Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and  Melo, Maasch and de la Fuente-Nunez review the current practices in use of artificial intelligence in the discovery of antibiotics and antimicrobials. They also provide details about the best-practices that should be engaged with during computational drug discovery, including open science and reproducibility. The Centers for Disease Control and Prevention estimates that 2.8 million infections are caused by antibiotic-resistant bacteria in the United States annually, leading to 35,000 deaths from such untreatable infections2. Current evidence also suggests that the solution may be part of the problem itself: antibiotics have been shown to cause significant damage to the gut microbiome, reducing species diversity and encouraging the evolution and dissemination of AMR genes3. Antibiotics under clinical trial are generally analogs to existing drugs for which AMR mechanisms have already emerged1, further underscoring the need for novel approaches in antibiotic discovery.Antimicrobial resistance (AMR) in clinically significant bacteria is undermining the efficacy of existing antibiotics, incurring concerning levels of global morbidity and mortality4. Between 2014 and 2019, only 14 new antibiotics were developed and approved5. In a survey of nearly 186,000 clinical trials for over 21,000 compounds, the probability of success for new drugs that treat infectious diseases was 25.2%6. For orphan drugs, i.e., those that treat rare infectious diseases, this probability dropped to only 19.1%6. This risk of failure drives corporations to pursue research and development with a higher guarantee of return on investment, opening the way for academia to initiate early stages of antibiotic design and optimization8.Compounding this issue, antibiotic development is a slow, expensive, and failure-prone process that can span over 10 years and cost hundreds of millions of dollars9. It is speculated that 1030–1060 drug-like chemicals exist10, while 20n variants exist per n-length canonical amino acid sequence. Although this immense combinatorial space presents a broad opportunity for computational antibiotic design, an exhaustive search cannot be achieved on a reasonable timescale. These challenges strongly incentivize the development of efficient heuristics and artificially intelligent algorithms for high-throughput antibiotic discovery. A prominent subdomain of computer science, artificial intelligence (AI) concerns the study and development of machines that are capable of learning, problem-solving, or mimicking other displays of reasoning akin to natural intelligence. For the purposes of this review, AI will generally pertain to machine learning (ML), the training of mathematical models to output predictions when presented with previously unseen data. The application of ML to drug discovery, and antibiotic discovery specifically, has been greatly facilitated by the public availability of empirical datasets (Table Accelerated antibiotic discovery will require computer-aided prospection for novel drugs with new mechanisms of action (MOAs)11. Leveraging advances in protein structure prediction and modeling, small-molecule antibiotic targets can be reliably described in atomic detail. Protein structures are then probed for binding sites, allowing large libraries of compounds to be used for automated large-scale docking and binding affinity studies in a process known as virtual screening (VS). This practice is now integral to many drug development pipelines, receiving ample attention from the ML community12. The most challenging step in VS is evaluating binding site affinity, driving the development of ML tools that significantly outperform traditional binding affinity prediction methods15. In recent years, deep learning (DL) has been used to successfully bypass docking and affinity estimation entirely, resulting in the identification of a small-molecule antibiotic active against multiple bacterial pathogens16.The integration of computational tools to expedite drug development has led to key advances for the rational design of bioactive compounds efficacious in animal models, thus demonstrating that computers can yield preclinical antibiotic candidates17 due in part to a relatively low propensity to induce AMR18. Research topics will be introduced by following the logical flow of an ML pipeline, starting with compound representation and progressing through trait prediction and novel compound design. ML innovation in general drug development will be reviewed where it has cross-over utility for antibiotic-specific applications. Trends in the literature and directions for future research will be discussed, including prospects for increasing data availability, computational–experimental collaboration, and innovation in interpretable ML (IML). Additionally, we provide an original analysis of open science practices among cited research and discuss the potential for best practices in open and reproducible ML to expedite antibiotic discovery.In this review, we will focus on the application of AI to the development of two major classes of bioactive compounds: small-molecule antibiotics and antimicrobial peptides (AMPs). The former, studied since the beginning of the twentieth century with the discovery of penicillin and in use for over 70 years, represents the majority of antibiotics in use today. The latter, a class of small proteins usually composed of 5 to 50 amino acids, is receiving increasing attention in research and clinical trials19. Aiming to computationally predict the activity of newly designed molecules, saving time and money by avoiding synthesis and experimentation on inactive compounds, researchers relied on computational representations of drug candidates to predict their properties. As it became apparent, the problem of representing biological or chemical data for use in computational models is in itself an important field of research. Likewise, it is an essential component of the computational drug discovery pipeline  drove over 50 years of research and innovationχ2 statistical tests or mutual information estimation, the search for reduced and information-rich representations has now fully integrated ML tools and principles. The efforts described below highlight how the theoretical and methodological advances made in diverse ML applications and interest areas can be adapted to aid ML-driven antibiotic development.From traditional dimensionality reduction techniques like principal component analysis (PCA) and singular value decomposition to feature selection approaches involving 25. A similar approach was taken to study and predict protein structures26. In an extensive benchmark study of available methods and datasets27, it was found that neural networks can enhance not only the process of describing a drug based on a set of molecular descriptors but also the determination of such molecular descriptors themselves. This work was extended to create a series of antimicrobial compounds that were correctly predicted as active despite being structurally distant from known antibiotics 16.A prominent example is the use of graph convolutional networks to leverage the geometry and connectivity of molecules to naturally translate them into graphs, using neural networks to learn from the chemical structure itself28. Alternatively, RNNs have been combined with reinforcement learning to autonomously create an embedded representation for drugs based on their SMILES representations29.While common in the fields of signal processing and natural language processing (NLP), recurrent neural networks (RNNs) have now been adapted to process simplified molecular-input line-entry system (SMILES) representations, which encode structures of chemical species using simple text strings. In one case, researchers used long short-term memory (LSTM) generative neural networks to learn from SMILES representations of known drugs and then used the trained neural network to generate new compounds30 and multiplicative-LSTM neural network31 have been trained to create embedded representations for peptide sequences. The latter led to an embedded representation that could be used to derive a protein’s secondary structure, thermal stability, deep mutational scanning classification, and even the functional impact of mutations31.RNNs have emerged as a natural embedding approach for AMP sequences, given their ability to parse sequence-based inputs. Based on a one-hot encoding of amino acid residues , both an LSTM-based autoencoder7 and attracting a variety of approaches32  and takes a weighted vote of each model into consideration to achieve the final prediction35. The underlying assumption behind ensemble learning is that errors made by one model will be compensated for by others, and this assumption has been confirmed in applications ranging from proinflammatory peptide identification36 to prediction of drug side effects37.In a recent effort to repurpose previously developed drugs as antibiotics39. Alternatively, deep neural networks have been used to predict antimicrobial properties from simplified residue representations of arbitrary amino acid sequences. In 2009, researchers combined 44 peptide descriptors traditionally used for QSAR studies and used them as inputs for an artificial neural network that predicted peptide activity against P. aeruginosa40. More recently, a 2020 study created a deep convolutional neural network model based on a simplified amino acid vocabulary that translated the natural 20 amino acids into pseudo residue types41. This model predicts antimicrobial activity in small peptides and is available in a web server. Extreme gradient boosting has been used for genome-based prediction of minimum inhibitory concentrations for 20 antibiotics against Klebsiella pneumoniae42 and 15 antibiotics against nontyphoidal Salmonella strains43. Using RNNs44, a combination of input representation and regression models were created to select peptide sequences with antimicrobial activity. Finally, through a variational autoencoder approach, peptide sequences were embedded in a latent space that was subsequently searched for new AMP sequences45.Classical ML techniques such as support vector machines (SVMs) have been applied to describe AMPs and quantify their MOAs9. The ability of AMPs to limit AMR development has been related to their varied MOAs46, which has led researchers to focus on classifying peptides and discovering new MoAs. Specifically, DBSCAN was used for cluster-based prediction of AMP activity against Gram-negative bacteria47, with promising candidates being synthesized and tested in vitro48.The variety of techniques utilized thus far correlates with an increasing focus on AMPs, which have been regarded as a major source of new antibiotics to tackle the development of resistance in microbesEscherichia coli49. Since patterns found by generalized linear models can be directly interpreted by analyzing the model weights, one can directly translate the model into actionable information for AMP design.The direct combination of experimental and ML techniques in a closed-loop approach has also benefited the development of new AMPs. Starting from a template with known antimicrobial activity and a series of homologous sequences, it was possible to train a generalized linear model to create new AMPs with 160-fold increased antimicrobial activity against 50. This approach can expand the search space for new drugs by avoiding direct comparisons between molecular descriptors and focusing instead on their effects on pathogens.While most ML-based antibiotic development approaches focus on creating new representations for drug candidates and new models to predict their activity based on molecular descriptors, the phenotypic drug discovery approach focuses not on describing the molecule itself but on its effects on target organisms. For example, a recent study used a random forest model to predict antimicrobial activity based on featurization of cell imaging, avoiding detailed description of the molecules themselves51 and subsequent concepts of drug-likeness and lead-likeness. Prediction of drug-likeness has been refined and increasingly automated over recent decades, with traits of interest including absorption, distribution, metabolism, excretion, and toxicity (ADMET)52. ML-based prediction of binding affinity can also accelerate high-throughput screening and structure-based drug lead optimization by pinpointing candidates with more favorable drug–target interactions, as discussed in recent reviews53.ML can yield