Analysis Model Research Articles

DySurv: dynamic deep learning model for survival analysis with conditional variational inference.

Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitudinal measurements from electronic health records to estimate the individual risk of death dynamically. DySurv directly estimates the cumulative risk incidence function without making any parametric assumptions on the underlying stochastic process of the time-to-event. We evaluate DySurv on 6 time-to-event benchmark datasets in healthcare, as well as 2 real-world intensive care unit (ICU) electronic health records (EHR) datasets extracted from the eICU Collaborative Research (eICU) and the Medical Information Mart for Intensive Care database (MIMIC-IV). DySurv outperforms other existing statistical and deep learning approaches to time-to-event analysis across concordance and other metrics. It achieves time-dependent concordance of over 60% in the eICU case. It is also over 12% more accurate and 22% more sensitive than in-use ICU scores like Acute Physiology and Chronic Health Evaluation (APACHE) and Sequential Organ Failure Assessment (SOFA) scores. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets. Our interdisciplinary framework successfully incorporates deep learning, survival analysis, and intensive care to create a novel method for time-to-event prediction from longitudinal health records. We test our method on several held-out test sets from a variety of healthcare datasets and compare it to existing in-use clinical risk scoring benchmarks. While our method leverages non-parametric extensions to deep learning-guided estimations of the survival distribution, further deep learning paradigms could be explored.

An Interpretable Machine Learning-Based Hurdle Model for Zero-Inflated Road Crash Frequency Data Analysis: Real-World Assessment and Validation

An Interpretable Machine Learning-Based Hurdle Model for Zero-Inflated Road Crash Frequency Data Analysis: Real-World Assessment and Validation

Depression among Asian American adolescents: a multifaceted examination of risk and protective factors

ABSTRACT Depression among adolescents is a public health concern. The present study examines the impact of sex, grade level, employment status, parental support, school-related influences, engagement in prosocial activities, and participation in risky behaviors on the mental well-being of Asian American youth. A secondary data analysis of the 2020 Student Drug Use Survey was conducted. Of the N = 2332 Asian American youth, 19.6% (n = 456) reported depression. Of these, 24.2% (n = 286) identified as female, and 14.5% (n = 163) identified as male. Furthermore, univariate logistic regression analysis and logistic regression model analyses were employed. The final logistic regression model was found to be significant and suggests that lack of parental support, involvement in prosocial activities, and involvement in risky behaviors can predict depression among Asian American high school youth. The findings of this research study hold significant implications for the development of targeted interventions aimed at addressing depression among Asian American high school students. Identifying specific predictors such as the lack of parental factors, engagement in prosocial activities, and participation in risky behaviors provides valuable insights for educators, mental health professionals, and policymakers. By recognizing these influential factors, interventions can be tailored to enhance parental involvement, promote prosocial activities, and mitigate risky behaviors, thus potentially reducing the prevalence of depression among Asian American youth in high school.

Point of care creatinine testing for early detection of renal dysfunction in Tanzanian HIV patients: a study protocol

BackgroundRenal dysfunction (RD) is more prevalent among people living with HIV (PLHIV) in sub-Saharan Africa (SSA) due to factors such as co-infections, hypertension, diabetes, and nephrotoxic antiretroviral drugs like tenofovir. Early detection of RD is critical but limited by resource constraints in many SSA countries, including Tanzania. Point-of-care (POC) tests, such as the Stat-Sensor Creatinine test by Nova Biomedical, present a cost-effective and non-invasive option for early detection. This study will evaluate the diagnostic performance of the Stat-Sensor creatinine test in detecting renal dysfunction in HIV care and treatment clinics (CTCs) in Tanzania. It will be conducted for a period of six months, from November 2024 to April 2025.MethodologyThe study will measure point-of-care creatinine using a rapid creatinine dipstick against the standard serum creatinine test. The diagnostic performance and agreement to diagnose renal dysfunction will be assessed using a standardized statistical approach: Bland-Altman analysis and linear mixed-effects models to test agreement between creatinine dipstick tests with serum creatinine. Receiver operating characteristics (ROC) statistics will be used to test the diagnostic performance of the creatinine dipstick test to diagnose renal dysfunction.DiscussionWe hypothesize that POC creatinine testing will show strong diagnostic performance, providing a reliable, rapid, and cost-efficient alternative for RD detection, leading to better patient outcomes and integration of POC tests into routine HIV care.Ethical clearanceThe study protocol was reviewed and approved by the Muhimbili University of Health and Allied Sciences Research and Publication committee in June 2024 with reference number, MUHAS-REC-05-2024-2275.

