Monitoring Of Progression Research Articles

Portable loop-mediated isothermal amplification device with spectrometric detection for rapid pathogen identification

With the rise in extreme weather due to global warming, coupled with globalization facilitating the spread of infectious diseases, there's a pressing need for portable testing platforms offering simplicity, low cost, and remote transmission, particularly beneficial in resource-limited and non-urban areas. We have developed a portable device using loop-mediated isothermal amplification (LAMP) with spectrometric detection to identify Salmonella Typhimurium DNA. The device utilizes the LinkIt 7697 microcontroller and a microspectrometer to capture and transmit spectral signals in real-time, allowing for improved monitoring and analysis of the reaction progress. We built a hand-held box containing a microspectrometer, thermoelectric cooler, ultraviolet LED, disposable reaction tube, and homemade thermal module, all powered by rechargeable batteries. Additionally, we conducted thorough experiments to ensure temperature accuracy within 1 °C under thermal control, developed a heating module with a LinkIt 7697 IoT development board to heat the DNA mixture to the reaction temperature within 3 min, and integrated foam insulation and a 3D-printed frame to enhance the device's thermal stability. We successfully demonstrated the amplification of Salmonella Typhimurium DNA with an impressive sensitivity of 2.83 × 10−4 ng/μL. A remote webpage interface allows for monitoring the temperature and fluorescence during the LAMP process, improving usability. This portable LAMP device with real-time detection offers a cost-effective solution for detecting Salmonella Typhimurium in food products. Its unique design and capabilities make it a promising tool for ensuring food safety.

Estimating Cognitive Workload Using Task-Related Pupillary Responses in Simulated Drilling in Cochlear Implantation.

Training of temporal bone drilling requires more than mastering technical skills with the drill. Skills such as visual imagery, bimanual dexterity, and stress management need to be mastered along with precise knowledge of anatomy. In otorhinolaryngology, these psychomotor skills underlie performance in the drilling of the temporal bone for access to the inner ear in cochlear implant surgery. However, little is known about how psychomotor skills and workload management impact the practitioners' continuous and overall performance. To understand how the practitioner's workload and performance unfolds over time, we examine task-evoked pupillary responses (TEPR) of 22 medical students who performed transmastoid-posterior tympanotomy (TMPT) and removal of the bony overhang of the round window niche in a 3D-printed model of the temporal bone. We investigate how students' TEPR metrics (Average Pupil Size [APS], Index of Pupil Activity [IPA], and Low/High Index of Pupillary Activity [LHIPA]) and time spent in drilling phases correspond to the performance in key drilling phases. All TEPR measures revealed significant differences between key drilling phases that corresponded to the anticipated workload. Enlarging the facial recess lasted significantly longer than other phases. IPA captured significant increase of workload in thinning of the posterior canal wall, while APS revealed increased workload during the drilling of the bony overhang. Our findings contribute to the contemporary competency-based medical residency programs where objective and continuous monitoring of participants' progress allows to track progress in expertise acquisition. Laryngoscope, 2024.

Identification potential biomarkers for diagnosis, and progress of breast cancer by using high-pressure photon ionization time-of-flight mass spectrometry

BackgroundIn this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC). MethodsA high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring. ResultsOn the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC. ConclusionCollectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.

Multimodal Technologies for Remote Assessment of Neurological and Mental Health.

Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.

Enhancing claustrophobia exposure therapy: A blockchain and NFT-enabled metaverse approach