a fuller aggregate picture of antibiotic therapeutic potential than simply predicting antimicrobial activity. Attempts to quantitatively distinguish the subsets of chemical space that have therapeutic potential from those which do not have yielded various schemas, including the introduction of the Rule of 5 in 1997Like many ML problems, drug-likeness prediction can be attempted using a wide array of algorithms. While experimental observations often require a specific methodology or well-established gold standard, diverse ML algorithms can often provide comparable performance for a given classification or regression problem. There is often no way to know a priori which algorithm will perform best, although theoretical knowledge can guide decision-making. Therefore, it is important to follow a rigorous model selection process that compares multiple algorithms  across several performance metrics that are salient to the particular use case. In this section, we note the use of diverse algorithms that have been applied to multiple drug-likeness prediction problems.52, incentivizing pre-trial in silico exploration. Host cell toxicity is a critical ADMET endpoint and a significant risk in antibiotic development, motivating the design of predictive tools for mammalian red blood cell toxicity, kidney cell toxicity, and other forms of eukaryotic cell damage. Hemolytic activity, or the ability to burst red blood cells, has been a major focus of therapeutic development given that numerous drugs enter the bloodstream. Prediction of hemolytic activity in AMPs and antimicrobial peptidomimetics has been explored using neural networks54, classification trees55, and gradient boosting classifiers56. Consensus model-based software for hemolytic activity prediction has also been released for general applications in drug development, with an emphasis on small molecules57 and saponins58. A feedforward fully connected neural network has demonstrated comparable performance to prior random forest models for the prediction of drug candidate cytotoxicity59. Deep Taylor Decomposition was used to identify the most significant features in DL-based cytotoxicity classification, with an emphasis on visualization to facilitate interpretability59. Additional antibiotic side effects can also be foreshadowed using ML, as has been done for the seizure-inducing potential of enoxacin, a broad-spectrum fluoroquinolone antibacterial60.Dangerous pharmacokinetic properties and toxicity are leading causes of clinical trial failure61. Protein solubility prediction has used neural network62, gradient boosting machine63, logistic regression classifier64, SVM65, and random forest models66. Degradation via the action of proteolytic enzymes is a significant concern when evaluating the stability of peptide-based antibiotics68. The in silico identification of putative proteolytic cleavage sites can inform AMP lead selection and guide sequence optimization for increased stability. Cleavage site prediction has been explored through the lens of drug development69 and other protein informatics applications using classification and regression mode SVM74, convolutional neural network75, conditional random field classifier76, and logistic regression models77. Similarly, the stability of drug-like chemicals has been modeled using an attention-based graph convolution neural network78 and Naive Bayes classifier79.The development of AMP-based antibiotics must also consider peptide solubility and stability, which are necessary for manufacture and efficacy. Pharmaceutically viable AMPs will be soluble, a trait that can be predicted from amino acid sequence80. Collateral damage to the gut microbiome has been proposed as one additional ADMET endpoint, and consensus model-based software has been released for ML prediction of microbiome damage58. Indeed, disruption to the microbiome is a significant side effect of antibiotics and has been implicated in AMR evolution3. For this particular endpoint, species-specific antimicrobial activity prediction may be the answer: ML can aid in the selection of candidates with high specificity for target pathogens and low activity against known commensals.As outliers to original drug-likeness definitions expand the boundaries of these criteria, new qualitative endpoints and quantitative thresholds have come under consideration9. Likewise, the need to track AMR emergence, mechanisms, and dynamics are raising new applied ML questions unique to computational antibiotic discovery, bacterial genomics, and infectious disease epidemiology. While ML-based AMR prediction may be clinically useful for informing AMR diagnosis and antibiotic prescription82, it may also be experimentally useful in the drug development process. We anticipate that ML approaches to AMR genomics in epidemiology and medicine will increasingly be adapted specifically for drug development purposes, e.g. ML-informed resistance evolution experiments for new lead compounds.Unlike most therapeutics, antibiotics are designed to kill a living target with the capacity for resistance evolution. The near-inevitability of AMR evolution thus adds an additional urgent consideration that is absent from most other drug development niches. Incentives to develop less resistance-prone countermeasures are drawing research to historically underexplored sources of inspiration for novel antibiotic design84. Small-molecule AMR has also been observed to coincide frequently with a collateral sensitivity to AMPs, yet rarely with AMP cross-resistance85. Together with the fact that protein target modifications are a common AMR mechanism, this suggests the large potential for cross-over between ML and traditional protein informatics in AMR research. However, the majority of existing ML models forgo this route in favor of pathogen genetic and genomic inputs. Although model design strategies are expected to diversify, the current state of ML for AMR learns from the bacterial genome rather than drug or molecular target features.Protein space is one such underexplored area that is expected to yield future antibiotics with minimal AMR risk. Antimicrobial host defense peptides, including encrypted AMPs released from precursor proteins through proteolytic cleavage, have notably emerged as reservoirs for low AMR-risk antibiotic templates due in part to a tendency to act on multiple cellular targetsK. pneumoniae42, E. coli88, P. aeruginosa89, Mycobacterium tuberculosis91, and Staphylococcus aureus86. While ML models of AMR may be trained on drug- and bacteria-specific data94, a more agnostic approach has been explored using a neural network to facilitate environmental metagenomic analysis95. However, predictive performance has been observed to vary significantly by antibiotic, target species, genomic data sampling method, and resistance mechanism complexity96, suggesting that AMR prediction may at times require relatively context-specific modeling. A free web server and standalone software have been released for SVM-based prediction of efflux-mediated AMR97. ML-assisted metagenomic analysis has implicated AMR genes associated with antibiotic-induced microbiome perturbations98. A novel combination of protein homology modeling and LASSO penalized logistic regression has been used to investigate the horizontal transfer of antibiotic resistance determinants from gut commensals to bacterial pathogens99.Pathogen genomic data have been used to build ML models of antibiotic susceptibility and resistance phenotypes in clinically relevant bacteria, including 81, IML can enable models to suggest causal factors in AMR at the organismal and population scale. Coupling ML with gene–protein structure mapping to investigate drivers of M. tuberculosis AMR evolution, interactions between genes conferring AMR were hypothesized to manifest as correlations in their weights and signs across the hyperplanes of an SVM ensemble100. An ML-integrated genome-scale model using data from microbial genome-wide association studies has enabled allele-parameterized flux balance analysis to reveal metabolomic insights into M. tuberculosis AMR101. Open-source software using protein orthology-based gene variant mapping has also been developed for interpretable AMR prediction96. Computationally characterizing the molecular signatures and population dynamics of AMR might help indicate which MOAs are overused and which present promising new avenues, even on a regional scale. Using training data from multiple countries, geographic analysis of predicted AMR genes revealed population dynamics that could be supported by national rates of multidrug-resistant tuberculosis and antibiotic prescription trends100.While “black-box” approaches may limit the utility of ML for AMR-risk reduction102. Like classical autoencoders, VAEs are trained to encode inputs to a compressed representation and then to decode an approximate reconstruction, learning the latent variables describing the training data in the process. However, VAEs are directed probabilistic models, learning continuous latent variables through a variational Bayesian approach to generative DL103. This section will note the use of several variations on these common generative architectures as applied to drug discovery.Generative DL can lend itself to computational antibiotic discovery in multiple ways. Here, we will focus on de novo molecular design, which often employs generative adversarial networks (GANs), variational autoencoders (VAEs), or related architectures. Comprised of dueling generative and discriminative models, GANs infer the probability distribution from which training data derive in order to construct novel samples from this distribution. Engaging in a two-player minimax game, both models are trained to optimize the error rate of the discriminator: while the generator is trained to maximize the likelihood that the discriminator fails to distinguish empirical data from synthetic data, the discriminator is trained to minimize this likelihood104, including inverse design of inorganic matter105 and graph-based neural network models for the NP-hard106 inverse protein folding problem107. Increasingly, generative DL is applied explicitly to drug discovery, whereby synthetic molecular designs are proposed from drug-like chemical spaces. De novo drug candidate design has been attempted with deep reinforcement learning coupling generative and predictive neural networks29, deep generative adversarial autoencoder architecture108, differentiable neural computer architecture with reinforcement learning and adversarial training110, deep neural networks coupled with Monte Carlo tree search111, and an autoencoder–GAN combination for both random and target-biased molecular design112. Given their suitability for sequential data, generative RNNs taking SMILES inputs have drawn attention in drug design114 and have demonstrated relatively broad, uniform, and complete coverage of chemical space116. Experimentally validated membranolytic anticancer peptides have been generated by both an LSTM RNN with transfer learning117 and a counterpropagation artificial neural network optimized by a genetic algorithm118.Generative DL has found diverse chemical and protein engineering applicationsE. coli than ampicillin119. Additional preliminary success in AMP discovery is described in a proof-of-concept study coupling a VAE with experimental validation45. A generative LSTM RNN with transfer learning has demonstrated success in reconstructing molecules known to target S. aureus after pretraining on a large generalized dataset and fine-tuning on a smaller set of target-specific bioactive molecules120. An RNN with unidirectional LSTM cells for de novo AMP design observed 82% of generated peptides to be putative AMPs, while only 65% of random permutations from the amino acid distribution of the training data were predicted to be antimicrobial30.A burgeoning interest in generative DL within chemical engineering, protein engineering, and drug development at large suggests that similar techniques may be increasingly applied to AMP and small-molecule antibiotic design. To date, a GAN has been used to generate an AMP with a significantly lower minimum inhibitory concentration against In this section, we present an argument for increasing openness and reproducibility in ML-based antibiotic discovery. This argument hinges on a two-pronged crisis: (1) the global public health crisis of AMR, slow antibiotic development rates, and emerging infectious diseases and (2) the reproducibility crisis currently plaguing AI. We conclude with an original