Latent Model Research Articles

Trajectory of haemoglobin A1c and incidence of cardiovascular disease in patients with type 2 diabetes mellitus.

To evaluate the association between changes in haemoglobin A1c (HbA1c) and the concurrent incidence of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) patients. We conducted a retrospective cohort study among T2DM patients with HbA1c measurement after T2DM diagnosis between August 2009 and September 2010. The patients were classified into six subgroups based on baseline HbA1c (<7%; 7%-7.9%; ≥8%) and age (<65; ≥65 years), and then clustered into classes by HbA1c trajectory and CVD incidence over the 12-year follow-up period using joint latent class mixture models. We explored the HbA1c trajectories and CVD incidences in each latent class. Multinomial logistic regression was used to compare the baseline characteristics among different latent classes. A total of 128 843 T2DM patients were included with a median follow-up period of 11.7 years. Ten latent classes were identified in patients with baseline HbA1c ≥ 8% and age <65 years, while seven classes were identified in the other five groups. Among all the identified latent classes, patients with fluctuating HbA1c trajectories, characterized by alternating periods of increase and decrease, had higher CVD incidences. Male patients, and patients with higher baseline HbA1c and use of antidiabetic drugs were more likely to have a fluctuating HbA1c trajectory. More specifically, patients aged < 65 years with younger age or a smoking habit, and patients aged ≥ 65 years with a longer duration of T2DM were more likely to have a fluctuating HbA1c trajectory. We found that T2DM patients with fluctuating HbA1c trajectories could have a higher CVD risk. Different trajectory-associated characteristics in age subgroups highlight the need for individualized management of T2DM patients.

Association between the atherogenic index of plasma trajectory and risk of heart failure among hypertensive patients: a prospective cohort study

BackgroundThe atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular events. However, it remains unclear whether hypertensive patients with long-term high AIP levels are at greater risk of developing heart failure (HF). Therefore, the aim of this study was to investigate the association between AIP trajectory and the incidence of HF in hypertensive patients.MethodsThis prospective study included 22,201 hypertensive patients from the Kailuan Study who underwent three waves of surveys between 2006 and 2010. Participants were free of HF or cancer before or during 2010. The AIP was calculated as the logarithmic conversion ratio of triglycerides to high-density lipoprotein cholesterol. Latent mixed modeling was employed to identify different trajectory patterns for AIP during the exposure period (2006–2010). Cox proportional hazard models were then used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for incident HF among different trajectory groups.ResultsFour distinct trajectory patterns were identified through latent mixture modeling analysis: low-stable group (n = 3,373; range, -0.82 to -0.70), moderate-low stable group (n = 12,700; range, -0.12 to -0.09), moderate-high stable group (n = 5,313; range, 0.53 to 0.58), and elevated-increasing group (n = 815; range, 1.22 to 1.56). During a median follow-up period of 9.98 years, a total of 822 hypertensive participants experienced HF. After adjusting for potential confounding factors, compared with those in the low-stable group, the HR and corresponding CI for incident HF in the elevated-increasing group, moderate-high stable group, and moderate-low stable group were estimated to be 1.79 (1.21,2.66), 1.49 (1.17,1.91), and 1.27 (1.02,1.58), respectively. These findings remained consistent across subgroup analyses and sensitivity analyses.ConclusionProlonged elevation of AIP in hypertensive patients is significantly associated with an increased risk of HF. This finding suggests that regular monitoring of AIP could aid in identifying individuals at a heightened risk of HF within the hypertensive population.

Classification of Student Leadership Profiles in Diverse Governance Settings: Insights from Pisa 2022.

Student leadership prepares students for responsibilities, such as taking on specific tasks and assuming leadership roles in their future personal and professional lives. Developing students' leadership profiles is among the important goals of educational systems aiming for future generations to take responsibility and advance their countries. With this perspective in mind, the PISA assessment includes items to measure students' leadership behaviors. This study aims to extract student leadership profiles from the leadership-related items in the PISA 2022 application, using data from Cambodia, Peru, Paraguay, and Guatemala, which have different governance systems and cultural characteristics. The second purpose of the research is to determine the distribution of the identified leadership profiles in these countries and explain them in the context of governance and cultural characteristics. Latent class analysis was used to determine student leadership profiles. Accordingly, two-class and three-class latent models were found to be the most suitable models to explain student profiles. While the distinction between student profiles is more pronounced in the two-class model, the three-class model provides more detailed information about student profiles. In this respect, two-class and three-class latent models are reported comparatively. In the two-class latent model, students are labeled as the "Shy or Lack of Self-Confidence Group" and the "Active Leader or Influential Group". In the three-class latent model, students are labeled as the "Moderate or Passive Leader Group", the "Strong Leader or Influential Group", and the "Avoidant or Leadership-Uncomfortable Group". In both models, it is one of the striking findings that Cambodian students are in the low leadership profile, and Peruvian students are in the high leadership profile.

