Trajectory Patterns Research Articles

Longitudinal Cervical Length Measurements and Spontaneous Preterm Birth in Singleton and Twin Pregnancies

Changes in cervical length in twin pregnancies exhibit various patterns, but it is unclear whether the mechanism underlying spontaneous preterm birth (sPTB) is consistent. The existence of detailed phenomena in singleton pregnancies is also unclear. To explore the different patterns in cervical length trajectories in singleton and twin pregnancies and to analyze whether the immunological mechanisms of sPTB are consistent among these cervical length patterns. This cohort study recruited pregnant individuals who received antenatal care and delivered at Peking University Third Hospital in Beijing, China, between January 1, 2014, and December 31, 2022. Individuals with singleton and twin pregnancies were included. Cervical length measurements and white blood cell (WBC) indicators. The primary outcome was sPTB. Longitudinal trajectory cluster analysis was used to identify patterns of changes in cervical length in singleton and twin pregnancies. A random-effects model with cubic spline was used to fit and compare the longitudinal trajectory of WBC indicators among early preterm birth, moderate to late preterm birth, and term birth. A total of 43 559 pregnant individuals were included; of these, 41 706 had singleton pregnancies (mean [SD)] maternal age, 33.0 [4.0] years) and 1853 had twin pregnancies (mean [SD] maternal age, 33.3 [3.6] years). Two distinct patterns of cervical length changes were observed in both singleton and twin pregnancies: shortened (21 366 singletons and 546 twins) and stable (20 340 singletons and 1307 twins). In singleton pregnancies, WBC count was associated with early sPTB in individuals with both shortened cervix (odds ratio [OR], 1.35; 95% CI, 1.00-1.82) and stable cervix (OR, 1.64; 95% CI, 1.07-2.50). However, for twin pregnancies, the association of WBC count (OR, 3.13; 95% CI, 1.58-6.18) with the risk of early sPTB was observed only in individuals with a shortened cervix. This study identified 2 distinct cervical length patterns: shortened and stable. These patterns revealed 2 preterm birth mechanisms in twin pregnancies, with the immunopathogenesis of sPTB found only in the shortened cervix pattern; in singleton pregnancies, maternal immune response was associated with a higher risk of sPTB regardless of a shortened or stable cervix.

Trajectory patterns and cumulative burden of CEA during follow-up with non-small cell lung cancer outcomes: A retrospective longitudinal cohort study.

Previous studies of non-small cell lung cancer (NSCLC) focused on CEA measured at a single time point, ignoring serial CEA measurements. This retrospective cohort included 2959 patients underwent surgery for stage I-III NSCLC. CEA trajectory patterns and long-term cumulative CEA burden were evaluated using the latent class growth mixture model. Four CEA trajectory groups were identified, named as low-stable, decreasing, early-rising and later-rising. Compared with the low-stable group, the adjusted hazard ratios associated with death were 1.27, 4.50, and 3.68 for the other groups. Cumulative CEA burden were positively associated with the risk of death in patients not belonging to the low-stable group. The 5-year overall survival (OS) rates decreased from 62.3% to 33.0% for the first and fourth quantile groups of cumulative CEA burden. Jointly, patients with decreasing CEA trajectory could be further divided into the decreasing & low and decreasing & high group, with 5-year OS rates to be 77.9% and 47.1%. Patients with rising CEA trajectory and high cumulative CEA were found to be more likely to develop bone metastasis. Longitudinal trajectory patterns and long-term cumulative burden of CEA were independent prognostic factors of NSCLC. We recommend CEA in postoperative surveillance of NSCLC.

TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions

A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input. To address this challenge, we propose TCNet, a hybrid network that effectively models spatio-temporal information from Trajectories and Correlated regions. TCNet's trajectory module transforms frames into aligned trajectories composed of continuous visual tokens. This facilitates extracting region trajectory patterns. In addition, for a query token, self-attention is learned along the trajectory. As such, our network can also focus on fine-grained spatio-temporal patterns, such as finger movement, of a region in motion. TCNet's correlation module utilizes a novel dynamic attention mechanism that filters out irrelevant frame regions. Additionally, it assigns dynamic key-value tokens from correlated regions to each query. Both innovations significantly reduce the computation cost and memory. We perform experiments on four large-scale datasets: PHOENIX14, PHOENIX14-T, CSL, and CSL-Daily. Our results demonstrate that TCNet consistently achieves state-of-the-art performance. For example, we improve over the previous state-of-the-art by 1.5\% and 1.0\% word error rate on PHOENIX14 and PHOENIX14-T, respectively. Code is available at https://github.com/hotfinda/TCNet

