1, GEMIN5, and SNRNP200. We also validated these stress-induced interactions in SH-SY5Y cells and further determined that SNRNP200 also associated with osmotic- and thermal-induced SGs. Finally, we identified SNRNP200 in cytoplasmic aggregates in amyotrophic lateral sclerosis (ALS) spinal cord and motor cortex. Collectively, our findings provide the first description of the Caprin-1 protein interactome, identify novel cytoplasmic SG components, and reveal a SG protein in cytoplasmic aggregates in ALS patient neurons. Proteomic data collected in this study are available via ProteomeXchange with identifier PXD023271.

The multi-functional RNA-binding protein G3BP1 and its potential implication in neurodegenerative disease

Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) is a multi-functional protein that is best known for its role in the assembly and dynamics of stress granules. Recent studies have highlighted that G3BP1 also has other functions related to RNA metabolism. In the context of disease, G3BP1 has been therapeutically targeted in cancers because its over-expression is correlated with proliferation of cancerous cells and metastasis. However, evidence suggests that G3BP1 is essential for neuronal development and possibly neuronal maintenance. In this review, we will examine the many functions that are carried out by G3BP1 in the context of neurons and speculate how these functions are critical to the progression of neurodegenerative diseases. Additionally, we will highlight the similarities and differences between G3BP1 and the closely related protein G3BP2, which is frequently overlooked. Although G3BP1 and G3BP2 have both been deemed important for stress granule assembly, their roles may differ in other cellular pathways, some of which are specific to the CNS, and presents an opportunity for further exploration.

TDP-43 proteinopathy alters the ribosome association of multiple mRNAs including the glypican Dally-like protein (Dlp)/GPC6

Amyotrophic lateral sclerosis (ALS) is a genetically heterogeneous neurodegenerative disease in which 97% of patients exhibit cytoplasmic aggregates containing the RNA binding protein TDP-43. Using tagged ribosome affinity purifications in Drosophila models of TDP-43 proteinopathy, we identified TDP-43 dependent translational alterations in motor neurons impacting the spliceosome, pentose phosphate and oxidative phosphorylation pathways. A subset of the mRNAs with altered ribosome association are also enriched in TDP-43 complexes suggesting that they may be direct targets. Among these, dlp mRNA, which encodes the glypican Dally like protein (Dlp)/GPC6, a wingless (Wg/Wnt) signaling regulator is insolubilized both in flies and patient tissues with TDP-43 pathology. While Dlp/GPC6 forms puncta in the Drosophila neuropil and ALS spinal cords, it is reduced at the neuromuscular synapse in flies suggesting compartment specific effects of TDP-43 proteinopathy. These findings together with genetic interaction data show that Dlp/GPC6 is a novel, physiologically relevant target of TDP-43 proteinopathy.

Integrated analysis of RNA-binding proteins in thyroid cancer

Recently, the incidence of thyroid cancer (THCA) has been on the rise. RNA binding proteins (RBPs) and their abnormal expression are closely related to the emergence and pathogenesis of tumor diseases. In this study, we obtained gene expression data and corresponding clinical information from the TCGA database. A total of 162 aberrantly expressed RBPs were obtained, comprising 92 up-regulated and 70 down-regulated RBPs. Then, we performed a functional enrichment analysis and constructed a PPI network. Through univariate Cox regression analysis of key genes and found that NOLC1 (p = 0.036), RPS27L (p = 0.011), TDRD9 (p = 0.016), TDRD6 (p = 0.002), IFIT2 (p = 0.037), and IFIT3 (p = 0.02) were significantly related to the prognosis. Through the online website Kaplan-Meier plotter and multivariate Cox analysis, we identified 2 RBP-coding genes (RPS27L and IFIT3) to construct a predictive model in the entire TCGA dataset and then validate in two subsets. In-depth analysis revealed that the data gave by this model, the patient's high-risk score is very closely related to the overall survival rate difference (p = 0.038). Further, we investigated the correlation between the model and the clinic, and the results indicated that the high-risk was in the male group (p = 0.011) and the T3-4 group (p = 0.046) was associated with a poor prognosis. On the whole, the conclusions of our research this time can make it possible to find more insights into the research on the pathogenesis of THCA, this could be beneficial for individualized treatment and medical decision making.

