Fly ash has been widely used as a cement substitute to improve the sustainability of concrete. Although the advantages of fly ash have been extensively documented, there is a gap in understanding why its use in mass concrete applications remains low in some countries, such as the Philippines. Thus, this work aims to understand the issues that impede waste utilization, particularly fly ash in the concrete construction industry, quantify the impact of the current practice, and identify opportunities for sustainable fly ash utilization. Endpoint impact analysis was conducted through the life cycle using SimaPro 9.3 to quantify the impacts on human health, ecosystem, and resources of 31 concrete mixtures of low, normal, and high strength design with 0 to 20% fly ash as cement replacement. In-depth, semi-structured interviews with key stakeholders were undertaken to determine the institutional, economic, social, and technological challenges related to the utilization of waste materials in large-scale concrete construction. More than 90% of the total impact of concrete contributes to damage to human health, primarily caused by global warming and fine particulate matter. The use of fly ash at 20% replacement by weight of cement benefits resources more significantly than human health and the ecosystem. The use of chemical admixture to improve strength has a significant impact on resources. High fly ash replacement for normal and high-strength concrete has a greater reduction in all endpoint categories than for low-strength design. Recommendations are proposed to maximize the beneficial impact of using fly ash in the concrete industry. As a popular construction material, the concrete industry will continue to play a significant role in global construction for an extended period3. Concrete’s vast production and consumption cycles have a severe environmental impact4, rendering the present industry unsustainable. It is critical for the concrete industry to develop strategies for the mitigation of environmental and societal impacts. One approach to achieving tangible sustainability is using waste materials as a substitute for the conventional components of concrete6. Various cement replacements are sourced from industrial by-products and agricultural and municipal solid wastes. The industrial by-products that have been utilized in place of using cement in concrete are silica fume7, coal fly ash8, bottom ash9, granulated blast-furnace slag10, and limestone powder11. Agricultural ashes from rice husks and palms are also used13. The most commonly utilized by-product as cement replacement is fly ash due to its magnitude, compatibility with cement, and relatively low cost14. It is a coal combustion by-product, accounting for a least 85% of the ash generated in coal-fired power plants15. However, the global average usage of fly ash is just 53.5% of the total output16. In 2008, it was estimated that the world produced 777 million tons of fly ash12. Fly ash can potentially improve the compressive strength and segregation of concrete17 as well as its many durability properties18. The use of fly ash as a cement replacement will minimize landfill disposal.Concrete is the world’s most consumed manufactured construction material20. Since 1990, LCA has been used in the construction sector as a critical technique for assessing the environmental effect of materials and the environmental performance of buildings22, as well as for evaluating various construction materials24. Vieira et al.25 performed an environmental assessment of fly ash and slag utilization in self-compacting concrete (SCC), considering strength and service life, and concluded that using slag is preferable. In addition, Celik et al.26 showed that 55% fly ash replacement by weight of cement creates highly workable SCC mixtures with low global warming potential for concrete manufacture. In comparison with concrete made with pure OPC, Chen et al.27 discovered that applying fly ash on pervious concrete displayed the highest overall performance in balancing several criteria, including cost, material properties, greenhouse house gas reduction, and energy. Using LCA, Hafez et al.28 concluded that transporting fly ash from China to the United Kingdom negates the environmental advantages of its replacement of cement. The study of Radwan et al.20 demonstrates that 50% and 70% OPC substitution will lower the impact by an average of 44% and 61%, regardless of the blending scheme of fly ash and/or slag.Several studies have utilized the life cycle analysis (LCA) approach to estimate the benefits of employing fly ash as a building material29. The selection between midpoint and endpoint methods for impact assessment depends on considerations of relevance and reliability30. In this study, preference is given to the endpoint method due to its greater relevance, as it allows to compare the different options investigated. Endpoint impact assessment has been used in the assessment of concrete gravity dams31 and concrete with recycled aggregates32. The endpoint method facilitates the generation of comparable single overall environmental score and damage-focused indicators, which can easily be understood by decision-makers21. Endpoints are often components that the public believes should be protected33.Life cycle impact assessments are typically conducted at the midpoint and endpoint levels. Notably, previous research in the field of impact assessment of concrete has predominantly focused on midpoint impacts35, there exists a gap in understanding why the utilization of fly ash in mass concrete applications remains low in some countries. According to the Philippine Department of Energy, the five biggest coal-fired power plants in the Philippines generate around 1.4 million tons of ash annually, with relatively poor utilization36. This is anticipated to increase as additional coal-fired power plant facilities are constructed nationwide. Fly ash that is not used is disposed of in ash ponds and landfills37. However, due to the metal and mineral composition of coal ash, its disposal poses a risk of surface and groundwater contamination38. The construction sector can use this massive waste resource39. It is important to understand the barriers that restrict the widespread adoption of these materials as a cement replacement in the concrete construction industry to maximize the use of this waste resource.Although several literatures have documented the advantages of fly ashThe aim of this work is to (1) quantify the impacts/benefits of fly ash utilization on human health, ecosystem, and resources, (2) explore the challenges that impede waste utilization in the concrete construction sector, particularly fly ash, and (3) identify opportunities for sustainable utilization of fly ash in mass concrete production. In this study, we employed both quantitative and qualitative methods. Quantitatively, we assessed the sustainability impact of current concrete production through a life cycle endpoint analysis. Qualitatively, we conducted key stakeholder interviews to identify barriers and motivations, aiming to develop a comprehensive roadmap for the future of sustainable concrete consumption. This study will contribute to existing fly ash concrete literature by unraveling the impacts of fly ash utilization on reducing the impact of mass concrete associated with human health, ecosystem, and resources. The study result will benefit countries with similar conditions and concrete industries with similar practices.Two major components are included in the general framework Fig.  used in 40 with minor adjustments. In-depth, semi-structured interviews were conducted with a diverse group of key stakeholders with decades of expertise in the concrete building industry. The semi-structured interview adhered to a general structure but allowed for an in-depth discussion of individual issues. The interview covers a wide range of issues, including specific industry conditions in the Philippines, current waste material use, and the possibilities and constraints of large-scale waste material use . The specific questions posed to the stakeholders are listed in Supplementary Table Interviews with key stakeholders were conducted to understand the motivation and barriers to using cement replacing material in the Philippine concrete construction industry, specifically, but not limited to, fly ash. The motivations and barriers to waste utilization in concrete construction were qualitatively identified using the interview structure developed by Henry and Kato42. It is a well-established tool for the environmental assessment of different concrete mixtures43. The SimaPro 9.3 LCA program and the EcoInvent 3.8 database were used to evaluate the concrete mixtures. SimaPro has been extensively utilized in the LCA of concrete and cement45. On the other hand, EcoInvent was recently regarded as one of the finest construction materials databases46. The life cycle study was conducted with local conditions in the Philippines. To assess the influence of fly ash on various concrete mix designs, a cradle-to-gate life cycle analysis of various concrete mixtures was made, similar to many previous studies47.This study employs the International Organization for Standardization (ISO) 14040 standard for LCA, which has been used in earlier investigationsThis research examines the impact of fly ash utilization on a wide range of concrete mixtures. The data were obtained from two batching plants located in the capital region of the Philippines. This includes 31 concrete mix designs covering low, normal, and high-strength concrete mixtures. The mixtures obtained from concrete batching plants used for mass concrete applications have a range of 2000 psi (13.8 MPa) to 10,000 psi (69.0 MPa). Concrete can be categorized into the following groups based on its 28-day compressive strength: low strength for concrete with a compressive strength below 20 MPa, normal strength for concrete with a compressive strength between 20 and 40 MPa, and high strength for concrete with a compressive strength above 40 MPa. In the Philippines, 3000 psi (20.7 MPa) or less is considered low strength, while 6000 psi (41.4 MPa) or higher is considered high strength for mass concrete applications. Table 2 content of 53.1%, Al2O3 content of 17.5%, and Fe2O3 content of 5.48%. The loss on ignition (LOI), as determined through testing conducted in accordance with ASTM C311, is recorded as 2.7. The provided chemical composition details were obtained directly from the fly ash supplier.The concrete mixtures investigated in this study incorporate varying percentages of fly ash as partial replacements for cement, specifically 0%, 10%, 15%, and 20%. These replacement levels align with the typical practices observed in the Philippine concrete construction industry. The fly ash utilized is sourced from coal-fired power plants in the Philippines and is mixed into the concrete as a mineral admixture at the concrete production plants. It is important to note that the fly ash is obtained from third-party suppliers. The specific type of fly ash employed in the batching plants is classified as Class F fly ash according to ASTM C618. The chemical composition of this fly ash includes a CaO content of 10.6%, SiO48. The system boundary, as