Pituitary adenoma is a common intracranial tumor that occurs in the pituitary gland that seriously affects the hypothalamus–pituitary–targeted organ axis system –3, and iGupta et al.). These data clearly demonstrate the importance of GH signaling pathway-based therapeutic treatment in GH-secreting pituitary adenomas. (ii) The second topic addressed the notion that the pattern change of six prolactin (PRL) proteoforms existed among five subtypes of pituitary adenomas, and different hPRL proteoforms might function in different PRL receptor-signaling pathways, which clearly demonstrates the importance and clinical value of PRL receptor-signaling pathway-based PRL proteoform pattern study in human pituitaries and pituitary adenomas . (iii) The third topic addressed the integration of proteomics and metabolomics to reveal metabolite–protein networks in adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas . Proteomic and metabolomic variations are a precious resource to clarify disease mechanisms and determine effective biomarkers . (v) The fifth topic addressed metabolomics as a promising approach to pituitary adenomas . Pituitary adenoma is an endocrine- and metabolic-related disease. Some metabolites have been studied; however, real metabolomics has not been extensively carried out in pituitary adenomas. (vi) The sixth topic addressed the new progress on different pathway mechanisms of dopamine and dopamine agonists (BRC and CAB) in prolactinomas , which will provide new evidence for dopamine signaling pathway-based personalized and precise treatment of prolactinomas. (vii) The seventh topic addressed the molecular network basis of invasive pituitary adenomas, namely, the invasiveness-related molecules, including pituitary tumor transforming gene (PTTG), vascular endothelial growth factor (VEGF), hypoxia-inducible factor-1a (HIF-1a), fibroblast growth factor-2 (FGF-2), and matrix metalloproteinases , which interact in a complex molecular network and are responsible for the invasiveness of pituitary adenomas (Yang and Li). Of course, the mechanism of the invasiveness of pituitary adenomas is very complex. Multiomics-based molecular network investigation might provide novel insights into the molecular mechanisms and therapeutic targets of pituitary adenoma invasiveness . (ix) The ninth topic addressed the epigenomics of pituitary adenomas, focusing on DNA methylation, histone modification, and transcript modification . In-depth investigation of the relationship between tumor epigenetics-related molecular pathways and clinical pathological characteristics might serve for clinical decision-making. (x) The 10th topic addressed the first ubiquitinomic profile and ubiquitination-involved signaling pathway network alterations, including ribosome, hippo signaling pathway, PI3K-AKT signaling pathway, and nucleotide excision repair pathway, in human pituitary adenomas . (xi) The 11th topic addressed mitogen-activated protein kinases  pathway-based drug therapeutic targets in pituitary adenomas , which in detail discussed the advances in understanding the role of MAPK signaling in pituitary tumorigenesis, and the MAPK pathway-based potential therapeutic drugs for pituitary adenomas. (xii) The 12th topic addressed biological roles and mechanisms of mitochondrial dysfunction pathway network and mitochondrial dynamics in pituitary adenomas, and current status of mitochondria-based biomarkers and targeted drugs for effective treatment of pituitary adenomas (Li and Zhan). (xiii) The 13th topic described the large-scale quantitative proteomic profile  and the protein molecular pathway network profile of non-functional pituitary adenomas, which were combined with transcriptomic data  to reveal 52 statistically significant pathways, including cGMP-PKG pathway, focal adhesion, and platelet activation signaling pathways . (xiv) The 14th topic addressed multiomics-based signaling pathway network alterations in non-functional pituitary adenomas , which analyzed nine sets of omics data, and provided a comprehensive and large-scale pathway network data for non-functional pituitary adenomas to understand the accurate molecular mechanisms and discover effective biomarkers for diagnosis, prognosis, and determination of therapeutic targets for pituitary adenomas.This present issue mainly focuses on the molecular network study of pituitary adenomas, which contains 14 topics: (i) The first topic addressed the idea that a tyrosine kinase inhibitor, imatinib, inhibited growth hormone (GH) secretion signaling via the PDGFR-β pathway, but did not affect cell viability and apoptosis, which might be used as an adjunct therapy to treat GH-secreting pituitary adenomas , mitochondrial dysfunctional pathway (Li and Zhan), GH-PDGFR-β signaling pathway , estrogen signaling pathway , cAMP-PDE pathway , and dopamine signaling pathway . Based on these given signaling pathways, some therapeutic targets and drugs have been discovered and FDA-approved for pituitary adenomas. However, one must realize that although many advances have been made, these signaling pathways have not been fully clarified in pituitary adenomas, and in-depth exploring diversity of each of these pathways might discover great potentials of these pathways for pituitary adenomas. Also, from a systematic viewpoint, the multi-target combination treatment within each pathway or with combining each pathway with other pathways will be superior to the single-target treatment. (ii) Multiomics has driven molecular network study in pituitary adenoma. This special issue involved in egigenomics , transcriptomics, proteomics , ubiquitinomics , nitroproteomics, phosphoproteomics, metabolomics , and multiomics-based integrative study  in pituitary adenomas, including GH-secreting adenoma, ACTH-secreting adenoma, PRL-secreting adenoma, and non-functional pituitary adenomas. Omics-based molecular network analysis has made significant advances in pituitary adenomas. Until now, transcriptomics and proteomics have been extensively studied in pituitary adenomas  (Yang and Li). However, one must note that these invasiveness-related molecules are derived from previous traditional studies and do not represent at all the entire molecule world of the invasive characteristics of pituitary adenomas. Indeed, the molecule world of invasive pituitary adenomas is very complex. We strongly recommend the use of multiomics to study pituitary adenoma invasiveness, which might be the right way to resolve its clinical invasiveness challenge for clarification of its molecular mechanisms, discovery of effective therapeutic targets, and determination of effective biomarkers for diagnosis and prognostic assessment. Some proteomics and transcriptomics between invasive and non-invasive pituitary adenomas have been performed to understand molecular mechanism and discover biomarkers of pituitary adenoma invasiveness . Also, dopamine receptor proteoforms , PDE proteoforms , and their involved molecular signaling pathways are discussed in this special issue. Proteoform studies will need in-depth insights into a proteome, which is the future direction of proteomics. We recommend the strengthening of proteoform-mediated molecular signaling pathway network studies in pituitary adenomas for precise treatment in the future.Molecular networks are an effective approach to annotate the interactome in pituitary adenomas for in-depth insight into its molecular mechanisms and discovery of effective biomarkers and therapeutic targets , 13. (i) et al.) –19; epig et al.) –23, and  et al.) , and PTMsiveness –26. (iv)siveness . The consiveness , 28. Clasiveness . For pitsiveness , includisiveness , and proIn summary, molecular network studies of pituitary adenoma have achieved significant advances. However, one must realize that this special issue contains only a fraction of the very important molecular network study of pituitary adenomas. This Research Topic serves as a spur to stimulate and encourage researchers who study molecular networks to come forward with its scientific merits to research and clinical practice of pituitary adenomas. Future issues will collect more multiomics-based molecular network studies with large-scale clinical information for basic research, translational research, and clinical practice in pituitary adenomas. We strongly believe that multiomics-based molecular network studies, molecular network-based therapeutic target and drug studies, and molecular network-based pattern biomarker studies , 32–37 wXZ conceived the concept, designed manuscript, wrote and critically revised the manuscript, and was responsible for its financial supports and the corresponding works. DD participated in the development of concept and critically revised the manuscript. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The Khorana score (KS) clinical algorithm is used to predict VTE risk in cancer patients. The study objective was to evaluate VTE and survival rates among patients newly diagnosed with cancer and stratified by KS in a real‐world population.