Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.Pre‐emergence herbicides are taken up by seeds before germination and by roots, hypocotyls, cotyledons, coleoptiles or leaves before emergence, whereas post‐emergence herbicides are taken up primarily by foliage and stems. Most modern pre‐emergence herbicides are lipophilic, but post‐emergence herbicides may be lipophilic or hydrophilic. The metabolic conversion of herbicides to inactive or active metabolites after plant uptake is of major importance for some compound classes. Several herbicides are proherbicides as for example some acetyl‐coenzyme A carboxylase (ACCase)‐inhibitors. The physicochemical characteristics of proherbicides and herbicides are usually unrelated. A major role can be attributed to the site of action at a cellular level. A great number of herbicides such as photosystem II (PS II)‐inhibitors, protoporphyrinogen oxidase (PPO)‐inhibitors or carotenoid biosynthesis inhibitors require light for activity. Others, such as cellulose‐biosynthesis and mitotic inhibitors seem to be primarily active in belowground organs. Several lipophilic barriers against the uptake of xenobiotics exist in aboveground and belowground plant parts. The relevance of these barriers needs, however, further clarification. Uptake and translocation models are valuable tools for the explanation of the potential movement of compounds. Many factors other than uptake and translocation have, however, to be considered for the design of herbicides. For post‐emergence herbicides, ultraviolet (UV) light stability, stability in formulations, and mixability with other agrochemicals have to be kept in mind while, in addition to the aforementioned factors soil interaction plays a major role for pre‐emergence herbicides. In our opinion, general physicochemical characteristics of pre‐ or post‐emergence herbicides do, unfortunately not exist yet. © 2021 The Authors.  A high number of parameters influence the fate of herbicides. General characteristics of pre‐ or post‐emergence herbicides can therefore not be defined yet. Some herbicides are incorporated into the soil to guarantee a better performance, while others are applied banded in rows only or post‐directed using special nozzles. We concentrate here on planted, arable crops and analyze pre‐emergence and post‐emergence herbicides in row crops other than rice. Flooding of rice fields with water for example leads to the requirement of products with special characteristics. Weed spectra in rice and crop management are different compared with other field crops.In a strict sense, pre‐emergence herbicides are applied prior to the emergence of the crop or weeds.Pre‐emergence or soil‐applied herbicides play a major role in wide‐row crops where early competition of weeds may hamper crop yields. They are common weed management tools in crops such as maize, soybean, cotton and sugar cane in many parts of the world.Another reason for their use is that farmers often face a heavy workload in the beginning of cropping seasons. They are forced to control weeds in different crops planted at around the same time such as maize and soybean in the United States. Pre‐emergence herbicides allow farmers to split their weed control management measures ideally across crops. They apply pre‐emergence herbicides primarily in maize and post‐emergence herbicides in soybeans later as soybeans tolerate some early weed competition better than maize. The wide range of soybean planting dates, row width in corn and herbicide resistance also contribute to this preference.Finally, pre‐emergence herbicides provide tools for the avoidance of weed resistance risks as they often inhibit different target sites compared with post‐emergence products. All these properties make them widespread tools.Post‐emergence herbicides are sprayed on the foliage of crops and weeds. They either control escapees of pre‐emergence applications and/or they are applied several times for the control of sequential flushes of emerging weeds. An important barrier for post‐emergence herbicides is the leaf cuticle with waxes and very long‐chain fatty acid (VLCFA) derivatives Fig. .Herbicides are often distinguished by their mobility within plants. They are either classified as contact herbicides or as systemic herbicides.Pre‐ and post‐emergence herbicides are usually mixed with other herbicides as single components, and they usually do not control the full potential weed spectrum. For post‐emergence herbicides, timing is critical. In principle, however, most products perform best against young weed stages, usually in the two to four‐leaf stage. Later stages often require either higher dose rates or weeds are not sufficiently controlled. Weed management guidelines are provided by extension services in many countries around the globe. We mention here just two examples in the United States which highlight best timings for farmers in Michigan, Ohio, Indiana and Illinois.Single herbicide applications for weed control will usually not provide sufficient weed control. Instead, sequential treatments of pre‐ and post‐emergence herbicides are common for the management of weeds germinating in flushes.Herbicides are not developed by an academic process which allows the distinction between their pre‐ or post‐emergence characteristics. Companies want to recover their expenses and they can only develop molecules which can be registered. When scientists tried to find common physicochemical principles of marketed herbicides in the past based on physicochemical parameters, they often ignored this fact. A major factor for the selection of herbicides is their marketability and acceptance by the customer. They may have some properties in common but not as a result of their initial design as pre‐ or post‐emergence herbicides in the first instance.Based on different sources, we estimate that the global market size for agrochemicals in the year 2019 was approximately 59 billion US$. The herbicide market that year amounted to approximately 30 billion US$. Neglecting all problems with an exact definition of the market for soil‐applied, pre‐emergence and pre‐plant herbicides and the possibility to use some compounds both pre‐ and post‐emergent and the special situation with rice herbicides, we assume a global turnover of soil‐applied herbicides at approximately 10 billion US$ and for post‐emergence products at 20 billion US$.The growth of the post‐emergence segment in recent decades was driven by the use of glyphosate in transgenic crops . The overall increase of herbicide resistance has triggered the need to return to more complex weed management systems using a sequence of pre‐emergence followed by post‐emergence products, often used in combinations.Some chemical classes are ideally suited for pre‐plant, pre‐plant incorporated, or pre‐emergence control. Examples are the chloroacetamides, thiocarbamates and dinitroanilines. The reason of their economic success over many decades has or had to do with the weed spectrum controlled, such as grass weeds in grass crops. Other compound classes are mostly or entirely post‐emergence compounds. Examples are acetyl‐coenzyme A carboxylase (ACCase) inhibitors such as aryloxyphenoxypropanoates and cyclohexanediones, auxins such as aryloxyalkanoic acid derivatives or the 5‐enolpyruvylshikimate‐3‐phophate synthase (EPSPS)‐inhibitor glyphosate. There are, however, compound classes as for example heterocycles in the group of hydroxyphenylpyruvate dioxygenase (HPPD)‐inhibitors from which both pre‐emergence and post‐emergence herbicides are in the market. Moreover, compounds from different compound classes such as atrazine, chlorsulfuron or mesotrione display pre‐ as well as post‐emergence activity at the same time.Commercial aspectsThe development of herbicides is not driven by the directed design of herbicides as pre‐ or post‐emergence products. Companies have to recover their expenses. This means the potential performance of a compound in the market, whether pre‐ or post‐emergence or both, is the actual driving force.Regulatory aspectsPhysicochemical parameters have to be determined as regulatory requirements, primarily for the avoidance of environmental risks. Common properties between herbicides result more from this reason rather than from the application timing.Which physicochemical parameters favor the product uptake by the weed?Herbicides have to overcome different lipophilic plant barriers. As a consequence, one would assume that only herbicides with lipophilic properties can penetrate these barriers. Some herbicides are, however, quite hydrophilic.Which factors enable the transport of an herbicide to its site of action?Uptake is the first step of a compound to its target. It has to be translocated within the plant, also.Links between mode of action (MoA) and application timingApparently, some compound classes are more suitable for soil application, others as foliar products. This fact can be correlated to some extent with some molecular MoAs.Synthetic approaches for the improvement of activityChemists are interested in guidelines for chemical synthesis. We will try to answer whether simple physicochemical rules can improve the performance of soil or foliar herbicides.This review addresses six different issues and objectives:2Costs for the development of new herbicide actives without crop tolerance traits amounted to approximately 340 million US$ in the year 2014.3Ka‐value, octanol–water partition coefficient (log Poct or just log P), binding to soil particles (KOC) and dissipation half‐life time (DT50) in soil. An easily understandable overview regarding the role of different parameters in soil–herbicide interactions was compiled by Ross and Lembi.In parallel to early toxicity tests and to early environmental fate studies, potential groundwater leaching has to be evaluated with very early screening tests. In consequence, basic parameters are checked for active molecules at early screening stages: water solubility, phttps://www.pesticidemodels.eu/pearl/pearl-eu-leaching-soil-exposure) provide early indications of groundwater leaching risks.These factors are especially important for pre‐emergence herbicides, as these are applied early in the season when temperatures are often relatively low and when microbial soil degradation is delayed. When pre‐emergence compounds are applied, most of the active compound will hit bare soil. Model calculations with programs such as PEARL (P < 2 and an effective applied dose rate below 200 g active ingredient (a.i.) ha−1.Companies screening for herbicides have set early targets for the selection of compounds in order to exclude compounds with ground water leaching risks. Ideally, a new compound should show soil degradation half‐life times < 90 days, a log Each factor can, however, influence the environmental fate of a compound. Lower dose rates and lower half‐life times will have positive effects. Each compound requires a specific analysis.Preliminary data under laboratory conditions with one or two soil types will never be sufficient. Actual registration trials have to run with many soil types and in a variety of sophisticated tests. High vapor pressures/volatility are not desired from a registration and an application view.As stressed