Childhood malnutrition is a major public health concern, as it is associated with significant short- and long-term morbidity and mortality. The objective of this review was to comprehensively review the evidence for the management of severe acute malnutrition (SAM) and moderate acute malnutrition (MAM) according to the current World Health Organization (WHO) protocol using facility- and community-based approaches, as well as the effectiveness of ready-to-use therapeutic food (RUTF), ready-to-use supplementary food (RUSF), prophylactic antibiotic use, and vitamin A supplementation. We searched relevant electronic databases until 11 February 2019, and performed a meta-analysis. This review summarizes findings from a total of 42 studies (48 papers), including 35,017 children. Limited data show some benefit of integrated community-based screening, identification, and management of SAM and MAM on improving recovery rate. Facility-based screening and management of uncomplicated SAM has no effect on recovery and mortality, while the effect of therapeutic milk F100 for SAM is comparable to RUTF for weight gain and mortality. Local food and whey RUSF are comparable to standard RUSF for recovery rate and weight gain in MAM, while standard RUSF has additional benefits to CSB. Prophylactic antibiotic administration in uncomplicated SAM improves recovery rate and probably improves weight gain and reduces mortality. Limited data suggest that high-dose vitamin A supplementation is comparable with low-dose vitamin A supplementation for weight gain and mortality among children with SAM. Childhood undernutrition includes wasting (weight-for-height z-score (WHZ) < −2SD), stunting (height-for-age z-score (HAZ) < −2SD), underweight (weight-for-age z-score (WAZ) < −2SD), and micronutrient deficiencies or insufficiencies . The curChildhood malnutrition is a major public health concern, since it is associated with significant morbidity and mortality . The conDespite the outlined interventions to manage childhood malnutrition , there ihttps://onlinelibrary.wiley.com/doi/full/10.1002/CL2.193.The protocol for this review is published with the Campbell Collaboration at To evaluate the effectiveness of community-based strategies, such as community-based mobilization, screening, follow-up, counselling, and education; to improve screening, identification, and management of SAM and MAM;To evaluate the effectiveness of facility-based strategies, such as facility-based screening, management, and periodic follow-up, to improve screening and management of SAM and MAM;To evaluate the effectiveness and relative effectiveness of various RUTF and RUSF for the management of SAM and MAM;To evaluate the effectiveness of prophylactic use of antibiotic to manage uncomplicated SAM;To evaluate the effectiveness of various doses of vitamin A supplement to manage children with SAM and MAM.The objectives of this review are:We included primary studies, including large-scale program evaluations, using experimental and quasi-experimental study designs that allow for causal inference. We included randomized controlled trials (RCTs), including both cluster and individual level randomization, quasi-experimental studies with non-random assignment to intervention and comparison groups, controlled before–after studies (CBA), and interrupted time series (ITS). We included studies targeting children under 5 years of age with SAM and MAM in low- and middle-income countries (LMIC). We used the following definition of MAM and SAM by WHO:SAM: weight-for-height z-score (WHZ) < −3 SD, weight-for-height (WFH) < 70% of the median National Center for Health Statistics (NCHS) or WHO reference, or mid-upper arm circumference (MUAC) < 115 mm or edema.Complicated SAM: SAM cases without appetite or with medical complications.Uncomplicated SAM: SAM children with successful standard appetite test, and without fever, clinical infections, or complications.MAM: weight-for-height z-score (WHZ) between −2 and −3 standard deviations (SD), WFH equal to 70–80% of the NCHS or WHO reference median, or mid-upper arm circumference (MUAC) of 115–125 mm.We excluded studies specifically conducted on HIV populations.The following interventions were considered and compared against the suggested comparison groups separately:Community-based strategies to screen, identify, and manage SAM and MAM compared to non-community-based strategies  versus no active surveillance; training of CHWs for community-based screening versus no training; community-based management with RUTF versus standard care practices).Facility-based strategies to screen and manage uncomplicated SAM according to the WHO protocol compared to other standards of care .Community-based management of children with uncomplicated SAM as outpatients with RUTF compared to standard diet, fortified blended flours (FBFs), or other locally produced foods.RUSF for MAM compared to standard diet, FBF, or other locally produced foods.Prophylactic use of antibiotics in children with uncomplicated SAM compared to no antibiotics.Vitamin A supplementation in the management of SAM and MAM with various doses and frequency of administration.We included studies that met our inclusion criteria, but only included studies in the analysis that reported on the predefined primary outcomes: recovery rate , weight gain (measured as grams/kg/day), relapse (measured as the proportion of children who re-enrolled after they had recovered at any time point reported by study authors), mortality (measured as the proportion of children dying under five years of age), case fatality rates . The secondary outcomes included height gain, MUAC gain, time to recover (measured as length of time between admission and discharge), stunting (defined as below minus two standard deviations from median height for age of reference population), wasting (defined as below minus two standard deviations from median weight for height of reference population), underweight (defined as below minus two standard deviations from median weight for age of reference population), infection incidence , adverse effects , hospitalization costs, and cost-effectiveness.http://www.who.int/ictrp/en/), ClinicalTrials.gov, and Epistemonikos (https://www.epistemonikos.org). We did not restrict our searches by date, language, or publication status. Search strategy available as We searched the following databases until 11 February 2019: Cochrane Database of Systematic Reviews (CDSR) and the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library; World Health Organization regional databases; The Campbell Library; MEDLINE (PubMed); EMBASE; CINAHL; Web of Science; POPLINE; CAB abstracts and Global Health; PAHO; IndMED (indmed.nic.in/indmed.html); and WHO Global Health Index. We also searched the WHO International Clinical Trials Registry Platform ;Duration of intervention (short-term (<3 months), medium-term (3–6 months), and long-term (6–12 months));Various formulations of supplementary foods;Setting ;Vitamin A supplementation dosage (different doses);Different antibiotics;Equity (low income and disadvantaged groups versus relatively high income groups).We planned to use the Chi2 test to assess subgroup differences. Due to the limited number of studies, we could not conduct the planned subgroup analysis; however, we did separately analyze the various supplementary foods that were compared with standard RUTF and standard RUSF.For RCTs, we used the Cochrane risk of bias tool , which aWe planned to conduct sensitivity analysis based on the risk of bias of the included studies by removing studies judged to be at high risk of bias for sequence generation, allocation concealment, and blinding of participants from the meta-analysis to determine whether the removal of studies with high risk of bias impacts the estimates.We summarized the quality of evidence according to the outcomes as per the grading of recommendations, assessment, development, and evaluation (GRADE) criteria . Grades Our search identified a total of 8451 potentially relevant titles from the electronic searches and 35 records from searching other sources. After removing duplicates, we screened 7684 records for eligibility and excluded 7618 on the basis of title and abstract. We obtained the full-text reports of the remaining 66 records, and of these, excluded 18 studies and included 42 studies (48 papers). We included a total of 42 studies from 48 papers), including 35,017 children  papers, ,61,62,63Two studies ,63 assesPrimary outcomes included recovery rate, weight gain, relapse, and mortality. None of the included studies reported case fatality rates. Among secondary outcome, included studies reported height gain, MUAC gain, time-to-recovery, stunting, wasting, underweight, adverse events, hospitalization, and cost effectiveness. The characteristics of the included studies are specified in All the studies  wereComparison 1: Community-based strategies to screen, identify, and manage SAM and MAM compared to non-community-based strategies.Two studies ,63 assesAmong secondary outcomes, integrated community-based management probably reduced length gain by 0.1 mm/day compared to standard management  and probably improved MUAC by 0.27 mm/day compared to the standard management . One study  reportedTwo studies ,63 reporThe forest plots for this comparison are provided in the Comparison 2: Facility-based strategies to screen and manage uncomplicated SAM according to the WHO protocol compared to other standards of care.Four studies ,30,35,50The forest plots for this comparison are provided in the Comparison 3: Facility-based strategies to screen and manage uncomplicated SAM according to the WHO protocol compared to other standards of care (inpatient treatment with RUTF compared to F100).2: 95%; very low quality outcome) and mortality  in facility-based treatment with RUTF compared to F100. Among secondary outcomes, there was no difference between RUTF and