Biomedical Modeling Research Articles

A new extension of the Gumbel distribution with biomedical data analysis

In the field of biomedical research, data characteristics often exhibit significant variability, challenging the applicability of classical Gumbel distribution for biomedical data modeling. To address this, this paper introduces a novel extension of the Gumbel model known as the odd beta prime Gumbel (OBP-Gum) model. Derived from the odd beta prime family, the new distribution exhibits greater kurtosis compared to the traditional Gumbel distribution. Importantly, the proposed distribution is designed to capture right-skewed, left-skewed, and nearly symmetric density functions, as well as increasing, decreasing, constant, and upside-down bathtub shapes for its hazard rate function, providing excellent curvature features for creating flexible statistical models for biomedical research. We derive the fundamental features of the OBP-Gum model, such as the quantile function, linear representations, moment generating function, moments, skewness, kurtosis, incomplete moments, and Rényi and Tsallis entropies. Parameter estimation for this new model is conducted using the maximum likelihood estimation method. A simulation study demonstrates the performance of the model parameters. The empirical findings, based on applications to two biomedical datasets, suggest that the OBP-Gum distribution outperforms existing models, particularly in handling extreme observations. Instead of relying on conventional models for decision-making, this research provides relevant stakeholders with an improved statistical distribution for more accurate biomedical data modeling.

A novel extended Kumaraswamy distribution and its application to COVID‐19 data

AbstractIn the field of biomedical research, data characteristics often exhibit significant variability, challenging the applicability of classical probability distributions for biomedical data modeling. In this research, we introduce a novel four‐parameter distribution called the odd beta prime Kumaraswamy (OBPK) distribution, derived from the odd beta prime generalized family of distributions and the Kumaraswamy distribution. This distribution exhibits greater flexibility compared with the traditional Kumaraswamy distribution. Importantly, the OBPK distribution offers symmetrical, right‐skewed, left‐skewed, bathtub, N‐shaped, J‐shaped, and reversed J‐shaped densities, as well as varied hazard functions, including increasing, decreasing, bathtub, increasing‐decreasing, upside‐down bathtub, and N‐shaped forms. These curvature patterns make it particularly useful for modeling biomedical data. We derive some basic properties of the OBPK distribution. Parameters are estimated using the maximum likelihood approach, and the performance of various estimators is assessed via Monte Carlo simulations. Finally, four applications to real data on COVID‐19 mortality rates from different countries are evaluated to demonstrate the importance of the OBPK model over other extended versions of the Kumaraswamy models. The empirical findings suggest that the OBPK model outperforms other extended versions of the Kumaraswamy models in these applications.

Biomedical modelling through path analysis approach

Since blood disease markers are one of the most prevalent health problems in this era, the aim of this study is to forecast pathological subjects from a population through biomedical variables of individuals using the currently produced path analysis (PA) model. In terms of the dataset, 539 subjects were used to implement this study. A mathematical approach based on the PA has been used to create a reliable biomedical model in this research that investigates if there exists a relation between the various anemia types and the biomedical variables through observational variables (the blood variables, age, and sex) and anemia types. Other linear approaches were taken into consideration for comparison, in terms of $R^2$ value of the model, which has a value of 0.699. The findings reveal that the model has great predictive potential. It is believed that the developed model, which includes observational variables, will help healthcare providers predictively plan appropriate treatment programs for their patients.

Non-Invasive Tools in Perioperative Stroke Risk Assessment for Asymptomatic Carotid Artery Stenosis with a Focus on the Circle of Willis.

This review aims to explore advancements in perioperative ischemic stroke risk estimation for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis (CoW) morphology based on the CTA or MR diagnostic imaging in the current preoperative diagnostic algorithm. Functional transcranial Doppler (fTCD), near-infrared spectroscopy (NIRS), and optical coherence tomography angiography (OCTA) are discussed in the context of evaluating cerebrovascular reserve capacity and collateral vascular systems, particularly the CoW. These non-invasive diagnostic tools provide additional valuable insights into the cerebral perfusion status. They support biomedical modeling as the gold standard for the prediction of the potential impact of carotid artery stenosis on the hemodynamic changes of cerebral perfusion. Intraoperative risk assessment strategies, including selective shunting, are explored with a focus on CoW variations and their implications for perioperative ischemic stroke and cognitive function decline. By synthesizing these insights, this review underscores the potential of non-invasive diagnostic methods to support clinical decision making and improve asymptomatic patient outcomes by reducing the risk of perioperative ischemic neurological events and preventing further cognitive decline.

Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91-100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.

Computational modeling for medical data: From data collection to knowledge discovery

<p>Biomedical data encompasses images, texts, physiological signals, and molecular omics data. As the costs of various data acquisition methods, such as genomic sequencing, continue to decrease, the availability of biomedical data is increasing. However, this data often exhibits high dimensionality, heterogeneity, and multimodal characteristics, necessitating the use of advanced computational modeling. Transforming raw data into meaningful biological insights is a critical aspect of computational modeling, which plays an increasingly important role in biomedical research in the era of big data. This review outlines the collection of various types of biomedical data and the challenges faced in data modeling, including high dimensionality, standardization, and privacy protection. Additionally, it addresses the complexity and interpretability of models used to guide knowledge discoveries. The review also discusses computational architectures such as parallel computing, cloud computing, and edge computing, which are essential to meet the demands of large-scale computation. Furthermore, it highlights the driving force of computational modeling in advancing medical research. With the foundation of big data, big models, and big computation, biomedical research is transitioning from experimental observation to theoretical deduction and data-driven approaches, profoundly impacting scientific research methodologies and paradigms. The development of biomedical computational modeling is steering medical research toward intelligent medicine, redefining the scientific research paradigm in biomedicine.</p>

#3258 MECHANISMS OF HEMOGLOBIN CYCLING IN ANEMIC END-STAGE RENAL DISEASE PATIENTS TREATED WITH ERYTHROPOIESIS-STIMULATING AGENTS

Abstract Background and Aims A considerable fraction of renal anemia patients treated with erythropoiesis-stimulating agents (ESAs) experience hemoglobin ‘cycling’ periods during which hemoglobin levels periodically over- and undershoot a specified target range [1]. This is an undesired condition, which also necessitates frequent adjustment of the drug dose. The causes of hemoglobin cycling are not understood on a fundamental level. Through a combination of biomedical simulations and quantitative data analysis, we aimed at identifying mechanisms of hemoglobin cycling and the physiological and treatment-related factors contributing to cycling. Method We developed a biomedical modeling and simulation scheme that captures the essential features of ESA therapy involving a minimal feedback model for the delayed hemoglobin response after ESA administration and a stereotypic ESA dosing algorithm [2]. In this model, hemoglobin cycling emerged as a consequence of the delayed feedback between a patient's physiological response to ESA administrations and the resulting dose adjustments, which mutually maintain themselves in a causal loop. Guided by this model, we developed a set of statistical indicators that can detect this type of self-sustained hemoglobin cycling in clinical time series, including the Pearson cross correlation between hemoglobin levels and given ESA doses and hemoglobin standard deviation. Results A model analysis showed that physiological and treatment-related delays in hemoglobin response and ESA dose adjustment and administration were a major driver of hemoglobin cycling. Such delays are caused, e.g., by the cell cycle and maturation times of erythroid progenitors in response to ESA administration on one hand and restrictions in ESA dose changes and typical administration intervals on the other hand. In clinical time series, this was reflected by a systematic delay between bursts of ESA administrations and hemoglobin cycles. We quantified 1134 such datasets from hemodialysis patients and found that more persistent cycling was associated with negative correlation coefficients between hemoglobin levels and ESA doses. Using model simulations with similar behavior, we found that many patient- and therapy-related factors affect the hemoglobin dynamics in several (sometimes competing) ways, obfuscating their overall effect. For example, a larger RBC lifespan generally both entails higher hemoglobin levels but also prolongs the effect of ESA misdosings. However, model simulations suggested that longer RBC lifespans generally have a favorable effect in requiring less ESA on average and preventing hemoglobin cycling. Moreover, the model showed that for a given patient, ESA-specific properties such as its half-life may have a ‘sweet spot’ range of values within which hemoglobin cycling was suppressed while still showing a desired effect on the hemoglobin dynamics. Furthermore, the choice of the hemoglobin target window had an impact on hemoglobin stability. If the window was chosen too small, more frequent dose adjustments could initiate and maintain a cycling behavior. Conclusion Hemoglobin cycling in renal anemia patients often emerges from a complex interplay of multiple physiological and ESA treatment-related factors, with delays between ESA administration, hemoglobin response and subsequent ESA dose adjustment being a key driver of cycling. Biomedical modeling and simulation can aid in systematically exploring these interplays and find improved dosing strategies.

Human Microbiomes and Disease for the Biomedical Data Scientist.

