AIS Cases Research Articles

Verification of Clot Burden Score Prognostic Value in Acute Middle Cerebral Artery Ischemic Insults

Abstract Background The Clot Burden Score (CBS) is a CT angiography-based grading system used to assess thrombus burden in acute ischemic stroke patients. CBS is a major determinant of stroke severity and outcome in anterior circulation stroke, offering advantages over invasive angiographic scores. Aim of the Work to investigate the prognostic value of CBS in predicting clinical outcomes using the modified Rankin Scale (mRS). Patients and Methods Type of study combined prospective and retrospective observational study. Sampling Method purposive sampling according to inclusion and exclusion criteria. Results The study included 40 patients with acute ischemic stroke, with a mean age of 58.35 ± 14.82 years. 60% of patients were male, and 40% were female. After 3 months, 17 patients (42.5%) achieved functional independence (mRS ≤ 2), while 23 patients (57.5%) had higher mRS scores (22 functionally dependent and 1 deceased). There was a statistically significant inverse correlation between CBS and mRS scores, indicating that patients with higher CBS scores had lower mRS scores. Receiver Operating Characteristic (ROC) curve analysis determined a CBS cutoff value of greater than 6 as predictive of a good prognosis, with high sensitivity and specificity. Conclusion The current study shows that CBS is a good predictor of patient outcome. It is a relatively easy and simple method in quantifying clot burden, apart from being systematic. This assists in selection of treatment strategies, and prognosticates patient outcome; and can be incorporated in routine radiological reporting of AIS cases.

Comunicación de moda e inteligencia artificial: El caso de Neural Fashion AI

Artificial intelligence is presented to society as a revolutionary tool capable of generating a change asunique as the democratization of Internet access at the beginning of the 21st Century. The different applications of AI are facilitating the development of marketing and communication strategies adapted to the needs of the public and the establishment of strong relationships with them. One of the most dynamic consumer markets is fashion communication, which is why we decided to delimit the applications of AI to brands in this sector. First goal was to identify the main resources and applications of AI that are being used to communicatewith the different stakeholders of fashion companies, particularly with the final consumer. Second objective was to recognize benefits and positive aspects along with the brakes and barriers that the application of this technology represents for the communication strategies of fashion brands. Thirdly, a case studyis offered to help academics and professionals understand how the fashion sector is receiving the helpof generative AI in the creation of campaigns. Through a combination of qualitative methods including3 Delphi interviews, a hemerographic research of professional publications and the Neural Fashion AI case study, the capacity of AI to point out a differentiating factor in the market that has to do withsustainability, product customization and optimization of company resources has been demonstrated. The main results highlight the contribution that AI makes to the efficiency of processes and to the achievement of brand objectives (customer satisfaction, loyalty, strengthening of positioning and brand image), expansion into new marketsand audiences, or the creation of innovative, impactful and attractive content.

Developing and Deploying End‐to‐End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

ABSTRACTArtificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real‐world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.

Big Tech, Small Tech, and the Dynamics of Technology Life Cycle: The Case of AI’s Evolution

Big Tech, Small Tech, and the Dynamics of Technology Life Cycle: The Case of AI’s Evolution

Rare variant association analyses reveal the significant contribution of carbohydrate metabolic disturbance in severe adolescent idiopathic scoliosis

BackgroundAdolescent idiopathic scoliosis (AIS), the predominant genetic-influenced scoliosis, results in spinal deformities without vertebral malformations. However, the molecular aetiology of AIS remains unclear.MethodsUsing genome/exome sequencing, we studied 368 patients with...

Patent Applications as Glimpses into the Sociotechnical Imaginary: Ethical Speculation on the Imagined Futures of Emotion AI for Mental Health Monitoring and Detection

Patent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.

