Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)

Exploring the relationship between illness perception, self-transcendence, and demoralization in patients with lung cancer: A latent profile and mediation analysis

Objective

This study examined the heterogeneity of illness perceptions in patients with lung cancer and evaluated the mediating role of self-transcendence in the relation between illness perception and demoralization.

Methods

A convenience sample of 477 patients with lung cancer was selected from three tertiary hospitals in Wuhan, China, between January and June 2024. Participants completed the Brief Illness Perception Questionnaire, Self-Transcendence Scale, and Demoralization Scale. Data were analyzed using Mplus 8.3 and SPSS 25.0.

Results

Three latent illness perception profiles were identified among patients with lung cancer: low (27.25%), moderate (40.04%), and high (32.71%). Mediation analyses revealed a partial mediation effect in the relation between illness perception and demoralization in the low versus moderate (SE ​= ​1.56, 95% CI ​= ​14.71, 20.86) and high versus low illness perception groups (SE ​= ​1.71, 95% CI ​= ​35.44, 42.71).

Conclusions

Patients with lung cancer exhibited heterogeneous illness perceptions, and self-transcendence partially mediated the relation between illness perception and demoralization. Promoting self-transcendence may help mitigate the negative impact of illness perceptions on demoralization. Clinical interventions aimed at reducing negative illness perceptions and enhancing self-transcendence should be prioritized in the care of patients with lung cancer.

A comprehensive multi-objective framework for the estimation of crash frequency models

A common and challenging data and modeling aspect in crash analysis is unobserved heterogeneity, which is often handled using random parameters and special distributions such as Lindley. Random parameters can be estimated with respect to each observation for the entire dataset, and grouped across segments of the dataset, with variable means, or variable variances. The selection of the best approach to handle unobserved heterogeneity depends on the data characteristics and requires the corresponding hypothesis testing. In addition to dealing with unobserved heterogeneity, crash frequency modeling often requires explicit consideration of functional forms, transformations, and identification of likely contributing factors. During model estimation, it is important to consider multiple objectives such as in- and out-of-sample goodness-of-fit to generate reliable and transferable insights. Taking all of these aspects and objectives into account simultaneously represents a very large number of modeling decisions and hypothesis testing. Limited testing and model development may lead to bias and missing relevant specifications with important insights. To address these challenges, this paper proposes a comprehensive optimization framework, underpinned by a mathematical programming formulation, for systematic hypothesis testing considering simultaneously multiple objectives, unobserved heterogeneity, grouped random parameters, functional forms, transformations, heterogeneity in means, and the identification of likely contributing factors. The proposed framework employs a variety of metaheuristic solution algorithms to address the complexity and non-convexity of the estimation and optimization problem. Several metaheuristics were tested including Simulated Annealing, Differential Evolution and Harmony Search. Harmony Search provided convergence with low sensitivity to the choice of hyperparameters. The effectiveness of the framework was evaluated using three real-world data sets, generating sound and consistent results compared to the corresponding published models. These results demonstrate the ability of the proposed framework to efficiently estimate sound and parsimonious crash data count models while reducing costs associated with time and required knowledge, bias, and sub-optimal solutions due to limited testing. To support experimental testing for analysts and modelers, the Python package "MetaCountRegressor," which includes algorithms and software, is available on PyPi.

Latent classes of acute grief reactions in the shadow of collective trauma and its predictors in bereaved adults

The current study explored grief reaction profiles after the October 7th, 2023, Israeli massacre regarding the loss of significant others. It investigated factors worsening pre-existing grief in 2,028 adult civilians, with 1,263 reporting pre- or post-massacre loss. Participants completed self-reports on prolonged grief disorder (PGD), post-traumatic stress disorder (PTSD), complex PTSD (CPTSD), cognitive emotional regulation (CER), and assumptive worldviews. Latent class analysis identified four classes of probable PGD among those with lifetime loss: "high PGD-both core and emotional pain" (35.9 %); "Medium PGD-emotional pain" (6.9 %); "medium to high PGD-high core, medium emotional pain" (23 %); and "low PGD-both core and emotional pain" (34.5 %). The subsample analysis of individuals experiencing lifetime loss with concurrent October 7th loss indicated three classes of probable PGD: "high PGD symptoms" (47.9 %), "high to medium PGD-high core with medium emotional pain symptoms" (15.9 %), and "low PGD symptoms" (36.2 %). The subsample analysis of those who experienced concurrent recent loss related to the October 7th attack showed two classes related to indicative of acute grief: "high PGD symptoms" (69 %), and "low PGD symptoms" (31 %). Negative CER coping strategies were associated significantly with the high PGD in all three groups, and with increased risk of PTSD and CPTSD symptoms compared to the "low PGD symptoms" group. Collective bereavement amid collective trauma can activate prior individual grief reactions, even without current losses from the traumatic event. Those experiencing loss tied to severe traumatic events may face higher susceptibility to developing pathological grief.

Exploring occupational well-being profiles, outcomes, and predictors among Chinese teachers: A mixed-methods approach using latent profile and decision tree analysis

Understanding the varied profiles of occupational well-being, their outcomes, and predictors is key to formulating effective strategies for enhancing teachers' occupational health and well-being. This study employed latent profile analysis (LPA) to identify distinct occupational well-being profiles and their outcomes among 366 Chinese teachers, and decision tree analysis to explore the factors predicting each profile. The results showed three occupational well-being profiles: burnout, engaged, and burnout-engaged. The "engaged" group exhibited normal ranges for depression and stress, along with mild anxiety. The "burnout" group demonstrated moderate depression and stress, coupled with severe anxiety. The "burnout-engaged" group was near the threshold of mild depression and moderate anxiety. The result of the decision tree model revealed that marital status, teaching experience, income, role as a class teacher, school type, and working hours significantly influenced these occupational well-being profiles. Specific combinations of variables were associated with each occupational well-being profile, offering a nuanced understanding of the risky and protective factors for teacher occupational well-being. By identifying distinct occupational well-being profiles among Chinese teachers and their outcomes, and elucidating the key predictors and their interrelations, this study provides insights into how to quickly screen for teachers in need of help at work, and how to design targeted interventions for different teachers.

Developmental interactions between peer victimization and depressive symptoms in adolescents: A latent change score analysis

Peer victimization and depressive symptoms are highly relevant risks during adolescence. Understanding the dynamic patterns of interactions between peer victimization and depressive symptoms as well as gender differences in these variables can improve intervention strategies for adolescents navigating this critical transition period. In the present study, a large sample of Chinese adolescents reported peer victimization and depressive symptoms in four survey waves at six-month intervals. A total of 2534 adolescents (51.9% boys, M = 12.98 ± 0.60 years) were included in the latent change score (LCS) analysis. The results supported the reciprocal effects model obtained in the full sample. Changes in peer victimization were influenced by prior changes in depressive symptoms over time, and changes in depressive symptoms were influenced by prior levels of peer victimization. There were also gender differences, with boys exhibiting depressive symptom-driven effects on peer victimization, while girls exhibiting peer victimization-induced depressive symptoms. The dynamic relationships between peer victimization and depressive symptoms that promote and constrain each other in adolescents are elucidated in this study. Differentiating effects on boys and girls is crucial for enhancing the effectiveness of practical interventions.

Direct and indirect genetic effects on early neurodevelopmental traits

BACKGROUND

Neurodevelopmental conditions are highly heritable. Recent studies have shown that genomic heritability estimates can be confounded by genetic effects mediated via the environment (indirect genetic effects). However, the relative importance of direct versus indirect genetic effects on early variability in traits related to neurodevelopmental conditions is unknown.

METHODS

The sample included up to 24,692 parent-offspring trios from the Norwegian MoBa cohort. We use Trio-GCTA to estimate latent direct and indirect genetic effects on mother-reported neurodevelopmental traits at age of 3 years (restricted and repetitive behaviors and interests, inattention, hyperactivity, language, social, and motor development). Further, we investigate to what extent direct and indirect effects are attributable to common genetic variants associated with autism, ADHD, developmental dyslexia, educational attainment, and cognitive ability using polygenic scores (PGS) in regression modeling.

RESULTS

We find evidence for contributions of direct and indirect latent common genetic effects to inattention (direct: explaining 4.8% of variance, indirect: 6.7%) hyperactivity (direct: 1.3%, indirect: 9.6%), and restricted and repetitive behaviors (direct: 0.8%, indirect: 7.3%). Direct effects best explained variation in social and communication, language, and motor development (5.1%-5.7%). Direct genetic effects on inattention were captured by PGS for ADHD, educational attainment, and cognitive ability, whereas direct genetic effects on language development were captured by cognitive ability, educational attainment, and autism PGS. Indirect genetic effects on neurodevelopmental traits were primarily captured by educational attainment and/or cognitive ability PGS.

