Mapping the distribution of Nipah virus infections: a geospatial modelling analysis.

BACKGROUND: Nipah virus is a zoonotic paramyxovirus responsible for disease outbreaks with high fatality rates in south and southeast Asia. However, knowledge of the potential geographical extent and risk patterns of the virus is poor. We aimed to establish an integrated spatiotemporal and phylogenetic database of Nipah virus infections in humans and animals across south and southeast Asia. METHODS: In this geospatial modelling analysis, we developed an integrated database containing information on the distribution of Nipah virus infections in humans and animals from 1998 to 2021. We conducted phylodynamic analysis to examine the evolution and migration pathways of the virus and meta-analyses to estimate the adjusted case-fatality rate. We used two boosted regression tree models to identify the potential ecological drivers of Nipah virus occurrences in spillover events and endemic areas, and mapped potential risk areas for Nipah virus endemicity. FINDINGS: 749 people and eight bat species across nine countries were documented as being infected with Nipah virus. On the basis of 66 complete genomes of the virus, we identified two clades-the Bangladesh clade and the Malaysia clade-with the time of the most recent common ancestor estimated to be 1863. Adjusted case-fatality rates varied widely between countries and were higher for the Bangladesh clade than for the Malaysia clade. Multivariable meta-regression analysis revealed significant relationships between case-fatality rate estimates and viral clade (p=0·0021), source country (p=0·016), proportion of male patients (p=0·036), and travel time to health-care facilities (p=0·036). Temperature-related bioclimate variables and the probability of occurrence of Pteropus medius were important contributors to both the spillover and the endemic infection models. INTERPRETATION: The suitable niches for Nipah virus are more extensive than previously reported. Future surveillance efforts should focus on high-risk areas informed by updated projections. Specifically, intensifying zoonotic surveillance efforts, enhancing laboratory testing capacity, and implementing public health education in projected high-risk areas where no human cases have been reported to date will be crucial. Additionally, strengthening wildlife surveillance and investigating potential modes of transmission in regions with documented human cases is needed. FUNDING: The Key Research and Development Program of China.

Transient inhibition of type I interferon enhances CD8 + T cell stemness and vaccine protection.

Developing vaccines that promote CD8 + T cell memory is a challenge for infectious disease and cancer immunotherapy. TCF-1 + stem cell-like memory T (T SCM ) cells are important determinants of long-lived memory. Yet, the developmental requirements for T SCM formation are unclear. Here, we identify the temporal window for type I interferon (IFN-I) receptor (IFNAR) blockade to drive T SCM cell generation. T SCM cells were transcriptionally distinct and emerged from a transitional precursor of exhausted (T PEX ) cellular state concomitant with viral clearance. T SCM differentiation correlated with T cell retention within the lymph node paracortex, due to increased CXCR3 chemokine abundance which disrupted gradient formation. These affects were due a counterintuitive increase in IFNψ, which controlled cell location. Combining IFNAR inhibition with mRNA-LNP vaccination promoted specific T SCM differentiation and enhanced protection against chronic infection. These finding propose a new approach to vaccine design whereby modulation of inflammation promotes memory formation and function. HIGHLIGHTS: Early, transient inhibition of the type I interferon (IFN) receptor (IFNAR) during acute viral infection promotes stem cell-like memory T (T SCM ) cell differentiation without establishing chronic infection. T SCM and precursor of exhausted (T PEX ) cellular states are distinguished transcriptionally and by cell surface markers. Developmentally, T SCM cell differentiation occurs via a transition from a T PEX state coinciding with viral clearance. Transient IFNAR blockade increases IFNψ production to modulate the ligands of CXCR3 and couple T SCM differentiation to cell retention within the T cell paracortex of the lymph node. Specific promotion of T SCM cell differentiation with nucleoside-modified mRNA-LNP vaccination elicits enhanced protection against chronic viral challenge.

PRRX1-OLR1 axis supports CAFs-mediated lung cancer progression and immune suppression.