analysis of open science practices among the publications cited in this review.121, sudden pathogen emergence, unexpected large-scale losses in quality of life and economic security122, and structural inequities that render some populations disproportionately vulnerable123 raise unique questions of urgency and justice in infectious disease control. These questions heighten the need for swift research and development, evoking calls for increased openness under global public health crises124. We argue that similar calls should extend to the global crisis of AMR evolution, and thus to computational antibiotic discovery.Accelerating antibiotic discovery through open information and technology exchange carries both practical and ethical weight. As evidenced by poignant examples from the COVID-19 pandemic, factors such as AMR125 signal a growing concern for transparency, reproducibility, and equitable access to information within the scientific community. Effective 2021, Plan S dictates that publications resulting from public and private grants of participating bodies “must be published in Open-Access Journals, on Open-Access Platforms, or made immediately available through Open-Access Repositories without embargo” . Nevertheless, open-access publishing addresses only one facet of computational openness and reproducibility. With stakes as high as they are in computational antibiotic discovery, we call for a more comprehensive set of open science best practices.The international movement toward open-access publishing represented by groups like cOAlition S126, and some argue that AI generally suffers from a reproducibility crisis, not unlike that of psychology127. Reproducibility challenges common to ML  can also have detrimental interactions with challenges unique to biomedicine 128.An open science regime that ensures computational reproducibility can accelerate ML-based antibiotic discovery through free public access to (1) source code, (2) training and testing data, and (3) published findings. Computational reproducibility facilitates the external validation of published claims while encouraging the dissemination of knowledge and methods. However, standards of openness and reproducibility in biomedical ML are still subject to debate126, an analysis of 400 general AI conference papers revealed that only 6% released code, 54% released pseudocode, and ~30% released test data127. Within ML for the life sciences and medicine specifically, a recent review found that 50% of 300 publications released software, while 64% released data129. A review of 511 studies found that papers applying ML to healthcare data underperformed relative to NLP, computer vision, and general ML on multiple metrics of reproducibility, including code release rates130. A systematic review of 415 studies on ML-based image analysis for COVID-19 diagnosis found that all publications contained serious methodological flaws or failed to report key information needed for reproducibility and substantiation of claims, such that not a single model was of clinical use131.Although releasing source code, training data, and testing data could mitigate reproducibility concerns while increasing the scientific value of AI research132. Exemplifying the conflict between researchers and publishers, the recent 2-year-long negotiation between the University of California system and Elsevier resulted in the largest deal for open-access publishing for scientific articles in North America133. Interestingly, the fields of medical and biological research are among the most accessible, with biology having the largest fraction of immediately free-to-read articles134.Confounding factors such as lack of incentives in academia or misaligned objectives in the private sector may further hinder the adoption of open science practices. While open-access journals continue to grow, many prestigious scientific journals charge premiums over publication fees in order to make articles open access. Authors then face a tough choice between funding their research or paying premiums to make their publications free to all readers. Indeed, a recent study showed that authors of open-access publications in US research institutions tend to have more access to funding and belong to more advanced career stages135 can still provide great advances to the field, finding a balance between open access and closed IP may prove to be a barrier in itself. Guidance may be found in the efforts of related fields to establish community-wide standards for responsible and reproducible ML publications, with the Checklist for Artificial Intelligence in Medical Imaging being a notable example136.Beyond publishing research findings, the release of source code and training and testing data may also raise conflicts regarding intellectual property (IP) and competitiveness in the private sector. Therefore, while industry-funded research for antimicrobial discoveryThis conversation in AI, and in biomedical ML specifically, motivated our analysis of code, data, and software release rates among models cited in this review Fig. . This anhttps://zitniklab.hms.harvard.edu/TDC/) provides free ML datasets to lower barriers to entry and accelerate drug development pipelines. The Open-Access Antimicrobial Screening Program extends the concept of openness to experimental methods by offering free compound screening services (https://www.co-add.org). Such creative counterexamples to the closed research paradigm will ideally become the norm in antibiotic discovery.Moving forward, inspiration can be found in projects taking a broad view of openness and reproducibility in drug discovery. The open-source Therapeutics Data Commons . To explore the extent to which research interest has changed over time, we queried PubMed by year for texts on ML and antibiotics, ML and cancer therapies, ML and cardiovascular drugs, or ML and drugs broadly defined (Fig. 137, querying for these applications provides relevant public health context for infectious disease applications. Further, a blanket query for AI and ML keywords with no additional qualifiers provides the most macroscopic view of research interest in these predictive methodologies, irrespective of application area. Exact Boolean search phrases can be found in Supplementary Table To assess the state of publishing on ML for antibiotic discovery, we measured trends among papers in PubMed, a public database maintained by the United States National Library of Medicine of the National Institutes of Health (ned Fig. . QueryinResults indicate increasing research interest in all areas over the first two decades of the twenty-first century, with the volume of ML literature focused explicitly on antibiotics and cancer drugs lagging behind broader drug development applications by nearly a decade. Surprisingly, publication counts for cardiovascular drugs and ML remain very low. As the general drug query did not double-count observations from the major disease groups explored, these results may suggest that broad applications have received greater research attention than disease group-specific applications. However, similar trends in cancer- and antibiotic-related publication rates suggest that antibiotics might not be disproportionately neglected. The prevalence of general-interest lines of inquiry might be due to the relative recency of ML for drug discovery, whereby the initial establishment phase lays the groundwork for future specialization. To that end, the significantly higher volume of general drug development applications represents a reservoir of research that is expected to have trickle-down impacts on disease group-specific research over time. Further, the proportion of AI and ML publications that feature applications in general drug discovery, antibiotic discovery, and cancer drug discovery have each increased throughout the twenty-first century. Our analysis also marks 2018 as a watershed moment for the use of ML for antibiotic discovery, coinciding with landmark papers in the field published that year together with preceding software developments.138. Increased data sharing from both successful and failed projects in the pharmaceutical industry has also been proposed as a means of accelerating research and development139. Existing data can also be further mined for new purposes, as exemplified by resources like the Drug Repurposing Hub140. While ML increasingly opens up new regions of chemical space to exploration, the repurposing of non-antibiotic pharmaceuticals could also be a promising avenue for antibiotic discovery1 that has already benefited from DL methods16.Over the third decade of the twenty-first century, prospects for ML-facilitated antibiotic discovery will partially hinge on data improvements. As larger data sources become publicly available, new ML questions can be pursued and ongoing questions can be revisited with greater rigor. While expanding public sources of experimental data will be crucial, federated learning across institutions may facilitate empirical dataset expansion without sharing private data, as has been done in other areas of biomedical ML129, suggesting that computational antibiotic discovery might similarly benefit from combined expertise. Increased coupling of in silico model testing with in vitro and in vivo validation—and even additional computational methods, e.g., molecular dynamics simulation141—will help ensure that published models are robust and yield experimentally actionable predictions. Interdisciplinary collaboration might also facilitate increasingly insightful predictions through biologically informed IML. As a response to the prevalent “black-boxing” of ML models’ internal decision-making, IML is an expanding focus in biomedical computation142 that has been used to elucidate antibiotic MOAs143. As firmer terminological and methodological standards alleviate significant confusion surrounding its diverse implementations144, IML is expected to enable greater human interpretability and causal inference in antibiotic discovery than opaque algorithms generally allow. Expanding interpretability for causal biological insights will surely require both computational creativity and biomedical domain knowledge.A recent review observed greater technical correctness among biomedical ML publications featuring collaborations across computer science, biology, and medicine145 may indicate the utility of ML-guided autonomous robotics in future antibiotic discovery. Creative integration of diverse lessons from NLP, computer vision, generative DL, computer-aided drug design, and other flourishing areas in ML research will play important roles in accelerating the urgent task of novel antibiotic discovery.Additional new avenues for ML-facilitated antibiotic discovery are expected to trickle in from algorithmic theory, robotic AI, and adjacent computational domains. While this review has focused on ML rather than embodied AI, recent attempts to deploy intelligent robots in chemical experimentationSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


