Since secreted proteins are easy to be detected in body fluids, urine, and saliva in clinical diagnosis, they play important roles in biomarkers for disease diagnosis and vaccine production. In this study, we propose a novel predictor for accurate high-throughput identification of mammalian secreted proteins that is based on sequence-derived features. We combine the features of amino acid composition, sequence motifs, and physicochemical properties to encode collected proteins. Detailed feature analyses prove the effectiveness of the considered features. Based on the differences across various species of secreted proteins, we introduce the species-specific scheme, which is expected to further explore the intrinsic attributes of specific secreted proteins. Experiments on benchmark datasets prove the effectiveness of our proposed method. The test on independent testing dataset also promises a good generalization capability. When compared with the traditional universal model, we experimentally demonstrate that the species-specific scheme is capable of significantly improving the prediction performance. We use our method to make predictions on unreviewed human proteome, and find 272 potential secreted proteins with probabilities that are higher than 99%. A user-friendly web server, named iMSPs , which implements our proposed method, is designed and is available for free for academic use at:  Secreted proteins (SPs) are the proteins that are released by a cell or tissue into the extracellular space. Generally, these proteins are produced through two pathways, namely the classical Endoplasmic Reticulum and Golgi routes  and the Recent years have witnessed a number of computation-based approaches in this field. In 2011, Hong et al. used physiochemical properties and amino acid composition features to predict whether a protein can be excreted into urine . Liu et The above-mentioned studies all contributed to the development of the research in secreted proteins. However, there still exist some shortcomings that need to be further investigated: (i) the structure-based methods, which could achieve high accuracy, are limited in real application due to the small number of known protein structures. Although sequence-based predictors are featured in their wide application, they often suffer from the unsatisfactory prediction performance; (ii) many methods used structure- or sequence-based features in order to construct feature matrix without analyzing the features in detail. That is, it is unknown whether these features could successfully differentiate secreted proteins from non-secreted proteins; (iii) some predictors simply predict general secreted proteins without considering the differences across various species of secreted proteins. Based on our investigation, the differences do exist and they help to recognize specific secreted proteins.In view of these three issues, we aim to focus on the challenge of proposing an accurate computational method for the identification of mammalian secreted proteins based on primary sequences. Instead of using general secreted proteins, we compile several sub-datasets of prevalent species of secreted proteins. The features which have been proved to be involved in secreted proteins are collected to encode secreted proteins. We analyze the differences between secreted proteins and non-secreted proteins in detail, especially across several mammalian species. In addition, Fisher-Markov selector together with incremental feature selection scheme is introduced to remove redundant features, as well as to explore optimal feature subset. Experimental results on benchmark datasets and independent testing dataset prove the effectiveness and generalization capability of our method. Additionally, we also make predictions on unreviewed human proteome and find potential secreted proteins with high confidence.In this paper, we encode the proteins by using three types of features, including amino acid composition (AAC), sequence motifs (MTF), and physicochemical properties (PCP). Before constructing the prediction model, we investigate the differences of the considered features between secreted proteins and non-secreted proteins. As shown in x-axis indicates the normalized values and y-axis stands for twelve properties. For instance, the distribution of secreted proteins against the non-secreted proteins varies obviously in hydrophobicity (Panel A). This phenomenon keeps consistent in SPs-H, SPs-M, SPs-B, and SPs-C. In SPs-H or SPs-B, a significant difference is found on the distributions of the entropy of formulation and protein kinase A. In SPs-M, the difference on protein kinase A is mild, but that on polarity is remarkable. In SPs-C and SPs-O, the majority of the considered physicochemical attributes show a big difference in secreted proteins against the non-secreted proteins.We further illustrate the distribution of physicochemical properties in H  and SPs-C (MCC 0.546 versus 0.410). These results illustrate the effectiveness of our feature selection scheme. The prediction performances of the detailed different numbers of features on six training sets are provided in M. musculus. When considering MCC, which is capable of balancing the measurements between sensitivity and specificity, the species-specific model all produce higher values. H. sapiens, M. musculus, and B. taurus, the improvements on AUC are about 0.018, 0.021, and 0.023. For SPs-C and SPs-O, the AUC values sharply increase from ~0.59 to ~0.78 and ~0.71 to ~0.83.Based on our previous investigation, different species of secreted proteins show various attributes in many aspects. Then, we are inspired to introduce species-specific strategy for the specific identification of various mammalian secreted proteins. When compared with universal models, species-specific ones are based on specific feature construction and optimal feature subsets. To investigate the effectiveness of this strategy, we compare these two kinds of models based on same benchmark training datasets over five-fold cross-validation. As shown in To evaluate the generalization capability of the proposed predictor as well as to compare with previous methods, we further test our method on the independent testing dataset. Recent years have witnessed several powerful predictors for identification of SPs, such as SecretomeP, NClassG+, and SRTpred. The criteria that used for selecting efficient methods include (1) the outputs of the predictors are scores and (2) the predictors can successfully predict an average length protein sequence with 200 residues within 30 min. As a result, we select two predictors, namely SecretomeP  and SRTpU) produces the MCC of 0.427, 0.455, 0.507, 0.359, 0.324, and 0.332 on six testing datasets respectively. On the former four testing datasets, our iMSP-U outperforms SecretomeP and SRTpred. On SPs-C and SPs-O’s testing sets, SecretomeP and SRTpred show much better than our iMSP-U. When adopting species-specific models  on the corresponding species-specific testing datasets, the prediction performance shows obvious improvements.http://www.inforstation.com/webservers/iMSP/. iMSP offers efficient high-throughput predictions for biologists. In this work, our new-compiled benchmark dataset was generated from UniProt . In the UniProt database, sequence similarity search programs are used to identify orthologs. Since H. sapiens and M. musculus secreted proteins occupy a large part of all secreted proteins, they would somehow influence other species of secreted proteins, such as B. taurus, C. lupus familiaris, and O. cuniculus. In our benchmark dataset, the number of secreted proteins in SPs-H and SPs-M is much higher than that of SPs-B, SPs-C, and SPs-O. As a result, the accuracy of the latter three species-specific models will be affected by that of the first two. The users are suggested to choose universal model for Bos, Canis and Oryctolagus proteins, and species-specific models for Homo and Mus proteins.We implement the proposed method as a public web server, named iMSP, which is deployed at U) and species-specific model (iMSP-H), we also calculate the probabilities of unreviewed human proteins to be secreted proteins. All of the proteins were ranked according to the predicted probabilities. Based on iMSP-H, we find that 7601 (14.77%) proteins have the probabilities higher than 0.8, while a large number of proteins are not secreted proteins , Mus musculus (M. musculus), Bos taurus (B. taurus), Canis lupus familiaris (C. lupus familiaris), and Oryctolagus cuniculus (O. cuniculus). The species-specific datasets are used to explore the differences across the various mammalian secreted proteins. Next, Blastclust [In this study, we collect 17,209 mammalian secreted proteins and 29,479 non-secreted proteins from UniProt. We take consideration of the prevalent several species, which include astclust  is used Amino acids are the fundamental elements of proteins. The features of amino acid composition (AAC) reflect the distribution of amino acids in proteins ,30. AAC Proteins in the same family tend to share similar attributes. These attributes are usually located on the highly conserved parts of the proteins. These conserved parts can be recognized by sequence patterns/motifs . In thisIn real-world cases, the imbalance on number of SPs to non-SPs would somewhat lead to potential bias on the selected motifs based on IG. Considering this, we further calculate the ratio of the difference value of IG (RDI) for target MTF ‘The physiochemical properties (PCP) of residues reveal microscopic environment of proteins. These microscopic environments includes protein energy, fore, and dynamics . For exaThe index data for twenty standard amino acids can be formulated as:In information theory, the existence of ‘bad’ (noisy or irrelevant) features will potential destroy the classifier or will lead to overfitting . TherefoIn this work, LIBSVM 3.20  is utiliTP is the number of correctly recognized secreted proteins, TN is the number of correctly recognized non-secreted proteins, FP is the number of incorrectly recognized secreted proteins, and FN is the number of incorrectly recognized non-secreted proteins. Since the abovementioned threshold-dependent measurements are sensitive to thresholds, we also adopt AUC (area under Receiver Operating Characteristic (ROC) curve), which has been proved to be a robust assessment criterion for imbalanced testing datasets [We assess our method using two statistical cross-validation methods, namely five-fold cross-validation and the independent test. A five-fold cross-validation is adopted for evaluating the performance of proposed predictor on the training dataset. First, we randomly divide the training dataset into five parts. In each run, four of them are used to train a classifier and test on the holdout fold. Then, we combine the predictions in all five iterations to compute the following threshold-dependent measurements: accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC). They are defined, as follows:datasets .Secreted proteins are widely spread in living organisms and cells. Featured by easily being detected in body fluids, urine, and saliva in clinical, they play important roles in potential biomarkers for disease diagnosis and vaccine production. In this study, we present a novel high-throughput predictor for the identification of mammalian SPs from primary protein sequences. We analyze the differences across various types of secreted proteins and non-secreted proteins by using considered features, including AAC, MTF, and PCP. When compared with the traditional universal model, the introduced species-specific scheme proves to be capable of improving the prediction performance for corresponding species of secreted proteins. Tests on independent testing dataset promise a good generalization capability of our proposed method. We also apply the proposed predictor to predict unreviewed human proteome. We list 272 potential secreted proteins, which are predicted with high confidence (≥99%), for further investigation by biologists.


