A retrospective study involving 1638 individuals in GnRH-a long-protocol and 621 in GnRH-antagonist protocol were conducted and a decision tree model analysis was used to analyze the cost-effectiveness. Both direct and indirect costs were calculated. As a result, during the fresh embryo transplantation cycles, there was no significant difference in the rate of ongoing pregnancy between the two protocols, the average cost of per ongoing pregnancy in the GnRH-antagonist protocol was $ 16970.85, and that in the GnRH-agonist long-protocol was $19902.24. The probability of cumulative ongoing pregnancy per start cycle was estimated at 60.65% for the GnRH-antagonist protocol and 71.6% for the GnRH-agonist long-protocol (P < 0.01). Considering the cumulative ongoing pregnancy rate, the mean costs per ongoing pregnancy were estimated at $8176.76 and at $7595.28 with GnRH-antagonist protocol and GnRH-agonist long protocol, respectively. In conclusion, in fresh embryo transplantation cycle, the GnRH-antagonist protocol has economic advantage. However, the GnRH-agonist long protocol is more cost effective considering the cumulative ongoing pregnancy rate in the fresh embryo and frozen embryo transplantation cycles.The gonadotropin releasing hormone agonist (GnRH-a) long-protocols and the GnRH-antagonist protocols are two commonly used protocols for  With the refinement and development of assisted reproductive technology (ART), increasing number of infertile couples seek ART. The European Society of Human Reproduction and Embryology (ESHRE) reported that the world-wide number of babies born as a result of ART has reached an estimated total of 8 million since the world’s first, Louise Brown, was born in July 19782. Up to now, ART is the most important method to treat infertility in the world.According to a World Health Organization (WHO) report, about 48.5 million couples are affected by infertility worldwide in 20103. Therefore, controlled ovarian hyperstimulation (COH) is an important process to obtain a set number of oocytes for IVF. The GnRH-agonists were introduced into IVF in the late 1980s and the GnRH-agonist long-protocol is still the most frequently used protocol in most centers worldwide4. The basic principle is to use gonadotropin-releasing hormone agonist (GnRH-a) to regulate pituitary and stimulate follicular growth with exogenous gonadotropin hormone, and avoid endogenous luteinizing hormone (LH) surge before oocyte retrieval6. Since 1990s, GnRH antagonists were used in COH, this protocol competitively blocks pituitary GnRH receptors, inducing a rapid, reversible suppression of gonadotrophin secretion and preventing and interrupting LH surges8. The GnRH-antagonist protocol have been widely adopted in IVF due to these advantages.Study has shown that cumulative live birth rates are increased with the number of oocytes obtained9. In China, the cost of IVF is on patients. High costs discourage low-income infertility couples from seeking ART treatment.IVF is a protracted and costly process. In most developed countries, IVF is covered by insurance or subsidized. This is not the case in developing countries10. Moolenaar et al. reported that most economic studies about ART were performed in countries from mainland Europe (38%) and the United States (34%)11. In China, there has been few economic researches on COH protocols. Herein, we performed a retrospective study on comparing the cost-effectiveness of the two protocols using a single center data in China. Choosing a cost-effective protocol, can not only ease the financial pressure on couples, but also provide reference for medical decision-making.While both protocols are commonly used today, little is known about their cost-effectiveness, especially in China. Studies have shown that hormonal stimulation covered the main part of the costs per cycleIndividuals who came to the Reproductive Center of Women’s Hospital, Zhejiang University School of Medicine for their first cycle of IVF treatment from 1 January 2015 to 31 December 2017 were included. Inclusion criteria were as follows: 20 < age ≤ 38 years, regular ovulatory cycles every 21–35 days, total antral follicle count (AFC) ≥ 5, first cycle of IVF treatment, COH planned using the GnRH-agonist long-protocol or the GnRH-antagonist protocol, IVF fertilization. Exclusion criteria were the use of donor oocytes or frozen-thawed oocytes for fertilization, other protocols for COH, ICSI fertilization. Patient demographics are presented in Table GnRH-agonist long-protocol: A short-acting GnRH-a  was administrated daily in the mid luteal phase of the preceding cycle. 14 days later, follicular ultrasonography, serum LH, FSH and E2 were examined and 150–300IU recombination follicle stimulating hormone  was initiated daily when FSH and LH < 5 IU/L and E2 < 50 pg/ml. GnRH-a was continued until trigger. During the stimulation, according to ovarian response evaluated by transvaginal ultrasonography and serum hormone level, dose of r-FSH was adjusted as needed, human menopausal gonadotropin (HMG) or recombinant luteinizing hormone(r-LH) or growth hormone (GH) was added as needed.GnRH-antagonist protocol: 150–300IU r-FSH was initiated on day 2 or 3 of the menstrual cycle until trigger. The dosage of r-FSH was adjusted and HMG or r-LH or GH was added according to the ovarian response evaluated by transvaginal ultrasonography and serum hormone level. 0.25 mg GnRH-A  was used daily until trigger when the leading follicles reached a mean diameter of 14 mm.For both protocols, if three follicles reached a mean diameter of 17 mm or two follicles reached a mean diameter of 18 mm, r-HCG  was administered subcutaneously. Oocyte retrieval was performed 36 h after HCG injection by transvaginal ultrasound-guided single-lumen needle aspiration. Luteal phase support was initiated on day 1 after oocyte retrieval. Fresh embryo transplantation was carried out 72 h after oocyte retrieval. Fresh cycles were canceled if patients had endometrial thickness <7 mm, high risk of Ovarian hyperstimulation syndrome (OHSS) , no available embryos or other personal reasons. For those patients without fresh embryo transplantation or without ongoing pregnancy after fresh embryo transplantation, if excess frozen embryos were available and pregnancy test was negative, the frozen embryo transplantation (FET) was performed until no embryos remained or ongoing pregnancy was achieved. Clinical pregnancy was defined as a gestational sac observed by vaginal ultrasound. Ongoing pregnancy was defined as a pregnancy continuing 12 weeks without miscarriage.We constructed a decision tree model to analyze the cost-effectiveness of the GnRH-agonist long-protocol and GnRH-antagonist protocol in the fresh embryo transplantation and frozen embryo transplantation cycle Fig. . Each roThe transition probabilities for the various health states in this decision tree model were derived from the clinical data of included infertility patients. The transition probability of each step is shown in Fig. This study was performed from a patient’s perspective. Both direct and indirect costs were included in the analysis. Direct medical costs included drug, ultrasound, laboratory, surgery and care costs. Medications included those used in ovulation stimulation trigger and luteal support. The cost of the drug was equal to the unit cost of the drug multiplied by the total amount used. When calculating the cost of IVF, it is prudent to include the treatment costs for complications, primarily OHSS. Therefore, the cost of treatment for OHSS was also calculated in the total cost of the patient. In this study, transportation costs were included as direct non-medical expenses. These were calculated as the average fare from each city in Zhejiang province to Hangzhou. Indirect costs included the cost of lost work. The cost of lost work was calculated according to the per capita disposable income. Intangible economic burden generally refers to the decline in the quality of life or other costs caused by illness in this case, IVF, and also included other costs not reflected in the direct and indirect costs. Thus, considering the difficulty in calculating these intangible costs, such costs were not included in this study. Cycle costs were relatively stable during the study period, so discounting is not considered. The specific costs are shown in Table The statistical software package SPSS22 was used for data analysis. When the measurement data matched the homogeneity of variance, the independent sample t test was used; when the data didn’t match the homogeneity of variance, the Mann-Whitney test was used. The chi-square test or Fisher’s exact probability method were used for the comparison of the rates, and P < 0.05 was considered statistically significant. Cost-effectiveness analysis and incremental cost-effectiveness analysis were used to evaluate the costs and effects between the two protocols. Sensitivity analyses were then performed to assess the stability of the model.The study was approved by the Ethics Committee of Women’s Hospital School of Medicine Zhejiang University. All patients meeting the inclusion criteria signed the informed consent. And this study complied with declaration of Helsinki/relevant guidelines for the study on humans.About 2259 infertility patients met inclusion criteria (1638 in GnRH-agonist long-protocol and 621 in GnRH-antagonist protocol). There were no differences in patient ages, BMI, duration of infertility, cycle day 3 LH levels, cycle day 3 E2 levels and AFC. The duration of gonadotrophin stimulation and dose of gonadotropin used, number of oocytes obtained and number of available embryos in GnRH-agonist long-protocol were higher than that in GnRH-antagonist protocol. The clinical pregnancy rate and ongoing pregnancy rate for GnRH-agonist long-protocol and GnRH-antagonist protocol in the fresh embryo transplantation cycle were not different . When all available embryos had been utilized, the rate of cumulative ongoing pregnancy per start cycle was 60.65% for GnRH-antagonist protocol and 71.6% for GnRH-agonist long-protocol (P < 0.01). The OHSS rate was lower in GnRH-antagonist protocol . The cumulative multiple pregnancy  rate and the cumulative ectopic pregnancy rate  in GnRH-agonist long-protocol was not different from that of GnRH-antagonist protocol. Data on patients’ demographic and infertility treatment-related characteristics are represented in Table In fresh embryo cycles, there were less amount of gonadotropin and few days of OHSS for GnRH-antagonist protocols. Therefore, the total cost was lower in GnRH-antagonist protocols (average cost $16970.85) than in GnRH-agonist long-protocol (average cost $19902.24) in fresh embryo cycles. In fresh embryo transfer cycles, there was no statistical difference in the ongoing pregnancy rate between the two protocols, therefore, the minimum cost method was used for analysis. When the cumulative ongoing pregnancy rate was taken into account in the long protocol group, the number of frozen embryo transfer cycle was higher, leading to a higher cumulative ongoing pregnancy rate, and consequently a higher cost. Using cost-effectiveness analysis, the mean costs per ongoing pregnancy were estimated at $8176.76 and at $7595.28 with GnRH-antagonist protocols and GnRH-agonist long-protocols, respectively. The incremental cost-effectiveness ratio (ICER) is an evaluation index commonly used in economics research. It refers to the ratio of cost difference and effect difference between different protocols. In this study, the ICER for GnRH-agonist long-protocol versus GnRH-antagonist protocol was estimated at $4375.95 for one more ongoing pregnancy  in this study was higher than the average hospitalization