Real-world Events Research Articles

Strike early-strike strong lipid-lowering strategy with PCSK9i in ACS patients. Real-world evidence from AT-TARGET-IT registry

Abstract Background/Introduction No data are available on early initiation of proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) in patients with acute coronary syndrome (ACS) in real-world. Purpose This study investigates the effects of PCSK9i started at time of ACS hospitalization on lipid control and major CV events in real-world. Methods The lipid control outcome was the percentage of patients reaching the LDL-C target of < 55 mg/dL at first lipid control. The clinical outcome was the incidence of composite major CV events (all cause death, non-fatal MI, non-fatal stroke, and ischemia-driven revascularization) during follow-up in relation to quartiles of LDL-C at first lipid control. Results We included 771 patients with ACS from AT-TARGET-IT registry, receiving PCSK9i prescription during hospitalization or at discharge. Median LDL-C was 137 mg/dL and decreased to 43 mg/dL at first lipid control. 527 (68.3%) patients achieved LDL-C target at the first lipid control at a median time of 37 days from hospitalization; of them, 404 (76.8%) were discharged on statin plus ezetimibe background therapy. Event curves through a median follow-up of 11 months across quartiles of LDL-C showed a stepwise lower risk of 4P-MACE, 3P-MACE, all-cause mortality, and ischemia-driven revascularization in lower quartile of LDL-C values at first lipid control (<23 mg/dL) and in patients reaching LDL-C <55 mg/dL. Conclusions Intensive and early lipid-lowering therapy using PCSK9i in patients with ACS (strike early strike strong strategy) is safe and effective in clinical practice and associated with a reduction of residual CV risk.

Safety assessment of sapropterin dihydrochloride: real-world adverse event analysis based on the FDA adverse event reporting system (FAERS)

ObjectiveSapropterin dihydrochloride is the first drug for the therapy of phenylketonuria, which is a rare disease that occurs one of 10,000–15,000 newborns. As a result, detailed and comprehensive reports on the safety of sapropterin in large, real-world populations are required. The purpose of this study is to undertake a complete analysis of sapropterin’s adverse events (AEs) using the FDA Adverse Event Reporting System (FAERS) database.MethodsWe retrieved reports of adverse events with sapropterin as the principal suspect from FAERS between the first quarter of 2008 and the first quarter of 2024. The Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN) were utilized to detect AE signals.ResultsThe study collected 4,953 suspected AE cases from the FAERS database, with sapropterin as the major suspect. A total of 130 positive signals were obtained utilizing the ROR, PRP, and BCPNN. The FAERS database revealed that common clinical AEs of sapropterin included vomiting, upper respiratory infection, rhinorrhea, and a reduction in amino acid concentrations. Furthermore, we detected probable unexpected adverse events (AEs) using disproportionality analysis, including gastroesophageal reflux disease, flatulence, influenza, ear infection, viral infection, pharyngitis streptococcal, spontaneous abortion, and nephrolithiasis.ConclusionBy analyzing huge amounts of real-world data from the FAERS database, we found potential novel AEs of sapropterin using disproportionate analysis. It is advantageous for healthcare professionals and pharmacists to focus on efficiently managing sapropterin’s high-risk adverse events, improving drug levels in clinical settings, and ensuring patient medication safety.

A real-world adverse events study of rimegepant from the FAERS database

ABSTRACT Background The primary objective of this research is to conduct an exhaustive evaluation of rimegepant with the Food and Drug Administration Adverse Event Reporting System (FAERS) repository. Research design and methods This study downloaded the reporting files related to rimegepant from the second quarter of 2020 to the first quarter of 2024. Disproportionality analysis was utilized to explore the relationship between rimegepant and adverse drug events (ADEs). Results This study analyzed 6659 ADE reports associated with rimegepant as the primary suspected (PS) drug. In the disproportionate analysis of system organ classes (SOCs), the significant signal for rimegepant was general disorders and administration site conditions. On the preferred terms level, 35 ADEs were identified following the exclusion. The top 5 ADEs with high signal strengths were medication overuse headache, nasal edema, tension headache, performance status decreased, and motion sickness. The most frequent ADEs were nausea, feeling abnormal, abdominal pain upper, somnolence, and hypersensitivity. In addition, we need to pay attention to the ADEs not mentioned in the instruction and clinically significant: vertigo, hyperacusis, somnolence, Raynaud’s phenomenon, motion sickness, panic attack, and fear. Conclusions This study highlights previously unrecognized safety concerns associated with the use of rimegepant. These novel safety aspects suggest that while these treatments can be effective, additional risks may need further exploration.

