Poria cocos on organ injuries caused by high-volume FR.  To explore the effect of different-volume fluid resuscitation (FR) on organ functions in severe acute pancreatitis (SAP) and to elucidate the therapeutic effect and mechanism of N = 30) were randomly divided into five groups: sham, model, low-volume FR (1.5 ml/kg/h), high-volume FR (10 ml/kg/h), and Poria cocos combined with high-volume FR ; serum or plasma indicators and histopathologic scores were compared to explore the effect and mechanism of different fluid volumes and Poria cocos on organ function in SAP.  1. Clinical study: retrospective analysis of thirty-one patients about the effect of titrated fluid resuscitation protocol (TFR) on the occurrence of acute kidney injury (AKI) secondary to SAP. 2. Experimental study: rats , serum creatinine (Cr), matrix metalloproteinase (MMP), and pathologic scores of the pancreas and kidney. High-volume FR increased ascites, MMPs, and kidney pathologic scores. Poria cocos reduced MMP release, relieved glycocalyx damage, and alleviated the pancreas and kidney injury aggravated by high fluid volume in SAP. Therefore, endothelial glycocalyx protection might be a new strategy in the treatment of SAP. TFR-associated lower fluid volume and velocity reduced the occurrence of AKI secondary to SAP. High volume might aggravate AKI via increased MMP release leading to endothelial glycocalyx damage and vascular endothelial dysfunction.  Acute pancreatitis (AP) is a common acute inflammatory disease in pancreas, and majority of this disease is mild and self-limited. The number of people attacked by AP goes up worldwide and 10%–20% of the mild cases progress to severe acute pancreatitis (SAP) with a mortality of 10% to 30% among those severe cases , 5. MortVascular leakage induced by inflammatory exudation increased the risk of hypovolemia, organ hypoperfusion, and extrapancreatic organ injuries like the kidney . Early fPoria cocos, a traditional Chinese medicine, has the functions of promoting water and dampness distribution and metabolism and promoting the spleen to metabolize water and dampness. Whether Poria cocos can alleviate organ dysfunction induced by high-volume fluid resuscitation needs further studies.Why does organ dysfunction still exist after partial or complete fluid resuscitation? Microcirculation dysfunction contributed to multiple complications in SAP . Fluid rPoria cocos on organ injuries caused by high-volume fluid resuscitation.Little randomized controlled trials focused on AKI secondary to SAP. This study retrospectively analyzed the effect of different volume especially in titrated fluid resuscitation protocol-based volume on the occurrence of AKI among SAP patients. Furthermore, we designed an experiment to explore the effect and mechanism of different-volume fluid resuscitation on organ injuries and to elucidate the therapeutic effect and mechanism of ChiCTR1800019855). SAP patients (≥18 years of age) admitted to hospital from June 2016 to May 2017 were included, and information was extracted from the electric medical record system including demographics, infusion volume, physiochemical measurements, complications, in-hospital death, and comorbidity. A total of 316 patients were identified, and 31 patients (TFR group 13 vs. the control group 18) were ultimately analyzed.A retrospective analysis of the effect of the initial titrated fluid resuscitation (TFR) on the occurrence of AKI in patients with SAP was conducted at the West China Hospital of Sichuan University . Based o(1) Inclusion Criteria. (a) Diagnosed as SAP based on the revised Atlanta classification [fication ; (b) adm(2) Exclusion Criteria. (a) Acute or chronic heart failure; (b) pulmonary hypertension and pancreatic portal hypertension; (c) chronic kidney disease; (d) pregnancy pancreatitis; and (e) cancer.Eligible patients were divided into the TFR group and control group. The TFR group received titrated fluid resuscitation (TFR) protocols established according to guidelines and previous study: (i) when three of the following five items met, fluid resuscitation was initiated at a rate of 3 ml/kg/h: (a) urinary output ≤ 0.5 ml/kg/h; (b) mean arterial pressure ≤ 65 mmHg; (c) heart rate ≥ 120 beats/min; (d) hematocrit ≥ 44%; and (e) blood lactic acid ≥ 4 mmol/L , 9, 11. In the control group, the start-up of fluid resuscitation and the adjustment of fluid volume and velocity were based on the physicians' judgment according to the condition of patients.Except for the different fluid resuscitation protocol, two-group patients appropriately received the routine management including analgesic, decompression, and nutrition based on the guidelines of AP .(1) Primary Outcomes. The primary outcome was the occurrence of AKI within 72 h after admission. AKI was diagnosed if one of the following two items met: (a) serum creatinine ≥ 1.5 times of baseline or ≥ 26.5 µmol/L; (b) urine less than 0.5 ml/kg/h lasted ≥ 6 consecutive hours [ve hours . Evaluatve hours , 23.(2) Secondary Outcomes. The secondary outcomes were organ failure, death, length of stay, fluid input, and output condition within the first 72 h of admission , intensive care unit (ICU) admission, ventilator use, pancreatitis complications, pancreatitis-related surgery, and severity of AP. Patients' demographics consisted of age, gender, etiology, and body mass index (BMI).Organ failure was diagnosed when one of three systems including respiratory, cardiovascular, or renal system scored 2 or more . Organ fFluid input and output conditions within the first 72 h of admission included the records of 24-hour input volume of intravenous fluid, 24-hour total input or output volume, and hourly urine output and intravenous fluid velocity to evaluate the responsiveness of fluid therapy. Heart rate, hematocrit, mean arterial pressure, and blood lactic acid were used to evaluate the responsiveness of fluid therapy and prognosis as well , 14.Computed tomography (CT) scan findings of pneumonia, pericardial effusion, pleural effusion, and pancreatitis complications including peripancreatic fluid collection, pseudocyst, acute necrotic collection, and walled-off necrosis were recorded for the prediction of organ failure. CT scores and grades were calculated based on the modified computed tomography severity index (MCTSI) score and the Balthazar CT grade , 25. AddPoria cocos boil-free granules were prepared and purchased from the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine . Pentobarbital sodium salt and sodium taurocholate were purchased from Chengdu Baoxin Biotechnology Co., Ltd  and Beijing bailingwei Technology Co., Ltd  respectively. The Rat Syndecan-1 ELISA kit, SD MMP ELISA kit, SD hyaluronan ELISA kit, and SD soluble CD44 ELISA kit were purchased from Shanghai Chenyi Industrial Co., Ltd . Y-type and Z-type disposable intravenous indwelling needles (24 G) were purchased from Jiangxi Sanxin Medical Technology Co., Ltd  and Henan Xinwei Medical Equipment Co., Ltd , respectively. The BL-420S biological function experimental system and PT-102 blood pressure sensor were provided by Chengdu Taimeng Technology Co., Ltd . Medical microtube  were purchased from Scientific Commodities, Inc. .This animal experiment performed abiding by legislation on the Care and Use of Experimental Animal was approved by the Animal Ethical and Welfare Committee of West China Hospital, Sichuan University (No. 2019175A), and the results were reported according to the ARRIVE Guidelines (ARRIVE Checklist). 30 male Sprague-Dawley rats (body weight: 320 ± 30 g) were purchased from Experimental Animal Center of Sichuan University . Animals were raised adaptively in cages (5 rats per cage) with free diet for one week in independent ventilation system at 21 ± 1°C room temperature and 12 h light/dark cycle.n = 6 per group): sham group (SHAM), model group (NFR), low-volume fluid resuscitation group (LFR), high-volume fluid resuscitation group (HFR), and Poria cocos combined high-volume fluid resuscitation group (FL + HFR). All rats were fasted for 12 hours and drank freely before operation. Rats were anaesthetized by 2% pentobarbital sodium solution , and one-fifth of the initial dose was administered intravenously per hour to maintain anesthesia. Then, rats were detained and heparinized (50 IU/mL) (vein needle at tail vein)  to establish infusion channel and left femoral artery  to continuously record mean arterial pressure and heart rate by the BL-420S biological function experimental system [Thirty rats were randomly divided into five groups  at the dose of 5 g/kg through the duodenal drug administration catheter. Next, all rats were conducted a two-hour observation and received euthanasia by overdose anesthesia finally.At 30 minutes after modeling, rats in the LFR (1.5 mL/kg/h), HFR (10 mL/kg/h), and FL + HFR (10 mL/kg/h) groups started the eight-hour fluid resuscitation of normal saline with the microinjection pump though the tail vein. At ten hours after fluid resuscitation start-up, all rats received euthanasia by overdose anesthesia. Blood sample were collected from the heart, centrifuged at 3000 rpm for 5 min at 21°C, and stored at −80°C. Serum examination of amylase, creatinine, blood urea nitrogen, and lactate were analyzed by the automatic biochemical analyzer  at the Chengdu Office Hospital of the people's Government of Tibet Autonomous Region. Plasma levels of Syndecan-1, matrix metalloproteinase (MMPs), soluble CD44, and serum hyaluronan were tested by enzyme-linked immunosorbent assay (ELISA) using ELISA kits according to product instruction. Arterial blood gas analysis was conducted by the automatic analyzer .Ascites was collected with a syringe after blood collection. Urine was collected by using a tube with a tape fixed to the external urethral orifice and measured by a syringe.