Open Access

Multiparametric and semiquantitative scoring systems for the evaluation of mouse model histopathology - a systematic review

BMC Veterinary Research20139:123

DOI: 10.1186/1746-6148-9-123

Received: 8 January 2013

Accepted: 19 June 2013

Published: 21 June 2013

Abstract

Background

Histopathology has initially been and is still used to diagnose infectious, degenerative or neoplastic diseases in humans or animals. In addition to qualitative diagnoses semiquantitative scoring of a lesion`s magnitude on an ordinal scale is a commonly demanded task for histopathologists. Multiparametric, semiquantitative scoring systems for mouse models histopathology are a common approach to handle these questions and to include histopathologic information in biomedical research.

Results

Inclusion criteria for scoring systems were a first description of a multiparametric, semiquantiative scoring systems which comprehensibly describe an approach to evaluate morphologic lesion. A comprehensive literature search using these criteria identified 153 originally designed semiquantitative scoring systems for the analysis of morphologic changes in mouse models covering almost all organs systems and a wide variety of disease models. Of these, colitis, experimental autoimmune encephalitis, lupus nephritis and collagen induced osteoarthritis colitis were the disease models with the largest number of different scoring systems. Closer analysis of the identified scoring systems revealed a lack of a rationale for the selection of the scoring parameters or a correlation between scoring parameter value and the magnitude of the clinical symptoms in most studies.

Conclusion

Although a decision for a particular scoring system is clearly dependent on the respective scientific question this review gives an overview on currently available systems and may therefore allow for a better choice for the respective project.

Keywords

Score Semiquantitative Histopathology Colitis Nephritis Hepatitis Encephalitis

Background

Histopathology has initially been and is still used today to diagnose infectious, degenerative or neoplastic diseases in humans or animals. These qualitative diagnoses are based on a sum of observable changes in the morphology of the analyzed tissue. The cognition of these changes is based on the pattern recognition of the observer and the comparison of these patterns with the known physiologic variation in tissue morphology in the respective species. Decades of experience in veterinary pathology show that this approach allows for reproducible qualitative diagnoses by the observer but can also be used for semiquantitative scoring of the lesions magnitude, i.e. on an ordinal scale for instance with a low, medium or high grade trichotomy which correlates with the clinical relevance of the lesions.

Absolute quantification of the lesions extent and severity is however difficult since two main problems hamper absolute quantification, i.e. on a rational scale with absolute values of 1, 2, 3 etc., using standard, non-automated histopathology. First, the detection method is not reliable enough. Despite intensive training and attempts to standardize nomenclature and the definition of lesions there are still unresolved issues in terms of interobserver variation which may be acceptable for qualitative and semiquantitative evaluation but not for absolute quantitation [1]. Second, in most circumstances it is impossible to objectively justify the interval between two values, thus a read out of histopathologic scoring on a rational scale is impossible.

Image analysis by automated calculation of the tissue area affected or cells present per area have been introduced to overcome this problem. These approaches aim at a reliable and reproducible histopathology read out in a rational scale to allow proper statistical processing and at an exclusion of an observer bias [2]. Image analysis approaches usually use one or only few two dimensional planar sections of the tissue of interest to measure three-dimensional objects. This two-dimensional approach thus may also lead to biased results. Stereology, which is based on systematic random sampling and estimates third dimensional information, has been developed to avoid this bias [3]. It can therefore be seen as the most sophisticated method for the quantification of histologic information. It is however by comparison a laborious and complex method which is established in only few laboratories.

Semiquantitative scoring systems are therefore still the most widely used methods to include histopathologic information in biomedical research. These scoring systems usually include multiple parameters which are separately quantified on an ordinal scale and finally combined in a total score. Average scores of the different experimental groups can then be compared by non-parametric statistical tests. The selection of the parameters should be based on the scientific hypothesis or question together with the current knowledge on the morphologic outcome of the investigated disease model. It may therefore be useful to design individual scoring system for each study which in the best possible way answers the particular scientific question. Standard scoring systems for specific disease models on the other hand allow for the comparison of the results of different studies.

Several standard scoring systems for different mouse models have been introduced or emerged in the past 20 years. Histopathologists are therefore repeatedly requested by cooperating scientists to evaluate the outcome of animal studies using standard scoring systems or to elaborate project specific scoring systems. The present review is intended to give a comprehensive overview on the currently most commonly used multiparametric, semiquantitative scoring systems for mouse model histopathology.

Results

Scoring systems for murine intestinal disease models

Eighteen original scoring systems for colitis models could be identified. Most of these scoring systems were designed for dextran sodium sulfate (DSS)-induced colitis models but 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis and several models of immunopathologic colitis were also used to establish scoring systems (Table 1, Figure 1). Eight scoring systems for DSS-induced colitis fulfilled all required parameters and were included in this review. Generally, all of the paper with original DSS colitis scoring systems had a high citation rate but the scoring system described by Cooper is one of the earliest system with the highest citation number up to date and can therefore be seen as a prototype for DSS-colitis scoring [4]. It separates the colon into three segments which are then scored by the parameters of crypt loss, inflammation and affected area. Although later studies refined and increased the number of histopathologic parameters the value of this initial study is the separation of the colon in three segments and the sophisticated approach to the establishment of the scoring system. Remarkably, parameters in this study were chosen and tested according to their correlation with the clinical symptoms of the mice. This is contrast to the vast majority of scoring systems presented in this review, which only rarely stated the rational for choosing the included parameters and did not perform a correlation with the clinical symptoms.
Table 1

Semiquantitative scoring systems for murine intestinal disease models

Disease model

Scoring system: parameters (scale width)

Citations

Colitis

DSS-induced [4]

Proximal + middle + distal colon: crypt loss (0–4), inflammation (0–3) both quantified by the affected area (0–4)

718

DSS-induced [5]

Extent, inflammation, necrosis, regeneration (each 3–0)

321

DSS-induced [6]

Crypt damage, area involved, regeneration (each 0–4), extent (0–3), inflammation (0–3),

255

DSS-induced [7]

Crypt loss (0–4), crypt distortion (0–4), epithelial hyperplasia (0–4), inflammation (0–3) each multiplied by percentage of involved area (0–4)

185

DSS-induced [8]

Hyperplasia (0–3), severity (0–3), ulceration (0,1), area involved (0–4)

147

DSS-induced [9]

Severity of inflammation, thickness of inflammation, epithelial damage character, extent of epithelial damage (each 0–3)

130

DSS-induced [10]

Epithelium (goblet cell/crypt loss), infiltration (each 0–4)

103

DSS-induced [11]

Infiltration, tissue damage (each 0–3)

89

TNBS-induced [12]

Inflammation, loss of goblet cells, vascular density, transmural infiltration, thickening of the colon wall (together 0–4)

788

TNBS-induced [13]

Crypt distortion, goblet cell loss, acute inflammation, chronic inflammation (each 0–2)

126

TNBS-induced [14]

Loss of mucosal architecture, cellular infiltration, muscle thickening (each 0–3), crypt abscesses (0,1), goblet cell depletion (0,1)

97

Acetic acid-induced [15]

Inflammation, bleeding, ulcer size, deepness, perforation (together 0–6)

132

TNBS-induced [16]

Percentage of area, crypt loss (both 0–4) number of follicles, edema, erosion/ulceration, infiltration (each 0–3)

57

HLA-B27 transgenic mice [17]

Inflammation, goblet cell loss, mucosal thickening, submucosal infiltration, architecture loss (each 0–4), ulcer, crypt abscess (each 0,1)

423

IL10-deficient mice [18]

Mucosal ulceration, epithelial hyperplasia (both 0–3), Lamina propria mononuclear infiltrate, Lamina propria neutrophil infiltrate (each 0–2)

423

IL10-deficient mice [19]

Inflammation/epithelial erosion/ulcers/hyperplasia/crypt abscesses/goblet cell depletion (together 0–4) multiplied by no. of affected colon segments (1–5)

555

MHC missmatch [20]

Active inflammation (0.5-3), chronic inflammation (0.5-3), villous architecture (1–3) multiplied by the affected area (0.5-4)

83

Amebic colitis [21]

Number of amoeba (0–5), ulceration (0-100%), inflammation (0–5)

58

Small intestinal diseases

Clostridial toxicosis [22]

Epithelial damage, hyperemia/edema, neutrophils (each 0–3)

202

Intestinal ischemia [23]

Normal mucosa (0), villous edema (1), subepithelial edema (2), epithelium loss at villi sides (3), denuded villi (4), loss of villous tissue (5), crypt infarction (6), transmucosal infarct (7), transmural infarct (8)

270

Intestinal ischemia [24]

Mucosal damage, inflammation, hyperemia/hemorrhage (each 0–5)

72

Jejunitis [25]

Villous length, villous tips, epithelium, inflammation, crypt loss (each 0–2), crypt abscesses, hemorrhage (each 0,1)

15

Bacterial ileitis [26]

Hemorrhage, villous atrophy/necrosis, edema, congestion, neutrophils, epithelial necrosis (each 0–3)

1

Gastric diseases

Helicobacter-induced [27]

Five areas multiplied by inflammation (0–3)

49

DSS, Dextran sodium sulfate; TNBS, 2,4,6-trinitrobenzene sulfonic acid, MHC, Major histocompatibility complex; HLA-B27, Human Leukocyte Antigen-B27, IL10, Interleukin 10.

https://static-content.springer.com/image/art%3A10.1186%2F1746-6148-9-123/MediaObjects/12917_2013_Article_688_Fig1_HTML.jpg
Figure 1

Flow chart visualizing the approach to identify 153 original, multiparametric, semiquantiative scoring systems by Pubmed data search.

When comparing all original colitis models it becomes obvious that a wide variety of appellation for the most common parameters inflammation, crypt and surface epithelial damage were used (Table 1). These differences in the nomenclature make it however difficult to directly compare the different scoring systems. Less often used parameters in the colitis scoring systems were goblet cell loss, regeneration, muscular and epithelial hyperplasia, edema and the separation between acute and chronic inflammation. In some cases these singularities of the respective scoring system seem to be dependent on the objectives of the study while in most cases the rationale for selection of the parameters was not given.

The number of identified scoring systems for small intestinal disease was significantly lower than for colitis models and had on average lesser citations (Table 1). Two independent scoring systems were identified for intestinal ischemia which include the comprehensible parameters of hyperemia and hemorrhage as well as inflammation and epithelial damage and in the case of the higher cited publication by Park et al. several other more sophisticated parameters [23, 24]. Only two scoring systems for small intestinal enteritis were detected which both included villous morphology, epithelial damage and inflammation as the main features (Table 1). Surprisingly, only one semiquantitative scoring system for gastritis was identified. Wang et al. scored the severity of Helicobacter-induced gastritis in a uniparametric scoring of five gastric areas. For the sake of completeness this studies was included in this review although it did not fulfill the criterion of multiple parameters [27].

