There is an ongoing debate about whether, and to what extent, males differ from females in their language skills. In the case of handwriting, a composite language skill involving language and motor processes, behavioral observations consistently show robust sex differences but the mechanisms underlying the effect are unclear. Using functional magnetic resonance imaging (fMRI) in a copying task, the present study examined the neural basis of sex differences in handwriting in 53 healthy adults . Compared to females, males showed increased activation in the left posterior middle frontal gyrus (Exner's area), a region thought to support the conversion between orthographic and graphomotor codes. Functional connectivity between Exner's area and the right cerebellum was greater in males than in females. Furthermore, sex differences in brain activity related to handwriting were independent of language material. This study identifies a novel neural signature of sex differences in a hallmark of human behavior, and highlights the importance of considering sex as a factor in scientific research and clinical applications involving handwriting.  All participants were native Chinese speakers and were right‐handed as assessed by a handedness inventory  as quickly and accurately as possible within 3 min. The score were calculated by this equation: score = attack −. A digit span task was employed for evaluating verbal working memory in which participants were asked to repeat a series of digits in forward (range 3 to 12 digits) and backward order (range 3 to 10 digits). The test was terminated when participants failed in two consecutive trials of the same length, and the working memory span was the number of digits in the last successful trials.Finally, a visual‐motor integration test was conducted to assess low‐level visual‐motor skill in which participants were asked to copy 12 geometric symbols varying in visual complexity as accurately as possible. Two independent evaluators assessed the similarity between samples and participants' responses using a 7‐point scale, and the score was the sum of each symbol. The inter‐rater reliability of the assessment was good (intra‐class correlation coefficients [ICC] = 0.86).There were no differences in all reading, cognitive and motor tests between males and females, suggesting that these abilities were well‐matched between groups. Detailed information of all the participants was presented in Table 2.2Modern Chinese Frequency Dictionary (Participants were instructed to perform a handwriting task (copying Chinese characters) and a control task (drawing nonsense symbols). Thirty characters were selected for presentation, of which one half were of “high frequency”  and the other half were of “low frequency” (< 5 times per million), according to the ctionary . Visual Self‐paced  writing is a commonly used task paradigm in investigations of the neural basis of handwriting  and three blocks of drawing symbols, in pseudo‐random order. Each block included visual presentation of instructions for 2 s followed by five trials. In each trial, a “+” symbol was first presented visually and centrally for 0.3 s, followed by presentation of a character stimulus for 1 s and then a response period of 4.7 s. Three blocks of central fixation each with 12 s duration were also interspersed among the task and control blocks as a “rest” condition. The total duration was 318 s. Detailed information about the experimental design has been reported previously  at the Beijing MRI Center for Brain Research of the Chinese Academy of Sciences. Functional MRI time series data with blood oxygenation level‐dependent (BOLD) contrast were acquired using a two‐dimensional, T2*‐weighted, gradient‐echo echo planar imaging (EPI) sequence  to the end of the last written or drawn stroke of the response. A 2  by 3 (stimulus type: high‐frequency character vs. low‐frequency character vs. symbols) analysis of variance (ANOVA) was conducted.3.1.2Writing quality was evaluated by two independent  examiners using a 7‐point scale (1 = very bad and 7 = very good) based on the natural written scripts. The inter‐rater reliability was high (ICC = 0.91). This assessment was based on six dimensions, including stroke form, slant, organization of radicals, neatness, average size and overall appearance . The fMRI time series data for each participant were first corrected for head motion, and the corrected images were co‐registered to the associated anatomical imaging data. The anatomical images were transformed into Montreal Neurological Institute (MNI) stereotactic space, and the resulting transformation parameters were then applied to yield fMRI time series data normalized in MNI space with cubic voxels at 2 mm × 2 mm × 2 mm spatial resolution. These images were then spatially smoothed using an isotropic Gaussian kernel template with 6 mm full‐width at half‐maximum. The data for one male participant were not examined further, because the exclusion criteria for head motion were exceeded (>2.5 mm translation or > 2.5° rotation). Data for another female participant were excluded due to substantial signal loss in the prefrontal cortex.Image preprocessing and statistical analyses were conducted using SPM8 freeware  corrected at the cluster level. Brain regions were estimated from the Talairach atlas  method was used to generate activation maps for handwriting characters and drawing symbols for each participant. The GLM design matrix included the block design time series convolved with a canonical hemodynamic response function. To minimize residual motion artifacts, head movement parameters (estimated with six degrees of freedom during the motion correction step) were included in the design matrix as nuisance covariates. The data were high‐pass filtered at 0.008 Hz. At the first level, writing activation maps reporting the contrast between copying characters and drawing symbols were generated for high‐frequency and low‐frequency characters for each participant. The activation maps were then entered into a random‐effect ANOVA to examine the effects of group and condition, and their interaction. To identify within‐group activation specific to handwriting, separate brain activation maps for males and females were computed separately, using one‐sample β estimates) were extracted from functional ROIs showing sex differences in activation for each participant, which were then correlated with the writing completion time in high‐frequency and low‐frequency conditions. Because the correlation analysis was done twice (high‐frequency and low‐frequency conditions), the significance threshold was set at p < .025, corresponding to p < .05 after applying the Bonferroni correction for multiple comparisons.Brain‐behavior correlation analysis was conducted to further confirm that the sex differences in brain activity are linked to real handwriting practice in daily life. Pearson's correlation coefficients were calculated between brain activation and post‐imaging pen‐and‐paper writing performance. To do this analysis, contrast estimates  analysis  were extracted from functional ROIs showing sex differences in functional connectivity for each participant, which were then correlated with the pen‐and‐paper writing time.44.14.1.1p = .619) and sex , nor the stimulus type by sex interaction  were significant. For writing duration, the main effect of stimulus type was significant , but the main effect of sex  and sex‐by‐stimulus type interaction  were not significant. Post hoc pairwise comparisons showed that symbol drawing duration was significantly longer than writing high‐frequency characters  and writing low‐frequency characters , but there was no significant difference in duration between the two‐character conditions .Figure 4.1.2SD) was 23.33 (6.66) for males and was 27.3 (5.5) for females, respectively, and females performed better than males   of high‐frequency characters was 53.09 ms (7.82 ms) for males and was 49.86 ms (8.98 ms) for females, while for low‐frequency characters, the time was 52.92 ms (9.48 ms) for males and was 48.98 ms (9.83 ms) for females. However, there were no significant sex differences for the writing time (high‐frequency: t(51) = 1.4, p = .168; low‐frequency: ).The mean writing quality score  of the dichotomous sex judgment was 0.62 (0.07), which was significantly higher than the chance level . For the masculinity/femininity evaluation of written script, the average score (and SD) was 3.35 (1.14) for males, and 4.35 (1.14) for females, respectively, with the female writing rated significantly more feminine (t(51) = 3.16, p = .002)  , corresponding to Exner's area, and the left postcentral gyrus (PoCG)   and the left PoCG .To verify that the differences in activation were specific to handwriting, activation in these two regions was also investigated in the symbol drawing condition. Independent two‐sample p = .026), but not in the low‐frequency condition   and the low‐frequency condition .Correlation analysis revealed that greater activity in Exner's area was marginally significantly correlated with slower writing completion time during pen‐and‐paper writing in the high‐frequency condition  and in females (AI = −0.22), but was bilateral in the low‐frequency condition in males (AI = −0.15) and in females (AI = −0.12). The activity in the cerebellum was right‐lateralized in both males (high‐frequency: AI = −0.55 and low‐frequency AI = −0.63) and females (high‐frequency AI = −0.57 and low‐frequency AI = −0.6). However, no significant gender differences in AI values were observed for any of the ROIs investigated.Figure 4.1.5p = .56) and the low‐frequency condition .As only Exner's area showed reliable sex differences across different writing status, it was selected as the seed for functional connectivity analysis. The result revealed that males had stronger functional connectivity between Exner's area and the right cerebellum (peak at 4 –72 −8) Figure a. No oth5in Chinese. Functional MRI measures revealed differences in the activation of the left posterior middle frontal gyrus (Exner's area), irrespective of language material. Moreover, functional connectivity between Exner's area and the cerebellum was different between males and females, suggesting the sex difference in neural integration underlying handwriting. The activity in Exner's area was also found to be correlated with actual writing behaviors, suggesting that Exner's area might be a neural substrate of sex differences in handwriting. Collectively, this study unfolds a novel neural signature of sex differences in a hallmark of human behavior, highlighting the role of sex in handwriting in scientific research and clinical application.The aim of this study was to explore the neural basis underlying sex differences in handwriting, a hallmark of human language and motor skill. Using fMRI in a copying task, this study compared brain activation and functional connectivity during handwriting between males and females whose age, education level, reading, cognitive and visual‐motor skills were well‐matched. In line with previous findings in other language systems . Moreover, the significant correlations between the activity in Exner's area and behavioral performance metrics reinforced the relevance of the brain activation in this area to writing, implying that Exner's area is a specific neural substrate of sex differences in handwriting.Evidence from pure agraphia (Anderson, Damasio, & Damasio, Alternatively, several fMRI studies have demonstrated that the activity in the middle frontal sulcus was modulated by word length in alphabetic languages (Rapp & Dufor, right‐lateralized or bilateral activation in VWFA, contrary to the fMRI study by Planton et al. (Brain lateralization provides a key biological account for sex differences in language processing. In the present study, lateralization analysis demonstrated that males exhibited left‐lateralized activation in Exner's area during handwriting, while females exhibited bilateral. This pattern is consistent with previous findings of sex differences in other language tasks (Baxter et al., n et al.  who repon et al. , which wn et al. , and thun et al. . This clThe study also investigated how and to what extent functional interactions between writing‐related regions contribute to sex differences in handwriting. The gPPI analysis indicated that the connectivity between Exner's area and the right cerebellum was stronger in males than in females. Specifically, males showed positive connectivity during the writing task compared to the symbol‐drawing baseline, whereas females showed a negative pattern. Cerebellum is a key neural substrate of handwriting that has been reported in multiple writing tasks, and it has been proposed that it supports the motor execution in handwriting (Planton et al., Another finding of this study is that males showed increased brain activity in the left postcentral gyrus compared to females. Like the pattern of Exner's area, this difference originated from lower activation in writing character relative to drawing symbols in females. Although the left PoCG has been consistently found to be involved in handwriting (Katanoda et al., Finally, our findings have methodological implications for neuroscience research of handwriting, supporting the view that sex differences should be taken into account in neuroscience research studies (Bale & Epperson, 6The sample size was relatively small for a sex differences study, which would increase the risk of false‐positive detection (Etchell et al., Secondly, development is a critical factor influencing the pattern of sex differences at both behavioral (Vlachos & Bonoti, Thirdly, we only measured the motor response duration at the whole character level, and thus it is unclear how and to what extent sex differences occur at the sub‐character level. According to the homothety principle, the motor time of individual letters would be invariant, although the duration of the whole word may vary (Viviani & Terzuolo, 7This study addressed the question of the neural basis of sex differences in handwriting, a unique form of language and motor behavior. Males and females were shown to have different utilization of a writing‐specific brain region, namely Exner's area, and this has a plausible link to behavioral sex differences in handwriting. The work has methodological implications for neuroimaging studies of handwriting in normal and patient participants.The authors declare that they have no conflict of interest.Y.Y., Z.T.Z., F.T., S.J.G., and H.Y.B. conceived and designed the experiment. Y.Y., J.J.L., C.Y.G., and Z.T.Z. performed the experiment. Y.Y., Z.T.Z., N.Z.W., J.J.L., and R.T. performed the data analyses. Y.Y., Z.T.Z., and H.Y.B. co‐wrote the paper. Y.Y., Z.T.Z, F.T., S.J.G., G.C.S., N.Z.W., R.T., J.J.L., and H.Y.B. discussed the data and commented on the manuscript.