F100 for height , MUAC , or wasting .Three studies ,59,62 asThe forest plots for this comparison are provided in the Comparison 4: Community-based management of children with uncomplicated SAM as outpatients with RUTF compared to standard diet, fortified blended flours (FBFs), or other locally produced foods.Fourteen studies ,51,54,552 50%; moderate quality outcome), energy-dense, home-prepared food , or high oleic RUTF . Standard RUTF probably improves weight gain by 0.5 g/kg/day  and by 5.5 g/kg/day when compared to F100 . There was no evidence of difference on weight gain when standard RUTF was compared with energy-dense, home-prepared food  and high oleic RUTF . There was no evidence of difference on mortality when standard RUTF was compared with non-milk/peanut butter-based RUTF , energy-dense, home-prepared food , high oleic RUTF , and elevated n3 PUFA RUTF   .2 63%; low quality outcome) and high oleic RUTF . Standard RUTF may improve height gain by 0.07 mm/day when compared to energy dense home food . There was no evidence of difference on MUAC gain when standard RUTF was compared with non-milk/peanut butter-based RUTF , energy-dense, home-prepared food , and high oleic RUTF . RUTF might reduce the time to recovery by 3.9 days when compared with F100  and by 1.2 days when compared with energy-dense, home-prepared food . There was no difference between standard RUTF and other foods for any of adverse events, including coughing , diarrhea , and fever . There was no difference between standard RUTF and other foods for hospitalization .Among secondary outcomes, there was no evidence of difference on height gain when standard RUTF was compared with non-milk/peanut butter-based RUTF , while RUSF probably reduces recovery rate when compared to whey RUSF by 4% . RUSF may improve recovery rate by 7% when compared to CSB . There was no evidence of difference on weight gain when RUSF was compared with local homemade food  and whey RUSF . When compared to CSB, RUSF may improve weight gain. . There was no evidence of difference on mortality when RUSF was compared to whey RUSF , CSB , and food supplement .Fourteen studies ,56,58,612: 72%; low quality outcome), whey RUSF , and CSB . RUSF may improve MUAC gain when compared with local or homemade food , whey RUSF , and CSB . RUSF may reduce time to recovery when compared to local or homemade food by 14 days . There was no evidence of difference on time to recovery when RUSF was compared with whey RUSF  and CSB . There was no evidence of difference on moderate stunting when RUSF was compared with local or homemade food . There was no evidence of difference on moderate wasting when RUSF was compared with whey RUSF  and CSB . RUSF probably reduces severe wasting by 26% . There was no evidence of difference on underweight status when RUSF was compared with local or homemade food . There was no difference between RUSF and other foods for fever , diarrhea , acute lower respiratory tract infection (ALRI) , other illnesses , any adverse events , and severe adverse events . RUSF may increase vomiting compared to other foods . There was no difference in hospitalization between RUSF and other foods .Among secondary outcomes, there was no evidence of difference on height or length gain when RUSF was compared with local or homemade food  and probably improves weight gain by 0.67 g/kg/day . Prophylactic antibiotic administration probably reduces mortality by 26% compared to the no antibiotics group (2 = 52%).Three studies ,38,64 cocs group  . There was no evidence of difference on length gain  or time to recovery  when antibiotic was compared to no antibiotic. Three studies reported adverse events, suggesting no evidence of difference on diarrhea  or fever  between the antibiotic and no antibiotic groups. Prophylactic antibiotic probably decreases ARI symptoms compared to the no antibiotics by 11% . Prophylactic antibiotic administration reduces hospitalization by 11% compared to no antibiotic .Among the secondary outcomes, prophylactic antibiotic administration probably improves MUAC by 0.06 mm/day compared to the control group , including 35,017 children. Thirty-three of the included studies were RCTs, six studies were quasi-experimental studies, and three studies were cost studies. All the studies were conducted in either community, hospital, health center, or nutrition rehabilitation center settings in LMICs, including Bangladesh, Mali, Malawi, Congo, Kenya, India, Niger, Senegal, Sudan, Burkina Faso, Zambia, Ethiopia, Sierra Leonne, Cameroon, Indonesia, and Cambodia.Two studies assessed integrated community-based strategies to screen, identify, and manage MAM and SAM compared to non-community-based strategies. Integrated community-based management probably improves recovery rate by 4% and probably decreases weight gain by 0.8 g/kg/day compared to non-community-based management, while mortality was similar between the two group. Four studies assessed facility-based strategies to screen and manage uncomplicated SAM compared to other standards of care. Findings suggest that there was no evidence of difference on recovery or mortality. Three studies assessed facility-based management of SAM with RUTF compared to F100. There was no evidence of difference on weight gain or mortality when facility-based RUTF was compared with F100. Fourteen studies compared community-based management of children with uncomplicated SAM with RUTF versus other foods. There was no evidence of difference on recovery rate when standard RUTF was compared to other foods. Standard RUTF probably improves weight gain by 0.5 g/kg/day when compared to non-milk/peanut butter-based RUTF and by 5.5 g/kg/day when compared to F100, with no evidence of difference on weight gain when standard RUTF was compared with energy-dense, home-prepared food and high oleic RUTF. There was no evidence of difference on mortality when standard RUTF was compared with other foods. Fourteen studies compared RUSF for MAM with other foods. There was no evidence of difference on recovery when RUSF was compared to local or homemade food, while RUSF probably reduces recovery rate when compared to whey RUSF by 4%. RUSF probably improves recovery rate by 7% when compared to CSB. There was no evidence of difference on weight gain when RUSF was compared with local homemade food and whey RUSF, while RUSF may improve weight gain by 0.49 g/kg/day when compared with CSB. There was no evidence of difference on mortality when RUSF was compared with other foods. Three studies compared prophylactic use of antibiotics in children with uncomplicated SAM with no antibiotics. Prophylactic antibiotic therapy improves recovery by 6%, probably improves weight gain by 0.67 g/kg/day, and probably reduces mortality by 26% compared to no antibiotics. Two studies compared high dose vitamin A supplementation with low dose vitamin A supplementation in children with SAM. There was no evidence of difference on weight gain and mortality when high-dose was compared to low-dose vitamin A supplementation. The majority of the outcomes were rated as either moderate or low quality outcomes. Outcomes were downgraded mainly due to study limitations, high heterogeneity, imprecision, and small sample size.To the best of our knowledge, this is the only comprehensive systematic review evaluating the interventions to manage acute malnutrition in children under five years of age in LMICs. Various systematic reviews have assessed the effectiveness of individual interventions for managing malnutrition in children. A previous systematic review  evaluateFindings from this review suggest that there are limited data comparing community-based management and facility-based management with other standard of care for SAM or MAM, suggesting some benefit of integrated community-based and outpatient management on improving recovery when compared to standard care and inpatient management. Existing cost data also suggest that community or outpatient management of children with uncomplicated SAM is the cost-effective strategy. Evidence also suggests that facility-based management of SAM with RUTF is similar to F100 on outcomes of weight gain and mortality. Existing evidence on RUTF suggests that standard RUTF is comparable with other foods for recovery and mortality for SAM; however, standard RUTF may improve weight gain when compared to non-milk/peanut butter-based RUTF and F100. Standard RUTF might also reduce recovery time when compared with F100 and energy-dense, home-prepared food. Existing data on RUSF suggest that RUSF may improve recovery and weight gain when compared with CSB for MAM. Data on prophylactic antibiotic administration in children with uncomplicated SAM suggest improved recovery rate and weight gain along with reduced mortality when compared to no antibiotic administration. Limited data suggest that high dose vitamin A supplementation is comparable with low dose vitamin A supplementation for weight gain and mortality among children with SAM.Future studies are needed to evaluate the effectiveness of community and facility-based strategies for screening, identifying, and managing SAM and MAM, including studies comparing the effectiveness of various community and facility-based strategies, such as active community-based surveillance; training of CHWs for community-based screening; and training of health facility staff to diagnose and treat children with uncomplicated SAM. Existing data on the effectiveness of vitamin A supplementation are also limited, hence future data are needed to evaluate the role of vitamin A supplementation with various doses and frequency of administration among children with SAM and MAM. Future studies assessing the effectiveness of interventions to prevent and manage malnutrition among children in LMIC should report pertinent nutrition specific outcomes, including stunting, wasting, underweight status, infections, and potential adverse effects.


