Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets.

BACKGROUND AND OBJECTIVES: Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. METHODS: We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics. RESULTS: The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient. CONCLUSIONS: We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.

Knockdown of YTHDF2 initiates ERS-induced apoptosis and cancer stemness suppression by sustaining GLI2 stability in cervical cancer.

Cervical cancer ranks fourth in women in terms of incidence and mortality. The RNA-binding protein YTH N6-methyladenosine RNA-binding protein F2 (YTHDF2) contributes to cancer progression by incompletely understood mechanisms. We show how YTHDF2 controls the fate of cervical cancer cells and whether YTHDF2 could be a valid target for the therapy of cervical cancer. Sphere formation and alkaline phosphatase staining assays were performed to evaluate tumor stemness of cervical cancer cells following YTHDF2 knockdown. Apoptosis was detected by flow cytometry and TUNEL assay. The compounds 4PBA and SP600125 were used to investigate the correlation between JNK, endoplasmic reticulum stress, tumor stemness, and apoptosis. Data from The Cancer Genome Atlas (TCGA) databases and Gene Expression Omnibus (GEO) revealed that GLI family zinc finger 2 (GLI2) might be the target of YTHDF2. The transcription inhibitor actinomycin D and dual-luciferase reporter gene assays were employed to investigate the association between the GLI2 mRNA and YTHDF2. Nude mouse xenografts were generated to assess the effects of YTHDF2 knockdown on cervical cancer growth in vivo. Knockdown of YTHDF2 up-regulated the expression of GLI2, leading to JNK phosphorylation and endoplasmic reticulum stress. These processes inhibited the proliferation of cervical cancer cells and their tumor cell stemness and promotion of apoptosis. In conclusion, the knockdown of YTHDF2 significantly affects the progression of cervical cancer cells, making it a potential target for treating cervical cancer.

A FHIR Native Radiology Informatics Platform.

FHIR is a new standard that is rapidly being adopted in healthcare. We describe and implement a Radiology informatics platform (RIS) that is FHIR native and incorporates the ability to execute AI algorithms to aid with the interpretation of scans. Our design utilises the FHIR workflow pattern as an application programming interface with functionality provided by independent micro services thus granting flexibility and expandability.

Investigation of Chaotic Behaviors of Fractional Order Love Model Without External Environment Effect.

In this paper, we focus on the nonlinear dynamic behavior of fractional order love model because the fractional order can reflect the 'memory dependency' of certain dynamic processes to a certain extent. The novel fractional order love model without external environment effect investigates two aspects: first, the chaotic dynamic of the used system when the system order is 2, and second, the smallest system order of fractional order love model that can generate chaotic behaviors. The simulation results show the fractional order love model can produce different results compared to the integer order model. While the fractional order love model still has chaotic behavior even the sum of the system order is equal to 2. Moreover, the smallest system order of fractional order love model having chaotic behavior is 1.7. The results indicate that two individuals can display love status even if the sum of the system order is less than 2 because the 'memory dependency' effects can greatly affect the emotional changes of human beings. The simulation results based on time series, phase portrait, power spectrum, Poincare map, maximal Lyapunov exponent and bifurcation diagram, and the conclusion is applied to the real life are also discussed.

Ferrocene/air double-mediated FeTiO3-photocatalyzed semi-heterogeneous annulation of quinoxalin-2(1H)-ones in EtOH/H2O.

With both ferrocene and air as the redox catalysts, for the first time, the low-cost natural ilmenite (FeTiO3) was successfully used for photocatalytic bond formations. Under the assistance of a traceless H-bond, and HCHO as the methylene reagent, a variety of imidazo[1,5-a]quinoxalinones were semi-heterogeneously photosynthesized in high yields with good functional group compatibility.

Paraformaldehyde as C1 Synthon: Electrochemical Three-Component Synthesis of Tetrahydroimidazo[1,5-a]quinoxalin-4(5H)-ones in Aqueous Ethanol.

A green and practical method for the electrochemical synthesis of tetrahydroimidazo[1,5-a]quinoxalin-4(5H)-ones through the three-component reaction of quinoxalin-2(1H)-ones, N-arylglycines and paraformaldehyde was reported. In this strategy, EtOH played dual roles (eco-friendly solvent and waste-free pre-catalyst) and the in situ generated ethoxide promoted triple sequential deprotonations.

HBx promotes hepatocellular carcinoma progression by repressing the transcription level of miR-187-5p.

