Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter. Existing genetic prediction tools typically assume that genetic variants contribute equally towards the phenotype. The authors develop eight prediction tools that allow the user to specify the heritability model, and show that these tools enable substantially improved prediction of complex traits. They will also enable more widespread use of precision medicine, for example, by enabling us to better identify subgroups of individuals with elevated risk of developing a particular disease, or those with lowest chance of responding to a particular medication7.There is a great demand for more accurate genetic prediction models of complex traits. Better models will, for example, improve our ability to investigate genetic architecture, detect genetic overlap between traits and search for gene–environment interactions9. The resulting predictions are called polygenic risk scores (PRS). They take the form P = X1β1 + X2β2 + … + Xmβm, where m is the total number of SNPs, while Xj and βj denote, respectively, the genotypes and estimated effect size for SNP j. Tools for constructing PRS differ in how they estimate the SNP effect sizes. The simplest way to construct a PRS is using effect size estimates from single-predictor regression . However, it is generally better to use an advanced prediction tool that estimates effect sizes using a multi-SNP regression model14.Many complex traits have high SNP heritability, which justifies the use of genome-wide, linear, SNP-based prediction modelsh2j], the expected heritability contributed by each SNP, varies across the genome15. Almost all existing advanced prediction tools automatically assume that E[h2j] is constant. We refer to this as the GCTA Model, because it a core assumption of the software GCTA .(8) In particular, the GCTA Model is assumed by any prediction tool that uses a multi-SNP regression model and assigns the same penalty or prior distribution to standardized SNP effect sizes16. However, the GCTA Model is suboptimal. Recently, we provided a method for comparing different heritability models using summary statistics from genome-wide association studies17. Across tens of complex traits, the model that fit real data best was the BLD-LDAK Model, in which E[h2j] depends on minor allele frequency (MAF), local levels of linkage disequilibrium and functional annotations.Advanced prediction tools start by making prior assumptions regarding how SNPs contribute toward the phenotype. These assumptions include specifying a heritability model, which describes how E is constant, the LDAK-Thin Model allows E[h2j] to vary based on the MAF of SNP j, while the BLD-LDAK Model allows E[h2j] to vary based on the MAF of SNP j, local levels of linkage disequilibrium and functional annotations17. Our previous work compared heritability models based on how well they fit real data17. Specifically, we measured their performance via the Akaike Information Criterion23 (AIC), equal to 2 K - 2logl, where K is the number of parameters in the heritability model and logl is the approximate log likelihood (lower AIC is better). Across the 12 models we considered, AIC was lowest for the BLD-LDAK Model, highest for the GCTA Model, and intermediate for the LDAK-Thin Model , 20 h (LDAK-Bolt-Predict) or 50 h (big_spLinReg and LDAK-BayesR-Predict), and require 35 Gb memory .R2 by on average 9% (s.d. 2%), while replacing the GCTA Model with the BLD-LDAK Model increases R2 by on average 14% (s.d. 2%). Moreover, when run assuming the BLD-LDAK Model, LDAK-Bolt-Predict outperforms our implementations of the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We note that the performances of LDAK-Bolt-Predict and LDAK-Bayes–Predict are very similar. For example, when run assuming the BLD-LDAK Model, the tools have average R2 0.080 and 0.081, respectively (s.d.s 0.001), and each tool produces the most accurate PRS for seven of the 14 phenotypes. Therefore, our decision to recommend LDAK-Bolt-Predict simply reflects its faster runtime.Figure R2/h2SNP indicates the accuracy of a PRS relative to the maximum possible accuracy. When we use 200,000 training samples, the PRS achieve between 13%  and 62% (height) of their potential. The lines of best fit suggest that if we had individual-level data for 400,000 samples, the PRS would explain between 23 and 78% of SNP heritability.Figure R2 for between 217 and 225 phenotypes (depending on tool), while switching from the GCTA Model to the BLD-LDAK Model increases R2 for between 223 and 225 phenotypes. LDAK-BayesR-SS has the highest average R2 of the four prediction tools, and produces the most accurate PRS for 137 of the 225 phenotypes.Now we use our four new summary statistic tools to construct PRS for all 225 UK Biobank phenotypes. To construct each PRS takes under 2 h (regardless of which tool we use) and requires <10 Gb memory. Supplementary Fig. R2 is 14% (s.d. 1%). Consistent with simulations  if we consider only the 106 phenotypes with R2 < 0.05, only the 51 phenotypes with 0.05 < R2 < 0.1, or only the 68 phenotypes with R2 > 0.1.Figure R2. Supplementary Fig. For our main analyses, we measured the accuracy of PRS using 31, then test them using UK Biobank data. Again, we find that for all phenotypes, the accuracy of PRS improves when we replace the GCTA Model with the LDAK-Thin or BLD-LDAK Model. This indicates that the improvements we observed in the main analyses are not an artifact of genotyping errors .For Supplementary Fig. For our main analyses, we used white British individuals from the UK Biobank both to train and test the PRS. For Supplementary Fig. 9. We have instead developed tools that allow the user to specify more realistic heritability models, and shown how these enable the creation of substantially more accurate PRS. Of our eight new tools, we recommend using LDAK-Bolt-Predict when analyzing individual-level data, and LDAK-BayesR-SS when analyzing summary statistics .Most existing prediction tools start with the assumption that each SNP contributes equal heritabilityR2 due to changing from the GCTA Model to the BLD-LDAK Model was 14% (s.d. 2%). We showed that this increase is equivalent to increasing the sample size by about a quarter. To provide further perspective, consider that the average increase when switching from using LDAK-Bolt-SS to LDAK-Bolt-Predict  was 2% (s.d. 2%), the average increase when switching from using directly-genotyped SNPs to imputed genotypes was 7% (s.d. 2%), the average increase when switching from using LDAK-Ridge-Predict to LDAK-Bolt-Predict  was 16% (s.d. 2%), while the average increase when switching from classical PRS to LDAK-Ridge-Predict  was 17% (s.d. 3%).When using LDAK-Bolt-Predict, the average increase in A strength of our study is that we have considered a variety of complex traits. These include continuous, binary and ordinal phenotypes, that have low, medium and high SNP heritability, and that are both closely and distantly related to diseases. Therefore, the fact that we increased prediction accuracy for almost all of the 225 phenotypes we analyzed, makes us confident that improvements will be observed for many more complex traits. Similarly, our new prediction tools have varying forms of prior distribution for SNP effect sizes. Therefore, the fact that prediction accuracy increased for all tools, indicates that if a new tool is developed with a superior prior distribution form, it is likely that this tool could also be made more accurate by improving the heritability model.32 and LDpred-funct33. AnnoPred is similar to LDAK-Bolt-SS. It assumes that SNP effect sizes have the prior distribution p0 N + (1-p0) δ0, then incorporates the chosen heritability model by allowing either σ2 or p0 to vary across SNPs32. Supplementary Fig. σ2), where σ2 varies across SNPs according to the chosen heritability model, then regularizes these estimates via cross-validation33. Supplementary Fig. We are aware of two existing summary statistic prediction tools where the user can specify the heritability model, AnnoPred + -p0 δ0, t + -p0 δ0, t17. We now recommend the same when constructing PRS. Based on average AIC, the BLD-LDAK, LDAK-Thin and GCTA Models rank first, second and third, respectively, which matches their order when ranked based on the average accuracy of the corresponding PRS. We additionally construct PRS assuming the GCTA-LDMS-I34 and Baseline LD Models35, those currently recommended by the authors of GCTA8 and LDSC36, respectively. Based on average AIC, these two models rank between the LDAK-Thin and BLD-LDAK Models . This is relevant because, at present, most successful applications of genetic prediction models38 involve PRS with small or modest R2. For example in psychiatric research, a PRS with R2 ≈ 0.05 was used to show that impulsivity is an endophenotype for attention deficit hyperactivity disorder39, a PRS with R2 ≈ 0.07 was used to show that individuals with chronic schizophrenia had higher-than-average genetic liability to schizophrenia40, a PRS with R2 ≈ 0.02 was used to identify clinically-defined subtypes of autism that have significantly different genetic liabilities41, a PRS with R2 < 0.05 was used to demonstrate that risk of developing emotional problems is moderated by an interaction between environmental sensitivity and type of parenting42, and a PRS with R2 ≈ 0.01 was used to demonstrate that stressful life events and childhood trauma are risk factors for the development of major depressive disorder43. Away from psychiatric research, Khera et al.5 demonstrated the utility of genetic risk prediction for atrial fibrillation, breast cancer, coronary artery disease, inflammatory bowel diseases and type 2 diabetes using PRS with R2 between 0.02 and 0.04.Although we observed improvement for almost all of the 225 UK Biobank phenotypes, we found that the relative advantage of our new prediction tools was largest for phenotypes with small and modest We finish by noting that the performance of our new prediction tools will increase as more realistic heritability models are developed. To date, most of the improvement in PRS accuracy has come from increasing sample size, algorithmic innovations or developing more effective forms of prior distribution for SNP effect sizes. Our work indicates that in future, more focus should be placed on improving the heritability model.We begin by explaining our new prediction tools. Note that before running each tool, it is necessary to estimate the expected heritability contributed by each SNP, given the heritability model. Our prediction tools then use these estimates to set the parameters of the effect size prior distribution for each SNP.X denote the matrix of genotypes , and Y denote the vector of phenotypes (length n). For convenience, the Xj and Y are standardized, so that Mean(Xj) = Mean(Y) = 0 and Var(Xj) = Var(Y) = 1. We assume that the chi-squared (one degree of freedom) test statistic for SNP j from single-SNP analysis is Sj = n rj2 / (1 – rj2), where rj = XjY/n is the correlation between SNP j and the phenotype . We consider prediction tools that use the linear modelβj is the effect size for SNP j, and β = T. Because Xj and Y are standardized, the heritability contributed by SNP j is h2j = βj2.Suppose there are n individuals and m SNPs. Let 17ajk are pre-specified SNP annotations, while the parameters τk are estimated from the data44. In total, we consider five heritability models . Then SumHer estimates the τk by regressing the Sj on the E[Sj]. For further details see our earlier publications44. Note that while SumHer can allow for confounding bias . As well as estimating τk, SumHer also reports ej, the estimate of E[h2j] obtained by replacing the τk