μm, and performed with hematoxylin and eosin (H&E) staining. Stained slicers were observed and scored by two independent pathologists blinded to grouping according to histopathological scoring criteria [Fresh tissue of the pancreas and kidney was fixed in 4% paraformaldehyde, dehydrated in gradient ethanol, embedded in paraffin, sectioned at a thickness 5 criteria , 29.U or Student's t test for nonnormally or normally distributed quantitative data, respectively. The multiple group comparison of normally distributed data was performed by one-way ANOVA with Bonferroni test for data in variance homogeneity and by the Welch test with ther Tamhane test for data in variance inhomogeneity. The same methods were used for repeated measurement data of mean arterial pressure and heart rate. The multiple-group comparison of nonnormally distributed data was performed by the KruskalWallis H test. Logistic and multiple line regression analyses were performed to assess impact factors of AKI. A two-sided p value < 0.05 was the statistical difference. Means replace the missing values.Data were presented as mean ± standard deviation and median (interquartile: p25–p75) for normally and nonnormally distributed quantitative data, respectively. Categorical data were presented as frequency and percentage. Results were analyzed with PEMS 3.1 for Windows  and reviewed by Dr. Hai Niu who came from College of Mathematics, Sichuan University. Two-group comparison was analyzed with Fisher's exact probability for categorical data and the Mann–Whitney Thirty-one patients (13 in the TFR group and 18 in the control group) were included in the analysis . No statμmol/L, p=0.029) and lower incidence of AKI  than those in the control group.The TFR group had a lower median serum creatinine , Marshall score , serum bilirubin , and higher oxygenation index  than in the control group. As for use of mechanical ventilation, the TFR group started later  with shorter duration  than that of the control group. No significant difference was found in the occurrence of POF, persistent multiple organ failure, length of stay, and mortality.At first 24 h after admission, the TFR group had lower organ failure  and fluid velocity  than those in the control group. The urine output of the TFR group in the first three days after admission was significantly lower than that of the control group: 24 h urine volume , 48 h urine volume , and 72 h urine volume . No significant differences was found between the two groups in hourly urinary output, interval time between admission and fluid resuscitation, duration of fluid resuscitation, and total volumes of acetated Ringer's sodium and lactated Ringer's solution.SAP patients in the TFR group received lower fluid volume . Hematocrit is related to severity and mortality rate of AP. The hematocrit level of the TFR group reached to the goal (35%–44%) of fluid therapy in the first 48 h  and 72 h . There were no significant differences between two groups in the use of antibiotic, comorbidity, pancreatic complications, pancreatitis-related surgery, and ICU admission.The TFR group had a shorter parenteral nutrition duration  and Marshall score  were risk factors for predicting AKI secondary to SAP  Effect of Inflammatory Response of SAP on Organ Function. The SAP model had obvious intravascular volume depletion, inflammatory exudation, and functional changes of the lung, kidney, and heart. Compared with the sham group (SHAM), the AP model (NFR) had observed elevated white blood cell, hematocrit, oliguria, lactate, and ascites presenting enhanced inflammatory injury, intravascular volume depletion, tissue perfusion dysfunction, and exudative effusion. Increased blood urea nitrogen and creatinine and decreased pH, partial pressure of oxygen, and arterial oxygen saturation were observed in the NFR group presenting the kidney and lung injuries (injuries . The meainjuries . In the injuries .(2) Effect of Low- and High-Volume Fluid Resuscitation on Organ Function. Both low- and high-volume fluid resuscitation alleviated the pathological changes in the pancreas, but low volume was better. Low-volume fluid resuscitation (LFR) decreased the blood urea nitrogen, creatinine, and kidney pathological scores than high-volume fluid resuscitation (HFR).HFR increased AKI. HFR improved pH, partial pressure of oxygen, lactate, and oliguria than LFR at 10 hours after AP onset, but increased ascites, blood urea nitrogen, creatinine, and kidney pathological scores aggravating exudative effusion and kidney function and pathological injury than LFR . In the Endothelial glycocalyx might be involved in organ injury exacerbated by HFR. HFR increased the release of matrix metalloproteinases (MMPs), leading to endothelial glycocalyx degradation and development of AKI . Plasma (1) Therapeutic Effect of Poria cocos on Organ Injury Caused by HFR. The Poria cocos-combined high-volume group (FL + HFR) could improve the arterial oxygen saturation, attenuate the inflammatory exudation, and moderate the renal injury aggravated by HFR. Blood urea nitrogen, creatinine, and renal pathological score were lower, and arterial oxygen saturation was higher in the FL + HFR group than that in the HFR group. Poria cocos could alleviate the morphological changes of the pancreas and kidney in inflammatory infiltration, edema, hemorrhage, and necrosis than those in the HFR group  Mechanism of Poria cocos Moderated Organ Injury Caused by HFR. Poria cocos might moderate kidney injury by decreasing MMPs release and reducing endothelial glycocalyx degradation and damage. Plasma MMPs in the FL + NFR group was lower than that in the NFR group. Serum hyaluronan in the FL + NFR group was higher than that in the NFR group. No significant difference of Syndecan-1 and soluble CD44 were found between two groups  was recommended as basic fluid therapy strategy. We found TFR, which is based on the concepts of EGDT, could reduce the fluid volume and velocity and ultimately reduce the AKI secondary to SAP. Meanwhile, increased fluid velocity increased the risk of AKI secondary to SAP. Then, we performed the experimental study to explore the effect of different-volume fluid resuscitation on AKI secondary to SAP and found high-volume exacerbated inflammatory exudation and worsened AKI. Endothelial glycocalyx degradation and vascular endothelial dysfunction might be the cause of high-volume-related AKI in SAP. We found traditional Chinese medicine of Poria cocos could reduce abdominal exudation and renal injury and improve arterial oxygen saturation. Previous study shows that Poria cocos could improve renal metabolism and reduce kidney injury [Poria cocos though promoting distribution and metabolism of fluid. So increased fluid volume, high-volume, particularly, increased the incidence of AKI. SAP patients with AKI possessed higher mortality but our analysis failed to found it [SAP patients with AKI accounted from 15.05% to 69.3% in the previous study and 38.7% in our study , 5. Fluiy injury , 34. We Poria cocos treatment group.Creatinine is regarded as an index to evaluate the kidney injury according to the Kidney Disease: Improving Global Outcomes and Modified Marshall Scoring System. Creatinine was involved in hemoconcentration and was used to predict the organ failure of AP . We founMean arterial pressure, urine output, heart rate, blood lactic acid, and hematocrit were regarded as indicators of fluid responsiveness measurements , 14. MeaBlood lactic acid can predict pancreatic infection and also was suggested as a goal of fluid resuscitation , 40. Blo2/FIO2 ratio), systolic blood pressure, and bilirubin were regarded as physiologic measures to assess the organ function [2/FIO2 ratio at the first 24 h in the TFR group compared with that in the control group. As for use of mechanical ventilation, the TFR group started later with shorter duration than the control group.There were many scoring systems to evaluate the severity and predict the prognosis of AP. Based on the Modified Marshall scoring system, we evaluated the organ damage and dysfunction between two groups . Oxygenafunction . Study rfunction . We founPorio cocos reduced the release of MMPs and alleviated the glycocalyx damage aggravated by high volume, but Porio cocos increased the serum hyaluronan level. Porio cocos improved the humoral metabolism and increased humoral fluid exchange between vascular and tissue which probably led to an increase in the serum hyaluronan level.Fluid resuscitation could restore the circulatory capacity, but perfusion recovery might be uneven which involved insufficient tissue oxygen supply and microcirculation disorders . Matrix There were some limitations in our study. This small-sample and retrospective study could not provide strong evidence on the effect of TFR involving lower volume that decreased AKI secondary to SAP. We cannot provide strong evidence on significant differences of glycocalyx degradation among different volume fluid resuscitation.Poria cocos reduced the MMP release, relieved the glycocalyx injury, and alleviated the pancreas and kidney injuries aggravated by high fluid volume in SAP. Thus, fluid volume optimization might reduce AKI, and endothelial glycocalyx protection might be a new strategy in the treatment of SAP [In conclusion, TFR associated with lower fluid volume and velocity reduced AKI secondary to SAP. High volume stimulating MMP release increased the endothelial glycocalyx degradation and aggravated AKI. t of SAP .