Scrutinizing Algorithms: Assessing Journalistic Role Performance in Chinese News Media’s Coverage of Artificial Intelligence

ABSTRACT Coverage of artificial intelligence and algorithms has been largely examined by scholars studying countries in the Global North that have historically supported conditions for critical journalism and watchdog journalistic role performances. However, it is unclear if the findings from such work would be applicable to authoritarian contexts that do not share those conditions. This study addresses this gap through a textual analysis of 23 journalistic investigations of AI and algorithmic systems published in Chinese news media between 2019 and 2023. We found that Chinese journalists were critical of multiple aspects of algorithmic systems and called for urgent AI governance at the nation-state level. Despite the technical nature of the issue, those journalists overwhelmingly employed traditional reporting techniques to uncover political and economic intersections—namely those resulting from the rise of tech companies and the algorithms they implement. Chinese journalists simultaneously performed the roles of being watchdogs and loyal facilitators by highlighting the risks posed by private platforms and their algorithms while casting the state as a protector and responsible steward of technological development. The study thus highlights the intricate roles necessary to perform critical journalism in authoritarian contexts, and the possibilities that the case of AI permits.

Movie Review Sentiment Analysis and AI-Story Generation Web Application

Abstract: This paper presents a web application designed to enhance the interaction between users and multimedia content through two innovative platforms: "MOVIE REVIEW SENTIMENT ANALYSIS" and "AI-STORY GENERATION." The first platform leverages the Fastai library and LSTM networks to perform sentiment analysis on user-submitted movie reviews, determining their polarity as positive or negative. This analysis directly influences the movie's aggregate rating, with the system capable of processing extensive textual input with high accuracy. Ratings are dynamically updated and stored in JSON format. The second platform, AI-STORY GENERATION, introduces a creative avenue for generating novel stories based on user prompts. It utilizes Natural Language Processing (NLP) techniques and a Generative Pretrained Transformer (GPT-2 Large) model, trained on tokenized movie scripts, to produce coherent and engaging narratives. The Flask web framework supports the application's backend, providing a robust and scalable foundation for user interaction and content delivery. This dual-platform approach not only demonstrates the practical application of advanced machine learning and NLP techniques in enhancing digital entertainment experiences but also showcases the potential for AI-driven tools to foster creative storytelling. The integration of sentiment analysis and story generation within a single application illustrates a novel use case of AI in the context of web-based entertainment, potentially paving the way for more personalized and interactive user experiences

Why and how is the power of Big Tech increasing in the policy process? The case of generative AI

Abstract The growing digitalization of our society has led to a meteoric rise of large technology companies (Big Tech), which have amassed tremendous wealth and influence through their ownership of digital infrastructure and platforms. The recent launch of ChatGPT and the rapid popularization of generative artificial intelligence (GenAI) act as a focusing event to further accelerate the concentration of power in the hands of the Big Tech. By using Kingdon’s multiple streams framework, this article investigates how Big Tech utilize their technological monopoly and political influence to reshape the policy landscape and establish themselves as key actors in the policy process. It explores the implications of the rise of Big Tech for policy theory in two ways. First, it develops the Big Tech-centric technology stream, highlighting the differing motivations and activities from the traditional innovation-centric technology stream. Second, it underscores the universality of Big Tech exerting ubiquitous influence within and across streams, to primarily serve their self-interests rather than promote innovation. Our findings emphasize the need for a more critical exploration of policy role of Big Tech to ensure balanced and effective policy outcomes in the age of AI.

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation

As applications of AI in medicine continue to expand, there is an increasing focus on integration into clinical practice. An underappreciated aspect of this clinical translation is where the AI fits into the clinical workflow, and in turn, the outputs generated by the AI to facilitate clinician interaction in this workflow. For instance, in the canonical use case of AI for medical image interpretation, the AI could prioritize cases before clinician review or even autonomously interpret the images without clinician review. A related aspect is explainability – does the AI generate outputs to help explain its predictions to clinicians? While many clinical AI workflows and explainability techniques have been proposed, a summative assessment of the current scope in clinical practice is lacking. Here, we evaluate the current state of FDA-cleared AI devices for medical image interpretation assistance in terms of intended clinical use, outputs generated, and types of explainability offered. We create a curated database focused on these aspects of the clinician-AI interface, where we find a high frequency of “triage” devices, notable variability in output characteristics across products, and often limited explainability of AI predictions. Altogether, we aim to increase transparency of the current landscape of the clinician-AI interface and highlight the need to rigorously assess which strategies ultimately lead to the best clinical outcomes.