OBJECTIVE: To investigate the mechanism by which cancer-associated fibroblasts (CAFs) affect the growth and immune evasion of lung cancer cells. METHODS: Initially, datasets comparing CAFs with normal fibroblasts were downloaded from the GEO dataset GSE48397. Genes with the most significant differential expression were selected and validated using clinical data. Subsequently, CAFs were isolated, and the selected genes were knocked down in CAFs. Co-culture experiments were conducted with H1299 or A549 cells to analyze changes in lung cancer cell growth, migration, and immune evasion in vitro and in vivo. To further elucidate the upstream regulatory mechanism, relevant ChIP-seq data were downloaded from the GEO database, and the regulatory relationships were validated through ChIP-qPCR and luciferase reporter assays. RESULTS: OLR1 was significantly overexpressed in CAFs and strongly correlated with adverse prognosis in lung cancer patients. Knockdown of OLR1 markedly inhibited CAFs' support for the growth and immune evasion of lung cancer cells in vitro and in vivo. ChIP-seq results demonstrated that PRRX1 can promote OLR1 expression by recruiting H3K27ac and H3K4me3, thereby activating CAFs. Knockdown of PRRX1 significantly inhibited CAFs' function, while further overexpression of OLR1 restored CAFs' support for lung cancer cell growth, migration, and immune evasion. CONCLUSION: PRRX1 promotes OLR1 expression by recruiting H3K27ac and H3K4me3, activating CAFs, and thereby promoting the growth, migration, and immune evasion of lung cancer cells.

Mapping the distribution of sandflies and sandfly-associated pathogens in China.

BACKGROUND: Understanding and mapping the distribution of sandflies and sandfly-associated pathogens (SAPs) is crucial for guiding the surveillance and control effort. However, their distribution and the related risk burden in China remain poorly understood. METHODS: We mapped the distribution of sandflies and SAPs using literature data from 1940 to 2022. We also mapped the human visceral leishmaniasis (VL) cases using surveillance data from 2014 to 2018. The ecological drivers of 12 main sandfly species and VL were identified by applying machine learning, and their distribution and risk were predicted in three time periods (2021-2040, 2041-2060, and 2061-2080) under three scenarios of climate and socioeconomic changes. RESULTS: In the mainland of China, a total of 47 sandfly species have been reported, with the main 12 species classified into three clusters according to their ecological niches. Additionally, 6 SAPs have been identified, which include two protozoa, two bacteria, and two viruses. The incidence risk of different VL subtypes was closely associated with the distribution risk of specific vectors. The model predictions also revealed a substantial underestimation of the current sandfly distribution and VL risk. The predicted areas affected by the 12 major species of sandflies and the high-risk areas for VL were found to be 37.9-1121.0% and 136.6% larger, respectively, than the observed range in the areas. The future global changes were projected to decrease the risk of mountain-type zoonotic VL (MT-ZVL), but anthroponotic VL (AVL) and desert-type zoonotic VL (DT-ZVL) could remain stable or slightly increase. CONCLUSIONS: Current field observations underestimate the spatial distributions of main sandfly species and VL in China. More active surveillance and field investigations are needed where high risks are predicted, especially in areas where the future risk of VL is projected to remain high or increase.

Humoral and cellular immune responses following Omicron BA.2.2 breakthrough infection and Omicron BA.5 reinfection.

The emergence of novel Omicron subvariants has raised concerns regarding the efficacy of immunity induced by prior Omicron subvariants breakthrough infection (BTI) or reinfection against current circulating Omicron subvariants. Here, we prospectively investigated the durability of antibody and T cell responses in individuals post Omicron BA.2.2 BTI, with or without subsequent Omicron BA.5 reinfection. Our findings reveal that the emerging Omicron subvariants, including CH.1.1, XBB, and JN.1, exhibit extensive immune evasion induced by previous infections. Notably, the level of IgG and neutralizing antibodies were found to correlate with subsequent Omicron BA.5 reinfection. Fortunately, T cell responses recognizing both Omicron BA.2 and CH.1.1 peptides were observed. Furthermore, Omicron BA.5 reinfection may alleviate immune imprinting induced by WT-vaccination, bolster virus-specific ICS+ T cell responses, and promote the phenotypic differentiation of virus-specific memory CD8+ T cells. Antigen-updated or T cell-conserved vaccines are needed to control the transmission of diverse emerging SARS-CoV-2 variants.