Precision medicine in breast cancer demands markers sensitive to early treatment response. Aerobic glycolysis (AG) upregulates lactate dehydrogenase A (LDH-A) with elevated lactate production; however, existing approaches for lactate quantification are either invasive or impractical clinically.Thirty female patients  with invasive ductal carcinoma were enrolled. Lactate concentration was quantified from freshly excised whole tumours with double quantum filtered (DQF) magnetic resonance spectroscopy (MRS), and Nottingham Prognostic Index (NPI), LDH-A and proliferative marker Ki-67 were assessed histologically.t = 2.2224, p = 0.0349) in grade III (7.7 ± 2.9 mM) than in grade II (5.5 ± 2.4 mM). Lactate concentration was correlated with NPI , but not with Ki-67  or tumour size . Lactate concentration was negatively correlated with LDH-A .There was a significantly higher lactate concentration (Our results showed that lactate concentration in whole breast tumour from DQF MRS is sensitive to tumour grades and patient prognosis. Aerobic glycolysis (AG), a central feature of tumour metabolism, fuels rapid growth with elevated glucose consumption and subsequent lactate production.6 Malignant transformation is underscored by genetic mutation, activating hypoxia inducible factor 1 (HIF-1) and subsequent upregulation of lactate dehydrogenase A (LDH-A), leading to increased conversion of pyruvate to lactate and the efflux of lactate through monocarboxylate transporter 4 (MCT4).7 The accumulation of lactate generates an acidic environment suppressing normal immunological functions while providing fuel to tumour cells, resulting in accelerated local invasion.8 The inhibition of lactate production induces tumour regression in cancer xenografts, underpinning the role of lactate concentration as a response marker.9The 10-year survival rate of breast cancer has improved substantially from 40.1 to 78.4% in the last 40 years,18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) sensitive to glucose uptake, suffers from the dependency on glucose delivery and exposure of patients to ionising radiation.10 Hyperpolarised 13C-pyruvate magnetic resonance spectroscopic imaging maps the conversion of pyruvate to lactate, but suffers from the high cost of labelled pyruvate and specialist equipment.11 Conventional 1H magnetic resonance spectroscopy (MRS) allows lactate quantification in neuro-oncology,12 but suffers from overwhelming contamination signal from lipid in breast. Double quantum filtered (DQF) MRS, a method recently available for clinical research,13 allows lactate quantification in high lipid tissue such as breast using standard hardware, but was so far only examined in proof of concept case studies.14Biochemical approaches, although capable of lactate concentration quantification, are performed on excised tissue or biopsy samples with limited spatial extent. We therefore hypothesise that lactate concentration derived from DQF MRS is sensitive to tumour grades and is associated with prognosis.To probe the hypothesis, we conducted a two-group cross sectional study examining the lactate concentration of whole tumour freshly excised from patients, with further comparison against histopathological findings Fig. .Fig. 1StThe study was approved by the North West–Greater Manchester East Research Ethics Committee (REC Reference: 16/NW/0032), and signed written informed consent was obtained from the patients prior to inclusion in the study.Thirty female patients  with invasive ductal carcinoma were enrolled in the study. Only patients undergoing wide local excision or mastectomy, and with a tumour size larger than 1 cm in diameter on ultrasound were eligible. Patients with previous breast malignancies or who had undergone prior neoadjuvant chemotherapy or neoadjuvant hormonal therapy were not eligible. In total, 50 patients consented to be included in the study from 53 consecutive patients in the Breast Unit at Aberdeen Royal Infirmary. From these 50 resected tumour specimens, 39 were scanned, with the remainder excluded because of either theatre delays or MRI scanner unavailability. Following histopathological examination of the excised tumour, alterations in tumour subtype or grade from biopsy that classified the tumour differently from grade II/III invasive ductal in type were excluded. In total, nine tumour specimens, including two lobular phenotype, five mixed phenotype and two grade I, were excluded.d,15 using the reported difference in lactate concentration of 2 mM between aggressive and non-aggressive tumours in xenografted mice models,16 and standard deviation (SD) of 3 mM determined from repeated phantom experiment. Therefore, to show a significant mean difference between groups at the 5% level with 80% power using an independent sample design would require 15 subjects per group.17Power calculation was based on Cohen’s 1-weighted anatomical images were acquired using standard 3D sequence with an isotropic voxel size of 1 mm, matrix size of 220 × 220, repetition time (TR) of 5.2 ms, echo time (TE) of 2.7 ms, imaging volume encompassing the specimen. Subsequently, the lactate spectrum was acquired from a single voxel snug-fit to the tumour using single voxel DQF PRESS sequence,13 with TR/TE of 1.25 s/144 ms, spectral editing frequency at 4.1 ppm and 512 averages. A reference spectrum was acquired from the same voxel using single voxel PRESS sequence with TR/TE of 1.25 s/144 ms, 16 averages. The total acquisition time for lactate spectrum was 11 min.After wide local excision or mastectomy, the fresh tissue  was immediately transported to the Aberdeen Biomedical Imaging Centre .19 Water and lactate amplitudes were quantified from reference and lactate spectra, respectively, using corresponding prior knowledge databases.20 The lactate concentration in the tumour was then computed from water and lactate amplitudes, literature breast tissue biochemical composition and relaxation properties on the 3 T scanner.21 The quantification of lactate concentration was conducted following written procedure and blinded from group allocation. Full details of quantification and spectra from breast tumour specimens are provided in All spectra were processed following standard procedures and quantified using AMARES algorithm22 Subsequently, immunostaining was performed in a single batch for Ki-67,23 LDH-A24 and LDH-B25 including appropriate positive controls. The immunostains were assessed semi-quantitatively by two pathologists independently (EH and IM), blinded from group allocation.Upon the completion of scanning, the specimen was immediately transported to the Pathology Department at Aberdeen Royal Infirmary . Normality was determined on all the collected data using the Shapiro-Wilk test. Two-sample The patient characteristics are shown in Table t = 2.2224, p = 0.0349) lactate concentration in grade III (7.7 ± 2.9 mM) than in grade II (5.5 ± 2.4 mM)  in grade III : 4.44–5.02) than in grade II   Ki-67 expression in grade III  than in grade II   LDH-A expression in grade II  than in grade III   LDH-B expression in grade III  than in grade II  (Table There was a significantly higher (M) Table . NPI was9) Table . There w%) Table . There w0) Table . There wρ = 0.3618, p = 0.0495, Table ρ = 0.3041, p = 0.1023, Table r = 0.1716, p = 0.3645, Fig. ρ = −0.3734, p = 0.0421, Table There was a significant correlation between lactate concentration and NPI  with diminished production of natural killer cells and apoptosis of T cells.8 Lactate concentration does not show significant correlation with crude tumour size or Ki-67, underscoring lactate concentration as a specific marker to AG. Proliferative activity marker Ki-67 reveals the rate of division of cellular nuclear materials28 (that is possibly discordant with the mitotic index29), while lactate enhances the survival advantage of tumour cells reflecting invasion capability.6 The negative correlation against LDH-A was likely the result of feedback inhibition30 and depletion of pyruvate and nicotinamide adenine dinucleotide hydrogen (NADH)31 in higher grade tumour. The observed higher expression of LDH-A has been previously found in less aggressive 67NR breast cancer cells under hypoxia in preclinical model16 and more differentiated tumours in patients with gastric cancer,32 while observed higher LDH-B in higher grade is in agreement with previous studies in breast cancer.33 However, carbonic anhydrase IX (CA9), a strong prognostic marker, has been recently shown to induce an acidic tumour extracellular pH in vivo,34 decoupling lactate production regulated by LDH and lactate concentration. Hence, lactate concentration is a sensitive prognostic marker, specific to the invasion capability resulted from an acidic extracellular environment.The correlation between lactate concentration and NPI indicates the prognostic value of lactate concentration in breast cancer, in line with elevated release of lactate in highly malignant breast cancer cells.26 Although triple-negative breast cancer is postulated to be more aggressive, there is no significant difference in lactate concentration between triple-negative  and non-triple-negative  in our grade III cohort, indicating no proven relation to this potential confounding factor. The deterioration of tissue after excision may affect the accuracy of lactate concentration quantification; however, the median time gap from excision to the middle of the DQF MRS scan was 25 min, significantly below the time frame for observing change in lactate concentration in postmortem tissues (3 h).35 The freshly excised tumour allows the use of a high sensitivity receiver head coil for improved measurement accuracy, while eliminating biological noise. Restriction of tumours to grade II and III with a size larger than 1 cm (24 specimens>2 cm) is to represent patient population likely receiving neoadjuvant therapy. The restriction of phenotype to invasive ductal carcinoma, the most common breast cancer, eliminates phenotype as a confounding factor, allowing adequate statistical power in a small patient cohort. The sample size is small; however, careful power calculation was conducted to assess the appropriate sample size16 before the evaluation of the lactate concentration from ex vivo tumours.The lactate concentration obtained from single voxel approach is an average over the whole tumour, and does not inform the spatial distribution of lactate within tumour, limiting the accuracy in the cases with large disparity between tumour core and rim.36 should be incorporated in further development to improve prognostic value. Future longitudinal studies on patients undergoing neoadjuvant chemotherapy will confirm the prognostic and treatment monitoring value of lactate concentration, and multicentre study with larger cohort size covering a complete range of phenotypes is essential for clinical adoption.The current acquisition of lactate spectrum was long (11 min) and only suitable for lactate concentrations higher than 1.5 mM, demanding future development work to improve the sensitivity. Lactate was measurable in all spectra, and quantification was limited to lactate spectra with CRLB below 30%. The overlap of the distribution in lactate concentration between groups limits the prognostic value of lactate concentration obtained using current methodology in breast cancer. MRS is well known to have high specificity but low sensitivity. Future technological improvement including digital hardware and adaptive signal processing for phased array coilsLactate concentration from DQF MRS is a non-invasive marker sensitive to breast tumour grade and is associated with NPI as demonstrated in a cross sectional study. Lactate concentration provides a valuable clinical research tool for potential identification of treatment targets and application to improve image guided neoadjuvant chemotherapy.Supplementary Information


