Fluid accumulation is associated with adverse outcomes such as acute kidney injury (AKI) in critically ill patients. This study aimed to describe the factors associated with AKI in individuals with influenza A H1N1 severe pneumonia, and explore the relation of fluid accumulation with AKI and mortality.We reviewed medical records of individuals with influenza A H1N1 severe pneumonia and no history of chronic kidney disease, attending a national referral center for respiratory diseases between November 2014 and May 2015. Demographic information, risk factors for AKI, physiologic and laboratory data, outcomes and information on fluid intake and output were recorded. Categorical variables were compared using the chi-square test. Quantitative variables were compared using the Mann-Whitney test. Factors associated with AKI and mortality were identified by binary logistic regression. Linear models of fluid accumulation rates for individuals and groups were estimated using segmented linear regression.2O on admission (p = 0.031); mortality (p = 0.037); and fluid accumulation ≥10% (fluid overload) at day 7 of hospitalization (p = 0.00026). Mortality was associated with older age (p = 0.009); nephrotoxic drugs (p = 0.034); and higher Pneumonia Severity Index score  on admission. The Deceased-AKI group had a higher rate of fluid accumulation (expressed as ml/kg/body weight) than the Survivors-No AKI group during the study period of 7 days . During the highest phase of fluid accumulation, the Survivors-No AKI group had a slower rate of fluid accumulation than the Survivors-AKI group .Of 60 patients studied, 43 developed AKI (71.6%). Male gender was protective for AKI (p = 0.019). AKI was associated with nephrotoxic drugs (p = 0.016); PEEP>10 cm HA high rate of fluid accumulation was associated with AKI and mortality. We support the approach of resuscitation in acute illness, with an early transition to neutral and then negative fluid balances. Influenza A H1N1 virus infection is associated with a spectrum of illnesses, ranging from upper respiratory infection, to multiple organ dysfunction and death. In critical patients with severe influenza A H1N1 disease, acute kidney injury (AKI) is a common complication. AKI is associated with development of chronic kidney disease , 3. In mThe association between FO and AKI has been consistently reported, but the cause-effect relationship remains unclear. The aim of this study was to describe the factors associated with the development of AKI in critically ill individuals with influenza A H1N1 severe pneumonia, and we had a particular interest in exploring the relation of fluid accumulation on the development of AKI and mortality.2/FiO2 < 200 mm Hg, and a positive result for the influenza A H1N1-rRT-PCR assay in nasopharyngeal swab or bronchoalveolar lavage samples.This study was conducted at the National Institute of Respiratory Diseases (INER), a national referral center in Mexico City. We retrospectively reviewed the medical records of individuals with severe pneumonia caused by influenza A H1N1 attending our institution between November 2014 and May 2015. Patients with influenza A H1N1 severe pneumonia were those with clinical data of respiratory distress, bilateral alveolar opacities in 2 or more lobes, a ratio of PaOThe retrospective review of medical records included demographic and anthropometric variables, comorbidities, clinical and laboratory data, initiation and termination dates of mechanical ventilation, days on intensive care unit (ICU), initial mechanical-ventilator settings and use of vancomycin or other nephrotoxic drugs. The Acute Physiology and Chronic Health Evaluation (APACHE) II score, as well as the Sequential Organ Failure Assessment (SOFA) and the Pneumonia Severity Index (PSI) scores were calculated on admission. Information on fluid intake and output was obtained from fluid balance monitoring charts. Daily fluid balance was determined from all intakes and outputs recorded. All available intake and output data from 7 days in hospital were included in the analyses. We computed fluid balance for each day using the sum of daily fluid intake (L) from which we subtracted total output (L). To quantify cumulative fluid balance in relation to body weight, we used the following formula: (∑ daily (fluid intake (L)–total output (L)) / body weight (in kilograms). We used the term ‘percentage of fluid accumulation’ to define the percentage of cumulative fluid balance adjusted for body weight. Baseline body weight was based on initial hospital admission weight . Total oThe relation of fluid accumulation with AKI and mortality was explored by comparing the frequency of these outcomes in individuals with fluid accumulation <10% versus those with fluid accumulation ≥10% (FO).2 on admission using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation; no history of chronic kidney disease (CKD); and ratio of partial pressure arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) <200 mm Hg on admission. Pregnant women were not included in the study. The Research and Ethics Committee of the INER approved the study and waived the requirement for informed consent due to the retrospective design of the study.We included individuals with diagnosis of severe pneumonia caused by influenza A H1N1, confirmed by real-time reverse transcription–polymerase chain reaction (rRT-PCR); age 18 or older; estimated glomerular filtration rate (eGFR) > 60 ml/min/1.73 mFluid overload was defined as fluid accumulation greater than or equal to 10% of the baseline body weight . Serum cDiagnosis of AKI was based on a rapid reduction of kidney function, defined by an absolute increase in adjusted sCr >0.3 mg/dL within 48 hours compared to baseline; a sCr increase of 1.5 times within 7 days compared to baseline; or a decrease in urinary output >0.5 ml/kg/hour for more than six hours. AKI stage was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) staging system . AKI staCategorical variables were compared using the chi-square test. Median and interquartile ranges (IQRs) were calculated for quantitative variables and differences between groups were analyzed using the Mann-Whitney test. Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained using logistic regression analyses. A two-sided P value <0.05 was considered to be significant. Statistical analyses were performed using SPSS version 20 .We explored if a fluid accumulation ≥10% body weight gain was a predictor of AKI or death. Then we analyzed if the rate of fluid balance increase, indicated by the slope of the relation of body weight gain/day, was a predictor of AKI or mortality. This analysis was performed considering total volume or volume per kg of body weight. We estimated individual linear models having one or more segmented relationships in the linear predictor. Individuals with no converging segments were assumed to have a single segment across the follow-up period of 7 days. Estimates of the slopes and the possibly multiple breakpoints for each patient were obtained by segmented linear regression, using Package segmented version 0.5–1.4 for the R statistical language, Muggeo V.M.R. .2/FiO2 >200 mm Hg). We thus included 60 individuals in the study. Of those, 39 were men (65%). The median age was 47.5 years, IQR, 44.2–50.9; 56.7% were obese; 21.7% had systemic arterial hypertension; 8.3% had diabetes mellitus; 6.7% had pneumopathies; and 38.3% had a smoking history. Only 1.7% of this cohort had been vaccinated against influenza. Sudden onset of influenza symptoms (<72 hours) was observed in 63.3% of the cases. The median APACHE II score on admission was 13.5 . Forty-three individuals developed AKI (the AKI group). Of those, 9 had AKI stage 1 (20.8%); 16 had AKI stage 2 (37.1%) and 18 had AKI stage 3 (41.7%). Four patients requiring replacement therapy (6.7%) received sustained low-efficiency dialysis, and all of them survived.During the period between November 2014 and May 2015, 184 individuals were diagnosed with severe pneumonia at our institution. Of those, 124 were deemed ineligible for this study because they did not fulfill the inclusion criteria  vs. the non-AKI group (n = 17) on admission are shown in 2O . Mortality was significantly more frequent in the AKI group . The AKI group had higher PSI, APACHE II and SOFA scores on admission, as well as longer hospital stays and mechanical ventilation periods than the non-AKI group, but these differences were non-significant. Laboratory values and gasometric variables on admission were not significantly different between both groups.Male gender was protective for AKI  = 0.16, CI, 0.03–0.82). AKI was associated with the use of nephrotoxic drugs ; and positive end-expiratory pressure (PEEP) level >10 cm HDemographic and clinical characteristics of deceased individuals and survivors are shown in 2/FiO2 and HCO3 levels on admission were lower in deceased individuals than in survivors, but differences were non-significant.Older age was significantly associated with mortality . The use of nephrotoxic drugs during hospitalization was significantly associated with mortality . The Pneumonia Severity Index on admission was significantly higher in deceased patients than in survivors . PaOCumulative fluid balance until day 7 of hospitalization was obtained for 53 individuals. We explored the relation of FO with AKI and mortality in this group. Fluid overload was associated with AKI . Mortality was more frequent in the group with FO, but the association was non-significant .In the group of 53 individuals, we also assessed the accumulated fluid balance per day (expressed in mL/Kg of body weight) during 7 days of hospitalization . The accIndividual values of fluid accumulation of all study participants during hospitalization are shown in We explored if the rates of fluid accumulation per day were related to the different outcomes. For this, individuals who did not develop AKI and survived, constituted the Survivors-No AKI group; individuals with AKI constituted the Survivors-AKI group; and deceased individuals with AKI constituted the Deceased-AKI group. Two patients who did not develop AKI, died during the first 3 days of hospitalization. These patients were not included in this analysis, as this time period was deemed insufficient for assessing the effects of fluid accumulation. Considering that Phase I was defined by the highest rate of fluid accumulation in all three groups, we compared the linear segment corresponding to this initial phase among groups . The Sur2O on admission [2/FiO2 in the AKI than in the non-AKI group, as well as lower PaO2/FiO2 in deceased individuals than in survivors, but lack of statistical significance in this parameter was probably affected by the inclusion criteria of PaO2/FiO2 <200 mm Hg on admission.We explored the risk factors associated with the development of AKI and the relation of the accumulated fluid balance on AKI and mortality, in a homogeneous population with primary acute respiratory distress syndrome caused by influenza A H1N1 virus infection. By using the KDIGO classification, we found that 71.6% of individuals developed AKI. This proportion is similar to that found in a Canadian cohort, of 66.7% using the RIFLE classification . AKI haddmission . Male gedmission , high bodmission , high SOdmission . In our In line with previous studies, mortality was significantly more frequent in the AKI group than in the non-AKI group , 19, 20.We found a significant association between FO and AKI. Likewise, death was more frequent in the group with FO, but the limited number of deceased patients may have provided insufficient evidence to establish a difference between groups. Fluid accumulation tended to have two phases during the study period of 7 days. The first phase was defined by a high rate of fluid balance increase, followed by a subsequent phase of a slower rate of fluid accumulation. The most relevant finding of our study was that the phase of higher fluid accumulation, was significantly associated with AKI and mortality. Our results are consistent with a previous study using the concept of "slope", and showing that the velocity of fluid accumulation is important, and the more rapidly fluid accumulation occurs, the higher the risk of dying . TherefoThe main limitation of our study is derived from the retrospective design. That is, we found an association of fluid accumulation with AKI and mortality, but the causal mechanism of fluid accumulation and the different strategies of fluid management as therapy for AKI should be evaluated in randomized controlled trials. Another limitation of our study is that we retrieved information about fluid balance and development of AKI during hospitalization, but a longer observation period would have provided additional information regarding the clinical outcome and the impact of AKI in the population studied. Namely, we are not reporting the proportion of individuals developing chronic renal disease in the group with AKI. An additional limitation of our study is that we were unable to determine if the positive fluid balance observed in some individuals was caused by oliguria, fluid administration or a combination of both. Finally, our study was conducted in a national referral center for respiratory diseases, and this represents a potential source of referral bias. As we included previously health individuals with severe pneumonia caused by influenza A H1N, most of them had no chronic comorbidities. By consequence, reported risk factors for the development of AKI in ICU patients such as past history of chronic heart failure, lymphoma, leukemia, cirrhosis , peripheOur results support previous findings in critically ill patients, indicating that fluid overload and rapid fluid balance increase are associated with AKI and mortality. Scientific evidence obtained from randomized clinical trials exploring more rational and flexible approaches to fluid therapy is required for minimization of the adverse consequences of fluid overload.S1 FileThis file includes the minimal anonymized data set necessary to replicate our study findings.(CSV)Click here for additional data file.


