Numerical evaluation of spinal reconstruction using a 3D printed vertebral body replacement implant: effects of material anisotropy.

Background and objective: Artificial vertebral implants have been widely used for functional reconstruction of vertebral defects caused by tumors or trauma. However, the evaluation of their biomechanical properties often neglects the influence of material anisotropy derived from the host bone and implant's microstructures. Hence, this study aims to investigate the effect of material anisotropy on the safety and stability of vertebral reconstruction. Material and methods: Two finite element models were developed to reflect the difference of material properties between linear elastic isotropy and nonlinear anisotropy. Their biomechanical evaluation was carried out under different load conditions including flexion, extension, lateral bending and axial rotation. These performances of two models with respect to safety and stability were analyzed and compared quantitatively based on the predicted von Mises stress, displacement and effective strain. Results: The maximum von Mises stress of each component in both models was lower than the yield strength of respective material, while the predicted results of nonlinear anisotropic model were generally below to those of the linear elastic isotropic model. Furthermore, the maximum von Mises stress of natural vertebra and reconstructed system was decreased by 2-37 MPa and 20-61 MPa, respectively. The maximum reductions for the translation displacement of the artificial vertebral body implant and motion range of whole model were reached to 0.26 mm and 0.77°. The percentage of effective strain elements on the superior and inferior endplates adjacent to implant was diminished by up to 19.7% and 23.1%, respectively. Conclusion: After comprehensive comparison, these results indicated that the finite element model with the assumption of linear elastic isotropy may underestimate the safety of the reconstruction system, while misdiagnose higher stability by overestimating the range of motion and bone growth capability.

Restricted linear association between night sleep duration and diabetes risk in middle-aged and older adults: a 7-year follow-up analysis from the China health and retirement longitudinal study.

Background: A rapid increase in the prevalence of diabetes is an urgent public health concern among older adults, especially in developing countries such as China. Despite several studies on lifestyle factors causing diabetes, sleep, a key contributor, is understudied. Our study investigates the association between night sleep duration and diabetes onset over a 7-year follow-up to fill information gaps. Method: A population-based cohort study with 5437 respondents used 2011-2018 China Health and Retirement Longitudinal Study data. Using self-reported night sleep duration from the 2011 baseline survey, information on new-onset diabetes was collected in follow-up surveys. Baseline characteristics of participants with vs. without new-onset diabetes were compared using Chi-square and Mann-Whitney U tests. Multivariable Cox regression models estimated the independent relationship between night sleep and new-onset diabetes. The addictive Cox regression model approach and piece-wise regression described the nonlinear relationship between night sleep and new-onset diabetes. Subgroup analysis was also performed by age, gender, body measurement index, dyslipidemia, drinking status, smoking, hypertension, and afternoon napping duration. Result: 549 respondents acquired diabetes during a median follow-up of 84 months. After controlling for confounders, night sleep duration was substantially linked with new-onset diabetes in the multivariable Cox regression model. The risk of diabetes is lower for respondents who sleep longer than 5 hours, except for those who sleep over 8 hours [5.1-6h Hazard ratios (HR) [95% confidence intervals (CI)] = 0.71 (0.55, 0.91); 6.1-7h HR = 0.69 (0.53, 0.89); 7.1-8h HR = 0.58 (0.45, 0.76)]. Nonlinear connections were delineated by significant inflection points at 3.5 and 7.5 hours, with a negative correlation observed only between these thresholds. With one hour more night sleep, the risk of diabetes drops 15%. BMI and dyslipidemia were identified as modifiers when only consider the stand linear effect of sleep duration on diabetes. Conclusion: This study establishes a robust association between night sleep and new-onset diabetes in middle-aged and older Chinese individuals within the 3.5-7.5-hour range, offering a foundation for early glycemic management interventions in this demographic. The findings also underscore the pivotal role of moderate night sleep in preventing diabetes, marking a crucial juncture in community medical research.

Association between bedtime and female infertility: a secondary analysis from a cross-sectional study.

Objective: To evaluate the association between bedtime and infertility and to identify the optimal bedtime for women of reproductive age. Methods: We conducted a cross-sectional study using data from 3,903 female participants in the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2020. The effect of bedtime on female infertility was assessed using the binary logistic regression in different models, including crude model and adjusted models. To identify the non-linear correlation between bedtime and infertility, generalized additive models (GAM) were utilized. Subgroup analyses were conducted by age, body mass index (BMI), waist circumference, physical activity total time, marital status, smoking status, drinking status and sleep duration. Results: After adjusting for potential confounders (age, race, sleep duration, waist circumference, marital status, education, BMI, smoking status, drinking status and physical activity total time), a non-linear relationship was observed between bedtime and infertility, with the inflection point at 22:45. To the left side of the inflection point, no significant association was detected. However, to the right of it, bedtime was positively related to the infertility (OR: 1.22; 95% CI: 1.06 to 1.39; P = 0.0049). Subgroup analyses showed that late sleepers with higher BMI were more prone to infertility than those with a lower BMI (BMI: 25-30 kg/m2: OR: 1.26; 95% CI: 1.06 to 1.51; P = 0.0136; BMI ≥ 30 kg/m²: OR: 1.21, 95% CI: 1.09 to 1.34; P = 0.0014). Conclusion: Bedtime was non-linearly associated with infertility, which may provide guidance for sleep behavior in women of childbearing age.