Abstract 4638: Integrated proteogenomic characterization of longitudinal glioblastoma

Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options and median patient survival time of almost one year. Despite multi-modal therapy including surgery, irradiation and chemotherapy, all patients experience tumor progression and recurrence. Our group proposed and validated the first single cell guided functional classification of primary GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, recurrent GBM remains therapeutically unresolved due in part to the diffusely invasive nature and in part to marked cellular heterogeneity of the tumor.The evolutionary trajectory of glioblastoma after therapy is a multifaceted biological process that extends beyond discrete genetic alterations alone. Here, we profiled by multi-omics platforms the largest dataset of matched primary and recurrent GBM including 123 longitudinal glioblastoma pairs, temporally separated by standard-of-care treatment. Genomics, proteomics, and phosphoproteomics all independently captured the loss of proliferative-progenitor characteristics and a significant upregulation of specialized neuronal and synaptic signaling programs in recurrent GBM. Proteomic and phosphoproteomic analyses revealed that the molecular transition from proliferative to neuronal state at recurrence is marked by coherent post-translational activation of the WNT/PCP signaling pathway and the BRAF protein kinase. Multi-omic analysis of Patient-Derived Xenograft (PDX) models mimicked the patterns of evolutionary trajectory, consisting of marked activation of neuronal signaling programs in recurrent patients. Inhibition of the BRAF kinase with small molecule inhibitors impaired both neuronal transition and migration capability of recurrent glioblastoma cells, which are the phenotypic hallmarks of glioblastoma progression after therapy. Accordingly, combinatorial treatment of temozolomide with BRAF inhibitor, vemurafenib, significantly prolonged the survival of mouse PDX models. This work provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance and highlights new therapeutic opportunities to effectively counter them in the clinic. Citation Format: Simona Migliozzi, Kyung-Hee Kim, Harim Koo, Jun-Hee Hong, Seung Min Park, Hyung Joon Kwon, Luciano Garofano, Young Taek Oh, Fulvio D'Angelo, Chan Il Kim, Anna-Luisa Di Stefano, Franck Bielle, Jinlong Yin, Marc Sanson, Do-Hyun Nam, Jason K. Sa, Anna Lasorella, Jong Bae Park, Antonio Iavarone. Integrated proteogenomic characterization of longitudinal glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4638.

Association between circadian physical activity trajectories and incident type 2 diabetes in the UK Biobank

Physical activity (PA) is linked to a decreased risk of type 2 diabetes mellitus (T2DM). However, the influence of circadian PA trajectories remains uncertain. This study aims to explore the optimal circadian PA trajectory pattern for reducing the risk of T2DM. Methods: A total of 502,400 participants were recruited from the UK Biobank between 2006 and 2010, and 102,323 participants provided valid accelerometer-captured acceleration data. After excluding individuals with prior T2DM, 99,532 participants were included in the final analysis. We initially investigated the association between PA intensity at 24 hourly time points and T2DM. Subsequently, PA trajectories were identified using K-means cluster analysis. Cox proportional hazard models were employed to estimate hazard ratios (HR). Four distinct PA trajectories were identified: consistently low, single peak, double peak, and intense trajectories. Compared to consistently low, single peak, double peak and intense PA trajectory reduced the risk of T2DM progressively. Sensitivity analyses, further excluding individuals with glycated hemoglobin (HbA1c) ≥ 6.5% or random glucose ≥ 11.1 mmol/L and adjusted for daily average acceleration, yielded consistent results. This confirms that the ideal circadian PA trajectory serves as a protective factor, independently of PA intensity. Subgroup analyses indicated that these effects were more pronounced in men and individuals with eGFR < 60 mL/(min*1.73 m2). In conclusion, ideal circadian PA trajectory patterns (especially intense and then double peak) reduced risk of T2DM.