Post-transcriptional modifications have been recently expanded with the addition of RNA editing, which is predominantly mediated by adenosine and cytidine deaminases acting on DNA and RNA. Here, we review the full spectrum of physiological processes in which these modifiers are implicated, among different organisms. Adenosine to inosine (A-to-I) editors, members of the ADAR and ADAT protein families are important regulators of alternative splicing and transcriptional control. On the other hand, cytidine to uridine (C-to-U) editors, members of the AID/APOBEC family, are heavily implicated in innate and adaptive immunity with important roles in antibody diversification and antiviral response. Physiologically, these enzymes are present in the nucleus and/or the cytoplasm, where they modify various RNA molecules, including miRNAs, tRNAs apart from mRNAs, whereas DNA editing is also possible by some of them. The expansion of next generation sequencing technologies provided a wealth of data regarding such modifications. RNA editing has been implicated in various disorders including cancer, and neurological diseases of the brain or the central nervous system. It is also related to cancer heterogeneity and the onset of carcinogenesis. Response to treatment can also be affected by the RNA editing status where drug efficacy is significantly compromised. Studying RNA editing events can pave the way to the identification of new disease biomarkers, and provide a more personalised therapy to various diseases. RNA modifications refer to alterations in the chemical structure of RNA molecules occurring after DNA transcription and synthesis by the RNA polymerase enzyme. They were first described in 1968 with the discovery of RNA methylation in Hela cells .A unique type of RNA modification in trypanosome mitochondrial mRNA was discovered ~ 30 years ago . The proSaccharomyces cerevisiae alanine tRNA . M. M41]. MIn humans, three ADAR enzymes are expressed Fig. b. ADAR1 RNA editing occurs primarily within noncoding regions, and only a small percentage takes place in coding regions resulting in amino acid change. In humans, A-to-I editing mostly occurs in introns and untranslated regions (UTRs) of protein coding genes . A-to-I Ala have been reported [MicroRNAs (miRNAs) are also processed by ADARs (ADAR1 and ADAR2) for A-to-I editing in order to control miRNA biogenesis , 84. In reported . Even threported .APOBEC1 has a preference for AU-rich sequences within mRNA 3′ UTRs . APOBEC3Classical molecular biology and sequencing techniques have contributed significantly in the discovery and early detection of RNA editing. The emergence NGS technologies in combination with accurate bioinformatic pipelines made possible the detection of thousands of new RNA editing sites , 93. Mathttps://github.com/zhqingit/giremi), RNAEditor (http://rnaeditor.uni-frankfurt.de/) and DeepRed (https://github.com/wenjiegroup/DeepRed) from RNA-seq data in the absence of relevant genomic data [High-throughput sequencing methods like RNA-seq have now become readily available in the scientific community, leading to a wealth of accumulating transcriptomic data of various tissues and cell populations . This exmic data , 80, 96.https://omictools.com/the-rna-editing-atlas-tool) is the first human inosinome atlas, comprising > 3.0 million A-to-I events identified in six tissues from three healthy individuals. Matched directional total-RNA-Seq and whole genome sequence datasets were generated and analysed within a dedicated computational framework, also capable of detecting hyper-edited reads. Inosinome profiles within the RNA Editing ATLAS are tissue specific and edited gene sets, consistently show enrichment of genes involved in neurological disorders and cancer. The RNA Editing ATLAS reports that overall, the frequency of editing varies, but is strongly correlated with the expression levels of ADAR [Furthermore, freely available databases are now available, where one can explore RNA editing collections, in humans and other model organisms. The RNA Editing ATLAS ( of ADAR .http://rnaedit.com/) presents a comprehensive collection of A-to-I editing sites in human, mouse, and fly transcripts [Moreover, the RADAR database  is another freely available database collecting > 4.5 millions of A-to-I editing events in 55 body sites of 150 healthy individuals from the GTEx project. In REDIportal, RNA editing sites can be searched by genomic region, gene name and other relevant features as the tissue of origin. Recently, REDIportal started collecting A-to-I events in non-human organisms [REDIportal  provides a comprehensive resource for the functional prediction of RNA editing in long noncoding RNAs (lncRNAs) in Homo sapiens, Macaca mulatta, Mus musculus and Drosophila melanogaster [Furthermore, LNCediting  implicated ADARs and A-to-I editing post-transcriptionally regulates circadian rhythm and sleep [Drosophila led to constitutive release of neurotransmitter in glutamatergic neurons promoting sleep [2+, whilst regulating Ca2+ influx; a prerequisite for normal neuronal function  and class switch recombination (CSR). AID plays an important role in antibody diversity through both SHM and CSR . Two modAPOBECs are heavily implicated in viral immune defense. APOBEC3G is the first family member that was identified as an antiviral factor and the most well studied , 122. InDifferent levels of antiretroviral activity among APOBECs have also been demonstrated against Simian immunodeficiency virus (SIV), adeno-associated virus type 2 (AAV-2), murine leukemia virus (MLV), equine infectious anemia virus (EIAV), and foamy viruses (FVs) , 125–128Apart from viral C-to-U DNA editing, APOBEC3-mediated C-to-U RNA editing is also observed in HIV genomic RNA . APOBECsADARs  target RNA viruses like measle virus, infuenza A virus, Rift Valley fever virus (RVFV), lymphocytic choriomeningitis virus (LCMV) and hepatitis C virus (HCV) by A-to-I hypermutation –137. HowAID/APOBEC proteins have the capacity to restrict LTR and non-LTR retrotransposons , 139. ReThe contribution of RNA editing in the brain and CNS is not always beneficial. Evidence have emerged implicating RNA editing in the pathogenesis of neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), Parkinson and Alzheimer diseases . In ALS,Cancer pathogenesis is primarily attributed to genetic mutations in proto-oncogenes and tumour suppressor genes, which transform a normal cell into a malignant cancer cell. The rapid progress of NGS technologies and the readily available transcriptomic data have revealed a significant contribution of RNA editing in the pathogenesis and progression of cancer Table  153, 15. TranscrAs mentioned above, editing sites fall within noncoding regions of the genome, and this is also true for cancer genomes. MiRNAs are also important editing targets in cancer . In glioEditing in the protein coding region is far less frequent, but it has major consequences in the regulation and function of the affected gene. Sixty recoding events have been identified in a large scale study that are associated with tumours . Other rCytidine deaminase mutations have also been observed in an array of human cancers . APOBEC kataegis has been associated with programmed cell-death receptor-1 (PD-1) overexpression, an immune checkpoint molecule, leading to immune tolerance and exhaustion [Another important role of RNA editing in cancer is its involvement in the ability of tumours to evade immune responses. APOBEC overexpression and haustion . Convershaustion .https://proteomics.cancer.gov/programs/cptac), Peng and colleagues present evidence that the A-to-I RNA editing events in cancer are manifested in protein diversity of cancer cells through changes in amino acid sequences. These intriguing observations suggest that RNA editing is a novel source of cancer protein heterogeneity.In cancers, RNA editing not only changes the sequence of RNAs and their expression but also contributes to proteomic diversity , 192. CoIncreasing evidence proclaims that the levels of RNA editing, along with the expression of adenosine and cytidine deaminases and specifically edited genes , could all be used as important prognostic biomarkers in the pathogenesis and progression of cancer , 171. DeOverall, the modulation and application of RNA editing is an area of great potential. Besides traditional deaminase inhibitors to control expression, molecular tools such as antisense oligonucleotides are potent and selective inhibitors of RNA editing on targeted RNAs . EngineeIn this review, we have discussed the diverse aspects of RNA editing, from its discovery to physiological function and involvement in human diseases. The road ahead still seems bright for RNA editing with new exciting findings from modern transcriptomics and new therapeutic developments in associated disorders.