How elections shape perceptions of ideal leadership.

Individuals hold internal leadership representations, termed leadership prototypes. We examined how these prototypes changed in reaction to the 2020 U.S. presidential election. A sample of Republicans (N = 200), Democrats (N = 200), and individuals who identified with neither major party (N = 200), surveyed eight times between October 2020 and January 2021, and reported their perceptions of the characteristics of the ideal leader. Results from a regression discontinuity in time and repeated measurement analyses found that the election altered two dimensions of the average U.S. leadership prototype. We specifically find participants' perceptions of Tyranny and Masculinity to decrease, that is, shifts to more Biden-like and less Trump-like leadership prototypes. Other dimensions of the leadership prototype remained stable, that is, charisma, sensitivity, dedication, intelligence, and dynamism. Analyses examined two boundary conditions of the effect: political identification and the acceptance of the election result as legitimate. Only perceived legitimacy was found to moderate the effect with the shift in leadership prototypes being driven by individuals who accepted the result of the election as legitimate. Our findings demonstrate the dynamic nature of leadership prototypes in response to real-world events and more broadly how an election can shape psychological perceptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Real-World Immune-Related Adverse Events in Patients With Early Triple-Negative Breast Cancer Who Received Pembrolizumab.

The addition of pembrolizumab to chemotherapy in high-risk early triple-negative breast cancer (TNBC) improves cancer outcomes. However, pembrolizumab induces varied immune-related adverse events (irAEs) where some can be severe or lifelong. This retrospective study describes real-world patterns of irAEs in patients with TNBC who received pembrolizumab. We evaluated irAEs in patients with TNBC from a comprehensive cancer center and a community hospital who received pembrolizumab with chemotherapy between 2021 and 2023, excluding those enrolled in clinical trials. We used national guidelines to grade toxicities. Logistic regression assessed the effect of clinicopathologic variables on irAEs adjusting for covariates. We identified 233 patients with a median age of 51 years, 62% had stage II TNBC, 35% had stage III TNBC, 25% were Hispanic, 21% were Black, and 42% were White. Eighty patients (34%) developed 100 separate irAEs. The most common irAEs were endocrinopathies (52%) and GI (23%); there were 26 grade ≥3 irAEs, which all resulted in hospitalization, the most common being GI (13 instances); 45 required systemic steroids, 16 required additional immunosuppressive therapy, and 32 patients discontinued pembrolizumab because of irAEs. Two patients who developed colitis eventually died due to complications. Most (67 instances) irAEs were unresolved at the time of last follow-up, but 55% (37/67) had improved to grade 1. No clinicopathologic factors were associated with the development or severity of irAEs. In this real-world diverse population, we observed rates of irAEs comparable with KEYNOTE-522, where endocrinopathies were the most prevalent, but GI irAEs were also prevalent and severe. This emphasizes a critical issue as pembrolizumab is increasingly being used in early TNBC and could have long-term survivorship implications.