Abstract WP278: Trends in GLP1RA and SGLT2i Use in Ischemic Stroke Patients With Diabetes

Background: The current AHA stroke prevention guidelines give Class 1 recommendations that patients with AIS and diabetes should receive glucose-lowering agents with cardiovascular benefit to reduce risk of MACE. The ADA recommends in patients with ASCVD, when prevention of further vascular events is the priority, a GLP1RA should be added; and when concern for heart failure or chronic kidney disease is the priority, a SGLT2i should be added. Objective: Among a cohort of patients with diabetes presenting with AIS, we analyzed rates of GLP1RA and SGLT2i use during hospitalization and/or outpatient follow-up. Methods: A retrospective review of AIS patients at a comprehensive stroke center between May 2016 and October 2022 was conducted using an institutional registry. Patients were identified to have diabetes as derived by the Charlson Comorbidity Index ICD 10 codes E10 through E14. We then identified patients receiving GLP1RA or SGLT2i and evaluated trends over time. Results: 4131 patients were identified of which 2045 (49.4%) had a diagnosis of diabetes. Of these patients 60/2045 (2.9%) were on GLP1RA while 35/2045 (1.7%) were on SGTLi for total of 4.6%. Annual trends were 2016 6/220 (2.7%) and 1/220 (0.4%), in 2017 6/347 (1.7%) and 3/347 (0.8%), in 2018 6/377 (1.6%) and 5/377 (1.3%), in 2019 3/240 (1.2%) and 1/240 (0.4%), in 2020 9/335 (2.7%) and 10/335 (3.0%), in 2021 11/322 (3.4%) and 10/322 (3.1%), in 2022 19/205 (9.2%) and 5/205 (2.4%) for GLP1RA and SGLT2i, respectively. Conclusions: In this retrospective analysis of 4131 AIS cases, use of GLP1RA and SGLT2i prior to 2019 remained around 2-3%. Following publication of the 2021 stroke prevention guidelines, GLP1RA use increased in 2022 to 9%. However, the study highlights the still-low rates of use of secondary MACE prevention despite Class 1 recommendations. This analysis serves as a call to attention that improving treatment of diabetes with ASCVD risk-reduction is a potential area of improvement.

Assessing the predictive value of neutrophil percentage to albumin ratio for ICU admission in ischemic stroke patients.

Acute ischemic stroke (AIS) remains a substantial global health challenge, contributing to increased morbidity, disability, and mortality. This study aimed at investigating the predictive value of the neutrophil percentage to albumin ratio (NPAR) in determining intensive care unit (ICU) admission among AIS patients. A retrospective observational study was conducted, involving AIS cases admitted to a tertiary hospital in Jordan between 2015 and 2020. Lab data were collected upon admission, and the primary outcome was ICU admission during hospitalization. Descriptive and inferential analyses were performed using SPSS version 29. In this study involving 364 AIS patients, a subset of 77 (21.2%) required admission to the ICU during their hospital stay, most frequently within the first week of admission. Univariable analysis revealed significantly higher NPAR levels in ICU-admitted ischemic stroke patients compared to those who were not admitted (23.3 vs. 15.7, p < 0.001), and multivariable regression models confirmed that higher NPAR (≥19.107) independently predicted ICU admission in ischemic stroke patients (adjusted odds ratio [aOR] = 4.85, 95% CI: 1.83-12.83). Additionally, lower GCS scores and higher neutrophil-to-lymphocyte ratio (NLR) were also associated with increased likelihood of ICU admission. In terms of predictive performance, NPAR showed the highest accuracy with an AUC of 0.885, sensitivity of 0.805, and specificity of 0.854, using a cutoff value of 19.107. NPAR exhibits an AUC of 0.058, significantly outperforming NLR (Z = 2.782, p = 0.005). NPAR emerged as a robust independent predictor of ICU admission in ischemic stroke patients, surpassing the predictive performance of the NLR.