OBJECTIVES/SPECIFIC AIMS: N/A. METHODS/STUDY POPULATION: N/A. RESULTS/ANTICIPATED RESULTS: N/A. DISCUSSION/SIGNIFICANCE OF IMPACT: There is an increasing need to foster cross-disciplinary research to address complex problems within healthcare. The Sinai Team-based Translational Education Program: the URM Propeller (STTEP-UP) is a NCATS funded program through the Icahn School of Medicine at Mount Sinai. Its goal is to facilitate URM post-doctoral trainees becoming innovative leaders in clinical and translational research. The program includes a team-based research component, where fellows collaborate on a project. This year, disciplines represented by the four fellows include Cardiology, Psychiatry, Neurology, and Pediatrics. Identifying a clinical question and designing an investigation was facilitated by group brainstorming meetings with program mentors. Fellows designed a project to identify medical testing and prescribing that were not clinically indicated throughout the healthcare system, with the goal of exploring whether an intervention, including provider education, could reduce ordering practices. In addition to regular in-person meetings, a licensed virtual learning environment and free web-based sharing platform were used to foster collaboration. Challenges faced throughout this process, included fellows struggling to find protected time, difficulties accessing broad sets of data across the healthcare system, and overcoming administrative barriers between departments. Strengths of this approach, included fellows learning new research strategies and feeling a deeper sense of commonality with their peers. Overall, this experience supports the idea that cross-disciplinary research improves the collaboration and education of emerging researchers. However, addressing logistical and systems-based barriers may better facilitate this education and research.


This review discusses different forms of nanomaterials generated from chitosan and its derivatives for controlled drug delivery. Nanomaterials are drug carriers with multiple features, including target delivery triggered by environmental, pH, thermal responses, enhanced biocompatibility, and the ability to cross the blood-brain barrier. Chitosan (CS), a natural polysaccharide largely obtained from marine crustaceans, is a promising drug delivery vector for therapeutics and diagnostics, owing to its biocompatibility, biodegradability, low toxicity, and structural variability. This review describes various approaches to obtain novel CS derivatives, including their distinct advantages, as well as different forms of nanomaterials recently developed from CS. The advanced applications of CS-based nanomaterials are presented here in terms of their specific functions. Recent studies have proven that nanotechnology combined with CS and its derivatives could potentially circumvent obstacles in the transport of drugs thereby improving the drug efficacy. CS-based nanomaterials have been shown to be highly effective in targeted drug therapy. Drug administration is generally performed to treat disease progression, except for autoimmune regulation . An ideaNanotechnology has produced an extremely important impact on nano-biomedicine and the diagnosis/treatment of disease . NanomatN-acetyl-d-glucosamine (GlcNAc) and d-glucosamine (GlcN) (Chitosan (CS), a biological and cationic polysaccharide, has been considered as a promising nanomaterial with extensive medical applications ,24. CS ie (GlcN) , and thee (GlcN) , with poN-acylation, N-alkylation, O-alkylation, and oxidation-Schiff base reaction , poly(N-isopropylacrylamide) (pNIPAA), and bovine serum albumin [The internal environment of the human body is complex and not static, and the environment of the lesion will also affect the development of the diseases . The pH  albumin . Based oOther environmentally responsive macromolecules such as polyacrylic acid embedded in chitosan can also serve as drug carriers with pH-responsive property . A typicThere are numerous meticulous modifications used to build special CS drug delivery vehicles. Prodrug and fluorescent tracer are also applied to the design of CS-based delivery vehicle to generate improved drug delivery systems . ProdrugMultidrug resistance (MDR) is a major obstacle in anticancer drug delivery, and the P-glycoprotein (P-gp) as a multidrug resistance-associated protein (MRP) is commonly overexpressed in cancer cell membranes. The MRP pumps out the anticancer drugs with broad spectrum and maintains low drug levels in the cytoplasm. Indomethacin (IDM) has been demonstrated to inhibit MRP pumping and glutathione-S-transferase activities, and thus it is possible to enhance the cytotoxicity of doxorubicin (DOX) and vincristine. Kima et al. prepared a chitosan oligosaccharide (CSO)-indomethacin (IDM) conjugate (CI) via amide bond to fabricate chemosensitizing nanoparticles (NPs) for tumor-targeted drug delivery . The cheThe development of nanomaterials is rapid, bringing unprecedented opportunities to the food, cosmetic, and pharmaceutical industries . DiffereN-acylation, N-alkylation, O-alkylation, and Schiff-base reactions as described above. The CS-based conjugates subsequently promote the formation of nanomaterials by self-assembly. CS derivatives that possess hydrophilic functions is an excellent drug carrier with good biocompatibility, mucoadhesive property, and intestinal absorption into the body . It is nCheng et al. employed the TMC as raw material, which was subsequently conjugated with the oleyl-NHS via amidation . The ampCovalent cross-linking refers to the coupling of two or more molecules together via covalent chemical bonds . CrossliCarbonnier et al. developed hollow CS/poly(acrylic acid) nanocapsules in size of sub 100nm for antibiotic therapy . The golSerratia marcescens with the anti-cancer properties. The prodigiosin encapsulated chitosan microspheres was prepared via covalently cross-linking, and the glutaraldehyde played an important role as a cross-linker. It presented a positive proportion between the encapsulation efficiency and the drug/polymer ratio. Considering the experience results, the integrated prodigiosin contained CS nanospheres had a good effect to kill the breast cancer cell line.Soboyejo et al. designed prodigiosin encapsulated CS microspheres for targeting drug delivery . ProdigiCS is a natural carbohydrate with polycationic property and able to gel with negatively charged compounds or specific polyanionic molecules to form nanoparticles . This ge3O4 as well as the drug 5-FU in the mesoporous SiO2 core and encapsulated the core in the shell of thermosensitive polymer poly(N-isopropylacrylamide) (PNIPAM) and cationic CS. The smart delivery vehicle was designed to effectively release drug at 45 °C under tumor microenvironment. Moreover, they mounted fluorescent molecules on nanocarriers for cell imaging. The multi-responsive drug carriers showed outstanding advantages in targeting sustainable drug release and disease diagnosis. Gold nanospheres, a kind of photo-absorbing agents, are able to generate heat from optical light energy for photothermal antitumor therapy. You et al. prepared a multifunctional TNYL-peptide modified by chitosan-stearic acid copolymer (CSO-SA) and NP encapsulated with the hollow gold nanospheres (HAuNS) and near-infrared (NIR) fluorescent tracer [Many nontoxic inorganic nanoparticles are developed for drug delivery because of their excellent and well-controlled physical and chemical properties . They art tracer . The TNYt tracer . The CDst tracer . The synt tracer  and layet tracer  cooperatThe diseases disrupt normal physiological processes in the human body, thereby requiring effective treatment schemes . The ideOral administration (OD) is the preferred dosage form for the development of new drugs because of its convenience, safety, and patient tolerance . Drugs aN-(2-Hydroxy- propyl)-3-trimethylammonium chloride was modified with chitosan (HTCC) and insulin as positively charged core, whereas thiolated hyaluronic acid (HA-SH) acts as anionic shell [Insulin, the protein hormone secreted by Islet beta cells in the pancreas, is capable of decreasing blood glucose levels and promoting the synthesis of glycogen, fats, and proteins . Patientic shell . The paric shell . The nanN-acetylneuraminic acid in gastric mucus, whereas polyethylene glycol (PEG) functions as a crosslinker, prolonging protein circulation in blood. It was found that the EGCG-loaded NPs were more effective against gastric tumors in vivo and in vitro.Gastric cancer, a commonly diagnosed cancer, exhibits malignant tumor originating from the gastric mucosa . A polypIn addition, Santos et al. designed a multifunctional tailorable composite system to deliver peptide drugs in oral based on the mucoadhesive chitosan . HorcajaInjection administration usually refers to injecting substances by intravenous, subcutaneous, intramuscular, and intra-arterial injections. Intravenous injection that directly injecting drugs into blood flow can ensure that all given drugs enter the systemic circulation and work quickly. Intramuscular or subcutaneous injections pass through layers of biofilm to reach the systemic circulation, with the feature of low bioavailability and slow efficacy. Arterial injection is mainly used for blood transfusion for severe illness. The chitosan nano-drugs are administrated by the parenteral injection to achieve therapeutic effects. Zhang et al. developed stepwise pH-responsive nanoparticles to release doxorubicin on demand . The nanInjection administration