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PubMed2004

Inferring pathways from gene lists using a literature-derived network of biological relationships.

MOTIVATION: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway. Interpreting such gene lists and relating them to pathways is a challenging task. Databases of biological relationships between thousands of mammalian genes can help in deciphering omics data. The relationships between genes can be assembled into a biological network with each protein as a node and each relationship as an edge between two proteins (or nodes). This network may then be searched for subnetworks consisting largely of interesting genes from the omics experiment. The subset of genes in the subnetwork along with the web of relationships between them helps to decipher the underlying pathways. Finding such subnetworks that maximally include all proteins from the query set but few others is the focus for this paper. RESULTS: We present a heuristic algorithm and a scoring function that work well both on simulated data and on data from known pathways. The scoring function is an extension of a previous study for a single biological experiment. We use a simple set of heuristics that provide a more efficient solution than the simulated annealing method. We find that our method works on reasonably complex curated networks containing approximately 9000 biological entities (genes and metabolites), and approximately 30,000 biological relationships. We also show that our method can pick up a pathway signal from a query list including a moderate number of genes unrelated to the pathway. In addition, we quantify the sensitivity and specificity of the technique.

Algorithms↗

PubMed2006

Proteomic pattern-based analyses of light responses in Arabidopsis thaliana wild-type and photoreceptor mutants.

Light critically affects the physiology of plants. Using two-dimensional gel electrophoresis, we used a proteomics approach to analyze the responses of Arabidopsis thaliana to red (660 nm), far-red (730 nm) and blue (450 nm) light, which are utilized by type II and type I phytochromes, and blue light receptors, respectively. Under specific light treatments, the proteomic profiles of 49 protein spots exhibited over 1.8-fold difference in protein abundance, significant at p <0.05. Most of these proteins were metabolic enzymes, indicating metabolic changes induced by light of specific wavelengths. The differentially-expressed proteins formed seven clusters, reflecting co-regulation. We used the 49 differentially-regulated proteins as molecular markers for plant responses to light, and by developing a procedure that calculates the Pearson correlation distance of cluster-to-cluster similarity in expression changes, we assessed the proteome-based relatedness of light responses for wild-type and phytochrome mutant plants. Overall, this assessment was consistent with the known physiological responses of plants to light. However, we also observed a number of novel responses at the proteomic level, which were not predicted from known physiological changes.

Arabidopsis↗

PubMed2006

Improved proteomic discovery by sample pre-fractionation using dual-column ion-exchange high performance liquid chromatography.

Clinically relevant biomarkers are urgently needed for improving patient diagnosis, risk stratification, prognosis and therapeutic treatments. There is a particularly compelling motivation for identifying protein-based indicators of early-stage disease for more effective interventions. Despite recent progress, the proteomic discovery process remains a daunting challenge due to the sheer heterogeneity and skewed protein abundances in biofluids. Even the most advanced mass spectrometry systems exhibit limiting overall dynamic ranges and sensitivities relative to the needs of modern biomedical applications. To this end, we report the development of a robust, rapid, and reproducible high performance ion-exchange liquid chromatography pre-fractionation method that allows for improved proteomic detection coverage of complex biological specimens using basic tandem mass spectrometry screening procedures. This form of sample simplification prior to global proteomic profiling, which we refer to collectively as 'fractionomics', increases the number and diversity of proteins that can be confidently identified in tissue and cell lysates as compared to the straight analysis of unfractionated crude extracts.

Animals↗

PubMed2026

Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study.

This pilot study investigated whether candidate protein signatures from oral rinse samples can distinguish patients with severe periodontitis (stage III/IV) and its subtypes, generalized and localized periodontitis, from non-periodontitis controls. Participants rinsed with phosphate-buffered saline, and samples were analyzed using a Proximity Extension Assay targeting 92 inflammatory and 92 immuno-oncology proteins. A machine learning approach using repeated nested cross-validation and SHAP was implemented to identify protein signatures. The study included 38 patients (18 with localized periodontitis and 20 with generalized periodontitis) and 16 controls. After data preprocessing, 54 samples and 141 proteins were retained. Proteins Gal-1, HGF, TNFSF14, CD27, and ARG1 distinguished periodontitis from controls (ROC-AUC = 0.85, 95% CI 0.82, 0.87). For generalized periodontitis, we found a protein signature including TNFSF14, Gal-1, STAMBP, MUC-16, S100A12, HGF, CASP-8, CD27, LAP TGF-β1, TNFRSF9, and uPA (ROC-AUC = 0.92, 95% CI 0.90, 0.94). For localized periodontitis, we identified ARG1 (ROC-AUC = 0.72, 95% CI 0.68, 0.76). No proteomic signature distinguishing generalized periodontitis from localized periodontitis was identified. This pilot study indicated that oral rinses are suitable for proteomic profiling, and there was a putative protein signature that could differentiate periodontitis, generalized periodontitis, and localized periodontitis from controls. These findings warrant validation in larger independent cohorts, including a clearly defined gingivitis group, before real-world non-invasive screening applications can be considered.