CONCLUSIONS

Results were consistent with differential contributions to neurodevelopmental traits in early childhood from direct and indirect genetic effects. Indirect effects were particularly important for hyperactivity and restricted and repetitive behaviors and interests and may be linked to genetic variation associated with cognition and educational attainment. Our findings illustrate the importance of within-family methods for disentangling genetic processes that influence early neurodevelopmental traits, even when identifiable associations are small.

Impact of hospital formulary intervention on carbapenem use: a segmented time-series analysis of consumption and a propensity score-matched non-inferiority study of treatment efficacy

BACKGROUND

Pharmaceutical formularies play a crucial role in guiding medication use by balancing clinical effectiveness and cost efficiency. Although formulary implementation has been increasing in Japan, comprehensive evaluations of its impact on both clinical and economic outcomes are limited. This study aimed to assess the effect of introducing an antimicrobial formulary at Yokohama City University Hospital on antibiotic usage and treatment outcomes in intra-abdominal infections.

METHODS

We conducted a segmented time-series analysis to evaluate changes in carbapenem usage, including doripenem, before and after formulary implementation in October 2018. Monthly antibiotic consumption was measured by antibiotic use density (AUD). The primary outcomes were changes in doripenem use and treatment efficacy for intra-abdominal infections. To assess treatment efficacy, we used non-inferiority analysis with propensity score matching based on age, sex, body mass index, cancer status, and baseline blood test results. The treatment outcomes were evaluated using predefined clinical indicators.

RESULTS

Following the formulary intervention, doripenem use significantly decreased from 10.8 to 4.9%, meropenem use slightly increased, and imipenem/cilastatin usage remained stable. Overall, carbapenem use significantly decreased during the study period. Treatment effectiveness for intra-abdominal infections remained non-inferior, with a higher proportion of patients classified as having an "effective" response post-intervention (86.6% vs. 79.4% pre-intervention). The confidence interval confirmed the non-inferiority margin, indicating no clinically significant reduction in treatment effectiveness following the formulary introduction.

CONCLUSIONS

The introduction of an antibiotic formulary at Yokohama City University Hospital effectively reduced the use of doripenem without compromising the effectiveness of treatment of intra-abdominal infections. These findings suggest that formulary management can be a valuable strategy for optimizing antibiotic use while maintaining clinical outcomes and contributing to improved antimicrobial stewardship in healthcare settings. Further research is warranted to explore the broader implications of formulary implementation in Japanese healthcare practices.

Psychometric Evidence and Measurement Invariance by College-Going Status for the Inventory of the Dimensions of Emerging Adulthood (IDEA)

Numerous versions exist of the Inventory of the Dimensions of Emerging Adulthood (IDEA), which is used to assess normative markers of development in the 18-29 age range. However, their comparative psychometric properties have not been adequately explored, and samples including non-college-going emerging adults are rare. In Study 1, confirmatory factor analyses using survey data from 429 college-going and non-college-going emerging adults (52% female, 44% non-white) showed the IDEA-Short Form (IDEA-SF) to be the most psychometrically and conceptually sound version, demonstrating good fit for the five-factor model proposed by Arnett's (2004) theory of emerging adulthood; initial evidence for concurrent validity of the IDEA-SF scores was also presented. Study 2 (N = 765, 40% non-college-going) tested and found evidence for internal reliability and convergent validity as well as measurement invariance across college-going and non-college-going emerging adults for the IDEA-SF, though evidence for discriminant validity was mixed. Study 3 (N = 817) used data from the openly-available EAMMI3 dataset to examine concurrent and discriminant validity evidence for the IDEA-SF scores with multiple measures of well-being and adult development, respectively; preliminary, albeit weak, evidence of both forms of external validity was found. Suggestions for future research employing and improving the IDEA-SF and clinical implications for mental health clinicians working with emerging adults are discussed.

Multimodal sleep signal tensor decomposition and hidden Markov Modeling for temazepam-induced anomalies across age groups

BACKGROUND

Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.

NEW METHOD

We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.

RESULTS

Jensen-Shannon Divergence (JSD) was employed to assess variability in hidden state transitions, with older subjects (50-66 years) under temazepam displaying heightened transition variability and network instability as indicated by a 48.57 % increase in JSD (from 0.35 to 0.52) and reductions in network density by 12.5 % (from 0.48 to 0.42) and modularity by 21.88 % (from 0.32 to 0.25). These changes reflect temazepam's disruptive impact on sleep architecture in older adults, aligning with known age-related declines in sleep stability and pharmacological sensitivity. In contrast, younger subjects exhibited lower divergence and retained relatively stable, cyclical transition patterns. Anomaly scores further quantified deviations in state transitions, with older subjects showing increased transition uncertainty and marked deviations in REM-like to NREM state transitions.

COMPARISON WITH EXISTING METHODS

This XAI-driven framework provides transparent, age-specific insights into temazepam's impact on sleep dynamics, going beyond traditional methods by identifying subtle, pharmacologically induced changes in sleep stage transitions that would otherwise be missed.

CONCLUSIONS

DREAMS supports the development of personalized interventions based on sleep transition variability across age groups, offering a powerful tool to understand temazepam's age-dependent effects on sleep architecture.

Development of the PRECIOUS Short-Form (PRECIOUS-SF) quality of care measure for children with serious illnesses

BACKGROUND

Rising number of children with complex medical conditions necessitate regular healthcare quality evaluation to achieve optimal outcomes. To address the need for a periodic and quick assessment of quality of care in serious childhood illnesses, we developed a short version of previously validated 45-item PaRental Experience with care for Children with serIOUS illnesses (PRECIOUS) measure.

METHODOLOGY

PRECIOUS was administered by parents of children living with serious illnesses at two time-points (baseline and two weeks) in an online survey. PRECIOUS Short-Form (PRECIOUS-SF) items were derived from the full PRECIOUS measure, which comprises five scales, using an exploratory factor analysis and best subset regression. The measurement properties of PRECIOUS-SF scales were assessed using the concurrent validity using Pearson correlation (r) with the PRECIOUS scales, internal consistency (Cronbach's α) within each scale, convergent validity with overall QoC rating, and test-retest reliability (intraclass correlation coefficient, ICC) between baseline and two-week responses.

RESULTS

PRECIOUS-SF included 10 items across four scales - (1) access to financial and medical resources (2), collaborative and goal-concordant care (3), caregiver support and respectful care and (4) reduction of caregiving stressors. A fifth and optional scale was suggested for hospitalization-specific processes. PRECIOUS-SF scales correlated strongly with corresponding PRECIOUS scales (r = 0.91 to 0.98) and demonstrated satisfactory internal consistency (α = 0.77 to 0.91) and test-retest reliability (ICCs > 0.70).

CONCLUSIONS

PRECIOUS-SF demonstrated internal consistency, convergent validity, test-retest reliability, and concurrent validity with PRECIOUS. PRECIOUS-SF offers a practical tool for routine quality of care assessment in pediatric serious illnesses for promoting timely service evaluation and quality improvement.

Profiles of innovative behavior and associated predictors among clinical nurses: a multicenter study using latent profile analysis

BACKGROUND

Innovative Behavior (IB) is a key prerequisite for nurses in solving clinical problems. However, existing research on IB among clinical nurses is relatively limited.

OBJECTIVE

To identify profiles and characteristics of IB among clinical nurses and explore the associated predictors, as well as the relationships with research outputs.

METHODS

A multicenter cross-sectional study was conducted on 354 clinical nurses in Shanghai from April 2023 to May 2023 (response rate 98.06%). IB was measured by the Innovative Behavior Scale for Nurses (IBSN), future time perspective was measured by the Future Time Perspective Scale (FTPS), and work engagement was measured by the Utrecht Work Engagement Scale-9 (UWES-9). Socio-demographic and professional data and research output indicators were measured by a self-designed questionnaire. We used latent profile analysis (LPA) by Mplus 7.0 to identify latent classes of IB. Ordinal logistic regression analysis was used to analyze the relevant predictors on the different profiles. And then Pearson's chi-squared was used to analyze the association between IB level and research output.

RESULTS

Among the respondents, individuals aged 25 to 35 accounted for 55.9%, and females comprised 94.6%. IB of clinical nurses can be identified into 3 groups: low-level (n = 108, 30.51%), moderate-level (n = 149, 42.09%), and high-level (n = 97, 27.40%) groups. Based on the results of LPA, marital status, education level, work experience, monthly income, night shifts, future time perspective scores, and work engagement scores can be the predictors of IB among different profiles. Statistically significant associations were found between IB level and research productivity, including publishing academic papers (χ2 = 15.307, p < 0.001), registering patents (χ2 = 17.163, p < 0.001), and winning Sci. & Tech awards (χ2 = 27.814, p < 0.001).