earlier, weeds can germinate over a long time period. This is why residual activity of a pre‐emergence herbicide is desired by the farmer. However, the molecule must not have a too high half‐life time as this may result in a potential groundwater contamination or in a carry‐over and selectivity problem in the following crop.Also, the pre‐emergence molecule has to reach underground seedling parts. In order to be adequately distributed in the soil – vertically and horizontally, some water solubility is required. Crop selectivity is sometimes dependent on positional effects and by the mobility of herbicides in soil. Roots of weed seedlings from a shallow depth may be affected, whereas crop seeds germinating from deeper soil areas are not. This kind of selectivity leads, however, to application restrictions, as, for example, is the case for metribuzin in sandy soils.As a result, all the regulatory requirements and the contrasting characteristics of a perfect soil herbicide make it quite difficult to develop a molecule for pre‐emergence applications. However, it is often possible to leverage a degree of placement selectivity with soil‐applied herbicides across a wider range of crops. Foliar herbicides, in contrast, are dependent on metabolic or target based selectivity which may be limited to specific crops only.In general, the requested parameters for post‐emergence compounds are very similar to those of pre‐emergence herbicides. The probability of a post‐applied herbicide to hit bare soil is, however, much lower, especially in areas where the vegetation allows a fast development of crops and weeds. Temperatures in spring crops are usually higher when post‐emergence compounds are applied and favor soil metabolism and degradation of a chemical. These factors usually lead to reduced leaching risks. However, some residual soil activity is desirable for post‐emergence herbicides as well in order to control later weed flushes. Drift risks into adjacent sensitive crops and ecosystems may, however, be higher for post‐emergence products. Some of the earlier‐mentioned physicochemical parameters serve many purposes and also play a role in herbicide uptake models.4The performance of an herbicide depends to a great extent on its uptake by weeds. Pre‐emergence herbicides are taken up by seeds before germination, by roots, by hypocotyls, by cotyledons (dicots), by the coleoptile (grasses) or by leaves Fig. .TrappCompounds entering the root have to pass several cell layers before they can be transported by the vascular tissue Fig. . The rhiIt is questionable from our point of view if lignin can prevent the apoplastic penetration of water and solutes into the root at all as herbicides are apparently easily transported through several layers with lignified cell walls as part of the apoplast.The apoplast is the extracellular, supposedly dead part of the plant, and it includes the cell walls. The composition, the pH in the apoplast and the cellulose architecture stay, however, under the control of the symplast. Solutes can be transported in the apoplast, the symplast and along transmembrane pathways.In two cell layers of the cortex, cell wall modifications can be found which were assumed to control the apoplastic transport of water and solutes: the Casparian strip or band. Casparian strips also occur in the endodermis, at the border between cortex and vascular bundle cylinder of many weed rhizomes Fig. .et al.Briza media L. roots.Near the end of the last century, Perumalla 4.1P > 1.5 for the optimum uptake of compounds by barley roots. Unfortunately, this figure is often extrapolated into other plant species. It is obvious that root endodermis characteristics can differ considerably for different species.Root uptake was analyzed in the past with radiolabeled substances or with specifically designed trials guaranteeing root uptake only. Briggs and BromilowDespite the lipophilic character of the endodermis and the high lipophilicity of many pre‐emergence herbicides, it was documented many decades ago that highly water‐soluble compounds can also be taken up by roots. This was either directly demonstrated with radiolabeled material as in the case of picloramArabidopsis thaliana (L.) Heynh. mutants led to new insights into the composition of the endodermis. In the past, various components were claimed to form the Casparian strip as the major barrier for the translocation of xenobiotics into the root: suberin, lignin, lignin‐like polymers, or all of them. Naseer et al.Arabidopsis. It has to be shown if this is true for other species as well.Recent investigations of genes involved in the endodermis formation of Arabidopsis roots improved our understanding of sequential developental stages.P‐values of known herbicides often leads to the assumption that lipophilic barriers prevent the apoplastic transport of water and xenobiotics as many soil herbicides are characterized by high log P‐values. Apoplastic, lipophilic barriers do, however, not exist at early growth stages of roots. And in fact, several highly hydrophilic herbicides are easily taken up by roots. Examples are several acetolactate synthase (ALS)‐inhibiting and auxinic herbicides. Even compounds which are regarded as ideal post‐emergence herbicides such as glyphosateThe consistency of the endodermis has to be discussed in this context also. It differentiates with the age of roots from the tip to its base. Von GuttenbergFor acropetally translocated herbicides, the actual barrier from the apoplast to the symplast within stems is, however, the plasmalemma of cortical transfer cells next to the xylem.These are quite frequent in stem nodal regions.As shown in the beginning of this section, compounds can also be taken up by the hypocotyl of dicots, by cotyledons or by early stem and leaf stages. In all these cases, an epidermis and parenchymatic cells have to be penetrated before a compound can enter the vascular bundle system.Particularly striking and of paramount significance for soil‐applied herbicides is the non‐aerial and less so the aerial hypocotyl as shown for example for mung bean and canola and the coleoptile of grasses.Different methods were employed to understand the uptake process including osmotic measurements, studies on the impact of pH, staining with cationic and anionic dyes, radiolabeled tracer studies and here also measurement of the size selectivity of penetration.In monocots, the stem differentiates usually relatively late compared to dicots. The first monocot structures developing opposite the root are in some species the mesocotyl and in all species the coleoptile. The young stem of most monocots is enclosed by leaves for many days after germination. The uptake of soil herbicides by the coleoptile of monocots was demonstrated by selectively applied or by radiolabeled molecules in the last decades of the twentieth century. Jablonkai and DutkaPost‐emergence herbicides enter plants primarily via leaves. The surface of plant organs is covered by structures which function as the first barriers to many xenobiotics Fig. . The mesSome post‐emergence herbicides such as glyphosate and glufosinate are, however, quite hydrophilic. Compared to most other post‐emergence herbicides, glyphosate is not taken up very well, however, once taken up it is readily translocated and is very effective. Some authors claim that there are two paths through the cuticle, one for polar and one for lipophilic compounds.Formulation chemists have used isolated plant cuticles for penetration studies and have shown that these cuticles can be penetrated. Temperature and solute characteristics play a role when characterizing permeability.l.We will highlight now a few major aspects of foliar agrochemical penetration, for more detailed overview publications we refer for biology to Hull,Foliar penetration via the cuticle is typically better with young leaves or fruits, or generally growing plants, at higher temperatures and very high relative humidity, for herbicides with low molecular weight, more linear structure, low melting point, and a high water‐solubility if at the same time molecular weight is low as well. Before cuticle absorption and migration of solutes can start, effective spray retention and coverage is needed particularly for difficult to wet crop and weed surfaces.The surface or epicuticular waxes does not play a role for foliar penetration beyond the effective area that might be reduced due to diffusion impedance of crystals.Highly relevant for good bioavailability of foliar applied herbicides is the molecular size of the solute as there is an exponential dependence of mobility in the cuticle on molar volume which itself follows for a given species and temperature in a log‐normal distribution.−1) or dicamba (221.04 g mol−1) and the high molecular weight herbicides like tembotrione (440.8 g mol−1) or saflufenacil (500.85 g mol−1). The former small ones are very mobile and volatile and do not need penetration enhancers or other adjuvants, while the large molecules need methylated seed oil‐based tankmix adjuvants or other emulsifiers or suitable penetration enhancers to act at commercially reasonable use rates.This exponential relationship was found over the entire range of foliar‐applied herbicides for all plant species studied, but the absolute permeability among species can vary by more than one order of magnitude for the speed of penetration.5Penetration alone will, however, not necessarily guarantee the successful movement of the herbicide to its molecular target. As Caseley and WalkernAChR) competitive modulatorsEntomologists and mycologists regard compounds taken up by the roots and transported by the xylem as systemic besides those transported in the phloem. This can be exemplified by the latest generation of fluorinated nicotinic acetylcholine receptor  or quackgrass (Elymus repens). Pre‐emergence products with acropetal translocation alone will usually not provide an acceptable control of perennial weeds. Most weed scientists do therefore not follow the systemicity definition of insecticide and fungicide scientists. For them, only basipetally translocated herbicides are true systemic herbicides.Several pre‐emergence herbicides are taken up by roots and are transported acropetally within the xylem before they hit their targets. Examples are molecular targets within chloroplasts such as photosystem II (PS II), protoporphyrinogen oxidase (PPO) or any of several carotenoid biosynthesis enzymes, such as phytoene desaturase (PDS). In contrast, many post‐emergence herbicides have to be transported basipetally within phloem sieve tubes for the most efficient control of weeds. This is especially needed for herbcides to control perennial weeds which spread with underground propagules such as Johnsongrass , and an increasing number of rotatable bonds. This may reflect the efforts of exploring hydrophobic interactions and different target‐binding structures for enhanced activities. Finally, by physicochemical profiling of launched products, the number and types of new lead structures in crop protection have significantly increased.The knowledge of physicochemical properties of agrochemical products is helpful for the design and selection of libraries in combinatorial chemistry, high‐throughput (HT) and virtual screening technologies or for discovering novel agrochemical lead structures. The trend of physicochemical data of active ingredients has shown, that there is an increasing percentage of agrochemical products with higher hydrophobicity  light stability


