Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Algorithms and statistical analysis for linear structured weighted total least squares problem

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

Analysis of the COVID-19, Outbreak in Brazil Using Topological Weighted Centroid: An Intelligent Geographic Information System Approach

This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Research status and prospects of the fractal analysis of metal material surfaces and interfaces

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma

Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.

Antioxidant and lipoxygenase inhibitory properties of a novel flavonoid from Pistacia chinensis Bunge and its molecular docking analysis

Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.

Depression weights in patients with gastric cancer:Bibliometric analysis as a weapon to chart the future of research

Bibliometric analyses are increasing in the field of gastric cancer.This letter discusses a recently published analysis that focused on the bidirectional relationship between depression and gastric cancer and evaluated the types of papers published in this field and the changes in the direction of research.There is an increasing need for new,clinically relevant studies of this association.

Global research trends and hotspots in overweight/obese comorbid with depression among children and adolescents: A bibliometric analysis

BACKGROUND Overweight/obesity combined with depression among children and adolescents(ODCA)is a global concern.The bidirectional relationship between depression and overweight/obesity often leads to their comorbidity.Childhood and adolescence represent critical periods for physical and psychological development,during which the comorbidity of overweight/obesity and depression may increase the risk of adverse health outcomes.AIM To evaluate the relationship between ODCA,we conduct a bibliometric analysis to aid in formulating prevention and treatment strategies.METHODS From 2004 to 2023,articles related to ODCA were selected using the Science Citation Index Expanded from the Web of Science Core Collection.Bibliometric analysis of relevant publications,including countries/regions,institutions,authors,journals,references,and keywords,was conducted using the online bibliometric analysis platforms,CiteSpace,VOSviewer,and bibliometrix.RESULTS Between 2004 and 2023,a total of 1573 articles were published on ODCA.The United States has made leading contributions in this field,with Harvard University emerging as the leading contributor in terms of research output,and Tanofsky being the most prolific author.The J Adolescent Health has shown significant activity in this domain.Based on the results of the keyword and reference analyses,inequality,adverse childhood experiences,and comorbidities have become hot topics in ODCA.Moreover,the impact of balancedrelated behavior and exploration of the biological mechanisms,including the potential role of key adipocytokines and lipokines,as well as inflammation in ODCA,have emerged as frontier topics.CONCLUSION The trend of a significant increase in ODCA publications is expected to continue.The research findings will contribute to elucidating the pathogenic mechanisms of ODCA and its prevention and treatment.

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

Machine learning based damage state identification:A novel perspective on fragility analysis for nuclear power plants considering structural uncertainties

Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Comparative transcriptome analysis identifies key regulators of nitrogen use efficiency in chrysanthemum

Nitrogen(N)is a limiting factor that determines the yield and quality of chrysanthemum.Genetic variation in N use efficiency(NUE)has been reported among chrysanthemum genotypes.We performed a transcriptome analysis of two chrysanthemum genotypes,'Nannonglihuang'(LH,N-efficient genotype)and'Nannongxuefeng"(XF,N-inefficient genotype),under low N(0.4 mmol L^(-1)N)and normal N(8 mmol L^(-1)N)treatments for 15 d and an N recovery treatment for 12 h(low N treatment for 15 d and then normal N treatment for 12 h)to understand the genetic factors impacting NUE in chrysanthemum.The two genotypes exhibited contrasting responses to the different N treatments.The N-efficient genotype LH had significant superiority in agronomic traits,N accumulation and glutamine synthase activity under both normal N and low N treatments.Low N treatment promoted root growth in LH,but inhibited root growth in XF.Transcriptome analysis revealed that the low N treatment increased the expression of some N metabolism genes,genes related to auxin and abscisic acid signal transduction in the roots of both genotypes,as well as genes related to gibberellin signal transduction in roots of LH.The N recovery treatment just increased the expression of genes related to cytokinin signal transduction in roots of LH.The expression levels of the NRT2.1,AMT1.1,and Gln1 genes related to gibberellin and cytokinin signal transduction were higher in roots of LH than in XF under different N treatments,suggesting that the genes related to N metabolism and hormone(auxin,abscisic acid,gibberellin,and cytokinin)signal transduction in roots of LH are more sensitive to different N treatments than those of XF.Co-expression network analysis(WGCNA)also identified hub genes like bZIP43,bHLH93,NPF6.3,IBR10,MYB62,PP2C,PP2C06 and NLP7,which may be the key regulators of N-mediated responses in chrysanthemum and play crucial roles in enhancing NUE and resistance to low N stress in the N-efficient chrysanthemum genotype.These results revealed the key factors involved in regulating NUE in chrysanthemum at the genetic level,which provides new insights into the complex mechanism of efficient nitrogen utilization in chrysanthemum,and can be useful for the improvement and breeding of high NUE chrysanthemum genotypes.