The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.

Computational biomedical modeling and screening for prediction of molecular mechanisms of Simiao Pill against hyperuricemia

Hyperuricemia (HUA) is a type of purine metabolism disease and Simiao Pill (SMP), a classic herbal prescription, has a long history of treating HUA. Nonetheless, the mechanisms underlying SMP against HUA remain unclear. We employed computational biomedical modeling and screening to explore the potential pharmacodynamic components and the mechanisms of SMP for HUA. We obtained 6,045 docking results between 403 herbal compounds and 15 HUA-related targets (average binding affinity −7.081 kcal/mol). We identified 34 signaling pathways based on the 15 HUA-related proteins. Nitric-oxide synthase 3 (NOS3) was found to be the most promising drug target of SMP against HUA, exhibiting the best average binding affinity of −8.05 kcal/mol. Chelerythrine and quercetin were predicted to be the most bioactive compounds for xanthine dehydrogenase (XDH), a known drug target, along with kihadanin A and limonin for NOS3. Of all SMP compounds, prenol lipids, steroids and alkaloids were predicted to be the three primary classes of compounds with potential for targeting NOS3 and XDH. SMP may treat HUA by mechanisms that involve multi-targets and pathways. Our study presented a novel horizon for exploring the mechanism of SMP against HUA and developing potential drugs. Further experimental research is required to verify our results.

Comprehensive survey on analysis and modelling of femur bone fracture for an operative planning

ABSTRACT Accidents and Osteoporosis are two prime reasons of femur bone fractures. Osteoporosis causes weakening of bone in elder patients. Proper treatment during surgery will help patients for early rehabilitation. This paper portrays a review of concerned literature identified with Biomedical Image analysis and modelling of femur bone fractures for an operative planning. Paper intended to review literature for Biomedical image preprocessing to identify the proper region of Interest (ROI) by using an appropriate image modality, building up a 3D model of femur bone structure using image database with software, analysis of created 3D model for applying implant and its parametric analysis for evaluation of virtual surgery and operative planning to surgeon depending on assessment report produced utilising earlier advances. Such sort of work will keep away from mismatches of fracture which may prompt genuine implanted complications for patients including secondary fractures, poor anatomical alignment and bone debilitating too. Paper will summarise conclusion of concern literature to guide new researcher to start with

Three-dimensional printing and 3D slicer powerful tools in understanding and treating neurosurgical diseases.

With the development of the 3D printing industry, clinicians can research 3D printing in preoperative planning, individualized implantable materials manufacturing, and biomedical tissue modeling. Although the increased applications of 3D printing in many surgical disciplines, numerous doctors do not have the specialized range of abilities to utilize this exciting and valuable innovation. Additionally, as the applications of 3D printing technology have increased within the medical field, so have the number of printable materials and 3D printers. Therefore, clinicians need to stay up-to-date on this emerging technology for benefit. However, 3D printing technology relies heavily on 3D design. 3D Slicer can transform medical images into digital models to prepare for 3D printing. Due to most doctors lacking the technical skills to use 3D design and modeling software, we introduced the 3D Slicer to solve this problem. Our goal is to review the history of 3D printing and medical applications in this review. In addition, we summarized 3D Slicer technologies in neurosurgery. We hope this article will enable many clinicians to leverage the power of 3D printing and 3D Slicer.

Interprofessional Consensus Regarding Design Requirements for Liquid-Based Perinatal Life Support (PLS) Technology.

Liquid-based perinatal life support (PLS) technology will probably be applied in a first-in-human study within the next decade. Research and development of PLS technology should not only address technical issues, but also consider socio-ethical and legal aspects, its application area, and the corresponding design implications. This paper represents the consensus opinion of a group of healthcare professionals, designers, ethicists, researchers and patient representatives, who have expertise in tertiary obstetric and neonatal care, bio-ethics, experimental perinatal animal models for physiologic research, biomedical modeling, monitoring, and design. The aim of this paper is to provide a framework for research and development of PLS technology. These requirements are considering the possible respective user perspectives, with the aim to co-create a PLS system that facilitates physiological growth and development for extremely preterm born infants.

Development of a Novel X-ray Compatible 3D-Printed Bone Model to Characterize Different K-Wire Fixation Methods in Support of the Treatment of Pediatric Radius Fractures.