in Eq. , where σ2e is the residual variance. They differ in their forms of prior distributions for SNP effect sizes . LDAK-Ridge-Predict and LDAK-Ridge-SS use single Gaussian distributions, βj ~ N and βj ~ N, respectively. LDAK-Bolt-Predict and LDAK-Bolt-SS use a mixture of two Gaussian distributions, βj ~ p N/p E[h2j]) + (1-p) N E[h2j]). LDAK-BayesR-Predict and LDAK-BayesR-SS use a mixture of a point mass at zero and three Gaussian distributions, βj ~ π1 δ0 + π2 N + π3 N + π4 N, where π1 + π2 + π3 + π4 = 1 and s = (π2/100 + π3/10 + π4)−1. The biological intuition behind the different prior distribution forms is explained in Supplementary Fig. h2j]=ej (the estimate from SumHer), and σ2e = 1-Σej. The remaining prior parameters  are decided using cross-validation, as explained below.Each of our new tools assumes that the error terms in Eq.  are normzes Fig. . big_spL46. The original version of the function is described in Prive et al.47; the most recent version is the same, except that it allows the user to provide penalty factors that transform the prior from βj ~ DE(λ) to βj ~ DE(λ / E[h2j]0.5). In summary, big_spLinReg estimates the βj using coordinate descent with warm starts49. Given a value for λ, the βj are updated iteratively (starting from zero) until they converge. Within each iteration, each βj within the strong set (the subset of predictors determined most likely to have non-zero effects49) is updated once, by replacing its current value with its conditional posterior mode. λ starts at a value sufficiently high that βj = 0 for all SNPs, then is gradually lowered to allow an increasing number of SNPs to have non-zero effects. big_spLinReg uses ten-fold cross-validation to decide when to stop reducing λ.big_spLinReg is a function within our R package bigstatsrβj and deciding values for p and f2 as the existing tool Bolt-LMM18. In summary, LDAK-Bolt-Predict uses variational Bayes to estimate the βj. Given values for p and f2, LDAK-Bolt-Predict updates the βj iteratively (starting from zero), until the approximate log likelihood converges. Within each iteration, each βj is updated once, by replacing its current value with its conditional posterior mean. LDAK-Bolt-Predict considers 6 values for p  and three values for f2 , resulting in 18 possible pairs for p and f2. First LDAK-Bolt-Predict estimates effect sizes for each of the 18 pairs using data from 90% of samples. Then it identifies which pair results in the best fitting model (based on the mean squared difference between observed and predicted phenotypes for the remaining 10% of samples). Finally, for the best-fitting pair, it re-estimates effect sizes using data from all samples. Note that whereas Bolt-LMM begins by using REML (restricted maximum likelihood)50 to estimate h2SNP, then sets E[h2j]=h2SNP/m, LDAK-Bolt-Predict does not require this step because it instead sets E[h2j] based on estimates from SumHer (see above). Supplementary Fig. LDAK-Bolt-Predict uses the same algorithm for estimating the p = f2 = 0.5. Therefore, LDAK-Ridge-Predict uses the same algorithm as LDAK-Bolt-Predict, except that it fixes p = f2 = 0.5 and it is no longer necessary to perform the cross-validation step. Supplementary Fig. 19 (Best Linear Unbiased Prediction).The prior distribution used by LDAK-Ridge-Predict matches that used by LDAK-Bolt-Predict when 11. However, we do not have sufficient resources to apply BayesR to the full UK Biobank data (we estimate that this would require ~900 Gb and weeks of CPU time). Therefore, LDAK-BayesR-Predict instead uses variational Bayes and cross-validation. The algorithm is the same as for LDAK-Bolt-Predict, except that it is now necessary to select suitable values for π1, π2, π3 and π4. In total, we consider 35 different combinations: the first is the ridge regression model  = ; the remaining 34 are obtained by allowing each of π2, π3 and π4 to take five values , with the restrictions π4 ≤ π3 ≤ π2 and π2 + π3 + π4 > 0. We investigated omitting the restriction π4 ≤ π3 ≤ π2, in which case there are 125 different triplets, however, we found that while this takes approximately four times longer to run, it did not significantly improve prediction accuracy. In Supplementary Fig. The existing tool BayesR estimates all parameters using Markov Chain Monte Carlo (MCMC)βj for one value of λ are used as the starting βj when λ is reduced), but instead, each of the ten cross-validation runs can be performed independently. Additionally, for the functions LDAK-Ridge-Predict, LDAK-Bolt-Predict and LDAK-BayesR-Predict, LDAK automatically creates a save-point every 10 iterations, so that the job can be restarted if it fails to complete within the allocated time.The runtimes reported in the main text  correspond to using a single CPU. However, for big_spLinReg, LDAK-Bolt-Predict and LDAK-BayesR-Predict, we also provide parallel versions. For LDAK-Bolt-Predict and LDAK-BayesR-Predict, the parallel versions utilize the fact that models corresponding to different parameter choices can be generated independently . For big_spLinReg, this is not possible , training summary statistics  and test summary statistics (computed using the remaining samples). In some cases, you will already have (or be able to construct) training and test summary statistics. However, most likely, you will only have full summary statistics, in which case you should first generate pseudo training and test summary statistics (see below).XjY can be recovered from the results of single-SNP regression , where n is the sample size and rj = XjY/n), while XjXl can be estimated from the reference panel . Note that in the equations below, Xj, Y and n vary depending on context. When using full summary statistics, Xj and Y contain genotypes and phenotypes for all samples, and n is the total number of samples. When using training (test) summary statistics, Xj and Y contain genotypes and phenotypes for only training (test) samples, and n is the number of training (test) samples.MegaPRS exploits that, in the absence of individual-level data, MegaPRS has three steps. In Step 1, it uses the reference panel to estimate SNP–SNP correlations. In Step 2, it constructs pairs of prediction models, first using the training summary statistics (we refer to these as the “training models”), then using the full summary statistics (the “full models”). In Step 3, it uses the test summary statistics to identify the most accurate of the training models, then reports effect sizes for the corresponding full model. For our analyses, each step took <30 min and required <10 Gb memory.cjl . MegaPRS saves the local, significant pairs in a binary file, which requires 8 bytes for each pair . For the UK Biobank data, there were 260 M local, significant pairs , and so the corresponding binary file had size 1.9 Gb.In Step 1, MegaPRS searches the reference panel for local pairs of SNPs with significant βj with its conditional posterior mean. This is possible because for all four tools, the posterior distribution for βj can be expressed in terms of XjY and XjXl. For example, if we write the prior distribution for LDAK-Bolt-SS in the form βj ~ p N + (1-p) N, then the conditional posterior distribution of βj has the form p’N + (1-p’)N, whereIn Step 2, MegaPRS uses the training and full summary statistics to estimates effect sizes for training and full prediction models, respectively. Pairs of training and test models correspond to different prior distribution parameters. MegaPRS constructs 11 pairs of models if running LDAK-Lasso-SS or LDAK-Ridge-SS, 132 pairs of models if running LDAK-Bolt-SS and 84 pairs of models if running LDAK-BayesR-SS . These differences accumulate over the genome, resulting in poor estimates of XjTXβ = ΣlXjTXlβl, and therefore poor estimates of the conditional posterior distribution of βj. To avoid these problems, MegaPRS uses sliding windows . If a window fails to converge within 50 iterations, MegaPRS resets the βj to their values prior to that window. We found this happened rarely. For example, our main analyses constructed 160,650 full models , and for only 990 of these (0.6%) did any of the ~12,000 regions fail to converge.When performing variational Bayes using summary statistics, we found it was not feasible to iterate over all SNPs in the genome. This was due to differences between estimates of β’ denotes the vector of estimated effect sizes for a model, MegaPRS calculates R = β’TXTY /(nβ’TXTXβ’)1/2, an estimate of the correlation between observed and predicted phenotypes for the individuals used to compute the test summary statistics. MegaPRS then constructs the final model by extracting effect sizes from the full model corresponding to the training model with highest R.In Step 3, MegaPRS uses the test summary statistics to measure the accuracy of the training models. If 13, LDAK-Ridge-SS with sBLUP20, LDpred-inf12 and LDpred-funct33, LDAK-Bolt-SS with LDpred251 and AnnoPred32, and LDAK-BayesR-SS with SBayesR14. When we run our new tools assuming the GCTA model, they perform at least as well as the corresponding existing tools.In Supplementary Fig. n samples, we wish to generate two sets of results, mimicking those we would obtain from first analyzing nA < n samples, then analyzing the remaining nB = n - nA samples. We can reword the task as follows. Let γ = T denote the vector of true SNP effect sizes from single-SNP analysis . Given XTY/n, the estimate of γ from all n samples, our aim is to generate XATYA/nA and XBTYB/nB, estimates of γ from nA and nB samples, respectively.Given results from single-SNP analysis using XATYA/nA and XBTYB/nB is a modified version of that proposed by Zhao et al.52. First we sample XATYA/nA from N, where V is the variance of XTY, then we set XBTYB/nB = (XTY – XATYA)/nB. In their method, Zhao et al. restrict to independent SNPs, leading them to derive V = I + XTYYTX/n2, where I is an identity matrix. However, as we wish to accommodate SNPs in linkage disequilibrium, we instead use V = XTX, as proposed by Zhu and Stephens53. If X’ denotes the (standardized) genotypes of the reference panel (size n’ x m), then an estimate of V is X’TX’n/n’, and therefore we achieve the desired sampling by setting XATYA/nA = XTY/n + (nB/nA)1/2X’T/n’1/2g, where g is a vector of length n’ with elements drawn from a standard Gaussian distribution.The method we use to generate R for the training models (measured using pseudo test summary statistics) mirror the estimates of R for the corresponding full models (measured using the independent test data), indicating that it is valid to use pseudo summary statistics to decide prior distribution parameters. However, we note two caveats. Firstly, we observe that estimates of R can be unreliable when calculated using a reference panel that was also used to create the pseudo summary statistics and/or to construct the prediction models. Therefore, when running MegaPRS using pseudo partial summary statistics, we ensure that the reference panel used in Step 3 is distinct to the reference panel used in Steps 1 and 2. Secondly, we found that estimates of R can be unreliable when there are strong effect loci within regions of long-range linkage disequilibrium . Therefore, when estimating R using pseudo summary statistics, we recommend excluding regions of long-range linkage disequilibrium (a list of these are provided at www.ldak.org/high-ld-regions).As explained above, our primary use of pseudo summary statistics is to construct and test training prediction models, in order to decide parameters of the effect size prior distribution. Supplementary Fig. 22, for which we have access to phenotype and genotype data via Application 21432. These phenotypes are: body mass index (data field 21001), forced