Pancreatic cancer is one of the most aggressive malignant tumors with poor prognosis. High‐intensity focused ultrasound (HIFU) is an effective and safe treatment option for advanced pancreatic cancer, however, the survival time of patients after the treatment was different. So, the purpose of this study was to evaluate the relationship between the high‐risk characteristics and prognosis of unresectable pancreatic cancer after HIFU treatment.This prospective study included 30 patients with unresectable pancreatic cancer who received HIFU at Beijing Friendship Hospital. Data on patients' tumor size, pain scores, peripheral blood lymphocyte subsets, CA19‐9 and contrast enhanced ultrasound (CEUS) features were collected to assess the relationship with overall survival (OS) after HIFU.p = 0.001). The diagnostic model showed that high post VAS score and decreasing of peripheral CD4+ T cells were significantly correlated with poor prognosis (p < 0.05), and showed good discrimination ability .The median OS from the start of HIFU treatment was 159 days, 95% confidence interval (95% CI): 108–210. The levels of pain were determined by visual analogue scale (VAS) score, and the quartile of the score decreased from 6  to 4  immediately after one session of the treatment (+ T cells are independent risk factors affecting the prognosis in patients with unresectable pancreatic cancer after HIFU treatment.HIFU can effectively relieve pain in patients with unresectable pancreatic cancer. Post treatment VAS and change of peripheral CD4 In contrast, the information of patient's peripheral blood lymphocyte subsets is easy to get in clinical work. Recent studies have shown that peripheral blood lymphocytes play an important role in predicting OS and evaluating the therapeutic effect.Therefore, this study aimed at evaluate the relationship between these high‐risk characteristics and prognosis of unresectable pancreatic cancer after HIFU treatment, in order to provide help for HIFU treatment of unresectable pancreatic cancer in the future.22.1This is an exploratory study, and prospectively enrolled patients with unresectable pancreatic cancer in our hospital from January 2020 to January 2022. All patients received regular pancreatic arterial infusion chemotherapy in the interventional radiology department of our hospital except for HIFU treatment. The inclusion criteria were as follows: (1) age of 18–75 years; (2) with normal coagulation function; (3) diagnosis had been confirmed by puncture biopsy or intraoperative exploration; (4) no other treatments are used on the day of HIFU treatment, in other words, HIFU and arterial infusion chemotherapy cannot be given on the same day to avoid interfering with the study results. The following patients were excluded from this study: (1) tumor could not be clearly displayed by the built‐in ultrasonic machine of the HIFU instrument; (2) with a poor general condition; (3) who were unable to cooperate or complete treatment, had heart, liver, lung, kidney, other organ failure; (4) had immune deficiencies. The primary endpoint of this study was OS, defined as the time from the first day of HIFU treatment until death from any cause. The study protocol was approved by the Human Ethics Review Committee of the Beijing Friendship Hospital (2019‐P2‐226‐02) and has been registered in the Chinese Clinical Trial Registry (ChiCTR2000029620). The study design and implementation were in compliance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all the patients before enrollment.2.2Peripheral blood lymphocyte subsets, pain's visual analogue scale (VAS) and contrast enhanced ultrasound (CEUS) features were collected within 1 h before a session of HIFU and after treatment. Each session of HIFU treatment was conducted for 5 consecutive days. Patients' general information, such as sex, age, stage, serum CA19‐9, body mass index (BMI), tumor size and existence of metastasis were obtained within 1 week before treatment. And then, the correlation between these risk factors and OS were analyzed. The flow chart of this study is as follows . Treatment was delivered with the HIFUINT‐9000 system , which consists of six self‐focusing transducers with a geometric radius of 200 cm and a treatment depth range of 0–140 cm, operating at a frequency of 1 MHz. The treatment and interval times were set at 200 and 400 ms respectively. The size of each treatment unit is 3 × 3 × 8 mm. During treatment, the patient lies supine on the treatment bed. Then the doctor controlled the treatment bed to the designated position, and when the tumor was clearly shown, treatment area can be drawn on the software. To ensure the safety, the treatment zone should be at least 5 mm away from the tumor edge , B cells (CD3− CD19+), NK cells (CD3− CD16+ CD56+), CD4+ T cells (CD3+ CD4+) and CD8+ T (CD3+ CD8+) cells were determined. In our hospital, their normal value ranges are as follows: T cells (58.60%–83.10%), B cells (3.50%–15.40%), NK cells (6.90%–37.90%), CD4+ T cells (27.10%–49.80%), CD8+ T cells (19.40%–41.10%), and CD4/CD8 ratio (0.70–2.00).Within 1 h before a session of HIFU and after treatment, 4 mL of venous blood was reserved from each patient. Monoclonal antibodies were labeled with fluorescent dyes to determine various lymphocyte envelope differentiation antigens. The relative counts (percentage) of T cells  was used as ultrasound contrast agent. 2.4 mL of SonoVue suspension was injected through the superficial vein of the elbow, and then the intravenous indwelling tube was quickly rinsed with 10 mL of sterile saline. The time intensity curve (TIC) was generated by the built‐in software of the Hitachi ARIETTA 70 ultrasound diagnostic instrument. Since the normal pancreatic tissue in this group of patients often atrophied, large blood vessels at the same level as the tumor were selected as references , and the relative rise intensity (rRI) was calculated, that is, intratumoral rise intensity (RI)/large blood vessel RI.2.72). The tumor size was defined as the maximum diameter of tumor. Based on the results of previous studies, patients were grouped according to their age <60 years or ≥60 years, tumor size <4 cm or ≥4 cm,Patients' general information, such as sex, age, body mass index (BMI), tumor size, tumor location, existence of metastasis and CA19‐9 were obtained. BMI was calculated using the following formula: weight (kg)/height , variables with a p‐value of <0.1 were analyzed using the multivariable Cox model. The receiver operating characteristic curve (ROC) was used to assess the discriminatory ability of the diagnostic model. p < 0.05 was considered statistically significant.SPSS 26.0 (IBM Corp.) and GraphPad Prism 9.0  were used for statistical analysis. An independent sample 33.1A total of 30 patients were enrolled in this study. The baseline characteristics of patients are summarized in Table 3.2p = 0.001, Figure One patient had no pain symptoms before and after HIFU treatment. Therefore, in the statistical analysis of pain changes, 29 patients with pain symptoms before treatment were counted. The quartile of VAS before HIFU treatment was 6  and decreased to 4  immediately after one session of the treatment; the difference was significant (3.3+ T cells increased from 30.30 ± 11.46 to 32.33 ± 14.26 after HIFU treatment (p = 0.013). There were no significant differences in other parameters.The results of peripheral blood lymphatic subsets before and after HIFU treatment are shown in Table 3.4Compared with pretreatment, 14 patients demonstrated a reduction of rRI after a session of HIFU treatment, and the other 16 patients did not.3.5The OS, which was calculated as the period from the start of HIFU, of all patients ranged from 33 to 318 days , with a 95% confidence interval (CI) of 108–210 days.3.63.6.1p = 0.004)  and moderate‐to‐severe pain group (VAS score ≥4). After HIFU treatment, the median OS estimates of the mild pain group and moderate‐to‐severe pain group were 217 days (95% CI: 170–252) and 117 days (95% CI: 107–165), respectively. Survival curves were plotted by the Kaplan–Meier method, and the curves of patients in the moderate‐to‐severe pain were worse than those in the mild pain group  Figure . However) Figure .3.6.2+ T cells had shorter OS   Figure . There w) Figure .3.6.3p = 0.010)  in the group with tumor size<4 cm, and 138 days (95% CI: 103–173) in the group with tumor size ≥4 cm. The Kaplan–Meier method showed that the survival curve of patients with tumor size ≥4 cm was worse than that of patients with tumor size <4 cm  Figure .3.6.4p = 0.489), gender (p = 0.425), tumor location (p = 0.517), CA19‐9 (p = 0.350), and rRI (p = 0.365)  and change of CD4+ T cell after HIFU  were independent predictors of OS  , mechanical effect, and cavitation effect of focused ultrasound can induce tumor immune response by releasing tumor‐related antigens.+ T cell proportion was related to the patient's OS.As we have mentioned, peripheral blood lymphocyte subsets were collected before and immediately after a session of treatment. Thus, the corresponding changes in peripheral blood lymphocyte subsets were closely related to the role of HIFU. The changes of peripheral blood lymphocyte subsets after HIFU treatment may reflect the overall immune status of patients, which may be the reason why the change of CD4CA19‐9 is the best validated biomarker in pancreatic cancer, which can be used to evaluate the prognosis and therapeutic effect, and the normal baseline level of CA19‐9 was associated with long‐term survival.Several limitations of the study should be acknowledged. First, all the patients received HIFU treatment at a single center. Second, the number of patients enrolled in this study was relative small, so our results should be confirmed by studies including larger sample sizes in the future. In addition, this study only analyzes the survival of parameters that are easy to obtain in clinical work after HIFU treatment, and did not conduct experimental research on the relevant mechanism, which still needs follow‐up research.5+ T cell ratio after HIFU treatment may be potential survival risk factors for patients with unresectable pancreatic cancer, which can be easily obtained in clinical work and help to evaluate patients' feedback on HIFU therapy early. We hope that these findings will provide new ideas for further application of HIFU treatment in patients with unresectable pancreatic cancer.HIFU can effectively relieve pain in patients with unresectable pancreatic cancer. Higher VAS score and decreased CD4Yu Yang: Conceptualization ; data curation (lead); investigation (lead); methodology ; writing – original draft (lead). Xian‐Quan Shi: Data curation (supporting); investigation (supporting); resources ; writing – review and editing . Guang Chen: Investigation (supporting); methodology (supporting); resources (supporting). linxue Qian: Conceptualization ; funding acquisition (lead); methodology ; resources ; supervision (lead); writing – review and editing (lead).This work was supported by the General Program of National Natural Science Foundation of China (grant number: 52273300).The author reports no conflicts of interest in this work.


