Self-management intervention aims to facilitate an individual’s ability to make lifestyle changes. The effectiveness of this intervention in non-dialysis patients with chronic kidney disease (CKD) is limited. In this study, we applied a systematic review and meta-analysis to investigate whether self-management intervention improves renoprotection for non-dialysis chronic kidney disease.We conducted a comprehensive search for randomized controlled trials addressing our objective. We searched for studies up to May 12, 2018. Two reviewers independently evaluated study quality and extracted characteristics and outcomes among patients with CKD within the intervention phase for each trial. Meta-regression and subgroup analyses were conducted to explore heterogeneity.2 = 0%), risk of dialysis , or change in eGFR . Moreover, self-management interventions were associated with a lower 24 h urinary protein excretion , a lower blood pressure level , a lower C-reactive Protein (CRP) level  and a longer distance on the 6-min walk  when compared with the control group.We identified 19 studies with a total of 2540 CKD patients and a mean follow-up of 13.44 months. Compared with usual care, self-management intervention did not show a significant difference for risk of all-cause mortality  contains supplementary material, which is available to authorized users. Chronic kidney disease (CKD) is a progressive disease that leads to End-Stage Renal Disease , cardiovascular morbidity and mortality . ApproxiCKD management includes slowing the progression to ESRD and decreasing the risk of cardiovascular complications through management of kidney function and CKD progression risk factors such as hypertension and diabetes . In addiDespite an established tradition of patient self-management of ESRD, and self-management being a well-established treatment strategy for other chronic conditions such as diabetes , 9 and hWe performed a systematic review according to a specified protocol (PROSPERO number: CRD42017059870 ). The reElectronic databases were searched using a strategy combining selected MeSH terms with keywords related to CKD and self-management intervention. We used English and Chinese language restriction .Relevant studies were identified by searching the following electronic databases from inception to 12th of May 2018: PubMed, MEDLINE, EMBASE, CINAHL, the Cochrane Library database, the Chinese Biomedicine Database (CBM), Chinese National Knowledge Infrastructure (CNKI), and Wanfang Database. Reference lists from relevant review articles and reviews were also searched.Studies were first screened according to title and abstract, and the full texts of any study considered relevant according to the selection criteria were assessed for eligibility by 2 independent reviewers (JS. H and JW. H). Disagreements between the reviewers concerning decisions to include or exclude studies were resolved by consensus, and if necessary, consultation with a third reviewer (YF. W).2 or markers of kidney damage, or both, of at least 3 months duration [We included randomized controlled trials of self-management intervention compared with usual care for adults (age 18 and above) who had been clinically diagnosed with chronic kidney disease. CKD was defined as a glomerular filtration rate (GFR) < 60 mL/min/1.73 mduration . We inclComparing self-management intervention with the standard CKD treatment during the follow-up period, the primary outcomes of this systematic review included all-cause mortality, number of patients progressing to ESRD, change in GFR, change in proteinuria excretion, and adverse events. Secondary outcomes included health literacy  and other indexes of CKD risk factors, including glycaemia, blood pressure, blood lipid concentration and C-reactive protein (CRP) level.Data extraction included details on the study characteristics , population characteristics , intervention  and theoretical frameworks. The comparators and outcomes assessed were extracted, and then tabulated.We used the recommended Cochrane risk of bias assessment tool , and theBecause of the between-study variance, we used a random-effects model for all analyses . EffectsWe examined the influence of various characteristics on the study-specific effect estimates by stratifying the analysis by self-management type: a) lifestyle modifications; b) medical-behavior modifications; c) multi-factorial modifications.I2 test [Statistical heterogeneity across studies was detected with the Cochrane Q statistic and an I2 test . In case, or Harbord’s test [p-value less than 0.05 was considered statistically significant. To assess the robustness of our meta-analyses, we conducted a trim and fill analysis. The trim and fill method is used to identify, and correct for, funnel plot asymmetry arising from publication bias.Funnel plot is a useful tool to visually assess the potential for publication bias. If publication bias had been present, then smaller (less precise) studies that had failed to show statistical significance would have been less likely to have been published. This is reflected as asymmetry in the funnel plot. Publication bias was also examined by visual inspection of funnel plots for asymmetry and either Egger’s linear regression test for dichotomous data , 17, or d’s test  for contData were analyzed using RevMan 5.3.3 and STATA 14.0.Computerized and manual searches resulted in 1737 unique citations, 1280 of which were excluded after reviewing their titles and abstracts. In total, 252 potentially eligible articles were retrieved for full-text review, and 233 articles were excluded. Both reviewers (HS. H. and JW. H.) agreed to include 19 publications in the present study. See Fig. Nineteen studies, with a total of 2540 CKD patients, were deemed eligible. The clinical and methodological characteristics of each study are summarized in Table Trials in this review comprised various kinds of self-management. We grouped trials into similar interventions . Lifestyle interventions were the most common, followed by medical related practice and multi-factorial interventions. Among the lifestyle intervention trials, 9 studies included interventions related to lifestyle modification, targeting nutrition management, weight management or physical exercise. Four studies included in interventions were related to medical-behavior modification, targeting medicine adherence, disease cognition and complication control; and 6 were related to multi-factorial modifications . The programs were delivered either face-to-face in an individual or group format, or via telehealth sessions . They were conducted by a range of professionals, including nurses, dieticians/nutritionists, certified exercise physiologists (CEP) and physicians Table .Six different self-management intervention theoretical frameworks were included in these articles: the Coventry, Aberdeen, and London—Refined (CALO-RE) taxonomy; components of the chronic care (CCM) model; the Trans-Theoretical Model (TTM), the Stanford Patient Education, the SMS program, and the Health Belief Model (HBM). When there was insufficient information, we attempted to contact the authors, but the response rate was poor.The risk of bias of included studies is summarized in Additional file 2 = 0%)     Fig. , or chanThe funnel plots, Egger’s regression asymmetry test, and Harbord’s regression asymmetry test each indicated no significant publication bias for any outcome. There was also no statistical heterogeneity for any of the outcomes , and a lower CRP level  than that of the usual care. However, there were no statistical differences in HbA1c or total cholesterol (TC) levels. Likewise, for the behavioral risk factor outcomes, patients who had received exercise management had longer distances on the 6-min walk  than the control group. Regarding body weight and BMI, diet management appeared no better than usual care alone.For the surrogate outcomes, meta-analysis showed that self-management interventions were associated with multi-factorial modification group was associated with a significant decrease in HbA1c  compared with the usual care. ; there were no specific descriptions for definition of all AEs; and no AEs occurred during the follow-up periods.Table 2.Although RRT has been available for decades in wealthy countries, most people with kidney failure have insufficient access to life-saving dialysis and kidney transplants. CKD care is an effective alternative, yet it has limitations: underutilization of health professionals in the care of patients with CKD, limited capacity for CKD surveillance, a general absence of national strategies to support CKD care, and poor integration of CKD care programs into national NCD control schemes . A globaTo date, 6 systematic reviews have studied CKD management in general. While Lopez-Vargas 2016  providedCardiovascular disease events, proteinuria and diabetes are associated with CKD progression, and the former have been the major cause of death in those with CKD. In our study, the results showed a significant drop in blood pressure and a lower 24 h urinary protein excretion among the self-management group. Long-term blood pressure drops reduce proteinuria and other indicators of structural damage. Early change in proteinuria may lead to an increased risk of ESRD and early death, and may also be associated with slower progression of kidney disease . In the We also found that self-management intervention led to additional kidney protection, due to lower CRP levels and better exercise capacity. Evidence shows that exercise training results in improved physical performance and functioning among patients with CKD . Also, rPatient-oriented self-management is the cornerstone of chronic disease management, and optimized self-management is fundamental to controlling risk factors and improving disease management. Seeking to facilitate behavioral change rather than providing a purely educational program , self-maOur study consisted of the following 6 theoretical frameworks, with each model having unique aspects. These ranged from uni-dimensional variables to complex multi-dimensional constructs. Noar  analyzedAdditionally, computer-based machine learning algorithms can identify intervention at a practice level in real time to allow more focused and immediate correction of bias in NCD management . For exaThis study has several strengths. First, the concept of self-management is debatable. Many prior studies have failed to distinguish it from health education or chronic disease management. Additionally, there are a variety of risk factors for CKD. Our study grouped similar risk factors together, and found that the effect of comprehensive intervention  is more effective. We believe this will be a new trend in future self-management intervention.Second, current reporting of intervention content in published research articles and protocols lacks consistent terminology, making replicability difficult and uncommon. We concluded that there are 6 types of self-management frameworks, and this can provide reference for future self-management decision making.Our study also has several limitations. First, the effects of interventions on lifestyle and risk factor modification may require years for their results to modify surrogate and hard outcomes. A methodological limitation of the studies was the short-term follow-up might not distinguish kidney outcome differences, with most of the included studies had a follow-up time shorter than 2 years. Second, there was heterogeneity in patient characteristics, trial designs and risk factor targets  among the included studies. The number of included studies also limited the power for further exploration with multi-variate meta-regression or multi-level subgroup comparisons . TherefoThe drawback of a manual literature review is the time-consuming step of screening articles to select those that fulfill the requirements. Accordingly, some studies have leveraged computer-based topic analysis approaches to support literature review , 45. In We observed that self-management intervention provided additional benefits for neither renal outcomes nor all-cause mortality, when compared with standard treatments during a follow-up of 13.44 months in patients with CKD non-dialysis. However, it does show benefits for urine protein decline, blood pressure level, exercise capacity and CRP level. Hence, self-management intervention was beneficial for changing modifiable risk factors  for the progression of kidney disease. It may have been beneficial in optimizing CKD patient outcomes and avoiding progression to ESRD, and thus may have improved survival.Integration and ensuring the sustainability of healthcare self-management plans requires a large sample of RCT research and a unified and precise self-management intervention framework. These resources will help determine the ideal implementation for interventions.Additional file 1:Figure S1. Summary for Risk of Bias of Included Studies. Figure S2. Risk of Bias Graph of Included Studies. Figure S3. Funnel Plots, Contour-enhanced Funnel Plots, and Egger/Harbord Regression. Table S1. Association between Self-management Intervention and Standard Care by Subgroups. Table S2. Effects of Self-management on CKD Risk Factors. Table S3. Structure and Content of Self-management. (DOC 268 kb)