Multiple oral insulin-sensitizing agents, such as metformin, thiazolidinediones, inositols, and berberine, have been proven safe and efficacious in improving the endocrine, metabolic, and reproductive abnormalities seen in polycystic ovary syndrome (PCOS), providing more options for healthcare providers and patients. These oral insulin sensitizers are more convenient, practical, and economic than agents that need to be injected. A comparison of the clinical effectiveness of the four different classes of oral insulin sensitizers in PCOS has not been explored, leading to clinical uncertainty about the optimal treatment pathway. The present study aims to compare the effects of oral insulin sensitizers on endocrine and metabolic profiles in women with PCOS.We identified randomized controlled trials for PCOS from a variety of databases, published from January 2005 to October 2020. Outcomes included changes in menstrual frequency, improvements in hyperandrogenism and glucolipid metabolism and adverse side effects. A random-effects network meta-analysis was performed.d-chiro-inositol was associated with a greater improvement in menstrual frequency  2.31–93.58]). Myo-inositol + d-chiro-inositol and metformin + thiazolidinediones combination therapies were superior to respective monotherapies in reducing total testosterone levels. Thiazolidinediones, metformin + thiazolidinediones, and myo-inositol + d-chiro-inositol were associated with a lower insulin resistance index (HOMA-IR) compared with that in metformin alone (mean differences: − 0.72 [95% CI (− 1.11)–(− 0.34)] to − 0.89 [95% CI (− 1.460)–(− 0.32)]). Metformin + thiazolidinediones treatment was associated with lower triglyceride levels compared with that in metformin and thiazolidinediones monotherapy, while thiazolidinediones was superior to metformin in increasing high-density lipoprotein cholesterol and decreasing fasting plasma glucose, triglycerides, low-density lipoprotein cholesterol, and gastrointestinal adverse events.Twenty-two trials comprising 1079 patients with PCOS were included in this study. Compared with metformin, treatment with myo-inositol + d-chiro-inositol and metformin combined with thiazolidinediones appear superior to metformin alone in improving insulin resistance and decreasing total testosterone. Myo-inositol combined with d-chiro-inositol is particularly efficacious in menstrual recovery. Thiazolidinediones and metformin combined with thiazolidinediones improve lipid metabolism better than metformin alone.Ours is the first study to report that for women with PCOS, myo-inositol combined with Trial registration PROSPERO CRD42020211524The online version contains supplementary material available at 10.1186/s12978-021-01207-7. d-chiro-inositol and metformin combined with thiazolidinediones appear superior to metformin alone in improving insulin resistance and decreasing total testosterone level. Myo-inositol combined with d-chiro-inositol is particularly efficacious in menstrual recovery. Thiazolidinediones and metformin combined with thiazolidinediones improve lipid metabolism better than metformin alone.This study aimed to compare the effects of oral insulin sensitizers on endocrine and metabolic profiles in women with polycystic ovary syndrome (PCOS). A random-effects network meta-analysis including 22 trials was conducted. For women with PCOS, myo-inositol combined with The online version contains supplementary material available at 10.1186/s12978-021-01207-7. Polycystic ovary syndrome (PCOS) is a common and complex endocrinopathy that affects 4–21% of women of reproductive age worldwide , 4. MetfThe methods and results are reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement . The proSystematic literature searches were performed in the following databases from January 2005 to October 23rd, 2020: PubMed, Embase, Web of Science, the Cochrane Central Register of Controlled Trial (CENTRAL), CBM Database, CNKI Database, the WanFang Database, the WeiPu Database, and ClinicalTrials.gov. We used different combinations of the following search terms: “polycystic ovary syndrome,” “metformin,” “thiazolidinediones,” “inositol,” and “berberine.” The PICO framework was used to identify relevant trials , withoutThe outcomes included menstruation frequency, parameters of hyperandrogenism , parameters of glucolipid metabolism, including fasting plasma glucose (FPG), fasting insulin (FINS), Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), and lipids . Obesity-related indexes, such as body mass index (BMI) and waist–hip ratio (WHR), and adverse events were also considered.Two independent reviewers (H.Z. and C.X.) screened the titles and abstracts and assessed the full texts of potential reports. The data extracted from the original trials included study, year, region, intervention, sample size, follow-up duration, and outcomes of interest Table . Risk ofI2 and P values, for which I2 > 50% or P < 0.05, indicated substantial heterogeneity. The random-effect NMA was subsequently performed to combine the direct and indirect comparisons of agents into one analysis with Stata software . We calculated the pooled estimates of MDs or ORs with 95% CIs in order to compare multiple interventions to each other. Network inconsistency was evaluated using the node-splitting method and inconsistency models, and significance was assessed at the 0.05 level. Finally, to summarize the probabilities, we calculated the surface under the cumulative ranking (SUCRA) curve to provide a summary statistic for cumulative ranking. The efficacy of each intervention was expressed as a percentage ) and MI (OR 16.51 [95% CI 2.56–106.64]). MI + DCI was ranked best at improving menstrual frequency.With respect to improving menstrual frequency, our TMA showed that Met + TZDs –(− 5.44)]) and Met + TZDs [(TMA: MD − 3.14 [95% CI (− 6.19)–(− 0.09)]; P = 0.44, I2 = 0%; NMA: MD − 3.66 [95% CI (− 6.60)–(− 0.72)] were superior to Met alone. The NMA revealed that MI + DCI was more efficacious than Met, MI, and DCI [MDs ranging from − 6.72 [95% CI (− 10.24)–(− 3.20)] for Met to − 7.70 [95% CI (− 14.90)–(− 0.50)] for DCI]). Treatment with TZDs was less efficacious than Met (MD 3.90 [95% CI 1.10–6.71]). The SUCRA values were as follows: Met + BBR (92.2%), MI + DCI (75.4%), and Met + TZDs (59.4%). No difference was found in comparisons of SHBG, AND, and mF-G scores.In terms of reducing TT, the TMA and NMA both revealed that Met + BBR [(TMA: MD − 11.53 [95% CI (− 16.91)–(− 6.15)]; P = 0.32, I2 = 0%) was superior to Met monotherapy, whereas the NMA revealed that Met + BBR was more efficacious than Met, MI, TZDs, and Met + TZDs (MDs ranging from − 1.85 [95% CI (− 2.76)–(− 0.94)] for Met to − 3.08 [95% CI (− 4.10)–(− 2.05)] for TZDs). Met + BBR ranked best among all the treatments at reducing BMI. However, both the TMA and NMA revealed that there was no significant difference between the groups with respect to WHR reduction.With respect to BMI reduction, the TMA showed that Met + BBR (MD − 1.85 [95% CI (− 2.76)–(− 0.94)]; P = 0.19, I2 = 37%) was more efficacious than Met alone. The NMA did not reveal any significant differences among the groups.With respect to lowering FPG, the TMA revealed that there were no significant differences between each of the investigated agents. The NMA revealed that TZDs (MD − 0.21 [95% CI (− 0.21)–(− 0.02)]) were more efficacious than Met. Additionally, with respect to lowering FINS, the TMA revealed that Met + TZDs (MD − 1.83 [95% CI (− 3.12)–(− 0.55)]; P < 0.00001, I2 = 97%), Met + TZDs (MD − 0.85 [95% CI (− 1.21)–(− 0.49)]; P = 0.04, I2 = 65%), and Met + BBR (MD − 0.25 [95% CI (− 0.36)–(− 0.14)]; P = 0.37, I2 = 0%) were more efficacious than Met alone. The NMA revealed that TZDs, Met + TZDs, and MI + DCI were all superior to Met (MDs ranging from − 0.72 [95% CI (− 1.11)–(− 0.34)] for TZDs to − 0.89 [95% CI (− 1.46)–(− 0.32)] for MI + DCI), with MI + DCI being ranked the best with a SUCRA value of 80.8%. The SUCRA values for the other agents were as follows: 79.9% and 68.6% for Met + TZDs and TZDs, respectively.With respect to decrease in HOMA-IR, the TMA showed that TZDs (MD − 0.92 [95% CI (− 1.64)–(− 0.19)]; P = 0.74, I2 = 0%; TC: MD − 0.30 [95% CI (− 0.53)–(− 0.07)]; P = 0.98, I2 = 0%) was more efficacious than Met alone. In terms of reducing levels of TG, the NMA showed that Met + TZDs was superior to Met (MD − 0.08 [95% CI (− 0.16)–(0.00)]) and TZDs (MD − 0.51 [95% CI (− 0.88)–(− 0.14)]), and was the best intervention among treatments. The NMA also showed that BBR (MD − 0.03 [95% CI (− 0.06)–(0.00)]) was more efficacious than Met. However, for TC no significant differences were found in the NMA.Parameters assessed for improvement in blood lipids included TG, TC, HDL-C, and LDL-C. In terms of reducing TG and TC levels, the TMA revealed that Met + TZDs (TG: MD − 0.24 [95% CI (− 0.43)–(− 0.06)]; P = 0.10, I2 = 57%) was associated with higher HDL-C than Met. The NMA revealed that treatment with TZDs was superior to Met in increasing HDL-C (MD 0.13 [95% CI 0.03–0.24]) and decreasing LDL-C (MD − 0.19 [95% CI (− 0.27)–(− 0.11)]) and was the best intervention among the different treatments.Furthermore, the TMA also suggested that MI  was inferior to Met. With respect to the incidence of peripheral edema, the TMA revealed that TZDs were more frequent than Met , no significant differences were found in the NMA. Furthermore, both the TMA and NMA did not show any significant differences between the different treatment regimens for the incidence rate of muscle spasms, while no drug increased odds of transaminase abnormalities. Of note, these results should be interpreted with caution since results for MI, MI + DCI, and BBR were based on a single trial, and in some of the trials the description of adverse events was subjective and unclear.In terms of the frequency of gastrointestinal adverse events during treatment, both TMA and NMA revealed that treatment with TZDs  as an adjunct therapy to improve irregular menses, hyperandrogenism, and glucolipid metabolism abnormalities in women with PCOS. The results obtained are based on 22 trials that included 1079 women, randomly assigned to eight different interventions. Overall, treatment with MI + DCI was associated with the best improvement in menstrual frequency. MI + DCI, Met + TZDs, and Met + BBR were superior to Met for TT reduction, while MI + DCI, Met + TZDs, and TZDs significantly lowered HOMA-IR than Met alone. TZDs were superior to Met in decreasing FPG, TG, LDL-C levels, and increasing HDL-C level, while Met + TZDs was associated with lower TG levels compared to Met and TZDs monotherapy. Furthermore, Met + BBR was more efficacious than Met alone in reducing BMI.Metformin is a classic insulin sensitizer, that inhibits hepatic glucose production, thereby decreasing glucose levels . Many stThiazolidinediones (TZDs) decrease hepatic and peripheral insulin resistance directly through activation of the nuclear hormone receptor PPARγ and have a well-documented effect of improving hyperglycemia and dyslipidemia. TZDs also improve the menstrual cycle and ovulation and reduce androgen levels in women with PCOS , 42. HowOur NMA revealed that the combination of Met + TZDs was more effective than Met alone at improving menstruation frequency, and reducing TT, HOMA-IR, and TG. The SUCRA value showed that Met + TZDs was the best intervention for reducing TG level. The TMA also demonstrated that Met + TZDs was superior to Met monotherapy at reducing FINS and TC level. Our previous NMA in overweight women with PCOS revealed that Met + TZDs was superior to Met in recovering menstrual function, whereas there were no evident differences in TT and FINS . DiffereInositol acts as a second messenger with insulin-like functions and is safe and well-tolerated . The twoBerberine (BBR), a natural isoquinoline alkaloid, has been studied in various randomized clinical trials in patients with PCOS and has been shown to be safe and promising for decreasing insulin resistance, lowering blood lipids, and restoring ovulation –55. In tOur findings reflect the comparative efficacy and safety of monotherapy versus a combination of different oral insulin sensitizers  in the treatment of PCOS. Previous related meta-analyses have mainly been of pairwise design that focused on individual agents and have given inconsistent results, our NMA incorporated both direct and indirect comparisons of interventions into a single analysis and provided a ranking of the available interventions. Through a comprehensive search and a rigorous review of the design of the randomized clinical trials, we aimed to reduce the likelihood of selection bias. We focused on the analysis of menstrual and ovulation abnormalities, hyperandrogenism, obesity, glucolipid metabolism, and adverse events in women with PCOS to provide a comprehensive reference for its clinical treatment.However, the present study still has some limitations that must be noted. First, very few literature reports met the inclusion criteria of treatment without contraceptives and/or intervention of ovulation inducing drugs to effectively verify the independent effects of drugs on efficacy. Second, there were markedly different dosages, durations of treatment, and small sample sizes that could contribute to sample bias, selection bias, and high heterogeneity. Third, some of trials did not have a specific analysis of women who dropped out or missed a follow-up.In conclusion, for women with PCOS, MI combined with DCI and Met combined with TZDs appear to be superior than Met alone in improving insulin resistance and decreasing total testosterone. MI combined with DCI appears to be particularly efficacious in menstrual recovery. TZDs and TZDs combined with Met seem to offer the additional advantage of improving lipid metabolism. However, our findings are limited by the small number and low quality of available studies, and therefore more rigorous and high-level RCTs are needed to further guide the clinical management of women with PCOS, especially those with insulin resistance.Additional file 1: Appendix S1. Details on study methods.