Claustrophobia is a widespread mental health issue that affects a significant portion of the population. Traditional approaches, such as exposure therapies, have shown efficacy but are limited by their inability to provide immersive and controlled environments for effective therapy. These methods often struggle to replicate the intensity and authenticity of claustrophobic scenarios and face challenges in data security and patient engagement. In this paper, we propose a blockchain and Non-Fungible Token (NFT)-based metaverse to create a decentralized, transparent, immersive, and secure platform for claustrophobia exposure therapy. Our approach integrates blockchain technology for decentralized data management and NFTs for incentivizing patient participation, offering a unique enhancement to traditional therapy methods. We develop four blockchain smart contracts to manage registration, access control, NFT management, progress monitoring, and incentives distribution. Our system architecture, sequence diagrams, and algorithms are presented to demonstrate the functionality of our solution. We validate our approach through functionality testing, cost evaluation, and security analysis. Specifically, we measure the gas consumption of the smart contracts, analyze transaction costs, and assess system security against vulnerabilities. Additionally, we leverage existing literature to support the effectiveness of NFTs as incentives and the role of blockchain in enhancing data security and participation. The results show improvements in therapy engagement and data security, validating the practical implications of integrating blockchain and metaverse technologies in mental health therapy. We discuss the practical implications, challenges, and limitations of our solution, along with guidelines for generalization. The source code is publicly available on GitHub.

Risk to reoffend changes over time: Improving correctional programming through progress monitoring.

Progress monitoring is integral to evidence-based practice. Correctional settings, especially the supervision of individuals who commit sexual offenses, elicit public concern; negative outcomes can be catastrophic. Using a prospective longitudinal study of 2,939 men with a history of sexual offenses undergoing community supervision, we examined different models of progress monitoring and how they should inform the assessment of risk for sexual recidivism. We found that the most recent assessment scores of the ACUTE-2007 and STABLE-2007 sexual recidivism risk tools provided the best information about reoffending risk compared to using (a) the worst period of adjustments (i.e., highest risk score), (b) the best period of adjustments (i.e., lowest risk score), or (c) a rolling average of scores. We also found that the latest STABLE-2007 scores incrementally predicted sexual recidivism beyond baseline risk as assessed by demographic and criminal history variables (Static-99R). We conclude that the risk for sexual recidivism changes over time and that community corrections is advanced by repeated assessment of dynamic (changeable) risk factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

2023 American College of Rheumatology (ACR)/American College of Chest Physicians (CHEST) Guideline for the Screening and Monitoring of Interstitial Lung Disease in People with Systemic Autoimmune Rheumatic Diseases.

We provide evidence-based recommendations regarding screening for interstitial lung disease (ILD) and the monitoring for ILD progression in people with systemic autoimmune rheumatic diseases (SARDs), specifically rheumatoid arthritis, systemic sclerosis, idiopathic inflammatory myopathies, mixed connective tissue disease, and Sjögren disease. We developed clinically relevant population, intervention, comparator, and outcomes questions related to screening and monitoring for ILD in patients with SARDs. A systematic literature review was performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A Voting Panel of interdisciplinary clinician experts and patients achieved consensus on the direction and strength of each recommendation. Fifteen recommendations were developed. For screening people with these SARDs at risk for ILD, we conditionally recommend pulmonary function tests (PFTs) and high-resolution computed tomography of the chest (HRCT chest); conditionally recommend against screening with 6-minute walk test distance (6MWD), chest radiography, ambulatory desaturation testing, or bronchoscopy; and strongly recommend against screening with surgical lung biopsy. We conditionally recommend monitoring ILD with PFTs, HRCT chest, and ambulatory desaturation testing and conditionally recommend against monitoring with 6MWD, chest radiography, or bronchoscopy. We provide guidance on ILD risk factors and suggestions on frequency of testing to evaluate for the development of ILD in people with SARDs. This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the screening and monitoring of ILD in people with SARDs.

2023 American College of Rheumatology (ACR)/American College of Chest Physicians (CHEST) Guideline for the Screening and Monitoring of Interstitial Lung Disease in People with Systemic Autoimmune Rheumatic Diseases.