vital capacity (3062), height50, impedance (23106), neuroticism score (20127), pulse rate (102), reaction time (20023), systolic blood pressure (4080), college education (6138), ever smoked (20160), hypertension (20002), snorer (1210), difficulty falling asleep (1200) and preference for evenings (1180). Starting with all 487k UK Biobank individuals, we first filtered based on ancestry 17, then filtered so that no pair remained with allelic correlation >0.0325 (that expected for second cousins). Depending on phenotype, there were between 220,399 and 253,314 individuals . From these, we picked 200,000 and 20,000 individuals to use for training and testing prediction models, respectively. For all analyses, we used adjusted phenotypes, obtained by regressing the original phenotypic values on 13 covariates . These covariates are age (data field 21022), sex31, Townsend Deprivation Index (189) and ten principal components . Supplementary Table When using our individual-level tools, we constructed prediction models for 14 phenotypes from UK BiobankThe UK Biobank provides imputed genotype data, but in general we restricted to the 628,694 autosomal SNPs with information score >0.9, MAF > 0.01 and present on the UK Biobank Axiom Array  or use summary statistics from the Neale Lab. Although we downloaded results for 283 phenotypes, in the main text, we restrict to the 225 phenotypes for which it was possible to generate a PRS with R2 > 0.01 (using any tool and any heritability model). We made this choice because it is difficult to reliably compare the performance of tools using PRS with very low, and often non-significant, R2. However, we note that for the 58 phenotypes we rejected, it remained that improving the heritability model increased R2 on average 93% of time, and that the average improvement in R2 was 19% (s.d. 3%) when we switched from the best existing tool to LDAK-BayesR-SS assuming the BLD-LDAK Model.When using our summary statistic tools, we constructed prediction models for 225 phenotypes from UK Biobank . Note that when using individual-level data prediction tools, for which we use a reference panel to estimate E[29, atrial fibrillation28, breast cancer31, inflammatory bowel disease26, prostate cancer30, rheumatoid arthritis24, schizophrenia25 and type 2 diabetes27. We chose these diseases as they were the ones for which we could find both cases in the UK Biobank and publicly-available summary statistics from a genome-wide association study that did not use UK Biobank data. We excluded SNPs with ambiguous alleles (A&T or C&G) or that were not present in our UK Biobank dataset, after which on average 470,000 SNPs remained .For the analysis in Supplementary Fig. For the analysis in Supplementary Fig. 54. In this case, average R2 reduces by ~3% .In Supplementary Fig. 19, Bolt-LMM18, BayesR11, lassosum13, sBLUP20, LDpred-funct33, LDpred251, AnnoPred32 and SBayesR14, we used the default settings of each software .Supplementary Fig. βj from single-SNP analysis. We considered six p value thresholds  and four clumping thresholds . We decided the most-suitable pair of thresholds via cross-validation. Similar to above, we first constructed 18 models using 90% of training samples, then tested these models using the remaining 10% of training samples. We then used the best-performing p value and clumping thresholds to construct the final model .When constructing Classical PRS, we used estimates of Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Similarly, higher POCUS use was observed in younger physicians. In conclusion, PE is frequently performed and rates of confidence are low for most PE signs, especially among young doctors and POCUS users. This detailed inventory suggests an ongoing shift towards POCUS integration in clinical practice.Physical examination (PE) has always been a corner stone of medical practice. The recent advances in imaging and fading of doctors’ ability in performing it, however, raised doubts on PE usefulness. Point-of-care ultrasonography (POCUS) is gaining ground in medicine with the detection of free fluids being one of its main applications. To estimate physicians’ confidence and use of PE and POCUS for the detection of abdominal or pleural free fluid, we conducted a cross-sectional survey. In all, 246 internal and emergency medicine physicians answered to the survey . Almost all declared to perform PE in case of suspected ascites or pleural effusion . The highest rates of confidence were observed in conventional PE signs . For the remaining signs, rates of confidence were less than 53%. Physicians with > 15 years of experience and POCUS-naïve doctors reported higher confidence in PE. Most of emergency and almost half of internal medicine physicians  attended a structured POCUS course. POCUS use was higher among trained physicians for both ascites (84% vs 50%,  Evenectively . Moreoveectively  and reduectively  when comectively . POCUS iectively . The useectively . POCUS hectively , 20, 21 ectively , 23. Theectively .If the use and perceived usefulness of general PE was generally high in preceding surveys , 26, no A web-based anonymous survey was designed to answer the following four questions: (1) use of PE (any sign) in case of suspected abdominal or pleural free fluid, (2) confidence in 4 signs of ascites  and 5 signs of pleural effusion , (3) use of POCUS and (4) frequency of radiologist referral for diagnosis.Participants replied using a 5-point Likert scale if they used  the previously reported PE signs in their common practice and how frequent (1 = “Never” to 3 = “Five to ten times” to 5 = “More than twenty times”) they had been using them in last three months. They additionally reported how confident  they were in these signs. For purposes of simplicity, answers on PE use were grouped in a dichotomic way in “Use” (i.e. “Strongly agree” to “Agree”) and “Don’t use” . Answer on frequency were grouped in “Often” (i.e. “Five to ten times” to “More than twenty times”) and “Seldom” (i.e. “Never” or “Less than five times”) and answers on confidence in “Confident” (i. e. “Very confident” and “Confident”) and “Unconfident” . To estimate the global confidence in PE, a 1 to 6 score was attributed to each PE sign as follows: “Very confident” = 1, “Confident” = 2, “Neutral” = 3, “Unconfident” = 4, “Completely unconfident” = 5; a value of 6 was attributed if PE signs were never used. A total score ranging from 9 to 54 could be obtained when all answers were added. Nine to 18 were considered as “High confidence”, 19 to 36 as “Intermediate confidence” and 37 to 54 as “Low confidence”. Finally, participants informed how frequent  they were using POCUS and they were referring for radiologist ultrasonography or chest X-ray. Answers were grouped in “Often” (i.e. “Almost ever” to “Often”) and “Seldom “ . Characteristics of participants were collected and included, among others, place of practice , obtained or targeted speciality, years of clinical practice and previous POCUS training. The original survey is available online (in French).The survey was hosted online by a commercial site (SurveyMonkey). A link to the survey was distributed by email to 197 GPs and to the heads of internal medicine (IM) and emergency medicine (EM) departments of hospitals in French-speaking cantons of Switzerland, who would subsequently distribute it to all doctors working in their facilities. The data were recorded anonymously; an email recall was planned, but failed to be sent because of Covid-19 pandemic and subsequent clinical priorities.Descriptive statistics were used to present characteristics of participants and results. Proportions were compared with Pearson chi-square test. A two-sided P value of less than 0.05 was considered to infer statistical significance. Statistical analyses were performed using SPSS, version 26. Local ethical committee confirmed that a formal approval was unnecessary for the present study.From November 2019 to January 2020, the survey was distributed to 197 GP and to the heads of IM and EM departments of 12 hospitals in French-speaking Switzerland, who relayed it to 667 in-hospital physicians (IHP). A total of 246 physicians answered to the survey (39 GP and 207 IHP) corresponding to a total response rate of 28.5%. Most participants (77.6%) came from the Geneva lake area, worked in hospitals , and obtained or targeted a specialisation in internal medicine (IM) (93.1%). It is worth noting that in Switzerland IM title is required for GP and that emergency medicine (EM) is a complementary certificate, often obtained after an IM speciality. Characteristics of participants are presented in Table P = 0.001; 35% vs 5% for ascites, P = 0.001). When compared with EM doctors, IM physicians tended to report a more frequent use of PE  and a greater global confidence in PE . In-hospital physicians reported a more frequent use of PE in the last three months than did GP  and greater devices availability  were reported by IHP when compared with primary-care physicians. In hospitals, a larger proportion of EM physicians declared to be POCUS-trained than did IM physicians . POCUS-trained physicians reported a lower use of PE than did untrained for both ascites  and pleural effusion . Global confidence in PE signs was also lower for POCUS-trained doctors . Moreover, POCUS-trained participants mentioned a greater POCUS use in case of suspected abdominal  or pleural free fluid  and lower rate of radiologist referral for diagnosis of ascites  and pleural effusion . The results are presented in Fig. Globally, less than two-third of respondents affirmed to frequently use POCUS in case of suspected abdominal or pleural free fluid . Half of participants (50.0%) affirmed having attended a structured POCUS course. More than three quarters (75.9%) of participants reported to easily access to US devices at their workplace. Greater proportions of POCUS-trained physicians  and POCUS use greater  in less experienced doctors. As a result, senior doctors declared referring more frequently to radiologist for diagnosis . The results are presented in Fig. Globally, most of physicians with > 15 years of experience were confident in PE (94.0% of high or intermediate confidence), whereas less than half affirmed to use POCUS to confirm presence of ascites (49.0%) or pleural effusion 42.0%). Proportion of confident in PE was significantly lower , rates of confidence were significantly lower for the other clinical signs. Interestingly, there was no apparent relationship between reported confidence and diagnostic performances, with lower confidence given to signs with better likelihood ratios (e.g., reduced tactile vocal fremitus and asymmetric chest expansion, see Table Almost 30 years after the advent of POCUS , this vaPOCUS was initially developed in intensive care and EM; it is much more integrated in clinical practice in these specialities when compared with IM, being on the contrary a late POCUS-adopter discipline . FurtherThe present study has several limitations. First, less than one third of contacted physicians answered to the survey. Although low, this is usual in such professional surveys  and sligIn conclusion, PE still occupies a central place in detection of free fluids. The established POCUS superiority over PE signs for fluid detection, however, has introduced a progressive change of practice that may become evident in the following generations of physicians. As it happened with the revolutionary advent of stethoscope 200 years ago, POCUS should be progressively integrated and enhance physical examination, instead of replacing it. For this reason, teaching efforts should be placed in valuable components of PE and in developing structured POCUS training.