Since the introduction of magnetic resonance imaging (MRI) to radiation therapy (RT), it has increasingly been adopted in RT treatment planning for target and organ-at-risk (OAR) definition due to superior soft tissue contrasts. Recently, the roles of MRI in RT have been advanced to tumor delineation in a multiparametric format, MRI-based treatment planning and dose calculation, MRI-guided treatment delivery, and outcome assessment using quantitative imaging metrics or radiomic features. These advancements are due to the development of dedicated MRI simulators, integration of MRI scanners with radiation treatment platforms, as well as technical developments and applications of 4D-MRI, tumor tracking, adaptive planning, and treatment response evaluation. The advancement of machine learning and artificial intelligence also brings about tremendous opportunities to transform the application of MRI in RT.This collection includes 16 articles that cover the following themes:Target volume delineation and treatment planning workflow using MRI as the primary imaging modalityMultiparametric MRI (mp-MRI), a combination of morphologic and functional imaging modalities, has shown the potential to increase the accuracy of tumor detection, localization, and characterization of cancer aggression. Integration of mp-MRI techniques into RT offers enormous opportunities to individualize RT adaptation based upon the individual patient's response to treatment.Iqbal et al. developed a densely connected U-Net to create super resolution spectroscopic images by training the T1 weighted images (T1WI) and the low-resolution 1H MR spectroscopic images together. They showed that the 1H spectra were maintained on retrospective in vivo data.MR spectroscopy imaging (MRSI) can describe the metabolism of different tissues. However, the spatial resolution is limited due to the very low concentration of the metabolites in tissues. Lee et al. performed a volumetric and voxel-wise analysis of the dominant intraprostatic lesions (DIL) defined from T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE) imaging respectively. The correlation was further classified according to tumor location and Gleason grade group. The data suggested that constructing a Boolean sum volume that incorporated T2WI and apparent diffusion coefficient (ADC) maps were reasonable for delineating the DIL on mp-MRI. The value of adding information provided by Ktrans maps remains investigational due to the repeatability and consistency of DCE scans. The interobserver variability also indicated the need to develop a consensus guideline on DIL delineation using mp-MRI.Techniques to generate synthetic CT from MRI and the clinical implementation of MRI only workflowSubstantial interest has developed around generating synthetic CT (sCT) from MRI in order to use MRI as the only, or primary, imaging modality in the RT workflow. Different methods have been introduced to create sCT images using bulk density, atlas-based, or voxel-based approaches. Deep machine learning algorithms such as a U-Net or Generative Adversarial Network can learn image features among different imaging modalities and have great potentials to generate highly accurate synthetic images.Choi et al. used a bulk anatomical density approach to develop a method for patient-specific quality assurance for MRI-only prostate RT. The three-class model  provided accurate dose calculations for verifying sCT for clinical use in MRI-only workflows. The model has currently been implemented as a quality assurance method in a multi-center trial of prostate stereotactic RT that includes an MRI-only study.Gupta et al. used a 3-channel U-Net trained on aligned MRI and CT pairs in sagittal planes to generate sCT images. The three channels represented Hounsfield Unit (HU) ranges of voxels containing air, soft tissue, and bone, respectively. The improved soft tissue contrast of sCT was proved with low mean absolute error difference between sCT and actual CT. The improved image quality was also beneficial for the online image registration with cone beam CT.Wang et al. generated sCT from T2WI of nasopharyngeal carcinoma patients using a 2D U-net algorithm. The deep U-net with 23 convolutional layers was used to generate sCT. The soft tissue, nasal bone, bone marrow, and the interface between bones and soft tissues were carefully evaluated.Greer et al. described a multi-center study for the implementation of an MRI-only prostate workflow. A sCT was created using an atlas-based method from whole pelvic T2WI scans with an isotropic 1.6 mm voxel. A CT scan was acquired subsequent to MRI only plan approval for patient specific quality assurance. The 3D gamma was calculated to evaluate the dose difference between sCT and actual CT, and gold fiducial marker positions were used to evaluate the image registration accuracy between sCT and actual CT. All 25 patients recruited were treated with MRI only workflow.Mittauer et al. developed an MRI-guided online adaptive radiotherapy (MRgoART) procedure for palliative care in RT. The electron density information was incorporated with either a bulk density override or deformable image registration of diagnostic CT to the MRI. The plan quality and treatment delivery efficiency were superior than the conventional method. Excellent clinical outcomes were observed and were in line with historical and sampled controls.Quantitative analysis of morphological and functional MRI and their applications in treatment response assessmentQuantitative MRI can reflect tissue characteristics. Imaging biomarkers from functional MRI can have prognostic and predictive values for progression free survival, overall survival, and distant metastases etc. Radiomic features, which are defined as the post-processing for extraction of textural information from medical images, can provide tremendous information to analyze and characterize the properties of tumor tissues and their physiological and pathological stages.Cao et al. analyzed MRI-derived gross tumor volume, blood volume, and ADC from pre-treatment and mid-treatment, as well as pre-treatment FDG PET metrics for locally advanced head and neck cancer (HNC) treated with chemoradiation. The mean ADC values from pre-RT and its change rate mid-treatment were significant higher and lower in p16– than p16+ locally advanced HNC tumors, respectively. These biomarkers had predictive values and compared favorably with FDG-PET imaging markers.In this collection, van Schie et al. analyzed T2 and ADC changes during treatment and compared patients with and without hormonal therapy, as the hypoFLAME trial patients received ultra-hypofractionated prostate radiotherapy with an integrated boost to the tumor in 5 weekly fractions. Significant ADC changes were observed in the tumor in patients without hormonal therapy. Such early response measured with quantitative MRI holds the potential to predict clinical outcome and guide treatment adaptation.Bagher-Ebadian et al. extracted discriminant radiomic features in the real radiomics-feature space and the latent-variable space from mp-MRI for prostate cancer. These features were used to construct an artificial neural network to classify the DIL from normal prostatic tissues.Li Z. et al. analyzed pre-treatment T1WI, T2WI, and DWI for esophageal squamous cell carcinoma patients undergoing concurrent chemoradiotherapy and identified the ADC texture features that can be used to predict the overall survival.Yu et al. also analyzed pre-treatment T1WI, T2WI to identify tumoral radiomic features that were used to predict patient eligibility for adaptive radiotherapy in advanced nasopharyngeal carcinoma (NPC) patients.Blackledge et al. studied 8 commonly used supervised machine-learning algorithms for tissue classification of mp-MRI of soft tissue sarcoma to quantify post-RT changes. Five out of eight algorithms achieved similar performance. Of the five methods, the Naïve-Bayes classifier was chosen for further investigation due to its relatively short training and prediction times.Considering post-treatment changes are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments, evaluation of the tumors defined using pre-treatment images could be limited to predict treatment response. Development of MR-LinacJelen et al. developed methods to quantify dosimetric characteristics of the Australian MRI-linac system.Recent commercial developments of MRI-guided RT platforms provide great opportunities for direct imaging guidance, tumor/OAR tracking during RT, and treatment adaptation. Two systems have received CE and/or FDA 510 k clearance so far: Unity  and MRIdian system . The Australian MRI-linac system is at the research prototype stage and has an inline orientation, with radiation beam parallel to the main magnetic field. Such inline design can help minimize magnetic field influence on dose deposition. Review papersKiser et al. discussed the challenges of interpretability and reproducibility of MRI data, the complexity of a variety of MR sequences, and the corresponding impacts on RT workflow, such as synthetic CT generation, image fusion, dose calculation, and prognostic values using radiomic features.There are two review papers in this collection. As MRI-guided RT, including adaptive RT, have advanced in the field, the community needs to develop protocols on how to make clinical decisions with funneling MRIgRT data. Li G. et al. reviewed two 4D-MRI techniques—respiratory-correlated (RC) and time-resolved (TR) 4D-MRI. The RC-4DMRI was reconstructed to provide one-breathing-cycle motion, while the TR-4DMRI provided an adequate spatiotemporal resolution to assess tumor motion and motion variation. Both techniques were also discussed in the context of their clinical applications in radiotherapy.Benefitting from advanced technologies of synthetic CT techniques and MR Linacs, the MR-solely RT workflow has been rapidly evolving and has been clinically implemented widely. It has potential to improve the therapeutic gains for certain disease sites through dose escalation with better tumor delineation and motion management. Randomized clinical trials have been promoted to investigate the effects of dose escalation on normal tissue toxicity, quality of life, as well as overall survival and local control for prostate cancer, locally advanced pancreatic cancer, etc. As MRI is playing an increasingly essential role in RT, opportunities arise to incorporate functional imaging into RT workflow. Considering the response of the ADC maps to radiation dose and relatively robust protocol for DWI acquisition, DWI-derived biomarkers have strong potentials for tumor delineation and response assessment, as evidenced in a series of articles published in this collection.NW, YC, and JC are the coeditors for this Research Topic.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.