Data-driven decisions about individual patients: The case of medical AI.

There are high hopes that clinical decisions can be improved by adopting algorithms trained to estimate the likelihood that a patient suffers a condition C. Introducing work on the epistemic value of purely statistical evidence in legal epistemology I show that a certain type of AI devices for making medical decisions about persons rely on purely statistical evidence and that it raises an important question about the appropriateness of relying on such devices for allocating health resources. If the argument I present is sound, then it suggests a radical rethinking of the use of prevalent types of AI devices as well as the use of statistical evidence in medical practice more generally.

Correlation exploration among CT imaging, pathology and genotype of pulmonary ground-glass opacity.

To analyse the clinical features, imaging manifestation, pathological typing and genetic testing results of patients undergoing surgery for ground-glass opacity (GGO) nodules, and explore the reasonable diagnosis and treatment program for GGO patients as to provide the basis for the establishment of GGO treatment process. This study is an exploratory study. 465 cases with GGO confirmed by HRCT, undergoing surgery and approved by pathologic diagnosis in Shanghai pulmonary hospital were enrolled in this study. All the patients with GGO were cases with single lesion. The relationship between the clinical, imaging, pathological and molecular biological data of single GGO were statistically studied. (1) Among 465 cases, the median age was 58 years and females were 315 (67.7%); there were 397 (85.4%) non-smoking, and 354 cases (76.1%) had no clinical symptoms. There were 33 cases of benign and 432 cases of malignant GGO. Significant differences were observed on the size, vacuole sign, pleural indentation and blood vessel sign of GGO between two groups (p < 0.05). Of 230 mGGO, there were no AAH, 13 cases of AIS, 25 cases of MIA and 173 cases of invasive adenocarcinoma. The probability of solid nodules in invasive adenocarcinoma was higher than that in micro invasive carcinoma, and the difference was statistically significant (p < 0.05). 360 cases were followed up with the average follow-up time of 6.05 months, and GGO of 34 cases (9.4%) increased. (2) In 428 adenocarcinoma samples approved by pathologic diagnosis, there were 262 (61.2%) lesions of EGFR mutation, 14 (3.3%) lesions of KRAS mutation, 1 (0.2%) lesion of Braf mutation, 9 (2.1%) lesions of EML4-ALK gene fusion and 2 (0.5%) lesions of ROS1 fusion. The detection rate of gene mutation in mGGO was higher than that of pGGO. During the follow-up period, genetic testing results of 32 GGO showed that EGFR mutation rate was 53.1%, ALK positive rate of 6.3%, KRAS mutation rate of 3.1% and no ros1 and BRAF gene mutation. No statistically significant difference was observed in comparison with unchanged GGO. (3) EGFR mutation rate of invasive adenocarcinoma was the highest (168/228, 73.7%), mainly in the 19Del and L858R point mutations. No KRAS mutation was found in atypical adenoma hyperplasia. No significant difference was observed on the mutation rate of KRAS between different types of GGO (p = 0.811). EML4-ALK fusion gene was mainly detected in invasive adenocarcinoma (7/9). GGO tends to occur in young, non-smoking women. The size of GGO is related to the degree of malignancy. Pleural depression sign, vacuole sign and vascular cluster sign are all characteristic images of malignant GGO. pGGO and mGGO reflect the pathological development of GGO. During the follow-up, it is found that GGO increases and solid components appear, which is the indication of surgical resection. The detection rate of EGFR mutations in mGGO and invasive adenocarcinoma is high. pGGO has heterogeneity in imaging, pathology and molecular biology. Heterogeneity research helps to formulate correct individualized diagnosis and treatment plans.