Scoring systems for murine osteoarthritis models

Seventeen original semiquantitative, multiparametric scoring systems were identified for murine osteoarthritis models (Table 2). Three of these, designated as osteoarthritis in Table 2, are scoring systems for human idiopathic arthritis and were transferred to the murine model to allow for comparisons of the model with the human disease. Of these the the score developed by Mankin et al. has by far the highest citation number which is most probably influenced by its common application in the description of human osteoarthritic lesion [28]. The majority of mouse specific systems were established in collagen-induced models while models of instability-induced or bacterial arthritis models were only rarely used. Of these, the scoring system by William et al. had the highest citation number [29]. Twelve of the thirteen osteoarthritis scoring systems include an evaluation of cartilage damage, nine included an infiltration parameter, seven included changes in perichondral bone structure and six studies evaluated the extend of synovial infiltration (Table 2). Other parameters like tidemark integrity, proteoglycan content of the cartilage, pannus formation or synovial hyperplasia were used only in the minority or single scoring systems. Again, in most of these descriptions of original scoring systems no rational for inclusion of the respective parameters is given but their usefulness can be comprehended by reflecting the general pathogenesis of osteoarthritis.
Table 2

Semiquantitative scoring systems for murine osteoarthritis models

Osteoarthritis model

Scoring system: parameters (scale width)

Citations

Arthritis [28]

Cartilage structure (0–6), cells (0–3), Safranin-O-stain (0–4), tidemark integrity (0–1)

1322

Arthritis [30]

Cartilage destruction (0–6), optional subgrading (subdivision in 2 subgrades)

197

Arthritis [31]

Synovial lining, resident cell density, inflammation (each 0–3)

49

Collagen-induced [29]

Extent of synovitis, cartilage loss, bone erosions (together 0–3)

713

Collagen-induced [32]

Inflammation, cartilage destruction, bone erosion (each 0–3)

273

Collagen-induced [33]

Infiltration in the exudate, infiltration of the synovial membrane, cartilage destruction, bone erosion (each 0–3)

198

Collagen-induced [34]

Joint exudate (0–5), proteoglycan depletion (0–3)

139

Collagen-induced [35]

Bone resorption, inflammation, cartilage damage (each 0–5)

95

Mycobacterium butyricum-induced [36]

Synovial thickening, infiltration, pannus formation (each 0–3)

95

Adjuvant-induced [37]

Percentage of affected area, synovial hyperplasia, cartilage destruction, bone erosion (each 0–3)

64

IL1-induced arthritis [38]

Synovial infiltration, proteoglycan depletion, cartilage damage (each 0–3)

41

Instability-induced [39]

Matrix structure, matrix staining, cellularity, subchondral bone (each 0–8)

85

Instability-induced [40]

Cartilage destruction (0–4), osteophyte formation (0–3)

85

Hereditary arthritis [41]

Articular cartilage structure (0–8), Toluidine blue-staining (0–6)

52

Bacterial arthritis [42]

Infiltration (0–4), pannus formation, cartilage destruction, extra-articular manifestations, tail lesion (each 0,1)

145

Cartilage repair [43]

Cell morphology (0–4), matrix staining (0–3), surface regularity (0–3), thickness (0–2), integration of donor cartilage (0–2)

819

Cartilage repair [44]

Cellular morphology (0,2,4), Safranin-O stain (0–3), structural regularity (0–3), structural integrity (0–2), thickness (0–2), bonding (0–2), hypocellularity (0–3), chondrocyte clustering (0–2), adjacent degeneration (0–3)

297

Cartilage repair [45]

Relative defect area (0–4), integration of repair tissue (0–3), matrix staining (0–4), cellular morphology (0–5), defect architecture (0–4), surface architecture, percentage of new bone (0–4), tidemark formation (0–4)

222

Cartilage repair [46]

Filled depth (0–4), integration (0–2), surface architecture (0–3), cell morphology (0–3), cellularity (0–2), tidemark formation (0–4), Toloudin blue stain (0–2)

130

Cartilage repair [47]

Defect filling (0–4), osteochondral reconstruction (0–2), matrix staining (0–4), cell morphology (0–4)

96

IL1, Interleukin 1.

Scoring systems for murine renal disease models

Fourteen original multiparametric, semiquantitative scoring systems for murine models of renal diseases fulfilled the required criteria for inclusion in this review (Table 3). Scoring systems for murine Lupus erythematous models were the dominant model in the category of renal disease models with four appearances. Austin et al. published the lupus nephritis score with the highest citation number [48]. It uses a complex scoring system with 10 parameters and a scale width of four and five respectively and was thus more sophisticated than the other scoring systems which used only four different parameters.
Table 3

Semiquantitative scoring systems for murine renal disease models

Renal disease model

Scoring system: parameters (scale width)

Citation

Lupus nephritis [48]

Activity index (glomerular/tubulointerstitial abnormalities (6-tier, each 0–4)); chronicity index (4-tier, each 0–3)

358

Lupus nephritis [49]

Mesangial thickening, extent of changes (together 0–4)

44

Lupus nephritis [50]

Glomerular cell proliferation, lobulation, hyaline droplets, macrophage infiltration (together 0–3)

25

Lupus nephritis [51]

Glomerular inflammation, proliferation, crescent formation, necrosis (each 0–3)

1

Toxic nephropathy [52]

Glomerular cellularity, hypertrophy, thrombosis, dilation (together 0–5)

4

Toxic nephropathy [53]

Glomerular injury, tubular cysts/casts, podocyte hyperplasia, interstitial inflammation (each 0–4)

33

Hypertension nephropathy [54]

Mesangial matrix, percentage of glomerular affection (each 0–4)

524

Diabetic-/Hypertension-induced glomerulosclerosis [55]

Arteriole hyalinization, glomerular sclerosis (0–4), interstitial volume (%)

152

Diabetic nephropathy [56]

Glomerulosclerosis, interstitial fibrosis (each 0–4)

15

Crescentic glomerulonephritis[57]

Fibrin deposition, immunoglobulin deposition, tubular damage, glomerular crescents (each 0–3)

8

MRL/MPJ mouse glomerulonephritis [58]

Glomerular infiltration, crescents, necrosis, tubular casts, interstitial infiltrates (each 0–4)

244

Age-associated changes [59]

Mesangial proliferation, sclerosis, hyalinization (together 0–5)

27

HIV-nephropathy [60]

Tubuloepithelial degeneration/regeneration, tubular casts, dilation, interstitial infiltration, glomerular sclerosis, collapse, podocyte hyperplasia (each 0–3)

47

Obstructive nephropathy [61]

Tubular dilation/atrophy, interstitial fibrosis (each 0–3)

4

HIV, Human immunodeficiency virus; MRL, Murphy Roths Large.

Glomerular cellularity and proliferation were the terms most commonly used in all renal scoring systems except one scoring system for obstructive nephropathy model [61]. In addition, one half of the systems included tubulointerstitial infiltration and fibrosis in the scoring system.

Scoring systems for murine models of neurologic disease

Twenty-two original scoring systems for murine models of central nervous system (CNS) disease were identified (Table 4). Scoring systems for experimental autoimmune encephalomyelitis (EAE) and stroke clearly dominated results. Due to the wide variety of diseases covered by the system the selection of parameters to be analyzed also had a wide variation and was clearly dependent on the pathophysiology of the disease. But again, the rationale for inclusion of parameters was not consistently given.
Table 4

Semiquantitative scoring systems for murine central nervous disease models

Disease model

Scoring system: parameters (scale width)

Citations

Stroke

Focal ischemia [62]

Ischemic neuronal damage (0–3)

2176

Focal Ischemia[63]

18 areas x neuronal injury (0–5)

23

Global ischemia [64]

Infarcts in 3 cerebral regions (0–4), hippocampus infarction (0–4)

100

Global ischemia [65]

Eight regions x neuronal cell los/gliosis/iron deposition/gliosis (0–3)

95

Peripheral nerve ischemia [66]

Edema, fiber regeneration (each 0–4)

21

Multiple sclerosis models

EAE [67]

Inflammation, neuronal degeneration (each 0–4)

160

EAE [68]

Inflammation (cells/cuff), axonal injury, axonal loss (each 0–4)

81

EAE[69]

Lesion severity, myelin loss/tissue injury, acute inflammation, chronic inflammation (each 0–5)

79

Theiler's murine encephalomyelitis virus [70]

Neuropil inflammation, demyelination, necrosis, meningeal inflammation (each 0–4)

77

EAE [71]

Infiltration, demyelination (together 0,1) in 16 regions

64

EAE [72]

Inflammation, necrosis (each 0–3)

53

EAE [73]

Inflammation (cuffs/100 m2), demyelination (lesions/mm2)

52

EAE [74]

Meningitis (0–2), perivascular cuffing (0–5), demyelination (0–3)

43

EAE [75]

Spinal cord demyelination (0–3), inflammatory cells (No./mm2)

22

Spinal cord trauma (SCT)

SCT [76]

Gray matter degeneration/infarction (0–4) in 5 μm serial sections

33

SCT [77]

150 μm intervals x Area affected by neuronal degeneration, malacia (0–4)

23

Encephalitis/Meningitis

  

Streptococcus meningitis [78]

Meningeal inflammation (0–3) x four regions

46

Trypanosoma encephalitis [79]

Meningitis, perivascular cuffing, neuropil infiltration (each 0–4)

41

Miscellaneous CNS diseases

Oxidative damage [80]

7 areas x necrosis (0–3)

27

Senescence [81]

Spongiosis (0–3), lipofuscin positive cells (%)

23

Spinocerebellar ataxia [82]

Molecular layer thickness, Purkinje cell loss (each 0–3)

10

EAE, Experimental autoimmune encephalomyelitis, SCT, Spinal cord trauma, CNS, Central nervous system.

A striking feature of CNS disease scoring systems was the relatively low number of ordinal scales for parameter´s magnitudes and the common inclusion of multiple anatomical sites into the scoring system (Table 4). This discrepancy is not addressed in the respective publications but may be based on the anatomical diversity of the CNS. Furthermore, the inclusion of absolute values like lesions/mm2 occurred significantly more often in CNS disease scoring than for other organs although this is not comprehensible in each case. Only one scoring system occurred for a peripheral nerve system disease which has been developed to evaluate peripheral nerve ischemia in a relatively simple two-tier system with a zero to four-scale [66].

Scoring systems for murine models of pulmonary diseases

Fourteen original semiquantitative, multiparametric scoring systems were identified for pulmonary diseases (Table 5). Of these pulmonary fibrosis and pulmonary inflammation were diseases with the highest number scoring systems and citations. For instance, the scoring system developed by Ashcroft, which is a relatively simple multiparametric but single scaled system, is commonly used for the evaluation of lung fibrosis [83]. Three scoring systems were developed for models of general acute lung inflammation (Table 5). They used the parameters of edema and anatomical site specific inflammation as parameters to evaluate the relative amount of inflammatory response. Similar parameters in a wide variety of combinations were used to develop scoring systems for diverse infectious pneumonia models. This variation is again in most cases not based on reasonable argument for the inclusion of a certain parameter in a certain model and therefore not in all cases clearly associated with the supposed pathogen-associated pathogenesis of the respective pneumonia.
Table 5

Semiquantitative scoring systems for murine models of pulmonary diseases

Pulmonary disease models

Scoring system: parameters (scale width)

Citations

Lung fibrosis [83]

Alveolar/bronchial wall thickening, structure distortion, fibrosis (together 0–8)

273

Lung fibrosis [84]

Fibroblastic foci, established fibrosis, intraalveolar macrophages (each 0–6)

116

Cystic fibrosis [85]

Lymphoid infiltrate (0–5), goblet cell hyperplasia (0–2), mucus retention (0–3), bronchiolitis (0–5), pneumonia (0–3), edema (0–2)

101

Ventilator-induced lung injury [86]

Alveolar congestion, hemorrhage, neutrophils in airspaces/vessel walls, alveolar wall thickness, hyaline membranes (each 0–4)

78

Pulmonary ischemia [87]

Edema, inflammatory cell infiltration, vascular congestion, alveolar hemorrhage (each 0–3)

5

Smoke-induced pneumopathy [88]

Alveolar emphysema, atelectasis, infiltration, hemorrhage, alveolar wall thickness, perivascular/peribronchiolar edema (each 0–3)

4

Lung inflammation [89]

Perivascular edema (0–3), perivascular/-bronchiolar inflammation (0–3), goblet cell metaplasia (0–2)

32

Lung inflammation [90]

Alvolear wall inflammation, perivenous regions, periarterial/peribronchial regions, venous/arterial endothelial lesion (each 0–3)

30

Acute lung inflammation [91]

Alveolar necrosis, vascular congestion, infiltration by neutrophils/ macrophages (each 0–4)

6

RSV pneumonia [92]

Peribronchiolitis, alveolitis, perivasculitis, hypertrophy of mucus-producing glands, eosinophilia (each 0–5).