Here, we provide data on the frequency of complications for both the standard “pull” and the non-endoscopic, radiologic assisted, We retrospectively identified all patients treated with DLI from October 2009 to January 2020 at the Movement Disorders Center. Patients features and demographics, PEG-J procedures, causes for any discontinuation, reported complications and mortality were collected. In this cohort, PEG-J replacements were performed using the standard “pull” procedure or the radiologic assisted “push” method. Descriptive statistical analysis, t-test and paired t-test with False Discovery Rate correction was performed.This retrospective study included 30 APD patients . Mean treatment duration was 35.6 (30.6) months. Overall, 156 PEG-J procedures were performed, and Nineteen patients (63.3%) had a total of 185 reported complications, 85 of which were peristomal complications. 17 (56.6%) underwent 100 replacement procedures due to complications. The most commonly reported complication for replacement was J-tube dislocation (36%). One patient discontinued treatment after 6 months, due to peripheral neuropathy. Six patients died for causes not related to DLI. PEG-J replacements performed with the “push” method had a higher turnover (5.6 vs. 7.6 mo.), but fewer reported complications (67 vs. 75%).The overall rate of complications was lower for “push” technique. This result might have been due to a higher replacement turnover that acted as a protective factor. Duodenal levodopa infusion (DLI) is a widely utilized treatment for patients with advanced Parkinson’s disease (APD).  It provimH&Y stage, DLI doses and causes of withdrawal. The last follow-up visit was recorded in January 2020.This observational retrospective study was carried out to collect data from APD patients treated with DLI at the our Movement Disorders Center. All reviewed files were of patients who met diagnostic criteria for PD  and who Descriptive statistical analysis was performed for collected data. A t-test, with False Discovery Rate (FDR) correction, was performed to compare all clinical features for “pull” and “push” techniques. A paired t-test with with False Discovery Rate (FDR) correction was performed to compare scheduled and unexpected replacements was performed.The study conformed to the Declaration of Helsinki guidelines and was approved by the local Ethics Committee Comitato Etico regionale (CER) Umbria (n. 4032/19).n = 44), serous secretions (n = 20), surgical wound granuloma (n = 9), peristomal edema (n = 6), purulent secretions (n = 3), sero-ematic secretions (n = 2), candida infection (n = 1). 17 patients (56.6%) underwent a total amount of 100 PEG-J replacements due to complications, of which 82 out of 100 were associated with the tubing system utilized , while the remaining 18 with patient related issues  (Table granulation tissue (rome n = ] (Table rome n = ] (Table p < 0.05), however, after the FDR correction for variability did not showed this result due to the low number of subjects for each group (Table The “pull” method was adopted for 41 procedures (from 2009 to 2014) and the “push” method for 79 (from 2015 to 2019). Complications led to tube replacement in 75% of cases with “pull” and 67% of cases with “push” technique. Mean PEG-J duration was of 7,6 and 5,6 months for the “pull” and “push” methods, respectively. A comparison of clinical features for “pull” and “push” technique is reported in Table Gastrointestinal issues are frequent, up to 64% in PD patients [4]. Compared to a large review on DLI safety,  in a 12 The lower rate of stoma complications in the “push” group, associated to the lower rate of unexpected replacements, could be due to the higher turnover of replacements, that in turn may have acted as a protective factor against major complications. Any deterioration of the tubing system can reduce clinical benefit, increase the risk of stoma related complications or even cause sudden PEG-J breaking. Thus, a shorter PEG-J half-life, or higher turnover, together with an easy-to-use replacement technique may exert a protective role for device related complications. By reducing the number of operators needed to replace a device and simplifying endoscopic procedures, the “push” technique takes less time and is also less invasive. Finally, by not requiring sedation, it eliminates the risks of exposure to anesthetic drugs. The major limitation of this study was the retrospective nature and the low number of included patients.We found that the overall complication rate was lower for the “push” technique and this may have been due to its higher turnover rate, suggesting that it may have acted as a protective factor.


Dissociation is problematic in its own right for patients with psychosis but may also contribute to the occurrence of psychotic experiences. We therefore set out to estimate in a large cohort of patients with psychosis the prevalence of dissociative experiences, and assess using network models the relationships between dissociation, its potential maintenance mechanisms, and mental health symptoms.902 patients with non-affective psychosis attending UK mental health services participated. Both an undirected model and a partially directed network model were estimated to identify potential relationships between ‘felt sense of anomaly’ dissociative experiences, paranoia, hallucinations, psychological wellbeing, sleep, and six potential maintenance mechanisms .617 patients (65.4%) had experienced at least one dissociative symptom regularly over the past fortnight, with the average number experienced being 8.9 (SD = 8.0). Dissociation had direct relationships with paranoia, hallucinations, low psychological wellbeing, cognitive appraisals, cognitive-behavioural responses to dissociation, perseverative thinking, and low alexithymia. Dissociation was a probable cause of hallucinations , with a trend towards also being a cause of paranoia .Approximately two-thirds of patients with psychosis experience regular dissociative experiences. Dissociation is associated with low psychological wellbeing, and it is likely to have a direct causal influence on psychotic symptoms. Catastrophic cognitive appraisals, cognitive-behavioural responses to dissociation, factors related to affect sensitivity, and perseverative thinking may contribute to the occurrence of dissociation. DissociThere have been three large clinical studies of dissociation in the context of psychosis. The largest reports longitudinal associations over decades between first-rank psychosis symptoms, depersonalisation, and derealisation in 167 schizophrenia spectrum and 156 ‘other psychosis’ patients . The othAll three studies used different assessments of dissociation. Used interchangeably to signify either a symptom or an aetiological process, the term ‘dissociation’ has attracted multiple definitions incorporating a diverse range of experiences that have led to use of different assessment instruments . There iWe drew on our findings in the general population to test psychological processes that may lead to the occurrence and maintenance of dissociative experiences . FactorsTherefore, the key aims of this study are to establish the prevalence of FSA-dissociative experiences in patients with psychosis, examine relationships with paranoia and hallucinations, and identify potential psychological causes of dissociation in order to inform therapeutic approaches. The aim of understanding dependencies between multiple variables at once – symptoms and their possible mechanisms – is one best met through network analysis. Such analyses are gaining increasing popularity in mental health research because they are well-suited to exploring complex sets of correlated variables  and gene22.1The design was a cross-sectional self-report questionnaire study. Ethical approval was received from London – City and East NHS Research Ethics Committee (19/LO/1394).2.2Participants were recruited from thirty-six NHS trusts in England. Inclusion criteria were: age 16 years or over, currently under the care of an NHS mental health service, with a diagnosis of non-affective psychosis, and willing and able to give informed consent to participate. Exclusion criteria were: insufficient English language to complete the questionnaires with support, or an affective psychosis diagnosis.Recruitment ran between 18th October 2019 and 19th March 2020. Datasets from 1038 participants were returned. Only cases with low levels of missing data in every measure  were retained for analysis, resulting in a participant group of 902 patients .Table 1D2.3Cronbach's alphas for each scale are shown in 2.3.1The Affect Intolerance Scale assesses respondents' attitudes towards negative emotions using 30 items rated on Likert scales from 1 “strongly disagree” to 6 “strongly agree”. Items form two factors: “threat expectancy”  and “avoid/suppress” (“I should avoid negative feelings”). Higher scores indicate greater intolerance of negative affect.2.3.2The ČEFSA measures dissociative experiences with a core phenomenological experience of a felt sense of anomaly (FSA) using 35 items . Items are rated for the past two weeks on Likert scales from 0 “never” to 4 “always”. Higher scores indicate higher levels of FSA-dissociation.2.3.3The CAD-P measures cognitive appraisals of dissociative experiences. Respondents answer according to how they think when “feeling strange, disconnected, unreal or ‘dissociated’”. Thirteen items (“This might last forever”) are rated on Likert scales from 0 “never” to 4 “always”. Higher scores indicate more frequent occurrence of catastrophic appraisals in response to dissociative experiences.2.3.4The General Self-Efficacy scale comprises ten items  rated for ‘how true … they are of you in general’ on four-point Likert scales from 1 “not at all true” to 4 “exactly true”. Higher scores indicate greater self-efficacy.2.3.5An adapted version of the OAQ was used to measure alexithymia. This comprised eleven items forming three factors: “difficulty identifying feelings” ; “difficulty describing feelings” (“I can describe my emotions with ease”); and “externally-oriented thinking” . Items are rated from 1 “strongly agree” to 5 “strongly disagree” (with one reverse-coded item). Higher scores indicate greater difficulty identifying, naming, and acknowledging one's emotional state.2.3.6The PTQ is a 15-item measure of repetitive negative thinking. Items ask how the respondent ‘typically’ thinks about negative experiences or problems, rated from 0 “Never” to 4 “almost always”. Higher scores indicate higher levels of ruminative