A health scare can be described as a campaign that attempts to alert the public of a particular substance or activity that can lead to a negative effect on health. A recent health scare to emerge relates to the health hazards associated with the use of e-cigarettes, which has caused widespread debate, which peaked towards the end of 2019. Health scares need to be studied in the context in which they occur, and one method of studying them is through social media. This paper identifies two key topics of discussion on Twitter, which consisted of pro-vaping and anti-vaping views. The paper then identifies influential users, frequently occurring words, hashtags, and websites related to this time period in order to gain insight into e-cigarette perceptions. The paper then reviews current scientific evidence and develops a flowchart for the general public, which can be used to for public reassurance and guidance. Electronic cigarettes or e-cigarettes are handheld battery operated devices that heat liquid and deliver an aerosol that simulates smoking. They may also be referred to as electronic nicotine delivery systems (ENDS) in certain literature. In conjunction with the increased use of these devices , a healtTobacco can be traced back thousands of years and was grown as a crop from around 5000–3000 BC, led by the communities in the Andes, South America . Native Americans would cultivate and smoke tobacco for medicinal and ceremonial purposes. Tobacco eventually spread around other continents and eventually all around the world . HencefoThe tobacco industry is argued to be one of the most profitable and deadly industries that exists with cigarette retail values in 2013 hitting $ 722 billion and there being 5.7 trillion cigarettes in circulation . In todaOur study sought to examine an influential time-point related to e-cigarettes from 2019 when there was a heightened interest in e-cigarette safety. We identified a gap in knowledge as no previous empirical work has conducted an analysis of Twitter data related to this time point. Moreover, this is an important topic to study because it aims to build an understanding of how social media may play a role in the global dissemination of amateur and unfounded speculation against accepted medical research. This type of research is increasingly important, as we find medical studies being socially challenged by various social media networks at an increasing rate. We utilised a mix of social network, automated text, and link analysis in order to identify network structures, influential users, the most utilised words, and hashtags. What was the overall shape of the network structure on Twitter related to the e-cigarette debate?What key themes and/or topics emerged when Twitter users conversing about e-cigarettes on Twitter?Who were the key stakeholders, and what types of content were they sharing on Twitter related to the e-cigarette debate?We sought to address the following research questions:A further objective of the study was to review existing literature in order to develop a flowchart for consumers to assess the safety of e-cigarettes by analysing public views from Twitter and by drawing up current advice from health authorities and domain experts. This flowchart is likely to be of interest to consumers and public health agencies across the world.We retrieved data from Twitter using NodeXL related to the time when the first study was published linking cancer and vaping in mice. NodeXL utilises the Search Application Programming Interface (AP)I. We retrieved data using the keywords “ecigarette” OR “e-cig” OR “ecig” OR “vaping”, and we were able to retrieve a sufficient amount of tweets. The tweets in the network were tweeted over the 17 h, 37 min period from Monday, 07 October 2019, at 21:20 Coordinated Universal Time (UTC), to Tuesday, 08 October 2019, at 14:58 UTC. The graph represents a network of 14,912 Twitter users.We utilised social network analysis, which is an established method of studying social media content, and a complete overview of network shapes and structures can be found elsewhere . Within The network graph highlights that there were different clusters of discussion taking place on Twitter during this time with two large groups and several smaller groups indicating a number of communities that had emerged related to this topic. The ten largest groups were labelled. There was also a sizeable isolates group (Group 1), which indicated that a number of Twitter users were tweeting about e-cigarettes without mentioning or replying to other Twitter users. In The most frequently occurring words in Group 1 are displayed in The most frequently occurring words in Group 2 (the second largest cluster of Twitter users) are highlighted in These words in Group 2 highlighted the polarisation on Twitter as certain hashtags were against the idea of any restrictions on vaping by governments, and Twitter users expressed this through the use of the hashtag “#wevapewevote”, whereas those against e-cigarettes would use hashtags such as “#vapeban”.The most frequently occurring hashtags overall are summarized in n = 341) [n = 229) titled Researchers find e-cigarettes cause lung cancer in mice in first study tying vaping to cancer. Further websites also appeared such as an article titled Expert reaction to study on ecig vapour and cancer in mice, which was published by the Science Media Centre (n = 124). Interestingly, the article contained reactions from two professors with expertise in tobacco research, and both noted that the study linking e-cigarettes and cancer to mice was potentially seriously flawed in its relevance for human vapers. Another article titled Juul Is Sued by School Districts That Say Vaping Is a Dangerous Drain on Their Resources published by the New York Times was also shared (n = 88). These results are summarized in The top URL shared on Twitter during this time was entitled “Lung Damage From Vaping Resembles Chemical Burns, Report Says” published in the New York Times (n = 341) . The othResearchers find e-cigarettes cause lung cancer in mice in first study tying vaping to cancer’. A citizen who tweeted the study and noted the findings of the study for the reason for “not following trends” received over 400 retweets and 1.8 thousand likes also became influential and had 6.1 thousand followers. The user was characterizing e-cigarettes as a societal trend with dissent towards those who were using e-cigarettes. A user replied to this tweet indicating that the fear around e-cigarettes seemed irrational when traditional e-cigarettes were still being sold. Other influential accounts consisted of The Centres for Disease Control (CDC) with 1.24 million followers who tweeted about cases of lung damage of users of mostly illicit e-cigarettes containing the Tetrahydrocannabinol (THC), and Gregory Conley, with 18.1 thousand followers, who is a tobacco harm-reduction advocate and supporter of e-cigarettes who was tweeting at this time. Influential users were ranked by the betweenness centrality algorithm using NodeXL. Influential users consisted of CNBC, which had 3.3 million followers, and the news anchor of Columbia Broadcasting System’s (CBS) Nightly Business Report, who had 31.3 thousand followers. The reason for the prominence of CNBC was because they had published an article on October 7th titled ‘Much of the content around this time related to Twitter users sharing the news story linking vaping and cancer in mice. Furthermore, our tweet clustering, word, and hashtag analysis revealed two further main themes taking place within the overall network related to Twitter users who were, broadly speaking, either pro-vaping or anti-vaping. In order to confirm this finding, we utilised content analysis to categorize tweets until thematic saturation occurred, and we found two additional main themes related to pro-vaping or anti-vaping views. Twitter users who were supporting vaping tweeted how using vaping products had helped them stop smoking and also highlighted how the current issue around vaping was very specific to certain illicit types of vaping products that contained Tetrahydrocannabinol (THC). Arguments were also made towards vaping serving a function in harm-reduction as it was comparably safer than smoking. Twitter users against vaping requested for people to stop using e-cigarettes and for tougher legalisation on vaping products. Below, we provide anonymized tweets related to these two main themes.There were a number of Twitter users who would share their own positive personal experiences of using e-cigarette devices:“I was a heavy smoker for many decades and knew I’d be dead in my 50 s and never get a chance to see my grandchildren. But then 5 years ago I started to use e-cigarettes and I may be able to see my grandchildren now”Other Twitter users were concerned about the increased focus on younger people using e-cigarettes:“The dangers of vaping and youth is completely overblown! Car crashes are actually the leading cause of death in younger people”There were also Twitter users who noted the dangers of cigarettes compared to e-cigarettes:“Right then, they will ban e-cigs because it causes deaths, but regular cigarettes don’t seem to be a problem, lol!”There were also Twitter users who criticised the