Hypertension may have some association with osteoporosis. This Mendelian randomization (MR) study aimed to explore the causal effect of blood pressure (BP) on bone mineral density (BMD), fall, and fracture.We used the genome-wide association study (GWAS) summary data among 330,956 European-descent individuals to identify 107 single-nucleotide polymorphisms (SNPs) as the instrumental variables of BP. MR analyses of these instruments were performed on 53,236 European individuals for the association with forearm BMD (FA-BMD), femoral neck BMD (FN-BMD), and lumbar spine BMD (LS-BMD); 451,179 European individuals for fall susceptibility; and up to 1.2 million individuals from European descent for fracture. Conventional inverse variance weighted (IVW) method was adopted to obtain the causal estimates of BP on different outcomes, while weighted median, MR-egger, and MR pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analyses.2 (95% CI: 0.013 to 0.063). This positive finding was also confirmed by weighted-median analysis  and MR-Egger analysis . However, there was no remarkable MR association between BP and other outcomes .Genetically high pulse pressure (PP) could significantly improve FA-BMD  in the IVW analysis, indicating that 1-SD increase in PP was associated with the improvement in FA-BMD levels by 0.038 g/cmOur findings reveal a potentially causal relationship between high PP and improved FA-BMD, which may provide new sights for the treatment of osteoporosis. Osteoporosis is one common systemic skeletal disease characterized by decreased bone mineral density (BMD) and increased risk of fracture –3. Its pHypertension is one common and strongly heritable disease, with high mortality and morbidity –12. It mIn this two-sample MR analysis, we used single-nucleotide polymorphisms (SNPs) strongly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) as instrumental variables. To our knowledge, this is the first two-sample MR study to explore the causal effect of blood pressure (BP) on forearm BMD (FA-BMD), femoral neck BMD (FN-BMD), lumbar spine BMD (LS-BMD), fall, and fracture.This MR study was conducted based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (−8 was used to denote genome-wide significance of blood pressure. The replication resources included a large blood pressure (BP) meta-analysis consortium (ICBP cohorts) and further cohorts with 1,000 Genomes data for GWAS (−8), including 24 SNPs for systolic blood pressure (SBP), 41 SNPs for diastolic blood pressure (DBP), and 42 SNPs for pulse pressure (PP)  among 330,956 European-descent individuals, a threshold of P < 5*10for GWAS . Blood p2<0.001, window size =10,000 kb) was conducted with the European samples from the 1,000 genomes project, and we estimated LD between SNPs. Among the pairs of SNPs with r2≥0.001, the SNP with a larger association P value would be excluded. The SNPs that were absent from the LD reference panel were also removed. Thus, three SNPs about SBP, four SNPs about DBP, and five SNPs about PP were excluded due to LD. Finally, 21 SNPs for SBP, 37 SNPs for DBP, and 37 SNPs for PP were used as the instrumental variables (2>0.9) were used as the instrumental variables.We excluded SNPs in strong linkage disequilibrium (LD) because they produce some bias. The clumping process  on BMD , fall, and fracture. MR estimates for instrumental variables were meta-analyzed by computing an inverse variance weighted (IVW) analysis for the primary analysis . We usedTo assess the potential violation of these assumptions, MR-Egger analysis was used to assess the directional pleiotropy based on the intercept . The preThe funders of this study had an important role in study design, data collection, data analysis, data interpretation, and writing of the report. All authors had full access to all data in the study and had final responsibility for the decision to submit for publication.We evaluated the causal effect of blood pressure including SBP , FN-BMD , or LS-BMD   for the association between DBP and fall, and one outlier (rs12628032) for the association between PP and fall. After excluding these outliers, DBP and PP still revealed no causal effect on the incidence of fall . This positive finding was also confirmed by weighted median, MR-egger, and MR-PRESSO analyses. However, no causal association was seen between BP and other outcomes .In this two-sample MR analysis, we found the casual effect between high PP and improved FA-BMD , indicating that 1-SD increase in PP was associated with the improvement in FA-BMD levels by 0.038 g/cmHypertension has important association with alterations in calcium metabolism, including increased calcium loss, compensatory activation of parathyroid gland, and increased movement of calcium from the bones . Long-laIn one cross-sectional study involving 270 postmenopausal Turkish women, hypertension was found to be significant predictors of osteopenia in a multivariate analysis  . A retro2, and weight , which was confirmed by multiple sensitivity analyses. The association between PP and BMD was explored after adjusting for sex, age, aged weight . In addiIt is very interesting to confirm that genetically high PP shows strong MR association with improved BMD. PP is defined as SBP minus DBP. High PP largely results from large-artery stiffness, while decreased PP is caused by low stroke volume, such as congestive heart failure and aortic valve stenosis . VasculaForearm BMD at the 1/3 radial site is commonly used to improve the prediction of hip fractures when considered together with FN-BMD. Forearm BMD represents the BMD of combined trabecular and cortical bone structures, indicating the better improvement of BMD than other sites of BMD after effective anti-osteoporosis treatments, which may account for the positive MR association only between PP and forearm BMD . In addiTo the best of our knowledge, this is the first two-sample MR study to find the positively casual association between PP and FA-BMD. The summary statistics of outcome phenotypes are retrieved from GWAS or genome-wide meta-analysis with huge sample size. This two-sample MR method allows for the estimation of the causal effects of BP on all outcomes while at the same time minimizing reverse causation bias and confounding factors. The intercepts for the MR-Egger analysis, except for the association between DBP and FN-BMD, indicate that there is no directional pleiotropy to influence other causal associations.Several limitations should be taken into consideration. Firstly, there is some heterogeneity between DBP (or PP) and fall, which may be caused by the selection of some instrumental variables. Secondly, this MR study reveals the potential causal effect of PP on FA-BMD, but null association is observed between BP and FN-BMD (or LS-BMD). The factors of this inconsistency remain elusive. Thirdly, it is not feasible to perform the MR analysis based on different age stratums because of the limitation of GWAS summary statistics. Fourthly, the MR analysis is restricted in the European-ancestry population, which may limit the generalizability of our finding to other populations.This two-sample MR reveals the potential causal effect of high PP on improved FA-BMD, suggesting the protective role of high PP for osteoporosis.http://www.gefos.org/) and the UK Biobank (https://www.ukbiobank.ac.uk/). The instructions to download data from (http://www.gefos.org/) are presented in The original data were available in GEnetic Factors for OSteoporosis Consortium , National Natural Science Foundation of China (81701382), Foundation of The First Affiliated Hospital of Chongqing Medical University (PYJJ2018-13), and Chongqing Yuzhong Nature Science Foundation of China (Grant No. 2018114).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study evaluates whether blood glucose response differs upon consuming rice cooked in a carbohydrate (carb)-reducing rice cooker. Rice cooked this way exhibited 19% reduced total carbohydrate (34.0 ± 0.3 vs. 27.6 ± 0.9 g/100 g rice) and 20% reduced total calorie  contents. We measured the blood glucose response  in 13 healthy participants after consuming 6 different rice types: regular white rice ), low-carb WR with equivalent weight as regular WR (low-carb WR (EW)), low-carb WR with equivalent carb as regular WR (low-carb WR (EC), regular mixed-grain rice (regular MR), low-carb MR (EW) as regular MR, and low-carb MR (EC) as regular MR. All rice samples were prepared in an electric carb-reducing rice cooker. Postprandial blood glucose, sensory, and appetite were assessed after each test meal. The incremental area under the curve of 15 and 30 min after rice consumption was significantly lower in low-carb WR (EW) than that in regular WR. These results suggest possible health benefits of low-carb WR in reducing early postprandial spikes in blood glucose level without significant differences in satiety and satisfaction. Rice is globally among the most popular food grains and is currently consumed by more than half of the global population ,2. WhiteReducing carbohydrate (carb) intake is attracting growing attention from people who want to eat healthily . RaisingCarb-reducing electric rice cookers theoretically reduce starch contents in rice. Recently, with the increasing number of patients with diabetes and people who want to consume healthy meals, the carb-reducing rice cooker is a promising device in the kitchen appliance market. In particular, interest in carb-reducing rice cookers is growing in Asia, where rice is the staple food. Various carb-reducing rice cookers are manufactured, and the effect of reducing starch varies from a minimum of 20% to a maximum of 50%. The basic principle of the carb-reducing rice cooker is to boil rice in excess water (3–4 times the weight of the rice), and starch contents from the rice are thus dissolved in the water and removed. Starch dissolved in rice is removed, resulting in low-carb rice. Low-carb rice cookers are divided into two types, the siphon and wash-down types, according to the method of draining the excess water . The sipThese carb reduction effects claimed by companies are based on food composition analysis, and no clinical research data are available on the effect of carb-reducing rice consumption on blood glucose response.The glucose response after consuming carbohydrate-containing food relative to a carbohydrate reference food, typically white rice or write bread, is normally measured using the glucose index (GI). The primary purpose of this study was to determine whether low-carb rice cooked in a siphon-type carb-reducing rice cooker could reduce blood glucose response in healthy adults. The secondary purpose was to perform sensory and appetite evaluation after low-carb rice consumption.This study was conducted to evaluate how consuming low-carb rice cooked in a carb-reducing rice cooker affects blood glucose response and appetite, and to perform sensory evaluation. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Institutional Review Board (IRBs) of Kyung Hee University in September 2021 (no. KHGIRB-21-420).We recruited 13 healthy participants (4 men and 9 women) from the campus and the community. The inclusion criterion was being at least 35 years old. Exclusion criteria were as follows: patients with uncontrolled diabetes abd people unable to perform a finger-prick blood test due to psychological fear. All subjects gave their informed consent to participate in the study before starting.Rice samples were cooked in a carb-reducing electric rice cooker . The carb-reducing rice cooker used in the study was a siphon type having the same composition as that of an electric rice cooker with only the inclusion of a tray . When coRice (white rice (WR): polished rice 300 g; mixed-grain rice (MR): 210 g polished rice + 90 g mixed-grain rice) was washed three times with sufficient distilled water ,19,20. LThe cooked rice was gently fluffed with a spatula and transferred into a container with a plastic lid for nutrient analysis. Freshly cooked rice was sent to Société Générale de Surveillance  for nutrient analysis.Subjects were provided with a verbal explanation of the study and directed to follow the instructions on the study description document. Participants were instructed to fast overnight for 12 h before their participation in the study. In addition, subjects were encouraged to eat their usual meals and conduct their daily activities the day before their participation. The participants visited the living lab 7 times, consumed 6 types of test foods, and performed a finger-prick blood glucose test, visual analog scale (VAS) test for the assessment of hunger, feeling of fullness, and desire to eat, and sensory evaluation. We followed the official method for determining the GI in foods instructed by the Organization for International Standardization (ISO) ,22 PartiThe six test rice types were as follows: (1) regular WR ), (2) low-carb WR with equivalent weight (EW) as regular WR , (3) low-carb WR with equivalent carb (EC) as regular WR , (4) regular MR , (5) low-carb MR (EW) , and (6) low-carb MR (EC) .The blood glucose levels of participants were measured with the finger-prick method using an Accu-Chek Performa Blood Glucose Meter . Each blood glucose level after the test meals was used to calculate the incremental area under the curve (iAUC) .Participants rated the six types of cooked rice for intensities of individual attributes, including appearance, flavor, taste, texture properties, and overall preference, on a 9-point structured scale (1 = extremely low intensity and 9 = extremely high intensity) . Cooked For the assessment of hunger, desire to eat (DTE), and feeling of fullness, we used validated VAS . ParticiHeight, body mass index, and body composition were measured using a stadiometer  and bioimpedance . Participants stood barefoot with minimal clothes on in a straight position, and palms were facing the thighs, so that the posture was clear.Sample size was determined on the basis of the method of the International Organization for Standardization . This sup-values of p < 0.05.All data are expressed as mean ± standard deviation (SD) values. The iAUC and blood glucose responses were analyzed at each test time using an independent Student’s t-test. Statistical analysis was performed using the IBM SPSS 25 statistical program . Statistical significance was set at Of the 17 screened participants, 13 eligible  were selected. iAUC blood glucose of the test foods. Blood glucose iAUC after low-carb WR (EW) intake was significantly lower at the time points of 0–15 and 0–30 min compared to regular WR . However, no significant difference could be observed in iAUC blood glucose between regular MR and low-carb MR.Sensory profile analysis could objectively evaluate the quality of the low-carb rice by quantifying appearances, flavor, taste, texture properties, and overall preference. The sensory evaluation of low-carb rice cooked in a carb-reducing rice cooker showed no significant difference with regular rice in appearance , flavor , taste , texture properties , and overall preference.Change in hunger, feeling of fullness, and desire to eat after low-carb rice intake were evaluated using the VAS . The VASThis study was conducted to evaluate whether consuming low-carb rice cooked in a carb-reducing rice cooker affects blood glucose response. In addition, we investigated the difference in sensory, hunger, fullness characteristics, and the DTE between low-carb and regular rice. Our results showed that the iAUC of blood glucose was significantly reduced at 15 and 30 min after low-carb WR intake compared to regular WR. Sensory characteristics, and hunger, fullness, and DTE 3 h after eating values showed no difference between low-carb rice and regular rice.Using a carb-reducing rice cooker produced ~20% fewer calories and carbohydrate contents than those of the regular rice cooking method. Moisture content is a determinant of the taste of rice, and the optimal amount of water for delicious rice is 1.2 to 1.3 times . SeveralLow-carbohydrate diets, which have recently attracted much attention, are effective in weight loss and the prevention of chronic diseases such as diabetes by regulating the glucose–insulin axis . In womeWR is among the most important staple foods for the global population ,2. AsianThe postprandial glucose response to carbohydrate meals is not only determined by the amount of available carbohydrate, but also by fat, protein, and various nutrients ,33. SoluTo comply with diabetic diets, a major determinant of the effect of the diet on blood glucose management, foods that are not palliative hinder sustaining it for a long period. Low-carb rice in this study had about 20% fewer calories and less carbohydrate than regular rice did , but there was no noticeable difference in the sensory evaluation  and the feeling of hunger, fullness, and DTE in consumption. When people are on a low-carbohydrate diet and consume less rice, fullness is normally low, and hunger and DTE are high, and the diet is usually accompanied by additional food intake, which leaves a low-carb diet with no health benefits. However, results of our study show that maintaining a low-carb diet with a carb-reducing rice cooker seems feasible because eating low-carb rice did not affect hunger or appetite.Visual cues for food can affect food intake ,40. In tThe flavor of low-carb rice is closely related to the sustainability of low-carb rice consumption in daily life. Even if there are foods that help in controlling blood glucose due to their low-carb content, it is difficult to maintain intake if they do not taste good. Therefore, low-carb rice does not exhibit sensory difference, such as regarding appearance, flavor, taste, texture properties, and preference, compared to regular rice. Common cooking principles of the carb-reducing rice cooker are as follows. First, put rice and excess water (about four times or more the weight of rice) in a rice cooker and boil them together. Next, after the starch in the rice had dissolved in the water, leaving only an appropriate amount of water, rice is cooked after removing the extra water. When the carb-reducing rice cooker was first released, it was mainly a wash-down type carb-reducing rice cooker that drained water to the bottom. In this rice cooker, soft- or liquid-type rice was prepared by exposing the rice to the water at the bottom. The siphon-type carb-reducing rice cooker, which was released later, removes rice water with the upper tray and cooks the rice. However, because of the cooking principle for reducing starch in the carb-reducing rice cooker, starch in the rice is removed using water, and low-carb rice can be cooked with a lot of water. Some limitations of this study should be noted. First, no difference in blood glucose response was found between regular and low-carb mixed rice. Second, the blood glucose response test was not performed 180 min after eating to see how long the effect can last. Third, the study was performed using the finger-prick blood test for convenience, but we thus could not assess insulin hormone concentrations for more accurately assessing the glycemic response.The strengths of the study are as follows. First, this is the first study to evaluate and publish the blood glucose response of low-carb rice cooked with a carb-reducing rice cooker. Second, we found a way to control blood glucose with low-carb rice using WR, the staple food of Asian countries. Third, there was no difference in the feeling of fullness, hunger, and DTE between the consumption of low-calorie low-carb WR and regular rice.The study is the first clinical trial to evaluate the glycemic response effect of low-carb rice cooked in a carb-reducing rice cooker. Low-carb and regular rice used in the study was cooked with a recipe that could produce the optimal flavor, and the low-carb rice had about 20% fewer calories and less carbohydrate than those of regular rice. Low-carb rice had no difference in sensory characteristics, fullness, hunger, and DTE compared to regular rice. These results indicate the health benefit of low-carb WR on postprandial spikes in blood glucose levels. The possibility of low-carb rice as a substitute for regular rice can be carefully claimed. Eating less carbohydrate  without feeling deprived contributes to improving the quality of life of people who need blood glucose control, such as patients with diabetes or impaired glucose tolerance.