We provide evidence-based recommendations regarding screening for interstitial lung disease (ILD) and the monitoring for ILD progression in people with systemic autoimmune rheumatic diseases (SARDs), specifically rheumatoid arthritis, systemic sclerosis, idiopathic inflammatory myopathies, mixed connective tissue disease, and Sjögren disease. We developed clinically relevant population, intervention, comparator, and outcomes questions related to screening and monitoring for ILD in patients with SARDs. A systematic literature review was performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A Voting Panel of interdisciplinary clinician experts and patients achieved consensus on the direction and strength of each recommendation. Fifteen recommendations were developed. For screening people with these SARDs at risk for ILD, we conditionally recommend pulmonary function tests (PFTs) and high-resolution computed tomography of the chest (HRCT chest); conditionally recommend against screening with 6-minute walk test distance (6MWD), chest radiography, ambulatory desaturation testing, or bronchoscopy; and strongly recommend against screening with surgical lung biopsy. We conditionally recommend monitoring ILD with PFTs, HRCT chest, and ambulatory desaturation testing and conditionally recommend against monitoring with 6MWD, chest radiography, or bronchoscopy. We provide guidance on ILD risk factors and suggestions on frequency of testing to evaluate for the development of ILD in people with SARDs. This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the screening and monitoring of ILD in people with SARDs.

Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors.

The Unified Parkinson's Disease Rating Scale (UPDRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity. The rating is crucial for disease progression monitoring and treatment adjustment. This study aims to advance the capabilities of PD management by developing an innovative framework that integrates deep learning with wearable sensor technology to enhance the precision of UPDRS assessments. We introduce a series of deep learning models to estimate UPDRS Part III scores, utilizing motion data from wearable sensors. Our approach leverages a novel Multi-shared-task Self-supervised Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework that processes raw gyroscope signals and their spectrogram representations. This technique aims to refine the estimation accuracy of PD severity during naturalistic human activities. Utilizing 526 min of data from 24 PD patients engaged in everyday activities, our methodology demonstrates a strong correlation of 0.89 between estimated and clinically assessed UPDRS-III scores. This model outperforms the benchmark set by single and multichannel CNN, LSTM, and CNN-LSTM models and establishes a new standard in UPDRS-III score estimation for free-body movements compared to recent state-of-the-art methods. These results signify a substantial step forward in bioengineering applications for PD monitoring, providing a robust framework for reliable and continuous assessment of PD symptoms in daily living settings.

Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer's risk-enhanced sample.

Understanding longitudinal change in key plasma biomarkers will aid in detecting presymptomatic Alzheimer's disease (AD). Serial plasma samples from 424 Wisconsin Registry for Alzheimer's Prevention participants were analyzed for phosphorylated-tau217 (p-tau217; ALZpath) and other AD biomarkers, to study longitudinal trajectories in relation to disease, health factors, and cognitive decline. Of the participants, 18.6% with known amyloid status were amyloid positive (A+); 97.2% were cognitively unimpaired (CU). In the CU, amyloid-negative (A-) subset, plasma p-tau217 levels increased modestly with age but were unaffected by body mass index and kidney function. In the whole sample, average p-tau217 change rates were higher in those who were A+ (e.g., simple slopes(se) for A+ and A- at age 60 were 0.232(0.028) and 0.038(0.013))). High baseline p-tau217 levels predicted faster preclinical cognitive decline. p-tau217 stands out among markers for its strong association with disease and cognitive decline, indicating its potential for early AD detection and monitoring progression. Phosphorylated-tau217 (p-tau217) trajectories were significantly different in people who were known to be amyloid positive. Subtle age-related trajectories were seen for all the plasma markers in amyloid-negative cognitively unimpaired. Kidney function and body mass index were not associated with plasma p-tau217 trajectories. Higher plasma p-tau217 was associated with faster preclinical cognitive decline.