Synthesis of Indol-3-yl Morpholino Derivatives Containing a Hydrazone Moiety as Potential Antiplant Virus Agents.

A series of indol-3-yl morpholino derivatives containing a hydrazone moiety were designed and synthesized using indole-3-carboxylic acid as the starting material. The antiviral activities of the synthesized compounds were systematically evaluated. Compound D35, optimized using the comparative molecular field analysis (CoMFA) model, exhibited the most potent inhibitory activity against Tobacco mosaic virus (TMV) in vivo with a 50% effective concentration (EC50) value of 69.9 mg/L and exceeded that of Ningnanmycin (142.4 mg/L). Molecular docking and molecular dynamics simulations revealed that compound D35 could form stable interactions with the TMV coat protein (CP), exhibiting a lower binding energy of -9.37 kcal/mol compared to that of Ningnanmycin (-8.55 kcal/mol). Microscale thermophoresis (MST) experiments further confirmed the stronger binding affinity of compound D35 for the TMV CP than Ningnanmycin. Additionally, transmission electron microscopy (TEM) demonstrated that compound D35 effectively disrupts the TMV particles and inhibits their spread. These collective findings strongly suggest that compound D35 has the potential to serve as a lead compound for the development of novel antiviral agents.

Study on Electrical Characteristics Analysis and Electrical Circuit Model Design of Vanadium Redox Flow Battery Systems Based on Current and Flow Rate Conditions

Study on Electrical Characteristics Analysis and Electrical Circuit Model Design of Vanadium Redox Flow Battery Systems Based on Current and Flow Rate Conditions

Does environmental policy stringency promote financial development? New policy insights from BRICS countries

Purpose Over the past few decades, BRICS nations have implemented several environmental legislations to combat environmental issues, along with the development of the financial sector. However, they still face the dual challenge of balancing financial development (FD) and reducing carbon emissions to achieve sustainable development. Hence, this study aims to explore how environmental policy stringency (EPS) influences FD in BRICS nations from 1993 to 2020. Design/methodology/approach The authors performed a panel data analysis to examine the effect of the EPS on FD. Firstly, they conducted a panel unit root test using the Levin–Lin–Chu and the Im–Pesaran–Shin unit root tests to check the stationarity of the data. Then, they used the fixed effect model for analysis. A feasible generalised least squares test is used to ensure the robustness of the results. Findings The EPS positively affects FD through financial markets and intuitions in BRICS nations, indicating that more stringent environmental policies of BRICS countries promote FD in these nations. Practical implications This study suggests that policymakers in BRICS nations should consider strengthening EPS, implying the dual benefit of aligning environmental sustainability goals with economic growth. Originality/value This study provides a deeper understanding by disassembling FD into financial markets and institutions, thereby establishing a nuanced relationship with EPS, which is highly relevant for addressing the distinctive financial systems and environmental challenges of BRICS nations. Therefore, this study fills this gap and offers new insights into the relationship between EPS and FD in these nations.

Ultrasound-guided thrombin injection for cardiac catheterization pseudoaneurysms: efficacy, safety, and predictors.

Percutaneous ultrasound-guided thrombin injection has become the gold standard treatment for pseudoaneurysms caused by cardiac catheterization. However, failure can occur in up to 25% of the procedures and little is known about its causes. To study the efficacy and safety of the technique and to determine possible risk factors responsible for the unsuccess. A cohort study was carried out based on data from medical records collected between December 2012 and June 2020 of 110 patients with the diagnosis of pseudoaneurysm, femoral or radial, secondary to cardiac catheterization. Clinical data, pseudoaneurysm morphological parameters, and technical factors related to catheterization were reviewed. Primary and global technical success rates were 85.5% and 100%, respectively. Age, neck, and anteroposterior diameter variables were predictors of primary failure on multivariate analysis. The odds ratio (OR) for age was 0.960 (95% confidence interval [CI]=0.927-0.995; P = 0.025), for anteroposterior diameter, OR was 2.023 (95% CI=1.144-3.578; P = 0.015), and for neck diameter, it was 4.625 (95% CI=1.023-20.904; P = 0.047). The receiver operating characteristic (ROC) curve was performed for the multivariate analysis model and the three predictors of failure. The area under the curve for the multivariate analysis model was 0.695, for age it was 0.675, for anteroposterior diameter it was 0.679, and for neck diameter it was 0.676. No complications were observed. Percutaneous ultrasound-guided thrombin injection is safe and effective for the treatment of pseudoaneurysms after cardiac catheterization. Largest anteroposterior diameter, largest neck diameter, and a younger age were independent predictors of primary failure with the technique.