Evidence‐informed practice versus evidence‐based practice educational interventions for improving knowledge, attitudes, understanding, and behavior toward the application of evidence into practice: A comprehensive systematic review of undergraduate health and social care students.11.1Over the past three decades, there has been increasing attention on improving healthcare quality, reliability, and ultimately, patient outcomes, through the provision of healthcare that that is influenced by the best available evidence, and devoid of rituals and tradition  created by Tilson et al., Knowledge: Knowledge refers to learners’ retention of facts and concepts about evidence‐informed practice and evidence‐based practice. Hence, assessments of evidence‐informed practice and evidence‐based practice knowledge might assess a learner's ability to define evidence‐based practice and evidence‐informed practice concepts, list their basic principles or describe levels of evidence.Attitudes: attitudes refer to the values ascribed by the learner to the importance and usefulness of evidence‐informed practice and evidence‐based practice to inform clinical decision‐making.Understanding: understanding refers to learners’ comprehension of facts and concepts about evidence‐based practice and evidence‐informed practice.Behavior: Behavior refers to what learners actually do in practice. It is inclusive of all the processes that a learner would use in the implementation of evidence‐informed practice and evidence‐based practice, such as assessing patient circumstances, values, preferences, and goals along with identifying the learners’ own competence relative to the patient's needs in order to determine the focus of an answerable question.The mode of delivery of the educational program could be in the form of workshops, seminars, conferences, journal clubs and lectures (both face‐to‐face and online). The content, manner of delivery and length of the educational program may differ in each of the studies to be included as there is no standard evidence‐informed practice/evidence‐based practice educational program.In this systematic review, evidence‐informed practice and evidence‐based practice educational interventions that are targeted towards health and social care postgraduate students or registered health and social care practitioners will be excluded. Comparison interventions will include educational interventions that do not advance the teaching of the principles and processes of evidence‐informed practice and/or evidence‐based practice in healthcare or no intervention.1.3Most efforts to apply evidence into healthcare practice have either been unsuccessful or partially successful  between evidence‐informed practice and evidence‐based practice educational interventions?2.Does participating in evidence‐informed practice versus evidence‐based practice educational interventions facilitate the use of evidence in practice ?3.Do both evidence‐informed practice and evidence‐based practice educational interventions targeted at undergraduate health and social care students affect patient outcomes ?4.What factors affect the impact of evidence‐informed practice and evidence‐based practice educational interventions ?Specifically, this systematic review will answer the following questions:33.13.1.1Based on the objectives and the outcome criteria for this systematic review, it is anticipated that relevant studies will have employed not only quantitative methodologies but also qualitative methods. Thus, this systematic review proposes to include both qualitative and quantitative research articles (a mixed‐methods systematic review).Specifically, this systematic review will follow the recommended steps by Sandelowski, Leeman, Knafl, & Crandel, We will then integrate the results of the two separate syntheses by means of an aggregative mixed‐methods synthesis. The two results  will be integrated by translating findings from the quantitative synthesis into qualitative statements, by use of Bayesian conversion. This is because, it is preferable to translate quantitative synthesis into qualitative statements, than it is from qualitative to quantitative, as problems may arise when converting verbal accounts  into numbers or quantities JBI . Figure 1.Randomized controlled trial: Studies in which participants are randomly assigned to intervention and comparison conditions.2.Quasi‐randomised controlled trials: Studies where participants are assigned to intervention and comparison conditions through a quasi‐random approach, such as birth date, students identification number, or date of the week.3.Quasi‐experimental controlled trial with individual level matching: Studies where participants in the intervention and comparison conditions are assigned to conditions in a non‐random manner . For such studies where participants in the intervention and comparison groups are not matched, there must be enough statistical information to enable us to evaluate pre‐test effect sizes for at least one outcome measure.4.Quasi‐experimental controlled trial with pre‐test adjusted outcomes: Studies where the intervention and comparison conditions are assigned in a non‐random manner, but the pretest differences between groups have been adjusted by the study authors. Examples include pre‐test‐adjusted post‐test means and regression coefficients from models that adjust for pretest.5.Quasi‐experimental controlled trial with pretest data: Studies where the intervention and comparison conditions are assigned in a non‐random manner, but pre‐test data are available for each outcome. For such studies, pre‐test data must be reported in a form that allows evaluation of the initial equivalence of the intervention and control groups on those variables through the calculation of effect size. For those outcomes with inapplicable pre‐test data, data for a close proxy of a pre‐test must be available.To be eligible for inclusion in this review, study designs must meet at least one of the following criteria:Other studies to be eligible for inclusion include, before and after studies, prospective and retrospective cohort studies. In order to determine the effects of the educational intervention, the comparison groups that were used in included studies will be analysed. Additionally, primary studies that use descriptive epidemiological study designs will be eligible for inclusion in this systematic review. Examples of eligible epidemiological study designs will include case series, individual case reports, and descriptive cross‐sectional studies, focus groups, and interviews. Furthermore, other qualitative approaches such as ethnography, phenomenology, and grounded theory will be eligible for inclusion. Biases and limitations associated with any included study design will be discussed in relation to the impact it may have on the effectiveness of the intervention.3.1.2To be eligible for inclusion in this systematic review, primary studies must include, as participants, undergraduate pre‐registered health and social care students in higher education (University) from any geographical area. Studies whose participants, are registered health and social care practitioners and postgraduate students will be excluded from this review.3.1.3In this systematic review, we will include studies that evaluate any formal evidence‐based practice and/or evidence‐informed practice educational interventions aimed at improving undergraduate pre‐registered health and social care students’ knowledge, attitudes, understanding and behavior in the application of evidence into healthcare practice. These two interventions will then be compared to determine whether the two concepts act together or individually to facilitate the application of evidence into practice. If it emerges that the two concepts act individually, we will determine which of them better facilitates the effective application of evidence into practice.As mentioned above, in this systematic review, an evidence‐informed practice educational intervention refers to any formal educational program that facilitates the application of but not limited to the evidence‐informed practice model described by McSherry, The mode of delivery of the educational programs may be in the form of workshops, seminars, conferences, journal clubs and lectures (both face‐to‐face and online). The content, manner of delivery and length of the educational program may differ in each of the studies to be included as there is no standard evidence‐informed practice/evidence‐based practice educational program.Evidence‐informed practice and evidence‐based practice educational interventions that are targeted at health and social care postgraduate students or registered health and social care practitioners will be excluded. Comparison conditions will include educational interventions that do not advance the teaching of the principles and processes of evidence‐informed practice and/or evidence‐based practice in healthcare.3.1.41.Participants’ knowledge about evidence‐informed practice and/or evidence‐based practice.2.Participants’ understanding of evidence‐informed practice and/or evidence‐based practice.3.Participants’ attitudes towards evidence‐informed practice and/or evidence‐based practice.4.Participants’ behavior towards evidence‐informed practice and evidence‐based practice.In this systematic review, studies will be eligible for inclusion if they address at least one of the following constructs as an outcome:As previously stated, the above constructs  will be defined based on the Classification Rubric for Evidence‐based practice Assessment Tools in Education (CREATE), developed by Tilson et al., Knowledge: Knowledge will refer to learners’ retention of facts and concepts about evidence‐informed practice and evidence‐based practice. Hence, assessment of evidence‐informed practice and evidence‐based practice knowledge might assess a learner's ability to define evidence‐based practice and evidence‐informed practice concepts, list their basic principles or describe the levels of evidence.Attitudes: attitudes will refer to the values ascribed by the learner to the importance and usefulness of evidence‐informed practice and evidence‐based practice to inform clinical decision‐making.Understanding: understanding will refer to learners’ comprehension of the facts and concepts relating to evidence‐based practice and evidence‐informed practice.Behavior: Behavior will refer to what learners actually do in practice. It is inclusive of all the processes that a learner uses in the application of evidence‐informed practice and evidence‐based practice, such as assessing the scientific evidence, patient circumstances, values, preferences, and goals along with identifying the learners’ own competence relative to the patient's needs in order to determine the focus of an answerable question.1.The use of a standardized questionnaire to evaluate knowledge, attitude, understanding, and behavior towards the application of evidence into practice. Examples