Humans↗

PubMed2006

Systematic identification of the subproteome of Escherichia coli cell envelope reveals the interaction network of membrane proteins and membrane-associated peripheral proteins.

Membrane proteins of Gram-negative bacteria are key molecules that interface the cells with the environment. Despite recent proteomic identification of numerous oligomer proteins in the Escherichia coli cell envelope, the protein complex of E. coli membrane proteins and their peripherally associated proteins remain ill-defined. In the current study, we systematically analyze the subproteome of E. coli cell envelope enriched in sarcosine-insoluble fraction (SIF) and sarcosine-soluble fraction (SSF) by using proteomic methodologies. One hundred and four proteins out of 184 spots on 2D electrophoresis gels are identified, which includes 31 outer membrane proteins (OMPs). Importantly, our further proteomic studies reveal a number of previously unrecognized membrane-interacting protein complexes, such as the complex consisting of OmpW and fumarate reductase. This established complete proteomic profile of E. coli envelope also sheds new insight into the function(s) of E. coli outer envelope.

Blotting, Western↗

PubMed2006

Novel susceptibility genes in inflammatory bowel disease.

The inflammatory bowel disease, Crohn's disease and ulcerative colitis, are polygenic disorders with important environmental interactions. To date, the most widely adopted approach to identifying susceptibility genes in complex diseases has involved genome wide linkage studies followed by studies of positional candidate genes in loci of interest. This review encompasses data from studies into novel candidate genes implicated in the pathogenesis of inflammatory bowel disease. Novel techniques to identify candidate genes-genome wide association studies, yeast-two hybrid screening, microarray gene expression studies and proteomic profiling, are also reviewed and their potential role in unravelling the pathogenesis of inflammatory bowel disease are discussed.

Adaptor Proteins, Signal Transducing↗

PubMed2003

The human pituitary proteome: the characterization of differentially expressed proteins in an adenoma compared to a control.

In order to clarify the basic molecular mechanisms that participate in the formation of human pituitary macroadenomas, this study, for the first time, describes the comparative proteomics between a pituitary adenoma tissue and a control tissue. A vertical, two-dimensional polyacrylamide gel electrophoresis system and PDQuest image analysis software were used to provide a high level of between-gel reproducibility and electrophoretic separation to accurately locate each differentially expressed protein. Mass spectrometry (MALDI-TOF and LC-ESI-Q-IT) and protein databases were used to characterize each differentially expressed protein. A total of 137 differential gel spots (37 increased spot volumes, 39 decreased, 19 new and 42 lost) were found when we compared an adenoma proteome to a control proteome. Seventy-one spots (20 increased, 27 decreased, 13 new, 11 lost), representing 39 differentially regulated proteins, were identified. Five differentially regulated proteins (prolactin, cellular retinoic acid-binding protein II, G-protein beta subunit 3, secretagogin and calreticulin) were also validated with results from a comparative transcriptomics study of pituitary adenomas and controls. The functional characteristics of these differentially expressed proteins provide a differential proteomic profile between a pituitary adenoma and a control.

Adenoma↗

PubMed2024

A Proteogenomic Pipeline for the Analysis of Protein Biosynthesis Errors in the Human Pathogen Candida albicans.

