Natural fibers are now becoming widely adopted as reinforcements for polymer matrices to produce biodegradable and renewable composites. These natural composites have mechanical properties acceptable for use in many industrial and structural applications under ambient temperatures. However, there is still limited understanding regarding the mechanical performance of natural fiber composites when exposed to in-service elevated temperatures. Moreover, nanoparticle additives are widely utilized in reinforced composites as they can enhance mechanical, thermal, and physical performance. Therefore, this research extensively investigates the interlaminar shear strength (ILSS) and flexural properties of flax fiber composites with graphene at different weight percentages  and exposed to in-service elevated temperatures . Mechanical tests were conducted followed by microscopic observations to analyze the interphase between the flax fibers and epoxy resin. The results showed that a significant improvement in flexural strength, modulus, and interlaminar shear strength of the composites was achieved by adding 0.5% of graphene. Increasing the graphene to 1.0% and 1.5% gradually decreased the enhancement in the flexural and ILSS strength. SEM observations showed that voids caused by filler agglomeration were increasingly formed in the natural fiber reinforced composites with the increase in graphene addition. Synthetic-fiber-based polymer composites are now being used in a number of industrial applications including civil engineering and construction, packaging, and automotive industries due to their high strength, stiffness, and durability. However, the manufacturing and use of synthetic fiber composites have some detrimental effects on the environment due to their nondegradable nature, reduced recyclability, and toxicity . MoreoveAmong many different types of plant fibers, flax is the most commonly used fiber for manufacturing natural fiber composites. Flax fibers are extracted from bast, which has a high cellulose percentage (approximately 30–76% by weight) as well as a low microfibril angle of 2 to 8 degrees. The cellulose percentage is responsible for the strength of the fibers while the microfibril angle controls the stiffness. Both contribute to the mechanical properties of the fibers and the performance of the composites . Xiong eOne limitation of natural fibers as effective internal reinforcements in polymeric composites is their lower thermal degradation property when compared with synthetic fibers . This prA number of investigations have evaluated the performance of synthetic-based FRP composites with and without nanofillers at in-service elevated temperatures under different loading conditions including tension, compression, interlaminar shear (ILSS), and flexure ,23,24,25This study investigates for the first time the effectiveness of graphene nanoparticles in improving the performance of flax-fiber-based natural composites at in-service elevated temperatures. The experimental investigation focuses on the flexural and interlaminar shear performance of hybrid flax-reinforced epoxy-based composites with different levels of graphene by weight  and tested at different levels of elevated temperature . The results of this study will provide comprehensive information on the effect of graphene addition on natural fiber composites and their performance at in-service elevated temperatures. Moreover, it will support the development and application of cost-effective and eco-friendly biocomposites for industrial and engineering applications.2/g and elastic modulus of 340 GPa were supplied by Sigma-Aldrich, Castle Hill, Australia. The unidirectional flax fibers with density of 200 g/m2 were supplied by Colan Composite Reinforcement, Huntingwood, NSW, Australia. Epoxy resin (R246TX) mixed with hardener (H160 Kinetix Medium Hardener) supplied by ATL Composites, Molendinar, Australia was used as a matrix in the manufacturing of the composites. The resin to hardener ratio used was 1:4 by weight, as recommended by the supplier. Graphene nanoparticles with average specific area of 300 mf) was maintained at 25%. This is based on a previous study, which showed that the weight proportion of the fiber content is in the range of 23% to 34% influenced by several factors including the resin and fiber type and the manufacturing process (hand layup and vacuum bagging processes) [The flax fibers were arranged in a longitudinal direction and prepared as a sheet with dimensions of 600 mm in length and 400 mm in width see a. These ocesses) . Our stuocesses) ,32. A meocesses) , Geren eocesses)  to produThe flexural and interlaminar shear properties of flax-fiber-reinforced epoxy composites with different graphene percentages under in-service elevated temperature were characterized following ASTM D790  and ASTMThe scanning electron microscope (SEM) JEOL JXA 840A  was used to examine the damage features, fracture surface, and fiber–matrix interface of the samples with and without graphene tested under different levels of temperature. SEM observations were also conducted to evaluate the distribution of the graphene nanoparticles within the composite matrix. The samples were carefully prepared by cutting around 10 mm by 10 mm from the tested specimens and then observed under the SEM.gT of the composite, indicating a change in the molecular chain movement of the epoxy matrix, the bonding strength of the fibers with synthetic resin deteriorates at elevated temperature. Thus, this bonding is unable to mediate load transfer from the matrix to the fibers and the role of graphene addition is insignificant, but some of the fibers that remain intact can bear some load at this temperature level. −6/°C) [−6 K−1 to −0.77 × 10−6 K−1 as reported by Gangineni et al. [−6 K−1 [gT temperature of 78 °C , where the influence of the graphene addition becomes insignificant.It is worth highlighting that the increase in graphene addition percentage indicates the high sensitivity of the composite samples with higher graphene content to elevated temperature due to other phenomena arising from the presence of graphene, which contributed to obtaining the trend of mechanical strength values for hybrid composites at elevated temperature. This trend of reduction in mechanical strength can be explained by the thermal effect from the vast differences in the thermal expansion coefficients of flax fiber, epoxy resin, and graphene particles in the hybrid composites. Flax fibers have a negative coefficient of thermal expansion in their longitudinal direction (−8 × 10−6/°C) . As for i et al. , while e [−6 K−1 . This meThe failure modes in flexure of hybrid flax-fiber-reinforced epoxy composites under elevated temperatures are shown in TF: This failure occurs when the load stress applied on a sample exceeds the material’s strength . This loFB: This failure occurs when local high stresses associated with the cracks of the cross-matrix and the intersection of the cracks of polymer matrix and ply interfaces occurred, as also observed by Jawaid et al. . This meFP: This failure occurs when the interfacial bonding is weak, causing the fibers to slip out of the matrix. Exposure of the hybrid composites to the in-service elevated temperature caused softening of the epoxy matrix allowing the fibers to slide in the direction of flexural loading. This type of failure was observed in specimens with various graphene weight percentages and tested at 80 and 100 °C see b.IF: Interlaminar failure is caused by the high interlaminar stresses developed between the layers of the fibers ,44. ThisED: This failure can be identified as a horizontal interlaminar crack initiated at the end of the specimens and propagated to the mid-span of the sample a. This fCF: This type of failure occurs by compressive stresses on the upper surface and was observed in specimens without graphene when tested at different levels of temperature, as can be seen in MD: This mode of failure appeared as permanent deformation in the specimens without graphene nanoparticles and tested at 80 °C and 100 °C see a. This pSD: Increasing the temperature up to 80 °C and 100 °C for composites without graphene and with graphene resulted in failure by severe horizontal interlaminar cracks. SD failure initiated at the mid-span and propagated towards the supports because of the high mid-span deflection under loading, as shown in The failure modes of composites with and without graphene in ILSS under elevated temperature can be categorized as (1) End-ply Delamination (ED), (2) Compressive Failure (CF), (3) Matrix Deformation (MD), and (4) Mid-span Delamination (SD). A description of these failure modes is provided below:The microstructure of flax fiber composites with various amounts of graphene at room and at elevated temperature was analyzed by Scanning Electron Microscope (SEM), as shown in In contrast, the specimens with higher graphene percentages (1.0% and 1.5%) showed a greater number of void formations caused by filler agglomeration induced by the increase in the viscosity of the epoxy resin see c. These gT for epoxy was also measured by DMA test, which was 64.6 °C. In the case of softening, although reducing the size of voids in all composites, regardless of filled or not, softening of the resin resulted in a continuous decrease in mechanical properties. For the control specimen tested at 60 °C, more pores were observed due to fiber pull-out, as shown in At a higher temperature, the SEM images were chosen at 60 °C because this temperature is the HDT of the epoxy at which the resin begins to soften . FurtherAdding graphene nanoparticles to the resin matrix changed the flexural behavior of the flax–epoxy composites. For instance, adding 0.5% of graphene to the neat resin increased the flexural strength and modulus by 62% and 47%, respectively, at room temperature a,b. ThisThe addition of graphene significantly enhanced the interlaminar properties of the flax fiber–epoxy composites. Adding 0.5% of graphene increased the ILSS by 250% compared with the composites without graphene because of the stronger fiber–matrix interface developed by adding nanoparticles, which allowed stress to transfer easily across the fibers. This was supported by the observed failure behavior, wherein the composites with 0.5% graphene failed by flexural failure b and notgT, indicating the start of its softening. Moreover, the specimens with graphene showed higher degradation loss in strength under elevated temperature than the specimens with no graphene, which was attributed to the extreme variation in the thermal expansion coefficients of the three phases that developed different internal thermal stresses at the interface of the samples with graphene. This resulted in a lower efficiency of transferring internal stresses through the interface of specimens with graphene than specimens without graphene particles. Unlike the findings observed at 40 °C, increasing the percentage of graphene particles at 60 °C briefly reduced the retention in flexural strength of the tested specimens, confirming the effect of graphene anisotropy on the thermal stresses developed at the interface when increasing the amount of graphene. In other words, the extent of thermal expansion was higher in the hybrid composites with more graphene, leading to further deterioration of the interface. A significant reduction in the flexural properties has been witnessed at 80 °C and 100 °C  properties of flax fiber epoxy composites at in-service elevated temperature. Hybrid composites with graphene ranging from 0, 0.5, 1.0, and 1.5% by weight of epoxy resin were prepared and tested at a temperature of 20 °C to 100 °C with increments of 20 °C. From the test results and observations, the following points are concluded:An increase in the flexural and ILSS strength of flax fiber–epoxy composites at room temperature was achieved with the addition of graphene nanoparticles. The addition of 0.5% graphene increased the flexural and ILSS strength of tested composites by 62% and 149%, respectively, due to the increase in bond strength at interface between the epoxy resin and flax fibers caused by the contribution of graphene nanoparticles. Increasing the amount of graphene from 0.5% to 1.0% and 1.5%, however, reduced the flexural strength and ILSS strength by 57% and 52%, and 142% and 135%, respectively, attributed to the filler agglomeration as witnessed from the SEM observations.The addition of graphene nanoparticles improved the flexural modulus of flax fiber composites by 50%, 53%, and 58%, respectively, for 0.5%, 1.0%, and 1.5% at room temperature. This improvement in the flexural stiffness resulted in the failure mode changing from ductile to a brittle manner due to the greater stiffness of the graphene.The addition of graphene nanoparticles has an insignificant influence on the flexural and ILSS strength of flax fiber composites at in-service elevated temperature. The flexural and ILSS strength retention of the composites with 0.5% graphene nanoparticles were 60% and 52%, respectively, at 60 °C, and only 18% at 100 °C. This is a result of the high difference in the thermal expansion of the hybrid composites with graphene, which contributes to the increase in thermal stresses and their effect on the bonding strength of the interface at in-service elevated temperature. In contrast, the composites without graphene retained their flexural strength and ILSS up to 80% and 60%, respectively, at 60 °C, with similar strength retention to the composites with graphene at 100 °C.The mode of failure in flexural samples with graphene changed from the fiber breakage at room temperature to fiber pull-out at elevated temperature showing that the matrix properties governed the failure at in-service elevated temperature. Similar failure behavior can be observed for flexural test specimens without graphene at room temperature. Under ILSS, the stronger fiber–matrix interface by adding graphene nanoparticles changed the failure behavior from interlaminar shear to tensile failure and fiber pull-out at low and moderate temperature, and from fiber pull-out and tensile failure to delamination failure at elevated temperature.It can be recommended that further studies are required to investigate the inclusion of graphene into natural fiber composites considering the durability and environmental behavior of this type of composite. Various types of filler can be also investigated to study the bonding mechanism between different fillers and fibers.