Additive manufacturing technologies are essential in biomedical modeling and prototyping. Polymer-based bone models are widely used in simulating surgical interventions and procedures. Distal forearm fractures are the most common pediatric fractures, in which the Kirschner wire fixation is the most widely used operative method. However, there is still lingering controversy throughout the published literature regarding the number of wires and sites of insertion. This study aims to critically compare the biomechanical stability of different K-wire fixation techniques. Different osteosyntheses were reconstructed on 189 novel standardized bone models, which were created using 3D printing and molding techniques, using PLA and polyurethane materials, and it has been characterized in terms of mechanical behavior and structure. X-ray imaging has also been performed. The validation of the model was successful: the relative standard deviations (RSD = 100 × SD × mean−1, where RSD is relative standard deviation, SD is the standard deviation) of the mechanical parameters varied between 1.1% (10° torsion; 6.52 Nm ± 0.07 Nm) and 5.3% (5° torsion; 4.33 Nm ± 0.23 Nm). The simulated fractures were fixed using two K-wires inserted from radial and dorsal directions (crossed wire fixation) or both from the radial direction, in parallel (parallel wire fixation). Single-wire fixations with shifted exit points were also included. Additionally, three-point bending tests with dorsal and radial load and torsion tests were performed. We measured the maximum force required for a 5 mm displacement of the probe under dorsal and radial loads (means for crossed wire fixation: 249.5 N and 355.9 N; parallel wire fixation: 246.4 N and 308.3 N; single wire fixation: 115.9 N and 166.5 N). We also measured the torque required for 5° and 10° torsion (which varied between 0.15 Nm for 5° and 0.36 Nm for 10° torsion). The crossed wire fixation provided the most stability during the three-point bending tests. Against torsion, both the crossed and parallel wire fixation were superior to the single-wire fixations. The 3D printed model is found to be a reliable, cost-effective tool that can be used to characterize the different fixation methods, and it can be used in further pre-clinical investigations.

Scientific advice and COVID-19 policy making in the UK: an oral history study

BackgroundThe COVID-19 pandemic has posed severe challenges to policy makers in all countries: these include uncertainty about the science of the disease, its epidemiology, and public behaviour, coupled with the need to act fast. This real-time study was undertaken to capture UK Government scientific advisers' perceptions of how scientific advice worked and to learn lessons about what works best in a fast-moving response to a novel epidemic. MethodsRegular semi-structured calls were recorded with ten prominent scientific advisers to elicit their roles in, and reactions to, the UK's COVID-19 response. Interviewees were recruited using the existing networks of the NIHR Health Protection Research Unit for Emerging and Zoonotic Infections. They were active in fields including biomedical research, modelling, and global health, and they included members of the Scientific Advisory Group for Emergencies (SAGE). In pandemic conditions interviewing was opportunistic: one participant spoke to us almost every 1–2 weeks, others at longer intervals. Interviews were transcribed and coded using a published analytical framework for the study of policy decision-making. Findings93 interviews were conducted between Feb 17, 2020, and July 22, 2021. We report interviewees' perceptions that scientific advice had not led to sufficiently rapid policy decisions, and that a lack of transparency was sapping public trust. Interviewees also drew attention to policy makers' failure in the early months of the pandemic to frame a policy goal, and the problems this posed for giving scientific advice. It also became clear that scientific advisers and policy makers operated in different intellectual worlds, and that useful advice was most likely to be given when individuals could span this gap and understand the agendas of each group. InterpretationThese findings provide empirical information about how science advice has worked, uncovering power dynamics and business processes that are not otherwise well understood. We argue that politicians abdicated responsibility by their early “follow the science” rhetoric, later renegotiated. The study would be strengthened if the perceptions of policy makers were also included. We requested interviews with eight policy makers, who declined due to unavailability. FundingUK Research and Innovation—National Institute for Health Research.

Patient-Specific Modelling of Blood Coagulation.

Blood coagulation represents one of the most studied processes in biomedical modelling. However, clinical applications of this modelling remain limited because of the complexity of this process and because of large inter-patient variation of the concentrations of blood factors, kinetic constants and physiological conditions. Determination of some of these patients-specific parameters is experimentally possible, but it would be related to excessive time and material costs impossible in clinical practice. We propose in this work a methodological approach to patient-specific modelling of blood coagulation. It begins with conventional thrombin generation tests allowing the determination of parameters of a reduced kinetic model. Next, this model is used to study spatial distributions of blood factors and blood coagulation in flow, and to evaluate the results of medical treatment of blood coagulation disorders.

Single Volume Image Generator and Deep Learning-Based ASD Classification.