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Associations of maternal sleep trajectories during pregnancy and adverse perinatal outcomes: a prospective cohort study

ObjectiveSleep problems are common in pregnant women and sleep is altered during pregnancy. However, the associations between sleep trajectory patterns and adverse maternal and neonatal outcomes are unclear. The current study aims to identify sleep trajectory patterns and explore their associations with adverse perinatal outcomes in a prospective cohort study. MethodsPregnant women (N = 232) completed the Pittsburgh Sleep Quality Index each trimester during pregnancy in Tianjin, China. Perinatal outcomes were extracted from the hospital delivery records. Latent class growth analysis (LCGA) described the trajectories of sleep timing, duration, and efficiency. Multivariable linear regression and multivariable logistic regression were employed to evaluate associations between sleep trajectory patterns and perinatal outcomes. ResultsTrajectories were identified for bedtime (early, 49.1%; delaying, 50.9%), wake-up time (early, 82.8% of the sample; late, 17.2%), duration (short, 5.2%; adequate 78.0%; excessive, 16.8%), and efficiency (high, 88.4%; decreasing, 11.6%). Compared with women in more optimal sleep groups, those in the late wake-up, excessive duration, and decreasing efficiency groups had babies with shorter birth lengths (β range, −0.50 to −0.28, p < 0.05). Moreover, women in the decreasing efficiency group had babies with lower birth weight (β, −0.44; p < 0.05). Women in the delaying bedtime group had greater odds of preterm delivery (OR, 4.57; p < 0.05), while those in the decreasing efficiency group had greater odds of cesarean section (OR, 3.12; p < 0.05). ConclusionsLess optimal sleep trajectory patterns during pregnancy are associated with perinatal outcomes. Therefore, early assessment of maternal sleep during pregnancy is significant for identifying at-risk women and initiating interventions to reduce perinatal outcomes.

Association of longitudinal trajectories of general and abdominal adiposity during middle age with mental health and well-being in late life: A prospective analysis

Single measures of adiposity markers, such as body mass index (BMI) and waist circumference (WC), are associated with adverse mental health outcomes; however, long-term patterns of adiposity and their health effects remain unclear. The current study assessed adiposity trajectories during a 14-year span beyond middle age and their relevance to mental well-being in late life, and the contribution of genetic and lifestyle factors to the trajectories. Based on a nationally representative sample with longitudinal anthropometric measures, adiposity trajectories were identified by latent mixture modeling, and logistic regression model was used to estimate their associations with mental well-being, with adjustment for confounders. Of the 3491 eligible participants included (mean [SD] age, 69.5 [8.9] years), five discrete BMI and four WC trajectory patterns were identified over 14 years. Compared with the low-stable BMI group (range, 22.8 to 22.9 kg/m²; representing stable healthy body weight), the high-stable group (range, 34.3 to 35.4 kg/m²; stable obese) was associated with increased risk of depression (odds ratio [OR], 1.63; 95 % CI, 1.28–2.07) and low subjective well-being (OR, 1.35; 95 % CI, 1.02–1.79). Compared with the low-stable WC group (range, 75 to 79 cm healthy WC), the high-increasing group (range, 114 to 121 cm) was associated with increased risk of depression (odds ratio [OR], 1.64; 95 % CI, 1.19–2.25) and low well-being (OR, 1.48; 95 % CI, 1.01–2.16). The adiposity trajectories, especially the high-stable/increasing groups, were driven by genetic factors in a dose-response manner, whereas the high/moderate-increasing groups were also behaviorally related. This longitudinal cohort study reveals that stably high trajectory patterns of central and general adiposity during middle age were associated with higher risk of depression and low well-being in late life. The findings indicate the importance of weight management beyond middle age, such as adherence to a healthy lifestyle, in promoting mental health and well-being.

The association of metabolic syndrome score trajectory patterns with risk of all cancer types.