26

Mycoplasma pneumonia [93]

Quantity/quality of (peri-)bronchial infiltrates, bronchial luminal exudate, perivascular infiltrate, parenchymal pneumonia (0–3)

56

Pneumocystis carinii infection [94]

Cyst number (0–4), inflammation (0–5)

87

Streptococcus pneumonia [95]

Bronchitis, edema, interstitial inflammation, intraalveolar inflammation, pleuritis, endothelialitis (each 0–4)

0

Scoring systems for myocardial, vascular and muscular disease models

Three original scoring systems for the evaluation of viral myocarditis were identified [9698]. All of them included the evaluation of the parameters of myocardial necrosis and inflammation (Table 6). In addition, two of them also included calcification as a parameter while fibrosis and Evans blue-staining as a marker of myofiber damage were used as a parameter of myocardial disease only once.
Table 6

Semiquantitative scoring systems for cardiovascular and muscle disease models

Disease model

Scoring system: parameters (scale width)

Citations

Myocardial diseases

EMCV-induced myocarditis [96]

Myocardial necrosis, infiltration, calcification, fibrosis (together 0–4)

50

Coxsackievirus-induced myocarditis [97]

Myocardial necrosis, infiltration, calcification (together 0–4)

33

Coxsackievirus-induced myocarditis [98]

Necrosis, inflammation, Evans blue-stain (each 0–4)

17

Dilated cardiomyopathy [99]

Myocardial necrosis, fibrosis (together 0–4)

20

Chronic cardiotoxicity [100]

Qualitative/quantitative myocardial degeneration score (each 0–4)

72

Vascular diseases

Aneurysm [101]

Extent of medial, adventitial disruption/size of lesion (together 0–6)

21

Atherosclerosis [102]

Medial erosion, foam cells, buried fibrous caps, chondrocyte-like cells, lateral xanthomas (each 0,1)

2

Vasculitis [103]

Infiltration, elastic lamina destruction, intimal thickening (together 0–3)

8

Muscle diseases

Ischemic necrosis [104]

Infiltration, necrosis, hemorrhage (together 0–10)

14

Trypanosoma myositis [105]

Number of parasites, eosinophilic infiltration (each 0–3)

0

EMCV, Encephalomyocarditis virus.

Three semiquantitative scoring systems for the most important human vascular diseases were identified: atherosclerosis, aneurysms and vasculitis (Table 6) [101103]. They all cover several aspects of the pathogenesis and pathophysiology of the diseases but have been generally rarely cited yet. The aneurysm scoring system grades the severity of the disease by the extent of medial and adventitial lesion together with the general size of the lesion [102]. The atherosclerosis scoring systems uses a 5-tier system with a 0–1 scale width [102], whereas the vasculitis score uses the parameters infiltration, elastic lamina destruction and intimal thickening, thus indicating that the system may only be useful for evaluation of larger vessel types [103].

Two scoring systems for muscular disease models were identified (Table 6). One recently published scoring system evaluated the extent of Trypanosoma-induced myositis [104] while the other scoring system was developed to quantify the extent of ischemia-induced muscle necrosis by the parameters necrosis, infiltration and hemorrhage [105].

Scoring systems for hepatic and pancreatic diseases

Ten original scoring systems for chronic hepatitis have been developed or used for the quantification chronic hepatic disease (Table 7). The scoring systems by Ishak and Knodell are both highly cited scoring systems and cover almost all possible histomorphologic changes in chronically inflamed livers [106, 107]. The two identified scoring systems for acute hepatitis quantify lesions by grading the extent of inflammation and necrosis, similar to the Ishak system for chronic hepatitis [108, 109].
Table 7

Semiquantitative scoring systems for murine hepatic and pancreatic disease models

Disease model

Scoring system: parameters (scale width)

Citations

Hepatic disease

Chronic active hepatitis [107]

Periportal bridging (0–10), intralobular necrosis, portal inflammation, fibrosis (each 0–4)

2,609

Chronic hepatitis [106]

Periportal/septal inflammation (0–4), confluent necrosis (0–6), focal necrosis/apoptosis/inflammation (0–6), portal inflammation (0–4), fibrosis (0–6)

2,001

Chronic hepatitis [110]

Mitotic activity, portal inflammation, ductular proliferation, councilman bodies, fibrosis (each 0–3)

107

Hepatic fibrosis [108]

Centrolobular vein/perisinusoidal space fibrosis (each 0–2), portal tract fibrosis (0–3), septa number (0–3), septa width (0–5)

135

Acute hepatitis [111]

Steatosis (0–4), necrosis, inflammation (each 0–2),

218

Acute hepatitis [109]

Portal/lobular inflammation (each 0–3)

41

Nutritional hepatopathy [112]

Hepatocyte degeneration, portal inflammation, portal fibrosis (each 0–3)

25

Alcohol-induced hepatopathy [113]

Steatosis (%), inflammation/necrosis/fibrosis (each 0–2)

8

Non-alcoholic Steatohepatitis [114]

Steatosis (0–3), hepatocellular ballooning (0–2), lobular inflammation (0–2)

1,149

Hepatic ischemia [115]

Location, necrosis (together 0–4)

12

Pancreatic disease

Acute pancreatits [116]

Edema, necrosis, inflammation, hemorrhage, fat necrosis (each 0–4)

307

Acute pancreatitis [117]

Edema, necrosis, inflammation, vacuolization (each 0–4)

144

Acute pancreatitis [118]

Edema, necrosis, inflammation, hemorrhage (each 0–4)

89

Acute pancreatitis [119]

Acinar-cell ghosts (%), acinar cells vacuolization/swelling (%)

42

Ischemia-induced acute pancreatitis [120]

Edema, necrosis, infiltration, hemorrhage, vacuolization (each 0–3)

14

Autoimmune pancreatitis [121]

Infiltration, necrosis, lipomatosis (0–4)

42

Insulitis [122]

Islet infiltration, destruction, atrophy (0–5)

49

Five scoring systems for the evaluation of acute pancreatitis have been identified (Table 7). The first and most commonly cited scoring system was published by Schmidt et al. [116]. It uses the five parameters edema, necrosis, inflammation, hemorrhage and fat necrosis to score the extent of pancreatic lesions. All four later developed scoring systems only marginally modified the parameters by omitting a single parameter or including vacuolization as an additional marker (Table 7). In addition, one multiparametric but several uniparametric (data not shown) scoring systems were identified for the quantification of insulitis in mice models. The scoring system by Papaccio et al. uses islet infiltration, atrophy and destruction as parameters for the evaluation of Isle of Langerhans inflammation [122].

Scoring systems for skin and ocular diseases and miscellaneous disease models

Murine psoriasis models are the only skin disease model with more than one identified scoring system (Table 8). Both scoring systems offer a wide variety of parameters for the evaluation of epidermal and dermal changes in models of this relevant human disease [123, 124]. In addition, scoring systems for dermal sclerosis, burn scars, atopic dermatitis and epithelial irritation were identified (Table 8).
Table 8

Semiquantitative scoring systems for murine models of eye, skin and miscellaneous diseases

Disease model

Scoring system: parameters (scale width)

Citations

Skin diseases

Burn scars [125]

Epidermal hyperplasia/hyperkeratosis, hair follicles, apocrine glands, smooth muscles, fibroplasia, vascular proliferation (each 0,1), collagen orientation (0–3)

26

Systemic sclerosis [126]

Dermal inflammation, thickened collagen bundles, dermal thickness (each 0–3)

18

Atopic dermatitis [127]

Epidermal hypertrophy, hyperkeratosis, parakeratosis, erosion, inflammation, edema, ulcer (each 0–4)

8

UV radiation-induced skin damage [128]

Epidermal thickness (0–3), dermal cellularity (0–3), dermal cyst changes (0–5)

7

Epithelial irritation [129]

Leukocyte infiltration (0–5), epithelial reaction (0,1)

1

Psoriasis [123]

Munro abscesses (1.5), hyperkeratosis (0.5), parakeratosis (1), length of rete ridges (0.5–1.5), lack of granular layer (1), acanthosis (1), dermis lymphocytic infiltrate (0.5–1.5), papillary papillae congestion (1), thinning above papillae (0.5)

18

Psoriasis [124]

Epidermal thickness, Stratum corneum thinning, extent of Stratum granulosum/parakeratosis/inflammation, microabscesses (each 0–3)

5

Ocular diseases

Diabetic retinopathy [130]

Inflammation (leukocytes/100 μm retina length), leukostasis (leukocytes per vessel lumen)

25

Oxygen induced retinopathy [131]

Blood vessel growth, tufts, tortuosity, extraretinal neovascularization, vasoconstriction (each 0–3), hemorrhage (0,1)

48

Bacterial endophthalmitis [132]

Inflammation, retinal architecture (each 0–4),

18

Autoimmune uveoretinitis [133]

Infiltration (0–4)

285

Endotoxin uveitis [134]

Inflammation (0–3)

47

Miscellaneous diseases

Abdominal adhesion [135]

Inflammation, fibrosis, necrosis/abscess, granulomas (each 0–3)

100

Abdominal adhesion [136]

Vessel number, neutrophil infiltration, neutrophils at site (each 0–3)

4

Abdominal adhesion [137]

Fibrotic matrix, collagen fibers, fibroblast proliferation (together 1–5)

n.r.

Embryonic development [138]

Grading (0–5) of 17 parameters

429

Wound healing [139]

Infiltration, granulation tissue, fibroblasts, collagen deposition (together 0–12)

369

Thyroiditis [140]

Number of inflammatory foci, parenchymal destruction (together 0–4)

77

Vaginitis [141]

Epithelial disruption, leucocyte infiltration, edema, vascular injection (each 0–4)

74

Esophagitis [142]

Epithelial damage/hemorrhage (0–4), inflammation (0–3)

6

Lymph node [143]

Heterophils, apoptotic histiocytes, sinus histiocytosis, follicular hyperplasia (together 0–5)

1

Spermatogenic activity [144]

Presence of spermatozoa/spermatides/germ cells/sertoli cells (together 0–10)

n.r.

n.r., not reported; UV, ultraviolet light.

Five original scoring systems for the evaluation of ocular diseases were identified. Two of these systems use only one parameter for the evaluation of autoimmune and endotoxin uveitis. For the sake of completeness these scoring systems are also displayed in Table 8, although they do not fulfill requirements for inclusion [133, 134]. Furthermore, the identified scoring system for diabetic retinopathy uses two parameters evaluated in absolute numbers of leukocytes per area [130].