depicted in Fig. The functional unit selected for this study is one cubic meter of concrete, a widely adopted measure in numerous Life Cycle Assessment (LCA) investigations concerning concreteThe system boundary adopted in this study encompasses the complete life cycle of cement and aggregates, including their extraction, processing, and transportation to batching plants. It should be noted that fly ash is a by-product of industrial activities, and thus, this study does not specifically evaluate the consequences associated with fly ash production. However, certain aspects related to fly ash preparation, such as minimal drying processes, were taken into consideration. Particular attention was given to transportation activities, as they are recognized as significant contributors to emissions associated with fly ash. The end-of-life strategy for concrete is beyond the scope of this investigation. Given that the concrete mixtures under examination were designed to meet similar strength requirements and functional specifications, factors related to the construction, service, and demolition stages were deemed to have no direct influence on the findings of this study.3 of energy is required for concrete mixing49.Cement is the primary ingredient of concrete. Local data for cement production were obtained from comprehensive Environmental Impact Assessment Reports of two major cement factories in the Philippines. The grinding process consists of 90% clinker, 5% gypsum, and 5% mineral filler. The information used to describe Portland cement production is based on the American Society for Testing and Materials’ technical standard (ASTM C150) for Portland cement. The Philippines’ local cement standard adopts the ASTM Standard. The dataset utilized for sand transformation describes a blend of riverbed and quarry sand. According to interviews with operators of ready-mix batching plants, roughly 80% of the aggregates come from riverbeds, and 20% are from quarries. For gravel, the dataset represents the operation of open-pit mechanized mining. Drilling, blasting, separation, and collecting are carried out on the mining site. The utilized chemical additives are superplasticizers. For admixture, worldwide data was utilized because most Philippine admixtures are imported from overseas. Type G polycarboxylate admixtures conforming to ASTM C494 are the most frequently used admixtures in the country. Global average data or GLO were used in Ecoinvent for the emission caused by this plasticizer. The electricity utilized is medium voltage Philippine electrical market. At the batching plant, 76–78 kWh/mThe transportation of cement-replacement materials in the context of life cycle analysis is a critical component of this study. In this investigation, all the examined components are transported via truck, with weights ranging from 16 to 32 tons. To estimate the transportation data, the latitude of the concrete batching plant’s suppliers  is utilized, and the land route is calculated using a geographical information system. The data obtained represents the average distances between the sources and the batching plants situated within the metropolitan area. The distances considered for the various materials are as follows: 150 km for cement, 121 km for fly ash, 36 km for gravel, and 100 km for sand. The presence of on-site water sources eliminates the necessity for water transportation. The mode of transport used in the EcoInvent database is specified as “transport freight lorry 16–32 metric tons euro 3,” and the geographical data for the rest of the world (RoW) is employed.48. The midpoint and endpoint impact analyses are the two most common assessment techniques used to determine the influence of concrete mixtures. The endpoint technique has advantages since it produces simple outcomes by evaluating damages to human health, the ecosystem, and resources  among concrete mixtures. This statistical analysis method allows for the determination of whether the mean difference between two sets of observations is statistically significant. In the context of this study, the t test is employed to examine whether the substitution of cement with fly ash has a substantial impact on reducing the associated endpoint impact across concrete mixtures. Endpoint impacts of concrete mixtures with similar compressive strength design, but different fly ash content is considered data pairs. The interpretation of the test results is based on a 95% level of confidence or a 5% level of significance. Under this threshold, a difference between the impacts is deemed significant if the ρ) is utilized in this study to explore the monotonic relationship between continuous or ordinal variables. The correlation coefficient can range from + 1 to −1, with its value indicating the strength of the association. A coefficient closer to + 1 indicates a stronger positive correlation between the variables. Spearman correlation provides valuable insights into the impact of concrete components on endpoint indicators, especially when prior knowledge of the relationship is limited. This method of analysis has also been used in a previous study on life cycle analysis of concrete52. The statistical software Minitab 21 was used to perform all statistical analyses.In addition, correlation analysis is conducted to discern the influence of specific concrete components on the endpoint damage indicators related to human health, ecosystem, and resources. Correlation analysis is a bivariate examination that evaluates the strength and direction of the association between two variables. Spearman’s correlation coefficient (All methods were carried out in accordance with relevant guidelines and regulations. The data gathering with stakeholders was conducted in accordance with the Data Privacy Act of the Philippines (Republic Act No. 10173). All subjects gave their informed consent for inclusion.The following sections discuss the results of endpoint impacts and key stakeholder interviews.t test comparison (Supplementary Tables p < 0.05) fly ash replacement. By substituting 20% fly ash for cement, the impact on human health falls dramatically at a 10% level of significance. This indicates that the use of fly ash will significantly lessen the negative effects of concrete production on human health. The extent of human health damage caused by global warming (65.12%) and fine particulate matter generation (33.44%) for a 27.6 MPa concrete, one of the most widely used concrete mix in the Philippines, is shown in Fig. 2 production has been widely discussed in scientific literature53. However, the influence of fine particulate matter should be examined as well. This is because studies have shown that tiny particles cause more life years to be lost. According to the findings of this investigation, fine particulate matter accounts for 33.44% of the total contribution by a 27.6 MPa concrete. Dust contributes considerably to PM10 and PM2.5, particularly as a result of cement manufacture, ready-mix facilities, and limestone mining54. Fine particulate matter is a critical health issue that needs to be addressed because it is a major source of the country’s air pollution levels. Current concentrations of PM2.5 in urban regions of the Philippines exceed the guidelines of the World Health Organization (WHO), according to published studies56. Air pollution continues to have a devastating impact on the health of Filipinos. Traffic enforcers in Metro Manila, for instance, are 124 times more likely to develop chronic obstructive pulmonary disease if continuously exposed to high PM2.5 concentrations56. Reduced cement use by replacing fly ash in manufacturing concrete batching plants will significantly minimize the generation of fine particulate matter by reducing the fly ash disposed of in landfills, as well as the emissions generated during cement production.The effects of using different fly ash replacements on human health for different concrete mix designs are illustrated in Fig. neration 3.44% forx, NH3, and SO2, which are anthropogenic atmospheric pollutants57. This causes acidification of rivers/streams and soil. All these harmful gases are released during the production of cement. In the Philippines, sustainability initiatives are yet to be incorporated into cement production. Acidification causes the mobilization of heavy metals and soil leaching, which has an adverse effect on terrestrial and aquatic animals58, as well as plants, by disturbing the food chain. The main factors influencing acidification are NOx and SO2 in air and H2S and H2SO4 in water systems59. The combustion of diesel fuels mainly generates ozone during the different stages of concrete’s life cycle. Other midpoint impacts account for less than 3% of total ecological damage. The impact of using fly ash at various 28-day compressive strengths on the ecosystem is similar to that of the impact on human health.The influence of utilizing fly ash as a replacement for cement on ecosystem damage Fig. c followst tests (Supplementary Tables p < 0.05). The figure also shows that when the concrete mix design with 20% fly ash replacement is projected at a higher compressive strength, a more significant reduction in environmental burden can be obtained. When fly ash replacement is increased from 15 to 20%, the reduction in resource degradation is substantial at the 95% confidence level (p < 0.05). Almost all the harm to resources in a representative 27.6 MPa concrete is caused by fossil fuel scarcity, accounting for 99% of the overall impact  at normal (27.6 MPa) and high-strength 41.4 MPa) concrete, there is a greater reduction in damages for all endpoint groups. However, at lower strength (13.8 MPa) concrete mix  and human health (ρ = 0.996).A contribution analysis shows that cement contributes the most to all the damage indicators. For example, for 13.8 MPa Fig. a and 41.63. For all the mixtures considered, the average admixture utilized was 1.27 ± 0.17% by weight of the binder. Interestingly, even this low percentage of admixture addition provides significant damage to resources. The impact associated with resources accounts for 16.79% of 41.4 MPa concrete . This demonstrates that enhancing plasticizer production (or decreasing consumption) is another viable option for sustainable concrete manufacture. Few researchers have examined the environmental effects of incorporating chemicals into concrete64. Xing et al.65 stated that there is insufficient evidence to conclude the relationship between chemical admixture use and each environmental effect indicator. Conversely, Ji et al.66 reported a substantial impact of plasticizers. Their research quantified that the contribution to resource damage of plasticizers is 44.9% when employed in typical off-site ultra-high-performance concrete. Plasticizers are often made from petroleum feedstocks, which throughout the manufacturing process, result in the production of certain chemical components, including SOx and NOx. These acidic gases, when emitted, will settle on land or water and modify the pH value, causing damage to the ecosystem67.The admixture dosage used in the concrete mixtures reported by the ready-mix batching plants ranges from 1.00 to 1.53% by weight of the binder used. Admixture is typically used to enhance specific concrete qualities