CONCLUSION

According to our research, clinical nurses have three unique IB profiles. The current level is predominantly at a moderate level, with less than 30% demonstrating a high level of innovation. It revealed that better socio-demographic status and professional characteristics, future time perspective, and work engagement positively influenced innovative behavior among clinical nurses. The findings also highlight the potentially important role of IB in contributing to nurses' research output.

PRACTICAL IMPLICATIONS

As far as we know, it might be the first study to employ LPA to clarify the heterogeneity in the levels of IB and their specific distribution among nurses. Our findings may provide a new viewpoint for promoting IB among clinical nurses. Nursing administrators should pay attention to IB of clinical nurses and develop targeted interventions to enhance their IB levels.

Longitudinal associations of flare and damage accrual in patients with systemic lupus erythematosus

OBJECTIVE

To estimate the prevalence of organ damage (damage) and flare and to examine longitudinal associations between flares and subsequent damage accrual, in patients with systemic lupus erythematosus (SLE).

METHODS

Patients enrolled in the Asia Pacific Lupus Collaboration cohort with ≥3 years of prospectively captured data were studied. Flares were assessed at routine visits, while damage ((Systemic Lupus International Collaborating Clinics/American College of Rheumatology) Damage Index) was assessed annually. Multivariable, multifailure survival analyses were carried out to quantify the association between flares and damage accrual.

RESULTS

1556 patients with SLE with a median (IQR) of 5.7 (3.9, 7.0) years of follow-up were studied. 39.5% (n=614) of patients had damage at enrolment, and 31.9% (n=496) accrued damage during the study observation period. The incidence of damage accrual during observation was ~58/1000 person-years. Overall, 74.1% (n=1153) of patients experienced a flare of any severity (mild/moderate or severe) at least once; 56.9% (n=885) experienced recurrent (≥2) flares. The risk of subsequent damage accrual in patients who experienced mild-to-moderate flare, after controlling for confounders, was 32% greater than in patients without flares (adjusted HR) (95% CI 1.32 (1.17 to 1.72)). The risk of damage accrual was greater if patients had severe flares (HR (95% CI) 1.58 (1.18 to 2.11)). For each additional flare, the risk of damage accrual increased by 7% (HR (95% CI) 1.07 (1.02 to 1.13)).

CONCLUSIONS

Flares independently increased the risk of damage accrual. Prevention of flares should be considered a necessary goal of SLE disease management to minimise permanent damage.

Fear of progression in Chinese patients after cardiac valve replacement: profiles, influencing factors, and mechanisms

AIMS

This study aims to investigate the latent profiles and influencing factors of fear of progression (FoP) among patients following cardiac valve replacement (CVR) and to explore the mechanisms of FoP and its primary influencing factors.

METHODS AND RESULTS

This cross-sectional study included 385 patients who had undergone CVR in Hunan Province, China. Data on FoP, Type D personality, death anxiety, medical coping modes, and family function were collected using appropriate questionnaires. A serial multiple mediation model was utilized to evaluate the multiple effects of family function on FoP. The average FoP score of the patients following CVR was 35.59 ± 8.21, with 191 exhibiting high FoP levels. Family function negatively affected resignation coping mode (β = -0.255, P < 0.001), death anxiety (β = -0.145, P < 0.001), and FoP (β = -0.363, P < 0.001). Resignation coping mode and death anxiety demonstrated a chain mediating role (β = -0.036, P < 0.001) between family function and FoP, accounting for 13.84% of the total indirect effect. Latent profile analysis identified four FoP profiles: a 'low-risk fear group' (8.05%), 'moderate-risk fear group' (42.33%), 'high-risk fear group' (34.54%), and 'severe-risk fear group' (15.06%). Patients with high levels of death anxiety, Type D personality, monthly household income <3000 RMB, and adoption of avoidance or resignation coping modes (all P < 0.05) were more likely to be classified into the 'Severe fear group.'

CONCLUSION

Patients who have undergone CVR exhibit heterogeneous profiles, with 49.60% showing high and severe FoP. Family members and clinical staff should focus on patients with a high risk of FoP and provide targeted prevention strategies and psychological care according to the characteristics and influencing factors of distinct FoP profiles.

Pitching kinematics have direct and indirect effects on pitch location in NCAA baseball

Kinematics and release parameters are important factors of throw location; yet an understanding of their relationship has yet to be achieved. This study sought to explore this relationship. Kinematic data were collected for 77 collegiate pitchers. Fifty-seven kinematic parameters were included in path analyses for horizontal and vertical plate locations. Release angles were set as mediating variables (MED) between independent and dependent variables. Eleven kinematic variables directly (13 indirectly) affected the vertical plate location, while 23 kinematic variables directly affected the horizontal plate location (10 indirectly). Linear mixed models revealed that lateral trunk flexion at ball release (R 2=0.908, BIC=-598, ICC=0.528) best explained vertical plate location. Trunk flexion at foot contact (R 2=0.944, BIC=-607, ICC=0.776), mediolateral center of mass displacement at foot contact (R 2=0.974, BIC=-573, ICC=0.918) and ball release (R 2=0.967, BIC=-593, ICC=0.865), and pelvis rotation at ball release (R 2=0.965, BIC=-588, ICC=0.895) models were identified for the horizontal plate location. Results indicate that the relationship between pitching kinematics, release conditions, and throw location is complex. Biomechanics can influence release parameters, which in turn impacts the throw location. This work may serve to understand better how biomechanics influence performance.

Cannabis use characteristics and associations with problematic use outcomes, quitting-related factors, and mental health among US young adults

OBJECTIVE

Given the changes in trends of cannabis use (e.g., product types), this study examined latent classes of young adult use and associations with use-related outcomes.

METHODS

We analyzed 2023 survey data among 4,031 US young adults (Mage=26.29, 59.4% female, 19.0% Hispanic, 13.5% Black, 13.6% Asian). Among those reporting past-month use (48.8%), latent class analysis (LCA) indicators included: days used (1-5; 6-20; 21-30), use/day (1; 2-4; ≥5), and type usually used (herb/flower; edibles; oils/vape; concentrates/other). Multivariable regressions examined class in relation to problematic use, quitting-related factors, and mental health, controlling for sociodemographics and state non-medical cannabis laws.

RESULTS

LCA identified 4 classes of cannabis use frequency and types used: 'infrequent-herb/edibles' (41.4%), 'frequent-herb' (16.8%), 'moderate-herb' (28.0%), and 'moderate-oil/other' (13.8%). In multivariable analyses (referent group: 'moderate-herb' class), 'frequent-herb' reported less problematic use (B=-0.18, 95%CI=-0.30, -0.07), while 'moderate-oil/other' reported greater (B = 0.39, 95%CI = 0.27, 0.51). 'Infrequent-herb/edibles' had lower odds of driving post-use of cannabis (aOR = 0.28, 95%CI = 0.22, 0.37) and cannabis/alcohol (aOR = 0.52, 95%CI = 0.35, 0.76), whereas 'frequent-herb' (aOR = 1.52, 95%CI = 1.02, 2.28) and 'moderate-oil/other' (aOR = 3.98, 95%CI = 2.72, 5.82) reported greater odds of driving post-cannabis/alcohol use. 'Moderate-oil/other' reported higher quitting importance (B = 0.59, 95%CI = 0.17, 1.01), while 'frequent-herb' reported lower (B=-0.33, 95%CI=-0.99, -0.18). 'Infrequent-herb/edibles' reported higher quitting confidence (B = 0.56, 95%CI = 0.20, 0.92), whereas 'frequent-herb' (B=-1.01, 95%CI=-1.45, -0.57) and 'moderate-oil/other' (B=-1.27, 95%CI=-1.74, -0.81) reported lower. 'Infrequent-herb/edibles' reported fewer mental health symptoms (B=-0.55, 95%CI=-0.93, -0.17), while 'moderate-oil/other' reported more (B = 1.03, 95%CI = 0.53, 1.52).

CONCLUSIONS

Preventing frequent and moderate use of cannabis, particularly of oils/concentrates, is crucial given the potential negative implications for problematic use, quitting, and mental health.

Development and preliminary validation of five miRNAs for lung adenocarcinoma prognostic model associated with immune infiltration

Our aim was to investigate the potential value of immune-related miRNA signaling in predicting clinical prognosis and immunotherapy. We first identified immune-related miRNAs in lung adenocarcinoma (LUAD), and then constructed a miRNA-based risk model by lasso regression modeling. Finally, we validated our findings using RT-qPCR in serum from LUAD patients and normal patients. Weighted gene co-expression network analysis (WGCNA) was used to screen the aberrantly expressed genes associated with immune scores, and then correlation analysis and prognostic analysis were used to identify and immune-associated miRNAs, and lasso-cox regression was used to construct an immune-associated 5-miRNA model. Risk score as an independent prognostic factor could accurately predict the prognosis of LUAD patients. Immunotherapy analysis revealed that patients with low-risk scores benefited more from anti-PD-1 and CTLA-4 therapy. Experimental validation showed that only miRNA-200b-3p was significantly differentially expressed in 91 cases of clinically collected cancer tissues and normal tissue serum. We constructed a 5-miRNA model that can be used for risk stratification of LUAD patients. Targeted therapy against miRNA-200b-3p is expected to be a prospective new strategy for the clinical treatment of LUAD.