Sex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC.Here, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses.By using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression.Sex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers. Differences in cancer occurrence and mortality between sexes are evident across tumor types; males exhibit higher rates of cancer incidence and often poorer response to treatment, including some forms of chemotherapy and immunotherapy , 2. WhilAcross tumor types analyzed, the largest sex differences in autosomal mutational profiles were seen in liver hepatocellular carcinoma (HCC), indicating that male and female HCC are etiologically distinct . FurtherSex-specific gene regulation may partially underlie differences between the sexes in disease prevalence and severity , 10. PreIn addition to sex differences in overall gene expression due to the wide effects of sex as a biological variable, genetic variants may alter gene expression in a sex-specific manner. A pan-cancer analysis of the TCGA dataset identified 128 germline variants altering gene expression levels (eQTLs) in HCC . eQTLs tTargeted approaches to the treatment of male and female HCC require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Here, we analyzed data from TCGA and The Genotype-Tissue Expression project (GTEx) to examine the sex-specific patterns of gene expression and regulation in HCC. Here, we have contrasted the sex-biased patterns of gene expression in HCC tumors with healthy and tumor-adjacent liver tissues, allowing us to detect sex differences in gene expression shared between and specific to the different tissues. We show that male and female HCC exhibit differences in the dysregulation of genes and germline genetic regulation of tumor gene expression. Importantly, these orthogonal approaches identify genes that converge in shared pathways, indicating sex-specific etiology in HCC. The results presented here have implications for the development of targeted therapies for male and female HCC.strip_reads function of XYAlign [FastQC [TRIMMOMATIC IlluminaClip [GTEx (release V6p) whole transcriptome (RNAseq) data (dbGaP accession #8834) were downloaded from dbGaP. TCGA LIHC Affymetrix Human Omni 6 array genotype data, whole exome sequencing (WES), and RNAseq data (dbGaP accession #11368) were downloaded from NCI Genomic Data Commons . In tota XYAlign . We used [FastQC  to assesminaClip , with thHISAT2 [Subread featureCounts [Sequence homology between the X and Y chromosomes may cause the mismapping of short sequencing reads derived from the sex chromosomes and affect downstream analyses . To overHISAT2 . Female reCounts . Reads oGATK  best practices [Picard Toolkit’s AddOrReplaceReadGroups and optical duplicates marked with Picard Toolkit’s MarkDuplicates . Base quality scores were recalibrated with GATK (v.4.0.3.0) BaseRecalibrator. Germline genotypes were called from whole blood Whole Exome Sequence samples from 248 male and 119 female HCC cases using the scatter-gather method with GATK HaplotypeCaller and GenotypeGVCFs [LiftOver tool [BAM files were processed according to Broad Institute ractices –23: ReadypeGVCFs . Affymett-test to test for the equality of means in patient age and cell-type proportions, and Fisher’s exact test to test to detect differences in risk factors and pathological classifications  expression values for each gene were obtained using EdgeR . Each exvoom [duplicateCorrelation function was used to calculate the correlation between measurements made between tumor and tumor-adjacent samples on the same subject, and this inter-subject correlation was accounted for in the linear modeling. As the paired tumor samples differed significantly between the sexes in terms of race, tumor grade, and HBV status,  analysis, filtered, untransformed read count data were quantile normalized and logCPM transformed with voom . From thlimma/voom pipeline [eBayes. An FDR-adjusted p-value threshold of 0.01 and an absolute log2 fold-change (FC) threshold of 2 were used to select significant DEGs.DEGs between comparisons were identified using the pipeline  by compup-value ≤0.1 and absolute log2(FC) ≥ 0.To reliably detect genes that are expressed in a sex-biased way in HCC but not in non-diseased liver or in tumor-adjacent tissue, we examined genes that were DE in the male vs. female tumor comparison using the previously described significance thresholds, but not in the male vs. female comparisons of normal or tumor-adjacent samples with a relaxed significance threshold of FDR-adjusted To detect genes that are dysregulated in tumors compared to matched tumor-adjacent samples in each sex, we identified DEGs in the tumor vs. tumor-adjacent comparison of males, females, and in the whole sample. DEGs that were identified in one sex but not in the other or in the combined analysis of both sexes were considered sex-specific. DEGs identified in the combined analysis were considered sex-shared. This approach allows the identification of high-confidence sex-specific events that are a result of the underlying biological differences as opposed to sampling or statistical power. ANOVA and Kruskal-Wallis tests were used to test for equality of fold changes of sex-shared and sex-specific DEGs across male, female, and all samples.NetworkAnalyst webtool [p-values for the overrepresentation of genes in regards to GO terms and KEGG and Reactome pathways. An FDR-adjusted p-value threshold of 0.01 was used to select significantly overrepresented GO terms and canonical pathways.We further analyzed the sex-shared and sex-specific tumor vs. tumor-adjacent DEGs as well as the sex-specific eQTL target genes (eGenes) to identify sex-shared and sex-specific pathways driving HCC etiology. We used the  webtool , which uSNPRelate [Gene expression values are affected by genetic, environmental, and technical factors, many of which may be unknown or unmeasured. Technical confounding factors introduce sources of variance that may greatly reduce the statistical power of association studies, and even cause false signals . Thus, iNPRelate  to perfoCis-acting  eQTLs were detected by linear regression as implemented in QTLtools v.1.1 [p-values for associations between the gene expression levels and the top-variants in cis: first, permutations are used to derive a nominal p-value threshold per gene that reflects the number of independent tests per cis-window. Then, QTLtools uses a forward-backward stepwise regression to determine the best candidate variant per signal [p-values were calculated to correct for multiple phenotypes tested, and an adjusted p-value threshold of 0.01 was used to select significant associations. To allow the comparison of effect sizes of sex-specific and sex-shared eQTLs across the sexes, the effects of each variant located within the 1 Mb cis-window were obtained using the QTLtools nominal pass.We used eQTL analyses to detect germline genetic effects on tumor gene expression. Similar to the DE analysis, we utilized combined and sex-stratified analyses to detect sex-shared and sex-specific effects. Germline genotypes and tumor gene expression data from 248 male and 119 female donors in the TCGA LIHC cohort were used in the eQTL analysis. Filtered count data was normalized by fitting the FPKM values of each gene and sample to the quantiles of the normal distribution. To account for technical confounders and population structure, 10 de novo PEER factors and three genotype principal components were used as covariates. ls v.1.1 . Variantr signal . FDR-adjSimilarly to the tumor vs. tumor-adjacent DEGs, eQTLs that were detected in one sex but not in the other or in the combined analysis were considered sex-specific, while eQTLs detected in the combined analysis were considered sex-shared. ANOVA and Kruskal-Wallis tests were used to test for equality of effect sizes of sex-shared and sex-specific eQTLs across male, female, and all samples.powereQTL [We used the R package owereQTL  to estimAnnotatr to annotate the genomic locations of eQTLs [We used the R package of eQTLs . Variantof eQTLs  annotatip-value ≤0.01 and an absolute log2 (FC)≥ 2), tumor-adjacent tissue , and HCC  to characterize the shared and unique sex differences that may drive the observed sex-biases in HCC occurrence and etiology  for the overrepresentation of functional pathways. We found that the sex-shared and sex-specific DEGs were enriched in pathways relevant to oncogenesis and cancer progression , with 63% of all eQTLs across the combined and sex-specific analyses being located within 20 kb of TSSs. On average, 384 variants were tested per gene. 31% of the unique shared and sex-specific cis-eQTLs in HCC were also identified as eQTLs in the liver data in the GTEx project analysis release V7, indicating shared tissue origin. Out of the total of 1595 unique associations, 75.7% were shared between the sexes. We detected 295 male-specific and 92 female-specific eQTLs. Since these associations were not detected in the unstratified analysis, they are likely not a result of differential power to detect associations due to different sample sizes, but exhibit effect modification by sex. Sex-specific associations exhibited differences in effect size between the sexes  being expressed in a sex-biased way. These sex differences point to distinct mechanisms underlying HCC oncogenesis between the sexes, and may partially underlie the observed sex-biases in HCC occurrence and onset. We detected 34 genes that were expressed in a sex-biased way in HCC tumors, but not in healthy or tumor-adjacent liver tissues. Some of these genes are of particular interest in the context of HCC: female-biased e in HCC , 37. Addsive HCC , and lowl in HCC . These gl stages , and NTSn in HCC . Furtherarcinoma , but hasarcinoma . Anotherimmunity . The oppBy sex-specific analyses of matched tumor and tumor-adjacent samples, we detected genes that are uniquely dysregulated in male and female HCC. Further examination of these genes revealed sex differences in the pathway activation, indicating that the molecular etiologies of male and female HCC are partly driven by distinct functional pathways. Males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment  was found to be under germline regulation in male HCC, but not in female HCC nor in the joint analysis of both sexes  among the 24 male-specific eGenes. PI3K-AKT is known to co-operate with Notch by triggering inflammation and immunosuppression [Sex-specific regulatory functions may underlie sex differences in cancer etiology, progression, and outcome. We detected sex differences in the germline genetic regulation of tumor gene expression in HCC, including 24 genes that were under germline regulatory control only in male HCC Fig. . Functioxes Fig. d. The eQ cancers , 53, inc cancers , 55. POGpression . Concurrpression –60. We fpression , is invopression , and is pression . The rolIn summary, we discovered differential regulatory functions in HCC tumors between the sexes. This work provides a framework for future studies on sex-biased cancers. Further studies are required to identify and validate sex-specific genetic effects on tumor gene expression and its consequences in HCC and other sex-biased cancers across diverse populations.Additional file 1: Figure S1. A multi-dimensional scaling plot of the TCGA LIHC tumor samples of each sex . Euclidean distances between samples were calculated based on 100 genes with the largest standard deviations between samples.Additional file 2: Figure S2. Estimation of statistical power in the combined (grey), male-specific (blue), and female-specific (red) eQTL analyses with a p-value level 0.01 and 384 variants. Increased power in the combined analysis allows the detection of sex-shared low-effect eQTLs.Additional file 3: Tables S1-S19.