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied in clinical or behavioral identification of ASD; most erstwhile models are inadequate in their capacity to exploit the data richness. On the other hand, classification techniques often solely rely on region-based summary and/or functional connectivity analysis of functional magnetic resonance imaging (fMRI). Besides, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image by considering the voxel time point of each subject separately. Then, to classify ASD and typical control participants, we evaluate four deep learning approaches with their corresponding ensemble classifiers comprising one amended Convolutional Neural Network (CNN). Finally, to check out the data variability, we apply the proposed CNN classifier with leave-one-site-out 5-fold cross-validation across the sites and validate our findings by comparing with literature reports. We showcase our approach on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, which outperforms the state-of-the-art methods. Hence, it is robust and consistent.

High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays.

Stem-cell-derived epithelial organoids are routinely used for the biological and biomedical modelling of tissues. However, the complexity, lack of standardization and quality control of stem cell culture in solid extracellular matrices hampers the routine use of the organoids at the industrial scale. Here, we report the fabrication of microengineered cell culture devices and scalable and automated methods for suspension culture and real-time analysis of thousands of individual gastrointestinal organoids trapped in microcavity arrays within a polymer-hydrogel substrate. The absence of a solid matrix substantially reduces organoid heterogeneity, which we show for mouse and human gastrointestinal organoids. We use the devices to screen for anticancer drug candidates with patient-derived colorectal cancer organoids, and apply high-content image-based phenotypic analyses to reveal insights into mechanisms of drug action. The scalable organoid-culture technology should facilitate the use of organoids in drug development and diagnostics.

Independent path‐based process recommendation algorithm for improving biomedical process modelling

Process recommendation is an important technique in business process management (BPM), which can be introduced to support modelling biomedical processes. However, most existing process recommendation algorithms in terms of behaviour (what tasks need to be executed in what order) suffer from the problem of state-space explosion when unfolding a process with many parallel patterns. To address this issue, the authors propose an independent path-based process recommendation algorithm to speed up the biomedical process recommendation while guaranteeing the accuracy. The experimental results of the proposed algorithm showed higher accuracy and better efficiency than the state-of-the-art algorithms.

Combination of Biodata Mining and Computational Modelling in Identification and Characterization of ORF1ab Polyprotein of SARS-CoV-2 Isolated from Oronasopharynx of an Iranian Patient

BackgroundCoronavirus disease 2019 (COVID-19) is an emerging zoonotic viral infection, which was started in Wuhan, China, in December 2019 and transmitted to other countries worldwide as a pandemic outbreak. Iran is one of the top ranked countries in the tables of COVID-19-infected and -mortality cases that make the Iranian patients as the potential targets for diversity of studies including epidemiology, biomedical, biodata, and viral proteins computational modelling studies.ResultsIn this study, we applied bioinformatic biodata mining methods to detect CDS and protein sequences of ORF1ab polyprotein of SARS-CoV-2 isolated from oronasopharynx of an Iranian patient. Then through the computational modelling and antigenicity prediction approaches, the identified polyprotein sequence was analyzed. The results revealed that the identified ORF1ab polyprotein belongs to a part of nonstructural protein 1 (nsp1) with the high antigenicity residues in a glycine-proline or hydrophobic amino acid rich domain.ConclusionsThe results revealed that nsp1 as a virulence factor and crucial agent in spreading of the COVID-19 among the society can be a potential target for the future epidemiology, drug, and vaccine studies.

Engineering geometrical 3-dimensional untethered in vitro neural tissue mimic

Formation of tissue models in 3 dimensions is more effective in recapitulating structure and function compared to their 2-dimensional (2D) counterparts. Formation of 3D engineered tissue to control shape and size can have important implications in biomedical research and in engineering applications such as biological soft robotics. While neural spheroids routinely are created during differentiation processes, further geometric control of in vitro neural models has not been demonstrated. Here, we present an approach to form functional in vitro neural tissue mimic (NTM) of different shapes using stem cells, a fibrin matrix, and 3D printed molds. We used murine-derived embryonic stem cells for optimizing cell-seeding protocols, characterization of the resulting internal structure of the construct, and remodeling of the extracellular matrix, as well as validation of electrophysiological activity. Then, we used these findings to biofabricate these constructs using neurons derived from human embryonic stem cells. This method can provide a large degree of design flexibility for development of in vitro functional neural tissue models of varying forms for therapeutic biomedical research, drug discovery, and disease modeling, and engineering applications.