STMN2, in the cerebellum of 95 FTLD-TDP cases and 25 non-neurological disease controls. Soluble TDP-43 was decreased in the cerebellum of FTLD-TDP cases but a concomitant increase in insoluble TDP-43 was not seen. Truncated STMN2 transcripts, an indicator of TDP-43 dysfunction, were elevated in the cerebellum of FTLD-TDP cases and inversely associated with TDP-43 levels. Additionally, lower cerebellar TDP-43 associated with a younger age at disease onset. We provide evidence of TDP-43 loss of function in the cerebellum in FTLD-TDP, supporting further investigation into this understudied brain region.Frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP) is a neurodegenerative disease primarily affecting the frontal and/or temporal cortices. However, a growing body of evidence suggests that the cerebellum contributes to biochemical, cognitive, and behavioral changes in FTLD-TDP. To evaluate cerebellar TDP-43 expression and function in FTLD-TDP, we analyzed TDP-43 protein levels and the splicing of a TDP-43 target, The online version contains supplementary material available at 10.1186/s40478-022-01408-6. Frontotemporal dementia (FTD) is an umbrella term for syndromes presenting with deficits in behavior, executive function, and language , 2. ApprC9orf72) gene as the most common genetic cause of FTD and amyotrophic lateral sclerosis (ALS)—often referred to as c9FTD/ALS—stoked interest in the cerebellum. The cerebellum of c9FTD/ALS cases is marked by robust dipeptide-repeat protein pathology and foci of repeat RNA transcripts [C9orf72 repeat expansion [C9orf72-associated pathology, leaving the causes of cerebellar dysfunction in FTLD-TDP unanswered.The cerebellum has historically been underappreciated in FTLD-TDP given the absence of TDP-43 inclusions and significant neurodegeneration in this neuroanatomical region. However, functional imaging studies show cerebellar involvement in cognitive abilities such as working memory, emotion, language, and attention processing , 7. In anscripts –14. Reponscripts , 16, andnscripts , 18, andxpansion –24. In aSTMN2 [STMN2 (tSTMN2), generated by the aberrant inclusion of a stop-codon containing cryptic exon, is detected in the central nervous system of ALS and FTLD cases with TDP-43 proteinopathy [tSTMN2 was also detected in the cerebellum of some ALS and ALS/FTLD cases [Previous studies discovered that TDP-43 suppresses the inclusion of cryptic exons in numerous transcripts , includiSTMN2 , 27. We inopathy . IntriguLD cases , suggestGiven the mounting evidence implicating cerebellar anomalies in FTLD-TDP, we evaluated TDP-43 splicing function and expression in the cerebellum of a cohort of well-characterized FTLD-TDP cases.Post-mortem brain tissue from individuals with neuropathologically confirmed FTLD-TDP and those without neuropathological features were provided by the Mayo Clinic Florida Brain Bank. All participants or their family members gave written informed consent, and all protocols were approved by the Mayo Clinic Institution Review Board and Ethics Committee. Sample size was determined based on the availability of tissue in our brain bank. A description of patient characteristics is provided Table tSTMN2 transcript were determined using 250 ng of RNA using the NanoString PlexSet platform. Data was analyzed using transcript nSolver4.0 software (NanoString Technologies) and normalized to hypoxanthine phosphoribosyltransferase (HPRT1), a housekeeping gene we have previously used to normalized transcript levels [tSTMN2 5’-AGAAGACCTTCGAGAGAAAGGTAGAAAATAAGAATTTGGCTCTCTGTGTGAGCATGTGTGCGTGTGTGCGAGAGAGAGAGACAGACAGCCTGC-3’, and HPRT1 (NM_000194.3), 5’- CTATGACTGTAGATTTTATCAGACTGAAGAGCTATTGTAATGACCAGTCAACAGGGGACATAAAAGTAATTGGTGGAGATGATCTCTCAACTTTAACTGG-3’.RNA was extracted from postmortem frozen cerebellum tissue using the RNAeasy Plus Mini Kit (Qiagen) per the manufacturer’s instructions. RIN was assessed using an Agilent 2100 bioanalyzer (Agilent Technologies), and only samples with an RNA integrity number  were used. Levels of the t levels . Probe sRadioimmunoprecipitation assay buffer (RIPA)-soluble and urea-soluble protein fractions were obtained from postmortem cerebellar tissue, as previously described . ApproxiAll statistical analyses were done using GraphPad Prism 9 (GraphPad Software). For each figure the type of analysis used, and the number of subjects is indicated in the figure and/or legend.tSTMN2 RNA and TDP-43 protein in the cerebellum between controls and FTLD-TDP cases, all FTLD-TDP cases combined and the three genotypes separately, were analyzed with single-variable (unadjusted) and multivariable linear regression models (adjusted). Multivariable models were adjusted for age at death, sex and RIN for tSTMN2 RNA, and for age at death and sex for TDP-43 protein. Both tSTMN2 RNA and TDP-43 protein were analyzed on the base 2 logarithmic scale due to their skewed distributions. The regression coefficients (β) and 95% confidence intervals (CIs) were estimated and interpreted as the difference in the means, bases on the 2 logarithmic scale, between all FTLD-TDP cases combined or the individual genotypes and the controls (reference group). P values less than 0.0125 were considered statistically significant after adjusting for the four different statistical test that were performed for all FTLD-TDP and the separate FTLD-TDP groups  vs. controls.To compare tSTMN2 RNA, age at disease onset, or disease duration after onset were evaluated in FTLD-TDP cases using single-variable and multivariable linear regression models. Both TDP-43 protein levels and tSTMN2 RNA were analyzed using the base 2 logarithmic scale. The multivariable model examining TDP-43 protein levels and tSTMN2 RNA was adjusted for age, sex and genotype. The model evaluating TDP-43 and age at disease onset was adjusted for sex and genotype, and the model assessing associations between TDP-43 protein and disease duration was adjusted for sex, age at onset and genotype. P values less than 0.0167 were considered statistically significant after adjusting for multiple comparisons.For associations between TDP-43 protein levels and To compare levels of soluble (RIPA-soluble) or insoluble (urea-soluble) TDP-43 protein a Mann–Whitney test was used.STMN2 missplicing, we used the NanoString PlexSet platform to measure tSTMN2 RNA in 95 FTLD-TDP cases ([C9orf72 repeat expansion and 33 with a mutation in progranulin (GRN)) and in 25 cognitively normal controls pathologically-confirmed to have no TDP-43 pathology  fraction, we measured cerebellar TDP-43 in soluble and insoluble fractions for a subset of FTLD-TDP cases and controls. More specifically, we selected the FTLD-TDP cases showing the most drastic depletion of soluble TDP-43 Fig. D as theytSTMN2 RNA production, we assessed whether detergent soluble cerebellar TDP-43 or tSTMN2 RNA levels associate with age of disease onset or disease duration in FTLD-TDP cases. Lower cerebellar TDP-43 associated with a younger age at disease onset in both unadjusted analysis  showed a significant accumulation of tSTMN2 RNA arguing that even a minor change in TDP-43 levels results in aberrant STMN2 splicing. These data suggest that tSTMN2 and other cryptic exon-containing transcripts normally suppressed by TDP-43 are sensitive markers of TDP-43 loss of function, thus providing a rationale to study their possible utility as biomarkers for distinguishing patients with FTD caused by TDP-43 proteinopathy vs. tauopathy—an endeavor of great importance to the FTD field.We noted that loss of soluble TDP-43 protein was more pronounced in the cerebellum of mutation carriers with the decrease in non-mutation carriers failing to reach statistical significance. However, each FTLD-TDP group (i.e., non-mutation carriers, tSTMN2 RNA associating with lower age at disease onset, in agreement with our earlier study finding a significant association between tSTMN2 in the frontal cortex and disease onset in a larger FTLD-TDP cohort [We additionally observed that lower levels of cerebellar TDP-43 associated with an earlier age at disease onset, suggesting that maintaining cerebellar TDP-43 levels may be protective in FTLD-TDP. We also noted a trend of higher P cohort . In combP cohort , 18, 31,P cohort –24, thesOverall, we conclude that TDP-43 loss of function may be more pervasive than initially appreciated, and that further study of the role of the cerebellum in the pathogenesis of FTLD-TDP is warranted.Additional file 1: Table S1. Associations of cerebellar tSTMN2 RNA with age at onset, and disease duration in FTLD-TDP cases.