study linking cancer and vaping:“If they re-ran the study and compared vaping and real cigarettes then vaping would be the safer alternative! #vapingsavedmylife”In the above tweet extract, the user employed the hashtag “vapingsavedmylife”, which was used by Twitter users to indicate the life-saving potential of vaping.There were also Twitter users who called for people to quit vaping:“Please stop vaping! – read this news story Lung Damage From Vaping Resembles Chemical Burns, Report Says”Other Twitter users called for parents to inform their children that e-cigarettes were unsafe:“Let your children know that vaping is NOT SAFE, doesn’t matter what their friends say, they’ve been brainwashed by big Tobacco!”There were also a number of general comments noting that e-cigarette use was likely to be dangerous: “I am not a medical professional, but most likely that vaping is not good for you”Another Twitter user noted:“I used to smoke 50 cigarettes a day and managed to just quit, that’s the best way to quit. Vaping is not safe.”From the tweet extracts above, we can see that there was a diverse range of views and comments noting that e-cigarettes were unsafe. This study showed the effects of a research study linking cancer in mice to vaping on Twitter. The study was tweeted by several mainstream media outlets and dominated discussions of e-cigarettes on Twitter. The study, however, was questioned by several experts as they noted that the study conducted on mice could not be generalised to humans. Our study highlighted the impact of academic research on Twitter when it is disseminated through the mainstream media. The publication of the study caused great concern over the safety of e-cigarettes on Twitter, and users of the platform were quick to point out that e-cigarettes were still a much safer alternative to traditional smoking. For instance, health authorities such as the National Health Service in the United Kingdom still noted that e-cigarettes contained a fraction of the risk when compared to that of traditional cigarettes . HoweverBy analysing public comments on Twitter, we were able to identify potential points of confusion by consumers, and combining our results with that of current medical advice led to the development of the flowchart above. Social media may be a source of information for a subset of the population, and it is important to analyse this information from a public health surveillance perspective. Our findings can also be contrasted with previous empirical work in this area. A study published in 2018  conducteThis paper conducted an analysis of current debates surrounding e-cigarette usage on Twitter. We identified two main themes that arose, which were either based on pro-vaping or anti-vaping views, and this method has been utilised in previous research . Our wor


SERPINB5, FERMT1, AGR2, SLC6A14 and TOP2A. These genes have been associated with growth, proliferation, invasion and metastasis in pancreatic cancer previously. Cumulatively, this demonstrates the applicability of PDX models and transcriptomic array to identify genes associated with growth and proliferation of pancreatic cancer.Pancreatic cancer remains among the most lethal cancers worldwide, with poor early detection rates and poor survival rates. Patient-derived xenograft (PDX) models have increasingly been used in preclinical and clinical research of solid cancers to fulfil unmet need. Fresh tumour samples from human pancreatic adenocarcinoma patients were implanted in severe combined immunodeficiency (SCID) mice. Samples from 78% of treatment-naïve pancreatic ductal adenocarcinoma patients grew as PDX tumours and were confirmed by histopathology. Frozen samples from F1 PDX tumours could be later successfully passaged in SCID mice to F2 PDX tumours. The human origin of the PDX was confirmed using human-specific antibodies; however, the stromal component was replaced by murine cells. Cell lines were successfully developed from three PDX tumours. RNA was extracted from eight PDX tumours and where possible, corresponding primary tumour (T) and adjacent normal tissues (N). mRNA profiles of tumour vs. F1 PDX and normal vs. tumour were compared by Affymetrix microarray analysis. Differential gene expression showed over 5000 genes changed across the N vs. T and T vs. PDX samples. Gene ontology analysis of a subset of genes demonstrated genes upregulated in normal vs. tumour vs. PDX were linked with cell cycle, cycles cell process and mitotic cell cycle. Amongst the mRNA candidates elevated in the PDX and tumour vs. normal were  Pancreatic cancer remains a cancer of unmet need, with a 5 year survival rate of approximately 7−9% . The surOne of the most common research tools for oncology research is the cell-line model. Cell lines and cell-line panels have formed the basis of much preclinical research previously. The limitations of conventional cell lines for pancreatic cancer research has been previously discussed, showing to be a poor predictor of clinical trial outcome . In pancPatient-derived xenograft (PDX) models have emerged as a tool for investigation of pancreatic cancer and become more utilised as they have been shown to more closely represent patient tumours . Due to Pancreatic cancer cell lines, while not mimicking the tumour–stromal interaction or pancreatic cancer’s dense desmoplasia offer a model to examine direct drug effects on the tumour cells. PDX models also offer the opportunity for the development of novel cell-line models. The establishment of pancreatic cancer cell lines using finely minced tissue fragments and specialised media components has been previously reported ,9. TheseThis study shows the comprehensive development of patient-derived xenograft models, including detailing the tumours that failed to proliferate as PDX models. From these novel models, primary cell lines were developed and characterised as human tumour cell lines. Using microarray screening this study sought to compare the mRNA profile of adjacent normal tissue, patient tumour tissues and PDX F1 tissue. This revealed the innate heterogeneity of the adjacent normal tissue and patient pancreatic tumour but showed patient-derived xenograft models more closely clustered together. More than 1400 genes were differential expressed between the tumour and adjacent normal tissue, with more than 3800 genes differentially expressed in the tumour–PDX comparison. In this study, we focused on genes that were differentially expressed in both comparisons and associated with disease initiation and disease progression characteristics.In total, 48 patient samples were collected from patients who ranged in age from 40 years to 80 years, with a total 29 males and 19 females. The baseline characteristics of the patients are given in Due the centralised nature of pancreatic cancer surgical resection procedures in Ireland, follow-up data was not available for all patients in the study, many of whom were treated in facilities other than the surgical hospital for their post-surgical care. Available follow up information regarding treatment and survival is given in Only 11 of 48 patient samples had recorded information regarding the patient presentation. Of those 11, nine patients presented with jaundice, obstructive jaundice or painless obstructive jaundice, 1 presented with abdominal pain and another patient was an incidental identification on a PET scan.Tumour material obtained was confirmed macroscopically by consultant histopathologist. The quantity of tumour available for research (biobanking and implantation) was variable and determined by the pathologist. The number of mice per patient implanted varied from 3 to 5 depending on the tumour type and quantity. In total, 48 patient samples were acquired over a 5 year period. Of these, 35 samples were PDAC, with three cholangiocarcinoma, three IPMN and six NET samples acquired; a full breakdown of samples acquired and engraftment status is given in The average number of days following implantation of patient tumour to palpate tumour is 78 days, ranging from 20 days to 306 days. The tumour growth rates in the first generation in mouse (F1 generation) varied both within tumours and between tumours with timelines for harvesting tumours ranging from 72 days post implant to 512 days post implant. Tumour growth rates in In total, 10 F1 generation PDX fresh frozen tumour samples were investigated for mutational status by Sequenom MassArray MALDI-TOF system, Given the small sample numbers, PDX growth and take rate cannot be attributed to a single factor but key operational factors including the immediate cooling of the sample following surgery, rapid implantation and sample selection and identification by histopathologistFrom 11 F1 PDX tumour pieces, three tumour cell lines were isolated: PIN 080, PIN 099 and PIN 127. Two further tumour cell lines failed to proliferate after initial sub-culturing: PIN 089 and PIN 091. The three tumour cell lines established were confirmed as human by IHC, as detailed in From seven patient tumours, two patient-derived stromal cell lines were isolated. The two stromal cell lines, Fibro-102 and Fibro 120, have been successfully cultured, cryo-preserved and restored. These cell lines grow with a fibroblast morphology. Preliminary data (unpublished) indicated phenotypic changes in pancreatic cancer cells  in response to co-culture with patient-derived stromal cells in comparison to cancer cell alone.PDX tumours are known to recapitulate the patient morphology. Additionally, as the tumours grow, within the first passage in the stroma is replaced with mouse tissue ,11,12. SMicroarray gene expression profiling was performed on GeneChip Human Gene 2.0 ST array on five triple-matched samples and three double-matched samples, detailed in In total, more than 5000 genes were differentially expressed in the comparisons on adjacent normal vs. tumour and tumour vs. PDX, taking a 1.5-fold cut off. The breakdown of the numbers of genes changed is given in In order to determine which biological process the differentially expressed genes were involved in, gene ontology analysis was completed on a short list of 89 genes ,14. Genep = 8.84 × 10-5 and 8.96 × 10-6 respectively). p = 3.00 × 10-4) was detected with a further 2.5-fold increase (p = 5.40 × 10-3) in the F1 PDX tumour material when compared to the F1 cohort. AGR-2 (p = 1.50× 10-3) in the patient tumour compared to the adjacent normal tissue and a further 5.09-fold (p = 3.00 × 10-4) increased in PDX F1 tissue compared to tumour material Solute Carrier SLC6A14 (p = 4.00 × 10-4) in a