cost for 30 diseases in 2018 according to the national bureau of statistics25. Toftager26et al. found that quality of life and psychosocial and physical well-being of patients used the GnRH-antagonist protocol was better than that used the GnRH-agonist protocol. But these are hard to quantify in terms of costs. And it’s difficult to calculate the impact on families and society of obtaining a healthy baby by IVF. Considering the different incidence of related complications during pregnancy, the treatment costs and nursing costs vary greatly. Therefore, in this study, we used the cumulative ongoing pregnancy rate as the effect of the study.Economic analysis of IVF is still challenging, and there is no unified view on the result indicators of the analysis. Existing economic studies often used ongoing pregnancy, live birth rates or quality-adjusted life years (QALYs) as result of the study. However, it is worth considering that QALYs of both husband and wife or child is used when taking QALYs as a result of IVF11. Economic model carried out before a trial is particularly useful in reducing unnecessary waste of research resources in evaluating techniques and interventions and improving the quality and efficiency of the research27. In our study, we developed a decision-tree model to evaluate the cost-effectiveness of the GnRH-agonist long-protocol and GnRH-antagonist protocol. The relevant transfer probability in the model was calculated using the data of infertile patients in the reproductive center of our hospital. In addition, we have made some reasonable assumptions for analysis, as shown in Table There are many economic analysis methods, but among the existing studies on economics in ART, 84% are cost-effectiveness analysis and 48% are model-based studiesThis is a retrospective study on the data available from infertility diagnosis and treatment in our reproductive center. The relevant probability and costs in the model are calculated based on the data of our single center. These results may differ from those of other centers, but can provide some guidance for patients and clinicians. In the future, large samples, multi-center prospective randomized controlled trials are needed to more thoroughly explore more economical and effective treatment protocols.In conclusion, if fresh embryo transfers are considered, the pregnancy outcomes between GnRH-agonist long protocols and GnRH-antagonist protocols are similar, but GnRH-antagonist protocols have lower cost. Therefore, in the fresh embryo transfer cycle, the GnRH-antagonist protocol has economic advantage and is worth recommending. However, GnRH-agonist long-protocol have higher success rates and higher costs when cumulative ongoing pregnancy rates are taken into account. The cost per ongoing pregnancy in the GnRH-agonist long-protocol cycles was lower than that in the GnRH-antagonist protocol cycles. Thus, cost-effectiveness analysis shows that the GnRH-agonist long-protocol is more cost-effective than the GnRH-antagonist protocol and may represent a cost-effective option from the perspective of patients. However, further large sample sizes and multi-center randomized controlled trials are needed.


We report the finding of alkaline phosphatase-immunoglobulin complex ) in a patient with persistently increased ALP activity. The identification of macro-ALP is important to rule out pathological causes of increased ALP activity and to avoid unnecessary diagnostic investigation. The patient was subsequently diagnosed with vitamin D deficiency, gallstone pancreatitis, and monoclonal gammopathy of undefined significance (MGUS). Macro-ALP can coexist with disease that can increase serum ALP activity. We report, for the first time, a case of macro-ALP in a patient with a monoclonal protein (M-protein). The tissue-nonspecific alkaline phosphatase (ALP) is the major fraction found in the serum and is coded by a single gene expressed in the liver, bone, and kidney. Intestinal alkaline phosphatase, which is about 25% of normal sera, is coded by a separate gene which is separate from the gene that codes for placental alkaline phosphatase and the placental-like isoenzyme . Raised serum ALP may be due to the liver ALP isoenzyme as a result of hepatic damage or cholestatasis. Increased bone-specific ALP can be caused by a number of pathologies affecting the bone: fracture, Paget's disease, osteomalacia, and bone metastases. Raised intestinal ALP has been reported in liver cirrhosis, diabetes, chronic kidney disease, and bowel ischemia and infarction. Alkaline phosphatase isoenzyme analysis is used to understand the changes in serum values of ALP and has many diagnostic advantages in identifying underlying pathology , 2.Rarely increased serum ALP activity will be due to an immunoglobulin-bound form of ALP (macro-ALP). Macro-ALP has reduced renal clearance usually due to a high molecular weight form of the enzyme. The importance for the diagnosis of macro-ALP lies in their ability to cause diagnostic confusion when plasma ALP activities are elevated, leading to unnecessary and potentially harmful investigations . We repoA 73-year-old man with a total ALP of 70 IU/L (reference range (RR) 30–130, using p-nitrophenyl phosphate as a substrate, Roche UK) was found to have a minor IgG kappa monoclonal band (M-band) of 2.8 g/L in 2009. His total ALP increased to 142 IU/Lin 2012 and to 178 IU/L in 2013 . His gamFollowing cholecystectomy, his ALP remained raised, and alanine aminotransaminase, bilirubin, gamma-GT, serum calcium, and renal function were within the reference limit. Liver screening for antinuclear antibody, mitochondrial antibody, smooth muscle antibody, and anti-liver-kidney microsomal antibody was negative. Referral to a hospital gastroenterologist and an abdominal ultrasound detected a post inflammatory pancreatic cyst but did not report liver disease.ALP isoenzyme analysis was carried out by the Sebia (UK) serum enzyme electrophoresis method. The SEBIA system utilizes the different degrees of sialylation of liver and bone isoenzymes to separate them. Wheat germ lectin presents a strong affinity for the sialic acid residues and consequently binds preferentially to the bone isoenzyme that is sialated to a higher degree. The separated ALP isoenzymes are visualized at 37°C and 50°C using an ALP-specific chromogenic substrate, 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium in aminomethyl propanol (AMP) buffer, with pH 10.1. Isoenzyme analysis of the patient's sera in 2018 identified an unusual form of ALP .n = 3) in the patient. Isoenzyme electrophoresis of the patient's sera before and after PEG precipitation showed that the unusual form was precipitated by PEG and identified an immunoglobulin-alkaline phosphatase macroenzyme complex  6000  were mixed, and the residual activity was measured. Fifteen patient sera with liver, bone, and intestinal alkaline phosphatase isoenzymes were used as controls. The average measured residual activity was 95.4% in controls (range 84.8–108.5), compared to 25.2% ( complex .The Sebia (UK) immunofixation method allows the separation and characterization of M-protein heavy and light chains using specific antisera. Following agarose gel electrophoresis, the gel is incubated with antisera to IgG, IgA, and IgM heavy chains and kappa and lambda light chains. The excess antisera are removed, the precipitated (immunofixed) immunoglobulin is visualised, and the M-protein is identified by staining. The patient's sera and three controls with total alkaline phosphatase in the range 200–300 IU/L were electrophoresed on the immunofixation gel . Following electrophoresis serum, ALP was visualised using the specific substrate for ALP. Visualisation was performed by a manual technique in which the gel was overlaid with the substrate and incubated at 37°C. A discrete band that was visualised by the ALP chromogenic substrate was identified in the patient's sera but not in controls . CompariThe discrete band found using specific substrate staining following electrophoresis on the immunofixation gel suggested a monoclonal (M) component to the ALP-immunoglobulin complex. The molecular weight of IgG is 150,000 and of the non-tissue-specific form of ALP is 186,000 . It is lThere have been several reports of macro-ALP in the literature. Reported macro-ALP were bound to IgG , 7 and tCharacterisation of the macro-ALP was performed by gel filtration , immunoeMacro-ALP has been reported in patients with ulcerative colitis , carcinoIsolated increase in ALP is associated with a variety of medical illnesses. In the first instance, a reasonable approach would be a careful medical and drug history and physical examination. The within-subject variation for ALP was reported as 6.4% , suggestThe finding of an isolated increase in ALP triggered several investigations in this patient. The increase was at first attributed to vitamin D deficiency. The persistent increase following repletion with vitamin D resulted in carrying out further investigations for liver disease and a referral to a hospital based specialist gastroenterologist. Conditions that favour the presence of a macro-ALP are the absence of symptoms, an isolated and persistently increased ALP, symptoms atypical for the abnormal level of the enzyme. However, as in this patient, the presence of macro-ALP can coexist with disease that can cause an increase in ALP, vitamin D deficiency, gallstone pancreatitis, and MGUS. Identification of macro-ALP in this context becomes of relevance in the correct interpretation of increased total ALP. Interestingly, macro-ALP in this patient raises the possibility of ALP bound to the M-protein as a cause of the immunoglobulin-ALP complex.β-crosslaps, and deoxypyridinoline characteristic of bone degradation as well as bone-specific alkaline phosphatase, osteocalcin, amino-terminal propeptide of type I collagen, and carboxy-terminal propeptide of type I collagen characteristic of bone formation [Recent studies have indicated an increased fracture risk in multiple myeloma around the time of presentation . MGUS usormation . In a goIn summary, we report a case of macro-ALP in a patient with IgG kappa MGUS. Diagnostic use of ALP isoenzyme characterisation is of value and can provide useful information on underlying pathology. The presence of macro-ALP can confuse the interpretation of increased total ALP and can lead to unnecessary investigation. This is particularly true in disease states, such as MGUS and multiple myeloma which can lead to pathology that can result in increased total ALP.Differential diagnosis of an isolated increased ALP can be a challenge and can trigger several investigationsWe report for the first time, macro-ALP in a patient with M-proteinMacro-ALP can coexist with several pathologies which can increase ALPIt is important to detect the presence of macro-ALP as they can cause diagnostic confusion leading to several unnecessary investigations