Metabolic syndrome (MetS) elevates cancer risk. However, a single MetS assessment does not fully reveal the long-term association with cancer. Inflammation, alongside MetS, could synergistically expedite both the onset and advancement of cancer. This study aims to investigate MetS score trajectories and cancer risk in a large, prospective cohort study. The authors prospectively examined the relationship between MetS score trajectory patterns and new-onset cancer in 44,115 participants. Latent mixture modeling was used to identify the MetS score trajectories. Cox proportional hazards regression models were used to evaluate the association between MetS score trajectory patterns and the risk of overall and site-specific cancers. Four MetS score trajectory patterns were identified: low-stable (n=4657), moderate-low (n=18,018), moderate-high (n=18,288), and elevated-increasing (n=3152). Compared to participants with a low-stable trajectory pattern, the elevated-increasing trajectory pattern was associated with an elevated risk of overall (hazard ratio [HR], 1.27; 95% confidence interval [CI], 1.04-1.55), breast (HR, 2.11; 95% CI, 1.04-4.34), endometrial (HR, 3.33; 95% CI, 1.16-6.77), kidney (HR, 4.52; 95% CI, 1.17-10.48), colorectal (HR, 2.54; 95% CI, 1.27-5.09), and liver (HR,1.61; 95% CI, 1.09-4.57) cancers. Among participants with chronic inflammation (C-reactive protein levels ≥3mg/L), the elevated-increasing trajectory pattern was significantly associated with subsequent breast, endometrial, colorectal, and liver cancers. Trajectories of MetS scores are associated with the occurrence of cancers, especially breast, endometrial, kidney, colorectal, and liver cancers, emphasizing the importance of long-term monitoring and evaluation of MetS. The association between long-term elevated metabolic syndrome (MetS) scores and a heightened risk of various cancers is a pivotal finding of our study. Our research further indicates that individuals with MetS, particularly when coupled with chronic inflammation, are at an increased risk of cancer. We propose that sustained monitoring and management of MetS could be beneficial in reducing cancer risk.

Characterization and trajectories of hematological parameters prior to severe COVID-19 based on a large-scale prospective health checkup cohort in western China: a longitudinal study of 13-year follow-up

BackgroundThe relaxation of the “zero-COVID” policy on Dec. 7, 2022, in China posed a major public health threat recently. Complete blood count test was discovered to have complicated relationships with COVID-19 after the infection, while very few studies could track long-term monitoring of the health status and identify the characterization of hematological parameters prior to COVID-19.MethodsBased on a 13-year longitudinal prospective health checkup cohort of ~ 480,000 participants in West China Hospital, the largest medical center in western China, we documented 998 participants with a laboratory-confirmed diagnosis of COVID-19 during the 1 month after the policy. We performed a time-to-event analysis to explore the associations of severe COVID-19 patients diagnosed, with 34 different hematological parameters at the baseline level prior to COVID-19, including the whole and the subtypes of white and red blood cells.ResultsA total of 998 participants with a positive SARS-CoV-2 test were documented in the cohort, 42 of which were severe cases. For white blood cell-related parameters, a higher level of basophil percentage (HR = 6.164, 95% CI = 2.066–18.393, P = 0.001) and monocyte percentage (HR = 1.283, 95% CI = 1.046–1.573, P = 0.017) were found associated with the severe COVID-19. For lymphocyte-related parameters, a lower level of lymphocyte count (HR = 0.571, 95% CI = 0.341–0.955, P = 0.033), and a higher CD4/CD8 ratio (HR = 2.473, 95% CI = 1.009–6.059, P = 0.048) were found related to the risk of severe COVID-19. We also observed that abnormality of red cell distribution width (RDW), mean corpuscular hemoglobin concentration (MCHC), and hemoglobin might also be involved in the development of severe COVID-19. The different trajectory patterns of RDW-SD and white blood cell count, including lymphocyte and neutrophil, prior to the infection were also discovered to have significant associations with the risk of severe COVID-19 (all P < 0.05).ConclusionsOur findings might help decision-makers and clinicians to classify different risk groups of population due to outbreaks including COVID-19. They could not only optimize the allocation of medical resources, but also help them be more proactive instead of reactive to long COVID-19 or even other outbreaks in the future.