possess obvious advantages in emergency situations because it works faster for severe or sudden illness. However, injection administration may cause some drawbacks including vascular injury, skin damage, serious bacterial and viral infection . Thus, iTopical medication can avoid systemic side effects caused by conventional oral and injection administration . It can N-acetylcysteine (NAC) on CS via amidation reaction between the primary amino groups of CS and the carboxylic acid group of NAC. The modification improved the solubility and performance of the CS vehicle. Cysteine is a compound containing a thiol group that may form disulfide bonds between molecules and tissues and the interaction prolongs the drug retention time. There are many sulfhydryl residues in proteins and mucus on the surface of tissues that may interact with thiol groups in the modified CS vehicle. CS played an important role as mucoadhesive component while the modified thiol group enhanced the effect of bioadhesion and precorneal retention. It has been proven to be a smart strategy that evidently prolong the residence time between the drug and the corneal. Shi et al. developed a novel block polymer composed of cationic CS and methoxy poly(ethylene glycol)-poly(ε-caprolactone) (MPEG-PCL) to deliver diclofenac in ocular diseases [Ophthalmic diseases are generally treated using topical instillation of active compounds. However, the method of treatment has been hindered by the inherent defense function of the eye. Drugs are thus degraded by metabolic enzymes in the ocular tissues. Yang et al. reported a curcumin-loaded lipid carrier for ophthalmic delivery . They codiseases . They atNasal administration is an alternative method of topical administration for anti-inflammatory, antibacterial, nasal congestion or hemostasis therapy . More imN-naphthyl-N,O-succinyl chitosan (NSCS) and N-octyl-N,O-succinyl chitosan (OSCS). CUR was loaded by dialysis that is a physical entrapment approach. The morphology of the micelles obviously changed in the different pH medium, and the release characteristics of CUR were found to be pH-dependent. The release of CUR significantly increased at GI tract pH levels. Taghdisi et al. reported an epirubicin (Epi) encapsulated in CS-modified PLGA NPs via physical adsorption [The intestine harbor various types of microorganisms that produce a variety of enzymes degrading compounds with specific structures such as polysaccharides containing β-glycosidic bonds . CS, a hsorption . Interessorption . They enCarcinoma involves the uncontrolled proliferation of cells. Surgery and chemotherapy usually are combined as a comprehensive strategy to conquer the tumor . ChemothCis-aconitic anhydride was used to covalently graft DOX onto TMC to form nanocomplex with release of DOX in a pH-sensitive pattern. IL-2 was combined with NPs via electrostatic adsorption without compromising its bioactivity. Folate (FA) has also been loaded on nanocarrier for targeted delivery. The sub-spherical nanocomplexes have an average size about 200 nm and are positively charged. The optimized combination therapy strategy showed improved antitumor efficacy and reduced in vivo size effects. Yin et al. developed hydrophilic NPs with cationic TMC co-delivered of DOX and interleukin-2 (IL-2) for enhanced antitumor efficacy . Cis-acoN-hydroxysuccinimide. The encapsulated DOX is released by pH-responsive swelling of the nanospheres. NO release is triggered by the encapsulated UCNPs and RBS, and the synergistic therapeutic effect demonstrated it was an effective method for cancer therapy.Shen et al. prepared integrated formulations combined DOX and Roussin’s black salt (RBS), a photosensitive nitric oxide (NO) into engineered nanospheres for tumor therapy . NaYF4:E2 as white powder. The MSN core was then loaded with TPT and then covered with poly (acrylic acid) (PAA) and CS as shell, and conjugated with QT on the surface of TPT loaded in the MSN core. Finally, the arginine-glycine-aspartic acid (cRGD) was attached to the integrated drug-containing NPs, which targeted to cancer cells via integrin receptor-mediated endocytosis. Drug release was triggered by inter-tissue/intracellular pH change and CS degradation. This strategy has been shown to be effective in breast cancer treatment.Murugan et al. designed a combinatorial nanocarrier for amalgamation of anti-tumor agents in breast cancer cells and introduced the drug topotecan (TPT) and quercetin (QT) into a single system . They prThere are many publications about chitosan-based nanomaterials for antitumor drug delivery in innovative ways ,153. EffGenes encoded specific proteins are essential for various physiological processes of the body and their mutation often results in disease . Gene thSmall interfering RNA (siRNA) is a double-stranded RNA of 20 to 25 nucleotides in length and has many important functions in biology . It primStride et al. developed a CS-deoxycholic acid nanodroplets loading on the magnetic nanoparticle for siRNA delivery . They coVaccine refers to biological products applied to prevent or control the occurrence and spread of infectious diseases . The natTumor cell lysates can act as antigens to stimulate dendritic cells to produce immunity. Kong et al. developed tumor cell lysate-loaded (TCL) CS nanovaccine to enhance antitumor immunity by targeting dendritic cells (DCs) . They moTirelli et al. developed a dual targeting vaccine vector. CS showed an electrolyte interaction with HA, which potentially played an important role to load the siRNA . SimultaDC-based vaccination is extremely limited because it demands multiple injections and ex vivo surgery. Han et al. developed a CS-based NP that achieving immunomodulation in vivo for cancer immunotherapy . The CS N-deacetylation of chitin that abundantly exists in the world, such as crustacean shells in ocean and vertebrates on land. CS has been developed and utilized as a high-value biomacromolecule, particularly in terms of biomedical applications. CS possesses good biocompatibility and low toxicity, immunity, and biodegradability, indicating it may serve as an effective drug delivery vehicle. The CS backbone consists of multiple free amino and hydroxyl groups that could be used as active sites, and versatile approaches have been reported for the construction of CS-based nanomaterials, such as nanogels, NPs, micelles, liposomes, nanofibers, and nanospheres. Taking advantage of the nanomaterials derived from CS, the drug delivery system has been extensively studied for oral and injection administration, topical delivery, colon-targeted drug delivery, carcinoma therapy, vaccine and gene delivery. It is worth mentioning that the clinical administration of CS-based drug vectors is still limited due to their potential risks. In summary, this review highlights recent advances in the development of CS-based nanomaterials for drug delivery. With novel CS derivatives inspired by glycochemistry and vast innovative materials brought by nanotechnology, CS-based nanomaterials would be fully explored for their promising biomedical applications, such as tissue engineering, wound dressing, drug delivery, and cancer diagnosis in the near future.First and foremost, CS is a linear and cationic polymer obtained from degradation and


The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes. With respect to the last decade, ten times more medical images are taken, increasing the number of images per body region per patient to 200–1000 . The advThis large image collection aids medical professionals and improves diagnosis. However, radiologists are challenged by the amount of data. They have to maintain a high interpretation accuracy of radiological images, but also maximize efficiency in terms of the increasing number of images per body region. Computer-based assistance is needed for image interpretation, categorization and annotation , as thesDeep learning techniques  have impThis paper evaluates the effect of several image enhancement techniques on the prediction accuracy rate on radiographs. To analyze this value, several classification schemes were acquired from the ImageCLEF 2009 Medical Annotation Task dataset. All images used at the training and testing stages were preprocessed with the various presented image enhancement techniques. Finally, the obtained image annotation performance accuracies are compared and discussed.Several approaches to Information Retrieval (IR) in Medical Domain as objective have been designed. KHRESMOI was a large EU-funded project aimed at creating a multilingual and multimodal-based search system for biomedical information and documentation . The GNUPositive results have been achieved by image preprocessing using input color enhancement techniques. In , superioThe aim of this presented approach is to apply several image enhancement techniques on radiographs, to increase the overall prediction accuracy of classification models. This is fundamental for implementing image retrieval systems.1121-127-732-500 and 1121-410-620-625, representing “Xray Analog Overview Image; Coronal Anteroposterior Supine; Lower Middle Quadrant; Uropoietic System” and “Xray Analog Low Beam Energy; Other Oblique Orientation; Left Breast; Reproductive Female System Breast”.The dataset adopted for evaluation was distributed at the ImageCLEF 2009 Medical Annotation task . The tra5 different classification schemes are used for evaluation, which were derived by using the complete IRMA code, as well as splitting the code to its’ four axes.The 13-digit code used for