The purpose of this article is to provide a systematic review of leadership and Job Demands-Resources (JD-R) theory. We have analyzed 139 studies that study the relationship between leadership and Job Demands-Resources (JD-R) theory. Based on our analysis, we highlight ways forward. First, research designs can be improved by eliminating endogeneity problems. Regarding leadership concepts, proper measurements should be used. Furthermore, we point toward new theory building by highlighting three main ways in which leadership may affect employees, namely by: (1) directly influencing job demands and resources, (2) influencing the impact of job demands and resources on well-being; and (3) influencing job crafting and self-undermining. We hope this review helps researchers and practitioners analyze how leadership and JD-R theory can be connected, ultimately leading to improved employee well-being and organizational performance. Inspirational leadership as a work resource might lead to positive attitudes, such as happiness at work.” Salas-Vallina and Fernandez , ensuring transparent and complete reporting . RQ1 focuses on the research methods and designs used by scholars who study leadership and JD-R theory. We then discuss which leadership concepts scholars have used, including transformational leadership, servant leadership, and Leader-Member Exchange (LMX) (RQ2). We end by analyzing how scholars have connected leadership to the various elements of JD-R theory . Hence, we aim to answer the following three research questions:Which research methods and designs have been used to analyze the leadership-JD-R relationship?Which leadership concepts have been used—and which have been ignored—when studying the leadership-JD-R relationship?How has the relationship between leadership and JD-R theory been conceptualized?Our goal for this review is to offer an agenda for future research on leadership and JD-R theory. This agenda aligns with the research questions. First, by studying the research methods and designs that scholars have used to analyze the leadership-JD-R relationship, we will show how scholars can improve methods and designs in future studies. Among others, we highlight that research designs can be improved by eliminating endogeneity problems  directly influencing job demands and resources, (b) influencing the impact of job demands and resources on well-being (strain and motivation); and (c) influencing job crafting. Future studies could use these three main ways to study the connections between leadership and JD-R in depth. In this way, we hope our review helps structure the knowledge on leadership and JD-R theory and provide theoretical ways forward Short, .Job Demands–Resources (JD-R) theory  explainsJob demands are job aspects that require sustained effort and are, therefore, associated with physiological and psychological costs  AND  but did not link it to JD-R theory. Furthermore, we focused on the academic published literature, leading to publication bias . Studying the methods and designs helps us uncover methodological strengths and weaknesses and provides directions for future studies.Scholars predominantly used surveys  used interviews instead of surveys  used a cross-sectional design. Such designs are ill-suited for causal inference. Longitudinal designs (26%) can better demonstrate causality and study long-term consequences of work stressors, although they can still suffer from endogeneity . In Transformational leadership is intuitively appealing and inspiring visions genuinely move people , job demands (9%) or related JD-R concepts like personal resources (6%). For example, Fernández-Muñiz et al. , p. 405 In addition to directly impacting job demands, resources, and job crafting, leadership can be a moderator. It can change the impact of job demands (9% of studies), job resources (1%), or personal resources (1%). Such moderating effects are interesting to study when working conditions are hard to change. For instance, Breevaart and Bakker  showed tIt is clear from the preceding analysis that scholars have connected leadership and the JD-R theory in various ways. We found 14 potential connections. In addition, We, therefore, provide a new, hopefully valuable, theoretical perspective on how leadership and JD-R theory can be connected. We see leadership as a construct located on a higher level than the dimensions of JD-R theory. Our review found various studies that use a similar conceptualization  and the link between job demands and strain (path 2b). Leadership can, for instance, strengthen the effects of personal resources. Leadership may help employees by stimulating them to use their resources, such as their proactive personality, which ultimately could increase engagement  and follower self-undermining (path 3b). Job crafting is a crucial self-reinforcing path in JD-R theory. People craft their jobs when they proactively change their job demands and resources. Wang et al.  and ThunThis article set out to perform a systematic literature review to answer questions about leadership and JD-R theory. We analyzed the methods and designs (RQ1), the leadership concepts (RQ2), and the ways scholars have connected leadership and JD-R theory (RQ3). We identified 139 studies that investigated leadership and JD-R theory. The findings suggest that leaders may influence employees in various ways. This multitude of potential connections shows the heuristic nature of the JD-R theory. However, it also makes it hard to develop cumulative knowledge. We have therefore structured this in three primary connections: (1) leadership can directly impact job demands, job resources, and personal resources, (2) leadership can moderate the link between job/personal resources and motivation as well as the link between job demands and strain, and (3) leadership can directly impact follower job crafting and self-undermining.Our review also identified gaps in the literature. We will discuss these gaps below and offer an agenda for future research on leadership and JD-R theory. To start, most scholars use cross-sectional surveys to study leadership and JD-R theory. Cross-sectional surveys help when we want to measure the prevalence of leadership behavior  directly impact job demands, job resources, and personal resources, (2) moderate the link between job/personal resources and motivation as well as the link between job demands and strain, and (3) directly impact follower job crafting and self-undermining. We also indicated methodological and theoretical research gaps that scholars can address to take the field forward. These include strengthening research designs, using proper measurements of leadership concepts, clarifying the mechanisms underlying the connections between leadership and JD-R, and researching negative leadership concepts. Studying leadership and JD-R theory should prove to be a timely and productive endeavor for researchers and practitioners alike.https://osf.io/fwgmz/.Our data and codebook are available on the Open Science Framework: LT conducted the data analysis. LT and AB wrote the manuscript. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The available packages and tools primarily focus on binary prediction (i.e. two classes). However, many real-world classification problems e.g. tumor subtype prediction, are multiclass tasks.Here, we present multiclassPairs, an R package to train pair-based single sample classifiers for multiclass problems. multiclassPairs offers two main methods to build multiclass prediction models, either using a one-versus-rest kTSP scheme or through a novel pair-based Random Forest approach. The package also provides options for dealing with class imbalances, multiplatform training, missing features in test data and visualization of training and test results.https://github.com/NourMarzouka/multiclassPairs.‘multiclassPairs’ package is available on CRAN servers and GitHub: Bioinformatics online. GeneA<GeneB, then Class1, else Class2, to predict one of two classes. Different implementations of binary kTSP classifiers are currently available through R packages, e.g. MetaKTSP  approach or through ensembled one-versus-rest kTSP classifiers generated by the switchBox package. multiclassPairs provides additional gene and rule selection methods tailored for multiclass and multiplatform problems.ReadData function, each workflow handles the subsequent steps differently, as described below and in further detail in the To build a pair-based multiclass predictor, multiclassPairs provides two methods, either an RF-based workflow or a one-versus-rest kTSP workflow. Both have the following steps: reading the input data and labels, selecting informative features, combining features as pairs, selecting informative pairs and constructing the final predictor model. After reading the input data and labels by sort_genes_RF function) and rules (sort_rules_RF function) and the training of the final RF model (train_RF function). To deal with class size imbalance, gene and rule selection are performed using both overall and class-versus-rest importance which can be applied separately to each platform/study to select features important in all platforms/studies. The train_RF function optionally filters the less informative rules via the Boruta package .multiclassPairs uses the RF algorithm which is capable of handling complex prediction tasks and can be used for feature selection. The fast RF implementation from the ranger package  is used  package  before bfilter_genes_TSP function) which can be performed using one-versus-rest Wilcoxon test or one-versus-one Dunn’s test. The train_one_vs_rest_TSP function combines the filtered genes into binary one-versus-rest rules and gives a score for each rule in each class, after which an optimal number of top-scoring rules for each class is selected and assembled into a final model. Rule scores can be obtained as one-versus-rest score or as the average of one-versus-one scores. The switchBox package is used for calculating the rule scores and to determine the optimal number of rules for each class through the Variance Optimization (VO) approach (true rules divided by number of rules for that class, or weighted class votes i.e. sum of the scores for the true rules divided by sum of rule scores for that class. Weighted votes are used by default to reduce the chance of ties. Vote ties are flagged and reported to the user. Similar to the RF workflow, gene filtering and rule scoring can be performed in a platform/study-wise manner.In the one-versus-rest scheme, one-versus-rest binary classifiers for each class are assembled in one model. A benefit of the one-versus-rest approach is the intuitive model interpretability. The workflow in this scheme starts with gene filtering (multiclassPairs provides heatmap plots to visualize the binary rules and prediction scores in the training and test datasets . For then = 3134 samples, We used a breast cancer gene expression dataset (Here, we introduce the R package multiclassPairs, enabling easy training and application of pair-based multiclass single-sample predictors using the established one-versus-rest kTSP scheme or an RF scheme. multiclassPairs is equipped with options to handle multiclass and multi-platform scenarios.btab088_Supplementary_DataClick here for additional data file.