The clinical features and prognosis of breast cancer can vary widely, depending on the molecular subtype. Luminal B breast cancers are usually either estrogen receptor-positive and/or progesterone receptor-positive with high proliferation of Ki67 index, or HER2 positive (HER2+). The authors compared the clinicopathologic factors and survival rates of different subtypes of luminal B breast cancer according to HER2 status. Between 2009 and 2013, 1131 cases of breast cancer were reviewed and characterized as 1 of 4 different molecular subtypes based on their immunohistochemical results: luminal A, luminal B, HER2+, or triple-negative breast cancer. From these, luminal B breast cancers were extracted and the clinical features and prognosis of the HER2- and the HER2 + subtypes were compared. Survival differed significantly based on the molecular subtype regardless of whether or not the patient received treatment with neoadjuvant chemotherapy. While patients with HER2- luminal B breast cancer who received neoadjuvant chemotherapy had better prognoses, patients with HER2 + luminal B breast cancer who did not receive neoadjuvant chemotherapy had better prognoses. Luminal B breast cancers showed different clinical outcomes and survival rates according to HER2 gene overexpression type. Physicians should consider these results when they establish a treatment strategy. Additionally, ER + and/or PR+, HER2 + breast cancers are classified as another type of luminal B breast cancer.,12 Therefore, luminal B breast cancers can be either ER + and/or PR + , HER2−, high Ki67 index, or ER + and/or PR + , HER2 + , with either high or low Ki67. Because they have different molecular signals, the treatment and prognosis for these 2 types are different, even if both are referred to as luminal B breast cancer.The molecular subtype of breast cancer can only be precisely identified through genetic profiling. However, they can be roughly classified using immunohistochemical (IHC) biomarkers, because there is a high correlation between the molecular subtypes and IHC results.The authors analyzed the clinical outcomes and prognosis of breast cancer based on the molecular subtypes and compared the oncologic outcomes of the different subcategories of luminal B breast cancer.A total of 1131 patients with breast cancer who underwent surgery and additional treatments at the Kyungpook National University Hospital  between 2009 and 2013 were included in this study. The mean follow-up period was more than 8 years, and all breast cancer-specific events were recorded. Clinical records and pathologic results were all reviewed and analyzed, and the cancers were classified into 4 different molecular subtypes based on their IHC biomarkers: luminal A, luminal B, HER2 + , and tipple-negative breast cancer (TNBC). The Ki67 proliferation index was considered high when > 15% of tumor cells showed nuclear immunoreactivity. The criteria in the ASCO/CAP 2016 guidelines were followed for the histopathological examination of 4 biomarkers.HER2- luminal B breast cancer) and ER + and/or PR + , HER2 + . The 2 subgroups were compared according to clinicopathologic characteristics and oncologic outcomes. The analysis also included identifying whether or not patients with luminal B breast cancer had received neoadjuvant chemotherapy (NAC).All luminal B breast cancers were categorized into 2 subgroups: ER + and/or PR TNBC + , HER2−, high Ki67 proliferation index  for breast lesions and axillary lymph nodes was also evaluated using mammography, ultrasonography, breast MR, and chest/abdominal CT.All procedures in this study that involved human participants were performed in accordance with the ethical review of the Institutional Review Board of the Kyungpook National University Chilgok Hospital (KNUCH 2015-05-205). The experimental protocol was also approved by the Institutional Review Board of the Kyungpook National University Chilgok Hospital and all experiments were performed in accordance with relevant guidelines and regulations.P value < .05 was considered to be statistically significant.All statistical analyses were performed using SPSS ver. 22.0 . Categorical variables were analyzed using the chi-squared test in univariate analysis, and oncologic outcomes were assessed using Kaplan–Meier analysis to identify factors affecting locoregional recurrence, distant metastasis, or death. A P = .015); distant metastasis (P < .001); death (P < .001) , luminal B (n = 251), HER2 + breast cancer (n = 111), and TNBC (n = 218). The clinical variables  did not show significant differences between the 4 groups. Different treatment strategies  had different outcomes, however, based on the tumor characteristics. HER2 + breast cancers and TNBC cancers showed worse oncologic outcomes than the luminal type breast cancers   Table . The incP = .001). After NAC, pCR was only achieved in the HER2−, luminal B breast cancer group. Therefore, HER2 + luminal B cancer patients who had received NAC were more likely to undergo additional chemotherapy after surgery (P = .014). The incidence of both locoregional recurrence and distant metastasis was higher in the HER2 + luminal B breast cancer group who had received NAC .There were 26 luminal B breast cancer patients who received NAC and 225 patients who did not receive NAC. The clinicopathologic variables of the patients with luminal B breast cancer are shown in Table P = .005, 0.036) , ER + , PR−, HER2 + breast cancer, and ER−, PR + , HER2 + breast cancer. In this study, as previously known, the clinical outcomes and prognosis were significantly different according to the molecular subtypes, and luminal type breast cancer generally had a better prognosis than HER2 + breast cancer and TNBC. Especially, in patients with breast cancer who received NAC, the pCR rate was significantly higher in HER2 + breast cancer and TNBC which have higher responsiveness to chemotherapy. And the survival outcomes between HER2− and HER2 + luminal B types of breast cancer were significantly different.In many clinical studies, luminal B breast cancer is considered as a single type when analyzing clinical relevance and outcomes.The analysis was conducted by dividing the patients into groups based on whether they had received NAC, considering the initial stage of each group. NAC was used in 6.9 % of luminal A-type (n = 38), 10.4 % of luminal B-type (n = 26), 9.9 % of HER2 + breast cancer type (n = 11), and 17.0 % of TNBC type (n = 37). Of the 26 patients with luminal B breast cancer who received NAC, pCR was only identified in the HER2− luminal B breast cancer patients and these patients had a better prognosis than the HER2 + luminal B breast cancer patients. However, these patients were diagnosed with breast cancer 7 to 10 years ago and the treatment strategy for HER2 + breast cancer has evolved since then. Therefore, further analysis is needed for patients with HER2 + breast cancer who received newer treatment modalities for HER2 + breast cancer. The role of NAC has been further expanded within breast cancer subtypes, and even the application of immunotherapy for breast cancer has led to more improved survival results. However, the role of NAC in the luminal B subtype is still not fully understood. Although there were only 26 patients with luminal B-type breast cancer who received NAC in this study, it is clear that the prognosis of HER2 + and HER2− luminal B-type breast cancer is significantly different under standard of care. Physicians need to understand that the prognosis can vary depending on the molecular expression, even if the cancers are classified into the same subtype, to establish individualized treatment strategies.It has been reported that NAC can increase the pCR rate in HER2-positive and TNBC, leading to significantly improved survival rates for patients.The prognosis for patients with luminal B breast cancer differs according to HER2 status. While the HER2- luminal B breast cancer had a better prognosis in patients who received neoadjuvant chemotherapy, the HER2 + luminal B breast cancer had a better prognosis in patients who did not receive neoadjuvant chemotherapy. These results provide clinical implications for establishing the most appropriate treatment strategy for luminal B breast cancer.Conceptualization: Jeeyeon Lee, Byeongju Kang, Ho Yong Park.Data curation: Jeeyeon Lee, Byeongju Kang.Formal analysis: Jeeyeon Lee.Investigation: Jeeyeon Lee, Byeongju Kang, Heejung Keum.Methodology: Jeeyeon Lee, Byeongju Kang.Resources: Jeeyeon Lee, Heejung Keum.Software: Jeeyeon Lee.Supervision: Jin Hyang Jung, Wan Wook Kim, Ho Yong Park.Validation: Byeongju Kang, Jin Hyang Jung, Wan Wook Kim, Ho Yong Park.Visualization: Jeeyeon Lee, Byeongju Kang.Writing – original draft: Jeeyeon Lee, Byeongju Kang.Writing – review & editing: Jin Hyang Jung, Wan Wook Kim, Heejung Keum, Ho Yong Park.