Due to the rapid increase in population, the use of automobile vehicles increases day by day, which causes a considerable increase in the waste tires produced worldwide. Research studies are in progress to utilize scrap tires and waste rubber material in several fields to cater the pollution problems in a sustainable and environmentally friendly manner. In this research, the shredded waste tires were used in concrete to replace fine aggregates in different percentages. The fine aggregates in the rubberized concrete were replaced 10%, 15%, and 20% by rubber. The stress–strain behavior of the concrete models is then determined and compared with the already established analytical models, i.e., Modified Kent and Park Model, Mander’s model, and Razvi and Saatcioglu Model. A total of 12 standard concrete cylinders and 18 models of each type of concrete, i.e., normal concrete, reinforced rubberized concrete with 10%, 15%, and 20% addition of rubber, were fabricated. Specimens fabricated in each replacement of rubber were laterally confined, employing 3 in (76 mm) and 6 in (152 mm) c/c tie spacing. The model and cylinders were subjected to uni-axial compression tests using Universal Testing Machine (UTM). The drop in compressive strength, stress–strain constitutive law, strain limits, and overall behavior of the rubberized reinforced concrete were explored experimentally. The results were then compared with the analytical results of the established models. The research can help explore the possible future for the use of rubberized concrete for the potential application as a structural material. Automobile industries are increasing continuously due to rapid increase in the usage of vehicles worldwide. Many waste tires are producing day by day, which causes a considerable increase in environmental pollution and proves to be great pressure on the present solid waste management system. So, disposal of waste rubber is most necessary to minimize and reduce the associated problems globally. The disposal rate is different in different countries; the USA discard about 1.1 million tires per person per year, while Australia disposes of 48 million tires every year. About 37 million tires are produced in England, while 200,000 tons of scrap rubber are discarded in Malaysia per year. There is no reliable data available in Pakistan regarding the production and disposal rate of waster rubber ,6,7. To At the material level, the authors performed research using rubber as an alternative to natural aggregates in concrete and found rubberized aggregate concrete’s tensile and compressive properties. It was concluded that replacing coarse aggregates entirely with chip rubber causes a 50% decrease in tensile and 85% decrease in compressive strength of concrete. Similarly, complete replacement of fine aggregates posted a 65% decrease in compressive strength and a 50% decrease in tensile strength of concrete. Later on, it was explored that the workability of rubberized concrete is quite adequate, but its unit weight is measurably less than that of plain concrete . ResearcIn recent research work, the effects of freeze-thaw process on rubberized concrete for pavement base and sub-base materials were investigated. It was found that the resilient modulus of rubberized aggregate concrete subjected to freeze-thaw cycles was higher than that of control samples subjected to tests at 25 °C constant temperature. Therefore, this economical and viable option of blend of RCA and rubber material can be used for pavement base and sub-base material . AuthorsMany research studies have been carried out in rubberized aggregate concrete production , where mThe above discussion concludes that addition of crumb rubber replacing coarse aggregates causes a decrease in workability, flexural strength, and split tensile strength of concrete . While iLimited studies are available on the structural behavior of rubberized concrete made of replacing crumb rubber by fine aggregate. Therefore, this research investigates the effect of replacing fine aggregate by crumb rubber investigating the stress–strain behavior of columns. Different models regarding the investigation of stress–strain behavior of the confined normal concrete were employed in this research for comparison. First, Kent and Park’s model is proposed , which wIn this research work, standard concrete cylinders and test specimens in reduced scale were built; column specimens had cross-sectional dimensions of 6 in × 6 in (152 mm × 152 mm) and 30 in (762 mm) span length, employing rubberized reinforced concrete. Natural fine aggregates were replaced 10%, 15%, and 20% by volume via scrap rubber. The prototype of the test specimen has a column’s cross-section of 18 in × 18 in (457 mm × 457 mm). The focus of this study is to explore the stress–strain behavior of rubberized reinforced concrete. The compressive strength of the rubberized and reference concrete was experimentally explored by testing the standard concrete cylinders and stress–strain relationship of the test specimens and compared with the available models developed for normal concrete.A mix ratio of 1:1.80:1.60 and water to cement ratio of 0.48 were used for casting conventional concrete cylinders. In addition, rubberized concrete cylinders employing 10%, 15%, and 20% scrap rubber as partial replacement of sand by volume were also fabricated. To find the behavior and relevant parameters of reinforced concrete employing rubber crumbs, three classes: A, B, and C, were selected for casting test specimens. Ordinary Portland cement, reinforcement, aggregates, crumb rubber, and mixing water were used in this research work. Crumb rubber was used in the form of dust obtained from retaining on sieve No. 200. The sieve analysis of coarse and fine aggregate is shown in First, the reference concrete cylinders were fabricated. Then, 10%, 15%, and 20% rubberized concrete cylinders and specimens were fabricated in three consecutive days and were placed in the water tank for 28 days of curing. For proper concrete placement, a tamping rod made of steel was used while concreting in standard concrete cylinders. However, a mechanical vibrator of a 1-inch diameter was used to remove voids and entrapped air during the fabrication of the test specimens.For observance and clarity of cracks during the tests, specimens were whitewashed. The concrete cylinders and specimens were then labelled as shown in A total of 12 standard concrete cylinders and eighteen test specimens were constructed for the evaluation of mechanical properties. The experimental investigation of the models revealed the stress–strain curve of reduced scale specimens built in reinforced concrete employing rubber crumb. The curves were compared with the previously determined models for normal reinforced concrete having the same mix ratio of 1:1.80:1.60 with a water to cement ratio of 0.48. The strength of crumb rubber concrete is determined experimentally by testing the specimens with partial replacement of fine aggregates via rubber. Variation in the behavior due to lateral ties’ spacing was also explored in this research by employing 3 in (76 mm) and 6 in (152 mm) spacing of transverse reinforcement.2/min) using the Universal Testing Machine (UTM) available at the Structural Engineering Laboratory, Civil Engineering Department, University of Engineering and Technology Peshawar, Khyber Pakhtunkhwa, Pakistan. Two linear variable displacement transducers (LVDTs) were placed on opposite sides of the specimen to measure average axial deformation. Test data were recorded and transferred to the computer accordingly. The UTM and LVDT utilized in this research study can be seen in The test specimens were examined experimentally under uni-axial load  that the increase in load caused an increase in vertical stresses, followed by failure and spalling of the cover concrete. Further increase in load caused the failure of core concrete followed by a buckling of the longitudinal reinforcement while the tie bar remained unopened in all three specimens of this type. The failure pattern is shown in Similarly, test results of 15RRC6 specimens and those of the analytical model, given in It was found in the specimens of rubberized reinforced concrete having 15% of rubber and tie bar of spacing 3 in (76 mm), that the increase in load causes an increase in vertical stresses, followed by failure and spalling of the cover concrete. Further increase in load causes the failure of core concrete followed by a buckling of the longitudinal reinforcement while the tie bar remained unopened in all three specimens of this type. The failure pattern is shown in The specimens’ results were also compared with the second model, i.e., Mander’s Model for normal concrete. Strain at peak and peak strength increased while the relative ductility, stiffness, and ultimate strain decrease. Experimental results from the tests of both types of specimens, i.e., 15RRC6 and 15RRC3 were also compared in It was noticed in the specimens of rubberized reinforced concrete having 20% of rubber and tie bar of spacing 6 in (152 mm) that the increase in load causes an increase in vertical stresses, followed by failure and spalling of the cover concrete. Further increase in load causes the failure of core concrete followed by a buckling of the longitudinal reinforcement while the tie bar remained unopened in all three specimens of this type. The failure pattern is shown in The results were also compared with the second model established for reference concrete. It can be seen from It was found in the specimens of rubberized reinforced concrete having 20% of rubber and tie bar of spacing 3 in (76 mm), that the increase in load causes an increase in vertical stresses, followed by failure and spalling of the cover concrete. Further increase in load causes the failure of core concrete followed by a buckling of the longitudinal reinforcement while the tie bar remained unopened in all three specimens of this type. The failure pattern is shown in At last, the test specimens’ behavior and parameters were compared with the model i.e., Modified Kent and Park Model. It can be observed from Various parameters of 20RRC6 specimens were compared in The stiffness of the models decreases by increasing the percentage of rubber. The decrease in stiffness of the members also depends upon the spacing of the lateral ties. However, the degradation does not follow a specific trend. The average stiffness of each percentage can be seen Rubber being a lightweight material as compared to sand; significant reduction in the weight of concrete was recorded due to partial replacement of sand via different percentages of rubber by volume. The effect of rubber addition on weight can be seen in It was found from the test data that the relative ductility decreases due to the incorporation of rubber in concrete as compared to corresponding normal concrete. It was explored that the decrease in relative ductility of the members also depends upon the spacing of the lateral ties. However, the degradation does not follow a specific trend. The relative ductility of each percentage can be seen in Due to less adhesion between the rubber and the cement matrix, the compressive strength generally decreases by increasing the addition of rubber. The compressive strength of all types of specimens can be seen in The experimental data recorded during testing of the concrete cylinders and reinforced test specimens shows that 20% rubber replacement via fine aggregates causes an 8.4% reduction in weight and 41.86% in compressive strength compared to normal concrete. The peak strength has increased from 7% to 14% concerning the Modified Kent and Park Model. The reinforced rubberized concrete stiffness decreased while the strain at peak increased.Likewise, the ultimate strain was decreased in each series, and the degradation of stiffness occurs at 50.53%, 64.72%, and 74.34% concerning normal concrete. Similarly, employing scrap rubber crumbs in concrete causes the relative ductility to decrease by 39.60%, 44.93%, and 51.27% compared with Modified Kent and Park Model, for 10%, 15%, and 20% volume replacement (3 inches spacing). It was also found that the relative ductility is reduced for specimens having 3 in (76 mm) c/c tie spacing in contrast to specimens having 6 in (152 mm) c/c tie spacing by using rubberized concrete due to confinement of lateral ties.The plain and reinforced concrete employing crumb rubber is under investigation worldwide at different sections and full-frame structure levels. The Poison’s ratio of rubberized aggregate concrete and its variation with percentage replacement of rubber is under exploration. It is recommended to build frame structures and various sections of structural members, i.e., beam and column of plain and reinforced concrete, and be tested under static and dynamic loading to know the performance parameters and behavior so that scrap rubber can be confidently used as building materials. This will serve as a guideline of reusability of rubber in construction industries and will minimize the associated problems like contamination of the environment and dumping site availability.With the advancement in artificial intelligence (AI), wide variety of civil engineering problems are solved using AI ,43,44,45This research study focused on exploring the stress–strain relationship of rubberized reinforced concrete test column specimens  and their comparison with the established analytical models. The following conclusions have been summarized from the current research work.