Three scoring systems for the evaluation of abdominal adhesions after traumatic or toxic irritation of the peritoneum could be identified. Interestingly, not all scoring systems for abdominal adhesion use fibrosis as a parameter for the grading of the adhesions [135137]. Finally, very helpful scoring systems for the evaluation of embryonic development and wound healing could be identified (Table 8).

Scoring systems for systemic diseases and transplant rejection

Three original scoring systems analyzing lesions associated with graft-versus-host disease (GvHD) are available (Table 9). The most cited scoring system by Hill et al. exclusively covers intestinal lesions associated with GvHD [145]. This system allows a very thorough analysis of intestinal lesion using a wide variety of parameters in the small and large intestine. The other two systems also cover intestinal lesions but provide additional parameters for the analysis of hepatic [146] or hepatic and skin lesions [147].
Table 9

Semiquantitative scoring systems for systemic disease models

Disease model

Scoring system: parameters (scale width)

Citations

Graft-versus-host disease (GvHD)

Intestinal acute GvHD [145]

Small intestine: villous blunting, crypt regeneration/apoptosis/loss, enterocyte loss, infiltration (each 0–4); Colon: crypt regeneration, colonocyte vacuolization, crypt apoptosis/destruction, infiltration (each 0–4)

389

Acute GvHD [146]

Small/large bowel: villous blunting, crypt regeneration/apoptosis/loss, luminal sloughing, infiltration, mucosal ulceration, epithelial/vacuolization (each 0–4), liver: portal infiltration, bile duct apoptosis/sloughing, parenchymal apoptosis/abscesses/mitoses, steatosis, cholestasis (each 0–4)

170

Acute GvHD [147]

Skin: epidermal damage, dermal collagen density, dermal infiltration, subcutaneous fat loss, hair follicle loss (each 0–2), intestine: crypt apoptosis, inflammation(each 0–4), liver: bile duct injury (0–4)

50

Hemorrhagic shock

Lung lesion [148]

Alveolar membrane thickening, congestion, edema, intraalveolar hemorrhage, interstitial, intraalveolar infiltration (each 0–3)

36

Intestinal, pulmonary lesions [149]

Intestine: % injury (number of edematous villi x 0,5 + number of villi with epithelial damage x 1), lung: number of neutrophils in 10 fields

2

Pulmonary, intestinal, renal, hepatic lesions [150]

Lung: atelectasis, hemorrhage, edema, congestion, inflammation, hyp-eraeration (each 0–3); ileal mucosal damage, (0–5), liver: congestion, necrosis, vacuolization (each 0–3), Kidney: epithelial swelling, tubular dilation, necrosis, edema, microthrombosis (each 0–3)

2

Immunotoxicity

[151]

Complete assessment of all lymphoid organs (each 0–4)

50

Transplant rejection

Banff classification for renal rejection [152]

Tubulitis, arteritis, mononuclear cell interstitial infiltrates, glomerulitis, interstitial fibrosis, tubular atrophy, glomerulopathy, mesangial matrix increase, vascular fibrous intimal thickening, arteriolar hyaline thickening (each 0–3)

1.727

Heart rejection 1990 [153]

Infiltration, myocyte damage (0–4)

1401

Heart rejection 2005 [154]

Infiltration, myocyte damage (0–3)

365

Lung rejection [155]

Inflammation (0–4), lymphocyte infiltration (0–1), bronchiolitis obliterans (0–4), Vascular rejection (0–1), vasculitis (0–1)

362

Skin rejection [156]

Acanthosis, ulceration, necrosis, inflammation, granulation tissue (0–5)

1

GvHD, Graft versus host disease.

Three original scoring systems have been developed for the analysis lesions associated with hemorrhagic shock (Table 9). The scoring system with the highest citation number only focusses on the pulmonary lesion and offers a variety of parameters for the evaluation of shock-induced lesions in the lung [148]. The other two scoring system also include parameters for lung evaluation but both offer additional parameters for the quantification of intestinal changes [149, 150] or in one case offer scoring systems for renal and hepatic lesions [150] associated with hemorrhagic shock.

The identified scoring system for immunotoxicity of toxins more or less demands the analysis of all immune organs but gives a good guideline in terms of the nomenclature of morphologic changes associated with the toxin application (Table 9) [151].

Finally, the three scoring systems which have been used for murine transplant rejection models have consistently been developed for the evaluation of tissues from human patients (Table 9). They are all used in mouse models unmodified to allow for better conclusions from the mouse models for the situation in the human patient.

Discussion

Extensive research of the literature identified 146 originally designed semiquantitative, multiparametric scoring systems for the histopathology of mouse models. These scoring systems cover almost all organs systems and a wide variety of disease models. Colitis and especially ulcerative colitis was the disease model with the largest number of different scoring system closely followed by experimental autoimmune encephalitis (EAE), lupus nephritis and collagen induced osteoarthritis.

The number of citations for the publication including the scoring system varied between few citations and up to 2176. The citation number clearly reflects the value of the scientific work shown in the papers and thus also indirectly reflects the quality of the included scoring systems. In some cases there is even clear evidence that the high citation number is directly based on the “gold” standard character of the scoring system and its regular use in mouse models or human tissues, for instance the score from Mankin et al. for osteoarthritis evaluation and the scores developed by Knodell and Ishak et al. for chronic hepatitis or the score by Cooper et al. for DSS-colitis [4, 28, 106, 107]. Nevertheless, after careful analysis of the publication it also became obvious that scoring systems in publications with a small number of citation also proofed to be of expedience for certain scientific question.

Assuming that the main function of scoring systems is the analysis of the influences of experimental factors on the microscopical tissue morphology the selected parameters should be consciously chosen to be able to reflect the potential changes. The lack of a rationale for the selection of the parameters was therefore an emerging and surprising finding during the literature search for this study. Although the selection of parameters in most scoring systems is comprehensibly based on the common knowledge on the pathogenesis of the disease modeled in the mouse, there is only rarely a clear statement or a line of argument for choosing a parameter. Even less often the correlation between scoring parameter value and the magnitude of the clinical symptoms or the differences in the extent of the experimental factor is given as for instance in the excellent study of Cooper et al. [4]. This lack is most probably due to the time-consuming work involved, but it may however tremendously increase the value and the scientific merit of the scoring system.

Conclusion

In summary, a final judgment of the quality and the usefulness of the scoring systems presented was not an aim of this study and is after all most probably not possible since the value of a scoring system clearly depends on the scientific question, the underlying hypothesis, the model characteristics and the pathogenesis of the disease. This review may however give an overview on currently available scoring systems and may therefore allow for a better choice for the respective project.

Methods

Selection of scoring systems

The systematic review was prepared according to the PRISMA guidelines [157]. All items were considered and can be viewed in Additional file 1. Scoring systems were identified by a comprehensive Pubmed search (http://www.ncbi.nlm.nih.gov/pubmed/) using a combination of the search terms “mouse”, “score”, “histopathology”. This led to the identification of 1479 publication by October 30, 2012 (Figure 1). Full text versions of all publications were obtained and analyzed for the description of multiparametric, semiquantitative, scoring systems for the histopathology of mouse models. Inclusion of a mouse scoring system in this overview was based on the fulfillment of six parameters.

First, the scoring system had to be based on the semiquantitative evaluation of histopathologic changes in murine tissues. Thus, approaches using digital image analysis for absolute quantification of lesion area, cell number or immunohistochemical signals or scoring systems with dominance of immunohistochemical markers as evaluation parameters were not included.

Second, only scoring systems evaluating more than one histomorphologic parameter were included in the review. Nevertheless, scoring systems with high citation numbers which combined several parameters in a uniparametric score were also included. For instance, if a highly cited scoring system integrated the presence and extent of crypt abscesses, epithelial sloughing and submucosal infiltration into a single score of 0 to 4 the study was also included.

Third, the scoring approach had to be comprehensibly described to allow for reproduction by the reader.

Fourth, the scoring system had to be originally designed for the presented study without citation of former publications. If former publications were cited as the source of the scoring system, the string of citations was followed back to the study originally describing the scoring systems. If scorings systems were not referenced to older studies but similar approaches were detected in earlier publication, only the older study was included in this review.

Fifth, the scoring systems were generally grouped by the organ affected and analyzed. Systemic diseases and transplantation models were included in separate groups. If the number of identified scoring systems for a specific disease model exceeded ten, only the then most cited scoring systems were included in this review.

Citation number was obtained using Thomson Reuters Web Science© (http://apps.webofknowledge.com).

Author’s contributions

RK had the idea of the project, performed the internet search, the data analysis and wrote the abstract.

Declarations

Acknowledgments

This study was supported by the FCT grant BD/43731/2008 and the DFG grant KL 2240/1-1. Literature search was tremenduously supported by Camillo Krawczyk, Freie Universität Berlin.

Authors’ Affiliations

(1)
Department of Veterinary Pathology, College of Veterinary Medicine, Freie Universität Berlin

