Tracing System Research Articles

The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PDF

Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days.

Privacy and contact tracing efficacy.

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.

Oil Palm Economic Benefit Distribution to Regions for Environmental Sustainability: Indonesia’s Revenue-Sharing Scheme

Palm oil is considered a key commodity in supporting the Indonesian economy, generating both domestic revenue and foreign exchange. Such revenue needs to be distributed equally for the benefit of the government and society. This paper discusses the opportunities and challenges of the proposed revenue-sharing scheme, DBH Sawit, sourcing from palm oil, particularly between central and local governments. It examines how the proposed scheme can be implemented and what strategies can reward regions for moving toward environmental sustainability practices. Research methods used in this paper include a literature study, stakeholder interviews, the SWOT method and analytical hierarchy processes. Results indicate that DBH Sawit is one of several fiscal instruments that could potentially incentivize sustainability. It has both strengths and anticipated opportunities, such as increased CPO production and global demand for palm oil. It faces, however, serious weaknesses, such as unauthorized oil palm plantations and the absence of traceability systems. It is also threatened by unfair market and global policies. This paper recommends diverse strategies to increase palm oil productivity, accelerate sustainability certification and develop a tracing system. To accelerate the adoption of environmental sustainability, the paper advocates an ecological-based fiscal transfer system.

A Web Application for Drug Traceability in Healthcare System using Blockchain

Abstract: In current situation, we come across various problems in pharmaceutical supply chain in India which can be solved with different ideas. Since a drug’s ownership changes from manufacturer to distributor and then to the pharmacist before reaching the customer . The manufacturers don’t know how their drug is being used. At the same time, consumers don’t know whether the drug has come from the rightful source including inaccurate information, lack of transparency and limited data provenance. It is difficult to detect counterfeits because these drugs pass through different complex distributed networks, thus forming opportunities for counterfeits to enter the authentic supply chain. Most existing track and trace systems are centralized leading to data privacy, transparency and authenticity issues in healthcare supply chains. In this project work, an end-to-end drug tracking system across the pharmaceutical supply chain is built as a Web Application. It attempts to prevent drug counterfeiting, add traceability, security, and visibility to the pharmaceutical supply chain using Blockchain technology and AWS S3 service. The system guarantees data provenance, eliminates the need for intermediaries and provides a secure immutable history of transactions to all stakeholders. We developed a Web application which provides traceability of drug manufacturers and other information to emulate the data tampering scenario. We also create a tampering application to exhibit the false transactions of a node. We perform testing and validation to recover the original data to evaluate it’s effectiveness to enhance traceability within pharmaceutical supply chain.

Epidemiological profile of COVID-19 in the French overseas department Mayotte, 2020 to 2021.

BackgroundDuring the COVID-19 pandemic, national and local measures were implemented on the island of Mayotte, a French overseas department in the Indian Ocean with critical socioeconomic and health indicators.AimWe aimed to describe the COVID-19 outbreak in Mayotte from March 2020 to March 2021, with two waves from 9 March to 31 December 2020 and from 1 January to 14 March 2021, linked to Beta (20H/501Y.V2) variant.MethodsTo understand and assess the dynamic and the severity of the COVID-19 outbreak in Mayotte, surveillance and investigation/contact tracing systems were set up including virological, epidemiological, hospitalisation and mortality indicators.ResultsIn total, 18,131 cases were laboratory confirmed, with PCR or RAT. During the first wave, incidence rate (IR) peaked in week 19 2020 (133/100,000). New hospitalisations peaked in week 20 (54 patients, including seven to ICU). Testing rate increased tenfold during the second wave. Between mid-December 2020 and mid-January 2021, IR doubled (851/100,000 in week 5 2021) and positivity rate tripled (28% in week 6 2021). SARS-CoV-2 Beta variant (Pangolin B.1.351) was detected in more than 80% of positive samples. Hospital admissions peaked in week 6 2021 with 225 patients, including 30 to ICU.ConclusionThis massive second wave could be linked to the high transmissibility of the Beta variant. The increase in the number of cases has naturally led to a higher number of severe cases and an overburdening of the hospital. This study shows the value of a real-time epidemiological surveillance for better understanding crisis situations.

Effective Contact Tracing System Minimizes COVID-19 Related Infections and Deaths: Policy Lessons to Reduce the Impact of Future Pandemic Diseases

One of the fundamental questions in the presence of Coronavirus Diseases 2019 (COVID-19) pandemic crisis and in general of new pandemic diseases is to design effective policy responses to reduce the impact in the initial phase of diffusion, when appropriate therapies and drugs lack. This study analyses a main case study given by Italy, one of the first European countries to be damaged of the impact of COVID-19 pandemic. In particular, this study focuses on health policy responses to the pandemic crisis across selected Italian regions that were the first areas to experience a rapid increase in confirmed cases and deaths of COVID-19. The analysis of early regional health policies, from January to July 2020 (during the first wave of the COVID-19 pandemic), reveals that some regions have managed pandemic crisis with appropriate health policy responses based on: a) a timely and widespread testing of individuals, b) effective units of epidemiological investigation in a pervasive contact-tracing system to detect and isolate all infected people. This health policy response has reduced total deaths and negative effects of COVID-19 on health of people during the first pandemic wave, when are not available pharmaceutical interventions, such as vaccines and other antiviral drugs. This evidence in the first wave of COVID-19 pandemic provides important lessons to design an effective public health policy to constraint future pandemic waves driven by new variants and new viral agents, when appropriate drugs are not ready.

Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.

A privacy-preserving blockchain-based tracing model for virus-infected people in cloud

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users’ rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts.

AgriFood supply chain traceability: data sharing in a farm-to-fork case

PurposeTraceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models.Design/methodology/approachThis research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production.FindingsTogether with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability.Originality/valueThe existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products.

Research Progress of Application of Information Tracing System in Disinfection Supply Center

Research Progress of Application of Information Tracing System in Disinfection Supply Center

COVID-19 Contact Tracing and the Operationalisation of Somatechnics

This article draws on the paradigm of media operationalism to understand the somatechnical construction of bodies during the COVID-19 pandemic. According to the concept of somatechnics, one’s experience with the social world is articulated through the available technologies and techniques required to and developed from using these technologies ( Sullivan and Murray 2016 ). By drawing on the case of the Service Victoria app, the digital COVID-19 contact tracing system launched by the Victoria State government in Australia, I focus on the transformative meaning of technologies and somatechnics and how subjectivity is being redefined through the lens of technological utilisation. I suggest that all human-related forms of relations (human-to-human and human-to-machine) have become secondary and give way to the synchronic data-to-data relation of the app. In the regime of operational media, the body is not just a historical and cultural construction but a techno-transactional object that supports the optimisation of automated-decision making. The recent operational-turn in media studies provides a useful pathway to rethink the changing meaning of body and the human/technologies entanglement.

Using Parametric Test to determine the Significance of Banded patterns in N-dimensional 0-1 dataset

Background: The identification of banded patterns in N-dimensional (ND) 0-1 dataset is one where all the elements in the dimensions are arranged such that the one’s entries are ordered about the center of dimensions. Reordering 0-1 datasets in order to determine banded patterns in data, allows for the identification of interesting pattern that are hidden in the data. The challenge is whether or not the identify banded patterns are significant. Previous work on the significance of banded patterns using parametric test was aimed at 2D and 3D banding algorithms, using a single banding algorithm in each case, which meant no work on the significance of banded patterns using different banding algorithms with different dimensions. Aim: To this end, this paper presents a comparison between ND banding algorithms for 2D and 3D. Method: The approach is to use parametric test on synthetic data, UCI and the real datasets taken from the cattle tracing system (CTS). The ND banding algorithms considered for 2D are:2D banding, barycentric (BC) and 2D sort, and for 3D are: exact-Euclidean, exact-Manhattan variations and the approximate. Results: The experimental results presented shows the significance of banded patterns with p value less than 0.05. However, the post-hoc test result shows a statistically significant difference between 2D banding and BC, 2D banding and 2D sort, exact-Euclidean and exact-Manhattan, exact-Euclidean and the approximate but no significant difference between BC and 2D sort as well as exact-Manhattan and the approximate.

A Blockchain-Based Human-to-Infrastructure Contact Tracing Approach for COVID-19

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.

Contact tracing and mobility pattern detection during pandemics – a trajectory cluster based approach

PurposeA wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations.Design/methodology/approachPandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients.FindingsIt is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data.Research limitations/implicationsAs the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose.Originality/valueThis paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.

Ultra-wide range and wavelength fixed trace gas detection technology based on single chamber multiplexing

The all-fiber photo-thermal interference (PTI) system based on hollow-core photonic crystal fiber (HC-PCF) has the advantages of wide dynamic range, ultra-sensitivity, and small volume, which is of great significance in gas trace analysis. Most of the previous PTI systems obtain the relationship between gas concentration and phase modulation by wavelength scanning and extracting the second harmonic signal absorbed by the gas. However, the residual modulation caused by the asynchronism between wavelength and modulation signal seriously affects the signal-to-noise ratio (SNR) and dynamic detection range of the system. In addition, wavelength scanning has limitations for detecting gas with irregular absorption lines. This paper proposes an all-fiber photo-thermal interference system with pump intensity external modulation. The system is not limited by the gas absorption line and has a high SNR. The factors affecting the dynamic range of the system are analyzed theoretically and experimentally. It is verified that increasing the injection gas pressure can significantly increase the detection limit of the system compared with increasing the pump light power. The redshift of the pump light can dramatically increase the detection limit of the system. A C2H2 gas trace system with the pump wavelength redshifted to 1530.41 nm was demonstrated. The detection lower limit of 9.24 ppm and an extensive dynamic detection range of five orders of magnitude were obtained.