Genome‐wide 5hmC profiling in cell‐free DNA acquired from CRC patients, adenoma patients, and healthy individuals revealed that the differential 5hmC‐modified regions were gathered into four clusters with no overlap, although there are a few overlapped genes shared between the different clusters. CRC patients with adenoma history showed exclusive 5hmC‐gain characteristics, which was consistent with the ‘parallel’ evolution hypothesis in adenoma. Approximately 85% colorectal cancers (CRCs) are thought to evolve through the adenoma‐to‐carcinoma sequence associated with specific molecular alterations, including the 5‐hydroxymethylcytosine (5hmC) signature in circulating cell‐free DNA (cfDNA). To explore colorectal disease progression and evaluate the use of cfDNA as a potential diagnostic factor for CRC screening, here, we performed genome‐wide 5hmC profiling in plasma cfDNA and tissue genomic DNA (gDNA) acquired from 101 samples (63 plasma and 38 tissues), collected from 21 early‐stage CRC patients, 21 AD patients, and 21 healthy controls (HC). The gDNA and cfDNA 5hmC signatures identified in gene bodies and promoter regions in CRC and AD groups were compared with those in HC group. All the differential 5hmC‐modified regions (DhMRs) were gathered into four clusters: Disease‐enriched, AD‐enriched, Disease‐lost, and AD‐lost, with no overlap. AD‐related clusters, AD‐enriched and AD‐lost, displayed the unique 5hmC signals in AD patients. Disease‐enriched and Disease‐lost clusters indicated the general 5hmC changes when colorectal lesions occurred. Cancer patients with a confirmable adenoma history segmentally gathered in AD‐enriched clusters. KEGG functional enrichment and GO analyses determined distinct differential 5hmC‐modified profiles in cfDNA of HC individuals, AD, and CRC patients. All patients had comprehensive 5hmC signatures where Disease‐enriched and Disease‐lost DhMR clusters demonstrated similar epigenetic modifications, while AD‐enriched and AD‐lost DhMR clusters indicated complicated subpopulations in adenoma. Analysis of CRC patients with adenoma history showed exclusive 5hmC‐gain characteristics, consistent with the ‘parallel’ evolution hypothesis in adenoma, either developed through the adenoma‐to‐carcinoma sequence or not. These findings deepen our understanding of colorectal disease and suggest that the 5hmC modifications of different pathological subtypes (cancer patients with or without adenoma history) could be used to screen early‐stage CRC and assess adenoma malignancy with large‐scale follow‐up studies in the future. Appr% worldwiet al. ). The sample with 5hmC‐DNA capture and enrichment efficiency higher than 100 folds and mapping rate of more than 90% was used for further analysis. Peaks with high enrichment and significance  in all samples were considered as highly reliable peaks and were combined into one unified catalog by the ‘mergePeak’ function from homer (version 4.9.1) [The raw sequencing reads were removed with low‐quality (less than Q20) reads and aligned to the human reference genome (hg19/GRCh37). Model‐based analysis of ChIP‐seq [n 4.9.1)  . The genomic annotation of 5hmC peak regions was performed using annotatr . The met2.4edger of the r package [P value < 0.05. A heatmap of r package (https://cran.r‐project.org/web/packages/pheatmap/index.html) was used to visualize hierarchical clustering and the distance in a heatmap figure [r package ‘topgo’ (http://www.bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO.pdf), and KEGG pathway enrichment was conducted using kobas online tools (http://kobas.cbi.pku.edu.cn/) [Genes with differential 5hmC levels were detected in all the tissue and plasma samples using the software  package . The denp figure . Gene Onedu.cn/) .33.1H‐test  Fig. . The 5hm3.3P value ≤ 0.01 , whereas the cfDNA 5hmC metagene profiles showed an overall reduction in CRC, consistent with previous studies [et al. [et al. [We sequenced cfDNA to generate genome‐wide 5hmC profiles in the plasma of CRC patients, precancerous AD patients, and HC individuals, and sequenced the gDNA in the corresponding CRC and AD tissues. The robust tumor‐associated gDNA and cfDNA 5hmC signatures were identified in the gene body and promoter regions of the CRC and AD groups compared with the HC group. The majority of genome‐wide hMR distribution in all three plasma samples were observed in the intragenic and promoter regions, whereas fewer were found in the intergenic regions, which is consistent with the results of previous investigations , 27. The studies , 42. MorAll the cfDNA DhMRs were gathered into four clusters: Disease‐enriched, AD‐enriched, Disease‐lost, and AD‐lost, with no overlap. This suggests that the cfDNA 5hmC modifications among HCs, AD patients, and CRC patients are quite distinct. None of the clusters could separate pathologic subtypes of stage I and II CRC or non‐advanced and advanced adenoma, and the comparison result in different stages CRC was similar to a previous investigation of CRC . The KEGThe 5hmC signature in the CRC group was more comprehensive and contained two features: one similar to the HC group (AD‐enriched and AD‐lost), and the other similar to the AD patients (Disease‐enriched and Disease‐lost). This was in agreement with previous evidence that a major part of CRCs evolved through the adenoma‐to‐carcinoma sequence, and the remainder did not , 43. AD 5In summary, we generated 5hmC profiles of cfDNA from Chinese CRC patients, AD patients, and HC individuals, as well as the gDNA 5hmC profiles for tumor and adenoma tissue. Large‐scale 5hmC changes in promoter and gene body regions were detected. All the cfDNA DhMRs were gathered into four clusters: Disease‐enriched, AD‐enriched, Disease‐lost, and AD‐lost, which did not overlap. CRC patients with a confirmable adenoma history segmentally gathered in the AD‐enriched cluster. This provides a comprehensive and global overview of epigenetic changes in colon disease states between adenoma and early‐stage CRC. It suggested that there are two pathologic subtypes of precancerous adenoma: one could progress to malignant lesion through adenoma‐to‐carcinoma sequence and the other not. The 5hmC modification changes with clear pathological evidences, that is, CRC with or without adenoma history, could expand the molecular biological knowledge of the colorectal disease process and benefit early‐stage CRC screening. This finding suggests the potential values of using 5hmC as diagnostic biomarkers to develop new strategies for CRC diagnosis through large‐scale studies on patients with clear colorectal disease history and long‐enough follow‐up surveys to evaluate the possibility of adenoma malignancy.Shuangxiu Wu, Wendy Wu, Lu Zheng, Gaojing Liu, Xiaoling Li, and Fuming Sun are the employees of BerryOncology Corporation. The other authors declare that they have no conflict of interest.JT and ZX conceived and designed the study. JT, SW, and ZX designed the methodology. LZ and XL performed the laboratory experiments. FS, GL, SW, and WW analyzed the data. WW and SW wrote and revised the manuscript. ZX, JT, CW, ML, and ZY involved in acquisition of material . JT, YY, SL, WS, XF, QC, and XT gave the important intellectual input. JT supervised the study.Fig. S1. Representative images of colorectal cancer (Stage I and Stage II), advanced tubular adenoma, Villus tubular adenoma and non‐advanced adenoma tissue.Fig. S2. General procedure of 5hmC sequencing profiling from gDNA and cfDNA.Fig. S3. Kruskal‐Wallis H‐test among CRC, AD and HC groups (A) for age distributions, (B) for gender distributions.Fig. S4. Heatmap of differentially 5hmC hMRs (FC ≥ 2 and p ≤ 0.01) with stage status  in Disease‐enriched cluster (A), in Disease‐lost cluster (B) and in AD‐lost cluster (C).Fig. S5. KEGG enrichment analysis of significant 5hmC regulated regions increased (left and red) and decreased (right and blue) between CRC and healthy control groups (A), between adenoma and healthy control groups (B), and between CRC and adenoma groups (C).Fig. S6. Volcano plots of all plasma 5hmC DhMRs (A) between CRC and healthy control groups, (B) between adenoma and healthy control groups and (C) between CRC and adenoma groups.Fig. S7. Heatmap of the top 20 DhMRs (A) between tumor and healthy control groups, (B) between adenoma and healthy control groups and (C) between tumor and adenoma groups.Click here for additional data file.Table S1. Clinical characteristics of colorectal cancer and precancerous adenoma patients and healthy controls.Click here for additional data file.Table S2. Annotated genes in 4 clusters, Disease‐enriched, AD‐enriched, Disease‐lost and AD‐lost.Click here for additional data file.Table S3. Annotated genes of differential 5hmC change regions (DhMRs) in the overlaps of 4 clusters.Click here for additional data file.Table S4. Statistics of differential 5hmC change regions (DhMRs) in cfDNAs in three comparisons, CRC vs HC groups, AD vs HC groups, CRC vs AD groups.Click here for additional data file.Table S5. Genes annotated in the top 20 differential hMRs (DhMRs) of cfDNA in three comparison sets, CRC group vs HC group, CRC group vs AD group and AD group vs HC group.Click here for additional data file.Table S6. GO enrichment and annotation of DhMRs of 4 clusters.Click here for additional data file.Supplementary MaterialClick here for additional data file.