Cancer is a complex disease that deregulates cellular functions at various molecular levels . Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset. Ovarian cancers are generally diagnosed at an advanced age as the early-stage disease is usually asymptomatic, and symptoms of the late-stage disease are nonspecific3. The anatomical location and the ovaries’ position are mainly responsible for asymptomatic and nonspecific nature of the disease. Since the ovaries have limited interference with the surrounding structures, ovarian cancer is hard to detect until the ovarian mass is significant, or metastatic disease supervenes. Due to the symptoms’ nonspecific nature, it often requires multiple consultations with a primary care physician and several investigations in finding the disease and for an appropriate therapy/treatment. In this context, the development of useful tools to better understand the complex pathogenesis is needed for effective cancer management and prognosis5.Ovarian cancer is a common and deadly gynaecological cancer with a high mortality rate in developed countries. It accounts for 5% of all cancer deaths in females in the UK8. Any study limited to any of these levels is insufficient to understand the complex pathogenesis of cancer. Integrated multi-omics analysis of data from these levels is essential to understand cellular malfunctions responsible for cancer and its progression holistically. Importantly, integrated multi-omics analysis, taking the advantage of various omic technologies , can identify reliable and precise biomarkers for diagnosis, treatment stratification and prognosis9. Recent advancements of omics and computational technologies, including deep learning, have boosted the research in integrated multi-omics analysis for precision medicine and cancer. In recent years, many research works12 have been published on integrated multi-omics analysis of cancer. These works are either on individual cancer18 or pancancer22. Most of these works integrated di-omics16, few of them integrated tri-omics23, and very few of them integrated tetra-omics24 data. There are a few integrated multi-omics analyses of ovarian cancer, including di-omics25 and tri-omics27 based analyses.Cancer is a complex and heterogeneous disease that deregulates cellular functions in different molecular levels, including DNA, RNA, proteins and metabolites. Importantly, molecules from different levels are mutually associated in reprogramming the cellular functions29 are becoming very useful and effective in integrated multi-omics analysis of cancer data. However, high dimensional omics data are normally imbalanced with a large number of molecular features and a relatively small number of available samples with clinical labels29. For example, DNA methylation and mRNA integrated dataset (TCGA) for ovarian cancer has 39,622 features  for common 481 samples with clinical labels. This imbalanced dimensionality of cancer datasets makes it challenging to use a machine learning (ML) or DL in integrated multi-omics analysis, especially in individual cancers as they have few samples. For instance, TCGA ovarian cancer RNAseq dataset has only 308 samples, whereas pancancer RNAseq dataset has 9081 samples 29. Algorithms for dimensionality reduction, such as autoencoder-based DL algorithms18, together with conventional solutions, such as principal component analysis (PCA)30, are possible solutions to the dimensionality problem. Importantly, considering the discontinuous and non-generative nature of traditional autoencoders, VAEs31 have emerged as DL-based generative models for compressed features learning or dimensionality reduction. There are many works which have used VAE in their studies. However, they are mostly mono-omics studies of individual cancer33 or pancancer35. OmiVAE29 is the only work that considered VAE for integrated multi-omics (di-omics) analysis of pancancer. Also, VAE may suffer in representing the input features due to uninformative compressed features and variance over-estimation in feature space36. None of the existing works has used MMD-VAE, and also their analyses are limited to cancer molecular subtypes classification only.Due to the increasing availability of large-scale multi-omics cancer data, machine learning approaches, especially DL approaches35 use unsupervised dimensionality reduction methods, separating the downstream analysis from the reduction method. However, dimensionality reduction in cancer multi-omics analysis is an intermediate step toward the downstream analysis, like classification . Separating the dimensionality reduction and model  learning may not be optimal for classification as datasets are not always suitable for a classification task. For example, a DNA methylation dataset that includes cancer and normal samples is readily applicable for classification due to their discriminative features. However, a DNA methylation dataset that only includes cancer samples may not be useful in classification. Due to lack of supervision during a dimensionality reduction process, some key features can be filtered before training the classifier, affecting the final performance38. In this context, supervised dimensionality reduction methods can be more useful in balancing multi-omics datasets and their integrated downstream analysis.Moreover, most of the existing works36. To the best of our knowledge, this is the first work that does a comprehensive mono-omics and integrated multi-omics  analysis of individual cancer (ovarian) using VAE or MMD-VAE, or both. The objectives of this work are three-fold. First, we have designed and developed a DL architecture of VAE and MMD-VAE that supports unsupervised and supervised learning of latent features from mono-omics, di-omics and tri-omics data. Second, we did a dimensionality reduction performance analysis of the developed DL architecture on ovarian cancer by cancer samples identification, molecular subtypes clustering and classification. As a dimensionality reduction technique, the performance of MMD-VAE or VAE depends on input features and sample size, not on the cancer type. Hence, we have tested the developed MMD-VAE and VAE using datasets with three different sample sizes  to demonstrate our findings’ robustness. Finally, a survival analysis of an existing ovarian cancer dataset has been carried out using the reduced or latent features sets.In this work, we did an integrated multi-omics analysis of ovarian cancer using VAE and MMD-VAEIn the following, we briefly discuss the datasets used, data preprocessing, VAE/MMD-VAE architecture, dimensionality reduction and survival analysis methods.39, one for mRNA, CNV/CNA and RNAseq, and two for DNA methylation. All the mono-omics datasets except the second DNA methylation dataset are from the TCGA Ovarian Cancer (OV) cohort43. The second DNA methylation dataset is from GDC TCGA Ovarian Cancer (OV) cohort44. It includes cancer and normal samples. We have concatenated these mono-omics data to form the di-omics and tri-omics datasets. The table’s ’Feature dimension’ and ’Sample size’ columns demonstrate that all the datasets are imbalanced with too many input features and relatively too few numbers of samples with clinical labels. For example, one tri-omics (CNV + DNA methylation + RNAseq) dataset has 72,885 input features with only 292 samples.We used mono-omics and multi-omics  data for the study. We generated multi-omics data using different combinations of high dimensional mono-omics data. Table Step 1 First, we intersected the mono-omics datasets to find the common and same size samples. We did two different intersections of the datasets  in Supplementary documents) using common sample IDs  to keep the maximum number of samples for the study. The intersection of CNVs, mRNA and DNA methylation datasets has found 481 samples, and the intersection of CNVs, DNA methylation and RNAseq has found 292 samples in common within the datasets.Step 2 We identified and removed the missing/zero/NA values in the four downloaded omics files. All the data files, except the RNAseq, had no missing/zero/NA values, and 212 input features or genes  with zero expression values were removed from the RNAseq dataset.Step 3 Non-normalised datasets, such as CNVs and RNAseq datasets, were normalised using the min-max technique. We used the min-max normalisation as unlike other techniques  it guarantees multi-omics features will have the same scale45. Thus, all the features will have equal importance in the multi-omics analysis.Step 4 Finally, we concatenated the normalised mono-omics datasets to form the di- and tri-omics datasets. Concatenations involving RNAseq, such as The second DNA methylation44 dataset is highly imbalanced  as it has only 10 normal samples compared to the 603 cancer samples (class ratio: 1.36:98.64). We used the Borderline-SMOTE SVM46 to reduce the class imbalance  by re-sampling of normal samples (10 to 283). After the re-sampling, we have 886 samples compared to the original 613 samples. This has increased class ratio (31.94:68.06) between the normal and cancer samples.The downloaded datasets are not ready  to be used in dimensionality reduction and integrated multi-omics analysis. They need to be preprocessed, such as the datasets’ sample sizes need to be same to integrate and generate di- and tri-omics datasets. We have preprocessed the downloaded datasets (TCGA Ovarian Cancer cohort) using a four steps method .31 is a deep generative model, which can learn meaningful data manifold from high dimensional input data. Unlike, standard autoencoders , a VAE encodes an input : an encoder encodes or generate each latent variable Step 2 (sampling): a sampler samples points from the latent space by sampling from the encoded or encoding distribution 47 for the regularisation term and jointly optimises the encoder and decoder using the following loss function that rely on the traditional evidence lower bound (ELBO) criterion: Step 3 (decoding) : a decoder decodes the sampled points from a conditional distribution Step 4 (backpropagation): finally, the calculated loss is backpropagated through the network to update the model accordingly.A VAE36:49. KL divergence naturally encourages the latent code x, making the code uninformative/unaware about the input. In this context, the encoder could fail to learn any meaningful latent representation of the input.Uninformative latent code/feature: The regularisation term  in loss function instead of the 50 can address the above issues. According to the MMD, two distributions are identical if and only if all of their moments are same. Unlike p(z) and q(z) are. We can use the kernel embedding trick to estimate MMD for two distributions as Eq.  with an input layer with nodes equal to the LFs (32/64/128), a hidden layer with nodes equal to the half of LFs, and an output layer with nodes equal to the class numbers .The encoder network comprised of an input layer and three hidden layers. The decoder network structure is the mirror image of the encoder structure. Notably, the encoder and decoder share the necessary bottleneck layers. We used the architecture for mono-omics, di- and tri-omics data, and the size of the bottleneck layers is same for all the datasets. However, the other layers’ sizes varied according to the omic count  and omic data. For example, for mono-omics data, such as mRNA data, the input and output layers are 12,043, and hidden layers sizes are 2048 and 1024. As shown in Fig. z given input sample x or simply N) in the bottleneck layer to make the sampling process differentiable and suitable for backpropagation. The sampled latent features vector (z/LFs) is the compressed lower-dimensional representation of omics or integrated multi-omics data.Encoder: The encoder network using two hidden layers encodes mono-omics data into a 1024 dimensional vector, di-omics data into two 1024 dimensional vectors and tri-omics data into three 1024 dimensional vectors. The encoding network for the DNA methylation data is different from the other omics data. For example, in the first hidden layer, each chromosome related DNA methylation data are encoded into corresponding vectors with 256 dimensions wheres for the others, input data are encoded into a 2048 dimensional vector. This encoding is to capture the intra-chromosome relationships, and second hidden layer for the DNA methylation data captures the inter-chromosome relationships. For di- and tri-omics data, the second hidden layer respectively concatenates two and three 1024 dimensional