Cardiovascular risk factors among older persons with cognitive frailty in middle income country

BACKGROUND Cognitive frailty,characterized by the coexistence of cognitive impairment and physical frailty,represents a multifaceted challenge in the aging population.The role of cardiovascular risk factors in this complex interplay is not yet fully understood.AIM To investigate the relationships between cardiovascular risk factors and older persons with cognitive frailty by pooling data from two cohorts of studies in Malaysia.METHODS A comprehensive approach was employed,with a total of 512 communitydwelling older persons aged 60 years and above,involving two cohorts of older persons from previous studies.Datasets related to cardiovascular risks,namely sociodemographic factors,and cardiovascular risk factors,including hypertension,diabetes,hypercholesterolemia,anthropometric characteristics and biochemical profiles,were pooled for analysis.Cognitive frailty was defined based on the Clinical Dementia Rating scale and Fried frailty score.Cardiovascular risk was determined using Framingham risk score.Statistical analyses were conducted using SPSS version 21.RESULTS Of the study participants,46.3%exhibited cognitive frailty.Cardiovascular risk factors including hypertension(OR:1.60;95%CI:1.12-2.30),low fat-free mass(OR:0.96;95%CI:0.94-0.98),high percentage body fat(OR:1.04;95%CI:1.02-1.06),high waist circumference(OR:1.02;95%CI:1.01-1.04),high fasting blood glucose(OR:1.64;95%CI:1.11-2.43),high Framingham risk score(OR:1.65;95%CI:1.17-2.31),together with sociodemographic factors,i.e.,being single(OR 3.38;95%CI:2.26-5.05)and low household income(OR 2.18;95%CI:1.44-3.30)were found to be associated with cognitive frailty.CONCLUSION Cardiovascular-risk specific risk factors and sociodemographic factors were associated with risk of cognitive frailty,a prodromal stage of dementia.Early identification and management of cardiovascular risk factors,particularly among specific group of the population might mitigate the risk of cognitive frailty,hence preventing dementia.

Taxation Models and Improvement Proposals for Personal Income Tax in the Sharing Economy

As an innovative economic model,the sharing economy has seen rapid growth globally in recent years.It has not only brought a profound impact on traditional economies but also injected new vitality and momentum into the sustainable development of the social economy.However,for the sharing economy,the process of collecting personal income tax is facing several issues,such as the ambiguity of tax policies regarding personal income,challenges in identifying taxpayers,and difficulties in defining income.To achieve the fairness and efficiency of personal income tax collection in the sharing economy,this study proposes optimized regulatory mechanisms and conducts in-depth discussions on the adjustment of personal income tax policies,innovation in tax management technology,and improvement in the quality of personal income tax services.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

Personality and Nomophobia: A Moderated Mediation Model of Self-Esteem and Non-Self-Determined Solitude

Background:With the development of information technology,mobile phone has brought much convenience to people’s lives but also caused many negative consequences due to excessive use,such as mobile phone addiction and nomophobia.Previous studies have explored the relationship between the Big Five Personality and proble-matic mobile phone use(PMPU).However,they focus on mobile phone addiction.Although there is a correlation between nomophobia and mobile phone addiction,the psychological structure is different.Therefore,it is neces-sary to explore the relationship between personality and nomophobia and the underlying mechanism.This study aims to examine the relationship between Big Five Personality and nomophobia,then construct a moderated mediation model to explore the mediation effect of solitude between Big Five Personality and nomophobia,as well as the moderation effect of self-esteem.Method:Data from 678 college students(351 females,51.77%)were collected.Participants completed the Big Five Personality Inventory,Solitude Behavior Scale,Nomophobia Scale and Self-esteem Scale.Analyses were conducted via mediation and moderated mediation.Results:Structural equation models revealed that solitude mediated the relationship between neuroticism and nomophobia.The results showed that neuroticism positively predicted solitude,which in turn positively predicted nomophobia.Four types of solitude partially mediated the relationship between neuroticism and nomophobia.We also found that self-esteem moderated the association between neuroticism and non-self-determined solitude.It is note-worthy that high self-esteem cannot protect people from negative factors.However,because of its characteristics,it is easy to receive more social information,and people high in neuroticism are sensitive to negative social infor-mation.It may cause maladaptive behavior.Conclusion:Findings demonstrated a process through which neu-roticism relates to nomophobia and a context under which these relationships may have occurred.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively.The current popular Person Search models,whether end-to-end or two-step,are based on anchor boxes.However,due to the limitations of the anchor itself,the model inevitably has some disadvantages,such as unbalance of positive and negative samples and redundant calculation,which will affect the performance of models.To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes,this paper proposes a Deformable-Attention-based Anchor-free Person Search model(DAAPS).Fully Convolutional One-Stage(FCOS),as a classic Anchor-free detector,is chosen as the model’s infrastructure.The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism,applied to guide the model adaptively adjust the perceptual.The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes.The experiment proves the adaptability of the Attention mechanism to the Anchor-free model.Besides,with an improved ResNeXt+network frame,the DAAPS model selects the Triplet-based Online Instance Matching(TOIM)Loss function to achieve a more precise end-to-end Person Search task.Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models,reaching 95.0%of mean Average Precision(mAP)and 95.6%of Top-1 on the CUHK-SYSU dataset,48.6%of mAP and 84.7%of Top-1 on the Person Re-identification in the Wild(PRW)dataset,respectively.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Mshpy23:a user-friendly,parameterized model of magnetosheath conditions

Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind−Magnetosphere−Ionosphere Link Explorer(SMILE)will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under various solar wind conditions after their respective launches in 2024 and 2025.Magnetosheath conditions,namely,plasma density,velocity,and temperature,are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions.We developed a userfriendly model of magnetosheath that parameterizes number density,velocity,temperature,and magnetic field by utilizing the global Magnetohydrodynamics(MHD)model as well as the pre-existing gas-dynamic and analytic models.Using this parameterized magnetosheath model,scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models.First,we created an MHD-based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities(1,5,10,15,20,25,and 30 cm)and 2 interplanetary magnetic field Bz components(±4 nT),and then parameterizing the results in new magnetosheath conditions.We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77,especially for plasma density and plasma velocity.Second,we compiled a suite of magnetosheath models incorporating previous magnetosheath models(gas-dynamic,analytic),and did two case studies to test the performance.The MHD-based model was comparable to or better than the previous models while providing self-consistency among the magnetosheath parameters.Third,we constructed a tool to calculate a soft X-ray image from any given vantage point,which can support the planning and data analysis of the aforementioned LEXI and SMILE missions.A release of the code has been uploaded to a Github repository.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Liver function in transgender persons:Challenges in the COVID-19 era

Transgender persons constitute a non-negligible percentage of the general population.Physical gender-transitioning in trans persons is mainly achieved with hormonal cross-sex therapy and sex reassignment surgeries that aim to align bodily appearance with gender identity.Hormonal treatment acts via suppressing the secretion of the endogenous sex hormones and replacing them with the hormones of the desired sex.The administration of testosterone is the typical masculinizing treatment in trans men,whilst trans women are routinely treated with estradiol agents in combination with anti-androgens or gonadotrophinreleasing hormone agonists if testes are present.Exogenous androgenic steroids,estradiol agents,and anti-androgens have been implicated in a series of hepatotoxic effects.Thus,liver integrity is a major concern with the long-term administration of cross-sex therapy.Hepatic tissue is susceptible to coronavirus disease 19(COVID-19)through various pathophysiological mechanisms.Special consideration should be paid to minimize the risk of hepatic damage from the potential cumulative effect of COVID-19 and gender-affirming treatment in transgender patients.Appropriate care is significant,with continuous laboratory monitoring,clinical observation and,if needed,specific treatment,especially in severe cases of infection and in persons with additional liver pathologies.The pandemic can be an opportunity to provide equal access to care for all and increase the resilience of the transgender population.

Depression and Suicidal Ideations among Older Persons Living with HIV/AIDS in Mbarara City, Southwest Uganda

Background: Due to the increase in longevity and use of antiretroviral treatment, Uganda has had a growing population of older persons living with HIV/AIDS. However, there is a paucity of information on the mental health of the elderly living with HIV/AIDS. In this cross-sectional study, we determined the prevalence of, and associated factors for depression and suicidal ideation among older persons living with HIV/AIDS in Mbarara city, southwest Uganda. Methods: Older persons (150 females, 115 males), with mean age = 64.2 (±5.1) years, accessing health services from three purposively selected HIV/AIDS care centers in Mbarara city, southwest Uganda were recruited. Data on depression and suicidal ideation were collected using a Patient Health Questionnaire (PHQ-9) validated in Uganda, and a structured questionnaire was used to collect data on clinical and socio-demographic characteristics. Data were analysed using logistic regression. Results: Approximately 8.3% and 12.1% had depression and suicidal ideation, respectively. The factors associated with lowering the likelihood of depression were: an increase in the number of family members they stayed with and having no having any problems with their ARVs. On the other hand, earning more than 100,000 Uganda shillings was associated with reducing the risk of suicidal ideations among the participants. Conclusion: Approximately 8 to 12 in 100 older persons living with HIV/AIDS in Uganda have experienced depression or suicidal ideation. Family support and financial control were instrumental factors associated with depression and suicidal ideations, respectively. We recommended strengthening family structures and creating more avenues for financial independence among older persons living with HIV/AIDS to reduce the burden of depression, and suicidal behaviours among this vulnerable population.

Gut Bacteria and Nutritional Status of Nigerian Children at an Internally Displaced Persons’ Camp in Benue State, Nigeria: A Pilot Study

This study was conducted to determine the gut bacteria and nutritional status of children (n = 30) aged 2 - 11 in Benue’s largest internally displaced persons (IDP) camp since information on this is lacking. Gut bacteria were identified using culture techniques, while Body Mass Index (Kg/m2), Weight-for-Height (WHZ), and Weight-for-Age (WAZ) z scores were computed from anthropometric measurements. Socio-demographic and economic variables were collected via structured questionnaires. IBM SPSS v25 was used to analyze the data, with p Salmonella spp., Shigella spp., and Escherichia coli compared to children from a nearby private school (n = 10), except for E. coli, where the prevalence was equal. The results for BMI revealed that 23 (57.5%) of the children had a healthy weight while 17 (42.5%) were underweight.WAZ z-scores were between (-0.02 - 2.51) with evidence of mildly underweight (20%) and mildly overweight (5%) children. WHZ z-scores were between -0.03 - 2.37, with moderately wasted (30%) and severely wasted (5%) found. To ensure better health outcomes for residents, conditions in the camp must be improved.