Unlocking human-like conversations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents.

BACKGROUND: Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes. OBJECTIVES: To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions. METHODOLOGY: A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria. RESULTS: Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing. CONCLUSIONS: The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.

Endocardial repolarization dispersion in BrS: A novel automatic algorithm for mapping activation recovery interval.

INTRODUCTION: Repolarization dispersion in the right ventricular outflow tract (RVOT) contributes to the type-1 electrocardiographic (ECG) phenotype of Brugada syndrome (BrS), while data on the significance and feasibility of mapping repolarization dispersion in BrS patients are scarce. Moreover, the role of endocardial repolarization dispersion in BrS is poorly investigated. We aimed to assess endocardial repolarization patterns through an automated calculation of activation recovery interval (ARI) estimated on unipolar electrograms (UEGs) in spontaneous type-1 BrS patients and controls; we also investigated the relation between ARI and right ventricle activation time (RVAT), and T-wave peak-to-end interval (Tpe) in BrS patients. METHODS: Patients underwent endocardial high-density electroanatomical mapping (HDEAM); BrS showing an overt type-1 ECG were defined as OType1, while those without (latent type-1 ECG and LType1) received ajmaline infusion. BrS patients only underwent programmed ventricular stimulation (PVS). Data were elaborated to obtain ARI corrected with the Bazett formula (ARIc), while RVAT was derived from activation maps. RESULTS: 39 BrS subjects (24 OType1 and 15 LTtype1) and 4 controls were enrolled. OType1 and post-ajmaline LType1 showed longer mean ARIc than controls (306 ± 27.3 ms and 333.3 ± 16.3 ms vs. 281.7 ± 10.3 ms, p = .05 and p < .001, respectively). Ajmaline induced a significant prolongation of ARIc compared to pre-ajmaline LTtype1 (333.3 ± 16.3 vs. 303.4 ± 20.7 ms, p < .001) and OType1 (306 ± 27.3 ms, p < .001). In patients with type-1 ECG (OTtype1 and post-ajmaline LType1) ARIc correlated with RVAT (r = .34, p = .04) and Tpec (r = .60, p < .001), especially in OType1 subjects (r = .55, p = .008 and r = .65 p < .001, respectively). CONCLUSION: ARIc mapping demonstrates increased endocardial repolarization dispersion in RVOT in BrS. Endocardial ARIc positively correlates with RVAT and Tpec, especially in OType1.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications.

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Temporary Immersion System for Biomass Production of Salvia spp.: A Mini-Review.

Salvia is a very valuable medicinal plant for the pharmaceutical industry. Tissue culture techniques can be used to increase the number of plants in a shorter time. Although protocols for in vitro propagation of more than 15 plant species have been developed, they are not yet efficient enough to increase mass propagation of plants. Therefore, the use of temporary immersion systems is necessary to increase the morphological quality of plants as well as their biomass in several Salvia species. In this chapter, progress in in vitro propagation in several Salvia species using liquid medium and automation is described.

Forced inspiratory suction and swallow tool (FISST): an automation of Valsalva maneuver variants for therapeutic interventions.

INTRODUCTION: The Valsalva maneuver and its modifications have been utilized across several conditions in medicine; however, there have been difficulties in its application. Thus, at the University of Texas Health Science Center in San Antonio, we designed and patented an affordable and accessible device that mimics Valsalva called "Forced Inspiratory Suction and Swallow Tool (FISST). AREAS COVERED: In this review, we discuss the premise for the design of FISST, based on applications of the law of conservation energy, the continuity equation, and Bernoulli's principle. We then detail the mechanism by which FISST stimulates hiccup cessation by increasing negative inspiratory pressure when drinking through the apparatus, causing diaphragmatic contraction and disruption of the hiccup reflex. We then detail the efficacy and future applications of FISST in addressing other pathologies. EXPERT OPINION: FISST has been used to address hiccups by utilizing its reverse-Valsalva effect to increase parasympathetic stimulation by increasing vagal tone. In a prospective study that we conducted on a cohort of 249 hiccup subjects worldwide, this tool achieved 92% effectiveness. Additionally, several cases, including a published case report, have found FISST successful in terminating supraventricular tachycardias (SVT). FISST may be further utilized in diagnosing or addressing various upper airway pathologies and should be explored further.