Online information about safety of medications during pregnancy and breastfeeding is shown to be conflicting, resulting in anxiety and abstaining from use. The aim of this study was to characterize questions to SafeMotherMedicine, a web-based medicines information service for pregnant and breastfeeding women, to identify target areas that could guide subsequent development of medicines information directed at pregnant and breastfeeding women.The SafeMotherMedicine database contains all questions received through the web-based service and their corresponding answers. A retrospective database analysis of questions received from January 2016 to September 2018 was performed, using descriptive statistics.A total of 11 618 questions were received including 5 985 questions (51.5%) concerning pregnancy, 4 878 questions (42.0%) concerning breastfeeding, and 755 questions (6.5%) concerning both conditions. The medications in question represented all therapeutic groups with paracetamol (7.0%), ibuprofen (4.1%), cetirizine (3.3%), desloratadine (3.2%) and meclizine (2.8%) being the top five. The 20 medications most frequently asked about for either pregnancy, breastfeeding or both pregnancy and breastfeeding, constituted half of all questions and were used to identify target areas. These included both symptomatic relief of common complaints, such as pain, nausea, and rhinitis, as well as treatment of chronic conditions such as allergy, psychiatric disorders, and asthma. Analysis of a subset of questions showed that most of these questions were asked before use of medications in a current pregnancy (49%) or during breastfeeding (72%). The questions concerned use of medications in all stages of pregnancy and breastfeeding. For 81.6% of the questions concerning pregnancy, and for 84.2% of the questions concerning breastfeeding, information of no or low risk for the foetus or the breastfed infant was provided by SafeMotherMedicine.We found that target areas for medicines information directed at pregnant and breastfeeding women included both symptomatic relief of common complaints as well as treatment of chronic conditions. The questions concerned a wide range of medications and involved use in all stages of pregnancy and breastfeeding. Our findings indicate that developing medicines information addressing the identified target areas will meet the information need for a large proportion of this patient group.The online version contains supplementary material available at 10.1186/s12884-022-05252-3. Use of medications among pregnant and breastfeeding women is common. An international study found that approximately 80% of pregnant women use medications , while tThe internet has been shown to be an important source for pregnant women seeking information about safety on use of medications , 12. HowSafeMotherMedicine is a web-based medicines information service directed towards pregnant and breastfeeding women. The service is run by the Regional Medicines Information and Pharmacovigilance Centre (RELIS), a Norwegian network of medicines information centres . RELIS iThe aim of this study is to identify target areas that could guide subsequent development of medicines information directed at pregnant and breastfeeding women, based on a review of questions to SafeMotherMedicine.st level has 14 main anatomical or pharmacological groups, and the ATC 5th level denotes the chemical or generic substance [In SafeMotherMedicine, all questions received through the web-based service and their corresponding answers are stored in a full-text database, searchable by staff only. The inquirer index the question with the relevant condition . The inquirer is also asked to describe medications, indication, dosage and duration of treatment, previous communication with physician, level of breastfeeding , and if the breastfeed child is healthy, sick, or premature. The medications in question are indexed by staff according to the Anatomical Therapeutic Chemical (ATC) classification system for medications  before tubstance .A retrospective, descriptive analysis of the SafeMotherMedicine database from January 2016 to September 2018 was performed. Only questions concerning conventional medications from the pharmaceutical industry were included, while questions concerning food, dietary supplements, cosmetics, and complementary therapies were excluded. The 20 medications most frequently asked about in the categories; pregnancy, breastfeeding and both pregnancy and breastfeeding, were categorized into therapeutic fields, that pointed out the target areas. This categorization was based on presumed indication, which again was based on ATC code as well as the authors’ experience from SafeMotherMedicine.Why the question was raised , other , symptoms in nursing child).When the question was submitted relative to use of the medication(s) in question , not described).st trimester, 2nd trimester, 3rd trimester/ 0-2 weeks, 2 weeks – 6 months, 6–12 months, > 12 months, not pregnant/breastfeeding yet, not described).Intended use of the medication(s) in question relative to stage of pregnancy or age of breastfed child .Advice that was given .A subset of 550 questions was randomly selected from the total material using Research Randomizer . The sizEach question could involve several medications, and each medication was assessed individually.Data for questions to SafeMotherMedicine was collected by Structured Query Language (SQL), transferred to Excel, and subsequently analysed with statistical software SPSS version 25 . For the subset of questions that was studied in more detail, a pilot study was performed based on the 30 initial questions in the study material. Seven questions were excluded from the pilot material because they did not concern medications. The remaining 23 questions concerned 29 medications. In the pilot study, consistency among the authors with regard to classification of questions was considered as excellent with an Intraclass Correlation Coefficient (ICC) of 0.97 for questions concerning pregnancy (n = 15), and 0.92 for questions concerning breastfeeding (n = 17), both p < 0,0001.In the study period, SafeMotherMedicine received 11 618 questions including 5 985 (51.5%) concerning pregnancy, 4 878 (42.0%) concerning breastfeeding, while 755 questions (6.5%) concerned both conditions. Excluding 1 084 (9.3%) questions that did not concern medications, a mean of 1.48 medications per question was observed. Sixty-four percent (63.7%) of the questions concerned only 1 medication, and the number of indexed medications in a question ranged between 1–9. Mean number of medications was comparable in questions concerning pregnancy (1.50), breastfeeding (1.43), and both conditions (1.65).The medications were distributed among all ATC-groups, with medications used for disorders related to the respiratory and nervous system being the most frequent. Top five medications were paracetamol (7.0%), ibuprofen 4.1%), cetirizine (3.3%), desloratadine (3.2%) and meclizine (2.8%). With regard to the main ATC-groups, questions concerning either pregnancy, breastfeeding or both pregnancy and breastfeeding had a comparable pattern of distribution, as illustrated in Supplementary Figure %, cetiriFigure Of a subset of 550 questions analysed in more detail, 138 questions were excluded, as they did not address use of specific medications, resulting in a remaining sample of 412 questions concerning in total 521 medications. Three hundred medications (57.6%) concerned pregnancy and 266 breastfeeding (51.1%). The majority of the questions associated with pregnancy involved women that asked about medications before use, or during use, in a current pregnancy  of questions regarding pregnancy, and for 211 (79.3%) questions regarding breastfeeding, no specific reason was given for the why the questions were raised to SafeMotherMedicine. In 23 (8%) of the questions concerning pregnancy, and in 28 (11%) of the questions concerning breastfeeding, the reason for raising the question to SafeMotherMedicine was that use of the medication was discouraged by health care professionals or product information, or that the information in other sources was conflicting. For all these questions, SafeMotherMedicine gave reassuring advice and did not advise avoidance. No pattern was found with regard to which medications that were associated with conflicting information on safety.For 245 of the 300 questions (81.6%) concerning pregnancy, SafeMotherMedicine provided information of no or low teratogenic risk, and for 224 of the 266 (84.2%) questions concerning breastfeeding, SafeMotherMedicine provided information of no or low risk for the breastfed infant. SafeMotherMedicine advised avoidance in 10 of 300 (3.0%) questions regarding pregnancy and in 8 of 266 (2.6%) regarding breastfeeding.To the best of our knowledge, this study is the first analysis of questions and their corresponding answers on use of medications from both pregnant and breastfeeding women received through a web-based service. We found that complaints such as pain, nausea, and rhinitis, as well as chronic conditions such as allergy, psychiatric disorders, and asthma are involved in a significant amount of the questions, making them potential target areas to guide subsequent development of medicines information. The questions in the study concerned all stages of pregnancy and breastfeeding and reassuring advice was provided in most cases.Half of all questions were constituted by the 20 medications most frequently asked about for each of the categories; pregnancy, breastfeeding and both pregnancy and breastfeeding, that together with the medications’ corresponding therapeutic field pointed out the target areas. The identified target areas include complaints well known to be prevalent among pregnant and breastfeeding women, in addition to those known to pose a risk if undertreated. These findings imply that by directing information efforts towards these target areas, tailored to apply to all stages of pregnancy or breastfeeding, the information need can be met for many pregnant and breastfeeding women. However, questions concerning medications from a wide range of therapeutic fields were received, indicating an information need that cannot be covered by information directed at target areas only.Medications used to treat complaints from the nervous system or respiratory system constitute a major part of the questions in our data material, including several medications also available over-the-counter (OTC), in line with previous studies –28. DespThere were differences in the therapeutic fields most frequently asked about between the three categories pregnancy, breastfeeding and both pregnancy and breastfeeding Fig. . This isFor use of medications during both pregnancy and breastfeeding, the high prevalence of questions concerning psychiatric disorders could be explained by planning of therapy due to chronic disease. This have also been shown by Pijpers et al., who found that use of psychotropic medications, in particular antidepressants, were important drivers to seek information . PsychiaWe found that women in most cases asked prior to use of a medication during pregnancy (67%) and breastfeeding (87%), reflecting a need of decision support prior to treatment initiation. This is similar to the findings from a North American Teratology Information Service (TIS), which reported that 80% of questions were asked before use of a medication during pregnancy or breastfeeding . BreastfStudies about calls from pregnant women to medicines information or teratology centres, has shown that most calls regarded exposures in the first trimester , 25, 28.The number of questions concerning use of medications during breastfeeding (42%) was somewhat higher in our material compared to what is reported from other TIS (34–39%) , 27, 28.Information about safety of medications in pregnancy and breastfeeding is available from multiple sources, and particularly online information has been shown to be frequently used by pregnant women , 36, desPregnant and breastfeeding women are among patient groups where there has been a focus on increasing the digital health literacy, which is the ability to find, understand, and appraise health information from electronic sources . By offeFew studies on web-based medicines information services to pregnant and breastfeeding women have been conducted, and this study is the first to systemize spontaneous questions about use of medications from pregnant and breastfeeding women in Norway, to identify the information need in this patient group.A limitation of the study is the lack of demographic data of the users of the service, which complicates the evaluation of the generalisability of the results. Another limitation is that information on stage of pregnancy and age of breastfed infant at time of use, and reason for raising a question to SafeMotherMedicine was not mandatory in the question form, and thus is missing in some of the questions. Lastly, questions to the service are individual and spontaneous, and does not necessary reflect questions from the general population of pregnant and breastfeeding women. However, the number of unique users on the SafeMotherMedicine webpage (approximately 45 000 users/year) compared to the number of births  needs to be studied among both users and non-users of the service. Sociodemographic information about users of SafeMotherMedicine has not been registered as the service is confidential, and it would be of interest to conduct a survey to compare the users of the service with the non-users.The identified target areas for medicines information directed at pregnant and breastfeeding women included both symptomatic relief of common complaints, such as pain, nausea, and rhinitis, as well as treatment of chronic conditions such as allergy, psychiatric disorders, and asthma. Our findings imply that developing medicines information directed at the identified target areas, tailored to apply to all stages of pregnancy or breastfeeding, will meet the information need of a large proportion of this patient group. The findings of this study could guide future development of medicines information to pregnant and breastfeeding women.Additional file 1. Additional file 2.Additional file 3. Additional file 4.