Runtime integration of machine learning and simulation for business processes: Time and decision mining predictions

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model complement it with information provided by DL models to overcome the unrealistic or oversimplified assumptions of traditional DESs. State-of-the-art techniques in BPM combine Deep Learning and Discrete Event Simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model.In this paper, we aim at taking a step further by introducing Rims (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions a posteriori, Rims provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions while respecting simulation execution, thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. In particular, the runtime integration ensures the accuracy of intercase features for time prediction, such as the number of ongoing traces at a given time, by calculating them during directly the simulation, where all traces are executed in parallel. Additionally, it allows for the incorporation of online queue information in the DL model and enables the integration of other predictive models into the simulator to enhance decision point management within the process model. These enhancements improve the performance of Rims in accurately simulating the real process in terms of control flow, as well as in terms of time and congestion dimensions. Especially in process scenarios with significant congestion – when a limited availability of resources leads to significant event queues for their allocation – the ability of Rims to use queue features to predict waiting times allows it to surpass the state-of-the-art. We evaluated our approach with real-world and synthetic event logs, using various metrics to assess the simulation model’s quality in terms of control-flow, time, and congestion dimensions.

P2.11A.01 Racial/ethnic Differences in Real-World Adverse Events and Survival Among Patients on Immunotherapy for Advanced NSCLC

P2.11A.01 Racial/ethnic Differences in Real-World Adverse Events and Survival Among Patients on Immunotherapy for Advanced NSCLC

Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system that has multiple contributions including a random batch selection mechanism to reduce interbatch correlation and a normalcy suppression block (NSB) which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block (CLB) is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. An extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate the superior anomaly detection capability of our approach.

Enhancing Autonomous Agents Through Narrative Memory: A Comprehensive Framework for Improved Performance and Interaction

The process of organizing information and experiences into a narrative style is known as narrative memory, and it presents a cutting-edge method for improving the performance of autonomous agents. This essay describes the idea of narrative memory, as well as its elements, construction of narrative structures, and uses. The four main elements of narrative memory are timelines, characters, events, and context. Events are important things that happen, such interactions or acts, and characters are people, other agents, and the autonomous agent. The context gives the underlying knowledge required to comprehend these events and individuals, while the chronology sets the order of occurrences, creating a cause-and-effect link. The process of creating narrative frameworks includes gathering data, identifying events, crafting stories, and refreshing memories. Three methods are used to collect data: external sources, user input, and sensors. This data is used to identify significant events, which are subsequently arranged into logical tales. New situations and experiences are constantly added to the narrative memory. Improved predictive analysis, easier learning and adaptability, and improved human-robot interaction are just a few of the uses of narrative memory in autonomous agents. Robots can interact with people more successfully if they can comprehend human narratives and reply with responses that are contextually relevant. Agents may learn and adapt by identifying patterns in their prior experiences, and they can make better decisions by forecasting future occurrences with the help of historical narratives. Managing the complexity of real-world events, addressing ambiguity and uncertainty, and guaranteeing the ethical use of narrative data are challenges in the implementation of narrative memory. In order to improve narrative memory frameworks, future research options include creating sophisticated story generating tools, integrating narrative memory with other memory systems, and working across disciplines. The goal of this roadmap is to enhance autonomous systems' comprehension, communication, and decision-making skills by utilizing the power of tales.

Diagnosis, Treatment Patterns, and Outcomes in Real-World Patients with RET Fusion-Positive Non-small Cell Lung Cancer in China.

Epidemiological studies on non-small cell lung cancer (NSCLC) have noted RET fusions as an oncogenic driver. However, real-world data on RET biomarker testing and treatment patterns in China remain limited. This study aimed to examine demographics, clinical and molecular features, and RET testing and treatment patterns and outcomes in patients with RET fusion-positive NSCLC. Utilizing real-world data from the Chinese Multi-center Lung Cancer Precision Medicine Registry, this retrospective cohort study focused on Chinese patients diagnosed with RET fusion-positive NSCLC between January 1, 2016, and November 30, 2021. The cohort was divided into early-stage and advanced-stage subgroups. Demographics, clinical and molecular profiles, treatment received, and outcomes including real-world event free survival (rwEFS), real-world progression free survival (rwPFS), and overall survival (OS) were assessed. The study included 121 patients with RET fusion-positive NSCLC, comprising 80 early-stage and 58 advanced-stage patients. High biomarker testing rates were observed at diagnosis (75% for early-stage, 78% for advanced-stage). RET testing was often conducted via tissue samples (95.9%) and next-generation sequencing (89.3%). KIF5B (57.0%) and CCDC6 (20.7%) were the most common gene fusion partners. The most frequent oncogenic mutations were TP53 (15.7%) and EGFR (6.6%). Platinum-based chemotherapy was the most common first-line treatment among advanced-stage patients. Median rwPFS was 9.22months for advanced-stage patients on first-line chemotherapy, and median OS was 30.7months for all advanced-stage patients. The 2-year rwEFS rate for early-stage patients was 86.0%, with a median OS of 91.9months. The study observed high biomarker testing rates at initial diagnosis for early- and advanced-stage RET fusion-positive NSCLC patients in China. The heterogeneous treatment pattern of advanced patients suggests the need for more precise, evidence-based treatment to guide clinical decisions. Given the existing therapeutic regimens fall short of adequately addressing treatment needs, targeted therapies are essential to improve outcomes.

Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning

Temporal Knowledge Graphs (TKGs) have garnered significant scholarly interest for their ability to dynamically represent the evolution of real-world events. However, the presence of the long tail effect in knowledge graphs necessitates the exploration of one-shot temporal knowledge graph reasoning. Concurrently, the issues of out-of-distribution and overfitting heavily impact the performance of models. Empirical evidence from real-world cases has demonstrated the significance of historical trend information in accurately predicting future events, and normalizing flows effectively address out-of-distribution and overfitting challenges. Thus, this paper proposes a novel approach named Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning (HNOT). Specifically, HNOT combine normalizing flows with a historical information aggregator to obtain more complex distributions, thereby resolving out-of-distribution and overfitting problems and capturing global representations of TKGs. In contrast to existing models that focus solely on entity and relationship modeling, we have introduced a novel historical trend information aggregator. This aggregator is designed to extract trend information from temporal knowledge subgraphs. By employing trend information as the primary modeling object, it facilitates the exploration of trend variations at different timestamps. This approach serves as an auxiliary source of global information. Subsequently, we integrate these two distinct types of information to achieve predictions of future events. Experimental results demonstrate the superiority of the proposed method over state-of-the-art baselines on three benchmark datasets.

Drug-Related Keratitis: A Real-World FDA Adverse Event Reporting System Database Study.

This study aimed to assess the drug risk of drug-related keratitis and track the epidemiological characteristics of drug-related keratitis. This study analyzed data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from January 2004 to December 2023. A disproportionality analysis was conducted to assess drug-related keratitis with positive signals, and drugs were classified and assessed with regard to their drug-induced timing and risk of drug-related keratitis. A total of 1606 drugs were reported to pose a risk of drug-related keratitis in the FAERS database, and, after disproportionality analysis and screening, 17 drugs were found to significantly increase the risk of drug-related keratitis. Among them, seven were ophthalmic medications, including dorzolamide (reporting odds ratio [ROR] = 3695.82), travoprost (ROR = 2287.27), and brimonidine (ROR = 2118.52), and 10 were non-ophthalmic medications, including tralokinumab (ROR = 2609.12), trazodone (ROR = 2377.07), and belantamab mafodotin (ROR = 680.28). The top three drugs having the highest risk of drug-related keratitis were dorzolamide (Bayesian confidence propagation neural network [BCPNN] = 11.71), trazodone (BCPNN = 11.11), and tralokinumab (BCPNN = 11.08). The drug-induced times for non-ophthalmic medications were significantly shorter than those for ophthalmic medications (mean days, 141.02 vs. 321.96, respectively; P < 0.001). The incidence of drug-related keratitis reached its peak in 2023. Prevention of drug-related keratitis is more important than treatment. Identifying the specific risks and timing of drug-induced keratitis can support the development of preventive measures. Identifying the specific drugs related to medication-related keratitis is of significant importance for drug vigilance in the occurrence of drug-related keratitis.

Safety assessment of basiliximab using real-world adverse event data from the FDA Adverse Event Reporting System Database: A retrospective observational study.

