Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to real world pathology data.The TNM ontology uses the Foundational Model of Anatomy for anatomical entities and BioTopLite 2 as a domain top-level ontology. General rules for the TNM classification system and the specific TNM classification for colorectal tumours were axiomatised in description logic. Case-based information was collected from tumour documentation practice in the Comprehensive Cancer Centre of a large university hospital. Based on the ontology, a module was developed that classifies pathology data.TNM was represented as an information artefact, which consists of single representational units. Corresponding to every representational unit, tumours and tumour aggregates were defined. Tumour aggregates consist of the primary tumour and, if existing, of infiltrated regional lymph nodes and distant metastases. TNM codes depend on the location and certain qualities of the primary tumour (T), the infiltrated regional lymph nodes (N) and the existence of distant metastases (M). Tumour data from clinical and pathological documentation were successfully classified with the ontology.A first version of the TNM Ontology represents the TNM system for the description of the anatomical extent of malignant tumours. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data. The staging procedure compiles several clinical and pathological parameters such as the location and the size of the A prerequisite for an evidence-based cancer treatment is a correct and unambiguous cancer diagnosis. Interdisciplinary expert groups, e.g. from clinical medicine, imaging, and pathology, have been working in close cooperation to establish criteria for precise tumour diagnoses . One of 1. Besides a growing number of reliable biomarkers, TNM classification and staging are the most important information for the therapy planning for patients with colorectal cancer [2 and predefined results using TNM stratified data3).By far, the most important coding system for tumour staging is the Tumour-Node-Metastasis (TNM) classification  for malil cancer –5 and otl cancer  or breasl cancer ), exceptThe TNM coding procedure requires advanced skills, encompassing both experience in tumour documentation and in-depth domain knowledge. The criteria for classification of the different primary tumour locations differ to the same extent as the underlying diseases. As a consequence, even expert coders and physicians for one organ system might encounter difficulties in the correct application or interpretation of TNM in a different organ system. Several combinations of tumour findings are difficult to encode due to ambiguous or overlapping criteria (non-disjoint definitions) or non-exhaustive definitions, which often result in cases where no TNM code or more than one TNM code is applicable to a given tumour state. A variety of problems with TNM coding has been described for different tumour locations. Main issues that arise in the practice of TNM coding derive from overly complex definitions of the underlying medical situation, which then result in interpretation problems even for experts –10. The The complexity of TNM is mainly due to the development of the TNM classification as an evolutionary process , which hDespite its importance and formal precision, to the knowledge of the authors, no formal representation of the complete TNM is available so far. Formal, i.e. computable representations would have several advantages over TNM’s current publication as a textbook. An initial attempt to represent staging of lung tumours and glioma tumours was not continued , 22. MorOne of the major requirements a formal representation of TNM could satisfy is the automatic classification of instance data obtained from clinical databases or mined from textual reports –26. ConsDescription logics is the method of choice for a formalization of TNM . AdvanceWith this work, we propose to close the gap of a missing formal representation by outlining and prototyping the TNM ontology (TNM-O). Following up on initial attempts in the breast cancer domain , the objth edition published in 2009. The AJCC has recently announced the release of the 8th edition of the TNM classification for the beginning of 20174. The part of the new version for lung cancer is already in use with its important changes satisfying urgent medical requirements [The canonical description of the TNM classification based on the anatomic extent of disease (EOD) is published by the UICC and the AJCC , 33. Theirements . The objirements .core TNM classification uses three descriptors: T (tumour), N , and M (distant metastasis). The extent of the disease is indicated by integer values resp. character modifiers: TX (Tumour cannot be assessed), T0 (No evidence of primary tumour), T1-4 , Tis (Carcinoma in situ); NX , N0 , N1-3 ; M0 (No distant metastasis), M1 (Distant metastasis). For some entities further subdivisions of the categories are possible indicated by lower case characters (e.g. N2a and N2b).The 5. It is not possible to list all single regions addressed by the TNM classification here  in 2003. As an extension of the International Classification of Diseases (ICD-10) [The specific medical denotation for the different descriptors is dependent on the localisation of the tumour, designated by the ICD-O localisation codelist see ). Howeve(ICD-10)  for tumo(ICD-10) . ICD-O iclinical (indicated as cTNM) and post-surgical pathological (pTNM) classification. pTNM codes can only be assigned to the disease after pathological assessment following surgery and is the most important diagnostic item for following (adjuvant) radio- or chemotherapy or their combination. The results from the clinical assessment have to be accurately discerned from the pathological assessment due to their different meanings and evidence levels.With an additional modifier, the TNM classification is divided into the pre-treatment carcinoma in situ the suffix “is” has to be used (“Tis”). As TNM allows putting an “X” wherever the information about the clinical or pathological situation is incomplete or inaccurate, incomplete code assignments become widespread (e.g. MX for “no statement on metastases possible”). In this work only the classes with the descriptors T, N, and M with the modifiers c and p are represented  [TNM-O, the TNM ontology presented here, uses the Foundational Model of Anatomy  for anatogy (RO) . They coThe design of BTL2 is top-level agnostic and has been influenced both by the Basic Formal Ontology (BFO and BFO2) and the Descriptive Ontology for Linguistic and Social Engineering (DOLCE) which is discussed in more detail in . BTL2 is6. In prior work, the TNM classification for breast tumours (ICD-O C50) had been formally represented [The development of TNM-O is an ongoing process. For this study, colorectal cancer was chosen as use case for several reasons. It is the third most common cancer worldwide and accounts for 9 % of all cancer incidence , 42, affresented . The selThe general rules of the TNM classification and the specific TNM classification for tumours of the colon and the rectum 8. It classifies breast tumour and colorectal tumour data based on the corresponding TNM ontologies. It reads either tabular input data from files or processes data from manual entry via a graphical user interface.A classifying tool for individuals (instances) derived from pathology reports was developed employing the OWL API (version 4.0.1)The objective of TNM-O is not to re-design an existing tumour classification into a new system. At the current level of development, TNM-O is the result of an ontological analysis of what has been developed by the medical community over a long period, followed by its translation into a formal language, incorporating ontological principles, in order to improve the development, maintenance, and application of the TNM classification system.In the following two sections, we describe (1) the TNM classification in detail as foundation of what has to be represented by TNM-O, (2) how the TNM classification artefacts are represented by information artefacts of TNM-O, (3) how these information artefacts are related to the actual tumour entities, and (4) how the patho-anatomical reality of tumour disease is constructed in terms of what is required for the TNM classification.aboutness relation [btl2:InformationObject as RepresentationalArtefact. Information reported on individual patients, e.g. as TNM-codes in patient records are thus individuals of these classes. Individuals from subclasses of InformationObject are related by btl2:represents to individuals of classes about the current disease state . The inverse relation is btl2:isRepresentedBy connects material or processual entities with the respective TNM-artefact.The relation between the artefacts of the TNM classification and the actual tumour diseases is denotational: the T code denotes the extent  of the primary tumour, the N code the extent of regional lymph node metastases, and the M code the existence of distant metastases. For TNM-O, we adopted an approach which is compliant with the Information Artefact Ontology from the OBO Foundry and recently published work on the relation , 45. In independently combined to a joint code. Classes are only dependent on the location of the primary tumour and additional modifiers c or p: e.g. cN1 for colon cancer has a different meaning than cN1 for breast cancer, and cT1 has a different meaning than pT1 for all locations where these codes are available). This characteristic is conserved in TNM-O. The class RepresentationalUnit is a superclass of organ specific classes separated in a clinical and a pathological branch.As the TNM classification is compositional, the individual classes of the three descriptors can be btl2:locatedIn, thus providing them with an exact topography and extent. The extent of primary tumours cannot only be described by their localisation (i.e. occupying space or infiltrating through layers of an organ) but can be further characterised by qualities, e.g. tumour size or infiltration patterns. These qualities are dependent on the localisation of the primary tumour and can substantially differ between them.For representing anatomical structure, TNM-O uses content from the Foundational Model of Anatomy, restricted to cancer-related anatomy as referred to by the TNM classification. All primary tumours individuals and metastases are then related to individuals anatomical entities by the relation regional lymph node depends on its proximity to a primary organ. An axillary lymph node is a regional lymph node of the breast gland but not of the colon. For all relevant organs, these regional lymph node groups are to be defined. Moreover, the formalisation of infiltrated regional lymph nodes depends on