However, only a subset of HNSC patients receiving cetuximab actually responds to treatment, underlining the need for a means to tailor treatments of individual patients. The aim of the present study was to investigate the effect of cetuximab treatment on tumour growth, on tumour partial oxygen pressure as measured by LiPc electron paramagnetic resonance oximetry and on the expression of proteins involved in tumour growth, metabolism and hypoxia. Two HNSCC cell lines, UT-SCC-2 and UT-SCC-14, were used to generate xenografts on female BALB/c (nu/nu) nude mice. Mice with xenografts were given three injections of intraperitoneal cetuximab or phosphate-buffered saline, and the tumour volume was recorded continuously. After treatment the tumour partial oxygen pressure was measured by LiPc electron paramagnetic resonance oximetry and the expression of epidermal growth factor receptor (EGFR), phosphorylated EGFR, Ki-67, MCT1, MCT4, GLUT1, CAIX and HIF-1α were investigated by immunohistochemistry. In xenografts from both cell lines (UT-SCC-2 and UT-SCC-14) cetuximab had effect on the tumour volume but the effect was more pronounced on UT-SCC-14 xenografts. A higher tumour oxygenation was measured in cetuximab-treated tumours from both cell lines compared to untreated controls. Immunocytochemical staining after cetuximab treatment shows a significantly decreased expression of EGFR, pEGFR, Ki67, CAIX and nuclear HIF-1α in UT-SCC-14 tumours compared to untreated controls. MCT1 and GLUT1 were significantly decreased in tumours from both cell lines but more pronounced in UT-SCC-14 tumours. Taken together, our results show that cetuximab treatment decreases the tumour growth and increases the tumour partial oxygen pressure of HNSCC xenografts. Furthermore we found a potential connection between the partial oxygen pressure of the tumours and the expression of proteins involved in tumour growth, metabolism and hypoxia.Head and neck squamous cell carcinoma (HNSCC) tumours are associated with high mortality despite advances in therapy. The monoclonal antibody cetuximab (Erbitux Head and neck squamous cell carcinoma (HNSCC) is a malignancy that is associated with high mortality despite advances in therapy. The treatment of HNSCC is heavily based on radiotherapy in combination with surgery and/or cytostatic drugs. However, radiotherapy and/or chemotherapy resistance and tumour recurrences are important clinical problems in the management of HNSCC.®) has been approved for the treatment of advanced HNSCC. Studies have shown an increase in overall survival of almost 20 months in HNSCC patients who received targeted therapy against EGFR in combination with radiotherapy compared to radiotherapy alone [The epidermal growth factor receptor (EGFR) is a transmembrane cell surface receptor found mainly in cells of epithelial origin. In malignant tumours EGFR overexpression is linked to poorer prognosis , 2. Apprpy alone . At the py alone , 4.Hypoxia, which is very common in solid tumours, is associated with treatment resistance, increased invasion and poor clinical outcome. Treatment success rates for HNSCC are known to be correlated to tumour oxygenation with low treatment success rate associated with tumour hypoxia, and thus a number of clinical studies have reported that the amount and the severity of hypoxia correlate with poor prognosis from radiation therapy –8. AccurIn the light of the importance of hypoxia in radiation therapy, several methods have been proposed to quantify tumour oxygenation . Invasiv2) in, e.g., mm-Hg in the tissue of interest , and it has been shown that the correlation between EPRI oximetry and, e.g., oxylite measurements is good both in terms of the spatial distribution pattern and the pO2 magnitude [A less explored method in the array of measuring techniques is electron paramagnetic resonance (EPR) oximetry that holds a number of advantages. EPR is a spectroscopic method for studies of paramagnetic species, such as radicals and transition ions , and EPRagnitude . For theagnitude –28.The aim of the current study was to investigate the effect of cetuximab treatment on tumour partial oxygen pressure as measured by lithium phthalocyanine (LiPc) EPR oximetry, on tumour growth, and on the expression of proteins involved in tumour growth, metabolism and hypoxia.In this study, two HNSCC cell lines UT-SCC-14 (tongue) and UT-SCC-2 (floor of mouth) from the University of Turku were used. These cell lines were cultured in Dulbecco’s modified Eagle’s medium supplemented with 2 mM glutamine, 1% non-essential amino acids, 100 IU/ml penicillin-G, 50 µg/ml streptomycin, and 10% foetal bovine serum . The cells were given fresh culture media twice per week and were subcultured at confluence after detaching the cells with 0.25% trypsin + 0.02% ethylenediaminetetraacetic acid at a weekly split ratio of 1:4. Cultures in passages 15–25 were used in all experiments. Cells were screened periodically for mycoplasma contamination using DAPI staining and/or the Universal Mycoplasma Detection Kit .6 cells suspended in 0.2 ml of phosphate-buffered saline (PBS) using ethical committee-approved standard protocols and procedures. All procedures were performed at the Linköping University’s animal facility and were approved by the local Animal Use and Care Committee (Dnr 126-10). Ten mice were injected with UT-SCC-14 and the remaining ten with UT-SCC-2. The mice were examined twice a week for development of tumours. Within each group of ten animals five received three intraperitoneal injections of cetuximab  at day 10, 13, and 16 after injection of tumour cells. The size of the tumours was recorded at an interval of 2–3 days. Tumour-bearing mice were sacrificed at day 21, and the tumour tissue was excised, fixed in formalin, and embedded in paraffin. The experiment was repeated twice.20 BALB/c (nu/nu) female nude mice were injected subcutaneously in both flanks with 5 × 10A TMA was constructed from formalin-fixed paraffin embedded tumours at the Swedish Science for Life Laboratory (SciLifeLab) facilities at the Department of Immunology, Genetics, and Pathology at the Rudbeck Laboratory of Uppsala University (Sweden), as previously described . In brieIHC and slide scanning were performed at the Swedish SciLifeLab facilities in accordance with protocols described elsewhere . In brieTo obtain high-resolution digital images, stained IHC slides were scanned with a 20× objective using an Aperio Scan Scope XT Slide Scanner . Scoring was performed blinded without knowledge of cell line or treatment by two investigators. A definite consensus score was determined by mutual agreement in a separate session. Staining for EGFR, pEGFR, MTC1, MTC4, CAIV and GLUT1 was predominantly membranous and was scored according to the percentage of positively stained cells and staining intensity . Ki67 was used as a proliferation marker and scored as follows: 0–10% = 1, 11–50% = 2, and >50% = 3).LiPc  crystals/aggregates were loaded into 23 G needles and compressed to create LiPc oximetry probes using a technique described previously –28. Each0. No EPR signal could be detected for the empty resonator.EPR measurements were performed 2–3 days after injection of the LiPc probes using a Bruker Elexsys E540 L band EPR spectrometer equipped with an E540 R36 L band resonator (36 mm sample access), an E540 GCL Triple axis coil set (gradient field strength up to 40 G/cm) and an EPR 066L-AMC L band Microwave Bridge. Mice were anaesthetised by an injection of mixture of ketamine and xylazine as per body weight and placed inside the resonator. EPR spectrometer parameters were: 36 mW applied microwave power, 0.1 G modulation amplitude, time constant 20.48 ms, 10 s sweep time (512 measurement points), centre field approximately 384 G with 3 G sweep width, similar to the settings used in earlier works , 28. DueThe recorded EPR spectra were imported into Matlab using the Easyspin toolbox  and analWe have previously screened a large number of HNSCC cell lines to determine their cetuximab sensitivity in vitro . For thiR = −0.13, P = 0.59) could be found, but in UT-SCC-14 xenografts a very weak correlation was observed .Tumour oxygenation for the xenografts was measured using EPR oximetry at day 21 and an oxygenation approximately 50% higher was measured in cetuximab-treated tumours in both cell lines compared to untreated controls , monocarboxylate transporter 4 (MCT4) and glucose transporter 1 (GLUT1) was determined by IHC.The MCT1 and GLUT1 expression was significantly decreased in the cetuximab-treated groups of both cell lines but differences were more pronounced in UT-SCC-14 xenografts Figs. . InteresThe protein expression of hypoxia inducible factor 1α (HIF1α) and carbonic anhydrase IX (CAIX), two proteins known to be involved in hypoxia, was also investigated.The nuclear expression of HIF-1α was decreased in UT-SCC-14 tumours after cetuximab treatment but was mostly unchanged in tumours from the UT-SCC-2 Fig. . HoweverThe CAIX expression was significantly reduced in cetuximab-treated UT-SCC-14 xenografts when compared to the untreated controls but in UT-SCC-2 xenografts cetuximab treatment did not change the expression of this protein Fig. .Oxygen is a key regulator and stimulant for cell growth and metabolism-related processes, and aerobic environment is important in maintaining healthy cell activities. Tumour development might however alter to a large extent the oxygenation status due to the poor vasculature of the architectural network and the environmental gradients around the blood vessels . In this2 values were evaluated. Digital filtration may cause additional broadening of up to 10% [Our method of choice for EPR oximetry was to use the particulate LiPc probes –28, whicp to 10% . Overmodp to 10% . The in-p to 10% .Tumour oxygenation has a spatial heterogeneity in HNSCCs  that migKRAS and BRAF (unpublished data). The UT-SCC-14 cell line, which showed a high sensitivity to cetuximab, exhibited a very high expression of both total and activated EGFR whereas the UT-SCC-2 cell line, which is less sensitive, had lower levels of EGFR. We and others have previously demonstrated that there is no correlation between the level of EGFR expression and cetuximab sensitivity [To improve the cure rates and expedite recovery of head and neck patients, it is important to identify reliable predictive biomarkers. Here, we investigated the tumour oxygenation and the expression of proteins involved in proliferation, metabolism and hypoxia in untreated and cetuximab-treated xenografts. In this study one cetuximab-sensitive cell line  with a high EGFR expression and one resistant cell line  with a low EGFR expression were used. Both were known to form xenografts in mice  and to hsitivity –38.In this study we investigated the influence of cetuximab on the EGFR expression and activation in vivo using IHC. In the cetuximab-sensitive xenografts investigated parameters  were significantly decreased and in the less sensitive tumours no significant differences were noticed. These data are in