® Clinformatics® DataMart database between 01/01/2012–09/30/2017 was used to identify adults with ≥ 1 hospitalization or ≥ 2 outpatient claims with a cancer diagnosis (index date). Only patients who were initiated on chemotherapy or radiation therapy were included. Patients were classified based on KS . Time‐to‐first VTE and survival were evaluated from the index date to the earliest among end of data availability or insurance coverage, death, or 12 months post‐index using Kaplan‐Meier (KM) analyses.Data from the OptumP < .001). Regardless of KS, patients with VTE had significantly lower survival rates than those without.A total of 2,488 (KS = 0); 2,125 (KS = 1), 1,074 (KS = 2), and 507 (KS ≥ 3) cancer patients were included. The 12‐month KM rates of VTE were 3.1%, 5.4%, 7.9%, and 14.9%  among KS = 0, 1, 2, and ≥ 3 cohorts, respectively. Corresponding adjusted hazard ratios (95% CIs) relative to the KS = 0 cohort were 1.72 (1.25‐2.38), 2.46 (1.73‐3.50), and 4.99 (3.40‐7.31) for the KS = 1, 2, and ≥ 3 cohorts, respectively  in a real‐world population. The present findings indicate that the rates of VTE significantly increased with KS, confirming its predictive ability. Moreover, VTE was associated with lower survival rates within each KS cohort. There is limited information on symptomatic VTE rates and how it may affect survival in patients with cancer particularly when stratified by KS. Therefore, the current study sought to assess VTE incidence and survival rates in a broad US population of patients newly diagnosed with cancer who received various cancer treatments across various strata of KS.22.1® Clinformatics® Data Mart database were used, with data from January 2012 through September 2017. Optum Clinformatics covers 12‒14 million annual lives of a large national managed care company affiliated with Optum in all census regions of the United States. It contains more than 36 months of historical data on patients . The database only contains de‐identified data that fully comply with the confidentiality requirements of the Health Insurance Portability and Accountability Act.Healthcare insurance claims from the Optum2.2This study used a retrospective cohort study design with the index date defined as the date of the first claim for a cancer diagnosis with ≥ 6 months of continuous eligibility before the index date (ie baseline period). The 45‐day period following the index date was used to assess cancer treatment initiation to avoid patients with inappropriately delayed therapy starts. The risk stratification period was defined as the 28 days prior to the initiation of cancer treatment (this period may overlap with the baseline period) during which laboratory values were collected for the KS calculation. The follow‐up was defined as the period between the index date and the earliest of end of data availability, end of insurance coverage, death, or 12 months post‐index, whichever came first.2.3 age at index ≥ 18 years; (2) ≥ 2 outpatient visits or ≥ 1 hospitalization with a diagnosis of cancer ; (3) having initiated chemotherapy  had a diagnosis of VTE during the 6‐month baseline period; (2) used anticoagulants any time prior to the index date or during the follow‐up period; or (3) had an inpatient surgery after the first cancer diagnosis, defined as a procedure code for abdominopelvic surgery, major surgery, and neurosurgery or orthopedic surgery during an inpatient stay Figure 1.Patients who met all inclusion and exclusion criteria were further stratified into four mutually exclusive cohorts based on their KS. More specifically, the KS was calculated based on the cancer's primary site at the time of initial diagnosis, BMI, and lab values for hemoglobin, platelet, and leukocyte count during the risk stratification period (28 days prior to the treatment start date). ICD‐9‐CM and ICD‐10‐CM diagnostic codes were used as a proxy for body mass index (BMI) ≥ 35 in the KS calculation.The present study population comprised all patients with cancer. A sensitivity analysis was conducted among the subgroup of patients with the same tumor types  that were used to develop the KS.2.4Study outcomes included VTE events and survival. VTEs were evaluated during the follow‐up period for each KS cohort and were defined as (1) a diagnosis of VTE in the primary position during a hospitalization, or (2) a hospitalization with a medical claim for a secondary diagnosis of VTE followed by an anticoagulant dispensing or administration ≤ 30 days post‐discharge of the first hospitalization, or another hospitalization or an outpatient visit with a primary or secondary VTE diagnosis, or (3) a primary or secondary outpatient VTE diagnosis followed by an anticoagulant dispensing or administration within 30 days after the outpatient visit. Survival was assessed among patients with vs without VTE during the follow‐up period for each KS cohort.2.5Baseline characteristics, overall and stratified by KS risk cohorts, were reported using descriptive statistics. More specifically, frequencies and proportions were used to summarize categorical variables; means, standard deviations, and medians were used to summarize continuous variables.Time to first VTE event was evaluated using Kaplan‐Meier (KM) analyses. Censoring was applied to patients without a VTE at the end of the observation period. KM rates of VTE were reported at three, six, nine, and 12 months and were compared between KS cohorts using hazard ratios from Cox proportional hazards models adjusting for the following covariates evaluated during the baseline period or on the index date: sex, age, type of insurance plan, region, year/month of index date, Elixhauser comorbidities with a proportion ≥ 5%,Survival at 12 months of follow‐up was also evaluated using KM analyses. Adjusted hazard ratios from Cox proportional hazard models  were used to compare survival between patients with and without VTE events.3A total of 6194 cancer patients met all inclusion criteria. Of these, 2488 (40.2%) were classified in the KS = 0 cohort, 2125 (34.3%) in the KS = 1 cohort, 1074 (17.3%) in the KS = 2 cohort, and 507 (8.2%) in the KS ≥ 3 cohort  and 1.72  times higher, respectively, compared to KS = 0 , almost four times higher in the KS = 2 cohort , and two times higher in the KS = 1 cohort , KS = 1 , KS = 2 , and KS ≥ 3  cohorts at 12 months of follow‐up , KS = 1 , KS = 2 , and KS ≥ 3 , patients without vs with VTE also had greater survival among patients with KS = 0 .Until recently, most guidelines did not recommend primary VTE prophylaxis in outpatients with cancer,et al suggest that risk factors are not taken into account when prescribing VTE prophylaxis for hospitalized patients with cancer, with 79% of high‐risk patients and 63% of low‐risk patients receiving primary VTE prophylaxis.et alCurrent guidelines from the National Comprehensive Cancer Network (NCCN) and the ASCO recommend primary VTE prophylaxis in most inpatients with cancer.In this study, the risk of VTE most sharply increased during the first three months after cancer diagnosis in all KS cohorts, confirming previous observations that the risk of VTE is highest during the first few months after cancer diagnosis.4.1The current study is subject to some limitations. First, included patients were treated with either chemotherapy or RT, and results may not apply to patients receiving other cancer treatments. Second, this claims‐based study may also be subject to residual confounding due to unmeasured confounders, such as cancer stage. Third, BMI was evaluated using insurance claims with BMI ICD codes, which may lead to underestimation of the KS. Fourth, in spite of the large size of the present study population and its nationwide geographical representation, findings may not be generalizable to patients with health insurance plans that substantially differ from those of the population analyzed in this study. Finally, healthcare claims may contain coding omissions and inaccuracies that may influence the absolute VTE rates reported. Our conservative approach, requiring at least two diagnostic codes or a diagnostic code and anticoagulant code makes underreporting of VTEs possible, but over‐reporting unlikely.5In this large real‐world retrospective analysis, over 25% of patients diagnosed with cancer had a KS of ≥ 2 (high risk of VTE), and these patients were three times more likely to develop VTE than patients with KS = 0 (low risk of VTE) at the time of their cancer diagnosis. Survival was particularly low among patients in higher KS categories, which might be explained by different types of cancer and advanced stages of cancer. Interestingly, we observed that among patients at low risk for VTE (KS = 0), those developing VTE are approximately seven times more likely to die prematurely relative to those who do not develop VTE. Moreover, patients at higher risk of VTE (KS ≥ 2) who developed VTE were approximately twice as likely to die relative to patients who did not develop VTE over 12 months of follow‐up. These results build on those of previous clinical studies by documenting the prognosis of patients falling into different risk categories in a real‐world setting. In light of the recent recommendation to offer primary VTE prophylaxis to high‐risk ambulatory patients with cancer,Parts of the finding in this manuscript was presented at the 60th American Society of Hematology (ASH) annual meeting held December 1‐4, 2018 in San Diego, CA, and 61st ASH held December 7‐10, 2019 in Orlando, Florida.The data for study participants were de‐identified and complied with the Health Insurance Portability and Accountability Act (HIPAA); therefore, no reviews by an institutional review board were required.GG, FL, SDM, and PL are employees of Groupe d'analyse, Ltée, a consulting company that provided paid consulting services to Janssen Scientific Affairs, LLC. for the conduct of the present study. DM is an employee of Janssen Scientific Affairs and shareholders of Johnson & Johnson. AAK, NMK, KMC, GHL, and MBS have received research funds from Janssen Scientific Affairs.All authors were responsible for the study design and the interpretation of the study results. FL, GG, and SDM were responsible for the data analysis. All authors reviewed the manuscript for intellectual content and approved the final draft.