Identifying psychosocial predictors and developing a risk score for preterm birth among Kenyan pregnant women

BACKGROUND

Preterm birth (PTB) is a leading cause of neonatal mortality, particularly in sub-Saharan Africa where 40% of global neonatal deaths occur. We identified and combined demographic, clinical, and psychosocial correlates of PTB among Kenyan women to develop a risk score.

METHODS

We used data from a prospective study enrolling HIV-negative women from 20 antenatal clinics in Western Kenya (NCT03070600). Depressive symptoms were assessed by study nurses using the Center for Epidemiologic Studies Depression Scale (CESD-10), intimate partner violence (IPV) with the Hurt, Insult, Threaten, Scream scale (HITS), and social support using the Medical Outcomes Survey scale (MOS-SSS). Predictors of PTB (birth < 37 weeks gestation) were identified using multivariable Cox proportional hazards models, clustered by facility. We used stratified k-fold cross-validation methods for risk score derivation and validation. Area under the receiver operating characteristic curve (AUROC) was used to evaluate discrimination of the risk score and Brier score for calibration.

RESULTS

Among 4084 women, 19% had PTB (incidence rate: 70.9 PTB per 100 fetus-years (f-yrs)). Predictors of PTB included being unmarried (HR:1.29, 95% CI:1.08-1.54), lower education (years) (HR:0.97, 95% CI:0.94-0.99), IPV (HITS score ≥ 5, HR:1.28, 95% CI:0.98-1.68), higher CESD-10 score (HR:1.02, 95% CI:0.99-1.04), lower social support score (HR:0.99, 95% CI:0.97-1.01), and mild-to-severe depressive symptoms (CESD-10 score ≥ 5, HR:1.46, 95% CI:1.07-1.99). The final risk score included being unmarried, social support score, IPV, and MSD. The risk score had modest discrimination between PTB and term deliveries (AUROC:0.56, 95% CI:0.54-0.58), and Brier Score was 0.4672. Women considered "high risk" for PTB (optimal risk score cut-point) had 40% higher risk of PTB (83.6 cases per 100 f-yrs) than "low risk" women (59.6 cases per 100 f-ys; HR:1.6, 95% CI:1.2-1.7, p < 0.001).

CONCLUSION

A fifth of pregnancies were PTB in this large multi-site cohort; PTB was associated with several social factors amenable to intervention. Combining these factors in a risk score did not predict PTB, reflecting the multifactorial nature of PTB and need to include other unmeasured factors. However, our findings suggest PTB risk could be better understood by integrating mental health and support services into routine antenatal care.

Stimulate to Remember? The Effects of Short Burst of Transcutaneous Vagus Nerve Stimulation (taVNS) on Memory Performance and Pupil Dilation

The decline in noradrenergic (NE) locus coeruleus (LC) function in aging is thought to be implicated in episodic memory decline. Transcutaneous auricular vagus nerve stimulation (taVNS), which supports LC function, might serve to preserve or improve memory function in aging. However, taVNS effects are generally very heterogeneous, and it is currently unclear whether taVNS has an effect on memory. In this study, an emotional memory task with negative events involving the LC-NE system was combined with the short burst of event-related taVNS (3 s) in younger adults (N = 24). The aim was to investigate taVNS-induced changes in pupil dilation during encoding and possible taVNS-induced improvements in (emotional) memory performance for early and delayed (24 h) recognition. Negative events were associated with increased pupil dilation and better memory performance. Additionally, real as compared to sham or no stimulation selectively increased memory for negative events. Short bursts of stimulation, whether real or sham, led to an increase in pupil dilation and an improvement in memory performance over time, likely due to the attention-inducing sensory modulation of electrical stimulation.

Exploring the bounded rationality in human decision anomalies through an assemblable computational framework

Some seemingly irrational decision behaviors (anomalies), once seen as flaws in human cognition, have recently received explanations from a rational perspective. The basic idea is that the brain has limited cognitive resources to process the quantities (e.g., value, probability, time, etc.) required for decision making, with specific biases arising as byproducts of the resource allocation that is optimized for the environment. While appealing for providing normative accounts, the existing resource-rational models have limitations such as inconsistent assumptions across models, a focus on optimization for one specific aspect of the environment, and limited coverage of decision anomalies. One challenging anomaly is the peanuts effect, a pervasive phenomenon in decision-making under risk that implies an interdependence between the processing of value and probability. To extend the resource rationality approach to explain the peanuts effect, here we develop a computational framework-the Assemblable Resource-Rational Modules (ARRM)-that integrates ideas from different lines of boundedly-rational decision models as freely assembled modules. The framework can accommodate the joint functioning of multiple environmental factors, and allow new models to be built and tested along with the existing ones, potentially opening a wider range of decision phenomena to bounded rationality modeling. For one new and three published datasets that cover two different task paradigms and both the gain and loss domains, our boundedly-rational models reproduce two characteristic features of the peanuts effect and outperform previous models in fitting human decision behaviors.

Drawing behaviour influences ongoing thought patterns and subsequent memory

For millennia, humans have created drawings as a means of externalizing visual representations, and later, to aid communication and learning. Despite its cultural value, we understand little about the cognitive states elicited by drawing, and their downstream benefits. In two preregistered experiments, we explored these states; Undergraduate participants (Ns = 69, 60) encoded words by drawing or writing, periodically describing their thoughts using multi-dimensional experience sampling, a tool for characterizing the features of ongoing thought. Subsequent memory was tested via free recall. Contrasted with writing, drawing improved memory, and evoked thoughts that were more visual and elaborative. Recall was also dictated by the emergence of these thought patterns, with the former most important when drawing. Our findings establish that drawing elicits unique thought patterns that promote successful memory, providing an explanation for drawing's influential role in our everyday lives.

Generalized Fused Lasso for Treatment Pooling in Network Meta-Analysis

This work develops a generalized fused lasso (GFL) approach to fitting contrast-based network meta-analysis (NMA) models. The GFL method penalizes all pairwise differences between treatment effects, resulting in the pooling of treatments that are not sufficiently different. This approach offers an intriguing avenue for potentially mitigating biases in treatment rankings and reducing sparsity in networks. To fit contrast-based NMA models within the GFL framework, we formulate the models as generalized least squares problems, where the precision matrix depends on the standard error in the data, the estimated between-study heterogeneity and the correlation between contrasts in multi-arm studies. By utilizing a Cholesky decomposition of the precision matrix, we linearly transform the data vector and design matrix to frame NMA within the GFL framework. We demonstrate how to construct the GFL penalty such that every pairwise difference is penalized similarly. The model is straightforward to implement in R via the "genlasso" package, and runs instantaneously, contrary to other regularization approaches that are Bayesian. A two-step GFL-NMA approach is recommended to obtain measures of uncertainty associated with the (pooled) relative treatment effects. Two simulation studies confirm the GFL approach's ability to pool treatments that have the same (or similar) effects while also revealing when incorrect pooling may occur, and its potential benefits against alternative methods. The novel GFL-NMA method is successfully applied to a real-world dataset on diabetes where the standard NMA model was not favored compared to the best-fitting GFL-NMA model with AICc selection of the tuning parameter (Δ A I C c > 13 ) $$ \Delta AICc>13\Big) $$ .

Dosing Regimen Recommendations for Sirolimus in Adult Liver Transplant Recipients: Insights from a Population Pharmacokinetic Model

Background

Sirolimus is a commonly used immunosuppressant administered after solid organ transplantation. It is characterized by a narrow therapeutic window and highly variable exposure, necessitating the identification of the sources of variability and design of individualized drug therapies.

Aim

This study aimed to perform a population pharmacokinetic (PK) analysis of sirolimus in adult liver transplant recipients and develop dosing regimen recommendations according to patient characteristics.

Methodology

A total of 216 measurements of whole blood sirolimus concentrations in 103 adult patients were obtained for analysis. Covariates influencing the PKs of sirolimus were investigated using a stepwise procedure. Monte Carlo simulations were conducted to recommend dosing regimens for patients with different levels of covariates.

Results

A one-compartment model with first-order elimination provided the best fit of the data. Hematocrit (HCT) significantly influenced the apparent clearance of sirolimus. Monte Carlo simulations showed that for patients with a low HCT level of 28%, dosing regimens of 1.5 mg qd or 1 mg qd alternating with 1.5 mg qd should be recommended. For patients with a normal HCT level, the recommended dosing regimens were 1 mg qd, 2 mg qod, or 0.5 mg qd alternating with 1 mg qd.