The Positive Predictive Value of Pediatric Stroke Diagnoses in Administrative Data: A Retrospective Validation Study.

This retrospective cohort study aimed to examine the positive predictive value (PPV) of pediatric stroke diagnoses in the Danish National Registry of Patients (DNRP) and the impact of different stroke definitions on the PPV. We included children registered with a stroke or stroke-related diagnosis in the DNRP between January 2017 through December 2020. Two assessors reviewed medical records and validated cases according to the American Heart and American Stroke Association (AHA/ASA) stroke definition. The level of interrater agreement was examined using kappa statistics. Validation by the AHA/ASA definition was compared with validation according to the definition in the International Classification of Disease 11th version (ICD-11) and the World Health Organization's definition. Stroke was confirmed in 120 of 309 included children, yielding an overall PPV of 0.39 (95% CI: 0.33-0.45). PPV varied across stroke subtypes from 0.83 (95% CI: 0.71-0.92) for ischemic stroke (AIS), 0.57 (95% CI: 0.37-0.76) for unspecified stroke, 0.42 (95% CI: 0.33-0.52) for intracerebral hemorrhage (ICH) to 0.31 (95% CI: 0.55-0.98) and 0.07 (95% CI: 0.01-0.22) for cerebral venous thrombosis and subarachnoid hemorrhage (SAH), respectively. Most non-confirmed ICH and SAH diagnoses were in children with traumatic intracranial hemorrhages (36 and 66% respectively). Among 70 confirmed AIS cases, 25 (36%) were identified in non-AIS code groups. PPV varied significantly across stroke definitions with the highest for the AHA/ASA definition (PPV = 0.39, 95% CI: 0.34-0.45) and the lowest for the WHO definition (PPV = 0.29, 95% CI: 0.24-0.34). Correspondingly, the incidence of pediatric AIS per 100.000 person-years changed from 1.5 for the AHA/ASA definition to 1.2 for ICD-11 and 1.0 for the WHO-definition. The overall interrater agreement was considered excellent (κ=0.85). After validation, stroke was confirmed in only half of the children registered in the DNRP with a stroke-specific diagnosis. Non-validated administrative data should be used with caution in pediatric stroke research. Pediatric stroke incidence rates may vary markedly depending on which stroke definition is used.

Simion and Kelp on trustworthy AI

Simion and Kelp offer a prima facie very promising account of trustworthy AI. One benefit of the account is that it elegantly explains trustworthiness in the case of cancer diagnostic AIs, which involve the acquisition by the AI of a representational etiological function. In this brief note, I offer some reasons to think that their account cannot be extended — at least not straightforwardly — beyond such cases (i.e., to cases of AIs with non-representational etiological functions) without incurring the unwanted cost of overpredicting untrustworthiness.

Autonomous weapons of pleasure. Media archaeology of automated killing in military and gaming technologies

ABSTRACT The article examines the relationship between the military model of autonomous weapons and the use of AI combat entities in computer games. The author explores how the technological and discursive model of autonomous weapons functions in the software created to entertain the players. The methodology of the article is based on Kittlerian media discourse analysis and media archaeology. The article focuses on a case of predatory AI that hunts the player in Alien: Isolation game. The author compares technologies of automated warfare (the Igloo White Operation, Predator and Reaper drones, Project Pigeon etc.) with the game to reveal that certain military aspects (automated tracking, killing, manhunt, mental manipulation etc.) recur in the software. The pleasure of playing games masks the military entrapment. The author explains how the software exploits the joys of being ‘immersed’ in a machine. The gameplay implements the cybernetic project of man-machine interaction, providing the player with an illusion of control. In reality, the AI system manages the player’s behaviour and emotions, making them enjoy being preyed on. The author ponders how the domesticated pleasure of playing against killing bots may be used to develop autonomous weapons and become weaponised against humans.