To study the trends of smoking-attributable mortality among the low and high educated in consecutive birth cohorts in 11 European countries.Register-based mortality data were collected among adults aged 30 to 79 years in 11 European countries between 1971 and 2012. Smoking-attributable deaths were estimated indirectly from lung cancer mortality rates using the Preston-Glei-Wilmoth method. Rate ratios and rate differences among the low and high-educated were estimated and used to estimate the contribution of inequality in smoking-attributable mortality to inequality in total mortality.In most countries, smoking-attributable mortality decreased in consecutive birth cohorts born between 1906 and 1961 among low- and high-educated men and high-educated women, but not among low-educated women among whom it increased. Relative educational inequalities in smoking-attributable mortality increased among both men and women with no signs of turning points. Absolute inequalities were stable among men but slightly increased among women. The contribution of inequality in smoking-attributable mortality to inequality in total mortality decreased in consecutive generations among men but increased among women.Smoking might become less important as a driver of inequalities in total mortality among men in the future. However, among women, smoking threatens to further widen inequalities in total mortality.The online version contains supplementary material available at 10.1186/s12963-021-00247-2. While people’s health in Europe has improved in the last few decades, the improvements are not experienced equally by all. Reducing educational inequalities in health has been one of the most important targets in public health . Among aHowever, it has been less studied how changes in smoking behavior have influenced trends in educational inequalities in mortality. The studies that measured the effect of smoking on inequalities in mortality have suggested increases in relative inequalities in smoking-related mortality in many European countries in recent decades –13. AlthIn this article, we studied trends in smoking-attributable mortality among the low and high-educated in consecutive birth cohorts in 11 European countries. We assessed whether educational inequalities in smoking-attributable mortality and their contribution to educational inequalities in total mortality have changed between older and younger generations in these countries. We used the Preston-Glei-Wilmoth (PGW) method to estimate smoking-attributable mortality.www.mortality.org). Most data comprise national populations, except for Italy, where we obtained data from Turin only; for Spain, from Barcelona only; and for the UK, from England and Wales only. Details on data sources can be found in Supplementary file Register-based mortality data were collected and harmonized for all-cause mortality and lung cancer mortality by sex, age , and educational level over the period 1971-2012 in 11 European countries : Up to lower secondary education , completed upper secondary education  and tertiary education . In the UK , only two groups could be distinguished, namely the “low”  and “high” (ISCED 5 and 6).birth cohort = period − age) in the mortality dataset by country, sex, and level of education. Because data were collected in 5-year age groups and 1- to 5-year periods, the birth cohorts reconstructed were in 6- to 10-year ranges with overlaps. For example, those of age 30 to 34 who died during 1980-1984 belong to birth cohort 1946-1954, and those of age 35 to 39 who died during the same period belong to birth cohort 1941-1949. In each country-sex-education level-age-period group, we excluded cells in which the number of deaths due to lung cancer was less than 10 in order to avoid erratic results due to small numbers. We further excluded birth cohorts with data for less than three age groups. As a result, our analysis covers 18 birth cohort groups, ranging from 1902-1910 to 1952-1961. We present birth cohorts by their median birth year. Detailed descriptions of the birth cohort construction are provided in Supplementary file Birth cohorts were reconstructed using age and period  to summarize overall mortality from smoking in each birth cohort. The final step calculated rate ratios (RR), rate differences (RD), and contributions of inequalities in smoking-attributable mortality to educational inequalities in total mortality. In the following, we describe each of these steps in more detail.The PGW method, introduced by Preston et al. , is baseM) in our study to lung cancer death rates among non-smokers (λ) in the Second Prospective Cancer Prevention Study (CPS-II) to get the proportion of lung cancer deaths attributable to smoking (β′) were published in the original articles and can be directly used without re-performing the analysis in further studies to obtain the proportion of other deaths. Finally, we combined the two fractions calculated above, weighted by the numbers of deaths from lung cancer (DL) and other causes (DO), to generate the overall smoking-attributable fraction for deaths from all causes  using parametric bootstrapping, assuming Poisson-distributed death counts and setting the number of repetitions to 1000. All analyses were performed using Stata V.15.1 SE.Table For men, smoking-attributable mortality was higher among the low educated than among the high educated in every country. Smoking-attributable mortality gradually decreased in consecutive birth cohorts among both low and high-educated men in most countries, except for low-educated men in Estonia and Hungary, where smoking-attributable mortality increased, and in Lithuania, where a decrease followed after an increase. Among women, smoking-attributable mortality was not always higher in the low educated than the high educated. Among high-educated women, smoking-attributable mortality decreased in consecutive generations in most countries. Among low-educated women, however, smoking-attributable mortality gradually increased in consecutive cohorts in most countries, except for Denmark where it declined from older generations at a very high level.Relative inequalities were present among men in all generations in all countries Fig. . RelativAbsolute inequalities Fig.  were staAbsolute inequalities in total mortality  urban regions compared with the national average , 32. MorThe strengths of our study include using a unique set of data on mortality by education, covering a wide range of time periods and countries. Most mortality data were based on individual mortality follow up, avoiding numerator-denominator bias . The appThe trends in smoking-attributable mortality in consecutive generations by educational level for men and women were, to some extent, consistent with the updated cigarette epidemic model, which shows that smoking-attributable mortality has decreased in men and increased or plateaued in women in the last decades of the twentieth century . Low-eduSeveral studies have examined the trends of smoking-related mortality in European countries by birth cohort and sex using age-period-cohort models , 38–40. Gregoraci et al.  comparedSeveral studies examined cohort patterns in smoking initiation and cessation and found rising disparities between groups of low and high educated , 41, 42,Our study found, in most counties, decreasing smoking-attributable mortality trends in consecutive birth cohorts among both low- and high-educated men and among high-educated women, but increasing smoking-attributable mortality among low-educated women. For men, based on these birth cohort-specific patterns, we expect that in most non-Eastern European countries smoking will become less important as a driver of inequalities in total mortality in the future, despite a continuing widening of relative inequalities in smoking-attributable mortality. Among men in Hungary, Estonia, and Lithuania, smoking may well remain an important driver of inequalities in total mortality in the near future. Among women, however, smoking threatens to further widen inequalities in total mortality. In order to avoid this, it is necessary to step up efforts to promote smoking prevention and cessation while not increasing health inequalities. Before implementation, interventions should be equity-checked and evaluated . PopulatAdditional file 1: Supplementary file 1 Overview of mortality data source. Supplementary file 2_a. Detailed birth cohorts constructed by country. Supplementary file 2_b. Age composition of each birth cohort. Supplementary file 3. Calculations of smoking-attributable mortality from the Preston-Glei-Wilmoth method. Supplementary file 4 Calculations of CCMFs. Supplementary file 5. CCMF results from less strict exclusion criteria. Supplementary file 6. Birth cohort-specific trends of total mortality. Additional file 8: Supplementary file 7. Table for rate ratio, rate difference and the fraction with 95% CIAdditional file 2.


N = 134, ≈ 1 hr of rs-fMRI/subject). We employed robust data-driven techniques to identify cerebral and cerebellar resting-state networks and extract descriptive summary measures of static and dynamic cerebro-cerebellar FC. We observed evidence linking BIS, BAS, sensation seeking, and lack of premeditation to the total strength and temporal variability of FC within networks connecting regions of the prefrontal cortex, precuneus, posterior cingulate cortex, basal ganglia, and thalamus with the cerebellum. Overall, our findings improve the existing knowledge of the neural correlates of impulsivity and the behavioral correlates of the cerebro-cerebellar system.Human and animal brain studies bring converging evidence of a possible role for the cerebellum and the cerebro-cerebellar system in impulsivity. However, the precise nature of the relation between cerebro-cerebellar coupling and impulsivity is far from understood. Characterizing functional connectivity (FC) patterns between large-scale brain networks that mediate different forms of impulsivity, and the cerebellum may improve our understanding of this relation. Here, we analyzed static and dynamic features of cerebro-cerebellar FC using a highly sampled resting-state functional magnetic resonance imaging (rs-fMRI) dataset and tested their association with two widely used self-reports of impulsivity: the UPPS-P impulsive behavior scale and the behavioral inhibition/approach systems (BIS/BAS) in a large group of healthy subjects ( Accumulating evidence from preclinical and neuroimaging studies proposes that the cerebellum regulates impulsive behavior through its interactions with brain regions that subserve control and reward processes. To further explore this proposal, we analyzed static and dynamic aspects of resting-state functional connectivity between the cerebellum and distinct large-scale brain networks using robust methods and evaluated them against a set of self-reported measures of impulsivity. We found compelling new evidence linking multiple forms of impulsivity to strength and temporal variability of FC between large-scale cerebral networks, involved in top-down control and reward, and the cerebellum. Our findings highlight the utility of combining static and dynamic FC approaches in furthering current understanding of cerebro-cerebellar coupling and the neurobiology of complex behaviors. Impulsivity is a multidimensional personality construct present to varying degrees in healthy individuals as well as diverse neuropsychiatric populations  has received attention for the comprehensive evaluation of interregional FC in the absence of tasks Sporns, . It has N = 134, 62 females). We decomposed the rs-fMRI data into separate cerebral and cerebellar resting-state networks (RSNs), using data-driven techniques, to account for the functional heterogeneity present in both structures. Then, we estimated static FC among the identified RSNs and used In this study, we aimed to characterize cross-sectional differences in static and dynamic cerebro-cerebellar FC, and test for potential associations with self-reported impulsivity. The uniform cytoarchitecture of the cerebellum suggests that cerebellar modules differ in the projection of their afferent and efferent connections to sensorimotor and higher cognitive networks while serving a unitary A total of 134 healthy participants  from the Neuroanatomy and Connectivity protocol (N&C), which is part of the Max Planck Institute Leipzig Mind-Brain-Body (MPILMBB) dataset, were included in this study. All included participants were healthy with no past or present signs of any neuropsychiatric condition, fulfilled the MRI safety requirements, and provided written informed consent prior to their participation  that regulates sensitivity to punishment and negative cues, and a behavioral approach system (BAS) that regulates sensitivity to desirable cues and nonpunishment . A higher score on the BIS scale indicates an increased sensitivity to negative outcomes of anticipated actions and hence a higher tendency to avoid them, whereas a higher score on the BAS scale indicates an increased sensitivity to rewards and desirable outcomes and hence a higher tendency to engage in goal-directed behaviors Gray, . The BAS2, imaging matrix = 88 × 88, 64 slices with 2.3-mm thickness, TR = 1,400 ms, TE = 39.4 ms, flip angle = 69, echo spacing = 0.67 ms, bandwidth = 1,776 Hz/Px, partial Fourier 7/8, no pre scan normalization, multiband acceleration factor = 4,657 volumes, duration = 15 min 30 s per run. Individuals were instructed to remain awake, during the resting-state scan, with their eyes open and to fixate on a crosshair. The use of four 15-min rs-fMRI scans per subject enhances the temporal signal-to-noise ratio (tSNR) in the data, improves the estimation of resting-state networks, and permits a more reliable modeling of functional connectivity dynamics in the brain.The resting-state fMRI acquisition parameters are described in full detail in Mendes et al. . In summaCompCor method to remove