of such questionnaires include, but are not limited to the Evidence‐Based Practice Belief (EBPB) Scale and Evidence‐Based Practice Implementation (EBPI) scales developed by Melnyk, Fineout‐Overholt, & May, 2.Examples of unstandardized instruments include, but are not limited to self‐reports from participants and researcher administered measures.Measurement of the above outcomes may be conducted using standardized or unstandardized instruments. This is because, to the researchers’ knowledge, there is no uniform tool for evaluating evidence‐based practice and evidence‐informed practice educational interventions. Specific measures will include, but not be limited to:Studies that measure the impact of evidence‐informed practice and/or evidence‐based practice educational programs on patient outcomes will be included.Examples of patient outcome indicators to be assessed include; user experience, length of hospital stay, nosocomial infections, patient and health practitioner satisfaction, mortality, and morbidity rates.In the current systematic review, no limit will be placed on the duration of the follow‐up. The rationale is to give room for studies with either short or long term follow‐up duration to be eligible for inclusion.This systematic review will include primary studies from any geographical area. However, due to language translation issues, only studies written in English will be included. Studies whose title and abstracts are in English and meet the inclusion criteria, but the full article is reported in another language will be included subject to the availability of translation services.To qualify for inclusion in the current systematic review, studies must have been published during the period from 1996 (the date when evidence‐based practice first emerged in the literature) , wildcards (such as?), phrase operators , and truncations (including *). This will be done in order to ensure search precision and sensitivity. In addition, our search strategy will have three sets of terms: the population, intervention(s) and outcomes. We will use limiting commands to narrow the results by dates, language, and type of study design. Below are examples of anticipated search terms to be used:3.2.2We will import the full set of search results directly into an Endnote Library. Where this is not possible, we will manually enter search results into the Endnote Library. An Endnote library will make it easier to identify duplicates and to manage references.3.2.3The current systematic review will utilize six strategies, in order to identify published and unpublished studies that meet the inclusion criteria described above. These strategies have been outlined below.1.Academic search completeAcademic search premierAMEDAustralian education indexBritish education indexCampbell systematic reviewsCanada bibliographic database (CBCA Education)CINAHLCochrane LibraryDatabase of Abstracts of Reviews on EffectivenessDissertation Abstracts InternationalEducation AbstractsEducation completeEducation full text: WilsonERICEvidence‐based program databaseJBI database of systematic reviewsMedlinePsycInfoPubmedSciELO (Scientific Electronic Library Online)ScopusElectronic Database Search2.GoogleGoogle scholarA web search using search engines3.OpenGrey (System for Information on Grey Literature in Europe)System for information on Grey LiteratureThe Society for Research on Educational EffectivenessVirginia Henderson Global Nursing e‐RepositoryGrey literature searchAppendix 3 illustrates the search strategy for the MEDLINE database searched on the EBSCOhost platform. We will modify the search terms and strategies for the different databases.4.Hand searching. The table of content of at least three journals that published most of the studies identified as eligible for inclusion will be hand searched for additional relevant studies. Examples include Worldviews on Evidence‐Based Nursing Journal, British Medical Journal, and the British Journal of Social Work.5.Track bibliographies of previously retrieved studies and literature reviews. The reference list of previously conducted systematic reviews, meta‐analysis and primary studies will be screened for other relevant studies.6.Contact leading authors. The Corresponding authors of identified eligible abstracts, whose full texts are unavailable will be contacted to request for full‐text reports.3.33.3.1The search output will be screened by two independent authors (either EAK and JBS or SH and RM) for relevant studies. The title and abstract of search output will first be screened, followed by the full text of articles with a seemingly relevant abstract. These articles will be assessed for eligibility using the pre‐specified inclusion and exclusion criteria. Studies that meet the inclusion criteria will be assessed independently by two authors (either SH and RM or EAK and JBS) for methodological validity using standardized critical appraisal instruments from the Joanna Briggs Institute Meta‐Analysis of Statistics Assessment and Review Instrument (JBI‐MAStARI). This includes, but not limited to, the JBI‐MAStARI checklist for case‐control studies, checklist for case reports, checklist for cohort studies, checklist for quasi‐experimental, checklist for randomised controlled trials, and checklist for analytical cross‐sectional studies. Any disagreements that may arise between authors will be resolved through discussion, if no agreement can be reached, a third author will be consulted.3.3.2Data will be extracted from included papers using standardized data extraction tools from JBI‐MAStARI. Information to be extracted from quantitative studies will include study design, interventions, population, outcomes of significance to the review questions, and specific objectives. See Appendix 1 for the quantitative data extraction form. While this form is currently generic, we will include information specific to this systematic review, such as methods used to assess the impact of evidence‐informed practice/evidence‐based practice educational interventions on patient outcomes.3.3.3For each included study, two authors (either RM and SH or EAK and JBS) will independently assess the risk of bias. This will be done using the Cochrane Collaboration's Risk of Bias tool , no attrition, or no evidence of differential attrition will be considered as having a low risk of bias. Use of Intention to Treat (ITT) analysis and methods of account for missing data  will be recorded.We will assess studies for reporting bias to determine whether there are inconsistencies between measured outcomes and reported outcomes. Studies will be considered as having a low risk of bias if the results section of publications clearly show that all pre‐specified outcomes are reported.3.3.4For continuous data, where outcomes on the same scale are presented, we will use mean difference, with 95% confidence interval. However, where outcome measures differ between studies, we will use the standardized mean difference as the effect size metric based on Hedges’ g, which is calculated using the formula below:SMD = Difference in mean outcome between groups ⁄ Standard deviation of outcome among participants.For dichotomous data, we will calculate the risk ratio  for the occurrence of an event. For the purpose of meta‐analysis, we will convert risk ratios to the standardized mean difference, by the use of David Wilson's practical effect size calculator.Meta‐regression will be used to assess the impact of moderator variables on the intervention effect size. Moderator analysis will be conducted if a reasonable number of eligible research articles are identified and if the required data is presented in the report.For studies with one control group versus two or more intervention groups, and all the interventions are regarded as relevant to the study, the following options will be used: 1) if the intervention groups are not similar, the sample size of the control group will be divided into two (or more based on the number of intervention groups) and will then be compared with the intervention groups 2) if the intervention groups are similar, the two groups will be treated as a single group. Therefore, two effect size estimates will be provided in this study. This is to ensure that participants in the control group are not “double counted”  of participants as units of analysis. In the event that cluster‐randomised trials  are identified as eligible, we will use standard conversion criteria as recommended in the Cochrane Handbook , data analysis will be conducted based on the available data.However, if data is missing not at random , we will impute the missing data with replacement values, and treat these values as if they were observed .3.3.7Heterogeneity will be assessed through the comparison of factors such as participant demographics, type of intervention, type of control comparators and outcome measures. Heterogeneity will be assessed and reported visually and by examining the I² statistic, which describes the approximate proportion of variation that is due to heterogeneity rather than sampling error. This will be supplemented by the Chi² test, where a P value < 0.05 indicates heterogeneity of intervention effects. In addition, we will estimate and present Tau², along with its CIs, as an estimate of the magnitude of variation between studies. This will provide an estimate of the amount of between‐study variation.3.3.8We will assess studies for reporting bias to determine whether there are inconsistencies between measured outcomes and reported outcomes. Studies will be considered as having a low risk of bias if the results section of publications clearly show that all pre‐specified outcomes are reported.3.3.9Narrative and statistical methods will be used to synthesise included studies. Data synthesis will be focused on calculating the effect sizes of the included studies. We will conduct meta‐analysis if our search yields sufficient (i.e. 2 or more) eligible studies that can be grouped together satisfactorily. A logical approach will be used when combining studies in meta‐analysis. Decisions on combining studies in meta‐analysis will be based on two reasons: 1) a sufficient number of eligible studies with similar characteristics 2) similar characteristics shared by those eligible studies may include the type of intervention and the targeted outcome of the intervention. Where a meta‐analysis is being conducted, we will employ the Comprehensive Meta‐analysis Software developed by Borentein, Hedges, Higgins, & Rothstein, 3.3.10Heterogeneity will be assessed through the comparison of factors such as participant demographics, type of intervention, type of control comparators and outcome measures. Heterogeneity will be assessed and reported visually and by examining the I² statistic, which describes the approximate proportion of variation that is due to heterogeneity rather than sampling error. This will be supplemented by the Chi² test, where a P value < 0.05 indicates heterogeneity of intervention effects. In addition, we will estimate and present Tau², along with its CIs, as an estimate of the magnitude of variation between studies. This will provide an estimate of the amount of between‐study variation. Sensitivity and subgroup analyses will also be used