line with observations from one of our recent in vitro studies in which three tongue cancer cell lines  were investigated. We found that cetuximab treatment resulted in a reduction in the EGFR and pEGFR expression in the most sensitive cell line LK0902. In contrast, cetuximab treatment had no effect on the EGFR and pEGFR expression of LK0824 cells, which exhibited an intermediate sensitivity to cetuximab, whereas an increase in EGFR and pEGFR was observed in the resistant cell line LK0412.We also investigated the expression of proteins involved in metabolism and hypoxia. The transporter MCT1 is known to be an importer of L-lactate and ketone bodies into cells. MCT1 has been proposed as a possible metabolic therapeutic target in HNSCC since an increased expression has been found in the most metabolic and proliferative active HNSCC cells . In thisThe HIF-1α expression was seen to be accumulated in the tumour mass specifically in the UT-SCC-14 xenografts, which might be due to the hypoxic condition present in the tumour mass. Cetuximab treatment showed reduction in the nuclear accumulation of HIF-1α, while the overall HIF-1α expression was not significantly altered. In line with these results, Li et al have shown in HNSCC cell lines that cetuximab can downregulate HIF-1α by inhibiting the transcriptional activity . More reCAIX is a transmembrane protein and is a tumour-associated isoenzyme. It is regulated by HIF-1α, overexpressed in the hypoxic tumours and is involved in cell proliferation . CAIX isGLUT1 facilitates the cellular uptake of glucose through the plasma membrane and it is overexpressed in most of tumours including HNSCC. In the present study, the GLUT1 expression was high in both UT-SCC-2 and UT-SCC-14 xenografts. Interestingly, after cetuximab treatment a decreased expression was found in both tumours but the decrease was more pronounced in UT-SCC-14. This result together with all our results in this study show a potential connection between increased oxygenation and decreases in the hypoxia dependent proteins GLUT1, MCT1, CAIX, HIF-1α (nuclear) and in Ki67 expression in HNSCC xenografts.Taken together, our results show that cetuximab treatment affects the tumour growth and the tumour partial oxygen pressure as measured by LiPc EPR oximetry. Furthermore, we found a possible connection between the partial oxygen pressure of the tumours and the expression of proteins involved in tumour growth, metabolism and hypoxia.Supplementary Information


The statement should have read “This study was supported by an Inha University Research grant”.In the article “Assessment of hepatocellular carcinoma risk based on peg-interferon plus ribavirin treatment experience in this new era of highly effective oral antiviral drugs”,


Fluid resuscitation during cardiac surgery is common with significant variability in clinical practice. Our goal was to investigate current practice patterns of fluid volume expansion in patients undergoing cardiac surgeries in the USA.t tests, to evaluate differences among respondent groups.We conducted a cross-sectional online survey of 124 cardiothoracic surgeons, cardiovascular anesthesiologists, and perfusionists. Survey questions were designed to assess clinical decision-making patterns of intravenous (IV) fluid utilization in cardiovascular surgery for five types of patients who need volume expansion: (1) patients undergoing cardiopulmonary bypass (CPB) without bleeding, (2) patients undergoing CPB with bleeding, (3) patients undergoing acute normovolemic hemodilution (ANH), (4) patients requiring extracorporeal membrane oxygenation (ECMO) or use of a ventricular assist device (VAD), and (5) patients undergoing either off-pump coronary artery bypass graft (OPCABG) surgery or transcatheter aortic valve replacement (TAVR). First-choice fluid used in fluid boluses for these five patient types was requested. Descriptive statistics were performed using Kruskal-Wallis test and follow-up tests, including The most commonly preferred indicators of volume status were blood pressure, urine output, cardiac output, central venous pressure, and heart rate. The first choice of fluid for patients needing volume expansion during CPB without bleeding was crystalloids, whereas 5% albumin was the most preferred first choice of fluid for bleeding patients. For volume expansion during ECMO or VAD, the respondents were equally likely to prefer 5% albumin or crystalloids as a first choice of IV fluid, with 5% albumin being the most frequently used adjunct fluid to crystalloids. Surgeons, as a group, more often chose starches as an adjunct fluid to crystalloids for patients needing volume expansion during CPB without bleeding. Surgeons were also more likely to use 25% albumin as an adjunct fluid than were anesthesiologists. While most perfusionists reported using crystalloids to prime the CPB circuit, one third preferred a mixture of 25% albumin and crystalloids. Less interstitial edema and more sustained volume expansion were considered the most important colloid traits in volume expansion.Fluid utilization practice patterns in the USA varied depending on patient characteristics and clinical specialties of health care professionals.The online version of this article (10.1186/s13741-017-0071-6) contains supplementary material, which is available to authorized users. Cardiac surgeries are commonly performed procedures that almost universally require fluid resuscitation during the intraoperative and perioperative period  involved in cardiovascular surgeries in the USA was developed to (a) examine the use of different fluid types for resuscitation  in patients undergoing cardiovascular surgery, (b) determine whether certain patient characteristics and/or practice settings influence the type of fluid utilized for resuscitation, (c) determine whether the fluid selected for resuscitation varies by clinical specialties of the treating HCPs, and (d) determine the fluids used to prime the CPB circuit in patients undergoing on-pump procedures.Volume expansion during CPB when not experiencing significant blood lossVolume expansion in the presence of blood loss during CPB when blood transfusion is not indicated (adequate hemoglobin [Hb])Volume maintenance during acute normovolemic hemodilution Volume expansion while patients were supported with extracorporeal membrane oxygenation (ECMO) or a ventricular assist device (VAD)Intraoperative volume expansion for off-pump coronary artery bypass graft (OPCABG) surgery or transcatheter aortic valve replacement (TAVR)This study was cross-sectional and collected survey data from 124 cardiothoracic surgeons, cardiovascular anesthesiologists, and perfusionists to investigate the patterns of fluid utilization in cardiovascular surgery. The online survey was conducted November 4 through 17, 2015, with an average survey completion time of 9 min. The 38-item self-administered questionnaire , third-generation HES or HES 130 , and blood-derived blood products other than albumin. The participants rated the frequency with which they used various fluid types for volume expansion using a 5-point scale . Participants were also asked to indicate the bolus volumes (mL) of the crystalloids and of colloids that they typically use for volume expansion. Participants rated the importance of certain colloid characteristics  and non-oncotic properties of albumin  on the patient treatment decision-making process using a 5-point scale . Four of the 38 questions addressed pump priming preferences for CPB circuits and were asked of perfusionists only.A total of 124 participants were recruited from the e-Rewards Medical panel. e-Rewards Medical is a leading provider of market research services to the professional health care community. The panel consists of HCPs who have opted to become members of the panel and were paid for their time. Email invitations for participation in this study were sent from e-Rewards to the non-probability sample of its panelists, meaning this sample set was not a random selection of all physicians. Of note, the panelists remained anonymous to the investigators in this study. The email invitation provided a general description of the survey topic  and a link by which to access the online survey. Each invitation contained a unique identifier that prevented any one respondent from taking the survey more than once. To qualify for participation, respondents had to specialize in cardiac surgery, adult cardiovascular anesthesiology, or be a perfusionist; had been in practice for at least 2 years since residency or training in the USA but not in the states of Minnesota, Vermont, West Virginia, Massachusetts, nor the District of Columbia as these states prohibit or limit compensation to physicians; and had performed or were involved in at least four cardiac bypass surgeries per month. Anesthesiologists who specialized in pediatrics were excluded because fluid management for pediatrics is different than for adults due to vast differences in the pathophysiology of their circulatory system. To ensure a minimum number of completed surveys were received from each group, subquotas were set for each clinical specialty: 50 surgeons, 50 anesthesiologists, and 50 perfusionists.This research project involved obtaining the opinions of physicians and perfusionists about their choices for the use of various fluids for volume expansion in five different hypothetical patient situations. No patient data were obtained, and no questions were asked of the participants that would help in identifying them. All participant data were de-identified. Hence, this study was exempt from requiring institutional review board approval under United States Code of Federal Regulations Title 45 Part 46.101(b)(2) by Copernicus Group Independent Review Board (CGIRB). The study did receive a formal Letter of Exemption from the CGIRB.T tests were used to evaluate differences across ratio variables . Statistical significance was assessed at the alpha level of less than 0.05. Descriptive analyses were performed using SPSS (Version 23.0).Most questions were based on 5-point scales and provided ordinal data which, by definition, are not normally distributed. As such, descriptive statistics were performed using a Kruskal-Wallis test to evaluate differences among the respondent groups on the ordinal measures. Follow-up tests were conducted to evaluate differences within and between patient scenarios. Of the 124 HCPs who completed the survey, 52 (41.9%) were anesthesiologists, 47 (37.9%) were surgeons, and 25 (20.2%) were perfusionists (Table P < .001), pulse pressure variation , and stroke volume variation .The five most commonly used indicators of volume status were blood pressure (77%), urine output (76%), cardiac output (74%), central venous pressure (73%), and heart rate 61%) . Higher percentages of surgeons than anesthesiologists chose HES and blood-derived products other than albumin, while no perfusionists chose any of those fluid types. The most frequently used adjunct fluid to crystalloids was 5% albumin  fluid for