Information regarding efficacy of the angiotensin II receptor blocker, telmisartan, for treatment of proteinuria in dogs is limited.To evaluate the antiproteinuric efficacy of telmisartan, as compared to enalapril, in dogs with chronic kidney disease and persistent, renal proteinuria.Thirty‐nine client‐owned dogs with chronic kidney disease and urinary protein‐to‐creatinine ratio (UPC) > 0.5 (if azotemic) or ≥ 1.0 (if nonazotemic).In this prospective, randomized, double‐masked clinical trial, dogs were block randomized, according to presence or absence of azotemia and systemic arterial hypertension, to receive telmisartan (1.0 mg/kg PO q24h), or enalapril (0.5 mg/kg PO q12h), and followed for 120 days. Up‐titration of study drug dosage on days 30 and 60, and addition of the other study drug at day 90, were performed if UPC > 0.5 was noted at these visits. Percentage change in UPC relative to baseline was calculated for all time points. Data are presented as median (range).P = .002). Among dogs persistently proteinuric at earlier visits, telmisartan remained superior to enalapril at days 60 (P = .02) and 90 (P = .02). No difference in percentage change in UPC between study groups was observed at day 120, when combination therapy was allowed. Combination therapy resulted in relevant azotemia in 4/13 (31%) dogs.Thirty‐nine  dogs were included. At day 30, percentage change in UPC was greater for telmisartan‐treated (−65% [−95% to 104%]) vs enalapril‐treated (−35% [−74% to 87%]) dogs (Telmisartan might be a suitable first‐line therapy for dogs with renal proteinuria. ACEangiotensin‐converting enzymeACEiangiotensin‐converting enzyme inhibitorAng IIangiotensin IIARBangiotensin receptor blockerCKDchronic kidney diseaseCrblood creatinine concentrationHcthematocritIRISInternational Renal Interest SocietyKblood potassium concentrationRAASrenin‐angiotensin‐aldosterone systemSBPsystolic arterial blood pressureUPCurinary protein‐to‐creatinine ratio%CrΔpercentage change in blood creatinine concentration relative to baseline value%HctΔpercentage change in hematocrit relative to baseline value%KΔpercentage change in blood potassium concentration relative to baseline value%UPCΔpercentage change in urinary protein‐to‐creatinine ratio relative to baseline value1Chronic kidney disease (CKD) affects up to 1.4% of the general canine populationAngiotensin‐converting enzyme inhibitors (ACEi), such as enalapril, decrease proteinuria in experimentally‐inducedAngiotensin II receptor blockers (ARBs) are commonly prescribed to human patients with renal proteinuria.The objective of this study was to determine the short‐term efficacy of telmisartan, compared to a standard dose of enalapril, for the reduction of proteinuria in dogs with persistent pathologic renal proteinuria. We hypothesized that telmisartan would produce a greater percentage reduction in UPC than would enalapril when administered for 30 days. As secondary objectives, we sought to evaluate the efficacy of a dosage‐escalation protocol for dogs in which “standard” dosages of either drug were unsuccessful in controlling proteinuria, and to determine whether treatment with a combination of telmisartan and enalapril for 30 days would lead to a clinically significant UPC reduction in dogs that were persistently proteinuric on “ceiling” dosages of either monotherapy. We hypothesized that telmisartan would lead to a greater and faster reduction in UPC than would enalapril, and that clinically important reductions in UPC would be noted with combination therapy. A final objective of this study was to evaluate the safety of telmisartan and enalapril when administered at progressively greater dosages and when coadministered to a sample of dogs with naturally‐occurring CKD.22.1This was a prospective, randomized, single‐center, double‐masked clinical trial. All procedures were approved by the Clinical Research Committee of the University of Georgia's College of Veterinary Medicine . Informed owner consent was obtained prior to enrollment.2.2Dogs with persistent, pathologic, renal proteinuria due to CKD were recruited prospectively from client‐owned dogs presented to the University of Georgia's Veterinary Teaching Hospital. Dogs of any age and body weight were considered if they had persistent proteinuria with UPC >0.5 if azotemic , or ≥1.0 if nonazotemic , documented in ≥2 urine samples collected ≥14 days apart, and abdominal ultrasound findings consistent with CKD.Dogs were excluded if any 1 or more of the following were identified: urolithiasis; urogenital neoplasia; evidence of hemorrhage, inflammation or bacteria on urine sediment; positive urine culture; positive heartworm antigen test within 3 months of identification of proteinuria or known lack of treatment with regular monthly heartworm preventive; findings suggestive of acute kidney injury, infectious nephropathy or lower urinary tract disease; average indirect systolic arterial blood pressure (SBP) <120 mm Hg; moderate hyperkalemia (blood potassium concentration [K] >6.5 mmol/L); history of having received renin‐angiotensin‐aldosterone system (RAAS) antagonists, corticosteroids, or both, in the 14 days preceding enrollment; and concurrent illness , for which specific treatment might result in mitigation of proteinuria. Dogs with hyperadrenocorticism and diabetes mellitus were not excluded if these diseases were considered clinically and biochemically controlled for at least 30 days prior to enrollment.2.3Dogs were categorized according to presence or absence of systemic arterial hypertension  and azotemia . Based on this categorization, dogs were assigned to 1 of 4 groups: nonazotemic, normotensive; nonazotemic, hypertensive; azotemic, normotensive; and azotemic, hypertensive. Within each group, dogs were block randomized in blocks of 4 with a 1 : 1 treatment allocation ratio .2.4Dog owners, investigators, and study personnel were masked to each dog's treatment group. One of the study investigators (BNL) was unblinded if removal of a dog from the study was necessary for the evaluation or treatment of an adverse event. After completion of each dog's study period, treatment allocation was revealed to 1 of the study investigators (BNL) to enable ongoing treatment recommendations for that dog prior to the conclusion of the study.2.5Dogs were randomized to receive telmisartan solution  at an initial dosage of 1.0 mg/kg PO in the morning and an equal volume of placebo in the evening, or enalapril suspension  at an initial dosage of 0.5 mg/kg q12h PO. Concentrations of telmisartan (10 mg/mL) and enalapril (5 mg/mL) allowed for equivalency of volume administered per kg body weight (0.1 mL/kg) regardless of treatment group. Enalapril was compounded by the UGA Veterinary Teaching Hospital Pharmacy into a suspension using preserved simple syrup and using the standards for compounding provided by the United States Pharmacopeia.2.6Dogs receiving amlodipine at the time of enrollment were not excluded. In those dogs for which persistent, severe systolic arterial hypertension 2.7This study consisted of 2 phases. Phase I was a 30‐day period during which dogs received telmisartan or enalapril at “standard” doses. Phase II was a subsequent 90‐day period during which monthly up‐titration of study drug dosage and subsequent addition of the other study drug was performed step‐wise to target UPC ≤0.5 in persistently proteinuric dogs. All dogs were reevaluated at the end of the 120‐day study period. The monitoring protocol used in the present study was adapted from recommendations of the American College of Veterinary Internal Medicine.Dirofilaria immitis antigen test  or a combined test for Anaplasma phagocytophilum, Anaplasma platys, Borrelia burgdorferi, and Ehrlichia canis and Ehrlichia ewingii antibodies and D. immitis antigen , and urine obtained by cystocentesis for urinalysis and urine culture, were collected if these data had not been obtained in the 14 days prior to enrollment. In addition, whole blood biochemical analyses  for variables indicative of renal function was performed to provide baseline values that would be directly comparable to follow‐up values. Dogs underwent abdominal ultrasonographic examination, performed by a board‐certified veterinary radiologist or a trainee under the supervision of a radiologist, if such an examination had not been performed within the 8 weeks preceding enrollment. Endocrine disease testing was performed if clinical signs or bloodwork findings were suspicious for hyperadrenocorticism or hypothyroidism. All laboratory analyses were performed by a single laboratory .At enrollment (day 0), physical examination data, indirect SBP, and urine for UPC measurement were obtained for all dogs. In addition, blood for measurement of hematocrit (Hct), serum biochemical analyses, as well as Systolic blood pressure was measured at all visits using Doppler ultrasonography , following an acclimation period of at least 10 minutes and before physical examination, venipuncture or cystocentesis, in a manner conforming to guidelines set forth by the American College of Veterinary Internal Medicine.2.8Scheduled rechecks were performed on days 7 ± 1 and 30 ± 2 for all dogs, at which time physical examination data, SBP, and whole blood for renal biochemical analyses were obtained. Urinalysis and UPC measurement were repeated on day 30 ± 2.In hypertensive dogs for which SBP ≥180 mm Hg was documented at day 7 ± 1, amlodipine was increased to 0.1 mg/kg PO q12h, and SBP and Cr rechecked at 7‐day intervals. At follow‐up visits, amlodipine dosage was increased in increments of 0.05 mg/kg q12h to a maximum dose of 0.3 mg/kg q12h, to target SBP <180 mm Hg.Dogs were removed from the study if an increase in Cr of ≥30% compared to baseline or moderate hyperkalemia was identified at any recheck, or if hypotension  was identified in a dog not receiving amlodipine. Dogs receiving amlodipine could remain in the study if dosage decrease or discontinuation led to resolution of hypotension. Study medication dosage decreases were not permitted.2.9Dogs with UPC ≤0.5 at day 30 ± 2 continued to receive study drug at the originally prescribed dosage and were not reevaluated until the end of the study (day 120 ± 2). For dogs with UPC >0.5 at day 30 ± 2, study drug dosage was increased to 2.0 mg telmisartan/kg q24h or 1.0 mg enalapril/kg q12h according to treatment group. At day 60 ± 2, UPC was reevaluated in dogs undergoing dosage up‐titration at day 30 and dogs either continued to receive study drug at the same dosage until final study recheck on study day 120 (those with UPC ≤0.5), or underwent dosage up‐titration to 3.0 mg telmisartan/kg q24h or 1.5 mg enalapril/kg q12h (those with UPC >0.5). At day 90 ± 2, UPC was reevaluated in dogs undergoing dosage up‐titration at day 60, and dogs either continued to receive study drug at the same dosage either alone (those with UPC ≤0.5), or additionally received “standard” dose of the other study drug  until the final study recheck.Dogs undergoing study drug up‐titration or addition of the other study drug were rechecked 7 days after each treatment adjustment , at which time physical examination data, SBP, and whole blood for biochemical analyses for variables indicative of renal function were obtained. Removal criteria for phase II were identical to those of phase I. As noted, any dog with UPC ≤0.5 at a scheduled recheck was not reevaluated until study end.All dogs underwent reevaluation on day 120 ± 2, at which time physical examination data, SBP, blood for measurement of Hct and biochemical analyses, and urine for urinalysis and UPC measurement were obtained.2.10Dogs that were randomized and received at least 1 dose of