License plate detection (LPD) is the first and key step in license plate recognition. State-of-the-art object-detection algorithms based on deep learning provide a promising form of LPD. However, there still exist two main challenges. First, existing methods often enclose objects with horizontal rectangles. However, horizontal rectangles are not always suitable since license plates in images are multi-oriented, reflected by rotation and perspective distortion. Second, the scale of license plates often varies, leading to the difficulty of multi-scale detection. To address the aforementioned problems, we propose a novel method of multi-oriented and scale-invariant license plate detection (MOSI-LPD) based on convolutional neural networks. Our MOSI-LPD tightly encloses the multi-oriented license plates with bounding parallelograms, regardless of the license plate scales. To obtain bounding parallelograms, we first parameterize the edge points of license plates by relative positions. Next, we design mapping functions between oriented regions and horizontal proposals. Then, we enforce the symmetry constraints in the loss function and train the model with a multi-task loss. Finally, we map region proposals to three edge points of a nearby license plate, and infer the fourth point to form bounding parallelograms. To achieve scale invariance, we first design anchor boxes based on inherent shapes of license plates. Next, we search different layers to generate region proposals with multiple scales. Finally, we up-sample the last layer and combine proposal features extracted from different layers to recognize true license plates. Experimental results have demonstrated that the proposed method outperforms existing approaches in terms of detecting license plates with different orientations and multiple scales. License plate recognition is a key technology for intelligent transportation systems. It has been widely applied in traffic surveillance and road management. Typically, license plate recognition consists of three procedures: license plate detection (LPD), character segmentation, and text recognition. Among them, LPD is the first and key step, since it directly affects follow-up tasks and determines the overall accuracy . As the inherent attributes of license plates  [To detect license plates in images, representative features should be extracted to distinguish the target license plates from the background. Traditional LPD methods carefully handcraft features based on  texture ,3,4,5, b texture ,7,8, col texture ,10,11 an texture ,13,14,15m (SIFT) , speededm (SIFT) , histogrm (SIFT) ,19,20). m (SIFT) .In the practical use, multiple kinds of features are often combined to improve the accuracy. For example, Yuan et al.  proposedIn recent years, deep learning methods based on convolutional neural networks (CNN) have achieved remarkable performance in general object-detection tasks. Driven by the great success, several methods ,24,25,26However, directly training the deep models for the LPD task may not achieve good enough performance. For the practical LPD applications, there still exist two main challenges.The first challenge is that the detected regions are not accurate enough. Unlike general object-detection, LPD is often the prerequisite for character recognition. Since the license plates should be rectified ahead of recognition, the localization needs to be highly accurate. However, general object-detection algorithms enclose objects by horizontal rectangles. As shown in Works for text detection ,35,36,37The second challenge is that the detection of license plates with multiple scales has not been well solved. A traditional way to improve the scale invariance is to use the scattering operator as in the works of ,40,41. TTo address the above-mentioned two problems, we propose a novel method of multi-oriented and scale-invariant license plate detection (MOSI-LPD) based on CNNs. The proposed MOSI-LPD tightly encloses license plates with bounding parallelograms, and is highly invariant to the scale discrepancy of license plates. In addition, the proposed MOSI-LPD is robust to challenging conditions with a comparable detection speed. The main contributions of our work can be summarized as follows.We propose novel strategies to tightly enclose the multi-oriented license plates with bounding parallelograms. Both the network architecture and the loss function are elaborately designed to directly regress bounding parallelograms from horizontal proposals. Our method significantly improves the localization precision and guarantees a high detection accuracy simultaneously.We design effective strategies to detect license plates with multiple scales. Multiple convolutional layers are exploited both for proposal generation and feature extraction. The priori knowledge regarding inherent shapes of license plates is considered for anchor box design. Our method is highly invariant to the scale discrepancy of license plates, and effectively detects tiny license plates that are only several pixels.http://cvrs.whu.edu.cn/projects/LASI-LPL/).We construct a large license plate dataset. The dataset contains more than 7000 images, and all the license plates are labeled by the exact edge points. The dataset is publicly available for related research  layer. Right after the first four convolutional layers, there is also a Max-pooling layer. To construct RPN, we slide a The combined license plate proposals raised by RPN act as the inputs of the detection sub-network. They are projected onto the combined layers of the up-sampled “Conv5-2x” layer and “Conv4” layer. Similarly, the fused features are fed into two fully connected layers. The first layer classifies the proposals as license plates or the background, and the second layer regresses functions that map the proposals to license plates positions. Final outputs of our MOSI-LPD are bounding parallelograms that tightly enclose the multi-oriented license plates.Our strategies to achieve multi-oriented and scale-invariant detection are detailed as follows.Existing methods enclose license plates with horizontal rectangles. However, for practical LPD applications, the license plates in images are multi-oriented reflected by rotation and perspective distortions. As illustrated in To achieve this goal, we first reformat edge point coordinates of the license plates. For each license plate, we reformulate the coordinates of the upper left point Please note that there are three key points in the reformulation strategy. (i) The first is that we enclose license plates with parallelograms rather than quadrilaterals to simplify the task and help model training. Although quadrilaterals can indicate precise locations, their structures are over-free without any geometric constraints. To form quadrilaterals, the network must learn all the edge points and the lack of geometric constraints brings great difficulty to model training. By contrast, to form parallelograms, the lower right point Equation  based onTo regress the license plate location parameterized by central point coordinates and relative positions pired by , we formW, we train the model on training batches N samples, where To learn the above-mentioned parameters denoted as For the RPN sub-network, we define the classification loss st R-CNN  to map aSince region proposals do not contain relative positions of the edge points, the symmetry constraints are dropped by setting For the detection sub-network, the classification loss To leverage the symmetry property of parallelograms, the geometric constraints are enforced through the symmetry loss Given that the regressing task is more complex and challenging in the detection sub-network, we enhance effects of the regression loss and the symmetry constraints by setting the weight parameters With the learned transformation functions, license plate proposals raised by RPN are mapped to three edge points of a nearby license plate parallelogram. The fourth edge point is inferred with  to form In practical LPD applications, license plates in images vary greatly in scale and it is difficult to achieve multi-scale detection. The original Faster R-CNN  deals wiFor a more in-depth investigation, we can observe that the detection performance is particularly poor on small license plates. The reason is that the receptive fields become larger as the network goes deeper. For the tiny license plates, the re-projected area into the original image may contain high proportion of redundant information. The lack of necessary information seriously interferes with the detection process. To improve the scale invariance of the network, we introduce two strategies in the following.Firstly, in the RPN sub-network, we generate proposals for candidate license plate regions on multiple output layers rather than only on the last convolutional layer. This is motivated by the fact that different convolutional layers have receptive fields with different resolutions. The lower layers have smaller receptive fields and extract local features. By contrast, the higher layers have bigger receptive fields and extract global features. Therefore, the lower layers are more suitable to detect small license plates, and the higher layers are more appropriate for detecting large license plates. In order to effectively detect license plates with multiple scales, we take advantage of both the lower “Conv4” layer and the higher “Conv5” layer when searching for region proposals. The results are combined to produce stronger proposals with complementary scales.In addition, to generate proposals that better match the license plate shapes, we design the anchor boxes by considering the inherent shapes of license plates. Specifically, we analyze the scale ranges of the license plates, and set scales of the anchor boxes as Secondly, in the detection sub-network, different convolutional layers with various resolutions are also exploited to extract better features. We up-sample the “Conv5” layer to the size of the “Conv4” layer via the deconvolution operation, and combine the up-sampled layer “Conv5-2x” with the “Conv4” layer. For the feature extraction of the raised proposals, we perform RoI pooling on the combined layers, which shows better performance than traditional method that projects the proposals only to the last “Conv5” layer. We take this step by leveraging the fact that “Conv5” features are more appropriate for detecting license plates with normal-to-large scales, but less representative for small license plates. To obtain more detailed clues about license plates with small scales, we exploit the finer-grained “Conv4” features to get more local information.We have conducted extensive experiments to evaluate the proposed MOSI-LPD. In this section, we first introduce