the aggregate of a localised primary tumour together with some metastasis in a regional lymph node of that organ in which the primary tumour is located. Thus, an infiltrated axillary lymph node is a regional lymph node metastasis for a breast tumour, but certainly not for a colon cancer. Distant metastases are, by definition, those located in a tumour aggregate that is not a regional lymph node of the primary tumour.What makes a lymph node a 9. Automatic classification was solely based on axioms defined in the colorectal TNM-O version 7 (TNM-O_colon_7.owl). The complete set of criteria is shown in Table We computationally classified data describing the extent of 291 colorectal cancer specimens into TNM, documented at the Institute of Surgical Pathology, Medical Center – University of Freiburg using a pathology information system. This data were re-coded as RDF-OWL instance data and classified into classes of TNM-O by an application based on the OWL API using an OWL classifierFor comparison of the ontology-based TNM classification with a manual expert TNM classification, the data were manually classified by a pathologist into TNM version 7.TNM-O is designed as a modular system of independent ontologies under BTL2. For every organ or organ system based module of the TNM classification system, TNM-O provides a set of specific ontologies. The TNM connecting ontology serves as a hub to import BTL2 as well as the organ and organ system specific TNM ontologies . In all of these cases, the clause “or ” allows other values that are not TNM representational units. In the remaining text, the namespace of the TNM ontology is suppressed for clarity:The representation of the TNM system is decomposed into the representational units T, N, and M, together with the location of the primary tumour. Thus, for every existing code Tn, Nn, and Mn in combination with a specific organ there exists one TumourOfColonAndRectumWith7OrMoreMetastaticRegional- LymphNodes subClassOfTumourAggregate andbtl2:isRepresentedBy only )PrimaryTumour, a subclass of MalignantAnatomicalStructure. The tumour characteristics relevant for the representational unit T of the TNM classification system are represented as location and qualities of PrimaryTumour. For colorectal tumours, the exact localization of the tumour in the gut wall, the quality of the tumour confinement with respect to neighbouring organs (confined or invasive), the quality of the assessment , are important:The primary tumour is represented as InvasiveTumourOfSubmucosaOfColonAndRectumEquivalentTo ColonAndRectumTumour and (btl2:isBearerOf some (Confinement and (btl2:projectsOnto some Invasive))) and (btl2:isIncludedIn someSubmucosaOfLargeIntestine)PrimaryTumour above is directly related to the corresponding representational unit as introduced in the section above.The specific tumour defined as subclass of InvasiveTumourOfSubmucosaOfColonAndRectumsubClassOfbtl2:isRepresentedBy some (ColonRectumTNM_T1 orColonRectumTNM_pT1) andbtl2:isRepresentedBy only )N, which describes the extent of infiltration of regional lymph nodes by the primary tumour. The anatomy of lymph nodes draining the colon and rectum was modelled according to clinical anatomical conventions. Metastatic regional lymph nodes can exactly be located by the exact subclass of infiltrated regional lymph node:The most complex part of the TNM classification of many primary tumour locations is the interpretation of the axis MetastaticLymphNodeOfColonAndRectumTumourEquivalentTo LymphNode and (btl2:hasPart someMetastasisOfColonAndRectumTumour)MetastaticRegionalLymphNodeOfColonAndRectumTumourEquivalentToMetastaticLymphNodeOfColonAndRectumTumour andColonAndRectumRegionalLymphNodeTumourAggregate) has to be considered as one (composite) entity. The representational unit N of the TNM classification of colorectal cancers depends on the count of metastatic regional lymph nodes and the presence of subserosal tumour deposits without regional lymph node metastases. The count of metastatic lymph nodes is represented by subclasses of CardinalityValueRegion:To define regional lymph node metastases of colorectal cancers, the aggregate of primary tumour and infiltrated lymph nodes around the colon and rectum  and ))M of the TNM classification system the existence and number of distant metastases are evaluated. The definition of distant metastases excludes regional lymph nodes as their localisation:For the representational unit DistantMetastasisOfColonAndRectumTumour EquivalentToMetastasisOfColonAndRectumTumour and )TumourOfColonAndRectumWithDistantMetastasisEquivalentToTumourOfColonAndRectumAggregate and (btl2:hasPart someDistantMetastasisOfColonAndRectumTumour)TumourOfMammaryGlandWithDistantMetastasissubClassOf )A posteriori comparison of the automatic classification results with a manual TNM coding based on the same findings from the pathology database by an experienced pathologist showed 100 % agreement. Table All instance data of 291 samples of colorectal cancer could be classified into classes of TNM-O on colorectal cancer. TNM is a globally accepted system to describe the anatomical extent of malignant tumours , 14. AltOver time, TNM has developed into a coding system, which had to accommodate both the pragmatics of coding and representational accuracy. The literature on ambiguities and difficulties of TNM in practice is abundant. The discussion of TNM for breast tumours illustrates the dilemma of its maintainers , 47, 48.Encoding clinical conditions using TNM as well as the selection of the right treatment according to TNM codes is daily routine in oncology. In order to assist in these difficult and time consuming decision processes, several systems have been proposed, usually based on text extraction from pathology reports and machine learning algorithms –26. The first version of the TNM Ontology (TNM-O), limited to mammary gland [This study is limited as far as we provide here a ry gland  and coloDue to the nature of the domain and the rich top-level ontology employed, the computational resources needed to classify the ontology are considerable. In order to alleviate performance issues, TNM-O will be provided as modules for different organ systems. Thus, the users can import only the modules of interest into their application context.Future research should evaluate the presented prototype ontology (i) by implementing further tumour locations, and (ii) by systematic application in clinical classification and retrieval scenarios. We will provide the formalization of TNM for other primary tumour locations in a modular way, so that users can select which part of the TNM-O they would like to use. In this way, we hope to reduce the computational resources already needed to a minimum.We presented a first version of an ontology (TNM-O) that represents the TNM tumour classification system. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data. This work provides a foundation for an exhaustive TNM ontology.1http://www.uicc.org2http://seer.cancer.gov/seerstat/databases/ssf/3http://seer.cancer.gov/csr/1975_2013/sections.html4https://cancerstaging.org/About/news/Pages/8th-Edition-Publication-Date-Announced.aspx5http://codes.iarc.fr/usingicdo.php6http://www.who.int/cancer/modules/en/7http://owlapi.sourceforge.net/8http://hermit-reasoner.com/9http://owlapi.sourceforge.net/


CorrectionAfter publication of the original article  the authThe original manuscript reads:Funding:This study was funded by the Korea Ministry of Environment (MOE) as “the Environmental Health Action Program” (grant number: KMIPA2015–8016).However, the following statement should have been included at the time of publication:Funding:This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMIPA2015-8062.


Once an eye with retinoblastoma is excised, accurate histopathological staging is essential in order to determine whether the child can leave the hospital completely cured, or may need chemotherapy or radiotherapy.Surgical treatment for retinoblastoma is limited to enucleation  or exenteration . Histopathological examination of such specimens yields important information which must be taken into account when considering prognosis and making decisions about future management and treatment.http://bit.ly/RBdatasetWhen histopathologists are dealing with cancer specimens, their reports should include standard and appropriate information items, commonly known as a dataset. In the UK, histopathologists use datasets produced by the Royal College of Pathologists. Data items are reviewed every two years and aligned with the international tumour, node, metastasis, (TNM) classification. The TNM classification for retinoblastoma has recently been updated from TNM7 to TNM8. The newest dataset for retinoblastoma from the Royal College of Pathologists (RCPath), released in early 2018, reflects the changes in the TNM classification. Visit The RCPath dataset for retinoblastoma records whether tumour is present in specific structures. Presence of tumour in any of these structures is considered a histological high-risk feature (HHRF). Retinoblastoma specimens with HHRFs indicate that the patient is at higher risk for metastasis (tumour spread) than a patient whose specimen does not have such features.anterior chamberiristrabecular meshworkSchlemm's canalciliary bodychoroid (above a certain threshold)scleraextraocular structuresretrolaminar optic nerve (including the cut end).Detection of HHRFs by the histopathologist influences management decisions. The histopathologist examining the specimen should therefore specifically seek such features. The structures where tumour presence is regarded as an HHRF are:The RCPath dataset for retinoblastoma includes recommendations for macroscopic examination and sampling of globe and exenteration specimens. Even if resources are limited, it is useful to sample the cut end of the optic nerve separately  as well as a ‘PO block’ of the globe (see panel). If the globe is being opened fresh to sample tumour for cytogenetics, the optic nerve should be sampled first. This will avoid artefactual contamination.The sampled optic nerve should be embedded transversely, so that a full cross-section can be evaluated. It can either be embedded on the true resection margin (surgeon's cut end) or the pathologist's cut end, as long as the orientation is known to the reporting pathologist.www.cehjournal.org.For further information on what to look for microscopically (with figures), read this article on This is a slice which includes the cornea, pupil and optic disc. It is typically obtained by making two parallel slices from anterior to posterior, one either side of the limbus. The orientation of the parallel slides may be sagittal, transverse or oblique, depending on the location of the tumour and any other pathology to be sampled.