Lactobacillus, Leuconostoc, and Weissella, the acetogenic Proteobacteria Acetobacter and Kozakia, Mycobacterium, two fungi (Cutaneotrichosporon and Cyphellophora), and at a lesser extent Methanobrevibacter and the protist Nannochloropsis. Cows carrying genes correlated with milk FA also had higher concentrations of butyrate, propionate and tyrosine and lower concentrations of xanthine and hypoxanthine in the rumen. Abundances of rumen microbial genes were able to explain between 76 and 94% on the variation of the most abundant milk FA. Metagenomics and metabolomics analyses highlighted that cows producing milk with contrasting FA profile under the same diet, also differ in their rumen metabolic activities in relation to adaptation to reduced rumen pH, carbohydrate fermentation, and protein synthesis and metabolism.Milk products are an important component of human diets, with beneficial effects for human health, but also one of the major sources of nutritionally undesirable saturated fatty acids (SFA). Recent discoveries showing the importance of the rumen microbiome on dairy cattle health, metabolism and performance highlight that milk composition, and potentially milk SFA content, may also be associated with microorganisms, their genes and their activities. Understanding these mechanisms can be used for the development of cost-effective strategies for the production of milk with less SFA. This work aimed to compare the rumen microbiome between cows producing milk with contrasting FA profile and identify potentially responsible metabolic-related microbial mechanisms. Forty eight Holstein dairy cows were fed the same total mixed ration under the same housing conditions. Milk and rumen fluid samples were collected from all cows for the analysis of fatty acid profiles (by gas chromatography), the abundances of rumen microbiome communities and genes (by whole-genome-shotgun metagenomics), and rumen metabolome (using 500 MHz nuclear magnetic resonance). The following groups: (i) 24 High-SFA  vs. 24 Low-SFA  cows, and (ii) 8 extreme High-SFA  vs. 8 extreme Low-SFA  were compared. Rumen of cows producing milk with more SFA were characterized by higher abundances of the lactic acid bacteria  Excessi Givens, . Given t Givens, , produciMilk fatty acid profiles generally present much higher concentrations of SFA and lower concentrations of PUFA than the FA profile in ruminant diets. This is partly due to the fat synthesis in the mammary gland, which produces a substantial amount of SFA with 4–16 atoms of carbon  compare the rumen microbiome between Holstein cows which, under the same diet, produce milk with contrasting FA profile, (ii) identify the rumen microbial taxa and genes impacting on individual FA and FA groups which are associated to human health, and (iii) identify metabolic-related microbial mechanisms controlling milk FA content. We hypothesized that differing microbial communities in the rumen of cows fed the same diet will influence the extent of rumen biohydrogenation and so potentially affect the milk FA profile. Given the overarching importance of diet in shaping rumen microbiome composition and its activity , University of Reading, UK; all experimental procedures used were licensed, regulated, and inspected by the UK Home Office under the Animals (Scientific Procedures) Act, 1996.n = 24 cows, 60.2–66.6% total FA) and highest range  of milk SFA contents within this population. After 5 days, milk samples were collected from these cows (n = 48) and sent for immediate analysis of fat and protein content by FTIR, whilst an aliquot was stored in −20°C until FA analysis by gas chromatography (GC). Simultaneously, ~50 mL of rumen liquid were taken from the same 48 cows by inserting a stomach tube  nasally and aspirating manually. This liquid was filtered through two layers of muslin and 5 mL strained rumen fluid still containing small particles were mixed with 10 ml phosphate buffered saline containing glycerol (30% v/v). These samples were stored at −80°C between collection and analysis. This technique is a practical means to sample a large number of cows for subsequent microbial community profiling, gene abundance analysis, and metabolite profiling; as well as previously presented sample preparation and storage techniques , throughout the study. Forage was comprised of 71% maize silage and 29% grass silage (on a dry matter basis). Concentrate feed was made of commercial pellet (blend), sodagrain (sodium hydroxide-treated wheat), Megalac  and salt. Nutrient composition, digestibility, metabolisable energy (ME) content, volatile compounds and fatty acid profiles are presented in Milk FA profiling was performed using GC coupled with flame ionization detection  according to previously described methods of esterification and methylation Viant, , and techttp://www.kegg.jp) followed the same procedure as previously described in Wallace et al.  were carried forward for downstream analysis.DNA was extracted from the rumen fluid samples; all 48 samples were prepared for sequencing. Illumina TruSeq libraries were prepared from genomic DNA and sequenced on a NovaSeq 6000 instrument by Edinburgh Genomics . Paired-end reads (2 × 150 bp) were generated, resulting in between 10 and 24 GB per sample (between 33 and 80 million paired reads). A metadata file indicating the correspondence between animal and metagenomics IDs as well as grouping information based on milk SFA is summarized in e et al.  and Roehe et al. . StatistFor taxonomic annotation, we constructed a custom database using Kraken (v.0.10.5)  and the relative abundance (percentage) of individual variable in each profile. Unless otherwise stated, all parameters used were the default.Taxonomic and gene abundances were compared individually or within a functional group  using a general linear model with adjustment for multiple comparisons using the Bonferroni correction .In order to identify the rumen microbial species and genes on individual FA and FA groups which are associated to human health (and in particular SFA), we used partial least squares (PLS) analysis  to identify the most important genes associated with the milk SFA content. For the metagenomics analysis, the PLS analysis accounts for multiple testing and the correlation between taxonomic levels or microbial genes as variables. The model selections were based on the variable importance for projection (VIP) criterion Wold, , wherebyA Bruker Avance HD 700 MHz  was used to acquire the NMR spectra from the ruminal biofluids  and goodness of prediction . The best models  were included in a second step of the pipeline where a selection of the samples with the lowest and highest concentration of the gene (n = 16) were used to identify metabolomics changes associated with the gene, used as predictor in each case. Metabolomic annotation was carried out using publicly available literature  and the Bovine Metabolome Database (http://www.cowmetdb.ca/cgi-bin/browse.cgi).For the metabolomics analysis, all spectra were pre-processed in MestReNova version 11.0.2–18,153  where it was phased and baseline corrected. The algorithm used for the baseline correction was Whittaker smoother; multipoint baseline correction was used if necessary  for High-SFA cows than Low-SFA cows .Dry matter intake was slightly higher  across the data, were 0.63 ± 0.08% of total FA for the 48 animals dataset and 0.62 ± 0.09% of total FA for the extremes 16 animals dataset  were found in higher concentrations (P < 0.01) in milk from the High-SFA than from Low-SFA cows (P < 0.01). ALNA and n-3 were both higher for Low-SFA milk whilst the relative difference in n-3 was smaller (P < 0.05). The transfer of linoleic acids , but not ALNA, from diet to milk was 1.19-fold higher in Low-SFA compared with High-SFA cows  with milk FA content.The rumen microbial communities were mainly composed of  animals  even whe animals . The FirP < 0.05) between SFA groups using the 48 (n = 37) and extreme animals with 37 and 18 genera, respectively, and showing a limited number of shared genera (n = 5) mostly within Actinobacteria (4/5), Denitrobacterium and the Firmicutes Jeotgalibacillus sp. (P < 0.05) in High-SFA animals, within the 48 animals, included low abundant Actinobacteria  and other bacteria and archaea. In addition, three methanogens , and two highly abundant genera (>0.01%), the protist Nannochloropsis and the Proteobacteria Burkholderia showed higher abundance (P < 0.05) in High-SFA animals (P < 0.05) in High-SFA animals, but only when the 16 extreme cows were compared, and were composed of 3 Actinobacteria including Mycobacterium, 3 lactic acid bacteria (LAB) within Firmicutes , 3 Proteobacteria  and fungi (5/13) such as Cutaneotrichosporon and Cyphellophora  based on PLS coefficients with one main branch grouping saturated FA and the second branch unsaturated FA together  with SFA groups and negatively correlated (negative PLS coefficient) with unsaturated FA groups  for a particular group of FA was determined using PLS analysis (VIP > 0.8). Different profiles of genera were identified within SFA  and UFA groups , Jeotgalibacillus (positive with C12:0 and C14:0), Kozakia (positive with C16:0 and negative with cis-PUFA), Cutaneotrichosphoron (negative with cis-MUFA), Cyphellophora (negative with ALNA and n-3 and positive with C16:0), and Sutterella (positive with cis-PUFA). Hafnia and Acetobacter genera showed contrasting correlation results within SFA or unsaturated FA. For example, Hafnia was positively correlated with C16:0 and cis-PUFA and negatively correlated with cis-MUFA. The two methanogen genera Methanobrevibacter and Methanotorris were negatively and positively correlated with n-3, respectively. Although Mycobacterium showed a positive correlation with SFA, this genus was negatively correlated with C16:0, n-3, ALNA, and cis-MUFA.Individual FA groups were correlated with a particular genus , such asP < 0.05) between Low-SFA and High-SFA animals  and also between extreme–low SFA and extreme-high SFA animals , respectively (n = 6) or energy production and conversion (n = 4) but none directly related to FA biohydrogenation. Genes involved in methanogenesis, and encoding for formylmethanofuran dehydrogenase (K00200-204), methyl-coenzyme M reductase (K00399-402) and tetrahydromethanopterin S-methyltransferase  were only significantly different when using the 48 animals.From the 1,630 microbial genes with a relative abundance above 0.001%, 273 and 40 genes were significantly different , the enriched functions related to higher relative abundance of genes in extremes–low SFA compared to extreme-high SFA , were cacis-PUFA did not group in the same main branch as other UFA. The genes which were significantly correlated with cis-PUFA by PLS were different for all FA tested and the gene with the highest VIP-value (>1.25) was involved in intracellular sulfate transport (K06020).A total of 39 genes were highly correlated with FA groups (VIP > 0.8), explaining from 76% (C14:0) to 94% (C16:0) of the variability in milk FA by PLS analysis , and wercis-MUFA.Within the 39 genes, the 8 genes which were positively correlated with SFA, and in general negatively correlated with unsaturated FA, were associated with stress resistance (K00383 and K07482), lactate metabolism (K03778), adaptation to new environments (K01126 and K06148), and cell division . On the other hand, 3 other genes which are involved in no stress responses (K03070 and K03596) and protein synthesis (K01875) were strongly positively correlated mostly with K01126  and K03778  and K06148 . In all cases, higher levels of the gene were associated principally with butyrate, propionate, tyrosine and NADP+ production. Conversely, these individuals had lower levels of hypoxanthine and xanthine in the rumen fluid .In total, rumen microbial genes which were significantly correlated to milk SFA concentrations were used to compare the metabolome of cows that they had high vs. low (8 vs. 8) abundance of the gene . The besluid see . The modbutyrate . HoweverLactobacillus, Leuconostoc, and Weissella), as well as acetogenic bacteria such as Acetobacter and Kozakia, and to a lesser extend Pediococcus (LAB), Komagataeibacter (Acetogen), Hafnia (Phytate degrader) and