Atomic deuterium (D) adsorption on free-standing nanoporous graphene obtained by ultra-high vacuum D Graphene (Gr) is at the forefront of cutting-edge science and technology. The conversion of semimetallic graphene into a semiconducting phase would open new perspectives for electronic devices and would provide new opportunities for large band gap semiconducting graphene-based photonics. The considerable effort on the chemical functionalization of graphene to induce a gap opening can entail the covalent modification of its planar carbon mesh. Atomic hydrogen/deuterium interaction at the basal plane of graphene breaks its local guration ,4,5,6,7.Although graphene has high thermodynamic stability and chemical inertness, it can be patterned ,9 or decSeveral methods have been employed to hydrogenate Gr, ranging from hot ,13,14 tocracking ,17,18, aWe herewith expose to atomic deuterium a three-dimensional (3D) warped free-standing 2D graphene, constituted by a compact, bi-continuous interconnected 3D arrangement of high-quality graphene veils, composed by one to two layers, weakly interacting among them ,20. The ecreases  thanks t [3 bond , as obse [3 bond ,6. NPGs  [3 bond ,24, wher [3 bond ,7. In thWe investigate pristine and deuterated NPG by Raman and photoelectron spectroscopies. Raman spectroscopy is a well-known sensitive technique to probe disorder and defects in graphene through defect-associated peaks. In fact, the Raman-forbidden D and D results ,26,27. Amponents ,29,30.Here, we present a spatially resolved Raman spectro-microscopy experiment in an innovative ultra-high vacuum setup, also hosting high-resolution X-ray photoelectron spectroscopy. We show that this combined optical and electronic spectroscopy approach allows us to demonstrate the almost defect-free configuration of pristine free-standing graphene as well as the highly homogeneous uptake of deuterium atoms, which leads to a significant lattice distortion from the planar C mesh toward the spThe disentanglement of the influence of sp devices ,32.The nanoporous graphene samples were synthesised by employing a nanoporous Ni-based chemical vapour deposition (CVD) method ,35,36,37Pristine NPG cleaning in UHV has been carried out by high-temperature annealing up to ∼620 20 ∘C, asnstrated . As a coThe Raman, XPS, and Auger Electron Spectroscopy experiments were performed at Sapienza University in Rome, in the SMART laboratory of the Department of Physics. This is a newly conceived, integrated experimental apparatus, constituted by three UHV-interconnected chambers in a novel set-up recently designed and assembled to combine optical and electron micro-spectroscopy all in UHV. The three vacuum chambers, hosting the micro-XPS, micro-Raman and preparation stages, respectively, had base pressures in the high 10The Raman experiments were carried out in the novel UHV setup in a backscattering configuration, by using as excitation source a single frequency Nd:YVONPG samples have been imaged by scanning electron microscopy (SEM) by using the apparatus of the CNIS laboratory at Sapienza University: field-emission Zeiss Auriga 405, nominal resolution of 1 nm at maximum magnification, beam energy of 9.5 keV and working distance of approximately 3.5 mm.One of the most effective spectroscopy techniques for determining the thickness, doping, strain, lattice deformation, and defects in 2D materials is the Raman technique, widely used in graphene ,39,40, iThe Raman spectra taken on the pristine and deuterated-NPG samples in UHV ambient are shown in  spectra . In partThe increased intensity of the D and D′ bands can be due to different type of effects in the graphene lattice, such as spaminants . In the ng sites . Furthergraphene ,48,49,50In order to probe the homogeneity of the deuteration process, Raman spectra of deuterated NPG have been acquired over a region of 10 × 10 p3 bonds ,27, althA further proof that the exposure to atomic D leads to the establishment of lattice distortion associated with non-planar D-to-C bonds, and also to the formation of other lattice defects, can be obtained by the analysis of the C 1s core level by XPS. The C 1s core level spectra for pristine clean NPG and deuterated NPG are shown in Pristine clean NPG is dominated by the huge, narrow, and slightly asymmetric spomponent  at 284.7s of NPG ,20 and amponents ,52,53.In the C 1s core level of deuterated NPG, the spar bonds ,6. The eere used ,6. It isere used ,55,56,57So far we have found that Raman and core-level photoemission confirm the establishment of spaterials ,59, in pdN(E)/dE b is char and electronic (XPS) spatially resolved spectroscopies, combined in a new and original micro-spectroscopy setup. The use of a high-temperature D


In the graphene/Bi2Se3 composite structure, the coupling of the hyperbolic phonon polaritons supported by Bi2Se3 with the surface plasmons supported by graphene can lead to the hybrid surface plasmon–phonon polaritons (SPPPs). Compared with that between undoped Bi2Se3, Casimir friction can be enhanced by more than one order of magnitude due to the contribution of SPPPs. It is found that the chemical potential that can be used to modulate the optical characteristic of SPPPs plays an important role in Casimir friction. In addition, the Casimir friction between doped Bi2Se3 is also studied. The friction coefficient between doped Bi2Se3 can even be larger than that between graphene-covered undoped Bi2Se3 for suitable chemical potential due to the contribution of unusual electron surface states. The results obtained in this work are not only beneficial to the study of Casimir frictions but also extend the research ranges of topological insulators.Casimir friction is theoretically studied between graphene-covered undoped bismuth selenide (Bi In nature, friction is a very common phenomenon, which extensively exists in the macroscopic to the microscopic world. However, the physical mechanism of friction in the microscopic world is different from that of the friction observed in our daily lives. In recent years, people have paid much attention to nanotribology with the rapid development of nanofabrication technology. Interestingly, there is one type of friction that appears between bodies in relative motion even without direct contact at the nanoscale, which is usually called noncontact friction ,4,5,6,7.The theoretical method of Casimir friction between two objects separated by a small gap was firstly derived by Pendry with the help of classical electromagnetic theory . Then, V1−xSbx alloy [2Te3, Sb2Te3 [2Se3 [2Se3 is also well-known for its bulk optical response [2Se3 with finite doping, the coupling of HPPs with Dirac plasmon confined at the surface can result in the appearance of hybrid modes [2Se3. As has been mentioned above, graphene plasmon can be coupled with different surface polaritons, so it is interesting to investigate the coupling between graphene plasmon and HPPs supported by undoped Bi2Se3.In the past decade, topological insulators have received extensive attention because of their exotic characteristic and important applications in the fabrications of new electronic devices ,43,44,45bx alloy  and obsebx alloy . Then, s, Sb2Te3 , and Bi2e3 [2Se3 ,49 compoe3 [2Se3 ,49,50,51response ,53,54,55response . For Bi2id modes . However2Se3. The low-frequency HPPs supported by Bi2Se3 can be coupled with SPs supported by graphene, and then resulting in the appearance of hybrid surface plasmon–phonon polaritons (SPPPs). We demonstrate that the SPPPs play an important role in the Casimir friction between graphene-covered Bi2Se3. Compared with that between undoped Bi2Se3, the Casimir friction between graphene-covered Bi2Se3 enhances about one order of magnitude in wide separation gaps. Furthermore, the hybrid SPPPs can be flexibly controlled by tuning the chemical potential of graphene that depends on the gate voltage. Therefore, it provides an opportunity to actively modulate the Casimir friction between topological insulators. Casimir friction between doped Bi2Se3 is also studied. Depending on the chemical potential, Casimir friction between doped Bi2Se3 can be larger or smaller than that between graphene-covered Bi2Se3 under the same separation distance. This study not only extends the research ranges of topological insulators but also provides an efficient method to control the Casimir friction.In this paper, we study the Casimir friction between graphene-covered undoped Bi2Se3 is shown in 2Se3 bulks are separated by a distance 2Se3 moves relatively to the bottom one along the x-axis with a velocity T, which is set to be T = 300 K in all the calculations. If the moving velocity of the top graphene-covered Bi2Se3 satisfies the conditions 2Se3. However, the contribution of the propagation wave can be ignored in the near-field region [xy plane, respectively. p-polarized electromagnetic waves. The symbol p-polarized reflection amplitude s-polarized reflection amplitude p-polarized wave often plays a dominant role in the near-field region, so the contribution from s-polarized waves can be excluded. The integral of q in Equation (1) is defined as force spectral density (FSD), which can be expressed byThe schematic of the Casimir force between graphene-covered Bi as f=γv . kB and d region . Therefod region (1)γ≈ℏ2π in Ref. , p-polar2Se3 parallel and perpendicular to the optical axis (z-axis), respectively. e is the electron charge, 2Se3 exhibits hyperbolic optical properties. The dielectric properties perpendicular  Rp=pnsitions . e is thgiven by ,60(5)εα2Se3 are shown in 2Se3 is type II HM  via 2Se3 is determined by its chemical potential The parameters used in Equation (5) are 2Se3, graphene, and graphene-covered Bi2Se3 are denoted in green dotted, red dashed, blue dot-dashed, and black solid lines, respectively. The chemical potential of graphene is fixed at 2Se3 decrease slowly as the distance increases, while the friction coefficients of hBN and graphene-covered Bi2Se3 decrease rapidly as the distance increases. Although the friction coefficient of Bi2Se3 is smaller than that of hBN when the distance is less than 25 nm, it has an obvious enhancement when covered with graphene. Compared with that of Bi2Se3, the friction coefficient between graphene-covered Bi2Se3 can increase more than one order of magnitude when the chemical potential of graphene is 0.2 eV.In  in Ref. . Here we2Se3, graphene, and graphene-covered Bi2Se3 are displayed in green dotted, red dashed, blue dot-dashed, and black solid lines, respectively. The chemical potential of graphene is set to be 2Se3. FSD has a strong peak corresponding to the resonance frequency of the HPPs on the surface of Bi2Se3 material, so the main contribution to quantum friction in Bi2Se3 comes from the HPPs. The FSD spectrum of graphene covers a very wide frequency range but with a relatively low value, which agrees that graphene can support p-polarized SPs with a wide frequency range. For graphene-covered undoped Bi2Se3, the HPPs supported by Bi2Se3 can be coupled with SPs supported by graphene, which results in the shift of the hybrid SPPPs toward higher frequencies. Therefore, the Casimir friction between graphene-covered Bi2Se3 is mainly from the SPPPs. We can qualitatively obtain the Casimir friction by judging the area covered by the curves of different configurations. Because Casimir friction is mainly from the exchange of the evanescent waves in the near-field region, we display the FSDs for different configurations to realize the contribution of different evanescent waves to Casimir friction in 2Se3 shown in 2Se3 is covered with graphene, the HPPs supported by Bi2Se3 can couple with SPs supported by graphene to form the SPPPs which is out of the hyperbolic band. The SPPPs move toward higher frequencies as the wave