annotation is known as the IRMA code and was proposed in . The IRMThe (T) technical classification scheme is the technical axis of the IRMA code. It consists of a 4-character string and denotes physical source, modality position, techniques and sub-techniques . The T-sThe (D) directional classification scheme is a 3-character string and denotes the orientation plane of the radiographs, such as coronal, sagittal and transversal . This scThe (A) classification scheme stands for the complete coding of anatomical regions which are present in the human body. The A-scheme defines nine major body regions, where each region has 2 hierarchical sub-regions . In totaThe (B) biological classification code categorizes the organic system scanned into ten major parts . The B-sIn this section, the three experiments adopted for enhancing visual representation before the classification and annotation of the radiographs are explained.For image recognition tasks, convolutional neural networks trained on large datasets produce favorable results. Considering the number of images in the ImageCLEF 2009 Medical Annotation Task, the adaptation of Transfer Learning with pre-trained neural, such as Inception-v3  and InceNumber of tiles: Contrast enhancement limit: 0.01Number of histogram bins: 256Range of output data: FullDesired histogram shape: UniformDistribution parameter: 0.4The first extra layer was obtained using the image processing technique: Contrast Limited Adaptive Historization Equation (CLAHE) . CLAHE iKernel ratio: 4Window ratio: 4Filter strength: 0.05The second layer was generated by applying the Non Local Means (NL-MEANS) preprocessing method. This is a digital image denoising method, based on a non local averaging of all present pixels in an image . The effThe augmented RGB-Image is obtained by adding the two layers to the original grayscale radiograph, as shown in There are variations regarding the height and width of the radiographs distributed for the ImageCLEF 2009 Medical Annotation Task. The upper and lower extremities are usually narrow with less width size, while head scans are wider with less height size. To obtain size similarity over all images, a fixed size was defined. All radiographs in the dataset were resized to [512 x 512] by padding the input images, which can be seen in Image Layering and Image Padding, are applied successively; the output image is shown in Both image layering and padding, as explained in subsections For the dCNNs, TensorFlow-Slim (TF-slim), a lightweight package for defining, training and evaluating models in TensorFlow  with preOptimizer: Root Mean Square Propagation (rmsprop)Number of epochs: Number of steps: Batch size: Learning rate: 0.01Learning rate decay type: Weight decay: 0.00004Model name: Inception-v3The pre-trained model Inception-v3  which waFor all other parameters not mentioned above, the default values as proposed in TF-Slim  were adoOptimizer: Root Mean Square Propagation (rmsprop)Number of epochs: Number of steps: Batch size: 32Learning rate: 0.01Learning rate decay type: Weight decay: 0.00004Model name: Inception-ResNet-v2The pre-trained model Inception-ResNet-v2  which isFor all other parameters not mentioned above, the default values as proposed in TF-Slim  were adoRandom forest (RF)  models wVLFEAT library .The dCNN systems Inception-v3 and Inception-ResNet-v2 were applied as image classification models. The traditional machine learning algorithm Random Forest (RF), trained with Bag-of-Keypoints visual representation, was adopted for performance comparison. Five classification schemes, each having different number of classes and categorization focus, were applied to evaluate the image enhancement techniques.This works shows that enhancing the radiographs before training and classification, proves to obtain positive results. This is observed for the models trained with the deep learning systems Inception-v3 and Inception-ResNet-v2, as well as the traditional combination of Bag-of-Keypoints and Random Forest. For all five classification schemes, better prediction accuracies were achieved when the enhanced radiographs were used.Prospective evaluation of annotating radiographs can be based on multi-modal image representation and hierarchical class annotation, as positive results have been presented in recent approaches.


Premature mortality is a meaningful indicator of both population health and health system performance, which varies by geography in Ontario. We used the Local Health Integration Network (LHIN) sub-regions to conduct a spatial analysis of premature mortality, adjusting for key population-level demographic and behavioural characteristics.We used linked vital statistics data to identify 163,920 adult premature deaths (deaths between ages 18 and 74) registered in Ontario between 2011 and 2015. We compared premature mortality rates, population demographics, and prevalence of health-relevant behaviours across 76 LHIN sub-regions. We used Bayesian hierarchical spatial models to quantify the contribution of these population characteristics to geographic disparities in premature mortality.LHIN sub-region premature mortality rates ranged from 1.7 to 6.6 deaths per 1000 per year in males and 1.2 to 4.8 deaths per 1000 per year in females. Regions with higher premature mortality had fewer immigrants and higher prevalence of material deprivation, excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status. Adjusting for all variables eliminated close to 90% of geographic variation in premature mortality, but did not fully explain the spatial pattern of premature mortality in Ontario.We conducted the first spatial analysis of mortality in Ontario, revealing large geographic variations. We demonstrate that well-known risk factors explain most of the observed variation in premature mortality. The result emphasizes the importance of population health efforts to reduce the burden of well-known risk factors to reduce variation in premature mortality.The online version of this article (10.1186/s12963-019-0193-9) contains supplementary material, which is available to authorized users. Dying prematurely—before an expected or average age of death—is a signal of unfulfilled life expectancy. Many premature deaths are considered to be avoidable through appropriate prevention or early and effective treatment , 2. AnalAnalysis of premature mortality in Ontario’s 14 large health service regions, known as Local Health Integration Networks (LHINs), revealed large geographic disparities in premature mortality . FurtherAnalysis of population health outcomes across the new LHIN sub-regions has not yet been done. Furthermore, no spatial analysis of Ontario mortality has been carried out at any small-area level. This gap reflects the complexity of working with small-area rates, which are often unstable and spatially autocorrelated, in part due to small population counts . RigorouWe used population-based mortality data to identify small-area disparities in Ontario premature mortality, using the newly formed LHIN sub-regions as the geographic unit of analysis. Further, we integrated linked demographic and behavioural data to quantify the impact of underlying population-level characteristics on premature mortality and to investigate how fully these population-level differences can explain patterns of premature mortality.We identified premature deaths from the Ontario Registrar General’s death file (ORG-D), a comprehensive database of population mortality data linked at ICES . ORG-D cLinked data from the census-derived Ontario Marginalization Index (ON-Marg) were used to measure material deprivation, a proxy of socioeconomic status . ON-MargImmigration, Refugees and Citizenship Canada’s (IRCC) permanent resident database was used to identify immigrants in each sub-region . IRCC caCancer Risk Factors Atlas of Ontario [We obtained behavioural risk factor data from the  Ontario . That pu Ontario .We defined our primary outcome, adult premature death, to include all deaths registered in ORG-D between 2011 and 2015 for decedents between the ages of 18 and 74. In doing so, we intended to capture adult deaths from all causes that occurred before full life expectancy was achieved. Specifically, this age range is based on the cut-off used by the Canadian Institute for Health Information –21. Betwz-scores based on the distribution across all 76 LHIN sub-regions.Population-level characteristics were measured to assess the influence of key sociodemographic and behavioural risk factors on premature mortality outcomes across Ontario. These characteristics were grouped into two categories: demographic variables, which included material deprivation and immigrant population size, and health-relevant behavioural factors, which included current alcohol consumption, excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status. All covariates were measured as sex-specific prevalence estimates at the sub-region level. For all statistical models, prevalence estimates were normalized to Material deprivation was operationalized as the proportion of each LHIN sub-region’s population in the highest provincial quintile of material deprivation. Material deprivation, which describes the likelihood that an individual cannot afford or attain necessary goods and services, is a measure of low socioeconomic status . ImmigraAll behavioural risk factors were measured in the 12-and-older population, according to pre-existing CCHS variable definitions , 17. CurWe calculated 5-year (2011–2015) annualized adult premature mortality rates for