HBV-associated hepatitis B virus x protein (HBx) plays multiple roles in the development of hepatocellular carcinoma. In our prior study, we discovered that miR-187-5p expression was inhibited by HBx. To investigate the underlying molecular mechanism of HBx-mediated miR-187-5p downregulation in hepatocellular carcinoma cells, effects of HBx and miR-187-5p on hepatoma carcinoma cell were observed, as well as their interactions. Through in vitro and in vivo experiments, we demonstrated that overexpression of miR-187-5p inhibited proliferation, migration, and invasion. Simultaneously, we observed a dysregulation in the expression of miR-187-5p in liver cancer cell lines, which may be attributed to transcriptional inhibition through the E2F1/FoxP3 axis. Additionally, we noted that HBx protein is capable of enhancing the expression of E2F1, a transcription factor that promotes the expression of FoxP3. In conclusion, our results suggest that the inhibitory effect of HBx on miR-187-5p is mediated through the E2F1/FoxP3 axis. As shown in this work, HBx promotes hepatoma carcinoma cell proliferation, migration, and invasion through the E2F1/FoxP3/miR-187 axis. It provides a theoretical basis for finding therapeutic targets that will help clinic treatment for HCC.

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation.

BACKGROUND AND PURPOSE: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. MATERIALS AND METHODS: The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. RESULTS: The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. CONCLUSION: To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework.

Distal radius fractures (DRFs) are one of the most common types of wrist fracture and can be subdivided into intra- and extra-articular fractures. Compared with extra-articular DRFs which spare the joint surface, intra-articular DRFs extend to the articular surface and can be more difficult to treat. Identification of articular involvement can provide valuable information about the characteristics of fracture patterns. In this study, a two-stage ensemble deep learning framework was proposed to differentiate intra- and extra-articular DRFs automatically on posteroanterior (PA) view wrist X-rays. The framework firstly detects the distal radius region of interest (ROI) using an ensemble model of YOLOv5 networks, which imitates the clinicians' search pattern of zooming in on relevant regions to assess abnormalities. Secondly, an ensemble model of EfficientNet-B3 networks classifies the fractures in the detected ROIs into intra- and extra-articular. The framework achieved an area under the receiver operating characteristic curve of 0.82, an accuracy of 0.81, a true positive rate of 0.83 and a false positive rate of 0.27 (specificity of 0.73) for differentiating intra- from extra-articular DRFs. This study has demonstrated the potential in automatic DRF characterization using deep learning on clinically acquired wrist radiographs and can serve as a baseline for further research in incorporating multi-view information for fracture classification.

A Food Waste-Derived Organic Liquid Fertiliser for Sustainable Hydroponic Cultivation of Lettuce, Cucumber and Cherry Tomato.

We previously reported a sustainable food waste management approach to produce an acceptable organic liquid fertiliser for recycling food waste called "FoodLift." This study follows our previous work to evaluate the macronutrients and cation concentrations in harvested structural parts of lettuce, cucumber, and cherry tomatoes produced using food waste-derived liquid fertiliser (FoodLift) and compare them against commercial liquid fertiliser (CLF) under hydroponic conditions. N and P concentrations in the structural parts of lettuce and the fruit and plant structural parts of cucumber appear to be similar between FoodLift and CLF (p > 0.05), with significantly different N concentrations in the various parts of cherry tomato plants (p < 0.05). For lettuce, N and P content varied from 50 to 260 g/kg and 11 to 88 g/kg, respectively. For cucumber and cherry tomato plants, N and P concentrations ranged from 1 to 36 g/kg and 4 to 33 g/kg, respectively. FoodLift was not effective as a nutrient source for growing cherry tomatoes. Moreover, the cation (K, Ca, and Mg) concentrations appear to significantly differ between FoodLift and CLF grown plants (p < 0.05). For example, for cucumber, Ca content varied from 2 to 18 g/kg for FoodLift grown plants while Ca in CLF-grown cucumber plants ranged from 2 to 28 g/kg. Overall, as suggested in our previous work, FoodLift has the potential to replace CLF in hydroponic systems for lettuce and cucumber. This will lead to sustainable food production, recycling of food waste to produce liquid fertiliser, and will promote a circular economy in nutrient management.

NUF2 Promotes Breast Cancer Development as a New Tumor Stem Cell Indicator.