LFDT-Fusion: A latent feature-guided diffusion Transformer model for general image fusion

For image fusion tasks, it is inefficient for the diffusion model to iterate multiple times on the original resolution image for feature mapping. To address this issue, this paper proposes an efficient latent feature-guided diffusion model for general image fusion. The model consists of a pixel space autoencoder and a compact Transformer-based diffusion network. Specifically, the pixel space autoencoder is a novel UNet-based latent diffusion strategy that compresses inputs into a low-resolution latent space through downsampling. Simultaneously, skip connections transfer multiscale intermediate features from the encoder to the decoder for decoding, preserving the high-resolution information of the original input. Compared to the existing VAE-GAN-based latent diffusion strategy, the proposed UNet-based strategy is significantly more stable and generates highly detailed images without adversarial optimization. The Transformer-based diffusion network consists of a denoising network and a fusion head. The former captures long-range diffusion dependencies and learns hierarchical diffusion representations, while the latter facilitates diffusion feature interactions to comprehend complex cross-domain information. Moreover, improvements to the diffusion model in noise level, denoising steps, and sampler selection have yielded superior fusion performance across six image fusion tasks. The proposed method illustrates qualitative and quantitative advantages, as evidenced by experimental results in both public datasets and industrial environments. The code is available at: https://github.com/BOYang-pro/LFDT-Fusion.

Toxic air pollution and cognitive decline: Untangling particulate matter

There is increasing evidence indicating air pollution is an important factor influencing the aging brain. However, much of this work measures air pollution using particulate matter (PM). Yet we know that the chemical components of PM are not consistent across space or time. Rather, the possible chemical mixtures of PM vary and are therefore not reliably measuring the same thing across studies. In this study we attempt to disentangle the effects of the components of measured PM by using estimates of concurrent exposures of 415 industrial air toxics, as well as 44 neuro- and developmental toxics. Using bivariate latent curve models, we leverage individual level panel data from the bi-annual Health and Retirement Study to test how these exposures relate to cognitive score trajectories of respondents across the years 2002–2012. We find that more exposure to neurotoxics was associated with faster rate of cognitive decline by 1.09 points (p < 0.05).

Insomnia mediates the relation between empathy and anxiety among nursing students: a latent moderated mediation model of self-compassion

BackgroundNursing students are faced with multiple challenges and have a higher probability of suffering from anxiety. The current study aims to explore the relation between empathy and anxiety, examining the mediation and moderation effects of insomnia and self-compassion, respectively.MethodsThis study employed a convenient sampling method, recruiting 1,161 nursing students (female = 923, male = 238, Meanage = 18.37, SDage = 2.38) from three universities in China. These students completed the questionnaires online, including General Anxiety Disorder -7 (GAD-7), Jefferson Scale of Physician Empathy-Nursing student (JSPE-NS), Youth Self-rating Insomnia Scale -8 (YSIS-8), and Self-Compassion Scale (SCS). The study employed latent variable structural equation models to analyze the relation and mechanisms between empathy and anxiety. Then, the mediated role of insomnia and the moderated role of self-compassion were examined.ResultsThe prevalence rates of anxiety and insomnia in the current sample are 18.24% and 26.76%, respectively. The results showed that empathy could negatively predict anxiety, with a significant mediating effect of insomnia between them (B = -0.081, p < 0.05, 95% CI [-0.197, -0.063]). Additionally, it was proven that self-compassion moderated the positive relation between insomnia and anxiety. With a higher level of self-compassion, the indirect effect of empathy on anxiety through insomnia was weaker (B = -0.053, p < 0.01, 95% CI [-0.095, -0.019]). When individuals showed a lower level of self-compassion, the indirect effect of empathy on anxiety through insomnia was stronger (B = -0.144, p < 0.01, 95% CI [-0.255, -0.059]).ConclusionThe analysis of this research proved that empathy was negatively related to anxiety, and insomnia served as a mediator between empathy and anxiety. Besides, the protective role of self-compassion on individuals’ mental health was identified. The findings of the study suggest that the education of nursing students should highlight the significance of fostering empathy and self-compassion. The intervention on insomnia may be helpful in reducing the levels of anxiety since insomnia is a risky factor for anxiety.