all 76 sub-regions. We report sex-specific rates, which have not been age-standardized, as deaths per 1000 per year. We also calculated “excess” deaths in each sub-region as the total number of premature deaths that would have been avoided if that sub-region experienced the premature mortality rate of North West Mississauga . Excess death counts reflect the absolute burden of premature mortality and have been included to contextualize the mortality rates observed in each LHIN sub-region.We used a Bayesian hierarchical approach to model the effect of sub-region on premature mortality, adjusting sequentially for demographic and behavioural covariates. Bayesian hierarchical models are commonly used in small-area disease mapping, because they stabilize small-area rate estimates while accounting for patterns of spatial autocorrelation that violate the assumptions of other approaches , 11. SpeWe fit two-level Bayesian hierarchical Poisson models. In the first level, the observed number of premature deaths in a given sub-region is decomposed to the age-standardized expected number of deaths in that region multiplied by a sub-region-specific standardized mortality ratio (SMR). We calculated the expected deaths for each sub-region using sex- and age-specific premature mortality rates for all Ontario. Age-standardizing the expected death counts accounts for differences in age structure across LHIN sub-region populations which may influence model estimates.The second level contains the covariates which determine a region’s mortality ratio, and includes both fixed and random effects. We used fixed effects to represent the effect of population-level demographic and behavioural covariates, and random effects to represent the direct system-level sub-region effect. The random effects contain both spatially structured and unstructured components, which vary independently. The full model specification and implementation is described in Additional file We specified three age-standardized, sex-stratified models. The first, unadjusted model, contained only random effects. The second, demographics-only model, adjusted for population material deprivation and immigrant proportion as of 2012. The third, demographics plus behaviours model, introduced five population-level behaviours: alcohol consumption, excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status. We fit models using Markov chain Monte Carlo (MCMC) sampling methods with non-informative priors.From each model, we extracted relative risk estimates and 95% credible intervals for all fixed-effect covariates, and adjusted SMR estimates for each individual sub-region. To compare models, we calculated the global variance in SMR estimates. This global variance statistic quantifies sub-region variation in premature mortality, after adjusting for model covariates. It can be used to calculate how much geographic disparity is explained by population differences.We also calculated the spatial component of variance, which ranges from 0 to 1 and is defined as the proportion of random effect variance attributable to the spatial random effect . A spatiI statistic.To ensure the appropriateness of our spatial models, we repeated our analysis using generalized linear mixed models with no structured spatial component. These model results were compared to output from the Bayesian models. The residual terms from these models were tested for spatial autocorrelation using a Moran’s Descriptive statistics were prepared using SAS software version 9.4 . Models Adult premature mortality rates by LHIN sub-region are mapped in Fig. Large geographic disparities were seen in both males and females. Mortality rates ranged from 1.71 (North West Mississauga) to 6.57  premature deaths per 1000 per year in males, and from 1.22 (North West Mississauga) to 4.83 (Northern) premature deaths per 1000 per year in females. In each sub-region, premature mortality rates were higher in males than in females.In males and females, low premature mortality rates were concentrated in south-central Ontario with a secondary cluster in southeast Ontario near the eastern (Québec) border. These clusters represent the metropolitan areas surrounding Toronto and Ottawa, both of which are large, urban centres. Premature mortality rates were comparatively higher throughout the rest of the province, particularly in the north.Table Thirty-nine sub-regions were classified as “Higher than Ontario” according to this approach, with 25 “Lower than Ontario” and 12 “Neither Higher nor Lower”. In general, sub-regions with higher premature mortality had fewer immigrants and higher prevalence of excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status.p ≤ 0.05) for males and females: material deprivation, immigrant population, alcohol consumption, and ever-smoked status. Increased prevalence of high material deprivation and ever-smoked status was associated with increased premature mortality risk, while large immigrant populations and higher prevalent alcohol consumption were associated with decreased mortality risk. The direction and approximate size of all effects were consistent between sexes.The results from the Bayesian hierarchical Poisson models are presented in Table Compared to the unadjusted model, adjusting for socioeconomic status and immigrant population size reduced sub-region variation in premature mortality by approximately 80%. Adjusting further for five health-relevant behaviours  reduced the variation by an additional 10%. The final model covariates explained close to 90% of sub-regional variation in premature mortality in males and females.For the final (demographics plus behaviours) model, the spatial component of variance was 0.608 for males and 0.570 for females. Since values closer to 1 indicate strong spatial structuring, this finding indicates that our models have residual spatial variance and suggests unresolved system-level disparities.I tests for these models showed significant residual spatial autocorrelation for the random effect-only model , but not for the demographics only  or demographics and behaviours model .The results of our analysis using generalized linear mixed models are available in Additional file We conducted the first spatial analysis of premature mortality using Ontario’s newly formalized LHIN sub-regions. We used Bayesian spatial analytic techniques to model the effect of LHIN sub-region on adult premature mortality in Ontario, adjusting for population-level demographic and behavioural characteristics. Our study showed large disparities in premature mortality at the LHIN sub-region level. Adjusting for socioeconomic status and immigrant population explained most of the geographic variation in premature mortality. Adjusting for five key behaviours further reduced sub-region variation; however, some variation remains unchanged. Additionally, we found empirical evidence that the spatial pattern of premature mortality outcomes in Ontario is not fully accounted for by population demographics or health behaviours.Our study revealed a clear geographic pattern of premature mortality, with low rates clustering around large urban centres in Ottawa and Toronto. This is consistent with evidence showing elevated mortality risk in rural environments –32. HoweTogether, demographic and behavioural population characteristics explained a large proportion of LHIN sub-region variations in premature mortality. However, residual spatial variation suggests that there is a systematic component underlying sub-region differences in premature mortality not explained by the population-level traits we have considered. This may be the result of system-level factors, such as access to care and risk factor control, which have been shown to modify mortality risk . The impWe used population-level prevalence estimates for all measured covariates, rather than individual-level risk factor data. As such, the patterns of premature mortality identified by our analysis may be sensitive to our use of LHIN sub-regions, and the same patterns may not be seen if the analysis was repeated with a different unit of aggregation. This is an instance of the modifiable areal unit problem  and may Comprehensive population-level vital statistics data reveal large geographic disparities in premature mortality in Ontario’s LHIN sub-regions. A majority of sub-regional variation can be explained by differences in population characteristics and behaviours, most notably material deprivation and immigrant status. However, residual variation shows a strong spatial component, which suggests that unexplained differences in premature mortality are systematic in nature. Future work should focus on quantifying the impact of system-level impacts  on premature mortality outcomes. Additionally, greater effort is needed to understand which interventions may mitigate the negative mortality effects of low socioeconomic status and key behavioural risk factors.Additional file 1:Figure S1. Excess premature deaths by sex and LHIN sub-region, Ontario, 2011–2015. Map showing the absolute number of excess deaths registered in Ontario LHIN sub-regions between 2011 and 2015 inclusive. Table S1. Results of generalized linear mixed models. Table showing the results of sensitivity analysis using generalized linear mixed models in place of Bayesian hierarchical spatial models. (PDF 1631 kb)Model specification and implementation. Provides a more detailed overview of the model specification and implementation processes employed in our analyses than is described in the main manuscript.


Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants.The online version of this article (10.1007/s00216-019-01615-6) contains supplementary material, which is available to authorized users. The indoor environment is increasingly gaining attention as an important source of human exposure to environmental contaminants including pesticides, polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), plasticizers, organophosphorus flame retardants, bisphenols, parabens, and other chemicals of concern for human health –7. PolluIn response to the growing interest in further development and harmonization of suspect and nontarget screening approaches and their application to the indoor environment, the NORMAN network  organized a collaborative nontarget screening trial on composite household dust. Each participating organization was requested to analyze the test sample using established mass spectrometry (MS) techniques in their laboratory, declare a number of substances present in the sample, and perform provisional identification using target, suspect, and nontarget screening approaches. Together with the test sample, mixtures of analytical standards suitable for liquid chromatography (LC) and gas chromatography (GC), respectively, were provided for the calculation of retention time index (RTI) and retention index (RI) information. RIs have a significant role in nontarget screening as they can be used to support or reject a candidate structure. In future studies, suspect lists with associated RIs will have a great value, e.g., in retrospective screening of compounds of emerging concern using data stored in digital archives.The study aimed to (a) evaluate progress in the field of suspect and nontarget screening of dust, (b) build an extensive database of semivolatile and nonvolatile organic contaminants that could be applied in future screening of the indoor environment, (c) assess the degree of complementarity of instrumental analytical techniques, and (d) present recommendations for successful suspect and nontarget screening. To our knowledge, this is the first collaborative effort on untargeted analysis of indoor dust.All dust samples were obtained from a larger, homogenized dust sample, which was a composite of residential dust obtained from household vacuum bags collected from homes around Toronto, Canada, in 2015. The dust was preprocessed by sieving with a coarse 1-mm sieve to aid in homogenization and stored at − 18 °C. The dust sample was collected to be used in another interlaboratory study aimed at halogenated flame-retardant contaminants in indoor dust and has been checked for homogeneity. Aliquots of 250 mg of the dust were transferred to brown glass vials that were dispatched in January 2016 with standard mixtures for use in the calculation of retention index information: alkane standards for GC-MS techniques and 10 substances for LC-MS techniques  Table m/z, intensity, intensity of blank, MS/MS data, type of workflow, proposed ID, molecular formula, CAS, and identification confidence level. Twenty-seven participants from 26 organizations representing 15 countries registered to participate in the study. Seventeen participants registered for LC-MS and GC-MS techniques, three only for the GC-MS, and seven for LC-MS only. Out of these, 20 datasets were received for the LC-MS techniques and 14 for the GC-MS techniques. One participant officially withdrew from the trial for both techniques and one for the GC-MS technique only. The participants’ list included institutions with various levels of experience in performing suspect and nontarget methods . Table All participants were requested to measure these standards and report the results by June 2016. The reporting was done using data collection templates that included details related to the chromatographic and mass spectrometric methods and related to the reported compounds, e.g., retention time (RT), v/v) for extraction of dust for LC-MS analysis. The extraction technique and cleanup techniques were not specified, but all laboratories were requested to process a procedural blank in parallel to the sample.The participants were instructed to use dichloromethane for extraction of dust for GC-MS analysis and dichloromethane:methanol  methods is presented in ESM Table ESI was mainly used with different collision-induced dissociation (CID) or higher-energy CID (HCD) energies. Some participants submitted MS data only. One participant used atmospheric pressure chemical ionization (APCI) and atmosphere pressure photoionization (APPI) in addition to ESI. All participants who measured in both positive and negative ESI modes did so in separate runs. Some participants used “all-ion” data-independent acquisition approaches (fragmentation without precursor ion selection).Most participants used time-of-flight (TOF), quadrupole TOF (QTOF), or ion mobility QTOF mass analyzers. Three used Orbitrap mass analyzers. The resolution of the TOF-based systems ranged from 10,000 to 42,000 and the resolution of the Orbitrap systems from 70,000 to 120,000.D to facilitate molecule identification.In general, the most commonly used workflows consisted of peak-picking and deconvolution by instrument vendor software and spectra matching to commercially available or in-house mass spectral libraries. One participant used open-source XCMS  and R-paAn overview of GC-MS methods is presented in ESM Table A range of MS analyzers were used. The most popular were (Q)TOF analyzers, used by half of the participants, followed by triple-quadrupole (QQQ) and single-quadrupole analyzers, used by four and three participants, respectively. One data contributor used a GC-Orbitrap system. Most of the GC-(Q)TOFs were HRMS systems, ranging widely in age and performance. The reported mass resolution ranged from 5000 to 60,000 and the mass accuracy from 5 to 20 ppm, with one exception at 200 ppm. The remaining instruments were low-resolution mass spectrometers (LRMS) that provide unit mass resolution. Electron ionization (EI) was by far the most common ionization technique, used by all but one participant. Complementary data were generated in chemical ionization (CI) and APCI mode by four and two participants, respectively.The most common suspect and nontarget screening workflow was peak-picking, EI library search (NIST and in-house libraries), manual spectra review, and (sometimes) interpretation. Several participants used peak and spectra deconvolution algorithms  to enhance spectra quality. Some of these labs also used RI matching (NIST or in-house databases) to increase confidence in their identification. In the absence of RI data, at least one participant utilized RT correlation (RT vs. molecular weight) to check the plausibility of proposed structures. Additional information on the workflows and libraries used by each laboratory is given in Tables In nontarget screening, there is a clear risk for misassignment, especially for compound classes that produce very similar spectra and for compounds that do not produce strong molecular ion signals. The latter may, for instance, occur in LC-MS for compounds that easily produce stable adducts (and no molecular ions) and in GC-MS for compounds that easily fragment. Thus, proper data curation is essential to reduce the number of misassigned compounds that are added to the final list of compounds found in the house dust sample and to the indoor dust contaminant suspect list.The level of data curation varied between the participants in the collaborative trial. Many participants applied in-house rules (basic to elaborate) for qualifying sample constituents for reporting and provided good documentation on what had been done. In several reported results, there were indications that some compounds had been misassigned, e.g., the same compound reported several times with different retention times, GC analyses with low molecular weight compounds reported with high retention indices, etc. It was therefore deemed necessary to perform additional curation of the reported data.Expert evaluation of submitted LC-HRMS spectra and experimental or predicted RTI information were used to curate the contaminants found through suspect and nontarget screening analyses, thereby increasing the identification confidence.tert-butyl-4-hydroxyanisole, and diacetoxy-di-tert-butoxysilane at level 4 confidence. However, the reported MS/MS spectra did not match the library or in silico spectra, and thus, those structures were likely incorrectly assigned and therefore were not included in the list of dust contaminants.Curation of the LC-MS data consisted of several steps. Only 50% volunteered to submit their raw chromatograms for further expert evaluation. Whenever MS/MS data were available for compounds reported with identification confidence level 3 or lower , and theD correlation within the FOR-IDENT platform  for compounds in the STOFF-IDENT database . These resources may be found at https://www.lfu.bayern.de/stoffident and https://water.for-ident.org −. The homolog pattern is shown in Fig. One of the participants  mixtures are exceptionally complex and the use of GC×GC greatly facilitates the separation of CP homolog groups as well as isomers, as demonstrated by Korytár et al. . Using tGC-APCI-MS has recently attracted attention and has proven valuable in NTS studies . Its maiThe main advantage of using a GC coupled with a single quadrupole mass spectrometer (in addition to the relatively low cost and availability) is that there are several easily exchangeable ionization techniques available that provide complementary information and increase the number of compounds that can be tentatively identified with a high level of confidence. One participant used three types of ionization EI, and methane-positive ion chemical ionization (PCI) and positive ion chemical ionization (NCI), all in full-scan mode. The identification workflow was based on the full-scan EI data and the use of mass spectral and LRI databases . Often this was sufficient for a tentative identification; however, many compounds displayed chimeric (mixed) spectra or spectra without a clear molecular ion. In such cases, the PCI and NCI data provided useful complementary information, PCI provided molecular ion or adduct ion information for compounds with high proton affinity, and NCI provided molecular ion information for compounds with high electron affinity and high molecular ion stability. NCI can also be used to verify the presence of electronegative atoms, mainly halogens, through inspection of chlorine and bromine EICs. These compounds can sometimes be tentatively identified by matching with entries in a “home-made” NCI library. Three examples of the complementary use of EI, CI, and LRI information are given in the following sections (i) verification of candidate structure, (ii) correction of proposed structure, and (iii) tentative identification based solely on NCI data.m/z 181) are shown in ESM Fig. m/z 450) of cyhalothrin isomers was confirmed through the use of the PCI result (window B) and NCI confirmed the presence of chlorine. The good agreement of the experimental LRI (2576) and NIST RI (2579) further strengthened the proposed structure.In the first example, a NIST library search indicated the presence of two isomers of cyhalothrin (MW = 449) in the dust extract. The total ion chromatogram (TIC), full-scan spectrum, and base peak chromatogram (m/z 163) with three peaks labeled with yellow arrows. Their manually extracted spectra were very similar and one of them is shown (panel B). A NIST search offered N-propyl benzamide as the most likely compound (86% match), but the calculated LRI (2485) did not match that of NIST (1526) indicating a misassignment. After applying some constraints , another candidate was found, dipropyleneglycol dibenzoate (three isomers), which had a much better LRI match (2445). Because the dipropyleneglycol dibenzoate EI spectrum lacks a molecular ion, PCI was used for