Screening for Delirium During Pediatric Brain Injury Rehabilitation

ObjectiveTo assess feasibility of routine delirium screening using the Cornell Assessment of Pediatric Delirium (CAPD) in children admitted for rehabilitation with acquired brain injury (ABI), report on the prevalence of positive delirium screens in this population, and explore longitudinal trends in CAPD scores and their association with rehabilitation outcomes. DesignRetrospective study. SettingPediatric inpatient rehabilitation unit. Participants144 children (median 10.8 years) with ABI (N=144). InterventionsNot applicable. Main Outcome MeasuresPercent compliance with twice daily delirium screening; prevalence of positive delirium screens; trajectories in CAPD scores and their relation with FIM for Children (WeeFIM) scores. ResultsScreening was feasible (mean 75% compliance for each of 144 children). Of 16,136 delirium screens, 29% were positive. 62% of children had ≥1 positive screen. Four primary patterns of CAPD trajectories were identified: Static Encephalopathy (10%), Episodic Delirium (10%), Improving (32%), and No Delirium (48%). Validity of these trajectories was demonstrated through association with WeeFIM and CALS outcomes. Younger age at admission was associated with positive delirium screens, and rehabilitation length of stay was significantly longer for the Improving group. ConclusionsDelirium occurs frequently in children with ABI during inpatient rehabilitation. Routine delirium screening provides clinically relevant information including the potential to facilitate early detection and intervention for medical complications. Longitudinal ratings of delirium symptoms may also have a role in developing a standardized definition for Post Traumatic Confusional State (PTCS) stage of recovery in children.

Understanding scholar-trajectories across scientific periodicals

Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly captures essential information about periodicals as well as complex (inter-)disciplinary structures of science. Based on this space, we study academic success through the prism of movement across scientific periodicals. We use a measure from human mobility, the radius of gyration, to characterize individual scholars' trajectories. Results show that author mobility across periodicals negatively correlates with citations, suggesting that successful scholars tend to publish in a relatively proximal range of periodicals. Overall, our framework discovers intricate structures in large-scale sequential data and provides new ways to explore mobility and trajectory patterns.

Prognostic implication of residual inflammatory trajectories in acute type I aortic dissection: dual-center prospective cohort study.

Peripheral platelet-white blood cell ratio (PWR) integrating systemic inflammatory and coagulopathic pathways is a key residual inflammatory measurement in the management of acute DeBakey type I aortic dissection (AAD); however, trajectories of PWR in AAD is poorly defined. Two AAD cohorts were included in two cardiovascular centers (2020-2022) if patients underwent emergency total arch replacement with frozen elephant trunk implantation. PWR data were collected over time at baseline and five consecutive days after surgery. Trajectory patterns of PWR were determined using the latent class mixed modelling (LCMM). Cox regression was used to determine independent risk factors. By adding PWR Trajectory, a user-friendly nomogram was developed for predicting mortality after surgery. Two hundred forty-six patients with AAD were included with a median follow-up of 26 (IRQ 20-37) months. Three trajectories of PWR were identified [cluster α 45(18.3%), β105 (42.7%), and γ 96 (39.0%)]. Cluster γ was associated with higher risk of mortality at follow-up (crude HR, 3.763; 95% CI: 1.126-12.574; P =0.031) than cluster α. By the addition of PWR trajectories, an inflammatory nomogram, composed of age, hemoglobin, estimated glomerular filtration rate, and cardiopulmonary time was developed and internally validated, with adequate discrimination [the area under the receiver-operating characteristic curve 0.765, 95% CI: 0.660-0.869)], calibration, and clinical utility. Based on PWR trajectories, three distinct clusters were identified with short-term outcomes, and longitudinal residual inflammatory shed some light to individualize treatment strategies for AAD.

Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months.