Consumer awareness and knowledge regarding use of non-steroidal anti-inflammatory drugs (NSAIDs) in a metropolitan area.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) have well-known adverse effects, and numerous studies have shown inappropriate behaviors regarding their use. The primary aim of this study was to analyze the knowledge, attitudes, and behaviors regarding the use of NSAIDs simultaneously in one of the largest and most populated areas of Italy, Naples. Methods: From 2021 December 14th to 2022 January 4th, a cross-sectional survey study was conducted among community centers, working places, and universities using a snowball sampling method. For inclusion in the study, the participants were required to be at least 18 years old and residents in the metropolitan area of Naples. Three multiple linear regression analysis (MLRA) models were developed by including variables that could potentially be associated with the following outcomes of interest: knowledge (Model I), attitudes (Model II), and behavior (Model III) regarding the use of NSAIDs. Results: Data were acquired from 1,012 questionnaires administered to subjects evenly divided by gender with an average age of 36.8 years and revealed that only 7.9% of the participants self-admittedly did not take NSAIDs, while approximately half the participants (50%) admitted to occasionally using them. The results showed a statistically significant correlation between attitudes regarding the appropriate use of NSAIDs and less knowledge. The regression analyses indicated that behaviors regarding the appropriate use of NSAIDs were statistically significant in younger respondents, non-smokers, and those without children. These interesting results showed that behaviors regarding the appropriate use of NSAIDs were significantly higher among respondents with less knowledge and more positive attitudes. Conclusion: According to the collected data and statistical analysis results, it is possible to identify factors that can greatly affect inappropriate behaviors regarding the use of NSAIDs and establish targeted prevention programs.

Cone Angles Quantify and Predict Affinity and Reactivity of Anion Complexes between Trifluoroborates and Rigid Macrocycles.

Steric manipulation is a known concept in molecular recognition but there is currently no linear free energy relationship correlating sterics to the stability of receptor-anion complexes nor to the reactivity of the bound anion. By analogy to Tolman cone angles in cation coordination chemistry, we explore how to define and correlate cone angles of organo-trifluoroborates (R-BF3-) to the affinities observed for cyanostar-anion binding. We extend the analogy to a rare investigation of reactivity and how it changes upon anion binding. The substituent on the anion is used to define the cone angle, θ. A series of 10 anions were studied including versions with ethynyl, ethylene, and ethyl substituents that were selected to tune steric bulk across the sp, sp2 and sp3 hybridized a-carbons bearing 0, 1 and 2 hydrogen atoms. A linear relationship between affinity and cone angle is observed for anions bearing substituents larger than the -BF3- headgroup. This correlation predicted affinities of two new anions to within ±5%. We explored how complexation affects the reactivity of fluoride exchange. The yield of fluoride transfer from R-BF3- to Lewis acid triphenylborane is correlated with cone angle. We predict that other rigid macrocycles, like commercially available bambusuril, could follow these trends.

Predictors of radiation-induced changes in arteriovenous malformation patients undergoing radiosurgery: Insights from a Malaysian linear accelerator cohort.

Background: Radiation-induced changes (RICs) post-stereotactic radiosurgery (SRS) critically influence outcomes in arteriovenous malformation (AVM) treatments. This study aimed to identify predictors of RICs, described the types and severity of RICs, and assessed their impact on patient's functional outcomes to enhance risk assessment and treatment planning for AVM patients. Methods: This retrospective study analyzed 87 AVM patients who underwent SRS at Hospital Kuala Lumpur between January 2015 and December 2020. RICs were identified through detailed magnetic resonance imaging evaluations, and predictive factors were determined using multiple logistic regression. Functional outcomes were assessed with the modified Rankin scale (mRS). Results: Among the cohort, 40.2% developed RICs, with radiological RICs in 33.3%, symptomatic RICs in 5.7%, and permanent RICs in 1.1%. Severity categorization revealed 25.3% as Grade I, 13.8% as Grade II, and 1.1% as Grade III. Notably, higher Pollock-Flickinger scores and eloquence location were significant predictors of RIC occurrence. There was a significant improvement in functional outcomes post-SRS, with a marked decrease in non-favorable mRS scores from 8.0% pre-SRS to 1.1% post-SRS (P = 0.031). Conclusion: The study identified the eloquence location and Pollock-Flickinger scores as predictors of RICs post-SRS. The significant reduction in non-favorable mRS scores post-SRS underscores the efficacy of SRS in improving patient outcomes. Their results highlighted the importance of personalized treatment planning, focusing on precise strategies to optimize patient outcomes in AVM management, reducing adverse effects while improving functional outcomes.