our dataset for training and testing. Next, we give implementation details and evaluation criteria. Finally, we compare and analyze performance between our MOSI-LPD and the baseline algorithms. The experimental results demonstrate that our MOSI-LPD tightly encloses the multi-oriented license plates with bounding parallelograms, and effectively detects license plates with multiple scales. Besides, our MOSI-LPD is highly robust to challenging conditions and achieves a comparable detection speed.To the best of our knowledge, existing license plate datasets annotate the license plates with horizontal rectangles. However, the information is not accurate enough to indicate actual positions of the multi-oriented license plates. Therefore, we constructed our own dataset. We manually collected 7284 images with totally 10,279 license plates, which had different orientations and multiple scales. We manually labeled the license plates with the exact four exact edge points. Since deep models should be trained with huge amounts of data, we further augmented the dataset by flipping, rotation , blurring, brightening, color and contrast enhancement, and noise-adding. These processes extended the dataset to a total number of 131,112 images with 185,022 license plates. We also collected some negative samples containing objects similar to license plates . We divided the images into training set and testing set by randomly sampling 75% of them for training and using the rest for testing. For each experiment, we built test subsets according to its aim.The environment to implement our algorithm was a desktop running Ubuntu with TitanX. We initialized the fundamental building blocks with the VGG16 model pre-trained on ImageNet , and weiSince there are no uniform criteria for LPD evaluation, we follow the text detection measurements and further include the intersection over union (precision, recall and f-measure, which are defined as:The text detection measurements consist of Since a major goal of our work is to enclose the multi-oriented license plates in a tighter way, we further take the In this section, we conducted a series of experiments to evaluate the proposed MOSI-LPD. The same experiments were also performed on corresponding baseline models for comparison. In the first experiment, we randomly sampled 10,000 images containing license plates with different orientations and multiple scales. Our MOSI-LPD was tested on the test subset for a brief overview on its performance. In the second experiment, we constructed three test subsets based on the skew degrees of license plates. These subsets were applied to evaluate the proposed strategy for multi-oriented detection based on bounding parallelograms. In the third experiment, we built three test subsets according to the scales of license plates. These subsets were used to evaluate the proposed strategy for scale-invariant detection. In the fourth experiment, we measured robustness of our MOSI-LPD to challenging data with blurs and noises. In the last experiment, we evaluated the detection speed of our MOSI-LPD. Implementation details and experimental results are presented and discussed in the following.We randomly sampled 10,000 images from all test data to construct the test subset “Dataset10000” for this experiment. License plates in “Dataset10000” had different orientations and multiple scales. We tested the proposed MOSI-LPD on this subset.MOSI-LPD is compared with those of state-of-the-art LPD methods:BOCO-LPD;Traditional method based on boundary features and color features , which iFaster R-CNN [Backbone region-based deep learning method of er R-CNN .For in-depth statistical analysis, we calculate precision, recall, f-measure and average IoU cf.  of the dMOSI-LPD significantly outperforms BOCO-LPD and Faster R-CNN on all the evaluation metrics. Considering the enclosure performance, the average overlap between detection results and ground-truth regions achieved by our MOSI-LPD is about BOCO-LPD and Faster R-CNN. This proves that our MOSI-LPD encloses the multi-oriented license plates in a much tighter way. Besides, considering the recognition performance, our MOSI-LPD obtains the highest precision and recall among these methods with the f-measure as high as 0.98. This proves that our MOSI-LPD achieves state-of-the-art detection performance in LPD.Overall, the experimental results prove that our MOSI-LPD tightly encloses the multi-oriented license plates with bounding parallelograms, and is highly invariant to the scale discrepancy of license plates.MO-LPD, is compared with the original Faster R-CNN [RRPN [TextBoxes++ [MO-LPD, Faster R-CNN, RRPN and TextBoxes++ on these three test subsets with visual inspection and statistical analysis.In this section, we evaluated our strategy for multi-oriented detection which aimed at tightly enclosing license plates with bounding parallelograms cf. . The proer R-CNN , and staNN [RRPN  and TexttBoxes++ . For thiMO-LPD tightly encloses the multi-oriented license plates with bounding parallelograms. As the skew degree of license plates increases, the horizontal rectangles predicted by Faster R-CNN contain much redundant information (shown in RRPN fail to enclose the corners of the license plates well (shown in MO-LPD still tightly enclose the multi-oriented license plates (shown in TextBoxes++ can also predict precise locations with quadrilaterals (shown in For visual inspection, shown in b, and thshown in c. In conshown in a. Pleaseshown in d. HoweveMO-LPD obtains higher average Faster R-CNN and RRPN: the average Faster R-CNN is less than 80% on the “Modest” test subset, and further drops to less than 60% on the “Severe” test subset. Similarly, the average RRPN is less than 85% on the “Modest” test subset, and further drops to less than 75% on the “Severe” test subset. However, MO-LPD retains the high MO-LPD improves the average Faster R-CNN and RRPN by around 25% and 15%, respectively. The tighter enclosure is attributed to the fact that the structures of parallelograms are more flexible than those of rectangles. As shown in For statistical analysis, MO-LPD is slightly lower than that of TextBoxes++, while the f-measure is much higher. The slight under-performance in MO-LPD over TextBoxes++ in recognition performance is significant, which is indicated by an about 5% increase in f-measure. The out-performance is attributed to the less complicated task and the benefit of geometric constraints. By estimating parallelograms via predicting three edge points, the last point is easily inferred based on the symmetry property. The task becomes simpler and the prediction difficulty is reduced. In addition, the geometric constraints between diagonal points are enforced in the loss to help model training. With the robust region-based architecture, the well-trained MO-LPD achieves accurate enclosure precision and high detection accuracy simultaneously.Please note that the MO-LPD encloses the multi-oriented license plates in an accurate way, and achieves high detection accuracy. The improved enclosure performance proves that our strategy for multi-oriented detection based on bounding parallelograms is effective.Overall, the experimental results demonstrate that MOSI-LPD with MO-LPD introduced in MOSI-LPD and MO-LPD on these three test subsets with visual inspection and statistical analysis.In this section, we evaluated our strategy for scale-invariant detection which aimed at detecting license plates with multiple scales cf. . We compMOSI-LPD manages the problem of multi-scale detection better than MO-LPD. As the scale of license plates decreases, MO-LPD fails to detect some license plates, especially the tiny ones. In contrast, our MOSI-LPD successfully detects license plates with multiple scales. For the tiny license plates that are hardly recognizable even by human eyes, our MOSI-LPD still figures them out.For visual inspection, MOSI-LPD and MO-LPD. We find that on all the three test subsets, our MOSI-LPD outperforms MO-LPD, and the improvement is particularly significant on small license plates. Specifically, the f-measure of MO-LPD is only 0.93 on the “Medium” test subset, and drops to 0.9 on the “Tiny” test subset. However, the f-measure of our MOSI-LPD always retains higher than 0.95. On the especially challenging “Tiny” test subset, MOSI-LPD improves the recall and precision of MO-LPD by around 10%.For statistical analysis, MOSI-LPD attains higher invariance to license plate scale discrepancy. The improved performance proves that our strategy to achieve multi-scale detection is effective.Overall, the experimental results demonstrate that our MOSI-LPD to challenging conditions. We manually blurred and added noise to 1821 unprocessed test images. Detection performance on the disturbed images and original ones were compared and analyzed with visual inspection and statistical analysis.In this section, we evaluated robustness of our MOSI-LPD is notably robust to tough environments. Although the license plates are quite indistinct in the disturbed images, our MOSI-LPD still successfully detects and tightly encloses them.For visual inspection, MOSI-LPD is almost not influenced by degradation of image quality: the f-measure on the blurred data decreases by as low as around 1% and the average For statistical analysis, MOSI-LPD is highly robust to challenging conditions. Despite the poor image quality, our MOSI-LPD still successfully detects and tightly encloses the license plates with bounding parallelograms.Overall, the experimental results demonstrate that our MOSI-LPD. For this experiment, our MOSI-LPD and the backbone framework Faster R-CNN [In this section, we evaluated the detection speed of our er R-CNN  were assMOSI-LPD is remarkable. For an input image of about MOSI-LPD is about 0.217s. Compared with Faster R-CNN, the well-known towards real-time detection network, our MOSI-LPD is just slightly slower. The nearly negligible decrease results from the modifications on the network architecture. However, since the extra time cost is as little as 0.041s, it almost does not affect the overall detection speed.MOSI-LPD almost retains the high detection speed of the original Faster R-CNN, and is very fast and promising for practical LPD tasks.Overall, the experimental results demonstrate that our In this paper, we proposed a MOSI-LPD based on CNNs. The proposed MOSI-LPD tightly encloses license plates with bounding parallelograms, which is much more accurate than traditional methods based on horizontal rectangles. In addition, our MOSI-LPD is highly invariant to the scale discrepancy of license plates. It can even detect tiny license plates that are only several pixels. Besides, our MOSI-LPD is robust to challenging conditions with a comparable detection speed.