in the fresh and hardened stateete Fig. , Fig. S268, but actual use in the industry has not been realized. With the country experiencing a surge in development, the industry must fully commit to its efficiency goals to ensure the optimal use of the country’s quarry resources.Another significant contributor to the damage is the aggregates (sand and gravel). The impact of aggregates is significantly higher in low-strength concrete mixtures than in high-strength concrete mixtures. For example, as shown in Fig. An in-depth, semi-structured interview with the various experts provided insight into the current institutional, economic, social, and technical aspects that impede the broader adoption of waste materials in concrete construction. The major points are summarized as follows:The experts indicated that there is a widespread belief in the construction industry that using waste material results in a lower-quality concrete product (compared to pure cement), which has a negative impact on the performance of concrete structures. Furthermore, the current regulatory framework does not actively promote the use of these waste materials in concrete production. This is seen as a significant barrier to its widespread use. In addition, industrial by-products are commonly associated with toxicity and health risks. Sustainability is also not well-appreciated in the industry due to a lack of awareness among concrete producers, contractors, and consumers about the benefits of sustainable concrete production and the impacts of unsustainable practices.One key economic impediment is an overemphasis on the initial cost of concrete construction, with the long-term benefits of waste material utilization being underappreciated. The interviewees mentioned that, to the best of their knowledge, many batching plant operators are concerned about the increased costs of integrating new technology, methods, and/or materials for large-scale concrete applications. Another major issue for certain waste by-products  is the country’s unstable supply. Only fly ash from coal-fired power stations is now accessible in a constant supply. The country’s high proportion of coal-fired power plants is one of the critical reasons for the widespread use of fly ash. Geographical issues are also important economically, as some waste goods must be transported by water due to the country’s archipelagic character. However, due to their durability benefits for the marine environment, the Philippines’ expansive coastlines present an outstanding potential for fly ash in marine concrete.Another hindrance is a lack of technical knowledge in managing innovative ingredients for concrete mixtures. Many concrete technicians are uncertified since certification is not needed by law. More research, testing, and collaboration between industry and academia are needed. There is a need for more research to develop effective quality control methods for ensuring consistent and reliable use of fly ash in concrete production. Because codes place a high value on strength, technology is scarce for testing durability. Concrete must be exceptionally durable due to the country’s extended wet season and high humidity. Certain by-products, such as fly ash, slag, and rice husk ash, may help in this respect. Furthermore, one expert interviewee observed a lack of strong linkage between the university and the industry. While the Philippines has done multiple studies on various waste materials, most of these have been conducted in academic laboratories and have not been scaled up for industrial application.High fly ash replacement demonstrates greater benefits at higher concrete strength levels, providing an opportunity for improvement in the Philippine concrete industry.Specifying concrete strength at later ages can maximize the environmental benefits of fly ash at high replacement, particularly in the context of mass concrete.Consideration of hot and rainy weather conditions is crucial for handling mass concrete, emphasizing the importance of observing and controlling concrete temperatures to prevent thermal stresses and cracking.Further research is needed to optimize the use of fly ash in concrete production, including determining ideal mix proportions and processing techniques. Enhanced collaboration between academia and industry is essential to improve current practices.Strengthening laws and regulations is necessary to integrate sustainability into concrete production, with active promotion of waste materials within the industry. A supportive environment that fosters sustainability in construction, encompassing legal and economic aspects, should be established.Policies governing the use of cement-replacing materials should consider sustainability analysis to provide comprehensive and informed guidelines. Current regulations and standards for concrete production in the Philippines may require further development to ensure sustainability, necessitating more comprehensive and stringent policies.There is significant room for improvement in current practices in the Philippine concrete industry, with the substitution of cement by waste materials like fly ash offering enhanced sustainability. Sustainable practices should be explored not only for cement replacement but also for other concrete materials, such as aggregates and chemical admixtures. The following are identified opportunities for sustainable fly ash utilization in the Philippines based on the results of impact analysis and key stakeholder interviews:Results of the life cycle endpoint analysis of 31 mass concrete mixtures in the Philippines demonstrated that cement plays a significant role in causing harm to human health, the ecosystem, and resources. Global warming and fine particulate matter generation are the most significant contributors to human health damage. Terrestrial acidification, ozone formation, and land use are the other major drivers of damage related to the ecosystem. A higher fly ash replacement (20%) in normal (27.6 MPa) and high-strength (41.4 MPa) concrete leads to a greater reduction in damages across all endpoint groups, but for lower strength concrete (13.8 MPa), the 10% fly ash replacement mix performs better in terms of environmental performance. The use of admixture in mass concrete production has a significant impact on resource damage, while the incorporation of fly ash proves advantageous in reducing damage associated with human health, ecosystem, and resources.Significant barriers to using fly ash as a cement substitute in mass concrete include institutional, economic, social, and technical dimensions. These barriers are primarily driven by the industry’s assumption that incorporating waste materials, such as fly ash, compromises concrete quality and the lack of recognition for the long-term benefits of waste material utilization in the Philippines. Furthermore, the weak connection between academia and industry hinders the implementation of state-of-the-art findings in large-scale industrial applications. It is essential to enhance laws and regulations that incentivize the use of waste materials to promote sustainable concrete production. Future research may explore the sustainability of other waste materials for mass concrete applications.Supplementary Information.


Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies. Several large-scale shRNA and CRISPR-Cas9 perturbation screenings have been conducted to identify novel cancer genes and dependencies 4. Through the analysis of these screens, researchers have made significant progress in unraveling the complex biology of cancer and identifying novel therapeutic targets 1. To analyze diverse experimental data such as gene expression, mutation, and drug response, a range of statistical and machine learning models have been developed, including models to identify associations between cell line viability and molecular features and models that predict drug responses based on genomic features 7. Moreover, several computational tools have been proposed for uncovering SL interactions, which are critical for discovering personalized cancer therapies that exploit cancer-specific vulnerabilities. Synthetic lethality refers to a genetic interaction between two or more genes where the loss of function, either through genetic alterations or inhibition, in all genes is required to cause cell death8. This concept provides a novel approach to combating cancer, but only a handful of inhibitors that target partner genes in SL interaction, such as BRCA1/2-PARP1, have received FDA approval for use as SL-based therapies11. Identifying clinically significant SL interactions is a challenging task through experimental validation, due to their rarity, as well as the high cost and time requirements associated with wet lab experiments 13. Therefore, various computational approaches have been developed to address this challenge and facilitate the identification of clinically relevant SL interactions in cancer cells. SLIdR is a rank-based statistical method that predicts SL pairs from shRNA perturbation screens in both pan-cancer and cancer-specific settings 2. MiSL is another computational tool that identifies SL partners for specific cancer mutations in particular cancer types using pan-cancer human primary tumors 14.Identifying cancer dependencies through analysis of large-scale perturbation studies is an active research field with the potential to discover new therapeutic targets for cancerous tumors 17. In addition, cancer genome-scale metabolic modeling has been employed to predict SL pairs in various cancer types19. The process of cancer constraint-based metabolic modeling involves tailoring a cancer-specific metabolic model from a generic model and omics data. This tailored model can then be subjected to various analyses based on objective functions such as biomass production, which is a well-established objective in cancer metabolic modeling due to its close association with cell growth rate21.Additionally, several approaches have incorporated genome-scale metabolic networks (GSMN) to investigate the effects of gene knockdown on cancer cell line growth rates and to predict cancer vulnerabilities and potentially identify novel targeted therapies22. In contrast, the gene minimal cut set (gMCS) algorithm searches for MCS in gene level 18. The ngMCS algorithm, an extension of gMCS, incorporates environmental factors in the SL prediction. By considering both gene function loss and nutrient absence, ngMCS incorporate both genetics and the microenvironment for predicting metabolic SL interactions 23. On the other hand, the exhaustive search algorithm aims to identify pairs of reactions whose deletion significantly reduces or nullifies the objective function compared to the wild-type model25. Fast-SL is an exhaustive search strategy that narrows the solution space to find SL pairs efficiently26.There are two metabolic network approaches for predicting SL pairs in different types of cancer: the minimal cut set (MCS) and exhaustive search algorithms. The MCS algorithm identifies sets of reactions that, if removed, would prevent the execution of a specific metabolic function, such as the biomass reaction. Klamt et al. developed the MCS algorithm for SL identification using the enumeration of shortest elementary