A Multi-Scale Thermal-Mechanical Numerical Method for Mini-Channel Heat Exchanger Subjected to Fluid Pressure Loads

Abstract This study proposes a novel multi-scale numerical method for thermal-mechanical analysis of mini-channel heat exchangers (MCHEs) under internal fluid pressure and temperature loads. The method comprises a macro-scale model for global analysis and a meso-scale model for detailed submodel analysis, specifically focusing on the internal fluid pressure effects within the MCHEs. The macroscopic model divides the MCHE into cover plate and homogenized regions subjected to pressure and temperature loads. To incorporate internal pressures into the homogenized MCHE model, mathematical equations are formulated to convert internal fluid pressures into equivalent strain loads. Additionally, a novel equivalent thermal expansion method is introduced, integrating internal fluid pressure loads by prescribing equivalent thermal expansion coefficients alongside spatially-varying nodal temperature fields within the MCHE. The meso-scale models with detailed channel patterns are assigned to specific portions of the homogenized region. The integration of the mesoscale model into the macroscopic framework is achieved through the application of the submodel method. Comparisons between the equivalent and actual MCHE models show that the proposed equivalent method can provide accurate predictions for thermal-mechanical deformations and stresses, and significantly reduce the computational expenses.

Leveraging Multi-Source Data for the Trustworthy Evaluation of the Vibrancy of Child-Friendly Cities: A Case Study of Tianjin, China

The vitality of a city is shaped by its social structure, environmental quality, and spatial form, with child-friendliness being an essential component of urban vitality. While there are numerous qualitative studies on the relationship between child-friendliness and various indicators of urban vitality, quantitative research remains relatively scarce, leading to a lack of sufficient objective and trustworthy data to guide urban planning and the development of child-friendly cities. This paper presents an analytical framework, using Heping District in Tianjin, China, as a case study. It defines four main indicators—social vitality, environmental vitality, spatial vitality, and urban scene perception—for a trustworthy and transparent quantitative evaluation. The study integrates multi-source data, including primary education (POI) data, street view image (SVI) data, spatiotemporal big data, normalized difference vegetation index (NDVI), and large visual language models (LVLMs) for the trustworthy analysis. These data are visualized using corresponding big data and weighted analysis methods, ensuring transparent and accurate assessments of the child-friendliness of urban blocks. This research introduces an innovative and trustworthy method for evaluating the child-friendliness of urban blocks, addressing gaps in the quantitative theory of child-friendliness in urban planning. It also provides a practical and reliable tool for urban planners, offering a solid theoretical foundation to create environments that better meet the needs of children in a trustworthy manner.

Feature screening for case-cohort studies in the presence of interval censoring

A great literature has been established for both feature screening under various situations and the analysis of case-cohort studies. Also several methods have been proposed for feature screening when one faces case-cohort studies but all of the existing methods apply only to right-censored failure time data. In this paper, we discuss feature screening for case-cohort studies in the presence of interval censoring, a type of censoring that occurs more generally and includes right censoring as a special case. For the problem, two methods, a model-based procedure and a model-free procedure, are proposed. The former, an inverse probability weighted joint likelihood screening approach, is developed under the proportional hazards model, the most commonly used model for failure time data analysis. The latter is a weighted smooth partial rank screening method that allows for both ultrahigh-dimensional covariates and covariate-dependent censoring. For the proposed methods, the sure screening properties are established under mild regularity conditions. An extensive simulation study is conducted to assess the performance of the proposed methods and indicates that they work well in practical situations.