The Scored Patient-Generated Subjective Global Assessment (PG-SGA) is a multidimensional tool to assess malnutrition and risk factors. The objectives of this study are to determine the validity of the Thai version of the Scored PG-SGA (Thai PG-SGA) and examine the correlations with selected nutritional parameters. . This observational analytic study included 195 cancer patients aged greater than 18 years at a university-affiliated hospital in Bangkok, Thailand. All patients were assessed for nutritional status by Thai PG-SGA in comparison to subjective global assessment (SGA). Anthropometry, body composition, and hand grip strength were evaluatedAccording to PG-SGA global assessment categories, 39% (75) of 195 cancer patients were well nourished, 27% (53) were moderately malnourished and 34% (67) of patients were severely malnourished. Thai PG-SGA had a sensitivity of 99.1% and a specificity of 86.0% at predicting SGA classification. PG-SGA numerical scores were significantly different between well-nourished and malnourished groups . The PG-SGA scores, nutritional status assessed by PG-SGA, and nutritional status assessed by SGA were correlated with weight, % weight loss in one month, body mass index, body fat, and hand grip strength (p < 0.001) respectively. Thai PG-SGA showed high sensitivity and good specificity in predicting malnutrition in Thai cancer patients. This tool demonstrated the correlations with anthropometric parameters, body composition, and muscle strength. Oncology patients are at risk of malnutrition throughout the course of the disease and its treatment . The prevalence of malnutrition in cancer patients ranged from 40% to 80% depending on the tumor type, location, stage, and therapy . Cancer and anti-cancer treatments could adversely affect the patient’s nutritional status, where they interfere with appetite and dietary intake . All of which leads to malnutrition and may cause an increased risk of complications, reduced response and tolerance of treatment, decreased quality of life, increased healthcare costs, and prolonged hospitalization . Therefore, screening, assessment, and monitoring of malnutrition is essential for triaging patients and provided timely intervention to improve clinical outcomes . The Subjective Global Assessment (SGA) is a well validated tool for assessing nutritional status which has generally been regarded as a standard tool based on the concept of medical history and physical examination . It has been used in several clinical settings and has been proven to correlate with clinical variables  . However, this assessment has a limitation of its subjective method in categorizing patient into three categories, which could lead to difficulty to detect small changes in nutritional risks .The Scored Patient-Generated Subjective Global Assessment (PG-SGA) is a 4-in-1 instrument facilitating proactive screening, assessment, monitoring, and interdisciplinary intervention triage . The PG-SGA was developed as a modification of the original SGA and has been widely used as a reference method for nutrition assessment in oncology patients . The scored PG-SGA consists of 2 components. Firstly, the patient-generated component was designed to be completed by patient. It incorporates four boxes on weight history, food intake, nutrition impact symptoms, and activities/function. These components are officially known as the PG-SGA Short Form and to reflect about 80-90% of the total PG-SGA score . Secondly, professional component includes 5 Worksheets addressing scoring the percentage of weight loss, disease and its relation to nutritional requirements, metabolic demand, physical examination, and the global category rating. The professional component was developed to be filled by the healthcare professionals . In addition, the PG-SGA provides a numerical scoring system which helps prioritizing patients to receive urgent interventions matched with their symptoms based on nutritional triage recommendations and monitoring changes in nutritional risks . The advantages of the PG-SGA are not only reducing time for patient interaction and shortening clinic flow but also potentially allowing proactive prevention of malnutrition by identifying and triaging for necessary interventions .Recently, the Thai version of the Scored PG-SGA (Thai PG-SGA) was officially established which included multiple translation processes according to the principles of good practice by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) . It was tested in cancer patients and healthcare professionals with the supervision of copyright holder and an international expert on translation and cultural adaptation of the PG-SGA. The results showed it has conceptual equivalence to the original English PG-SGA and considered easy to use and comprehensible by cancer patients and healthcare professionals . Utilization of this tool in each own country’s language could better reflect nutrition status of cancer patients as well as promote meta-analysis and inter-country comparison of nutrition status in cancer patients since the Scored PG-SGA was translated and culturally adapted to several languages . Nonetheless, at present, the validity of Thai PG-SGA and its association with important clinical parameters has not been assessed.The aims of this study were to validate Thai PG-SGA by comparing with SGA in categorizing nutritional status of cancer patients and evaluate the association of this tool with anthropometry, body composition, and hand grip strength. This study was a cross-sectional design to validate the Thai version of the Scored PG-SGA with the SGA in assessing nutritional status in cancer patients and concurrently analyze the relationship of the assessment outcomes with body weight, body mass index, percentage of weight loss, percentage of body fat, muscle mass, and hand grip strength.ParticipantsCancer patients at the outpatient and the inpatient departments of Division of Therapeutic Radiation and Oncology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand were recruited into the study by convenience sampling between February and April 2017. Eligibility criteria were as follows: age greater than 18 years, having anticancer treatment (chemotherapy and/or radiotherapy and/or surgery), and agreed to participate in the study. Patients having physical limitation or cognitive impairments, being pregnant, or being unable to read and write in Thai, were excluded.The study was approved by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University . All eligible participants were informed about the study protocol and gave their written consent before participating in the study.Nutritional assessmentThe nutritional status of all cancer patients was assessed using Thai PG-SGA  and SGA  by a trained dietitian experienced in using both tools. In the evaluation process, the trained dietitian applies strict criteria to the standard protocols and making measurements with great care in categorizing nutritional status by PG-SGA and SGA. The details of category rating and numerical scoring system of PG-SGA and SGA were addressed by Jager-Wittenaar (2017) and Detsky (1987) respectively. Permission by copyright owner of PG-SGA and SGA were granted. Thai PG-SGA is now available at www.pt-global.org. Each patient was either classified as well nourished (category A), moderately malnourished or suspected of being malnourished (category B), or severely malnourished (category C). The total score of PG-SGA were the sum of scores from patient-generated component and professional component, where the higher score indicating higher severity of malnutrition. The score of 0-1 suggests no intervention required, 2-3, educating patient and family is recommended, 4-8, requiring intervention as indicated by symptoms, and the score of 9 or more implying critical need for intervention. The score was also affected by age, diagnosis, and stages of cancer. These factors were retrieved from medical records.Anthropometric assessment2 (kg/m2). The anthropometric measurement was evaluated by the same researcher throughout the study.Body weight (kg), body fat (%) and muscle mass (kg) were measured by a bioelectrical impedance analysis (BIA) machine . The body weight was recorded in kilogram to the nearest 0.1 kg. A stadiometer was used for measuring height and recorded in centimeter to the nearest 0.1 cm. Body mass index was calculated as body weight in kg per height in mFunctional assessment o. The test was administered with the dominant hand. The patients squeezed the handgrip dynamometer as hard as possible for three consecutive measurements with a 30 seconds rest period between each squeeze to allow for optimal recovery. The mean of the three trials were used as the output measure for the test. All measurements were performed by the same investigator to avoid inter-observer variation. Hand grip strength (HGS) was measured using a mechanical grip dynamometer (T.K.K. 5001 Grip A). All study patients performed the test while sitting comfortably with the arm by their side of the body and the elbow flex at 90Statistical analysisAll data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22.0 for Windows. Descriptive statistics were reported as frequency, percentage, mean ± standard deviation (SD) or median and interquartile range. A contingency table was used to determine the sensitivity, specificity, and predictive value of the Thai version PG-SGA compared to SGA. Data between well-nourished and malnourished group were compared. The continuous clinical variables with normal distribution  and skewed distribution (age and muscle mass) were analyzed by independent t-test and Mann-Whitney U test respectively. Chi-square tests were used for categorical clinical variables. Correlations between PG-SGA scores, nutritional status classified by PG-SGA/SGA and clinical variables were reported as the Pearson correlation coefficient and Spearman’s rho correlation. The strength of the correlation for the absolute value of r: 0.00 - 0.19 “very weak”, 0.20 - 0.39 “weak”, 0.40 - 0.59 “moderate”, 0.60 - 0.79 “strong”, and 0.80-1.00 “very strong”. A p-value < 0.05 was considered statistically significant.Clinical characteristics and prevalence of malnutrition of study participantsA total of 195 cancer patients were recruited. There were more females (63%) than males (37%), with median age of 58 years old. The majority of patients were diagnosed with breast cancer, followed by head and neck cancer. According to Thai PG-SGA, 75 (39%) of patients were well-nourished (PG-SGA A), 53 (27%) were moderately/suspected malnutrition (PG-SGA B) and 67 (34%) were severely malnutrition (PG-SGA C). Detailed patient characteristics