vectors and produces an encoded 512-dimensional vector. The encoder’s final hidden layer fully connects to two output layers. These two layers of the size of latent code or features (32/64/128) are part of the bottleneck layers and represent the mean z as the input and passes through three hidden layers, and finally outputs the reconstructed vector M is the number of chomosomes, CE is the binary cross-entropy between input data , which is: nll- negative log likelihood which can be calculated as mean of N, which is: 29. The binary cross-entropy based classification loss (Decoder: The decoder network takes the latent feature vector ng Eqs.   and 6)  z as theentclass1pt{minimaWe used a batch normalisation technique in each fully connected block to implement the VAE/MMD-VAE DL architecture. This is to address the internal covariate shift   by normalising layer inputs52. Thus, it stabilises the learning process and significantly improves the learning speed. As the activation function, we used the rectified linear units (ReLU) for the hidden layers, the sigmoid for the decoder’s output layer and the softmax for classifiers’ output layer. We built the model using PyTorch (version 1.5.0). The implementations of the models used in this paper are available on GitHub (https://github.com/hiraz/MMD-VAE4Omics).The VAE/MMD-VAE architecture does all the activities illustrated in Fig. 53. Hence, correct clustering and classification of ovarian cancer samples and molecular subtypes are important for improved disease management. Authors in53 have identified four ovarian cancer transcriptional ) subtypes, which may have clinical significance. These four subtypes of high grade serous ovarian cancer (HGS-OvCa) are named as Immunoreactive, Differentiated, Proliferative and Mesenchymal54. The datasets used in this work are about HGS-OvCa, and the clinical data include these molecular subtypes for most of the samples. Although these molecular subtypes are transcriptional , they can be used for other omics data analysis due to their correlation or association with transcriptional data58. For example, authors in55 have reported that DNA methylation is often negatively associated with gene expression in promoter regions, while DNA methylation is often positively associated with gene expression in gene bodies.Cancer samples identification and molecular subtypes are useful in prognostic and therapeutic stratification of patients and improved management of cancersz or LFs) can be used to cluster and classify cancer samples, subtypes, including existing transcriptional or molecular subtypes of ovarian cancer. The performance of clustering and classification exploiting z can demonstrate the dimensionality reduction capability of VAE or MMD-VAE. We demonstrated the dimensionality reduction capability of VAE and MMD-VAE using the latent features learned from the mono-omics, integrated di-and tri-omics data of ovarian cancer, and used for the followings:Clustering We can use the LFs learned (unsupervised and supervised) by the VAE/MMD-VAE models to cluster samples into cancer vs normal and molecular subtypes. We used a two- and three-dimensional embedding of the mono-omics, di- and tri-omics features for the selected samples, and visualised the clustered samples using scatter plots. Two dimensional (2D) and three dimensional (3D) embedding of the omic(s) features for the selected samples are accomplished by selecting first 2 and 3 LFs from the learned LFs  and MMD-VAE/VAE performs in molecular subtypes clustering.VAE or MMD-VAE generated latent and compressed features  LFs inferring and predicting survival subgroups of HGS-OvCa. We followed a 3-step process . NbClust can calculate up to 30 indices or metrics to determine the optimal number of clusters in a data set. It also identifies the best value for K by the majority rule. In our all 11 datasets  model for each of the LFs produced by the VAE/MMD-VAE  and identify the best model for a training dataset. In each round of the validation, 60% data were used for training and rest 40% were left out from the training as the test dataset. Finally, we used the test dataset to predict the survival subgroup or risk labels. We used the Cox-PH model and Kaplan-Meier (KM) survival curves to evaluate survival prediction performance. We used the following three metrics for the evaluation:Predicting survival group labels for new samples: After having the survival subgroups labels from K-means clustering, we used an SVM-based classifier . We then predicted survival using the labels of the test set. We then computed the C-index using concordance function of R’s survival package64. Similarly, we calculated the C-index only considering the clinical features .P value of Cox-PH regression The Cox-PH models built on training datasets compute log-rank p values for the models. Also, we plotted the Kaplan-Meier survival curves of the two survival subgroups (predicted) and calculated the log-rank p-value of the survival difference between them.Brier score Brier score function measures the accuracy of probabilistic prediction65. In survival analysis, it measures the mean of the difference between the observed and the estimated survival beyond a certain time66. The score ranges between 0 and 1, and a smaller score indicates better accuracy of the prediction. We used the R Package survAUC67 to compute the Brier score.Identifying prognostic biomarkers from LFs: We did all the above analyses using the LFs as their compressed representations simplify the survival analysis and molecular subtyping. However, we need to map these LFs back to their corresponding input features to identify potential molecular biomarkers. We mapped the associated input features for each clinically relevant LF using a linear model and filtered the features with zero or insignificant input feature values. Next, we estimated the correlation between the CRLFs and their corresponding input features. Finally, we used the filtered correlation data for hierarchical clustering (colour map) of LFs and their input features.Identification of robust survival subgroups of ovarian cancer (HGS-OvCa) can significantly improve patient care. Existing molecular subtypes of HGS-OvCa, such as transcriptional molecular subtypesWe used the developed DL architecture of VAE/MMD-VAE for cancer samples identification, molecular subtypes clustering and classification, and survival analysis using the TCGA ovarian cancer datasets. The results demonstrate the performance of the VAE and MMD-VAE in dimensionality reduction and survival analysis.68. We reported the results only for We trained and tested the developed VAE and MMD-VAE models with three different bottleneck layers . We have selected the first 2 LFs of the 128 LFs to cluster the samples into two groups . The two-dimensional embedding of the DNA methylation dataset’s input features was plotted on scatter plots for PCA, t-SNE, VAE and MMD-VAE. As illustrated in Fig. Molecular subtypes clustering: We clustered the transcriptional subtypes using the LFs learned through unsupervised and supervised VAE and MMD-VAE models. For the LFs learned via unsupervised model, we have selected the first 2 LFs of the learned 128 LFs to cluster the molecular subtypes. The two-dimensional embedding of the mono omic, di-omics and tri-omics datasets’ input features were plotted on scatter plots for PCA, t-SNE, VAE and MMD-VAE. Figure 9 in Supplementary documents presents the results of 2 LFs-based molecular subtypes clustering. As seen in Fig. 9b–i in Supplementary documents, all the dimensionality reduction methods poorly clustered the samples into four subtypes using the mono- and tri-omics datasets. This result is expected as the original omics datasets are not discriminative or well representative of the transcriptional subtypes. As Fig. 9a in Supplementary documents illustrates, even the most relevant transcriptional dataset (mRNA) do not represent the transcriptional subtypes. Hence, the unsupervised PCA, t-SNE, VAE and MMD-VAE models struggle to cluster the transcriptional subtypes. In this context, we can use the supervised versions of these models, especially VAE and MMD-VAE. We used the supervised VAE and MMD-VAE models to learn the task-oriented  or guided LFs from the mono-, di- and tri-omics datasets. We have selected the first 2 LFs of the learned 128 LFs to cluster the molecular subtypes. Figure 58. For the same reason, the use of integrated non-transcriptional and transcriptional data helps to maintain a similar performance or improve the performances of the transcriptional subtypes clustering and classification. For example, the accuracy of MMD-VAE using mRNA is 93.8%, which has been maintained (93.7%) in case of the integrated CNV-mRNA, and improved to 95.5% in case of the integrated CNV-mRNA-methylation datasets. The confusion matrices in Fig. 10b,d,f in Supplementary documents for MMD-VAE illustrate similar results. As a dimensionality reduction algorithm, in majority datasets, MMD-VAE shows better performance compared to VAE. This performance difference could be due to the MMD-based loss function. We tested the supervised MMD-VAE and VAE on omics datasets with three different sample sizes  to demonstrate our findings’ robustness. We used the learned LFs to classify the transcriptional subtypes and presented the classification accuracies in Table 5 in Supplementary documents. As seen in the Table, both VAE and MMD-VAE show robust classification accuracy for the same omics data with different sample sizes.Cancer samples identification: We used an SVM-based classifier to identify the cancer samples from the normal samples using the LFs learned through the unsupervised PCA, t-SNE, VAE and MMD-VAE. Table 3 in Supplementary documents presents the classification performances for the DNA methylation dataset of 886 samples (GDC cohort). All the models except t-SNE have more than 99% classification accuracy with very high precision (0.99), recall (0.99) and f1 score (0.99). The discriminative features  of the DNA methylation data is the main reason for this classification performance. Molecular subtypes classification: Like the transcriptional subtypes clustering, we used the LFs learned through the unsupervised and supervised VAE and MMD-VAE models in molecular subtypes classification. For the unsupervised setting, we also compared the classification performance of the LFs learned through VAE and MMD-VAE with of the LFs learned through PCA and t-SNE. Table 4 in Supplementary documents presents the classification performance of an ANN-based classifier utilising the LFs learned via these unsupervised models from mono-, di- and tri-omics datasets. As we can see from the table, the classifier using the PCA and t-SNE generated LFs poorly classify the existing transcriptional subtypes in all omics datasets. On the other hand, the classifier using the VAE and MMD-VAE generated LFs can classify the transcriptional subtypes for mono-omics (mainly mRNA), di- and tri-omics data with higher accuracies in the range of 73.2–81.44%. However, the performances may not be acceptable in many real-life applications. Lack of discriminative features within the omics datasets for the transcriptional subtypes is the main reason for the low accuracies. Supervised learning of the LFs can improve the classification performance. In the supervised setting, VAE or MMD-VAE and the classifier jointly learn the LFs using the transcriptional subtypes as the supervisory guidance. We trained the joint models on the mono-, di- and tri-omics datasets and tested the models separately. Table The dimensionality reduction performance results of VAE and MMD-VAE in clustering and classification demonstrate the followings:in any downstream