Gliomas are the most frequent type of primary brain tumours. Low grade gliomas  may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas . In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process. Gliomas are the most frequent type of brain tumours, as they represent approximately 30% of all central nervous system tumours and about 80% of all malignant brain tumours . The terLGGs are incurable primary tumours, usually occurring in frontal and temporal lobes. Typically LGG patients present only epileptic seizures, while other symptoms  are less common. Usually LGGs are slowly-growing, infiltrative tumours, but the prognoses for LGGs patients are diverse. Some of those tumours grow very slowly for years, while others progress rapidly causing major neurological deficits and subsequent death. Because of the unpredictable clinical course, treatment strategies for LGG vary from the “wait and see” approach to gross total resection followed by immediate radiotherapy or chemotherapy. Reports –5 providMost LGG patients die due to the transformation of the tumour into a higher grade one, which is a process known as malignant transformation, anaplastic transformation or malignant progression. Median survival of LGG patients is between 5 and 10 years , comparee.g. [The time of occurrence of malignant transformation differs among patients. The results vary among clinical studies with a 5-year malignancy-free survival rates  from 30 to 70% –19. There.g. , 21. Rade.g. .It was reported that LGGs displaying preoperative contrast enhancement had a significant increased risk of recurrence , thus the.g. [There has been a lot of activity on mathematical modelling of LGGs in the last years, e.g. –33. LGGse.g. .Our mathematical model describes the change of the tumour cell density in time and space due to the interplay of net proliferation and net diffusion of cancer cells, as in some previous works , 36.It has been suggested that malignant transformation of LGGs may be induced by a high cell density focus , 37–39. Lcrit initiating a non-reversible damage to the microenvironment [Thus we base our model of malignant transformation on the assumption that the first step in this phenotypic transition is the growth of the tumour density beyond a certain critical level ironment . Beyond ironment , 43 and τ to acquire a more aggressive high grade behaviour.Malignant transformation cannot be reversed, once the transformation is triggered cells can not change their phenotype to a less aggressive one because of the accumulation of new mutations. We assume that after the onset of malignant transformation cells take some time ρL, DL and ρH, DH for LGG and HGG, respectively).Cells before and after transformation differ in dynamic properties, which is reflected in the model by different proliferation and motility rates  cells is described by a function τ and having the following form:T is the total tumour density, Lcrit is the density of tumour cells triggering malignant transformation and Δcrit is the width (or sensitivity) of the switch function. In what follows, we express Lcrit in terms of the maximal cellular density K, that is Lcrit = βK for some β ∈ .The density of LGG cells is described by a non-negative function h0 at the centre of the tumour x = 0, i.e.σ is a measure of the spread of LGG cells.As in  we assumK is estimated by taking the typical astrocyte size to be about 10 μm in diameter leading to a value 108cells/mm3 [The maximal tumour density ells/mm3 , 46.ρL, DL and ρH, DH quantify the overall biological aggressiveness of gliomas growth, e.g. proliferation rates ρL and ρH are based on the observable values of tumour cells doubling times. We assume LGG proliferation rate ρL to be larger than 0.0001/day, which is a value 10 times smaller than the smallest proliferation rate observed in study of Gerin et al. [ρL we take a value 0.008/day, which is the smallest value of proliferation rate observed for HGGs [2/day. These values are, respectively, around three times smaller than minimal and three times larger than the maximal values for LGG diffusion coefficients estimated in [e.g. [2/day. These values are close to minimal and maximal diffusion rates estimated in [The parameters n et al. . As an ufor HGGs . The difmated in . For HGGin [e.g. , 45 and mated in .Lcrit triggering microenvironment damage and the malignant transformation is taken to be around 60% of the carrying capacity K in agreement with previous estimates [crit is arbitrary chosen to be 5%.The value of critical density stimates , 30. Theτ needed for a high grade tumour to arise corresponds to the time required for the development of hypoxia in the presence of a high cellularity, the generation of transient hypoxic events leading to the development of more aggressive phenotypes and higher genetic instability leading to new mutations. Since typical angiogenesis times are of the order of 1-2 weeks we can estimate τ to be of the order of a few months. Thus, we assume that typical values of τ should be in the range 100-200 days.The time h0 leading to relevant symptoms and disease detection is difficult to estimate as it can vary a lot depending on the tumour location. The normal physiological value of cellularity of brain tissue is around 10-15%. LGGs are characterised, among others, by an increased cellularity with respect to the healthy brain tissue. We will assume that the minimal mean density leading to glioma diagnosis is around 0.3K as in [h0 to be no smaller than 0.3K. On the other hand, as we consider only tumours before transformation, see K, which corresponds to minimal density causing the onset of LGG cells transformation as discussed previously.The tumour cell density 3K as in . It meanA retrospective study of the volumetric growth of LGGs was developed to verify the potential of the mathematical model to describe the malignant transformation. Initially, for the presented study 82 patients diagnosed with LGG and followed with MRI scans at the Bern University Hospital between 1990 and 2013 were considered. The study was approved by Kantonale Ethikkommission Bern , the approval number: 07.09.72. The data was analysed anonymously.The criteria for inclusion of the patients into study were: (i) first biopsy/surgery confirmed LGG , (ii) second biopsy/surgery confirmed HGG , (iii) availability of at least 5 MRI scans before the histological confirmation of the malignant transformation, (iv) no treatment given in the period of study and (v) no decrease of total tumour size observed in the absence of treatment. Among all considered patients, 32 had confirmed malignant transformation and 8 satisfied all of the inclusion criteria of the study. D1, D2, D3) according to three reference orthogonal planes  were measured and the tumour volumes were estimated using the ellipsoidal approximation: V = (D1 ⋅ D2 ⋅ D3)/2, following the standard clinical practice [V using the equation MTD = (2V)1/3.Radiological glioma growth was quantified by the measurements of the tumour diameter on successive T2 (or FLAIR) MRI scans. The three largest tumour diameters  were fitted at once with starting point chosen visually.ity  to 2.1944 years (for ρH = 0.08/day). For the typical value of ρH = 0.042/day (see ρL = 0.001/day) to 1.0556 year (for ρL = 0.008/day). This is an expected outcome of the model since in previous works [For the mean value of proliferation rate /day see  virtual us works , 57, 58 e.g. spots of contrast enhancement on T1+Gd MRI scans). Although an inclusion of treatment into the model and further analysis are needed, our results indicate that it is better to use more aggressive intervention earlier, trying to prevent malignant transformation than to wait and treat already transformed and fast-growing tumour cells. One can base the treatment decisions on the estimates of the tumour aggressiveness and potential time to malignant transformation which can be derived from imaging [This is an interesting result which can have an influence on treatment planing since in many cases more intensive therapies such as radiotherapy or even significantly less toxic chemotherapy are reserved until there are signs of progression  ≥ d. Then it is detectable for all positive times ifu is increasing ifr with respect to time we obtain the tumour growth velocity:t ≪ 1 and when t ≫ 1. First we investigate the long time behaviour of Eqs t we obtainρt). This means that for large times lack of restriction on the density leads to slower tumour growth. This might seem a surprising result, but in fact it not so much. Although density limitation due to the competition slower cell division it also forces cells to move to the area with less density leading to faster increase in tumour radius.The virtual tumour is detectable at time t ≪ 1 we expand the right-hand side of t = 0. We havet and using h0/d) we arrive att → 0:r0 or, being precise, on the term ln(h0/d). In particular for σρ > D there exists a ratio h0/d for which this velocity is minimal.The behaviour of the radius and velocity of tumour for small times is even more important. The maximal tumour density is relatively small and we expect better agreement of the results with the full model. In order to obtain asymptotic approximation of the tumour radius for t → 0 the value of tumour radius and tumour growth velocity tend to r0 and It is easily seen that for Here we intend to provide some analytical estimates for the onset of malignant transformation as we believe that knowing the approximated time of this transformation could help in making clinical decisions .tOMT. However instead of considering partial differential equations, we would like to obtain algebraic formula feasible to solve in common programmes like Microsoft Excel, available in clinics.The onset of malignant transformation can be estimated from numerical simulations of tOMT, S of the onset of malignant transformation as the time when the LGG cell density hits the value Lcrit − Δcrit. Let us recall that h0 < Lcrit − Δcrit, see Sec. Typical values and ranges of the model parameters. As function u attains its maximum in x = 0, we will calculate tOMT in the following way:tOMT. The solution of Based on tOMT strongly depends on tumour density at center, that is where the cellular density is highest. For sufficiently small LGG cell diffusion coefficient we can approximate the evolution of tumour density at x = 0 by the logistic equation instead of solving nonlinear partial differential equation. Thus, we consider L ≤ u(t), where u is a solution totOMT analytically asWe observe that We have computed estimates of the onset of malignant transformation for first six patients for which the occurrence of malignant transformation was observable radiologically in tumour size, see In this paper we addressed the process of malignant transformation of low-grade gliomas, what is the main reason for the disease lethality. The early detection of malignant transformation could improve the therapeutical management and prevent the misdiagnosis of actual tumour grade. It can have major therapeutic implications, namely the tumour with undetected malignant transformation would be treated less aggressively than necessary and as a result an increase in mortality will occur.et al. in [To our knowledge up to now the only mathematical model accounting for the LGG malignant transformation was formulated by Swanson t al. in . In thatRecently it has been hypothesised that malignant transformation may be triggered by the change of the tumour microenvironment due to the elevation of the cell density in a specific tumour areas , 33, 39.In this paper for the first time we try to use this concept in a quantitative way to describe the full process of the malignant transformation from a LGG to a HGG. We describe this process in a minimal way using a model of two coupled FKEs in which total tumour density is a driving force of phenotypic change. Interestingly, the model is able to reproduce the main features of the transition from low grade into high grade glioma. We presume that in our mathematical framework we can treat the tumour consisting of both LGG and HGG cells see  as WHO gpost-hoc estimates of the onset of malignant transformation using the values of fitted parameters for individual patients. All estimated onsets of malignant transformation appears in medically viable time period, however there have been a significant delay from these times to the visible change in the velocity of tumour radius growth suggesting that there is a visible increase in detectable tumour size when the already transformed cells form the substantial part of the tumour. Importantly, the obtained values do not overestimate medically confirmed malignant transformation time. Thus, we can interpret the estimated values as the earliest times when malignant transformation could occur.We were also able to fit the model to the retrospective volumetric data of LGG patients who underwent malignant transformation and obtained a very good agreement. Based on this results we can treat our new model as a first step to the investigation of malignant progression as a function of patient-specific coefficients. We suggest combining the use of the early imaging and the results of mathematical modelling. To be specific, first we simplified our model of evolution of LGGs and obtained analytical equations for tumour radius and velocity before the onset of malignant transformation. As a result, we were able to provide an early approximation of the onset of malignant transformation based on the patient-specific parameters. Although e.g. a standard linear regression method. Subsequently, having parameters describing initial LGG density and tumour growth rates, the estimate of malignant transformation time can be computed from et al. in [e.g. [Let us note that the estimated time of onset of malignant transformation depends crucially on three biologically relevant parameters: proliferation rate, motility rate and mean initial density. The last one is essential to estimate in an unambiguous way the time of malignant transformation before it occurs. In general, we would like to provide predictions of time to malignant transformation for individual patients using data of only few medical examinations (MRI scans). However, our research shows that in order to do so, we would need not only volumetric data, but also imaging data at least from the first diagnostic MRI, based on which an initial tumour density could be estimated. Clearly, the proliferation and diffusion rates for LGG cells could be estimated from a few MRIs (possibly three) using t al. in  using thin [e.g. , estimatin [e.g.  as well in [e.g. , 23, 65.in [e.g. , 23, 65.Using such approach, the results obtained from imaging data and fitting the model opens the doors for the treatment personalisation. The estimations of the time of malignant transformation can possibly assist in selecting the best treatment. In particular, cases of fast-growing tumours or those which initial density was found to be significant should be followed with imaging thoroughly and early treatments strategies should be taken into account. One could consider applying treatment in the time of predicted malignant transformation. By eradicating glioma cells either by surgery or chemotherapy, the tumour cells density will be reduced resulting in prolonged malignant transformation-free survival and as a consequence overall survival, as well. In the future we would like to address the problem of optimal therapeutical schedulings taking into account the results of this study. In order to do so, the mathematical model may be improved by inclusion of more biological details.We also believe that further understanding of the dynamics of the malignant transformation of LGGs may enable the development of more effective treatment strategies aimed at prolonging recurrence and delaying the arising of malignancy. Thus, further studies aimed at improving the understanding of the evolution of the malignant transformation, coupling multimodal imaging with mathematical models and studying the impact of optimised therapeutical schedules on the time to malignant transformation are necessary.S1 FileContaining data of LGG patients followed at Bern University Hospital selected in the study, see also Sec. Patients data.(XLSX)Click here for additional data file.