Candida albicans is a diploid pathogen known for its ability to live as a commensal fungus in healthy individuals but causing both superficial infections and disseminated candidiasis in immunocompromised patients where it is associated with high morbidity and mortality. Its success in colonizing the human host is attributed to a wide range of virulence traits that modulate interactions between the host and the pathogen, such as optimal growth rate at 37 °C, the ability to switch between yeast and hyphal forms, and a remarkable genomic and phenotypic plasticity. A fascinating aspect of its biology is a prominent heterogeneous proteome that arises from frequent genomic rearrangements, high allelic variation, and high levels of amino acid misincorporations in proteins. This leads to increased morphological and physiological phenotypic diversity of high adaptive potential, but the scope of such protein mistranslation is poorly understood due to technical difficulties in detecting and quantifying amino acid misincorporation events in complex protein samples. We have developed and optimized mass spectrometry and bioinformatics pipelines capable of identifying rare amino acid misincorporation events at the proteome level. We have also analyzed the proteomic profile of an engineered C. albicans strain that exhibits high level of leucine misincorporation at protein CUG sites and employed an in vivo quantitative gain-of-function fluorescence reporter system to validate our LC-MS/MS data. C. albicans misincorporates amino acids above the background level at protein sites of diverse codons, particularly at CUG, confirming our previous data on the quantification of leucine incorporation at single CUG sites of recombinant reporter proteins, but increasing misincorporation of Leucine at these sites does not alter the translational fidelity of the other codons. These findings indicate that the C. albicans statistical proteome exceeds prior estimates, suggesting that its highly plastic phenome may also be modulated by environmental factors due to translational ambiguity.

Candida albicans↗

PubMedDate not supplied

Comparative plasma proteome analysis of lymphoma-bearing SJL mice.

In SJL mice, growth of RcsX lymphoma cells induces an inflammatory response by stimulating V(beta)16+ T cells. During inflammation, various serum protein levels can increase (e.g., acute phase reactants) or decrease (e.g., albumin), and most of these altered proteins are thus potential biomarkers. Although blood plasma is a valuable and promising sample for biomarker discovery for diseases or for novel drug targets, its proteome is complex. To address this, we have focused on a comprehensive comparison of the plasma proteomes from normal and RcsX-tumor-bearing SJL mice using the 1D-Gel-LC-MS/MS method after removing albumin and immunoglobulins. This analysis resulted in the identification of a total of 1079 nonredundant mouse plasma proteins; more than 480 in normal and 790 in RcsX-tumor-bearing SJL mouse plasma. Of these, only 191 proteins were found in common. The molecular weights ranged from 2 to 876 kDa, covering the pI values between 4.22 and 12.09, and included proteins with predicted transmembrane domains. By comparing the plasma proteomic profile of normal and RcsX-tumor-bearing SJL mice, we found significant changes in the levels of many proteins in RcsX-tumor-bearing mouse plasma. Most of the up-regulated proteins were identified as acute-phase proteins (APPs). Also, several unique proteins i.e., haptoglobin, proteosome subunits, fetuin-B, 14-3-3 zeta, MAGE-B4 antigen, etc, were found only in the tumor-bearing mouse plasma; either secreted, shed by membrane vesicles, or externalized due to cell death. These results affirm the effectiveness of this approach for protein identification from small samples, and for comparative proteomics in potential animal models of human disorders.

14-3-3 Proteins↗

PubMed2004

Isotope-differentiated binding energy shift tags (IDBEST) for improved targeted biomarker discovery and validation.

Mass spectrometry has proved to be an important tool for protein biomarker discovery, identification and characterization. However, global proteomic profiling strategies often fail to identify known low-abundance biomarkers as a result of the limited dynamic range of mass spectrometry (two to three orders of magnitude) compared with the large dynamic range of protein concentrations in biologic fluids (11 to 12 orders of magnitude for serum). In addition, the number of peptides generated in such methods vastly overwhelms the resolution capacity of mass spectrometers, requiring extensive sample clean-up (e.g., affinity tag, retentate chromatography and/or high-performance liquid chromatography) before mass spectrometry analysis. Baiting and affinity pre-enrichment strategies, which overcome the dynamic range and sample complexity issues of global proteomic strategies, are very difficult to couple to mass spectrometry. This is due to the fact that it is nearly impossible to sort target peptides from those of the bait since there will be many cases of isobaric peptides. IDBEST (Target Discovery, Inc.) is a new tagging strategy that enables such pre-enrichment of specific proteins or protein classes as the resulting tagged peptides are distinguishable from those of the bait by a mass defect shift of approximately 0.1 atomic mass units. The special characteristics of these tags allow: resolution of tagged peptides from untagged peptides through incorporation of a mass defect element; high-precision quantitation of up- and downregulation by using stable isotope versions of the same tag; and potential analysis of protein isoforms through more complete peptide coverage from the proteins of interest.