analysis  unsupervised dimensionality reduction is useful if the input dataset is discriminative , otherwise supervised dimensionality reduction is necessary, andintegrated dimensionality reduction and multi-omics analysis of data may improve or maintain the similar performance of their mono-omics counterparts exploiting their association, without confounding each other.We did a comprehensive survival analysis using eleven datasets, including mono-omics and multi-omics data, particularly for the samples with existing transcriptional subtypes and inferred survival/risk groups. Considering the space limitation, we presented a subset but enough of the results . We did a two-stage survival analysis of the samples (481 and 292) using the two inferred subgroups. In the first stage, we plotted Kaplan-Meier survival curves for all the samples. As seen in the Kaplan-Meier survival curves . Potential reasons for the higher p values or insignificant differences between the predicted survival groups for the datasets could be (i) the smaller sample size with few events to identify the differences and (ii) too much compression may have obscured the clinically relevant features.In the second stage, we predicted survival subgroup labels using an SVM-based classifier splitting the samples into training and test data using a 60/40 split ratio. After predicting survival groups for the test datasets, we ran two multivariate Cox-PH regressions  on the training samples, then predicted survival using the labels of the test datasets. For the clinical co-variates, we considered three clinicopathological characteristics of the considered patients: (i) age at diagnosis, (ii) clinical or FIGO stage, and (iii) grade. We calculated C-indexes, Brier scores and models’ p-values for the training and held-out test samples for the multivariate Cox-PH regressions. As seen in Table 7 in Supplementary documents, the training samples generated moderately high C-indexes in between Impact of inferred subgroups on survival: In case of mono-omics and multi-omics data, inferred subgroups combined with the clinical covariates  improve the survival prediction. For example, subgroups learned using the MMD-VAE on CNV mono-omics data combined with clinical features shows higher C-index value (0.63) than the C-index value (0.62) for the clinical features. However, the improvement is not that significant. In summary, inferred subgroups, combined with the clinical features, demonstrate survival prediction performance higher than or similar to the clinical features.mono-omics vs multi-omics based LFs in survival: Similar to molecular subtypes classification, multi-omics based LFs may incrementally  improve the survival subgroups classification accuracy compared to mono-omics based LFs (shown in Table 6 in Supplementary documents). However, multi-omics  based LFs and subgroups predicted based on them do not improve the survival prediction performances than their mono-omics counterparts. Potential reasons for the lower performance using multi-omics based LFs could be due to (i) smaller sample size with few events  to identify the differences, (ii) too much compression  may have obscured the clinically relevant features.Finally, we presented a simple method to identify potential prognostic biomarkers from the clinically relevant LFs using a linear model. Figure 69. Thus, we can identify the other responsible gene or cgp island that could jointly work as a multi-omics prognostic biomarker. However, the linear mapping is not the best solution to identify a set of biomarkers from the learned CRLFs as it is only identifying correlations values within From Fig. Integrated multi-omics data analysis of cancer is vital for a comprehensive understanding of cellular functions, responsible for cancer and its development. DL algorithms have become a useful and popular tool for integrated multi-omics analysis of cancer data in recent years. However, imbalanced high dimensional multi-omics data makes a DL based integrated multi-omics analysis difficult, especially in individual cancer. DL based dimensionality reduction techniques, such as variational autoencoder is a potential solution to this dimensionality issue. In this work, we have designed and developed VAE and MMD-VAE based dimensionality reduction techniques for mono-omics and multi-omics data analysis of cancer. These techniques can learn latent features from any omics data using unsupervised and supervised training process. Then, we used the developed techniques to learn latent features from the mono-omics, di-omics and tri-omics datasets of ovarian cancer. Finally, we used the learned latent features to analyse ovarian cancer through cancer samples identification, molecular subtypes clustering and classification and survival analysis. The results, especially subtype clustering and classification and survival subgroups prediction demonstrate that integrated di- and tri-omics based LFs can perform better or similar to their mono-omics counterparts based LFs. Also, molecular subtypes clustering and classification results show that MMD-VAE is outperforming VAE in most datasets. Notably, the results demonstrate that unsupervised dimensionality reduction is useful in the downstream analysis  when the input dataset is discriminatory, otherwise supervised dimensionality reduction is needed. Finally, inferred and predicted survival subgroups results show a significant survival difference between the two subgroups. However, multi-omics based LFs and subgroups predicted based on them do not improve the survival prediction performances than their mono-omics counterparts. One of the potential reasons for this could be a sub-optimal number of LFs or dimension of the selected dimension space. Moreover, the straightforward integration (concatenation based) strategy, treating omics measurements from different platforms equally and performing integration in a parallel fashion may not always be useful. The linear model used for mapping between CRLFs and input features may not be the best solution for biomarkers identification. There is significant scope for future work in these areas.The residual disease is one of the strongest predictors for outcome estimation after any cancer therapy or treatment. Hence, VAE or MMD-VAE based integrated multi-omics analysis on predicting residual disease for ovarian cancer will be one of our future works.Supplementary Information.


The brain systems underlying placebo analgesia are insufficiently understood. Here we performed a systematic, participant-level meta-analysis of experimental functional neuroimaging studies of evoked pain under stimulus-intensity-matched placebo and control conditions, encompassing 603 healthy participants from 20 (out of 28 eligible) studies. We find that placebo vs. control treatments induce small, widespread reductions in pain-related activity, particularly in regions belonging to ventral attention (including mid-insula) and somatomotor networks (including posterior insula). Behavioral placebo analgesia correlates with reduced pain-related activity in these networks and the thalamus, habenula, mid-cingulate, and supplementary motor area. Placebo-associated activity increases occur mainly in frontoparietal regions, with high between-study heterogeneity. We conclude that placebo treatments affect pain-related activity in multiple brain areas, which may reflect changes in nociception and/or other affective and decision-making processes surrounding pain. Between-study heterogeneity suggests that placebo analgesia is a multi-faceted phenomenon involving multiple cerebral mechanisms that differ across studies. The neural mechanisms of placebo analgesia are not fully understood. Here the authors conducted a large scale meta-analysis of individual data from fMRI studies of pain and placebo conditions. Placebo analgesia is the most robust and best-studied type of placebo effect3. A growing number of neuroimaging studies elucidate the brain correlates of placebo analgesia. These studies, and meta-analyses of their findings, have provided evidence for the involvement of brain regions linked to nociceptive processing, including early pain-gating mechanisms, but also to decision-making, cognitive appraisal, reward/motivation, emotional regulation7, and other forms of learning and social cognition8 relevant for health behaviors.Placebo effects contribute substantially to treatment outcomes in both medical research and clinical practice. A better understanding of the underlying mechanisms is thus important for optimizing drug development and clinical care4, and the lack of large-sample assessments hampers the detection of small to moderate effects9 and makes it difficult to identify precisely which structures are consistently altered by placebo treatment. Previous meta-analyses have all relied on published coordinates of activation peaks. These incomplete summaries of the full activation maps provide only approximate information on replicability across studies and are susceptible to bias10. These limitations can be overcome by meta-analyses based on single-participant, whole-brain images, which are sometimes referred to as ‘mega-analyses’11. As meta-analyses on participant-level data are preferable in terms of statistical power and risk-of-bias11, a mega-analysis of placebo-induced brain activity can be expected to foster convergence in our understanding of placebo analgesia, to provide novel insights into the underlying neural mechanisms, and guide the development of predictive methods of individual placebo analgesia from neuroimaging data, which would be of crucial importance both from a clinical and drug development point of view. Here, we conducted a systematic participant-level meta-analysis of 20 independent neuroimaging studies on experimental placebo analgesia. Based on whole-brain activation patterns in a total of N = 603 healthy participants, we mapped the effects of placebo treatment on pain-related brain activity and identified neural correlates of individual differences in behavioral placebo analgesia.Nevertheless, the results of these studies vary substantiallyN = 603 participants from 20 studies, see ref. 7 and Table 12, only maps with white-matter regions masked out were available.All included studies (Outlier screening (see Supplementary Methods and Results) indicated pain ratings that were too low for inclusion in six participants . Problematic image features were found in 12 (2.0%) participants. These include imaging artifacts (six participants), extreme values (four participants), or likely errors in first-level analysis (two participants). These outlier participants were retained in our analysis ; with the largest located in the insula, bilaterally . The τ-statistic indicated considerable between-study heterogeneity in pain-related activity throughout most of the brain  effect on pain-related brain activity, as compared to the matched control conditions , left fusiform cortex and cerebellum Fig. .To further follow up on potential sources of between-study heterogeneity, we explored the possibility of explaining heterogeneity through study-level experimental features, such as the method of placebo induction. A preliminary comparison of placebo induction methods (conditioning and suggestions versus suggestions only) showed no significant differences in placebo-related brain activity after correction for multiple comparisons , the secondary somatosensory cortex  was also negatively correlated with analgesia but was only significant without correcting for multiple comparisons . Without correcting for multiple comparisons, positive correlations , in the orbitofrontal cortex , and the prefrontal pole .Positive correlations between