Involvement of the immune system in tumour progression is at the forefront of cancer research. Analysis of the tumour immune microenvironment has yielded a wealth of information on tumour biology, and alterations in some immune subtypes, such as tumour-associated macrophages (TAM), can be strong prognostic indicators. Here, we use optical tissue clearing and a TAM-targeting injectable fluorescent nanoparticle (NP) to examine three-dimensional TAM composition, tumour-to-tumour heterogeneity, response to colony-stimulating factor 1 receptor (CSF-1R) blockade and nanoparticle-based drug delivery in murine pulmonary carcinoma. The method allows for rapid tumour volume assessment and spatial information on TAM infiltration at the cellular level in entire lungs. This method reveals that TAM density was heterogeneous across tumours in the same animal, overall TAM density is different among separate pulmonary tumour models, nanotherapeutic drug delivery correlated with TAM heterogeneity, and successful response to CSF-1R blockade is characterized by enhanced TAM penetration throughout and within tumours. Tumour-associated macrophages (TAM) can be used as prognostic indicators in cancer. Here, the authors establish a platform for high-throughput 3D microscopy in murine lung carcinoma that allows to visualize TAMs infiltration throughout the entire lung, response to CSF-1R blockade and nanoparticle drug delivery. Moreover, tumour biology can vary between the surface and the centre of a single nodule with regard to proliferating and quiescent cells that may affect TAM distributionTumour microenvironments often include vast numbers of seemingly normal host cells, including a diverse immune cell population, which can potently regulate cancer progression1346783 (and often >125 mm3)172 and a depth of ∼100 μm before resolution is degraded by light scattering212223Imaging represents a unique approach to visualize tumours in three-dimensional (3D) and is typically done at two extremes: (i) the whole body level, covering entire organs, yet with the inherent limitation that minimum detectable tumour size is typically >1 mmex vivo molecular dyes, and intravenously delivered labels, we were able perform whole-organ tumour burden, host-cell analysis and drug-delivery assessment within days. We were specifically interested in addressing the following questions: (i) can tissue clearing and fluorescence microscopy be used to measure tumour burden with sufficient sensitivity; (ii) what is the heterogeneity of TAM infiltration across metastatic lung tumoursDriven by the biological question of how heterogenous TAM recruitment is to lung adenocarcinoma, we set out to develop a rapid whole-lung imaging protocol that reveals details on macrophage infiltration throughout the entire intact mouse lung. Furthermore, our approach maintains the ability to image cellular detail at desired locations throughout all lobes. Using preserved fluorescent protein, 3 than healthy brain tissue27While computed tomography (CT) offers noninvasive detection of tumour nodules in the lung of live animals , resolutTo image pulmonary carcinoma, we applied the clearing and imaging technique in a lung adenocarcinoma model, in which tumour cells from a KRAS and p53 mutant mouse (KP) were injected intravenously29303 tumour, × 10) so as to contextualize tumour, TAM and vascular architecture. We found that 2–6-mm-thick sample chambers were advantageous for high-resolution spot imaging, since a thin chamber allows the lobes to spread apart such that and any tumour in the chamber can be imaged with minimal distortion, scattering or absorption penalties through thicker tissues. Direct comparison of unrestricted and 2 mm chambers yielded similar results  when only a few cancer cells were present , and about half were alternatively activated M2-like macrophages (n=12) of the NP+ signal is due to CD11b+ CD11c+ F4/80+ TAM, and the remainder due to other phagocytes. Flow cytometry indicated that NP-positive cells were predominantly CD11b+ CD11c+ F4/80+ Ly6C− and thus resembled TAM phenotypically . Using this relationship, fluorescence density in other wide-field images was then used to infer cellular TAM density.Building on previous reports of macrophage-selective uptake of dextran-coated iron oxide NPs35er mouse  model torophages . Neutropypically . In clea R=0.89, . Computassed GFP . Thus, Nssed GFP . A conve−3 within a tumour, but this varied sevenfold among tumours of a similar size. In other words, across 1 mm3 tumours, TAM density varied from 8,000 to 60,000 in other similarly sized nodules. No discernible pattern was found in anatomic location or vascular features that predicted TAM density. We extended this analysis to a Lewis lung carcinoma model−3 tumour P<0.0001; KP-GFP: 21,000 TAM mm−3 tumour P=0.0001; one-way analysis of variance) than in the non-GFP KP model , the average number of pulmonary metastases was 44 and ranged from 29 to 57. To expand the analyses, we investigated several hundred tumour nodules in 21 different mice . For exaKP model . In sum,in vivo, tumours were grown for 21 days and mice were then treated with 30 mg kg−1 PLX3397 i.p. for 7 days. Lungs were then prepared for imaging.The innate immune composition of the tumour microenvironment has emerged as an attractive therapeutic target and efforts are underway to modulate TAM numbers or molecular phenotypes to control cancer progression3113, whereas it was ∼93 mm3 in the control group . Correlating tumour volume with TAM density for all tumour nodules ; however the average TAM density among tumours remained unchanged from the 16,500 TAM mm−3 observed in the untreated cohort , likely reflecting a response to tumour cell death and repair. These data indicate that CSF-1R inhibitors can effectively control lung cancer progression without affecting TAM density in whole tumours, but can profoundly alter TAM spatial distribution within lesions and/or their polar phenotype414243 nodules  revealedd cohort  revealedd cohort  averagedco-glycolic acid)-b-polyethyleneglycol (PLGA-b-PEG) and the co-encapsulated fluorescent PLGA-BODIPY-TMR. Drug-loaded NPs were intravenously injected 18 h before killing. 2= 0.94; Prior research has shown that nanoencapsulated chemotherapeutics accumulate in cancers via the enhanced permeability and retention effect and can thus potentiate efficacy, while minimizing systemic toxicities3135To achieve optimal imaging from cleared tissue in orthotopic tumour sites, we experimented with several different methods of labelling cellular compartments: genetic reporters (different fluorescent proteins), intravenously injected intravital stains  and post-clearing staining  . Our dat48By applying the clearing method to whole lungs, we were able to quantify lung tumour burden and observe TAM characteristics, such as density and tumour infiltration. This enabled detailed visualization of TAM and revealed notable heterogeneity in TAM density among lung tumour nodules in a single mouse. We quantified changes in tumour burden and TAM density in a KP pulmonary tumour model, as well as a GFP-labelled KP model in response to CSF-1R blockade . We alsoThe described method could also be adapted to other tumour and organ models and be expanded to other fluorescent protein reporter cells and mice515354565258Post-clearing labelling techniques are constantly advancing with antibodies24n=21), B6.129 P-Cx3cr1GFP/+ (n=1) and NOD.CB17-Prkdcscid/MERTKGFP/+ (n=1). All protocols were approved by the institutional IACUC review board. Mice were 5–7-week-old females at the time of tumour implantation. The tumour models consisted of the mutant Kras/P53 models29LSL G12D/+; p53fl/fl mice following infection with an adenovirus expressing Cre recombinase by intratracheal administration. KP cells were maintained in Iscove's DMEM media supplemented with 10% fetal bovine serum and 5% penicillin/streptomycin and all cell lines were routinely tested for contamination using mouse antibody production testing (VRL Laboratories) and assaying for mycoplasma . For tumour implantation, cells were injected into unanaesthetized B6 mice via tail vein injection of a single bolus of 2.5 × 106 cells in 100 μl PBS. Parental KP1.9 cells were also transduced in vitro with a lentiviral vector expressing dLNGFR and eGFP using a bidirectional promoter system5 cells were injected into B6 mice. Animals were killed and lungs harvested for imaging between 12 and 40 days. LLC cell line was purchased from the ATCC , which was administered i.p. at 30 mg kg−1 daily for 7 days. No exclusion criteria were applied.All lung clearing experiments were conducted in C57BL/6 mice  was then perfused through the right ventricle at the same rate. The lungs were then inflated with 4% FA (2 ml) through the trachea. The lungs were then removed, placed in a conical tube containing 15 ml FA, and allowed to fix for an additional 3 h at room temperature (rt), with occasional application of vacuum to degas the lungs. The lungs were transferred to a glass vial containing 10 ml CUBIC solution and rocked for 24–48 h at 37 °C. Lungs were then mounted in fresh CUBIC solution between glass slides. Cleared lungs were stored at 4 °C with little loss in GFP or VT680 signal or resolution over several weeks. Nuclear labelling was performed with 4′,6-diamidino-2-phenylindole, dihydrochloride (DAPI) (Life technologies) or SYTO13 (Thermo Scientific) and airways were labelled with streptavidin-Alexa Fluor 750 (Thermo Scientific).The TAM-targeting NP (Ferumoxytol-VT680XL) was injected by tail vein 18 h before being killedCUBIC reagent 1 solution: Water (145 ml) was added to a flask containing quadrol ethylenediamine  followed by addition of Urea . The mixture was placed on a heater/stirrer and warmed to about 50 °C until the solution became homogeneous. After cooling to room temperature, Triton X-100  was then added. The solution was then degassed under sonication and then vacuum. Lung tumour images acquired in CUBIC reagent 1 solution were determined to be equivalent to those acquired in the sucrose/urea-based CUBIC reagent 2 solution.2x–y coverage, and was suitable for two-photon imaging. × 20 NA 0.95 or × 40 NA 1.15 water objectives were also used when higher resolution was desired. All images were acquired in sequential mode and acquisition of stacks was started at maximum depth to offset bleaching effects. A 1 mm3 tumour could be imaged at cellular resolution  in ∼10 min. Wide-field grids were acquired from both sides of the lungs in about 13 min per side. Grids were assembled in Image J, when necessary, mediastinum was digitally removed for presentation  between two microscopy grade slides , filled with clearing media, and sealed with silicone sealant .3D renderings and movies were obtained using Bitplane Imaris and FEI Amira software. Two-dimensional images and co-localization plots were generated in Imagej. Cell counting in two-dimensional in cleared tissue and histological sections was performed with CellProfiler and in 3D with Bitplane Imaris. GraphPad Prism was used for graph generation and statistical analysis. Box plots are illustrated such that the box represents the 25th–75th percentile and whiskers represent the full range. Groups of tumours and animals were compared with an unpaired t-test.