To explore distinct longitudinal trajectories of resourcefulness among initial ischemic stroke patients from diagnosis to 12 months, and to identify whether sociodemographic factors, disease-related factors, self-efficacy, family function, and social support can predict patterns in the trajectories of resourcefulness. A prospective longitudinal study was conducted. Initial ischemic stroke patients who met inclusion and exclusion criteria were followed up when still in hospital (Preparing for discharge, Baseline, T1), at 1 month (T2), at 3 months (T3), at 6 months (T4), at 9 months (T5) and 12 months (T6) (±1 week) after discharge. General information, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), General Self-Efficacy Scale (GSES), General Family Functioning Subscale (FAD-GF), and Social Support Rate Scale (SSRS) were used in T1. The Resourcefulness Scale© was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. Logistic regression was used to identify predictors of resourcefulness trajectories. Three longitudinal trajectories of resourcefulness were identified and named as the high-stable class (38.9%, n=71), fluctuation class (41.2%, n=75), and low-stable class (19.9%, n=36), respectively. Dwelling areas (x2=6.805, P=0.009), education (x2=44.865, P=0.000), monthly income (x2=13.063, P=0.001), NIHSS scores (x2=44.730, P=0.000), mRS scores (x2=51.788, P=0.000), Hcy (x2=9.345, P=0.002), GSES (x2=56.933, P=0.000), FAD-GF (x2=41.305, P=0.000) and SSRS (x2=52.373, P=0.000) were found to be statistically significant for distinguishing between different resourcefulness trajectory patterns. Lower education (OR=0.404), higher NIHSS(OR=6.672) scores, and higher mRS(OR=21.418) scores were found to be risk factors for lower resourcefulness, whereas higher education(OR=0.404), GSES(OR=0.276), FAD-GF(OR=0.344), and SSRS(OR=0.358) scores were identified as protective factors enhancing resourcefulness. This study obtained three patterns of trajectories and identified their predictive factors in initial ischemic stroke. The findings will assist health care professionals in identifying subgroups of patients and when they may be at risk of low resourcefulness and provide timely targeted intervention to promote resourcefulness.

The trajectory and transition pattern of intention to practice medicine among medical students in China

There has been a growing concern about the career intentions (whether medical students have the intention to practice medicine) due to the increasingly serious problem of medical students attrition. Career intentions have the potential to promote medical students' career commitment and encourage them to stay in the medical profession. Moreover, a series of dynamic career intentions during medical education can be served as an early guide to the medical students' future career choices (whether medical students finally choose to practice medicine). However, few studies focus on the dynamics of career intentions among medical students. In this study, we utilized data from a large-scale national survey of medical students conducted in China from 2020 to 2022 to curve the trajectory of the intention to practice medicine among medical students during their undergraduate medical education by using multilevel growth model. Furthermore, we applied latent Markov model to estimate the transition matrix of the intention across each academic year during the undergraduate medical education. Our findings revealed a trajectory curve with a peak during the second year in the intention to practice medicine. In addition, we identified three latent states of career intention including “strong intention”, “wavering” and “weak intention”, and further found two distinct transition patterns between individuals with strong career intentions and those without strong career intentions. The transition patterns play a crucial role in understanding the changes in the trajectory of medical students’ intentions and determining the optimal timing for interventions to prevent medical student attrition. Our study offers a comprehensive understanding for the dynamics of the career intention among medical students, which has practical implications for medical educators and institutions to address the issues of medical student attrition.

LSTM Auto Encoder 이용한 접근 이상 항적 탐지 모형

With the rise in aviation demand and the emergence of Urban Air Mobility, developing a safe aviation system in urban areas is becoming increasingly important. This study addresses the challenge of detecting anomalous flight trajectories, which can be influenced by environmental factors. We propose a novel Long Short-Term Memory-Auto Encoder (LSTM-AE) model that processes both environmental and trajectory data but only reconstructs trajectory data in its output. This approach was validated by assessing the average reconstruction error for specific trajectories. Additionally, the model's ability to identify anomalies was confirmed by evaluating the Area under the ROC curve (AUC) for typical anomalous trajectories, such as go-around maneuvers. Our findings indicate that the proposed LSTM-AE model effectively learns trajectory patterns in relation to environmental variables and shows enhanced anomaly detection capabilities compared to traditional AE and LSTM-AE models. These results contribute to the development of advanced models that incorporate a wider range of environmental factors, enhancing safety in urban air travel.

Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectories

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

Associations of body mass index trajectory, waist circumference trajectory, or both with type 2 diabetes mellitus risk in Chinese adults: The China-PAR project.