behavioral placebo analgesia and brain activity, i.e. increasing brain activity with stronger placebo response, did not reach statistical significance  nucleus15. A tendency towards negative associations was found for the lateral geniculate, medial geniculate, and pulvinar nuclei, which are known for their predominantly visual and auditory roles15.Activation for painful stimulation compared to baseline (averaged across placebo and control conditions) showed activation of multiple expected cortical and subcortical regions . In the insula, placebo reduced activity in bilateral middle short gyrus and right posterior short gyrus, corresponding to the dorsal anterior/mid-insula, as well as a trend towards reduced activity in the right anterior long gyrus . Thalamic nuclei showed tendencies towards placebo-induced decreases in areas strongly activated in pain. The strongest decreases were found in the VPL, a primary target of the spinothalamic tract, and the habenula. Observed placebo effects in other regions tended to be smaller.Network similarity analysis indicated that placebo treatment reduced activity in the ventral attention and the somatomotor networksAs with the main effects of placebo vs. control, network similarity-based analysis of regions correlated with placebo analgesia indicated that activity in the ventral attention and somatomotor networks was negatively correlated with behavioral placebo responses Fig. , i.e., sN = 603) participant-level voxel-based neuroimaging meta-analysis of placebo analgesia, involving the majority of eligible experimental neuroimaging studies. Our results provide a reliable, aggregated view of the size, localization, significance, and heterogeneity of placebo-effects on pain-induced brain activity. In a previous paper, we focused on the question of whether placebo analgesia involves changes in the neurologic pain signature (NPS)17, a machine-learning based weighted, multi-voxel summary metric (covering about 10% of the brain), that can be interpreted as a neuromarker of nociceptive pain. This previous study revealed that behavioral placebo analgesia was associated with significant but small effects in the NPS, pointing to the relevance of other brain areas and networks. Accordingly, characterizing this potentially broader set of changes was the key focus of this voxel-wise whole-brain investigation  how strongly do placebo treatments affect the same systems involved in nociception and pain generation (as indicated by the contrast control > placebo); and (ii) which systems are engaged by placebo treatments (as indicated by the contrast placebo > control) and may therefore reflect top-down modulatory mechanisms.Our study-as-random-effect results provide strong evidence for placebo-associated reductions of pain-related activity in some brain areas linked to nociception and pain and indicate that these are generalizable across experimental paradigms. We also provide strong evidence that the degree to which pain-related activity is reduced in these brain areas correlates with the magnitude of behavioral hypoalgesia across individuals.18, including the thalamic VPL complex the posterior insula and, moreover, in the habenula  the NPS may not perfectly characterize nociceptive processing in this sample, and some nociceptive pain-related effects may be missed; (2) it cannot provide a broad view of effects across the brain, including areas like the habenula and many cortical areas; and (3) it tests a distributed pattern response and is not suited to identify placebo effects in VPL (or any other region) in particular. The present findings of effects in specific thalamic and other regions are complementary and important, in that they provide a region-level inference about effects in neuroanatomically defined nociceptive pathways. Thus, overall, we believe that placebo effects affect the circuitry involved in pain generation to some degree, in a manner that varies across individuals more than across studies in the present dataset, but also includes other brain effects beyond nociception that may be important for the emotions, decision-making, and behaviors surrounding pain.These findings complement previous findings of small but statistically significant placebo-induced reductions in the NPS25 also showed reduced activity during placebo analgesia, particularly in strong placebo responders  suppression of striatal prediction errors or other aversive circuits27.One of the strongest effects was found in the left putamen, which de-activated with placebo in proportion to analgesia. This is in line with multiple studies reporting correlations between placebo analgesia and both fronto-parietal and limbic fronto-striatal pathways12), cerebellar effects were not reported in previous meta-analyses of placebo analgesia, possibly due to insufficient cerebellar coverage across studies. Here, the dorsomedial cerebellum showed a profile of responses to painful events, reductions with placebo, and correlations with the magnitude of placebo analgesia. Some cerebellar regions have been linked to pain, and others to other cognitive, affective, and motor processes29 and patients with cerebellar infarctions show reduced placebo analgesia30. Cerebellar reductions and correlations are centered in vermis areas V and (to a lesser degree) II33, which are associated with somatomotor and limbic cortical networks, respectively. Thus, the best interpretation of cerebellar effects here is that they are related partly, but not exclusively, to somatomotor networks and pain. Placebo hypoalgesia-related activity changes in the VL nucleus, a target of cerebellothalamic tract15, also suggest that fronto-cerebellar connectivity may pose a promising novel target for future in-depth studies on the mechanisms of placebo analgesia.Interestingly, each of the main analyses revealed prominent placebo-related reductions in cerebellar regions. While this in line with some previous findings . These regions tend to be localized in the frontoparietal network (FPN). These increases were statistically significant only in the fixed effect analysis and involved the right DLPFC (with subthreshold activation on the left side), as well as the bilateral intraparietal sulcus. While the fixed-effect analysis provides limited generalizability, this result is very much in line with previous neuroimaging studies highlighting the importance of the DLPFC in initiating and maintaining the top-down effect of treatment expectation on nociceptive processing and pain. E.g. activity in the DLPFC precedes and scales with activity changes in downstream pain modulatory areas and prevents the extinction of once learned placebo analgesia35).The lack of FWER-corrected increases might be attributed to the reduced power in the study-as-random-effect analysis, which is very conservative. However, the heterogeneity analysis indicates that the between-study variance is significantly higher than expected in key fronto-parietal areas Fig. . This su36. The degree to which prefrontal systems are required for analgesia may vary. For example, in ref. 37, placebo analgesia was predicted by fronto-parietal activity in regions associated with emotion regulation but not working memory. Emotion regulation, in particular reappraisal strategies involving self-generated positive contexts for experiences, appears to involve fronto-parietal networks in reducing negative affect. Other recent studies have also found correlations between placebo analgesia and DLPFC connectivity39  and opioid binding in prefrontal cortex42. By contrast, other studies have found that mindfulness practice can reduce pain without fronto-parietal activation or appreciable deactivation in spinothalamic targets43. These strategies focus on acceptance without judgment rather than active re-contextualization, which may be another important component of placebo analgesia.Between-study heterogeneity was statistically significant throughout the frontal lobe Fig. , which m44. Our results suggest that placebo effects are not restricted solely to either sensory/nociceptive or cognitive/affective processes, but likely involve a combination of mechanisms that may differ depending on the paradigm and other individual factors. Understanding the neural and neurochemical pathways underlying this variability will pave the way to systematically utilize/modulate placebo responses in a context-, patient-, and disease-specific manner. Fostering the therapeutic processes underlying placebo effects in clinical settings promises to boost the efficacy (and tolerability) of analgesic drug treatments. Likewise, controlling and homogenizing placebo responses during drug development can enhance the assay sensitivity in clinical trials. Finally, biomarkers based on the types of brain alterations we identify here, and reported in other studies45, may help to dissect placebo from analgesic drug responses in pre-clinical trials.In sum, placebo analgesia may involve multiple alterations in appraisal systems, reflecting multiple underlying mechanisms46 (and therefore MNI-templates47) and different spatial smoothing kernels were used. This study therefore trades off spatial precision for generalizability across scanners, populations, pipelines, and paradigms. Finally, some brain regions, notably the orbitofrontal cortex (including the vmPFC), the inferior cerebellum and the top-most part of the brain were not fully covered . Here, we used the same data set to map placebo responses across the brain. In contrast to the previous analysis, which was restricted to the NPS as a neuromarker of nociceptive processing, this manuscript now focuses on voxel-wise brain-activity. This allows us to investigate placebo effects on individual regions and the distribution of effects across the brain, including in regions associated with affective and cognitive processes beyond nociception.The present study is a systematic meta-analysis of participant-level data across 20 published studies. A previous paper on this dataset7, we performed a systematic literature search to identify experimental functional magnetic resonance imaging (fMRI) investigations of placebo analgesia . Criteria for study eligibility were: (a) published in peer-reviewed journal in the English language; (b) original investigation; (c) human participants; (d) functional neuroimaging of the brain during evoked pain; (e) pain delivered under matched placebo and control conditions. Definitions of placebo and control conditions (see Supplementary Methods and Results) were identical to our previous meta-analysis7. Investigators of eligible studies were contacted and invited to share data. We collected single-participant, first-level, whole-brain standard-space summary images of pain response  from the original analyses, as published, as well as corresponding pain ratings, experimental design parameters, and demographic data , i.e., the effect of painful stimulation compared to baseline, as estimated in the original analyses . Based on this outcome, we performed three comparisons: (i) main effect of pain vs baseline, averaging placebo and control conditions; (ii) pain-related activity acquired under matched placebo and control conditions (placebo–control); and (iii), for studies that manipulated placebo vs. control within-subject, correlations across individuals between the effect of placebo treatment on brain activity and behavioral placebo analgesia .52 stimulus conditions were excluded. For studies that involved left- and right-lateralized stimulation53, strong and weak placebo conditions55, or early- and late heat-pain periods57 maps were averaged on subject level, as in the previous analysis7 (see Supplementary Table 48 only pooled estimates of the main effect