−1 collagenase type I  in RPMI-1640 for 30 min at 37 °C. Digested samples were filtered ; washed in PBS with 0.5% BSA and 2 mM EDTA; incubated with Fc-block  for 15 min at 4 °C; and labelled with antibodies as indicated for 45 min at 4 °C. Flow cytometry  labelled tumour cells (CD45− EpCAM+), TAM (CD45+ CD11b+ Ly6C- Lin- CD11c+ F4/80+), lymphocyte-like cells (CD45+ CD11b- Lin+), along with CD45- EpCAM- host-cell populations. Antibodies included EpCAM ; CD45 , F4/80 , CD11c , Ly6C ; and CD11b . The lineage (Lin) antibody mix contained anti-CD90.2 (clone 53–2.1), anti-B220 (clone RA3-6B2), anti-NK1.1 (clone PK136), anti-CD49b (clone DX5), anti-Ter119 (cloneTER-119) and anti-Ly6G (clone 1A8) . 7-aminoactinomycin D  excluded dead cells. VT680 fluorescence was directly assessed using the LSRII flow cytometer, FlowJo v.8.8.7  and MATLAB.As described previouslyTumour-bearing mice were injected with the fluorescent NP 18 h before killing. In the same manner as for clearing, mice were mice were anaesthetized with isofluorane and perfused with PBS followed by 4% FA through the right ventricle. Lungs were further fixed for 3 h, washed with PBS and placed in 30% sucrose in PBS overnight. The lung tissue was embedded in O.C.T. compound (Sakura Finetek) and snap-frozen in a 2-methlbutane bath on dry ice. Serial 20 μm thick sections were prepared and stained with CD68  and CD206  followed by biotinylated anti-rat IgG (1:100) and streptavidin-DyLight 594 . KP 1.9 and RAW 264.7 cells were seeded overnight at 20,000 cells per well in an eight-well glass slide (Millipore). Cells were fixed in 4% FA for 15 min at rt, fixative was quenched with glycine and cells were permeabilized with 0.5% Tween-20 for 5 min at rt. Cells were blocked for 30 min with Odyssey blocking buffer (Licor) and incubated with anti-CSF-1R-Alexa Fluor 594 antibody conjugate  in Odyssey buffer for 1 h. Cells were washed with PBS for 1 h. To identify nuclei, the sections were counterstained with 4′,6-diamidino-2-phenylindole, dihydrochloride (DAPI) . For analysis, the images were captured by using a fluorescence microscope (Olympus BX63) equipped with Neo sCMOS Monochrome Camera (ANDOR).−1 dose of DTX was injected intravenously. After 18 h, the animal was killed and prepared for clearing.Using a formulation previously describedThe data that support the findings of this study are available from the corresponding author upon reasonable request.How to cite this article: Cuccarese, M. F. et al. Heterogeneity of macrophage infiltration and therapeutic response in lung carcinoma revealed by 3D organ imaging. Nat. Commun.8, 14293 doi: 10.1038/ncomms14293 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary FiguresVolume rendering of 1 mm3 tumor, acquired at 10x.Volume rendering of tumor and airway, acquired at 20x.Volume rendering of a whole tumor-bearing lung.


The aim of the present study was to examine the relationship between tumour cell expression of total and phosphorylated STAT1 (ph-STAT1) and STAT3 (ph-STAT-3), components of tumour microenvironment and survival in patients with invasive ductal breast cancer.Immunohistochemical analysis of total and ph-STAT1, and STAT3 were performed on tissue microarray of 384 breast cancer specimens. Tumour cell expression of STAT1 and STAT3 at both cytoplasmic and nuclear locations were combined and identified as STAT1/STAT3 tumour cell expression. These results were related to cancer specific survival (CSS) and phenotypic features of the tumour and the host.P≤0.001) and PR (both P <0.05), reduced tumour grade (P=0.015 and P<0.001 respectively) and necrosis (both P=0.001). Ph-STAT1 was associated with increased general inflammatory infiltrate (P=0.007) and ph-STAT3 was associated with lower CD4+ infiltration (P=0.024). In multivariate survival analysis, only high ph-STAT3 tumour cell expression was a predictor of improved CSS (P=0.010) independent of other tumour and host-based factors.High ph-STAT1 and ph-STAT3 tumour cell expression were associated with increased ER (both STAT1 and STAT3 tumour cell expression appeared to be an important determinant of favourable outcome in patients with invasive ductal breast cancer. The present results suggest that STAT1 and STAT3 may affect disease outcome through direct impact on tumour cells, counteracting aggressive tumour features, as well as interaction with the surrounding microenvironment. Breast cancer is the most frequent malignancy in women, accounting for 25% of all female cancers, with more than a million women diagnosed annually. It is also the leading cause of cancer death in women with >500,000 deaths in 2012 worldwide [Components of tumour microenvironment, including tumour stroma and tumour inflammatory cell infiltrates are now recognised to play a key role in cancer progression and survival, and represent interactions between the tumour and the host , 3. HoweThe IL-6/Janus-activated kinase can trigger tyrosine phosphorylation of both STAT1 and STAT3 through homo- or hetero-dimerization of the signal transduction subunit gp130 , 8, howeSTAT1 and STAT3 have a complex interaction with both tumour cells and the tumour microenvironment including immune infiltrates such that STAT1 and STAT3 are thought to play opposite roles in tumorigenesis, regulating distinct gene signatures . STAT1 hDespite the fact that several experimental studies suggest that STAT1 and STAT3 play a critical role in breast cancer tumorigenesis, the prognostic value of these proteins in patients with breast cancer remains unclear. Five studies have examined the prognostic value of STAT1 in breast cancer, using either total STAT1 or phosphorylated STAT1 (ph-STAT1)  Table . High toAlso, commensurate with their role in regulating cytokine-dependent inflammation and immunity, the relationship between STAT1 and STAT3 and components of tumour microenvironment is unclear. Therefore, the aim of the present study was to examine the relationship between total and phosphorylated STAT1 and STAT3 tumour cell expression, components of the tumour microenvironment and survival in a mature cohort of patients with invasive ductal breast cancer.Total and ph-STAT1 and STAT3 expression in tumour cells were quantified using the weighted histoscore, taking the staining intensity and percentage into account . The IHC staining of total and ph-STAT1 and STAT3 was homogenous in both the cytoplasm and nuclei of tumour cells, which is consistent with previous reports . The staP=0.421). Expression of total STAT1 and ph-STAT1 within the nucleus correlated strongly with their expression within the cytoplasm .The histoscore of total STAT1 expression ranged from 0-200 within the cytoplasm and from 0-220 within the nucleus, with cytoplasmic and nuclear expression in 270 patients (70%) and 268 patients (70%) respectively. The histoscore for ph-STAT1 expression ranged from 0-190 within the cytoplasm and from 0-225 within the nucleus, with cytoplasmic and nuclear expression in 350 patients (91%) and 374 patients (97%) respectively. Total STAT1 cytoplasmic expression was not correlated with ph-STAT1 nuclear expression (P<0.001). Expression of total STAT3 and ph-STAT3 within the nucleus correlated strongly with their expression within the cytoplasm .The histoscore of total STAT3 expression ranged from 0-280 within the cytoplasm and from 0-293 within the nucleus, with cytoplasmic and nuclear expression in 375 patients (98%). The histoscore of ph-STAT3 expression ranged from 0-150 within the cytoplasm and from 0-250 within the nucleus, with cytoplasmic and nuclear expression in 359 patients (93%) and 376 patients (98%) respectively. Total STAT3 tumour cell expression correlated strongly with ph-STAT3 tumour cell expression , PR status (P=0.048), and negatively with increased tumour grade (P=0.015). Similarly, ph-STAT3 tumour cell expression was not associated with patient age, tumour size and Her-2 status, though borderline significant associations with reduced LVI (P=0.055) and BVI (P=0.052) were observed. High ph-STAT3 tumour cell expression was positively associated with ER status (P<0.001), PR status (P=0.015) and negatively with increased tumour grade (P<0.001).The relationship between ph-STAT1 and ph-STAT3 tumour cell expression and clinicopathological characteristics was shown in Table P=0.001), and was positively associated with the generalised inflammatory infiltrate as measured using Klintrup–Mäkinen (K-M) grade (P=0.007). Similarly, high ph-STAT3 tumour cell expression was not associated with TSP and tumour budding. High ph-STAT3 tumour cell expression was negatively associated with tumour necrosis (P=0.001) and cellular inflammatory infiltrate as measured using CD4+ helper T-lymphocytes (P=0.024). High ph-STAT1 and ph-STAT3 tumour cell expression were also significantly associated with reduced tumour recurrence (P=0.003 and P=0.001 respectively).Within the tumour microenvironment, high ph-STAT1 tumour cell expression was not associated with tumour stroma percentage (TSP) and tumour budding. High ph-STAT1 tumour cell expression was negatively associated with tumour necrosis (P=0.435)   Figure . High ph) Figure . The meaP<0.001)   Figure . High ph) Figure . The meaP=0.002) and ph-STAT3 (P<0.001) tumour cell expression were associated with improved CSS. In multivariate analysis, high ph-STAT1 tumour cell expression was not independently associated with CSS (P=0.193). In contrast, high ph-STAT3 tumour cell expression was independently associated with improved CSS  independent of other variables, including nodal status, tumour necrosis, LVI, BVI, CD8+ T-lymphocyte infiltrate, CD138+ plasma cell infiltrate, and tumour budding  independent of LVI, BVI, CD68+ macrophage infiltrate, CD8+ T-lymphocyte infiltrate, tumour budding and locoregional treatment . High ph-STAT3 was significantly associated with improved CSS in luminal A (n=174) (P=0.005) and B (n=92) tumours (P=0.017). The small Her-2 positive subtype cohort (n=30) precluded meaningful analysis.The relationship between ph-STAT1 and ph-STAT3 tumour cell expression and CSS using Kaplan-Meier log rank test, with relevance to different molecular subtypes, was examined Figure . High phIn the present study, increased tumour cell expression of both ph-STAT1 and ph-STAT3 was associated with improved survival and the phenotypic characteristics of the tumour, in particular the low tumour grade and lack of tumour necrosis. Therefore, activation of tumour STATs may be an important mechanism by which the tumour cells mitigate the development of an aggressive phenotype in patients with invasive ductal breast cancer.To our knowledge no previous study have conducted a comprehensive analysis of total and phosphorylated STAT1 and STAT3 expression in patients with ductal breast cancer. Ph-STAT1 and ph-STAT3 were strongly associated with each other independent of cellular location. In multivariate analysis, ph-STAT1 was independently associated with prolonged CSS, however when ph-STAT3 was also included in the model only ph-STAT3 remained independently associated with CSS. These results suggest that ph-STAT3 is the dominant STAT protein associated with improved survival in patients with invasive ductal breast cancer.The observation that STAT1 is associated with improved survival may be explained by its role in promoting apoptosis and inhibition of proliferation . STAT1 iThe results of the present study are consistent with the majority of previous reports where 6/10 studies reported that ph-STAT3 was associated with improved outcome  α-dependent transcription in human glioblastoma cells lines . HIF-1 iTaken together, the results of the present study would suggest that both STAT1 and STAT3 act as tumour-suppressor proteins in patients with ductal breast cancer. STAT1 has long been implicated in growth suppression , 45 as lIn the present study, although high ph-STAT1 and ph-STAT3 were associated with improved outcome, the clinical utility of targeting these pathways remains to be established. However, it is of interest that clinical trials evaluating Il-6/JAK/ STAT inhibitors in breast cancer patients are under way .Potential limitations of the present study were that a specific examination of STAT1 and STAT3 in stromal tissue and in inflammatory cells was not carried since Chan and colleagues have reported that STAT1 expression may vary between tumour cells and the associated stroma, and have reported a selective loss of STAT1 expression in breast cancer cells but not in the surrounding stromal cells during tumour progression. Thus the increase in STAT1 levels in the subset of breast cancer cases that exhibit low STAT1 expression in the neoplastic cells could be explained by selective upregulation of STAT1 transcription in the stromal cells alone .In conclusion, STAT1 and STAT3 tumour cell expression appears to be an important determinant of favourable outcome in patients with invasive ductal breast cancer. The present results suggest that STAT3 may affect disease outcome through the direct impact on tumour cells counteracting aggressive tumour features, as well as interaction with the surrounding microenvironment.Patients presenting with invasive ductal breast cancer at Glasgow Royal Infirmary, Western Infirmary, and Stobhill Hospital, in the West of Scotland, between 1995 and 1998, and who had formalin-fixed paraffin embedded tissue blocks of the primary tumour available for evaluation were studied (n = 384). The study was approved by the Research Ethics Committee of the West Glasgow University Hospitals NHS Trust (REC reference is 07/s0704/61) and was performed according to the REMARK guidelines .Clinicopathological data included age, tumour size, tumour grade, lymph node status, and type of surgery and use of adjuvant treatment  were retrieved from the routine reports. Tumour grade was assigned according to Nottingham Grading System. The ER and PR status were assessed on tissue microarrays (TMA) using immunohistochemistry with Dako ER antibody and Leica PR antibody, and scored according to the American Society of Clinical Oncology and College of American Pathologists guidelines with a cut-off value of 1% positive tumour nuclei . Her-2 sLymph (LVI) and blood (BVI) vessel invasion were assessed, on 2.5 μm thick sections, using IHC staining with the lymphatic endothelial marker D2-40  diluted 1:100 and vascular endothelial marker Factor VIII  diluted 1:100 as previously described .Full-section haematoxylin and eosin (H&E) slides were used to score general local inflammatory infiltrate according to Klintrup-Mäkinen (KM) grade  and tumoIndividual immune cells infiltrate was assessed using IHC staining on TMA sections for macrophages, helper and cytotoxic T-lymphocytes, and plasma cells using CD68, CD4, CD8 and CD138 antibodies respectively, as previously described .2 size was drawn at five highest budding areas. The highest bud count per field was used as the number of buds.Full-section H&E slides were also used to score the tumour stroma percentage (TSP) and tumour budding as previously reported -61. BrieImmunohistochemical expression of total STAT1, Y701 phosphorylated STAT1 (ph-STAT1) total STAT3 and Y705 phosphorylated STAT3 (ph-STAT3) were carried out using a previously constructed TMA. Sections of 2.5 μm thickness from each TMA block were placed on silanized glass slides. Sections were dewaxed in xylene before being rehydrated using graded alcohols. Antigen retrieval for all STATs isoforms was performed using Tris-EDETA buffer (pH 8) for 20 minutes before cooling for 20 minutes. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide for 20 minutes before rinsing in water. Normal horse serum at dilution 1:10 was applied for 30 minutes at room temperature as a blocking solution. TMA sections were then incubated overnight at 4°C with the primary antibodies as following: total STAT1 (STAT1 (42H3) Rabbit monoclonal antibody, code 9175, Cell Signaling Technology, USA) at a concentration of 1:100; ph-STAT1  at a concentration of 1:150; total STAT3  at a concentration of 1:200; Ph-STAT3 (Y705) antibody (P-STAT3 (Y705) Rabbit Ab, code 9131L, Cell Signaling Technology, USA) at a concentration of 1:200. Sections were then washed in TBS for ten minutes. Envision (Dako) was then added to the sections for 30 minutes at room temperature before washing in TBS for ten minutes. DAB substrate was added for five minutes until colour developed before washing in running water for ten minutes. Slides were then counterstained in haematoxylin for 60 seconds and blued with Scotts’ tap water before being dehydrated through a series of graded alcohols. Cover slips were applied using distrene, plasticizer, xylene (DPX). Negative and positive controls were included in the staining runs.Stained TMA sections were scanned using a Hamamatsu NanoZoomer  at x20 magnification and visualization was carried out using Slidepath Digital Image Hub, version 4.0.1 . Assessment of total STAT1, ph-STAT1, total STAT3 and ph-STAT3 expression within the cancer cell cytoplasm and nucleus was performed by a single examiner (FJG) blinded to clinical data at x20 magnification  using the weighted histoscore. The weighted histoscore provides an assessment of the percentage and density of staining and is calculated as follows: 0x% not stained + 1x% weakly stained + 2x % moderately stained + 3x % strongly stained. This gives a range of scores from 0 to 300. Total and ph-STATs expression within the cytoplasm and nucleus were calculated separately. To ensure reproducibility of scoring, 15% of tumours for each antibody was co-scored by a second investigator (J.E.) blinded to other data. The intraclass correlation coefficient (ICCC) was 0.852 and 0.831 for cytoplasmic and nuclear total STAT1 respectively, and 0.797 and 0.871 for cytoplasmic and nuclear ph-STAT1 respectively. The ICCC was 0.795 and 0.801 for cytoplasmic and nuclear total STAT3 respectively, and 0.814 and 0.782 for cytoplasmic and nuclear ph-STAT3 respectively, indicating good agreement.For the purpose of statistical analysis, patients were split into two groups on the basis of the mean value of cytoplasmic and nuclear STAT1/STAT3 weighted histoscore, as low cytoplasmic and low nuclear STAT1/STAT3 expression and high cytoplasmic or high nuclear STAT1/STAT3 expression. In order to identify the impact of cellular STAT1/STAT3 expression at both cytoplasmic and nuclear location, an expression code was developed (STAT1/STAT3 tumour cell expression) as follows: patients with both low cytoplasmic and nuclear expression were classified as the low tumour cell expression group, patients with either cytoplasmic or nuclear expression is low were classified as the moderate tumour cell expression group, and patients with both high cytoplasmic and high nuclear expression were classified as the high tumour cell expression group. These analyses have been applied for total and for ph-STAT1 separately and total and ph-STAT3 separately.P-value of <0.1 were entered into a multivariable model using a backwards conditional method. A P-value <0.05 was considered statistically significant. All analyses were performed using SPSS version 22.0 (IBM SPSS).Subsequently, the relationship between clinicopathological characteristics, ph-STAT1 tumour cell expression and ph-STAT3 tumour cell expression were examined using the Chi-square test for linear trend. The relationship between total and ph-STAT1 tumour cell expression, total and ph-STAT3 tumour cell expression and cancer-specific survival was examined using Kaplan-Meier log-rank analysis. Univariate survival analysis was performed using Cox proportional hazards regression. Variables with Patients were routinely followed-up following surgery. Date and cause of death was cross-checked with the cancer registration system and the Registrar General (Scotland). Death records were complete until 31st of May 2013 and that served as the censor date. Cancer-specific survival was measured from the date of primary surgery until the date of death from breast cancer.