Targeting dendritic cell activation: the therapeutic impact of paeoniflorin in cortosteroid-dependent dermatitis management.

This study investigates the mechanism through which paeoniflorin inhibits TSLP expression to regulate dendritic cell activation in corticosteroid-dependent dermatitis treatment. Utilizing databases like TCMSP, we identified paeoniflorin's components, targets, and constructed networks. Molecular docking and gene enrichment analysis helped pinpoint key targets and pathways affected by paeoniflorin. In vitro and in vivo models were used to study CD80, CD86, cytokines, T-cell activation, skin lesions, histopathological changes, TSLP, CD80, and CD86 expression. Our study revealed paeoniflorin's active constituent targeting IL-6 in corticosteroid-dependent dermatitis. In vitro experiments demonstrated reduced TSLP expression, CD80, CD86, and cytokine secretion post-paeoniflorin treatment. In vivo, paeoniflorin significantly decreased skin lesion severity, cytokine levels, TSLP, CD80, and CD86 expression. The study highlights paeoniflorin's efficacy in inhibiting TSLP expression and suppressing dendritic cell activation in corticosteroid-dependent dermatitis, suggesting its potential as a therapeutic intervention. Additionally, it offers insights into the complex molecular mechanisms underlying paeoniflorin's anti-inflammatory properties in treating corticosteroid-dependent dermatitis.

Epidemiological characteristics and antibody kinetics of elderly population with booster vaccination following both Omicron BA.5 and XBB waves in China.

After the termination of zero-COVID-19 policy, the populace in China has experienced both Omicron BA.5 and XBB waves. Considering the poor antibody responses and severe outcomes observed among the elderly following infection, we conducted a longitudinal investigation to examine the epidemiological characteristics and antibody kinetics among 107 boosted elderly participants following the Omicron BA.5 and XBB waves. We observed that 96 participants (89.7%) were infected with Omicron BA.5, while 59 (55.1%) participants were infected with Omicron XBB. Notably, 52 participants (48.6%) experienced dual infections of both Omicron BA.5 and XBB. The proportion of symptomatic cases appeared to decrease following the XBB wave (18.6%) compared to that after the BA.5 wave (59.3%). Omicron BA.5 breakthrough infection induced lower neutralizing antibody titers against XBB.1.5, BA.2.86, and JN.1, while reinfection with Omicron XBB broadened the antibody responses against all measured Omicron subvariants and may alleviate the wild type-vaccination induced immune imprinting. Boosted vaccination type and comorbidities were the significant factors associated with antibody responses. Updated vaccines based on emerging severe acute respiratory syndrome coronavirus 2 variants are needed to control the Coronavirus Disease 2019 pandemic in the elderly.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Human Breast Milk Exosomal miRNAs are Influenced by Premature Delivery and Affect Neurodevelopment.

SCOPE: This study investigates the exosomal microRNA (miRNA) profiles of term and preterm breast milk, including the most abundant and differentially expressed (DE) miRNAs, and their impact on neurodevelopment in infants. METHODS AND RESULTS: Mature milk is collected from the mothers of term and preterm infants. Using high-throughput sequencing and subsequent data analysis, exosomal miRNA profiles of term and preterm human breast milk (HBM) are acquired and it is found that the let-7 and miR-148 families are the most abundant miRNAs. Additionally, 23 upregulated and 15 downregulated miRNAs are identified. MiR-3168 is the most upregulated miRNA in preterm HBM exosome, exhibiting targeting activity toward multiple genes involved in the SMAD and MAPK signaling pathways and playing a crucial role in early neurodevelopment. Additionally, the effects of miR-3168 on neurodevelopment is confirmed and it is determined that it is an essential factor in the differentiation of neural stem cells (NSCs). CONCLUSION: This study demonstrates that miRNA expression in breast milk exosomes can be influenced by preterm delivery, thereby potentially impacting neurodevelopment in preterm infants.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

EP300 regulates the SLC16A1-AS1-AS1/TCF3 axis to promote lung cancer malignancies through the Wnt signaling pathway.