a patient needing volume expansion during CPB when not experiencing significant blood loss (scenario 1) was crystalloids, followed by 5% albumin and 25% albumin, respectively , while no HCP chose HES 130. When the first fluid choice was 5% albumin, the most frequently chosen adjunct fluid was crystalloids , HCPs chose 5% albumin most frequently as the first choice of IV fluid Fig. . CrystalP < .05) for volume maintenance during ANH. Again, 5% albumin was the most frequently used adjunct fluid to crystalloids  was crystalloids, followed by 5% albumin and then 25% albumin Fig. . AnestheP < .05), more surgeons preferred 25% albumin than anesthesiologists . Only surgeons utilized HES fluids for this scenario. When the first fluid choice was 5% albumin, crystalloid was the most frequently chosen adjunct fluid , HCPs preferred 5% albumin and crystalloids equally as the first choice of IV fluid . Crystalloid fluid was the most preferred first choice of IV fluid, followed by 5% albumin and then 25% albumin Fig. , and 5% The most important colloid traits that influenced the decision to use colloids for volume expansion seemed to be “less interstitial edema” and “more sustained volume expansion” Fig. . Most peThis cross-sectional study provided insights on the patterns of fluid utilization in cardiovascular surgery from a survey of 52 cardiovascular anesthesiologists, 47 cardiothoracic surgeons, and 25 perfusionists in the USA. This survey examined the fluids chosen for volume resuscitation by these 124 HCPs to treat five different hypothetical patient scenarios. The 25 perfusionists were surveyed to determine the solutions that they utilized to prime the CPB circuit. There were remarkable variability in clinical practice and a lack of consensus about the use of various fluid types in cardiovascular surgery patients.In this survey, the most commonly preferred indicators of volume status were blood pressure, urine output, cardiac output, central venous pressure, and heart rate. Anesthesiologists preferred transesophageal echocardiography, pulse pressure variation, and stroke volume variation as indicators of volume status significantly more frequently than surgeons. Different fluid types were chosen as the first choice of IV fluids depending on the clinical context of the patients. For example, crystalloid fluid was the predominant first choice for patients needing volume expansion during CPB without bleeding (scenario 1), for fluid maintenance during ANH (scenario 3), and for intraoperative volume expansion during OPCABG or TAVR (scenario 5). On the other hand, 5% albumin was the primary fluid choice for patients needing volume expansion in the presence of blood loss during CPB not requiring transfusion (scenario 2) or during ECMO or VAD (scenario 4). When choosing colloids for volume expansion, HCPs felt that “less interstitial edema” and “more sustained volume expansion” were more important colloid traits than “faster volume expansion,” “better respiratory function,” and “less weight gain.” “Other blood-derived products” were chosen infrequently (less than 10%) as the first choice for fluid resuscitation by both university- and non-university-affiliated physicians; the only situation in which it was chosen by more than 10% of the respondents was for patients on ECMO and VAD.In addition, there were differences in practice patterns among the clinical specialties of the treating HCPs. Significantly larger numbers of surgeons, relative to anesthesiologists, preferred 25% albumin for patients who need volume expansion in the presence of blood loss (scenario 2), for fluid maintenance during ANH (scenario 3), and for volume expansion during EMCO or VAD (scenario 4). Sixty percent of the perfusionists in this study preferred to use only crystalloids for priming the CPB circuit, while the other 40% used a mixture of albumin and crystalloids. The data from this study demonstrate that volume repletion practices vary dramatically and clinical data do not allow for consensus recommendations. While there are no large, randomized trials available to inform HCPs of definitive protocols for optimal volume resuscitation during cardiovascular surgeries, it is well accepted that fluid overload increases the risk of major complications after CABG . HES should be given with caution or possibly avoided altogether due to warnings from the FDA . In addition, perfusionists predominately preferred crystalloids to prime the CPB circuit; one third of the perfusionists preferred 25% albumin mixed with crystalloids for priming.Additional file 1:Albumin Surgical Utilization Survey. (DOCX 415 kb)Additional file 2: Figure S1.a . CPB, cardiopulmonary bypass; HES, hydroxyethyl starch. aResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient not experiencing significant blood loss when volume expansion is indicated during cardiovascular surgery with CPB? (JPEG 168 kb)Frequency of adjunct fluid use for patients needing volume expansion during CPB when not experiencing significant blood loss when the first choice is crystalloidsAdditional file 3: Figure S2.a when first fluid choice is 5% albumina (n = 52) and b when first fluid choice is crystalloidsb (n = 39). CPB cardiopulmonary bypass; Hb, hemoglobin; HES, hydroxyethyl starch. aResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient not experiencing significant blood loss when volume expansion is indicated during cardiovascular surgery with CPB? bResponses to the following question: How often do you use each of the following as an adjunct to your first choice in a patient for volume expansion in the presence of blood loss when blood transfusion is not indicated (adequate Hb) during cardiovascular surgery with CPB? (ZIP 186 kb)Frequency of adjunct fluid use for patients needing volume expansion in the presence of blood loss during CPB when blood transfusion is not indicated (scenario 2) Additional file 4: Figure S3.a . HES, hydroxyethyl starch. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient for volume maintenance during acute normovolemic hemodilution (autologous blood collection)? (JPEG 167 kb)Frequency of adjunct fluid use for patients needing volume maintenance during acute normovolemic hemodilution when first fluid choice is crystalloidsAdditional file 5: Figure S4.a when first fluid choice is albumin 5%a (n = 33b) and b when first fluid choice is crystalloidsa (n = 31b). ECMO, extracorporeal membrane oxygenation; HES, hydroxyethyl starch; VAD, ventricular assist device. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient who needs volume expansion during ECMO or VAD? bNo statistical tests were performed due to small sample size. (ZIP 169 kb)Frequency of adjunct fluid use for expansion during ECMO or VAD (scenario 4) Additional file 6: Figure S5.a . HES, hydroxyethyl starch; OPCAB, off-pump coronary artery bypass surgery; TAVR, transcatheter aortic valve replacement. aResponses to the following question: How often do you use the following as an adjunct to your first choice for a patient who needs intraoperative volume expansion for OPCAB or TAVR? (JPEG 167 kb)Frequency of adjunct fluid use for intraoperative volume expansion for OPCABG or TAVR when first fluid choice is crystalloids


Twitter channels are increasingly popular at medical conferences. Many groups, including healthcare providers and third party entities  use these channels to communicate with one another. These channels are unregulated and can allow third party commercial entities to exert an equal or greater amount of Twitter influence than healthcare providers. Third parties can use this influence to promote their products or services instead of sharing unbiased, evidence-based information. In this investigation we quantified the Twitter influence that third party commercial entities had in 13 major medical conferences.We analyzed tweets contained in the official Twitter hashtags of thirteen medical conferences from 2011 to 2013. We placed tweet authors into one of four categories based on their account profile: healthcare provider, third party commercial entity, none of the above and unknown. We measured Twitter activity by the number of tweet authors per category and the tweet-to-author ratio by category. We measured Twitter influence by the PageRank of tweet authors by category.We analyzed 51159 tweets authored by 8778 Twitter account holders in 13 conferences that were sponsored by 5 medical societies. A quarter of all authors identified themselves as healthcare providers, while only 18% could be identified as third party commercial entities. Healthcare providers had a greater tweet-to-author ratio than their third party commercial entity counterparts (8.98 versus 6.93 tweets). Despite having less authors and composing less tweets, third party commercial entities had a statistically similar PageRank as healthcare providers (0.761 versus 0.797).The Twitter influence of third party commercial entities (PageRank) is similar to that of healthcare providers. This finding is interesting because the number of tweets and third party commercial entity authors required to achieve this PageRank is far fewer than that needed by healthcare providers. Without safety mechanisms in place, the Twitter channels of medical conferences can devolve into a venue for the spread of biased information rather than evidence-based medical knowledge that is expected at live conferences. Continuing to measure the Twitter influence that third parties exert can help conference organizers develop reasonable guidelines for Twitter channel activity. Medical conference organizers must strike a balance with commercial entities . Third parties are needed to offset the cost of many national scientific meetings and provide valuable information about the latest developments in the field ,2. ConcuThis model has not been replicated in the increasingly popular Twitter hashtag channels (channels) ,6. TwittIn this investigation, we used established methods to explore the Twitter influence that third parties have in the Twitter channels of thirteen prominent medical conferences from 2011–2013. In the unregulated realm of Twitter channels, we hypothesized that third parties have a comparable Twitter influence score as any other group.We identified five (5) medical societies that promoted the Twitter channels of their respective annual meetings. These societies assigned a conference-specific hashtag for each channel and registered each with the Healthcare Hashtag Project . We queried the HHP database for all conference-specific tweets using the pre-assigned hashtags. We performed five (5) separate analyses to identify the types of content within our data set. First we categorized each tweet as an “original” or “retweet”. We defined “original tweets” as messages composed by the same author who tweeted the message. We defined “retweet” as a message composed by a different author than the one who tweeted the message. Realizing that many medical conferences expose attendees to products/services offered by third party commercial entities, we defined “advertisements” as those tweets that were soliciting the attendees to a) use a product or service or b) visit an exhibition booth to learn more about a product/service. Moreover many of these products/services are offered by commercial entities that are publicly traded corporations. Thus we defined “financial tweets” as those messages whose content contained information about the underlying financial security (stock) of a particular commercial entity. In our fourth analysis we identified tweets that contained hyperlinks (URLs) to multimedia files (pictures or videos or both); we defined such tweets as “enhanced”. Finally, we calculated word frequencies in the remaining tweets that were not categorized as “advertisements” or “financial” in order to identify the most popular scientific topics that were discussed within each medical discipline. In order to quantify the influence that third parties exert in Twitter channels, we categorized every tweet author/account holder mentioned within the body of a tweet (@mentions) into one of four categories: a) healthcare provider (HCP), b) third party commercial entity (third party), c) unclear identity, and d) none of the above. To ensure inter-rater reliability when categorizing Twitter account holders, we performed a Light’s kappa statistic on a different set of previously published Twitter data . The LigWe assessed Twitter activity using two methods. In the first method, we measured the number of distinct account holders per category that authored at least one tweet in one of the 13 conferences analyzed. We defined a high Twitter activity as that category with the largest number of account holders.In the second method, we measured the total number of tweets authored by account holders in each category. We calculated the tweet:author ratio by dividing the number of tweets composed by the total number of authors within a particular category. We ascribed the greatest Twitter activity to that category with the highest ratio.Keller, Leavitt et al, and Antoniadis et al defined influence as one’s ability (user A) to affect the behavior of another (user B) –22. On TWe calculated the PageRank of every account holder that was mentioned (@mentions) in the body of a tweet. The @mentions include both the replies to and retweets of one’s original tweet. Originally developed by Page, Brin, Motwani and Winograd, the PageRank is a link-based algorithm and considered by Williams, Baldwin, and Rubel to be the best measure of social media influence –26. As dThe tweets collected from the HHP contained identifying information or links to such information. The same identifying information is freely available to the general public through the Library of Congress ,36. TwitNevertheless, the identifying information within each tweet was as critical to our investigation as our ethical use of it. Therefore, we designed our methods in accordance with the United States Department of Homeland Security’s 2012 Menlo Report–a guide for investigators performing “communication technology research” . We alsoWe performed content analyses using NOD Analytics (goo.gl/mfziXG). We used WordItOut (worditout.com) to graphically represent the popular scientific topics in each Twitter channel. We calculated frequencies per category for: 1) number of Twitter accounts that authored tweets, 2) number of Twitter accounts that were mentioned within a tweet, 3) number of tweets composed. We performed chi-square tests to compare these data using JMP Pro version 10.0.0 . We calculated PageRank using the NodeXL plugin (nodexl.codeplex.com) for Microsoft Excel 2013 . Median and interquartile ranges for the PageRank were calculated and compared using the Kruskal-Wallis test. Each group needed to have at least 8671 @mentions in order to have achieved an 80% power to detect a 0.2 difference in PageRank. To mitigate any future concern about the lack of reproducibility of our results, we 1) did not perform subgroup analyses of Twitter influence by conference and 2) followed recent guidelines that make “classical hypothesis testing more congruent with evidence thresholds for Bayesian tests” . As a reThis investigation conforms to STROBE guidelines for observational research and SAMPL guidelines for statistical reporting ,45.We collected 51159 tweets, authored by 8778 Twitter account holders, in 13 conferences, sponsored by 5 medical societies, from 2011 to 2013 . Our datIn every Twitter channel there were more original tweets composed (N = 30310) than retweets generated (N = 23643). The overall original tweet:retweet ratio was 1.28:1: the lowest ratio belonging to the American Society of Oncology’s annual meetings and the highest to the Digestive Disease Week meetings . There were very few advertisements: the non-advertisement:advertisement ratio in our data set was 19.48:1 . A similUnsurprisingly the most common scientific topic in the entire data set revolved around the “patient” . Popular topics in the American College of Cardiology Twitter channels were three clinical trials and three medications. The clinical trials were: CORONARY , PREVAIL (sponsored by Boston Scientific Corporation) and HPS2-THRIVE . The popular medications were: Cangrelor (by The Medicines Company), Niacin , and Rivaroxaban . Popular topics in the American Society of Oncology Twitter channels were specific diseases . We did not identify specific medications or clinical trials as popular topics in #asco11, #asco12, and #asco13.Medications and diseases were the popular topics in the American Society of Nephrology Twitter channels. Aliskiren , tolvaptan , and cincalcet  competed with CKD (chronic kidney disease), AKI (acute kidney injury), and ESRD/dialysis for popularity. In the Twitter channels of Digestive Disease week, the popular diseases discussed were IBD (inflammatory bowel disease), IBS (irritable bowel syndrome) and GERD . Uniquely popular in the #ddw11, #ddw12, and #ddw13 channels were explicit mentions of third party commercial entities . Lastly, the only appearance of a governmental agency amongst the list of popular topics was in the #aad12 and #aad13 channels  Figs .Nearly 61% of the authors had a Twitter profile that identified them . In thisDespite being the greatest number of authors, those with unclear identities did not compose the greatest number of tweets . The tweIn our data set, a total of 3316 Twitter accounts were mentioned a total of 39997 times . HealthcThird party commercial entities had a statistically similar PageRank as healthcare providers  despite having significantly fewer authors  and significantly less Twitter activity . This suggests that third parties are equally influential in the Twitter channels of scientific meetings as healthcare providers; a parity that is difficult to achieve at live conferences. Admittedly, there are no investigations that measure third party influence at live conferences. Perhaps the lack of data is due to conference organizers’ financial reliance on third parties to sponsor their conferences. In 2009, third parties gave close to $850 million dollars of sponsorships to various medical conferences [Conference organizers do not depend on the financial support of third parties to maintain active Twitter channels. Creating and registering a conference-specific hashtag and composing tweets are free. Yet not one of the eight conference organizers (in any of the 13 conferences studied) implemented any safeguards to limit third party “detailing” [Jalali, Wood, and others have suggested that conference organizers learn how their respective Twitter channels are being used/misused in order to curtail the Twitter influence that third parties have within them ,31,44,46Conference organizers should publicly state in their Twitter channel that third party entities should declare themselves as such in their respective Twitter profiles Conference organizers should insist that third parties compose tweets that disseminate scientific facts and not solicitations for products/servicesIf third parties wish to solicit for a product/service, they should include an additional hashtag in the body of their tweet  to allow participants within the channel to filter out such tweetsConference organizers should encourage third parties to restrict their Twitter activity to coincide with their live “hours of operation”Conference organizers should task independent individuals/groups to annually measure the PageRanks for each Twitter account mentioned (@mentions) within their conference-specific hashtagConference organizers should target third party accounts with abnormally high PageRanks for further education about best-practices within their respective Twitter channelThese recommendations would align third party activities in Twitter channels with their activities at live conferences. These recommendations are not mandates, but rather reasonable suggestions that are neither burdensome to conference organizers nor offensive to third party commercial entities. These recommendations are made with the same intentions as those that guide physician activity on Twitter ,47. In bThe PageRank of @mentions has been used by a number of Twitter researchers and is considered the closest estimation of Twitter influence ,30,32,33A common misperception is that the number of followers or impressions  is an accurate measure Twitter influence. Any metric that uses the number of followers and/or tweets often results in false calculations of Twitter influence ,22,52. BTwitter researchers do not perform content analyses to measure influence ,26. NeitThere were 3413 Twitter accounts that could not be identified because their Twitter profiles were vague or empty. These accounts generated only 24.7% of the total tweets analyzed. We consciously avoided using alternative methods to identify these accounts. In accordance with recommendations by Farnan and McKee, we assumed that account holders with vague profiles wanted to remain anonymous ,53. To rPerhaps the greatest strength of this investigation is its breadth  and depth (51159 tweets). Chaudhry et al conducted an analysis of 12644 tweets from 2 conferences sponsored by one medical society while Jalali et al analyzed 10937 tweets from 4 conferences sponsored by as many medical societies. . We measAs with many studies that study Twitter activity, we were unable to directly measure influence. We used a proxy metric to measure Twitter influence: the PageRank. However, the PageRank is considered one of the best and most commonly used markers of social media influence –27,30–34Using the PageRank as a surrogate marker, third party commercial entities exert an equal Twitter influence as healthcare providers in the Twitter channels of medical conferences. Without safety mechanisms in place, Twitter channels can devolve into a venue for the spread of biased information rather than evidence-based medical knowledge, as seen at live conferences. Continuing to measure the Twitter influence that third parties exert can help conference organizers develop reasonable guidelines for Twitter channel activity.