References

  1. Renshaw AA, Gould EW: Measuring errors in surgical pathology in real-life practice - Defining what does and does not matter. Am J Clin Pathol. 2007, 127 (1): 144-152.PubMedGoogle Scholar
  2. Riber-Hansen R, Vainer B, Steiniche T: Digital image analysis: a review of reproducibility, stability and basic requirements for optimal results. APMIS. 2012, 120 (4): 276-289.PubMedGoogle Scholar
  3. Boyce RW, Dorph-Petersen KA, Lyck L, Gundersen HJG: Design-based Stereology: Introduction to Basic Concepts and Practical Approaches for Estimation of Cell Number. Toxicol Pathol. 2010, 38 (7): 1011-1025.PubMedGoogle Scholar
  4. Cooper HS, Murthy SNS, Shah RS, Sedergran DJ: Clinicopathological Study of Dextran Sulfate Sodium Experimental Murine Colitis. Lab Invest. 1993, 69 (2): 238-249.PubMedGoogle Scholar
  5. Dieleman LA, Ridwan BU, Tennyson GS, Beagley KW, Bucy RP, Elson CO: Dextran Sulfate Sodium-Induced Colitis Occurs in Severe Combined Immunodeficient Mice. Gastroenterology. 1994, 107 (6): 1643-1652.PubMedGoogle Scholar
  6. Dieleman LA, Palmen MJHJ, Akol H, Bloemena E, Pena AS, Meuwissen SGM, van Rees EP: Chronic experimental colitis induced by dextran sulphate sodium (DSS) is characterized by Th1 and Th2 cytokines. Clin Exp Immunol. 1998, 114 (3): 385-391.PubMedPubMed CentralGoogle Scholar
  7. Murthy SNS, Cooper HS, Shim H, Shah RS, Ibrahim SA, Sedergran DJ: Treatment of Dextran Sulfate Sodium-Induced Murine Colitis by Intracolonic Cyclosporine. Digest Dis Sci. 1993, 38 (9): 1722-1734.PubMedGoogle Scholar
  8. Mahler M, Bristol IJ, Leiter EH, Workman AE, Birkenmeier EH, Elson CO, Sundberg JP: Differential susceptibility of inbred mouse strains to dextran sulfate sodium-induced colitis. Am J Physiol-Gastr L. 1998, 274 (3): G544-G551.Google Scholar
  9. Hudert CA, Weylandt KH, Lu Y, Wang JD, Hong S, Dignass A, Serhan CN, Kang JX: Transgenic mice rich in endogenous omega-3 fatty acids are protected from colitis. P Natl Acad Sci USA. 2006, 103 (30): 11276-11281.Google Scholar
  10. Obermeier F, Kojouharoff G, Hans W, Scholmerich J, Gross V, Falk W: Interferon-gamma (IFN-gamma)- and tumour necrosis factor (TNF)-induced nitric oxide as toxic effector molecule in chronic dextran sulphate sodium (DSS)-induced colitis in mice. Clin Exp Immunol. 1999, 116 (2): 238-245.PubMedPubMed CentralGoogle Scholar
  11. Hartmann G, Bidlingmaier C, Siegmund B, Albrich S, Schulze J, Tschoep K, Eigler A, Lehr HA, Endres S: Specific type IV phosphodiesterase inhibitor rolipram mitigates experimental colitis in mice. J Pharmacol Exp Ther. 2000, 292 (1): 22-30.PubMedGoogle Scholar
  12. Neurath MF, Fuss I, Kelsall BL, Stuber E, Strober W: Antibodies to Interleukin-12 Abrogate Established Experimental Colitis in Mice. J Exp Med. 1995, 182 (5): 1281-1290.PubMedGoogle Scholar
  13. Dohi T, Fujihashi K, Rennert PD, Iwatani K, Kiyono H, McGhee JR: Hapten-induced colitis is associated with colonic patch hypertrophy and T helper cell 2-type responses. J Exp Med. 1999, 189 (8): 1169-1179.PubMedPubMed CentralGoogle Scholar
  14. Appleyard CB, Wallace JL: Reactivation of Hapten-Induced Colitis and Its Prevention by Antiinflammatory Drugs. Am J Physiol-Gastr L. 1995, 269 (1): G119-G125.Google Scholar
  15. Macpherson BR, Pfeiffer CJ: Experimental Production of Diffuse Colitis in Rats. Digestion. 1978, 17 (2): 135-150.PubMedGoogle Scholar
  16. ten Hove T, van den Blink B, Pronk I, Drillenburg P, Peppelenbosch MP, van Deventer SJH: Dichotomal role of inhibition of p38 MAPK with SB 203580 in experimental colitis. Gut. 2002, 50 (4): 507-512.PubMedPubMed CentralGoogle Scholar
  17. Rath HC, Herfarth HH, Ikeda JS, Grenther WB, Hamm TE, Balish E, Taurog JD, Hammer RE, Wilson KH, Sartor RB: Normal luminal bacteria, especially bacteroides species, mediate chronic colitis, gastritis, and arthritis in HLA-B27/human beta(2) microglobulin transgenic rats. J Clin Invest. 1996, 98 (4): 945-953.PubMedPubMed CentralGoogle Scholar
  18. Madsen KL, Doyle JS, Jewell LD, Tavernini MM, Fedorak RN: Lactobacillus species prevents colitis in interleukin 10 gene-deficient mice. Gastroenterology. 1999, 116 (5): 1107-1114.PubMedGoogle Scholar
  19. Berg DJ, Davidson N, Kuhn R, Muller W, Menon S, Holland G, ThompsonSnipes L, Leach MW, Rennick D: Enterocolitis and colon cancer in interleukin-10-deficient mice are associated with aberrant cytokine production and CD4(+) TH1-like responses. J Clin Invest. 1996, 98 (4): 1010-1020.PubMedPubMed CentralGoogle Scholar
  20. Burns RC, Rivera-Nieves J, Moskaluk CA, Matsumoto S, Cominelli F, Ley K: Antibody blockade of ICAM-1 and VCAM-1 ameliorates inflammation in the SAMP-1/Yit adoptive transfer model of Crohn's disease in mice. Gastroenterology. 2001, 121 (6): 1428-1436.PubMedGoogle Scholar
  21. Houpt ER, Glembocki DJ, Obrig TG, Moskaluk CA, Lockhart LA, Wright RL, Seaner RM, Keepers TF, Wilkins TD, Petri WA: The mouse model of amebic colitis reveals mouse strain susceptibility to infection and exacerbation of disease by CD4(+) T cells. J Immunol. 2002, 169 (8): 4496-4503.PubMedGoogle Scholar
  22. Pothoulakis C, Castagliuolo I, Lamont JT, Jaffer A, Okeane JC, Snider RM, Leeman SE: Cp-96,345, a Substance-P Antagonist, Inhibits Rat Intestinal Responses to Clostridium-Difficile Toxin-a but Not Cholera-Toxin. P Natl Acad Sci USA. 1994, 91 (3): 947-951.Google Scholar
  23. Park PO, Haglund U, Bulkley GB, Falt K: The Sequence of Development of Intestinal Tissue-Injury after Strangulation Ischemia and Reperfusion. Surgery. 1990, 107 (5): 574-580.PubMedGoogle Scholar
  24. Lane JS, Todd KE, Lewis MPN, Gloor B, Ashley SW, Reber HA, McFadden DW, Chandler CF: Interleukin-10 reduces the systemic inflammatory response in a murine model of intestinal ischemia/reperfusion. Surgery. 1997, 122 (2): 288-294.PubMedGoogle Scholar
  25. de Koning BAE, van Dieren JM, Lindenbergh-Kortleve DJ, van der Sluis M, Matsumoto T, Yamaguchi K, Einerhand AW, Samsom JN, Pieters R, Nieuwenhuis EES: Contributions of mucosal immune cells to methotrexate-induced mucositis. Int Immunol. 2006, 18 (6): 941-949.PubMedGoogle Scholar
  26. Stewart-Tull DES, Coote JG, Thompson DH, Candlish D, Wardlaw AC, Candlish A: Virulence spectra of typed strains of Campylobacter jejuni from different sources: a blinded in vivo study. J Med Microbiol. 2009, 58 (5): 546-553.PubMedGoogle Scholar
  27. Wang X, Sturegard E, Rupar R, Nilsson HO, Aleljung PA, Carlen B, Willen R, Wadstrom T: Infection of BALB/c a mice by spiral and coccoid forms of Helicobacter pylori. J Med Microbiol. 1997, 46 (8): 657-663.PubMedGoogle Scholar
  28. Mankin HJ, Dorfman H, Lippiell L, Zarins A: Biochemical and Metabolic Abnormalities in Articular Cartilage from Osteo-Arthritic Human Hips .2. Correlation of Morphology with Biochemical and Metabolic Data. J Bone Joint Surg Am. 1971, 53 (3): 523.PubMedGoogle Scholar
  29. Williams RO, Feldmann M, Maini RN: Antitumor Necrosis Factor Ameliorates Joint Disease in Murine Collagen-Induced Arthritis. P Natl Acad Sci USA. 1992, 89 (20): 9784-9788.Google Scholar
  30. Pritzker KPH, Gay S, Jimenez SA, Ostergaard K, Pelletier JP, Revell PA, Salter D, van den Berg WB: Osteoarthritis cartilage histopathology: grading and staging. Osteoarthr Cartilage. 2006, 14 (1): 13-29.Google Scholar
  31. Krenn V, Morawietz L, Burmester GR, Kinne RW, Mueller-Ladner U, Muller B, Haupl T: Synovitis score: discrimination between chronic low-grade and high-grade synovitis. Histopathology. 2006, 49 (4): 358-364.PubMedGoogle Scholar
  32. Joosten LAB, Helsen MMA, Saxne T, van de Loo FAJ, Heinegard D, van den Berg WB: IL-1 alpha beta blockade prevents cartilage and bone destruction in murine type II collagen-induced arthritis, whereas TNF-alpha blockade only ameliorates joint inflammation. J Immunol. 1999, 163 (9): 5049-5055.PubMedGoogle Scholar
  33. Delgado M, Abad C, Martinez C, Laceta J, Gomariz RP: Vasoactive intestinal peptide prevents experimental arthritis by downregulating both autoimmune and inflammatory components of the disease. Nat Med. 2001, 7 (5): 563-568.PubMedGoogle Scholar
  34. Adkison AM, Raptis SZ, Kelley DG, Pham CTN: Dipeptidyl peptidase I activates neutrophil-derived serine proteases and regulates the development of acute experimental arthritis. J Clin Invest. 2002, 109 (3): 363-371.PubMedPubMed CentralGoogle Scholar
  35. Bendele A, McComb J, Gould T, McAbee T, Sennello G, Chlipala E, Guy M: Animal models of arthritis: Relevance to human disease. Toxicol Pathol. 1999, 27 (1): 134-142.PubMedGoogle Scholar
  36. Taniguchi K, Kohsaka H, Inoue N, Terada Y, Ito H, Hirokawa K, Miyasaka N: Induction of the p16(INK4a) senescence gene as a new therapeutic strategy for the treatment of rheumatoid arthritis. Nat Med. 1999, 5 (7): 760-767.PubMedGoogle Scholar
  37. Helyes Z, Szabo A, Nemeth J, Jakab B, Pinter E, Banvolgyi A, Kereskai L, Keri G, Szolcsanyi J: Antiinflammatory and analgesic effects of somatostatin released from capsaicin-sensitive sensory nerve terminals in a Freund's adjuvant-induced chronic arthritis model in the rat. Arthritis Rheum. 2004, 50 (5): 1677-1685.PubMedGoogle Scholar
  38. Alten R, Gram H, Joosten LA, van den Berg WB, Sieper J, Wassenberg S, Burmester G, van Riel P, Diaz-Lorente M, Bruin GJ, et al: The human anti-IL-1 beta monoclonal antibody ACZ885 is effective in joint inflammation models in mice and in a proof-of-concept study in patients with rheumatoid arthritis. Arthritis Res Ther. 2008, 10 (3): R67.PubMedPubMed CentralGoogle Scholar