This study analyzed adverse drug events (ADEs) associated with basiliximab, sourced from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, spanning the first quarter of 2004 to the fourth quarter of 2023. We collected ADE data for basiliximab from 2004 Q1 to 2023 Q4. After standardization, we employed several signal quantification methods for analysis, such as the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propensity for Neural Networks (BCPNN), and empirical bayes geometric mean (EBGM). In this analysis of 1520 ADEs reports citing basiliximab as the primary suspect, we identified 295 preferred terms across 24 system organ classifications (SOCs). The 3 most prevalent SOCs were investigated (n = 1403, ROR 2.84, PRR 2.54, IC 1.34, EBGM 2.54), infections and infestations (n = 1198, ROR 2.85, PRR 2.59, IC 1.37, EBGM 2.59), and renal and urinary disorders (n = 903, ROR 6.01, PRR 5.48, IC 2.45, EBGM 5.47). Increased blood creatinine and pyrexia were the most frequently reported adverse events (AEs) associated with basiliximab, and cytomegalovirus infection also demonstrated significant signal intensity. Notably, this study revealed some adverse reactions beyond basiliximab drug instructions, such as mitral valve calcification, diastolic dysfunction, pelvic fluid collection, testicular swelling, soft tissue necrosis, and muscle necrosis. Although basiliximab offers therapeutic benefits, it carries the risk of several adverse reactions. Clinicians should monitor patients for signs of increased serum creatinine level, fever, cytomegalovirus infection, anaphylactic shock, mitral valve calcification, diastolic dysfunction, pelvic fluid collection, testicular swelling, soft tissue necrosis, muscle necrosis, and other events during clinical use.

Temporal Graph Network for continuous-time dynamic event sequence

Continuous-Time Dynamic Graph (CTDG) methods have shown their superior ability in learning representations for dynamic graph-structured data, the methods split the sequential updating process into discrete batches to reduce the computation costs, as a result, the message constructor in existing CTDG methods cannot be optimized by gradient descent and is designed to be parameter-free. In particular, this layer fails to embed complex event subgraphs and ignores the structure information, while most real-world events are structured and complex. For example, a paper publication event in an academic graph contains different relations like authorship and citations. Furthermore, the corresponding nodes could not receive position-wise messages to make precise representation updates. To tackle this issue, we propose a new method called Temporal Graph Network for continuous-time dynamic Event sequence (TGNE) with a structure-aware message constructor to update node representation with complex event subgraph, by treating message construction and delivery as a message-passing process, in this way, the message constructor can be formalized as a graph neural network layer. TGNE extends the input of CTDG methods to subgraphs with complex structures and preserves more information in message delivery. Extensive experiments demonstrate that the proposed method can achieve competitive performance on traditional tasks on bipartite graphs and event sequence learning tasks on heterogeneous graphs.

Leveraging Collaborative Partnerships to Enhance NWS and Emergency Management Communications through Exercising

Abstract Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat—a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat and observe how local decision-makers interpreted and utilized the impact-based decision support service (IDSS) graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world IDSS graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.

A real-world drug safety surveillance study of lenvatinib from the FAERS database

ABSTRACT Background There is a need to determine lenvatinib-associated real-world adverse events (AEs) as its adverse effects may result in its discontinuation. Research design and methods Lenvatinib-associated AEs were analyzed and quantified and risk signals from the first quarter of 2015 to the fourth quarter of 2023 were detected through data mining. Potential targets for lenvatinib-associated cholecystitis, cholangitis, and hepatic encephalopathy were identified by data mining. Result 68 Preferred Terms (PTs) with an important imbalance were kept. Unexpected AEs, such as immune-mediated hepatitis, portal vein thrombosis and adrenal insufficiency were associated with the use of lenvatinib use. Lenvatinib alone was more strongly associated with adrenal insufficiency than lenvatinib and pembrolizumab combination. Hepatic encephalopathy was more strongly correlated with drug use when Lenvatinib was administered to male patients with hepatocellular carcinoma. Most AEs occurred during the first month after treatment, with a median onset time of 41 days. FGFR4, PDGFRA, and KIT (Lenvatinib targets) are potentially linked to cholecystitis, cholangitis, and hepatic encephalopathy. Conclusions We identified Lenvatinib-associated AEs and discovered new AEs that will be useful for clinical monitoring and risk assessment.