Conclusion

Based on our population PK model of sirolimus in adult liver transplant recipients, which has the largest sample size to date, we recommend to tailor dosing regimens to various HCT levels in such patients.

Unveiling the Sorption Properties of Graphene Oxide-M13 Bacteriophage Aerogels for Advanced Sensing and Environmental Applications

GraPhage13 aerogels (GPAs) are ultralow density, porous structures fabricated through the self-assembly of graphene oxide (GO) and M13 bacteriophage. Given GPA's high surface area and extensive porous network, properties typically associated with highly adsorbent materials, it is essential to characterize its sorption capabilities, with a focus on unlocking its potential for advanced applications in areas such as biomedical sensing and environmental monitoring. Herein, the water, ethanol and acetone sorption properties of GPA were explored using dynamic vapor sorption (DVS). GPA was found to be highly hygroscopic, with a sorption capacity of 0.68 ± 0.02 g/g, double that of conventional desiccant silica gels and 20% higher than GO laminates. This remarkable sorption capacity, along with its sorption kinetics, was influenced by both GPA's morphology and the strong interactions between the water molecules and the functional groups on the GO within GPA. The low hysteresis and stability of GPA during repeated sorption-desorption cycles highlight the reversibility of water sorption. While GPA shows lower capacity for ethanol and acetone, its tuneability presents opportunities for improving acetone sorption, and its ethanol sorption capacity exceeds that of similar carbon-based materials. These findings underscore GPA's capability and versatility in vapor adsorption, paving the way toward its integration into graphene-based devices for sensing applications.

Comparations of efficacy and safety of rituximab, calcineurin inhibitors and cyclophosphamide in primary membranous nephropathy: a single-center retrospective analysis

BACKGROUND

To compare the efficacy and safety of rituximab (RTX), calcineurin inhibitor (CNI) and cyclophosphamide (CTX) plus glucocorticoids in the treatment of primary membranous nephropathy (PMN).

METHODS

Totally 478 biopsy-proven PMN patients in single center were retrospectively included. After 1:1 propensity score matching (PSM), 258 patients were included in RTX, CNI or CTX group (86 patients in each group).

RESULTS

After PSM, there were no differences on serum creatinine, eGFR, serum albumin, urine protein, anti-PLA2R antibody levels among groups. The follow-up duration was 12 (10.5, 18) months in CNI group, 12 (12, 18) months in CTX group and 12 (12, 18) months in RTX group. Throughout entire follow-up period, 39 patients (45.3%) in CNI group, 47 patients (54.7%) in CTX group, and 59 patients (68.6%) in RTX group achieved total remission (TR, either complete remission or partial remission). The survival curve showed a higher rate of TR in RTX group than CNI group (p = 0.018). A relapse occurred in 15 of 39 (38.5%) patients in CNI group, significantly higher than CTX group (4.3%, p < 0.001) and RTX group (3.4%, p < 0.001). In CNI group, 36% patients had a ≥ 25% decline in eGFR.

CONCLUSIONS

RTX may be more effective than CNI in inducing remission in PMN and showed similar efficacy to CTX. CNI may have a high risk of proteinuria relapse and eGFR decline.

Association analyses of the measurements of the photopic negative response evoked by two ISCEV protocols

PURPOSE

To perform association analyses between the measurements of photopic negative response (PhNR) evoked by two ISCEV protocols.

METHODS

A total of 172 eyes from 72 post-operative pediatric cataract patients and 24 healthy children were enrolled. The amplitude and peak time of PhNR were analyzed in three eye groups, 1. healthy controls; 2. fellow eyes of unilaterally affected patients; 3. affected eyes. PhNR responses were measured with skin-electrodes and evoked by the ISCEV standard protocols of PhNR and light-adapted 3.0, referred to as PhNR1 and PhNR2. The correlation coefficients between PhNR1 and PhNR2 measurements were calculated. The generalized estimating equation (GEE) model of PhNR1, with PhNR2 as a predictor, was evaluated after adjusting for correlation between paired eyes.

RESULTS

Both the amplitude (P = 0.025) and the peak time (P = 0.036) of PhNR1 showed a significant difference among the three eye groups, which was not observed in PhNR2. The four correlation coefficients (Pearson, Intraclass, Lin's and Kendall's) between z-score transformed PhNR1 and PhNR2 measurements were generally moderate: 0.52, 0.52, 0.52, 0.36 for amplitude (P < 0.001), and 0.57, 0.57, 0.57, 0.36 for peak time (P < 0.001). The amplitude of PhNR1 cannot be precisely predicted by PhNR2, with a mean absolute percentage error (MAPE) of 36.7%, while the peak time of PhNR1 can be precisely predicted with a MAPE of 3.9%.

CONCLUSIONS

PhNR1 appears to be a more sensitive measure than PhNR2 for detecting eye group differences. Further research is needed to confirm this and explore its clinical applications. PhNR1 may not be entirely replaced by PhNR2 due to moderate correlation and low prediction precision in amplitude.

The employee's perception of psychological safety: construct dimensions, scale development and validation

Although organisations can effectively resist risks and enhance organisational performance by creating a Perception of Psychological Safety (PPS) climate, there is a lack of PPS scales developed under a multidimensional integrated interaction perspective. Based on a review of relevant theories, this paper clearly defines the concept of PPS and proposes a three-dimensional model of PPS, including organisational- level PPS, team- level PPS, and dyadic- level PPS. In this paper, we develop and validate a scale for measuring PPS in the workplace using 12 startups in a relatively economically developed region in the southeastern coastal region of China as the empirical target. The results of the empirical analyses show that the scale we developed has a good level of reliability and consistency, as well as good content validity, convergent validity, and discriminant validity.

Predictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery

Purpose

To address the prevalence and risk factors of postoperative chronic opioid dependence, focusing on the development of a predictive scoring system to identify high-risk populations.

Methods

We analyzed data from the Taiwan Health Insurance Research Database spanning January 2016 to December 2018, encompassing adults undergoing major elective surgeries with general anesthesia. Patient demographics, surgical details, comorbidities, and preoperative medication use were scrutinized. Wu and Zhang's scores, a predictive system, were developed through a stepwise multivariate model, incorporating factors significantly linked to chronic opioid dependence. Internal validation was executed using bootstrap sampling.

Results

Among 111,069 patients, 1.6% developed chronic opioid dependence postoperatively. Significant risk factors included age, gender, surgical type, anesthesia duration, preoperative opioid use, and comorbidities. Wu and Zhang's scores demonstrated good predictive accuracy (AUC=0.83), with risk categories (low, moderate, high) showing varying susceptibility (0.7%, 1.4%, 3.5%, respectively). Internal validation confirmed the model's stability and potential applicability to external populations.

Conclusion

This study provides a comprehensive understanding of postoperative chronic opioid dependence and introduces an effective predictive scoring system. The identified risk factors and risk stratification allow for early detection and targeted interventions, aligning with the broader initiative to enhance patient outcomes, minimize societal burdens, and contribute to the nuanced management of postoperative pain.

Positive and Negative Affect Differentially Predict Individual Differences and Intra-Individual Changes in Daily Cognitive Failures in Younger and Older Adults

(1) Background: Cognitive failures, including lapses in attention, memory, and executive functioning, can negatively affect daily performance and well-being. Negative and positive affectivity have been implicated in cognitive functioning, yet their relationship with cognitive failures remains underexplored. This study investigates the impact of positive and negative affect on cognitive failures, using daily diary methods to examine both within-person and between-person associations in a sample of younger adults from Singapore and adults across the lifespan from the United States (US). (2) Methods: Participants (Singapore: N = 253, US: N = 1726) completed daily diaries over seven (Singapore) or eight (US) consecutive days. Multilevel modelling was used to analyse both within- and between-person relationships between affect and cognitive failures, controlling for demographic and socioeconomic variables. (3) Results: In both the Singapore and US samples, negative affect was consistently positively associated with cognitive failures at both levels (SG within-person: β = 0.21, p < 0.001; SG between-person: β = 0.58, p < 0.001; US within-person: β = 0.08, p < 0.001; US between-person: β = 0.28, p < 0.001), supporting the influence of negative affective experiences on cognitive lapses. However, positive affect showed no significant associations with daily cognitive failures in the Singapore sample (within-person: β = 0.01, p = 0.683; between-person: β = -0.04, p = 0.484) and only a between-person negative association in the US sample (within-person: β = 0.02, p = 0.157; between-person: β = -0.11, p < 0.001). (4) Conclusion: These findings suggest that positive and negative affect differentially influence individual differences and intra-individual changes in daily cognitive failures among both younger and older adults.

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes.

Kernel-based hierarchical structural component models for pathway analysis on survival phenotype

BACKGROUND

High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.