Biomarkers↗

PubMed2006

Comparative proteomic analysis of Helicobacter pylori strains associated with iron deficiency anemia.

Helicobacter pylori is known to cause chronic gastritis, peptic ulcer, and gastric cancer, and has also been linked to iron deficiency anemia (IDA). To determine whether H. pylori clinical isolates correlate with the prevalence of H. pylori-associated IDA, we compared the proteomic profiles of H. pylori strains isolated from antral biopsy specimens of H. pylori-positive patients with or without IDA. Fifteen strains, including eight non-IDA and seven IDA strains, were cultured under iron-rich and iron-depleted conditions and then analyzed for protein expression profiles by 2-DE. The distances between two H. pylori strains were determined on the basis of similarities between their expression patterns of 189 protein spots, and a phylogenetic tree was constructed. The results revealed that the IDA strains formed a cluster separate from that of six non-IDA strains, with two non-IDA strains between the clusters. H. pylori strain 26695 was located in the non-IDA cluster. Protein spots displaying similar expression patterns were clustered, and 18 spots predominantly expressed in IDA strains were identified by MALDI-TOF analysis. These data indicate that the non-IDA and IDA strains can be distinguished by their protein expression profiles, suggesting that the polymorphism of H. pylori strains may be one of the factors determining the occurrence of H. pylori-associated IDA.

Adolescent↗

PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗

PubMed2025

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (β = 0.50, 95% CI: 0.07-0.93, p < 0.01), with anxiety positively correlating with depression (r = 0.62, p = 0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC = 0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium↗

PubMed2004

Proteomics and cancer diagnosis: the potential of mass spectrometry.

Proteomic approaches to the identification of novel biomarkers for cancer diagnosis and staging have traditionally relied on the identification of differentially expressed proteins between tumor cells and their normal counterparts based on the patterns of protein expression observed by two-dimensional gel electrophoresis (2D-PAGE). Recent advances in mass spectrometry and in the informatics and statistical tools necessary to interpret mass spectrometric data have revolutionized the approach to defining new tumor markers. The combinations of SELDI mass spectrometry, retentate affinity chromatography, and statistical algorithms for pattern recognition have engendered a great deal of interest in 'proteomic profiling' as a diagnostic tool. However, the ability of new mass spectrometers to provide unambiguous identification of low abundance proteins from mixtures as complex as human serum also provides a mechanism for the discovery and mechanistic validation of small sets of specific proteins that are amenable to more traditional formats for clinical assays.

Algorithms↗

PubMedDate not supplied

Analysis of the dorsal spinal cord synaptic architecture by combined proteome analysis and in situ hybridization.

The proteomic analysis of tissue samples is an analytical challenge, because identified gene products not only have to be assigned to subcellular structures, but also to cell subpopulations. We here report a strategy of combined subcellular proteomic profiling and in situ hybridization to assign proteins to subcellular sites in subsets of cells within the dorsal region of rat spinal cord. With a focus on synaptic membranes, which represent a complex membrane protein structure composed of multiple integral membrane proteins and networks of accessory structural proteins, we also compared different two-dimensional gel electrophoresis systems for the separation of the proteins. Using MALDI mass spectrometric protein identification based on peptide mass fingerprints, we identified in total 122 different gene products within the different synaptic membrane subfractions. The tissue structure of the dorsal region of the spinal cord is complex, and different layers of neurons can be distinguished neuroanatomically. Proteomic data combined with an in situ hybridization analysis for the detection of mRNA was used to assign selected gene products, namely the optical atrophy protein OPA-1, the presynaptic cytomatrix protein KIAA0378/CAST1, and the uncharacterized coiled-coil-helix-coiled-coil-helix domain containing protein 3 (hypothetical protein FLJ20420), to cell subsets of the dorsal area of the spinal cord. Most striking, KIAA0378/CAST1 mRNA was found only sparsely within the dorsal horn of the spinal cord, but highly abundant within the dorsal root ganglion. This finding, combined with the identification of KIAA0378/CAST1 within the synaptic membrane fraction of the spinal cord at the protein level, are consistent with the reported presynaptic localization of CAST, predominantly within the tissue we investigated primarily attributable to primary afferent sensory neurons. Our approach may be of use in broader studies to characterize the proteomes of neural tissue.

Animals↗

PubMed2025

Plasma proteomic markers of pain and emotional dysfunction in fibrous dysplasia/McCune-Albright syndrome.