vector increases. Therefore, we can obtain that SPPPs dominate the Casimir friction between graphene-covered undoped Bi2Se3.To further analyze the physical mechanism of Casimir frictions for different configurations, we show the photon exchange functions of these configurations in 2Se3 with chemical potentials Because the characteristic of SPs excited by graphene as well as the coupled SPPPs is dependent on the chemical potential of graphene, we investigate the effect of chemical potential on the Casimir friction. In 2Se3 bulks. The friction coefficients with separation distances of 10, 20, and 30 nm are plotted in black solid, blue dashed, and red dotted lines, respectively. In In 2Se3 is fixed at 2Se3 can enhance the Casimir friction significantly between graphene-covered Bi2Se3. It is also possible to actively modulate the Casimir friction between such graphene/Bi2Se3 composite structures by controlling the gate voltage.In 2Se3 in the previous sections. Then, we continue to study the Casimir friction between doped Bi2Se3 without covering graphene. Such a configuration can be realized by moving graphene away in 2Se3 bulk behaves like an anisotropic material, while the two layers denoted in orange represent the top and bottom surface states. The electron surface states which behave as massless Dirac fermions exist when Bi2Se3 is doped; it plays a similar role as graphene in the graphene-covered undoped Bi2Se3 configuration. The fundamental density response functions of the surface states are the sheet conductivity We have investigated the Casimir friction between graphene-covered undoped Birelation (6)σBq,ω2Se3 by employing Equation (1).Under the condition of random-phase approximation for Dirac fermions, ytically :(7)Pq,ω=2Se3, which is similar to that shown in 2Se3 which is shown in 2Se3 is larger than that between undoped Bi2Se3 when the separation distance is less than 20 nm. However, when the separation is larger than 20 nm, the difference in Casimir friction between undoped Bi2Se3 and doped Bi2Se3 is very small.In 2Se3 for different separation distances 2Se3 has little impact on the surface states that dominate the Casimir friction at such a small distance. The Casimir friction, in this case, is even larger than that between graphene-covered Bi2Se3 when the chemical potential is larger than 0.1 eV, which can be seen in 2Se3 are not as sensitive to the chemical potential as those between graphene-covered undoped Bi2Se3.In 2Se3. Owing to the coupling of the HPPs supported by Bi2Se3 to the SPs excited by graphene, the Casimir friction between the proposed structures shows a significant enhancement compared to those between undoped Bi2Se3 materials. The Casimir friction can be modulated actively by tuning the chemical potential of graphene. It is found that the maximum value of Casimir friction can be obtained by choosing the appropriate chemical potential. Furthermore, Casimir friction between doped Bi2Se3 is also studied. When the chemical potential is larger than 0.1 eV, Casimir friction between doped Bi2Se3 is even larger than that between graphene-covered undoped Bi2Se3. Although more and more schemes have been proposed to enhance the Casimir force, it is still challenging to observe such a small force exactly in an experiment. Because it may play an important role in the nanoelectromechanical systems (NEMS), it is feasible to observe the Casimir force in the NEMS successfully. The results of this study are of great help to extend the research range of Casimir frictions and are meaningful to understanding the application of ultra-sensitive force detection of topological insulators.In summary, we have studied the Casimir friction between graphene-covered Bi


Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein–protein complexes obtained by popular docking software. To this aim, we generated Bioinformatics Advances online.https://doi.org/10.5281/zenodo.4012018. The programs underlying this article will be shared on request to the corresponding authors.The docking models are available at  Protein–protein interactions (PPIs) are pivotal to the most diverse biological processes, including signal transduction, electron transfer and immune response. Proteins interact with each other in a specific manner and increasing evidence is revealing that perturbation of such interactions frequently leads to defective phenotypes. Mutations associated to human genetic disorders, for instance, commonly alter the interaction between proteins, rather than their folding and stability , the latter one embodying in atom–atom or residue–residue potentials the statistical occurrences observed in databases of experimental 3D structures . HoweverNowadays, over 100 scoring functions are available from the CCharPPI web server  or used Herein, we present the results of an ML approach we developed to exploit all the scoring functions that we could collect from public sources . To thisWe performed a data analysis and employed ML algorithms within the scikit learn python library (For each of the 230 protein–protein complexes (targets) in the protein–protein docking BM5  and 6 339 793 incorrect DMs. For these targets, we calculated the number of clashes for each of the 30 000 generated DMs. As in CAPRI, clashes were defined as contacts between non-hydrogen atoms separated by a distance below 3 Å. For each target, we discarded DMs with a number of clashes greater than the average number of clashes plus 2 standard deviations for that target. Overall, we discarded 243 061 DMs, which left us with a total of 6 146 939 DMs, on average 28 859 ± 218 DMs per target.2.2.2Since databases containing DMs generated by docking software are highly unbalanced, to train the ML algorithms we built a ‘balanced’ dataset by applying a random undersampling selection of the incorrect DMs to match the number of correct ones. We performed this undersampling case by case, meaning that for each target we matched its number of correct DMs with the same number of randomly sampled incorrect DMs. In this way, we produced a balanced dataset, Bal-BM5, where each target has the same number of correct and incorrect DMs. The number of DMs per target is quite variable in the Bal-BM5 dataset, as it depends on the number of correct DMs available for each specific target. The number of correct DMs per target ranges from 2 to 600, with the same number of incorrect ones. On average the number of DMs per target is 346 (with a standard deviation of 358), while the median value is 214. Only DMs that passed the check on clashes were included in the Bal-BM5 dataset. The choice of undersampling, in order to generate a balanced dataset, is due to the fact that oversampling generally relies on interpolation for generating new samples of the minority class, thus depending on the overlap of the label distributions. As the label distributions overlap differently here with the different features used, interpolation would risk to generate noise in the dataset.The targets and DMs selected in the above steps were separated into training and validation sets using two different strategies. As a first strategy, all the 161 targets included in the benchmark 4 (BM4) were assigned to the training set, Bal-BM4, while the remaining 52 targets, corresponding to the BM5 update, relatively to BM4, were assigned to the validation set, Bal-BM5up. Alternatively, for the cross-validation (CV) approach, we randomly selected 64 targets from the Bal-BM5 dataset to produce a validation set, while the remaining 149 targets were used for training. This process was repeated 10 times.2.2.3As the typical case in docking challenges is having a small fraction of correct DMs in the total ensemble of DMs, we decided to build unbalanced datasets for validation and testing of the trained ML models. To this end, we selected 3000 DMs per target, out of those generated in the docking step and that passed the check on clashes. For each target, we selected up to 600 DMs with acceptable or better quality, and picked up randomly incorrect DMs until 3000 DMs in total were collected into the 3K-BM5 dataset. We fixed a maximum value for the number of correct DMs, 600, to avoid biasing the training of the ML algorithms toward a few cases with a too large number of correct DMs. Strategies similar to those used to split the whole Bal-BM5 dataset into training and validation datasets were used to extract validation datasets from the 3K-BM5 dataset. Specifically, the 52 targets corresponding to the BM5 update were assigned to the 3K-BM5up dataset or, as an alternative, for the CV approach 64 targets were randomly selected to produce a validation dataset, repeating this process ten times.2.2.4For an independent testing, we used the CAPRI Score_set, made of 15 targets from the CAPRI Rounds 13–26, with a total of 19 013 DMs, of which 2166 (11%) are correct. This BM set contains DMs predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, being thus highly diverse, and include dimers and multimers . Ninety-two of such features come from the CCharPPI server . We did Z-scores using σ and μ are the mean and the standard deviation of the corresponding scoring function on the training set.The values of these scoring functions were normalized as It is widely acknowledged that a feature selection step, i.e. the removal of irrelevant and weakly relevant features, may improve the performance of a predictor, in terms of accuracy, simplicity and/or speed  presenting a variable number of DMs per target (346 on average) but equally distributed between correct and incorrect DMs, the latter (the 3K dataset) consisting in a dataset of 3000 DMs per target, with a variable number of correct and incorrect DMs (see Section 2). As a consequence of their definition, the 3K dataset is F1 score (F1_corr).The default training of the classifiers was performed on the targets of the BM4 balanced dataset, Bal-BM4, while validation was performed on the targets included in the BM5 update both using the 3K and the balanced datasets, 3K-BM5up and Bal-BM5up. The comparative performance of the three classifiers trained on Bal-BM4 and tested on the Bal-BM5up and 3K-BM5up datasets is reported in The performance of all the classifiers on the Bal-BM5up dataset is quite satisfactory, with MCC values ranging between 0.44 and 0.67, and differences between them become less dramatic. However, RF still significantly outperforms the SVM classifier, while performing slightly better than the PRC one.In light of the good performance exhibited by the RF classifier, four more tree-based approaches, XgBoost, Gradient Boosting, Decision tree and AdaBoost, were tested on the 3K-BM5up dataset. However, evaluation metrics shown in  show tha3.1.2F1 score and MCC values .As the RF classifier was more promising than the SVM and PRC ones, we further tested it using a CV approach. The results of a 10-fold CV on the whole Bal-BM5 dataset  are comp3.1.3An advantage of the RF is the possibility of retrieving the features important for classification. We used a forward selection protocol to determine the threshold of importance for a feature to influence the classification. We evaluated the importance of the features for the two different training approaches described before, which is training on Bal-BM4 with validation on the Bal-BM5up and 10-fold CV on Bal-BM5. Monitoring performance metrics showed that 1% is a suitable importance threshold to optimize the classifier performance .Overall, 16 features with an importance above 0.01 were common to the two selection approaches see  and wereOne feature has an importance around 0.2 for both the approaches, being one order of magnitude larger than any other one. It is the CONSRANK score, a consensus score, which reflects the conservation (or frequency) of the