Effect of fuel compositions coupled with engine configuration parameters on a gasoline engine based on sensitivity analysis

ABSTRACT A gasoline engine is determined by many factors, the coupling of them makes it difficult to quantify the influence of each parameter on the gasoline engine. In order to find an effective way to improve the performance of a high-intensification gasoline engine, it is necessary to quantify the sensitivity factors of gasoline engines’ performance. In this paper, engine configuration parameters and fuel compositions were the inputs, and torque was the output. Models were developed by support vector machine for correlation analysis. Then, the models were used as agent models for Sobol sensitivity analysis to quantify the sensitivity factors of the engine performance. All the modeling results showed that the coefficient of determination was more than 0.9, and the mean absolute percentage error was less than 3%, indicating they were viable for sensitivity analysis. Sensitivity analysis results showed that the average influence weight of engine configuration parameters was 84.19%, much higher than that of fuel compositions. The two parameters with the most significant impact on engine performance are ignition timing and intake pressure, with average weights of 53.41% and 21.00%, respectively. With the increase of engine speed, the weight of intake pressure decreased, and the weight of ignition timing increased. Among the fuel compositions, the influence weight of oxygenated components was relatively higher. The effect of fuels with low combustion velocity on engine performance decreased with engine speed increased. In addition, there were interactions between the input parameters investigated in this paper, both ignition timing and intake pressure interacting most significantly with the fuel compositions at the engine speed of 5200rev/min. The second-order interaction accounted for about 90% of total interactions, indicating no need to consider higher-order interactions. In summary, support vector machine coupled Sobol method is feasible for sensitivity analysis in the field of internal combustion engine, which can effectively identify and quantify the role of each parameter itself and the interactions between the parameters under multi-parameter coupling and provide the basis and direction for the optimization of the gasoline engine.

Research on Runway Capacity Evaluation of General Aviation Airport Based on Runway Expansion System

To enhance the operational management capabilities of general aviation airports, this paper proposes a method for evaluating the runway capacity of general aviation airports based on the runway expansion system. Firstly, it provides a brief introduction to the flight rules of general aviation airports and arrival and departure flight procedures with symmetrical characteristics, which serve as a theoretical basis for establishing the runway expansion system. Subsequently, a runway expansion system that covers symmetrical flight activities such as departure and arrival under a visual flight rule and an instrument flight rule is proposed, providing a conceptual model for evaluating the runway capacity of general aviation airports. On this foundation, the classical space–time analysis model is improved to establish a single runway arrival, departure, and mixed operation capacity evaluation model for general aviation airports. Finally, the reliability and rationality of this method are verified through case evaluations and three sets of numerical experiments with symmetrical relationships. The experiments demonstrate that this method can better reflect the actual conditions of the runways at general aviation airports while ensuring flight safety, and it can provide a reference for related research.

Psychological distress among students in Egypt and Jordan during the initial months of the Gaza war

BackgroundPsychological distress has significantly impacted students in Egypt and Jordan. These countries have faced many challenges, including the COVID-19 pandemic, the fallout from the conflict in Syria, and the war in Ukraine. These crises have had far-reaching consequences, affecting the economy, food security, and energy supplies, particularly with the increased number of refugees in these countries. Amid these existing complexities, the ongoing war in Gaza further exacerbates the situation, compounding mental health challenges. Therefore, this study aimed to analyze how the war in Gaza impacted the mental health of students in Egypt and Jordan.MethodsWe conducted a cross-sectional study involving students from Egypt and Jordan between December 2023 and January 2024. The questionnaire incorporated the Brief Symptom Inventory 18 (BSI-18) to assess the overall general distress score, as well as the domains of anxiety, somatization, and depression.ResultsA total of 1509 Jordanian and Egyptian students were included in the study, of which 66% were female. Approximately 18% of the students had an elevation (≥ 50) in their BSI-18 total score. Females scored significantly higher in general distress [30 (18–44.25) vs. 24 (11–41), p < 0.001], and all three dimensions. The univariate predictors for elevated general distress (≥ 50) were being Egyptian (OR 1.49, 95% CI 1.08 to 2.08, p = 0.014), watching social media multiple times a day (OR 3.04, 95% CI 1.97 to 4.69, p < 0.001), and having a Palestinian connection (friend, neighbor, colleague, or relative) (OR 1.63, 95% CI 1.23 to 2.16, p < 0.001). These three predictors were retained in the backward stepwise multivariate regression analysis model. The univariate predictors for moderate and marked general distress (≥ 57) were watching social media multiple times a day (OR 3.26, 95% CI 1.78 to 5.99, p < 0.001) and having a Palestinian connection (OR 1.49, 95% CI 1.05 to 2.14, p = 0.026). Only the former was retained in the backward stepwise multivariate regression analysis.ConclusionGeneral distress has increased among students in Egypt and Jordan throughout the War in Gaza. Mental, psychological, and social support activities are necessary for these students, particularly those watching social media multiple times daily. The development of coping skills to manage the additional stressors of war and socioeconomic status necessitates further research within this group of students.