OBJECTIVE

This study aims to develop the HisCoM-KernelS model to identify survival phenotype-related pathways by accommodating complex, nonlinear relationships between genes and survival outcomes, while accounting for inter-pathway correlations.

METHODS

We applied HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset, comprising 4,498 protein-coding genes mapped to 186 KEGG pathways from 148 PDAC samples. Kernel machine regression was used to model pathway effects on survival outcomes, incorporating hierarchical gene-pathway structures. Model parameters were estimated using the alternating least squares algorithm, and the significance of pathways was assessed through a permutation test.

RESULTS

HisCoM-KernelS identified several pathways significantly associated with pancreatic cancer survival, including those corroborated by previous studies. HisCoM-KernelS, especially with the Gaussian kernel, showed a better balance of detection rate and number of significant pathways compared to four other existing pathway-based methods: HisCoM-PAGE, Global Test, GSEA, and CoxKM.

CONCLUSION

HisCoM-KernelS successfully extends pathway-based analysis to survival outcomes, capturing complex nonlinear gene effects and inter-pathway correlations. Its application to the TCGA PDAC dataset emphasizes its utility in identifying biologically relevant pathways, offering a robust tool for survival phenotype research in high-throughput sequencing data.

A retrospective analysis of perioperative medications for opioid-use disorder and tapering additional postsurgical opioids via a transitional pain service

AIMS

To investigate perioperative opioid requirements in patients on methadone or buprenorphine as medication for opioid-use disorder (MOUD) who attended a transitional pain clinic (Personalized Pain Program, PPP).

METHODS

This retrospective cohort study assessed adults on MOUD with surgery and attendance at the Johns Hopkins PPP between 2017 and 2022. Daily non-MOUD opioid use over 6 time-points was evaluated with regression models controlling for days since surgery. The time to complete non-MOUD opioid taper was analysed by accelerated failure time and Kaplan-Meier models.

RESULTS

Fifty patients (28 on methadone, 22 on buprenorphine) were included with a median age of 44.3 years, 54% male, 62% Caucasian and 54% unemployed. MOUD inpatient administration occurred in 92.8% of patients on preoperative methadone but only in 36.3% of patients on preoperative buprenorphine. Non-MOUD opioid use decreased over time postoperatively (β = -0.54, P < .001) with a median decrease of 90 mg morphine equivalents (MME) between the first and last PPP visit, resulting in 46% tapered off by PPP completion. Older age and duration in PPP were associated with lower MME, while mental health conditions, longer hospital stays and higher discharge opioid prescriptions were associated with higher MME. The average time to non-MOUD opioid taper was 1.79× longer in patients on buprenorphine (P = .026), 2.75× in males (P = .023), 4.66× with mental health conditions (P < .001), 2.37× with chronic pain (P = .031) and 3.51× if on preoperative non-MOUD opioids; however, higher initial MOUD level decreased time to taper (P = .001).

CONCLUSIONS

Postoperative opioid tapering utilizing a transitional pain service is possible in patients on MOUD.

Fine particulate matter and intima media thickness: Role of endothelial function biomarkers

Background

Ambient fine particulate matter (PM2.5) is a risk factor for atherosclerosis disease. We aimed to assess whether nitric oxide stable metabolites (NOx) and l-arginine mediate the association between PM2.5 and carotid intima media thickness (cIMT) increase.

Methods

We selected 251 participants from the control group of GEA (Genetics of Atheroslerosis Disease Mexican) study (2008-2013) in Mexico City. Mediation models were carried out using pathway analyses, a special case of structural equation models.

Results

The median concentration of PM2.5 area under the curve (auc) was 25.2 µg/m3 (interquartile range: 24.2-26.4 µg/m3). Employing participants with observed values for both biomarkers (n = 117), the total effect of PM2.5auc on mean cIMT at bilateral, right, and left was 19.27 µm (95% confidence interval [CI]: 5.77, 32.78; P value = 0.005), 12.69 µm (95% CI: 0.67, 24.71; P value = 0.039), and 25.86 µm (95% CI: 3.18, 48.53; P value = 0.025) per each 1 µg/m3 increase of PM2.5auc. The direct effect of PM2.5auc (per 1 µg/m3 increase) was 18.89 µm (95% CI: 5.37, 32.41; P value = 0.006) for bilateral, 13.65 µm (95% CI: 0.76, 26.55; P value = 0.038) for right, and 24.13 µm (95% CI: 3.22, 45.03; P value = 0.024) for left. The indirect effects of NOx and l-arginine were not statistically significant showing that endothelial function biomarkers did not mediate PM2.5 and cIMT associations. Although l-arginine was not a mediator in the PM2.5 and cIMT pathway, a decrease in l-arginine was significantly associated with PM2.5auc.

Conclusions

In this study of adults from Mexico City, we found that PM2.5 was associated with an increase in cIMT at bilateral, left, and right, and these associations were not mediated by endothelial function biomarkers (l-arginine and NOx).

Maturational changes in frontal EEG alpha and theta activity from infancy into early childhood and the relation with self-regulation in boys and girls

There is increasing interest in examining the development of frontal EEG power in relation to self-regulation in early childhood. However, the majority of previous studies solely focuses on the brain's alpha rhythm and little is known about the differences between young boys and girls. The aim of the current study was therefore to gain more insight into the neural mechanisms involved in the emergence of self-regulation. The sample consisted of 442 children and data were collected at approximately 5 months, 10 months, and around 3 years of age. Latent growth curve models indicated that,while the neurobiological foundations of self-regulation are established during infancy,it is the maturation of the frontal alpha rhythm that contributes to variations in both observed and parent-reported self-regulation. In addition, it appears that boys might have a greater reliance on external regulation than girls during early childhood, as evident by higher scores of girls on both measures of self-regulation. More insight into the role of external regulators in brain maturation can help to implement interventions aimed at establishing bottom-up self-regulatory skills early in life, in order to provide the necessary foundations for the emergence of top-down self-regulatory skills in the preschool period.

Using Latent Class Analysis to Characterize Sickness Signaling in Relation to Familism and Public and Private Religiosity in a Stratified US Sample

BACKGROUND

In response to contracting an infection, individuals usually display a suite of external signs (including sickness behavior) as an outward indication of illness. This context-dependent phenomenon seems to weigh the benefits and costs of eliciting sympathy by indicating sickness versus hiding signs of illness to avoid exposing others to potential infection. In a dynamically social species like humans, non-kin may be as likely to respond to these signs with care as family members, particularly fellow church members. We explore the relative contributions of religiosity and familism in shaping self-reported sickness signaling styles as two dimensions central to human altruism using latent class analysis (LCA).

METHODS

LCA was used to characterize the signaling styles of the study participants. Data come from a large 2018 survey (n = 1259) of sickness and health behaviors among US adults. We used denomination public (church attendance) and private religiosity (time spent in prayer, meditation, etc.) and the God Locus of Health Control scale to assess the impact of God on health. Sickness signaling style was assessed with the SicknessQ and three additional items. Covariates included age, gender, education, and income.

RESULTS

We identified four classes (Familiar, Moderate, Gregarious, and Stoic) tied to signaling styles. The Familiar Signaling class displayed sickness verbally to familiar others, were the oldest, and were least guided by an internal sense of religion. The Moderate Signaling class was younger and had lower public and private religiosity (except regarding health issues) than the Stoic and Gregarious Signaling classes. The Gregarious class signaled to both close others and strangers and scored highest in familism and religiosity. The Stoic class did not verbally signal but indicated sickness worsening when around both close others and strangers, were less likely to be married or endorse private religiosity, and were least likely to recall recent illness as severe.

CONCLUSION

The signaling classes strongly resembled aspects of the introvert-ambivert-extrovert spectrum. We conclude that variation is important at multiple levels, including personality types, and potentially prevents the loss of immunological diversity.

Foreground bias: Semantic consistency effects modulated when searching across depth

When processing visual scenes, we tend to prioritize information in the foreground, often at the expense of background information. The foreground bias has been supported by data demonstrating that there are more fixations to foreground, and faster and more accurate detection of targets embedded in foreground. However, it is also known that semantic consistency is associated with more efficient search. Here, we examined whether semantic context interacts with foreground prioritization, either amplifying or mitigating the effect of target semantic consistency. For each scene, targets were placed in the foreground or background and were either semantically consistent or inconsistent with the context of immediately surrounding depth region. Results indicated faster response times (RTs) for foreground and semantically consistent targets, replicating established effects. More importantly, we found the magnitude of the semantic consistency effect was significantly smaller in the foreground than background region. To examine the robustness of this effect, in Experiment 2, we strengthened the reliability of semantics by increasing the proportion of targets consistent with the scene region to 80%. We found the overall results pattern to replicate the incongruous effect of semantic consistency across depth observed in Experiment 1. This suggests foreground bias modulates the effects of semantics so that performance is less impacted in near space.