to investigate possible sources of heterogeneity.3.3.111.Unpublished studies2.Studies with outlier effect sizes3.Studies with a high risk of bias4.Studies with missing information  for methodological validity using the JBI Qualitative Assessment and Review Instrument (JBI‐QARI). Any disagreements that may arise between authors will be resolved through discussion, if no agreement could be reached, a third author will be consulted.Data will be extracted from included papers using standardized data extraction tool, namely, the JBI‐QARI. Information to be extracted from qualitative studies will include population, study methods, details about the phenomena of interest, outcomes of significance to the review question and specific objectives. See Appendix 2 for the qualitative data extraction form. While this form is currently generic, we will include information specific to this systematic review, such as methods used to assess the impact of evidence‐informed practice/evidence‐based practice educational interventions on patient outcomes.Where possible, qualitative research findings will be pooled using JBI‐QARI. This will involve the synthesis or aggregation of findings to generate a set of statements that represent that aggregation. Findings will be assembled based on their quality, and also, by grouping findings with similar meanings together. We will then perform a meta‐synthesis of these groups or categories so as to produce a single set of comprehensive synthesized findings. In the event that textual pooling is not possible, we will present findings in narrative form.Finally, results from both the quantitative review and the qualitative review will be integrated using the JBI Mixed Methods Aggregation Instrument (MMARI). The integration will be achieved by translating findings from the quantitative review into qualitative results through the use of Bayesian conversion to generate synthesized results.44.1Results of the searchIncluded studiesExcluded studies4.2Allocation (selection bias)Blinding (performance bias and detection bias)Incomplete outcome data (attrition bias)Selective reporting (reporting bias)Other potential sources of biasEffects of interventions5Summary of main resultsOverall completeness and applicability of evidenceQuality of the evidencePotential biases in the review processAgreements and disagreements with other studies or reviewsImplications for practiceImplications for research1.Content and Systematic Review methodology: Ms. Elizabeth Adjoa Kumah is a registered general nurse who has worked mainly in the critical care setting as a nurse supervisor and patient advocate. She has been actively engaged in teaching healthcare students in the clinical setting and serving as a mentor. She is currently pursuing a Ph.D. Health program, with evidence‐informed practice educational interventions as the area of research focus. She brings knowledge about the content both in terms of teaching healthcare students about the application of research evidence into practice and theoretically for improving knowledge of evidence‐informed practice and how it enhances evidence‐based practice skills, attitude and behavior in the educational setting. Elizabeth is passionate about improving the standard of patient care and patient outcome, which she believes could be achieved by the effective and consistent implementation of evidence‐informed practice. She will also help with the methodological aspects of the systematic review.2.Content and Systematic review methods: Professor Robert McSherry will bring both methodological as well as content expertise relating to evidence‐informed practice and the development of teaching programs to the team. His area of expertise is around the evidence‐informed practice, patient safety, quality and clinical governance using practice development. Practice development is about promoting person‐centred care and approaches, which Rob has integrated effectively within both educational and research programs. He is the co‐author of a book on systematic reviews and has over thirty years’ experience as a registered general nurse. Robs educational and professional expertise has been recognized and rewarded internationally and nationally. He was awarded the highly prestigious National Teaching Fellow award in the UK in 2011.3.Content and systematic review methods: Dr. Josette Bettany‐Saltikov will bring significant expertise in Systematic review methods and content to this systematic review, both in terms of knowledge about evidence‐based practice and knowledge about developing educational programs. She has taught systematic review methods to university students at all levels for over 15 years. She has also published a book on how to conduct a systematic review and has been involved in three Cochrane reviews, one of which she led. She has authored a number of systematic reviews on diverse topics published in other journals and has significant experience of developing educational programmes from her teaching experience as a university Senior lecturer for 23 years.4.Content and systematic review methods: Professor Sharon Hamilton will bring expertise in systematic reviewing. She is the director of the Teesside Centre for Evidence‐Informed Practice: A Joanna Briggs Institute Centre of Excellence, and has conducted a number of qualitative and quantitative reviews. Sharon is a registered nurse and has research expertise in the evaluation of clinical interventions.5.Information retrieval: Mrs. Julie Hogg brings Information retrieval expertise to the team. Julie is an Academic Librarian at Teesside University and will carry out a thorough and systematic search of the literature.6.Statistical analysis: Mrs. Vicki Whittaker is a very experienced statistician with over 18 years of experience in teaching and advising students and academics on their research projects and clinical trials. She has been involved in data analysis and meta‐analysis of numerous research projects and systematic reviews.The review team declares no potential conflicts of interest.Characteristics of studiesCharacteristics of included studiesCharacteristics of excluded studiesCharacteristics of studies awaiting classificationCharacteristics of ongoing studiesExcluded studiesStudies awaiting classificationOngoing studiesTeesside University, UKThis review forms part of a Ph.D. programme, which is supported and funded by Teesside University, Middlesbrough.No sources of support providedFeedback


Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case–control design. We employed several approaches to statistically analyze the data and the methods indicated that the case–control differences were subtle but most distinguishable in metastatic case–control pairs in the period 0–3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case–control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes. In Norway, there were about 3200 new cases of lung cancer in 2017 where around half were diagnosed in women2. Among women, there is a high proportion of adenocarcinomas3; approximately 80% of those diagnosed with known stage have metastasis at diagnosis4; and survival is related to stage at diagnosis2. The majority of these cancers is attributed to exposure to tobacco smoke5, and the incidence rate of lung cancer in women in Norway has increased in recent years2.Lung cancer is the most commonly diagnosed cancer (2.1 million new cases in 2018) and the leading cause of cancer death worldwide11. Still, as earlier diagnosis is essential for improved prognosis, these markers should ideally be identified in a readily obtainable matrix at a time early in the progression of the malignancy.Understanding key molecular markers of lung carcinogenesis and identifying biomarkers for risk stratification and early detection is essential for reducing lung cancer mortality. Several studies have successfully identified clinically relevant biomarkers in tumor or in blood at time of diagnosis, like EGFR mutations or PD-L1 expression13. Related to lung cancer, epigenetic markers that largely reflect past and current tobacco smoking have been established as blood-based markers of lung cancer risk using prospective designs15. Still, little is known regarding peripheral changes in gene expression prior to clinical manifestation of lung cancer16.Prospective study designs allow for exploration and characterization of early functional genomic events, and recent studies have indicated that there are such molecular signals in blood that can be detected years before cancer diagnosis. As exemplified for breast cancer, modulated trajectories in gene expression linked to breast cancer, especially related to metastatic cancers, have been identified in blood prior to the clinical diagnosis17, estimated proportions of white blood cells in blood as well as their relative ratios could be related to future cancer risk. Indeed, ‘omics data’, exemplified by DNA methylation data in blood, has been used to predict a neutrophil-to-lymphocyte ratio (NLR) that was related to risk of several cancers20.Gene expression and other ‘omics data’ based on peripheral blood samples are influenced by the underlying distribution of white blood cells at the time of blood sampling, and algorithms can estimate the proportions of such cell populations. Considering that certain cell populations have been linked to elevated cancer risksThis study aimed to identify transcriptomic signals in blood during years prior to lung cancer diagnosis. We also assessed differences in blood cell populations that were estimated from microarray data. To address the prospective design of the study, we employed several approaches to statistically analyze the data with focus on potential signals in samples donated closer to diagnosis and from metastatic cases.21. Women aged 30–70 years were randomly selected from the National Registry and invited to participate in the study through a mailed invitation letter to their home address that also included a detailed questionnaire. Women that agreed to participate have been followed-up regularly with consecutive questionnaires. The questionnaires have covered self-reported anthropometry and lifestyle variables, including detailed information on past and concurrent smoking. Based on the questions related to smoking history, we constructed a comprehensive smoking index (CSI)22 as a stringent variable to represent smoking exposure. CSI scores were obtained using duration of smoking , intensity , and time since smoking cessation  and fitting the following model to our data: X2 = (1 − 0.5/τdur∗) (0.5/τtsc∗) ln(int + 1), where τ is the estimated half-life parameter, and δ is an estimated lag time parameter describing tsc and total duration as follows: tsc∗  = max and dur∗  = max(dur + tsc − δ) − tsc∗ .The Norwegian Women and Cancer study (NOWAC) is a nationally representative cohort study initiated in 199123. At the time of blood donation, the participants also filled out a one-page questionnaire covering information about recent and current smoking habits. Blood samples were donated at family doctors into RNA stabilizing PAXgene tubes that were sent by overnight mail to the Department of Community Medicine, UiT, Tromsø, Norway. Upon arrival, tubes were frozen immediately at − 80 °C.We conducted a case–control study nested within the NOWAC study among those participants who had donated a blood sample in 2003–2006 Through linkage with the Cancer Registry of Norway we identified 134 participants who had been diagnosed with lung cancer between 2004 and 2011, after they donated a blood sample. Thus, the time from blood donation to diagnosis ranged from 0.2 to 7.2 years. Of the 134 identified cases, 100 were diagnosed with metastatic cancer at the initial diagnosis. For each case, one cancer-free control was randomly drawn from NOWAC participants with available blood samples and matched on birth year and blood sample collection batch. All participants gave written informed consent and this study was approved by the Regional Committee for Medical and Health Research Ethics in Northern Norway and the Norwegian Data Inspectorate. The research has been conducted according to the principles expressed in the Declaration of Helsinki.24. Samples were analyzed using the IlluminaHuman HT-12 expression bead chips and Illumina GenomeStudio 1.9.0 was used to assess the quality of each array. Of the 268 samples analyzed, six case–control pairs were excluded due to laboratory quality measures before original probe values were background corrected using negative controls (R package limma: function nec)25. Further, probes reported to have poor quality from Illumina, no annotation or detected in < 10% of samples were removed and values were quantile normalized (R lumi:lumiN) and log2 transformed (R lumi:lumiT)26. Annotation of preprocessed data was obtained using R lumi:nuID2RefSeqID and R illuminaHumanv4.db package. The statistical analyses were performed using 11,610 annotated and unique transcripts in 128 case–control pairs.Microarray service was provided by the Genomics Core Facility at the Norwegian University of Science and Technology (NTNU). Briefly, total sample RNA was isolated from the whole blood samples in PAXgene tubes using established protocols27 and the LM22 signature matrix to estimate the proportions of 22 white blood cell populations (WBCs) in samples based on the gene expression profiles. Cell types with mean across all samples > 5% for the relative fractions were included in comparisons across case–control status, smoking status and periods of years to diagnosis. An NLR in each sample was calculated based on the fraction of neutrophils to the summed estimated proportions of lymphocyte  and alterations in moving windows in time . Specifically, the ‘curve group’ method designate genes in predefined groups of genes according to their respective means within three set time windows (days to diagnosis) where the value for each case–control pair is represented by their gene-wise difference in expression values. Six potential curve groups  are predefined according to ranked average case–control difference in expression values within the time windows. In the dots provided here for illustration, diagnosis is to the right; Period 1 is closest to diagnosis and the number listed first is the respective period with the highest mean. For example, a gene with increasing differences in expression levels between cases and controls when approaching time of diagnosis would be designated to the curve group ‘123’. In our data, the case–control differences were divided into the three predefined groups according to days to diagnosis for the case. To ensure similar numbers of cases in each time window we defined the following cutoffs: < 1093, 1093–1783, 1783 < days. If there are more genes in a curve group than expected by chance, a time trend is considered present12. The analyses were performed separately for metastatic case–control pairs which resulted in 36, 31, 33 case–control pairs in the three set time windows, respectively. From these analyses, unranked lists of genes in the significant curve groups were extracted. The results are not presented for the non-metastatic groups due to the small number of case–control pairs in the three time windows .Differences in expression values in each case–control pair across time were explored using descriptive and exploratory methods described by Lund et al.12 and Holden et al.13. From these analyses, p-values from testing mean differences in log2 gene expression for case–control pairs across days from blood sampling until time of diagnosis using LITS are presented.The ‘local in time statistics’ (LITS) method uses sliding time windows and the data set was divided into overlapping periods that each contained a set of 30 metastatic cases that were consecutive in time across the time to diagnosis . In this dataset, this division resulted in 71 time periods and each period duration ranged 475–1069 days. We focused on hypothesis testing of case–control differences for cases with metastasis according to time to diagnosis. The null hypothesis of the test was that the expectation for differences in log2 gene expression for the case–control pairs was zero for all genes in all time periods. The null distribution is estimated by permutation of the case and control status in each case–control pair. Detailed method descriptions are presented by Lund et al.28. Linear regression models adjusting for the matched pairs were estimated for all subjects as well as restricted to (i) pairs where the case was metastatic, and (ii) metastatic cases sampled within 3 years prior to diagnosis, which is approximately overlapping the period represented by the time window closest to diagnosis in the curve group method described above. From these analyses, separate lists of the 100 genes with the lowest p-values were extracted (‘Top100 lists’). Additionally, separate regression models were estimated for the cumulated cases until each respective year of the interval between blood donation and diagnosis.Differences in expression values between cases and controls were identified using linear models for microarrays (R limma)Non-linear examinations of gene-wise differences in expression levels according to time to diagnosis in cases adjusting for the matched pairs were explored using natural spline regression (3 degrees of freedom) and moderated F-tests for each gene (R limma). The regressions examined the difference in gene expression values for each transcript and each case–control pair  across the time window from blood sampling to diagnosis of the case. Analyses were performed for all subjects as well as restricted to (i) metastatic case–control pairs and (ii) metastatic cases sampled within three years prior to diagnosis. From these analyses, separate lists of the 100 genes with the lowest p-values were extracted (‘Top100 lists’).29.Gene names, Entrez IDs, and accession numbers for genes in the identified lists from the different analyses were extracted using R packages lumi and org.Hs.eg.db. Lastly, the identified gene lists were compared between approaches mentioned above and functional explorations of molecular signatures as gene ontologies within each list were examined by overrepresentation analyses (R clusterProfiler)Stratified analyses by histological subtypes  were not performed due to low sample sizes.Case–control differences in proportions of WBCs including the NLRs estimated from gene expression values were assessed using regressions . Further, we used regression splines to evaluate any non-linear trends in proportions of WBCs as well as NLR across years to diagnosis.30 and open source packages from R and the Bioconductor project. Wilcoxon and Kruskal–Wallis tests were used to test for group differences.All statistical analyses were performed using RCharacteristics of the NOWAC participants are summarized in Table The most prevalent populations of WBCs were neutrophils, CD8 T cells, and monocytes Table . The proAnalyses of curve groups demonstrated that there were time trends for certain groups of genes in cases with metastatic cancer p = 0.04; Table ; Table 42 gene expression for metastatic case–control pairs with time to diagnosis demonstrated that p-values were lowest, although not significant, closest to diagnosis , respectively . For the hundred genes with lowest p-values in the CSI-adjusted analyses, the overlap with the Top100 lists for the unadjusted analyses of all pairs, metastatic pairs, and metastatic pairs with < 3 years to diagnosis was 76, 58, and 23, respectively.We identified seven lists of genes of interest: Top100 genes for case–control differences in the total study sample, the corresponding Top100 genes for metastatic case–control pairs, and the Top100 genes for metastatic case–control pairs in the last 3 years before diagnosis; the Top100 genes for non-linear associations with time to diagnosis in all cases and the corresponding Top100 genes for metastatic cases; and genes overrepresented in curve groups 123 and 321. For these lists, the overrepresentation analyses of gene ontology categories demonstrated differences in categories among the different approaches Fig.  and inclThese exploratory analyses of microarray gene expression data in peripheral blood focusing on time-dependent processes in lung cancer cases and their matched controls indicated small differences in gene expression during the years prior to diagnosis. Overall, the methods indicated that case–control differences were most distinguishable in metastatic case–control pairs in the period 0–3 years prior to diagnosis. The genes and gene ontology categories identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case–control differences. The enriched processes included categories related to both metabolic processes and immunological responses. White blood cell populations in blood estimated from the gene expression values indicated that neutrophils and NLR might be disrupted in cases overall (independent of time to diagnosis) and that natural killer cells were lower in current smokers. Only few of the genes identified were among those that have been previously linked to tobacco exposure. Overall, the results could indicate disruption of immunological processes in blood of lung cancer cases and that there is a relation to time to diagnosis and metastatic cancer.Of the different curve groups, the case–control differences were increasing and decreasing with time towards diagnosis in the curve groups ‘123’ and ‘321’, respectively, and both gene groups indicated that the period closest to diagnosis (0–2 years) was when case–control differences diverged the most. The LITS method indicated that the gene expression for cases and controls were most dissimilar in the periods around 2.5 in addition to around 4.6 years prior to diagnosis. The non-linear regressions could have indicated diverging trajectories, but this approach did not indicate a specific period where case control differences were more pronounced nor were the direction of effect estimates consistent across top genes. When assessing differentially expressed genes when comparing cases and matched controls while restricting the analysis to cases diagnosed with metastatic cancers within two and three years of diagnosis, 61 and 27 genes were indicated, respectively (20 overlapped). Overall, case–control differences were more pronounced for metastatic cases and controls across the statistical approaches. When comparing all case–control pairs, no