In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics , resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and  Despite standardized uptake values (SUV) reflecting regional FDG uptake, intrinsic networks derived from FDG-PET using independent-component analysis (ICA) could reflect long-distance metabolic brain connections. Based on this data-driven technique, the characteristics of metabolic resting-state networks (RSN) could be utilized to observe a cognitive decline in Alzheimer’s disease and amyotrophic lateral sclerosis (Positron emission tomography (PET) with clerosis , 2017. Aclerosis . The neuclerosis .The integrated PET/MRI system could simultaneously obtain both fMRI and FDG PET images . With thIn order to shorten the time of scan, some clinical research using the integrated PET/MRI system prefer to run MRI sequences during the FDG uptake phase (the first 40 min after the radiotracer administration). However, the acoustic, thermal, and electromagnetic effect imposed by MRI on the human brain may result in physiological inference on quantitative FDG uptake curve. Although it has been assessed on phantoms that the stability of PET quantitation was not affected during simultaneous MRI scan, even when an aggressive sequence such as fast-spin echo MRI sequence was performed , the impTherefore, the principal purpose of this work is to systematically evaluate the impact of simultaneous fMRI scan on the quantification of brain metabolism using an integrated PET/MRI system. We designed a randomized self-control study on healthy volunteers using clinically relevant imaging protocols. We will focus on the additional effect induced by fMRI sequence pulsing on voxel-wise energy consumption, spatial distribution of metabolic networks as well as the correlation between regional glucose metabolism and functional metrics within and across different resting-state functional networks.18F-FDG injection, with a dosage of 3.7 MBq/kg per subject. Immediately before the scanning began, all subjects underwent a blood glucose level measurement, and all measured values were below 8 mmol/L.All subjects were provided with written informed consent to undergo procedures approved by the Medical Research Ethics Committee of Xuanwu Hospital, Capital Medical University . A total of 24 healthy subjects  with ages of 31–66 years were enrolled in this study. The subjects were all right-handed and free of personal or family history of psychiatric or neurological disease, diabetes, renal failure, claustrophobia, and other MRI-related exclusion criteria. Prior to the scan, the participants were directed to fast for at least 6 h before the 18F-FDG PET/MRI scanning was performed on an integrated PET/MRI system  equipped with a 24-channel phase-array head coil  for brain segmentation. The following 22-min experimental scan was separated into MRI-off and MRI-on modes, with each one lasting for 11 min. In the MRI-off mode, no MRI sequence was performed. In the MRI-on mode, a gradient-echo echo-planar pulse sequence  was performed for resting-state BOLD-fMRI measurements lasting for 10 min. In order to fairly evaluate the impact of BOLD-fMRI on FDG-PET, all subjects were divided randomly into one of two groups who received either protocol A or B. For the protocol A group, the MRI-off mode was performed ahead of the resting-fMRI, while for the protocol B group, the scan order was reversed see .The PET list mode data had a 511-KeV energy drift correction applied to correct the temperature-induced counting loss during MRI scanning . Dynamic1.Data on post-processing for PET and MRI images is summarized in 2 (3 and were used to mask out the regions of interest (ROIs). The mean value of each metric was calculated in each of the eight ROIs.The pre-processing of resting-fMRI was performed using the Data Processing and Analysis for Brain Image (DPABI) tool2 . After sR) between rCMRGlu and fMRI metrics was calculated using the python package SciPy4. Across-subject correlation was calculated of each pair of rCMRGlu and ALFF, fALFF, DC, and ReHo at the network level.After normalization of focal FDG uptake for body weight and injected dose, the relative standardized uptake value ratios (SUVr) with reference to the cerebellum and white matter was calculated, respectively. To quantify the change of SUVr during the time course of MRI-on and MRI-off mode acquisitions, SUVr slope was derived by linear fitting of the relevant SUVr values from each 2-min frame images with time. An analysis of regional cerebral metabolic rate of glucose (rCMRGlu) at each voxel was performed . To allo5. The optimal number of components of the principal component analysis was set to five, which was estimated using the GIFT dimensionality estimation tool. Group ICA was used to derive task-based regional activation, treating MRI-on mode as a paradigm. First, the mixing matrix was estimated, which has a unique partition for each object. The component graph of each task was calculated by projecting a single task data onto the inverse of the mixing matrix partition. Task-specific time courses and images were used to make group and inter-group inferences  and rCMRGlu among the eight networks between MRI-on mode and MRI-off mode were calculated with one-way ANOVA analysis and paired two-sample t-test, respectively.Statistical analysis was performed using DPABI and Statistical Product and Service Solutions . Paired two-sample WB/SUVCB) distribution maps , while the SUVr slope significantly increased when comparing the MRI-on mode with the MRI-off mode . The same measurement using white matter as a reference tissue was performed, and a similar result was confirmed . ReHo provided significantly higher correlation coefficients with rCMRGlu compared to the other metrics . No difference was found between the other metrics . The highest correlation coefficients between rCMRGlu and all fMRI metrics were found in the visual network  and the default network .The spatial correlation maps between rCMRGlu and four fMRI metrics, as well as the results of the correlation analysis over each of the eight resting-state networks, are shown in 18F-FDG. It is commonly considered that the FDG-PET data acquired during this plateau phase of the uptake curve mainly represents the neuronal activity that occurred during the preceding early uptake phase. Thus, SUVr for the plateau phase is supposed to be steady even when fMRI scan is synchronously performed. Our results found no difference in either mean SUVr or voxel-wise SUVr compared between MRI-on and MRI-off periods, which supported this inference. However, an obvious increase of SUVr slope was detected in the MRI-on period across the whole brain, especially in gray matter, located in sensorimotor, attention, control, default, and auditory networks. This network-wise metabolic change reflects a short-term FDG uptake elevation due to the fMRI scan. We assume that this phenomenon was mainly in favor of a neuronal origin whose activation mostly locate in gray matter regions and may also be due to other unspecific physiological inferences, such as the temperature-dependent acceleration of metabolic rates. However, with only a minor amount of free FDG available in the blood pool, fMRI scan in the steady state could only produce a limited, short-term effect on the trend of FDG elevation, which could be presented by the elevation of SUVr slope but does not affect the calculation of an accumulative SUVr value to statistical significance.To the best of our knowledge, this work is the first study to systematically assess the impact of simultaneous fMRI scan on FDG-PET in human brain with an integrated PET/MRI system. Our protocol is a self-control study design following the recommended clinical routine, where MRI sequences were performed 30–40 min after the injection of In the network-wise comparison, when treating fMRI scan as a stimulation task, the affected components were located in the default, auditory, visual, and language networks, which were commonly regarded as “higher-order” cognitive networks. A previous study reported that 13 meaningful RSNs could be detected from FDG-PET data acquired 10–30 min post-injection. Among them, seven networks could be detected by both modalities, including default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks . In our In previous studies, the possible mechanism underlying neurometabolic coupling was explained by temporally synchronized cerebral blood flow and energy utilization based on the theory that resting-state glucose and oxygen metabolism were closely linked . Strong Our study reports a synthesis effect of MRI scan on quantitative PET in an integrated system. Among all possible factors, acoustic MRI noise resulting from echo-planar imaging should be regarded as the main concern. This sequence is normally accompanied by a gradient-shifting noise with a sound level greater than 100 dB . Studies18F-FDG and venous blood sampling . The patients/participants provided their written informed consent to participate in this study.YS, ZW, QX, and YZ contributed to the conception and design of the study. YS, ZW, SS, QX, and QG performed the formal analysis. YS and ZW wrote the first draft of the manuscript. BC and HY contributed to project administration and visualization. JL, YZ, and MZ contributed to supervision and validation. JL contributed to funding acquisition. All authors contributed to manuscript revision and read and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Epithelial-mesenchymal transition (EMT) plays a vital role in tumor metastasis and drug resistance. It has been reported that EMT is regulated by several long noncoding RNAs (lncRNAs). We aimed to identify EMT-related lncRNAs and develop an EMT-related lncRNA prognostic signature in kidney renal clear cell carcinoma (KIRC).  In total, 530 ccRCC patients with 611 transcriptome profiles were included in this study. We first identified differentially expressed EMT-related lncRNAs. Then, all the samples with transcriptional data and clinical survival information were randomly split into training/test sets at a ratio of 1 : 1. Accordingly, we further developed a twelve differentially expressed EMT-related lncRNA prognostic signature in the training set. Following this, risk analysis, survival analysis, subgroup analysis, and the construction of the ROC curves were applied to verify the efficacy of the signature in the training set, test set, and all patients. Besides, we further investigated the differential immune infiltration, immune checkpoint expression, and immune-related functions between high-risk patients. Finally, we explored the different drug responses to targeted therapy (sunitinib and sorafenib) and immunotherapy (anti-PD1 and anti-CTLA4).  A twelve differentially expressed EMT-related lncRNA prognostic signature performed superior in predicting the overall survival of KIRC patients. High-risk patients were observed with a significantly higher immune checkpoint expression and showed better responses to the targeted therapy and immunotherapy.  