other genera . These genera explained between 67 and 88% of the variability of each gene using PLS analysis (Microorganisms strongly correlated with these 5 genes (VIP > 0.8) were mostly composed of the 3 LAB , rumen microbial biohydrogenation of dietary FA, desaturase enzyme activity in the mammary gland, and release from body fat stores   , as well as the abundance of rumen metabolites in cows carrying these genes (metabolomics), which explain variation in the main milk SFA and UFA, using data and samples from 48 dairy cows. The present work also increases the number of rumen microbiome and metagenome results for dairy cattle and provides further information on possible mechanisms impacting milk FA profile , Proteobacteria (Acetobacter and Kozakia) and Actinobacteria (Mycobacterium), fungi , and to a lesser extent  protists (Nannochloropsis), and methanogens (Methanobrevibacter).A number of rumen microorganisms, have been found at higher abundance in the rumen of cows that produced milk with higher concentrations of SFA and were also positively correlated to SFA, C12:0, C14:0, or C16:0 and/or negatively correlated to Lactobacillus, is higher in animals fed high-grain diets, and associated with lower rumen pH, because of the intake of rapidly fermentable carbohydrates, such as starch , which were found at higher concentrations in the rumen of cows producing High-SFA milk, are known to be involved in acetate production   is a lipase producing yeast  was rather surprising given that Nannochloropsis is known to synthesize and store high concentrations of long chain n-3 PUFA in their triacylglycerol , and cell wall modification (K01126) or ABC transporter (K06148), which are important for LAB survival in lower pH environments . In addition, microbial genes explaining most of the variability in milk FA, included those associated with lactic acid metabolism (K03778), and genes related to stress resistance and environmental adaptation by the synthesis of glutathione (K00383), which were mainly carried in LAB. This was coupled with the higher abundance of the cofactor NADPH/NADPAcetobacter, and Kozakia, were positively correlated with the contrasting groups of FA ; although the mechanisms controlling acetate concentration in the rumen, an important source for milk FA synthesis, are still unclear  were identified by metabolomics as highly correlated with microbial genes which were abundant in High-SFA animals, but milk yield between Low-SFA and High-SFA animals was similar. Differences in microbial and liver metabolisms related to tyrosine and propionate could explain this lack of effect on milk yield, in contrast with previous results . The diversity and abundances of rumen microbial genes explained from 76% (for C14:0) to 94% (for C16:0) of the variation in milk fatty acid profiles, thus highlighting that the rumen microbial environment is highly correlated with milk nutritional quality.To conclude, the present study revealed that some microbial taxa, genes and their activities within the rumen microbiome differ substantially between cows which, under the same diet and housing conditions, produce milk at the high range  than in the low range  of the nutritionally undesirable SFA concentrations; while simultaneously show higher concentrations of the nutritionally beneficial https://figshare.com/s/b5413ccaf25f9b0d9f87. Metagenomics raw data can be downloaded from the European Nucleotide Archive under accession PRJEB33080. A metadata file giving information on the animal experiment is associated to PRJEB33080 and also summarized in The animal study was reviewed and approved by the animal experiment was conducted at the Centre for Dairy Research (CEDAR), University of Reading, UK, all experimental procedures used were licensed, regulated, and inspected by the UK Home Office under the Animals (Scientific Procedures) Act, 1996.MA and SS: conceptualization and project management. MA, IC-L, MM-O, MW, and RS: formal analysis. MA, MM-O, and SS: original writing. SS, IC-L, MM-O, RD, DH, RR, MW, and MA: review and editing. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.The Comprehensive Antibiotic Resistance Database (CARD;  Mycobacterium tuberculosis  new tools for classification of reference data and genome annotation results and (v) new efforts toward community biocuration of AMR molecular reference data.In the century since Alexander Fleming isolated penicillin ,2 and larculosis  or collarculosis . Others rculosis , tool , a phttp://purl.obolibrary.org/obo/ro) and NCBITaxon . The ARO is the primary ontology in CARD as it includes detailed descriptions of the molecular basis for antibiotic resistance, encompassing known AMR determinants , drug targets, antibiotic molecules and drug classes, and the molecular mechanisms of resistance. The ARO is organized into three major branches: Determinant of Antibiotic Resistance (ARO:3000000), Antibiotic Molecule (ARO:1000003) and Mechanism of Antibiotic Resistance (ARO:1000002). Each resistance determinant described by the ARO  must include an ontological connection to each of these three branches. Additional, minor ARO branches detail other aspects of AMR: Antibiotic Target (ARO:3000708), for describing antibiotic-sensitive wild-type bacterial components; Antibiotic Biosynthesis (ARO:3000082), for describing in vivo antibiotic synthesis by bacterial cells or communities; and, Resistance-Modifying Agents (ARO:0000076), for describing antibiotic adjuvants, inhibitors of resistance enzymes, and antibiotic potentiators which help restore a susceptible phenotype. Since our previous update and in collaboration with the Genomic Epidemiology Ontology (GenEpiO.org), we have added a new AMR Phenotype Terminology branch (ARO:3000045) to the ARO containing 133 terms describing clinical AMR phenotypes, laboratory microbial susceptibility testing and testing reference standards. Overall, each entity in ARO uses semantic relationships within and between these branches to provide the full biochemical context for each AMR determinant, some of which have been updated , with four such ontologies being central to its operation: the ARO, the CARD Model Ontology (MO), the CARD Relations Ontology  Relations Ontology) (Ontology )  reductases .CARD curation occurs continuously, with monthly updates released by a team of biocurators. CARD curation involves both a descriptive component (i.e. an ontology term) and a functional component (i.e. AMR detection models with associated reference sequences). The curation process primarily involves regular review of the available scientific literature, as described in detail below, to determine applicable additions and modifications. Enforced curation guidelines provide the necessary context to ensure proper hierarchical classification, defined semantic relationships and data standardization. For example, when a new resistance determinant is identified, a biocurator places it within the ARO with the appropriate ontological relationships to indicate the AMR gene family, resistance mechanism and observed drug-class resistance. The biocuration team additionally annotates each ARO term with supplemental information from external references, including relevant publications (via NCBI PubMed ), chemicCARD’s primary curation paradigm is as follows: to be included in CARD an AMR determinant must be described in a peer-reviewed scientific publication, with its DNA sequence available in GenBank, including clear experimental evidence of elevated minimum inhibitory concentration (MIC) over controls. AMR genes predicted by in silico methods, but not experimentally characterized, are not included in CARD’s primary curation. Yet, our data harmonization efforts in 2019 that involved a comparison of ResFinder  beta-lactamase and hydrolysis of antibiotic conferring resistance. To address this issue, we have added a new ARO classification tagging paradigm, where our expert curators manually ‘tag’ certain terms in the ARO as particularly informative for interpretation. We designed seven types of classification tags: four primary tags used to index and classify genome or metagenome annotation results  and three secondary tags to track adjuvants or the complexities of antimicrobial efflux   has relationships to 28 ontology terms within the ARO, including expanded CARD’s curation paradigm as follows: every curated AMR determinant must have an ontological path including each of the four primary ARO classification tags, i.e. the AMR Gene Family to which that determinant belongs, the Resistance Mechanism, the Drug Class(es) to which resistance is conferred, and the specific Antibiotic with a demonstrably elevated MIC. This tagging allows easy interpretation of resistome predictions  checks or review of AMR gene nomenclature. Pathogen AMR review involves systematic review of the AMR literature for specific pathogens, with reviews completed in the last 2 years for Acinetobacter baumannii, Chlamydia trachomatis, Clostridioides difficile, Escherichia coli, Mycoplasma genitalium, Neisseria gonorrhoeae and Pseudomonas aeruginosa. Biocuration of M. tuberculosis AMR will be a major focus in 2020, including harmonization with ReSeqTB  the task of keeping CARD both comprehensive and up-to-date is daunting. CARD addresses this problem using three approaches:  ReSeqTB , as CARD ReSeqTB  for compS′) cut-offs, discontinuing use of less discriminatory BLAST expectation values (E). The chosen bit score cut-off reflects a human curated value that aims to differentiate between putative functional homologs and other similar proteins with different functions. Bit score cut-offs are selected based on values that perform this discrimination when the curated reference sequence is compared by BLAST against CARD itself and against GenBank's non-redundant database, with hand inspection to determine a value that correctly classifies matches as homologs of similar antimicrobial function (i.e. ≥ bit score cut-off) or similar proteins with different function or AMR Gene Family membership (i.e. <bit score cut-off). We had determined that the asymptotic nature of the BLAST expectation value (E) gave it very low discriminatory power between different β-lactamase gene families (nearly ⅓ of CARD’s content), but that the linear nature of the BLAST bit score (S′) allowed this level of discrimination.AMR determinants  are associated with ARO terms and AMR detection models in CARD, thus providing the interpretive context (ARO), reference sequence data and bioinformatics parameters for prediction of AMR determinants from raw DNA sequence. The latter is described by CARD’s Model Ontology MO, , which iCARD now also includes two additional model types, the rRNA gene variant model (RVM) and the protein overexpression model (POM). The RVM is functionally similar to the PVM, except it works for rRNA mutations and therefore uses a nucleotide reference sequence and a BLASTN bit score cut-off. The POM is also similar to the PVM, but predicts protein overexpression based on the presence of mutations often associated with regulatory proteins. POM reflects how certain proteins contributed to AMR with and without mutations and is most often applied to efflux complexes, where wild-type proteins result in low or basal expression, whereas key mutations result in overexpression and clinical resistance ,37. Unlibwt) uses Bowtie2 . To generate these data, we analyzed pathogen genomes with RGI to produce a predicted resistome for each, tracking allelic variation, ARO classification, and prevalence among pathogens, genomes, plasmids, and whole genome shotgun (WGS) assemblies. In total, CARD Resistomes & Variants includes in silico surveillance of 82 pathogens of public health and AMR relevance, including each pathogen from the World Health Organization's (WHO) Global Priority List of Antibiotic-Resistant Bacteria  covering 1656 AMR detection models from 82 pathogens. CARD Resistomes & Variants are not included in CARD’s primary curation nor used as reference sequences, except that CARD’s RGI version 5 can optionally incorporate these data to increase reference sequence diversity for mapping of metagenomic reads, to provide epidemiological context for interpretation of metagenomic data, and to provide novel