Soluble Immune Checkpoint-Related Proteins in Blood Are Associated With Invasion and Progression in Non-Small Cell Lung Cancer.

BackgroundImmune checkpoint inhibition therapy has been achieved significant success in the treatment of non-small cell lung cancer (NSCLC). However, the role of soluble immune checkpoint- related proteins in NSCLC remains obscure.MethodsWe evaluated the circulating levels of 14 immune checkpoint-related proteins panel (BTLA, LAG-3, GITR, IDO, PD-L2, PD-L1, PD-1, HVEM, Tim-3, CD28, CD27, CD80, CD137 and CTLA-4) and their associations with the risk of invasive disease and the risk of NSCLC in 43 pre-invasive (AIS), 81 invasive NSCLC (IAC) patients and matched 35 healthy donors using a multiplex Luminex assay. Gene expression in tumors from TCGA were analyzed to elucidate potential mechanisms. The multivariate logistic regression model was applied in the study. ROC(receiver operator characteristic) curve and calibration curve were used in the performance evaluation.ResultsWe found that sCD27, sCD80, CD137 and sPDL2 levels were significantly increased in IAC cases compared to AIS cases (P= 1.05E-06, 4.44E-05, 2.30E-05 and 1.16E-06, respectively), whereas sPDL1 and sPDL2 levels were significantly increased in NSCLC cases compared to healthy controls (P=3.25E-05 and 1.49E-05, respectively). Unconditional univariate logistic regression analysis indicated that increased sCD27, sCD80, sCD137, and sPDL2 were significantly correlated with the risk of invasive diseases. The model with clinical variables, sCD27 and sPDL2 demonstrated the best performance (AUC=0.845) in predicting the risk of IAC. CD27 and PDCD1LG2 (PDL2) showed significant association with cancer invasion signature in TCGA dataset.ConclusionOur study provides evidence that soluble immune checkpoint-related proteins may associate with the risk of IAC, and we further established an optimized multivariate predictive model, which highlights their potential application in the treatment of NSCLC patients. Future studies may apply these biomarkers to test their predictive value of survival and treatment outcome during immunotherapy in NSCLC patients.

Research discovery and visualization using ResearchRabbit: A use case of AI in libraries

One of the most challenging tasks of any research is literature review in which a researcher is constantly switching between different modes of searching and browsing papers. The researchers may find and explore some relevant papers via keyword searching or stumble upon some required papers via Wikipedia or at a conference, which leads to looking at the references or citations of that paper, which again leads to more citation mining and before one knows it might be lost. It often feels like tumbling down the rabbit hole, as each relevant paper that is found opens up yet more avenues to explore and dig out the endless possibilities. Research Rabbit is a new literature mapping tool that helps with doing all this complex literature review in a seamless way and provide quick visualizations to help in exploring the literature “forest”?

Novel Technologies as Potential Catalyst for Democratizing Urban Heritage Preservation Practices: The Case of 3D Scanning and AI

The conflict between heritage protection and urban infrastructure development rationales creates a context for inclusion, participation and dialogue of different heritage-related communities. However, developed in the pre-computer age of administrative practice, are often incapable, partially or completely, to accommodate the ‘new-era’ community oriented participatory practices. In this article, authors discuss the mutual effects of IT in the process of democratization of urban heritage preservation. The authors create and argue the conceptual model of distributed ledger technologies (DLT) in participatory UHP. The model demonstrates how technologies can become catalysts for democratization in situations when the regulatory and administrative change (on its own) is too inert. The article hypothesizes that novel technological developments which aim at or have the potential for increasing community involvement and democratization of administrative practice, exert their effects directly through technology-based participatory practices.