inter-residue contacts featured by a given model in the whole decoys ensemble it belongs to Selected features thus include statistical potentials, energy-based potentials as well as consensus scores, i.e. they include scoring functions from all the representative approaches in the field .3.1.4The hyperparameters that can be optimized to improve the performance of a RF classifier are: the number of trees, the minimum number of samples to split the nodes and the weights assigned to each class. We tested different values for all these three hyperparameters, finding out that the number of trees does not affect significantly the performance of our classifier; therefore, it was kept at the default value of 100 in sklearn. The optimal value for the minimum number of samples to split the nodes was instead found to be 10, versus a default value of 2 from sklearn, while the optimal weights for the Class 0 (incorrect) and Class 1 (correct) were identified as 1.2 and 0.1, respectively. These optimal values were used for the training of the final classifier (see below).3.1.5After selecting the features and optimizing the RF hyperparameters, we trained our final RF classifier, that we named CoDES, with selected features and optimal hyperparameter values, on the Bal-BM4 dataset. Evaluation of the features importance in the final classifier showed a substantial agreement with the trend we had found on the preliminary classifier, with the CONSRANK score still having by far the highest importance and three common features with the Bal-BM4/Bal-BM5up in the top five positions for importance . While the recall of correct solutions is even enhanced for the Score_set  and a percentage of correct models ≤2.5% for five more targets . Results are shown in 3.1.6A widely used metric for evaluating the performance of a scoring function is the ability to have correct DMs ranked at the top-N positions for each target. This kind of metric responds to the demand from researchers to have a limited number of putative solutions to be further explored by computational or experimental approaches. It can also been referred to as the ‘success rate’ and is the standard metric in the CAPRI experiment. Several recently developed ML methods for the scoring of DMs have been evaluated/tested in terms of success rate, therefore, we will use in the following such a metric for comparing the CoDES performance with them, on datasets for which their results have been made available.Performance in terms of top-10 success rate is available for two recently developed ML algorithms , for theThe comparative performance of CoDES, GraphRank, iScore and the best CAPRI scorer, for each target in the respective CAPRI round, is reported in A further comparison can be made with other two ML methods for which less detailed results on the Score_set, i.e. the total number of targets with at least one correct, high- or medium-quality DMs ranked within the top-10 positions, are available. These methods are: a RF classifier being a combination of eight energy potentials ; and an Testing on Score_set showed overall that CoDES is not only able to top rank correct solutions, but also to single out DMs of medium- and high quality. This outcome, maybe unexpected as the classifier was trained to only distinguish correct decoys from the incorrect ones independently from their detailed quality, could actually reflect overall an effective distinction between good and bad decoys, hidden in the features used for training the model.A comparison similar to those above was also feasible with the recently developed IRaPPA ML method, combining physico-chemical and statistical potential descriptors for the scoring of DMs, using ranking SVM, an efficient approach for information retrieval (While application of ML is dramatically boosting the efficiency of structural bioinformatics tools , severalvbab042_Supplementary_DataClick here for additional data file.


AbstractVisual diagnostic tests must have a high degree of consistency in their measurements (high reliability) to ensure accurate assessment of perceptual abilities. The current study assessed test–retest reliability and practice effects in the Leuven Perceptual Organisation Screening Test (L-POST) in 144 healthy volunteers, with time intervals between 0 and 756 days. We used Pearson's and intraclass correlation analysis, Bland–Altman analysis and multilevel modelling. Results from our analyses converged and supported an adequate reliability of the L-POST. Multilevel modelling demonstrated an absence of practice effect, suggesting that the L-POST is suitable for repeat administration. This study suggests that the L-POST has adequate reliability and is suitable for repeat administration even at short intervals. This study provides the basis for a more systematic evaluation for neuropsychological assessments, which can lead to the development of more reliable assessment batteries. Visual perception is the extraction and interpretation of visual cues from the environment. It underlies our ability to acquire object knowledge and to interact effectively with others. However, it is easily affected by brain damage and neurological disorders  and can be followed up by more in-depth testing of specific visual functions. The test is freely available at www.gestaltrevision.be/tests and is described in detail in Torfs et al.  s et al. .r = .77, p < .001) using a sample of 20 stroke patients. However, test–retest correlations for the subtests varied widely, and about half were not significantly different from zero, suggesting large confidence intervals around the observed correlations. Because samples vary randomly, it is plausible that the obtained correlation is much larger or smaller than the true population correlation. The smaller the sample size, the greater the likelihood of obtaining a spuriously small correlation coefficient. It is therefore uncertain if the low correlations reflect a genuine poor test–retest reliability or are extreme estimates due to their small sample size. Schonbrodt and Perugini  and rarely ever at the time interval used in traditional reliability studies. It is therefore important to evaluate test–retest reliability at multiple time intervals and across multiple sessions to increase the ecological validity of a reliability study. Multilevel modelling allows for comparison of more than two sessions and the inclusion of additional predictors such as the time between test sessions  and the number of previous sessions , while at the same time controlling for differences in baseline performance between individuals. Together, multilevel modelling and Bland–Altman analyses allow an in-depth examination of any practice effect if one exists.The current study aims to evaluate test–retest correlations of the L-POST in a sufficiently large sample. Additionally, we evaluate practice effects through Bland–Altman analyses and multilevel modelling.Our convenience sample consisted of 144 healthy volunteers . Participants’ age ranged between 18.34 and 83.48 years . The mean time spent in education was 14.36 years (SD = 4.62). Participants’ country of residence was as follows: Belgium (58), Hungary (24), Italy (11), Turkey (10), Germany (9), United States of America (9), Taiwan, People’s Republic of China (6), Israel (3), Netherlands (3), Slovakia (2), United Kingdom (2), Australia (1), Aland Islands (1), Switzerland (1), Spain (1), France (1), Portugal (1), and Singapore (1). Most participants reported normal (66) or corrected-to-normal (75) vision. Three participants reported having an eye condition, but no further details were provided. None of the participants reported having any neurological disorders. All procedures were approved by the Commission for Medical Ethics of the University of Leuven (ML8800).https://psytests.be/clinicians/ and is described in detail in Torfs et al.  on their own device  at their preferred time and location. Sessions where participants reported a high level of interruptions or technical issues were excluded from the analyses. We included 320 sessions of our 144 participants: two sessions from 132 participants, three sessions from nine participants, six sessions from one participant, 10 sessions from one participant, and 13 sessions from one participant. The duration between sessions ranged between 4 minutes and 756 days, with a median of 16 days (interquartile range = 0–104 days).N = 144). The duration between the first and second sessions has a median of 26 days (range = 6 minutes to 756 days). Comparisons were made through test–retest correlations, Bland–Altman analysis and intraclass correlations (ICC). We calculated Pearson’s correlation for the continuous variable ‘total L-POST score’. Correlation coefficients exceeding 0.70 would be considered a demonstration of acceptable reliability  and 23 for the total score (1/3 of 0–70). For calculation of the ICC estimates and 95% confidence intervals, we used a two-way single-measurement mixed model with absolute agreement between scores of both sessions and under the expectation that a score of one session would generalise to other sessions. We followed Koo and Li’s . The 95% confidence interval (CI) for the mean difference was −1.14 to 0.62. The upper and lower limits of agreement (LoA) for total scores were 10.20  and –10.72  respectively Fig. . The meaThe estimated ICC for the total score was 0.7 with a 95% confidence interval of 0.61 to 0.77. This indicates ‘moderate’ to ‘good’ reliability of the total score. At the level of subtests, ICC estimates varied between 0.20 and 0.73, with relatively large confidence intervals, meaning that the reliability of the subtest scores ranged between ‘poor’ and ‘good’ reliability (Table t(173) = 109.85, p < .001). Within this model, the effect of session number was not significant  = −0.31, p = .760), neither was the effect of time between sessions  = 1.02, p = .308) or the interaction effect of session number and time between sessions  = −0.16, p = .870). Despite the non-significant fixed effects, our multilevel model explained 70.60% of the variance of total score . Indeed, the variance explained by the fixed effects was only 0.26% , suggesting that most of the variance was explained by the random effect. In other words, it suggests high inter-individual variability in baseline scores and little effect of repeated testing and the time between sessions.Trajectories of changes in the individual scores over multiple sessions are presented in Figure Test–retest correlation between the total scores of 0.70 suggests adequate reliability of the L-POST. This is in line with a previous report  = 26.01, p < .001, ω2 = .06). Despite a potential ceiling effect, our Bland–Altman analyses and multilevel modelling reached convergent results as the correlation analyses, supporting an adequate reliability of the L-POST. Future inclusion of patient populations would diversify the sample and minimise ceiling effects. Second, data were collected online with little control or knowledge of the test conditions. We compensated for this lack of control by using strict inclusion criteria: only data with no reported technical issues or interruptions were included in the analyses.A limitation of the current study is that ceiling effects in L-POST scores might have negatively affected the correlations. The high average level of education in the sample might also have resulted in higher scores. A previous study showed a significant but small effect of education levels on L-POST performance (In conclusion, this study demonstrated adequate reliability of L-POST and the absence of a practice effect. The total score is sufficiently reliable, whereas meaningful interpretation of a change in subtest scores is only advisable when the difference is at least two points.