Using classification tree analysis, we evaluated the most useful magnetic resonance (MR) image type in the differentiation between early and progressed hepatocellular carcinoma (eHCC and pHCC).We included pathologically proven 214 HCCs (28 eHCCs and 186 pHCCs) in 144 patients. The signal intensity of HCCs was assessed on in‐phase (T1in) and opposed‐phase T1‐weighted images (T1op), ultrafast T2‐weighted images (ufT2WI), fat‐saturated T2‐weighted images (fsT2WI), diffusion‐weighted images (DWI), contrast enhanced T1‐weighted images in the arterial phase (AP), portal venous phase (PVP), and the hepatobiliary phase. Fat content and washout were also evaluated. Fisher's exact test was performed to evaluate usefulness for the differentiation. Then, we chose MR images using binary logistic regression analysis and performed classification and regression tree analysis with them. Diagnostic performances of the classification tree were evaluated using a stratified 10‐fold cross‐validation method.p < 0.05), and AP and T1in were finally chosen for creating classification trees (p < 0.05). AP appeared in the first node in the tree. The area under the curve, sensitivity and specificity for eHCC, and balanced accuracy of the classification tree were 0.83 (95% CI 0.74–0.91), 0.64 , 0.94 , and 0.79 (95% CI 0.70–0.87), respectively.T1in, ufT2WI, fsT2WI, DWI, AP, PVP, fat content, and washout were all useful for the differentiation  image type in differentiating between early and progressed hepatocellular carcinoma. The arterial phase image is the most useful MR image type, and T1‐weighted images in phase the second. First, it becomes possible to predict prognosis prior to treatment. The prognosis of eHCC patients undergoing radiofrequency ablation and surgery is better than that of pHCC patients.Liver Imaging Reporting and Data System (LI‐RADS) is a well‐known system for diagnosing HCC.For detection of eHCC, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) using Gadolinium ethoxybenzydiethylenetriamine pentaacetic acid  is more useful than intravenous contrast‐enhanced CT and CT during hepatic arteriography/arterial portography.Classification tree is a kind of prediction model in machine learning that take the form of yes or no questions. One of its advantages is to produce human‐readable rules regarding how to classify a given example.22.1This retrospective study was approved by our Institutional Review Board, and the requirement for informed consent was waived. We reviewed our institution's pathology records between March 2008 and June 2021, and 471 pathologically proven HCCs through liver resection (34 eHCCs and 437 pHCCs) in 290 patients were enrolled in this study. Both eHCC and pHCC were diagnosed according to the General Rules for the Clinical and Pathological Study of Primary Liver Cancer (5th and 6th editions) and the criteria of International Consensus Group for Hepatocellular Neoplasia. Among these lesions, 190 HCCs (190 pHCCs) that were over 2 cm in diameter were excluded, because eHCC is usually 2 cm in diameter or less.2.2MR imaging was performed on 140 HCCs using a 3‐Tesla (T) system , on 25 HCCs using a 3‐T system , on 19 HCCs using a 3‐T system , on 19 HCCs using a 1.5‐T system , on 10 HCCs using a 1.5‐T system , on 1 HCC using a 1.5‐T system . First, ultrafast half‐Fourier acquisition single‐shot turbo spin‐echo or single‐shot fast spin echo T2‐weighted images (ufT2WI) were obtained, followed by fat‐suppressed fast/turbo spin‐echo T2‐weighted images (fsT2WI), diffusion‐weighted images (DWI), and T1‐weighted images (T1WI) [in‐phase (T1in) and opposed‐phase (T1op)]. FsT2WI and DWI were obtained after DCE‐MRI in some cases. The MRI scanning parameters are shown in Data DCE‐MRI was obtained using the standard dose of Gd‐EOB‐DTPA (0.025 mmol/kg body weight) injected at a rate of 2 ml/s followed by 50 ml of 0.9% saline at the same rate. The pre‐contrast image (Pre) was obtained before contrast agent injection. The DCE‐MRI was obtained using the fluoroscopic triggering method (FLTM) in 60 HCCs and the fixed time method (FTM) with multiple phase images in 154 HCCs. From the multiple arterial phase FTM images, two radiologists  selected late arterial phase images  because late arterial phase images are strongly preferred for evaluation of HCC.2.3Above‐mentioned two radiologists who were blinded to the patients' pathological findings qualitatively determined whether lesions were high‐, iso‐, or low‐intensity compared with the adjacent liver parenchyma. The presence of fat was considered positive when the SI on T1op was lower than that on T1in. The presence of washout was considered positive in any enhancing observation when temporal reduction in enhancement in whole or part relative to composite liver tissue from AP to PVP.2.4p value <0.05 at Fisher's exact test in order to choose MR image types for creating classification trees.The CLTA and statistical analysis consisted of three steps. The first step was to determine the MR image types for creating classification trees. The useful MR image types for differentiating between eHCCs and pHCCs were analyzed using Fisher's exact test after making a 2 × 2 table. Diagnostic performance metrics such as sensitivity and specificity for eHCC, and balanced accuracy were also calculated. Then, binary logistic regression analysis was performed using variables with a The second step was to create the classification tree with MR image types chosen in the first step. Classification and regression tree analysis using the stratified 10‐fold cross‐validation method was performed. We performed 100 trials and chose the trial with the 50th highest AUC in order to avoid the effects of data splitting in the cross‐validation method.There were not enough cases to construct a validation set to optimize hyperparameters. We therefore set the minimum number of cases in a leaf to 10% of the total number of cases so that we obtain reproducible trees between folds. If we make more complex trees, trees obtained were different between folds in the cross‐validation method. The ratio of eHCC to pHCC was 0.15, and when one class is rare in a classification problem, trees often do not work well at diagnosing that class. Therefore, we set “class_weight” (a hyperparameter in Scikit‐learn) to “balanced” to automatically adjust the class weights to be inversely proportional to the class proportions in the training dataset.The third step was to assess diagnostic performance metrics of the tree: area under the curve (AUC), sensitivity and specificity for eHCC, balanced accuracy. We calculated 95% confidence interval (95% CI) for them using 2000 iterations of the stratified bootstrap method. The diagnostic probability of a case assigned by a classification tree is the class proportion of the leaf node to which the case is assigned. We did not average the AUC values of each fold in the cross‐validation method because each test set contained only two or three eHCCs. Instead, we calculated the overall AUC (AUC merge): to sort the individual scores from all folds together into a single ROC curve and then compute the AUC.To evaluate the usefulness of the above‐mentioned tree, we performed two comparisons. First, we compared the AUC of the above‐mentioned tree to that of the tree created with the MR image in the first node by itself. Second, we calculated the AUC values for all combinations of useful MR image types chosen by Fisher's exact test in the first step. We compared the maximum AUC values among them to that of the above‐mentioned tree. The AUC comparison was performed using Delong test.p value <0.05 was considered to indicate statistical significance.The binary logistic regression was performed using the vcd package,33.1The inter‐reader agreement between the two radiologists was good or excellent: Cohen's kappa coefficients were 0.84 for T1in, 0.85 for T1op, 0.77 for fat, 0.95 for ufT2WI, 0.89 for fsT2WI, 0.91 for DWI, 0.84 for AP, 0.82 for PVP, 0.77 for washout, and 0.91 for HBP.p < 0.05, Table p < 0.05, Table The SI on T1in, ufT2WI, fsT2WI, DWI, AP, PVP and the presence of washout and fat were all significantly useful for the differentiation . The maximum AUC among all combinations of useful MR image types chosen by Fisher's exact test was obtained with a combination of AP, T1in, and PVP . However, there was no significant difference between this and the AUC value of the combination of AP and T1in (p = 0.173). Receiver operating characteristic curves are shown in Figure The AUC value of the combination of AP and T1in was significantly higher than that of AP , but some eHCCs showed high intensity (36% in our study). It is important to diagnose eHCCs that show high intensity on AP, but we could not find a successful combination of MR images in our study. Instead, the tree of AP and T1in diagnosed two types of HCCs as pHCC, but with different probabilities Figure , and impIn contrast, fsT2WI was not included in the tree, although it had almost the same sensitivity and specificity as AP. This might be because fsT2WI had a stronger correlation with AP than T1in (Spearman's rank correlation coefficient = 0.64 between fsT2WI and AP and − 0.24 between T1in and AP). As a result, fsT2WI might not have provided additional information to that provided by AP in this study. However, this study included only resected HCCs, and their number was small. Therefore, further study is necessary. In addition, combining AP with fsT2WI properly may facilitate better differentiation. For example, fsT2WI can be used instead of AP when appropriate AP images are not available because of factors such as motion artifacts and inappropriate scan timing.Thus, there is a large variety of MR image types that provide overlapping and sometimes contradictory information. CLTA revealed that in clinical practice AP is the most useful type of MR image and T1in the second most useful, among the eight useful types of MR images. There have been several studies that revealed useful MR image types for the differentiation between eHCC and pHCC,This study has several limitations. First, only lesions pathologically proven through surgery were