modes in a dual representation of metabolic networksHowever, these methods exhibit certain limitations. Firstly, these approaches primarily lean on metabolic models contextualized with expression data, often overlooking the inclusion of mutation data. Integrating mutation data could potentially yield improved SL predictions across different cancer types. Secondly, metabolic network-based SL prediction algorithms typically present SL pairs in a deterministic manner, whereas the incorporation of a concept of statistical significance would be more desirable for addressing data uncertainty and imprecision of methodological approaches.This paper presents the development of SL-scan, a computational pipeline that integrates metabolic network modeling and mutation data to identify SL pairs in various cancer types. The approach involves tailoring a generic human metabolic model using iMAT to construct a cancer-specific metabolic model for each cancer cell line. Gene knockouts are then simulated for all genes, and simulated dependency scores are estimated based on the constructed cancer metabolic models for each cell line separately. We then predicted potential SL interaction by evaluating the relationship between the mutation statuses of a driver gene across different cell lines and the simulated dependency scores of a potential SL partner gene. Our results demonstrated that SL-scan outperformed existing SL prediction algorithms in metabolic modeling and successfully identified putative SL pairs in various cancer types, as confirmed by benchmarking against CRISPR, and drug perturbation data analysis. These findings provide valuable insights into the discovery of novel metabolic-based cancer therapies.https://depmap.org/portal/download/all/. The gene expression data set consists of the log2 transformed transcript per million (TPM) values of 19,221 protein-coding genes from 1406 cell lines across 33 cancer types. We obtained the mutation data from the MAF file CCLE_mutations_21Q4.csv, which contains information on all somatic point mutations called in the DepMap cell lines 27. We used Achilles_gene_effect.csv (22Q2) for the CRISPR data set, which includes the effects of CRISPR screening on 18,018 genes in 957 cancer cell lines3. The drug perturbation data used in this study is the primary PRISM Repurposing dataset, which contains the results of pooled-cell line treatment with chemical perturbation for 4,686 compounds against 578 cell lines. The values reported in this dataset represent the response of each compound-cell line combination, indicating how the compound influenced cell viability. This data reflects the growth inhibitory effects of various compounds on cancer cell lines, measured in terms of log-fold changes in cell viability compared to DMSO-treated cells 28. For the integration of shRNA-related information, we incorporated the DRIVE_ATATRiS_data.rds  dataset. This dataset encompasses cancer dependency scores originating from the effects of shRNA-induced silencing on 7,837 genes across 398 distinct cancer cell lines. For generating cancer-tailored models, the generic input metabolic model Recon2.v04, obtained from https://www.vmh.life/files/reconstructions/Recon/2.04, was used 29. Recon2.v04 includes a total of 2140 genes, 5063 metabolites, and 7740 reactions. For our analysis, we specifically focused on the metabolic genes contained in the Recon2.v04 gene list. As a result, the overlap of mutation data, CRISPR dataset, and the metabolic network gene list resulted in 1674 shared genes. Likewise, the intersection of the expression dataset, CRISPR dataset, and mutation data comprised 850 unique cancer cell lines. To ensure that the culture medium does not impact the predictive potential of the SL algorithm, we restricted our analysis to cancer cell lines that were cultured in the RPMI culture medium. Additionally, we only included cancer cell lines that had at least ten observations in the gene expression data. To implement this, we followed the step-by-step procedure outlined in the article by Jamialahmadi et al.30. We used the MutSig files to define significantly mutated genes in TCGA for individual cancer types. Supplementary Table The gene expression data, mutation data, CRISPR, and drug perturbation data sets used in this study were obtained from the Depmap project Flux Balance Analysis (FBA) is a linear programming technique extensively employed in analyses of metabolic network. FBA facilicates understanding of cellular metabolism by optimizing the flow of metabolites through reactions within metabolic networks. The constrained optimization problem of FBA is given as follows:In this formulation, 'c' denotes a vector of coefficients that indicates the respective contributions of individual reactions to the objective function, x is the reaction flux vector, S is the stoichiometric matrix, and l and u are lower bounds and upper bounds for reaction fluxes, respectively. The solution x captures the optimized reaction fluxes under the above constraints.S) is a key component in FBA, representing the stoichiometry coefficients of each metabolite participating in the network reactions. Each row of the matrix corresponds to a metabolite, and each column corresponds to a reaction. The elements of S indicate the stoichiometric coefficients of each metabolite in a given reaction. The stoichiometric matrix forms the basis for mapping the connectivity and flow of metabolites in the network.The stoichiometric matrix (33.The Gene-Protein-Reaction Association (GPRA) rules specify a connection between genes, proteins, and reactions within a metabolic model. These rules define which genes encode enzymes responsible for catalyzing specific reactions. GPRA rules enable the translation of genetic information into the context of metabolic reactions, thereby facilitating the integration of gene expression data into metabolic models34. The iMAT algorithm used both Upper Bound (UB) and Lower Bound (LB) reaction expression thresholds. For each cancer type, we conducted a grid search to identify the first suitable UB and LB thresholds. These thresholds were determined based on their ability to successfully solve an FBA simulation with a non-zero objective function value. This iterative process allowed us to optimize the UB and LB values for each specific cancer type, ensuring accurate modeling of cellular metabolism and achieving meaningful simulation results. The createTissueSpecificModel function in the COBRA toolbox was used to construct the cancer-specific models, taking into account the UB and LB thresholds, a reaction expression vector, the generic model, and a core set of reactions as inputs. This approach has proven to be useful in cancer metabolic modeling (Fig. 35.We used gene expression data to tailor the Recon2.v04 human metabolic model and create cancer-specific metabolic models using the iMAT algorithm, which has been shown to maintain more cancer-specific genesing Fig. b 30,33,336.We verified the structural soundness of all constructed models for both linear programming and quadratic programming problems using the verifyCobraProblem function. Additionally, we used the verifyModel function, a built-in function of the COBRA toolbox, to ensure the reaction flux consistency, mass balance, and the presence of necessary fields in the resulting models. Using Opdam et al.'s definition of the 56 metabolic-based functionalities essential for cancer cell proliferation, we evaluated each constructed model. We compared the number of functionalities of each model against 100 random models with the same size. A cancer model construction was considered successful if its number of passed functionalities is larger than 99% of the numbers of functionalities passed by random modelshttps://github.com/RamanLab/FastSL. The FastSL method uses the default cutoff value of 0.01. The ngMCS related functions were implemented using supplementary files of the corresponding article23. All the SL prediction algorithms were restricted to searching for cut sets or SLs with a maximum of two genes. Moreover, a time limit of four minutes is designated for solving the optimization problems of gMCS, MCS, and ngMCS algorithms. These algorithms were configured to predict a maximum of 1000 SL pairs. For translating reaction-level SL pairs to gene-level pairs for the FastSL and MCS algorithms, we used the 'findGenesFromRxns' function of the COBRA toolbox. This function applies the GPRAs of the Recon2.v04 metabolic model. For the ngMCS algorithm, which predicts metabolites involved in SL pairs, we initially identified the reactions using the predicted metabolites with the 'findRxnsFromMets' function of the COBRA toolbox. Subsequently, we then converted these reactions into genes using the 'findGenesFromRxns' function(Fig. We employed MCS, gMCS, ngMCS, and FastSL algorithms for the SL identification. We implemented MCS and gMCS using the COBRA-toolbox's built-in functions. We followed the guidelines outlined on the relevant GitHub page to use FastSL, which can be accessed at The SL-scan has the following steps:Step 1: We converted each cancer cell line's gene expression into a metabolic model. To minimize the Taxicab norm of reactions in the FBA problem of a constructed model using the COBRA Toolbox, we used the optimizeCbModel function with the minNorm parameter set to 'one', which solves an LP problem that minimizes the sum of absolute values of fluxes, subject to the constraints of the FBA problem. Mathematically, the optimization problem is defined as:Step 2: We conducted simulations to assess the growth rate of the metabolic models both before and after each gene knockout from the KO-gene list. This process generated a KO-simulated score matrix. For genes not included in the KO-gene list but present in the generic model, we assumed that they had no inhibitory effect on growth. Therefore, we assigned a value of 1 to those genes, indicating that their knockout had no impact on the model's growth rate compared to the wild type (Fig. Step 3: We then used the mutation dataset to stratify cancer cell lines into mutated and wild groups for each driver gene. We define driver genes as metabolic genes that harbor at least one damaging mutation in at least two cell lines for specific cancer types.Step 4: To compare the KO-simulated scores between the wild-type and mutated groups, we conducted a t-test for each pair of KO and driver genes. To enhance the efficiency of the SL-scan pipeline in the exhaustive search, we implemented a filtering to exclude driver genes that had a mean greater than 0.95 in the mutated group. In these cases, we assumed a P-value of one. Furthermore, we introduced uniform noise ranging from 1e-12 to 1e-11 to both the wild-type and mutated groups. This adjustment was made to accommodate the metabolic network's tendency to generate KO ratio values with a standard deviation of zero in a small fraction of cases. Furthermore, we used the Benjamini and Hochberg approach to address multiple testing issues and control for false discovery error rate (Fig. To assess the accuracy of the GSMN-based SL pair predictions, we constructed a gold standard using CRISPR perturbation data. We divided the cell lines into wild-type and mutated groups for each driver gene and conducted a t-test on