  39. Rudolphi K, Gerwin N, Verzijl N, van der Kraan P, van den Berg W: Pralnacasan, an inhibitor of interleukin-1 beta converting enzyme, reduces joint damage in two murine models of osteoarthritis. Osteoarthr Cartilage. 2003, 11 (10): 738-746.Google Scholar
  40. Kamekura S, Hoshi K, Shimoaka T, Chung U, Chikuda H, Yamada T, Uchida M, Ogata N, Seichi A, Nakamura K, et al: Osteoarthritis development in novel experimental mouse models induced by knee joint instability. Osteoarthr Cartilage. 2005, 13 (7): 632-641.Google Scholar
  41. Huebner JL, Hanest MA, Beekman B, TeKoppele JM, Kraus VB: A comparative analysis of bone and cartilage metabolism in two strains of guinea-pig with varying degrees of naturally occurring osteoarthritis. Osteoarthr Cartilage. 2002, 10 (10): 758-767.Google Scholar
  42. Bremell T, Abdelnour A, Tarkowski A: Histopathological and Serological Progression of Experimental Staphylococcus-Aureus Arthritis. Infect Immun. 1992, 60 (7): 2976-2985.PubMedPubMed CentralGoogle Scholar
  43. Wakitani S, Goto T, Pineda SJ, Young RG, Mansour JM, Caplan AI, Goldberg VM: Mesenchymal cell-based repair of large, full-thickness defects of articular cartilage. J Bone Joint Surg Am. 1994, 76 (4): 579-592.PubMedGoogle Scholar
  44. O'Driscoll SW, Keeley FW, Salter RB: The chondrogenic potential of free autogenous periosteal grafts for biological resurfacing of major full-thickness defects in joint surfaces under the influence of continuous passive motion. An experimental investigation in the rabbit. J Bone Joint Surg Am. 1986, 68 (7): 1017-1035.PubMedGoogle Scholar
  45. Sellers RS, Peluso D, Morris EA: The effect of recombinant human bone morphogenetic protein-2 (rhBMP-2) on the healing of full-thickness defects of articular cartilage. J Bone Joint Surg Am. 1997, 79 (10): 1452-1463.PubMedGoogle Scholar
  46. Fortier LA, Mohammed HO, Lust G, Nixon AJ: Insulin-like growth factor-I enhances cell-based repair of articular cartilage. J Bone Joint Surg Br. 2002, 84 (2): 276-288.PubMedGoogle Scholar
  47. Pineda S, Pollack A, Stevenson S, Goldberg V, Caplan A: A semiquantitative scale for histologic grading of articular cartilage repair. Acta Anat (Basel). 1992, 143 (4): 335-340.Google Scholar
  48. Austin HA, Muenz LR, Joyce KM, Antonovych TA, Kullick ME, Klippel JH, Decker JL, Balow JE: Prognostic Factors in Lupus Nephritis - Contribution of Renal Histologic Data. Am J Med. 1983, 75 (3): 382-391.PubMedGoogle Scholar
  49. Wang BY, Yamamoto Y, El-Badri NS, Good RA: Effective treatment of autoimmune disease and progressive renal disease by mixed bone-marrow transplantation that establishes a stable mixed chimerism in BXSB recipient mice. P Natl Acad Sci USA. 1999, 96 (6): 3012-3016.Google Scholar
  50. Miyazaki T, Ono M, Qu WM, Zhang MC, Mori S, Nakatsuru S, Nakamura Y, Sawasaki T, Endo Y, Nose M: Implication of allelic polymorphism of osteopontin in the development of lupus nephritis in MRL/lpr mice. Eur J Immunol. 2005, 35 (5): 1510-1520.PubMedGoogle Scholar
  51. Keil DE, Peden-Adams MM, Wallace S, Ruiz P, Gilkeson GS: Assessment of trichloroethylene (TCE) exposure in murine strains genetically-prone and non-prone to develop autoimmune disease. J Environ Sci Heal A. 2009, 44 (5): 443-453.Google Scholar
  52. Chen SM, Mukoyama T, Sato N, Yamagata SI, Arai Y, Satoh N, Ueda S: Induction of nephrotoxic serum nephritis in inbred mice and suppressive effect of colchicine on the development of this nephritis. Pharmacol Res. 2002, 45 (4): 319-324.PubMedGoogle Scholar
  53. Zheng ZY, Schmidt-Ott KM, Chua S, Foster KA, Frankel RZ, Pavlidis P, Barasch J, D'Agati VD, Gharavi AG: A Mendelian locus on chromosome 16 determines susceptibility to doxorubicin nephropathy in the mouse. P Natl Acad Sci USA. 2005, 102 (7): 2502-2507.Google Scholar
  54. Raij L, Azar S, Keane W: Mesangial Immune Injury, Hypertension, and Progressive Glomerular Damage in Dahl Rats. Kidney Int. 1984, 26 (2): 137-143.PubMedGoogle Scholar
  55. Bader R, Bader H, Grund KE, Mackensenhaen S, Christ H, Bohle A: Structure and Function of the Kidney in Diabetic Glomerulosclerosis Correlations between Morphological and Functional Parameters. Pathol Res Pract. 1980, 167 (2–4): 204-216.PubMedGoogle Scholar
  56. Yabuki A, Tahara T, Taniguchi K, Matsumoto M, Suzuki S: Neuronal nitric oxide synthase and cyclooxygenase-2 in diabetic nephropathy of type 2 diabetic OLETF rats. Exp Anim Tokyo. 2006, 55 (1): 17-25.Google Scholar
  57. Lai PC, Smith J, Bhangal G, Chaudhry KA, Chaudhry AN, Keith JC, Tam FWK, Pusey CD, Cook HT: Interleukin-11 reduces renal injury and glomerular NF-Kappa B activity in murine experimental glomerulonephritis. Nephron Exp Nephrol. 2005, 101 (4): E146-E154.PubMedGoogle Scholar
  58. Watson ML, Rao JK, Gilkeson GS, Ruiz P, Eicher EM, Pisetsky DS, Matsuzawa A, Rochelle JM, Seldin MF: Genetic-Analysis of Mrl-Lpr Mice - Relationship of the Fas Apoptosis Gene to Disease Manifestations and Renal Disease-Modifying Loci. J Exp Med. 1992, 176 (6): 1645-1656.PubMedGoogle Scholar
  59. Yumura W, Sugino N, Nagasawa R, Kubo S, Hirokawa K, Maruyama N: Age-Associated Changes in Renal Glomeruli of Mice. Exp Gerontol. 1989, 24 (3): 237-249.PubMedGoogle Scholar
  60. Gharavi AG, Ahmad T, Wong RD, Hooshyar R, Vaughn J, Oller S, Frankel RZ, Bruggeman LA, D'Agati VD, Klotman PE, et al: Mapping a locus for susceptibility to HIV-1-associated nephropathy to mouse chromosome 3. P Natl Acad Sci USA. 2004, 101 (8): 2488-2493.Google Scholar
  61. Park HC, Yasuda K, Ratliff B, Stoessel A, Sharkovska Y, Yamamoto I, Jasmin JF, Bachmann S, Lisanti MP, Chander P, et al: Postobstructive regeneration of kidney is derailed when surge in renal stem cells during course of unilateral ureteral obstruction is halted. Am J Physiol-Renal. 2010, 298 (2): F357-F364.Google Scholar
  62. Pulsinelli WA, Levy DE, Duffy TE: Regional Cerebral Blood-Flow and Glucose-Metabolism Following Transient Forebrain Ischemia. Ann Neurol. 1982, 11 (5): 499-509.PubMedGoogle Scholar
  63. Barber PA, Hoyte L, Colbourne F, Buchan AM: Temperature-regulated model of focal ischemia in the mouse: a study with histopathological and behavioral outcomes. Stroke. 2004, 35 (7): 1720-1725.PubMedGoogle Scholar
  64. Thoresen M, Bagenholm R, Loberg EM, Apricena F, Kjellmer I: Posthypoxic cooling of neonatal rats provides protection against brain injury. Arch Dis Child. 1996, 74 (1): F3-F9.Google Scholar
  65. Sheldon RA, Sedik C, Ferriero DM: Strain-related brain injury in neonatal mice subjected to hypoxia-ischemia. Brain Res. 1998, 810 (1–2): 114-122.PubMedGoogle Scholar
  66. Iida H, Schmelzer JD, Schmeichel AM, Wang Y, Low PA: Peripheral nerve ischemia: reperfusion injury and fiber regeneration. Exp Neurol. 2003, 184 (2): 997-1002.PubMedGoogle Scholar
  67. Fenyk-Melody JE, Garrison AE, Brunnert SR, Weidner JR, Shen F, Shelton BA, Mudgett JS: Experimental autoimmune encephalomyelitis is exacerbated in mice lacking the NOS2 gene. J Immunol. 1998, 160 (6): 2940-2946.PubMedGoogle Scholar
  68. Kassis I, Grigoriadis N, Gowda-Kurkalli B, Mizrachi-Kol R, Ben-Hur T, Slavin S, Abramsky O, Karussis D: Neuroprotection and immunomodulation with mesenchymal stem cells in chronic experimental autoimmune encephalomyelitis. Arch Neurol-Chicago. 2008, 65 (6): 753-761.PubMedGoogle Scholar
  69. Butterfield RJ, Blankenhorn EP, Roper RJ, Zachary JF, Doerge RW, Teuscher C: Identification of genetic loci controlling the characteristics and severity of brain and spinal cord lesions in experimental allergic encephalomyelitis. Am J Pathol. 2000, 157 (2): 637-645.PubMedPubMed CentralGoogle Scholar
  70. Murray PD, McGavern DB, Lin XQ, Njenga MK, Leibowitz J, Pease LR, Rodriguez M: Perforin-dependent neurologic injury in a viral model of multiple sclerosis. J Neurosci. 1998, 18 (18): 7306-7314.PubMedGoogle Scholar
  71. Bright JJ, Du CG, Coon M, Sriram S, Klaus SJ: Prevention of experimental allergic encephalomyelitis via inhibition IL-12 signaling and IL-12-mediated Th1 differentiation: An effect of the novel anti-inflammatory drug lisofylline. J Immunol. 1998, 161 (12): 7015-7022.PubMedGoogle Scholar
  72. Soilu-Hanninen M, Roytta M, Salmi A, Salonen R: Therapy with antibody against leukocyte integrin VLA-4 (CD49d) is effective and safe in virus-facilitated experimental allergic encephalomyelitis. J Neuroimmunol. 1997, 72 (1): 95-105.PubMedGoogle Scholar
  73. Zhang J, Li Y, Cui YS, Chen JL, Lu M, Elias SB, Chopp M: Erythropoietin treatment improves neurological functional recovery in EAE mice. Brain Res. 2005, 1034 (1–2): 34-39.PubMedGoogle Scholar
  74. Tsunoda I, Kuang LQ, Theil DJ, Fujinami RS: Antibody association with a novel model for primary progressive multiple sclerosis: Induction of relapsing-remitting and progressive forms of EAE in H2(s) mouse strains. Brain Pathol. 2000, 10 (3): 402-418.PubMedGoogle Scholar
  75. Lu ZQ, Hu XQ, Zhu CS, Wang DJ, Zheng XP, Liu QT: Overexpression of CNTF in Mesenchymal Stem Cells reduces demyelination and induces clinical recovery in experimental autoimmune encephalomyelitis mice. J Neuroimmunol. 2009, 206 (1–2): 58-69.PubMedGoogle Scholar
  76. Sirin BH, Ortac R, Cerrahoglu M, Saribulbul O, Baltalarli A, Celebisoy N, Iskesen I, Rendeci O: Ischaemic preconditioning reduces spinal cord injury in transient ischaemia. Acta Cardiol. 2002, 57 (4): 279-285.PubMedGoogle Scholar
  77. Sheng HX, Spasojevic I, Warner DS, Batinic-Haberle I: Mouse spinal cord compression injury is ameliorated by intrathecal cationic manganese (III) porphyrin catalytic antioxidant therapy. Neurosci Lett. 2004, 366 (2): 220-225.PubMedGoogle Scholar