Multiple new subtypes of breast cancer (BRCA) are identified in women each year, rendering BRCA the most common and rapidly expanding form of cancer in females globally. NUF2 has been identified as a prognostic factor in various human cancers, regulating cell apoptosis and proliferation. However, its role in BRCA prognosis has not been clarified. This study explored the role of NUF2 in breast cancer development and prognosis using informatic analysis combined with in vivo intracellular studies. Through the online website TIMER, we evaluated the transcription profile of NUF2 across a variety of different cancer types and found that NUF2 mRNA was highly expressed in BRCA patients. Its transcription level was found to be related to the subtype, pathological stage, and prognosis of BRCA. The R program analysis showed a correlation of NUF2 with cell proliferation and tumor stemness in the BRCA patient samples. Subsequently, the association between the NUF2 expression level and immune cell infiltration was analyzed using the XIANTAO and TIMER tools. The results revealed that NUF2 expression was correlated with the responses of multiple immune cells. Furthermore, we observed the effect of NUF2 expression on tumor stemness in BRCA cell lines in vivo. The experimental results illuminated that the overexpression of NUF2 statistically upregulated the proliferation and tumor stemness ability of the BRCA cell lines MCF-7 and Hs-578T. Meanwhile, the knockdown of NUF2 inhibited the abilities of both cell lines, a finding which was verified by analyzing the subcutaneous tumorigenic ability in nude mice. In summary, this study suggests that NUF2 may play a key role in the development and progression of BRCA by affecting tumor stemness. As a stemness indicator, it has the potential to be one of the markers for the diagnosis of BRCA.

Lambda-vector modeling temporal and channel interactions for text-independent speaker verification.

Most of the current excellent models in speaker verification are ResNet-based deep models and attention-based models. These models have a general weakness, which is the large number of parameters and high hardware requirements. On the other hand, many deep structures only generate embedding features from the features extracted by the last frame-level layer, which causes shallow features and channel-related features to be ignored. To solve these problems, this paper proposed a shallow speaker verification model based on Lambda-vector, its main structure is composed of three Lambda-SE modules. The module extracts long-distance dependencies between frame-level features and channel-related interaction information to enhance representation of features. Meanwhile, so that adequately mine the information in deep and shallow features, the model introduces multi-layer feature aggregation to fuse the features of different frame-level layers together. It can increase the detailed information in the deep features and improve the model's ability to represent complex information. The experimental results on the public datasets Voxceleb1 and Voxceleb2 show that the model has more stable training speed, fewer model parameters, and better identification performances than baseline models.

In Patients with Acute Ischemic Stroke and Cancer: The Shorter Interval, the Higher D-Dimer.

OBJECTIVES: Acute ischemic stroke in cancer patients is uncommon. The study was aimed to identify the relationship of patients' characteristics and the interval time between the diagnosis of stroke and cancer. METHODS: The clinical features of acute ischemic stroke patients with cancer were retrospectively analyzed from May, 2016 to April, 2021. Categorical data was compared between groups using chi-square test. Hematological biomarkers were compared using Mann-Whitney U test. RESULTS: A total of 70 acute ischemic stroke patients with cancer were identified. The median interval time between the diagnosis of acute ischemic stroke and cancer was 53.0 months. Patients with interval < 53.0 months and > 53.0 months were regarded the short interval group and the long interval group, respectively. Between the short and long interval groups, there was no significant differences in respect to sex, age, chemotherapy, hypertension, diabetes, smoking, atrial fibrillation and dyslipidemia. The medians of homocysteine, high-sensitivity C-reactive protein and fibrinogen were also not significantly different between the two different interval groups. D-dimer in the short interval group was higher than that in the long interval group (216 vs. 142 ng/mL, p = 0.037). The long interval group had more surgery for cancer than the short interval group (94.3% vs. 57.1%, p = 0.000). CONCLUSION: In conclusion, in patients with ischemic stroke and cancer, patients with short interval time between the diagnosis of ischemic stroke and cancer had higher D-dimer than patients with long interval time.

Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.

OBJECTIVES:  To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work. METHODS:  The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test. FINDINGS:  Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified. INTERPRETATION:  A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial.

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.

A review of medical image data augmentation techniques for deep learning applications.

Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren't typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state-of-the-art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

[Analysis on quality value transfer of substance benchmarks of Qingwei San].

By preparing 10 batches of substance benchmarks freeze-drying powder( lyophilized powder),the methodology of the characteristic spectrum and the content of index component for substance benchmarks of Qingwei San was established. The characteristic peaks and the similarity range of the characteristic spectrum,the contents and the transfer rate range of isoferulic acid,palmatine and paeonol,and the paste-forming rate range were determined to define key quality attributes of substance benchmarks of Qingwei San. In the10 batches of substance benchmarks of Qingwei San,the similarity of characteristic spectrum was higher than 0. 90. In further comparison of the characteristic peak information,a total of 16 characteristic peaks were identified,including 5 characteristic peaks from Cimicifugae Rhizoma,5 characteristic peaks from Coptidis Rhizoma,2 characteristic peaks from Angelicae Sinensis Radix and 4 characteristic peaks from Moutan Cortex. The content of isoferulic acid was 0. 10%-0. 18%,with the average transfer rate of 49. 82%±4. 02%. The content of palmatine was 0. 17%-0. 31%,with the average transfer rate of 15. 84% ±2. 39%. The content of paeonol was 0. 41%-0. 75%,with the average transfer rate of 23. 41%±3. 23%. The paste-forming rate of the 10 batches of substance benchmarks were controlled at 27%-33%,with the transfer rate between the theoretical paste-forming rate and the actual paste-forming rate was 86. 59%±3. 39%. In this study,the quality value transfer of substance benchmarks of Qingwei San was analyzed by the combination of characteristic spectrum,the content of index component and the paste-forming rate. A scientific and stable evaluation method was preliminarily established,so as to provide the basis for subsequent development and quality control of relevant preparations of Qingwei San.