k-mer algorithms (i.e. signature sub-sequences) for pathogen-of-origin and plasmid-association predictions for AMR genes or metagenomic reads . To maintain a clear distinction between characterized AMR alleles and in silico predictions, these two forms of data are accessible on different parts of the CARD website and via separate download files.The AMR reference data included in CARD is derived exclusively from peer-reviewed publications, following CARD’s curation paradigm. Thus, CARD biocuration precludes putative AMR determinants or variants not validated by clinical or experimental data. To wit, CARD reference sequences do not include computationally predicted alleles lacking an experimental demonstration of elevated MIC over controls. Yet, assessment of sequence diversity is important for epidemiological investigations, evolutionary studies, mapping of metagenomic sequencing reads  and consBacteria . For eacBacteria , retainiBacteria . These rCARD uses the custom ‘Broad Street’ schema for storage and curation , named fhttps://card.mcmaster.ca) is updated monthly (with a few exceptions) and provides tools for browsing and searching the ARO, AMR detection model parameters and reference sequences, CARD Resistomes & Variants (https://card.mcmaster.ca/genomes) data with Prevalence calculations , and tracking of changes for each release. The website also includes a built-in BLAST instance for comparing sequences to CARD reference sequences and a web instance of RGI for resistome prediction with data visualization tools . The download section (https://card.mcmaster.ca/download) includes CARD reference sequence data , CARD Resistomes, Variants and Prevalence data , RGI software downloads for command line usage, and all ontologies . Full documentation and open source code for the RGI is additionally available at the publicly accessible CARD GitHub (https://www.github.com/arpcard/rgi), which includes a wrapper for use with the Galaxy bioinformatics framework, a monitored issue tracker, plus instructions for using RGI via the Conda software packaging system. The ARO is additionally available through the Open Biomedical Ontologies’ OBO Foundry (http://purl.obolibrary.org/obo/aro).The CARD curation team continuously updates the database on a development server and prior to release, rigorous QC scripts are implemented to validate these data before porting it to the publicly available website. These QC steps verify the use of external identifiers, publication citations, AMR detection model parameters and imposed rules for the ontology structure. Any detected issues are resolved prior to release. After QC, the public CARD website (card@mcmaster.ca and software or data releases are announced via Twitter (@arpcard) and the CARD-L mailing list (see http://arpcard.mcmaster.ca/about). In response to the 2019 European Commission's Joint Research Centre (JRC) AMR Databases Workshop, we have established the ‘AMR_Curation’ public repository for collective curation of AMR genes and mutations involving the majority of AMR database curators  with an active and monitored curation issue tracker, a parallel AMR curation mailing list, editable Google Spreadsheet List of AMR Databases and Software, and curated Wikipedia list of AMR Databases all accessible at https://github.com/arpcard/amr_curation. We encourage researchers, software developers and AMR data curators to use this repository and associated resources to submit, discuss and resolve AMR curation issues.The CARD biocuration and development teams are available for contact at CARD has evolved substantially since our initial release  and prevgkz935_Supplemental_FilesClick here for additional data file.


Molecular clustering at the plasma membrane has long been identified as a key process and is associated with regulating signalling pathways across cell types. Recent advances in microscopy, in particular the rise of super-resolution, have allowed the experimental observation of nanoscale molecular clusters in the plasma membrane. However, modelling approaches capable of recapitulating these observations are in their infancy, partly because of the extremely complex array of biophysical factors which influence molecular distributions and dynamics in the plasma membrane. We propose here a highly abstracted approach: an agent-based model dedicated to the study of molecular aggregation at the plasma membrane. We show that when molecules are modelled as though they can act (diffuse) in a manner which is influenced by their molecular neighbourhood, many of the distributions observed in cells can be recapitulated, even though such sensing and response is not possible for real membrane molecules. As such, agent-based offers a unique platform which may lead to a new understanding of how molecular clustering in extremely complex molecular environments can be abstracted, simulated and interpreted using simple rules. The plasma membrane (PM) has long been identified as a key component of cellular machinery. Its function is not limited to separating intra-cellular organelles from the extracellular environment; it is also recognized broadly as the principal instigator and coordinator of inter-cellular interactions, 2. The In practice, molecular clustering has been highlighted as important in regulating signal transduction at the PM. Its study has benefited considerably from the rise of fluorescence microscopy and more recently super resolution methods–16, enabConsidering the current interest of the community in molecular aggregation at the PM, its understanding at a more theoretical level remains relatively understudied and simplified models are generally lacking or focusing on a specific activation pathway in a given cell type. Agent-bas if they can. We show that this apparent behaviour can be exploited to construct simplified models which recapitulate clustering profiles without the new to incorporate a full biophysical model. Interestingly, we also demonstrate that a modification of the environment, for instance a filamentous actin enriched environment, leads to distinct modifications of the clustering patterns as observed in cells.Our hypothesis is that all competing factors affecting a given molecular population distribution at the cellular membrane can be summarised as a single “desire for clustering” assigned to each agent (i.e. molecule). We demonstrate that this overall “desire for clustering” translated in an agent-based model, is sufficient to recapitulate clustering behaviours observed in cellular membranes. While in reality, molecules are not able to sense their neighbours at distances of tens or hundreds of nanometres, we show that they behave Any agent-based model relies on three levels of abstraction. It consists of an observer (or user) defining an environment (the PM in our case) in which agents  evolve following pre-set rules. The model thus allows studying the fate of these agents over time (the third level of abstraction).2 region of interest (ROI: the PM). Initially all proteins are randomly distributed forming a completely spatially random (CSR) distribution. The boundaries of the ROI are toroidally wrapped to ensure a constant number of molecules. The mobility characteristics of each molecule are updated every 10 ms. The simulations are run for 5 minutes  as most cellular processes occur in this range of timescales. In all cases studied, n = 30 simulations were generated.In practice, for simulations we consider 2000 agent-molecules diffusing freely (i.e. following Brownian motion) in a 2D 3 x 3 μmAlthough a multitude of cellular processes have been shown to promote molecular clustering in cellular membranes, our initial hypothesis is that they can be summarised by a unique parameter: the “desire for clustering” given to each agent. This “desire” parameter results from a combination of competing cellular processes ranging from self-affinity, membrane lipid domains–32, the The method by which we generate a “desire for clustering” for each agent is by imposing a rate of Brownian motion, which is dependent on the immediate density of that agent’s local molecular environment. All agents possess a theoretical “target value” for clustering (defined as a certain number of neighbours within a 100 nm radius) and the closer its local density is to the targeted value, the slower the agent. Hence, whilst maintaining the assumption of molecules diffusing freely following Brownian motion in the PM, the diffusion coefficient (DC) of each molecule is fixed according to the local level of clustering.ith localisation, the local density is estimated based on the “LR” value, i.e. the linearised localised Ripley’s K function:A is the area of the ROI, n is the total number of molecules in the ROI, for i ≠ j, δi,j= 1 if the molecule j is contained in a circle of radius R centred on molecule i and δi,j= 0 otherwise. Here, we define R = 100 nm, as molecular clusters in cellular membranes have been shown to be typically below the diffraction limit of light (< 200nm)[LR is a mathematically well-defined quantity, considered as an accurate measure of local density and routinely used for spatial point pattern (SPP) cluster analysis [LR value equals R . This is the starting point of the simulations (For each frame and for each (< 200nm), 35–37. analysis , 38–40. ulations .L100. For each agent-molecule i, the measured value of L100,i can therefore be compared to that target, from which the step displacement (D) per second is updated independently for each agent-molecule in every frame. Each molecule is thus attributed an independent D as a result of the level of clustering (L100), ranging from a pre-defined maximum (Dmax) to a minimum value when the agent reaches the targeted L100. Although the resulting diffusion is an approximation for Brownian motion, the difference in order of magnitude between step displacement (given the update/frame rate) and the length scale at which we study cluster formation, ensures this approximation is robust. Multiple functions can be chosen to map the dependence of D on L100. For the Standard Condition, we focus on a linearly converging profile in which a molecule i that has been attributed L100,i >= L100,target is set as fully immobile (i.e. Dmin = 0) as illustrated in The target value for the clustering of all agents is simply defined as a specific value of L100 per frame of the simulation, serving as a measure of the overall level of clustering within the ROI [min = 0 (i.e. fully immobile). We varied the targeted L100 from 7 encircled molecules  up to 69 encircled molecules (L100 ~ 317) with 2 molecule increments –47 and Dcrements . The ranL100 target value applied. We can extract two instructive parameters from these curves: the convergence time . We verified that this cut-off point was not in fact a slow convergence by repeating the simulations  over a total 30 minutes, which demonstrates no sign of convergence    . Interesctively) , with slL100 targets which do not converge for slower diffusing molecules, whilst forming larger aggregates  and over a larger range of target values. As they travel larger distances, fast moving molecules are more likely to be caught within the range of influence of a spontaneous nucleation site  Movies. gregates . For insSo far, we have chosen to vary the displacement (or per frame step size) for each molecule over time, which is a robust approximation of Brownian motion at the length scale at which we are studying cluster emergence versus frame update. However, a more general approximation consists in a random walk in which the per-frame step sizes are drawn randomly (and independently) from a normal distribution centred on the targeted diffusion coefficient. Using this approach, we were able to show that not only molecular aggregation can be achieved but also that similar mode  are observable .We also studied the impact of the shape of displacement curves themselves on the output structures . Surprismin for non-converging cases. We set Dmin as the expected per frame displacement of a cluster containing all molecules in the ROI , is necessary to understand better the mechanism behind the emergence of molecular aggregation, it is unlikely to recapitulate all the variations existing at the cell