significant genes were indicated according to FDR adjusted p-values. In summary, exploring alterations with time in gene expression demonstrated that the magnitude of differences vary according to time to diagnosis.31 and lung cancer diagnosis is preceded by many symptoms especially in the last year before diagnosis is set32. A large Danish population-based study investigating trajectories across individuals prior to cancer diagnosis found that lung cancer had up to 30 prior disorders and that most of them accumulated 1–2 years prior to the cancer diagnosis33. Thus, the detected alterations could be systemic signals related to lung cancer progression per se, or to any of the related diseases. The ontology categories identified for the included lists indicated several cellular processes, including categories related to both metabolic processes and immunological responses. These could indicate that the systemic signatures of exposure or disease-related processes are linked to these cellular processes in circulating white blood cells.Summarizing the different approaches, the period closest to diagnosis (approximately 0–3 years prior to diagnosis) was indicated as the period with the more pronounced case–control differences. This period could be expected based on knowledge of the progression of lung cancer disease. The lung cancer tumor grows for several years34. Further, proportions of monocytes increased in all cases closer to diagnosis and such cells in blood samples have been related to lung cancer and presence of late stage disease35. Still, the systemic presence and function of macrophages and monocytes prior to clinical manifestation of the disease is not clear. Although proportions of natural killer cells were not different across case–control comparisons, this cell type was associated with exposure to smoking as estimated proportions of natural killer cells were lower in current smokers, which is also observed from cell counts in blood37. Of note, the differences in natural killer cells across smoking categories here correspond to the observation of estimated fractions based on DNA methylation in the same samples38.The proportions of macrophages were higher in metastatic cases as compared to controls and the differences were more pronounced closer to diagnosis. Macrophages have been shown to be tumor promoting and present in tumors40 and several immune cells in peripheral blood have been shown to have prognostic value for several cancers17. The estimated proportions of neutrophils constituted a large fraction of WBCs in samples but was lower as estimated from gene expression than what is typical in blood42 as well as estimated from DNA methylation in the same samples38. Still, their proportions were higher in cases as compared to controls although there was no indication of enhanced differences among metastatic case–control pairs nor with time to diagnosis. The role of neutrophils in carcinogenesis is both pro-tumor and anti-tumor but it is also plausible that their increased presence in blood is a secondary inflammatory response to the carcinogenesis43.Elevated systemic inflammatory responses are important indicators of cancer development and progression42. Further, NLRs derived from ‘omics data’ has indicated that an elevated NLR (estimated from DNA methylation data) was linked to increased risk of future lung cancer19 and lower lung cancer survival20. Thus, these observations support that there are systemic immunological responses detectable in blood prior to clinical cancer diagnosis, and that using ‘omics data’ as digital cytometry can be useful as predictive markers.Related to the elevated proportions of neutrophils in cases as compared to their controls, the estimated NLRs were higher in cases. A higher NLR ratio in blood has previously been associated with poorer survival in lung cancer cases and thus an elevated ratio in blood samples from prospective cases could also be expectedF5, TLR5 and C19orf59 have been previously observed differently expressed in whole blood of non-small cell lung cancer (NSCLC) patients compared to controls44 in one study and SLC25A5 in another study of blood samples from NSCLC cases and controls45. None of the 20 genes were among those 29 or 8 genes that were identified in blood drawn at diagnosis from patients with NSCLC46 or adenocarcinomas47, respectively. The 20 genes of highest interest in these prospectively sampled blood samples could indicate a specific gene profile in blood prior to lung cancer diagnosis, which would thus be different from those genes identified at diagnosis or later in the disease progression. Accordingly, the FGFR3 gene, a gene that has been linked to lung carcinogenesis and identified as a therapeutic target48, was the gene with the lowest p-values in the spline regressions according to time to diagnosis but not when comparing case–control differences. Conversely, these 20 genes could also indicate a non-specific blood profile indicating systemic responses to any cancer developing. Still, the 20 genes indicated here were not among those 50 indicated in a profile related to later breast cancer in a similar prospective case–control study related to breast cancer, also within the NOWAC cohort24. Of note, genes of interest identified in blood and in matched tumor samples from the same persons has been compared for women with breast cancer in the NOWAC cohort and the biological processes and expression patterns appeared to vary49. The processes indicated in tumor samples were enriched for genes involved in hallmarks of cancer, while processes indicated in blood samples (sampled with the same protocol as this study) were enriched for either general cellular processes or specific immune responses. Thus, circulating profiles of samples in studies related to cancer appear to differ between cancer sites and differ from local molecular signals related to the developing tumor. Further assessments focusing on time to diagnosis and cancer stage are warranted to assess whether disruption of expression of specific genes identified in prospective studies could contribute to risk stratification, diagnostic characterization or indicate genes as therapeutic targets.The different statistical approaches yielded different lists of top genes with variable degree of overlap. The genes identified from approaches focusing on time to diagnosis and metastatic cancer were distinct from those focusing on the case–control differences in all pairs. Among the 20 genes differentially expressed in blood sampled within both 2 or 3 years prior to diagnosis in metastatic cases, 50. Three genes  among the ten genes with the lowest p-values in the comparisons of all case–control pairs were among those 1270 differently expressed genes observed when comparing current and never smokers in the meta-analyses. Further, among the hundred with the lowest p-values in the case–control comparisons in this study, fourteen  were also identified in the meta-analyses. When investigating metastatic cases under three years, none of the top ten were among the list linked to current smoking, but ten  of those 100 with the lowest p-values were linked to smoking in the meta-analyses50. Thus, the genes identified linked to lung cancer case–control status, especially when focusing on short time to diagnosis and metastatic cancer, have not been strongly linked to smoking in other populations.To better explore whether the identified genes were linked to exposure to smoking we compared our genes of interest to a large previous meta-analyses of gene expression in 10,233 participants51. Thus, genes identified in exploratory studies of blood transcriptomes could signify systemic signals of local diseases but as gene expression in blood samples to a large extend is influenced by white blood cells it is likely that signals reflect systemic immune responses. Further, blood transcriptome profiles have been shown to distinguish between several pulmonary diseases44 and as the disease entails accumulation of many disorders in the time close to clinical diagnosis33, signals as those in this study can represent interesting circulating markers during the development of lung cancer.The ontology categories indicated from each statistical approach differed considerably. Many ontology categories with the highest number of genes overrepresented indicated immunological functions and were identified from the lists from linear regressions of metastatic cases < 3 years prior to diagnosis and the spline regressions of all and metastatic case–control pairs. Both the estimated proportions of neutrophils, macrophages and monocytes as well as the ontology categories from genes of interest indicate disruption of immunological processes in blood and that there is a relation to time to diagnosis and metastatic disease for the magnitude of differences. Using blood ‘omics data’ to reveal characteristics of the immune system has been highlighted as part of the development of diagnostic biomarkers and personalized treatment optionsThis study used descriptive, exploratory methods as well as common statistical approaches to explore how differences in gene expression in case–control pairs vary across time between blood sampling and lung cancer diagnosis. By focusing on the time aspects and metastatic cancers while considering the matched pairs, this study might have captured genes of interest that were not apparent from the methods focusing solely on the case–control differences and thus have indicated genes related to metastatic disease and dynamic processes. Further, the signals could indicate a period where perturbations start and indicate functional processes disturbed in blood. The curve group approach included hypothesis testing of curve trajectories while the LITS method is more flexible as it does not assume predefined trajectories. Adjustment of smoking was not feasible in the explorative methods, but when the baseline linear model was adjusted for CSI scores, the overall results did not change substantially. Using established methods in combination with new statistical methods, this study demonstrated subtle time-dependent changes in gene expression profiles in blood prior to clinical diagnosis.across different individuals. I.e. we assume divergence in gene expression for different persons across time to diagnosis and is not a longitudinal sample although it was analyzed as such. Additionally, the interpretation of study findings are further hampered by the limited sample size of the study. Finally, the blood samples represent snapshots of the circulating immune cell activity and should be interpreted as such.Notably, the prospective case–control design of this study only allowed for investigations of changes in gene expression according to time to diagnoses that are common Combining approaches focusing on time to diagnosis and metastatic disease revealed distinct signals related to these features and the results could reflect systemic immune responses or disturbed distributions of blood immune cells. These results supported that genes of interest indicated in explorative analyses of prospective blood samples could potentially be linked to systemic signals of disease-related processes.Supplementary Figures.Supplementary Tables.