study medication comprised the intention‐to‐treat sample. Dogs that were confirmed to have met all eligibility criteria comprised the per‐protocol sample.2.11%UPC), calculated by subtracting baseline UPC from recheck UPC and dividing the difference by baseline UPC, at each of days 30 ± 2, 60 ± 2, 90 ± 2, and 120 ± 2. Additional efficacy outcomes included Δ%UPC at the maximum‐tolerated study drug dosage ; the proportion of dogs and odds of achieving UPC reduction ≥50%, and proportion and odds of dogs achieving UPC ≤0.5 at each of days 30 ± 2, 60 ± 2, 90 ± 2, and 120 ± 2; as well as time‐to‐UPC reduction ≥50% and time‐to‐UPC ≤0.5.Primary outcome variables related to efficacy were percentage change in UPC compared to baseline (Δ%Cr), K (Δ%K), Hct (Δ%Hct) and SBP (Δ%SBP) at each of days 30 ± 2, 37 ± 1, 67 ± 1, 97 ± 1, and 120 ± 2, calculated in the same manner as Δ%UPC, as well as removal and adverse events. An adverse event was defined as any unfavorable or unintended observation recorded during the study.Safety outcomes of interest included percentage change from baseline in Cr , with the number of enrolled dogs at the time of analysis used as the information fraction (t*).Interim analysis was scheduled for January 2019, the expected end of the study period. It was determined a priori that the trial would be terminated if a statistically significant difference in Δ2.12.3t test. Nonnormally distributed data are presented as median (range) and compared using the Wilcoxon rank sum test. Proportion of dogs and odds of achieving a given endpoint were compared using the Fisher's exact test. Time‐to‐event analyses were performed using the Kaplan Meier estimator function and the log‐rank test.Statistical analyses were performed using commercially available software packages . A significance level of 0.05 was used for all analyses. Data were examined for normality by visual assessment of histograms and normal quantile plot, and the Shapiro‐Wilk test. Normally distributed data are presented as mean ± SD and compared between groups using the Student's %UPC at day 30, controlling for baseline UPC. Four strata were created based on baseline UPC quartiles . An alternative model, using arbitrarily defined cut points  was also tested. The permutation test was performed by permuting the class labels within each stratum, recalculating the Wilcoxon rank sum test statistic, and comparing the test statistic for the null hypothesis of no difference between the treatment groups on the original data to the distribution on the stratification‐permuted datasets.To test the influence of magnitude of baseline proteinuria on response to therapy, a stratified, nonparametric permutation test was performed on ΔAll analyses were performed on the intention‐to‐treat sample, meaning that the baseline randomization was used for primary evaluation of treatments, and treatment modification information was not used for primary comparisons. Data from dogs not evaluated at a given time point according to study protocol or due to removal from the study were treated as missing. Select efficacy outcome variables were also compared in the per‐protocol sample, and are reported as such.3Recruitment, enrollment and follow‐up were carried out from January 1, 2015 to April 4, 2019.Planned interim analysis, performed using data collected through December 31, 2018 from 39 dogs for which data from the first 30 days (Phase I) of study were available, was conducted in January 2019, at which time criteria for trial termination were met and enrollment was ceased. Six dogs that were actively enrolled at the time of interim analysis were followed to the end of phase II in a double‐masked manner. For these 6 dogs, UPC reduction relative to baseline ≥50% and UPC ≤0.5 were noted in n = 6 and n = 4, respectively. Investigators remained masked to individual drug assignments, as well as to the identity of the superior drug, until all dogs completed the entire study period.B. burgdorferi  and therefore excluded from the per‐protocol sample, leaving 38 dogs in the latter.A total of 48 dogs were screened for eligibility, of which 39 were included in the intention‐to‐treat sample and were randomized to receive enalapril (n = 19) or telmisartan  dogs completed the 120‐day study period  and at study day 90 in the 2 remaining dogs, from which the second study drug was withheld. Three days after initiation of combination therapy (day 93), the owner of the fourth dog discontinued both study drugs; this dog was subsequently removed on day 97 for progressive azotemia. Two of the 4 dogs with known protocol deviations were removed prior to the next scheduled UPC measurement. For the 2 remaining dogs, 1 in each treatment group, because these deviations resulted in withholding of the second study drug, any potential impact would be limited to day 120 data.Relevant deviations from study protocol occurred in 4 cases. In 3 , a deviation occurred when the prospect of protocol‐dictated dosage adjustments or additions raised safety concerns in a patient experiencing a plateau in Δ3.2P = .39).Ten dogs  were treated with amlodipine during the study. Median (range) dosage of amlodipine administered at each dog's final recheck was 0.21 (0.09‐0.35) mg/kg/day and 0.13 (0.09‐0.35) mg/kg/day for the enalapril and telmisartan groups, respectively. Median dosage of amlodipine at final recheck was not significantly different between groups . When controlled for baseline UPC, the difference in Δ%UPC remained significant, regardless of the cut‐points applied .At day 30, telmisartan‐treated dogs experienced significantly greater median reduction in proteinuria than did enalapril‐treated dogs  and 90 .Values for UPC at baseline and on study days 30, 60, and 90 are presented in Figure %UPC at maximum‐tolerated tested dosage was compared between groups. Dogs receiving telmisartan to target UPC ≤0.5 experienced significantly median greater reduction in UPC than those receiving enalapril for the same purpose  had UPC ≤0.5 at day 120 .Combination therapy was tested in 13 dogs . Of these, 4  were removed from the study at day 97 for Δ%UPC at day 120, and the proportion of dogs or odds of achieving UPC reduction from baseline ≥50%  or in combination with hyperkalemia (1/10). Two (10%) of 20 dogs treated with telmisartan alone and 4 (21%) of 19 dogs treated with enalapril alone experienced Δ%Cr >30%. Of these, 1 (telmisartan‐treated) dog had concurrent vomiting, diarrhea, bacteriuria, and a positive urine culture , raising concern for possible pyelonephritis. Four (31%) of 13 dogs receiving combination therapy  were removed for Δ%Cr >30%. In all cases, Cr returned to baseline after discontinuation of study drugs; however, 1 dog that was receiving 3 mg of telmisartan/kg q24h and 0.5 mg of enalapril/kg q12h required in‐hospital treatment.No significant differences in Δnt Table . However%Hct were observed between groups at earlier time points, telmisartan‐treated dogs in which enalapril was added had significantly greater Δ%Hct reduction 1 week  and 1 month  after starting combination therapy than did those receiving the opposite combination.While no significant differences in Δ3.8Owner‐reported adverse events are summarized in Table 4B. burgdorferi infection) in which it was administered, while 4 (24%) of 17 dogs treated with enalapril alone experienced an increase in UPC.In the present study, treatment with telmisartan at a dosage of 1 mg/kg PO q24h led to significantly greater median reduction in UPC and a greater proportion of dogs with UPC reduction from baseline ≥50% after 30 days, compared to treatment with 0.5 mg enalapril/kg PO q12h. At progressively higher dosages of each drug, telmisartan's antiproteinuric effects were superior to those of enalapril, although only data from dogs refractory to lower dosages were included in these comparisons. Further, treatment with telmisartan resulted in UPC reduction in all but 1 dog  in 9 (56%) of 16 dogs treated with 0.5 mg enalapril/kg q12‐24 hours for 6 months, and in no dogs treated with placebo for 6 months.In a preclinical study of healthy dogs, treatment with 1 mg telmisartan/kg PO q24h attenuated the systolic pressor response to exogenous Ang I by a significantly greater degree than placebo, enalapril (0.5 mg/kg q12h), and losartan . These data, in addition to those of the present clinical trial, suggest that telmisartan might provide more clinically relevant RAAS blockade, and therefore, superior antiproteinuric effects, compared to enalapril. Similar observations have been made in human subjects, for whom a meta‐analysis of 20 randomized, controlled clinical trials including >25 000 people favored telmisartan over placebo, ACEi, other ARBs, non‐RAAS‐blocking antihypertensive drug therapy, or no medication, for the improvement of proteinuria or albuminuria.When the present study was designed, the use of combination ACEi/ARB therapy was relatively common in human medicine and associated with beneficial hemodynamic effects.%Cr >30% and in 1 case, substantial hyperkalemia. Of these, 2 dogs  were receiving the starting dosage at the time of removal, and the remainder were receiving higher study drug dosages. There were no significant differences in Δ%Cr or Δ%K between treatment groups at any evaluated timepoint.Azotemia, hyperkalemia, and acute kidney injury are well‐described potential adverse effects of RAAS blockade in patients with renal or cardiac disease.The present study was neither specifically designed nor adequately powered to assess the relative effects of telmisartan and enalapril on SBP. Nonetheless, excluding dogs receiving amlodipine , there was a significantly greater reduction in SBP in telmisartan‐, vs enalapril‐treated dogs at several study timepoints as early as study day 30. Clinical data regarding telmisartan's antihypertensive efficacy in dogs is limited to that of a single case series of 5 dogs with refractory systemic hypertension.Ang II modulates erythropoeiesis, as its signaling regulates renal transcription of erythropoietin to increase proliferation of early erythroid progenitors.There are several limitations to this study. Because renal biopsies were not performed, we are unable to describe our sample in terms of specific histologic diagnoses. However, treatment randomization makes it unlikely that dogs with specific conditions (eg, immune‐complex glomerulonephritisIn conclusion, after 30 days of therapy using the dosages and formulations tested, telmisartan treatment led to a greater percentage reduction in UPC than did enalapril treatment, and telmisartan treatment produced a clinically relevant reduction in UPC in a greater proportion of dogs and a shorter period of time. For dogs remaining proteinuric while receiving standard dosages of these medications, the antiproteinuric effects of increasing dosages of telmisartan were superior to those of similarly increasing dosages of enalapril; however, these data are limited by the smaller number of dogs evaluated at the later phases of the present study. No clear differences in the safety profiles of these medications, when administered alone, were observed. These data suggest that telmisartan is a suitable first‐line choice for RAAS inhibition in dogs with renal proteinuria.Dr Bianca Lourenço was the recipient of a Boehringer Ingelheim Postdoctoral Scholarship. Drs Amanda Coleman and Scott Brown have served as paid consultants for Boehringer Ingelheim Vetmedica GmbH.Authors declare no off‐label use of antimicrobials.Approved by the Clinical Research Committee of the University of Georgia's College of Veterinary Medicine, approval number CR‐399.Authors declare human ethics approval was not needed for this study.