physiological noise from residual data from the previous denoising step  that exhibit unique time course profiles  as implemented in the GIFT toolbox software To isolate the cerebellum from the brain, we generated a cerebellar mask in MNI152 space using the standard cerebellar MNI152 anatomical template from FSL. Concatenated cerebellar rs-fMRI data across all subjects and scans were demeaned and analyzed using principal components analysis (PCA) to reduce the dimensionality to 100 subject-level PCs (retaining > 99% of the variance in the data) and 25 group-level PCs. Then, we applied the Infomax algorithm 20 times using the ICASSO toolbox to automatically estimate and select the most reliable set of 25 independent components in the cerebellum. We then used the group information guided ICA, or GIG-ICA, to estimate subject-specific ICs and time series. GIG-ICA extracts subject-specific ICs and time series with better accuracy and correspondence than dual regression  on temporally concatenated BOLD time series from all runs and subjects as implemented in the hidden Markov model multivariate autoregression (HMM-MAR) toolbox i, denoted Vi, as the unbiased frequency-weighted standard deviation across the states, in which the subject-specific state frequency of occurrence values were used as weight factors. This way more frequently visited states contributed more to temporal variability of FC strength. The temporal variability of FC strength was calculated using the following formula:Sik is the cerebro-cerebellar FC strength of cerebral RSN i in state k, and the weight factor fk is the frequency of occurrence of state k. The rationale behind computing temporal variability of cerebro-cerebellar FC using the subject-specific dynamic FC states was based on the observation that static FC matrices highly resembled the frequency-weighted mean of the dynamic FC matrices: cosine similarity > 0.98 on average for full correlation matrices and > 0.94 on average for partial correlation matrices  that included the impulsivity scales as predictors and the static and dynamic FC measures as response variables in a multiple linear regression framework. In addition, age, gender, and mean framewise displacement were included as nuisance covariates. To test for significance and correct for multiple comparisons, we applied nonparametric permutation testing with 10,000 permutations and a maximum z-statistic procedure to obtain family-wise error adjusted Furthermore, to evaluate the replicability of our inference framework, we used repeated stratified fivefold cross-validation to split the initial sample into training  and testing  subsamples where the proportions of males and females were preserved in each split. Cross-validation was repeated 100 times with a different randomization in each repetition. This ensured that all subjects took part in the training and testing phases across all folds and repetitions. GLMs were refitted to the training data and then used to predict the outcome in the testing data. We only included the variables that exhibited significant associations in the previous step  while controlling for age, gender, and mean framewise displacement. Finally, we reported the median values of the explained variance obtained in the training data, denoted VIF < 2 except for the UPPS-P negative and positive urgency subscales. Accordingly, in order to avoid potential multicollinearity effect due to the strong association between the two subscales , we used factor analysis to obtain one urgency factor while preserving a sufficient amount of variance. The final set of self-reported impulsivity measures included six variables: UPPS-P urgency, UPPS-P lack of premeditation, UPPS-P lack of perseverance, UPPS-P sensation seeking, behavioral inhibition system (BIS) scale, and behavioral approach system (BAS) scale.Summary statistics of demographic and behavioral data are provided in Resting-state fMRI data from the cerebral cortex and subcortex were decomposed into 30 ICs out of which 25 ICs were identified as RSNs based on visual inspection of the localization of spatial activation in the gray matter (GM) and illustrated in see Resting-state fMRI data from the cerebellum were decomposed into 25 ICs, out of which 14 ICs were visually identified as cerebellar RSNs and illustrated in The details of statistically significant findings are reported in z = −3, β = −0.31, p = 0.038, z = −3.1, β = −0.29, p = 0.033, p values were family-wise error adjusted.Statistically significant associations were observed when quantifying static FC using partial correlation coefficients. Particularly, results revealed a significant negative correlation between the behavioral inhibition system (BIS) scale and the cerebro-cerebellar strength of the basal ganglia network, BGN  and the behavioral inhibition system (BIS) scale, which measures sensitivity towards unpleasant cues and punishment. This indicates that increased striato-cerebellar FC strength is associated with a lower tendency to avoid or inhibit actions with possibly negative outcomes such as punishment. We believe that this is in line with previous findings suggesting that the involvement of the cerebellum in inhibitory control mechanisms is mediated by its interaction with the basal ganglia , Award ID: TRAIL-ANR-10-LABX-57.Click here for additional data file.


Rigorous molecular characterization of biological systems has uncovered a variety of gene variations underlying normal and disease states and a remarkable complexity in the forms of RNA transcripts that exist. A recent concept, competitive endogenous RNA, suggests that some non-coding RNAs can bind to miRNAs to modulate their role in gene expression. Here, we used several platforms, integrating mRNA, non-coding RNAs and protein data to generate an RNA-protein network that may be dysregulated in human glioblastoma multiforme (GBM). Publicly available microarray data for mRNA and miRNA were used to identify differentially expressed miRNAs and mRNAs in GBM relative to non-neoplastic tissue samples. Target miRNAs were further selected based on their prognostic significance, and the intersection of their target gene set with the differentially expressed gene set in Venn diagrams. Two miRNAs, miR-637 and miR-196a-5p, were associated with poor and better prognosis, respectively, in GBM patients. Non-coding RNAs, ENSG00000203739/ENSG00000271646 and TPTEP1, were predicted to be miRNA target genes for miR-637 and miR-196a-5p and positively correlated with the selected mRNA, CYBRD1 and RUFY2. A local protein interaction network was constructed using these two mRNAs. Predictions based on the ENSG00000203739/ENSG00000271646-miR-637-CYBRD1 and TPTEP1-miR-196a-5p-RUFY2 regulation axes indicated that the two proteins may act as an oncogene and tumor suppressor, respectively, in the development of GBM. These results highlight competitive endogenous RNA networks as alternative molecular therapeutic targets in the treatment of the disease. Glioblastoma multiforme (GBM) is the most aggressive central nervous system tumor in adults, and the prognosis is bleak. Conventional therapies, including surgery, radiotherapy, and chemotherapy with temozolomide have not resulted in significant improvement in the survival outcomes of patients with GBM. Median overall survival is 15–23 months, and 5-year survival is <6%  team, sponsored by the National Cancer Institute (NCI), published DNA copy number, gene expression, and DNA methylation analysis for 206 GBMs. Rigorous analysis of these data has led to some critical insights in the development of human gliomas. GBMs can now be classified as one of four molecular subtypes based on transcriptome expression data: classical, neural, mesenchymal, and proneural. Subsequent analysis of the methylation status of DNA promoter regions in 272 GBMs revealed two major glioma-CpG island methylation phenotypes . Finally, a total 1,122 gliomas samples were divided into EPIC1  has also been described , represents a novel regulatory mechanism between non-coding and coding RNAs. The theory suggests that lncRNAs, cirRNAs, and pseudogenes can act as “molecular sponges” to compete for miRNAs and effectively modulate their functions. The competition for miRNAs is mediated by miRNA binding sites or miRNA response elements . This crmiR-196a-5p and miR-637, their target mRNAs encoding a putative oncogene and tumor suppressor, and non-coding RNAs regulating the miRNA activity. We show how a fundamental biological question, which genes and miRNAs are differentially expressed in human GBM, can provide the basis for the construction of a molecular network including RNAs and proteins that might drive aspects of GBM development. Such “excavation” of molecular datasets is the key for the advancement of novel therapies in the treatment of the disease.With the development of high-throughput gene sequencing and chip technology, analyzing molecular data has become an extremely meaningful but challenging task. An increasing number of R language packages and bioinformatics analysis tools have become more user friendly for a broader range of investigators. Here, we used some of these tools to analyze miRNA and mRNA datasets to determine where they might converge in the development of human GBM. Our results led us to two miRNAs, MicroRNA expression profiles in GSE25631 from the publicly available NCBI GEO database, which had been collected using the Illumina GPL8179 platform (Human v2 microRNA Expression Beadchip), were analyzed. The GSE25631 dataset includes 82 primary GBM surgical specimens and 5 non-neoplastic brain tissue samples from areas surrounding arteriovenous malformations as controls. mRNA expression profiles were obtained from the GSE4290 dataset . CyTargetLinker , a plug-http://metascape.org), a web-based online bioinformatics resource that aims to provide tools for the functional interpretation of large lists of genes or proteins (Kyoto Encyclopedia of Genes and Genomes  (KEGG) pproteins , was alsproteins  on DEGs proteins . For DEGhttps://string-db.org/) to obtain a protein-protein interaction (PPI) network , a newly developed interactive web server, was used to analyze differences in expression between tumor and normal samples using RNA sequencing data . The intersection between candidate genes and predicted target non-coding RNAs was made.Analysis using LncBase v.2 was performed to predict the target non-coding RNAs of differentially expressed microRNAs in GBM . To acquhttps://string-db.org/)  . PPI netP < 0.05 and |logFC| ≥ 2) between GBM and non-neoplastic brain. Of these miRNAs, 27 were up-regulated and 40 were down-regulated in GBM relative to control samples  was associated with worse OS in patients  was associated with better OS in patients  identified using Cytoscape (https://reactome.org/) (To understand the possible function of ytoscape . The resytoscape . We furtme.org/)  22)..miR-196-To identify differentially expressed genes in GBM relative to non-neoplastic brain, we performed analysis on the GSE4290 dataset containing mRNA expression profiles . A totalCDK1, CCNB2, and CDC20  gene family, which prevents apoptotic cell death. Finally, HMMR encodes a protein involved in cell motility.Using the STRING protein databases, we generated a PPI network for the top 20 hub genes with the highest degrees of connectivity . The topnd CDC20  are invomiR-196-5p/miR-637, we generated a venn diagram illustrating the intersection between the target genes of miR-196-5p/miR-637 and the DEGs , for example, have completed a comprehensive genomic analysis known as the PanCancer Atlas. This project published a total of 27 top bioinformatics papers. These articles are undoubtedly innovative and encouraging. But such efforts are merely the beginning. Here, we laid the groundwork for a new strategy to explore the complicated molecular mechanisms underlying the development of GBM or other diseases. The next important step is to verify our approach using functional experiments to confirm our model in GSE25631, GSE4290, GSE90604, GSE65626.The datasets generated for this study can be found in the JZ, YK, and JW contributed to the conception of the study. JZ and YK contributed to experimental technology and experimental design. JZ, ZS, YK, QL, and HL performed the data analyses. JZ, YK, and JW wrote the manuscript. YW and JW supervised the study.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.