Pancreatic pseudocysts and walled-off pancreatic necrosis arise as a complication of pancreatitis. Multiple fluid collections are seen in 5-20% of the patients who have walled-off peripancreatic fluid collections. There is a paucity of data regarding the role of endoscopic transmural drainage in the management of multiple pancreatic fluid collections. In this case report, we present the case of a 72-year-old male with three walled-off pancreatic fluid collections in the setting of acute necrotizing pancreatitis. The patient underwent simultaneous endoscopic ultrasound-assisted cyst gastrostomy and cyst duodenostomy and aggressive irrigation without index endoscopic necrosectomy of the three peripancreatic fluid collections. Significant improvement in the size of the fluid collections was seen on the computed tomography scan, as well as a remarkable immediate clinical improvement after 24 hours of the endoscopic intervention. Walled-off pancreatic fluid collections (PFCs) are fluid collections that arise as a consequence of pancreatic injury and complicate both acute and chronic pancreatitis. Walled-off PFCs include pseudocysts and walled-off pancreatic necrosis (WOPN). The disruption of the pancreatic duct or side branches results in the collection of fluid that may or may not contain debris . PancreaA 72-year-old male presented to an outside hospital with epigastric pain, nausea, and vomiting. Physical examination revealed epigastric tenderness. Laboratory evaluation showed leukocytosis and elevated lipase  and amylase  with normal aspartate transaminase, alanine transaminase, and alkaline phosphatase. Initial imaging evaluation by ultrasound, computed tomography (CT) of the abdomen, and magnetic resonance cholangiopancreatography showed cholelithiasis with evolving necrotizing pancreatitis and normal intrahepatic, common bile, and pancreatic ducts without evidence of choledocholithiasis. Conservative management was initiated for gallstone pancreatitis; however, the patient failed to improve and was subsequently transferred to our tertiary care center. The patient’s abdominal pain worsened, and he was unable to tolerate anything by mouth due to early satiety, nausea, and vomiting. CT scan of the abdomen revealed a 9.9 x 5.7 cm fluid collection involving the pancreatic body and tail and a 5.2 x 3.6 cm fluid collection along the pancreatic head. Total parenteral nutrition was started. On Day 11, the patient’s clinical condition deteriorated with fever, leukocytosis, and increased epigastric pain. Repeat CT abdomen showed an increased size of peripancreatic fluid collections, which now appeared more well-defined with rim enhancing walls. Mild to moderate ascites was also noted. The largest collection along the pancreatic body and tail, previously measured 9.9 x 5.7 cm, was now 11.2 x 7.0 cm Figure . The smaEndoscopic ultrasound (EUS) with color Doppler was performed and it showed complex peripancreatic fluid collections with debris inside Figure .EUS confirmed that the WOPN were adjacent to the stomach and duodenum with the distance measuring less than 1 cm and there were no associated pseudoaneurysms. There was no communication of the PFCs with the pancreatic duct. Esophagogastroduodenoscopy (EGD) showed esophagitis in the mid-esophagus, likely from reflux related to partial gastric outlet obstruction from the cysts compressing the stomach. Noticeable bulges from the gastric wall in the proximal stomach and antrum were seen corresponding to the fluid collections seen on the CT scan. Duodenal bulb compression related to another cyst was also seen Figure .EUS demonstrated two cystic collections adjacent to the gastric lumen and one cystic collection adjacent to the duodenal lumen. The patient underwent simultaneous EUS-assisted cyst gastrostomy and cyst duodenostomy using three lumen apposing metal stents  Figure .Afterwards, all the stents were dilated with through-the-scope (TTS) controlled radial expansion (CRE) dilators. The collections were copiously irrigated with sterile water on index EUS intervention with plans for follow-up repeat necrosectomy. This was followed by rapid improvement in the patient’s symptoms and discontinuation of total parenteral nutrition (TPN) within 72 hours as his diet was eventually progressed from clear liquids to solids. CT of the abdomen 24 hours post-endoscopic management showed the decreased size of peripancreatic fluid collections . Pancreat Disord. 1999, 98:A1115). Each of the six patients had two pseudocysts. Half of the pseudocysts were drained via cyst gastrostomy and the other half were drained via cyst duodenostomy. No complications were seen in the cyst gastrostomy group. One patient from the cyst duodenostomy group developed a superinfection and required surgical drainage and debridement. They concluded that treatment of multiple pancreatic pseudocysts by endoscopic drainage is a safe and effective option.A retrospective study of 165 patients with pancreatic pseudocysts was conducted by Sharma, et al.  over a pOur case illustrates that simultaneous endoscopic drainage of up to three pancreatic walled-off fluid collections is a safe, effective, and minimally invasive alternative to surgical drainage in fluid collections with a mature wall within 1 cm of the gastrointestinal lumen. This approach may result in appropriate resource utilization including a decreased length of hospital stay and duration of TPN use. Further large-scale prospective studies are needed to establish the use of endoscopic drainage for the simultaneous management of multiple walled-off pancreatic fluid collections.


The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis.A  Automated OCT images analysis allows detection and quantitative assessment of retinal abnormalities , 2. The Fluid segmentation in OCT images is important for both AMD and diabetic macular edema (DME). With respect to the reported segmentation and quantification of fluid, morphological and pathological features, various aspects have been considered in the literature. These include retinal abnormality type such as AMD/DME, automated/semi-automated (with user interaction), 2D/3D and supervised/not machine learning-based. In –11, semiet al . Element x in A is expressed as x which can be interpreted as it is t% true, i% indeterminacy, and f% false [t% fluid, f% non-fluid and the confidence of this assignment is (1 − i)%. For using the concept of NS in image processing, an image should be transformed into the neutrosophic domain. The general method for this transformation was proposed by Guo et al [Suppose that f% false . This inuo et al . For OCTE(A) for image segmentation in graph cut is defined in R(A) versus the boundary property term B(A). In regional term R(A), the individual penalties for assigning pixel p to object and background, Rp(obj) and Rp(bkg), respectively, are considered. Ap is the label assigned to pixel p. Coefficient Bp,q >= 0 is interpreted as a penalty for discontinuity between p and q [Graph cut is a semi-automated global optimization method for image segmentation. In this method, each pixel in image is represented by a node in the graph, each node (pixel) is linked to its neighboring nodes with the edge cost defined by the difference between pixel gray levels. Also, two additional nodes referred as source and sink are added to represent the foreground and background, respectively. Each node of the graph is connected to source and sink with the edge cost corresponding to the probability that corresponds to how the intensity of that pixel fits into intensity probablities of foreground and background models computed from user seed points. Segmentation problem can be viewed as a binary labeling in graph cut framework which is equivalent to finding the minimum cut in the constructed graph from input image. The energy function  p and q , 49. OurT becomes more distinct, which is suitable for segmentation. Also, it was shown that segmentation in NS domain is more robust against noise. In OCT scans with a great deal of noise, it may be a good idea to transform images to NS domain and then apply fluid and layer segmentation methods. This transformation can be useful in OCT image analysis in two main aspects: 1) some abnormalities in OCT scans such as hard exudate regions and hyper-reflective structures affect conventional segmentation methods. These regions can be handled in NS domain to decrease their effects on segmentation. In this research, a new method is proposed to transform OCT scans to NS domain which is consistent with the layered structure of OCT images. In the next step, proposed layer and fluid segmentation methods are applied to images in NS domain. 2) Indeterminacy set in NS domain is used in our proposed cost function for fluid segmentation. As it will be discussed later, our proposed cost function penalizes noisy pixels. Here, pixels with high indeterminacy values are assumed as noisy pixels. The proposed method to transform OCT scans to NS domain is presented in Algorithm 1.In , it was Algorithm 1 Proposed image transformation to NS domaing (input image), Output: T, I.1: Inputs: 2: Compute 3: Consider a rectangular Gaussian filter with the dimension of .4: Rotate the filter in 10 different directions to cover 180 degrees of rotation.T to compute 10 filtered images: FIk = 1…10.5: Apply all filters to image I as: I = mink|T − FIk|.6: Compute T set as follows:7: Apply λ-correction operation to 8: Compute Entropy(It) − Entropy(It−1)| < 0.001 go to 10, otherwise go to 5.9: If |10: End.T set in step 2. Therefore, the proposed method assigns high memberships to pixels in fluid regions and low memberships to pixels in hard exudate regions and hyper-reflective structures such that these regions do not mislead the segmentation methods. Note that proposed layer and fluid segmentation methods are applied to T set. 2) A new definition of indeterminacy set is proposed. In indeterminacy definition of conventional NS, the greater the difference between each pixel and the mean of its neighbors in the square window, the greater the indeterminacy. In this definition, higher indeterminacy is assigned to pixels near the OCT layers and the boundaries even though these are not noisy pixels. In the proposed indeterminacy definition (steps 3-6), instead of considering the difference between each pixel and the mean of its surrounding window, the minimum difference between each pixel and the mean of its neighbors in 10 different directions is considered. By this way, for pixels located in layer boundaries, a filter in horizontal direction is selected in step 6 which results in the lowest difference. Thus, the indeterminacy of these pixels is not increased. 3) Instead of α − mean and β − enhancement operations in conventional NS, λ-correction operation (λ = 0.7 is chosen based on experiments) is proposed to decrease the noise effect as defined in T and I sets are used in the subsequent steps of segmentation and F is ignored. T and I sets of a transformed OCT scan to NS domain.To achieve better segmentation results, several novel approaches are applied to conventional NS transformation which are summarized as follows: 1) Since fluid regions are darker than other regions, the inverse of normalized intensity is considered as H for the calculation of vertical gradient of each pixel using T is the transformed image in NS. This filter is selected based on the fact that ILM pixels are located in the boundary of dark region (background above ILM) and bright region (retina tissue below ILM). The proposed weight computation between any two arbitrary pixels  and  is defined by MaxG is the maximum gray level of the image and R is a set of R pixels above . In this paper R is set to 40 based on experiments. Based on the filter H, the maximum of VerGrad is 2 * MaxG. So, the maximum of VerGrad + VerGrad is 4 * MaxG. Pixels which are located in the first layer have the maximum vertical gradient. Therefore, the minimum weight will be assigned between these pixels and they have the highest chance to be selected by the shortest path algorithm. The problem is that in abnormal OCT scans there are pixels in other layers which also have the maximum vertical gradient. mean(R); note that this term is very small for the pixels in ILM. Therefore, it is guaranteed that the pixels in ILM always have the minimum weight.Layer segmentation is not the main contribution of this paper. However, to improve the fluid segmentation results, ILM and RPE, the first and last retinal layers, respectively, are segmented. Since the background regions (the regions above ILM and below RPE) are very similar to fluid regions in both brightness and texture, these regions are ignored after the ILM and RPE layers are segmented. The proposed ILM/RPE segmentation methods for OCT scans of EAMD subjects are derived from the layer segmentation method based on graph shortest path in  which waH in ILM segmentation is used, RPE can be segmented. In EAMD subjects, two abnormalities referred as sub-retinal and sub-RPE fluids are the main challenges in RPE segmentation. These challenges are addressed in this paper as follows:The procedure for RPE segmentation in EAMD subjects is a more challenging task compared to ILM. In OCT scans of normal eyes, the main feature of RPE pixels is that these pixels are located in the boundary of a bright region (retina tissue above RPE) and a dark region . In normal eyes, if the inverse of filter U represents a set of U pixels below  and D denotes the vertical distance between  and ILM  is the height of ith pixel in RPE. When the RPE is elevated, a pick (or picks) are created. This pick is determined by steps 3-9 which corresponds to a point with the maximum curvature. Then, the left and right sides where the curve begins and ends are found in steps 11-15 and 17-21, respectively. Finally, left and right sides are connected by 1D linear interpolation which leads to the flattened RPE. Two samples of the layer segmentation results are shown in For sub-RPE fluid, RPE is elevated by fluid. In these cases, the