Global trends of purinergic receptors and depression:A bibliometric analysis from 2003 to 2023

BACKGROUND Depression significantly threatens human health.Purinergic receptors are reported to be associated with depression.However,there is no bibliometric research in this field have been published.AIM To provide some reference for the further research in the field of purinergic receptors and depression utilizing bibliometric analysis.METHODS Relevant researches were retrieved from the Web of Science Core Collection database.The period of the search was from January 1,2003 to December 31,2023.The CiteSpace(6.2.R7)and VOSviewer(1.6.19)were applied to identify the main contributors of countries,authors,institutions,references and journals.Besides,we evaluate keywords to assess the hotspots and trends over the previous 2 decades.RESULTS Totally,247 articles were identified,showing an increasing trend over time.The most productive country,institution,and journal in this field are China,Harvard University,and Biological Psychiatry,respectively.Liang SD and Rodrigues,Ana Lucia S were the most prolific authors.Burnstock G ranked first among the cited authors.The cooperation among countries and disciplines is crucial.The P2X7 receptor provides promising prospects for treating depression and further studies are warranted to validate the scope and significance of depression therapeutic strategies.CONCLUSION This study provides an overview of the worldwide research status and future trends in purinergic receptors and depression.P2X7 receptor is considered an appropriate target for the treatment of depression,as well as neurological diseases.It is implied that based on purinergic system,the future prospects for interventions aimed at depression treatment are promising,showing the way for both augmentation strategies and new drug treatments in the context of the pharmacology of depression.

Transcriptomic and metabolomic analysis of changes in grain weight potential induced by water stress in wheat

The sink strength of developing ovaries in wheat determines the grain weight potential.The period from booting to the grain setting stage is critical for ovary growth and development and potential sink capacity determination.However,the underlying regulatory mechanism during this period by which the wheat plant balances and coordinates the floret number and ovary/grain weight under water stress has not been clarified.Therefore,we designed two irrigation treatments of W0(no seasonal irrigation)and W1(additional 75 mm of irrigation at the jointing stage)and analyzed the responses of the ovary/grain weight to water stress at the phenotypic,metabolomic,and transcriptomic levels.The results showed that the W0 irrigation treatment reduced the soil water content,plant height,and green area of the flag leaf,thus reducing grain number,especially for the inferior grains.However,it improved the grain weight of the superior and inferior grains as well as average grain weight at maturity,while the average ovary/grain weight and volume during–3 to 10 days after anthesis(DAA)also increased.Transcriptomic analysis indicated that the genes involved in both sucrose metabolism and phytohormone signal transduction were prominently accelerated by the W0 treatment,accompanied by greater enzymatic activities of soluble acid invertase(SAI)and sucrose synthase(Sus)and elevated abscisic acid(ABA)and indole-3-acetic acid(IAA)levels.Thus,the sucrose content decreased,while the glucose and fructose contents increased.In addition,several TaTPP genes(especially TaTPP-6)were down-regulated and the IAA biosynthesis genes TaTAR1 and TaTAR2 were up-regulated under the W0 treatment before anthesis,which further increased the IAA level.Collectively,water stress reduced the growth of vegetative organs and eliminated most of the inferior grains,but increased the ABA and IAA levels of the surviving ovaries/grains,promoting the enzymatic activity of Sus and degrading sucrose into glucose and fructose.As a result,the strong sucrose utilization ability,the enhanced enzymatic activity of SAI and the ABA-and IAA-mediated signaling jointly increased the weight and volume of the surviving ovaries/grains,and ultimately achieved the tradeoff between ovary/grain weight and number in wheat under water stress.