verification and a pseudo-molecular ion (MH) was found. Dipropyleneglycol dibenzoates had also been detected by other participants, which further strengthens the tentative identification.The second example is illustrated by ESM Fig. The final example is illustrated by Fig. High complementarity between the techniques used by the participants in the collaborative trial was observed Fig. . GC-MS pAll LC-MS participants were using positive ion ESI. A few were also using negative ion ESI, APCI, or APPI. However, application of APPI only resulted in the discovery of two unique compounds. This does not mean that a majority of LC-MS-amenable compounds in dust respond well in positive ion ESI. On the contrary, several environmental contaminants respond well in negative ion ESI, APCI, or APPI, but poorly in positive ion ESI . Thus, tAmong the GC-MS techniques, the use of 2D separations and complementary soft (CI) and traditional (EI) ionization proved valuable. Two participants using GC×GC-APCI-MS and GC×GC-EI-MS, respectively, collectively found more than 1000 compounds. The use of CI did not, in isolation, lead to identification of many compounds, mainly due to lack of commercial CI spectral libraries. However, GC-PCI-MS still proved valuable as it provided molecular ion information that could confirm candidate structures obtained using GC-EI-MS. Similarly, negative ion CI was used to confirm the presence of halogen substituents. This technique has also been used to guide discovery workflows aimed at finding and identifying halogenated environmental contaminants . The PCIKow values below zero were reported -HRMS/MS. A question then arises—Is the small overlap between data generated using similar hardware due to differences in data handling? Most likely, this is part of the reason. However, it is also likely that the experience and time invested by the participants have significantly influenced the outcome.Almost all LC-MS data originated from target analysis or suspect screening approaches. If we look at the self-reported categorization of workflows, 14% of the reported data was generated using target analysis, 46% using a suspect screening workflow, and 40% using a NTS workflow. However, only one third of the compounds that were self-classified as NTS data had supporting MS/MS information, which is generally required for a tentative identification. Of the remaining 187 compounds, 11 were related to organophosphate and phthalate esters, which had been listed as suspects by the organizers, and 125 were related to glycols that are well-known contaminants in house dust. Besides these, 50 compounds  were generated using NTS workflows that included MS/MS confirmation.Consequently, the majority of the confirmed and tentatively identified compounds stem from suspect screening using candidate lists or searches of ESI mass spectral libraries. Different groups have access to different suspect lists and different mass spectral libraries, which often are vendor specific. Obviously, this influenced the compounds identified by the various participants. This may also be one of the major reasons for the poor overlap between the LC-MS and GC-MS datasets. The LC-ESI-MS libraries are usually relatively small and rich in compounds relevant for the life sciences, pharmaceutical research, and environmental and forensic toxicology, while the GC-EI-MS libraries are large, more diverse, and also include a large range of industrial chemicals. Thus, many of the reference mass spectra in the LC-MS libraries are missing in GC-MS libraries, and vice versa.One of the main outcomes of the collaborative effort is the generation of an extensive database with house dust contaminants. More than 2300 compounds were identified or tentatively identified of which close to 1700 could be assigned exact chemical structures. The database greatly expands our knowledge base of contaminants in house dust. As a comparison, a recent compilation of house dust contaminants by Zhang et al.  includedwww.norman-network.net, “databases” tab). Next to unique identifiers such as StdInChIKey, MS Ready InChIKey, SMILE, and QSAR model-predicted ecotoxicological limit values, exact masses of expected adduct ions in both positive and negative ionization modes and related RTIs are now available for more than 40,000 substances (as of October 2018). Work is currently in progress on obtaining experimental and predicted mass fragments for all compounds to support identifications of these suspects in the NORMAN Digital Sample Freezing Platform .The list of dust contaminants generated in the collaborative trial will be amended with additional compounds from the above cited studies and with indoor air contaminants reported by members of the NORMAN Association. This will result in a NORMAN indoor environment contaminants suspect list, which will be shared through the NORMAN suspect list exchange program. NORMAN partners are also working on compiling and curating a master list termed “SusDat” containing information needed in NTS workflows for screening of known environmentally relevant compounds  is an even larger well-curated repository of compound information. It contains many potential candidate lists, including the NORMAN lists, but also contains useful additional data to support identification, such as physicochemical properties, literature references, patent data, functional uses, and (eco)toxicological data. NORMAN and U.S. EPA closely cooperate on further development of SusDat and the two databases are interlinked.The above and other similar initiatives are in progress to expand the support of suspect screening and NTS workflows, including MS spectra predictions, and prioritization of compounds found in such studies. A recent paper by Rager et al.  illustraAccess to dedicated suspect lists through NORMAN, U.S. EPA, and other web resources will make it possible to better utilize the information generated by less commonly used ionization techniques, such as CI, APCI, and APPI, and thereby increase the coverage of contaminants in indoor environment samples. It will also increase the overlap of the chemical domains covered by LC-MS and GC-MS techniques. Detection and tentative identification of compounds by two or more independent analytical method greatly enhances the identification confidence.The parallel use of EI and PCI may yield library searchable spectra and molecular ion information, which, when combined with RI information, can result in a level 2 identification confidence. The use of NCI and negative ion APCI and ESI can be used to selectively screen for halogenated compounds , which oBased on the results of the collaborative trial, the most powerful combination of instrumental techniques seems to be ESI-HRMS/MS and GC×GC-EI-(HR)MS. Most of the reported compounds stem from either of those techniques. However, with the easy access to tailored suspect lists, it may prove fruitful in the future to complement those techniques with one or more CI techniques. Many top-end LC-HRMS systems allow both LC-APCI and GC-APCI analysis to be performed on the same platform, which may be worth exploring further.Although not tested in the collaborative trial, liquid chromatography–based multidimensional techniques  MS and LC ion mobility MS/MS) and supercritical fluid chromatography (SFC) are also expected to provide enhanced separation power and peak capacity. Current developments in multidimensional data evaluation software may unravel the full potential of such techniques.D values [Suspect screening using suspect lists and molecular formula generated using LC-HRMS information will, initially, have a high rate of false positives. The number of false positives can be subsequently reduced using various discriminators. One of the most effective discriminators is chromatographic retention indices. An automated routine was developed by partners of the NORMAN network to predict LC RTIs using a set of calibration compounds  or use iD values , 30, andL coefficient). A linear regression model created using the curated GC-MS data had an r2 = 0.95.Although the NIST library of EI-MS spectra includes GC RI information, it is not always easy to use. Some participants in the collaborative trial did not appear to use this information, which resulted in misassignments. Some MS software allows the use of RI information for ranking of the NIST spectral search hit list, which reduces the probability of false positives. However, even if the NIST database contains more than 72,000 compounds, RIs are still lacking for many compounds encountered in a complex matrix, such as house dust. A simple QSRR, such as the Abraham general solubility model developed for curation of the collaborative trial, may then be used for prediction of LRIs. In many cases, a sufficiently good prediction can be achieved by a single-parameter retention model, using linear regression of the GC retention times vs. the analyte vapor–hexadecane partition coefficients  are available that can be used to discriminate between potential candidates and to reduce the percentage of false positives. Methods for GC×GC retention indexing and prediction have recently been developed and tested , 54.Several participants used in silico tools , and Mass Frontier) for fragment confirmation, thereby raising the identification confidence. In this context, the development of automatic routines for suspect list and database curation and generation of “MS-ready” structures is of importance . These twww.casmi-contest.org/2016/). Data source ranking (number of PubChem references or patents) is supported in MetFrag [In addition, further ranking of candidate chemicals using data source ranking or functional use filtering has proven effective. The latest CASMI  challenge showed that the success rate of high-throughput (semiautomated) identification routines could be increased from 34 to 70% by including metadata ( MetFrag  and data MetFrag . Chemica MetFrag .https://www.norman-network.com/?q=node/236). Suspect screening against all the compounds in the NORMAN SusDat database allowed for provisional identification of additional 476 compounds not reported by the participants.The poor overlap between the GC-MS and LC-MS-derived datasets from individual participants is hypothesized to be mainly influenced by the varying experiences of the laboratories, the time spent for analyzing the data, and varying access to tools such as mass spectral libraries, dust-relevant suspect lists, and data-processing tools, rather than instrumental limitations. This hypothesis was confirmed after uploading selected LC-MS data to the recently developed Norman DSFP and perform suspect screening using the complete list of compounds identified in the study. Briefly, the dust LC-MS raw data files (from different vendors) were converted to mzML format, imported to DSFP together with instrumental metadata, contributor details, and retention times of the retention index calibrants. An internally standardized procedure of peak-picking and componentization (using previously optimized parameters) is utilized to create a component list, which then can be matched against any list of suspected substances considering their exact mass, fragmentation, and retention time plausibility through QSRR RTI models. The tentative results were obtained in a short time and the number of identified compounds exceeded on average 500. Additional compound lists can be found at the NORMAN Suspect List Exchange (https://water.for-ident.org), as previously discussed. It uses retention time/RTI, accurate mass/empirical formula, and mass spectra information of uploaded (suspects or nontarget screening) mzML files or raw data files from LC-MS vendors, which are compared with the information of compounds in the STOFF-IDENT database . The FOR-IDENT platform gives on this basis molecular identification suggestions with prioritization levels.The FOR-IDENT platform offers another open access tool ESM 2(XLSX 231 kb)