A 79‐year‐old former smoking Japanese man was admitted to our hospital with a 2‐year history of dry cough and dyspnoea on exertion. High‐resolution computed tomography of the chest revealed reticulation and perilobular opacity with bronchial wall thickening and ground‐glass opacities (GGOs) in both lungs, in addition to subpleural dense consolidation  predominantly in the bilateral upper lobes. Serum immunoglobulin G4 (IgG4) was elevated (348 mg/dl). Lung biopsy specimens obtained by video‐assisted surgery revealed a mixture of usual interstitial pneumonia (IP) and non‐specific IP pattern admixed with PPFE. In addition, immunohistochemical staining of IgG4 showed numerous IgG4‐positive plasma cells. Consequently, he was diagnosed with IgG4‐positive IP associated with PPFE. We initiated a combination therapy with prednisolone and cyclosporine as a calcineurin inhibitor. During prednisolone tapering, his clinical conditions and GGOs improved gradually over 12 months. However, reticular opacities and PPFE‐like lesions remained unchanged, and pulmonary function test findings slightly deteriorated. We describe a case of immunoglobulin G4‐positive interstitial pneumonia associated with pleuroparenchymal fibroelastosis, confirmed by video‐assisted thoracoscopic surgery. Combination therapy with prednisolone and cyclosporine showed long‐term efficacy and safety. Immunoglobulin G4‐related disease (IgG4‐RD) is a recognized systemic fibro‐inflammatory disease characterized by elevated serum IgG4 concentrations, infiltration of IgG4‐positive plasma cells and fibrosis, which affects various organs including the lungs.1] of 2.02 L and 79.5% of predicted) with diffusion capacity for carbon monoxide . Examination of bronchoalveolar lavage fluid (BALF) revealed alveolar macrophages, 84.6%; lymphocytes, 14.0%; neutrophils, 0.4%; and eosinophils, 0%, with increased total cells and a CD4/CD8 ratio of 3.0. Cultures of sputum and BALF were negative for fungal, bacterial or mycobacterial pathogens. Lung biopsy specimens obtained by VATS revealed dominantly usual IP and non‐specific IP (NSIP) pattern in the lower lobe, in addition to PPFE lesions in the upper lobe . On the other hand, the serum IgG4 level decreased from 348 to 49 mg/dl. Also, serum KL‐6 and SP‐D decreased from 1065 to 579 U/ml and from 313 to 107 ng/ml, respectively. The patient had no relapse or adverse effects in the subsequent 12 months.A 79‐year‐old former smoking Japanese man was admitted to our hospital with a 2‐year history of dry cough and dyspnoea on exertion. He had no history of recurrent lung infections such as non‐tuberculous mycobacterial infection. There was no evidence of specific physical findings suggestive of autoimmune diseases. High‐resolution computed tomography (HRCT) of the chest revealed reticulation and perilobular opacity with bronchial wall thickening and patchy ground‐glass opacities (GGOs) in the bilateral lower lobes, in addition to subpleural dense consolidation (PPFE‐like lesion) predominantly in the bilateral upper lobes Figure . Laborate Figure . There we Figure , but no e Figure . Conseque Figure . Howevere Figure , and valWe described a case of IgG4‐positive IP associated with PPFE, diagnosed histopathologically by VATS. Tanaka et al. have already described a case of IgG4‐positive IP mimicking idiopathic cellular NSIP without other IgG4 organ involvement.As described by Ikeda et al.,None declared.The authors confirm that appropriate written informed consent of the patients was obtained for publication of this case report and accompanying images.


The pulse waveform of intracranial pressure (ICP) is its distinctive feature almost always present in the clinical recordings. In most cases, it changes proportionally to rising ICP, and observation of these changes may be clinically useful. We introduce the higher harmonics centroid (HHC) which can be defined as the center of mass of harmonics of the ICP pulse waveform from the 2nd to 10th, where mass corresponds to amplitudes of these harmonics. We investigate the changes in HHC during ICP monitoring, including isolated episodes of ICP plateau waves.Recordings from 325 patients treated between 2002 and 2010 were reviewed. Twenty-six patients with ICP plateau waves were identified. In the first step, the correlation between HHC and ICP was examined for the entire monitoring period. In the second step, the above relation was calculated separately for periods of elevated ICP during plateau wave and the baseline.R = 0.45, p = 0.022) with HHC (3.64 ± 0.46). During the ICP plateau waves , we found a significant decrease in HHC .For the values averaged over the whole monitoring period, ICP (22.3 ± 6.9 mm Hg) correlates significantly (The good correlation between HHC and ICP supports the clinical application of pressure waveform analysis in addition to the recording of ICP number only. Mean ICP may be distorted by a zero drift, but HHC remains immune to this error. Further research is required to test whether a decline in HHC with elevated ICP can be an early warning sign of intracranial hypertension, whether individual breakpoints of correlation between ICP and its centroid are of clinical importance. Intracranial compliance (ICC) describes the capacity of intracranial compartments to contain an additional volume with no significant change in intracranial pressure (ICP) . ICC is The pulse waveform of ICP is reflected by the fluctuations of the cerebrospinal fluid generated by the systole and diastole of the cardiac cycle. Visual inspection of the ICP pulse waveform usually reveals three distinct peaks: P1, percussion wave; P2, tidal wave; and P3, dicrotic wave , 18 Fig, indicatOther than classifying ICP pulse waveform based on visual inspection , it is pThese concepts are potentially attractive because “continuous monitoring of intracranial compliance by computerized analysis of the ICP waveform may provide an earlier warning of neurological decompensation than ICP per se and, unlike pressure–volume index (PVI), does not require volumetric manipulation of intracranial volume” .In this study, we aim to investigate the correlations between HHC and ICP, ABP, cerebral perfusion pressure (CPP), heart rate (HR), and PRx, the cerebral pressure reactivity index  for (1) The study comprised patients after traumatic brain injury (TBI), treated in the Neurosciences Critical Care Unit (NCCU) in Addenbrooke’s Hospital, Cambridge, UK, from 2002 to 2010. The use of the data for anonymous audit and publication was approved by the institutional Research Ethics Committee (29 REC 97/291). Informed consent for this retrospective analysis was waived.We selected 26 patients with 86 ICP plateau waves from the 325-patient database of multimodality brain monitoring. Table https://icmplus.neurosurg.cam.ac.uk) was used for online data recording. Computerized data monitoring which supports critical care management is a standard clinical practice in Cambridge.ICP was monitored using an intraparenchymal micro-sensor  inserted in the frontal white matter via a cranial access device . Zeroing was standard and performed at the time of implantation of the ICP sensor—wet transducer tip zeroed against the atmospheric pressure. ABP was measured through a line inserted into the radial or femoral artery with a zero calibration at the level of the right atrium. All patients were positioned with head elevation to 30° during the monitoring period, as per standard intensive care protocols for conservative management of severe head injury . All indHHC was calculated in the following way. First, ICP waveform was divided into 10-s-long non-overlapping segments. Fourier transform was applied to decompose the segment of ICP into separate sinusoids, each with a different frequency and amplitude. The spectrum generated by the Fourier transform shows the amplitudes of these sinusoids plotted against their respective frequencies. The first harmonic  corresponding to the pulse (heart rate) was found as a frequency within the range of 40–180 beats per minute for which the amplitude was maximal. From the amplitude spectrum, we extracted only the fundamental component and its higher harmonics. Our amplitude spectrum was plotted against harmonic numbers instead of frequencies Fig. . This apPlateau waves were identified during post-recording offline analysis and inspection of the data. The identification criteria were an ICP elevation above 35 mm Hg, an average relative ICP increase of greater than or equal to 15 mm Hg, matched by a CPP decrease of greater than or equal to 10 mm Hg , 32. Thep > 0.1 for all variables). To identify a breaking point on ICP-HHC characteristics, a scatter plot and an error bar chart  were obtained for each patient’s individual recording as well as for pooled data.The relationships between HHC and the other parameters were investigated using multivariate regression analysis. The patient’s identification number was included in the model using binary dummy variables recoding. Partial correlation coefficients, which take into account repeated observations, were calculated . MoreoveTable An exemplary HHC error chart and a scatter plot obtained from a 10-h recording of ICP performed in a single patient are presented in Fig. This study shows that patient-averaged HHC correlates well with ICP outside of plateau waves and confirms findings from previous reports , 31. ThiThe main goal of this study was to investigate if the HHC indicates clinically relevant intracranial hypertension. This was answered affirmatively. An outcome correlation was not included, because the study comprised patients with plateau waves individually selected from a large database, resulting in a small subgroup of patients.This is the first report assessing the correlation of HHC with monitored parameters of brain physiology. Thus, there are no comparative studies examining these pulse wave metrics to date. However, research in the computerized analysis of ICP pulse waveform by a group from the Department of Neurosurgery at Baylor College of Medicine, Houston, USA, focused on another centroid, namely HFC. It is the power-weighted average frequency within the 4- to 15-Hz band of the ICP power density spectrum , 22, 31.Robertson et al.  did not The second paper from the Houston Group 6 years later, using a different dataset, includes a subgroup of patients who go on to develop refractory intracranial hypertension . One of It is a daily clinical observation that ICP pulse waveforms at a given ICP value differ within and between patients. This may have many causes including patient age, distribution of intra- or extracellular brain oedema/swelling, cerebral blood flow and cerebrovascular autoregulatory function, degree of hyperventilation, time after injury, and inflammatory reactions.It is also known that the typical three-peak appearance of the ICP pulse wave gets lost with increasing pulse amplitude during rising ICP , 25, turWe are not aware of any study that identified breakpoints in the ICP-HHC correlation. We showed that such a breakpoint exists and its occurrence