the CRISPR gene dependency scores between the two groups of cell lines for each KO gene. Next, we employed a hypergeometric test to determine whether the CRISPR predicted SL interactions were enriched in the list of SL-scan SL interactions Fig. d-e.We validated the predictions of GSMN-based SL pairs by cross-checking with the PRISM dataset. We classified cell lines according to the mutation status of driver genes and subsequently conducted t-tests on the PRISM log fold-change values of perturbed genes. We evaluated the enrichment of PRISM-predicted SL interactions among SL-scan predictions using hypergeometric tests Fig. d and f.37. Similar to our analysis on the CRISPR dataset, we employed mutation data from cancer cell lines to identify driver genes for each cell line. Subsequently, a t-test was employed to assess the influence of gene silencing in cancer cells harboring driver genes with damaging mutations compared to those without such mutations.We employed ATARiS cancer dependency scores to evaluate the influence of gene silencing through shRNA reagents on cancer cell linesTo thoroughly analyze the involvement and the presence of targets within the context of our predicted SL pairs, we focused on the top 1st quantile of frequently KO genes as determined by the SL-scan pipeline. We provided the associated PubMed IDs to indicate supporting literature for each KO gene. In addition, we also annotated whether a driver gene is a significantly mutated gene in the TCGA dataset using the MutSig files for each cancer type. A gene with a q-value of 0.05 or lower was identified as a significantly mutated gene in TCGA.The goal of this step was to discover significant biological processes connected with cancer-driver genes. To this end, a ranked gene list was generated for each driver gene. This involved compiling a list of SL partner genes alongside their corresponding t-statistic values in relation to the driver gene Fig. g. AdditiAfter applying filtration steps to identify relevant cancer types, we have selected a total of 16 distinct types of cancer for inclusion in our study. Due to the inability to provide viable models in the iMAT grid search for UB and LB thresholds, Gastric, and Lymphoma cancers were excluded from the subsequent analyses see "" sectionTo identify SL interactions between driver genes and KO genes, our initial step involved constructing a matrix through simulating gene knockout effects using metabolic models. We considered all the metabolic genes within the Recon2.v04 model, resulting in a testing of 1674 KO genes across all cancers. Additionally, the number of driver genes varied for each cancer type, as depicted in Figure S3.To validate SL predictions with CRISPR data, we used Achilles dependency scores and mutation datasets, along with the Recon 2.v04 model gene list. Table Supplementary Table Supplementary Tables ALPI-TYMS and PKM-DHFR, treated with nolatrexed and proguanil respectively. Panel b displays lung cancer SL pairs GUCY1A2-TYMS and PDE10A-TYMS, treated with leucovorin and capecitabine. Panel c depicts gastric cancer SL pairs KMT2C-TYMS and KMT2C-TYMS, treated with trifluridine and capecitabine. Refer to Supplementary Table To identify potential drugs for SL pairs, an exhaustive search was conducted using the PRISM dataset, similar to the CRISPR gold standard approach. No substantial drug was found among the SL pairs predicted by the FastSL, MCS, or gMCS algorithms. On the contrary, we discovered 10 different drugs for 14 different SL pairs using the SL-Scan and the PRISM dataset. Figure Supplementary Table KMT2C-PTDSS1 SL pair emerges prominently in the bile duct, brain, breast, gastric, lung, and head and neck cancers, while the LRP2-PTDSS1 SL pair is prevalent in breast, ovarian, lung, and head and neck cancers. The shared knockout gene in these recurring SL pairs may suggest its cross-cancer importance. Additionally, the driver genes KMT2C and LRP2 rank among the most frequent driver genes. The recurring patterns of these SL pairs in the SL-scan outcomes, visually represented in Fig. The ranking score was determined for the partner genes of each driver gene in the predicted SL pairs using the SL-Scan approach see "" sectionThe SL-scan pipeline was developed to identify SL interactions by integrating mutation data and metabolic network modeling for different types of cancer. The pipeline involved the use of a generic human metabolic model to construct cancer-specific metabolic models, followed by exhaustive prediction of SL pairs for each cancer type. Specifically, the SL-scan pipeline begins by identifying a minimal list of reactions and translating them into corresponding genes. The pipeline then performs KO simulations for each gene in cancer metabolic models and evaluates the statistical association with mutation data.Focusing on the common gene list between all considered approaches, the SL-scan approach successfully predicted 83 concordant SL pairs with the CRISPR analysis, significantly higher than the number of SL pairs predicted by other approaches. In particular, SL-scan exhibited the highest concordance in 13 different types of cancer. The MCS and FastSL methods also predicted one SL pair each, which concurred with the CRISPR analysis. However, the gMCS approach did not predict any SL pairs concordant with CRISPR. Additionally, we performed hypergeometric tests to assess the enrichment of CRISPR SL pairs in the SL pairs reported by SL-scan and other methods. The SL-scan method showed significant enrichment for lung cancer, ovarian cancer, and leukemia were found to be significant, while other methods did not reach statistical significance in any cancer type.RRM1 regulates DNA replication and ATR inhibition vulnerability, while RRM2 drives aggressive prostate cancer and hepatocellular carcinoma, suggesting their importance for targeted therapies 39. On the other hand, SLC7A6 and SLC25A19 play crucial roles in cancer by affecting amino acid transport, energy metabolism, and potential therapeutic targets41. Additionally, PLD, is known to be associated with lung, liver, and breast cancers 43.Furthermore, we discovered that KO genes linked to numerous driver genes across various cancer types play significant roles in cancer-related processes (Supplementary Table SLC15A2-CYP3A4 for breast cancer. Previous studies have shown that inheriting the CYP3A4*1B allele may increase susceptibility to early-onset menarche, which is a known risk factor for breast cancer 44. Furthermore, studies have reported that the expression levels of SLC15A2 RNA were lower in lung cancer tissues than in normal tissues 45. Another example of a promising SL pair predicted by SL-scan is PLCG1-LDHAL6A in bile duct. The literature supports the role of the gene LDHAL6A, which has been found to have significantly higher expression in malignant diffuse peritoneal mesothelioma tissues compared to normal mesothelial tissues 46. Another example of an SL interaction predicted by the SL-scan approach is between ACADVL and FABP1 in pancreatic cancer. Studies have shown that FABP1 staining significantly increases in pancreatic adenocarcinoma samples compared to normal samples47.The SL-scan approach has identified some promising SL pairs that were also found by the CRISPR analysis too. For example, we identified the SL pair KMT2C-TYMS in gastric cancer. The analysis of the PRISM data also identified the interaction between the driver gene KMT2C and the drug Capecitabine, an oral prodrug of 5-fluorouracil that targets TYMS and is commonly used in the treatment of advanced gastric cancer 49. KMT2C, a frequently mutated driver gene, has been reported in the literature to have an impact on gastric cancer progression. Mutations in KMT2C are also associated with increased PD-L1 positivity, indicating the existence of PD-L1 protein that can inhibit immunological responses against cancer cells50. Another example is the PKM-DHFR SL pair predicted for skin cancer by the SL-scan approach. The driver gene PKM is critical in tumor metabolism and has been found to be overexpressed in various cancers, promoting tumor cell proliferation and metastasis 51. Furthermore, Proguanil, an anti-malaria drug that targets DHFR, has been suggested to play a role in preventing some cancers by enhancing EGFR degradation and inhibiting its downstream signaling pathway to induce autophagy 53.The concordant results between the SL-scan and PRISM methods provide valuable insights from the literature. For example, we were able to predict the SL pair 54.Additionally, we performed a gene set enrichment analysis on partner genes for each driver gene and discovered key biological processes for each gene set, explaining each SL pair's likely biological process. Some of the most common biological processes include the carboxylic acid metabolic process, the organic acid metabolic process, and the oxoacid metabolic process, which are known as likely biological processes in cancers, and it was also reported in a study that genes differentially expressed in a mouse model of hepatocellular carcinoma were also enriched for the GO terms oxoacid, carboxylic acid, and organic acid metabolic processesSL-scan is an approach to SL prediction that differs from other metabolic model-based methods in several ways. Firstly, instead of constructing models per cancer type, SL-scan builds models on a per-cell-line basis, enabling better modeling of heterogeneity in input expression data. Secondly, SL-scan leverages mutation data to enhance its SL predictions and gain a deeper understanding of the genetic landscape of cancer and its relationship with KO experiments. Finally, SL-scan provides statistical significance measures for predicted SL pairs, which sets it apart from deterministic approaches that solely predict SL pairs without any statistical measures.The SL-scan approach has some limitations that need to be addressed to improve its ability to identify SL pairs and provide a better understanding of cancer dependencies. One such limitation is that it only considers metabolic genes, which may not be sufficient to capture the interactions between different types of genes involved in cancer development and progression. Therefore, it is important to incorporate other types of biological networks, such as gene regulatory networks, to identify SL pairs in a larger space of genes. Another challenge in using metabolic network modeling is the integration of gene expression data. Since gene expression data is inherently heterogeneous, it can be challenging to develop context-specific models that accurately reflect the underlying genetic state of the cell lines. Additionally, incorporating mutation data into context-specific metabolic models can also be difficult due to the lack of robust algorithms for this purpose. These challenges highlight the need for continuous refinement of computational approaches that can integrate diverse biological data sources.To summarize, our study introduced the SL-scan pipeline, which outperformed other approaches based on metabolic network modeling. This approach has the potential to identify more promising SL interactions, opening up possibilities for the development of new and more effective metabolic therapies for cancer.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.