Our study demonstrates that the twelve differentially expressed EMT-related lncRNA prognostic signature could act as an efficient prognostic indicator for KIRC, which also contributes to the decision-making of the further treatment. As a fundamental and reversible biological process in physiological and pathological conditions, epithelial-mesenchymal transition (EMT) has been identified in three different types . Type I Furthermore, the EMT mechanism is also involved in mediating drug resistance –9. For eKidney cancer is the 6th most common cancer in both sexes and the most common urogenital tumor , accountThe long noncoding RNA (lncRNA), defined as RNA transcripts with longer than 200 nucleotides and little protein-coding ability , have behttps://portal.gdc.cancer.gov/). Notably, the transcriptional profiles were downloaded in the fragments per kilobase of per million (FPKM) format and then merged as an eligible matrix. The corresponding clinical data was downloaded in the bcr xml format and then merged as a clinical information matrix. Besides, the gene annotation files were acquired from the Ensembl database (http://asia.ensembl.org/index.html) to transfer the Ensembl ID in the matrix to the gene symbol and annotate genes for its transcripts type, such as mRNA and lncRNA. More importantly, EMT-related gene list was downloaded from the dbEMT 2.0 database (http://dbemt.bioinfo-minzhao.org/). All the downloaded data was proceeded by R program version 4.1.0 [The transcriptional expression profiles and corresponding clinical information contained survival time, and clinicopathological characteristics for the KIRC were retrieved from TCGA-GDC  is the expression value normalized by FPKM of the ith lncRNA in the signature, and coe(i) is the estimated regression coefficient of it. Besides, all samples in both the training set and test set obtained a risk score calculated by the predict function in the R program, and we set the medium value of the risk score in the training set as the cut-off value to stratify patients with KIRC that the higher risk score represented a high risk, and the lower risk score was grouped into low risk.Having identified and screened differentially expressed EMT-related lncRNAs, we merged it with its corresponding clinical survival data. All the samples with transcriptional data and clinical survival information were randomly split into training/test sets at a ratio of 1 : 1. Following this, we performed univariate Cox regression in the training group to identify prognostic EMT-related lncRNAs with a significance of We first described the distribution of the risk scores and the proportion of risk stratification in the training set, the test set, and all samples and visualized the survival status of each patient with different risk scores through risk analysis, and then, we performed the Kaplan-Meier method to plot the survival curves for the patients in the training set, test set, and all patients. The log-rank test was carried out to examine the survival-predicting availability of this signature at the same time. Following this, we sort the clinicopathologic information of each patient, considering age, gender, stage, grade, and risk score as the alternative prognostic factors, and then compared the predicting capability of these prognostic factors by the 5-year multivariate ROC curves; simultaneously, the area under the curve (AUC) for each prognostic factors including age, gender, stage, grade, and risk score was calculated and compared with each other in the training set, test set, and all patients.We were interested in the correlation between these EMT-related lncRNAs included in the signature and their correlated EMT-related mRNAs. Thus, we extracted the correlation between these two from the results of the Pearson correlation test and visualized it as an interaction network by using Cytoscape (version 3.8.0) .χ2 test was applied to examine the difference of clinicopathologic status between these two groups. Moreover, all patients were divided into subgroups according to each clinicopathologic characteristic, including age (age > 65 and ≤ 65), gender , stage (stage I-II and stage III-IV), and grade (G1-2 and G3-4). Then, survival analysis was employed to test the efficacy of this signature in all different subgroups.Further investigating the differences of these clinicopathologic characteristics between high- and low-risk groups might contribute to learning the potential correlation between the risk score and clinical characteristics. All patients were divided into two groups as a high- or low-risk group; and then, the Having verified the efficacy of this signature, we were interested in the immune infiltration, immune-related functions, and immune checkpoint expression between high- and low-risk patients. Thus, we separately applied the Wilcoxon test to compare the immune infiltration and immune checkpoint expression status. Immune-related functions were investigated by single set gene set enrichment analysis (ssGSEA) . Also, tχ2 test or Fisher's exact test was applied to explore the heterogeneity between the training set and test set. It seemed that there was no significant difference between the training set and the test set.The study flow was shown in the graphic abstract. A total of 611 transcriptome profiles containing 72 normal tissues and 539 tumor tissues from 530 KIRC patients were downloaded and sorted. For those samples sequenced multiple times, we took the average of them as their transcriptional data. Besides, all samples were randomly split into training and test set at a ratio as 1 : 1, and the characteristics of the samples in the training set, test set, and all samples are shown as p.adj < 0.001. Following this, 358 differentially expressed EMT-related mRNAs  rate of ccRCC was 91%, 74%, 67%, and 32% separately for AJCC stages I, II, III, and IV, respectively . The OS Epithelial-mesenchymal transition (EMT), first introduced by Newgreen et al. in 1979 , was disFurthermore, researchers focused on the cell plasticity with the stemness of tumor cells . They beIn the present study, we focused on the EMT-related lncRNAs in KIRC and identified and developed a twelve-EMT-related lncRNA prognostic signature in KIRC. Following this, we verified the prognostic signature in the test set by log-rank test, risk analysis, subgroup analysis, and time-dependent multivariate ROC curves. Interesting, in the subgroup analysis, this prognostic signature showed an outstanding performance in almost all clinicopathological subgroups, which indicate the universality of our prognostic signature. Besides, in the comparison for the prognostic signature with common-used clinicopathological characteristics, including age, gender, grade, and stage, this prognostic signature showed a superior AUC. All these verified the accuracy and sensitivity of this EMT-related lncRNA signature. More importantly, though high-risk patients stratified by this signature had a worse OS, we found that the high-risk patients showed a more sensitive drug response to both sunitinib and sorafenib, which might contribute to the clinical decisions.Furthermore, the differential immune infiltration, immune checkpoint expression, and immune-related functions were engaging, consistent with the current opinion. Zhou et al. reported that the activation of EMT was associated with the upregulation of the immune checkpoint expression  , which mAC084876.1 has been identified as glycolysis-related lncRNA with prognostic value in KIRC by Cao et al. [IL10RB-DT has been reported associated with immune infiltration in KIRC [LINC02154 was discovered significantly differentially expressed between normal and tumor tissues in laryngeal cancer [PSORS1C3 is a significant lncRNA in immune-mediated diseases that have been identified regulated by glucocorticoids (GC) and showed great prognostic values in bladder cancer [SUCLG2-AS1 has been reported with prognostic value in clear cell carcinoma, gastric cancer, and triple-negative breast cancer [AC068338.3 has been reported as a significant prognostic factor associated with immune infiltration in lung adenocarcinoma [Besides, we constructed an EMT-related lncRNA-EMT gene network, which may provide potential regulatory mechanisms of EMT. Further experimental examination is needed to explore the interaction network in the future. Notably, in these twelve EMT-related lncRNAs, o et al. . IL10RB- in KIRC . LINC021l cancer , 46. PSOr cancer , 48. SUCt cancer –51. AC06arcinoma , 53. HowThere are some limitations in our study. Firstly, there lacks an independent validation set. Besides, we revealed a twelve differentially expressed-EMT-related-lncRNA prognostic signature for ccRCC with sufficient bioinformatical analysis and statistical-method based verification, which lacks relevant experimental verification. Further experimental verification is required to explore the molecular mechanism behind the association between this signature with immune infiltration, immune checkpoint expression, drug response to targeted therapy and immunotherapy.In summary, we successfully developed a twelve differentially expressed EMT-related lncRNA prognostic signature, which could robustly predict the overall survival and prognosis of KIRC patients. The prognostic signature performed best compared to other frequently used prognostic clinicopathologic factors in predicting the overall survival. However, large-scale, multicenter prospective researches need to be carried out to confirm our results in the future.


ABSTRACT IMPACT: By identifying clear gaps in our knowledge of racial and ethnic disparities in antibiotic-resistant infections, this research is informing the design of (a) community-based interventions and (b) patient-centered research studies that we are currently leading to address these disparities and improve human health. OBJECTIVES/GOALS: Antibiotic resistance (AR) is widely considered to be the next global pandemic. As with COVID-19, the potential for AR to disproportionately impact racial/ethnic minorities is a major concern. Our goal was to identify gaps in knowledge of AR disparities in order to inform the types of interventions that might be most appropriate to address this. METHODS/STUDY POPULATION: We reviewed the literature to examine evidence of racial/ethnic disparities in (a) infections with the most concerning drug-resistant bacteria in the United States, and (b) underlying social-economic or behavioral factors that could contribute to such infections. We searched PubMed and Google Scholar to identify studies published in English between August 1973 - August 2020. We used keywords that included: antibiotic resistance, antibiotic-resistant infections, antibiotic-seeking behavior, prescription/non-prescription antibiotic use, antibiotic education, or health literacy AND race, ethnicity, or socioeconomic status. We screened all abstracts to identify US-based studies that assessed (a) or (b) above. RESULTS/ANTICIPATED RESULTS: We identified 11 studies investigating racial/ethnic disparities for 5 of the 17 drug-resistant bacteria flagged in the CDC’s 2019 Antibiotic Resistance Threats Report. Black, Hispanic, and lower-income individuals were found to be at higher risk of some community-acquired antibiotic-resistant infections. We identified multiple factors that may contribute to disparities in AR-related morbidity and mortality, including reported differences in antibiotic use, higher likelihood of living in crowded/multigenerational homes, more frequent employment in potentially high exposure settings (e.g. slaughterhouses), lower health literacy, and more frequent underlying comorbidities, which increases risks for hospitalization and subsequent acquisition of drug-resistant infections. DISCUSSION/SIGNIFICANCE OF FINDINGS: Given the small number of studies on this topic, educational interventions that aim to raise awareness of this issue must target not only the public but also researchers. Community-based interventions that seek to address disparities in ‘antibiotic resistance literacy’ among minority and underserved groups could be particularly impactful.


MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge. Medical imaging plays an important role in disease identification and treatment planning. Generally, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are used to monitor the disease progression and diagnosis. However, segmentation of affected regions and identification of disease is sometimes affected by the degraded image quality. Automatic segmentation of cancerous regions in brain MRI is a challenging task  due to tIn summary, pre-processing techniques such as skull stripping, de-noising, bias field correction, and registration are widely used to prepare brain MRI data for automatic brain tumor segmentation and analysis. Afterwards, the learning models are trained on this pre-processed data . EspeciaThe major contributions of this work are as follows:A permutation of different pre-processing techniques for brain tumor segmentation is investigated and results are reported.It is demonstrated analytically and confirmed experimentally that the Gibbs ringing artifact removal is an important pre-processing technique which improves segmentation performance in brain MRI.Finally, a robust pre-processing framework is proposed for the automatic brain tumor segmentation.In practice, the acquired MRI images consist of both brain and non-brain tissue, i.e., skull, eyes, and spinal cord , and canThe presence of skull in brain MRI may affect the learning rate and computational complexity of the learning model. In addition, the presence of eyes and other non-brain tissues in MRI images of the training dataset may lead to misclassification and unexpected results . TherefoBrain MRI volume might be acquired by different MRI scanners or by the same scanner with different protocols which causes the intensity inhomogeneity problem, i.e., different intensity ranges for the same tissue type. Intensity normalization is the process of scaling the intensities of the image to a reference or standard scale . The metQuite often, the brain MRI acquisition process is subjected to noises which affect quality of the obtained MRI images, and it becomes difficult to distinguish between the abnormal tissues from the normal ones. Some of the de-nosing methods include anisotropic diffusion filtering (ADF) , non-locAnother inherent characteristic of the MRI acquisition process is the magnetic field variation which may also have negative effects (artifacts) on the images . ArtifacKellner et al.  proposedThis paper focuses on the investigation and analysis of different pre-processing techniques to enquire the impacts of pre-processing techniques separately and jointly on the segmentation performance of the deep learning model. In the first step, Gibbs ringing artifact removal technique is applied and its effects on the segmentation accuracy are studied. To the best of our knowledge, it is the first time that Gibbs ringing artifact removal is used as a pre-processing step for brain MR image analysis using a deep learning model. The detailed discussion about these pre-processing techniques is given in the following sections.BraTS focuses on evaluation of the state-of-the-art methods by providing 3D brain MR images along with ground truth labels annotated by expert physicians for the automatic segmentation of brain tumors . We utilDifferent combinations of pre-processing techniques were explored for the performance improvement, and it was inferred that bias field distortion correction followed by Gibbs ringing artifact removal of brain MR images offers a robust pre-processing sequence for brain tumor segmentation. The results were benchmarked with the existing top-scoring techniques of the BraTS-2018, BraTS-2015, and BraTS-2013 challenges. To validate the proposed framework, other pre-processing techniques, i.e., intensity normalization, bias field correction, histogram equalization along with Gibbs ringing artifact removal were also employed.Brain MR images are usually collected from different institutions and hospitals for dataset development . To addrV represents the observed image, u is uncorrupted image, f is bias field, and n denotes noise which is assumed to be the Gaussian and independent. After using the notation û = log u and assuming a noise free scenario the model becomes:As discussed in S* denotes the smoothing factor. According to [f(x), an iterative scheme is designed to converge, i.e., According to Tustison , the iterding to , insteadnth iteration is the sum of the first n residual bias fields, i.e.,Thus, the total bias field estimate at the Similar to Kamnitsas et al. , N4ITK  was emplNoise removal and histogram equalization are important and classical image processing techniques to improve the quality of acquired image. In this study, the adaptive histogram equalization (AHE)  was usedFT {.} denotes the Fourier transform, xG and yG are two weighting functions, given as:Gibbs ringing artifact is an artifact that degrades the MR image quality, which is also called truncation, spectral leakage, or ringing artifact. It typically appears near sharp high contrast boundaries in the MR images and is also known as Gibbs phenomenon . Gibbs axG + yG = 1, where the artifact free images, i.e., xJ = yJ remain unchanged as given in Equation (6), which ensures that the minimal smoothing is introduced [Using this approach, the high frequency components along the direction are enhanced and due to the normalization troduced . The restroduced  are showThe top row of l is the softmax output of the 3D U-Net and m is one hot encoding of the ground truth labels (segmentation maps). Additionally, i represents the voxel in training patch and K represents the tumor class . In Equation (9), i,kl and i,km denote the ground truths and output (softmax output) at voxel i of class k, respectively, where i belongs to I (voxel in training patch) and k belongs to K (one of the tumor classes). The fully convolutional nature of our network, i.e., 3D U-Net, allows the use of arbitrary-size images as we segment the entire patient data at once. In the first stage, several pre-processing techniques were applied in different sequences (separately and jointly). Based on experimental analysis, it was inferred that each pre-processing technique has a significant effect on the segmentation accuracy of brain MRI. However, correcting the brain MRI from bias field distortion (using (4)) followed by Gibbs ringing artifact removal gives us improved and generalized results as shown in (11). Brain MR Images were corrected from bias field using Equation (10).The proposed framework is depicted in nÎ is a bias field corrected MR image using N4ITK [In (10), ng N4ITK . To remong N4ITK  techniquFT {.} denotes the Fourier transform, Î denotes a bias field corrected MR image, xG and yG are weighting functions as given in (7) and (8), and J is the resultant MR image. The complete BraTS-2018 dataset was pre-processed using (10) and (11), i.e., bias field correction followed by Gibbs ringing artifact removal, which we called sequence-6, as there were 10 sequences in total which are discussed in later sections.In (11), In this work, different pre-processing techniques for automatic brain tumor segmentation in brain MR images were evaluated. Experiments were performed on BraTS  datasets and 10 different sequences were evaluated in this work.Gibbs ringing artifact removed MR images vs. original images, i.e., the images in BraTS- were used initially for training the 3D U-Net and results are recorded in To confirm and validate the importance of Gibbs ringing artifact removal as a pre-processing technique, original data results (produced by U-Net) compared with pre-processed data, i.e., Gibbs ringing artifact removed along with others pre-processing techniques results, are shown in The training and testing data split were 80% and 20%, respectively. For results comparison, the standard BraTS metric was used, i.e., dice score. A total of 10 sequences were used for training and testing purposes. All sequence results, i.e., training and testing are shown in The sequences (order of pre-processing techniques) along with training and testing dice score of each sequence are shown in In sequence-3, the BraTS-2018 dataset was again pre-processed by two different pre-processing techniques, however, this time AHE replaced the N4ITK  techniquThe dataset was fed to network and results were recorded after training. Sequence-7 comprises three pre-processing steps, i.e., N4ITK [The proposed methodology (sequence 6), i.e., bias field corrected MRI data followed by Gibbs ringing artifact removal techniques  was compThe permutations of different pre-processing techniques used in this work and their prediction results are depicted in Pre-processing the input training data is an essential step for training the deep learning model for automatic brain tumor segmentation. In the present work, different pre-processing techniques, i.e., bias field correction, intensity normalization, histogram equalization, and Gibbs ringing artifact removal were employed to analyze the effect of pre-processing techniques and find out the optimal pre-processing technique (sequence-6) to train the deep network, i.e., 3D U-Net. We also investigated whether bias field correction followed by Gibbs ringing artifact removal gives us improved segmentation results.To the best of our knowledge, using the Gibbs ringing artifact removal as a pre-processing technique to train the deep learning model was first used by this work. To confirm the confidence of Gibbs ringing artifact removal as a pre-processing technique, Gibbs ringing artifact removal was employed before bias field correction and after bias field correction. It was inferred that the results of Gibbs ringing artifact removal as a preprocessing technique are promising regarding the improvement of segmentation accuracy. Therefore, the use of Gibbs ringing artifact removal is strongly recommended as a pre-processing technique and also segmentation results are improved when Gibbs ringing artifact removal is followed by bias field correction (N4ITK).It can be noted that Gibbs ringing artifact removal from MR images produced improved results on whole and core regions, however, this technique did not improve the the enhancing region’s accuracy on the BraTS dataset . The enhIn this work, the Gibbs ringing artifact removal was applied to brain MR images with other pre-processing techniques to train the model. While, in future, this work can be further extended to apply the Gibbs ringing artifact removal to other parts (organs) of the body acquired with MRI modality.


Cybercriminals are constantly on the lookout for new attack vectors, and the recent COVID-19 pandemic is no exception. For example, social distancing measures have resulted in travel bans, lockdowns, and stay-at-home orders, consequently increasing the reliance on information and communications technologies, such as Zoom. Cybercriminals have also attempted to exploit the pandemic to facilitate a broad range of malicious activities, such as attempting to take over videoconferencing platforms used in online meetings/educational activities, information theft, and other fraudulent activities. This study briefly reviews some of the malicious cyber activities associated with COVID-19 and the potential mitigation solutions. We also propose an attack taxonomy, which  will help guide future risk management and mitigation responses. I.1https://www.marketwatch.com/story/zoom-microsoft-cloud-usage-are-rocketing-during-coronavirus-pandemic-new-data-show-2020-03-30, last accessed June 4, 2020.COVID-19, which is also referred to as novel coronavirus, 2019-nCoV, or SARS-CoV-2, is among the worst pandemics in recent times and has resulted in numerous countries introducing travel bans, social distancing, lockdowns, and stay-at-home orders 2https://www.todayonline.com/singapore/close-400-covid-19-related-scams-reported-s14-million-cheated-january-april, last accessed June 4, 2020.3https://www.scamwatch.gov.au/types-of-scams/current-covid-19-coronavirus-scams, last accessed June 4, 2020.4https://www.consumer.ftc.gov/blog/2020/04/covid-19-scam-reports-numbers, last accessed June 4, 2020.Corresponding security and privacy risks have also been observed. For example, Singapore’s Minister for Home Affairs indicated that between January and April 2020, “a total of 394 scams related to Covid-19 were reported and victims were cheated of at least SGD 1.4 million”.To the best of our knowledge, this study is the first attempt to provide an overview of cyberattacks prevalent during the COVID-19 pandemic. However, possible new attacks could have been perpetrated because the pandemic was still ongoing when this research was being conducted.