Effects of Annealing on Electrochemical Properties of Solvothermally Synthesized Cu2SnS3 Anode Nanomaterials.

Cu2SnS3, as a modified material for high-capacity tin-based anodes, has great potential for lithium-ion battery applications. The solvothermal method is simple, convenient, cost-effective, and easy to scale up, and has thus been widely used for the preparation of nanocrystals. In this work, Cu2SnS3 nanoparticles were prepared by the solvothermal method. The effects of high-temperature annealing on the morphology, crystal structure, and electrochemical performance of a Cu2SnS3 nano-anode were studied. The experimental results indicate that high-temperature annealing improves the electrochemical performance of Cu2SnS3, resulting in higher initial coulombic efficiency and improved cycling and rate characteristics compared with those of the as-prepared sample.

Clinical features and survival of patients with multiple primary malignancies.

BACKGROUND: Multiple primary malignancies (MPM) are characterized by two or more primary malignancies in the same patient, excluding relapse or metastasis of prior cancer. We aimed to elucidate the clinical features and survival of MPM patients. AIM: To elucidate the clinical features and survival of MPM patients. METHODS: A retrospective study of MPM patients was conducted in our hospital between June 2016 and June 2019. Overall survival (OS) was calculated using the Kaplan-Meier method. The log-rank test was used to compare the survival of different groups. RESULTS: A total of 243 MPM patients were enrolled, including 222 patients with two malignancies and 21 patients with three malignancies. Of patients with two malignancies, 51 (23.0%) had synchronous MPM, and 171 (77.7%) had metachronous MPM. The most common first cancers were breast cancer (33, 14.9%) and colorectal cancer (31, 14.0%). The most common second cancers were non-small cell lung cancer (NSCLC) (66, 29.7%) and gastric cancer (24, 10.8%). There was no survival difference between synchronous and metachronous MPM patients (36.4 vs 35.3 mo, P = 0.809). Patients aged > 65 years at diagnosis of the second cancer had a shorter survival than patients ≤ 65 years (28.4 vs 36.4 mo, P = 0.038). Patients with distant metastasis had worse survival than patients without metastasis (20.4 vs 86.9 mo, P = 0.000). Following multivariate analyses, age > 65 years and distant metastasis were independent adverse prognostic factors for OS. CONCLUSION: During follow-up of a first cancer, the occurrence of a second or more cancers should receive greater attention, especially for common concomitant MPM, to ensure early detection and treatment of the subsequent cancer.

Analysis on quality value transfer of substance benchmarks of Qingwei San / 中国中药杂志

By preparing 10 batches of substance benchmarks freeze-drying powder( lyophilized powder),the methodology of the characteristic spectrum and the content of index component for substance benchmarks of Qingwei San was established. The characteristic peaks and the similarity range of the characteristic spectrum,the contents and the transfer rate range of isoferulic acid,palmatine and paeonol,and the paste-forming rate range were determined to define key quality attributes of substance benchmarks of Qingwei San. In the10 batches of substance benchmarks of Qingwei San,the similarity of characteristic spectrum was higher than 0. 90. In further comparison of the characteristic peak information,a total of 16 characteristic peaks were identified,including 5 characteristic peaks from Cimicifugae Rhizoma,5 characteristic peaks from Coptidis Rhizoma,2 characteristic peaks from Angelicae Sinensis Radix and 4 characteristic peaks from Moutan Cortex. The content of isoferulic acid was 0. 10%-0. 18%,with the average transfer rate of 49. 82%±4. 02%. The content of palmatine was 0. 17%-0. 31%,with the average transfer rate of 15. 84% ±2. 39%. The content of paeonol was 0. 41%-0. 75%,with the average transfer rate of 23. 41%±3. 23%. The paste-forming rate of the 10 batches of substance benchmarks were controlled at 27%-33%,with the transfer rate between the theoretical paste-forming rate and the actual paste-forming rate was 86. 59%±3. 39%. In this study,the quality value transfer of substance benchmarks of Qingwei San was analyzed by the combination of characteristic spectrum,the content of index component and the paste-forming rate. A scientific and stable evaluation method was preliminarily established,so as to provide the basis for subsequent development and quality control of relevant preparations of Qingwei San.