retrospectively analyzed. This resulted in selection bias and imbalanced data. Second, because this was a retrospective study, the MRI machines and protocols were mixed. In particular, differences in b‐values on DWI and delay times on HBP might be an issue. However, we evaluated signal intensities qualitatively, and these differences would not have had a significant impact, as the results were consistent with previous studies.In conclusion, CLTA revealed that in clinical practice AP is the most useful MR image type and that T1in the second in the differentiation between eHCC and pHCC. This is clinically important because it becomes possible to predict prognosis prior to treatment. In addition, there remains room for discussion on eHCC treatment policies. We may treat eHCC and pHCC in different ways in the future.Fumihito Ichinohe: Formal analysis (lead); investigation ; writing – original draft (lead). Daisuke Komatsu: Investigation . Akira Yamada: Investigation . Takanori Aonuma: Data curation . Ayumi Sakai: Data curation . Shimizu Marika: Data curation . Masahiro Kurozumi: Data curation . Akira Shimizu: Data curation . Yuji Soejima: Data curation . Takeshi Uehara: Data curation . Yasunari Fujinaga: Conceptualization (lead); supervision (lead); writing – review and editing (lead).No specific funding was disclosed.The authors declare that there is no conflict of interest.This study was approved by our institutional ethics committee (no.2591). Informed consent was waived because this study was a retrospective study.Data S1Click here for additional data file.


Stress hyperglycemia ratio (SHR) reflects the acute blood glucose change in response to acute illnesses or injuries, including pneumonia. We aimed to investigate the associations of SHR with systemic inflammation and clinical outcomes in diabetic inpatients with pneumonia on admission.A multicenter and retrospective study was conducted among diabetic inpatients with pneumonia on admission via electronic medical records from 2013 to 2019 in Ruijin Hospital, Shengjing Hospital, and China‐Japan Friendship Hospital.9/L in Q4 vs 7.6 × 109/L in Q1, 7.9 × 109/L in Q2, and 8.0 × 109/L in Q3, p < .001), higher neutrophil‐to‐lymphocyte ratio , higher C‐reactive protein , higher procalcitonin , and higher D‐dimer . Excluding patients with hypoglycemia on admission in the analyses, there were still distinct J‐shaped associations between SHR and adverse clinical outcomes in patients with different severity of pneumonia, especially in those with CURB‐65 score for pneumonia severity  ≥ 2. In the multivariable regression model, predictive value for adverse clinical outcomes was higher when SHR was taken as a spline term than as quartiles in all patients , and when SHR as a spline term instead of fasting blood glucose was included in patients with CURB‐65 ≥ 2 .The study included 1631 diabetic inpatients with pneumonia on admission. Patients of the fourth quartile (Q4) of SHR on admission showed significantly elevated systemic inflammation compared with those of the first quartile (Q1), second quartile (Q2), or third quartile (Q3) of SHR, including more white blood cells  was associated with elevated systematic inflammation in diabetic inpatients with pneumonia on admission.In the absence of hypoglycemia on admission, SHR was of J‐shaped associations with adverse clinical outcomes in diabetic inpatients with pneumonia of different severity.In diabetic inpatients with pneumonia on admission, predictive value of SHR for adverse clinical outcomes was greater than that of fasting blood glucose and was further elevated when the nonlinear association was taken into consideration. Among previous studies, much attention was paid to the hazard of severe stress hyperglycemia to clinical outcomes but little was paid to the relative deficiency of stress blood glucose. In addition, management of blood glucose in patients with and without diabetes, in patients from different medical settings, and in patients with different severity of illness is not the same.Recently, several studies highlighted the value of stress hyperglycemia, which presents the absolute glycemia change, in the analyses of prognoses in acute physiological events including pneumonia.22.11C (HbA1C), for the calculating of SHR, and CURB‐65  scores on admission; (5) complete demographic information ; and (6) definite clinical outcomes . Exclusion criteria were as follows: (1) absolute hypoglycemia on admission (FBG ≤ 3.9 mmol/L); and (2) current pulmonary tuberculosis. Totally, 1631 cases were included in the analyses   aged no less than 18 years old; (2) pneumonia on admission; (3) diabetes; (4) available fasting blood glucose (FBG) and hemoglobin A) Figure . The eth2.21C according to the published formula eAG (mg/dL) = 28.7 × HbA1C (%) − 46.7.The diagnosis of diabetes was based on American Diabetes Association.2.3rcompanion were employed in group comparisons for categorical variables with p values adjusted by Bonferroni correction in the intragroup comparisons. Associations between SHR on admission and composite end point were explored using univariable and multivariable logistic regression analyses, and the results were listed as odds ratio (OR) with 95% confidence interval (CI). Restricted cubic splines based on logistic regressions were used to examine the potential nonlinear associations between SHR and composite clinical outcome. Considering the sample size of this study, five knots automatically located by R in certain percentiles were used in the spline analyses. Predictive values of SHR in different statistical forms were evaluated and compared by area under curve (AUC) of receiver operating characteristic (ROC) using R package pROC. Two‐sided significance level of 0.05 was selected for all analyses. Statistics were analyzed and displayed using R version 4.2.0 .Continuous variables were presented as range or median (interquartile range). Categorical variables were shown as count (percentage). Comparisons among groups for continuous variables were performed using the Kruskal–Wallis H‐test and further pairwise comparisons were done using Wilcoxon rank‐sum test. Chi‐square test with pairwiseNominalIndependence function in R package 33.1p < .001), longer length of hospital stay , higher mechanical ventilation rate , and higher in‐hospital mortality . Thus, more patients in Q4 reached the composite end point compared with the other three groups .In this study, 1631 diabetic inpatients with pneumonia on admission were included between January 2013 and December 2019; 1579 (96.8%) patients had previous diagnoses of diabetes, most of which were type 2 diabetes in those with recorded type of diabetes . The other 52 (3.2%) patients were of newly diagnosed diabetes. SHR on admission ranged from 0.17 to 3.12 and was divided into quartiles for the further analyses. Basic characteristics of patients were summarized an compared among patients of the first quartile (Q1), the second quartile (Q2), the third quartile (Q3), and the fourth quartile (Q4) of SHR, as shown in Table 3.29/L in Q4 vs 7.6 × 109/L in Q1, 7.9 × 109/L in Q2, and 8.0 × 109/L in Q3, p < .001), higher NLR , higher CRP , higher PCT , and higher D‐Dimer . No significant differences in ESR level were found among groups.As shown in Figure 3.3p < .001). In the multivariable logistic regression models, we adjusted ORs of SHR by age, sex, chronic comorbidities , current smoking status, and CURB‐65 scores. As shown in Figure p < .001). Taken Q2 as a reference, patients in Q3 and Q4 were of higher risk of developing adverse clinical outcomes . What is more, in patients with CURB‐65 ≥ 2, Q1 possessed a slightly but significantly increased risk over Q2 .Univariable and multivariable logistic regression analyses were done to determine the association between SHR and adverse clinical outcomes in all patients and patients of different severity classified by CURB‐65 scores Figure . In the 3.4p‐nonlinear < .001) and in all groups in the multivariable analyses . In view of the fact that there was no commonly accepted threshold of low SHR, we roughly divided SHR into two parts based on median 0.889. In the univariable and multivariable logistic regression analyses, we found that when SHR was under median, risk of reaching composite endpoint significantly increased as SHR decreased; and when SHR was over median, risk of reaching composite endpoint significantly increased as SHR increased , we took FBG as a continuous variable in the comparisons. As shown in Table p = .002) or as a continuous variable  in all patients, and also larger than that of SHR as a categorical variable in patients with CURB‐65 ≥ 2 . In the multivariable logistic regression model, predictive value of the model containing SHR as a spline term was larger than that of the model containing SHR as a categorical variable in all patients . These results indicated the added predicted value of low SHR in the analysis of adverse clinical outcomes. As to the AUC difference between SHR and FBG, SHR in different forms all showed increased AUC in contrast with FBG (p < .05) in the univariable logistic regression model in all patients. In the multivariable logistic regression model, only the model containing SHR as a spline term possessed larger AUC than that of the model containing FBG .Considering the nonlinear correlation between SHR and composite end point, we compared the predictive value of SHR as a spline term with that of SHR as a categorical variable (from Q1 to Q4) or as a continuous variable. In addition, we also compared the predictive value of SHR in different forms with FBG, which reflected the total blood glucose level with acute and routine blood glucose combined. Because FBG was linearly correlated with risk of reaching composite endpoint , Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top‐Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100), and Cultivation Project of Shanghai Major Infectious Disease Research Base (20dz2210500).The authors declare no conflicts of interest in this work.Figure S1. Flow chat of the study. SHR, stress hyperglycemia ratio.Click here for additional data file.