•Identify various COVID-19-related cyber threats,•Develop a new taxonomy of attacks and their effects on security goals, and•Discuss the potential mitigation strategies to counter the identified threats.This study attempts to map some of these attacks based on categories see . We use The remainder of this paper is organized as follows. II.Cybersecurity is the process of securing assets, networks, programs, and data from any unauthorized access or attack. The evolving nature of attacks makes cybersecurity one of the challenging research areas. To understand information flow within cybersecurity, an important aspect is gaining familiarity with a few key terms, namely, adversary or threat agent, threat, risk, attack, vulnerability, security policy, assets, and countermeasures. Brief descriptions of these terms are provided in At present, only few studies have highlighted the effects of COVID-19 in terms of cybersecurity because the majority of the current studies have mostly focused on security, privacy and trust aspects in wireless sensor networks (WSNs) To date, only a few approaches have been proposed to mitigate privacy concerns. Reference The other studies that highlighted the cybersecurity issues that arised owing to this pandemic include the work of III.In this section, we will present the taxonomy of COVID-19 related cyber incidents see , and disA.Recent statistics have shown that the number of COVID-19-themed cyberattacks has increased in the past weeks and months, as shown in 1)a:Europol reported a steady increase in DDoS attacks during the pandemic. These attacks have substantial practical consequences because the number of Internet users also increases owing to social distancing, work-from-home environments, and online educational activities  b:Spyware is a type of malware used to clandestinely obtain covert information of other systems. This threat has been observed in the current COVID-19 pandemic. For example, COVID-19-related tracker-based apps were reportedly embedded in spyware-based programs to track the activity of users. A popular malicious app is Corona Live 1.1.2)a:Malware, such as ransomware, are malicious programs designed to facilitate a broad range of nefarious activities b:Apart from COVID-19-themed malware designed to facilitate illicit financial gains, we also observed an increase in the number of COVID-19-themed gray marketing activities. Examples include attempts to sell personal protective equipment (PPE) or other COVID-19-related products at astronomical prices, or sell counterfeit and unapproved equipment and products. Approximately 2,000 online links were discovered by Interpol and other intelligence agencies between March 3 and 10, 2020 3)a:Telecomputing  is becoming a norm in the current COVID-19 pandemic, in which organizations offer flexible work arrangements to their employees. Given that these employees rely heavily on phone and Internet communications to carry out their business operations, including healthcare advisories, such a communication channel can also be, and have been, exploited by cyber criminals. For example, cyber criminals have been reported to hijack or impersonate business and personal communications via voice phishing , robocall scams, and other technical support scams. Cyber criminals have also been reported to abuse voice over IP (VoIP) services to scam individuals into paying for non-existent services or hand over their personal information  b:The existing social distancing requirements have resulted in the closure of such organizations as universities, government agencies, and other non-essential services. This closure has resulted in the significant use of online systems and platforms, such as online learning management systems (LMS) and video conferencing applications and tools . Several incidents, some of which are highly publicized, have been reported, in which cyber criminals identify and exploit vulnerabilities in the aforementioned systems and platforms. One popular but vulnerable platform was reportedly hacked owing to weak security and password mechanism. Consequently, the attackers were able to hijack video conference sessions or gain access to conferencing contents.c:Phishing is also a common attack threat observed during the COVID-19 pandemic. RiskIQ 11)a:Several infodemic campaigns have also been observed on popular social media platforms, such as Facebook, WhatsApp, and LinkedIn, where fake or misleading information were posted. Examples include claims of ayurvedic medicine being effective against COVID-19 or drinking tea or cow urine can prevent COVID-19 transmission B.All the previously discussed threats serve the same purpose, which is to disrupt security goals and exploit potential vulnerabilities in various sectors, such as health care, entertainment, education, business, banking, and essential government services. The brief descriptions of these security goals and effects are as follows.1)Confidentiality ensures that information is accessible only to authorized people and is commonly achieved through encryption, in which information is hidden to the outside world but accessible to participating users. For financial benefits, hackers utilize various type of techniques, such as ransomware, to gain unauthorized access to user devices and encrypt and lock personal files on their mobiles and PCs. These incidents result in considerable financial losses to individuals and organizations 2)The main goal of integrity is to safeguard data from any intentional or accidental changes by authorized/unauthorized users 3)Availability ensures that data and resources are readily available to authorized users, particularly during emergencies 4)Authenticity IV.This section provides guidelines for individuals working from home to minimize attacks. We also discuss the potential mitigation approaches to counter future pandemic-themed cyberattacks see also  and 6.TAA.One of the potential approaches to mitigate ransomware attacks is to vet third-party apps and educate users, thereby enabling them to identify trusted or reputable sources . App ratings can also be another indication whether apps are trustworthy. However, this approach will not work for new apps, particularly in pandemic-type situations.B.VoIP service providers can play an effective role in mitigating scam call threats, such as assisting to raise user awareness and actively identify and block potentially fraudulent or scam callers . Although not all users are cyber aware, free educational campaigns, such as not sharing personal information through voice calls and disregarding online offers that are too good to be true , could be intensified during pandemics.The other possible mitigation strategy involves the design and development of anti-spam detectors based on artificial intelligence (AI). Using the data from previous pandemics, an AI-based bot can be developed to answer calls (instead of users) and verify whether an incoming call is a spam or not.C.Evidently, we need collective effort from different countries and governments during pandemics, such as the current COVID-19 emergency. To combat pandemic-themed cyber threats, effort and countermeasures are required from the international community, including the establishment of an international task force to facilitate the sharing of current cyber threat intelligence .The importance of financial support cannot be understated in international collaboration activities . However, many other competing priorities are present during pandemics. Hence, the support of the community and international organizations should be sought to fund mitigation initiatives. For example, financial support from such organizations as the International Monetary Fund (IMF) can be used to develop tools and skills to mitigate cyber threats.D.To counter infodemic campaigns, we need the support and involvement of a broad range of stakeholders, such as social media platforms. However, determining whether posted contents are fake can be challenging, particularly when relating to ongoing pandemics. Hence, computer and social scientists and healthcare professionals have roles in collaborating and designing approaches  to considerably identify and classify fake or misleading news.E.Given the increased use of systems at homes due to social distancing measures, effort should be exerted to ensure that home systems are patched and secure. For example, patching operating systems and applications is one of the key cyber mitigation strategies recommended by the Australian Signals Directorate’s Australian Cyber Security Centre Security organizations can also play a role, such as by not charging subscriptions for their security products  during pandemics.F.Risk management framework is an effective method to access, mitigate, and evaluate risks associated with the threat. Several risk management frameworks are available such as for scada systems V.The most pronounced impact of COVID-19 is the shift of the cyber security landscape from an enterprise to a home environment. The fortuitous shift has provided many new opportunities to hackers and cybercriminals, thereby resulting in an increased risk of vulnerability exploitation. During the COVID-19 pandemic, a new wave of cyberattacks was recorded. Working from home has increased the risk of cyberattacks owing to various reasons, which is highlighted in User awareness is critical to mitigate and reduce the risk of such cyberattacks in the future. We summarized the key user awareness guidelines in Emerging technologies . We used these categories to present potential mitigation solutions. The cyberattack taxonomy and potential mitigation strategies can also facilitate cyberattack prevention effort plannings in future pandemics. In the future, we intend to extend the proposed taxonomy and propose risk management model for these pandemics.


Major depressive disorder (MDD) and cocaine use disorder (CUD) are related with disability and high mortality rates. The assessment and treatment of psychiatric comorbidity is challenging due to its high prevalence and its clinical severity, mostly due to suicide rates and the presence of medical comorbidities. The aim of this study is to investigate differences in brain derived neurotrophic factor (BDNF) and cortisol plasmatic levels in patients diagnosed with CUD-primary-MDD and CUD-induced-MDD and also to compare them to a sample of MDD patients (without cocaine use), a sample of CUD (without MDD), and a group of healthy controls (HC) after a stress challenge.n = 6), CUD (n = 15), CUD-primary-MDD (n = 16), CUD-induced-MDD (n = 9), and 21 HC. Psychiatric comorbidity was assessed with the Spanish version of the Psychiatric Research Interview for Substance and Mental Disorders IV (PRISM-IV), and depression severity was measured with the Hamilton Depression Rating Scale (HDRS). Patients were administered the Trier Social Stress Test (TSST) before and after the biological measures, including BDNF, and cortisol levels were obtained.A total of 46 subjects were included: MDD  had a large effect size (0.77).After the TSST, Cohen's Results suggest a different pathogenesis for CUD-induced-MDD with higher levels of cortisol and BDNF compared with CUD-primary-MDD. Such variations should imply different approaches in treatment. Mental and substance use disorders (SUD) are related with 7% of the global burden of disease as measured in disability-adjusted life years (DALYs) and increasing significant mortality rates . In womeThe assessment and treatment of psychiatric comorbidity is challenging because of its high prevalence , 3 and cAt the international level, cocaine is one of the most widely used illicit drugs. The 2021 United Nations Office on Drugs and Crime World Report estimated that around 20 million individuals aged 15–64 years (0.4%) had consumed cocaine during 2019 . Its useIndividuals taking cocaine are reported to present a high risk of depression . The eupDue to the reasons mentioned above, clinicians are faced with having to distinguish between CUD-primary-MDD and CUD-induced-MDD in cocaine consumers. In a similar manner to psychiatric disorders, there are, however, no valid biomarkers for their correct identification. The diagnosis of MDD (induced/primary) is based on the subjective identification of clinical symptoms, and there are no clear standards for differential diagnosis. A recent study comparing DSM-5 criteria only found that “changes in weight or appetite” had a differing prevalence among the two disorders . The ideThe hypothalamic-pituitary-adrenal (HPA) axis is involved in the pathogenesis of MDD . TraditiThe BDNF belongs to the peptide family involved in neural plasticity, neurogenesis, and neural survival . It alsoBoth MDD and SUD are complex diseases that result from changes in differing physiological systems. Thus, to better understand their pathophysiologies, the combined study of different systems and networks is required. In this regard, a recent paper by Chen et al. finds that combining the results of serum BDNF, cortisol, and interferon-gamma could help in making an accurate diagnosis of MDD .At present, it is crucial to perform accurate diagnoses of CUD-primary-MDD and CUD-induced-MDD. As the monoamine hypothesis of depression is proven insufficient to explain differences between both types of MDD , it is en = 6), (ii) CUD (n = 15), (iii) CUD and primary MDD (n = 16), and (iv) CUD and induced MDD (n = 9). All patients were recruited at the addiction treatment facilities of the Parc Salut Mar Institute of Neuropsychiatry and Addiction in Barcelona, Spain.In this cohort study, the sample included subjects with (i) MDD  were included from a database of subjects willing to participate in medical research projects at the Pharmacology Unit of the Hospital del Mar Institute of Medical Research (IMIM), Barcelona, Spain. In the HC group, the exclusion criteria were any Axis I psychiatric disorder, family history of depression, and any SUD except nicotine.HCs . Participants were required to be drug-free before the experimental session. The subjects remained sitting/lying in a calm laboratory environment during the session with restricted social interactions. The TSST was performed at 13.00 hours. This was carried out to (i) assure a similar waking time for all participants the day of the test, (ii) control activities that could affect HPA axis functioning, (iii) avoid heterogeneity of the cortisol response, and (iv) assure a period of rest before the protocol was administered , 36.At baseline assessment, a closed-ended questionnaire was used to record participants' sociodemographic characteristics, family history, medical assessment, history of substance use, and previous psychiatric treatment. Psychiatric diagnoses were performed according to DSM-IV-TR criteria with the Spanish version of the Psychiatric Research Interview for Substance and Mental Disorders IV (PRISM-IV) . PRISM wThe TSST is an acute stress test that consists of two tasks: public speaking and a mathematical task . ParticiHeart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate (RR), and temperature were monitored by Dash 3,000 monitor  at different times: before the test (pre-TSST); immediately after (post-TSST); and after 30 (post30-TSST), 60 (post60-TSST), and 90 min (post90-TSST). At the same time points, blood samples were collected from the subjects.To assess cortisol levels, 5 ml of peripheral blood sample was centrifuged at 4,000 rpm for 10 min. The serum obtained was frozen at −20°C until analysis was conducted by electrochemiluminescence, using an Immulite-2000 XPi analyzer (Siemens).BDNF was obtained before (pre-TSST), immediately after (post-TSST), and at 90 min (post90-TSST). Five milliliters of peripheral blood sample was centrifuged at 4,000 rpm for 10 min. The serum obtained was frozen at −20°C until analysis, which was performed with 500 microliters of serum by ELISA and the kit Human BDNF Quantikine ELISA Kit of R&D-Vitro SA and polyclonal antibodies.p-values to guarantee a family-wise error rate of 0.05 was based on the multivariate t distribution of the vector of test statistics.A descriptive analysis of all variables of interest was carried out separately in each of the study groups. For this purpose, the mean, median, standard deviation, and range were calculated. Repeated-measure ANOVA models were used to analyze the intragroup and intergroup changes of both the cortisol and BDNF concentrations. The models included group condition as a main factor in addition to all two- and three-way interactions. The computation of simultaneous confidence intervals and adjusted post hoc pairwise comparisons. Cohen's d was used to quantify the effect size of the pairwise differences among study groups . Cohen's d is a standardized score, analogous to a z score. Following Cohen's effect size conventions, only differences higher than a medium effect size (d ≥ 0.50) were considered of relevance.Next, one-way ANOVA models were fitted to compare the study groups with the mean of the variables. The model assumptions  were checked with residual plots and the Levene (homoscedasticity) and Kolmogorov-Smirnov tests, respectively. If assumptions held and group differences were statistically significant, the Bonferroni test was applied for the All data were analyzed using the IBM Corp. Released 2013 IBM SPSS Statistics for Windows, Version 22.0 . In the case of the group comparisons, statistical significance was set at 0.05 (to protect against type I errors), and for model assumption tests at 0.1 (to protect against type II errors).The clinical protocol was approved by the local Research Ethical Committee CEIC-Parc de Salut Mar, Barcelona, Spain (2009/3494/I and 2012/4751/I), and the study was conducted in accordance with the Declaration of Helsinki and Spanish laws concerning clinical research. Volunteers were financially compensated. All subjects gave written informed consent prior to their participation in the study.A total of 67 subjects were included in the study to assess possible differences in BDNF and cortisol levels during the TSST. The main sociodemographic and clinical characteristics of the sample are described in More than 76% of the total sample were single men aged >32 years. All groups had low HDRS scores, and the depressed groups had more than one MDD episode with a similar age of onset. Family history of depression and current treatment with antidepressants were also more prevalent in these groups. In the CUD groups, the age of onset of problematic cocaine use was very similar, and current nicotine use was >73%.Five out of six subjects in the MDD group were on varying types of antidepressants. In the CUD-induced and primary-MDD groups, the majority of patients were also on antidepressant treatment. The CUD-primary-MDD patients were treated with SSRIs although in the induced-MDD group other types of antidepressants were prescribed. Types of antidepressants in the study groups are described in .Changes in HR, SBP, DBP, and RR before and after the TSST are depicted in All groups showed a similar response pattern during the TSST follow-ups with the CUD-induced-MDD presenting the highest cortisol concentrations in post-60 TSST and the CUD-primary-MDD the lowest cortisol concentrations in post-90 TSST .p-values >0.117 when comparing groups in each assessment ; no differences, however, were reported when performing Bonferroni's post-hoc comparisons  in the levels of cortisol and BDNF in primary and induced depression. As the diagnosis of depression is based on clinical criteria, sometimes with suboptimal rates of validity and accuracy, the detection of measurable biomarkers has implications in the study of the pathophysiology of depression and the introduction of effective treatments.As previously reported, the monoamine theory of depression cannot completely explain the pathogenesis of induced depressions , and othMoreover, when analyzing BDNF plasma levels, we observed similar differences with higher concentrations of BDNF at 90 min after the TSST in the CUD-induced-MDD compared with the CUD-primary-MDD and MDD without cocaine use. Traditional research describes lower levels of BDNF in depressive patients . In our The use of cortisol and BDNF levels as markers to differentiate cocaine-induced and primary depressions could help in the design of personalized treatments. They would permit the correct selection of antidepressant, thus avoiding prolonged periods before patient response to treatment . NeverthOur findings do not signify a causal model, and it was not possible to clarify whether the differences in cortisol and BDNF levels were primary or secondary to induced/primary depression. Could a previously disrupted HPA axis be a marker of depression risk? Another study reported that patients with CUD presented a previous childhood history (parent neglect), higher scores in depression severity (measured by the SCL-90), and greater levels of ACTH and cortisol in plasma than HCs. The authors concluded that early life events (neglect and poor attachment to parents) influenced HPA axis function, and additionally, such individuals presented increased vulnerability to depression and substance use . In thisMore comprehensive knowledge regarding dual depression biomarkers and the differences between primary and induced depression are essential to introduce effective treatment, particularly as the improvement of depressive symptoms requires at least 4 weeks after commencement of antidepressant therapy. Moreover, previous research, in accordance with the present results, demonstrates differential neurobiological processes underlying induced and primary MDD , 49 suggThere are several limitations to this study. First, the sample size was small for all groups. Indeed, the strict inclusion criteria made it difficult to find pure cocaine/depressed-only patients. For this reason, although our results suggest biochemical differences between CUD-primary-MDD and CUD-induced-MDD, such findings should be confirmed by the analysis of a larger set of samples. A second limitation is that the MDD patients were under remission, and differences could, therefore, be underestimated. The decision to include patients in remission was made due to ethical reasons in order not to expose individuals with depression to a stress situation (even in a controlled environment). This means that the differences observed should be considered as trait markers or risk factors to develop depression and not clinical depression itself. In previous research, the TSST is useful to discriminate risk factors in stress response for healthy controls , the genReliable biomarkers are needed to detect and diagnose depression subtypes. One strength of the study is that these molecules could be analyzed routinely. Future studies investigating their involvement in the outcome and response to treatment are warranted.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and the clinical protocol was approved by the local Research Ethical Committee CEIC-Parc de Salut Mar, Barcelona, Spain (2009/3494/I and 2012/4751/I) and the study was conducted in accordance with the Declaration of Helsinki and Spanish laws concerning clinical research. Volunteers were financially compensated. All subjects gave written informed consent prior to their participation in the study. The patients/participants provided their written informed consent to participate in this study.MT was the principal investigator of the grants supporting the research. MT and MF were responsible for the study concept and design. MT, MF, JM-P, and FF designed the protocol. FF, RR-M, CP-M, and EP selected the participants. JM-P, CP-M, EP, and MF conducted the TSS test sessions. JM-P and KL performed the statistical analysis and FF, JM-P, MF, and MT interpreted findings. FF, JM-P, and MB wrote the initial draft of the manuscript. MT and MF provided critical revision of the manuscript for key intellectual content. All authors have read and agreed to the published version of the manuscript.This work was supported by grants from the Instituto de Salud Carlos III–ISCIII Red de Trastornos Adictivos 2016 (RD16/0017/0010 and RD16/0017/0003); Fondo de Investigación Sanitaria (FIS) ; National R + D+I and funded by the Instituto de Salud Carlos III (ISCIII) and the European Regional Development Fund (FEDER) grant Juan Rodes (JR 16/00020); Ministerio de Sanidad, Política Social e Igualdad, Plan Nacional Sobre Drogas (PNSD) (2012I054); Suport Grups de Recerca AGAUR-Gencat ; Ministerio de Economía y Competitividad (MTM2015-64465-C2-1-R); ISCIII-Redes de Investigación Cooperativa Orientadas a Resultados en Salud (RICORS); Red de Investigación en Atención Primaria de Adicciones (RIAPAd), grant number RD21/0009/0001. The funding agencies had no role in study design, data collection, interpretation, and no influence on the writing.FF has received travel grants during the last 3 years from Lundbeck, Otsuka, Indivior, Pfizer, Gilead and Servier; she has also received grant/research support from Indivior and Servier. MT has been consultant/advisor and/or speaker for Gilead Sciences, Merck Sharp & Dohme Corp, Indivior, Mundipharma Pharmaceutics, Servier and Adamed. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.