Does grit predict thriving or is it the other way around? A latent cross-lagged panel model on the triarchic model of grit and the 5Cs of positive youth development.

Grit, defined as passion and perseverance to achieve long-term goals, has been associated with adolescents' optimal academic and psychological functioning. However, the role of grit in adolescents' positive youth development (PYD)-characterized by the 5Cs known as competence, confidence, connection, character, and caring-remains unknown. Using latent cross-lagged panel modeling, this study examined the longitudinal relations between the triarchic model of grit (TMG) dimensions (i.e., perseverance of effort, consistency of interest, and adaptability to situations), and the 5Cs of PYD while controlling for specific socio-demographic covariates (gender, age, SES, and school type). Data were obtained from 1620 Filipino adolescents (Mage = 17.15; SDage = 1.00) in November 2021 and May 2022. Results show that grit dimensions predicted subsequent PYD dimensions, but not vice versa. Specifically, while perseverance and adaptability were linked to higher levels of competence, confidence, and character, consistency was associated with lower levels of competence and confidence six months later. Results highlight the importance of grit's dimensions in facilitating PYD characteristics among adolescents, especially efficacy-related Cs (competence and confidence). Theoretical and practical implications in promoting adolescent thriving and well-being are discussed.

Attitude, Habit Strength, and the Relationship Between Intention and Leisure-Time Physical Activity Behavior Among College Students

ABSTRACT According to the dual-process theories, many scholars proposed that physical activity behavior tends to be governed by an interplay of intentional planning and habitual action. The major purpose of this study was to determine the moderation effects of habit strength on the relationship between leisure time moderate-to-vigorous physical activity (MVPA) intention and behavior and the impacts of cognitive and affective attitude on PA habit strength, utilizing a latent interaction modeling approach. A total of 1803 undergraduate students completed this study. All variables were measured using validated self-report questionnaires. Structural equation modeling and Hayes’ PROCESS program (model 1) were conducted to address the research questions. Habit strength significantly and positively moderated the effects of intention on leisure time MVPA behavior (β = .08, p < .01). The association between intention and PA behavior became stronger as habit strength increased. Affective attitude positively and significantly influenced habit strength (β = .71, p < .01). The effect of cognitive attitude on habit strength was significant and negative with a small effect size (β = –.08, p < .01). Findings indicated that habit strength positively moderated the relationship between PA intention and behavior, suggesting that stronger habit strength may amplify the association between PA intention and actual PA behavior. Affective attitude toward PA significantly influenced habit strength, whereas cognitive attitude exhibited minimal negative impact on habit strength. These findings contribute to our understanding of the interaction effects between deliberative and automatic processes in regulating PA behavior, as well as the nuanced relationship between attitude and habit development.

Trajectory model to analyze the effect of multi-metal exposures on sperm parameters and sex hormones of the MARHCS cohort in China