  78. Gerber J, Raivich G, Wellmer A, Noeske C, Kunst T, Werner A, Bruck W, Nau R: A mouse model of Streptococcus pneumoniae meningitis mimicking several features of human disease. Acta Neuropathol. 2001, 101 (5): 499-508.PubMedGoogle Scholar
  79. Kennedy PGE, Rodgers J, Jennings FW, Murray M, Leeman SE, Burke JM: A substance P antagonist, RP-67,580, ameliorates a mouse meningoencephalitic response to Trypanosoma brucei brucei. P Natl Acad Sci USA. 1997, 94 (8): 4167-4170.Google Scholar
  80. Sheldon RA, Jiang XN, Francisco C, Christen S, Vexler ZS, Tauber MG, Ferriero DM: Manipulation of antioxidant pathways in neonatal murine brain. Pediatr Res. 2004, 56 (4): 656-662.PubMedGoogle Scholar
  81. Chan YC, Hosoda K, Tsai CJ, Yamamoto S, Wang MF: Favorable effects of tea on reducing the cognitive deficits and brain morphological changes in senescence-accelerated mice. J Nutr Sci Vitaminol (Tokyo). 2006, 52 (4): 266-273.Google Scholar
  82. Oz G, Nelson CD, Koski DM, Henry PG, Marjanska M, Deelchand DK, Shanley R, Eberly LE, Orr HT, Clark HB: Noninvasive detection of presymptomatic and progressive neurodegeneration in a mouse model of spinocerebellar ataxia type 1. J Neurosci. 2010, 30 (10): 3831-3838.PubMedPubMed CentralGoogle Scholar
  83. Ashcroft T, Simpson JM, Timbrell V: Simple Method of Estimating Severity of Pulmonary Fibrosis on a Numerical Scale. J Clin Pathol. 1988, 41 (4): 467-470.PubMedPubMed CentralGoogle Scholar
  84. Nicholson AG, Fulford LG, Colby TV, du Bois RM, Hansell DM, Wells AU: The relationship between individual histologic features and disease progression in idiopathic pulmonary fibrosis. Am J Resp Crit Care. 2002, 166 (2): 173-177.Google Scholar
  85. Davidson DJ, Dorin JR, Mclachlan G, Ranaldi V, Lamb D, Doherty C, Govan J, Porteous DJ: Lung-Disease in the Cystic-Fibrosis Mouse Exposed to Bacterial Pathogens. Nat Genet. 1995, 9 (4): 351-357.PubMedGoogle Scholar
  86. Nishina K, Mikawa K, Takao Y, Shiga M, Maekawa N, Obara H: Intravenous lidocaine attenuates acute lung injury induced by hydrochloric acid aspiration in rabbits. Anesthesiology. 1998, 88 (5): 1300-1309.PubMedGoogle Scholar
  87. Makiuchi A, Yamaura K, Mizuno S, Matsumoto K, Nakamura T, Amano J, Ito KI: Hepatocyte growth factor prevents pulmonary ischernia-reperfusion injury in mice. J Heart Lung Transpl. 2007, 26 (9): 935-943.Google Scholar
  88. Yang YL, Tang GJ, Wu YL, Yien HW, Lee TS, Kou YR: Exacerbation of wood smoke-induced acute lung injury by mechanical ventilation using moderately high tidal volume in mice. Resp Physiol Neurobi. 2008, 160 (1): 99-108.Google Scholar
  89. Zeldin DC, Wohlford-Lenane C, Chulada P, Bradbury JA, Scarborough PE, Roggli V, Langenbach R, Schwartz DA: Airway inflammation and responsiveness in prostaglandin H synthase-deficient mice exposed to bacterial lipopolysaccharide. Am J Respir Cell Mol Biol. 2001, 25 (4): 457-465.PubMedGoogle Scholar
  90. Curtis JL, Byrd PK, Warnock ML, Kaltreider HB: Requirement of Cd4-Positive T-Cells for Cellular Recruitment to the Lungs of Mice in Response to a Particulate Intratracheal Antigen. J Clin Invest. 1991, 88 (4): 1244-1254.PubMedPubMed CentralGoogle Scholar
  91. Eveillard M, Soltner C, Kempf M, Saint-Andre JP, Lemarie C, Randrianarivelo C, Seifert H, Wolff M, Joly-Guillou ML: The virulence variability of different Acinetobacter baumannii strains in experimental pneumonia. J Infection. 2010, 60 (2): 154-161.Google Scholar
  92. Barends M, van Oosten M, de Rond CGH, Dormans JAMA, Osterhaus ADME, Neijens HJ, Kimman TG: Timing of infection and prior immunization with respiratory syncytial virus (RSV) in RSV-enhanced allergic inflammation. J Infect Dis. 2004, 189 (10): 1866-1872.PubMedGoogle Scholar
  93. Cimolai N, Taylor GP, Mah D, Morrison BJ: Definition and Application of a Histopathological Scoring Scheme for an Animal-Model of Acute Mycoplasma-Pneumoniae Pulmonary Infection. Microbiol Immunol. 1992, 36 (5): 465-478.PubMedGoogle Scholar
  94. Beck JM, Warnock ML, Curtis JL, Sniezek MJ, Arraj-Peffer SM, Kaltreider HB, Shellito JE: Inflammatory responses to Pneumocystis carinii in mice selectively depleted of helper T lymphocytes. Am J Respir Cell Mol Biol. 1991, 5 (2): 186-197.PubMedGoogle Scholar
  95. Lammers AJ, de Porto AP, de Boer OJ, Florquin S, van der Poll T: The role of TLR2 in the host response to pneumococcal pneumoniain absence of the spleen. BMC Infect Dis. 2012, 12 (1): 139.PubMedPubMed CentralGoogle Scholar
  96. Dong R, Liu P, Wee L, Butany J, Sole MJ: Verapamil Ameliorates the Clinical and Pathological Course of Murine Myocarditis. J Clin Invest. 1992, 90 (5): 2022-2030.PubMedPubMed CentralGoogle Scholar
  97. Hiraoka Y, Kishimoto C, Kurokawa M, Ochiai H, Sasayama S: Effects of Polyethylene-Glycol Conjugated Superoxide-Dismutase on Coxsackievirus B3 Myocarditis in Mice. Cardiovasc Res. 1992, 26 (10): 956-961.PubMedGoogle Scholar
  98. Wang YX, Da Cunha V, Vincelette J, White K, Velichko S, Xu Y, Gross C, Fitch RM, Halks-Miller M, Larsen BR, et al: Antiviral and myocyte protective effects of murine interferon-beta and -alpha 2 in coxsackievirus B3-induced myocarditis and epicarditis in Balb/c mice. Am J Physiol-Heart C. 2007, 293 (1): H69-H76.Google Scholar
  99. Kanda T, Araki M, Nakano M, Imai S, Suzuki T, Murata K, Kobayashi I: Chronic Effect of Losartan in a Murine Model of Dilated Cardiomyopathy - Comparison with Captopril. J Pharmacol Exp Ther. 1995, 273 (2): 955-958.PubMedGoogle Scholar
  100. Rahman A, More N, Schein PS: Doxorubicin-Induced Chronic Cardiotoxicity and Its Protection by Liposomal Administration. Cancer Res. 1982, 42 (5): 1817-1825.PubMedGoogle Scholar
  101. Ghazalpour A, Wang XP, Lusis AJ, Mehrabian M: Complex inheritance of the 5-lipoxygenase locus influencing atherosclerosis in mice. Genetics. 2006, 173 (2): 943-951.PubMedPubMed CentralGoogle Scholar
  102. Bennett BJ, Wang SS, Wang XP, Wu XH, Lusis AJ: Genetic Regulation of Atherosclerotic Plaque Size and Morphology in the Innominate Artery of Hyperlipidemic Mice. Arterioscl Throm Vas. 2009, 29 (3): 348-A318.Google Scholar
  103. Yamada A, Miyazaki T, Lu LM, Ono M, Ito MR, Terada M, Mori S, Hata K, Nozaki Y, Nakatsuru S, et al: Genetic basis of tissue specificity of vasculitis in MRL/lpr mice. Arthritis Rheum. 2003, 48 (5): 1445-1451.PubMedGoogle Scholar
  104. Carter WO, Bull C, Bortolon E, Yang L, Jesmok GJ, Gundel RH: A murine skeletal muscle ischemia-reperfusion injury model: differential pathology in BALB/c and DBA/2N mice. J Appl Physiol. 1998, 85 (5): 1676-1683.PubMedGoogle Scholar
  105. Nascentes GAN, Meira WSF, Lages-Silva E, Ramirez LE: Immunization of Mice with a Trypanosoma cruzi-Like Strain Isolated from a Bat: Predictive Factors for Involvement of Eosinophiles in Tissue Damage. Vector-Borne Zoonot. 2010, 10 (10): 989-997.Google Scholar
  106. Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, Denk H, Desmet V, Korb G, MacSween RN, et al: Histological grading and staging of chronic hepatitis. J Hepatol. 1995, 22 (6): 696-699.PubMedGoogle Scholar
  107. Knodell RG, Ishak KG, Black WC, Chen TS, Craig R, Kaplowitz N, Kiernan TW, Wollman J: Formulation and Application of a Numerical Scoring System for Assessing Histological Activity in Asymptomatic Chronic Active Hepatitis. Hepatology. 1981, 1 (5): 431-435.PubMedGoogle Scholar
  108. Chevallier M, Guerret S, Chossegros P, Gerard F, Grimaud JA: A histological semiquantitative scoring system for evaluation of hepatic fibrosis in needle liver biopsy specimens: comparison with morphometric studies. Hepatology. 1994, 20 (2): 349-355.PubMedGoogle Scholar
  109. Siegmund B, Lear-Kaul KC, Faggioni R, Fantuzzi G: Leptin deficiency, not obesity, protects mice from Con A-induced hepatitis. Eur J Immunol. 2002, 32 (2): 552-560.PubMedGoogle Scholar
  110. VanNieuwkerk CMJ, Elferink RPJO, Groen AK, Ottenhoff R, Tytgat GNJ, Dingemans KP, Weerman MAVB, Offerhaus GJA: Effects of ursodeoxycholate and cholate feeding on liver disease in FVB mice with a disrupted mdr2 P-glycoprotein gene. Gastroenterology. 1996, 111 (1): 165-171.Google Scholar
  111. Nanji AA, Khettry U, Sadrzadeh SMH: Lactobacillus Feeding Reduces Endotoxemia and Severity of Experimental Alcoholic Liver (Disease). P Soc Exp Biol Med. 1994, 205 (3): 243-247.Google Scholar
  112. Curran TJ, Uzoaru I, Das JB, Ansari G, Raffensperger JG: The Effect of Cholecystokinin-Octapeptide on the Hepatobiliary Dysfunction Caused by Total Parenteral-Nutrition. J Pediatr Surg. 1995, 30 (2): 242-247.PubMedGoogle Scholar
  113. Sancho-Bru P, Bataller R, Fernandez-Varo G, Moreno M, Ramalho LN, Colmenero J, Mari M, Claria J, Jimenez W, Arroyo V, et al: Bradykinin attenuates hepatocellular damage and fibrosis in rats with chronic liver injury. Gastroenterology. 2007, 133 (6): 2019-2028.PubMedGoogle Scholar
  114. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, Ferrell LD, Liu YC, Torbenson MS, Unalp-Arida A, et al: Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005, 41 (6): 1313-1321.PubMedGoogle Scholar
  115. Cavalieri B, Perrelli MG, Aragno M, Ramadori P, Poli G, Cutrin JC: Ischaemic preconditioning modulates the activity of Kupffer cells during in vivo reperfusion injury of rat liver. Cell Biochem Funct. 2003, 21 (4): 299-305.PubMedGoogle Scholar
  116. Schmidt J, Rattner DW, Lewandrowski K, Compton CC, Mandavilli U, Knoefel WT, Warshaw AL: A Better Model of Acute-Pancreatitis for Evaluating Therapy. Ann Surg. 1992, 215 (1): 44-56.PubMedPubMed CentralGoogle Scholar
  117. Rongione AJ, Kusske AM, Kwan K, Ashley SW, Reber HA, McFadden DW: Interleukin 10 reduces the severity of acute pancreatitis in rats. Gastroenterology. 1997, 112 (3): 960-967.PubMedGoogle Scholar