BOSS: Bones, organs and skin shape model.

A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.

Field Evaluation of an Automated Pollen Sensor.

Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus (Oak) and Pinus (Pine) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93-0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor's weed and grass pollen identification algorithms require further improvement.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization.

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.

[Twenty years in the 21st century: temporal and spatial evolution of Chinese medicinal processing equipment].

Against the backdrop of "Internet+" and Made in China 2025, Chinese medicinal processing equipment embraces various opportunities and develops to an unprecedented level. In the 20 years of the new century, the processing equipment has gradually developed in the direction of high efficiency, energy saving, environmental protection, integration, and automation, and this field has tended to highlight the establishment and application of the linkage production line for the processing of Chinese medicinal decoction pieces. Integrating automation control technology, online detection technique, and the internet of things technology, the online detection system of Chinese medicinal processing equipment and the computer information management system of Chinese medicinal proces-sing are the mainstream development trends of Chinese medicinal processing equipment. Standard Chinese medicine processing equipment is the prerequisite for the standardization of processing parameters. A standard system for processing equipment and processing parameters is the key to the modernization of Chinese medicinal decoction pieces. This paper summarized the research and application of Chinese medicinal processing equipment in the 20 years of the 21 st century and predicted the development trend, which is expected to serve as a reference for the technological innovation and development of the processing equipment.

Automating insect monitoring using unsupervised near-infrared sensors.

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.

Integration of technology to clinical teaching:The impact of mobile and web-based software automation designed for midwifery students on motivation, time management and anxiety levels.

AIM: This study was performed to assess the impact of a mobile and web-based software system named Midwifery Clinical Automation (EBEKO) and designed by researchers to be used in clinical practices on motivation, time management and anxiety level. METHOD: The sample of this randomized controlled trial consisted of 60 midwifery students who were randomly and equally assigned to the experimental and control groups. Data were collected between February and May 2019 using "Student Introduction Form", "Motivation Resources and Problems Scale (MRPS)", "Time Management Inventory (TMI)" and "Spielberger's State-Trait Anxiety Inventory (STAI I-II). EBEKO application was designed as a software system ensuring mutual interaction between students and instructors. Students in the experimental group and all instructors of the department of midwifery were trained in terms of using the system. Students were asked to upload the data regarding midwifery care, monitoring and trainings utilized within the clinical practices to the EBEKO. Data uploaded to the system were transferred to the screen designed for the instructors who were in charge. Instructors assessed these forms (for pregnant women/puerperal women/newborns etc.), demanded revisions, approved or returned. Approved forms corresponded to the number of forms needed by the students in the study to graduate. All students in the experimental group used EBEKO system throughout their education period (14 weeks). No interventions were made on the students in the control group. Data were analyzed through the descriptive statistics (frequency, percentage, mean, standard deviation, min-max values), and t-test in the dependent and independent groups. RESULTS: Students in the experimental and control groups were found to have similar motivation, time management and anxiety level prior to the use of EBEKO. However, after the use of the application, the mean post-test motivation and time management scores of the students in the experimental group significantly increased compared to the control group (p<0.001, p<0.05), but no significant difference regarding the anxiety levels was present between the groups (p>0.05). CONCLUSION: EBEKO was found to positively affect the motivation and time management of midwifery students.

The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology.

In recent years, numerous applications for artificial intelligence (AI) in cardiology have been found, due in part to large digitized data sets and the evolution of high-performance computing. In the discipline of cardiac electrophysiology (EP), a number of clinical, imaging, and electrical waveform data are considered in the diagnosis, prognostication, and management of arrhythmias, which lend themselves well to automation through AI. But equally relevant, AI offers a unique opportunity to discover novel EP concepts and improve clinical care through its inherent, hierarchical tenets of self-learning. In this review we focus on the application of AI in clinical EP and summarize state-of-the art, large, clinical studies in the following key domains: (1) electrocardiogram-based arrhythmia and disease classification; (2) atrial fibrillation source detection; (3) substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias; and (4) predicting outcomes after cardiac resynchronization therapy. Many are small, single-centre, proof-of-concept investigations, but they still show ground-breaking performance of deep learning, a subdomain of AI, which surpasses traditional statistical analysis. Larger studies, for instance classifying arrhythmias from electrocardiogram recordings, have further provided external validation of their high accuracy. Ultimately, the performance of AI is dependent on the quality of the input data and the rigour of algorithm development. The field is still nascent and several barriers will need to be overcome, including prospective validation in large, well labelled data sets and more seamless information technology-based data collection/integration, before AI can be adopted into broader clinical EP practice. This review concludes with a discussion of these challenges and future work.