Prevalence of mandibular asymmetries in the pediatric population of Jazan: A radiographic analytical study.

Background: Facial asymmetry results from variation in mandibular linear and angular dimensions on the right and left sides of the face. Mandibular asymmetry is of great significance to oral surgeons and orthodontists as it directly impacts the facial profile of an individual. Aim: The present study aimed to measure the prevalence of mandibular asymmetry and its fluctuations during the mixed dentition growth phase in healthy children aged 6-8 years in the Jazan region of Saudi Arabia. Method: This retrospective observational study was conducted by measuring linear asymmetrical measurements of mandible on orthopantomograms of 390 healthy children (182 boys and 208 girls, aged 6-8 years) with mixed dentition. Linear measurements from orthopantomograms were obtained using a standardized digitizer. Two sets of mandibular measurements were recorded, alongside subjective assessments of mandibular first molar development. An independent t-test was employed to assess the significance between measurements on both sides, while one-way ANOVA was used to demonstrate facial asymmetry significance among different age groups. Result: The result of this study revealed a significant statistical difference (p-value≤ 0.05) for both sides of the mandible across two dimensions: condylar and ramus height (p value = 0.03) and mandibular length (p value = 0.04). The asymmetry index resulted in no asymmetry among most of the included subjects. However, compared to the other three linear measurements, many seven-year-old participants possess mandibular asymmetry on condylar height (54.5 %). Conclusion: Within the limitation it could be concluded that children in growing age have a significant mandibular asymmetry (mainly 7 years), which, however, is only seldom clinically significant. Hence, treatment plan should be cautiously planned.

Paddy insect identification using deep features with lion optimization algorithm.

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

Addressing the Gap: Racial Disparities and Public Health Strategies in the Epidemiology of Gastrointestinal Stromal Tumors.

Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.

Potency Assay Variability Estimation in Practice.

During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.

Syntactic Variation in Reduced Registers Through the Lens of the Parallel Architecture.

Diversion from the syntactic norm, as manifested in the absence of otherwise expected lexical and syntactic material, has been extensively studied in theoretical syntax. Such modifications are observed in headlines, telegrams, labels, and other specialized contexts, collectively referred to as "reduced" registers. Focusing on search queries, a type of reduced register, I propose that they are generated by a simpler grammar that lacks a full-fledged syntactic component. The analysis is couched in the Parallel Architecture framework, whose assumption of relative independence of linguistic components-their parallelism-and the rejection of syntactocentrism are essential to explain properties of queries.

Analysis and classification of tea varieties using high-performance liquid chromatography and global retention models.

As a result of their metabolic processes, medicinal plants produce bioactive molecules with significant implications for human health, used directly for treatment or for pharmaceutical development. Chromatographic fingerprints with solvent gradients authenticate and categorise medicinal plants by capturing chemical diversity. This work focuses on optimising tea sample analysis in HPLC, using a model-based approach without requiring standards. Predicting the gradient profile effects on full signals was the basis to identify optimal separation conditions. Global models characterised retention and bandwidth for 14 peaks in the chromatograms across varied elution conditions, facilitating resolution optimisation of 63 peaks, covering 99.95 % of total peak area. The identified optimal gradient was applied to classify 40 samples representing six tea varieties. Matrices of baseline-corrected signals, elution bands, and band ratios, were evaluated to select the best dataset. Principal Component Analysis (PCA), k-means clustering, and Partial Least Squares-Discriminant Analysis (PLS-DA) assessed classification feasibility. Classification limitations were found reasonable due to tea processing complexities, involving drying and fermentation influenced by environmental conditions.

New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under Q -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

Improvements on stability criteria for linear systems with a time-varying delay via novel delay-dependent Lyapunov functionals.

This work investigates the less conservative stability conditions for linear systems with a time-varying delay. At first, augmented Lyapunov-Krasovskii functionals(LKFs) are constructed with state vectors that have not been utilized in the existing works, and an augmented zero equality that can be derived according to the augmented vector is proposed. By utilizing them, a stability condition is proposed in the form of a linear matrix inequality. And, by using novel delay-dependent LKFs and the introduced ones, improved results are obtained than the previous result. The addition of the delay-dependent LKFs increases the number of decision variables in the results. Therefore, any vectors of integral inequalities utilized in the proposed criterion are appropriately adjusted to reduce computational complexity. To check the excellence and validity of the proposed results, several numerical examples are applied.