The conceptual design of intelligent spatial decision support system for the fishery-industry logistic

This paper describes the conceptual framework of ISDSS (Intelligence Spatial Decision Support System) for the fishery industry logistic. The decision problem involved is complex due to the fish’s seasonal, perishable, size variability characteristics as raw material. The ISDSS will be supported by three main models, i.e. raw material origin selection, distribution route selection and product tracking and tracing. Multiple criteria decision models will be used for raw material selection. Multi-objective optimization will be used for optimum route selection, considering time, distance, fish quality, and spatial information. Internet of Things (IoT) model will be used for product tracking and tracing system. The geographical information system (GIS) integration with the DSS will also be supported by Artificial Intelligence Technique, i.e. data mining in classifying and clustering the raw material characteristic and origin data. The data collection will be supported by the Internet of Things (IoT). The best technique for each model will be investigated further based on the field experiment and simulation.

Live system call trace reconstruction on Linux

Live system call traces provide essential information in analyzing modern malware. Prior work demonstrated how system call traces can be used to differentiate benign from malicious applications. For example, ransomware invokes file system API to remove users’ access to their sensitive data, and asks for a ransom to restore the access privileges.Unfortunately, current methods and tools focus on offline reconstruction using memory dumps of the entire system. While it is possible to use such methods in live analysis by pausing execution and tracking system calls, it severely hinders the system performance. In this paper, we present the design and implementation of our method to trace system calls in Linux-based systems. We show how using our method enables obtaining and analyzing the system calls performed by real ransomware. To build a system call trace for a given process, we first retrieve the process context from a raw live memory image. Next, we analyze this context and identify the last invoked system call. We repeat this process generating a full trace.We prototype our approach as a LibVMI tool using only memory read APIs and compare the accuracy of our method with that of strace, an intrusive and dedicated system call tracer. We show that we achieve the same level of accuracy, yet, without impacting the performance of the traced program. Further, we demonstrate how non-intrusive tracing has a better performance compared to trap-based tracing when using LibVMI traps and interrupts APIs to hook system call events. Finally, we provide an analysis of system calls performed by mature ransomware and demonstrate how our extracted traces can be used to identify them.

On Image-Processing-Based Identification Method of Express Logistics Information

As a modern comprehensive information platform for integrated statistical analysis of express shipments information and for express shipments management decision-making, the express logistics track and trace system needs to use artificial intelligence (AI) technology and image processing technology to automatically extract the text content of express logistics documents. Existing express shipments information identification models usually have problems such as less-than-ideal performance in detecting single characters or small text regions of express logistics documents, high human resource cost for character-level markup, and low speed and accuracy of text recognition. In response, this paper studies the image-processing-based identification method of express logistics information. It presents a recognition process for pre-processing text images of express logistics documents, along with a detailed description of denoising, greyscaling and binarisation methods. While proposing an enhancement strategy for Chinese characters in the section of handwritten Chinese, this paper constructs a model for recognition of express shipping document texts based on bidirectional long-short term memory (LSTM) and attention mechanism. In this way, we fully mined key semantic information of express logistics document texts. The experimental results verify the effectiveness of the constructed model.

Dichotomous Roles of Smooth Muscle Cell-Derived MCP1 (Monocyte Chemoattractant Protein 1) in Development of Atherosclerosis.

Smooth muscle cells (SMCs) in atherosclerotic plaque take on multiple nonclassical phenotypes that may affect plaque stability and, therefore, the likelihood of myocardial infarction or stroke. However, the mechanisms by which these cells affect stability are only beginning to be explored. In this study, we investigated the contribution of inflammatory MCP1 (monocyte chemoattractant protein 1) produced by both classical Myh11 (myosin heavy chain 11)+ SMCs and SMCs that have transitioned through an Lgals3 (galectin 3)+ state in atherosclerosis using smooth muscle lineage tracing mice that label all Myh11+ cells and a dual lineage tracing system that targets Lgals3-transitioned SMC only. We show that loss of MCP1 in all Myh11+ smooth muscle results in a paradoxical increase in plaque size and macrophage content, driven by a baseline systemic monocytosis early in atherosclerosis pathogenesis. In contrast, knockout of MCP1 in Lgals3-transitioned SMCs using a complex dual lineage tracing system resulted in lesions with an increased Acta2 (actin alpha 2, smooth muscle)+ fibrous cap and decreased investment of Lgals3-transitioned SMCs, consistent with increased plaque stability. Finally, using flow cytometry and single-cell RNA sequencing, we show that MCP1 produced by Lgals3-transitioned SMCs influences multiple populations of inflammatory cells in late-stage plaques. MCP1 produced by classical SMCs influences monocyte levels beginning early in disease and was atheroprotective, while MCP1 produced by the Lgals3-transitioned subset of SMCs exacerbated plaque pathogenesis in late-stage disease. Results are the first to determine the function of Lgals3-transitioned inflammatory SMCs in atherosclerosis and highlight the need for caution when considering therapeutic interventions involving MCP1.