Primary care is a critical partner for antimicrobial stewardship efforts given its high human antibiotic usage. Peer comparison audit and feedback (A&F) is often used to reduce inappropriate antibiotic prescribing. The design and implementation of A&F may impact its effectiveness. There are no best practice guidelines for peer comparison A&F in antibiotic prescribing in primary care.To develop best practice guidelines for peer comparison A&F for antibiotic prescribing in primary care in high income countries by leveraging international expertise via the Joint Programming Initiative on Antimicrobial Resistance—Primary Care Antibiotic Audit and Feedback Network.We used a modified Delphi process to achieve convergence of expert opinions on best practice statements for peer comparison A&F based on existing evidence and theory. Three rounds were performed, each with online surveys and virtual meetings to enable discussion and rating of each best practice statement. A five-point Likert scale was used to rate consensus with a median threshold score of 4 to indicate a consensus statement.The final set of guidelines include 13 best practice statements in four categories: general considerations (n = 3), selecting feedback recipients (n = 1), data and indicator selection (n = 4), and feedback delivery (n = 5).We report an expert-derived best practice recommendations for designing and evaluating peer comparison A&F for antibiotic prescribing in primary care. These 13 statements can be used by A&F designers to optimize the impact of their quality improvement interventions, and improve antibiotic prescribing in primary care.The online version contains supplementary material available at 10.1186/s13756-023-01279-z. Rising antimicrobial resistance (AMR) poses a threat for modern medicine and society as a whole. In 2019, an estimated five million global deaths were associated with bacterial AMR .Primary care is responsible for 80–90% of human antibiotic usage, making general practitioners and pediatricians critical partner for antimicrobial stewardship , 5. It iThe Joint Programming Initiative on Antimicrobial Resistance—Primary Care Antibiotic Audit and Feedback Network (JPIAMR-PAAN) is an international network of over 40 members representing 15 countries with expertise in the fields of antimicrobial stewardship, primary care, and implementation science  with three rounds of surveys and feedback meetings to identify and develop best practice guidelines for peer comparison A&F of antibiotic prescribing in primary care settings of high-income countries –22.This study presents results from expert consensus and did not require review or approval from an ethics review board. All members of JPIAMR-PAAN were intended panelists of the present MDP, with expertise in the fields of antimicrobial stewardship, primary care, and/or implementation science. JPIAMR-PAAN members represent 15 high-income countries in Australia, Europe, and North America  was adopted as the consensus rating tool with the median threshold of percent agreement used for consensus , 24. FrePanelists of the MDP underwent three rounds of online surveys and consensus rating  and two coordinators , reviewed existing guidance on developing effective A&F interventions, and drafted an initial questionnaire with multiple choice and open-ended questions (Survey 1a). The core team drew on Brehaut et al.’s 15 suggestions for optimizing effectiveness , and incth, 2022. Median Likert scores were obtained for each statement and subsequently used to facilitate the first virtual meeting.Invitation to complete Survey 1a was emailed to all JPIAMR-PAAN members on March 28th, 2022. Members were invited to respond and/or suggest additions or modifications to the survey. Results were reviewed by the process facilitators, and an initial list of consensus statements was produced (Survey 1b). This initial list of best practice statements was sent by email to all JPIAMR-PAAN members for consensus rating and feedback on April 11All JPIAMR-PAAN members were invited to attend the first virtual feedback session on April 21st, 2022. The median Likert score of each statement was presented by the process facilitators (AX and BM), followed by a facilitated discussion and clarification of feedback. Using suggestions and feedback generated from the virtual meeting, a modified set of statements was produced by the process facilitators (Survey 2). All network members were invited to complete Survey 2 for consensus rating and provide open-text feedback on May 30th, 2022.Following an iterative approach, the results from Survey 2 were used to facilitate discussion of the second virtual meeting on June 14th, 2022, with prioritization on statements that did not reach consensus. Feedback generated from the second meeting and Survey 2 were then used to produce the final set of statements (Survey 3). This final set of statements was distributed to all JPIAMR-PAAN members for a final round of consensus rating on July 11th 2022. All statements with a median Likert score >  = 4.0 were included in the final set of best practice recommendations.We administered four online surveys and two virtual meetings in total as part of the MDP. All four surveys had a response rate of 54% (20 out of 37), with 12 panelists completing all four surveys. Meeting #1 and #2 had 14 and 13 panelists present, respectively; with eight panelists attending both meetings. Overall, seven panelists participated in all aspects of the MDP by responding to all surveys and attending both meetings. The final product includes four categories and 13 best practice recommendations for designing and evaluating A&F interventions for antibiotic prescribing in primary care. Table The best practice recommendations below are presented with a summary of internal discussions and comments around each statement.Antibiotic audit and feedback interventions in primary care should be framed as quality improvement projects within a supportive environment.There exists substantial variability in antibiotic prescribing in primary care , 26. A&FPrior to initiating an antibiotic audit and feedback intervention in primary care, consider potential barriers to success such as local data availability, data validity, expected engagement of feedback recipients, perceived patient expectations for antibiotics, and other situational factors.information to intention and ultimately link that intention to the desired behavior [. Clinicians need to accept the data presented to them, understand how to interpret the data and the expected action to take as a result. Gaps or weaknesses in any of these components of the cycle will create an intention-to-action gap reducing the desired effect; in this case resulting in no reduction in inappropriate antibiotic prescribing [A&F interventions consist of proceeding through one or more cycles of establishing best practice criteria, measuring current practice, feeding back findings, implementing changes, and further monitoring of data . All aspbehavior . Cliniciscribing .Strategies to optimize reach and engagement of an antibiotic audit and feedback intervention in primary care include; utilizing an opt-out approach to delivery of feedback reports, offering of continuing medical education credits, financial incentives, and facilitated peer group discussions.The starting point for A&F interventions is that they are important quality improvement initiatives. The poorest performing clinicians benefit the most from A&F, but are also least likely to volunteer . For thiAll primary care prescribers, regardless of practice type or prescribing volume, should be included in antibiotic prescribing audit and feedback interventions.While there was general agreement that receiving A&F should follow an opt-out approach (statement 3), however, discussions during the MDP meetings revealed a lack of consensus whether A&F programs should preferentially include a subset of prescribers . MDP participants articulated that selecting appropriate prescribers to be involved in A&F is context dependent. As discussed above A&F is a quality improvement initiative that should be standard practice in all jurisdictions. All prescribers may benefit from their data with peer comparisons and/or comparison with standard of care, with opportunities to improve. The goal of antibiotic A&F at the population is to shift the mean towards lower inappropriate antibiotic use, and avoiding regression to the mean whereby lower prescribers increase their antibiotic use. Targeting only the highest prescribers, or lowest performing physicians, may avoid regression to the mean as well as be more cost effective , 13. We Feedback indicators for antibiotic prescribing in primary care should target reductions in antibiotic initiations, prolonged antibiotic duration, and/or unnecessary broad-spectrum antibiotics.As demonstrated by the CP-FIT model, designers of feedback interventions should consider importance, controllability, and relevance . Data inTotal antibiotic use is highly correlated with unnecessary or inappropriate use , and in Antibiotic feedback reports in primary care should enable and support behaviour change by providing guidance and educational resources.Feedback intervention designers should consider how supporting resources and guidance use behavioural techniques to support practice change. Providing antibiotic guidelines or patient directed educational materials may not by itself significantly improve the effect of A&F , 13, howThe optimal data source for antibiotic audit and feedback in primary care is credible, valid, routinely collected, and comprehensive for the region; ideally containing prescription, diagnostic, and clinical data.The CP-FIT suggests that data collection and analysis for feedback should be automated, accurate, and accepted by recipients . One of Benchmarks or achievable targets for peer comparisons for antibiotic prescribing in primary care should be indicator-specific and based on national and/or local performance data of high performing peers.et al. effectively compared physicians to their top decile of their peers for unnecessary antibiotic prescribing for viral infections [In the absence of being able to accurately define inappropriate antibiotic prescriptions within the data used for feedback, the optimal target, or benchmark, that should be used is controversial and depends on the indicator used, data availability, and the existence of local antibiotic prescribing guidelines. Previous interventions have had varied comparators. A study by Meeker fections . Hallswofections  comparedfections . Given tfections . We encoAntibiotic audit and feedback in primary care should be displayed such that recipients can understand their performance and desired actions within seconds.Primary care clinicians are busy, manage a broad range of patients, and have multiple competing priorities for quality improvement. Designers of A&F interventions should minimize extraneous cognitive load for feedback recipients by prioritizing the relative importance of feedback contents and employ user-friendly designs . Overly Antibiotic audit and feedback reports in primary care should be repeated with updated data over time. The optimal frequency is not known but can depend on local factors such as data availability and seasonality of prescribing.A&F provided more than once is generally more effective . ProvidiAntibiotic feedback in primary care should be ideally delivered by multiple strategies including verbal, paper, and/or electronic means.The Cochrane review on A&F identified larger effect sizes when feedback is provided through both written and verbal communication . The comAntibiotic feedback should be delivered to primary care prescribers from a respected authority figure or colleague.A common criticism of A&F by recipients is the credibility of the information provided . CP-FIT Individual-level antibiotic feedback should be delivered confidentially to primary care prescribers, and the opportunity for peer discussion should be provided and encouraged.Where possible, individual feedback, opposed to group or regional level feedback, should be provided to address specificity. The personalized nature of A&F facilitates the desired behaviour change and is less likely to be discounted by the recipients. Engaging with the feedback through social interaction is based on educational research on improved adult learning through social construction compared to passively received materials. Peer discussion and active delivery of feedback are both supported by CP-FIT to improve effectiveness of feedback interventions . FurtherAntibiotic A&F for primary care clinicians is a potentially effective population level intervention to improve antibiotic use and combat rising AMR. However, the design and delivery of these interventions are critical to their success. The best practice statements found in this study provides 13 recommendations for designers of antibiotic primary care A&F interventions which builds on existing evidence, best practices for A&F in general , the CP-Best practice recommendations for A&F exist and this work builds on those recommendations to provide more specificity related to A&F on antibiotics in primary care. Table Inappropriate antibiotic prescribing behaviour is complex and driven by habit, fear, time constraints, and perceived patient expectations. Rarely, is it related to a simple knowledge gap to be affected by education alone . Multifawww.jpiamr-paan.org.We have included a toolkit to facilitate the application of these 13 best practice recommendations as well as examples of applying the toolkit to previously published trials (Additional file There are limitations to this guidance document. Not all statements will apply to all interventions depending on local context and data availability. There are important gaps in the evidence with some aspects more heavily influenced by previous experience and expert opinion. The modified Delphi process provides some rigour to these opinions and we highlighted within each statement areas of gaps in evidence which should be prioritized for future research. AMR is a global problem that impacts low and middle income countries disproportionately. However, the etiology of rising AMR, and actions required to combat them, have important differences compared to high income countries. These recommendations focus on A&F specifically for high income countries and may not be appropriate for low or middle-income countries.We encourage antimicrobial stewardship and public health programs to utilize this guidance and the checklist provided in Additional file Additional file 1: Supplementary materials.