Association of magnesium intake with predicted atherosclerotic lesions and cardiovascular risk in young adults based on PDAY score: a cross-sectional study

BackgroundMagnesium deficiency has been shown to accelerate atherosclerosis. We hypothesized that dietary magnesium intake at a young age is associated with future atherosclerotic lesions and cardiovascular risk in a large, nationally representative cohort of U.S. adults.MethodsWe included U.S. adults aged 20 to 34 years old from the National Health and Nutrition Examination Survey (NHANES) 2007 to 2018, a population-based cross-sectional study. Dietary magnesium intake was assessed using 24-hour diet recalls. Atherosclerotic lesions in the young adult population were predicted by the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) score that was based on age, sex, smoking status, lipids, blood pressure, and obesity. Information on cardiovascular disease (CVD) risk factors including hypertension, non-HDL-C dyslipidemia, and diabetes was also collected. We used multivariable logistic analysis models to test the association between magnesium intake levels and the PDAY score and CVD risk factors, respectively, after adjusting for several potential confounding factors.Results7,244 eligible participants were included in the analysis. The magnesium intake level was classified into three categories based on the tertile distribution in the population (i.e., ≤ 224, 225–340, and ≥ 341 mg/day). Compared with the lowest tertile, the multivariable-adjusted odds ratio (OR) and 95% confidence interval (95% CI) for the PDAY score were 0.83 (95% CI, 0.72 to 0.96) and 0.60 (95% CI, 0.49 to 0.74) in the second and the third tertiles of magnesium intake, respectively (P value for trend < 0.001), and there was a negative dose-response relationship (test for trend P value < 0.001). In addition, the highest dietary magnesium intake was significantly inverse associated with non-HDL-C dyslipidemia compared with the lowest magnesium intake (OR = 0.65; 95% CI, 0.46 to 0.91).ConclusionsDietary magnesium intake is inversely associated with the risk of future cardiovascular events assessed by the PDAY score and non-HDL-C dyslipidemia in young adulthood years.

What Is Needed to Determine and Potentially Improve the Clinical Effectiveness and Cost-Effectiveness of Robot-Assisted Reconstructive Urinary Tract and Pelvic Floor Surgery?-ICI-RS 2024.

Robot-assisted (RA) surgery has many advantages for urinary tract and pelvic floor reconstruction (UTPFR) but its application is only slowly increasing. Studies are limited in number, patients and follow-up duration. The low-volume high-complexity nature of UTPFR warrants specific research to evaluate the potential role of the RA approach. During its 2024 meeting in Bristol, the International Consultation on Incontinence-Research Society (ICI-RS) reviewed the current literature on RA UTPFR. Priority research questions and strategies were explored in a proposal. Four different topics were discussed. (1) Large multicentre prospective studies on RA UTPFR are needed to evaluate clinical effectiveness and safety. The RA approach needs to be prioritized in specific procedures and patients, to first improve patient care where gains are highest. (2) Models for cost-utility analysis for RA UTPFR are needed to (re)direct limited resources as efficiently as possible in a quickly changing technological field. (3) A modular curriculum for training UTPFR surgeons to become RA certified needs to be developed, to allow valid interpretation and comparison of outcomes. (4) Technological improvements to address tissue handling, accessibility, efficiency and sustainability are needed. Digitalization can be a gamechanger for RA UTPFR through telesurgery (training and expertize coverage), augmented reality and artificial intelligence providing intra-operative assistance, assessment and feedback. The proposal discussed the need for studies on clinical and cost effectiveness, safety, and training curriculum development for RA UTPFR. Recent technological developments hold great promise for RA UTPFR. High-priority research questions were identified.