comparison between tumour tissue and adjacent normal material and a further 3.2-fold increase (p = 1.07 × 10-2) in the F1 PDX cohort compared to the tumour tissue. TOP2A (p = 2.00 × 10-4) in the tumour compared to adjacent normal tissue (p = 2.00 × 10-4) and a further 3.8-fold increase in the PDX F1 tissue compared to the patient tumour. MUC1 has been shown to be overexpressed in pancreatic cancer [t. AGR-2 C was inc SLC6A14 D showed e. TOP2A E showed c cancer , and hasc cancer . In our c cancer .Pancreatic cancer tissue and adjacent normal tissue (N) were obtained from patients undergoing surgical resection at St Vincent’s University Hospital. After initial macroscopic pathological confirmation, material remaining after diagnostic sampling was cold transferred in RPMI 1640 medium containing 1% Penicillin-Streptomycin, 1% fungazone to DCU. Transfer time between hospital and implantation was on average 2 h or less.Collection of patient material was approved by St Vincent’s University Hospital Research Ethics Committee. All animal work received ethical approval from the DCU Research Ethics Committee (DCUREC/2012/202) and was licensed by Department of Health (B100-4501).3) and rinsed with fresh serum-free RPMI media following transport. Severe combined immunodeficiency (SCID), CB17/lcr-Prkdcscid/lcrCrl mice  were implanted subcutaneously with fresh patient tumour material. Depending upon the size and type of tumour material, 3–5 mice were implanted per patient sample. Under anaesthesia  a small incision was made in the skin of the left flank of the animal. The tumour piece was placed in the pocket under the skin and the wound sealed with a single staple. The animals were monitored post-surgery, and staple removal was within 10 days. Animals were monitored weekly for body weight and tumour development. Mice were monitored for tumours development for up to 1 year post implantation. Animal welfare monitoring criteria included tumour volume, tumour axis, body weight and condition. There tumour volume and tumour axis limits were set as <2000 mm3, and <20 mm respectively. A decrease in body weight of >10% resulted in increased monitoring with body weight decrease of 20% resulting in humane euthanasia.The tumour was cut into implant-sized pieces  for 24 h and then transferred to the liquid nitrogen (LN2) for long-term storage. For snap freezing, a piece of tumour was minced with a scalpel, placed in a cryovial and placed directly into LN2, then stored at −80 °C. For FFPE, a central slice was preserved in 10% neutral buffered formalin overnight and processed subsequently in 70%, 90%, 100% ethanol followed 100% xylene (1 h each). Following processing the tumours were embedded in paraffin. FFPE samples of PDX tumours were reviewed by consultant histopathologist to validate maintenance of tumour phenotype in comparison with original patient tumour.Following humane euthanasia of the mouse, the tumour was divided for cryopreservation, formalin-fixed paraffin embedding (FFPE) and snap frozen. For cryopreservation, implant-sized tumour pieces were stored in freezing media (RMPI/FCS/DMSO—50/40/10% v/v) in a cryovial and placed in a Mr Frosty3) was minced and placed in Collagenase/Hyaluronidase  at 4 °C for 1 h and then transferred to 37 °C for 30 min. Following digestion, the sample was centrifuged 200 g for 5 min and the supernatant removed. The digested material was suspended with complete media (DMEM/F-12 Hams supplemented with 10% FCS 1% PenStrep). Samples were monitored for growth and fed as required. Tumour cells were isolated from first generation PDX tumours while fibroblasts were isolated directly from patient tumours. Tumour cell lines were confirmed as human by IHC.A small piece of tumour  staining was performed using the DAKO Autostainer. Deparaffinisation and antigen retrieval were performed using Epitope Retrieval 3-in-1 solution (pH 6)  was used. For epitope retrieval slides were heated to 97 °C for 20 min and then cooled to 65 °C.Following either deparaffinisation or epitope retrieval as outlined above slides were immersed in 1× wash buffer. On the autostainer (DAKO), slides were blocked for 10 min with 200 µL HRP Block (DAKO). Slides were washed with 1× wash buffer and 200 µL of the primary antibody were added to the slides for 30 min. The primary antibody used was Abcam Anti-Mitochondria antibody 113–1 (ab92824), a human-specific antibody . The ant® microscope slides and allowed to attach overnight at 37 °C, 5% CO2. Slides were washed 3 times in PBS and cells were then fixed in cold 4% paraformaldehyde for 15 min and washed with PBS. Immunostaining was carried out using the Dako Autostainer as per Cells were seeded directly on SuperfrostMass spectrometry-based single-nucleotide polymorphism genotyping technology  was used for identification of hotspot, potentially clinically relevant nonsynonymous somatic mutations as previously described . The gen2 using a mortar and pestle. Briefly, tissue sample was placed in a metal mortar with a small volume of LN2 and ground quickly. When powdered the powder was transferred to a clean Eppendorf tube. This was done before the LN2 completely evaporated and while the powder was still frozen.Snap-frozen sections of PDX tumours, original patient material and adjacent normal tissue were prepared for RNA extraction by grinding samples under LNg for 15 min. The aqueous phase was then separated using a micropipette and places in a new Eppendorf. 500 µL of isopropanol was added to the aqueous phase, allowed to sit at room temperature for 10 min, after which it was spun at 12,000× g at 4 °C for 10 min. The RNA pellet was then washed using 75% ethanol and spun at 4 °C at 7500× g for 5 min. Ethanol was removed from pellet, which were allowed to air dry, and were then reconstituted in nuclease free water.Once homogenised, RNA samples were prepared using Trizol, according to manufacturer’s guidelines. Briefly, powdered tissue was lysed in 1 mL of Trizol reagent. Sample was allowed to homogenise at room temperature for 15 min. Per 1 mL of Trizol reagent used, 200 µL of chloroform was added to the Eppendorf , sample was mixed and allowed to sit for 5 min. At 4 °C samples were spun at 12,000× Samples were quantified using a Nanodrop  and quality determined using Agilent Bioanalyser. Samples with a RNA Integrity Number (RIN) number greater than 8 were most suitable for microarray analysis.® Human Gene 2.0 ST Array while rotating at 60 rpm for 16 h at 45 °C. After hybridization, the microarray was washed using the Affymetrix Fluidics Station according to the manufacturer’s protocol. The chips were scanned in an Affymetrix 3000 7G scanner.Preparation of cRNA, hybridization, and scanning of microarrays was performed according to the manufacturer’s protocol . In brief, 300 ng of total RNA extracted from homogenised patient samples isolated using Trizol was converted into double-stranded cDNA by reverse transcription. Biotin-labelled cRNA was generated by converting the cDNA sample using the Genechip WT plus reagent kit . Labelled cRNA was hybridized to the Affymetrix GeneChipp-value < 0.05 and an overall FDR F-Test: <0.005.Differential gene analysis expression was carried out using Applied Biosystems Transcriptome Analysis Console (TAC) software 4.0.2. Resulting gene lists were filtered for +/- 2-fold changes, a Patients ages and diagnosis characteristics are broadly representative of the earlier-stage pancreatic cancer patient population at large. In Ireland, the median age of diagnosis is 70–74 years for the entire pancreatic patient population . In thisPDAC samples which were treatment naïve formed tumours in vivo 78% of the time. This is somewhat higher that previously reported rates of 61% (sub-cutaneous) and 62% (orthotopic) for primary pancreatic cancer samples, though these models were established in nude mice rather than SCID mice ,27. The From seven tumours collected where the patient had received neo-adjuvant treatment, one formed a sub-cutaneous tumour in vivo. Dorado et al. examined three experimental techniques for the establishment of pancreatic PDX models, subcutaneous, orthotopic and intraperitoneal. Of the bank of 11 samples collected in that study, one had received neo-adjuvant therapy and formed a sub-cutaneous tumour, but did not form a tumour intraperitoneal nor at the orthotopic site .Previously, our group examined 10 primary patient tumour samples and nine matched normal adjacent specimens and 10 matched F1 and F2 generation PDX tumours using high-resolution mass spectrometry. MS identification allowed for the isolation of the human only protein, representing the tumour cells, and these were tracked from patient tumour, to F1 and F2 generation PDX tumours. Between patient primary and F1 tumour 32 proteins were upregulated and 113 downregulated. In comparison, between F1 and F2 generation of PDX tumour, only eight human-specific proteins were differentially expressed when analysed by quantitative label-free differential analysis. This demonstrates the fidelity of tumour phenotype once engrafted . This isEight PDX tumour samples were interrogated by Affymetrix Microarray technology in matched comparison to patient tumour and adjacent normal tissue. Primary patient tumour cluster together with PDX tumours clustering together but separate from the primary tumour. RNASeq data in ovarian PDX models demonstrate this differential clustering is predominantly due to the loss of human stroma and the growth of murine stroma . By compSERPINB5, had the largest fold change in the T vs. F1 analysis and in the top ten differentially expressed genes in the T vs. N analysis. The SERPIN superfamily of proteins is known to consist of 37 members in humans, with 13 members of the SERPINA family and 13 members of the SERPINB family [SERPINA1, SERPINB5 and SERPINE1. SERPINA1 was increased in T vs. N (3.6-fold), but decreased in T vs. F1 (5.0-fold). SERPINE1 was decreased in T vs. N analysis (3.6-fold) and further decreased in T vs. F1 comparison (4.9-fold). SERPINB5 has been previously associated with pancreatic cancer. In a meta-analysis of publicly available datasets of transcriptome data for human pancreas specimens and a GEM mouse models Bhasin et al. included SERPINB5 as one the of the genes in a five gene panel for discriminating PDAC and early precursor lesions. Other genes included in this panel were TMPRSS4, AHNAK2, POSTN, ECT2 [TMPRSS4 showed fold changes of 9.5 and 2.1 in the N vs. T and T vs. F1 respectively. Similarly, AHNAK2 fold changes were 2.6 (N vs. T) and 2 (T vs. F1), POSTN 15.5 (N vs. T) and -69.9 (T