Low Birth Weight in Cameroon: Research and Analysis of Factors Associated with Their Occurrence in Douala Maternity Wards

Introduction: Low birth weight (LBW) is defined by the World Health Organization (WHO) as a birth weight strictly below 2500 g, whatever the term of pregnancy. It constitutes a major public health problem, both in developed and developing countries, due to its magnitude and its strong association with infant morbidity and mortality. Main objective was to study the factors associated with the occurrence of small-for-gestational-age newborns in Douala. Methodology: We carried out a cross-sectional analytical study with prospective data collection using a technical pretested sheet in the maternity wards of the Douala General Hospital, the Laquintinie Hospital, and the District hospitals of Deido, Nylon and Bonassama over a period of 4 months (January to April 2020). We were interested in any newborn, born alive, vaginally or by cesarean section, of low weight, seen in the first 24 hours from a full-term single-fetal pregnancy whose mother had given her consent. Our sampling was consecutive and non-exhaustive. We excluded newborns whose term was unclear and those with congenital malformations or signs of embryo-foetopathy. Data collection was done using survey sheets. Statistical analyzes were carried out with CS Pro 7.3 and SPSS version 25.0 software. The Student, Chi-square and Fischer tests were used to compare the means of the variables, the percentages with a significance threshold P value Results: During the study period, 305 full-term newborns were included, divided into 172 boys and 133 girls. The percentage of small-for-gestational-age newborns was 9.8%;after multivariate analysis by logistic regression to eliminate confounding factors, we found maternal factors associated with small for gestational age newborns;maternal age less than 20 years, primiparity, gestational age (37 - 38), a delay in prenatal visits greater than 14 weeks, anemia in pregnancy, positive toxoplasmosis serology in pregnancy, a body mass index of Conclusion: Our study revealed the potential determinants of low birth weight at term in the Cameroonian urban context and specifically in Douala.

Functions of Uni- and Multi-citations： Implications for Weighted Citation Analysis

Purpose： （1） To test basic assumptions underlying frequency-weighted citation analysis： （a） Uni-citations correspond to citations that are nonessential to the citing papers; （b） The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. （2） To explore the degree to which citation location may be used to help identify nonessential citations. Design/methodology/approach： Each of the in-text citations in all research articles published in Issue 1 of the Journal of the Association for Information Science and Technology （JASIST） 2016 was manually classified into one of these five categories： Applied, Contrastive, Supportive, Reviewed, and Perfunctory. The distributions of citations at different in-text frequencies and in different locations in the text by these functions were analyzed. Findings： Filtering out nonessential citations before assigning weight is important for frequency-weighted citation analysis. For this purpose, removing citations by location is more effective than re-citation analysis that simply removes uni-citations. Removing all citation occurrences in the Background and Literature Review sections and uni-citations in the Introduction section appears to provide a good balance between filtration and error rates. Research limitations： This case study suffers from the limitation of scalability and generalizability. We took careful measures to reduce the impact of other limitations of the data collection approach used. Relying on the researcher＇s judgment to attribute citation functions, this approach is unobtrusive but speculative, and can suffer from a low degree of confidence, thus creating reliability concerns. Practical implications： Weighted citation analysis promises to improve citation analysis for research evaluation, knowledge network analysis, knowledge representation, and information retrieval. The present study showed the importance of filtering out nonessential citations before assigning weight in a weighted citation analysis, which may be a significant step forward to realizing these promises. Originality/value： Weighted citation analysis has long been proposed as a theoretical solution to the problem of citation analysis that treats all citations equally, and has attracted increasing research interest in recent years. The present study showed, for the first time, the importance of filtering out nonessential citations in weighted citation analysis, pointing research in this area in a new direction.

Interdisciplinary perspectives on diabetes and microcirculatory dysfunction:A global bibliometric analysis

BACKGROUND The prevalence of diabetes and its association with microcirculatory dysfunction presents a significant challenge in contemporary global health.Addressing this nexus is crucial for developing targeted therapeutic interventions.AIM To trace the progression and delineate the current state of interdisciplinary research concerning diabetes and microcirculation.METHODS Employing a bibliometric approach,this study scrutinizes 12886 peer-reviewed publications retrieved from the PubMed and Web of Science databases.The focus is on elucidating the research trajectory and thematic concentrations at the confluence of diabetes and microcirculation.RESULTS Research outputs have surged since 2011,with the United States,China,and the United Kingdom leading in the quantity and quality of publications.This analysis revealed that journals such as Diabetes Care and The New England Journal of Medicine,along with top research institutions,have significantly contributed to advancing the understanding of microvascular processes affected by diabetes.The central themes identified include inflammation,oxidative stress,and endothelial dysfunction,which are critical in mediating the microvascular complications of diabetes.CONCLUSION This bibliometric evaluation reveals an evolving landscape focusing on diabetes and microcirculatory dysfunction.The complexity of diabetic microvascular issues encouraged multidisciplinary research strategies that are imperative for global health outcomes.