The appropriate assessment of threat and safety is important for decision‐making but might be altered in old age due to neurobiological changes. The literature on threat and safety processing in older adults is sparse and it is unclear how healthy ageing affects the brain's functional networks associated with affective processing. We measured skin conductance responses as an indicator of sympathetic arousal and used functional magnetic resonance imaging and independent component analysis to compare young and older adults' functional connectivity in the default mode (DMN) and salience networks (SN) during a threat conditioning and extinction task. While our results provided evidence for differential threat processing in both groups, they also showed that functional connectivity within the SN – but not the DMN – was weaker during threat processing in older compared to young adults. This reduction of within‐network connectivity was accompanied by an age‐related decrease in low frequency spectral power in the SN and a reduction in inter‐network connectivity between the SN and DMN during threat and safety processing. Similarly, we found that skin conductance responses were generally lower in older compared to young adults. Our results are the first to demonstrate age‐related changes in brain activation during aversive conditioning and suggest that the ability to adaptively filter affective information is reduced in older adults. We measured skin conductance responses as an indicator of sympathetic arousal and used functional magnetic resonance imaging and independent component analysis to compare young and older adults’ functional connectivity in the default mode (DMN) and salience networks (SN) during a threat conditioning and extinction task. While our results provided evidence for differential threat processing in both groups, they also showed that functional connectivity within the SN – but not the DMN – was weaker during threat processing in older compared to young adults. Our results are the first to demonstrate age‐related changes in brain activation during aversive conditioning and suggest that the ability to adaptively filter affective information is reduced in older adults. Threat and safety learning are most commonly assessed by measuring psychophysiological signals of arousal, such as the skin conductance response, which indicate the sympathetic nervous system's defensive response to direct, impending threat. Previous studies investigating threat conditioning in older adults have shown no age‐related differences in older adults' behavioural or psychophysiological differential responses to conditioned stimuli beyond a general reduction in arousal  and default mode network  were selected from a previously published study, which used the same experimental paradigm and acquired data using the same equipment and procedures , incidental findings (two data sets: macular degeneration and empty sella syndrome), and trauma, that was not disclosed during screening but after data acquisition (one data set: experience of terrorist attack). The final data set used in the analysis contains data from 23 participants  and projected onto a screen, which could be viewed with a mirror attached to the head coil.Each block started with 15 sec of background presentation to allow the electro‐dermal response to settle and the participants to habituate. During each experimental block, 20 stimuli  were presented for 3 sec and followed by 15 sec of background. Two colours, blue and orange, served as backgrounds and were randomly associated with each experimental block. All stimuli were presented in a randomised order using Presentation software . Prior to scanning, the stimulation voltage was adjusted to individual tolerances following established procedures to ensure that stimulation was highly uncomfortable, but not painful . Electrodermal activity was measured on the medial phalanges of the index and middle fingers of the participant's left hand using pre‐gelled carbon snap electrodes , amplified and low‐pass filtered (0.1 Hz) using an EDA100C amplifier .https://www.python.org/) script using the Neurokit module (https://neurokit.readthedocs.io). The electrodermal data were analysed using the cvxEDA module (https://github.com/lciti/cvxEDA). cvxEDA uses a convex optimization approach to model the electrodermal activity of the recorded skin conductance response (SCR) and to derive estimates for phasic sudomotor nerve activity (SNA), tonic changes, and noise  in RStudio (v.1.2.1335) using predictive mean matching with 20 iterations of 100 imputations  = 4,000 msec; echo time (TE) = 2.89 msec; flip angle = 6°; FOV = 256 mm, GRAPPA acceleration factor = 3. Functional images were acquired using a T2*‐weighted echo‐planar image sequence with the following parameters: 45 slices; 2.5 × 2.5 × 2.7 mm voxel size; TR = 3,000 msec; TE = 30 msec; FOV = 192 mm; flip angle = 90°. Functional images were acquired in four runs, each corresponding to one experimental block.Images were acquired with a Siemens Magnetom Trio 3T scanner and a 32‐channel head coil at the Centre for Advanced Imaging at the University of Queensland. For each participant, a T1‐weighted volumetric anatomical MRI was acquired with the following parameters: 176 slices sagittal acquisition MP2‐RAGE; 1 mmhttp://www.fil.ion.ucl.ac.uk/spm). Images were realigned to the mean image for head‐motion correction and then spatially normalised into standard stereotaxic space with a voxel size of 2 mm3  using segmented white and grey matter T1 maps. Head movement and rotation in the three dimensions did not exceed 2 mm or 2° and no dataset had to be excluded from analysis. Finally, the functional images were spatially smoothed with an 8‐mm full width half maximum Gaussian kernel.Brain activation was assessed using the blood oxygenation level dependent (BOLD) effect  using the Group ICA of fMRI Toolbox  with the within‐subjects factors stimulus  and phase , and the between‐subjects factor group  separately for each IC of interest.https://fsl.fmrib.ox.ac.uk; Winkler, Ridgway, Webster, Smith, & Nichols, p < .001 FWE).To identify group differences in network recruitment as well as recruitment of potentially compensatory areas in older adults, we also conducted non‐parametric tests based on the t‐statistics for comparisons of each IC of interest between young and older adults. For each session, back‐reconstructed individual maps of the SN‐IC and DMN‐IC were compared using FSL's randomise  and the CS‐  following each phase. Two paired t‐tests comparing expectancy ratings for the CS+ and CS‐ following conditioning and extinction showed that after conditioning older adults were correctly expecting the US following CS+ but not CS‐presentations  whereas their expectancy ratings did not differ significantly between conditioned stimuli after extinction  = 6.7, p = .01, 2 = .133) and phase  = 6.5, p = .01, 2 = .129). The factor group almost met the significance criterion  = 3.5, p = .07, 2 = .073). No interactions were significant . Together, these results show that participants responded faster during conditioning than during extinction but slower in response to CS+ than to CS‐  and phase , and between‐subjects factor group  resulted in significant main effects of stimulus  = 5.1, p = .03, 2 = .107) and stimulus  = 5.9, p = .02, 2 = .12). The factor phase almost met the significance criterion  = 3.5, p = .07, 2 = .08). No interactions were significant . Together, these results show that young adults produced higher overall skin conductance responses and provide evidence for successful differential conditioning in both groups  and phase , and between‐subjects factor group  resulted in a significant main effects of group  = 20.7, p < .001, 2 = .277). No other effects or interactions were significant .The 2x2x2 ANOVA of the beta‐estimates for the DMN‐IC showed a significant main effect of stimulus  = 5.7, p = .02, 2 = .096) and group  = 4.9, p = .03, 2 = .082). The factor phase almost met the significance criterion  = 3.1, p = .09, 2 = .054). No other effects or interactions were significant  and that both groups showed greater SN‐IC connectivity in response to the CS+ compared to the CS‐ .The 2x2x2 ANOVA of the beta‐estimates for the SN‐IC showed significant main effects of stimulus  = 42.5, p < .001, 2 = .321). The factor phase almost met the significance criterion  = 3.9, p = .05, 2 = .041). No other effects or interactions were significant . The mean correlation between the DMN‐IC and SN‐IC was −.25 (SD = .26) during the conditioning and − .35 (SD = .3) during the extinction phases for older adults compared to −.59 (SD = .26) during the conditioning and − .61 (SD = .25) during the extinction phases for younger adults).A 2 × 2 ANOVA of functional network connectivity between the DMN‐IC and the SN‐IC with within‐subjects factor phase  and between‐subjects factor group  resulted in a significant main effect of group (t(44) = 3.9, p = .009; 0.08–0.16 Hz: t(44) = 4.1, p = .005) and extinction (0–0.08 Hz: t(44) = 3.5, p = .03; 0.08–0.16 Hz: t(44) = −3.1, p = .13) phases. The results further show significantly higher power in both networks across both experimental phases for older, compared to younger, adults in the high frequency bins .Two‐sided t‐tests  of the spectral power across time bins for each network further revealed significantly higher power in the SN‐IC (but not the DMN‐IC) for younger, compared to older adults, in the low frequency bins during conditioning (0–0.08 Hz: 4The aim of this study was to investigate age‐related differences in behavioural, psychophysiological, and neural responses to threat and safety, as well as to test for evidence of compensatory recruitment in older adults. All three types of data provided evidence of differential responding to threat and safety across both age groups. However, in contrast to young adults, older adults showed a general reduction in skin conductance responses and in SN connectivity irrespective of stimulus or experimental phase. Our results further showed age‐related changes in the SN, but not the DMN, including reduced connectivity in the anterior cingulate cortex, insula, and thalamus, and reduced power in the low frequency spectrum in older, compared to younger, adults. In addition, our results demonstrate that connectivity in the SN and DMN is less strongly anticorrelated in older, compared to younger, adults. Our results provide no evidence of compensatory activity in older adults.The psychophysiological pattern of our results is in line with findings of previous studies, which report overall reduced skin conductance responses in older compared to young adults (LaBar et al., One potential explanation for the reduction in skin conductance responses is based on our fMRI data. The neuroimaging results demonstrate reduced task‐related connectivity in the SN in older adults; in particular, a reduction in the connectivity between the thalamus and the anterior cingulate cortex. Previous studies have established that the anterior insula is engaged in the perception and anticipation of salient stimuli whereas the anterior cingulate cortex is more strongly associated with behavioural adaptation (Ham, Leff, de Boissezon, Joffe, & Sharp, Interestingly, our results do not show a significant group difference in task‐related DMN connectivity. Previous studies generally report an inability of older adults to decrease activity in the DMN during externally directed tasks (Brown, Hakun, Zhu, Johnson, & Gold, Finally, our results show that in older adults, connectivity within the DMN is significantly less anticorrelated with connectivity within the SN. In addition, our results show that low frequency BOLD‐related power in functional connectivity is reduced in older, compared to young, adults in the SN, but not the DMN. Together, these findings suggest that during threat and safety responding older adults do not engage the SN to the same degree as young adults and that this lower SN activity is not due to the DMN or the interaction between the DMN and SN.Taken together, our results highlight important age‐related changes in threat and safety processing. A central idea in current cognitive neuroscience is that prediction of afferent signals is central to the brain's computational architecture (Bubic, von Cramon, & Schubotz, In summary, our results are the first to show age‐related differences in brain activation during aversive conditioning. In particular, our findings highlight the role of the SN in the processing of aversive prediction errors and sympathetic nervous system responses. However, this study can only provide a starting point and further research into the ultimate causes of age‐related changes in SN connectivity, prediction error processing, and aversive conditioning are necessary.The authors declare no conflicts of interest.


Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle‐ and older‐aged women. Using novel applications of brain‐age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth. In this study, we identified regional brain‐aging patterns linked to parity in 19,787 middle‐ and older‐aged women. A higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior. An overview of the diagnoses is provided in the UK Biobank online resources (http://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=41270), and the diagnostic criteria are listed in the ICD10 diagnostic manual (https://www.who.int/classifications/icd/icdonlineversions). A group of two hundred two participants was excluded based on MRI outliers  and 9 had missing data on number of previous childbirths, yielding a total of 19,787 participants that were included in the analyses. Sample demographics are provided in Table The sample was drawn from the UK Biobank (www.ukbiobank.ac.uk), and included 21,928 women. A total of 1,885 participants with known brain disorders were excluded based on ICD10 diagnoses . In addition, participants with SD ± 4 on the global MRI measures mean cortical or subcortical gray matter volume were excluded , yielding a total of 19,796 participants with T1‐weighted MRI data. Only participants who had data on number of previous childbirths in addition to MRI were included, and the final sample used in all subsequent analyses (unless otherwise stated) counted 19,787 participants.A detailed overview of the UK Biobank data acquisition and protocols is available in papers by Alfaro‐Almagro et al.  and Mill2.3XGBoost regressor model, which is based on a decision‐tree ensemble algorithm (https://xgboost.readthedocs.io/en/latest/python), was used to estimate global and regional brain age based on the MRI data. XGboost includes advanced regularization to reduce overfitting . Randomized search with 10 folds and 10 iterations was run to optimize parameters, using all imaging features as input. Scanned parameters ranges were set to maximum depth:  and applied it to the newly added participants , yielding a global brain‐age estimate for each individual. Next, we calculated each participant's brain‐age delta by subtracting chronological age from estimated brain age. This measure provides an estimation of an individual's brain aging pattern relative to normative aging trajectories . For each identified cluster of MRI features, separate prediction models were run with 10‐fold cross validation, providing cluster‐specific brain‐age delta estimates for each individual. To investigate model prediction accuracy, R2, root mean square error (RMSE), and mean absolute error (MAE) were calculated for each model, and correlation analyses were run for predicted versus chronological age. Associations with number of previous childbirths were investigated using separate regression analyses with cluster‐specific brain‐age delta estimates as the dependent variable, and number of childbirths as the independent variable. Chronological age was included as a covariate, and p‐values were adjusted for multiple comparisons using false discovery rate correction  was utilized to investigate number of childbirths as the independent variable. Age was included as a covariate. All statistical analyses were conducted using Python 3.7.0.To further identify specific regions associated with number of childbirths, the hierarchical clustering procedure was repeated on the MRI features within the cluster showing the strongest association with number of previous childbirths. For each identified subcluster, brain‐age prediction models were run to generate subcluster‐specific brain‐age estimates for each individual. Next, we tested the association between subcluster‐specific brain‐age estimates and number of childbirths. For the subcluster showing the strongest association, a linear regression was run to test the difference in subcluster‐specific brain aging between parous and nulliparous women. Subcluster‐specific brain age was entered as the dependent variable, and a binary variable for parity/nulliparity was used as the independent variable. Age was included as a covariate. To identify the unique contribution of each region contained in this cluster, separate brain‐age models were run with each MRI feature as input, and regression models were run with feature‐specific brain age as the dependent variable, and 33.1N = 10,907) to the newly added participants . When applied to the test set, the modeled age prediction showed an accuracy of R2 = 0.34, RMSE = 6.00, and Pearson's r  = 0.58, 95% confidence interval (CI) = , p < .001. Corresponding to our previous results, an association was found between a higher number of previous childbirths and less apparent brain aging in the sample of newly added participants: β = −0.13, SE = 0.03, t = −4.07, p = 4.79 × 10−5.To replicate our findings described in de Lange et al. , we applβ) only, and one including intercept, linear, and quadratic terms (γ). For these analyses, the brain‐age delta estimations were first corrected for chronological age using linear regression . The results from the fit including the linear term only showed a significant linear effect , while the results from the fit including both terms showed that only the linear term was significant . The two fits are shown in Figure β = −13.44 ± 3.35, γ = 0.83 ± 3.35, F = 8.08, p = 3.11 × 10−4).To test for nonlinear relationships, polynomial fits were run for number of childbirths and brain age delta: one including intercept and a linear term  and nulliparous  women. The results showed less apparent brain aging in parous relative to nulliparous women , with an effect size of d = 0.12 ± 0.02 (SE).As subcluster 2 showed the strongest association with number of previous childbirths, a linear regression was performed to test for differences in subcluster 2‐specific brain aging between parous  and women who did not (N = 234). A linear regression showed no association between gestational diabetes and subcluster 2 brain‐age estimates , and repeated the main analysis including all covariates. The results showed an association of β = −0.12, SE = 0.02, t = −5.42, p = 5.92 × 10−8 between number of childbirths and subcluster 2.To control for potential confounding factors, the analyses of number of previous childbirths versus subcluster 2 brain‐age estimates were rerun including assessment location, education, body mass index (BMI), diabetic status, hypertension, smoking and alcohol intake, menopausal status , and oral contraceptive (OC) and hormonal replacement therapy (HRT) status (previous or current user vs. never used) as covariates. A total of, 16,512 women had data on all variables and were included in the analyses. The results showed an association of β = 0.12, SE = 0.16, t = 0.71, p = .48, 95% CI = ; women with male firstborn: 630, women with female firstborn: 778). Number of previous childbirths and age at first birth correlated r = −0.294, p = 6.90 × 10−296 (corrected for age). To test for an association with brain aging, an analysis was run with Subcluster 2 brain age as the dependent variable and age at first birth as the independent variable, including all covariates . No association was found .As fetal sex has been shown to influence cognitive function during pregnancy, we extracted the available data from UK Biobank including sex of the firstborn child for 1,408 women to test for effects of fetal sex on brain aging. In this subsample, a linear regression showed no association between fetal sex and subcluster 2 brain‐age estimates  and the full model (LL2). The results showed that only the accumbens contributed uniquely to the association with number of previous childbirths, as shown in Table β = −0.098, SE = 0.020, t = −5.001, p = 5.766 × 10−07, indicating that the association was not solely driven by this region.To investigate the unique contributions of each region in subcluster 2 to the association with previous childbirths, separate brain‐age prediction models were run with each feature as input, yielding 11 region‐specific brain‐age estimates. Table m Wilks,  as 2∆LL,As a cross check, we investigated associations between previous childbirths and regional volumes in subcluster 2. Separate analyses were run with the volume measure for each region as dependent variables and number of previous childbirths as the independent variable, including age, assessment location, education, BMI, diabetic status, hypertension, smoking and alcohol intake, menopausal status, and OC and HRT status as covariates. In all, 16,516 women had data on all variables and were included in the analysis. The associations between number of previous childbirths and regional volume corresponded to the associations with brain‐age estimates, as shown in Table 4The results showed that a higher number of previous childbirths were associated with less apparent brain aging in striatal and limbic regions, including the accumbens, putamen, thalamus, hippocampus, and amygdala. The most prominent effect was seen in the accumbens, which is part of the ventral striatum and a key region in the mesolimbic system involved in reward processing and reinforcement learning (Haber & Knutson, reduced hippocampal neurogenesis has been reported during the postpartum period, coinciding with enhanced memory performance in primiparous compared to nulliparous rodents (Kinsley & Lambert, In addition to the regions overlapping with the maternal circuit, we found a link between hippocampal brain aging and previous childbirths. This association concurs with animal studies showing enhanced hippocampal neurogenesis in middle age in parous relative to nulliparous rats (Eid et al., Pregnancy represents a period of enhanced neuroplasticity of which several underlying mechanisms could confer long‐lasting effects on the brain. Fluctuations in hormones including estradiol, progesterone, prolactin, oxytocin, and cortisol are known to influence brain plasticity (Barha & Galea, In conclusion, the current study replicates preceding findings showing less apparent brain aging in multiparous women (de Lange et al.,