may be a warning sign with a need to decrease ICP or a sign of improvement in case of an increasing HHC with decreasing ICP. This topic may spark some research interest based on this study and even expand the armamentarium of modalities in clinical brain monitoring in the future.The possible advantage of the HHC over the HFC is its independence of heart rate. Potential increases in the HFC can be induced by increases in the HR, which can occur with rising ICP and during plateau waves . This efOur study shows that the HHC was found to be lower during plateau waves when compared to the entire monitoring time. This drop additionally occurs between baseline and peak plateau waves. HR increases in the presented material were not large enough to match concerns of compounding with HHC.st harmony and the pulse amplitude is mainly composed of low-frequency components, because the amplitude of the 1st harmony is the most significant in the Fourier spectrum of the PPW (pressure pulse wave)” [To the knowledge of the authors, there are no publications on the analysis of ICP pulse waveform with the use of the HHC. One study in the neurosurgical literature reported that only harmonics nos. 1 to 9 of the pulse pressure wave were of significant size. This publication further concluded that “the close correlation between the amplitude of the 1e wave)” . This isThe second article which we found had examined some harmonics of the ICP waveform in relation to different ICP values . It repoWe suggest that HHC deserves further investigation because of its potential clinical utility and the paucity of investigations available. The HHC decrease with rising ICP can again be attributed to the decrease of the content of higher harmonics, which eventually causes the HHC to decrease.Finally, the main source of error in the recording of mean ICP is zero drift of the micro-transducer. HHC is not affected by the zero drift therefore it may be useful in the detection of intracranial hypertension irrespective of the drift of the ICP micro-transducer.Our data show that HHC is not correlated with the cerebral autoregulation, assessed with the PRx, in the entire monitoring period. The only statistically significant finding was a negative correlation between HHC and PRx during plateau waves, which probably indicates worsening of the autoregulation with decreasing compliance. We conclude that cerebral autoregulation and compliance are not directly linked and represent independent markers of cerebral function which can be variably impaired after brain injury.The study population consisted of patients with plateau waves and did not include all patients with ICP elevated above a certain threshold . Although it can be seen as a limitation, intracranial hypertension is usually treated rapidly in clinical settings , 26, 37,It is necessary to take into account that the selected group of patients under study may have tended towards those with prominent intracranial hypertension. However, in this work, we focused on the analysis of plateau waves as an interesting and well-described phenomenon triggered by active vasodilatation and increase in cerebral blood volume leading to elevation in ICP and decrease in CPP. These changes in cerebral blood volume, along with the reduced intracranial compliance, are known to be associated with changes in the shape of the ICP pulse waveform , 10, whiIt is also important to take into consideration the fact that an analysis of ICP elevated above a certain threshold could mask the observation of the presence of breaking point on the ICP-HHC characteristic in the current study. Further research on a larger number of patients will allow us to answer the question of what is the significance of the breaking point and whether it occurs for ICP increases above the assumed threshold  or it is specific for ICP plateau waves only.This study supports the importance of the computerized analysis of the ICP pulse waveform. The HHC decreases at high ICP values. A decrease in the HHC may be a strong descriptor of intracranial hypertension. Detection of changes in the HHC may also help identify a significant zero drift of the ICP transducer. Further work should focus on relations between centroids’ shifts and clinical outcomes following TBI.


Fiber-reinforced polymer (FRP) composites have been widely used for strengthening or constructing structures due to their excellent corrosion resistance and high tensile strength. An emerging hybrid structural member form with FRP composites—which consist of a steel section as internal reinforcement, an external FRP wrap/tube, and concrete filled between them (referred to as FRP-confined steel-reinforced concrete (FCSRC) systems)—has attracted increasing research interest. To date, the concept has been adopted to strengthen/repair steel structures or used as new hybrid structural members ). The FRP confinement and composite action between the three components in FCSRCs result in the excellent performance of the hybrid member. This paper presents a state-of-the-art review of FCSRCs for structural applications. The gaps in knowledge and future research opportunities on FCSRC structural members are also identified. Fiber-reinforced polymer (FRP) composites have been widely used as alternatives to steel reinforcement or strengthening materials in engineering structures due to their excellent corrosion resistance and tensile properties ,13,14,15Among the various novel forms of hybrid structural members containing FRP composites, one of the most common is concrete-filled FRP tubes CFFTs; , in whicThe concept of FCSRC structural members was first proposed by Liu et al.  with theUp to date, FCSRC systems have been adopted to strengthen/repair steel structures ,36,52,55The concept of FCSRC structural members was first proposed by Liu et al.  to strenIn addition to their use as a rehabilitation technique for steel structures, FCSRC systems have also gradually developed into significant structural elements in new structures . In such a hybrid structural member, the prefabricated FRP tube not only acts as a confining device but also serves as an in-site formwork for casting concrete. Moreover, both the confinement provided by the FRP tube and the support from the surrounding concrete can effectively restrain local and overall buckling of the internal steel section, leading to the full exploitation of the load-carrying capacity of the steel section. In general, structural capabilities are similar whether FCSRC systems are used for structural repairing/strengthening or new construction. Currently, a number of experimental and theoretical investigations have been conducted to gain an in-depth understanding of the behavior of this novel form of hybrid member under various loadings  ,55,56,57On the other hand, the concept of FCSRC systems has been applied to the buckling restrained braces (BRBs) that are commonly used as passive energy dissipation devices in seismic zones  ,66,67,68As shown in In building construction, square or rectangular columns may be preferred to circular columns due to aesthetical and other reasons. However, it is well-known that not all the concrete in a square or rectangular FRP tube is effectively confined, especially the concrete close to the flat sides. This hinders the application scope of FRP-confined concrete columns with a non-circular cross section. As shown in I-shaped (or H-shaped) steel sections are one of the most common forms in steel structures, and they have recently been used in FCSRC columns see  32,34,3,53,55,56In most cases, I- or H-shaped steel columns are fully encased by the concrete and the composite columns are fully confined by an FRP jacket along with the column height ,53,55,56Studies on FCSRC columns with a cruciform steel section have been limited see  41,42,4,47,49. TIn comparison with I-shaped steels and cruciform section steels, channel steels and steel plates are less popular in FCSRC columns due to the asymmetry of their cross sections and other reasons. To the authors’ best knowledge, only He and Chen  and Yu eIn addition, in the study of Yu et al. , encasedFRP composites can be classified into two types according to their tensile strain capacity: large rupture strain (LRS) FRPs (up to 8%) and small rupture strain FRPs . Conventional FRPs include CFRPs, GFRPs, aramid FRPs (AFRPs), and basalt FRPs (BFRPs). Generally, CFRPs have the highest ultimate strength and modulus of elasticity, while BFRPs have the lowest ultimate strength and modulus of elasticity, as shown in High-strength materials are often preferred when the weight and/or size of structures needs to be reduced. However, the use of high-strength materials has so far been rather limited because they significantly reduce the ductility of members. High-strength steel  is more susceptible to buckling failure than normal-strength steel. Such buckling failure should be avoided in structures because it portends that the yield strength of high-strength steel cannot be fully utilized and the ductility of the structural member can be greatly compromised. Efforts have been made to prevent or delay the local and overall buckling of steel in structural columns, such as (i) filling a steel tube with concrete ,70,71, . It was In recent years, scholars have extended the in-filled concrete in FCSRC columns from ordinary concrete to new types of concrete , which has considerably expanded the application scope of FCSRC columns. The following sections summarize the latest studies on FCSRC columns with new types of concrete.High-strength concrete (with a compressive strength of 50–120 MPa) has become increasingly attractive due to its high strength and high modulus of elasticity. Previous studies have revealed that FRP confinement can improve both the strength and ductility of high-strength concrete; however, a strain-softening segment often appears in the stress–strain behavior of FRP-confined high-strength concrete. The interaction between the encased steel and the high-strength concrete confined with FRP is also an important issue to be explored for FCSRC columns. Up to date, there have been few studies on the compressive behavior of FCSRC columns using high-strength concrete ,53. In tExtensive research have been carried out to understand the material and structural performance of recycled aggregate concrete ,82,83,84It is well-known that FRPs provide passive confinement to concrete cores subjected to lateral dilation ,13,14,15Different from stub FRP-confined composite columns, slender FRP-confined composite columns encounter secondary bending moments, which reduce their load-carrying capacity. This is because the increased slenderness changes the failure mode of the column from a loss of cross-sectional strength to a loss of member stability, thus leading to a reduction in the FRP confinement efficiency. Therefore, the effect of column slenderness on the compressive behavior of columns is an important issue to be addressed. To the best knowledge of the authors, most of the research on FCSRC columns has focused on stub columns, and studies on slender FCSRC columns are limited ,46,48,50Karimi et al. ,38 invesIn practical applications, columns are inevitably subjected to combined compression and bending. The load-carrying capacity of a column is generally reduced by a bending moment due to uneven stress distribution. Huang et al.  investigModels for the load capacity and ultimate axial strain of FCSRC columns/respective confined concrete are summarized in ‘Type I’ considers the contributions of unconfined concrete and profile steel in the nominal load capacity  columns . As the  columns  proposed‘Type II’ considers the contributions of confined concrete and profile steel in the load capacity  columns ,48,49,54 columns  and Xiong et al.  added a g et al. , which wg et al. , the comg et al.  were fulg et al.  to predin et al. .