BRAFV600E/RAS mutations) in metastatic colorectal cancer (mCRC) is mainly based on mCRC patients with proficient mismatch repair (pMMR) tumors. It is uncertain whether these biomarkers have the same prognostic value in mCRC patients with deficient mismatch repair (dMMR) tumors.Current knowledge on prognostic biomarkers  and a large French multicenter cohort (2007–2017). All mCRC patients with a histologically proven dMMR tumor were included.BRAFV600E mutation was present in 47% of tumors and 30% harbored a RAS mutation. Multivariable regression analysis on OS showed significant hazard rates (HR) for known prognostic factors as age and performance status, however showed no significance for Lynch syndrome , BRAFV600E mutational status , and RAS mutational status , with similar results for PFS.In our real‐world data cohort of 707 dMMR mCRC patients, 438 patients were treated with first‐line palliative systemic chemotherapy. Mean age of first‐line treated patients was 61.9 years, 49% were male, and 40% had Lynch syndrome. BRAFV600E and RAS mutational status are not associated with prognosis in dMMR mCRC patients, in contrast to pMMR mCRC patients. Lynch syndrome is also not an independent prognostic factor for survival. These findings underline that prognostic factors of patients with dMMR mCRC are different of those with pMMR, which could be taken into consideration when prognosis is used for clinical decision‐making in dMMR mCRC patients and underline the complex heterogeneity of mCRC. V600E and RAS mutation status on survival outcomes with pooled individual patient data from the large Dutch and French deficient mismatch repair (dMMR) / microsatellite instability‐high (MSI‐H) metastatic colorectal cancer (mCRC) cohort. Lynch syndrome, BRAFV600E and RAS mutational status are not independently associated with overall survival in dMMR/MSI‐H mCRC patients treated with palliative first‐line chemotherapy, in contrast to proficient MMR mCRC patients. This could be taken into consideration when prognosis is used for clinical decision‐making in dMMR mCRC patients and underline the complex heterogeneity of mCRC.An original research article to provide insight in the prognostic value of Lynch syndrome, BRAF BRAFV600E mutated tumors can be effectively treated with a combination of encorafenib (BRAF inhibitor) plus cetuximab (anti‐EGFR).The heterogeneity of CRC is expressed by many molecularly‐defined subgroups with differences in response to treatment and prognosis.Immune checkpoint inhibitors (ICIs) have shown a marked improvement in PFS and OS in patients with dMMR/MSI‐H mCRC.BRAFV600E mutation and uncertain results of Lynch syndrome and RAS mutations due to low number of included patiens with dMMR/MSI‐H mCRC.BRAFV600E and RAS mutation status and the effect of treatment regimens on survival outcomes with pooled individual patient data from the largest Dutch and French dMMR/MSI‐H mCRC cohorts up until now.Data of mCRC patients with dMMR/MSI‐H tumors are scarce because of the low incidence in the metastatic setting and previous studies show conflicting results regarding prognosis of the 22.1BRAFV600E  and RAS status, if determined during daily clinical practice. Status of BRAFV600E and RAS was established by next generation sequencing, according to national guidelines, in almost all cases. The NCR data were pseudonomized and consent was obtained by an opt‐out approach. Data of the French cohort was collected in the period of 2007–2017 in 18 French centers by local physicians and/or clinical research associates.Comité de Protection des Personnes Ouest III and, due to the retrospective nature of the study and since most patients were deceased, informed consent was waived.This observational cohort study combined a nationwide population‐based Dutch cohort and a French multicenter observational cohort of adult dMMR/MSI‐H mCRC patients. For the Dutch cohort individual data were collected in the period of 2014–2019 by well‐trained data managers of the Netherlands Comprehensive Cancer Organization (IKNL) and registered in the Netherlands Cancer Registry (NCR). Data collection included all Dutch centers by linkage with the Dutch Nationwide Pathology Databank (PALGA), thereby capturing every patient with histologically proven CRC.In both cohorts, all consecutive patients with histologically proven dMMR and/or MSI‐H mCRC were included. Patients with a concurrent malignancy interfering with the prognosis and patients with short follow‐up (≤15 days) were excluded. In addition, inconclusive cases with discordance between MSI and MMR immunohistochemistry status (MSI/pMMR or MSS/dMMR) were not included in the study.2.2MMR and/or MSI status was only known if tested in daily clinical practice and was obtained by analysis of the four MMR proteins expression by immunohistochemistry and/or DNA MSI testing in accredited laboratories according to international guidelines.BRAFV600E status and MLH1 promotor hypermethylation status (details available in supplements) in both the Dutch and the French cohort, as previously described.Whether patients were identified with Lynch syndrome or sporadic dMMR/MSI‐H was based on a tailored approach by MMR protein expression, family history, 2.3In the Dutch cohort, the NCR was linked to the National Municipal Personal Records Database in January 2021 to obtain the most recent information on vital status. In the French cohort, the vital status was updated until September 2019. OS was defined as treatment initiation of palliative first‐line (OS1), second‐line (OS2) or third‐line (OS3) treatment until death. PFS was defined as survival from treatment initiation of first‐line (PFS1), second‐line (PFS2) or third‐line (PFS3) treatment until progression or death, whichever occurred first.A new line of therapy was defined when a new systemic therapy was initiated, including change of therapy due to toxicity or progression. It was not considered an event for PFS if a new line of therapy was initiated without documented progression or death. Adjuvant therapy was only considered a line of therapy when progression occurred during the adjuvant chemotherapy or within 6 months after start of treatment. Patients treated with ICIs and non‐standard chemotherapy  were only included for analyses in treatment lines preceding these therapies. Sensitivity analyses were performed with censoring of these patients at the start of ICI and/or local treatment.2.4Median follow‐up time was analyzed with reversed Kaplan–Meier analyses. The primary endpoint was survival on first‐line treatment, for which PFS1 and OS1 were analyzed. Kaplan–Meier curves were obtained for univariable analysis, and Cox regression analysis was used for multivariable analysis. Patients were censored at date of last follow‐up for patients alive and without disease progression.BRAFV600E and RAS mutational status, Lynch syndrome status, World Health Organization performance score (WHO PS), chemotherapy regimen, targeted therapy and with stratification for country.Cox proportional hazard regression models with 95% confidence intervals (CIs) included preselected factors, based on literature and expert opinion: age at diagnosis mCRC, sex, sidedness of primary tumor , primary tumor resection, grade, T‐stage, N‐stage, adjuvant therapy, metachronous or synchronous mCRC .To study chemosensitivity, PFS analyses were restricted to first‐line patients. Subgroups for molecular status and Lynch syndrome status were analyzed when at least 20 patients were present in each of the arms. Additionally, PFS and OS analyses were performed from start second‐line and third‐line treatments . A 33.1The combined Dutch and French cohorts included a total of 707 patients of which 180 (25%) patients received