classified by PG-SGA categories are shown in Comparison of clinical variables between well-nourished and malnourished patientsMalnourished cancer patients had significantly higher average percentage of weight loss in one month and lower handgrip strength compared to those who classified as well-nourished (p <0.001). On the contrary, actual body weight, BMI, % body fat and muscle mass did not differ between well-nourished and malnourished patients. Moreover, the mean PG-SGA scores were significantly higher in malnourished group (16.3 ± 4.9) than well-nourished group (4.2 ± 2.4) .Validity of the Thai version of the Scored PG-SGA The ability of the Thai version of the Scored PG-SGA to predict SGA is shown in Association between PG-SGA scores, nutritional status and clinical variablesFor the anthropometric assessment, percentage of weight loss in one month had positive correlation with PG-SGA scores , nutritional status assessed by PG-SGA and SGA . In contrast, actual body weight, BMI, and % body fat showed inverse correlation with PG-SGA scores, nutritional status assessed by PG-SGA and SGA. In addition, functional assessment by handgrip strength showed negative correlation with PG-SGA scores, nutritional status identified by PG-SGA and SGA.This was the first study to determine the validity of the official Thai translation and cultural adaptation PG-SGA in predicting malnutrition as compared to SGA in terms of sensitivity and specificity. Thai PG-SGA was shown to be valid at identifying malnutrition among Thai cancer patients. The results are consistent with Bauer et al., (2002) who stated that the Scored PG-SGA had 98% sensitivity and 82% specificity when compared to SGA. This may be explained by the similarity of study design which was conducted in oncology setting and experience of dietitian in using the tool. When comparing with SGA as the gold standard, PG-SGA seems to have a lower specificity may be due to the scores obtained from the nutrition impact symptoms and other factors (box 3). In this part, any symptoms affecting eating that patient reports get scored and all points are additive (maximum 24 points in this section) . These extensive range of symptoms provided by PG-SGA may identify more patients at risk of malnutrition. Being able to identify nutrition impact symptoms at the early stage could be beneficial for proactively preventing malnutrition allowing timely intervention during cancer and anti-cancer treatments. Another possibility for the difference between PG-SGA and SGA is the contribution of the assessment of weight change where weight information in PG-SGA is addressed along a continuum – 6 months (chronic), 1 month (intermediate), and past two weeks (acute) change but that of SGA change is assessed at 2 time-points, only in the past 6 months and 2 weeks.The prevalence of malnutrition assessed by Thai PG-SGA was high (62%) probably due to the severity of cancer in majority of malnourished patients and inclusion of hospitalized cancer patients. The study findings indicated that cancer patients with advanced stage had a higher prevalence of malnutrition which was comparable to the figure reported by Bauer et al., (2002) in which 76% of cancer patients were found to be malnourished as classified by PG-SGA. In this study, mean PG-SGA scores were significantly higher in malnourished compared to well-nourished patients which was consistent with previous studies . The PG-SGA numerical scores showed positive correlation with nutritional status assessed by PG-SGA and SGA, in the anticipated direction. The percentage of weight loss in one month was significantly higher in malnourished than well-nourished group. It also had significantly positive correlation with PG-SGA numerical scores, nutritional status assessed by PG-SGA and SGA. This finding was consistent with Bauer et al., (2002) which reported significant correlation between PG-SGA scores and % weight loss in 6 months . Even though, the time interval of % weight loss of the present study is shorter, the results follow the same direction. In this study, actual body weight and BMI were not significantly different between well-nourished and malnourished patients. This was also reported by Gabrielson et al., (2013) that body weight was not significantly different between group (p = 0.218). Another study conducted in 148 lung cancer patients also showed the same trend that body weight and BMI alone failed to identify malnutrition in the patients . Actual body weight and BMI were negatively correlated with PG-SGA scores, nutritional status assessed by PG-SGA and SGA as expected. However, the findings indicated that BMI and body weight alone may have limitations in predicting malnutrition since malnourished cancer patients may have normal or overweight BMI range. Therefore, malnutrition can happen at any BMI while, in some cases, body fat could mask loss of lean body mass .Body composition analysis by BIA showed no significant difference of % body fat and muscle mass between well-nourished and malnourished patients. Similar results were found in study by Laky et al., (2008) with no significant difference of fat mass and fat-free mass between well-nourished and malnourished gynecologic cancer patients. In addition, % body fat was found negatively correlated with PG-SGA scores, nutritional status classified by PG-SGA and SGA. Although the correlation of PG-SGA scores, and nutritional status identified by PG-SGA/SGA with muscle mass did not show statistical significance, the direction of the association demonstrated that the higher the PG-SGA scores and the degree of malnutrition, the lower the muscle mass. Regarding functional assessment of upper extremities by hand grip dynamometer, it was found that well-nourished had significantly higher average HGS than malnourished patients. This is consistent with the finding in a prospective cross-sectional study conducted in 189 cancer patients to examine muscle strength that well-nourished patients had significantly higher HGS than malnourished ones (30.4±10.4 vs. 22.9±11.1 kg) . The correlation analysis showed that HGS had significantly negative correlation with nutritional status assessed by both tools and PG-SGA scores. These data would suggest that the lower the hand grip strength, the higher the prevalence of malnutrition and PG-SGA scores. Similar trend was found in several previous studies. The study by Ozorio et al., (2017) in 101 gastrointestinal cancer patients found inverse correlation between HGS and PG-SGA (r= -0.52). Another study by Flood (2014) conducted in 217 hospital patients reported negative correlation between HGS and PG-SGA scores . HGS has advantages i.e. easy, non-invasive and most feasible bed side method, as well as an indicator of muscle function . Until now, there is no reliable and validate cutoff value of HGS available for cancer patients and there is no standard protocol for HGS assessment . This might limit the generalizability of study results since there are different methods in assessing HGS in terms of techniques used and types of instrument. In addition to malnutrition, there are various factors related to muscle weakness such as disease severity, comorbidities and medical treatments .From these findings, the correlations between clinical variables and nutritional status assessed by both tools ranged from strong to weak correlations. The results revealed strong correlation between percentage of weight loss in one month and PG-SGA numerical scores and moderate correlation with nutritional status classified by PG-SGA/SGA. Therefore, percentage of weight loss in one month is shown to be significant clinical parameter, a better indicator than other clinical variables. The results also suggested that nutritional status could not be determined by using any single clinical parameter alone because each parameter has different limitation in nutrition assessment. Therefore, data collection from a variety of domains are necessary for nutrition assessment to determine appropriate diagnosis of malnutrition . The systematic review showed that PG-SGA could serve as a nutritional assessment tool as it covers all components of the definitions of malnutrition as published by European Society for Clinical Nutrition and Metabolism (ESPEN) and the American Society for Parenteral and Enteral Nutrition (ASPEN) . It also has several advantages as a nutritional instrument in comparison to SGA in terms of numerical scoring system rather than category. In addition, it provides extensive range of nutritional impact symptoms which often experienced by oncology patients .The strength of this study is that some clinical parameters were evaluated by anthropometric and functional assessment. In addition, there were various kinds of cancer patients with all cancer staging and treatment from both outpatient and inpatient department enrolled to the study.The principal limitation of this study is the acquiring of participants by convenience sampling and the exclusion of cancer patients who had cognitive impairments and physical limitation that prevented them from completing the Scored PG-SGA. This limitation may influence the study results and limit the generalizability. Another potential limitation of this study is that the nutritional assessment was evaluated by trained dietitian only which may affect to the results. However, it avoids inter-rater variability. Further study is needed to assess interobserver reliability or reproducibility of Thai PG-SGA evaluation among health professionals by a standard method such as a test-retest model. More could be explored for validate the use of Thai PG-SGA in specific type of cancer as well as different treatment regimens. Currently, the Thai PG-SGA has only paper-based version. Development of an application-based tool may yield a better result in terms of reducing time spent, accuracy of PG-SGA score calculation, and convenience of data collection.In conclusion, the results of this study suggest that Thai PG-SGA is a valid nutritional instrument in identifying malnutrition among cancer patients. This tool could serve as a suitable assessment method for cancer patients in Thailand. The nutritional status assessed by Thai PG-SGA is well correlated with percentage of weight loss in one month. The other clinical variables, while having a weak to moderate correlation, were also partially contribute to nutrition diagnosis. Therefore, the multidimensional tools which include assessment of various key clinical variables could be used to identify appropriate nutritional status. th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund)This study was supported by the 90No conflict of interest in this study.