Self-stigma is highly prevalent in serious mental illness (SMI) and is associated with poorer clinical and functional outcomes. Narrative enhancement and cognitive therapy (NECT) is a group-based intervention combining psychoeducation, cognitive restructuring and story-telling exercises to reduce self-stigma and its impact on recovery-related outcomes. Despite evidence of its effectiveness on self-stigma in schizophrenia-related disorders, it is unclear whether NECT can impact social functioning.This is a 12-centre stepped-wedge cluster randomized controlled trial of NECT effectiveness on social functioning in SMI, compared to treatment as usual. One hundred and twenty participants diagnosed with schizophrenia, bipolar disorder or borderline personality disorder will be recruited across the 12 sites. The 12 centres participating to the study will be randomized into two groups: one group (group 1) receiving the intervention at the beginning of the study (T0) and one group (group 2) being a control group for the first 6 months and receiving the intervention after (T1). Outcomes will be compared in both groups at T0 and T1, and 6-month and 12-month outcomes for groups 1 and 2 will be measured without a control group at T2 . Evaluations will be conducted by assessors blind to treatment allocation. The primary outcome is personal and social performance compared across randomization groups. Secondary outcomes include self-stigma, self-esteem, wellbeing, quality of life, illness severity, depressive symptoms and personal recovery.NECT is a promising intervention for reducing self-stigma and improving recovery-related outcomes in SMI. If shown to be effective in this trial, it is likely that NECT will be implemented in psychiatric rehabilitation services with subsequent implications for routine clinical practice.NCT03972735. Trial registration date 31 May 2019.ClinicalTrials.gov  Mental illness stigma refers to the negative beliefs, emotional reactions and attitudes towards people with serious mental illness (SMI) endorsed by the general population . PeopleSeveral psychosocial interventions have been designed to reduce self-stigma and its impact on patient’s outcomes, with preliminary results on self-stigma, insight and self-efficacy . NarratiIn summary, NECT has demonstrated effectiveness on self-stigma and subjective aspects of recovery  in participants with psychotic-related disorders and SZ. Its effectiveness on social function and in other stigmatized SMI conditions (bipolar disorder (BD), borderline personality disorder (BPD)) are, however, still unclear. Similarly, NECT’s effectiveness in improving depression, treatment adherence, wellbeing and personal recovery is still unknown.The objectives of the present study are to investigate (i) NECT effectiveness on social functioning in SMI participants, (ii) NECT effectiveness on self-stigma, psychiatric symptoms, depression, well-being, subjective quality of life and personal recovery, (iii) whether insight into illness and cognitive functioning at baseline predict improvements at follow-up and (iv) whether the effects on primary and secondary outcomes persist after 6 and 12 months’ follow-up.The primary hypothesis to be tested is whether NECT significantly improves social functioning in comparison with the control group. The secondary hypotheses are to determine if, when compared to the control condition, NECT results in significant improvements in self-stigma, psychiatric symptoms, depression, wellbeing, self-esteem, subjective quality of life and personal recovery.This stepped wedge cluster randomized controlled study is multi-centric, prospective, interventional and exploratory. For ethical purposes, we have chosen a stepped wedge cluster randomized trial design, with a waiting list type control condition. Previous research from the French national REHABase network showed that (i) most patients were dissatisfied with their interpersonal relationships at the time of admission and asked for interventions improving social function  and (ii)The total duration of the study will be 18 months to allow for recruitment of the target number of participants, delivery of the intervention, follow-up assessments and data analysis using intention-to-treat methods. A schematic overview of the study design is presented in Fig. Eligible participants will be adults (age > 18) diagnosed with schizophrenia, schizoaffective disorder, schizotypal disorder, schizophreniform disorder, bipolar I or II disorder or borderline personality disorder (DSM-5 criteria ). For thd = 0.50, alpha level of 0.05) without allowing for intra-cluster correlations and the cluster and time effects found in stepped-wedge designs ),t and covariance analysis with repeat measures ANCOVA) and with non-parametric tests  in case of significant deviations from the normal distribution. The primary analysis was done by intention to treat.Intention-to-treat analyses will be conducted in the first instance. The analyses will be conducted using parametric tests for normal distributed data (Student’s d) will be calculated using the effsize package [We will analyse all outcomes using multilevel regression models with time point and intervention status as fixed effects and clusters as a random effect. Timepoint will be included as a categorical variable . The sta package . Size ef package . The lea package . The levThe present trial has not yet started. The recruitment will begin on September 1, 2019, with an estimated study completion date of April, 3, 2023. Protocol version 1.5 was written on April 2, 2019, and approved by the relevant ethical committee (CPP Nord-Ouest I) on April 26, 2019.NECT has shown effectiveness in reducing self-stigma in patients with psychotic-related disorders in a range of contexts in previous research. Considering the prevalence of moderate to high self-stigma in SMI and its impact on functional outcomes, NECT appears to be a promising intervention to complete the basket of services proposed by psychiatric rehabilitation centres . If sho


Compliance with social distancing and staying-at-home, stress , anxiety , and depressive symptoms  were collected. The associations between mental health symptoms and compliance were examined by multivariable regression models. Of the 1501 respondents , 74.2%, 72.7%, and 59.7% reported avoiding going out, going to crowded places, and attending social gatherings of more than four people, respectively. Most respondents had stayed-at-home for at least four of the past seven days . Adoption, perceived effectiveness, and perceived compliance with social distancing were associated with lower stress levels and less anxiety and depressive symptoms . However, more days stayed-at-home were associated with more depressive symptoms . The long-term psychological impact in relation to social distancing and staying-at-home requires further investigation.The success of public health measures for controlling the coronavirus disease 2019 (COVID-19) pandemic relies on population compliance. We analyzed compliance with social distancing and its associations with mental health. The Hong Kong COVID-19 Health Information Survey was conducted from 9–23 April 2020 on 1501 adults randomly sampled for landline telephone interviews ( Driven by the increasingly frequent worldwide transportation of those with asymptomatic patients, the infection is spreading more rapidly and widely than ever ,4. Solidock down ,7. EngagThe prevalence of anxiety and depression have increased during the COVID-19 pandemic . PsycholHong Kong had its first confirmed case on 23 January 2020 . Public The Hong Kong COVID-19 Health Information Survey (CoVHInS) was a population-based dual sampling landline and online survey conducted from 9–23 April 2020. The target population was general Hong Kong residents aged 18 or above. Social Policy Research Limited, a reputable local survey agency, was commissioned to conduct the survey.We adopted two-stage random sampling in the landline survey using the Web-based Computer-Assisted Telephone Interview system (Web-CATI). Residential telephone directories that covered approximately 76% of Hong Kong residents ,22 were The online survey was conducted on a representative panel of Hong Kong residents. This panel was previously formed by inviting local mobile phone users to join. All mobile phone numbers , which covered over 90% of Hong Kong residents, received an invitation message. These numbers were generated using the Numbering Plan for Telecommunication Services in Hong Kong provided by the Office of the Communications Authority (OFCA). A total of 100,079 residents covering diverse socio-economic backgrounds joined the panel. Stratified random sampling by sex and age was adopted to select a random list of panel participants, who were then invited to join the online survey by an invitation text message. Participants self-administered the questionnaire via Web-CATI. The response rate was 61.7% (1001 of 1623 eligible panel participants).Ethics approval was granted by the Institutional Review Board (IRB) of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 20-238). Informed consent was obtained from all respondents before answering the questions.The adoption of social distancing measures (yes/no) since the first case confirmed in Hong Kong included: (i) avoid going out, (ii) avoid going to crowded places, (iii) avoid going to high-risk places , (iv) avoid social gatherings of more than four people , (v) avoid greetings with physical contact such as handshaking, hugging and kissing, and (vi) keep 1.5 meters from others in public. The total number of social distancing measures was calculated (ranged 0–6). Perceived overall effectiveness of and perceived compliance with the above social distancing measures were measured on a scale ranging from 0–10 with higher scores indicating higher perceived effectiveness and compliance. Stay-at-home, which was voluntary, was measured by the number of days at home except for essential tasks in the past seven days. Personal protection measures including: (i) wearing surgical mask, (ii) washing hands with alcohol-based sanitizers, (iii) using alcohol to clean daily necessities, and (iv) adding water to household drainage system in the past seven days were recorded.Stress level in the preceding month (from early-March to mid-April 2020) was assessed by the four-item Perceived Stress Scale (PSS-4) . PSS-4 cAnxiety and depressive symptoms in the past two weeks (from late-March to mid-April 2020) were assessed using the four-item Patient Health Questionnaire (PHQ-4). PHQ-4 consists of the two-item General Anxiety Disorder (GAD-2) and the two-item Patient Health Questionnaire (PHQ-2) ,26. GAD-Sex, age, marital status , current living arrangement , and socioeconomic status (SES) including educational attainment , employment status  and monthly personal income  of the respondents were recorded.t-test to compare sociodemographic characteristics and mental health symptoms between the landline telephone and online self-administrated samples. To improve the representativeness of the sample, all data were weighted according to provisional figures obtained from the Census and Statistics Department on the sex, age, and education attainment distributions of Hong Kong’s general adult population in 2016. Multivariable linear regression was used to examine the associations with sociodemographic characteristics of number of social distancing measures adopted, number of days stayed-at-home and perceived compliance with social distancing measures. The association of mental health symptoms including stress, anxiety and depression with the number of measures adopted, number of days stayed-at-home and perceived effectiveness and compliance were calculated by multivariable linear (for stress) and logistic (for anxiety and depression) regressions. In regression model 1, potential sociodemographic confounders including sex, age, educational attainment, current employment status, and monthly income were adjusted. We additionally adjusted for the four personal protection measures in regression model 2. The association between personal protection measures and mental health symptoms were analyzed by multivariable regression models adjusted for sociodemographic factors and social distancing. Effect modifications by age  and education attainment  on the associations between mental health symptoms, stay-at-home