A comprehensive model for measuring real-life cost-effectiveness in eyecare: automation in care and evaluation of system (aces-rwm™).

This paper describes a holistic, yet simple and comprehensible, ecosystem model to deal with multiple and complex challenges in eyecare. It aims at producing the best possible wellbeing and eyesight with the available resources. When targeting to improve the real-world cost-effectiveness, what gets done in everyday practice needs be measured routinely, efficiently and unselectively. Collection of all real-world data of all patients will enable evaluation and comparison of eyecare systems and departments between themselves nationally and internationally. The concept advocates a strategy to optimize real-life effectiveness, sustainability and outcomes of the service delivery in ophthalmology. The model consists of three components: (1) resource-governing principles (i.e., to deal with increasing demand and limited resources), (2) real-world monitoring (i.e., to collect structured real-world data utilizing automation and visualization of clinical parameters, health-related quality of life and costs), and (3) digital innovation strategy (i.e., to evaluate and benchmark real-world outcomes and cost-effectiveness). The core value and strength of the model lies in the consensus and collaboration of all Finnish university eye clinics to collect and evaluate the uniformly structured real-world outcomes data. In addition to ophthalmology, the approach is adaptable to any medical discipline to efficiently generate real-world insights and resilience in health systems.

Twenty years in the 21st century: temporal and spatial evolution of Chinese medicinal processing equipment / 中国中药杂志

Against the backdrop of "Internet+" and Made in China 2025, Chinese medicinal processing equipment embraces various opportunities and develops to an unprecedented level. In the 20 years of the new century, the processing equipment has gradually developed in the direction of high efficiency, energy saving, environmental protection, integration, and automation, and this field has tended to highlight the establishment and application of the linkage production line for the processing of Chinese medicinal decoction pieces. Integrating automation control technology, online detection technique, and the internet of things technology, the online detection system of Chinese medicinal processing equipment and the computer information management system of Chinese medicinal proces-sing are the mainstream development trends of Chinese medicinal processing equipment. Standard Chinese medicine processing equipment is the prerequisite for the standardization of processing parameters. A standard system for processing equipment and processing parameters is the key to the modernization of Chinese medicinal decoction pieces. This paper summarized the research and application of Chinese medicinal processing equipment in the 20 years of the 21 st century and predicted the development trend, which is expected to serve as a reference for the technological innovation and development of the processing equipment.

Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England.

BACKGROUND: Online reviews may act as a rich source of data to assess the quality of dental practices. Assessing the content and sentiment of reviews on a large scale is time consuming and expensive. Automation of the process of assigning sentiment to big data samples of reviews may allow for reviews to be used as Patient Reported Experience Measures for primary care dentistry. AIM: To assess the reliability of three different online sentiment analysis tools (Amazon Comprehend DetectSentiment API (ACDAPI), Google and Monkeylearn) at assessing the sentiment of reviews of dental practices working on National Health Service contracts in the United Kingdom. METHODS: A Python 3 script was used to mine 15800 reviews from 4803 unique dental practices on the NHS.uk websites between April 2018 -March 2019. A random sample of 270 reviews were rated by the three sentiment analysis tools. These reviews were rated by 3 blinded independent human reviewers and a pooled sentiment score was assigned. Kappa statistics and polychoric evalutaiton were used to assess the level of agreement. Disagreements between the automated and human reviewers were qualitatively assessed. RESULTS: There was good agreement between the sentiment assigned to reviews by the human reviews and ACDAPI (k = 0.660). The Google (k = 0.706) and Monkeylearn (k = 0.728) showed slightly better agreement at the expense of usability on a massive dataset. There were 33 disagreements in rating between ACDAPI and human reviewers, of which n = 16 were due to syntax errors, n = 10 were due to misappropriation of the strength of conflicting emotions and n = 7 were due to a lack of overtly emotive language in the text. CONCLUSIONS: There is good agreement between the sentiment of an online review assigned by a group of humans and by cloud-based sentiment analysis. This may allow the use of automated sentiment analysis for quality assessment of dental service provision in the NHS.