‘Type III’ considers the contributions of three parts  in the load capacity  columns ,47,56,90 columns ,90 and R columns ,90, the ress fsu b, which ress fsu c. Furtheress fsu d to predStudies on the flexural behavior of FCSRC beams have been rather limited. Up to date, only Zakaib and Fam  have invRecently, FRP composites with high stiffness have been adopted to prevent the local and overall buckling failure of slender steel members ,96,97,98MacEachern and Sadeghian  tested 2Similarly, Feng et al.  demonstrFeng et al.  built fiSeveral studies have focused on the cyclic performance of BRBs. Sun et al.  investign et al.  suggesteAssembled BRBs usually involve FRP wraps. They can be assembled or disassembled by wrapping or cutting the external FRP wraps, respectively, making repair or inspection easier. Research related to this form of BRBs has been extensively carried out ,66,67,68The cyclic behavior of assembled BRBs with FRP wrapping has attracted the attention of some researchers. El-Taiwil and Ekiz , Deng et(1)Using FRP confining devices and filling materials (the concept of FCSRC systems) to strengthen existing (corroded and buckled) steel columns is feasible: the load-carrying capacity of reinforced steel columns can be restored or even significantly increased to some extent due to the dual restraint of concrete and FRP tubes. In this case, in addition to the FRP-wrapping-based wet layup process, the split-tube construction process is also recommended.(2)FCSRCs have been developed into significant structural elements in new structures . The FRP confinement and composite action between the three components  generally result in the superior structural performance of FCSRC structural members.(3)Previous studies have primarily focused on the behavior of circular, square, and rectangular FCSRC columns subjected to concentric or eccentric compression. The investigated parameters included the cross-sectional shapes, the strength grades of the steel or the concrete, the slenderness ratio, and the load eccentricity.(4)In most cases, the buckling of inner steel section  can be effectively delayed or prevented by the surrounding concrete and the external FRP confining tube no matter its configuration, so the post-yield strength of steel can be fully exploited, further indicating the validation of the FCSRC system.(5)In addition to the FRP full wrapping strengthening, FRP partial wrapping strengthening has been used in FCSRC columns, and relevant research has indicated the feasibility of using FRP strips to confine concrete-encased steel columns.(6)New types of materials have been adopted in FCSRC columns. For instance, PET FCSRC columns exhibit much better deformation capacity than FCSRC columns made of conventional FRPs. PET FCSRC columns can fully exploit the strength of high-strength materials including high-strength steel and high-strength concrete.(7)Pre-stress could eliminate stress lag, and the strength of expansive concrete-based FCSRC columns is higher than that of FCSRC columns with ordinary concrete.(8)A number of models have been proposed for the load capacity and ultimate axial strain of FCSRC columns. These models are classified into three types based on the estimation of the contributions of three different components in FCSRC columns . However, these models have never considered the additional confinement from the steel section. The accuracy of these models also requires further evaluation.(9)Currently, research on FCSRC beams is rather limited. A previous study indicated the excellent flexural performance of FCSRC beams.(10)The concept of FCSRC systems has been applied to buckling restrained braces. Previous studies on FCSRC BRBs have focused on behavior under the axial compression or reversed cyclic loading, and they have demonstrated the feasibility of FRP strengthening technique for BRBs.Previous studies have indicated that FCSRC structural members are promising. This paper has presented a state-of-the-art review of FCSRC structural members in strengthening existing structures/constructing new structures and buckling restrained braces. Based on the literature review, several key conclusions can be drawn:(1)The effects of full and partial encasement of steel sections in surrounding concrete need to be explored.(2)The long-term structural performance of FCSRC structural members under extreme conditions  needs to be further explored.(3)Future efforts can focus on the development of FCSRC structural members made of high-performance materials.(4)The buckling behavior of steel sections may counteract the strength enhancement caused by their confinement; however, most models still assume that the steel is an idealized elastic–perfectly plastic material. Thus, it requires further investigation regarding the two aspects, and respective design standards need to be established.(5)Research on FCSRC beams is rather limited. The fatigue performance of FCSRC beams needs to be explored.(6)The bond behavior between the steel section and concrete in an FCSRC member needs to be understood. The combined use of bolt connections can result in full composite action. The interfacial bond between FRP and the concrete could be enhanced by using a proper device (such as a rough surface of the FRP with resin ribs or sand-coating).(7)The fire resistance of exterior FRP coatings is very important when FCSRC structural members are used in residential premises. It is necessary to adopt an effective approach  to impr(8)It is necessary to develop new types of FCSRC BRBs that are more efficient and inexpensive.(9)FCSRCs are generally used as columns, beams, and buckling restrained braces in high-rise buildings or infrastructures. FCSRCs have been less used in spatial structures to date, which deserves further investigation.(10)Future work should address the issue of FRP layer protection against mechanical damage.In order to facilitate the wide application of FCSRC systems in structural applications, it is necessary to gain an in-depth understanding of the structural performance of FCSRC systems subjected to various forms of loading. The gaps in knowledge and future research opportunities on FCSRC systems are identified and discussed below:


MET gene alterations are known to be involved in acquired resistance to epidermal growth factor receptor inhibition. MET amplifications present a potential therapeutic target in non-small cell lung cancer. Although next-generation sequencing (NGS) and fluorescence in situ hybridization (FISH) are conventionally used to assess MET amplifications, there are currently no clinically defined cut-off values for NGS, with FISH still being the gold standard. A collective of 20 formalin-fixed paraffin-embedded lung cancer tissue samples (mean age 64 years) were selected based on increased MET gene copy number (CNV) status or the presence of mutations detected by NGS  and were further assessed by FISH . Of these, 17 tumor samples were MET-amplified and one patient was found to have a MET rearrangement by NGS, while two samples had no MET gene alteration. In contrast to the NGS result, FISH analysis showed only one highly amplified sample and 19 negative samples. The single highly amplified case detected by FISH was also positive by NGS with a fold change (FC) of 3.18 and a mean copy number (CNMV 10−100%) of 20.5. Therefore, for the assessment of MET amplifications using the QIAGEN NGS workflow, we suggest detecting amplified cases with an FC value of ≥ 3.0 and a CNMV 10−100% value of ≥ 20.0 by FISH. In summary, NGS allows for DNA- and RNA-based analysis of specific MET gene amplifications, point mutations or rearrangements. MET encodes for a receptor tyrosine kinase which is a ubiquitously expressed cell surface receptor consisting of an extracellular alpha-chain disulfide-bonded to a membrane spanning beta-chain  – C-terminal (Abcam) as well as a review of the literature did not provide evidence of the validity of MET antibodies for the purposes of prescreening for MET alterations , 35.MET amplification. Additionally, copy number arrays  as well as another NGS panel  will be used to validate the results. Further clinical data are needed to define clinically relevant MET alterations and to establish a testing algorithm for routine diagnostics.In the next step we are going to increase the cohort size, including cases with negative, low-level, intermediate-level and high-level ® RSC FFPE Plus DNA Kit (Promega). The library preparation was performed according to standard laboratory instructions and sequenced using the custom-made targeted resequencing panel (nNGM version 1.0) and the Gene Reader (QIAGEN) workflow. The results were analyzed with the QIAGEN bioinformatics software packages QCI-A and QCI-I. The cases were selected with respect to the quality parameters specified by the analysis software (QCI-A). Sequencing was performed on Gene Reader (QIAGEN) with a resulting sequence coverage of >100×. The detailed parameters of the bioinformatics NGS workflow for the detection of CNVs were set in the QCI-A software according to The samples were previously analyzed using NGS. After macrodissection of the tumor tissue, DNA was isolated using the MaxwellMET amplification status was assessed using ZytoLight® FISH-Tissue Implementation Kit (ZytoVision®) with a dual-color FISH probe set (ZytoLight® SPEC MET/CEN7) targeting MET and CEN7 on formalin-fixed, paraffin-embedded tissue sections. The ZytoBrite Hybridizer (Zytomed) was used for hybridization following the ZytoLight® protocol. The analysis was made using fluorescence microscopy (Carl Zeiss Microscopy GmbH).Evaluation of the FISH analysis was performed and modified according to the criteria of Schildhaus et al.  Table 5Table 5.To calculate the analytical sensitivity and specificity, FISH analysis was assumed to be the gold standard. The calculation was performed according to the criteria of the “Deutsche Akkreditierungsstelle” (DakkS) .Analytical sensitivity is a measure of the rate of true positive cases. It thus describes the ratio of correctly positive results to the total number of positive results.The analytical specificity describes the rate of correctly negative results. It is a measure of the ratio of correct negative results to the total number of negative results.