of pain were available (see Supplementary Methods and Results for details).Non-painful or mildly painful58 to assess the risk-of-bias of included studies. (Supplementary Methods and Results and Supplementary Table We applied the Cochrane risk of bias tool7 (see Supplementary Methods and Results for details). Voxels missing in >10% of participants (n > 60) or outside of the MNI152 brain-template  were excluded from analysis.Images underwent systematic quality control, as described previouslyg (Hedges’ grm for within-subject studies60), a small-sample bias-corrected version of Cohen’s d, commonly used in meta-analysis58. Furthermore, we used Pearson’s r to assess correlations between placebo analgesia and its effects on brain activity in studies with a within-subject design . Study-level effect size estimates were summarized using the generic inverse-variance (GIV) weighting method, accounting for study as a random effect62. Pearson’s r was transformed to Fisher’s Z for across-study averages and tests of statistical inference. Between-study heterogeneity in effect size was estimated using the τ-statistic, which represents the study-level standard deviation of effect sizes62. Effect size summaries and standard errors were used to calculate z-62 and pseudo-z-scores63, the latter was based on smoothened  instead of raw standard errors, as described in ref. 63. Voxel-wise p-values were obtained by performing a non-parametric permutation-test of the pseudo-z statistic63, correcting for multiple comparisons at Family Wise Error (FWER) level, according to the maximum-z method63. Permutation testing was performed at 1500 random permutations, small (p < 0.01) p-values were approximated by tail-fitting a generalized Pareto distribution64. To perform a robust enhancement of spatially extended activations and, at the same time, allowing for simple z-score-based thresholding, we performed probabilistic threshold-free cluster enhancement (pTFCE)65. Both enhanced and unenhanced z-score maps were thresholded at an FWER-corrected alpha level of p < 0.05. For visualization, unenhanced z-score maps were thresholded at an uncorrected alpha level of p < 0.05. All p-values presented are two-tailed.Outcome assessment was performed in a mass-univariate fashion, separately for each brain voxel. To account for between-study differences in the scaling of pain reports and imaging data, we used standardized effect sizes rather than the raw values. Standardization of effect sizes (mean response to pain and its difference between placebo and control conditions) was based on the between-subject-level standard deviation of pain-related brain activity, separately for each study, by using Hedges’ 14 (resting-state), as well as (ii) insular sub-regions (anatomy based)66, and (iii) thalamic nuclei (anatomy based)67, i.e., the most prominent brain regions involved in pain processing13. Note that the large-scale functional connectivity networks are based on resting-state data, thus reverse-inference and direct associations to task-based activity should be performed carefully. On the other hand, large-scale resting-state networks have de-facto evolved as standard means for brain-wide localization68. Accordingly, we use these canonical networks solely for localization purposes. Obtained participant-level cosine similarity values were summarized using the GIV method, with statistics based on t-tests across studies, treating study as a random effect. No correction for multiple comparisons was performed for the atlas-based analyses due to their exploratory nature. Cosine similarity is equivalent to Pearson’s correlation except for mean-centering, so it remains sensitive to the overall level of activation across the brain and thus reflects absolute normalized activity levels in the regions/networks tested rather than relative activity across regions.To aid the interpretation of results, we utilized cosine similarity in an exploratory analysis comparing the participant-level contrast maps with brain-parcellation atlases representing (i) canonical large-scale functional connectivity networkshttps://github.com/canlab/CanlabCore), and custom functions implementing the GIV method. Further analysis details are provided in the Supplementary Methods and Results.All analyses were performed with MATLAB 2016b, SPM12, the CANlab Core Tools neuroimaging analysis toolbox  was used to create illustrations of statistical parametric maps. All neuroimages shown follow the neurological convention . Effect sizes are interpreted as small, moderate, and large according to the recommendations by CohenFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Few studies assess dog ownership and walking with both self-reported or perceived and audited or objective walkability and physical activity measures. Across two years, we examined both types of walkability and activity measures for residents living within 2km of a “complete street”—one renovated with light rails, bike lanes, and sidewalks. Audited walkability (Irvine–Minnesota Inventory) was more consistently related to dog ownership and walking groups than perceived walkability . Self-reported leisure walking was much higher (289–383 min per week) among dog walkers than among other groups (100–270 min per week), despite no difference in accelerometer-measured light or moderate-to-vigorous physical activity (MVPA). Furthermore, the most powerful difference between groups involved single-family detached home residence, which was much lower among non-dog-owners (44%) than among non-dog-walkers or dog walkers . Given discrepancies across walkability and activity measures, we recommend future use of walkability audits and objectively measured physical activity over the current emphasis on self-report measures. We also urge greater attention to increased densities of housing, which may negatively affect dog ownership levels unless compensating supports for dog ownership and walking are created by public health messaging, dog-friendly policies, and dog-friendly housing and community design. Dogs provide important sources of psychological and behavioral benefits for their human companions ,2,3. RecAmong studies in a meta-analysis that examined dog walking, 24% of respondents owned a dog, with a range that varied considerably across studies, from 10% to 57%. Past research shows that both family and housing characteristics are cited as important reasons to get  or relinA meta-analysis of 17 studies of dog owners found that a median of 59% of owners reported walking their dog, although variability was high, ranging from 3% to 80% . SeveralU.S. residents aged 60 or older reported a significant 17.2 more minutes of walking per week than non-dog-owners , and thoTwo studies of adult dog walkers that used objective measures revealed greater activity among dog walkers. In Baltimore and Seattle, dog walkers accrued 35 min per day in accelerometer-measured moderate-to-vigorous physical activity, which was a significant 2 min/day more than non-dog-owners and 8 min/day more than non-dog-walkers . In the These results suggest that sometimes dog walkers, compared with non-dog-walkers, do not achieve additional physical activity and when they do, the amounts vary substantially across studies. When objective measures are used, the differences across dog owner and walker groups are more modest than when self-reports are used. The results also suggest that it may be important to separate dog owners into those who do and do not walk their dogs when examining walking amounts. The varied results also suggest that it is worthwhile to identify neighborhood environmental features that might encourage dog ownership and walking.When neighborhood walkability is assessed by self-reported walkability, the results have been mixed. For example, San Diego dog walkers, compared with non-dog-walkers, reported better neighborhood aesthetics and more places for walking but did not differ in perceived crime problems in the neighborhood . The bivFew studies have examined neighborhood physical features in relation to walking dogs among adults. In Seattle and Baltimore, after sampling neighborhoods by land use variables that represent high  and low walkable neighborhoods, walkability was higher for non-dog-owners than dog walkers and higher for dog walkers than non-dog-walkers . A similIn sum, the current study examines whether perceived and audited walkability and activity differentiate across three dog owner and walker groups, with separate analyses across two years.Data for this study are from the Moving Across Places Study (MAPS) in Salt Lake City, Utah, USA. The study was designed to assess whether a new light rail transit line encourages walking to transit. Data were collected before (2012) and after (2013) the completion of the 4.2 km line through the middle of the neighborhood. The study was also able to investigate perceived and audited walkability and how walkability was related to other types of activities, such as biking and use of parks and community centers. Although several parks were located within the neighborhood, none had any dog-specific areas or fencing.Blocks within 2 km of the rail line were randomly selected to create the sample. Adults 18 or older were eligible for recruitment if they intended to live there for at least a year, spoke English or Spanish, were not pregnant, and could walk a few blocks. Recruitment was typically conducted door-to-door, although some participants were recruited at community events if otherwise eligible . WithinAs noted in Jensen et al., the 54-item Neighborhood Environment Walkability Scale—Abbreviated (NEWS-A)  providedWe chose the Irvine–Minnesota Inventory because it provided a comprehensive micro-level walkability scale, which focused on each block face—both sides of the street between intersections. As noted from Jensen et al. , 40 itemWe selected the two walking items from the International Physical Activity Questionnaire  . ParticiParticipants wore Actigraph GT3X+ accelerometers  and GPS units for approximately one week to provide objective measures of physical activity. We used definitions for accelerometer non-wear time and thresholds for light and MVPA based on Troiano et al.’s standards set for the analysis of the National Health and Nutrition Examination Survey (NHANES). Specially, accelerometer non-wear time was defined as 60 min of zero accelerometer counts per minute but allowing for spikes of up to 2 min of 100 counts per minute. Light activity was defined as 100 to 2019 counts per minute and MVPA as 2020 counts per minute or higher . TroianoParticipants were asked if they owned a dog and, if so, if they walked the dog “at least a couple of times a week”. Participants were assigned to one of three categories: non-dog-owners, non-dog- walkers, and dog walkers.Chi-square analyses tested the differences across the three groups of dog ownership and walking for dichotomous variables. One-way ANOVAs with Hochberg GT2 post-hoc tests for unequal cell sizes tested differences for continuous predictor variables . The datA multinomial logistic regression tested multivariate differences across the three groups. Prior to conducting the multinomial regression, ordinary least squares regressions were used to test for multicollinearity. In 2013, the audited measures of access and aesthetics showed multicollinearity (>30 collinearity scores with two variables weighing >0.50 on the variance proportion table) . TherefoIn 2012, 63.1% were non-dog-owners, 28% owned and walked their dog, and 9% owned but did not walk their dog. Similar results appeared in 2013, with 60.8% non-dog-owners, 30% owners who walked their dog, and 9.1% who owned but did not walk their dog.Among perceived measures, in 