best supportive care alone  had a mean age of 74.6 years, 12% had proven or suspected Lynch syndrome, 47% had a WHO PS status of 2+ and tumors harbored a BRAFV600E mutation in 75% and a RAS mutation in 10% of cases , while Lynch dMMR/MSI‐H tumors more often harbored a RAS mutation (65%)  compared to the Dutch cohort  Figure . Most paBRAFV600E mutation, 26.3 months (95% CI: 19.7–36.9 months) for tumors harboring a RAS mutation, and 19.6 months (95% CI: 14.4–39.6 months) for RAS and BRAFV600E wildtype . Median PFS1 was 5.1 months (95% CI: 3.9–6.5 months), 7.1 months (95% CI: 3.7–10.2 months) and 5.9 months (95% CI: 3.7–10.2 months), respectively (p = 0.16). Median OS1 and PFS1 for Lynch dMMR/MSI‐H mCRC patients were 35.1 months (95% CI: 26.1–40.1 months) and 7.5 months (95% CI: 5.5–10.2 months) versus 14.2 months  and 5.1 months  for sporadic dMMR/MSI‐H mCRC patients. Median OS1 for young (<60 years) and elder (≥60 years) dMMR/MSI‐H mCRC patients stratified for Lynch syndrome status did not show significant outcomes   and median PFS1 6.0 months (95% CI: 5.0–6.7) for mCRC patients with dMMR/MSI‐H mCRC treated with palliative first‐line chemotherapy. Median OS1 was 14.7 months (95% CI: 11.4–20.7 months) for tumors harboring a ) Figure .RAS mutations were observed, including 101 KRAS mutations and 5 NRAS mutations. KRAS mutations were most often G13D (27%), G12D (25%) or A146 (20%)  in patients with other KRAS mutations  Figure . Median s Figure .3.4In multivariable regression analyses higher age, higher N‐stage, liver involvement and a WHO PS of two or higher were associated with worse OS in dMMR/MSI‐H mCRC patients treated with first‐line chemotherapy with or without targeted therapy Table , while r3.5Median PFS1 for patients treated with a first‐line palliative chemotherapy was 6.0 months (95% CI: 5.0–6.7 months), for second‐line 3.8 months (95% CI: 3.1–4.4 months) and for third‐line 3.6 months (95% CI: 2.3–5.1 months) , resection status of primary tumor (72% vs. 91%) and Lynch syndrome (24% vs. 52%), potentially due to differences in country guidelines or patient selection; in France patients were selected in expert centers and in the Netherlands patients were selected from all centers.The median OS for patients with first‐line systemic therapy was 19.3 months from the start of first‐line treatment in our study. The median OS from other studies differed from 9 months to 39 months. A direct comparison to these studies is difficult due to differences in patient characteristics such as the number of patients with Lynch syndrome, inclusion of patients with initially resectable disease, administration of ICI and inclusion of trial or cohort patients.The median PFS for patients with a first‐line of palliative systemic therapy in our study was 6.0 months, which was comparable to other studies on dMMR/MSI‐H mCRC with a PFS varying from 4 to 6 months in dMMR/MSI‐H mCRC,BRAFV600E mutation in patients dMMR/MSI‐H mCRC.BRAFV600E mutation as a driver for poor prognosis in dMMR/MSI‐H patients.BRAFV600E tumors and an important impact on the prognosis.Our study is in agreement with most studies showing no inferior prognosis for tumors with a RAS mutation showed a trend towards better prognosis in univariate analysis, which is remarkable compared to the known inferior prognosis of RAS mutations in pMMR/MSS mCRC patients.RAS‐mutated tumors and Lynch syndrome and younger age. The Lynch population is relatively young, which could be an important driver for better survival. The univariate effect is mitigated in multivariable analysis, with no significant effect on prognosis, consistent with other studies.KRAS mutations in dMMR/MSI‐H mCRC is different compared to pMMR/MSS with relatively fewer KRAS codon 12 mutations and more KRAS A146 mutations.KRAS mutations, KRAS A146 mutation has been suggested as a distinct molecular subgroup with worse clinical outcomes and its underlying exon with mucinous/rare histological subtype.KRAS A146 mutation, although the sample size is small (N = 11).Patients with tumors harboring a RAS mutational status, age and Lynch syndrome could be of important knowledge when investigating subgroups in dMMR/MSI‐H mCRC patients. A subgroup of patients with a tumor harboring a RAS mutation did not show a significant survival increase in KEYNOTE‐177 to either pembrolizumab or chemotherapy.BRAFV600E mutation, RAS mutation or Lynch syndrome on OS, however more research is warranted in this matter.Lynch syndrome showed a superior survival in univariable analyses but not in multivariable analysis, potentially also due to the strong correlation between Lynch syndrome and young age. This is in agreement with other studies.n = 67) compared to anti‐EGFR (n = 36).The effect of different treatment regimens on survival is uncertain in patients with dMMR/MSI‐H mCRC. No randomized controlled trial has been conducted to primarily analyze efficacy of different treatment regimens in dMMR/MSI‐H mCRC patients, and subgroups in cohort studies are often small due to the low incidence of dMMR/MSI‐H tumors in metastatic setting. Tougeron et al. have reported the largest series on chemosensitivity and presented no significant differences in chemotherapy and/or targeted therapy regimens, although a trend was seen in favor of anti‐VEGF ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; software ; validation ; visualization ; writing – original draft ; writing – review and editing . Frederieke H van der Baan: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; supervision ; validation ; writing – original draft ; writing – review and editing . Romain Cohen: Writing – review and editing . Thomas Aparicio: Writing – review and editing . Christelle De La Fouchardiere: Writing – review and editing . Thierry Lecomte: Writing – review and editing . Cornelis J.A. Punt: Writing – review and editing . David Sefrioui: Writing – review and editing . Rik Verheijden: Methodology ; writing – review and editing . Geraldine Vink: Conceptualization ; project administration ; resources ; supervision ; writing – review and editing . Emerens Wensink: Writing – review and editing . Aziz Zaanan: Writing – review and editing . M. Koopman: Conceptualization ; supervision ; writing – review and editing . David Tougeron: Conceptualization ; methodology ; project administration ; resources ; supervision ; writing – original draft ; writing – review and editing . Jeanine Roodhart: Conceptualization ; funding acquisition ; methodology ; project administration ; supervision ; visualization ; writing – original draft ; writing – review and editing .The author(s) received no specific funding for this work.AZ: Pierre Fabre, Merck, Baxter, Havas Life, Amgen, Roche. CdlF: None. CJP: Nordic Pharma. RC: Servier Institute, Nuovo‐Soldati Foundation, ARC Foundation for Cancer Research, MSD. DS: Servier, Ipsen, Astella, Roche. DT: Amgen, MSD, Pierre Fabre, Merck, Roche, Bayer, Ipsen. EW: None. FvdB: None. GV: BMS, Merck, Servier, Personal Genome Diagnostics, Bayer, Sirtex, Pierre Fabre, Lilly. JR: Bayer, BMS, Merck‐Serono, Pierre Fabre, Servier, HUB4organoids, Cleara Biotech. KZ: None. MK: Nordic Farma, Merck‐Serono, Pierre Fabre, Servier, Bayer, Bristol Myers Squibb, Merck, Roche and Servier. RV: None TL: Amgen, Servier, Sanofi. TA: Roche, Sirtec, Sanofi, Amgen.Comité de Protection des Personnes Ouest III and, Due to the retrospective nature of the study and since most patients were deceased, informed consent was waived. The study was performed in accordance with the Declaration of Helsinki.Dutch data were pseudonomized and consent was obtained by an opt‐out approach. The French data collection was approved by the ethical committee Appendix S1.Click here for additional data file.