Objective: To investigate the regulatory mechanism of EP300 in the interaction between SLC16A1-AS1 and TCF3 to activate the Wnt pathway, thereby promoting malignant progression in lung cancer. Methods: In lung cancer cell lines, SLC16A1-AS1 was knocked down, and the impact of this knockdown on the malignant progression of lung cancer cells was assessed through clonogenic assays, Transwell assays, and apoptosis experiments. The regulatory relationship between EP300 and SLC16A1-AS1 was investigated through bioinformatic analysis and ChIP experiments. The expression of SLC16A1-AS1 and TCF3 in 56 paired lung cancer tissues was examined using RT-qPCR, and their correlation was analyzed. The interaction between TCF3 and SLC16A1-AS1 was explored through bioinformatic analysis and CoIP experiments. Activation of the Wnt/ß-catenin pathway was assessed by detecting the accumulation of ß-catenin in the nucleus through Western blotting. The role of EP300 in regulating the effect of SLC16A1-AS1/TCF3-mediated Wnt/ß-catenin signaling on lung cancer malignant progression was validated through in vitro and in vivo experiments. Results: SLC16A1-AS1 is highly expressed in lung cancer and regulates its malignant progression. EP300 mediates histone modifications on the SLC16A1-AS1 promoter, thus controlling its expression. SLC16A1-AS1 exhibits specific interactions with TCF3, and the SLC16A1-AS1/TCF3 complex activates the Wnt/ß-catenin pathway. EP300 plays a critical role in regulating the impact of SLC16A1-AS1/TCF3-mediated Wnt/ß-catenin signaling on lung cancer malignant progression. Conclusion: EP300 regulates the SLC16A1-AS1/TCF3-mediated Wnt/ß-catenin signaling pathway, influencing the malignant progression of lung cancer.

Serotonergic modulation of vigilance states in zebrafish and mice.

Vigilance refers to being alertly watchful or paying sustained attention to avoid potential threats. Animals in vigilance states reduce locomotion and have an enhanced sensitivity to aversive stimuli so as to react quickly to dangers. Here we report that an unconventional 5-HT driven mechanism operating at neural circuit level which shapes the internal state underlying vigilance behavior in zebrafish and male mice. The neural signature of internal vigilance state was characterized by persistent low-frequency high-amplitude neuronal synchrony in zebrafish dorsal pallium and mice prefrontal cortex. The neuronal synchronization underlying vigilance was dependent on intense release of 5-HT induced by persistent activation of either DRN 5-HT neuron or local 5-HT axon terminals in related brain regions via activation of 5-HTR7. Thus, we identify a mechanism of vigilance behavior across species that illustrates the interplay between neuromodulators and neural circuits necessary to shape behavior states.

The global distribution and the risk prediction of relapsing fever group Borrelia: a data review with modelling analysis.