and compliance with social distancing were assessed using the interaction terms. Analyses were performed using STATA version/MP 15.1 .We used Chi-squared test and n = 500) and the online self-administrated sample (n = 1001) . Being female was associated with more days stayed-at-home  and perceived compliance with social distancing . Compared with respondents in full-time work, those who were economically inactive spent more days at home and reported higher compliance with social distancing . Having higher income was associated with increased perceived compliance with social distancing (p for trend <0.001).p < 0.001). Consistently, personal protection measures, including mask wearing, use of alcohol to clean daily necessities and adding water to the household drainage system were associated with lower stress levels and lower risks for anxiety and depressive symptoms  (p for interaction = 0.005) and depressive (p for interaction = 0.008) symptoms especially among respondents with older age (60+ years). More days stayed-at-home was associated with a higher stress level especially among respondents with primary or lower education attainment (p for interaction = 0.014). < 0.01) . NeverthWe have provided the first evidence on compliance with non-pharmaceutical community containment strategies including stay-at-home and social distancing and their associations with mental health symptoms during the COVID-19 pandemic. Random samples from landline and mobile phone increased the representativeness of the study. The effectiveness of public health interventions depends on the degree of population engagement and compliance in practice. Avoiding going out and avoiding social gatherings were mostly practiced, which may have resulted in the achievement of suppressing the first wave of community outbreak in February and March (first eight weeks) and in mitigating the second wave, due to imported cases, in April and May . Most reBeing female, older age, and having higher SES were associated with higher levels of perceived compliance with social distancing measures. Without the need for work-related essential tasks, the practice of stay-at-home and social distancing is easier for economically inactive people . The early reported higher mortality rate among older adults (aged 60+)  might haThe perceived effectiveness of and compliance with social distancing were associated with lower risks of mental distress. Consistently, personal protection measures including mask wearing and household disinfection were associated with less stress and depressive symptoms, adjusted for social distancing. The association between social distancing and mental health remained robust after additional adjustment for personal protective measures. Perceived effectiveness and compliance with public health measures in preventing the infection for oneself and family members, and contributing to the safety and health of the community and country may strengthen feelings of security and alleviate mental distress related to the pandemic. A repeated cross-sectional study conducted in January, February and March 2020 has reported that perceived susceptibility to worries about being infected was decreasing, while confidence in protecting oneself was increasing in Hong Kong . PersonaIn contrast, staying at home for a longer time was associated with increased risks of depressive symptoms. Reverse causation is possible in which respondents with depressive symptoms might have tended to stayed-at-home. However, a crowded living environment, restricted face-to-face social interactions and restricted outside activities have led to a physically inactive and sedentary lifestyle for months, which may contribute to boredom, low mood, or mental distress. The elderly and less educated population might be more vulnerable regarding the processing of health information and sudden changes in social rules, especially in the digital world where public communication is mostly performed online ,34. We fSome limitations of the study should be noted. Causal relations could not be inferred in this cross-sectional study, where respondents with psychological distress were less likely to comply with public health measures. Pre-existing psychological problems were not collected, and residual confounders were not able to be excluded. Recall bias may exist in self-reporting measurements. The reasons for non-compliance with social distancing need further investigation for further public directives to be put into practice. The PSS-4 has a satisfactory construct validity  but a soCompliance with social distancing and stay-at-home is higher among female, older and educated respondents. Compliance with and perceived effectiveness of social distancing were associated with lower levels of stress, anxiety and depressive symptoms. More days stayed-at-home, however, may increase the risk of depressive symptoms, especially in older adults. Public health interventions are needed to protect the “new normal” in a future with or without COVID-19. The long-term psychological impact in relation to social distancing and stay-at-home requires further investigation. Social support and targeted interventions for the psychological well-being of the most underprivileged community members are urgently needed.


Providers in Arkansas wrote 105.4 opioid prescriptions for every 100 persons in 2017-nearly twofold greater than the average U.S. rate of 58.7 opioid prescriptions . This makes AR the second highest opioid prescribing state. AR-IMPACT (Arkansas Improving Multi-Disciplinary Pain Care and Treatment) is a tele-video case conference education model which was designed to improve AR providers’ knowledge and behavior in pain management and improve awareness of opioid-sparing alternatives. Conference panelists include a geriatrician, psychiatrist, physical therapist, psychologist, pharmacist, a chronic pain specialist and various guest speakers such as the Arkansas Drug Director. During weekly tele-video conferences, relevant actionable cases were presented regarding treating diverse patients who have chronic pain. AR-IMPACT provided hundreds of practitioners with up-to-date data and education regarding opioid use and misuse, methods and resources regarding titrating and eliminating opioids, and viable alternative pain-management solutions such as motivational interviewing and dry-needling. This poster will present the background of the opioid crisis in rural Arkansas followed by the specifics of the development and application of the AR-IMPACT tele-video model. Specific older adult cases studies, quantitative and qualitative outcomes, feedback from presenters and participants, and lessons learned will be reviewed. Specifically, lessons learned center around the practitioners’ culture of pain management and how that culture must evolve and change. New methods of pain management must be taught to practitioners and patients and adopted as the new norm. This will demonstrate that programs like AR-IMPACT are crucial in helping make that cultural change happen.


Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds. The term compound promiscuity is often used with opposing connotations including the presence of non-specific, artificial, or otherwise undesired binding effects on the one hand, and the ability to engage in specific interactions with multiple targets on the other . The latGiven the dual role of multi-target activity for polypharmacology-driven drug discovery, on the one hand, and chemical biology, on the other, one would like to better understand molecular characteristics that distinguish non-promiscuous and promiscuous compounds. However, rationalizing multi-target activity at the molecular level of detail is still in its early stages. While molecular property analysis has indicated that high hydrophobicity or the presence of positively-charged groups are often implicated in undesired target engagement and ensuing side effects ,12 our cPromiscuity analysis substantially benefits from computational support. For example, ligand-based computational approaches have been used to predict new targets of known active compounds ,20. ThesDifferent types of computational predictions have focused on systematically distinguishing between promiscuous and non-promiscuous compounds using machine learning (ML) ,23. ThesFor compounds from the medicinal chemistry and patent literature, experimental test frequencies are generally unknown. For promiscuity analysis, this causes a potential bias in cases where assay frequencies of related compounds significantly differ. For example, if a given compound was tested only against a few targets but a structural analog against many different ones, the latter compound would more likely to display multi-target activity than the former for statistical reasons. Hence, the availability of test frequency information lends further credence to compound promiscuity assessment and predictions. This information can be obtained for compounds from biological screening campaigns, which has been a major motivation for our current investigation. Herein, we present newly designed high-confidence compound sets for promiscuity analysis extracted from biological screening data and report diagnostic ML studies under varying conditions to distinguish between multi-target and corresponding single-target compounds.Compounds with single- or multi-target activity were extracted from PubChem BioAssay  using inImportantly, for the assessment of compounds with multi-target activity, potentially false positive activity annotations should be avoided as much as possible. To exclude questionable activity annotations, screening compounds were disregarded that yielded one or more alerts in computational filters for pan assay interference compounds (PAINS)  from RDKThe initially obtained set of PubChem assays contained both biochemical and cell-based assay formats and was therefore termed the “mixed” set. From this set, a subset exclusively comprising biochemical single-protein assays was extracted and . Assay set statistics are provided in For both mixed and biochemical assays, compound promiscuity degrees (PDs) were calculated, which were defined as the number of targets a compound was active against. Two different PD thresholds were applied for defining multi-target compounds (MT-CPDs) including PD ≥ 5  and PD ≥ 3 . For each MT-CPD, a group of corresponding single-target compounds (ST-CPDs) was collected from the initial pool of all compounds . For each target of an MT-CPD, a ST-CPD active against this target, but reported to be inactive against the other targets of the MT-CPD, was randomly selected, if available. Following this procedure, the maximal number of ST-CPDs corresponding to each MT-CPD was determined. For only very few MT-CPDs no corresponding ST-CPDs were identified (and these MT-CPDs were thus omitted). For most MT-CPDs, experimentally confirmed ST-CPDs were not sampled for all targets. Importantly, for each assembled MT-/ST-CPD group, all compounds were tested against all targets, thus ensuring data completeness. For compound comparison, Tanimoto similarity  was calck values were selected from k ∈ .As a molecular representation, the Morgan fingerprint with radius of 2 from RDKit was calculated for each compound. As a control, the standard MACCS structural key (fragment) fingerprint  was usedRF is a supervised ML algorithm generating an ensemble of independently derived decision trees using bootstrapping for random training data selection. The majority vote of all decision trees determines the predicted class label for test compounds. The scikit-learn RF implementation was used with default hyper-parameter settings (version 0.23.2), except for the number of decision trees  and the minimum number of samples required to split an internal node , which were optimized during training.SVM is another supervised ML algorithm that constructs a hyperplane H in chemical feature space to best separate training instances with different class labels maximizing the margin between them. If linear separation is not possible in a given feature space, projection into higher-dimensional feature space is carried out where linear separation might be feasible. The projection is generated without explicit mathematical mapping through using kernel functions. The relationship between training errors and margin size is controlled by the regularization