Intensified coastal development is compromising the health and functioning of marine ecosystems. A key example of this is the Red Sea, a biodiversity hotspot subjected to increasing local human pressures. While some marine‐protected areas (MPAs) were placed to alleviate these stressors, it is unclear whether these MPAs are managed or enforced, thus providing limited protection. Yet, most importantly, MPAs in the Red Sea were not designed using climate considerations, likely diminishing their effectiveness against global stressors. Here, we propose to tailor the design of MPAs in the Red Sea by integrating approaches to enhance climate change mitigation and adaptation. First, including coral bleaching susceptibility could produce a more resilient network of MPAs by safeguarding reefs from different thermal regions that vary in spatiotemporal bleaching responses, reducing the risk that all protected reefs will bleach simultaneously. Second, preserving the basin‐wide genetic connectivity patterns that are assisted by mesoscale eddies could further ensure recovery of sensitive populations and maintain species potential to adapt to environmental changes. Finally, protecting mangrove forests in the northern and southern Red Sea that act as major carbon sinks could help offset greenhouse gas emissions. If implemented with multinational cooperation and concerted effort among stakeholders, our portfolio of climate‐tailored approaches may help build a network of MPAs in the Red Sea that protects more effectively its coastal resources against escalating coastal development and climate instability. Beyond the Red Sea, we anticipate this study to serve as an example of how to improve the utility of tropical MPAs as climate‐informed conservation tools. 2) by protecting high blue carbon areas, which are large mangrove forests that uptake CO2 from the atmosphere via photosynthesis and sequester it in sediments.A marine protected area (MPA) design for the Red Sea that buffers against the effects of global climate change includes coral reefs that vary in bleaching susceptibility over time: from unbleached/healthy, to heat‐stress‐ and upwelling‐induced bleaching, to recovering‐from‐bleaching reefs. Crucially, such MPA design also accounts for seascape genetics by ensuring these reefs are genetically and physically  connected to promote recovery and adaptation. Finally, a climate‐informed MPA design helps offset carbon dioxide emissions (CO The second and third projects—AMAALA and The Red Sea Project (TRSP)—are intended to become luxurious tourism destinations in Saudi Arabia, attracting an additional one million people on an annual basis , will stretch over ~35% of the entire eastern periphery of the basin, and overlap with some proposed marine protected areas  located within the basin and one near the Strait of Bab‐al‐Mandeb in Djibouti Figure , althougMass coral bleaching—when corals across entire reefs or regions simultaneously lose the symbiotic algae, revealing the coral's white skeleton—mainly occurs in response to anomalously high water temperature Glynn, . DependiWhile the need to incorporate connectivity in the design of MPAs in the Red Sea has been mentioned previously  that may partially offset anthropogenic carbon emissions  to identify the main connectivity pathways and the presence of genetic breaks. Finally, we map the location of mangrove forests and seagrass meadows as well as the carbon sequestration potential of mangroves to identify areas of high blue carbon capacity. MPA candidate areas are then proposed based on these climate‐tailored approaches. Beyond scientific assessments, we also provide practical recommendations for the integration of these approaches in marine conservation to improve the resilience of MPAs in the Red Sea and in other marine tropical environments worldwide.22.1The Red Sea coastal areas can be divided into four thermal regions reflecting the north‐south water temperature gradient and an east‐west upwelling pattern Figure . The higCrucially, each thermal region has a distinct bleaching history. The southern hot zone experienced widespread coral bleaching in 1998, stretching from Yemen to Eritrea and Sudan , and other benthic organisms , corals  and 6.8 ± 1.7 g m2 year−1 over 10 cm (centimeters) for mangroves and seagrass meadows, respectively  over 10 cm  in the mangroves of the Red Sea could represent another effective mechanism to sink atmospheric CO2, up to 20 times more than their carbon burial rates  in the design of tropical MPAs.in situ loggers to map thermal regions. Some examples of bleaching databases are the ReefBase voluntary bleaching database (http://reefbase.org), the expanded global observation bleaching database  and fisheries species to use connectivity in MPA design to simultaneously maintain ecosystem function, goods, and services for coastal nations. Yet, the effects of global warming on larvae should also be considered as they may develop faster and spend less time in the water column or experience high mortality rates leading to failed recruitment, impacting the level of genetic connectivity between individual MPAs , especially if adjacent to other vegetated ecosystems, such as saltmarshes and macroalgae habitats. Additionally, monetizing carbon sequestration through the establishment of a global blue carbon market .Additionally, a bleaching‐resilient, better‐connected MPA network for coral reefs should also be expanded to include vegetated coastal ecosystems with high blue carbon capacity to fight the effects of global warming. Considerable research effort needs to be dedicated to blue carbon science to better quantify the value of the carbon sequestration of marine vegetated ecosystems and their ability to offset CO5The marine policy incentive of this study resides in the creation of a portfolio of climate‐tailored approaches that could boost climate change mitigation and adaptation. One effective way of communicating this portfolio is to produce maps, such as Figure Yet, such a portfolio needs to consider legal, political, and institutional requirements to be successful (Green et al., Overall, a MPA design integrating the three climate‐tailored approaches will likely offer long‐term resilience to the marine ecosystems in the Red Sea that face greater climate instability and human pressure. Creating such a portfolio for other marine tropical environments represents one avenue to systemically incorporate climate‐mediated data in the core design of marine conservation efforts in tropical regions of the world.All authors declare no competing interest.All authors conceived the main ideas and contributed to the collective writing process led by L. Gajdzik. All authors revised the manuscript and gave final approval for publication.Supplementary MaterialClick here for additional data file.


In the original article, there was an error. The error concerned reported exposure percentages.Alcohol, paragraph three:A correction has been made to reported percentages in “In search of ethanol sensitivity windows for craniofacial development, zebrafish embryos were exposed to a—rather extreme—regime of 10% ethanol during defined developmental stages  deficiency are more frequent (Jiang et al., The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


In their metanalysis, Duan et al. address the role of indirect calorimetry (IC) in nutritional therapy in critically ill patients .Their fi We thank Professor Honore et al. for their interest in our meta-analysis . Our stuWe fully agree with what Professor Honore and colleagues pointed out that clinicians need to understand not only indications of IC but its limitations . EspeciaCardiopulmonary bypass can remove part of VCO2, which is considered the most important technical factor affecting the reliability of IC measurement . HoweverAlthough CRRT may be more complicated due to factors such as replacement fluid, Jonckheer et al. proposed a simple blood gas analysis based on the circuit to quantify CO2 that was removal during continuous venovenous hemofiltration. In their study, the CO2 content in ultrafiltration is available by blood gas analysis, and the CO2 content is converted from mmoL to mL with the help of the ideal gas law (pV = nRT). The authors performed IC in 4 different states  and found that CO2 removal by CVVH led to a change in REE of only 3%, which makes a correction factor unnecessary in this setting . Althoug


What is the topic of this review?Activation of brown adipose tissue with G protein‐coupled receptors as key druggable targets as a strategy to increase energy consumption and reduce fat mass.What advances does it highlight?2 consumption leading to enhanced nutrient uptake and a reduction in fat mass.GPR120 is a fatty acid receptor highly expressed in brown adipose tissue. Its activation by selective ligands increases brown adipose tissue activity. This is mediated by changes in mitochondrial dynamics resulting in increased O3‐adrenergic receptors. Determination of additional receptors expressed by brown, white and brite (brown‐in‐white) fat can lead to new pharmacological treatments to activate BAT. GPR120 is a G protein‐coupled fatty acid receptor that is highly expressed in BAT and further increases in response to cold. Activation of this receptor with the selective agonist TUG‐891 acutely increases fat oxidation and reduces fat mass in mice. The effects are coincident with increased BAT activity and enhanced nutrient uptake. TUG‐891 stimulation of brown adipocytes induces intracellular Ca2+ release which results in elevated O2 consumption as well as mitochondrial depolarisation and fission. Thus, activation of GPR120 in BAT with ligands such as TUG‐891 is a promising strategy to increase fat consumption.The identification of druggable targets to stimulate brown adipose tissue (BAT) is a strategy to combat obesity due to this highly metabolically active tissue utilising thermogenesis to burn fat. Upon cold exposure BAT is activated by the sympathetic nervous system via β  It is present and active in human adults and contributes to total energy expenditure , which is simulated by noradrenaline released by the sympathetic nervous system in response to cold exposure  levels, which contributes to defective thermogenesis   cascade , which interestingly was identified as cold‐induced in adipose tissues as well as being more highly expressed in BAT compared to WAT at thermoneutrality (Rosell et al., 2+ in brown adipocyte mitochondria. Furthermore, its activity may be subject to control by the proton gradient of the inner mitochondrial membrane (Shao et al., GPR120 activates a number of signal transduction pathways that have the capacity to modulate metabolic function in brown adipocytes (Figure 1.7in vivo actions of TUG‐891, although mostly through the receptor, have some effects in Gpr120 knockout mice. Although the reduction of body weight due to TUG‐891 observed in wild type mice was not apparent in the knockout, there was an increase in the uptake of fatty acids from lipoprotein‐like particles in the receptor‐ablated mice. One of the GPR120‐independent effects found for TUG‐891 was a direct activation of UCP1. The action of the ligand was similar to that of oleate and other long‐chain fatty acids to relieve the natural GDP‐dependent inhibition of UCP1 (Fedorenko, Lishko, & Kirichok, GPR120 ligands can have off‐target effects and this raises the possibility of additional beneficial or detrimental actions. The 22 consumption and mitochondrial dynamics. The receptor is also expressed in white adipocytes and contributes to the conversion to brite adipocytes. As impaired signalling of GPR120 is associated with human obesity development, agonists for this receptor represent a promising strategy to reduce obesity by increasing lipid combustion. The potential of GPR120 ligands to have additional beneficial effects should also be considered as this receptor mediates anti‐inflammatory effects and the ligands could have off‐target effects such as direct activation of UCP1.Transcriptomic analysis to identify new receptors with the potential to activate brown adipocytes has been shown to be a valid approach. GPR120 activation enhances the metabolic activity of BAT and cultured brown adipocytes including nutrient uptake, OThe author declares no conflicts of interest.