Background: Blood-based tumor mutational burden (bTMB) was recently found to be suboptimal in predicting overall survival (OS) benefits of atezolizumab over docetaxel among patients with advanced non-small cell lung cancer (NSCLC). The maximum somatic allele frequency (MSAF) is an indicator of the proportion of tumor-derived plasma DNA, which could affect the concordance between bTMB and tissue-based TMB. Therefore, we aimed to evaluate the utility of MSAF, alone or in combination with bTMB, to identify NSCLC patients with or without survival benefit from atezolizumab over docetaxel.Methods: We analyzed the individual patient-level data from the randomized POPLAR and OAK studies. The bTMB and MSAF were derived from the pre-treatment blood-based genomic data.Results: In both the bTMB-high  and bTMB-low subgroups, atezolizumab significantly improved OS compared with docetaxel . Among patients with a low MSAF , OS significantly favored atezolizumab , whereas OS with atezolizumab was similar to that with docetaxel in the MSAF-high subgroup . Among patients from the bTMB-low and MSAF-high subgroup, OS was numerically worse with atezolizumab than with docetaxel ; in contrast, OS was significantly improved with atezolizumab compared with docetaxel in those with either a high bTMB or low MSAF . Consistent findings were obtained for progression-free survival data.Conclusions: MSAF alone or in combination with bTMB can effectively distinguish patients with or without survival benefit from atezolizumab compared with docetaxel. MSAF and the combined bTMB-MSAF classification may become practical predictive markers for atezolizumab in advanced NSCLC. Tumor mutational burden (TMB) is being extensively studied as a promising biomarker for predicting the efficacy of immune checkpoint inhibitors (ICIs) –3. MountThe maximum somatic allele frequency (MSAF) is a useful bioinformatics tool for estimating the amount of tumor fraction of cell-free DNA in peripheral blood samples . PreviouIt has been suggested that a low MSAF could contribute to a poorer consistency between bTMB and tTMB . For insIn this pooled analysis of the randomized POPLAR (NCT01903993) and OAK (NCT02008227) studies , 14, we N = 287 and N = 850, respectively)   and OAK NCT02008227) studies (227 studictively) , 14. Thectively) , 14. As ctively) , mutatioOS was defined as the time from the date of randomization to the date of death from any cause. PFS was defined as the time from the date of randomization to the date of disease progression (per RECIST v1.1) or death from any cause, whichever occurred first. OS and PFS hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were estimated using multivariable Cox proportional hazards models adjusted for baseline covariates .To identify the optimal cutoff values for bTMB (or MSAF), we constructed multivariable Cox models including bTMB (or MSAF), treatment group, and their interaction term, adjusted for the aforementioned baseline covariates. We then applied the Chow test  to deterP < 0.05 was considered statistically significant. All statistical analyses were performed using R v. 3.5.1 (http://www.r-project.org).The predictive accuracy of prognostic models was investigated using a time-dependent ROC analysis. A two-sided P < 0.001; P < 0.001; interaction test P = 0.023; As both the POPLAR and OAK studies demonstrated significantly improved OS with atezolizumab , 14, we P < 0.001; P = 0.500; interaction test P = 0.017; We then evaluated the efficacy of atezolizumab vs. docetaxel in MSAF-high and MSAF-low cases defined by various cut-points of MSAF. P = 0.009). OS was found significantly better with atezolizumab than with docetaxel in the bTMB-low and MSAF-low subgroup , the bTMB-high and MSAF-high subgroup , and the bTMB-high and MSAF-low subgroup , along with a larger HR in the bTMB-low and MSAF-high than in the MSAF-high subgroup  but not the docetaxel arm  than with docetaxel .The combined bTMB-MSAF classification can also identify patients who had no PFS benefit from atezolizumab. Among the bTMB-low and MSAF-high subgroup, PFS was significantly worse with atezolizumab  than with docetaxel  of the Sun Yat-sen University Cancer Center and the Third Affiliated Hospital of Sun Yat-sen University.YC and ZW designed this study. YC, SS, and ZW collected the data and performed statistical analysis. All authors drafted the manuscript, read, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


NDUFAF6 gene [We read with interest the article by Baide-Mairena et al. about three siblings with early-onset Leigh syndrome due to compound heterozygous variants in the AF6 gene . We haveWe do not agree with the statement that thiamine is the only compound effective in Leigh-syndrome . In casel-arginine [Since the basal ganglia lesions were not only hyperintense on T2/FLAIR sequences but also on diffusion-weighted images (DWI) , we shouarginine . It is aarginine . Since SSince Leigh syndrome may manifest clinically with seizures , we shouOverall, this interesting study could be more meaningful by providing more extensive information about the phenotype of the included siblings, in particular if any of them ever had developed seizures, if the ADC maps were hyper- or hypointense on cerebral MRI, and if the discussion about treatment strategies in patients with Leigh syndrome was revised.There are no conflicts of interest.No funding was received.JF: design, literature search, discussion, first draft, FAS: critical review, literature search.


Static FC in Wake and Stage-2 Sleep (NREM2) were largely similar. By contrast, FC dynamics across wake and sleep differed, with transitions between FC states occurring more frequently during wakefulness than during NREM2. Evidence of slower FC dynamics during sleep is discussed in relation to sleep-related reductions in effective connectivity and synaptic strength.The transition from wakefulness to sleep is accompanied by widespread changes in brain functioning. Here we investigate the implications of this transition for interregional functional connectivity and their dynamic changes over time. Simultaneous EEG-fMRI was used to measure brain functional activity of 21 healthy participants as they transitioned from wakefulness into sleep. fMRI volumes were independent component analysis (ICA)-decomposed, yielding 42 neurophysiological sources. Static functional connectivity (FC) was estimated from independent component time courses. A sliding window method and  The transition from wakefulness to sleep is highly organized into a continuum of heterogeneous stages that change dynamically over the course of a single night and is associated with marked alterations in brain function and cognition. There is, for example, a marked reduction in long-range inter-cortical functional connectivity (FC) in the descent from alert wakefulness to deep, slow-wave sleep –3, and aOne important means of studying the brain’s FC and its relation to sleep is through simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The method is particularly powerful as EEG provides a gold-standard means of identifying qualitatively distinct sleep stages and fMRI provides a whole-brain, albeit indirect, measure of brain activity in the form of the blood oxygenation level dependant (or BOLD) signal. To date, most studies of functional connectivity (FC) and sleep have compared inter-regional BOLD correlations  observed during wakefulness with those observed during sleep –15, an aNeurologically intact healthy adult individuals were scanned at 3-Tesla as they transitioned from wakefulness to sleep. Sleep status was monitored throughout the scanning period via simultaneously collected EEG. Our analysis focused on time-dependent, or dynamic, changes in inter-regional FC measured by fMRI, rather than time-invariant FC. Whole-brain patterns of FC alternate between periods of stability and instability, potentially reflecting transitions into and out of successive, qualitatively distinct, connectivity states . Such tiIn this study, we investigated temporal characteristics of functional connectivity during wakefulness and sleep. Using a sliding-window analysis of functional connectivity dynamics, we hypothesized that whole-brain functional connectivity state transitions would occur less frequently during sleep than during wakefulness. These insights would be overlooked by conventional methods  that assume a stable functional connectivity configuration over time and may potentially serve as biomarkers for altered states of awareness such as anaesthesia, persistent vegetative state, and coma.Thirty-five healthy right-handed adults  participated in this study. All participants were screened based on a-priori exclusion criteria including, shift workers, history of head injury or seizures, diagnosis of a sleep disorder, neurological, psychiatric or chronic physical pain, high body mass index (> 25), taking prescription medications, and excessive caffeine, nicotine, or alcohol consumption see . ParticiDetails pertaining to EEG-fMRI acquisition and data preprocessing can be found in http://mialab.mrn.org/software/gift/) in MATLAB to decompose data into functional networks. In the first step, Principal Components Analysis (PCA) reduced subject-specific data to 65 components. Data were then aggregated across participants. The Infomax algorithm was then applied to obtain 65 maximally independent components (ICs). A high model order was used to optimally separate noise and source components, as well, to ensure a spatially fine-grained parcellation of cortical and subcortical brain regions [All wake and sleep volumes were submitted to a group-level spatial ICA implemented in the GIFT toolbox ( regions . Subject regions . The 42 IC time courses (TCs) were then further post-processed to maximize signal-to-noise ratio. First, estimates of translational and rotational motion and their temporal derivatives were regressed out of individual TCs using linear regression. Second, residualized TCs were despiked by replacing any spike greater than 3 standard deviations (STDV) with values equal to 3 STDV and low-pass filtered using a Butterworth filter with a cut-off frequency of 0.15 Hz. Last, a Fisher z-transformation was applied, and the resultant TCs were used for further analyses.k states. Subject-specific IDX vectors were parsed out of the group-level IDX vector, and used to estimate four different dynamic connectivity metrics including the number of transitions, inter-transition interval, frequency of connectivity state expression, and mean dwell time (see SI for more information).Building on a previously published approach, dynamic FC was estimated with a sliding window approach . To incrhttp://mialab.mrn.org/software/gift/) was conducted to decompose the data into functional networks. Subject-level time courses (TCs) were generated via back-reconstruction, and then filtered, detrended, and despiked. 42 neurophysiologically plausible sources were identified  who had sufficient consolidated sleep (bare minimum of 5 minutes) were included in the analysis see . Group-lentified . Static  p = .05 . Five coK-means clustering was then applied to the concatenated set of windowed FC matrices to produce an unsupervised, or data-driven, identification of repeating functional connectivity states. For this, we followed the precedent of Allen et al. [k = 7). Owing to the fact subject-specific mean FC was removed from individual windowed FC matrices , clusteg [k see .Four measures of functional connectivity dynamics were computed separately for participants and conditions . Two measures were repertoire-wide in that they were based on the complete set (or repertoire) of 7 states, and two measures were state-specific in that they were based on each of the 7 states separately. Repertoire-wide measures included: (a) the number of transitions, computed as the total number of state transitions evidenced by a participant in a condition corrected for the total number of windows in that condition for that participant; and (b) inter-transition interval, computed as the average number of consecutive instances of the same state before a transition to a different state. State-specific measures included: (c) frequency, computed as the proportion of one participant’s windows classified as instances of a particular state; and (d) mean dwell time, measured as the average number of consecutive windows classified as instances of a particular state.M = .15; SD = .033) than during wakefulness  = -2.49, p < .05; see M = 7.08; SD = 1.96) than during wakefulness  = -2.95, p < .01; see With respect to repertoire-wide measures, the number of transitions was lower during NREM2 , three of seven states showed a difference in frequency across Sleep and Wake . For twoConsistent with our predictions, qualitative changes in whole-brain functional connectivity occurred less frequently over time when participants were asleep compared to when they were awake. This sleep-related slowing of connectivity dynamics was most clearly reflected by a decrease in the number of transitions between qualitatively distinct connectivity states during sleep relative to wakefulness, and accordingly, by an increase in the length of time between transitions. These findings were robust across multiple clustering solutions (see SI for figures) and were not easily attributed to differences in non-neurophysiological artifacts such as motion and cardiac pulse rate (see SI for figures).Repertoire-wide changes in connectivity dynamics, as reflected in the number of state transitions and the length of the inter-transition interval across sleep and wakefulness, were driven largely by changes in the dynamics of three individual states. Of particular interest were sleep-related increases in the frequency and dwell time of connectivity State-1, a state marked by increased inter-cortical connectivity and decreased cortical-subcortical connectivity relative to the participant’s own mean FC. This is consistent with differences in the mode of operation of cortico-subcortical circuitry in NREM2 compared to wake, whereby at the cellular level, the brain oscillates between up-states (involved in the generation of slow oscillations), separated by down-states (periods of synaptic quiescence) –26. At tIn terms of state-specific expression of connectivity states, evidence that dynamic transitions between qualitatively distinct connectivity states occur more frequently during wakefulness than sleep is broadly consistent with the view that dynamic exploration of connectivity state space provides a foundation for cognitive function , and parInterestingly, no connectivity states were specific either to sleep or wakefulness. In the context of a 7-state cluster solution, all participants expressed all states while asleep, and all but 2 participants expressed all states when awake. Although there were subtle differences in the frequency of specific states across sleep and wakefulness, there was no indication that sleep onset was accompanied by a fundamental change in the state repertoire. As such, the most pronounced differences between sleep and wakefulness were evident in temporal rather than spatial aspects of functional brain connectivity. Thus, a dynamic measure of sleep-wake states may be more appropriate for studying whole-brain connectivity.Evidence that the spatial properties of the state repertoire were largely conserved across sleep and wakefulness contrasts with previous reports of marked changes in state topology given changes in awareness or arousal. In one report , dynamicAlthough differences between wake and sleep were most evident in dynamic aspects of functional connectivity, there were a selected number of static intra- and inter-network connections that varied across conditions. In both conditions, static FC was marked by strong positive correlations within functional networks, and weaker positive correlations and anti-correlations between functional families. Comparing conditions, there were a small number of inter-network connections that were stronger in NREM2 than Wake. These differences included stronger connections between three intra-visual networks. This result converges with previous findings that showed an increase in BOLD fluctuations within the visual network during light sleep when compared to wakefulness . We alsoDynamic FC revealed interesting temporal features of the data that would have otherwise been obscured using static methods. Our results suggest that: (a) dynamic fluctuations between inter-cortical and cortical-subcortical might be a useful marker of arousal; and (b) reduced connectivity between cognitive control and default mode networks might reflect reduced awareness. The latter is consistent with previous studies showing a decoupling of the anterior and posterior nodes of the DMN, observed only previously during deep sleep, which is restored again during REM sleep . HoweverS1 File(DOCX)Click here for additional data file.S2 File(PDF)Click here for additional data file.