BackgroundAt present, several cross-sectional studies have found that exposure to metal/metalloid elements is closely associated with male reproduction. However, the long-term effects of metal exposure on male reproduction have not been explored. MethodsIn 2013, 796 volunteers were recruited, followed by first and second follow-ups in 2014 and 2015. Urine, semen, and blood samples were collected at each stage to examine urinary metal/metalloid levels, sperm parameters, and sex hormones. Initially, the latent class trajectory model (LCTM) was utilized to analyze the trajectories of urinary metals. Subsequently, the effects of urinary metal trajectories on semen parameters and sex hormones were examined using the linear mixed model. Finally, the impact of urinary metal trajectories on the classification of semen quality (normal or abnormal) was evaluated using the generalized linear mixed model. ResultsAmong the 18 metals/metalloids studied, trajectories were formed by 6 of them (Li, Al, Fe, Zn, As, Rb). Further analysis using the linear mixed model and the generalized linear mixed model revealed that Li was negatively correlated with semen volume, and sperm motility (P < 0.05). The maximum-decreasing trajectory group had a detrimental effect on semen quality (OR = 1.75, 95%CI: 1.22, 2.53) compared to the minimum-stable trajectory group. Al showed negative associations with sperm concentration, total sperm count, and normal morphology (P < 0.05). Rb was positively associated with progressive motility (P < 0.05). The high-stable trajectory group exhibited a protective effect on semen quality (OR = 0.66, 95%CI: 0.49, 0.90) compared to the low-stable trajectory group. Additionally, Fe was observed to have a negative relationship with follicle-stimulating hormone (FSH) (P < 0.05), and Rb exhibited a negative correlation with progesterone (P) (P < 0.05). ConclusionOur three-year cohort study provides new evidence that Li and Al have a negative impact on semen quality, whereas Rb is associated with beneficial effects. Additionally, Rb and Fe are endocrine disruptors of sex hormones.

Extreme temperatures, PM2.5 and trajectories of impaired thyroid hormone sensitivity: A longitudinal study of patients with schizophrenia

BackgroundThe climate change scenario has witnessed an increase in extreme temperature events (ETEs), including heat waves and cold spells, and a heightened occurrence of compounding with fine particulate matter (PM2.5). However, the impact of this phenomenon on the sensitivity to thyroid hormones (THs) in humans is unclear, especially in a group as specific as schizophrenia. MethodsA longitudinal study was constructed using longitudinal measurements of thyroid function in schizophrenia in the Anhui Mental Health Center. The latent growth mixture model was applied to assess the optimal trajectory of change in impaired THs sensitivity. We then used logistic regression to explore associations between heat waves, cold spells, and PM2.5 with impaired THs sensitivity trajectories in the total population and different gender and age subgroups. Furthermore, the effect of the frequency, intensity, and duration of ETEs in the above associations was explored, as well as an assessment of the interaction between ETEs and PM2.5. ResultsAmong 931 participants, we identified two classifications of trajectories of impaired THs sensitivity: “Low-stable” (n = 836, 89.80 %) and “Rise-slight down” (n = 95, 10.20 %). Logistic regression showed significant associations between each additional day of heat waves (≥3 days with temperature thresholds above the 95th percentile) and cold spells (≥3 days with temperature thresholds below the 5th percentile) and “Rise-slight down” trajectory, with odds ratios (95 % confidence intervals) of 1.06 (1.02, 1.10) and 1.19 (1.14, 1.24), respectively, and the strength of this association increased with the intensity and duration of ETEs. Subgroup analyses indicated that the association was more pronounced in males and the age group above 40 years. Furthermore, PM2.5 was found to interact with heat waves, with high concentrations exacerbating the effects of heat waves. ConclusionsOur findings suggest that mitigating both ETEs and PM2.5 exposures may bring health co-benefits in preventing thyroid impairment in schizophrenia.

The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities

The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.

CRISPR-Cas9-mediated genome editing delivered by a single AAV9 vector inhibits HSV-1 reactivation in a latent rabbit keratitis model

CRISPR-Cas9-mediated genome editing delivered by a single AAV9 vector inhibits HSV-1 reactivation in a latent rabbit keratitis model

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning-Based Content Analysis Study.

Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking. Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions. We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling. We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention. The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Data-driven transient lift attenuation for extreme vortex gust–airfoil interactions

We present a data-driven feedforward control to attenuate large transient lift experienced by an airfoil disturbed by an extreme level of discrete vortex gust. The current analysis uses a nonlinear machine-learning technique to compress the high-dimensional flow dynamics onto a low-dimensional manifold. While the interaction dynamics between the airfoil and extreme vortex gust are parametrized by its size, gust ratio and position, the wake responses are well captured on this simple manifold. The effect of extreme vortex disturbance about the undisturbed baseline flows can be extracted in a physically interpretable manner. Furthermore, we call on phase-amplitude reduction to model and control the complex nonlinear extreme aerodynamic flows. The present phase-amplitude reduction model reveals the sensitivity of the dynamical system in terms of the phase shift and amplitude change induced by external forcing with respect to the baseline periodic orbit. By performing the phase-amplitude analysis for a latent dynamical model identified by sparse regression, the sensitivity functions of low-dimensionalized aerodynamic flows for both phase and amplitude are derived. With the phase and amplitude sensitivity functions, optimal forcing can be determined to quickly suppress the effect of extreme vortex gusts towards the undisturbed states in a low-order space. The present optimal flow modification built upon the machine-learned low-dimensional subspace quickly alleviates the impact of transient vortex gusts for a variety of extreme aerodynamic scenarios, providing a potential foundation for flight of small-scale air vehicles in adverse atmospheric conditions.