Considerable clinical heterogeneity in idiopathic pulmonary fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes would improve our understanding of the pathogenesis of IPF and could allow for a biomarker-driven personalised medicine approach. We aimed to identify clinically distinct groups of patients with IPF that could represent distinct disease endotypes.We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets . We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets .−5).We identified three clusters of patients with IPF with statistically significant differences in lung function (p=0.009) and mortality (p=0.009) between groups. Gene enrichment analysis implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: HR 4.25, 95% CI 2.14 to 8.46, p=3.7×10We have identified blood gene expression signatures capable of discerning groups of patients with IPF with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF. The clinical course of idiopathic pulmonary fibrosis (IPF) is highly heterogeneous, which has prompted speculation that the disease may consist of multiple ‘endotypes’.Gene expression profiles could be used to identify these endotypes but previous studies have been limited by sample size, ability to validate and clinical interpretation.By combining and clustering multiple gene expression datasets, we identified three distinct clusters of patients with IPF with significant clinical differences between groups, as well as differences in gene expression that implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity.We went on to develop a 13-gene cluster classifier that was able to predict mortality in two validation cohorts of patients with IPF.Our findings support the hypothesis of multiple endotypes of IPF and highlight distinct underlying biological mechanisms that could inform a precision medicine strategy for IPF.Idiopathic pulmonary fibrosis (IPF) is a complex, ultimately fatal disease, characterised by progressive scarring of the lungs, with a median survival of 3–5 years postdiagnosis.Disease endotypes are subtypes of a disease as defined by a particular pathophysiological mechanism. It has been speculated that distinct endotypes of IPF exist,Transcriptomic data can be used to define disease endotypes, as similar transcriptomic profiles in affected individuals may reflect common underlying biological mechanisms. Previous transcriptomic analyses of patients with cancer have been particularly successful in defining clinically significant patient subgroups, which have led to improvements in treatment.In this study, we aimed to conduct the largest unsupervised clustering analysis of available transcriptomic datasets to date, with independent validation, to identify clinically distinct groups of patients with IPF. We hypothesised that these groups could represent individuals with different endotypes of IPF. Rather than undertake single dataset analyses, we co-normalised and pooled multiple datasets together to increase the sample size and enhance statistical power. Additionally, we used classification to develop a method to accurately assign additional individuals with IPF to one of these groups. This classifier displayed the ability to predict survival in IPF and so we then compared the performance of our classifier in independent validation datasets to a previous method of outcome prediction in IPF.The design of our study is shown in 10.1136/thoraxjnl-2021-218563.supp1Supplementary dataAs the discovery datasets originated from different studies and the transcriptomic data were collected using varying platforms, there would have been considerable technical  differences in gene expression between them. As such, the discovery datasets required adjustment before they could be combined and clustered. We co-normalised the discovery datasets using the COmbat CO-Normalisation Using conTrols (COCONUT) method,We used R V.3.4.0 and the Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL)2 tests for count data, analysis of variance for non-skewed continuous data, Kruskal-Wallis tests for skewed continuous data and survival analysis methods for time-to-event data . For this we used as many of the genes in their signature as were present in the validation datasets. Similarly, each subject was assigned into one of our discovery clusters, which were each classed as low risk/high risk based on the discovery stage findings. Survival analysis methods were used to determine which method performed best at predicting survival  across clusters (p=0.009). Subjects in cluster 1 had a similar median predicted DLCO to those in cluster 3, whilst subjects in cluster 2 had the greatest median predicted DLCO, indicating that these individuals had relatively preserved lung function. Additionally, there was a significant difference in average score from the gender, age and physiology (GAP) index for IPF mortality (p=0.006),With all studies combined and unclustered individuals removed , there wGene enrichment analysis revealed that cluster 1 was significantly enriched for biological mechanisms relating to metabolic changes, including electron transport and cellular respiration . ClusterWe used the pooled, co-normalised gene expression data for all 196 subjects who were successfully clustered in the discovery analysis to train a gene expression-based cluster classifier . The claCO, although the difference in DLCO between validation clusters was not statistically significant (p=0.069). Cox PH models (−5). However, the difference in survival over time between clusters 1 and 3 was not statistically significant .There were 194 subjects with IPF across the three validation cohorts. Expression levels for all 13 genes used in the classifier were available in all three validation cohorts. We used the classifier to assign each individual to a cluster and compared phenotypic traits across clusters . As in tH models  estimateet al to predict outcome in IPF using a 52-gene signature.et al were present in the GSE27957 dataset and 50/52 (96.2%) were available in the GSE28042 dataset. Overall, there was 68.3% agreement between the two methods . This model had a C-index  of 0.664 (95% CI 0.590 to 0.737). SAMS , suggesting an improvement in predictive ability. A likelihood ratio test between the two models gave a p value of 0.005, suggesting that the improvement in predictive ability when including the classifier’s risk predictions was statistically significant. The multivariate Cox model containing SAMS’ risk predictions had a C-index of 0.760 (95% CI 0.684 to 0.837), which suggested an improvement over the Cox model containing only age, sex, ancestry, FVC and DLCO, although the likelihood ratio test p value between these two models was not statistically significant (p=0.105).The risk predictions made using the classifier remained statistically significant (p=0.007) after adjusting for age, sex, ancestry, FVC and DLCO , with anBy applying new statistical methods for data co-normalisation and machine learning to multiple publicly available datasets, we identified three clusters of patients with IPF with statistically significant differences in lung function and survival. As the clustering in this study was undertaken independently of clinical data, yet significant differences in clinical traits were observed between clusters, this suggests that they may be representative of distinct and clinically relevant endotypes of IPF.In this study, we used datasets in which the gene expression had been measured from whole blood samples. However, as IPF is a lung disease, characterised by damage to the alveolar epithelium, patterns of gene expression identified in blood may not reflect the underlying pathology of the disease and may instead reflect downstream effects or the presence of confounders, such as secondary infections or treatment effects. Nonetheless, blood is more accessible than a lung-specific tissue/cell type and the expression of a gene in blood is often a significant predictor of the expression of that gene in lung.The genes that were most differently expressed in subjects in cluster 1 were significantly enriched for biological mechanisms related to electron transport and cellular respiration. Recent findings appear to suggest that metabolic dysregulation could be a contributing factor to fibrosis, although its role is not yet fully understood.Among the biological pathways that were significantly enriched for cluster 2 were pathways related to apoptosis and cell cycle. It has been previously reported that apoptosis is increased in alveolar epithelial cells of patients with IPF but decreased in myofibroblasts,Cluster 3 was enriched for terms related to the immune system response. The role of the immune system in IPF has been controversial in the past; failed immunomodulatory therapies in IPF, some of which have led to worse outcomes, have led to speculation that certain immune responses are protective while others are harmful.−5), which was consistent with the findings in the discovery stage (p=0.008). In addition, while survival information was not directly available for the individuals in cluster 1 in the discovery stage, the significantly low average DLCO and high average GAP score for the individuals in that cluster is consistent with the poor survival that was observed for cluster 1 in the validation stage. As the classifier appears to have the ability to assign subjects who are at a lower risk of death into cluster 2 and the subjects who are at a greater risk of death into the other two clusters, it could potentially be used to predict survival in IPF.The 13-gene expression-based cluster classifier was able to assign the subjects with IPF from the validation datasets to clusters with statistically significant differences in survival between clusters 2 and 3  allowed for three datasets to be combined,There were several limitations to this study. First, as we relied on the use of publicly available data, some clinical variables were relatively underpowered due to missingness within the data or having not been reported in all studies. In particular, survival information was only available in one of the three discovery cohorts and two of the three validation cohorts, which may have limited our ability to clinically distinguish clusters 1 and 3 in terms of survival. In addition, we lacked detailed data for clinically significant traits such as patient reported outcomes, lung function decline over time and the incidence rate of acute exacerbations. Additionally, we did not possess information regarding the background therapy of the subjects with IPF. However, for the three cohorts with survival data available, we were able to glean from the original papers that the patients with IPF were either treatment-naïve populations (GSE93606) or that there were only a small proportion that were receiving immunosuppressive therapy at the time of the blood collection (GSE27957 and GSE28042). In addition, these populations were not given anti-fibrotics and so treatment effects are unlikely to have been driving the large differences in survival that were observed between clusters. These limitations highlight the need for a single large prospective study on this topic with more comprehensive phenotyping.A further weakness of our study is that each participating cohort of subjects with IPF was subject to survival bias, as only subjects who survived long enough to enrol into each study could have contributed their transcriptomic data to it. This could have restricted the level of heterogeneity of IPF that we were able to capture in the study and limited the generalisability of our findings.Additionally, COCONUT makes the assumption that the healthy controls across the different studies came from the same statistical distribution and so all differences between healthy controls across studies must have been due to non-biological variation. This means that any large differences in confounding factors between the groups of healthy controls would have restricted the efficacy of the co-normalisation. However, there were no significant differences in age (p=0.187) or sex (p=0.477) between the healthy controls across the three studies.If the clusters identified in this study do truly represent endotypes of IPF, it may be worth speculating about the nature of these endotypes. As IPF is a complex disease, with many known common genetic and environmental exposures, it is unlikely that it would behave under a traditional discrete endotype model and instead more likely that it would behave under a more complex model, such as the palette model described by McCarthy.To conclude, these results could support the hypothesis of multiple endotypes of IPF as there appear to be at least two clinically distinct groups of patients with IPF that can be identified through cluster analysis of transcriptomic data. As these clusters were defined using expression from groups of genes that were significantly enriched for many different biological pathways and processes, they could be representative of distinct pathophysiological states. Additionally, a classifier with the ability to assign additional individuals with IPF to one of the clusters was developed. With further development, this classifier could be a useful tool in outcome prediction in IPF as well as helping us gain a better understanding of the underlying biological processes that may be driving the observed differences in survival.