Unmanned ground vehicle (UGV) based automated construction progress measurement of road using LSTM

PurposeCurrent solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.Design/methodology/approachThe crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.FindingsIn controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.Research limitations/implicationsThe implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.Originality/valueThis first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

Label-Free Monitoring of Endometrial Cancer Progression Using Multiphoton Microscopy

Endometrial cancer is the most common gynecological cancer in the developed world. However, the accuracy of current diagnostic methods is still unsatisfactory and time-consuming. Here, we presented an alternate approach to monitoring the progression of endometrial cancer via multiphoton microscopy imaging and analysis of collagen, which is often overlooked in current endometrial cancer diagnosis protocols but can offer a crucial signature in cancer biology. Multiphoton microscopy (MPM) based on the second-harmonic generation and two-photon excited fluorescence was introduced to visualize the microenvironment of endometrium in normal, hyperplasia without atypia, atypical hyperplasia, and endometrial cancer specimens. Furthermore, automatic image analysis based on the MPM image processing algorithm was used to quantify the differences in the collagen morphological features among them. MPM enables the visualization of the morphological details and alterations of the glands in the development process of endometrial cancer, including irregular changes in the structure of the gland, increased ratio of the gland to the interstitium, and atypical changes in the glandular epithelial cells. Moreover, the destructed basement membrane caused by gland proliferation and fusion is clearly shown in SHG images, which is a key feature for identifying endometrial cancer progression. Quantitative analysis reveals that the formation of endometrial cancer is accompanied by an increase in collagen fiber length and width, a progressive linearization and loosening of interstitial collagen, and a more random arrangement of interstitial collagen. Observation and quantitative analysis of interstitial collagen provide invaluable information in monitoring the progression of endometrial cancer. Label-free multiphoton imaging reported here has the potential to become an in situ histological tool for effective and accurate early diagnosis and detection of malignant lesions in endometrial cancer.

A novel control chart scheme for online social network monitoring using multivariate nonparametric profile techniques

Online social networks have become indispensable in modern life, facilitating knowledge sharing, social communication, and business marketing. To gain a deeper understanding of individuals' behavior within social networks, researchers have undertaken essential analytical tasks such as change point detection. Recently, nonparametric change point detection methods have attracted researchers' attention due to their generality and flexibility. However, existing methods exhibit limitations, including overlooking network structure, reliance on case-based network attributes, and neglecting the dynamic nature of data, which may have correlations in evolving social networks. In this study, we propose a novel multivariate mixed-effects nonparametric profile control (MENPC) algorithm to address these limitations. The advantage of MENPC relies on its unique point of view in approaching network data, where it incorporates the dynamic nature of data into the monitoring process without assuming internal independence of networks over time. Additionally, it takes into account the network structure by considering both nodal and network-level attributes. Furthermore, by introducing an updating trick formula, the proposed algorithm simplifies computations, effectively balancing memory and speed for online monitoring. We evaluate the effectiveness of MENPC through comprehensive numerical experiments using the degree correlated stochastic block model to simulate interactions in evolving online social networks. The results demonstrate MENPC's superior performance in terms of expected detection delay, showcasing its accuracy and efficiency in comparison to competing approaches including Wilson, and eigenvalue methods. Applying MENPC to the Enron email network dataset further confirms its significant progress in social network monitoring, expanding its potential for various applications.

Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities

Automated progress monitoring of construction sites using cameras has been proposed in recent years. Although previous studies have tried to identify the most informative camera views according to 4D BIM to optimize installation plans, video collection using fixed or pan-tilt-zoom cameras is still limited by their inability to adapt to the dynamic construction environment. Therefore, considerable attention has been paid to using camera-equipped unmanned aerial vehicles (CE-UAVs), which provide mobility for the camera, allowing it to fit its field of view automatically to the important parts of the construction site while avoiding occlusions. However, previous studies on optimizing video collection with CE-UAV are limited to the scanning of static objects on construction sites. Given the growing interest in construction activities, the existing methods are inadequate to meet the requirements for the collection of high-quality videos. In this study, the following requirements for and constraints on collecting construction-activity videos have been identified: (1) the FOV should be optimized to cover the areas of interest with the minimum possible occlusion; (2) the path of the UAV should be optimized to allow efficient data collection on multiple construction activities over a large construction site, considering the locations of activities at specific times; and (3) the data collection should consider the requirements for CV processes. Aiming to address these requirements and constraints, a method has been proposed to perform simulation-based optimization of path planning for CE-UAVs to allow automated and effective collection of videos of construction activities based on a detailed 4D simulation that includes a micro-schedule and the corresponding workspaces. This method can identify the most informative views of the workspaces and the optimal path for data capture. A case study was developed to demonstrate the feasibility of the proposed method.