Intravenous fluid management of trauma patients is fraught with complex decisions that are often complicated by coagulopathy and blood loss. This review discusses the fluid management in trauma patients from the perspective of the developing world. In addition, the article describes an approach to specific circumstances in trauma fluid decision-making and provides recommendations for the resource-limited environment. Fluid resuscitation of trauma patients has been an ongoing challenge, constantly reviewed and debated, resulting in recommendations changing for the use of crystalloids/colloids/packed red blood cells/warm fresh whole blood and clotting factors. Other challenges, such as limited resources, impact the practitioners’ choice of fluid—the best fluid available does not always equate to the best fluid for the patient, especially where long transfers and no blood availability are concerned. These decisions and management strategies appear relevant for further discussion and research, as this fluid resuscitation attempts to provide adequate organ perfusion and oxygen delivery in a system compromised by the physiological consequences of injury. Several questions have arisen from this topic: Which fluid is best, how much should be given, and do specific injuries call for different strategies ? Achieving balance in the resuscitation period is challenging, particularly the volume administered. More fluid is not always better, in fact, quite the contrary –3. Much There are three distinct groups of trauma patients, but often there is an overlap. Most commonly encountered is the combination of blunt trauma and head injury associated with motor vehicle collisions. While the general Advanced Trauma Life Support (ATLS) management approach to the three groups remains similar, fluid therapy strategies differ. The literature suggests patients with penetrating injuries, particularly to the thoracoabdominal region, have better outcomes with restrictive clear fluid resuscitation policies, permitting a systolic blood pressure (BP) between 60 and 70 mmHg until the patient can be taken to the operating theater . Once heThe exception to the guidelines above is the polytrauma patient (blunt or penetrating) with traumatic brain injury (TBI). In order to preserve adequate cerebral perfusion pressure and prevent secondary brain injury, one needs to target a mean arterial pressure (MAP) of greater than 80 mmHg  .The ongoing debate as to which group of fluid  is bestIn the resource-limited environment, the use of cheaper crystalloid solutions is still recommended due to lack of data showing significant outcome benefits of more expensive synthetic colloids. A selection of different crystalloid solutions is often not available in resource-limited settings, making the available solution the only and best choice. In a Cochrane review, the use of hypertonic saline for the resuscitation of trauma victims has failed to show any benefit over isotonic or near-isotonic crystalloids and two adequately powered trials investigating mortality as an endpoint were halted early due to futility. Controversy, however, still continues fueled by animal studies demonstrating benefits that have not been reflected as outcome benefits in human studies . HeterogSeveral trials show either no benefit, or in some cases worse outcomes, with albumin thus making this solution not recommended in the resuscitation of trauma patients –22.The physiological impact of the volume of fluid infused may be as, or even more important than the type selected , 23–26. Therefore, until such time as blood and blood products are available, clear fluid resuscitation should be limited to only that which is necessary to maintain adequate organ perfusion. Several factors influence decisions at this point of the resuscitation. Trauma units with easy access to on-site blood and blood products should commence resuscitation of patients with massive blood loss with these products from the start. In environments where blood products are limited, the authors suggest judicious use of clear fluids to sustain organ perfusion while avoiding the negative effects of excess fluid. Response to fluid administration and determining the need for further fluid administration is discussed in the next section.As defined by the Advanced Trauma Life Support course, classification of patients into those that respond to initial fluid resuscitation versus those that only transiently respond or do not respond at all is important. The response to intravenous fluid resuscitation is assessed using physiological markers of improvement such as blood pressure, heart rate, decreasing lactate and normalizing base deficit with adequate control of bleeding. Responders are considered those that demonstrate these physiological improvements, whereas transient responders show an initial improvement followed by further physiological deterioration. Non-responders are those that show continued physiological deterioration despite initial fluid resuscitation. The distinction requires vigilance and repeated clinical assessments to identify those patients with re-bleeding, or ongoing bleeding, and initiation of blood product resuscitation together with surgical intervention. What may be regarded as acceptable physiological parameters will vary depending on many factors including the age, underlying medication and comorbidities of the patient.See Fig. The goal of resuscitation is to achieve adequate tissue perfusion and oxygenation while correcting any coagulopathy. Packed red blood cells, and to some extent hemoglobin-based oxygen carriers (HBOCs), help to achieve the former while component therapy attempts to deal with the coagulopathy. Whole blood may achieve both objectives . Current(R) , used in isolation as well as in combination with blood products, or as a bridge to blood transfusion [(R)  compound, which to date has had the most success with fewest serious adverse events [Modified hemoglobin solutions are not substitutes for blood as they do not possess the metabolic functions of erythrocytes. They act purely as oxygen carriers . Large-snsfusion , 35. Hemnsfusion , 34. Sere events . FurtherTrauma-induced coagulopathy  is a relMonitoring coagulation in trauma-induced coagulopathy has been made easier with the use of point-of-care testing, namely viscoelastic assays. Recent evidence shows that it improves survival in patients requiring massive blood transfusions compared to those monitored by more traditional assays as described above . While cTry and identify risk factors and priorities in trauma patients early .Consider early administration of blood products in a ratio of 1:1:1 or 1:1:2 if available.In the absence of blood products, use clear fluid resuscitation. Preferably, a balanced salt solution should be used (such as Ringer’s lactate or PlasmaLyte); however, care should be taken not to mix this fluid with blood transfusions.When using clear fluids in resuscitation, vigilance is required to provide only that fluid which is necessary to maintain perfusion. Excessive clear fluid administration has negative consequences such as dilution of coagulation factors, tissue edema, hyperchloremic metabolic acidosis and organ dysfunction.Point-of-care testing should be used whenever possible to guide component therapy replacement to correct coagulopathy.In rural LMIC or long-distance transfer situations with no access to blood, a synthetic colloid may be of benefit in reducing subsequent edema and bowel anastomotic breakdown.Recommendations for current best practices:The consequences of under- or over-resuscitation with intravenous fluids are both detrimental , 44. HypHemostasis and correction of coagulopathy (ongoing blood product replacement no longer required) .Evidence of improving microcirculatory flow  .Hemodynamic stability .Following the initial resuscitation phase, physiological targets may change despite the overall objective of adequate tissue perfusion remaining the primary goal. The post-resuscitation period may be considered after:At this stage, most patients will no longer be responsive to rapid fluid administration, with normalization of markers for volume status (barometric or volumetric preload indicators) and markers for fluid responsiveness (pulse pressure variation (PPV), stroke volume variation (SVV), passive leg raise (PLR) test) .Importantly, in certain circumstances where patients remain fluid responsive, curtailment of ongoing fluid resuscitation may be necessary if the tolerance to further intravenous fluid is deemed detrimental to physiological processes  . AlternaR) phase comes organ support (O) and stabilization (S), and finally evacuation (E) of excess fluids may be needed in some patients  phThese are broad guidelines, and exceptions do exist. Urine output has not been included in our recommendations due to the many influencing factors, although it may be one of the only parameters to measure, particularly in resource-limited settings. The renal response to hypovolemia is multifactorial and depends on a combination of renal blood flow, renal perfusion pressure and plasma oncotic pressure. The type of resuscitation fluid is important as it may influence oncotic pressure. In addition, the neurohormonal control of renal function may cloud the clinical picture and appropriate retention of water may be interpreted as renal dysfunction when this may be an appropriate physiological fluid conservation mechanism. As outlined by Peeters et al., previous studies have not found a correlation between urine output and invasively derived physiological variables. Also, several studies have pointed to the inaccuracy of using urine output as a resuscitation target and its limitation in identifying fluid responders. This situation is complicated further in trauma patients in the presence of increased intra-abdominal pressure. The limitations of using urine output should be understood by attending physicians.Fluids should be treated as drugs: Not only the type of fluid is important, but also the dose, the administration speed, the duration and de-escalation . There aIn providing maintenance fluids, care should be taken to avoid causing tissue edema. This requires limitation of crystalloid administration, which can only really be achieved in the post-resuscitation period. Crystalloid fluid administration is not without hazards. Excessive crystalloid administration is associated with edema of skin, abdominal organs , kidneys  and heart  . The ideDuring the post-resuscitation phase, crystalloids are not only required for fluid supplementation, but also as vehicles for administration of medication, including antibiotics, sedation and inotropes/vasopressors. The fluid required for the administration of these solutions together with those required for nutrition should as a guide not exceed 2 ml/kg/h. 0.9% “normal” saline has often been the fluid of choice for this purpose; however, concerns about both the sodium and chloride load may favor other “balanced” fluids , 50, 51.It