  118. Kusske AM, Rongione AJ, Ashley SW, McFadden DW, Reber HA: Interleukin-10 prevents death in lethal necrotizing pancreatitis in mice. Surgery. 1996, 120 (2): 284-289.PubMedGoogle Scholar
  119. Cuzzocrea S, Mazzon E, Dugo L, Serraino I, Centorrino T, Ciccolo A, Van de Loo FAJ, Britti D, Caputi AP, Thiemermann C: Inducible nitric oxide synthase-deficient mice exhibit resistance to the acute pancreatitis induced by cerulein. Shock. 2002, 17 (5): 416-422.PubMedGoogle Scholar
  120. Dembinski A, Warzecha Z, Ceranowicz P, Dembinski M, Cieszkowski J, Pawlik WW, Tomaszewska R, Konturek SJ, Konturek PC: Effect of ischemic preconditioning on pancreatic regeneration and pancreatic expression of vascular endothelial growth factor and platelet-derived growth factor-A in ischemia/reperfusion-induced pancreatitis. J Physiol Pharmacol. 2006, 57 (1): 39-58.PubMedGoogle Scholar
  121. Kanno H, Nose M, Itoh J, Taniguchi Y, Kyogoku M: Spontaneous Development of Pancreatitis in the Mrl/Mp Strain of Mice in Autoimmune Mechanism. Clin Exp Immunol. 1992, 89 (1): 68-73.PubMedPubMed CentralGoogle Scholar
  122. Papaccio G, Nicoletti F, Pisanti FA, Bendtzen K, Galdieri M: Prevention of spontaneous autoimmune diabetes in NOD mice by transferring in vitro antigen-pulsed syngeneic dendritic cells. Endocrinology. 2000, 141 (4): 1500-1505.PubMedGoogle Scholar
  123. Baker BS, Brent L, Valdimarsson H, Powles AV, Alimara L, Walker M, Fry L: Is Epidermal-Cell Proliferation in Psoriatic Skin-Grafts on Nude-Mice Driven by T-Cell Derived Cytokines. Brit J Dermatol. 1992, 126 (2): 105-110.Google Scholar
  124. Kvist PH, Svensson L, Hagberg O, Hoffmann V, Kemp K, Ropke MA: Comparison of the effects of vitamin D products in a psoriasis plaque test and a murine psoriasis xenograft model. J Transl Med. 2009, 7: 107.PubMedPubMed CentralGoogle Scholar
  125. Singer AJ, Thode HC, McClain SA: Development of a histomorphologic scale to quantify cutaneous scars after burns. Acad Emerg Med. 2000, 7 (10): 1083-1088.PubMedGoogle Scholar
  126. Yamamoto T, Takagawa S, Katayama I, Mizushima Y, Nishioka K: Effect of superoxide dismutase on bleomycin-induced dermal sclerosis: Implications for the treatment of systemic sclerosis. J Invest Dermatol. 1999, 113 (5): 843-847.PubMedGoogle Scholar
  127. Watanabe T, Hamada K, Tategaki A, Kishida H, Tanaka H, Kitano M, Miyamoto T: Oral Administration of Lactic Acid Bacteria Isolated from Traditional South Asian Fermented Milk 'Dahi' Inhibits the Development of Atopic Dermatitis in NC/Nga Mice. J Nutr Sci Vitaminol (Tokyo). 2009, 55 (3): 271-278.Google Scholar
  128. Lee HJ, Kim JS, Song MS, Seo HS, Moon CJ, Kim JC, Jo SK, Jang JS, Kim SH: Photoprotective Effect of Red Ginseng against Ultraviolet Radiation-induced Chronic Skin Damage in the Hairless Mouse. Phytother Res. 2009, 23 (3): 399-403.PubMedGoogle Scholar
  129. Hjortsjo C, Saxegaard E, Young A, Dahl JE: In vivo and in vitro irritation testing of low concentrations of hydrofluoric acid. Acta Odontol Scand. 2009, 67 (6): 360-365.PubMedGoogle Scholar
  130. Gubitosi-Klug RA, Talahalli R, Du YP, Nadler JL, Kern TS: 5-lipoxygenase, but not 12/15-lipoxygenase, contributes to degeneration of retinal capillaries in a mouse model of diabetic retinopathy. Diabetes. 2008, 57 (5): 1387-1393.PubMedPubMed CentralGoogle Scholar
  131. Higgins RD, Yu K, Sanders RJ, Nandgaonkar BN, Rotschild T, Rifkin DB: Diltiazem reduces retinal neovascularization in a mouse model of oxygen induced retinopathy. Curr Eye Res. 1999, 18 (1): 20-27.PubMedGoogle Scholar
  132. Ramadan RT, Ramirez R, Novosad BD, Callegan MC: Acute inflammation and loss of retinal architecture and function during experimental Bacillus endophthalmitis. Curr Eye Res. 2006, 31 (11): 955-965.PubMedGoogle Scholar
  133. Caspi RR, Roberge FG, Chan CC, Wiggert B, Chader GJ, Rozenszajn LA, Lando Z, Nussenblatt RB: A New Model of Autoimmune-Disease - Experimental Autoimmune Uveoretinitis Induced in Mice with 2 Different Retinal Antigens. J Immunol. 1988, 140 (5): 1490-1495.PubMedGoogle Scholar
  134. Cousins SW, Guss RB, Howes EL, Rosenbaum JT: Endotoxin-Induced Uveitis in the Rat - Observations on Altered Vascular-Permeability, Clinical Findings, and Histology. Exp Eye Res. 1984, 39 (5): 665-676.PubMedGoogle Scholar
  135. Tyrell J, Silberman H, Chandrasoma P, Niland J, Shull J: Absorbable Versus Permanent Mesh in Abdominal Operations. Surg Gynecol Obstet. 1989, 168 (3): 227-232.PubMedGoogle Scholar
  136. Corona R, Verguts J, Schonman R, Binda MM, Mailova K, Koninckx PR: Postoperative inflammation in the abdominal cavity increases adhesion formation in a laparoscopic mouse model. Fertil Steril. 2011, 95 (4): 1224-1228.PubMedGoogle Scholar
  137. Suga H, Teraoka S, Ota K, Komemushi S, Furutani S, Yamauchi S, Margolin S: Preventive effect of pirfenidone against experimental sclerosing peritonitis in rats. Exp Toxicol Pathol. 1995, 47 (4): 287-291.PubMedGoogle Scholar
  138. Brown NA, Fabro S: Quantitation of Rat Embryonic-Development Invitro - a Morphological Scoring System. Teratology. 1981, 24 (1): 65-78.PubMedGoogle Scholar
  139. Greenhalgh DG, Sprugel KH, Murray MJ, Ross R: Pdgf and Fgf Stimulate Wound-Healing in the Genetically Diabetic Mouse. Am J Pathol. 1990, 136 (6): 1235-1246.PubMedPubMed CentralGoogle Scholar
  140. Braley-Mullen H, Sharp GC, Medling B, Tang HW: Spontaneous autoimmune thyroiditis in NOD.H-2h4 mice. J Autoimmun. 1999, 12 (3): 157-165.PubMedGoogle Scholar
  141. Eckstein P, Jackson MCN, Millman N, Sobrero AJ: Comparison of Vaginal Tolerance Tests of Spermicidal Preparations in Rabbits and Monkeys. J Reprod Fertil. 1969, 20 (1): 85.PubMedGoogle Scholar
  142. Poplawski C, Sosnowski D, Szaflarska-Poplawska A, Sarosiek J, McCallum R, Bartuzi Z: Role of bile acids, prostaglandins and COX inhibitors in chronic esophagitis in a mouse model. World J Gastroentero. 2006, 12 (11): 1739-1742.Google Scholar
  143. Bondarenko A, Hewicker-Trautwein M, Erdmann N, Angrisani N, Reifenrath J, Meyer-Lindenberg A: Comparison of morphological changes in efferent lymph nodes after implantation of resorbable and non-resorbable implants in rabbits. Biomed Eng Online. 2011, 10: 32.PubMedPubMed CentralGoogle Scholar
  144. Johnsen SG: Testicular biopsy score count a method for registration of spermatogenesis in human testes. Normal values and results in 335 hypogonadal males. Hormones. 1970, 1: 2-25.PubMedGoogle Scholar
  145. Hill GR, Crawford JM, Cooke KR, Brinson YS, Pan LY, Ferrara JLM: Total body irradiation and acute graft-versus-host disease: The role of gastrointestinal damage and inflammatory cytokines. Blood. 1997, 90 (8): 3204-3213.PubMedGoogle Scholar
  146. Cooke KR, Hill GR, Crawford JM, Bungard D, Brinson YS, Delmonte J, Ferrara JLM: Tumor necrosis factor-alpha production to lipopolysaccharide stimulation by donor cells predicts the severity of experimental acute graft-versus-host disease. J Clin Invest. 1998, 102 (10): 1882-1891.PubMedPubMed CentralGoogle Scholar
  147. Kaplan DH, Anderson BE, McNiff JM, Jain D, Shlomchik MJ, Shlomchik WD: Target antigens determine graft-versus-host disease phenotype. J Immunol. 2004, 173 (9): 5467-5475.PubMedGoogle Scholar
  148. Murao Y, Loomis W, Wolf P, Hoyt DB, Junger WG: Effect of dose of hypertonic saline on its potential to prevent lung tissue damage in a mouse model of hemorrhagic shock. Shock. 2003, 20 (1): 29-34.PubMedGoogle Scholar
  149. Ahmadi-Yazdi C, Williams B, Oakes S, Moore FD: Attenuation of the Effects of Rat Hemorrhagic Shock with a Reperfusion Injury-Inhibiting Agent Specific to Mice. Shock. 2009, 32 (3): 295-301.PubMedPubMed CentralGoogle Scholar
  150. Mees ST, Gwinner M, Marx K, Faendrich F, Schroeder J, Haier J, Kahlke V: Influence of sex and age on morphological organ damage after hemorrhagic shock. Shock. 2008, 29 (6): 670-674.PubMedGoogle Scholar
  151. Kuper CF, Harleman JH, Richter-Reichelm HB, Vos JG: Histopathologic approaches to detect changes indicative of immunotoxicity. Toxicol Pathol. 2000, 28 (3): 454-466.PubMedGoogle Scholar
  152. Racusen LC, Solez K, Colvin RB, Bonsib SM, Castro MC, Cavallo T, Croker BP, Demetris AJ, Drachenberg CB, Fogo AB, et al: The Banff 97 working classification of renal allograft pathology. Kidney Int. 1999, 55 (2): 713-723.PubMedGoogle Scholar
  153. Billingham ME, Cary NR, Hammond ME, Kemnitz J, Marboe C, McCallister HA, Snovar DC, Winters GL, Zerbe A: A working formulation for the standardization of nomenclature in the diagnosis of heart and lung rejection: Heart Rejection Study Group. The International Society for Heart Transplantation. J Heart Transplant. 1990, 9 (6): 587-593.PubMedGoogle Scholar
  154. Stewart S, Winters GL, Fishbein MC, Tazelaar HD, Kobashigawa J, Abrams J, Andersen CB, Angelini A, Berry GJ, Burke MM, et al: Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J Heart Lung Transpl. 2005, 24 (11): 1710-1720.Google Scholar
  155. Yousem SA, Berry GJ, Brunt EM, Chamberlain D, Hruban RH, Sibley RK, Stewart S, Tazelaar HD: A Working Formulation for the Standardization of Nomenclature in the Diagnosis of Heart and Lung Rejection - Lung Rejection Study-Group. J Heart Transplant. 1990, 9 (6): 593-601.Google Scholar
  156. Gaucher S, Nicco C, Jarraya M, Batteux F: Viability and Efficacy of Coverage of Cryopreserved Human Skin Allografts in Mice. Int J Low Extr Wound. 2010, 9 (3): 132-140.Google Scholar
  157. Moher D, Liberati A, Tetzlaff J, Altman DG: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009, 62 (10): 1006-1012.PubMedGoogle Scholar

Copyright

© Klopfleisch; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.