Progressive Monitoring of Micro-Dumps Using Remote Sensing: An Applicative Framework for Illegal Waste Management

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

Validating a Curvature-Based Marker of Cervical Carotid Tortuosity for Risk Assessment in Heritable Aortopathies.

Cervical arterial tortuosity is associated with adverse outcomes in Loeys-Dietz syndrome and other heritable aortopathies. A method to assess tortuosity based on curvature of the vessel centerline in 3-dimensional space was developed. We measured cervical carotid tortuosity in 65 patients with Loeys-Dietz syndrome from baseline computed tomography angiogram/magnetic resonance angiogram and all serial images during follow-up. Relations between baseline carotid tortuosity, age, aortic root diameter, and its change over time were compared. Patients with unoperated aortic roots were assessed for clinical end point (type A aortic dissection or aortic root surgery during 4 years of follow-up). Logistic regression was performed to assess the likelihood of clinical end point according to baseline carotid tortuosity. Total absolute curvature at baseline was 11.13±5.76 and was relatively unchanged at 8 to 10 years (fold change: 0.026±0.298, P=1.00), whereas tortuosity index at baseline was 0.262±0.131, with greater variability at 8 to 10 years (fold change: 0.302±0.656, P=0.818). Baseline total absolute curvature correlated with aortic root diameter (r=0.456, P=0.004) and was independently associated with aortic events during the 4-year follow-up (adjusted odds ratio [OR], 2.64 [95% CI, 1.02-6.85]). Baseline tortuosity index correlated with age (r=0.532, P<0.001) and was not associated with events (adjusted OR, 1.88 [95% CI, 0.79-4.51]). Finally, baseline total absolute curvature had good discrimination of 4-year outcomes (area under the curve=0.724, P=0.014), which may be prognostic or predictive. Here we introduce cervical carotid tortuosity as a promising quantitative biomarker with validated, standardized characteristics. Specifically, we recommend the adoption of a curvature-based measure, total absolute curvature, for early detection or monitoring of disease progression in Loeys-Dietz syndrome.

Teaching strategies to facilitate social and cognitive processes in online learning environments

ABSTRACT This study used the Community of Inquiry framework to examine how university instructors designed, facilitated, and supported social and cognitive processes in online courses, and how instructors and students experienced these processes. In early 2021, 25 online focus groups were organized with instructors (n = 52) and students (n = 44) from all faculties of a large Dutch university to discuss this. In terms of design, instructors restructured their courses (e.g. reduced group sizes to intensify interaction and implemented a flipped classroom approach) and communicated more explicitly about the course structure and expectations to better enable students to self-regulate their learning process. During the course, instructors orchestrated cognitive and social processes by facilitating discourse (e.g. breakout room activities and collaboration activities) and offering direct support (e.g. online progress monitoring and “being there” for students). The analysis illustrated how teaching presence plays a crucial role in fostering cognitive and social processes in online courses. This study contributes to a better understanding of the role of teaching presence in a community of inquiry, while providing insights that can inform the development of effective online teaching strategies.