To identify the trajectories of body mass index (BMI) and waist circumference (WC), and assess the associations of BMI trajectory, WC trajectory, or the two combined, with type 2 diabetes mellitus (T2DM) risk in Chinese adults. This study was based on a prospective project-the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR). A total of 54 434 participants (39.21% men) who were measured on at least two occasions were included. Three slowly increasing trajectory patterns were identified for BMI, and four for WC, by latent mixed modelling. A nine-category variable was derived by combining the WC trajectory (low, moderate, moderate-high/high) and the BMI trajectory (low, moderate, high). Logistic regression models were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). The risk of developing T2DM increased with elevated BMI or WC trajectory levels (all ptrend <0.001). The risks were 2.85 (2.59-3.14) for high BMI trajectory and 4.34 (3.78-4.99) for high WC trajectory versus low trajectory groups, respectively. The association was more pronounced among younger individuals (pinteraction <0.001). In the joint analysis, compared to participants with low WC and BMI trajectory, those with moderate-high/high WC combined with high BMI trajectory had the highest risk of T2DM (OR 3.96, 95% CI 3.48-4.50); even those who maintained moderate-high/high WC but low BMI trajectory showed a higher T2DM risk (OR 3.00, 95% CI 2.31-3.91). This study suggests that simultaneous dynamic and continuous monitoring of BMI and WC may contribute more than single measurements to predicting T2DM risk and determining preventive strategies.

Association of the trajectory of plasma aldosterone concentration with the risk of cardiovascular disease in patients with hypertension: a cohort study

The purpose of this study was to determine the long-term pattern of plasma aldosterone concentration (PAC) trajectories and to explore the relationship between PAC trajectory patterns and cardiovascular disease (CVD) risk in patients with hypertension. Participants were surveyed three times between 2010 and 2016, and latent mixed modeling was employed to determine the trajectory of PAC over the exposure period (2010–2016). A Cox regression analysis was used to examine the association between PAC trajectory patterns and the risk of CVD (stroke and myocardial infarction). Hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were calculated and reported. During a median follow-up of 4.10 (3.37–4.50) years, 82 incident CVD cases (33 myocardial infarction cases and 49 stroke cases) were identified. Among all three PAC models, the high-stability PAC pattern exhibited the highest risk of CVD. After full adjustment for all covariables, HRs were 2.19 (95% CI 1.59–3.01) for the moderate-stable pattern and 2.56 (95% CI 1.68–3.91) for the high-stable pattern in comparison to the low-stable pattern. Subgroup and sensitivity analyses verified this association. The presence of a high-stable PAC trajectory pattern is associated with an elevated risk of CVD in hypertensive patients. Nevertheless, more studies are warranted to confirm these findings.

Persistently high HBsAg levels during HBeAg-seropositive stage predict lower risk of hepatocellular carcinoma in chronic hepatitis B patients.

High hepatitis B surface antigen (HBsAg) level predicts hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) patients with low viral load. The role of longitudinal HBsAg levels in predicting HCC in HBeAg-positive CHB patients remains unknown. HBeAg-positive CHB participants from the REVEAL-HBV cohort with ≥2 HBsAg measurements before HBeAg seroclearance were enrolled. Group-based trajectory modelling identified distinct HBsAg trajectory groups during a median of 11 years of HBeAg-positive status. Cox regression models were applied for investigating independent predictors of HCC and estimating adjusted hazard ratio (HRadj) with a 95% confidence interval (CI). A p-value less than 0.05 was considered statistically significant. A total of 319 patients were enrolled and classified by HBsAg trajectory patterns as (A) persistently high group (n = 72): HBsAg persistently ≥104 IU/mL, and (B) non-stationary group (n = 233): low HBsAg at baseline or declining to <104 IU/mL during the follow-up. Group B had higher proportions of abnormal ALT levels, HBV genotype C and basal core mutation than group A (p < 0.05); age at entry and gender were comparable. The annual incidence of HCC in group A and group B were 0.37% and 1.16%, respectively (p = 0.03). In multivariate analysis, age >40 years (HRadj [95% CI] = 4.11 [2.26-7.48]), genotype C (HRadj [95% CI] = 4.39 [1.96-9.81]) and the non-stationary group (HRadj [95% CI] = 3.50 [1.49-8.21]) were independent predictors of HCC. Basal core promoter mutation was the only risk factor of HCC in the persistently high HBsAg group (HRadj [95% CI] = 32.75 [5.41-198.42]). Patients with persistently high HBsAg levels during HBeAg-seropositive stage represent a unique population with low risk of HCC development.