Longitudinal trajectories of symptom change during antidepressant treatment among managed care patients with depression and anxiety

Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. Three depression subgroups showed symptom improvement, while three showed nonresponses. Similar patterns were observed in the six anxiety subgroups. Multinomial regression was used to associate patient characteristics with trajectory subgroup membership. Compared to patients in the remission group, factors associated with depressive symptom nonresponse included older age and lower depression severity.

Discovery of biophysical rate laws from the electronic health record enables real-time liver injury estimation from transaminase dynamics

Alanine (ALT) and aspartate (AST) aminotransferases are standard-of-care biomarkers for liver injury though their temporal dynamics during injury and resolution remain incompletely characterized. Here, we analyze aminotransferase kinetics to determine whether rate laws can be ascertained during acute liver injury agnostic to etiology. From 6.5 million AST and ALT measurements in 91,086 patients, we identify a single rate-limiting step in transaminase decline enabling the discovery of plasma clearance rates of AST (1.13 days-1) and ALT (0.47 days-1). These rates highlight that transaminases lag real-time liver injury on timescales relevant to clinical decision-making. To resolve this delay, we introduce a correction for AST and ALT, the hepatocyte injury index (HIX, hix.massgeneral.org), which yields a real-time estimate of liver injury. For both liver biopsies and choledocholithiasis, the HIX better distinguishes persistent versus resolved liver injury than transaminase values alone. The HIX can enable more timely clinical decisions for patients with acute liver injury.

Interpersonal and Trauma-Related Guilt moderate the relationship between intensity of combat experiences and suicidality

Suicide rates in military-affiliated communities remain elevated since the dawn of the Global War on Terror, despite substantial efforts by clinicians and researchers. While some risk factors have been identified, mixed results need to be clarified. The current study builds on previous research by testing a structural equation model of suicide risk associated with combat experiences that by incorporates risk factors with the most empirical support (combat experiences, guilt, PTSD, depression, and the Interpersonal Psychological Theory of Suicide [IPTS] factors of Perceived Burdensomeness, Thwarted Belongingness, Acquired Capability), using improved measures, in a more representative sample of Post-9/11 deployers. The models were evaluated separately for each of two different conceptualizations of guilt (trauma-related and interpersonal) as moderating factors. The results show that higher levels of guilt, whether trauma-related or interpersonal, strengthened the relationship between combat experiences and pathology. In contrast to previous studies, intensity of combat experiences was indirectly linked to suicidality through pathology and the IPTS constructs of Perceived Burdensomeness and Acquired Capability. The most prominent pathway to suicidal thoughts and behaviors in both guilt models traveled from combat experiences through PTSD and Perceived Burdensomeness, providing a clear target for clinical and organizational interventions.

Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation without the selected completely at random assumption

Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain, and there is value in discovering positives among the unlabeled (e.g., viable drugs among untested compounds). Most PU learning algorithms make the selected completely at random (SCAR) assumption, namely that positives are selected independently of their features. However, in many real-world applications, such as healthcare, positives are not SCAR (e.g., severe cases are more likely to be diagnosed), leading to a poor estimate of the proportion, α, of positives among unlabeled examples and poor model calibration, resulting in an uncertain decision threshold for selecting positives. PU learning algorithms vary; some estimate only the proportion, α, of positives in the unlabeled set, while others calculate the probability that each specific unlabeled instance is positive, and some can do both. We propose two PU learning algorithms to estimate α, calculate calibrated probabilities for PU instances, and improve classification metrics: i) PULSCAR (positive unlabeled learning selected completely at random), and ii) PULSNAR (positive unlabeled learning selected not at random). PULSNAR employs a divide-and-conquer approach to cluster SNAR positives into subtypes and estimates α for each subtype by applying PULSCAR to positives from each cluster and all unlabeled. In our experiments, PULSNAR outperformed state-of-the-art approaches on both synthetic and real-world benchmark datasets.

Time to lymphoma treatment within 24 months in 'watch and wait' follicular lymphoma is associated with inferior outcomes: A multicentre analysis

Some 'watch and wait' (W&W) FL patients suffer from rapid progression in a short term. Herein, we sought to identify these patients and also develop a risk score to screen them at diagnosis. Between 2008 and 2022, a total of 411 FL patients managed by the W&W strategy from 16 cancer centres were retrospectively enrolled in this study, and their time to lymphoma treatment (TLT) and progression-free survival (PFS) were evaluated. Thirty-five percent of W&W FL patients experienced TLT within 24 months (TLT24) after diagnosis. Their 5-year PFS rate was significantly lower than those without treatment at 24 months (62.3% vs. 89.5%). In multivariable analysis, five factors were identified as independent predictors of TLT24: stages III-IV, β2 microglobulin ≥3 mg/L, lymphocyte-to-monocyte ratio <3.8, bone marrow involvement and spleen enlargement (above umbilical line). Their AUCs for TLT24 were 0.76 (95% CI, 0.70-0.82) in the training cohort and 0.76 (95% CI, 0.67-0.85) in the validation cohort respectively. Risk groups were also associated with PFS (p < 0.001). In FL patients initially managed by W&W, TLT24 was associated with poor outcomes. This multivariable model helps screening for predicting TLT24, which may be useful to identify candidates for early interventional treatment.

Exploring proportions of spontaneous and goal-directed self-talk - It is not always the one or the other

The dichotomous classification of self-talk statements into goal-directed as a more controlled type of self-talk and spontaneous as a more uncontrolled type of self-talk might be an oversimplification. To address this issue, two studies were conducted aiming to explore the idea that the distinction between the two self-talk types should rather be proportional and not mutually exclusive. In Study 1, football players took part in a penalty competition and were subsequently asked to state the self-talk they had before and after the penalty. In Study 2, table tennis players took part in two activities (i.e., a precision task and a competitive set) and were subsequently asked to state the self-talk they had before the individual rounds in each activity. In both studies, the players subsequently rated for each self-talk statement the extent to which it was spontaneous and the extent to which it was goal-directed. The majority of self-talk statements were rated to some extent as both spontaneous and goal-directed. For Study 1, paired-sample t-tests showed that before a penalty kick self-talk was more goal-directed than spontaneous and after the penalty kick it was more spontaneous than goal-directed. In the more exploratory Study 2, multilevel regression analyses showed that the two types of self-talk could not predict sports performance. While the results support the usefulness of the distinction between spontaneous and goal-directed self-talk, treating the two types of self-talk as proportional rather than dichotomous might reflect more accurately the mental activity. Key words: dual-process, organic self-talk, self-regulation, System 1, System 2.

Expectation to rewards modulates learning emotional words: Evidence from a hierarchical Bayesian model

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanisms underlying emotional language processing, but have failed to recognize that languages are acquired in changing environments. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations to rewards modulated the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increased, individuals responded faster and exhibited higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and a preference for learning negative information.

Exposure-lag response of surface net solar radiation on lung cancer incidence: a global time-series analysis

Background

Recently, the impact of solar radiation (RAD) on diseases worldwide has garnered growing attention. However, the association between RAD and lung cancer remains largely unknow and no consensus has been reached. The aim of this study was to investigate the lag exposure-response of RAD on lung cancer and provide robust scientific evidence for updating prevention and treatment strategies of lung cancer.

Methods

Data of RAD were obtained from Google Earth Engine, which was post-processed by European Centre for Medium-Range Weather Forecasts (ECMWF). Lung cancer incidence, smoking prevalence and socio-demographic index (SDI) were obtained from Global Burden of Disease (GBD). Spearman's rank correlation tests and linear regression analyses were performed to investigate the relationship between RAD and lung cancer incidence. Additionally, a distributed lag non-linear model (DLNM) was utilized to reveal the lag effects of RAD on lung cancer incidence.

Results

There were 204 countries and territories and selected subnational locations with information recorded in GBD and radiation exposure was calculated in 272 countries and territories. After excluding missing and abnormal data, as well as Kashmir and Western Sahara which were two disputed districts, this study included 186 countries from 1992 to 2019. After adjusted for smoking and SDI, the Spearman's correlation coefficient ranged from -0.630 to -0.581. In the DLNM for lung cancer adjusted for smoking and SDI, the maximum relative risk (RR) was 1.013 [95% confidence interval (CI): 1.011-1.014], at RAD exposure of 12,760,000 with 5.8 lag years, while the minimum RR was 0.973 (95% CI: 0.947-0.992) at RAD exposure of 12,845,000 with 8.0 lag years.

Conclusions

The global rise in lung cancer incidence has been notably associated with low exposure to RAD, whereas the defensive influence of sunlight against lung cancer demonstrated hysteresis. This study shows that properly exposure to sunlight is a possible strategy for lung cancer prevention, which provides scientific support for the formulation of future health strategies. It is also crucial in epidemiological research as it offers a novel pattern for identifying additional potential risk factors for diseases.