Mercury, a highly toxic element, has been only found in rice samples collected in the mining activity zone . The experimental results indicated that the heavy metals and arsenic found in rice collected from mining activity zone were higher than those in rice harvested from normal cultivated areas like the Red River Delta. The heavy metals and arsenic content in Vietnamese rice samples were also compared with the concentration of heavy metals in other foreign rice samples in some recent publications. The estimated daily intake through rice consumption was calculated and compared with the level proposed by the Food and Agriculture Organization of the United Nations. The results indicated that the provisional daily intake of Cd was higher than the level proposed by FAO, while the intake of other heavy metals was in an acceptable range of CODEX standard.In this work, twelve heavy metals and arsenic, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, and Zn, in a rice sample collected from some areas of Vietnam have been quantified and implemented by using multiple analytical platforms such as ICP-MS, AAS, and mercury analyser. Seventy rice samples collected from the Red River Delta and mining zone activity were analysed. Concentration of heavy metals and arsenic in rice was analysed after appropriated sample digestion using internal or external calibration curves. The mean concentration (mg kg In Vietnam, rice is the most important and widely cultivated crop, which is the main food product for internal consumption and also for export. Vietnam is among the top ten countries in the world that grow and export rice . In addiTo date, many analytical methods like flame atomic absorption spectrometry (F-AAS) , graphitIn this work, the multielemental analysis of heavy metals and some toxic elements in rice samples was implemented by using different analytical techniques such as flame atomic absorption spectrometry (F-AAS), inductively coupled plasma-dynamic reaction cell-quadrupole mass spectrometry (ICP-DRC-QMS), and cold vapour-atomic absorption spectrometry . Seventy rice samples were taken from different area production (the Red River Delta and mining activity zone) and concentration of arsenic and heavy metals was analysed after microwave-assisted acidic digestion. The validation of the developed methods was performed by rice-based matrix certified reference  materials and spiking experiments as well. The developed method was applied to the assessment of heavy metal contaminant in rice samples collected from some rice fields in Red River Delta and in mining activity zone in Vietnam. Besides, heavy metals and arsenic can be harmful to human health when foodstuff containing these elements is regularly used in the diet; therefore, human exposure to these elements through rice consumption was assessed and implemented.−1), cadmium , cobalt , chromium , copper , iron , manganese , mercury , nickel (Ni 1000 mg L−1), lead , selenium , zinc , and indium  for ICP-MS were purchased from Sigma Aldrich (Singapore). HNO3, HCl, H2SO4, and HClO4 were collected from Merck . Other chemicals were purchased from Sigma Aldrich . Deionised water was purified by Milli-Q purify system . The working standard solution was prepared by dilution of stock solution in diluted nitric acid. Indium standard solution for ICP-MS from Sigma Aldrich (Singapore) was used as an internal standard for ICP-DRC-QMS measurement. Rice-based certified reference material, ERM-BC 211 certified for arsenic, and fish-based certified reference material, DORM 2 certified for mercury, were purchased from Institute for Reference Materials and Measurements and European Commission and National Research Council, Canada, respectively.Single element standard including arsenic . The optimum operating conditions of ICP-DRC-MS are shown in 40Ar35Cl+ on arsenic measurement and argon-based polyatomic ion interferences in selenium measurement. The standard mode was employed for cadmium measurement.A NexION 2000 ICP-DRC-QMS  was used for multielemental analysis. The sensitivity and selectivity of the ICP-DRC-QMS were daily checked by using a specific tuning solution from Perkin Elmer . Sensitivity of all target elements was optimized and daily checked by using in-house mixture standard solution . Automatic Mercury Analyzer Hg-201 (Sanso Company) was used. After digestion, 5 mL of sample was added in the reaction vial with the addition of 1 mL of stannous chloride (SnCl2-10%) to reduce Hg2+ to Hg0. Then, the Hg vapor generated is conducted into a flask containing NaOH at 5 N, which neutralizes the acid vapours, and a flask containing an ice bath, in which water vapour is condensed. Thereupon, the Hg vapours are transported to the photon-absorption cell to measure absorbance of 253.7 nm. In the analysis of total Hg, concentration of total Hg in sample solution was calculated by calibration curve.Total mercury in rice samples was determined by cold vapor atomic absorption spectrometry (CV-AAS) according to the method of Hirokatsu Akagi with some modifications . In brieRice samples were collected from different sites including the rice fields in the Red River Delta and activity mining zone in 2018 and 2019 harvest seasons. Rice samples were coded TN1-TN35 and R1-R35 collected from mining activity areas (Thai Nguyen province) and Red River Delta (Ha Nam province), respectively. The maps of both sampling sites are shown in (1) For ICP-DRC-QMS and F-AAS Measurement. Dried rice sample was grilled in the food processor and re-grilled on the agate mortar and pestle. 0.2000 gram of rice was weighted on the analytical balance and transferred into microware PPFE vessel and subjected for sample digestion by microwave oven. In brief, approximately 2 mL of concentrated nitric acid (65%) and 1.0 mL of hydrogen peroxide (34% by volume) were poured into digestion vessel. Afterward, the rice sample was digested in dedicated microwave oven  by controlled temperature program. After complete digestion, the vessels were cooled to 50°C. Samples were then cooled to room temperature and transferred to 50 mL volumetric flasks and made up to volume with deionised water. These solutions were then analysed by ICP-DRC-QMS using In as internal standard .(2) For CV-AAS Measurement of Mercury. Technically, approximately 100 mg of each rice sample was weighed in a 50 mL digestion tube, and 1 mL of deionized water was added plus 2 mL mixture of concentrated HNO3 and HClO4 (1 /1: v/v) and 5 mL of H2SO4 and heated for 30 minutes in a hot plate at temperature of 230°C. After cooling, the solution was transferred into 50 mL volumetric flask and made up with deionized water to a final volume of 50 mL. The final digestion solution was subjected for analysis of mercury by CV-AAS.For the quality control, rice-based certificated reference material  was used for analysis of arsenic. DORM-2 was used for validation of analytical method for mercury and spiking experiments for other elements. Digestion of the CRM was prepared by the same preparation procedure for the rice samples. The total number of blank and quality control samples contained at least 20% of total samples analysis for each analysis batch.The data analysis was performed by using data analysis function in the Microsoft Excel version 2020 . The confident interval level was set at 95%. The human exposure to these elements was calculated as suggestion of FAO.The average estimated daily intake of these compounds for the Vietnamese consumer through the consumption of rice and rice products was calculated according to equation  as proporice is the estimated daily intake of these elements (mg day−1 kg−1 body weight) through rice consumption. Mean values of body weight for Vietnamese adult man and woman are 58 kg and 45 kg, respectively. The average rice consumption of Vietnamese people is 589 gram per person per day [EDI per day . The aveμg/kg) including multielements such as Cd, Zn, Cu, Pb, Ni, Cr, Mo, Se, As, and In as internal standard  were prepared in diluted nitric acid. All elements were analysed by ICP-MS as indicated in 40Ar16O+ on 56Fe+ measurement and 40Ar14N1H+, 39K16O+, 40Ar15N+, etc. on 55Mn+ measurement) [Seven independent solutions (from 1.000 to 50.000 urement) . In addiAs can be seen in −1). The comparison of the concentration of each element in both sampling areas is shown in Rice samples were analysed after acidic microwave-assisted digestion using oven or other preparation methods as mentioned before. In total, 70 rice samples were analysed and the concentration of these elements in rice samples is reported in −1, dried weight) of the elements in rice samples decreased on the order of Mn (19.268) > Fe (13.624) > Zn (8.163) > Cu (3.138) > Ni (0.384) > Cr (0.296) > Co (0.279) > As (0.115) > Cd (0.111) > Pb (0.075) > Hg (0.007) > Se (<LOD).The total content of heavy metals and arsenic in the analysed rice samples is presented in As can be clearly seen in The estimated daily intake of these elements through rice consumption was calculated by using equation  and pres−1 according to European Council and USA Food and Drug Administration regulars and Vietnam standard has been set a maximum concentration of total As in rice as well . As can be seen in −1 and range is from 0.052 mg kg−1 to 0.328 mg kg−1. All analysed rice samples containing arsenic were below Vietnam national standard. However, only a few rice samples  containing As were of higher maximum level according to European Council and USA Food and Drug Administration. Moreover, the speciation analysis of As-related compounds in these rice samples should be taken into account in order to distinguish inorganic and organic arsenic species. The mechanism uptake of total arsenic and arsenic compounds in rice species was summarized in a recent publication [As, as a metalloid, is toxic to plants and animals as well. Rice absorbs As more efficiently than other cereals, mainly from contaminated paddy soil and irrigation water and other sources like pesticides or fertilizers. Rice grown on the As contaminated paddy soil can accumulate elevated levels of arsenic and increase the transfer of it from soil to rice. In addition, maximum residual level of inorganic arsenic in rice is below 0.2 mg kglication . Several−1 kg−1 (range 0.0005–0.0033 mg day−1 kg−1) and 0.0015 mg day−1 kg−1 (range 0.0007–0.0043 mg day−1 kg−1). The provisional tolerable weekly intake (PTWI) of arsenic according to CODEX standard is 0.0021 mg kg−1 body weight per day. Therefore, estimated daily intake of arsenic through rice consumption in this study was lower than CODEX standard.Humane exposure to arsenic may include inhalation of dust in the air, water and food consumption, and dermal contact of soil and water. The estimated daily intake of As from rice consumption grown in the mining activity zone and in Red River Delta is shown in −1 and mean concentration was 0.111 mg kg−1. The average concentration of Cd in 70 rice samples was below CODEX standard for polished rice (0.4 mg kg−1 dried weight) with the exception of three samples collected from mining activity zone  [−1kg−1 and 0.0014 mg day−1kg−1, respectively. The potential of human exposure to Cd through rice consumption harvested in mining activity zone was also higher than other areas. The provisional tolerable daily intake of Cd is 0.00082 mg kg−1 according to CODEX. Therefore, the estimated daily intake of Cd through rice in this study was higher than that from CODEX standard. Therefore, the action for mitigation of such element uptake in rice like immobilization in soil, bioremediation, and molecular biotechnology should be paid attention to.In this study, the concentration of Cd in 70 rice samples ranged from 0.005 to 0.480 mg kgnd TN12) , 35. In nd TN12) . The ave−1 and ranged from 0.004 to 1.223 mg kg−1. Other previous studies showed that the linearity correlation between concentration of chromium in rice and spiked soil has been observed [−1day−1 [−1 kg−1 (0.0001–0.0124 mg day−1 kg−1) and 0.0039 mg day−1 kg−1 (0.0001–0.0160 