A novel query execution time prediction approach based on operator iterate-aware of the execution plan on the graph database

Query execution time prediction is essential for database query optimization tasks, such as query scheduling, progress monitoring, and resource allocation. In the query execution time prediction tasks, the query plan is often used as the modeling object of a prediction model. Although the learning-based prediction models have been proposed to capture plan features, there are two limitations need to be considered more. First, the parent–child dependencies between plan operators can be captured, but the operator’s branch independence cannot be distinguished. Second, each operator’s output row is its following operator input, but the data iterate transfer operations between operators are ignored. In this study, we propose a graph query execution time prediction model containing a plan module, a query module, a plan-query module, and a prediction module to improve prediction effectiveness. Specifically, the plan module is used to capture the data iterate transfer operations and distinguish independent of branch operators; the query module is used to learn features of query terms that have an influence on the composition of operators; the plan-query interaction module is used to learn the logical correlations of plan and query. The experiment on datasets proves the effectiveness of the operator iterate-aware and query-plan interaction method in our proposed graph query execution prediction model.

Integrating Traditional Chinese Medicine and Artificial Intelligence for insomnia: A promising frontier

Insomnia, a common condition that significantly impacts health and daily life, is often treated with medications that can have adverse effects. Traditional Chinese Medicine (TCM) offers non-drug therapies for insomnia, but their diverse methods and efficacy require systematic evaluation. This letter explores the integration of TCM and Artificial Intelligence (AI) to enhance insomnia treatment. AI can personalize treatment plans, provide predictive analytics, monitor progress, and enhance research, potentially increasing the efficacy of TCM interventions. This combined approach promises more effective, personalized, and adaptive treatments for insomnia.

Retinal thickness and microvascular alterations observed by optical coherence tomography in antineutrophil cytoplasmic antibody-associated vasculitis: a cross-sectional study.

As an autoimmune disease, antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) often affects multiple organs, including the ocular system. This study aims to investigate differences in retinal thickness (RT) and retinal superficial vascular density (SVD) between patients with AAV and healthy controls (HCs) using optical coherence tomography angiography (OCTA). Currently, these differences are not clear. A total of 16 AAV individuals (32 eyes) and 16 HCs (32 eyes) were recruited to this cross-sectional study conducted in the First Affiliated Hospital of Nanchang University from June 2023 to September 2023. The study protocol conformed with the tenets of the Declaration of Helsinki (as revised in 2013). Each image observed by OCTA was divided into 9 regions using the Early Treatment Diabetic Retinopathy Study (ETDRS) subzones as a guide. In the full layer, the RT of AAV patients was found to be significantly reduced in the inner superior (IS, P<0.001), outer superior (OS, P=0.003), inner temporal (IT, P=0.003), and outer temporal (OT, P<0.001) regions; inner RT was significantly lower in the IS (P=0.006), OS (P<0.001), inner nasal (IN, P=0.005), outer nasal (ON, P<0.001), and center (C, P=0.01) regions than that in HCs. Outer RT of AAV patients showed a reduction in the IS (P<0.001), as well as IT (P=0.008), and OT (P<0.001) regions. No statistically significant differences were seen in the different subregions in other different layers (P>0.05). Only the inner inferior (II) and outer inferior (OI) regions of SVD in AAV patients did not differ significantly from controls. All other regions showed a reduction in SVD. The details are as follows: IS (P<0.001), OS (P<0.001), IT (P=0.005), OT (P<0.001), IN (P<0.001), ON (P<0.001), and C (P=0.003). According to receiver operating characteristic (ROC) curve analysis, the full IS region [area under the curve (AUC): 0.8892, 95% confidence interval (CI): 0.8041-0.9742, P<0.001] had the highest diagnostic value for AAV-induced reduction in RT. The IS (AUC: 0.9121, 95% CI: 0.8322-0.9920, P<0.001) region was also the most sensitive to changes in SVD of AAV individuals. In addition, we found that SVD in the IN region (r=-0.4224, 95% CI: -0.6779 to -0.0757, P=0.02) as well as mean visual acuity (r=-0.3922, 95% CI: -0.6579 to -0.0397, P=0.03) of AAV patients were negatively correlated with disease duration. However, we did not find an association between SVD and RT in this study. The findings from OCTA indicated a reduction in RT and SVD among patients with AAV. OCTA allows for the evaluation of AAV-related ocular lesions and holds promise for monitoring of disease progression through regular evaluations.