membrane. We run additional simulations with a desire for clustering following a non-linear decay (quadratic) to verify the robustness of clusters emergence. As in the linear case, we observe molecular aggregation .One of the advantages of performing simulations such as the agent-based model presented here, is that it enables studying the impact of any modifications in the environment architecture on the agents: e.g. would agent behaviour be different in the presence of obstacles or boundaries within the ROI. This approach is particularly interesting in the case of the cellular membrane whose nanoscale architecture is well characterised. Our framework can therefore be used to understand the impact of known cellular structures on the basal level molecular aggregation determined only by the desire for clustering. Here, we focus on the role played by the actin meshwork situated directly beneath the PM. In particular, we compare our Standard Condition with an actin enriched environment. Certainly, as previously stated, the desire for clustering encompasses complex, intertwined and diverse cellular processes, including the potential role of the actin mesh. However, here we are able to study the effect of an increase in filamentous actin added to the desire for clustering as defined in our Standard Condition or basal level of clustering. The environment here remains static, with actin mesh added during the whole time course.Actin is a fibrous polymer protein which forms a key component of the cell skeleton. It allows the cell to generate forces through actin polymerisation which serves as a highway for molecular trafficking by motor proteins and enhances the cells’ structural stability–56. ThisIn practice, actin fibres are translated into our agent-based model as reflective boundaries arranged in a regular grid, within the ROI. We vary the meshwork density from relatively sparse (1000 nm spacing) to more dense cases (500 nm and 250 nm spacing). 7 conditions were simulated with: 7, 15, 25, 35, 45, 55 and 65 encircled molecules targets. Representative examples of the molecular distributions obtained after 5 mins simulations are illustrated in max. Our simulations reach the cut-off point for a target value smaller than 55, 65 and 65 encircled molecules for 250 nm, 500 nm and 1000 nm mesh spacing respectively. In all cases, the cut-off values consist of smaller values than in the absence of a mesh (i.e. in the Standard Condition). However, although the digitized behaviour is preserved, with varying pass band width depending on the mesh density, our results also suggest that the actin mesh alone, in particular its density, impacts on the characteristics of the molecular aggregates for the same fixed target value. The presence of a dense actin mesh results in agent-molecules reaching a clustered distribution over a very limited range target values, whilst systematically forming more numerous smaller clusters when converging  are thought to act locally with often intertwined and competing effects on molecular aggregation. Their complex relationships remain to be fully characterised and more processes are likely to be identified with technological advancement in microscopy. In fact, there is no clear consensus in the field on the role of each in regards of the other, as wellBecause of these complex factors, complete and general molecular models, for example atomistic MD simulations of the whole plasma membrane-cortex system, have proven impossible to realise. We suggest in this work that cellular biology will benefit vastly from conceptual models such as agent-based approach as a way to improve our understanding of key cellular processes (e.g. protein clustering at the PM) similarly to other fields with high level of complexity like economics. The ageInterestingly, our simulations highlight that for small variations of the “desire for clustering” applied to molecules, drastically different clustering outcomes are obtained. This mimics observations made in cells where small changes at the PM are associated with significant modifications of the molecular distributions, possibly facilitating signalling or effector functions. Our resAgent based models have particular advantages over other methods e.g. MD simulations, which make them particularly useful for applications studying membrane protein distributions and dynamics. Firstly, they do not require complete knowledge of the structure of the system under investigation. They are well suited to the study of emergent phenomena—where macroscale architecture is generated from underlying local forces and they provide a simple and intuitive conceptual framework for understanding how biophysical phenomena (e.g. actin) impact molecular behaviour. In that context, agent based models might be useful for the design of pharmacological interventions designed to alter molecular nanoscale organisation due to their ability to predict the macroscale outcome of a local perturbation. For example, new therapeutic concepts involve the modification of plasma membrane clustering in the context of disease.S1 Figa Molecular distribution from t = 0 min to t = 30 min. b. Resulting (TIF)Click here for additional data file.S2 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65 encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S3 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65, (vi) 80, (vii) 90 encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S4 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65 encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S5 Figa. Non-converging scenario where a molecule i is immobile for L100,i = L100,target only. b. Converging scenario with Dmin>0. c. Example of final updates frame for target set at 7, 25, 45, 65 encircled molecules, in the non-converging scenario. d. Example of final updates frame for target set at 7, 25, 45, 65 encircled molecules in the converging scenario with Dmin>0.(TIF)Click here for additional data file.S6 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65, encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S7 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65 encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S8 Figa. Examples of the molecular distributions obtained after 5 min simulations with the target values set at (i) 7, (ii) 25, (iii) 45, (iv) 55, (v) 65 encircled molecules. b. Corresponding c. Convergence time and d. Error as a function of the number of molecules for each L100 target .(TIF)Click here for additional data file.S9 Figa. decay used for the displacement calculation of each molecule at each frame. b. Example of the molecular distributions obtained after 5 min. c. Corresponding (TIF)Click here for additional data file.S1 MovieTarget value: 41 encircled molecules.(AVI)Click here for additional data file.S2 MovieTarget value: 41 encircled molecules.(AVI)Click here for additional data file.S3 MovieTarget value: 41 encircled molecules.(AVI)Click here for additional data file.S4 MovieTarget value: 41 encircled molecules.(AVI)Click here for additional data file.S5 MovieTarget value: 35 encircled molecules.(AVI)Click here for additional data file.


Since the beginning of 2020, the ongoing coronavirus disease COVID-19) pandemic has resulted in the deaths of more than 250,000 patients in the European Union/European Economic Area (EU/EEA) and the United Kingdom alone [9 pandemiAspergillus spp. infection in an immunocompetent COVID-19 patient [Clostridioides difficile infections in hospitalised patients [While experts have warned of the link between COVID-19 and AMR -8, studi patient . However patient ,17, and patients . In a raThese different experiences may just be the consequence of previous antibiotic prescribing and infection prevention and control (IPC) practices that resulted in varying background AMR prevalence, in particular in healthcare settings, in different countries ,20. StilWe summarised various determinants that may result in either an increase or, inversely, a decrease in AMR and found them to be balanced (Table). The truth is that the impact of the COVID-19 pandemic on AMR will only become clear in the coming months and years as data gradually become available. Changes in AMR will most likely vary depending on the setting—e.g. ICUs vs other hospital units, hospital vs community settings—and possibly between countries.On the occasion of European Antibiotic Awareness Day (EAAD), the European Centre for Disease Prevention and Control (ECDC) will publish its annual reports on surveillance of AMR from the European Antimicrobial Resistance Surveillance Network (EARS-Net) and on surveillance of antimicrobial consumption in humans from the European Surveillance of Antimicrobial Consumption Network (ESAC-Net). Data up to 2019 are already available from the ECDC Surveillance Atlas of Infectious Diseases . The WorUnfortunately, more recent data are not yet available from these networks to assess the impact of COVID-19 on AMR. As of December 2018, only nine EU/EEA countries used machine-to-machine links for reporting data from clinical laboratory information management systems to a national database on EU-notifiable diseases and on AMR, respectively . While tIn addition, specific studies will need to be performed to assess changes in antibiotic prescribing, IPC practices and their effect on AMR as a consequence of the COVID-19 pandemic. One important point that will require consideration is that the effects of changes in antibiotic prescribing and IPC practices on AMR is unlikely to be immediate, and that the necessary delays to observe these effects—as well as the thresholds above which changes in antibiotic prescribing and IPC result in an effect—will need to be taken into account when analysing data . We shouOn 18 November, we will celebrate the 13th EAAD, in partnership with the World Antimicrobial Awareness Week, from 18 to 24 November 2020. Much has happened in Europe since the first EAAD in 2008: repeated awareness campaigns took place, EU and national action plans were developed in most EU/EEA countries and regulatory actions, policy initiatives and interventions were enacted at EU and national levels. One example of EU regulatory action is provided in this issue by Opalska et al., who reviewed all EU post-authorisation procedures of harmonising product information for antibiotics from 2007 to 2020. The study found that the majority resulted in a restriction of indications for antibiotics, which could have contributed to decreasing their consumption and, ultimately, AMR . The autEnterococcus faecium from bloodstream infections that are resistant to vancomycin almost doubled between 2015 and 2019. Resistance to carbapenems—a last-line group of antibiotics—continues to be a concern, with several countries reporting carbapenem-resistance proportions above 10% in Klebsiella pneumoniae, and very much higher in Pseudomonas aeruginosa and Acinetobacter species bloodstream infections [A. baumannii. Authors found that seven European countries report an endemic situation, while another nine report regional or inter-regional spread of specifically carbapenem-resistant A. baumannii, with national capacities for its surveillance and containment varying depending on the country [A. baumannii, 15 had national recommendations or guidelines for its control and only eight countries had a national plan for its containment—only one of which was one of the seven endemic countries.As suggested last year by Peñalva et al., there are signs that the many actions and initiatives implemented at EU and national levels may start to show their effects on antimicrobial consumption and AMR trends . Neverth country . For exaThe COVID-19 pandemic reminds us that compliance with IPC measures is critical to ensure the safety of hospitalised patients. Most IPC measures that are essential for controlling the spread of SARS-CoV-2 also contribute to reducing the spread of antimicrobial-resistant bacteria; these, together with antimicrobial stewardship programmes, must be maintained and strengthened. In the midst of the COVID-19 pandemic, we certainly must not give up on our efforts to prevent and control AMR and must stay united to preserve the effectiveness of antimicrobials.