Inhibition of the futalosine pathway for menaquinone biosynthesis suppresses Chlamydia trachomatis infection.

Chlamydia trachomatis, an obligate intracellular bacterium with limited metabolic capabilities, possesses the futalosine pathway for menaquinone biosynthesis. Futalosine pathway enzymes have promise as narrow-spectrum antibiotic targets, but the activity and essentiality of chlamydial menaquinone biosynthesis have yet to be established. In this work, menaquinone-7 (MK-7) was identified as a C. trachomatis-produced quinone through liquid chromatography-tandem mass spectrometry. An immunofluorescence-based assay revealed that treatment of C. trachomatis-infected HeLa cells with the futalosine pathway inhibitor docosahexaenoic acid (DHA) reduced inclusion number, inclusion size, and infectious progeny. Supplementation with MK-7 nanoparticles rescued the effect of DHA on inclusion number, indicating that the futalosine pathway is a target of DHA in this system. These results open the door for menaquinone biosynthesis inhibitors to be pursued in antichlamydial development.

Live-imaging rate-of-kill compound profiling for Chagas disease drug discovery with a new automated high-content assay.

Chagas disease, caused by the protozoan intracellular parasite Trypanosoma cruzi, is a highly neglected tropical disease, causing significant morbidity and mortality in central and south America. Current treatments are inadequate, and recent clinical trials of drugs inhibiting CYP51 have failed, exposing a lack of understanding of how to translate laboratory findings to the clinic. Following these failures many new model systems have been developed, both in vitro and in vivo, that provide improved understanding of the causes for clinical trial failures. Amongst these are in vitro rate-of-kill (RoK) assays that reveal how fast compounds kill intracellular parasites. Such assays have shown clear distinctions between the compounds that failed in clinical trials and the standard of care. However, the published RoK assays have some key drawbacks, including low time-resolution and inability to track the same cell population over time. Here, we present a new, live-imaging RoK assay for intracellular T. cruzi that overcomes these issues. We show that the assay is highly reproducible and report high time-resolution RoK data for key clinical compounds as well as new chemical entities. The data generated by this assay allow fast acting compounds to be prioritised for progression, the fate of individual parasites to be tracked, shifts of mode-of-action within series to be monitored, better PKPD modelling and selection of suitable partners for combination therapy.

Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development.

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.

Automated in vivo drug screen in zebrafish identifies synapse-stabilising drugs with relevance to spinal muscular atrophy.

Synapses are particularly vulnerable in many neurodegenerative diseases and often the first to degenerate, for example in the motor neuron disease spinal muscular atrophy (SMA). Compounds that can counteract synaptic destabilisation are rare. Here, we describe an automated screening paradigm in zebrafish for small-molecule compounds that stabilize the neuromuscular synapse in vivo. We make use of a mutant for the axonal C-type lectin chondrolectin (chodl), one of the main genes dysregulated in SMA. In chodl-/- mutants, neuromuscular synapses that are formed at the first synaptic site by growing axons are not fully mature, causing axons to stall, thereby impeding further axon growth beyond that synaptic site. This makes axon length a convenient read-out for synapse stability. We screened 982 small-molecule compounds in chodl chodl-/- mutants and found four that strongly rescued motor axon length. Aberrant presynaptic neuromuscular synapse morphology was also corrected. The most-effective compound, the adenosine uptake inhibitor drug dipyridamole, also rescued axon growth defects in the UBA1-dependent zebrafish model of SMA. Hence, we describe an automated screening pipeline that can detect compounds with relevance to SMA. This versatile platform can be used for drug and genetic screens, with wider relevance to synapse formation and stabilisation.