2012, non-dog-owners reported better pedestrian infrastructure and more pleasing aesthetics than non-dog-walkers. Although perceived crime differed across the three groups in the one-way ANOVA, the post-hoc tests did not reveal any pairwise differences. Nevertheless, the same pattern as obtained above was evident, namely that non-dog-owners reported the least crime problems and non-dog-walkers reported the most crime problems. In addition, non-dog-owners reported less time spent walking for leisure than dog walkers, and these two groups did not differ in reported time spent walking to get places.Among audited measures in 2012, non-dog-owners lived in areas that auditors rated to have less accessibility and less crime. Pedestrian infrastructure audited conditions were better among non-dog-owners than among either non-dog-walkers or dog walkers.In 2013, perceived measures showed that self-reported leisure walking was lowest for non-dog- walkers and significantly lower than that for non-dog-owners, which was significantly lower than that for dog walkers. Although perceived pedestrian infrastructure and aesthetics showed a similar pattern as in 2012, with non-dog-owners perceiving better pedestrian infrastructure and aesthetics than non-dog-walkers, the results were not statistically significant.Among audited measures in 2013, the combined access/aesthetics score was lower for non-dog-owners than for the two groups of dog owners. Traffic hazards were lower around the homes of non-dog-walkers than for either non-dog-owners or dog walkers. However, crime indicators were higher among non-dog-walkers than for non-dog-owners or dog walkers. Although pedestrian infrastructure, as in 2012, was better for non-dog-owners than for non-dog-walkers, and the one-way differences across the three groups were significant, no pairwise comparisons were significant.In general, perceived walkability variables were significant 3 out of 10 times and audited walkability variables were significant 7 out of 9 times. Although dog walkers reported more leisure walking than non-dog-walkers in both years, accelerometry-assessed light and MVPA showed no significant differences across the three groups.In addition, females were less likely to be in the non-dog-walker group than the non-dog-owner group in both 2012 and 2013 for analyses involving perceived walkability and activity. Females were also less likely to be in the non-dog-walker group than the non-dog-owner group in 2012 for the analysis involving audited walkability and activity measures in 2012.In the multivariate analyses, the only perceived measure that contributed significant amounts of unique variance was self-reported leisure walking. Recall from The significant audited measures, in 2012, show that pedestrian infrastructure, aesthetics, and traffic hazards significantly differentiated dog walkers from non-dog-owners. For these two groups, dog walkers had lower scores on pedestrian infrastructure, higher scores on aesthetics, and higher scores on traffic hazards, consistent with the pattern of results from These results partially replicate past research but also go beyond past research in the ability to present both self-reported and more objective measures and in replicating analyses over time. For the physical activity correlates, the literature review showed that most prior studies utilized self-reported physical activity measures. The results of the current study show that dog walkers report the most minutes walking for leisure and non-dog-walkers report the least number of minutes, with non-dog-owners in between. In fact, dog walkers reported between 57 and 283 more minutes per week of leisure walking than other groups . This isThis study also provided both trained-rater-assessed and self-reported walkability in relationship to dog owning and walking. Here, the findings partially replicate the two studies of audited walkability among adults. Coleman et al. assessed high- and low-walkability neighborhoods in Seattle and Baltimore, and Koohsari et al. assessed high- and low-walkability cities in Japan ,33. ColeIn terms of perceived walkability, the results of the current study echo the findings of other studies that infrequently found strong links between dog walking or ownership and better perceived walkability. In San Diego, Hoerster et al. found that dog walkers reported more places for walking and pleasant aesthetics than non-dog walkers but these results were reduced to non-significance when controls were added to the model; perceived crime did not differ . In CalgWhen comparing how audited and perceived walkability measures relate to dog owning and walking, the results suggest that perceived measures of walkability did not serve the same function in supporting dog ownership and walking as did audited measures of the physical environment. From the univariate relationships in In fact, the most consistent predictor across all multivariate models is whether the participant lived in single-family detached housing. Only 42% of non-dog-owners lived in detached housing compared with 70% of dog walkers and 81% of non-dog-walkers. Similarly, McCormack et al. reported detached housing for 69.8% of non-dog-owners, 86.7% of non-dog walkers, and 86.9% of dog walkers . Lail etAs the world urbanizes, denser housing options are needed to provide affordability, environmental benefits, and healthy walking opportunities . One uniThe current study balances strengths of objective measures for audits and accelerometry against inevitable limitations. We did not choose our sample area to vary along dimensions of walkability, given that the goal of the underlying project was to investigate one bounded geographic area. In addition, our self-reported walking did not specify walking with dogs in the neighborhood, although most dog walking is done in one’s neighborhood . Nor didGiven that our study demonstrated that residents said they achieved more leisure walking time when they were dog walkers but that accelerometer measures did not confirm these results, it is important to consider possible reasons. Dog walks are complex events in which the amount of physical activity may not be recalled accurately because the activity is interwoven with other experiences. A dog walk can become a “dog-stand” while chatting with neighbors or a “dog-sit” while owners appreciate watching the sunset or dogs cavorting together. Humans may overestimate how much physical activity they accrue on dog walks, given the natural diversions into more sedentary activity along the way. Dog walkers who choose to set specific goals for steps or physical activity minutes during dog walks may want to verify their activity amounts against objective measures, such as pedometers or activity watches. If activity goals fall short, the dog and owner might enjoy extending their walk, or the owner may compensate with more time on the exercise bike back home. However, the advice to consult activity feedback might backfire, if, as a reviewer suggested, the feedback transforms the complex enjoyable walk into a less enjoyable, externally motivated quest by humans for more steps. We acknowledge that there are many parts of a dog walk beyond physical activity that make them healthy for dogs and their owners—pleasurable routines, neighborly interactions, reduced stress, psychological well-being, and enhanced bonds ,3,53. FuThe current study revealed a number of relationships between walkability and physical activity among our three groups: non-dog-owners, non-dog-walkers, and dog walkers. More audited walkability than perceived walkability indicators were related to dog owning and walking groups. Furthermore, dog walkers reported high levels of leisure walking, but these high levels were not corroborated by objective accelerometer measures. We encourage future researchers to replicate these results. If replicable, there needs to be more public health efforts to assess the effects of encouraging dog walkers to track their activity using step or activity counters, to see if this aids in physical activity goals and dog walking enjoyment. These monitoring efforts could be complemented by housing policy and community design efforts to make dog ownership and walking an easier choice than may be experienced by many residents who live in dwellings that are not single-family detached homes. In addition, techniques that encourage physical activity to become habitual, such as providing attractive dog parks or encouraging group walks, may be needed to turn occasional walks into measurable physical activity increases.


New/novel psychoactive substances (NPS) are defined as new narcotic/psychotropic drugs which are not controlled by the United Nations’ 1961 Narcotic Drugs/1971 Psychotropic Substances conventions, but which may pose a public health threat [NPS are a reason of current/potential public health concern and typically include: synthetic cannabinoids, cathinone derivatives, psychedelic phenethylamines, novel stimulants, synthetic opioids, tryptamine derivatives, phencyclidine-like dissociatives, piperazines, GABA-A/B receptor agonists, a range of prescribed medications, psychoactive plants/herbs, and a large series of image- and performance-enhancing drugs . Users The accelerating speed of NPS appearance  is beingBoth the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and the United Nations Office on Drugs and Crime (UNODC) include an index NPS in their database only when the emerging NPS is seized, chemically analyzed and reported to them. Between 2009 and 2017, a total of 803 NPS were reported to the UNODC by 111 countries/territories. Conversely, by the end of 2017, the number of EMCDDA NPS was over 670, of which 632 were notified after 2004; most molecules were synthetic cannabinoids, synthetic cathinones, phenethylamine derivatives and synthetic opioids  and Enhancing Police EPS/NPS project (http://www.npsproject.eu/) are currently being adapted and refined for deep-web monitoring. The earliest of the possible appearance of a new substance might be evidenced on the deep, followed by a migration to the open, web . EventuaThe hidden-web drug marketplaces and crypto markets are increasingly being used for the anonymous sale of drugs, including NPS. Recent legislation, such as the May 2016 UK Psychoactive Substances Act, has been created to tackle NPS diffusion . As commWadsworth et al.  elegantlAnalyzing the information/data made available by e-psychonauts requires the management of ‘high-volume, high-velocity, and high-variety’ range of modifications in the NPS market, hence of ‘big data’ . The use of automatic web crawler software such as the University of Hertfordshire (UK)-coordinated NPS.finder has allowed both scanning and identification activities of a constant flow of drug-related data, providing a nearly real-time update ,12. CurrSo far, an excess of 4200 different NPS have been identified, mostly being phenethylamines and synthetic cannabimimetics ,4. To imSo far, the NPS.Finder has crawled on the open web only. Future studies by our group will be expanding drug searches on less accessible areas of the web, such as the deep web and the most elusive darknet . A qualiIt is here concluded that the open-, deep- and dark-web areas’ monitoring activities may well possess the potential to identify a wide range of novel/previously undescribed NPS. Better levels of misusing drugs’ clinical pharmacological-related knowledge are needed so that properly tailored management/treatment strategies and guidelines can be drawn up and made available. Finally, strengthening multidisciplinary collaboration between clinicians and bioinformatics may prove useful in better assessing the NPS-associated public health risks.