thinking.2.3.7Cognitive-behavioural responses to dissociation were measured using six items (“I try to keep busy”). Higher scores on the RTD indicate greater use of such responses to dissociation. Items are rated from 0 “never” to 4 “always” for how the respondent typically acts when feeling dissociated.2.3.8The R-GPTS assesses paranoia using two subscales: ideas of reference and persecution. The persecution subscale of the R-GPTS was used in this study. This comprises ten items , rated over the past month on five-point Likert scales from “0 - not at all” to “4 – totally”. Higher scores indicate higher levels of paranoia: a score above 18 indicates ‘severe’ levels.2.3.9The SCI is a clinical screening tool evaluating insomnia. The two-item short-form version asks how many nights a week the respondent typically had a problem with their sleep, and to what extent has poor sleep troubled them in general, in the past month. Responses are scored from 4 to 0, with lower scores indicating poorer quality sleep.2.3.10The SPEQ comprises four scales which each assess a key psychotic experience. The hallucinations scale (SPEQ-H) was used in this study. This asks respondents to rate how frequently they have recently had particular experiences (“How often do you: hear noises or sounds when there is nothing about to explain them?”) using six-point Likert scales . This was adapted to include two further items assessing voice-hearing: ‘How often do you… hear voices saying words or sentences when there is no one around that might account for it’ and ‘…hear two or more unexplained voices talking to each other’. Higher scores indicate higher levels of hallucinatory experiences.2.3.11The WEMWBS measures psychological wellbeing (“I've been feeling cheerful”) over the past two weeks using 14 items rated from 1 “none of the time” to 5 “all of the time”. Higher scores indicate greater psychological wellbeing.2.4Analyses were conducted in R, version 3.6.1 . MultiplPackages ‘bootnet’ (v1.3) and ‘qgraph’  were useThe final causal graph was calculated by averaging the results of 50,000 sample DAGs, obtained by using the BiDAG package to run the partition Markov Chain Monte Carlo algorithm  for 10 m3n = 310; 34.37%) of participants scoring above cut-off for a persecutory delusion, and 162 (17.96%) scoring in the ‘very severe’ range of the R-GPTS Persecution scale. There were high levels of hallucinatory experiences: a third reported hearing ‘voices saying words or sentences’ daily or several times a week , and a quarter reported ‘hear[ing] two or more unexplained voices talking to each other’ daily or several times a week . The scores also suggest many of the participants had significantly poor sleep quality , whilst a quarter  rated themselves as having good sleep (the maximum score). The WEMWBS score indicates psychological wellbeing on the boundary between ‘low’ and ‘average’ ranges, with n = 313 (34.70%) scoring below 40, a level suggestive of depression (pression .n = 1), ‘non-epileptic attack disorder’ (n = 1), and ‘historical diagnosis of depersonalisation’ (n = 1).Only three participants had a recorded dissociative disorder diagnosis: ‘dissociative disorder’  participants endorsed at least one item, 243 (26.94%) participants endorsed more than 25% of the items, 96 (10.64%) participants endorsed over 50%, and 29 (3.22%) endorsed over 75% of the items. A total of 285 (31.60%) participants did not endorse any ČEFSA items. n = 617), the mean number of items endorsed was 8.93 (SD = 8.01), and the median was 6 items. Taking endorsement of any of the five items in a factor as endorsement of that factor, the mean number of factors (i.e. different dissociation symptom types) endorsed by this subgroup was 3.94 (SD = 2.12), and the median number of factors endorsed was four. Anomalous Experience of Emotion, Altered Sense of Connection, and Altered Sense of Agency were the most likely to have at least one symptom being reported as occurring regularly .Of those who endorsed at least one item on the scale . In 99.42% of the 13.75% of sampled DAGs a direct edge from paranoia to dissociation was present, and this had an average strength of 0.29 (90% CI = 0.10–0.62). In the opposite direction, from dissociation to paranoia, there was a pathway present in 86.25% of the 50,000 sampled graphs, with an average strength of 0.48 (CI = 0.22–0.60). Of these, 93.40% contained a direct edge from dissociation to paranoia. The average strength of this was 0.25 (CI = 0.00–0.52). Therefore, for the relationship between paranoia and dissociation, there is a non-significant indication (found in more than 50% but less than 90% of sampled DAGs) that dissociation has a moderate influence on paranoia. The direction of influence whereby paranoia affects dissociation was found in fewer than 50% of sampled DAGs, indicating a low probability of this direction of effect.The results can be interpreted as in the following example: a pathway (direct or indirect) The only edge between dissociation and another variable reaching the 90% threshold for inferring probable direction of effect was with hallucinations. Here, it was probable that dissociation is a causal factor for hallucinations, with moderate effect size .Sleep and psychological wellbeing both had negative correlations with dissociation. For sleep, the direction of influence reached the 90% threshold for inferring dissociation was a probable cause of poor sleep. However, this effect was largely indirect, with fewer than 10% of the sampled DAGs with a pathway between dissociation and sleep containing the direct pathway. Inspection revealed that pathways between dissociation and sleep commonly (i.e. >50% of the 96.86% of DAGs) included perseverative thinking, hallucinations, paranoia, cognitive appraisals, and affect intolerance. In contrast, the result for wellbeing was ambiguous in terms of direction, but the vast majority of effect was via the direct pathway.As in the undirected model, dissociation had direct relationships (over the 50% threshold) with all potential maintenance mechanisms except general self-efficacy and sleep. It is a point of difference between the two networks that the DAGs analysis found a direct relationship between dissociation and affect intolerance, where none was found in the undirected network, although it should be noted that this was only marginally above the 50% threshold .In terms of likely direction of influence, no relationship between dissociation and a candidate maintenance factor reached the 90% threshold. There were trends towards dissociation being a causal influence for nearly all mechanism factors except general self-efficacy, and particularly in the case of cognitive appraisals, cognitive-behavioural responses to dissociation, and perseverative thinking which were influenced by dissociation in over 80% of sampled DAGs. The correlations between dissociation and general self-efficacy and alexithymia were negative.4I lose track of my surroundings”). The next most common were experiences of an altered sense of connection (“I feel as if I'm experiencing life from very far away”) and anomalous experience of emotion (“I don't fully experience emotions”). These findings are in contrast to the total of three co-morbid dissociative disorder diagnoses recorded in this group's clinical notes, which itself is far lower than previous estimates . Our view is that these results suggest dissociation may be maintained by a process related to affect sensitivity and an interacting process reinforcing dissociation via cognitive and behavioural responses to the experience. Direct relationships between dissociation and affect intolerance and alexithymia (i.e. self-rated detection of emotion) indicate an affect sensitivity process may be relevant, whilst direct and close inter-connections found between dissociation, cognitive appraisals, cognitive-behavioural responses to dissociation, and perseverative thinking are consistent with a general cognitive-behavioural perspective. Potentially linking the two hypothesised processes, perseverative thinking  had a direct relationship with affect intolerance (a key factor in the hypothesised affect sensitivity process). General self-efficacy was directly linked to perseverative thinking and responses to dissociation, suggesting this factor might indirectly influence the maintenance of dissociation.It is important to note that, as expected, whilst these relationships were robust, the results of the DAGs analysis do not unambiguously find that these potential maintenance factors causally influence dissociation. In all cases, the relationship between these variables and dissociation did not reach the 90% threshold for drawing conclusions about direction of influence. Indeed, the majority of these relationships  showed strong trends towards dissociation having causal influence. Unlike relationships between dissociation and psychotic symptoms, the question of how to interpret ambiguous directions of influence between potential maintenance factors and dissociation is not as clearly indicated by the existing literature. For example, it may be that there are sufficient individual differences in the response to dissociation  that heterogeneity presents as a weaker overall causal effect from each mechanism to dissociation at a group level.It is arguably a limitation of the current study that only two (positive) psychotic symptoms were measured. As psychosis research and intervention strategies increasingly take a symptom-specific approach , it woul5The implication of models proposing that dissociation causes psychotic experiences is that reducing dissociation may therefore alleviate them. Recent publications reflect increasing clinical interest in addressing this hypothesis. For example, The authors do not have participant consent to share the data collected in this study.10.13039/100010269Wellcome Trust via a Clinical Doctoral Fellowship to EČ (grant number 102176/B/13/Z). DF was supported during this work by an 10.13039/501100000272NIHR Research Professorship (NIHR-RP-2014-05-003) and is an NIHR Senior Investigator. AE is funded by the 10.13039/100010269Wellcome Trust (200796) and supported by the 10.13039/501100013373NIHR Oxford Health Biomedical Research Centre and a NIHR Senior Investigator Award. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.The work was supported by the The funder had no role in the study design, collection, analysis or interpretation of data, writing of the report, or in the decision to submit the article for publication.The authors declare no conflicts of interest.