A predictive model for therapy failure in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitor therapy

ABSTRACT

Although tyrosine kinase inhibitor (TKI) therapy has markedly improved the survival of people with chronic-phase chronic myeloid leukemia (CML), 20% to 30% of people still experienced therapy failure. Data from 1955 consecutive patients with chronic-phase CML diagnosed by the European LeukemiaNet recommendations from 1 center receiving initial imatinib or a second-generation (2G) TKI therapy were interrogated to develop a clinical prediction model for TKI-therapy failure. This model was subsequently validated in 3454 patients from 76 other centers. Using the predictive clinical covariates associated with TKI-therapy failure, we developed a model that stratified patients into low-, intermediate- and high-risk subgroups with significantly different cumulative incidences of therapy failure (P < .001). There was good discrimination and calibration in the external validation data set, and the performance was consistent with that of the training data set. Our model had the better prediction discrimination than the Sokal and European Treatment and Outcome Study long-term survival scores, with the greater time-dependent area under the receiver-operator characteristic curve values and a better ability to redefine the risk of therapy failure. Our model could help physicians estimate the likelihood of initial imatinib or 2G TKI-therapy failure in people with chronic-phase CML.

Magnitude and predictors of obstetric complications during delivery among postpartum women in Ethiopia: evidence from PMA Ethiopia longitudinal survey

INTRODUCTION

The majority of pregnancy-related complications were preventable. However, the magnitude of complications during pregnancy and childbirth was high in sub-Saharan Africa. There was limited evidence on the magnitude of obstetric complications during childbirth and its predictors among postpartum women in Ethiopia.

METHODS AND MATERIALS

The research used secondary data sources based on performance monitoring for action in Ethiopia's second cohort longitudinal survey. It was a national survey that was conducted in three large, predominantly agrarian regions (Oromia, Amhara, and SNNP) and one urban region (Addis Ababa) of Ethiopia between 2021 and 2023. It was conducted to track pregnant women's use of essential maternal and newborn health services. It involved the enrollment of pregnant women and tracking them at six weeks, six months, and one year after giving birth. The data collected at the beginning of the survey and six weeks after childbirth were used for the analysis. Sample weights were applied to account for differences in sample allocation and response rates across regions and urban/rural areas. Multi-collinearity and intra-cluster correlation were assessed before fitting the multilevel models to ensure the accuracy of the estimates. A multi-level logistic regression model was used to assess the magnitude and predictors for the occurrence of obstetric complications during delivery. The model fit was evaluated using Akaike's Information Criterion and Bayesian Information Criterion. Adjusted odds ratio with its 95% confidence interval was used to measure the strength of association for this study.

RESULTS

The magnitude of obstetric complications during delivery was found to be 33.86% with 95% CI (31.56, 36.24. Among the obstetric complications that occurred during delivery, about 15.73%with 95% CI(14.02, 17.61) of women experienced bleeding, 4.14% with 95% CI (3.30, 5.17) had their membrane rupture but labor did not start within 24 h, 2.29% with 95% CI(1.67, 3.13) had their membrane rupture before 9 months, 3.95% with 95% CI(3.16, 4.93) had faced malpresentation or malposition of the baby, 12.70% with 95% CI (11.18, 14.39) had prolonged labor lasting more than 12 h, and 12.40% with 95% CI (10.83,14.17) had convulsions. The odds of occurrence of obstetric complication during delivery among women from severely food insecure households were 1.88 times [AOR = 1.88; 95% CI (1.22, 2.90)] more likely to occur than women from food secure households. Moreover, the odds of occurrence for obstetric complications during delivery among women who had complications during their pregnancy were 2.39 times [AOR = 2.39; 95% CI (1.81, 3.16)] more likely to occur as compared to those women who had no complication during their pregnancy. On the other hand, women's who had 1-4 live births given before this delivery were 0.61 times [AOR = 0.61; 95% (0.43, 0.88)] less likely to develop complications as compared to those women's who had no prior live birth.

CONCLUSION AND RECOMMENDATION

Delivery-related obstetric complications during delivery in Ethiopia were high. Approximately one-third of postpartum women in Ethiopia experienced obstetric complications during delivery. Based on this study, women's from severely food insecure households, those women's who had no prior live birth, and women's who had complications during their pregnancy were more likely to develop delivery-related obstetric complications. Thus, policy makers and program implementers who were working on maternal and newborn health should give special attention for women's from severely food insecure households, women's who had no prior live births, and those women's who encountered obstetric complications during their pregnancy to decrease the occurrence of obstetric complications during delivery. In Ethiopia, various governmental organizations, such as the Ministry of Health and the Ministry of Agriculture, are dedicated to addressing food insecurity and improving nutritional access. The country has launched several effective nutritional programs, including the Productive Safety Net Program, the Targeted Supplementary Feeding Program, and the National Nutrition Program, aimed at alleviating food insecurity and enhancing nutrition. Ongoing efforts are crucial to tackle the food insecurity experienced by women, which can help to reduce obstetric related complications of women's during delivery. It is crucial for everyone involved in maternal and newborn health to prioritize addressing the factors that lead to obstetric complications during delivery in women in order to reach the goal of ending all preventable maternal and newborn deaths by 2030.

Evaluating phasic transcutaneous vagus nerve stimulation (taVNS) with pupil dilation: the importance of stimulation intensity and sensory perception

The efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) as a non-invasive method to modulate physiological markers of noradrenergic activity of the Locus Coeruleus (LC), such as pupil dilation, is increasingly more discussed. However, taVNS studies show high heterogeneity of stimulation effects. Therefore, a taVNS setup was established here to test different frequencies (10 Hz and 25 Hz) and intensities (3 mA and 5 mA) during phasic stimulation (3 s) with time-synchronous recording of pupil dilation in younger adults. Specifically, phasic real taVNS and higher intensity led to increased pupil dilation, which is consistent with phasic invasive VNS studies in animals. The results also suggest that the influence of intensity on pupil dilation may be stronger than that of frequency. However, there was an attenuation of taVNS-induced pupil dilation when differences in perception of sensations were considered. Specifically, pupil dilation during phasic stimulation increased with perceived stimulation intensity. The extent to which the effect of taVNS induces pupil dilation and the involvement of sensory perception in the stimulation process are discussed here and require more extensive research. Additionally, it is crucial to strive for comparable stimulation sensations during systematic parameter testing in order to investigate possible effects of phasic taVNS on pupil dilation in more detail.

Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma

Background

Aberrant fatty acid metabolism (FAM) plays a critical role in the tumorigenesis of human malignancies. However, studies on its impact in lung adenocarcinoma (LUAD) are limited.

Methods

We developed a prognostic signature comprising 10 FAM-related genes (GPR115, SOAT2, CDH17, MOGAT2, COL11A1, TCN1, LGR5, SLC34A2, RHOV, and DKK1) using data from LUAD patients in The Cancer Genome Atlas (TCGA). This signature was validated using six independent LUAD datasets from the Gene Expression Omnibus (GEO). Patients were classified into high- and low-risk groups, and overall survival (OS) was compared by Kaplan-Meier analysis. The signature's independence as a prognostic indicator was assessed after adjusting for clinicopathological features. Receiver operating characteristic (ROC) analysis validated the signature. Tumor immune microenvironment (TIME) was analyzed using ESTIMATE and multiple deconvolution algorithms. Functional assays, including CCK8, cell cycle, apoptosis, transwell, and wound healing assays, were performed on MOGAT2-silenced H1299 cells using CRISPR/Cas9 technology.

Results

Low-risk group patients exhibited decreased OS. The signature was an independent prognostic indicator and demonstrated strong risk-stratification utility for disease relapse/progression. ROC analysis confirmed the signature's validity across validation sets. TIME analysis revealed higher infiltration of CD8+ T cells, natural killers, and B cells, and lower tumor purity, stemness index, and tumor mutation burden (TMB) in low-risk patients. These patients also showed elevated T cell receptor richness and diversity, along with reduced immune cell senescence. High-risk patients exhibited enrichment in pathways related to resistance to immune checkpoint blockades, such as DNA repair, hypoxia, epithelial-mesenchymal transition, and the G2M checkpoint. LUAD patients receiving anti-PD-1 treatment had lower risk scores among responders compared to non-responders. MOGAT2 was expressed at higher levels in low-risk LUAD patients. Functional assays revealed that MOGAT2 knockdown in H1299 cells promoted proliferation and migration, induced G2 cell cycle arrest, and decreased apoptosis.

Conclusions

This FAM-related gene signature provides a valuable tool for prognostic stratification and monitoring of TIME and immunotherapy responses in LUAD. MOGAT2 is identified as a potential anti-tumor regulator, offering new insights into its role in LUAD pathogenesis.

Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand

Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.

Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan-Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.