Developing a Machine-Learning Prediction Model for Infliximab Response in Crohn's Disease: Integrating Clinical Characteristics and Longitudinal Laboratory Trends.

Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging. This study aims to establish a prediction model based on patients' clinical characteristics using a machine-learning approach to predict the long-term efficacy of infliximab (IFX). Three cohorts comprising 746 patients with CD were included from 3 inflammatory bowel disease (IBD) centers between June 2013 and January 2022. Clinical records were collected from baseline, 14-, 30-, and 52-week post-IFX treatment. Three machine-learning approaches were employed to develop predictive models based on 23 baseline predictors. The SHapley Additive exPlanations (SHAP) algorithm was used to dissect underlying predictors, and latent class mixed model (LCMM) was applied for trajectory analysis of the longitudinal change of blood routine tests along with long-term IFX therapy. The XGBoost model exhibited the best discrimination between long-term responders and nonresponders. In the internal training and testing set, the model achieved an AUC of 0.91 (95% CI, 0.86-0.95) and 0.71 (95% CI, 0.66-0.87), respectively. Moreover, it achieved a moderate predictive performance in the independent external cohort, with an AUC of 0.68 (95% CI, 0.59-0.77). The SHAP algorithm revealed disease-relevant laboratory measurements, notably hemoglobin (HB), white blood cells (WBC), erythrocyte sedimentation rate (ESR), albumin (ALB), and platelets (PLT), alongside age at diagnosis and the Montreal classification, as the most influential predictors. Furthermore, 2 distinct patient clusters based on dynamic laboratory tests were identified for monitoring the long-term remission. The established prediction model demonstrated remarkable discriminatory power in distinguishing long-term responders from nonresponders to IFX therapy. The identification of distinct patient clusters further emphasizes the need for tailored therapeutic approaches in CD management.

A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.

Neuropeptide Y Y2 Receptors in Sensory Neurons Tonically Suppress Nociception and Itch but Facilitate Postsurgical and Neuropathic Pain Hypersensitivity.

Neuropeptide Y (NPY) Y2 receptor (Y2) antagonist BIIE0246 can both inhibit and facilitate nociception. The authors hypothesized that Y2 function depends on inflammation or nerve injury status. The authors implemented a battery of behavioral tests in mice of both sexes that received (1) no injury; (2) an incision model of postoperative pain; (3) a spared nerve injury model of neuropathic pain; and (4) a latent sensitization model of chronic postsurgical pain. In addition to Y2 gene expression assays, spinal Y2 G-protein coupling was studied with guanosine-5'-O-(3-[35S]thio)triphosphate ([35S]GTPγS) binding assays. The authors report that intrathecal BIIE0246 increased mechanical and cold hypersensitivity, produced behavioral signs of spontaneous nociception and itch, and produced conditioned place aversion and preference in normal, uninjured mice. BIIE0246 did not change heat hypersensitivity or motor coordination. Conditional (sensory neuron-specific) Y2 deletion prevented BIIE0246-induced mechanical and cold hypersensitivity, nocifensive behaviors, and aversion. Both conditional deletion and pharmacologic blockade of Y2 reduced mechanical and thermal hypersensitivity after incision or nerve injury. SNI did not change the sensitivity of Y2 G-protein coupling with the Y2 agonist peptide YY (3-36) (PYY3-36), but increased the population of Y2 that effectively coupled G-proteins. Intrathecal PYY3-36 failed to reduce spared nerve injury- or incision-induced hypersensitivity in C57BL/6N mice. Incision did not change Npy2r gene expression in dorsal root ganglion. The authors conclude that Y2 at central terminals of primary afferent neurons provides tonic inhibition of mechanical and cold nociception and itch. This switches to the promotion of mechanical and thermal hyperalgesia in models of acute and chronic postsurgical and neuropathic pain, perhaps due to an increase in the population of Y2 that effectively couples to G-proteins. These results support the development of Y2 antagonists for the treatment of chronic postsurgical and neuropathic pain.