vs. F1), ECT2 2.6 (N vs. T) and 2.7 (T vs. F1). RNA-Seq analysis by Mao et al. of 10 PDAC tumour samples and adjacent benign tissue also detected SERPINB5. Their analysis showed SERPINB5 to be overexpressed in PDAC tumour in comparison to adjacent normal tissue [SERPINB5 expression may correspond with invasive tumours [SERPINB5 to be increased at the protein level when human-specific proteins were identified with significantly increased expression in PDX F1 tumours compared to PDAC tumour tissues, as determined by quantitative label-free mass spectrometric analysis [Serpin family B member 5, B family . SERPINAB family . In our TN, ECT2 . The othl tissue . Also in tumours . Previouanalysis .FERMT1, encoded protein is involved in integrin signalling and linkage of the actin cytoskeleton to the extracellular matrix and has been shown to be significantly over-expressed in colon cancer [FERMT1 by lenti-viral vector increased proliferation and radiation resistance in vitro [FERMT1 is a putative downstream gene target of EPS8 [EPS8 were not seen in our data set, FERMT1 changes were seen. To date, FERMT1 changes have not previously been directly shown in pancreatic cancer.Fermitin family member 1, n cancer . FERMT1 n cancer . In oesoin vitro . A study of EPS8 . While cAGR2, gene was expressed in pancreatic cancer tissues and cell lines, though this is not expressed in chronic pancreatitis samples. In vitro, AGR2 silencing by siRNA knockdown showed a decrease in cancer cell proliferation and invasion and increased gemcitabine sensitivity of resistant cells [AGR2 was associated with poorer survival and increased metastasis [AGR2 expression level [Ramachandran et al. showed that Anterior Gradient 2, nt cells . In breatastasis . In pancon level .SLC6A14 knock out mice showed a marked delay in tumour formation and tumour size, and analysis of the tumour material revealed evidence of amino acid starvation in the slc6a14-/- mice suggesting a key role from SLC6A14 in both tumour development and growth [SLC6A14 as a potential prognostic biomarker of pancreatic cancer. Interestingly CENPF and SERBINB5 were also included in this panel of prognostic genes. Both of these genes were also identified in our panel [Solute carrier family 6 member, SLC6A14 encoded protein is a sodium- and chloride-dependent neutral and basic amino acid transporter . In bread growth . In 2019ur panel . SLC6A14ur panel .TOP2A) gene encodes for a nuclear enzyme that regulates the topological structure of DNA and is involved in chromosome condensation, chromatid separation and torsional stress relief during DNA transcription and replication. TOP2A has been associated with many solid cancers such as breast and prostate cancers [TOP2A amplification [Topoisomerase IIα ( cancers ,54,55. Hfication . Pancreafication .This study details the establishment of a PDX biobank containing pancreatic ductal adenocarcinoma, intraductal papillary mucinous neoplasm, cholangiocarcinomas and neuroendocrine tumour including one PDX of a neo-adjuvant treated PDAC model. From this biobank, a further of three primary tumour cell lines and two primary patient-derived stromal cell lines were established.This panel of PDX tumours with matched patient tumour and adjacent normal tissue was investigated by Microarray. A subset panel of 89 genes were identified and shortlisted as differentially upregulated in tumour compared to normal and further upregulated in PDX F1 tumours. Potentially this panel indicates genes highly associated with tumour growth and development. These genes were predominantly associated with proliferation, cell cycles and mitotic processes, suggesting a significant role in tumour proliferation, progression and tumour engraftment in vivo.


Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated.Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas . The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation.P = 0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P < 0.05). For distinguishing cartilaginous tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.61, while the classifier with multi-parametric MRI parameters improved to 0.81(P < 0.05).For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (The combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma. Osteosarcoma is the most common primary malignant bone tumour, which tends to occur in children and adolescents, and is one of the main causes of cancer death and disability in these age groups worldwide . NeoadjuMagnetic resonance imaging (MRI) has become the most important diagnostic method for the local staging of primary bone tumours and the detection of postoperative tumour recurrence . DiffusiMachine learning has been used in the study of cancer, including breast, lung, and colon cancer, showing great diagnostic and prognostic potential . HoweverThis study was conducted with the approval of the Ethics Committee of the First Affiliated Hospital of Sun Yat-Sen University. Written informed consent was obtained from the patients or their parents before MRI.n = 7), chondroblastic (n = 4), and fibroblastic (n = 1) osteosarcoma.We prospectively recruited 12 patients  with primary osteosarcoma who were admitted to the First Affiliated Hospital of Sun Yat-Sen University from August 2011 to March 2012. Eight patients had primary osteosarcoma of the distal femur and 4 had primary osteosarcoma of the proximal tibia. The histological types included osteoblastic  at Department of Radiology of the First Affiliated Hospital of Sun Yat-Sen University.2. Finally, we performed a delayed enhanced scan with the same parameters as the axial non-enhanced T1WI sequence, and the subtracted images were automatically generated.T1-weighted imaging (T1WI) scanning adopted the axial spin-echo sequence with repeat time (TR) = 659 ms and echo time (TE) = 11 ms. T2-weighted imaging (T2WI) scanning adopted coronal, sagittal, and axial fast spin-echo sequences with or without fat suppression . Axial DWI was performed using the single-shot spin-echo echo-planar imaging sequence with the following scan parameters: TR = 3200 ms, TE = 82 ms, echo-planar imaging (EPI) factor = 3, b-values = 0, 800 s/mmAll the axial plane scans were perpendicular to the longitudinal axis of the body and parallel to the tibial plateau. The field of view and the centre of the layer were consistent (with the largest cross-section of the tumour as the centre of the layer), with slice thickness = 5 mm and interslice gap = 1 mm.The resected gross specimens from limb-salvage surgery were fixed in 10% buffered formaldehyde solution, and sectioned to axial slices with a thickness of 5 mm corresponding to the axial MRI layers. Section-by-section coregistration was performed between MRI and the specimens by a radiologist and an experienced musculoskeletal pathologist to select 6–10 well-matched specimen sections from each patient. Rectangular tumour tissue samples ranging from 10 × 15 mm to 15 × 20 mm were drawn on these specimen sections corresponding to the homogeneous signal intensity areas on T1WI, T2WI, and DWI. Depending on the size of tumour, 9 to 24 sampling areas were selected from each patient, and a total of 127 tissue samples were obtained from the 12 resected specimens. These tissue samples were fixed, decalcified, dehydrated, embedded with paraffin, sectioned, and stained with hematoxylin and eosin (H&E).In this study, microscopically viable sarcomatous cells, tumour osteoid, tumour bone, viable chondrosarcomatous cells with cartilaginous matrix, sarcomatous cells necrosis, post-necrotic collagen, liquefactive necrosis, blood spaces, and secondary aneurismal bone cysts (ABC) were recorded for all tissue samples by pathologists blinded to the MRI findings. Areas with tumour cell necrosis less than 10% were defined as the tumour viable areas, while areas with tumour cell necrosis greater than or equal to 90% were defined as the tumour necrotic areas. Areas with tumour cartilage greater than 50% were defined as the cartilaginous tumour, while areas with tumour cartilage less than or equal to 50% were defined as non-cartilaginous tumour. Thus, all the tissue samples were classified as non-cartilaginous tumour viable areas, cartilaginous tumour viable areas, non-cartilaginous tumour necrotic areas, tumour necrosis cystic/haemorrhagic and secondary ABC areas, and tumour post-necrotic collagenised areas. Among them, the non-cartilaginous viable tumour areas and cartilaginous viable tumour areas belong to the survival areas of tumour, while the non-cartilaginous tumour necrosis areas, collagen areas after tumour necrosis, tumour necrosis cystic/haemorrhagic and secondary ABC areas are classified as the nonviable areas of tumour  2  were placed on T2WI, subtract-enhanced T1WI (ST1WI), and the ADC maps which were coregistered to the histological sampling areas; this was performed jointly by two experienced radiologists (MLS and ZHG). The size of ROIs was in the range of 50–250 mmhttps://scikit-learn.org/stable/). The models were constructed with rADC values only, or with all the above normalised parameters, for comparisons between different feature inputs. Performance of the models was evaluated by 5-fold cross-validation. We calculated the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to evaluate the classification performance [Our previous studies have demonstrated by statistical methods that the differences in ADC values among the cartilaginous tumour survival areas, the post-necrotic collagenised areas, and the tumour necrosis cystic/haemorrhagic and secondary ABC areas were in significant . Thus, iformance . Using tA total of 12 patients with osteosarcoma were enrolled in this study, and the baseline clinical characteristics are shown in Table The number and proportion of samples of the aforementioned five histopathological types of osteosarcoma after NACT are shown in Table Among the 127 tissue samples, there were 102 homogeneous histological areas in 12 patients, including 38 of non-cartilaginous viable tumour, 25 of non-cartilaginous tumour necrosis, 14 of cartilaginous viable tumour, 14 of tumour necrotic cystic/haemorrhagic and secondary ABC, and 11 of post-necrotic collagen. In order to avoid inaccurate calculation of MRI parameters due to tissue heterogeneity, the remaining 25 heterogeneous histological areas, characterised by partial necrosis (10% < necrosis < 90%), were excluded from the analysis.Table P = 0.0933).The results showed that machine learning models constructed with rADC, rT2WI, and rST1WI signal intensity achieved better performance than those using rADC