mg day−1 kg−1) for Vietnamese woman and man, respectively.Cr was detected in 74.5% of rice samples  with mean concentration 0.296 mg kgobserved , 37. Tha[−1day−1 . The est−1 and ranged from 0.001 mg kg−1 to 1.369 mg kg−1. The average concentration of Co in the Red River Delta rice samples was 0.008 mg kg−1 (0.001–0.018 mg kg−1). Meanwhile, the average concentration of such element in rice sampled in mining activity zone was 0.512 mg kg−1 (0.021–1.369 mg kg−1). As a result, the concentration of Co in rice samples collected from mining activity zone was higher than that in the Red River Delta rice samples. It could be explained by accumulation of such element in rice from irrigation water, contaminated soil, or other sources in mining activity area [−1 kg−1 and 0.0028 mg day−1 kg−1. Moreover, no literature reported the provisional tolerance daily intake of such element to human health.The average concentration of Co in 70 rice samples was 0.275 mg kgity area . The conity area . The ave−1 and range 0.118 to 7.754 mg kg−1. The average concentration of Cu in this study was higher than that in Vietnamese rice samples reported from previous publication [−1 kg−1 and 0.0319 mg day−1 kg−1, respectively.Cu was detected in all collected rice samples with average concentration 3.138 mg kg−1 and range was from 0.03 to 0.177 mg kg−1. Concentration of Pb in all analysed rice samples was below maximum residual level according to CODEX standard (0.2 mg kg−1 dried weight for whole commodity). The concentration of Pb in rice samples collected from mining activity zone was also higher than that in the Red River Delta. The high concentration of Pb contained in rice collected in/nearby mining activity zone could be explained by transfer of this element from agricultural soil to rice in these areas [−1 kg−1 (0.0001–0.0018 mg day−1 kg−1) and 0.001 mg day−1 kg−1 (0.0001–0.0023 mg day−1 kg−1), respectively. The estimated daily intake of Pb through rice consumption was lower than intake level according to CODEX (0.003 mg day−1 kg−1).The average concentration of Pb in all analysed rice samples was 0.075 mg kgse areas , 40. The−1 and the range was from 7.4 to 42.65 mg kg−1. The higher concentration of Mn  was found in the rice samples collected from mining activity zone compared to concentration of this element in Red River Delta rice samples. In addition, Mn concentration in rice samples in this study was higher than that in previous publications but it was lower than that in Vietnamese brown rice [The mean Mn concentration in rice samples was 19.19 mg kg−1 (range from DL to 1.234 mg kg−1). Compared to previous study, Phuong et al. reported that the mean concentration of Ni in Vietnamese white rice samples was 0.869 mg kg−1. That means the finding of this study about concentration of Ni was three times lower. The concentration of Ni detected in rice in this study was lower than that in Chinese white rice in terms of comparison.In this study, the average concentration of Ni in 70 rice samples was 0.384 mg kg−1. The range of Zn concentration was from 5.341 to 11.653 mg kg−1. Interestingly, the concentration of Zn in rice samples collected from both areas, in the Red River Delta and in mining activity area, was the same concentration level. Compared with Zn in Korean white rice, Thai white rice, and Australian white rice, the level of Zn in Vietnamese white rice was two times lower. However, the concentration of Zn in Vietnamese white rice and Chinese white rice was observed to be at the same level. The estimated daily intake of Zn through rice consumption for Vietnamese woman and man was 0.1068 mg day−1 kg−1 and 0.0826 mg day−1 kg−1, respectively.The average concentration of Zn in rice samples in this study was 8.155 mg kgIn this work, the concentration of Se and Hg was also determined in rice samples. In case of selenium, the concentration of selenium in all rice samples was below method detection. Meanwhile, mercury at low concentration was presented in a few rice samples, all of which collected from mining activity area. Therefore, determination of mercury in a large number of rice samples collected from mining activity zone as well as in contaminated soil, or irrigation water, should be conducted in order to have reliable evidence to confirm. The estimated daily intake of Se and Hg was not also presented in this study.In this study, the concentration of heavy metals such as Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Se and toxic elements like arsenic and mercury in rice samples collected from Red River Delta has been analysed and presented. The finding in this study indicated that the concentration of all analysed heavy metals in rice samples grown in mining activity area was higher than the concentration of heavy metals in rice samples collected in the Red River Delta except Se and Zn. In addition, the human exposure to these elements has also been investigated and implemented. The provisional daily intake of all investigated heavy metals was in an acceptable range according to CODEX standard except Cd and As. Further investigation of human exposure to two elements, Cd and As, should be conducted regarding the chemical forms, mechanism uptake in the rice species, and toxicology of these elements.


A number of non‐low‐density lipoprotein cholesterol lipid abnormalities are associated with type 2 diabetes and insulin resistance, which may lead practitioners to use medications targeting these abnormal lipid fractions despite a lack of evidence or guideline recommendations.Among 382 921 US patients with type 2 diabetes , 95 995 (26%) were on some nonstatin lipid‐lowering medication—19 265 (5%) on niacin, 32 919 (9%) on a fibrate and 69 513 (18%) on fish oil. Use of all three medications was stable over time and higher in patients with cardiovascular disease and with higher triglyceride levels, although even among patients with triglyceride levels <2.3 mmol/L, 6% were on a fibrate and 17% were on fish oil.As clinical trials demonstrate little to no cardiovascular benefit from taking these medications, greater attention is needed to focus the use of lipid‐lowering medications to those with proven benefit. A number of non‐low‐density lipoprotein cholesterol lipid abnormalities are associated with type 2 diabetes and insulin resistance, which may lead practitioners to use medications targeting these abnormal lipid fractions despite a lack of evidence or guideline recommendations. Among 382 921 US patients with type 2 diabetes , 95 995 (26%) were on some nonstatin lipid‐lowering medication—19 265 (5%) on niacin, 32 919 (9%) on a fibrate and 69 513 (18%) on fish oil. Use of all three medications was stable over time and higher in patients with cardiovascular disease and with higher triglyceride levels, although even among patients with triglyceride levels <1.7 mmol/L, 5% were on a fibrate and 16% were on fish oil. As clinical trials demonstrate little to no cardiovascular benefit from taking these medications, greater attention is needed to focus the use of lipid‐lowering medications to those with proven benefit. The last patient visit was used for analysis. To examine use over time, the last patient visit in each quarter was used. We compared demographics, comorbidities and lipid levels for those patients on versus not on each of the medications using standardized differences . All analyses were performed with SAS version 9.4 (SAS Institute).32, and 68.7% had established ASCVD , who formed the analytic cohort. Patients with lipid data were more likely to be older and with ASCVD Table . Mean agVD Table . Use of 4In a large cohort of patients with T2D, we found that lipid levels were not markedly abnormal. Non‐LDL‐C lipid‐lowering medications were not infrequently used in patients with T2D, with an even greater use in those with established atherosclerosis. Furthermore, in patients with triglyceride levels <1.7 mmol/L, we found that 5% were on a fibrate and 16% were on fish oil and 5% of all patients were on niacin. Finally, we found general stability in these rates over time, indicating that practice patterns do not appear to be rapidly changing. Greater attention is needed to focus lipid‐lowering strategies to those with proven cardiovascular benefit.A prior analysis examined use of nonstatin lipid‐lowering medications in elderly patients with coronary artery disease from 2007 to 2011 and found that 5% of patients were on fibrates and 3% were on niacin.There are limitations to our analysis that merit further discussion. First, we do not have pretreatment lipid levels to know whether a patient's triglycerides were markedly elevated, making treatment with fish oil and/or fibrates appropriate. Second, while over‐the‐counter fish oil and niacin are often documented in electronic health records, we cannot know whether a patient was taking these supplements without the knowledge of the physician. Third, fibrates appear to have some benefit in reducing nonfatal cardiovascular events in statin‐intolerant patients, and fish oil has previously been recommended in cardiology guidelines for secondary prevention of coronary artery disease. As such, some patients with established ASCVD were likely given these medications for these reasons. Furthermore, with publication of the trial with high‐dose icosapent ethylIn conclusion, we found that medications that target non‐LDL‐C lipid fractions are commonly used in patients with T2D, which likely reflects the underlying lipid abnormalities associated with T2D/insulin resistance. As clinical trials have thus far demonstrated little cardiovascular benefit from taking these medications, greater attention is needed to focus the use of lipid‐lowering medications to those with proven benefit.NDW received research support through institution from Amgen, Boehringer Ingelheim, Amarin, Novo Nordisk; advisory board from Novaris, Amarin; speaker fees from Sanofi, Amarin; and consulting honoraria from Boehringer Ingelheim. LAL received research grants from Amgen, Esperion, Kowa, Regeneron/Sanofi, The Medicines Company and personal fees from Amgen, HLS, Kowa, Merck, Regeneron/Sanofi, The Medicines Company. MK received research grants from AstraZeneca, Boehringer Ingelheim; other research support from AstraZeneca; and consulting honoraria from AstraZeneca, Amgen, Bayer, Boehringer Ingelheim, Novo Nordisk, Sanofi, GSK, Merck (Diabetes), Eisai, Intarcia, Novartis and Glytec. The remaining authors report no relevant disclosures to the current manuscript.SVA, KG, NDW, VH, AG, LAL and MK contributed to design of the work; SVA, NDW and MK contributed to data acquisition; KG and SVA contributed to data analysis; SVA, KG, NDW, VH, AG, LAL and MK contributed to interpretation of analyses; SVA contributed to drafting the work; KG, NDW, VH, AG, LAL and MK contributed to critical revisions.Table S1‐S2Click here for additional data file.


To the Editor:p = 0.13). More importantly, after removing this included study [We thank Dr. Honore and his colleagues for their attention on our study in Critical Care . First, ed study  showed t