gradient information is lost and it is not possible to distinguish RPE pixels. Therefore, shortest path method does not work here. For this problem, after RPE segmentation, it is flattened by the proposed Algorithm 2. In this Algorithm first all pixels in RPE are considered as a vector in which The proposed ILM and RPE segmentation guarantees that intra-retinal, sub-retinal and sub-RPE fluids are located between these layers. Therefore, in the proposed fluid segmentation method, just the region between ILM and RPE is considered. In some cases, the layer segmentation errors affect the fluid segmentation algorithm. In Section IV, we will analyze the error of the layer segmentation methods and their effects on fluid segmentation.Algorithm 2 RPE FlatteningRPE, W = 150, Tr = 401: Inputs: All pixels in Pick, PickStart, PickEnd2: Output:for i = 1:length(RPE) do       ▷ Find a point with the maximum curvature3: L = 0;R = 0;4:  forj = 1:Wdo5:  L = L + RPE(i) − RPE(i − j);6:   R = R + RPE(i + j) − RPE(i);7:   Curvature(i) = L + R;8:  Pick = maxi(Curvature(i))9: C = 1;Curr = Pick10: whileC = = 1 do         ▷ Find the left side of curvature11: ifRPE(Curr) < RPE(Curr − Tr) then12:  Curr = Curr − 1;13:   elseC = 0;14:  PickStart = Curr;15:  C = 1;Curr = Pick16: whileC = = 1 do         ▷ Find the right side of curvature17: ifRPE(Curr) < RPE(Curr + Tr) then18:  Curr = Curr + 1;19:   elseC = 0;20:  PickEnd = Curr;21:  22: Connect PickStart to PickEnd using 1D linear interpolation23: End.In this paper, graph cut is developed for fluid segmentation. In this Section, following cases are explained: 1) Why the proposed fluid segmentation method is based on graph cut, 2) what are the limitations of graph cut for this application, and 3) how we address these limitations.Optimization based on variational methods relies on approximating numerical approaches that must be very carefully designed to achieve the robustness. Also, convergence of such methods is a non-trivial issue. In contrast, graph cut image segmentation relies on powerful combinatorial optimization methods which are very straightforward, robust and repeatable numerically . Also, iBeside the computational benefits of graph cut, there are three limitations in graph cut in our problem of interest, i.e., fluid segmentation: 1) Basically, graph cut is a semi-automated method and needs user seed points for regional term and hard constraints while we focus on developing a fully-automated method for fluid segmentation. 2) Graph cut is well-developed for the optimization of binary cost functions and consequently binary image segmentation while in OCT scans there are at least 13 different texture structures in retinal layers. 3) Graph cut is very sensitive to seed points. In this research, an automated method (without needing user interaction) is proposed for seed initialization in Algorithm 3. It considers the fluid regions as object and tissue regions as background. Automated seed points obtained from Algorithm 3 are depicted in Algorithm 3 Automated seed initialization in NS domain.g (input image), Number of fluid pixels: FN (= 500) and number of tissue pixels: TN (= 700)1: Inputs: Fl, Ti.2: Output: seed point sets 3: Compute:4: α − mean and β − enhancement operations proposed in [T.5: Apply the posed in  to subseEntropy(Ii) − Entropy(Ii−1)| < 0.001 go to 7, otherwise go to 4.6: If |k − means clustering on the subset T to have a coarse partitioning .9: Fluid seed points: select k − 2) clusters, select (Npi × TN) pixels from cluster i(i = 3…k) randomly and save them in Ti set: 10: Tissue seed points: from remaining  since it is a binary segmentation which considers fluid regions as object and non-fluid regions as background. Therefore, the cost function proposed in [Ap = {′O′,′ B′} represents the label of pixel p which is either object (fluid) or background (tissue) and Ap. λ1 and λ2 are the weights for region and boundary terms, respectively. ϕ maps pixel intensities to higher dimension. For this cost function parameters are set with λ1 = 1012, λ2 = 108 and σ = 0.1. Based on Mercer’’s theorem, any continuous, symmetric and positive semidefinite kernel function can be expressed as a dot product in a high-dimensional space, without any need to know explicitly the mapping [Obtained seed points from Algorithm 3 are presented for graph cut in  for fluientation . Proposeentation  segmentsposed in  cannot b mapping , 51. In  mapping .Final fluid segmentation scheme is described in Algorithm 4.Algorithm 4 Proposed fluid segmentation method.T and I sets in NS domain with Algorithm 1.1: Transform the input OCT scan to T.2: Segment ILM and OPL layers of 3: Initialize seed points for fluid and tissue with Algorithm 3.4: Construct a graph for the proposed cost function with the same way proposed in .5: Minimize Tr pixels.6: Ignore segmented regions above ILM and below RPE and regions under 7: Return all segmented regions as fluid (object) in graph cut segmentation.The proposed methods satisfy certain properties. These are stated and proven below.Property 1. Proposed weight computation in Proof. In sub-retinal fluid, there are pixels above fluid regions with high vertical gradients (similar to RPE pixels) which mislead the shortest path algorithm. Therefore, shortest path algorithm follows a path above sub-retinal fluid instead of RPE (see n pixels which mislead the shortest path method. These pixels are depicted in P′(1), …, P′(i), P′(i + 1), …, P′(n)}. These pixels are referred as “above RPE” pixels. There is another set {P(1), …, P(i), P(i + 1), …, P(n)} referred as “under RPE” pixels which should be selected by shortest path method. Note that corresponding pixels in these sets have the same column number. Each pixel P(i) has a column and row number of ai and bi, respectively. Therefore, it is represented by . What we are showing here is how the proposed weight computation leads the shortest path algorithm to select “under RPE” pixel set instead of “above RPE”. RPE see . SupposeD, D′, U and U′ parameters it is clear that:MaxG is same for both sets. To evaluate the relation between VerGrad terms two cases are possible:In In this case:Therefore:In this case:U is a set of pixels in choroid region while U′ is in fluid region. Therefore, inequality β parameter is multiplied to the left side. Therefore, it can be concluded that:It may be mentioned that Therefore,P(1), …, P(i), P(i + 1), …, P(n)}). Therefore, shortest path method does not mislead for “above RPE” pixels.From It should be noted that although inequality Property 2. Initialized seeds by Algorithm 3 lead to more accurate results in fluid segmentation in graph cut.Proof. In Algorithm 3, OCT images are first transformed to NS domain in steps 3-6. In this transformation, high memberships are assigned to fluid pixels and then noisy pixels are penalized with an averaging filter of dimension 11 × 11. This filter is used in α − mean and β − enhancement operations. After that, k − means clustering is applied which leads to a coarse partitioned image with k clusters (k − 2) clusters. Based on step 10 of Algorithm 3, i). Therefore,clusters . Based oBy substituting It is proven that In Section IV, the robustness of the proposed method in Algorithm 3 will be shown quantitatively.Property 3. Proposed cost function for graph cut penalizes the high indeterminacy pixels to decrease the effect of these pixels in segmentation.Proof. The effect of noise is considered with indeterminacy set I in NS domain in Algorithm 1. Based on the graph construction procedure in [Rp(′B′) is the weight between pixels and Object terminal node. In Rp(′B′)). If these pixels are noisy ), their strong connection will be weaker by multiplier (1 − I) in edure in  for regiTo show the effectiveness of the proposed cost function, the segmentation results of this cost function are compared with the cost functions in , 44 qualμm/pixel along the length and 3.87μm/pixel along the width. Fluid regions were segmented by two UMN ophthalmologists (DDK and PMD). The experimental procedures involving human subjects described in this paper were approved by the Institutional Review Board (IRB) at the University of Minnesota. The second dataset is from the OPTIMA Cyst Segmentation Challenge and contains 4 subjects with 49 images per subject where the image resolution varies from 512x496 to 512x1024. This dataset can be found at: http://optima.meduniwien.ac.at/challenges/optima-segmentation-challenge-1/.In this work, two datasets have been used for the evaluation of the proposed fluid segmentation method. The first dataset is a local dataset from the UMN ophthalmology clinic containing 600 OCT scans collected from 24 EAMD subjects which were taken using the Heidelberg Spectralis imaging system. We have selected 25 scans with the highest area of fluid among about 100 scans in each subject. Scans are obtained by averaging 12-19 frames with the resolution of 5.88ILM/RPE segmentation separates retinal tissue from background. After applying fluid segmentation method, just the segmented regions between ILM and RPE are considered. Therefore, if ILM and RPE are segmented below and above fluid regions, respectively, these regions will be lost and these cases are considered as layer segmentation errors which are reported in The 2D fluid segmentation results of the proposed method are compared with the ground truth annotated by two experts, graph cut (GC)  and kernNC) in this method should be assigned accurately. Here, NC = 7 is considered which leads to better results. Tables For having a fair comparison, in both GC and KGC, segmented ILM and RPE layers are used in post processing. Also, in GC, the automated seed points obtained from Algorithm 3 are used since GC is basically an interactive method. Segmentation results in UMN and Optima datasets are depicted in Figs In 3D OCT volumes, if there are fluid regions in consecutive 2D scans, fluid volumes are obtained. In 21 OCT volumes (out of 24 volumes) in local UMN dataset there are fluid regions in subsequence scans; therefore, 3D fluid volumes can be segmented. Graph cut is very sensitive to seed points. Changes in seed points lead to significant changes in segmentation results see . In the In this study, correlation between observers referred as grader 1 (G1) and grader 2 (G2) is analyzed and reported in One of the challenges of the automated seed initialization method in Algorithm 3 is to determine the clusters for fluid and tissue seed selection. Based on the transformation method in Algorithm 1, the fluid regions have the maximum intensity. After sorting in step 8 of Algorithm 3, the first cluster  is the best candidate for fluid seed selection. The challenge is that in some scans with very dark background, this cluster contains only background regions. In such cases, fluid regions are assigned to second or maybe third cluster. Therefore, fluid seed selection from the first cluster lead to incorrect seeds and low accuracy segmentation. To address this problem, fluid seed points are selected from the first and second clusters. For normal scans (without fluid), a few fluid seed points are selected in step 9 of Algorithm 3. This leads to small segmented fluid regions in graph cut segmentation and these regions are ignored in step 6 of Algorithm 4. Therefore, OCT scans without fluid are detected as normal scans automatically.To evaluate the performance of the proposed method on OCT data acquired from other imaging devices, a Cirrus OCT dataset from Optima challenge 2015 including 512 Bscans from 4 subjects is used. From OCT Bscans, three types of fluid including intraretinal, subretinal and sub-RPE are clinically distinguishable. Intraretinal fluid are revealed as contiguous fluid-filled spaces containing columns of tissue. Some times, these spaces appear as separated hyporeflective cystoid pockets. When these regions are too small, they are referred as micro-cyst regions. Accumulation of a clear or lipid-rich exudate underneath retinal space creates subretinal fluid. Note that intraretinal and subretunal fluid types are common in both AMD and DME subjects. In early and neovascular AMD, detachment of the RPE layer along with the overlying retina from the remaining Bruch’s membrane occurs which is referred as Pigment Epithelial Detachment (PED). This is due to the accumulation of sub-RPE fluid. This type of abnormality appears as serous, fibrovascular and drusenoid. In this research, the collected UMN dataset contains all three fluid types while datasets from Optima challenge contain only intraretinal fluid. Intraretinal and subretinal fluids are segmented by Algorithms 1, 3 and 4. Sub-RPE fluid is segmented by the proposed RPE segmentation and RPE flattening (Algorithm 2). It may be worth mentioning that, the proposed methods for sub-RPE fluid segmentation is not affected by the intensity corresponding to fluid regions which leads to the segmentation of any type of RPE elevation.In line with advanced EAMD treatment methods, OCT has emerged as an essential adjunct for the diagnosis and monitoring of this disease and the ability to accurately segment fluid as an EAMD biomarker is a prerequisite for ophthalmologists in treatment process. In this research, an automated algorithm for 3D fluid volume segmentation in subjects with EAMD pathology has been proposed based on graph cut, graph shortest path and neutrosophic sets. To show the efficiency of the proposed method, it was tested on two OCT datasets. Results show that fluid volumes obtained from our proposed algorithm closely correlated to the manual segmentations of the two ophthalmologist experts in 3D fluid volume and achieve better performance in comparison with prior methods with respect to dice coefficient, sensitivity and precision measures in 2D individual scans. Future efforts will be directed towards fine-tuning the algorithm for OCT images obtained from other manufacturers and DME subjects which do not have RPE elevation. Finally, reproducibility studies between segmentation following repeat imaging can be addressed as another future work.