Analyzing HPV Vaccination Service Preferences among Female University Students in China: A Discrete Choice Experiment.

Despite being primary beneficiaries of human papillomavirus (HPV) vaccines, female university students in China exhibit low vaccination rates. This study aimed to assess their preferences for HPV vaccination services and evaluate the relative importance of various factors to inform vaccination strategy development. Through a literature review and expert consultations, we identified five key attributes for study: effectiveness, protection duration, waiting time, distance, and out-of-pocket (OOP) payment. A D-efficient design was used to create a discrete choice experiment (DCE) questionnaire. We collected data via face-to-face interviews and online surveys from female students across seven universities in China, employing mixed logit and latent class logit models to analyze the data. The predicted uptake and compensating variation (CV) were used to compare different vaccination service scenarios. From 1178 valid questionnaires, with an effective response rate of 92.9%, we found that effectiveness was the most significant factor influencing vaccination preference, followed by protection duration, OOP payment and waiting time, with less concern for distance. The preferred services included a 90% effective vaccine, lifetime protection, a waiting time of less than three months, a travel time of more than 60 min, and low OOP payment. Significant variability in preferences across different vaccination service scenarios was observed, affecting potential market shares. The CV analysis showed female students were willing to spend approximately CNY 5612.79 to include a hypothetical 'Service 5' (a vaccine with higher valency than the nine-valent HPV vaccine) in their prevention options. The findings underscore the need for personalized, need-based HPV vaccination services that cater specifically to the preferences of female university students to increase vaccination uptake and protect their health.

Managing Type 2 Diabetes During the COVID-19 Pandemic: Scoping Review and Qualitative Study Using Systematic Literature Review and Reddit.

The COVID-19 pandemic impacted how people accessed health services and likely how they managed chronic conditions such as type 2 diabetes (T2D). Social media forums present a source of qualitative data to understand how adaptation might have occurred from the perspective of the patient. Our objective is to understand how the care-seeking behaviors and attitudes of people living with T2D were impacted during the early part of the pandemic by conducting a scoping literature review. A secondary objective is to compare the findings of the scoping review to those presented on a popular social media platform Reddit. A scoping review was conducted in 2021. Inclusion criteria were population with T2D, studies are patient-centered, and study objectives are centered around health behaviors, disease management, or mental health outcomes during the COVID-19 pandemic. Exclusion criteria were populations with other noncommunicable diseases, examining COVID-19 as a comorbidity to T2D, clinical treatments for COVID-19 among people living with T2D, genetic expressions of COVID-19 among people living with T2D, gray literature, or studies not published in English. Bias was mitigated by reviewing uncertainties with other authors. Data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Data from the Reddit forums related to T2D from March 2020 to early March 2021 were downloaded, and support vector machines were used to classify if a post was published in the context of the pandemic. Latent Dirichlet allocation topic modeling was performed to gather topics of discussion specific to the COVID-19 pandemic. A total of 26 studies conducted between February and September 2020, consisting of 13,673 participants, were included in this scoping literature review. The studies were qualitative and relied mostly on qualitative data from surveys or questionnaires. Themes found from the literature review were "poorer glycemic control," "increased consumption of unhealthy foods," "decreased physical activity," "inability to access medical appointments," and "increased stress and anxiety." Findings from latent Dirichlet allocation topic modeling of Reddit forums were "Coping With Poor Mental Health," "Accessing Doctor & Medications and Controlling Blood Glucose," "Changing Food Habits During Pandemic," "Impact of Stress on Blood Glucose Levels," "Changing Status of Employment & Insurance," and "Risk of COVID Complications." Topics of discussion gauged from the Reddit forums provide a holistic perspective of the impact of the pandemic on people living with T2D, which were found to be comparable to the findings of the literature review. The study was limited by only having 1 reviewer for the literature review, but biases were mitigated by consulting authors when there were uncertainties. Qualitative analysis of Reddit forms can supplement traditional qualitative studies of the behaviors of people living with T2D.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BackgroundThe widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. MethodsThis study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. ResultsModel constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = − 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. ConclusionsThis study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.