RNA-Binding Proteins and the Complex Pathophysiology of ALS

Genetic analyses of patients with amyotrophic lateral sclerosis (ALS) have identified disease-causing mutations and accelerated the unveiling of complex molecular pathogenic mechanisms, which may be important for understanding the disease and developing therapeutic strategies. Many disease-related genes encode RNA-binding proteins, and most of the disease-causing RNA or proteins encoded by these genes form aggregates and disrupt cellular function related to RNA metabolism. Disease-related RNA or proteins interact or sequester other RNA-binding proteins. Eventually, many disease-causing mutations lead to the dysregulation of nucleocytoplasmic shuttling, the dysfunction of stress granules, and the altered dynamic function of the nucleolus as well as other membrane-less organelles. As RNA-binding proteins are usually components of several RNA-binding protein complexes that have other roles, the dysregulation of RNA-binding proteins tends to cause diverse forms of cellular dysfunction. Therefore, understanding the role of RNA-binding proteins will help elucidate the complex pathophysiology of ALS. Here, we summarize the current knowledge regarding the function of disease-associated RNA-binding proteins and their role in the dysfunction of membrane-less organelles.

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

Establishment and validation of an RNA binding protein-associated prognostic model for ovarian cancer

Background

Ovarian cancer (OC) is one of the most common gynecological malignant tumors worldwide, with high mortality and a poor prognosis. As the early symptoms of malignant ovarian tumors are not obvious, the cause of the disease is still unclear, and the patients’ postoperative quality of life of decreases. Therefore, early diagnosis is a problem requiring an urgent solution.

Methods

We obtained the gene expression profiles of ovarian cancer and normal samples from TCGA and GTEx databases for differential expression analysis. From existing literature reports, we acquired the RNA-binding protein (RBP) list for the human species. Utilizing the online tool Starbase, we analyzed the interaction relationship between RBPs and their target genes and selected the modules of RBP target genes through Cytoscape. Finally, univariate and multivariate Cox regression analyses were used to determine the prognostic RBP signature.

Results

We obtained 527 differentially expressed RBPs, which were involved in many important cellular events, such as RNA splicing, the cell cycle, and so on. We predicted several target genes of RBPs, constructed the interaction network of RBPs and their target genes, and obtained many modules from the Cytoscape analysis. Functional enrichment of RBP target genes also includes these important biological processes. Through Cox regression analysis, OC prognostic RBPs were identified and a 10-RBP model constructed. Further analysis showed that the model has high accuracy and sensitivity in predicting the 3/5-year survival rate.

Conclusions

Our study identified differentially expressed RBPs and their target genes in OC, and the results promote our understanding of the molecular mechanism of ovarian cancer. The current study could develop novel biomarkers for the diagnosis, treatment, and prognosis of OC and provide new ideas and prospects for future clinical research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00777-1.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Using artificial intelligence to identify anti-hypertensives as possible disease modifying agents in Parkinson's disease

PURPOSE

Drug repurposing is an effective means of increasing treatment options for diseases, however identifying candidate molecules for the indication of interest from the thousands of approved drugs is challenging. We have performed a computational analysis of published literature to rank existing drugs according to predicted ability to reduce alpha synuclein (aSyn) oligomerization and analyzed real-world data to investigate the association between exposure to highly ranked drugs and PD.

METHODS

Using IBM Watson for Drug Discoveryâ (WDD) we identified several antihypertensive drugs that may reduce aSyn oligomerization. Using IBM MarketScanâ Research Databases we constructed a cohort of individuals with incident hypertension. We conducted univariate and multivariate Cox proportional hazard analyses (HR) with exposure as a time-dependent covariate. Diuretics were used as the referent group. Age at hypertension diagnosis, sex, and several comorbidities were included in multivariate analyses.

RESULTS

Multivariate results revealed inverse associations for time to PD diagnosis with exposure to the combination of the combination of angiotensin receptor II blockers (ARBs) and dihydropyridine calcium channel blockers (DHP-CCB) (HR = 0.55, p < 0.01) and angiotensin converting enzyme inhibitors (ACEi) and diuretics (HR = 0.60, p-value <0.01). Increased risk was observed with exposure to alpha-blockers alone (HR = 1.81, p < 0.001) and the combination of alpha-blockers and CCB (HR = 3.17, p < 0.05).