hyper-parameter C, which was optimized using values of 0.1, 1, 10, 50, 100, 200, 400, 500, 750, and 1000. SVM classifiers were generated with scikit-learn using the linear or Tanimoto kernel . During Test calculations using the alternative fingerprint representations produced similar results and consistently the same trends. Therefore, results obtained with the Morgan fingerprint are presented below.A standard double cross validation procedure was applied for each prediction task. In each of the 10 independent external cross validation trials, ST-CPDs of an individual MT-/ST-CPD group were first randomly sampled to balance the dataset and the resulting MT-/ST-CPD pairs were then randomly divided into 80% training and 20% test (sub)sets. For hyper-parameter optimization, MT-/ST-CPD pairs from training sets were randomly divided into 80% and 20% subsets for 10 independent internal cross validation trials. The best performing model was then used to predict test set compounds for the respective external cross validation trial. In addition, the same cross validation procedure was carried out with 50% training and 50% test set data splits, which produced very similar results, with deviations mostly within one standard deviation and consistently low variability of individual trials. Thus, the results were stable for these different test set sizes. Five performance measures were applied including balanced accuracy ) , MatthewTP, TN, FP, and FN stand for true positives, true negatives, false positives, and false negatives, respectively.Precision reports the proportion of TP among all positive predictions, while recall reports the proportion of recovered TP relative to all positive instances in the dataset:y-axis) to the FP rate  at different classification thresholds. The diagonal corresponds to random class label prediction and an area under the ROC curve (AUC) value of 0.5. Increasing AUC values > 0.50 indicate increasing prediction accuracy, AUC = 1.0 resulting from a perfect prediction.In addition, predictions were assessed using receiver operating characteristic (ROC) curves , which cClose structural relationships between screening compounds were detected by applying a molecular fragmentation-based algorithm for the systematic extraction of analog series from compound datasets . Using tThe data curation procedures described above led to the elimination of ~10% of the pre-selected compounds that might potentially cause assay artifacts . The bioPrior to balancing of these datasets for ML calculations, we examined the test frequencies of the sampled compounds. The distributions are shown in ML calculations were carried out under three different conditions. First, compounds from original (“full”) datasets were used. Second, 50% of MT-/ST-CPD groups were randomly removed . These calculations were carried out to examine the influence of dataset size reduction on prediction accuracy and served as a control for the third condition. In the third case, 25% of MT-/ST-CPD groups with ST-CPDs most similar to other ST-CPDs and 25% of groups with MT-CPDs most similar to other MT-CPDs were discarded , which also resulted in a 50% net reduction in dataset size. Hence, NN relationships of both MT- and ST-CPDs were detected using calculated Tanimoto similarity values and systematically reduced.The analysis of NN relationships among MT- and corresponding ST-CPDs was complemented by the systematic detection of structural analogs (sharing the same core structure) in the different datasets. For compounds from biochemical assays applying the PD ≥ 5 threshold for MT-CPDs, 59% of the MT- and 40% of the ST-CPDs were found to have structural analogs. When the PD ≥ 3 threshold was applied the proportions slightly increased to 64% of the MT- and 52% of the ST-CPDs. For compounds from mixed assays applying the PD ≥ 5 threshold, structural analogs were identified for 62% of the MT- and 56% of the ST-CPDs. Thus, analog relationships were similarly distributed in the datasets. However, structural analogs were detected for ~10–20% more MT- than ST-CPDs, consistent with the stronger tendency of MT-CPDs to form NN relationships with other MT-CPDs than observed for ST-CPDs, as discussed above. Notably, for approximate half of the compounds from all sets, no structural analogs were detected, indicating that the compounds were overall structurally diverse, as typically expected for collections of biological screening compounds.Diagnostic ML models were built on the basis of chemical structure to systematically distinguish between MT- and ST-CPDs under different conditions, as detailed above. The principal idea behind diagnostic ML was to determine prediction accuracy as an indicator of the presence of structural features that systematically differentiate between promiscuous and non-promiscuous compounds. If such features or patterns do not exist, ML models based upon chemical structure could not possibly be predictive. Thus, diagnostic ML did not aim to optimize model performance but associate observed prediction accuracy with varying calculation conditions to determine the presence or absence of features that might differentiate between promiscuous and non-promiscuous compounds. Given our system setup, removal of NNs from datasets systematically reduced close structural relationships and therefore served as an important control.We also determined the target coverage of MT-CPDs extracted from biochemical and mixed assays. Therefore, target annotations of MT-CPDs were recorded and compound-based target networks were generated. In these networks, targets were represented as nodes, which were connected by an edge if they shared a pre-defined number of MT-CPDs. ML models were first generated for MT- and ST-CPDs from biochemical assays, which represented single-target assays probing direct compound-target interactions. Initially, datasets obtained for the PD ≥ 5 threshold were used. Results of the predictions are reported in To examine the potential influence of the PD threshold for defining MT-CPDs, the analysis was then repeated for datasets resulting from PD ≥ 3. The same trends were observed but prediction accuracy was slightly lower than for the PD ≥ 5 datasets, consistent with the assumption that increasingly promiscuous MT-CPDs should be easier to distinguish from ST-CPDs. Results obtained for the PD ≥ 3 datasets are provided as In light of the findings discussed above, we further extended diagnostic ML analysis to compounds from mixed assays applying the PD ≥ 5 threshold. These assays included different formats such as biochemical and cell-based assays, which increased assay heterogeneity and, thus, decreased the confidence level of target annotations compared to biochemical assays. On the other hand, dataset size and target coverage by mixed assays further increased compared to the biochemical assay subset. Results of the predictions on datasets from mixed assays are reported in However, ML models derived from all datasets were predictive, strongly indicating the presence of structural features distinguishing between MT- and corresponding ST-CPDs. If test frequencies of compounds are unknown, which is typically the case for compounds reported in the medicinal chemistry literature, promiscuity analysis generally includes a factor of uncertainty, due to data sparseness. Accordingly, PD values might be under-estimated and calculated differences in promiscuity remain uncertain. As long as the ultimate goal of chemogenomics efforts is not reached , data incompleteness will continue to play a critical role in the assessment of target specificity vs. multi-target activity, which is of equal relevance for chemical biology and drug discovery. By focusing promiscuity analysis on large volumes of screening data, the data incompleteness issue can be addressed by taking test frequencies and negative assay results directly into account, as demonstrated herein. Compared to exploring multi-target activities in the very large space of currently more than 10,000 targets for which medicinal chemistry has identified active compounds, focusing the analysis on screening data also comes at a price. Target space defined by screening campaigns is typically smaller and, in addition, there are uncertainties associated with activity measurements, due to screening assay noise. Hence, from this point of view, promiscuity analysis of compounds from medicinal chemistry and screening sources are complementary in nature. Further increasing the level of confidence for the assignment of single- vs. multi-target activity for promiscuity analysis via diagnostic ML has been a prime motivation for our current study. Therefore, we have devised a data curation protocol focusing the analysis on compounds from different types of assays that were extensively tested in comparably large numbers of assays. Furthermore, by equally taking positive and negative assay readouts into account, groups of MT- and corresponding ST-CPDs were assembled ensuring data completeness in each case. Moreover, care was taken to remove all screening compounds with potential assay liabilities from further consideration, hence reducing the likelihood of including false positive active compounds in the analysis as much as possible. Datasets from biochemical and mixed assays were assembled under stringent selection criteria and then further modified on the basis of NN analysis to assess the influence of NN relationships between active compounds on predictions. The influence of dataset size reduction was determined by generating equally sized reduced datasets through random compound or NN removal, lending further credence to the analysis.Given the premise of diagnostic ML, models were built on the basis of chemical structure to systematically distinguish between MT- and corresponding ST-CPDs under varying conditions using specifically modified datasets. Notably, for such diagnostic ML analysis, there is no external or experimental validation scheme that might be applicable. The ML calculations were complemented by NN analysis and revealed that diagnostic models were generally predictive and that NN relationships between MT- and ST-CPDs had a strong influence on the predictions. On the basis of our findings, the picture is emerging that MT- and ST-CPDs have distinguishing structural features that can be detected via ML. These features can at least in part be associated with NN relationships. In other words, many MT-CPDs are more similar to other MT-CPDs than corresponding ST-CPDs and vice versa. Investigating whether these structural relationships might depend on the presence or absence of general structural signatures of compound promiscuity or of structural features that are confined to given compound classes and target combinations provides interesting opportunities for future research resulting from our analysis.In this work, we have systematically assessed the ability of ML to distinguish between promiscuous and non-promiscuous compounds from biological screening assays explicitly taking test frequencies and positive, as well as negative assay readouts, into account. Diagnostic ML models based upon chemical structure were consistently able to distinguish between MT- and corresponding ST-CPDs, providing evidence for the presence of differentiating structural features, and a strong positive influence of NN relationships on prediction accuracy was detected. MT-CPDs were often more similar to each other than to corresponding ST-CPDs, and the same applied to ST-CPDs, albeit to a lesser extent. These relationships supported the predictions. The results reported herein were encouraging from two points of view. First, predictive performance and equivalent trends were consistently observed under different calculation conditions and for compounds from biochemical assays and more heterogeneous mixed assays. Second, the observed trends essentially paralleled earlier findings from complementary studies on compounds from medicinal chemistry sources. Thus, taken together, these investigations assign a high level of confidence to the conceptual framework of promiscuity analysis reported herein. Our predictions demonstrated that MT-CPDs from screening sources, which were not further optimized for known activities, could generally be predicted with reasonable to high accuracy, hence providing a basis for practical applications in chemical biology and drug discovery.