In January 2001, I wrote an editorial on this page with the title "Coffee, Samba, Football, AIDS and Social Inequality in Brazil." The first paragraph described a contradiction within the Brazilian National Health System: "The most important newspapers and magazines worldwide  have been publishing the tremendous results from the Brazilian AIDS program. This is a combination of preventive measures and free distribution of antiretroviral drugs with an impressive fall in hospitalizations and mortality. Now, the Brazilian program has become the paramount public health effort for halting the AIDS epidemic to be followed in other places, especially the sub-Saharan African countries. In contrast, the Health Department of São Paulo State is warning about increasing case-fatality rates of tuberculosis. How can this contradiction be explained for a non-Brazilian?"Imagine if I published the same editorial again, just changing the phrase "about increasing case-fatality rates of tuberculosis" by "the recent incidence of Zika virus infection and new cases of microcephaly". Readers might think that they could detect an instance of self-plagiarism. Unfortunately, this is not a lazy way to write a comment by copying an older one. The fact is that the players can change, but the play is the same on the stage of Brazilian public health.The question transcribed above was answered in that editorial in 2001: "for us, Brazilian physicians and medical researchers, the answer is very easy. AIDS is a disease that has afflicted people with real prestige in Brazilian circles of power. In summary: journalists, popular music stars, soap-opera actors and actresses, physicians, scientists, military officers, priests and other well-born citizens who traveled to the USA during the early 1980s and were infected by sexual intercourse in New York City and San Francisco. After that, they spread the virus and disease among Brazilians who had never dreamed of going up the steps on an airplane. Until the early 1990s, in São Paulo City, the incidence of AIDS cases continued to be greater among affluent people than among poor ones. As the spread of AIDS became a menace to the abovementioned professions, it was easy to pressure the Ministry of Health, State Departments of Health, and other authorities to increase the budget for AIDS control and treatment, including sometimes a shift of resources from other programs".The Lancet, the federal budget for AIDS has been increasing and the budget for controlling vectors like Aedes aegypti is still declining.For more than a decade, the Brazilian AIDS program was self-proclaimed as "an example for the world". In contrast, the control over dengue has deteriorated year by year. Despite the manifest failure of control over the dengue epidemic, which has been conceded by most of the communicable disease epidemiologists in Brazil, in an article in One year ago, the voracious appetite for more money propelled the Brazilian authorities, physicians and AIDS activists to stay on the front-line of the fight against AIDS by advocating adoption by the Brazilian National Health System of a strategy called "pre-exposure prophylaxis" for HIV control. This is a very controversial proposal that will cost $ 13,000/year/per capita. I was invited to wrote an op-ed article in the most-read Brazilian newspaperThe basis of my reasoning was that scientific articles supporting the use of pre-exposure prophylaxis presented several limitations, not only from a strictly scientific view, but also from a strategic point of view. In my view, this proposal was a step backwards in relation to preventive efforts towards halting both AIDS and also other sexually transmitted diseases.Folha de S.Paulo was to refer to Arnold Relman , who wrote a seminal editorial about the concept of the medical-industrial complex. He described this situation as similar to the one of the "military-industrial complex" that Dwight Eisenhower described during his US Presidential term in the 1950s.My reasoning in that article in In fact, we are under heavy pressure from the "medical- industrial complex," now using UNAIDS as its spokesperson, to take money from other activities such as vector control, in order to put it into the pockets of the AIDS medical industrial complex. What is incredible in this story is that the proposal for pre-exposure prophylaxis relates only to one medicine: Truvada, manufactured by Gilead, in California. The leftist watchdogs who start barking if there is even a one-day delay in free distribution of any HIV medication did not even whimper about any conflict of interest in this case.One year after this debate, the same newspaper, Folha de S. Paulo published an article signed by the "Vice Brazil Organization" regretting that the Brazilian National Health System still did not include pre-exposure prophylaxis as free medicine. Furthermore, the author stimulated readers to start lawsuits against the Brazilian National Health System to get free drugs so that people could indulge in unsafe sexual intercourse.At the same time that pre-exposure prophylaxis was becoming a motive for great concern within the media located in São Paulo and Rio de Janeiro, less fortunate people in the poorest areas of the country were suffering from dengue, chikungunya and Zika. To the best of my knowledge, no one involved in AIDS activism is either creating some networks for supporting parents and children afflicted by the consequences of Zika infection, or is demanding more funds for combating zika infection.Aedes aegypti outbreak using military metaphors. She blamed her predecessors, despite the fact that this is her second term. It would be better for us if the President, the Brazilian Congress, the Ministry of Health, the media, the medical establishment and leftist activists could only acknowledge that we have been doing things wrongly for a long time now. Zika is only the play of the season of social inequalities in Brazil.An editorial by Richard Horton in The Lancet took the view that the Zika epidemic is an opportunity for Brazil to change its public health system.


LOC_Os01g68500, was identified firstly through gene-based haplotype analysis, gene expression and knockout transgenic verification.Genome-wide association mapping revealed a novel QTL for shoot length across multiple environments. Its causal gene, qSL-1f on chromosome 1 could be stably detected across all three cultivation methods in the whole population and indica subpopulation. Through gene-based haplotype analysis of the annotated genes within the putative region of qSL-1f, and validated by gene expression and knockout transgenic experiments, LOC_Os01g68500  was identified as the causal gene for SL, which has a single-base variation (C-to-A transversion) in its CDS region, resulting in the significant difference in SL of rice. LOC_Os01g68500 encodes a DUF538 (Domain of unknown function) containing protein, and the function of DUF538 protein gene on rice seedling growth is firstly reported in this study. These results provide a new clue for exploring the molecular mechanism regulating SL, and promising gene source for the molecular breeding in rice.Strong seedling vigor is an important breeding target for rice varieties used in direct seeding. Shoot length (SL) is one of the important traits associated with seedling vigor characterized by rapid growth of seedling, which enhance seedling emergence. Therefore, mining genes for SL and conducting molecular breeding help to develop varieties for direct seeding. However, few QTLs for SL have been fine mapped or cloned so far. In this study, a genome-wide association study of SL was performed in a diverse rice collection consisting of 391 accessions in two years, using phenotypes generated by different cultivation methods according to the production practice, and a total of twenty-four QTLs for SL were identified. Among them, the novel QTL The online version contains supplementary material available at 10.1007/s00122-023-04497-6. Rice is the staple foods for more than half of the world population, and rice production plays a critical role in world food security. However, increasing threats to rice production from labor shortage, energy scarcity, decline of water table and change in climatic conditions drive to search for an alternative cultivation system to traditional puddle transplanting /seedling height is one of the important traits characterized by rapid growth of seedling, which can enhance seedling emergence , encoding a DUF538 (Domain of unknown function) containing protein was identified as the causal gene underlying qSL-1f, which controlled SL at the seedling stage. Sequence analysis revealed that a single-base transversion (C-to-A) in its CDS region caused the significant difference in SL. To our knowledge, it is the first time to discover the function of DUF538 protein gene on rice seedling growth. These results provide a new clue for exploring the molecular mechanism regulating SL and promising gene source for the molecular breeding of rice with strong seedling vigor.In this study, we conducted GWAS of SL using a subset of the Rice Diversity Panel 2 (sRDP2) consisting of 391 rice accessions, which has been genotyped using 700 K SNPs  consisting of 391 rice accessions from 56 countries were used for GWAS in this study (Table S1). These rice accessions were selected from the Rice Diversity Panel 2 (RDP2) consisting of 1568 rice accessions based on their diversity and origins : the seeds were pre-germinated and sown in plastic trays (35.0 cm × 23.0 cm × 6.0 cm); Method 2 (GSF): the seeds were pre-germinated and sown in the paddy field; Method 3 (DST): the seeds were directly sown in plastic trays without pre-germination. GST were conducted in May 2018, while GSF and DST were conducted between late April and early May 2019. For GST and GSF, the soaked seeds were placed in a 7 cm petri dish with two layers of wet filter paper, and put into a growth chamber with 30 °C and relative humidity of 70% for germination in dark. After 2 days, the germinating seeds were sown into plastic trays filled with fine