Agro-residues are usually discarded as landfill, or burnt or left to decompose in the orchard. The efficient use of these walnut agro-residues would be a strategy that simultaneously helps to preserve the environment and boosts the economic outcome for farmers and companies. While some studies have reported on the content of bioactive compounds in walnut husks, little or nothing is known to date about the bioactive compounds in the buds and bark. Potentially, if walnut parts are used as a valuable source of bioactive compound, they might still be reused for other purposes. The identification and quantification of new phenolics between the different parts of the plant was carried out. It provided valuable data on their phenolic contents, and demonstrated where the extraction of individual phenolics would be meaningful. These data also show origin-related phenolic contents across the cultivars, and thus these phenolic profiles might serve to define the origins of different walnut cultivars. The study will help to propose new directions for further studies essential for agro-food, cosmetics and pharmacy industries.Juglans regia L. A comparison across six different cultivars grown in Slovenia was also carried out: ‘Fernor’, ‘Fernette’, ‘Franquette’, ‘Sava’, ‘Krka’ and ‘Rubina’. A total of 83 compounds were identified, which included 25 naphthoquinones, 15 hydroxycinnamic acids, 8 hydroxybenzoic acids, 13 flavanols, 2 flavones, 1 flavanone and 19 flavonols. For the first time, 38 phenolics in the husks, 57 phenolics in the buds and 29 phenolics in the bark were presented in J. regia within this study. Naphthoquinones were the major phenolics determined, approximately 75% of all analysed phenolics in the inner husk, 85% in the outer husk, 50% in buds and 80% in bark. The highest content of phenolics was found in the walnut buds, followed by the bark, the inner husk and the outer husk. On the basis of these high phenolic contents, walnut husks, buds and bark represented valuable by-products of the walnut tree. These data also show origin-related phenolic contents across the cultivars, and thus these phenolic profiles might serve to define the origins of different walnut cultivars.The present study was designed to identify and quantify the major phenolic compounds (phenolics) in the inner and outer husks, buds and bark of the Persian walnut,  Juglans regia L.) is the second most cultivated tree nut worldwide. Walnuts are native to the region stretching from the Balkans eastward to the Himalayas and southwest China. Nowadays, it is widely cultivated across Europe −–H2O–180), and trihydroxytetralone galloyl hexoside by fragmentation ions m/z 331 and 271, as reported previously for walnut leaves −, 147 [M–H–CO]−, 157 [M–H–H2O]− and 129 [M–H–CO–H2O]−; regiolone through its fragmentation pattern of MS2 ions m/z 159 [M–H–H2O]−, 175 and 131 [M–H–H2O–CO]−; 4,5,8-trihydroxynaphthalene-5-D-glucopyranoside through its fragmentation pattern of MS2 ions m/z 331 [M–H–C10H8O3]− and 271 [M–H–C12H12O5]−, and MS4 ions m/z 211 [M–H–C14H16O7]− and 169 [M–H–C16H18O8]−; 1,4,8-trihydroxynaphthalene-1-D-glucopyranoside through its fragmentation pattern of MS2 ion m/z 327 [M–H–C10H8O3]− and MS3 ions m/z 183 [M–H–C16H16O7]− and 225 [M–H–C14H14O6]−; bis-juglone through its fragmentation pattern of MS2 ions m/z 345 [M–H–H2O]−, 317 [M–H–H2O–CO]− and 301 [M–H–CO2]−; and p-hydroxymethoxybenzobijuglone through its fragmentation pattern of MS2 ions m/z 383 [M–H–CH4O]− and 355 [M–H–CH4O–CO]−, as reported by Huo et al. [Juglans mandshurica. These compounds were previously reported in J. mandshurica, but are reported here for the first time in the walnut J. regia.Other phenolics identified through their fragmentation patterns included: hydrojuglone and its derivatives through the distinct fragment ions MSo et al.  in Juglap-cumaroylquinic acid through its fragmentation pattern of MS m/z 337, MS2 m/z 163, 191 and 173, as reported by Liu et al. [p-coumaric acid derivatives through the p-coumaric acid fragmentation pattern after being broken down, through the fragmentation patterns of ions m/z 163 and 119, as reported by Liu et al. [n ion m/z 193 and MSn+1 ions m/z 149 and 117, as reported by Vieira et al. [n ion m/z 179 (caffeic acid–H), as reported by Vieira et al. [The 15 hydroxycinnamic acids identified through their fragmentation patterns included: neochlorogenic acid (3-caffeoylquinic acid) through its fragmentation, in addition to an external standard; 3-u et al.  and Vieiu et al. ; p-coumau et al.  and Vieiu et al. ; ferulica et al.  and Šukoa et al. ; and cafa et al. .m/z 169 and 125, as reported by Li and Seeram [bis-(hexahydroxydiphenoyl)-glucose through its fragmentation pattern of MS2 ions m/z 301 and 275, and MS3 ions m/z 257, 229 and 185, as reported by Medic et al. [m/z 257, 229 and 185, as reported by Singh et al. [Seven phenolics were identified for hydroxybenzoic acids: gallic acid derivatives, through the gallic acid fragmentation pattern after being broken down, through the fragmentation pattern of ions d Seeram  and Šukod Seeram ; bis-catechin and (−)epicatechin, thus suggesting that standards are needed when determining either of those compounds; epicatechin and catechin derivatives through the (+)catechin and (−)epicatechin fragmentation patterns after being broken down, through the ions m/z 245, 205 and 179, as seen in standard fragmentation patterns; and procyanidin dimers and procyanidin dimer derivatives through their characteristic fragmentation of MSn m/z 577 and MSn+1 m/z 425, 407 and 289 [There were 13 flavanols identified through their fragmentation patterns: (+)catechin and (−)epicatechin through their fragmentation patterns, in addition to an external standard, which produced fragment ions  and 289 ,30.The two flavones identified were santin and 5,7-dihydroxy-3,4-dimetoxyflavone, through their fragmentation patterns according to Yan et al. . Both sam/z 151 and 177.The flavanones included the identification of one compound: naringenin, through its fragmentation in addition to an external standard, through the fragment ions 2 ions m/z 316, 317 and MS3 ions m/z 179, 191; (ii) quercetin and quercetin glycosides through their clear fragmentation pattern of MS2 m/z 301 and MS3 m/z 179, 151; and (iii) kaempferol and kaempferol glycosides through their fragmentation pattern of MS2 m/z 284 and 285 and MS3 m/z 255 and 227, as reported by Santos et al. [2 ion m/z 285 and MS3 ions 165, 119 and 93, as reported by Chen et al. [Rhamnus davurica [The flavonols included the identification of three groups of compounds: (i) myricetin glycosides through their fragmentation pattern of MSn et al. . Kaempfedavurica , but thiThe highest contents of phenolics were in the walnut buds, followed by the bark, the inner husk and the outer husk, as shown in The highest relative contents of hydroxycinnamic acids and flavones were seen for the inner husk, with the highest relative contents of hydroxybenzoic acids, flavanols, flavanones and flavonols for the walnut buds, as shown in The total naphthoquinones were the major phenolic group determined for the inner and outer husks, and for the buds and bark as well. These represented approximately just over 50% of all of the identified phenolics in the buds, 75% in the inner husk, 80% in the bark and 85 % in the outer husk, as shown in While the content of phenolics is usually higher in the peel of fruit compared to the flesh , here, iInterestingly, the three Slovenian cultivars of ‘Sava’, ‘Krka’ and ‘Rubina’ had similar naphthoquinone contents in the walnut outer husk that were also higher than for the French cultivars ‘Fernor’, ‘Fernette’ and ‘Franquette’, which were also similar for their total naphthoquinone contents. The same was seen for the walnut inner husk, where the Slovenian varieties showed higher total naphthoquinone content than the French cultivars. This information that the Slovenian cultivars had higher total naphthoquinone contents than the French cultivars might also be useful in the future determination of the genetic origins of a cultivar, as cultivars that are bred in different climates might have specific naphthoquinone contents, as previously reported by Medic et al. . The sumLooking at individual naphthoquinones, in all these plant tissues, juglone was most abundant in the walnut inner husk, as can be seen in J. regia, or any other species for the first time, that may be unique to Juglandaceae family. To the best of our knowledge, this is the most complete study to describe the levels of the various phenolics for walnut husk, buds and bark. Furthermore, this is the first report to provide not only characterisation and quantification of the phenolics for walnut buds, but also a detailed characterisation and quantification of the separate husk layers . These data demonstrate the levels of the phenolics in these different walnut tissues, which are classified as agro-residuals. When considering the different walnut tissues, the total phenolic contents  were higher than previously reported for walnut shoots, leaves and kernels. This justifies the use of the husk, buds and bark as sources of phenolics. Furthermore, the Slovenian cultivars showed higher total naphthoquinone contents in the outer and inner walnut husks compared to the French cultivars. This information might be useful for the future determination of the genetic origin of a cultivar and also for the authentication of the walnuts belonging to each region, country, etc., as cultivars bred in different climates appear to show some specific variations in their naphthoquinone contents. Consequently, the present study provides useful information not only for agro-food industry  but also for the cosmetic and pharmaceutical industries. A total of 83 individual phenolics and the total phenolics content were identified and quantified for the inner and outer husks, buds and bark of six walnut cultivars. These 83 phenolics comprised 25 naphthoquinones, 15 hydroxycinnamic acids, 8 hydroxybenzoic acids, 13 flavanols, 2 flavones, 1 flavanone and 19 flavonols. Thirteen naphthoquinones have been reported for the walnut


Scientific Reports 10.1038/s41598-021-87926-0, published online 15 April 2021Correction to: The original version of this Article contained errors.In the Materials and methods section, under the subheading ‘Data collection’,18.”“With the maximum score being 6, satisfaction is considered high for a score above 5.4now reads:18. This copyrighted questionnaire is the property of Franklin Dexter and the University of Iowa Research Foundation.”“With the maximum score being 6, satisfaction is considered high for a score above 5.4Additionally, in the legend of Figure 2,15.”"ISAS, Iowa Satisfaction with Anaesthesia Scalenow read:15 (copyrighted by Franklin Dexter and the University of Iowa Research Foundation).""ISAS, Iowa Satisfaction with Anaesthesia ScaleThe original Article has been corrected.