BACKGROUND: The recent discovery of emerging relapsing fever group Borrelia (RFGB) species, such as Borrelia miyamotoi, poses a growing threat to public health. However, the global distribution and associated risk burden of these species remain uncertain. We aimed to map the diversity, distribution, and potential infection risk of RFGB. METHODS: We searched PubMed, Web of Science, GenBank, CNKI, and eLibrary from Jan 1, 1874, to Dec 31, 2022, for published articles without language restriction to extract distribution data for RFGB detection in vectors, animals, and humans, and clinical information about human patients. Only articles documenting RFGB infection events were included in this study, and data for RFGB detection in vectors, animals, or humans were composed into a dataset. We used three machine learning algorithms (boosted regression trees, random forest, and least absolute shrinkage and selection operator logistic regression) to assess the environmental, ecoclimatic, biological, and socioeconomic factors associated with the occurrence of four major RFGB species: Borrelia miyamotoi, Borrelia lonestari, Borrelia crocidurae, and Borrelia hermsii; and mapped their worldwide risk level. FINDINGS: We retrieved 13 959 unique studies, among which 697 met the selection criteria and were used for data extraction. 29 RFGB species have been recorded worldwide, of which 27 have been identified from 63 tick species, 12 from 61 wild animals, and ten from domestic animals. 16 RFGB species caused human infection, with a cumulative count of 26 583 cases reported from Jan 1, 1874, to Dec 31, 2022. Borrelia recurrentis (17 084 cases) and Borrelia persica (2045 cases) accounted for the highest proportion of human infection. B miyamotoi showed the widest distribution among all RFGB, with a predicted environmentally suitable area of 6·92 million km2, followed by B lonestari (1·69 million km2), B crocidurae (1·67 million km2), and B hermsii (1·48 million km2). The habitat suitability index of vector ticks and climatic factors, such as the annual mean temperature, have the most significant effect among all predictive models for the geographical distribution of the four major RFGB species. INTERPRETATION: The predicted high-risk regions are considerably larger than in previous reports. Identification, surveillance, and diagnosis of RFGB infections should be prioritised in high-risk areas, especially within low-income regions. FUNDING: National Key Research and Development Program of China.

Integrative temporal multi-omics reveals uncoupling of transcriptome and proteome during human T cell activation.

Engagement of the T cell receptor (TCR) triggers molecular reprogramming leading to the acquisition of specialized effector functions by CD4 helper and CD8 cytotoxic T cells. While transcription factors, chemokines, and cytokines are known drivers in this process, the temporal proteomic and transcriptomic changes that regulate different stages of human primary T cell activation remain to be elucidated. Here, we report an integrative temporal proteomic and transcriptomic analysis of primary human CD4 and CD8 T cells following ex vivo stimulation with anti-CD3/CD28 beads, which revealed major transcriptome-proteome uncoupling. The early activation phase in both CD4 and CD8 T cells was associated with transient downregulation of the mRNA transcripts and protein of the central glucose transport GLUT1. In the proliferation phase, CD4 and CD8 T cells became transcriptionally more divergent while their proteome became more similar. In addition to the kinetics of proteome-transcriptome correlation, this study unveils selective transcriptional and translational metabolic reprogramming governing CD4 and CD8 T cell responses to TCR stimulation. This temporal transcriptome/proteome map of human T cell activation provides a reference map exploitable for future discovery of biomarkers and candidates targeting T cell responses.

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized.

BACKGROUND: Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization. METHOD: Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score. RESULTS: A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell. CONCLUSION: A model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

In situ single-cell profiling sheds light on IFI27 localisation during SARS-CoV-2 infection.

The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.

Assessing the journey of calcium supplementation: A mendelian randomization study on the causal link between calcium levels and sleep disorders.

BACKGROUND & AIM: Sleep disorder is a growing concern, and calcium supplementation is often recommended as a potential intervention for sleep disorders. However, the causal relationship between calcium levels and the incidence of sleep disorders remains unclear. Mendelian randomization techniques utilizing genetic variants that affect calcium levels, can provide valuable insights into causality. This study aims to examine the association between calcium levels and sleep disorders in a diverse population that includes both adolescents and adults, and investigate the effects of calcium levels on sleep disorders. METHODS: Mendelian randomization analysis was conducted using data from UK Biobank and FinnGen datasets. The inverse-variance weighting (IVW) was selected as the primary method. In addition, traditional mediation analysis was performed on a subset of the NHANES data spanning from 2007 to 2018. RESULTS: Our findings provide evidence supporting a causal relationship between calcium intake and reduced risk of sleep disorders (beta = -0.079, SE = 0.0395, P = 0.0457). While not reaching statistical significance, other MR methods such as weighted median and Mr-Egger exhibited similar directional trends. Analysis of the NHANES cohort revealed a negative association between calcium levels and the prevalence of sleep disorders in male, black, and physically active populations. However, this association was not observed in other demographic groups. CONCLUSION: Our results suggested that there is no significant correlation between calcium levels and sleep disorder in non-exercise populations. This raises concerns about the long-term high-dose calcium supplementation in clinical practice, which requires further investigation.