C. elegans P7.p cell divisions is controlled by the Wnt/β-catenin asymmetry pathway . This pathway includes the β-catenin-like proteins SYS-1 and WRM-1, POP-1/TCF, and the Nemo-like-kinase, LIT-1 . The Wnt/β-catenin asymmetry pathway ensures different ratios of SYS-1 to POP-1, controlling the differential transcription of Wnt target genes between daughters of an asymmetric cell division. Because our genetic data indicate an antagonism between LRP-2 and LIN-17 similar to that between CAM-1 and VANG-1 and LIN-17 , we wanted to determine if LRP-2 can control the asymmetric localization of SYS-1 between the daughter cells of P7.p during anaphase of the first cell division. The initial establishment of vulval polarity can be observed through the localization of VENUS::SYS-1 (VNS::SYS-1), localized in a high (P7.pa)/low (P7.pp) pattern in the wild-type worm, reciprocal to the localization of POP-1/TCF .The polarity of the et al. 2008) that VNS::SYS-1 asymmetry in P7.p daughter cells is often lost in lin-17(n671) and lin-18(e620) mutants. These mutants display two aberrant patterns of VNS::SYS-1 localization as well as the wild-type pattern, though less frequently. The two deviant localization patterns include one in which both P7.pa and P7.pp express equal amounts of VNS::SYS-1 and a reversed VNS::SYS-1 pattern in which P7.pp is enriched with VNS::SYS-1. By observing VNS::SYS-1 localization in a lin-17(n671); lrp-2(gk272) background we see that the aberrant localization of SYS-1 is suppressed to a similar degree to that of lin-17(n671); cam-1(gm122) and lin-17(n671); vang-1(ok1142). This observation confirms LRP-2 controls vulval cell polarity by antagonizing LIN-17 in a similar fashion to CAM-1 and VANG-1, and that the effect of LRP-2 is at the level of P7.p rather than its progeny.It was previously reported (Green Strains:N2MT1306: lin-17(n671) MT1488: lin-17(n671); unc-13(e1091)PS5840: lin-17(n671); cam-1(gm122); qIs95[pSYS-1::VENUS::SYS-1] PS5787: lin17(n671); vang-1(ok1142); qIs95[pSYS-1::VENUS::SYS-1] lin17(n671); lrp-2(gk272) double mutant was constructed by crossing VC543lrp-2(gk272) males with strain MT1488: lin-17(n671); unc-13(e1091) hermaphrodites.The JK4062: lin-17(n671); qIs95[pSYS-1::VENUS::SYS-1]lin17(n671); lrp-2(gk272); qIs95[pSYS-1::VENUS::SYS-1] line was created by crossing VC543lrp-2(gk272) males with JK4062: lin-17(n671); qIs95[pSYS-1::VENUS::SYS-1] hermaphroditesThe


Efforts to address the complex global problem of antimicrobial resistance (AMR) highlight the need for imagination and innovation. However, nursing has not yet leveraged its potential to innovate to prevent AMR advancing.This paper focuses on the initial phase of an ongoing research and development study that seeks to foster nursing imagination and innovation by enhancing the meaningfulness of AMR for practising nurses and by facilitating their creative ideas.This aim is addressed through application of arts and humanities approaches, in particular the use of visualisation, co-design and historical methods, underpinned by the Design Council Double Diamond process model. The first phase with 20 UK participants explored how hospital and community-based nurses understand and respond to the priorities and consequences of AMR within their everyday working lives.Nurses varied in their conceptualisations of AMR and in their depictions and explanations of its meaning and priority within everyday practices. Some saw infection prevention and control as bound up with AMR, whereas others differentiated in the context of specific work activities. Insights into related reasoning and practice tactics were also generated.The initial project phase provides a basis for fostering nursing innovation in this important field. Before outlining one approach to fostering innovation in nursing, it would be useful to consider the concept of innovation itself. Within the context of health service delivery and organisation, first the spark, then the sweat ideation. The limitation of this way of thinking, however, is that it tends to highlight isolated sparks rather than ongoing creativity in ways of thinking, seeing and improving practice.Nevertheless, this only covers part of the story. It is necessary to ask how such innovation comes about, where it comes from and who is involved. One response to this is to associate innovation with invention and even genius. The famous maxim on the latter (often attributed to Thomas Edison), that ‘genius is 2% inspiration and 98% perspiration,’ typifies imaginari’, to ‘picture to oneself’ (Lexico (Oxford) English Dictionary 2020), ‘imagination’ thus places visualising centre-stage. Applied to innovation in nursing this entails the capacity to attend to (and engage in) ongoing dialogue between nursing as is and could be, and to either envisage and generate new ideas, or envisage adopting/adapting new knowledge and technologies in meaningful ways. As such, parts of this proactive envisaging go on in the mind’s eye, and Within this ambit we would argue that innovation in nursing may be more usefully considered as an ongoing response to a process of dialogue between practice as it is, and practice as it could or should be. At the heart of this dialogue is imagination in nursing. Stemming from the Latin verb ‘AMR is now recognised as a global problem of high magnitude . Within The need for nursing to harness its power to this end is pressing. Nurses constitute the largest professional healthcare workforce globally and, typically, nurses have numerous daily interactions with healthy and ill individuals, family members, community groups and other care professionals. Thus, they have many potential opportunities to enact antimicrobial stewardship (AMS) practices such as education to help lessen inappropriate demand for antibiotics or ensuring that these drugs are prescribed and administered optimally. However, to date, the profession has not yet leveraged its full potential to prevent AMR advancing or to countenance the consequences of failure. Survey research in the UK and beyond e.g.  indicateA number of AMR toolkits exist for staff e.g.  but, perHow can relevant arts- and humanities-based approaches help nurses to re-envisage their infection control practice ecologies in response to antimicrobial resistance?In addressing this overarching question, four subsidiary questions are also explored, namely:How do groups of hospital- and community-based nurses understand and respond to the priorities and consequences of antimicrobial resistance (AMR) within the context of their everyday working lives?How can co-design and visualisation-based approaches help these nurses to identify and construct sets of meaningful practices that optimise present prevention of AMR?How can co-design, visualisation, history and other relevant arts and humanities approaches help nurses to re-imagine and re-envisage their infection control practice ecologies in a future with minimal or no effective antibiotics?What priority issues and other questions does this initial enquiry raise, and how can these best inform policy and planning, education and further research?The Re-envisaging Infection Practice Ecologies in Nursing through Arts and Humanities Approaches (RIPEN) study seeks to address the issues outlined above by starting from where practising nurses are in their daily working lives. In doing so RIPEN uses, and reflexively evaluates, a combination of primarily visual-based qualitative methods drawn from design, art, history, health services, and policy research. The former three disciplines in particular are rooted in the arts and humanities and their approaches offer potential for different ways of thinking and seeing. Using these different windows and mirrors for the issue of AMR may have the potential to foster nursing imagination and innovation, and this rationale drives the study’s main question:The study is designed around two practice ‘labs’, one in Glasgow with a small group of mainly hospital-based participants and one in London with a small group of mainly community-based participants. Over the course of a year the participants within each lab are attending four workshops that address each of the above subsidiary study questions in turn. In between the workshops participants can also participate in shared activities online through a virtual learning environment. Following these workshops, a final integrative ‘policy lab’ will be convened whereby the research team, a sub-group of participants from both lab sites, the study advisory group and an invited range of policy experts and arts and humanities academics will address the latter part of Question 4.http://www.designcouncil.org.uk/news-opinion/design-process-what-double-diamond; see The underpinning conceptual model is the four-stage Design Double Diamond process model , while the defining and delivery stages aim more to focus down . As the initial discovery stage is now completed, this paper primarily focuses on methods and findings relating to Question 1.The study’s design and conduct are also informed by participatory co-design thinking , buildinAs Activity 1 approached conceptualisation by asking individual participants to think of resistance to antibiotics and its causes and effects, then draw how they pictured AMR in their mind see –4. SubseActivity 2 was designed primarily to elicit the main daily ‘touchpoints’ for participants in terms of AMR, infection prevention and control (IPC) or both within their working lives. Individuals completed an eight-frame storyboard  how they conceptualise AMR, (b) related AMR work, and (c) whether/how this differs from IPC work. The lens used is normalisation process theory , an implementation theory whose first construct (coherence) posits the importance of differentiation, specification and identification work to make sense of the relationship between new and existing practices. As such, it has particular relevance for learning about the coherence of AMR as a concept in practice as is and as could be.Thus, visual methods were central to the workshop activities to help participants situate, visualise and explain their current thinking and practice relating to AMR in a meaningful way. In the process, implicit knowledge could be made explicit. In turn, this could facilitate mutual reflection on action  and promSuch methods create a rich mix of visual, textual and audio data pertaining to AMR and nursing infection practice ecologies. Data analysis and synthesis of workshop outputs is driven by the main workshop questions, with extensive use of matrices that integrate the visual and textual material. These matrices facilitate consideration of manifest and latent content  and reseFor this exploratory study we wished to recruit around 10 nurses for each lab, with London focusing primarily on community nurses and Glasgow on hospital-based nurses. We sought to recruit mixed groups in terms of work settings and relative degrees of generalism, specialism and experience, as it was felt that this would enrich the nature and scope of dialogue. For the latter reason and to incorporate external perspectives we also sought participation from two patient/public representatives and two representatives from professions allied to nursing (AHPs) for each lab.Study information was disseminated widely via the UK nursing press, professional and organisational networks, and volunteers responded by self-referring. The majority of participants were recruited this way and we had to decline participation from several specialist IPC nurses in order to ensure group balance. To help recruit more generalist community nursing participants in London we gained Integrated Research Approval System permission (19/HRA/0118) from two NHS trusts. The workshops took place on university premises and ethical permissions were obtained from the five collaborating universities.The profile of the final groups of participants is summarised in This section presents findings contextualised through some analytic commentary.As the collated responses in