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

Adverse events associated with inclisiran: a real-world disproportionality analysis based on the FAERS database

ABSTRACT Background Randomized clinical trials have reported some safety profiles in inclisiran, but adverse events in real-world remain insufficient. We aim to evaluate the safety of inclisiran in real-world by collecting the data from the FDA Adverse Event Reporting System database. Methods Disproportionality analysis was performed by utilizing both Frequency method and Bayesian method to mine adverse event signals of inclisiran. A positive signal was deemed significant when adverse event met the criteria of the aforementioned methods simultaneously. Results We gathered a total of 2309 adverse event reports. Among these cases, adverse events were more common in females and ≥ 65 years age group. After data analysis, 51 positive signals from 11 system organ classes were identified, involving “Musculoskeletal and connective tissue disorders,” “General disorders and administration site conditions,” “Gastrointestinal disorders,” etc. At the preferred term level, the top three frequently reported adverse events were arthralgia, injection site pain and myalgia. We also found some uncommon but significantly strong adverse event signals (bladder discomfort and sinus pain) which should be taken prudently. Conclusions In this study, we analyzed the real-world adverse events of inclisiran more comprehensively and reported some new adverse events, hoping that can offer more safety information for clinical medication.

Triplet-set feature proximity learning for video anomaly detection

The identification of anomalies in videos is a particularly complex visual challenge, given the wide variety of potential real-world events. To address this issue, our paper introduces a unique approach for detecting divergent behavior in surveillance videos, utilizing triplet-loss for video anomaly detection. Our method involves selecting a triplet set of video segments from normal (n) and abnormal (a) data points for deep feature learning. We begin by creating a database of triplet sets of two types: a-a-n and n-n-a. By computing a triplet loss, we model the proximity between n-n chunks and the distance between ‘a’ chunks from the n-n ones. Additionally, we train the deep network to model the closeness of a-a chunks and the divergent behavior of ‘n’ from the a-a chunks.The model acquired in the initial stage can be viewed as a prior, which is subsequently employed for modeling normality. As a result, our method can leverage the advantages of both straightforward classification and normality modeling-based techniques. We also present a data selection mechanism for the efficient generation of triplet sets. Furthermore, we introduce a novel video anomaly dataset, AnoVIL, designed for human-centric anomaly detection. Our proposed method is assessed using the UCF-Crime dataset encompassing all 13 categories, the IIT-H accident dataset, and AnoVIL. The experimental findings demonstrate that our method surpasses the current state-of-the-art approaches. We conduct further evaluations of the performance, considering various configurations such as cross-dataset evaluation, loss functions, siamese structure, and embedding size. Additionally, an ablation study is carried out across different settings to provide insights into our proposed method.

The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

In sociological research based on online public opinion, scholars often overlook the delay and combinatory nature of online responses to real-world events. This study aims to explore the delayed and combinatory responses of online public opinion to the intensity of the COVID-19 pandemic. Specifically, we seek to answer the following questions: (a) Is there a temporal delay in the response of online public opinion to the intensity of the pandemic? (b) Does this delay exhibit general characteristics of social networks, such as combinatory effects and higher-order interactions? To address these questions, we employ natural language processing techniques to extract online public opinion data and utilize statistical and machine learning-based causal inference methods for analysis. The findings indicate that online public opinion’s response to the intensity of COVID-19 is not immediate but rather exhibits a long-term lag. Identical COVID-19 intensity data can trigger multiple delayed public opinion responses, while a single delayed public opinion datum may be influenced by multiple preceding COVID-19 intensity data points. This delayed response of online public opinion and its higher-order network characteristics result in a waveform structure of real-world impacts influenced by online public opinion. We also utilized machine learning causal inference techniques to investigate the sensitivity differences in online public opinion responses to COVID-19 during various time periods.