Fetal overgrowth and weight trajectories during infancy and adiposity in early childhood.

BACKGROUND: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood. Little is known about how infancy growth trajectories affect adiposity in early childhood in LGA. METHODS: In the Shanghai Birth Cohort, we followed up 259 LGA (birth weight >90th percentile) and 1673 appropriate-for-gestational age (AGA, 10th-90th percentiles) children on body composition (by InBody 770) at age 4 years. Adiposity outcomes include body fat mass (BFM), percent body fat (PBF), body mass index (BMI), overweight/obesity, and high adiposity (PBF >85th percentile). RESULTS: Three weight growth trajectories (low, mid, and high) during infancy (0-2 years) were identified in AGA and LGA subjects separately. BFM, PBF and BMI were progressively higher from low- to mid-to high-growth trajectories in both AGA and LGA children. Compared to the mid-growth trajectory, the high-growth trajectory was associated with greater increases in BFM and the odds of overweight/obesity or high adiposity in LGA than in AGA children (tests for interactions, all P < 0.05). CONCLUSIONS: Weight trajectories during infancy affect adiposity in early childhood regardless of LGA or not. The study is the first to demonstrate that high-growth weight trajectory during infancy has a greater impact on adiposity in early childhood in LGA than in AGA subjects. IMPACT: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood, but little is known about how weight trajectories during infancy affect adiposity during early childhood in LGA subjects. The study is the first to demonstrate a greater impact of high-growth weight trajectory during infancy (0-2 years) on adiposity in early childhood (at age 4 years) in subjects with fetal overgrowth (LGA) than in those with normal birth size (appropriate-for-gestational age). Weight trajectory monitoring may be a valuable tool in identifying high-risk LGA children for close follow-ups and interventions to decrease the risk of obesity.

The Depressive Tendency Questionnaire for Chinese Middle School Students: Development and Initial Validation.

Purpose: Depressive tendency refers to the early stage of the onset of depression disorder, which can also have a negative impact on individuals' quality of life. However, there are many numerous depression-related scales that are primarily used to evaluate depression disorders with relatively severe symptom severity. And considering the characteristics of the Chinese cultural background, relatively few corresponding scales are currently developed. This study aimed to develop and validate a depressive tendency questionnaire for Chinese middle school students, based on the psychological structure of depressive tendency in the context of Chinese culture. Methods: The initial version of the questionnaire was developed through literature reviews, open surveys, and in-depth interviews. The questionnaire was revised based on the predictions made from 865 middle school students (M=15.84, SD=1.50). Finally, the formal questionnaire was developed through valid administration to 1205 students (M=15.63, SD=1.82), using confirmatory factor analysis, reliability, and validity testing, as well as measurement invariance analysis. Results: The questionnaire consisted of 30 items, including negative self, cognitive function, personal reserve, and social support. The results of confirmatory factor analysis showed that the questionnaire fitted the data with well construct validity. Measurement invariance indicates no gender and grade differences in the questionnaire scores. The questionnaire demonstrates good reliability and validity, meeting the requirements of psychometrics. Conclusion: From a theoretical point of view, we clarified the psychological structure of depressive tendencies among Chinese middle school students, providing a theoretical and empirical basis for subsequent related research. From a practical point of view, the formal questionnaire plays a positive role in the smooth development of mental health counseling, education, and other work in middle schools.