The European Fault-Source Model 2020 (EFSM20): geologic input data for the European Seismic Hazard Model 2020

Abstract. Earthquake hazard analyses rely on seismogenic source models. These are designed in various fashions, such as point sources or area sources, but the most effective is the three-dimensional representation of geological faults. We here refer to such models as fault sources. This study presents the European Fault-Source Model 2020 (EFSM20), which was one of the primary input datasets of the recently released European Seismic Hazard Model 2020. The EFSM20 compilation was entirely based on reusable data from existing active fault regional compilations that were first blended and harmonized and then augmented by a set of derived parameters. These additional parameters were devised to enable users to formulate earthquake rate forecasts based on a seismic-moment balancing approach. EFSM20 considers two main categories of seismogenic faults: crustal faults and subduction systems, which include the subduction interface and intraslab faults. The compiled dataset covers an area from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland. It includes 1248 crustal faults spanning a total length of ∼95 100 km and four subduction systems, namely the Gibraltar, Calabrian, Hellenic, and Cyprus arcs, for a total length of ∼2120 km. The model focuses on an area encompassing a buffer of 300 km around all European countries (except for Overseas Countries and Territories) and a maximum of 300 km depth for the subducting slabs. All the parameters required to develop a seismic source model for earthquake hazard analysis were determined for crustal faults and subduction systems. A statistical distribution of relevant seismotectonic parameters, such as faulting mechanisms, slip rates, moment rates, and prospective maximum magnitudes, is presented and discussed to address unsettled points in view of future updates and improvements. The dataset, identified by the DOI https://doi.org/10.13127/efsm20 (Basili et al., 2022), is distributed as machine-readable files using open standards (Open Geospatial Consortium).

Influence of Digital Economy on Urban Energy Efficiency in China

The digital economy (DE) is characterized by invention, low energy consumption, cross-sector integration, and open sharing. It can effectively enhance social production methods, influence consumer behavior, and provide new pathways to enhance total factor energy efficiency (TFEE). This paper studies 280 Chinese cities, employing the entropy method and data envelopment analysis (DEA) model to evaluate and analyze urban DE and TFEE. It also constructs a system generalized method of moments model (SGMM model) and a threshold regression model (TR model) to examine the impact of the DE on TFEE in China. The main study findings include the following: (1) The regression results of the SGMM model indicate that the effect of DE on TFEE in Chinese cities shows a U-shaped trend. (2) The regression results of the TR model further confirm a U-shaped association connecting DE and TFEE, with the threshold estimated at 0.304. (3) The economic factors and industrial structure have a major impact on inhibiting the improvement of TFEE, whereas technological advancements and environmental regulations significantly facilitate its improvement.

A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies.

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities-structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI-in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of cross-modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age-related sources replicates across two independent and separately analyzed datasets. The same set also presented age-adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset.

Longitudinal change in cervical length measured after prophylactic cerclage in predicting spontaneous preterm birth.

To evaluate the value of serial post-cerclage cervical length measurements in predicting preterm birth. This was a retrospective study of women with a singleton pregnancy who underwent prophylactic transvaginal cerclage from 2017 to 2023. Prophylactic cerclage was performed in women with an obstetric history of cervical insufficiency. After cerclage, all participants underwent routine transvaginal cervical measurement every 2-4 weeks until 32-34 weeks of gestation. Trajectory patterns of changes in cervical length were identified through latent class trajectory modeling. The association of patterns with spontaneous preterm birth and pregnancy interval was analyzed by multivariable logistic regression analysis and Cox proportional hazards regression models. The specific characteristics of changes in cervical length associated with preterm birth were identified. We involved 225 women and acquired a total of 1248 measurements of cervical length. Two distinct trajectories of cervical length change along gestation were identified: Class 1 (persistent shortening of cervix, n = 52 [23.11%]) and class 2 (stable cervix, n = 173 [76.89%]). Women in class 1 were highly associated with spontaneous preterm birth, with a hazard ratio (95% confidence interval [CI]) of 2.00 (1.36, 2.96). The average shortening rate ≥0.24 cm per week and cervical length ≤1.1 cm was significantly associated with spontaneous preterm birth at different gestational age. Serial sonographic cervical length measurements after prophylactic cerclage are valuable in predicting preterm birth.