MDSCs and TAMs accumulation and their activities are potentially valuable therapeutic goals. In this study, we show that negative immune checkpoint molecule B7‐H3 is significantly overexpressed in human head and neck squamous cell carcinoma (HNSCC) specimen as compared with normal oral mucosa. Using immunocompetent transgenic HNSCC models, we observed that targeting inhibition of B7‐H3 reduced tumour size. Flow cytometry analysis revealed that targeting inhibition of B7‐H3 increases antitumour immune response by decreasing immunosuppressive cells and promoting cytotoxic T cell activation in both tumour microenvironment and macroenvironment. Our study provides direct in vivo evidence for a rationale for B7‐H3 blockade as a future therapeutic strategy to treat patients with HNSCC.Immature myeloid cells including myeloid‐derived suppressor cells (MDSCs) and tumour‐associated macrophages (TAMs) promote tumour growth and metastasis by facilitating tumour transformation and angiogenesis, as well as by suppressing antitumour effector immune responses. Therefore, strategies designed to reduce  HNSCC is the seventh most common cancer worldwide, with more than 600,000 new cases reported each year Since the mid‐1990s, it has been shown that MDSC recruitment at the tumour site is associated with an increased rate of metastasis and recurrence Recent reports indicate that immune checkpoint blockade antibodies (e.g. anti‐CTLA‐4 and anti‐PD‐1) enhance T cell antitumour activity and have produced exciting and durable results in treatment of a number of cancers Pten and Tgfbr1 may contribute to the tumour‐immunosuppressive microenvironment with concomitantly significant increase in MDSCs and TAMs. Moreover, we found that the blockade of B7‐H3 significantly reduces MDSCs and TAMs, as well as promoting IFN‐γ secretion of cytotoxic T cells in our HNSCC mouse model. These findings indicate that targeting B7‐H3 may be a potentially effective therapeutic approach to treat patients with HNSCC.In this study, we demonstrated that a significant increase in B7‐H3 expression is an important immunosuppressive mechanism in human and mouse HNSCC. Oncogene activation by the conditional knockout of Tgfbr1/Pten 2cKO mice  were maintained, genotyped and induced with oral application of tamoxifen (2 mg/kg in 200 μl corn oil by five consequent days) as previously described Tgfbr1flox/flox; Ptenflox/flox; K14‐CreERtam−/− mice from the same cage with same dose tamoxifen were used as wild‐type control The time‐inducible tissue‐specific in vivo monoclonal antibody  purchased from BioXcell  was stored at 4°C in a concentration of 6.96 mg/ml. The working solution was further diluted in PBS with a final concentration of 1 mg/ml immediately before use. The in vivo isotype control  was used for prophylactic tumorigenesis experiments. After oral gavage with tamoxifen (2 mg/kg) for five consequent days (day 1 to day 5), the Tgfbr1/Pten 2cKO mice were intraperitoneally injected with 0.3 mg of MJ18 every other day starting from day 14 . Isotype control mice received isotype IgG1 . All animals were routinely inspected and monitored every other day. Tumour size was measured with a micrometer calliper and photographed every other day. The end‐point was determined according to a systematic evaluation by the veterinary doctor. The mice were killed at the end of the study, the immune organ and the tumour were harvested as soon as possible, and tissues were fixed in paraffin overnight or frozen at ‐80°C for immunostaining or Western blot analysis.The antimouse B7‐H3‐blocking The single‐cell suspension was obtained from the spleen, lymphocyte node (LN), blood and tumour of HNSCC mouse model as previously described Tgfbr1/Pten 2cKO mice were lysed in a T‐PER buffer containing 1% phosphatase inhibitors and complete mini cocktail . Detailed procedures of immunoblotting were described previously T705, CXCL1, CCL2 and GAPDH.Spontaneous tumours that developed in On approval from the School and Hospital of Stomatology of Wuhan University Medical Ethics Committee, the informed consents were obtained from the patients. Further details have previously been described The tissue array sections were stained as per the previous protocol Tumour sections were hydrated in alcohol, washed three times in PBS, retrieved using sodium citrate in a pressure cooker, blocked with 2.5% bovine serum album in PBS buffer for 1 hour at 37°C and then incubated with primary antibody or isotype‐matched IgG controls overnight at 4°C. The next day, slides were incubated with fluorochrome‐conjugated secondary antibodies  and mounted in Vectashield with 4′,6‐diamidino‐2‐phenylindole . Fluorescence images were then captured using a Zeiss CLSM‐310 fluorescence microscope.anova followed by the post hoc Tukey multiple comparison tests and unpaired t‐test was used to analyse the differences in positive cells and immunostaining within each group. Two‐tailed Pearson's correlation statistic was used for correlation between B7‐H3, CD11b, CD33, CD68 and CD163. Kaplan–Meier analysis followed by log‐rank test was used to compare the survival of patients in the two groups. Mean values ± S.E.M. with a difference of P < 0.05 were considered statistically significant .As reported previously, hierarchical clustering was performed using Cluster program with average linkage based on Pearson's correlation, visualized using the TreeView program. Data analyses were carried out using GraphPad Prism version 5.0 for Windows . One‐way P < 0.05, fold change >1.5). In a meta‐analysis consisting eight data sets of head and neck cancer gene expression profiling, the mRNA level of CD276 was significantly increased in HNSCC compared with the control counterpart  and dysplasia (P < 0.001). We further analysed the clinicopathological characteristics of HNSCC. Of interest, Kaplan–Meier survival analysis indicated that high expression of B7‐H3 confers poor overall survival in patients with HNSCC  leads to a fast and full penetration of HNSCC tumorigenesis in these mice +Gr1+ and CD11b+F4/80+ cell populations was found in the tumour and spleen Tgfbr1/Pten 2cKO mouse model.Tgfbr1/Pten 2cKO mice. The mice received an intraperitoneal injection of B7‐H3‐blocking antibody  or isotype control rat IgG1  every other day  at the tumour site and in the blood The rapid‐fire clinical successes from blocking the ligands of B7 superfamily, such as CTLA‐4 and PD‐1, have opened prospects for extending this potential cancer immunotherapy by inhibiting more recently discovered checkpoint ligands and receptors + T cells may be suppressed in cancer through the recruitment of MDSCs +CD8+ T cells in the spleen and LN. Similar results were also reported indicating that B7‐H3 directly augments CD8+ T cell effector function and may play an important role in overcoming CD8+ T cell immunoregulation to acquire aggressive growth B7‐H3 blockade may offer a feasible antitumour function by reinvigorating antitumour immune response. B7‐H3 inhibits T cell activation and proliferation and is correlated with clinicopathological features in tumour, which suggested that B7‐H3 may play a role as an adversary in antitumour immunity Tgfbr1/Pten 2cKO mice are functionally recruiting myeloid cells, such as Ccl2, Ccl3, Cxcl1 and Csf1+HLA−DR−/low MDSCs in non‐small‐cell lung carcinoma (NSCLC) Immature myeloid cells, including MDSCs and TAMs, are essential components of the tumour stroma which are deeply intertwined in the course of tumour development and tumour recurrence In summary, the present study identified that B7‐H3 may serve as a potential biomarker for HNSCC and its overexpression may confer poor prognosis of patients with HNSCC. Using immune competent HNSCC mouse model, we have directly observed that targeting B7‐H3 decreased immature myeloid cells (MDSCs and TAMs) and promoted T cell activation in tumour microenvironment and macroenvironment. Thus, our study strongly suggests that B7‐H3 may be a potential target for immune therapy for patients with HNSCC.The authors declare no conflict of interest.Figure S1. CD276 gene increased in human HNSCC.Click here for additional data file.