The relationship between chronic PTSD,cortical volumetry and white matter microstructure among Australian combat veterans

Background:Posttraumatic stress disorder(PTSD)has been associated with volumetric and white matter microstructural changes among general and veteran populations.However,regions implicated have greatly varied and often conflict between studies,potentially due to confounding comorbidities within samples.This study compared grey matter volume and white matter microstructure among Australian combat veterans with and without a lifetime diagnosis of PTSD,in a homogenous sample assessed for known confounding comorbidities.Methods:Sixty-eight male trauma-exposed veterans(16 PTSD-diagnosed;mean age 69 years)completed a battery of psychometric assessments and underwent magnetic resonance and diffusion tensor imaging.Analyses included tractbased spatial statistics,voxel-wise analyses,diffusion connectome-based group-wise analysis,and volumetric analysis.Results:Significantly smaller grey matter volumes were observed in the left prefrontal cortex(P=0.026),bilateral middle frontal gyrus(P=0.021),and left anterior insula(P=0.048)in the PTSD group compared to controls.Significant negative correlations were found between PTSD symptom severity and fractional anisotropy values in the left corticospinal tract(R^(2)=0.34,P=0.024)and left inferior cerebellar peduncle(R^(2)=0.62,P=0.016).No connectome-based differences in white matter properties were observed.Conclusions:Findings from this study reinforce reports of white matter alterations,as indicated by reduced fractional anisotropy values,in relation to PTSD symptom severity,as well as patterns of reduced volume in the prefrontal cortex.These results contribute to the developing profile of neuroanatomical differences uniquely attributable to veterans who suffer from chronic PTSD.

Association between maternal gestational diabetes and allergic diseases in offspring:a birth cohort study

Background Previous studies have linked gestational diabetes(GDM)with allergies in offspring.However,the effect of specific glucose metabolism metrics was not well characterized,and the role of polyunsaturated fatty acids(PUFAs),a modifier of metabolism and the immune system,was understudied.We aimed to investigate the association between maternal GDM and allergic diseases in children and the interaction between glucose metabolism and PUFAs on allergic outcomes.Methods This prospective cohort study included 706 mother–child dyads from Guangzhou,China.Maternal GDM was diagnosed via a 75-g oral glucose tolerance test(OGTT),and dietary PUFAs were assessed using a validated food frequency questionnaire.Allergic disease diagnoses and the age of onset were obtained from medical records of children within three years old.Results Approximately 19.4%of women had GDM,and 51.3%of children had any allergic diseases.GDM was positively associated with any allergic diseases(hazard ratio[HR]1.40;95%confidence interval(CI)1.05–1.88)and eczema(HR 1.44;95%CI 1.02–1.97).A unit increase in OGTT after two hours(OGTT-2 h)glucose was associated with an 11%(95%CI 2%–21%)higher risk of any allergic diseases and a 17%(95%CI 1–36%)higher risk of food allergy.The positive associations between OGTT-2 h glucose and any allergic diseases were strengthened with decreased dietary a-linolenic acid(ALA)and increased n-6 PUFAs,linoleic acid(LA),LA/ALA ratio,and n-6/n-3 PUFA ratio.Conclusions Maternal GDM was adversely associated with early-life allergic diseases,especially eczema.We were the first to identify OGTT-2 h glucose to be more sensitive in inducing allergy risk and that dietary PUFAs might modify the associations.

Clinically adaptable machine learning model to identify early appreciable features of diabetes

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood sugar.This disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,etc.The aim of this study was to propose a machine learning based prediction model that detected diabetes at the beginning.Methods In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,Sylhet.Then,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this questionnaire.However,we accumulated patient records of these two datasets.In the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive model.The performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating characteristic.Finally,these outcomes were interpreted using Shapley additive explanations methods and identified relevant features for happening diabetes.Results In this work,different classifiers were shown their performance where ET outperformed any other classifiers with 97.11%accuracy for the Sylhet Diabetes Hospital dataset(SDHD)and MLP shows the best accuracy(96.42%)for the collected dataset.Subsequently,HGBC and LGBM provide the highest 94.90%accuracy for the combined datasets individually.Conclusion LGBM,stacking,HGBC,RF,ET,bagging,and GBC might represent more stable prediction results for each dataset.

Sharing Results with Clinical Trial Participants： Insights from an Online Survey of Chinese Consumers

To the Editor：Clinical trial participants have a right to know the results from the trials that they enable. European and North American research,however,shows that trial results are rarely shared with participants. Given the increase in industry-sponsored trials in China,we conducted the first investigation into Chinese consumer views about sharing the clinical trial results with participants.

基于家庭的早期干预对2岁儿童体质指数的影响：随机对照试验

目的评估基于家庭的早期干预对2岁儿童体质指数（BMI）的影响。设计随机对照试验。背景健康开端项目（Healthy Beginnings Trial）实施时间是2007至2010年，地点是澳大利亚悉尼社会和经济落后地区。