Auricularia and Tremellan) and Crataegus flavonoids promote the balance of dyslipidemia, which have a positive biological regulating effect on intestinal flora. In this study, the extraction of water‐soluble polysaccharides from Auricularia and Tremellan was investigated and optimized firstly. Polysaccharides and flavonoids were then combined to study the effects on the mediating role of abnormal blood lipid concentration and intestinal flora in vivo. The rats were divided into 10 groups, the NC , HM (model), PCI (Simvastatin control), PCII (Fenofibrate control), AAP (Auricularia auricular Polysaccharide), TFP (Tremella fuciformis Polysaccharide), HF (Crataegus Flavonoid), LDC (Low‐dose combination), MDC (Medium dose combination), and HDC (High‐dose combination), used to explore the impact of polysaccharides and flavonoids complex on state of blood lipid, liver, and intestinal flora of dyslipidemia rats. The results showed that the combination of polysaccharides and flavonoids could significantly decrease the levels of triglyceride (TG), total cholesterol (TC), low‐density lipoprotein (LDL‐C), and increase the level of high‐density lipoprotein cholesterol (HDL‐C). It also significantly decreased the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and improved liver morphology. What is more, the HDC favorably alters the intestinal microflora balance, promotes intestinal integrity and mobility, and inhibits the growth of harmful bacteria such as Escherichia coli/Shigella and Clostridium compared with HM group. In brief, the combination of polysaccharides and flavonoids had a synergistic effect on the remission of dyslipidemia, and promoted health by improving lipid metabolism, protecting liver tissue, and regulating the intestinal flora in hyperlipidemia rats.Both edible fungal polysaccharides ( The combination of polysaccharides and flavonoids had a synergistic effect on the remission of dyslipidemia and promoted health by improving lipid metabolism, protecting liver tissue, and regulating the intestinal flora in hyperlipidemia rats. Hyperlipidemia is a condition of abnormal lipid metabolism in the body which is due to environmental, genetic, and other factors or the factors' interaction. Hypolipidemic drugs are mainly used to control blood lipids within a normal range for a long time, and cannot completely cure hyperlipidemia. Clinically, statins, clobetin, nicotinic acid, and bile acid integrated resins are often used to prevent and treat hyperlipidemia with good results, but they have side effects inevitably Hu & Ji, . BecauseTremella and Auricularia polysaccharide can improve hyperlipidemia  and medicinal mushrooms have been widely used in Asia as part of traditional diet and medicine, and the utilization of their medicinal properties in naturally produced pharmaceuticals , Tremella fuciformis polysaccharides (TFP), and Crataegus flavonoids (HF) to probe their effects on the levels of blood lipids and the regulation of the intestinal flora.Therefore, we hypothesized combination of fungal polysaccharides and flavonoids could reduce blood lipids and positively regulate intestinal flora. To test the hypotheses, we combined the three components of 22.1Auricularia auricula and tremella used in the experiment were provided by Zhongkang Vegetable Planting Co., Ltd. The liquid nitrogen freezing pulverization method is used to pretreat the raw materials. The Crataegus flavonoids were provided by the Nutrition and Functional Food Laboratory of Hebei University of Science and Technology.High‐fat diet formula: 78.8% of basic feed, 10% of lard, 10% of yolk powder, 1% of cholesterol, and 0.2% of cholate; normal diet, provided by Hebei Medical University.2.2SPF Wistar rats (weighing 200 ± 20 g) were purchased from the Experimental Animal Center of Hebei Medical University (SCXK (JI) 2018‐004, 1410014, Shijiazhuang, China). All experiments were conducted following the Guide for the Care and Use of Laboratory Animals of National Institutes of Health. The animals were maintained in an environment with controlled temperature (20–25°C) and humidity (50 ± 5%), with food and water available at any time and natural light. The health of the rats was monitored every day. Before blood collection, rats were anesthetized with ether, and the other rats were euthanized by cervical dislocation under anesthesia with isoflurane.The 90 rats were divided into 10 groups randomly, and the rats were given preventive treatment. According to previous studies  orthogonal design was tested , with the assistance of ultrasound (150 w), we selected five levels to optimize the single factor for polysaccharide extraction conditions of 2.3.3Under each factor , with the assistance of ultrasound (150 w), we selected five levels for a single‐factor experiment. According to the results of the single‐factor experiment, a response surface methodology experiment was carried out to obtain the optimal synchronous extraction parameters, taking the extraction rate of polysaccharide as the response value, and the ultrasonic temperature (A), ultrasonic time (B), material–liquid ratio (C) were independent variable ; B: acetonitrile; A: B = 82: 18; flow rate: 1.0 mL/min; column temperature is 25°C; injection volume is 10 μL; wavelength is 245 nm.Polysaccharide samples were treated with 4 mol/L TFA (trifluoroacetic acid), 0.5 mol/L PMP‐methanol solution, 0.3 mol/L NaOH solution, and 0.3 mol/L HCl solution, respectively, for HPLC detection. HPLC conditions: SHISEIDO C18 column , mobile phase: A: 0.1 mol/L KH2.5During feeding, the body weights of rats were measured once weekly. The liver, spleen, heart, and kidney were weighed at the eighth week after the start of the feeding period. The following formula of organ index was determined , and the arteriosclerosis index (AI) was calculated.2.7The liver of the same part (hepatic lobule) of the rat soaked in 4% paraformaldehyde fixative was taken, and then dehydrate → paraffin embedding → section → HE staining → optical microscope to observe whether the liver tissue structure is degenerated  according to the manufacturer's protocols. The V3–V4 region of the bacteria 16S rRNA gene was amplified by PCR  using primers 338F (5′‐ACTCCTACGGGAGGCAGCAG‐3′) and 806R (5′‐GGACTACNNGGG TATCTAAT‐3′). PCR reactions were performed in triplicate   and 33.13.1.1A. auricula and the results of the orthogonal experiments  = 98.08%, and the index of variation C.V.% = 1.24, the equation of the fitting model is highly reliable. In summary, the model equation can be used to predict and analyze the extraction conditions of Tremella polysaccharide. The results also showed that the influence of each factor is B > A > C, that is, time > temperature > material–liquid ratio.The analysis of variance for the experimental results of the Box–Behnken design is presented in Table By comparing the response surface analysis chart and the contour map Figure , it can According to the typical analysis data, the optimal extraction process conditions, ultrasonic time 53.70 min, ultrasonic temperature 63.00°C, and material–liquid ratio 1:73 were carried out. The maximum expected extraction rate of TFP reached 19.00%, which is in line with theoretical predictions, and the values are similar. This is not much different from the results of Zou and Hou .3.2A. auricula and Tremella were analyzed. The results showed the monosaccharide composition of AAP, mannose: glucuronic acid: glucose: galactose: xylose: fucose (8.64: 1.00: 13.47: 1.69: 2.13); The monosaccharide composition of TFP was as follows: mannose: glucuronic acid: glucose: xylose: fucose , indicating that long‐term consumption of a high‐fat diet will cause a significant increase in the body weight of rats. This result is consistent with the studies of Zou et al. . Compared with the HM group, HDC group's liver index decreased by 9.6% (p < .05) and spleen index decreased by 20.8% (p < .01), which are shown in Figure High‐fat diets cause liver and spleen damage in rats, and liver and spleen index increased. Compared with the NC group, liver index in the HM group was increased, and the difference was significant (p < .01), TFP, HE, and LDC groups also had a reduction effect on perirenal fat index (p < .05). Compared with the HM group, only the HDC group had the most obvious effect of reducing the total fat coefficient, which decreased by 21.5% and had statistical significance (p < .05). Thus, it can be explained that the HDC group may have an effect of fat index loss  of the rats in HDC group (0.63 ± 0.10 mmol/L) which was compared with HM group (0.45 ± 0.10 mmol/L).The TC and TG levels of AAP, TFP, HF, LDC, MDC, and HDC groups were decreased significantly compared with HM group as shown in Figure With the regulation of blood lipids in rats, the average value of AI of rats in each group showed different changes. The larger the value of AI, the higher the risk of atherosclerotic  compared with HM group. This effect was even better than PCI and PCII groups on AST level. However, TFP group and HF group were not reduced significantly different (p > .05), indicating that the combined use of the AAP and HF extract has a protective effect on the liver health of hyperlipidemia rats. This result of flavonoids and polysaccharides is consistent with the treatment of hepatitis  was 6124. As shown in Figure 3.7.2p < .05) as shown in Table Alpha diversity can reflect the abundance and diversity of the microbial community , while the abundance of Bacteroides decreased compared with the NC group and model group, but there was no significant difference. At the genus level, the top 49 genera are listed in Figure Alistipes , Helicobacter (0.40% vs. 0.06%), Desulfovibrio (0.74% vs. 0.24%), Dorea (0.02% vs. 0.13%), and Escherichia coli/Shigella (0.12% vs. 0.01%).The classification of sequences from the samples resulted in nine different phyla that were identified in this study Figure . After t3.7.5Bacillus, Clostridiaceae 1, and Clostridium sensu stricto 1 were enriched in the NC group, β‐proteobacteria, Burkholderia, Oxalobacteraceae, and Ruminococcaceae UCG 013 were enriched in the HM group, Micrococcus, Micrococcaceae, Eubacterium cholerae, Holdemanella, and Streptococcus sp GDLAMI SD were enriched in the PCI group, whereas Fecal bacillus, Lactococcus, Bacillus pectineus, Lactobacillus acidophilus, and Lactococcus lactis were enriched in the HDC group.According to linear discriminative analysis (LDA), LefSe was performed to obtain the cladogram representation and the predominant bacteria in the intestinal microbiota in the four groups as shown in Figure 4A. auricular polysaccharides and Tremella polysaccharide extraction with ultrasound assisted. The composition of A. auricula polysaccharide is different from Zeng et al.  compared with NC group, which indicated that the lipid‐reducing medicine had a significant influence on the liver organ and reduced blood lipids in the meanwhile. This result was consistent with Ahmad et al. , active polysaccharides, could improve and regulate the intestinal flora which was used as an oral adjuvant for intestinal disorder flora. Kaakoush and Morris ; data curation (lead); methodology ; software ; validation ; writing – original draft (lead). Tao Zhou: Conceptualization ; data curation ; methodology ; software ; validation . Yu‐fei Yuan: Conceptualization ; data curation ; methodology ; software . Dan‐dan Li: Investigation ; resources ; supervision . Bin‐bin Gong: Investigation ; resources ; supervision . Shan Gao: Project administration ; resources ; supervision . Qi–jia Chen: resources ; writing – review and editing . Yan‐dong Li: Investigation ; project administration ; resources (lead). Xue Han: Formal analysis ; funding acquisition ; project administration ; resources ; supervision ; writing – review and editing .The present work was supported by grants from the Key Research and Development Program of Hebei Province (No. 21327125D), the Science and Technology Plan Program of Shijiazhuang (No. 221500052A) and the National Natural Science Foundation of China (No. 22001056).The authors declare that there are no conflicts of interest.Table S1Click here for additional data file.


Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations  across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments.Genome-wide association studies were conducted using a globally diverse safflower (The online version contains supplementary material available at 10.1007/s11032-022-01295-8. Carthamus tinctorius L.) is a member of the Compositae family, grown as a vegetable, cut flower, herbal medicine, animal feed, birdseed, and oilseed, etc. in over 60 geographical regions covering the Middle East, Africa, America, Europe, and Asia  in safflower has been sequenced with genes being isolated and cloned , which is also referred to as the traits phenotypic plasticity Bradshaw . IdentifIn Australia, crop production is challenged by spatial drought patterns due to seasonal rainfall and high temperatures , including elite cultivars, breeding lines, and landraces. Accessions information and the field trial experiment design are previously described  and standard error of prediction (SEP) were 0.93 and 0.7% for seed protein content and 0.95 and 1.2% for seed total oil content.Days to flowering (DF) was recorded as the number of days from sowing to 25% of the plot flowering. Plant height (PH) was measured at the late flowering stage from the ground surface to the top of the plot canopy in cm. Seed weight (SW) was measured as random 500 achenes from the whole plot in grams. Grain yield was measured as yield per plot (YP) in kilograms harvested by machine. Seed protein (PR) and seed oil content (OL) were determined by near-infrared reflectance spectroscopy  with calibration by the Dumas nitrogen combustion method for protein , and the Soxhlet extraction for oil . The NIR prediction models R-squared  for each trait at each site were calculated by a single site linear mixed model with safflower accessions fitted as fixed effects. The model was illustrated as:Ymijk is the phenotypes of accessions m in rep j at row i, column k; µ is the overall mean, gm is the fixed accession genetic effect, and Rj is the replicate effect; ri is the row effect, ck is the column effect, and ɛmijk is the residual, including the AR1 × AR1 covariance structure to adjust spatial variation.where Pearson’s correlation at each site was calculated based on the BLUEs of each trait. BLUEs were used as the “phenotypes” for the GWAS.To assess the G × E level for each trait, the four sites were combined, and the genetic effect associated with accessions was decomposed into two components, the genetic effect of accessions and the interaction effect between accessions and sites (G × E effect), which were assumed to be homogenous for all sites. The linear mixed model was:Yijk is the phenotype of accession k in rep j at site i, µ is the overall mean, Si is the fixed i-th site effect, Rj is the fixed replicate effect, Gk is the random accession genetic effect, SGik is the random G × E effect, and ɛijk is the residual. Two models, including and excluding the G × E effect, were compared, and a log-likelihood ratio test was used to test the significance of the G × E effect for each trait  was estimated as the ratio of the genetic effect of accessions to total genetic variance, calculated as rGoverall = where A heterogeneous variance structure was also fitted in the linear mixed model, which assumes that accessions genetic effect is independent at each site. It can be illustrated as:rGij = i and j. Similar to the above, high genetic correlations between two sites indicated a low G × E interaction. The significance of the genetic correlation between two sites was tested for deviation from 1 using likelihood ratio tests. If rGij significantly differed from 1, it suggested the ranking of accessions at the two sites was different. Akaike information criterion (AIC) was used to compare the fitness of models 2 and 3.where the terms are the same as above, with the site as a fixed effect and the accession and trial replicate effects both nested within sites as a random effect with different variance for each site. The residual variance was also nested within the site, with the AR1 × AR1 covariance structure used to adjust spatial variation across columns and rows. The genetic correlation of the accession effect between two sites was calculated as A total of 349 accessions were genotyped using a genotyping-by-sequencing assay as described in , plant height (PH), days to flowering (DF), 500 seed weight (SW), seed protein (PR), and oil content (OL) are shown in Fig. Pearson correlations between traits at each site showed that YP was positively correlated with PH (0.34–0.479) and negatively correlated with PR (− 0.208– − 0.405). SW was negatively correlated with OL (− 0.518– − 0.505), PR (− 0.55– − 0.383), and PR was positively correlated with OL (0.19–0.639) over all sites. However, DF is positively correlated with PH at sites 1, 3, and 4 (0.464–0.62) and negatively correlated with PH at site 2  among sites were observed for SW and OL , indicating low G × E interaction for those traits, while low rGoverall for YP (0.48) indicated a strong G × E interaction. G × E levels were moderate for PH, DF, and PR, with rGoverall value ranging from 0.67 to ~ 0.79 . The model including G × E interaction effects had a higher log-likelihood than the model excluding G × E interactions, and the G × E interaction effects were all significant (Supplementary Table rGij) between pairwise site combinations varied; however, traits with high rGoverall also had high rGij between pairwise sites  was not significantly different from 1. As the rest traits all had rGij ≥ 0.80 between sites 3 and 4, suggesting that those two sites could be treated as a single site with limited re-ranking. Genetic correlations for PH were uniformly high between pairwise sites (rGij ≥ 0.80), while the genetic correlations for DF and YP varied, with those for YP being the most variable.To account for differences in G × E interactions between sites, a linear mixed model assuming heterogeneous genetic and residual variances (model 3) was adopted. The AIC was lower for all traits for model 3 than for model 2, suggesting that model 3 fitted the data better  revealed a strong population structure among 318 accessions , respectively. Locus 28,935 was associated with low SW and high OL at sites 2 and 3 (or site 4) . Our results showed that G × E patterns differed between safflower traits. The identification of site-shared and site-specific MTAs in GWAS provides knowledge to broaden our understanding of the genetic basis of G × E interactions for important safflower traits.r = 0.476)  had an impact on safflower agronomic traits. Safflower is normally sown in Winter in Australia to maximize the usage of the available water from Winter and early Spring rain  Oz , indicat.476) Oz . The neg.476) Oz .rGoverall was observed for DF and PH, the pairwise genetic correlation showed that lines were reranked strongly for DF at site 2 compared with other sites. The genetic divergence of DF among the accessions in response to water stress at flowering implied DF is important in developing drought tolerant varieties  by all three GWAS approaches, of which 18 were shared across sites, and 12 were site-specific, indicating the complex genetic control of this trait. Studies with canola showed that 24 candidate genes were involved in fatty acid biosynthesis , days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and seed protein content (PR) in a globally diverse safflower collection grown in four field trials. The heterogenous mixed linear model (MLM) fitted data better and provided a detailed estimation of the G × E pattern. We observed that different water stress conditions impacted the performance of each of these traits differently, with low overall G × E observed for OL and SW and high overall G × E for YP. In total, 92 MTAs were identified with large effects MTAs detected for OL, SW, and PR across all sites. Site-specific MTAs were detected for all traits with differed allelic effects, suggesting these MTAs could be associated with trait G × E. Five MTAs were associated with multiple traits. The uniform GWAS thresholds used in the study could have impacted the number of significant SNP identified for complex traits. This study has provided new insights into the genetic architecture of the traits studied, and it presents opportunities to exploit the MTA identified in breeding programs to increase yield stability and local adaptation in safflower.Supplementary file1 (DOCX 973 KB)Supplementary file2 (XLSX 121 KB)Below is the link to the electronic supplementary material.