Pain in Fibrous dysplasia/McCune-Albright syndrome (FD/MAS) remains poorly understood and inadequately managed due to uncertainties regarding clinical or biological drivers. This cross-sectional pilot study aimed to use plasma proteomics to identify markers that inform on molecular pathways associated with pain and emotional symptoms in FD/MAS. Seventeen individuals (15 females, 2 males), aged 16 to 63 years, with confirmed diagnoses of monostotic FD, polyostotic FD, or MAS participated in a single study visit conducted at Boston Children's Hospital and Massachusetts General Brigham. During the visit, participants completed validated questionnaires assessing neuropathic pain characteristics, pain interference, anxiety symptoms, depression symptoms, and perceived stress, and provided plasma samples. These samples were analyzed for 57 proteins using Olink proximity extension assay. Associations between protein concentrations and symptom scores were evaluated using Spearman's correlations with false discovery rate correction (|r| > 0.5, p < 0.05). After FDR correction, the concentrations of seven proteins (TNF-α, LTA, CCL19, CSF2, CCL2, CCL4, CCL7) significantly correlated with pain interference, HADS-depression scores, or perceived stress. Four protein concentrations (TNF-α, CCL19, CSF2, CCL7) significantly correlated with multiple clinical measures. This pilot study identified several pain-associated proteins in individuals with FD/MAS, suggesting that proteomic profiling may be a promising approach for discovering pain biomarkers. Larger, longitudinal studies are needed to validate these results and investigate whether targeting immune pathways can alleviate pain and improve emotional health in FD/MAS.

Humans↗

PubMed2004

VIP receptor antagonists inhibit mammary carcinogenesis in C3(1)SV40T antigen mice.

The effects of a vasoactive intestinal peptide (VIP) receptor antagonist on mammary carcinogenesis were investigated using the C3(1)SV40T antigen (ag) mice. Ten microg/day VIPhybrid (VIPhyb) administered daily subcutaneously increased significantly the survival of C3(1)SV40Tag mice. At 5.2 months, VIPhyb significantly reduced the mammary tumor burden in C3(1)SV40Tag mice relative to control animals. 125I-VIP bound with high affinity to mouse mammary tumor homogenate. Because (Lys15, Arg16, Leu27)VIP1-7GRF8-27 (VPAC1 selective) but not Ro25-1553 (VPAC2 selective) inhibited specific 125I-VIP binding to mammary tumor membranes with high affinity, VPAC1 receptors predominate. By RT-PCR, VPAC1 receptor mRNA was detected in mammary tumors. By Western blot, a major 60 Kdalton band was detected in mammary tumor extracts using VPAC1 receptor antisera. By immunocytochemistry, VPAC1-R immunostaining was detected in the cytosol and plasma membrane but not the nucleus of fixed mammary tumor tissue. Using laser capture microdissected tumor cells and surface enhanced laser desorption/ionization (SELDI) techniques on mammary tumor cells, the proteomic profile was altered in mice treated with VIPhyb. Because VPAC1 receptor antagonists increase the survival and reduce the tumor burden in C3(1)SV40Tag mice, they may function as chemopreventive agents in mammary cancer.

Animals↗

PubMed2004

Delivery systems and molecular targets of mechanism-based therapies for GBM.

Glioblastoma multiforme (GBM) is the most common malignant brain tumor of adults and is in great need of novel diagnostic and therapeutic approaches. Diagnosis is beginning to consider a tumor's genetic status and in the future may incorporate gene expression or proteomic profiles. Genetic alterations in gliomas that are being used in classification include TP53 and retinoblastoma pathway disruption, PTEN mutations, epidermal growth factor receptor amplification and 1p/19q losses. Molecular mechanisms are being exploited to treat glioblastoma multiforme. Tyrosine kinase inhibitors directed at epidermal growth factor receptor (ZD1839, OSI-774) are being explored. Farnesyltransferase inhibitors (R115777) block activation of the ras pathway and may be effective. Antagonists of the endothelin receptor (e.g., atrasentan) expressed on blood vessels may block the high degree of angiogenesis in gliomas. Tumors lacking methylthioadenosine phosphorylase are sensitive to inhibitors of de novo adenosine synthesis (SDX-102) since they lack a salvage pathway. Future goals are to tailor therapies to a tumor's molecular, proteomic or genomic status ,and manage glioblastoma multiformes as in chronic diseases in a multidisciplinary clinical setting.

Antineoplastic Agents↗