alone in all three classification tasks. For distinguishing non-cartilaginous tumour survival from tumour nonviable, using only the rADC value, we obtained a sensitivity of 88%, a specificity of 89%, an accuracy of 89%, and an AUC of 0.93. Using rADC, rT2WI, and rST1WI signal intensity values improved the sensitivity to 97%, the specificity to 92%, the accuracy to 94%, and the AUC to 0.97 Table . The comP < 0.05).For distinguishing tumour survival from tumour nonviable, using rADC, rT2WI, and rST1WI, we obtained a sensitivity of 94%, a specificity of 78%, an accuracy of 85%, and an AUC of 0.90. However, using rADC alone, the sensitivity was reduced to 82%, the specificity was reduced to 69%, the accuracy was reduced to 75%, and the AUC was reduced to 0.83 (Table P < 0.05).For a more challenging discrimination task, namely distinguishing cartilaginous tumour survival from tumour nonviable, the classifier constructed with rADC, rT2WI, and rST1WI signal intensity values provided good classification rates . This classifier outperformed the classifier constructed using rADC alone, with significant AUC difference of 0.20 (Table In this study, we proposed a noninvasive and accurate method that evaluates the necrosis of osteosarcoma after NACT by combining mpMRI with machine learning. The results showed that our method could distinguish tumour necrosis and tumour survival more accurately, compared with a single MRI parameter.In distinguishing non-cartilaginous tumour survival from tumour nonviable, the machine learning based classification with rADC alone achieved an AUC of 0.93In our previous study, we have reported that the mean ADC values in non-cartilaginous tumour viable areas were significantly lower than those in non-cartilaginous tumour necrotic areas and cystic/haemorrhagic necrotic areas . HyperceThe results of this study indicate that ADC has limited ability to discriminate between cartilaginous tumour survival and tumour nonviable using machine learning techniques, while mpMRI significantly improved the discriminatory ability in this task. Our previous study demonstrated that cartilaginous tumour survival areas have high ADC values, indistinguishable from post-necrotic collagenic tissue or cystic/haemorrhagic necrosis . The higOur results also showed that mpMRI can significantly improve the ability of machine learning models to differentiate tumour necrosis from tumour survival compared with the use of ADC alone, even if living cartilage tumours component was taken into account. This may be because mpMRI provides more valuable information for the construction of machine learning models. There is evidence that multi-parametric imaging with different functional MRI parameters provides detailed information about cancer hallmarks , includiAmong the limitations of this study, we should mention that we only investigated the ability of mpMRI to classify the necrosis and survival of pathological types after NACT for osteosarcoma, but did not evaluate the response or necrosis rate of patients. However, our encouraging results provide evidence that can form the basis for a subsequent assessment of response or necrosis rate. A second limitation of this study is that we investigated the ability of mpMRI parameters to discriminate between different types of tissue based only on the local region averages. Correlation between MRI and the pathological information of entire specimen slices may provide more precise information. Future studies with larger samples are needed to investigate the relationship between MRI findings and histopathological information, so as to improve the NACT response evaluation in different subtypes of osteosarcoma.In summary, machine learning methods incorporating the information of T2WI, enhanced T1WI, and ADC could effectively distinguish tumour necrosis from tumour survival, or from the necrosis of the tumour extracellular matrix/stroma, after chemotherapy. Our study suggests that machine learning applied to mpMRI may provide an objective and reliable method for NACT response evaluation in osteosarcoma.


A very recent study by Zhang et al. published inNaturedemonstrates that gasdermin E  is a tumour suppressor by activating caspase-independent pyroptosis to enhance anti-tumour immunity.1In the meantime, a study by Wang et al. published inNaturealso demonstrates the anti-tumour effect caused by gasdermin A3 (GSDMA3) to induce pyroptosis requires both cytotoxic T cells and CD4+T helper cells.2The authors intriguingly showed that pyroptosis-induced inflammation triggers robust anti-tumour immunity, thus gasdermin is a promising novel therapeutic target for tumour treatment .3 The gasdermin family includes GSDMD, GSDMA, GSDMB, GSDMC, GSDME, and DFNB59. GSDMD is identified as the pyroptotic substrate of caspase-1/4/5/11 and multiple gasdermin-N domains can bind membrane lipids and bear a membrane pore-forming activity with the inner diameters of 10–14 nm of most gasdermin pores.4 The gasdermin pores disrupt the balance of the cell membrane to induce water inflow and cell swelling, as well as the release of many proinflammatory cytokines.5 GSDME can be cleaved by caspase 3 to switch apoptosis to pyroptosis. Although the expression level of GSDME is suppressed with the potential as a tumour suppressor, it is still unclear whether and how GSDME acts as a tumour suppressor.Pyroptosis is one kind of programmed necrosis mediated by the gasdermin family.1 Zhang et al. addressed the issue of GSDME function in tumour treatment in vitro and in vivo. GSDME function of tumour suppression was mediated by activating caspase-independent pyroptosis, which depended on an involvement of the enhancement of phagocytosis of tumour cells by tumour-associated macrophages, as well as the number and functions of tumour-infiltrating natural-killer and CD8+ T lymphocytes.1The Judy Lieberman group demonstrated that GSDME function was reduced in 20 of 22 tested cancer-associated GSDME mutation.Gsdme messenger RNA and/or protein in primary breast cancers and colorectal cancer to seek the tumour model with GSDME high or poor expression level. Gsdme was knocked out in highly expressing cell lines , or stably expressed in poorly expressing cell lines . The tumours knocked out for Gsdme in highly expressing cells grew much faster accompanied by fewer CD8+ T and NK cells, as well as tumour-associated macrophages. Meanwhile, tumour-infiltrating lymphocytes (TILs: CD8+ T and NK cells) from Gsdme−/− tumours also expressed less granzyme B (GzmB), perforin (PFN), interferon-γ (IFNγ), and TFN. The overexpression of Gsdme in cells with poorly expressing of Gsdme had the opposite effect. These results indicated that GSDME has the function of tumour suppression and TIL promotion.Using The Cancer Genome Atlas database, the authors first analysed the expression levels of + T or NK cells, indicating that killer lymphocytes mediate tumour suppression of GSDME. To determine whether killer lymphocytes induced pyroptosis, the authors used human NK line YT or NK-92 incubated with empty-vector and hGSDME-overexpressing HeLa cells. The results showed that NK cells induced pyroptosis in caspase-dependent and caspase-independent manners. Moreover, the authors used ethylene glycoltetraacetic acid (EGTA) to inhibit cytotoxic granule release and PFN with the results that EGTA completely blocked pyroptosis. All these results indicated that pyroptosis activated by killer cells depended on cytotoxic granule release.Next, the authors wanted to know whether the tumour suppression of GSDME was mediated by enhanced immune function. The authors provided further evidence that GSDME-mediated tumour inhibition was reduced significantly in mice lacking either CD8+ T or NK cells) was enhanced in tumours with the overexpression of wild-type GSDME. All these results provided evidence that cleavage of GSDME at D270 to disturb cell membranes via pore formation was required for tumour suppression.Furthermore, the authors speculated that Gzm proteases were involved in NK-induced pyroptosis by cleavage of GSDME. Based on the results of co-incubation GzmB and GSDME or GSDME D270A mutant, the authors determine that GzmB cleaved GSDME at D270, the same site of caspase 3. Then, the authors hypothesised that mutation of this residue in tumours should abrogate tumour suppression. B16 and 4T1E cells were used to test this hypothesis. The results showed that only tumours overexpressing wild-type GSDME reduced tumour growth, while tumours overexpressing D270A GSDME or empty vector grew with no difference. The authors provided further evidence that the function of killer lymphocytes (CD81 elegantly illustrates how GSDME acted as a tumour suppressor by inducing pyroptosis in melanoma, triple-negative breast cancer, and colorectal cancer tumours. The enhancement of anti-tumour killer-cell cytotoxicity was necessary and essential for tumour inhibition of GSDME. GSDME cleavage at D270 by GzmB/caspase 3 promoted pore formation to induce pyroptosis and suppress tumour growth through the enhancement of anti-tumour adaptive immunity.Altogether, the study by Zhang and colleagues2 established a bioorthogonal chemical system to enable the controlled release of a drug from an antibody–drug conjugate in mice. Using this bioorthogonal system, the authors observed membrane enrichment of gasdermin N domains after GSDMA3 conjugated to 60-nm nanoparticles and pyroptotic morphology, which phenomenon was deficient when GSDMA3 was mutant with E14K and L184D. Furthermore, the tumour regression induced by GSDMA3 conjugated to nanoparticles was inhibited by IL-1β antibody markedly. The authors also demonstrated that treatment with GSDMA3 conjugated to nanoparticles could synergize with checkpoint blockade anti-PD1 to prevent tumour growth. All these results indicated that pyroptosis induced by GSDMA3 could trigger robust antitumour immunity and GSDMA3 was a novel target for tumour treatment.In the meantime, Feng Shao and Zhibo Liu groupsNature2 gave us novel insights that gasdermin N domains could enrich on the cell membrane to form pores and induce pyroptosis. Antitumour immunity was triggered by inflammation induced by pyroptosis, which was essential for tumour suppressor of gasdermin. Thus, it is a promising way to develop therapeutic strategies depending on gasdermin function, such as the use of the DNA methylation inhibitor decitabine, or combined therapeutics of gasdermin and checkpoint blockade, in order to eradicate tumours by eventually activating robust antitumour immunity.In a word, the two papers recently both published in