must be remembered that intravenous fluids are drugs providing both electrolytes and water. While calculating these requirements, the patient’s medication and feed also need to be incorporated to avoid excessive volumes of each.Static markers of cardiac preloadOnly half of the ICU patients with hemodynamic instability are able to “respond” to fluid loading, which is explained by the shape of the Frank–Starling curve . On the 2.Dynamic markers of volume responsivenessConsidering the Frank–Starling relationship, the response to volume infusion is more likely to occur when the ventricular preload is low, rather than when it is high. Unfortunately, none of the measures of cardiac preload enables to accurately predict fluid responsiveness: Neither the central venous pressure (CVP), the pulmonary artery occlusion pressure (PAOP), nor the left ventricular end-diastolic area (LVEDA) can discriminate between responders and non-responders to fluid therapy , 53, 54.The alternative method for predicting volume responsiveness is simply to induce a change in cardiac preload and to observe the resulting effects on stroke volume or cardiac output or any available surrogate, i.e., to perform a “functional assessment” of the cardiac function . This isObserving the respiratory variation of hemodynamic signals has emerged as an alternative for assessing volume responsiveness without administering fluid. The concept is based on the assumption that the cyclic changes in right ventricular preload induced by mechanical ventilation should result in greater cyclic changes in left ventricular stroke volume when both ventricles operate on the steep rather than on the flat portion of the Frank–Starling curve, i.e., in case of biventricular preload preserve.Numerous studies have consistently demonstrated that the magnitude of respiratory variation of surrogates of stroke volume allows predicting fluid responsiveness with accuracy . Pulse pRespiratory variation of the pulse contour-derived stroke volume measured by the PiCCO or by the FloTrac/Vigileo  or by the LiDCOplus , 65, 66.Respiratory variation of the subaortic flow assessed by echocardiography and respiratory variation of the descending aortic blood flow assessed by esophageal Doppler , 67, 68.Other heart–lung interaction indices like respiratory variation of inferior or superior vena cava diameter (echocardiography), although limitations exist , 68–70.The passive leg raise (PLR) test carries an excellent ability to serve as a test of preload responsiveness, demonstrated in patients with acute circulatory failure , 72. A 1The end-expiratory occlusion test. The end-expiratory occlusion test can be used in patients with low lung compliance , 75.Ultrasonography: This bedside modality has advantages of repeatability, being noninvasive, with the ability to assess dynamic changes in the inferior vena cava (IVC) diameter, left ventricular outflow tract stroke volume variation, and estimate cardiac ejection fraction . This prThe following other surrogates of stroke volume respiratory variation can be used at the bedside:If ultrasonography is available, then we advise using dynamic changes in IVC together with other clinical parameters. Preferably, if the skills are available, combining aortic velocity–time integral with dynamic changes from a PLR test should be performed.In the absence of ultrasonography, dynamic markers of volume resuscitation should be attempted such as a PLR test, although this may have limited utility in the emergency setting due to patient injuries and pain.If it is unable to utilize ultrasonography or PLR testing, other dynamic markers of fluid responsiveness can be attempted although these may be impractical in the emergency setting. Repeated “mini-boluses” of intravenous fluid (100–250 ml) can be used, provided that vigilance regarding excessive or inappropriate clear fluid administration is maintained.Recommendations for current best practices:Infants and children suffer from trauma, particularly vehicular trauma, with an increasing incidence in the developing world . ChildreRestoration of circulating volume is a priority with the establishment of vascular access via a peripheral line, central  line or intra-osseous line. The access should not be established distal to a site of injury  as the resuscitation fluid will extravasate into the injured area .The principles of goal-directed therapy apply equally well to children as to adults. Initial resuscitation should be with 20 ml/kg of balanced crystalloid . There iThe role of all clear fluids is limited in trauma resuscitation due to their adverse effects of dilutional coagulopathy and anemia and generation of edema that hinders tissue perfusion and promotes organ dysfunction . The volume of clear fluid should not exceed 40 ml/kg . AdminisDuring the maintenance phase of resuscitation, children appear to be at risk of hyponatremia. This seems to be due to administration of excessive volumes of hypotonic solutions such as ½ strength Darrow’s solution  with 5% dextrose resulting in hyponatremia and potentially fatal cerebral edema . A clearThe World Health Organization defines “elderly” as a chronological age of 65 years or more. Age is only one criterion in the assessment of overall health leading to the concept of biological age based on organ dysfunction and/or chronic disease . DespiteCardiovascular changes of aging include stiffening of the arterial circulation and loss of compliance of the left ventricle. The elderly thus tolerate hypo- and hypervolemia poorly. Volume loss reduces preload resulting in ventricular under-filling and a disproportionate drop in cardiac output. Over-hydration is as dangerous due to the lack of ventricular compliance predisposing to the development of edema, particularly pulmonary edema . AssessmClear fluid administration should initially be limited to 20 ml/kg with early consideration given to the administration of blood and blood products. Careful re-evaluation and vigilant monitoring should to performed to determine if further fluid administration is required; particularly if underlying heart disease is suspected. Due to the likelihood of underlying coronary and cerebral artery disease in the elderly, consideration should be given to maintaining hemoglobin levels above 9 g/dl and mean arterial pressure above 70 mmHg, particularly if the patients comorbidities are unknown .Appropriate and effective initial resuscitation of victims of burns is vital for survival and reduction of morbidity and mortality . The deeThe rate of fluid administration will initially be rapid with up to half the daily requirement given in the first 6 h. The use of colloid solutions is controversial. Hyperoncotic colloids worsen outcome, but the role of albumin or synthetic colloids  is less clear –104. ColEnteral resuscitation is effective if commenced within 6 h. Placement of a feeding tube should be part of the resuscitation protocol for burns. Delay of feeding for more than 6 h will result in an increasing feeding failure due to gastroparesis and ileus. Maintenance of enteral feeding maintains the gut associated lymphoid tissue that participates in maintenance of immunity at all epithelial surfaces including the skin. A standard formula may be used starting at 2 ml/kg/h and increasing incrementally every 3 h until the goal rate calculated for each patient is reached .Two simple investigations should be used to monitor the effectiveness of resuscitation from burns. The first is the hematocrit, which may be as high as 70% on admission after extensive burns. Failure to reduce the hematocrit below 40% within the first 6 h is an accurate indicator of poor prognosis. The second is urine output, which should be maintained at around 1 ml/kg/h. Development of acute renal failure carries a very poor prognosis with extensive burns . A more Crush injury is seen in victims of motor vehicle collisions who are entrapped and have limbs compressed, resulting in direct muscle trauma followed by a reperfusion injury when freed. Similar injury is seen in prolonged immobilization  and entrapment in collapsed buildings after natural disasters . South AWith diffuse injury, such as community sjambok assaults, the surface area of the body injured should be quantified as for burns. A surface area of >18% carries increased risk of renal dysfunction . AggressTraditionally, 0.9% saline is used for fluid loading. Alternatives, to limit the occurrence of hypernatremia and hyperchloremic acidosis, include 0.45% saline and alternating 0.9% saline and 5% dextrose. Balanced solutions such as modified Ringer’s lactate are not recommended due to concerns regarding hyperkalemia, but this risk may be offset by the hyperchloremic acidosis seen with large-volume saline administration .Should presentation be delayed, creatinine and potassium should be measured while initial fluid loading occurs, as the kidneys may have been damaged beyond immediate recovery. Failure to produce urine after initial fluid loading associated with an elevated urea, creatinine and potassium indicates the need for urgent renal replacement therapy. Further fluid loading should not be administered, as the absolute volume overload that arises in the absence of urine output will result in pulmonary edema with hypoxia requiring intubation and ventilation. There is no role for loop or osmotic diuretics, and the use of sodium bicarbonate to induce alkaline diuresis is also not supported by evidence .Trauma, particularly vehicular, or due to intimate partner violence is a common cause for maternal and fetal morbidity and mortality. In the developing world, neonatal intensive-care facilities are limited so maternal considerations take precedence in resuscitation until fetal viability is likely .Pregnancy duration of more than 20 weeks makes aortocaval compression a realistic cause of hypotension during resuscitation, emphasizing the need for maintaining a 20° left lateral tilt . Fluid aShould delivery occur during resuscitation, significant blood loss may occur due to post-partum hemorrhage. Oxytocin availability may be limited due to expense and requirement for refrigeration. Misoprostol is an accepted alternative, but is only available in an oral form that may need to be administered rectally during resuscitation . It shouFluids are drugs and should be managed as such. Appropriate early fluid resuscitation in trauma patients is a challenging task. Care should be taken in selecting both the type and volume to promote appropriate perfusion and oxygen delivery, avoiding the adverse effects seen when giving too little or too much. Ongoing fluid strategies following resuscitation should incorporate dynamic markers of volume status whenever possible. All aspects of fluid administration should be incorporated into daily fluid plans, including feeding and infusions of medications. A sound knowledge of the differences and physiological consequences of specific trauma groups is essential for all practitioners delivering care for trauma patients .