Objective: The present study was conducted to compare neurocognitive profile in patients with borderline personality disorder (BPD) and bipolar II disorder (BD-II) and to find whether BPD can be classified as one of bipolar spectrum disorders.Method: A total of 35 patients with BPD and 35 euthymic patients with BDII disorder were selected by convenience sampling method. These 2 groups were compared with 30 healthy individuals using neurocognitive battery tests that assessed cognitive flexibility and set-shifting, response inhibition, problem-solving, decision-making, and sustained and selective attention. Data were analyzed using independent t test, X2 and ANOVA.Results: Patients with euthymic BDII and BPD had poorer performance than the healthy group in most neurocognitive domains (p<0.05). Both patient groups showed similar functions in cognitive flexibility and set-shifting, decision-making, sustained and selective attention, and problem-solving (p<0.05). BPD patients had more elevated response inhibition deficits than BD-II patients (P<0.05). Also, BPD patients had poorer performance in planning compared to BD-II patients (P<0.05). Conclusion: The results provided empirical support for previous findings which have reported that patients with BPD and BD-II show neurocognitive dysfunctions. Despite the similarity between these 2 clinical groups in terms of neurocognitive profile in this study, more extensive studies are needed to confirm the hypothesis that BPD can be conceptualized as one of bipolar spectrum disorders. Precise distinction between borderline personality disorder (BPD) and bipolar disorders (BD) is one of the main challenges among clinical therapists and psychopathologists , 2. Bipo Conditions through identifying neurocognitive functions that are similar or different in the 2 disorders. Results of many studies that had often been conducted separately, showed that neurocognitive function is impaired in patients with BPD and bipolar spectrum disorder. One study showed that attention, memory, and executive function are cognitive domains that were mostly impaired in patients with BD . A meta-compared to healthy controls. However, there were no significant difference between the 2 subtypes .One research reported that patients with bipolar I and II showed impairment in cognitive domains, such as inhibition, set-shifting, verbal fluency, and visual recall, One systematic review and meta-analysis in patients with BD (type I or II) and healthy controls identified 6 domains of executive function, including set-shifting, inhibition, planning, verbal fluency, working memory, and attention. In general, results showed that patients with BD-I performed worse than healthy group and patients with BD-II demonstrated impairment in verbal fluency, working memory, set-shifting, and attention .  Research studies indicated that BPD was characterized by deficits in nonverbal executive function and nonverbal memory , recoverStudies that were conducted separately found bipolar and BPD to be associated with multiple abnormalities in prefrontal attentional networks and mechanisms of inhibitory control which may contribute to impulsivity and poor affect regulation . HoweverIn this study, 35 patients with BD-II and 35 with BPD were consecutively selected from the outpatient population of Kamkar-Arabnia hospital in Qom, Iran, using the following inclusion criteria: age 18-65 years, diagnosis of BD-II or BPD according to SCID  and SCIDThe researcher provided a detailed description of how the test was going to be performed for each participant prior to testing. Also, the participants were entitled to leave the test at any time if they decided to leave the study. Moreover, participants were assured about the anonymity and confidentiality of the information. Ethical code was obtained from Ethics Committee of Qom University of Medical Sciences (IR.MUQ.REC.021). Neurocognitive AssessmentPatients completed neurocognitive battery tests: (1) Wisconsin Card Sorting Test , which Statistical AnalysisData were analyzed using by SPSS 21.0. The 3 groups were compared on demographical and clinical variables using ANOVA and x^2 test. Neurocognitive variables were normally distributed as assessed by the Kolmogorov-Smirnov test. To decrease the risk of type I errors, ANOVA was conducted with all neurocognitive variables as dependent variables and groups as factor. As neurocognitive tests are naturally correlated, this procedure was considered superior to the Bonferroni inequality correction that would increase type II errors. When significant main effects were present, Tukey post hoc test was used to clarify group effects. Also, the effect sizes (Cohen d) were calculated to find the difference between the 3 groups. Pearson correlation coefficients and Spearman correlation (in nonparametric variables) were used to test the associations between clinical-demographic and neurocognitive variables.Clinical-demographic variables of participants are summarized in According to The first finding of this study was that patients with BD-II and BPD had poorer performance than the healthy group on most neurocognitive domains. This result agrees with previous studies that showed patients with euthymic BD demonstrate deficits in cognitive functions , 33. BasAlso, these results are consistent with previous findings that indicated patients with BPD had impaired cognitive functions , 34. Both patient groups showed similar functions in cognitive flexibility and set-shifting, response inhibition, decision-making, sustained and selective attention planning, and problem-solving. However, it seems that Patients with BPD had more elevated response inhibition deficits (and as a result more impulsivity) than BD-II patients, as indicated by vast amount of commission errors on CPT. This finding indicates that patients with BPD had more impulsivity than BD-II patients. Direct neurocognitive comparison studies of BPD and BD-II are still limited. However, one study showed patients with BPD are more impulsive than BD-II patients . Moreover, patients with BPD had more errors on TOL than bipolar patients, indicating that BPD patients had poorer performance in planning compared to BD-II patients. One study reported that BD patients had poorer performance in strategy formation and planning than BPD patients and healthy group. Also, BPD patients showed deficits in planning compared to BD patients and healthy group . In the present study, patients with BPD and BD-II performed equally compared to control group on total moves, total time, time violation and test time tasks on TOL, which measured planning. These findings are inconsistent with some of the results obtained from previous studies. Beblo et al, using the Tower of Hanoi test, reported that BPD patients required more moves and significantly longer time to accomplish the task . FurtherHowever, BPD patients had more errors compared to BD-II patients and healthy group in the TOL tasks. Nevertheless, healthy group had higher total score than BD-II and BPD patients. These results highlighted previous findings that indicated BPD and BD patients have deficits in problem-solving . It seemAs programming and problem-solving are among the main areas of the frontal lobe execution, it seems that prefrontal lobe malfunctioning has a role in the phenomenology of cognitive impairment in borderline personality disorder.Also, Fue et al, in their study, reported reduced prefrontal activation during TOL and verbal fluency task in patients with bipolar depression. Based on these results, the researchers concluded which planning and problem-solving dysfunctions are related to the impairment of the prefrontal cortex in patients with bipolar depression . The abilities of planning and problem-solving in individuals with BPD and BD-II have not been extensively studied. However, it seems that some defects in problem-solving abilities of these patients lead to impaired function in daily life. For example, 1 study that was performed on women with BPD revealed that inappropriate problem-solving strategies are predictors of parasuicidal behavior in these patients . In patiThis study had several limitations. First, BPD and BD-II patients were not assessed for the presence of comorbid psychiatric disorders, such as other personality disorders and axis-I disorders. In addition, one of the factors that might have had an impact on our finding was use of psychiatric drugs in BPD and BD-II Patients. Some studies have indicated that psychiatric medicines, such as antipsychotics, can affect cognitive function in patients . AnotherOur findings suggest that may be the same brain performance in borderline and bipolar-II patients leads to similar neurocognitive profiles and deficits. Categorizing BPD as a BD subtype has raised considerable debate, and further studies are needed to better understand the differences and similarities between the two disorders, particularly in cognitive functions.