Because there was no culture done, there was nothing done, so we just prescribe any antibiotic and that’s not helping .By contrast, the next grouping .There is a tension here between the merits of conceiving the bigger picture and the scope of any one health profession to act and innovate. This is amplified when viewing the Department of Health’s comprehensive but rather overwhelming factorial network depictions .The depictions in grouping  focused As a professional working in that field I do feel that often I’m bashing my head off a brick wall trying to get the message across, and experience quite a lot of frustration, hence the brick wall .A staff nurse’s depiction of a disappointed antibiotic superhero facing victorious germ protagonists can be seen as part of visual genre with extensive historical roots .The interview also provides insight into another practice tactic relating to Frame E:I mean the other thing that I try to do is try and play up the bad side of antibiotics … You’re saying ‘here’s something which has possibly got some benefit but has also got clear risks and they’re not just big world risks of antimicrobials, they’re risks to you that you’re not going to feel great if you start taking this antibiotic’. Then weigh up the balance then .Within the context of tackling AMR by reducing inappropriate prescription of antibiotics, this tactic can be understood as an example of ‘exnovation’, which The related interview captures an example of a routine related to Frame B that is innovative in terms of serving more than one purpose:Sometimes I park my car – there it is down the street. I need to sanitise my hands, put everything away, move onto the next house. The next house is going to have a whole new load of animals, and you know, I don’t mean germs in a rude way, but you know it is, everyone’s home is different and everyone has different things and if they want to give you a cup of tea and use their mug and so you are constantly being aware of potential for infection, I guess. And do your talking therapy work and then lots of other people in between .While the vast majority of activities are seen in terms of IPC practice, administration of the depot injection (Frame D) involves a drug known for neutropenic effects and the nurse sees this as having related IPC and AMR considerations .Certain patients I go to see that are on clozapine or other high dose antipsychotics that do frequently get prescribed antibiotics, you do need to be aware of physical health .The combination of visualisations and explanations here gives insights into how AMR is understood and contextualised within a broader topography of dynamic microbial and related IPC interactions involving humans, animals and diverse environments. This resonates with A contrasting context, the world of a recently qualified community mental health nurse, is depicted in The latter phenomenon is very much in evidence in the storyboard of an IPC consultant nurse, where role and setting diversity is the norm .Figure I’ve also put a yellow sticker which indicates that it’s about infection prevention and control and AMR, because for me hydration is vitally important for all of us … if we don’t look after ourselves, at the end of the day, we are also the patients. And we might end up being that little old lady who’s not got a good fluid intake habit developed over a lifetime and gets those unnecessary antibiotics .The same conception is evident in relation to hygiene:So, for me, IPC and AMR are really intertwined in ways that I think a lot of people don’t really see. So for example in the previous exercise one of the participants had a bucket and a mop to show a cleaner and chose to say that that was just an infection prevention and control intervention, but actually it does remain an AMR intervention because by cleaning you are keeping down the bioburden in that room and therefore the potential resistance, microbial resistance, to each other. The latter passage also shows the awareness amongst participants of differences in the way they are conceiving and acting in relation to AMR, and the mutual learning that took place within the workshops.This storyboard also highlights the tendency we found for IPC specialist nurses to see IPC as bound up with AMR, right through to the importance of staff self-hydration in counteracting AMR (see Frame C).The picture boards – allowing me to fully understand different roles and the implication of infection and AMR.This reflected a very strong generic theme in feedback, namely learning about the topic in detail and in overview through individual and group reflections:.Great to be able to take time out to think about the bigger picture, and put each person's role into overall contextNevertheless, the diversity of roles, contexts and experience also highlighted challenges:Interesting to observe the differences of understanding of AMR, antibiotics and antimicrobials in a group of health professionals – so much work to be done!The emphasis on visual aspects in the methods was very well received with many participants being agreeably surprised at their ability to successfully convey their thinking in these forms. Some adjustment was often necessary though:It took me a while to change my way of thinking from text to picture based.Comments from participants (via the anonymous feedback form) were all very positive in regard to the meaningfulness and value of the activities, with the storyboarding being seen as particularly useful:The workshop sought to use imaginative, generative methods to gain initial understandings of nurses’ conceptions of AMR and what it means to them within the context of their working lives, that is, nursing as is. Findings suggest a wide range in conceptions of AMR and in how it is seen  to be manifest in practice. For some staff  there often seemed no differentiation between IPC and AMR in the context of daily work; one was seen as very much bound up with the other. Other staff clearly differentiated the two in the context of specific work activities. In addition to perceived variations in conceptions of AMR within teams and across contexts, variations in the levels of priority being given to it were often highlighted. Seen through the lens of NPT , these fAt one level such findings may seem unremarkable – variation is a common feature of health services. However, as reflected in the integrative review by By helping to capture implicit conceptions, contexts, tactics, commonalities and differences in relation to AMR and IPC in the world of practice, these methods are useful in illuminating how people view and respond to an essentially invisible concept such as AMR. They are also productive in illuminating why they see it in a particular way. When the IPC consultant nurse argues that re-hydrating through a mid-morning drink see  is also The individual methods being used in the RIPEN study are not in themselves new. For instance, they share elements with experience-based co-design and participatory visualisation approaches. Rather, where some claim to innovation may be made is in the inter-disciplinary combinations of methods outlined above, and in their application to this particular context . Part of the value of an exploratory study such as this lies in trying and testing ideas and new combinations of methods in context.I’ve really enjoyed the creativity and the lateral thinking, and how to put science and art, joining the two together. They are seen as different but they can be very collaborative and informative. It’s been revelatory and enlightening in a lot of ways.Working with the arts based method really makes you think about what you are doing and probably challenges, maybe people like me who probably think about things in a kind of probably one-dimensional method whereas this has made me think on a much wider scale and really made me think about what we are going to do in future.Experiences of different practitioners very useful and allowed me to introduce new ways of working in regards to my practices in infection control areas.Indeed, as this paper goes to press and the workshop phases complete, more evidence of cumulative impacts for participants is emerging:In concluding, we believe that the initial phase of this study has yielded some novel insights into nurses’ thinking and practice in relation to AMR. These understandings provide a platform for the ongoing study to foster innovation in nursing practice, and in doing so to also encourage related development of arts and humanities approaches in research and education in this field. This study seeks to show that such approaches are relevant to nurses’ work in addressing AMR within a range of contexts; for nursing is diverse and lives across homes, hospitals and hinterlands, through hands and feet, and in hearts and heads where imagination and innovation can thrive given nourishment. As Rafferty urges in relation to these many facets of nursing, ‘let’s not get boxed in’ .Nursing has not leveraged its full potential to address antimicrobial resistance and it needs to facilitate nurses’ innovation in this area of practiceBy using arts and humanities approaches this research and development project aims to address these needsThe initial phase used primarily visual methods to explore how hospital and community nurses understand and respond to AMRNurses varied in their conceptualisations of AMR and in their practice responses to itThe potential for further co-design work to harness nurses’ creativity and imagination is highlighted


Personalized cancer therapy matches the plan of treatment with specific molecular alterations. This is the result of pivotal advances in our knowledge of tumor biology focused on the mechanisms underpinning uncontrolled tumor growth, evasion from host immune—surveillance, cell survival, migration, and the ever-increasing number of available targeted and immune therapies.BRAF, ERBB2 (HER2), MET, RET, NTRK and ROS1 in addition to EGFR and ALK analysis [Nowadays, next-generation sequencing (NGS) is widely used for the molecular profiling of solid tumors. Over the past few years, this multigene approach has proved to be winning over a single gene sequential strategy. In many academic centers, NGS can offer a comprehensive genomic profile (CGP) to identify all four classes of alterations  in hundreds of genes. The NGS approach is particularly useful in NSCLC, where the number of actionable gene alterations is high ,2 and thanalysis . Moreoveanalysis . Recentlanalysis . Howeveranalysis ,9.In this continuously evolving perspective, the implementation of large NGS panels is the most pragmatic way to investigate clinically useful molecular markers simultaneously. CGP can detect concurrent or uncommon alterations in frequently mutated genes, reflecting tumor genomic heterogeneity and complexity. Studies from different centers reported successful implementation of CGP ,11,12. IIn this scenario, a multidisciplinary molecular tumor board (MTB) represents a valuable tool to support patient’s physicians to foster precision medicine strategies. MTB is a dynamic team, gathering professionals from different specializations, including oncologists, pathologists, molecular biologists, geneticists, radiotherapists, surgeons, translational scientists, pharmacologists, and bioinformaticians. In these meetings, all the information regarding the patient’s history, imaging, histopathology, laboratory results, and CGP are summarized and reviewed. CGP results can also be evaluated by experts using cancer-related databases ,15,16. HTo overcome these gaps, dedicated artificial intelligence (AI) tools are necessary . AI offeThe implementation of MTB has been recently reported as a valuable clinical tool in different scenarios ,24,25,26In conclusion, objective data are consistent with the indication that patients with advanced/metastatic disease often have distinct molecular alterations that may require matched treatments rather than standard therapies derived from non-biomarker-based populations ,30. In t