soil (GST in 2018) or in paddy field (GSF in 2019). For DST, the seeds were directly sown into plastic trays filled with fine soil without pre-germination. After 14 days of growth in natural environment, the shoot length (SL) of seedling were measured with a ruler. In this study, three replicates with 10 plants per accession were adopted in SL evaluation, and the average air temperature was 28.7 °C, 23.6 °C and 24.1 °C under GST, GSF and DST, respectively.For knockout transgenic (KO) lines, the healthy and filled seeds of the KO lines and their wild-type line were incubated at 49 °C for 96 h to break dormancy. After sterilized with 3% sodium hypochlorite solution, the seeds were soaked in distilled water for 24 h. The pre-germinated seeds were sown in black plastic culture boxes (12 cm × 8.6 cm × 11 cm) filled with 0.1% Yoshida nutrient solution (July 2022) or fine soil (June 2023), then put into a growth chamber with 30 °C and relative humidity of 70% for growth (12 h light/12 h dark). After 14 days, the SL of seedling were measured. Four replicates with 20 plants per lines were adopted in SL evaluation.indica, japonica and aus    of 343 rice accessions  and purified using RNeasy Plant Mini Kit .actin was used as endogenous normalized genes for mRNA. All reactions were run in triplicate. Primers used to amplify the selected genes are listed in Table S2.RNA reverse transcription reactions were performed using the Prime Script TM RT reagent kit . The primers for qRT-PCR were designed by Primer Premier 5.0. Quantitative real-time PCR was carried out using the SYBR Premix ExTaqTM kit , following the manufacturer’s instructions, on a BioRad CFX 96 Real-Time System. The LOC_Os01g68460 and LOC_Os01g68500 single-guide RNA (sgRNA) sequences were cloned using pYLgRNA-OsU3, respectively, as described previously  AGG at the 3′ end; the target site sequence of LOC_Os01g68500 was 5′-CGGATTTCAGGCACGACGAA-3′, which contained a PAM of GGG at the 3′ end. The positive plasmids were electroporated into Agrobacterium tumefaciens EHA105 and then introduced into calli of the cultivar Nipponbare via Agrobacterium-mediated genetic transformation, respectively.In order to validate the candidate gene for SL, we conducted the knockout transgenic experiments. To generate the CRISPR/Cas9 vectors, 2 generation, the homozygous positive transgenic plants of the candidate genes were selected by gene cloning and sequencing. The seeds of the homozygous positive plants were used to evaluate SL as described above, and the wild-type plants (Nipponbare) were used as control.At the Tt-test or Duncan’s multiple range test was conducted using SPSS10.0 to detect the differences in SL and expression levels of the candidate gene between or among the tested rice accessions.A Large variations in SL were observed in sRDP2 population using the three cultivation methods Table S1), ranging from 22.0 to 50.2 cm, with an average of 34.3 cm and a variation coefficient of 16.3% under the GST; from 17.0 to 41.2 cm, with an average of 29.2 cm and a variation coefficient of 15.4% under the GSF; from 12.5 to 34.2 cm, with an average of 23.3 cm and a variation coefficient of 16.3% under the DST. The SL in 391 rice accessions displayed a continuous and normal distribution  under all of the three cultivation methods; while the SL of aus group was significantly higher than that of japonica group under the GST and DST (P < 0.05) and indica group under GST (P < 0.05), but not significantly different from that of indica group under GSF and DST, and japonica group under GSF (P > 0.05)  Fig. d–f.P < 0.0001), with correlation coefficients of 0.726, 0.769 and 0.787 between GST and GSF, GST and DST, GSF and DST, respectively, suggesting there were stably QTLs associated with SL.The pairwise correlations of SL between different cultivation methods were highly correlated  decay in tested rice accessions   more than 5% in the sRDP2 population, 446,536 SNPs were selected for GWAS from the 700 K SNPs dataset in the Open Rice GWAS Platform  are firstly reported in the present study, and the other six QTLs co-localized with the previously identified SL QTLs . The average SLs of the lines carrying Hap2qSL−1f were 35.3, 29.7 and 24.1 cm, which were significantly longer than that of the lines carrying Hap1qSL−1f, with the average SLs of 29.0, 24.5 and 19.2 cm under the cultivation method of GST, GSF and DST, respectively.Significant differences in SL were observed between the two main haplotypes of -1f Fig. b–d underqSL-1f   of 343 rice accessions used in this study  and LOC_Os01g68500 (Os01g0913100), showed significant differences in SL among their haplotypes, respectively  , and SL exhibited significantly different between the two haplotypes identified based on this variation. Hap1LOC_Os01g68500 is consistent with the allele of Nipponbare   lines and Nipponbare (the wild type) . In nutrient solution, the SL of Nipponbare was 20.5 cm, while that of the LOC_Os01g68500 KO lines were 16.4 cm and 15.2 cm , with an average reduction of 22.9% and 10.0% in nutrient solution and soil culture, respectively, indicating that LOC_Os01g68500 is the causal gene for SL in rice.To validate the effect of the candidate genes on SL, CRISPR/Cas9 was applied to knock out pe) Fig. b, c, but cm Fig. b; in soiqSL-1d and qSL-1f would be beneficial to improve seedling vigor. For qSL-1f, there were two main haplotypes underlying its causal gene LOC_Os01g68500, of which Hap2LOC_Os01g68500 exhibited longer SL . To analyze the pyramiding effects of the two stable QTL, the rice accessions in the population were grouped according to their haplotypes of the QTL/gene and three haplotype combinations were found  at the position of + 594, causing an amino acid substitution from alanine to serine, and resulting in a significant difference in SL between Hap1LOC_Os01g68500 and Hap2LOC_Os01g68500  encodes a DUF containing protein which belongs to DUF538 family (http://rapdb.dna.affrc.go.jp). DUF538 proteins were widely distributed in plants  proteins and affected the bacterial growth by binding to the bacterial membranes  was identified to alter trichome branch formation in Arabidopsis  synergistically modulated plant growth and trichome development in Arabidopsis through the transcriptional regulation of GLABRA1, one of the central hub genes for trichome development , indicating that this domain is important for the gene functions and these conserved amino acids in the domain may play important roles for this domain, although they performed their specific functions in different plant species. To our knowledge, it is the first time to discover the function of DUF538 protein gene on rice seedling growth. Further study is needed to understand the regulation mechanism of LOC_Os01g68500 on SL.The utilization of natural variation in genes is important for rice breeding. Map-based cloning is the main approach to explore the functional genes with natural variation, but usually uses populations developed by two parents, in which contains only two kinds of alleles and the obtained one may not be the optimal. In order to make better use of cloned genes for molecular breeding, it is necessary to understand their haplotypes, so that the favorable haplotype can be obtained. A gene-based haplotype analysis can use to discover variations in specific genes, which helps to determine the types of mutation in a gene, and explore how the gene affects the trait.LOC_Os01g68500, two main haplotypes were identified in 343 diverse rice accessions used in this study, in which Hap1LOC_Os01g68500 exhibited short SL, while Hap2LOC_Os01g68500 exhibited long SL  showed significant differences in SL. Combined with gene expression analysis, it was easy to obtain the candidate gene underlying qSL-1f. Further knockout transgenic experiments confirmed that LOC_Os01g68500 is causal gene for SL. These results suggest that GWAS combined with gene-based haplotype analysis is an effective way to identify the candidate gene, as well as its favorable haplotype/allele.In this study, there are 54 annotated genes within the region of Supplementary file 1 (PDF 125 KB)Supplementary file 2 (PDF 71 KB)Supplementary file 3 (PDF 768 KB)LOC_Os01g68460 a, The mutation types of LOC_Os01g68460. b and c The shoot length of the knockout transgenic (KO) lines of LOC_Os01g68460 were not significantly different from that of their wild-type Nipponbare (Nip) in nutrition solution (b) and soil culture (c). The same letter upon the histogram indicates no significant difference at P = 0.05 based on Duncan’s multiple range test. Scale bar, 3 cm (PDF 105 KB)Fig. S1 Mutation types and shoot lengths of the knockout transgenic lines of qSL-1d. a The main haplotypes of qSL-1d.  #The significant SNP position (bp); b–d Boxplots for shoot length based on the haplotypes of qSL-1d under GST (b), GSF (c) and DST (d). Numbers in parenthesis indicate the number of rice accessions with the haplotype. The black horizontal lines represent the median value; the upper side and lower side of the box represent the upper quartile and lower quartile, respectively; the whiskers represent the range of data, and small circle represents outlier. Double asterisk indicates the significant difference in shoot length at P = 0.01 based on t-test. (PDF 95 KB)Fig. S2 Differences in shoot length between the major haplotypes of Arabidopsis, Ptom.006G00815 in Populus, GL6 in Maize and Celosia DUF538 were downloaded from the corresponding references, respectively . The multiple sequence alignments were performed using MEGA6 and GeneDoc (PDF 233 KB)Fig. S3 The multiple sequence alignments between LOC_Os01g68500 and the four reported DUF538 proteins from other plants. The conserved amino acids are highlighted in dark color, and gaps are marked with dashes. The red line indicates the DUF538 domain (33-142 amino acids) of LOC_Os01g68500 protein. The amino acid sequence of LOC_Os01g68500 protein was collected from the Rice Genome Annotation Project website (http://rice.Plantbiology.msu.edu/). The full-length protein sequences of SVB in Below is the link to the electronic supplementary material.