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PubMed_ArguminSci

Id Subject Object Predicate Lexical cue
T1 119-277 DRI_Background denotes The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections.
T2 278-464 DRI_Background denotes Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD).
T3 465-584 DRI_Background denotes However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care.
T4 585-739 DRI_Outcome denotes To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results.
T5 740-987 DRI_Outcome denotes The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e.
T6 988-1146 DRI_Outcome denotes time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data.
T7 1147-1437 DRI_Approach denotes As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD.
T8 1438-1664 DRI_Outcome denotes We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality.
T9 1665-1774 DRI_Approach denotes The computational pipeline can serve as an important resource in the development of new treatment modalities.

Goldhamster2_Cellosaurus

Id Subject Object Predicate Lexical cue
T1 425-426 CVCL_S361|Finite_cell_line|Mus musculus denotes C
T2 650-652 CVCL_5M23|Cancer_cell_line|Mesocricetus auratus denotes we
T3 663-665 CVCL_8754|Cancer_cell_line|Homo sapiens denotes an
T4 663-665 CVCL_H241|Cancer_cell_line|Homo sapiens denotes an
T5 762-764 CVCL_8754|Cancer_cell_line|Homo sapiens denotes an
T6 762-764 CVCL_H241|Cancer_cell_line|Homo sapiens denotes an
T7 896-898 CVCL_R842|Spontaneously_immortalized_cell_line|Cyprinus carpio denotes GI
T8 988-992 CVCL_0047|Telomerase_immortalized_cell_line|Homo sapiens denotes time
T9 1150-1151 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T10 1162-1164 CVCL_5M23|Cancer_cell_line|Mesocricetus auratus denotes we
T11 1212-1213 CVCL_S361|Finite_cell_line|Mus musculus denotes C
T12 1231-1232 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T13 1324-1334 CVCL_C410|Hybridoma|Mus musculus denotes activation
T14 1438-1440 CVCL_5M23|Cancer_cell_line|Mesocricetus auratus denotes We
T15 1490-1500 CVCL_C410|Hybridoma|Mus musculus denotes activation
T16 1505-1506 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T17 1507-1512 CVCL_ZE35|Undefined_cell_line_type|Mus musculus denotes mouse
T18 1522-1523 CVCL_S361|Finite_cell_line|Mus musculus denotes C
T19 1566-1568 CVCL_8754|Cancer_cell_line|Homo sapiens denotes an
T20 1566-1568 CVCL_H241|Cancer_cell_line|Homo sapiens denotes an
T21 1705-1707 CVCL_8754|Cancer_cell_line|Homo sapiens denotes an
T22 1705-1707 CVCL_H241|Cancer_cell_line|Homo sapiens denotes an

GoldHamster

Id Subject Object Predicate Lexical cue
T1 46-59 CHEBI:33281 denotes antimicrobial
T3 86-107 1496 denotes Clostridium difficile
T4 86-107 D016360 denotes Clostridium difficile
T5 86-117 D003015 denotes Clostridium difficile infection
T6 86-117 D003015 denotes Clostridium difficile infection
T10 153-174 1496 denotes Clostridium difficile
T11 153-174 D016360 denotes Clostridium difficile
T12 153-184 D003015 denotes Clostridium difficile infection
T13 153-184 D003015 denotes Clostridium difficile infection
T16 210-221 D000900 denotes antibiotics
T17 210-221 D000900 denotes antibiotics
T18 210-221 CHEBI:33281 denotes antibiotics
T19 359-364 CHEBI:27026 denotes toxin
T20 365-375 D000906 denotes antibodies
T21 449-456 D004194 denotes disease
T22 449-456 D004194 denotes disease
T23 548-559 D000900 denotes antibiotics
T24 548-559 D000900 denotes antibiotics
T25 548-559 CHEBI:33281 denotes antibiotics
T26 900-906 UBERON:0000344 denotes mucosa
T27 1039-1045 CL:0000084 denotes T cell
T28 1189-1200 C001520 denotes lanthionine
T29 1189-1200 CHEBI:25013 denotes lanthionine
T30 1189-1200 C001520 denotes lanthionine
T31 1201-1211 D008025 denotes synthetase
T32 1222-1228 PR:Q9NS86 denotes LANCL2
T33 1222-1228 PR:Q9JJK2 denotes LANCL2
T34 1222-1228 PR:000009662 denotes LANCL2
T37 1290-1316 D015212 denotes inflammatory bowel disease
T38 1290-1316 D015212 denotes inflammatory bowel disease
T39 1303-1308 UBERON:0000160 denotes bowel
T42 1318-1321 PR:Q9UKU7 denotes IBD
T43 1318-1321 PR:000003598 denotes IBD
T44 1340-1351 D000900 denotes antibiotics
T45 1340-1351 D000900 denotes antibiotics
T46 1340-1351 CHEBI:33281 denotes antibiotics
T47 1395-1400 CHEBI:27026 denotes toxin
T48 1401-1411 D000906 denotes antibodies
T49 1475-1481 PR:Q9NS86 denotes LANCL2
T50 1475-1481 PR:Q9JJK2 denotes LANCL2
T51 1475-1481 PR:000009662 denotes LANCL2
T52 1482-1489 CHEBI:34922 denotes pathway
T53 1505-1512 MGI:87853 denotes a mouse
T54 1507-1512 10090 denotes mouse
T55 1507-1512 D051379 denotes mouse
T56 1535-1544 D007239 denotes infection
T57 1535-1544 D007239 denotes infection
T58 1589-1600 D015431 denotes weight loss
T59 1589-1600 D015431 denotes weight loss
T60 1605-1622 CL:0009002 denotes inflammatory cell

Inflammaging

Id Subject Object Predicate Lexical cue
T1 0-118 Sentence denotes Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection.
T2 119-277 Sentence denotes The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections.
T3 278-464 Sentence denotes Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD).
T4 465-584 Sentence denotes However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care.
T5 585-739 Sentence denotes To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results.
T6 740-1146 Sentence denotes The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e. time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data.
T7 1147-1437 Sentence denotes As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD.
T8 1438-1664 Sentence denotes We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality.
T9 1665-1774 Sentence denotes The computational pipeline can serve as an important resource in the development of new treatment modalities.
T1 0-118 Sentence denotes Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection.
T2 119-277 Sentence denotes The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections.
T3 278-464 Sentence denotes Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD).
T4 465-584 Sentence denotes However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care.
T5 585-739 Sentence denotes To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results.
T6 740-1146 Sentence denotes The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e. time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data.
T7 1147-1437 Sentence denotes As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD.
T8 1438-1664 Sentence denotes We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality.
T9 1665-1774 Sentence denotes The computational pipeline can serve as an important resource in the development of new treatment modalities.