Sarcoidosis is a granulomatous disease of unknown origin with multisystem involvement. It mainly affects the lungs and intrathoracic lymph nodes, featuring noncaseating granulomas . The cliCalatroni et al. recommended systematic screening of renal function, including serum creatinine, urine analysis, and serum calcium levels. A kidney transplant is considered an acceptable option for patients with uremic sarcoidosis, and complications seem to be similar to those associated with a kidney transplant for other types of renal failure.The diagnosis of sarcoidosis is formulated by multidisciplinary evaluation of clinical, radiological, and immunological findings , especiaSedki et al. suggest lung imaging and measurement of serum ACE concentrations at the initial evaluation of sarcoidosis. However, further investigation is warranted in patients with intrahepatic cholestasis, negative for anti-mitochondrial antibodies, and with liver biopsy evidence of granulomas, especially if these are predominantly lobular. Sedki et al. recommend a primary bile cirrhosis-specific anti-nuclear antibody (ANA) (gp210 and sp100) test in the case of ANA positivity.No specific and sufficiently accurate biomarkers have yet been identified for the clinical management of sarcoidosis. Among diagnostic markers, angiotensin-converting enzyme (ACE)  has a coFauter et al. analyzed this complication in 12 patients with histological evidence of hepatic sarcoidosis. They highlighted the ineffectiveness of PH management and/or sarcoidosis therapy in these patients, concluding that a liver transplant could be the best therapeutic option when corticosteroids fail.A major complication of hepatic sarcoidosis is portal hypertension (PH), a prognostic marker associated with high morbidity and mortality. Despite extensive research, the etiology and pathogenesis of sarcoidosis remain largely unknown. Several triggers have been implicated in its development, including autoantigen-specific T cells, antibodies producing B lymphocytes, and autoimmune inflammation. Sarcoidosis is not classified as an autoimmune disorder, although autoimmune components play an important role in its pathogenesis.Rizzi et al. reviewed the relationship between sarcoidosis and autoimmunity, highlighting the role of humoral immunity in its pathogenesis. Although patients with sarcoidosis are inclined to develop specific autoantibodies, this is postulated to occur by molecular mimicry. In susceptible individuals, it occurs when there is a similarity between a foreign and a self-peptide, which promotes the activation of autoreactive T and B cells. Clinical improvement with anti-CD20 monoclonal antibody therapy, observed in some refractory cases of sarcoidosis, supports this argument. B-cell depletion has provided insights into the importance of autoimmunity in this immunological context. Several pathogenetic pathways and genetic predispositions are common to autoimmune diseases. The review article of Malkova et al. describes a genetic predisposition to chronic progressive sarcoidosis that prevents antigen elimination and promotes autoimmune inflammation, impairing the immune system or activating autoimmune disorders. Fibrosis is associated with fibroblast activation after the differentiation of leukocytes which release cytokines in a setting of chronic inflammation.Patterson et al. suggested that failure to clear antigens contributes to cardiac sarcoidosis and that the distribution of lymph node activity in the thoracic region differs over time. According to the study, lymph nodes are more likely to be involved in the progression of sarcoidosis in patients with chronic disease.Irrespective of organ involvement, the etiopathogenesis of fibrosis and failure of affected organs in patients with progressive sarcoidosis is still debated. Zhao et al. performed genome-wide miRNA profiling in the lymph nodes of patients with sarcoidosis, comparing it with the profiles of patients with tuberculosis lymphadenitis (TBLN) and demonstrating the high diagnostic value of such profiles for sarcoidosis. MiR-185-5p is still useful in the differential diagnosis of TBLN and sarcoidosis, particularly when patients with the latter disease are in stage I or II.It has been reported that lymph nodes are often involved in sarcoidosis, and a number of studies have been carried out using samples obtained by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) to explore the pathogenesis and establish diagnostic biomarkers. The purpose of this Research Topic is to present some new experimental findings and updated reviews on organs rarely affected by sarcoidosis and the relationship between autoimmune diseases and sarcoidosis. At the threshold of a new era of personalized treatment, the discovery of new therapeutic targets could help prevent the development of chronic inflammation and tissue damage in these patients.The author confirms being the sole contributor of this work and has approved it for publication.


Several observational studies have found that idiopathic pulmonary fibrosis (IPF) is often accompanied by elevated circulating C-reactive protein (CRP) levels. However, the causal relationship between them remains to be determined. Therefore, our study aimed to explore the causal effect of circulating CRP levels on IPF risk by the two-sample Mendelian randomization (MR) analysis.We analyzed the data from two genome-wide association studies (GWAS) of European ancestry, including circulating CRP levels  and IPF . We primarily used inverse variance weighted (IVW) to assess the causal effect of circulating CRP levels on IPF risk. MR-Egger regression and MR-PRESSO global test were used to determine pleiotropy. Heterogeneity was examined with Cochran's Q test. The leave-one-out analysis tested the robustness of the results.IVW = 1.446, 95% CI 1.128–1.854, P = 0.004).We obtained 54 SNPs as instrumental variables (IVs) for circulating CRP levels, and these IVs had no significant horizontal pleiotropy, heterogeneity, or bias. MR analysis revealed a causal effect between elevated circulating CRP levels and increased risk of IPF (ORThe present study indicated that elevated circulating CRP levels could increase the risk of developing IPF in people of European ancestry.The online version contains supplementary material available at 10.1186/s12931-022-02309-x. Idiopathic pulmonary fibrosis (IPF) is an aggressive, irreversible lung disease marked by scar formation caused by an atypical response to epithelial injury . IPF has risk of cardiovascular disease, psoriatic arthritis, type 2 diabetes, and cancer [IPF was initially considered an inflammatory disease . Later, d cancer –14. And d cancer . Severald cancer  and may d cancer , 17. Intd cancer . In shorMR analysis is a new epidemiological approach that uses genetic variations as instrumental variables (IVs) to estimate the causal relationship between exposure and outcome , 20. GivIn short, the present research intended to assess the causal effect of circulating CRP levels on the risk of developing IPF by a two-sample MR approach using the summary statistics from two large sample genome-wide association studies (GWAS) of European ancestry.https://gwas.mrcieu.ac.uk/ .To identify genetic loci related to circulating CRP levels, we utilized data from a large-scale GWAS meta-analysis of 88 studies  . This GWP = 5 × 10–8 threshold and r2 < 0.001 [To use genetic variation to assess the causal association between exposure (circulating CRP levels) and outcome (IPF), it must satisfy three critical assumptions for Ivs : (i) IVs < 0.001 . Then, wthe MR-PRESSO global test [P < 0.05 was considered significant.A two-sample MR analysis was utilized to examine the genetic relationship between circulating CRP levels and IPF risk. Inverse variance weighted (IVW)  was utilbal test  and MR-Ebal test . Secondlbal test . Additiobal test . We perfIVW = 1.446, 95% CI 1.128–1.854, P = 0.004) (Table P = 0.003), weighted median  and weighted mode  methods also yielded results consistent with the IVW method (Table We obtained 54 SNPs as IVs to assess the genetic association of circulating CRP levels with IPF, and the causal effect of each SNP on IPF is shown in the forest plot Fig. . Then, wPIVW = 0.204, PMR Egger = 0.242, Table P = 0.170, Table P = 0.143, Table Subsequently, we performed sensitivity analyses to assess our results. Firstly, Cochran's Q test results suggested no heterogeneity among IVs (Our study explored the causal effect of circulating CRP levels on the risk of IPF using a two-sample MR analysis. The results showed that elevated circulating CRP levels could lead to an increased risk of IPF. Moreover, sensitivity analysis suggested that our results were robust.Our results provide one piece of evidence for the previously controversial conclusion. A proteomics study by Niu et al. identified that CRP might be a potential specific biomarker for IPF . And anoPrevious studies have identified environmental pollutants, dust, inflammatory responses, and oxidative stress as potential causes of IPF , 37. In Our study has several advantages. Firstly, the present study is the first MR study to assess that elevated circulating CRP levels could increase the risk of IPF and that this association has a causal effect. Secondly, this MR study is based on two large samples of GWAS data from European populations, which provides us with sufficient power to estimate the causal relationship. Thirdly, the MR analysis reveals a long-term effect of genetically determined circulating CRP levels on IPF risk, which is unlikely to be influenced by confounders.Also, there are some limitations to the study. Firstly, our findings are mainly based on participants of European ancestry and may not apply to populations of other races. Secondly, although we did not find the presence of horizontal pleiotropy, there may be residual bias because the exact function of most of these SNPs is unknown. Thirdly, because our study utilized GWAS summary data and not individual-level data, we were unable to stratify our analysis by other factors such as age and gender. In addition, the circulating CRP levels we studied are genetically controlled, so our results reflected the effect of long-term circulating CRP levels on IPF rather than a short-term response to inflammation.Overall, our study indicated that elevated circulating CRP levels could increase the risk of developing IPF. This result probably provides new insight into the understanding of the pathogenesis of IPF. However, further pathological and biochemical studies are needed to investigate further the profound relationship of increased risk of IPF by elevated circulating CRP levels.Additional file 1. The 57 SNPs related to circulating CRP levels at a genome-wide significant level.Additional file 2. The 56 SNPs in IPF GWAS.Additional file 3. IVs for circulating CRP levels.