Novel Machine Learning Analysis Algorithm of DNA Methylation Patterns Identifies Cerebral Palsy with Concurrent Epilepsy.

Background: Spastic cerebral palsy, the most common pediatric-onset disabling condition with an estimated prevalence of 0.2% in children, is a complex condition characterized by stiff movement, muscle contractures, and abnormal gait that can diminish quality of life. Spastic CP accounts for approximately 83% of all CP cases and frequently co-occurs with other complex conditions, like epilepsy. An estimated 42% of spastic CP cases have co-occurring epilepsy. Unfortunately, CP is often difficult to diagnose. Although most children with CP are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with CP diagnosis often not confirmed until 5 years of age. New bioinformatic approaches to identify CP earlier are needed. Recent studies indicate that altered DNA methylation patterns associated with CP may have diagnostic value. The potential confounding effects of co-occurrent epilepsy on these patterns are not known. We evaluated machine learning classification of CP patients with or without co-occurring epilepsy. Results: Whole blood samples were collected from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8). A novel Support-Vector-Machine learning algorithm was developed to identify methylation loci that have ability to classify CP from controls in the presence or absence of epilepsy. This algorithm was also employed to measure classification ability of identified methylation loci. After preprocessing of data, isolation of important methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way scheme, encapsulating epilepsy diagnoses. The classification ability was similarly assessed. CP Classification performance was evaluated with and without inclusion of epilepsy as a feature. Median F1 scores were 0.67 in 4-class comparison, and 1.0 in the binary classification, outperforming Linear-Discriminant-Analysis (0.57 and 0.86, respectively). Conclusion: This novel algorithm was able to classify study participants with spastic CP and/or epilepsy from controls with significant performance. The algorithm shows promise for rapid identification in methylation data of diagnostic methylation loci. In this model, Support Vector Machines outperformed Linear Discriminant Analysis in classification. In the evaluation of epigenetics-based diagnostics for CP, epilepsy may not be a significant confounding factor.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews.

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Enhancing tunnel crack detection with linear seam using mixed stride convolution and attention mechanism.

Cracks in tunnel lining structures constitute a common and serious problem that jeopardizes the safety of traffic and the durability of the tunnel. The similarity between lining seams and cracks in terms of strength and morphological characteristics renders the detection of cracks in tunnel lining structures challenging. To address this issue, a new deep learning-based method for crack detection in tunnel lining structures is proposed. First, an improved attention mechanism is introduced for the morphological features of lining seams, which not only aggregates global spatial information but also features along two dimensions, height and width, to mine more long-distance feature information. Furthermore, a mixed strip convolution module leveraging four different directions of strip convolution is proposed. This module captures remote contextual information from various angles to avoid interference from background pixels. To evaluate the proposed approach, the two modules are integrated into a U-shaped network, and experiments are conducted on Tunnel200, a tunnel lining crack dataset, as well as the publicly available crack datasets Crack500 and DeepCrack. The results show that the approach outperforms existing methods and achieves superior performance on these datasets.

RIBAP: a comprehensive bacterial core genome annotation pipeline for pangenome calculation beyond the species level.

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.

Analyzing Children's Weight Growth Variations and Associated Factors in Ethiopia, India, Peru, and Vietnam: Using Fractional Polynomial Mixed-Effects Model.

Background: Children's growth is increasingly considered a key mediator of later life outcomes. When examining weight growth, the correlation between repeated observations on the same subject must be regarded as well-modelled. This study aimed to analyze children's weight growth variations and associated factors in Ethiopia, India, Peru, and Vietnam using a fractional polynomial mixed-effects model. Methods: This study used longitudinal data from the Young Lives Cohort Study conducted from 2002 to 2016 in Ethiopia, India, Peru, and Vietnam. The study included 7,140 children of 1 to 15 years old A fractional polynomial mixed-effects model was used to analyze the data. Results: Ethiopian, Peruvian, and Vietnamese children had significantly higher average body weights than children in India (1.426, P<0.001; 1.992, P<0.001; 1.334, P<0.001, respectively). Girl children's average body weight was significantly 0.15 times less than that of boys (-0.148; P=0.027). The average weight of rural children was significantly 0.671 times less than that of urban children (0.671, P<0.001). Children from Peru and Vietnam had higher rates of weight change than those from India. However, the rate of weight change was lower in Ethiopian children than in Indian children. Children from urban areas had a significantly higher rate of weight gain than those from rural areas. Conclusion: Country, sex, residence, parental education, household size, wealth, good drinking water, and reliable power affected children's longitudinal weight growth. Therefore, WHO and the nation's health ministry should monitor children's weight growth status and these associated factors to plan future action.