CONCLUSIONS

We present evidence that a computational approach can efficiently identify leads for disease-modifying drugs. We have identified the combination of ARBs and DHP-CCBs as of particular interest in PD.

The role of hnRNPs in frontotemporal dementia and amyotrophic lateral sclerosis

Dysregulated RNA metabolism is emerging as a crucially important mechanism underpinning the pathogenesis of frontotemporal dementia (FTD) and the clinically, genetically and pathologically overlapping disorder of amyotrophic lateral sclerosis (ALS). Heterogeneous nuclear ribonucleoproteins (hnRNPs) comprise a family of RNA-binding proteins with diverse, multi-functional roles across all aspects of mRNA processing. The role of these proteins in neurodegeneration is far from understood. Here, we review some of the unifying mechanisms by which hnRNPs have been directly or indirectly linked with FTD/ALS pathogenesis, including their incorporation into pathological inclusions and their best-known roles in pre-mRNA splicing regulation. We also discuss the broader functionalities of hnRNPs including their roles in cryptic exon repression, stress granule assembly and in co-ordinating the DNA damage response, which are all emerging pathogenic themes in both diseases. We then present an integrated model that depicts how a broad-ranging network of pathogenic events can arise from declining levels of functional hnRNPs that are inadequately compensated for by autoregulatory means. Finally, we provide a comprehensive overview of the most functionally relevant cellular roles, in the context of FTD/ALS pathogenesis, for hnRNPs A1-U.

History of artificial intelligence in medicine

Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now that AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which are reviewed in further detail by several other articles in this issue of Gastrointestinal Endoscopy.

Summoning a New Artificial Intelligence Patent Model: In The Age Of Pandemic

To combat the fast-moving spread of the pandemic we need an equally speedy and powerful tool. On the forefront against COVID-19, for example, AI technology has become a digital armament in the development of new drugs, vaccines, diagnostic methods, and forecasting programs. Patenting these new, nonobvious, and efficient technological solutions is a critical step in fostering the research and development, the huge investments as well as the commercial processes. This article considers the challenges of the current patent law as they apply to AI inventions in general and especially in the age of a global pandemic. The article proposes a novel solution to the hurdles of patenting AI technology by establishing a new patent track model for AI inventions (including the inventions that are made by AI systems and creative AI systems themselves). Unlike other publications promoting either complete abandonment of AI related patents, or advocating to maintain current patent laws, or recommending minor adjustment to patent laws, this article suggests a novel model of separate patent venue solely targeting AI inventions. The argument of this article is based on four pillars: the difficulty of having a patent-eligible subject matter, the hurdle of the "blackbox" conundrum, the confusion of who is "a person of ordinary skills in the art" ("POSITA"), and the criticality of establishing a new AI patent track model, a crucial step, especially during a global epidemic. The first pillar of the argument is the difficulty of having a patent-eligible subject matter in AI inventions. We therefore propose the new AI patent track model that would extend the scope of patent protection to cover creative AI systems, including both the algorithms and trained models, and AI-made inventions in order to, inter alia, incentivize investments of the "Multi-Players". The second pillar of the argument of the argument is the hurdle posed by the "blackbox" conundrum of AI systems that undermines the explainability and transparency of the inventions. In analogy to already existing rules applied to microorganism patents that are hard to describe, we advise a depository rule for AI working models to sufficiently describe the otherwise inexplicable inventions. The third pillar arises from the confusion of who is a person of ordinary skills in regard to the nonobviousness assessment of AI inventions. We submit an alternative standard of "a skilled person using an ordinary AI tool in the art" under the new track model to enable the evaluation of the patentability of complex AI inventions. The fourth pillar of the argument is the criticality of establishing a new AI patent track model on the grounds that the current patent law regime has posed substantial hurdles and uncertainties for patenting AI inventions with regard to almost all patentability requirements. We analyzed each of the requirements to demonstrate that most, if not all, aspects of patent law are not suitable in the AI era; only a revolutionary new patent model specific for AI inventions could solve all the concerns while maintaining the patent incentive for innovations. Our model also suggests an expedited examination with the aid of AI tools and a shortened patent lifetime in light of the fast AI development and technology elimination speed. The article concludes with the hope to harness AI technology for the wellbeing of humanity, in general and especially during tough times in the current COVID-19 era and in general.