Increased secretion of proinflammatory cytokines, such as tumor necrosis factor-alpha (TNFα), is often associated with adipose tissue dysregulation, which often accompanies obesity. High levels of TNFα have been linked to the development of insulin resistance in several tissues and organs, including skeletal muscle and the liver. In this study, we examined the complex regulatory roles of TNFα in murine hepatocytes utilizing a combination of global proteomic and phosphoproteomic analyses. Our results show that TNFα promotes extensive changes not only of protein levels, but also the dynamics of their downstream phosphorylation signaling. We provide evidence that TNFα induces DNA replication and promotes G1/S transition through activation of the MAPK pathway. Our data also highlight several other novel proteins, many of which are regulated by phosphorylation and play a role in the progression and development of insulin resistance in hepatocytes. Tumor necrosis factor-alpha (TNFα) is primarily secreted by a hyperplastic and hypertrophic adipose tissue, typically associated with obesity . The oveThe direct stimulation of glucose uptake and metabolism is mediated by insulin. Insulin is the single hormone responsible for the regulation of blood glucose concentration, acting through activation of the canonical glycolytic pathway in target cells . InsulinHepatocytes have been shown to be particularly sensitive to Free Fatty Acids (FFAs). Elevated circulatory levels of FFAs have been demonstrated to induce a dysregulation of the canonical insulin signaling pathway, diminishing the phosphorylation of insulin receptor substrates 1 and 2 (IRS1 and 2) . ElevateCharacterizing the downstream effectors of TNFα signaling in hepatocytes and elucidating subsequent biological implications is fundamental for a systemic understanding of obesity-associated pathologies including the development of T2DM. In this study, we used global proteomics and phosphoproteomics approaches to identify key proteins that are significantly regulated by, or in response to, TNFα exposure. Our data reveal that TNFα inhibits fatty acid and lipid metabolism, likely contributing to the accumulation of FFAs, which further contribute to insulin resistance. Moreover, we show that cell cycle proteins are significantly regulated and activated by TNFα treatment. Our data suggest that TNFα acts as a stimulator for entry in G1/S of the cell cycle. These results introduce a novel perspective on the functional roles of TNFα on hepatocytes, and can serve as a future reference for the development of therapeutic targets aiming to alleviate hepatic insulin resistance.To date, the molecular implications of TNFα-mediated inflammation in hepatocytes remain largely unexplored. Previous reports have, however, suggested that TNFα induces a sharp decrease in glucose uptake within as little as 1 h of treatment. Indeed, we observed a significant decrease in glucose uptake in AML12 murine hepatocytes after 2 and 8 h of 10 ng/uL TNFα treatment, with a recovery of glucose uptake at 24 h of single TNFα treatment compared with 0 h of treatment .Mus musculus database. Statistical analysis was then performed in the Perseus software [To explore the progressive effects of TNFα in the proteome of hepatocytes, AML12 murine hepatocytes were treated with 10 ng/uL TNFα and collected at 2 h (2H), 8 h (8H), and 24 h (24H) after initial treatment (0H). AML12 cells were subsequently homogenized and total protein was prepared as previously described . A totalsoftware  . Quantified proteins only were used for the subsequent analyses presented herein, and represent the most comprehensive dataset to date for characterizing protein regulations in AML12 in response to TNFα .To visualize our global data, while obtaining a large-scale perspective of the regulatory patterns of the proteins quantified, a correlation map of our global dataset was generated. Hierarchical clustering based on “one minus Pearson correlation” metric was applied, in which clear clusters were observed. The distinct areas of high and low correlation values indicate a noteworthy variance of LFQ values relative to the identified proteins B.Despite the protein-dependent variations in LFQ values observed, 2126 of the 2609 proteins identified were quantified in all four time-points studied, indicating highly consistent data acquisition across all samples analyzed C. This cTo explore the time-resolved molecular and biological ramifications of TNFα treatment, we focused on the statistically significant proteins curated from our global dataset.p < 0.05) using the ANOVA test. From the 2609 quantified proteins, a total of 357 met our p-value cutoff  analysis ,17 of alTo unveil the dynamics of the protein landscape of AML12 cells during TNFα exposure, we plotted a heatmap of all significant proteins across the time points studied. To characterize the expression patterns observed, the plotted proteins were clustered by k-means, in which proteins with similar regulatory profiles were clustered together. This method allows for the grouping of proteins that are likely involved in similar functional roles, thus allowing for a more thorough analysis of the underlying functional links of the identified proteins. Eight distinct clusters were observed in our dataset, numbered 1 to 8, of which cluster “1” had a considerably larger number of proteins compared with the other clusters D.From the eight clusters identified in our heatmap, we decided to focus on four specific clusters that showed a clear time-dependent trend in their expression levels; namely, clusters “1”, “4”, “7”, and “8” were selected, and gene ontology was subsequently performed with the proteins from each cluster.Cluster “1” showed a distinct downregulation at the 24H time point, with little variation between 0H and 8H of treatment. Unsurprisingly, the proteins grouped in this cluster were primarily involved in metabolic processes, with the “monocarboxylic acid metabolic process”, “flavonoid glucuronidation”, and “cofactor metabolic process” being highly enriched A. Our reThe upregulation of translation is the primary pathway enriched from proteins in cluster “2” B. NotablAlthough the regulatory patterns of clusters “7” and “8” are distinct, with cluster “7” showing decreased expression levels at 8H followed by overexpression at 24H, compared with the linear increase in expression levels observed in cluster “8”, both have overlapping biological processes. Clusters “7” and “8” contain proteins involved in cell cycle progression, with “mitotic cell cycle progression”, “DNA strand elongation involved in DNA replication”, and “pre-replicative complex assembly involved in nuclear cell cycle DNA replication” being the most prominent C,D. AmonInterestingly, clusters “2”, “3”, “5”, and “6” are also involved in DNA replication, translation, and metabolic processes . Taken tPhosphorylation events are fundamental in protein signaling and key regulators in diverse cellular pathways, including glucose uptake in response to insulin . TNFα diOur phosphoproteome dataset consisted of a total of 3287 proteins, mapped from 13,563 peptides, containing 12,798 phospho (STY) sites. When analyzing protein phosphorylation data, it is crucial that the modified site remains distinct, as protein regulation is site-dependent, and different phosphorylation sites often have different consequences in protein functionality and localization. Thus, for this dataset, we utilized intensity values of each phospho STY site for downstream analysis. Briefly, class I sites  were selected. We identified a total of 5638 unique class I sites, from which 235 unique phosphorylated sites are reported here for the first time . It is iFor downstream data processing, we conducted a statistical analysis in a similar fashion to that described in our global study. Significantly regulated class 1 phospho (STY) site intensities were considered as sites with p-values lower than 0.05 by ANOVA test. A total of 649 sites passed our criteria, and their z-score values were plotted as a heatmap B. A cleap < 0.05) enriched, the majority being related to protein transcription and translation, and cell cycle progression  protein was also upregulated in our data at 24H, compared with 0H of treatment. PML is a key component of subnuclear structures known as PML Nuclear Bodies (PML-NB) . PML-NBsMus musculus, were processed with the PHOTON tool. Statistical analysis was then performed on functionality scores of the resulting output matrix. ANOVA significant (p < 0.05) proteins were selected for subsequent analyses , Origin Recognition Complex Subunits (ORCS), MCM proteins, and several CDKs. All these proteins individually showed a significantly higher functionality score at 24H when compared with 0H of TNFα treatment . “DNA syCluster “4” was characterized by a significant increase in the functionality score of its proteins at 2H of treatment, and remain constant thereafter. This cluster had the greatest variety of enriched biological processes, ranging from “exocytosis” and “toxin transport” to “wound healing” . In a siProteins that constituted Cluster “7” showed a linear increase in their functionality score with respect to the duration of TNFα treatment B. GO ana2 (fold-change) of the same proteins identified in our global data. With the exception of cluster 4, most of the overlapping proteins showed a similar regulation at the protein level, as predicted by the PHOTON analysis  form a protein complex that, in late G1, localize to origin recognition complex (ORC) sites on the chromatin and act as helicases, unwinding DNA and promoting the formation of replication forks, thus acting upstream of PCNA .The localization and subsequent activation of MCM proteins is dictated by phosphorylation events, regulated by CDK2/Cyclin E activity . Indeed,Consistent with our omics results, PHOTON analysis predicted the involvement of several kinases in the activation and modulation of the identified kinases—particularly, the CMGC family kinases, of which ERK and CDK are prominent members. Similarly, the significant enrichment scores of pathways involved in DNA strand elongation and mitotic processes further support our hypothesis that TNFα not only leads to insulin resistance, but also induces cell cycle progression.As a whole, our data suggest that TNFα promotes cell cycle progression through the activation of the MAPK/ERK pathway, which leads to the activation of cell cycle regulators. Our data reveal that TNFα exposure leads to the phosphorylation of RAF, MEK1/2, and ERK proteins. We also report overexpression and phosphorylation of cyclins and CDKs; specifically, Cyclin D and E, which stimulate the expression of Rb, hyperphosphorylated in our results, culminating in the transition from G1 into S-phase. We also identified several MCM proteins being differentially phosphorylated and upregulated in response to TNFα, reinforcing the stimulatory roles of TNFα on DNA replication and cell cycle progression C.Murine AML12 (CRL-2254) hepatocytes  were cultured in DMEM:F12 (ATCC) supplemented with FBS (ATCC), Insulin-Transferrin-Selenium and dexamethasone . Three biological replicates (n=3) were treated with additional 10 ng/mL TNFα, for a total of 0H, 2H, 8H, or 24H ,46,47. AGlucose uptake assay was performed using the Glucose Uptake-Glo . Assay was performed following manufacturer’s recommendations.g for 15 min, supernatant was removed, and pellets were dried in a vacuum centrifuge for one min.Cells were washed three times with 1 × PBS (4 °C), collected by scraping and subsequentially resuspended in 100 mM ammonium bicarbonate (ABC) supplemented with protease and phosphatase inhibitors. Samples were homogenized for 90 s at 6500 rpm using Precellys CK28 homogenization vials . Protein concentration was subsequently calculated by bicinchoninic acid (BCA) assay . Four volumes of cold acetone were used to precipitate 500 μg of total protein at −20 °C overnight. Samples were then centrifuged at 17,300× Samples were fully resuspended in 50 µL of 10-mM DTT in 8-M urea and incubated in a thermomixer for 1 h at 37 °C. A total 50 µL of alkylation reagent mixture (97.5% acetonitile (ACN), 0.5% triethylphosphine, 2% iodoethanol) was added to each sample, and again incubated for 1 h in a thermomixer at 37 °C. After alkylation, samples were dried in a vacuum centrifuge and resuspended in 200 µL of 0.05 µg/uL Lys-C/Trypsin (Promega) dissolved in 25-mM ABC. Samples were transferred to a barocycler , in which proteolysis was carried out. Peptides were desalted with the Pierce Peptide Desalting Spin Columns . A total of 20 μg of peptides from each sample were saved for global analysis. The remainder was used for phosphopeptide enrichment, performed with PolyMac spin tips , following manufacturer’s recommendations.Dried samples were reconstituted in 3% ACN, 0.1% Formic Acid (FA) and separated using an Acclaim PepMap 100 C18 analytical column (75 μm ID × 50 cm) packed with 2-μm, 100-Å PepMap C18 medium (Thermo Fisher Scientific) by reverse-phase using a Dionex UltiMate 3000 RSLC coupled with the Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific). A 160-min gradient was used for the separation of peptides for global analysis. Sample injection was carried out using 2% mobile phase solution B (80% ACN with 0.1% FA in water). Mobile phase solution B was increased in a linear fashion until 27% B was reached at 110 min, 40% B at 125 min, and 100% B at 135 min. At this point, concentration of B was held constant for 10 min before returning back to 2% B and maintained at 2% B until the end of the run. Phosphopeptides were separated using 120-min gradient. Samples were injected at 2% B. For LC separation, solution B was increased linearly, reaching 30% B at the 80 min mark, followed by an increase of B to 45% B at 91 min and then 100% B at 93 min, at which point it was held constant for 5 min before reverting back to 2% B until the end of the run. MS analysis were performed with the orbitrap detector, with a MS1 resolution of 120,000 and MS2 resolution at 1500. Quadrupole isolation and a scan range of 375 to 1500 m/z were used. Data-dependent acquisition MS/MS was performed, with a dynamic exclusion duration of 60 s. HCD was used for fragmentation, with HCD collision energy set to 30%.Mus musculus database using the MaxQuant platform (Ver). Lys-C/Trypsin enzymes were selected for specific digestion, with 2 missed cleavage allowance. Variable modifications were set for “methionine oxidation” and for phosphoproteomics “STY phosphorylation”; “Iodoethanol” was selected as fixed modification. False discovery rate (FDR) of peptides and proteins identification was set to a standard value of 1%. Additionally, 10 ppm was selected as the main search peptide tolerance value. Peptide quantitation was performed using “unique plus razor peptides”. The proteomics results were processed and analyzed using the Perseus biostatistics platform for subsequent statistical analysis. “Contaminants”, “reverse”, and “only identified by site” proteins were filtered out, and LFQ intensity values were Log2 transformed. Proteins were then filtered based on 2 minimum valid values in at least one treatment group. Missing values were imputed based on the normal distribution of LFQ values. Intensity values and probability scores were used for phosphoprotein analysis. The raw phospho STY data file was filtered for proteins with a localization probability ≥ 0.75, and 2 valid intensity values in at least one time point. Missing values were again imputed with values drawn from the normal distribution. PHOTON analysis was then performed in Perseus, following the developer’s recommendations. Briefly, the STRING mouse protein–protein interaction network was imported, and only high confidence interactions (>0.9) were selected for PPI network construction. The network was then annotated with the phosphor STY site table, and the resulting signaling functionality scores were used for the downstream statistical analysis. Statistical significance was inferred based on ANOVA test. Proteins with a p-value ≤ 0.05 were considered significantly regulated. Gene ontology (GO) was performed using Metascape [Raw MS/MS data were searched against the Uniprot etascape  online s


Despite the recognition of the importance of indoor microbial exposures on children’s health, the role of different microbial agents in development and aggravation of respiratory symptoms and diseases is only poorly understood. This study aimed to assess whether exposure to microbial aerosols within the indoor environment are associated with respiratory symptoms among children under-5 years of age.I2 statistics using RevMan 5.3.A systematic literature search was conducted on PubMed, Web of Science, GreenFILE, ScienceDirect, EMBASE and Cochrane library through February 2020. Studies that investigated the exposure–response relationship between components of the indoor microbial communities and respiratory symptoms among under-five children were eligible for inclusion. A random-effect meta-analysis was applied to estimate pooled relative risk (RR) and 95% confidence interval (CI) for study specific high versus low microbial exposures. The potential effect of individual studies on the overall estimate was evaluated using leave-one-out analysis, while heterogeneity was evaluated by P = 0.001]. The association was strongest with exposure to a combination of Aspergillus, Penicillium, Cladosporium and Alternaria species . Stratified analysis revealed an increased risk of wheeze  from any microbial exposure.Fifteen studies were eligible for inclusion in a meta-analysis. The pooled risk estimate suggested that increased microbial exposure was associated with an increased risk of respiratory symptoms [pooled relative risk (RR): 1.24 , Microbial exposures are, in general, associated with risk of respiratory symptoms. Future studies are needed to study the indoor microbiome more comprehensively, and to investigate the mechanism of these associations.The online version contains supplementary material available at 10.1186/s12940-021-00759-2. The microbial community within indoor environments such as dwellings where humans, especially under-five children, spend more than 90% of their time, consists of a wide range of microorganisms including bacteria, fungi, and viruses –4. The mMost epidemiological studies have been heterogeneous in assessing different respiratory health effects associated with exposure to specific microbial components, especially endotoxins, and culturable molds –17 whileRespiratory health effects have been the subject of recent research among preschool children , 26 and AND “damp” OR “fungi” OR “mold” OR “mould” OR “fungal” OR “fungus” OR “bacteria” OR “virus” OR “microbial” OR “microbiome” OR “microbial diversity” OR “microbial load” OR “microbial burden” OR “microbiota” OR “biodiversity” AND “respiratory symptoms” OR “allergy” OR “hay fever” OR “cough” OR “fever” OR “difficulty breathing” OR “wheeze” OR “allergic rhinitis” OR “sinusitis” OR “asthma”. Title and abstract of each article was evaluated independently and differences on which publication(s) to include were clarified by recourse to a third reviewer. A snowball search was also carried out by screening reference lists of publications and reviews.The protocol for this meta-analysis was reported using MOOSE guideline  and regiStudies eligible for inclusion in the meta-analysis were epidemiological reports in humans with exposure–response relationship between indoor microbial aerosols and respiratory symptoms. Inclusion criteria were: (I) qualitative or quantitative assessment of indoor microbial aerosols. Studies that reported exposure to specific microbial communities  or presence/absence of visible molds were included; (II) studies conducted among children aged ≤ 5 years; (III) respiratory symptoms  and/or asthma adequately defined and described; (IV) full text articles in English originally published in peer reviewed journals. Literature reviews, abstracts, letters to the editor, case reports, and non-human studies were excluded.Assessment of articles was performed in EndNote databases. All duplicates were removed and studies were selected based on title or abstract for full text-screening. For studies that were excluded, the reasons for exclusion were listed.From the studies that met the inclusion criteria, the following information was extracted: (1) name and initials of the first author, (2) year of publication, (3) country, (4) type of sample, (5) sampling equipment, (6) microbial agent(s), (7) analytical method, (8) level and measure of exposure, (9) respiratory outcome definition, (10) number of cases, (11) sample size and (12) effect estimate. All incongruities from the data extracted were resolved by a third author.Two team members (AGF and NJ) assessed the methodological quality and risk of bias of the included studies using the Cochrane Collaboration guidelines  and the All statistical analysis was carried out using Review Manager 5.3. We applied the inverse of variance method for weighting and computed the summary effect estimates by first log-transforming all relative risk (RR) and 95% confidence interval (CI) for high vs low category (as reference) of microbial exposure. The standard error (SE) was estimated based on the formula:I2 test statistics. Where I2 statistics ≥ 50%, a random effect model was employed, otherwise, a fixed effect model , P < 0.0001; I2 = 0% , P < 0.00001; I2 = 0%] (model 1). Three studies  (model 2). The study-specific estimates based on two studies  (model 3) ; TFC and wheeze (B); Aspergillus spp. and wheeze (C); Penicillium spp. and wheeze (D); Cladosporium spp and wheeze (E); Endotoxin and wheeze (F). Figure S2. Funnel plots with pseudo-95% confidence limit for any IM exposure and allergic rhinitis among U-5C (A); visible mold and allergic rhinitis (B); Endotoxin and allergic rhinitis (C). Figure S3. Funnel plots with pseudo-95% confidence limit for any IM exposure/endotoxin and asthma among U-5C.