Unsupervised Local Discrimination for Medical Images

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified by optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performance.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort

PURPOSE

To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results.

MATERIALS AND METHODS

A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions.

RESULTS

The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively.

CONCLUSIONS

The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelligence (AI) model based on deep learning was constructed, and a comparison between the AI model and traditional manual counting was performed. In total, 918 FISH images from 320 consecutive invasive breast cancers were analysed and automatically classified into 5 groups according to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% (157/184) with a mean average precision of 0.735. In Group 5, the most common group, the consistency was as high as 95.90% (117/122), while the consistency was low in the other groups due to the limited number of cases. The causes of this inconsistency, including clustered HER2 signals, coarse CEP17 signals and some section quality problems, were analysed. The developed AI model is a reliable tool for evaluating HER2 amplification statuses, especially for breast cancer in Group 5; additional cases from multiple centres could further improve the accuracy achieved for other groups.

Generalizability and Diagnostic Performance of AI Models for Thyroid US

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature

PURPOSE

The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable to that of pre-surgical biopsies in benign-malignant differentiation of musculoskeletal tumors (MST).

METHODS

We first conducted a systematic review of literature to get the respective overall diagnostic accuracies of fine-needle aspiration biopsy (FNAB) and core needle biopsy (CNB) in differentiating between benign and malignant MST, by synthesizing data from the articles meeting our inclusion criteria. To compared against the accuracies reported in literature, we then invited 4 radiologists, respectively with 2 (A), 6 (B), 7 (C), and 33 (D) years of experience in interpreting musculoskeletal MRI to perform diagnostic tests on our own dataset (n = 62), with and without assistance of a previously developed DL algorithm. The gold standard for benign-malignant differentiation was histopathologic confirmation or clinical/radiographic follow-up.

RESULTS

For FNAB, a meta-analysis containing 4604 samples met the inclusion criteria, with the overall diagnostic accuracy reported to be 0.77. For CNB, an overall accuracy of 0.86 was derived by synthesizing results from 7 original research articles containing a total of 587 samples. On our internal MST dataset, the invited radiologists, respectively, achieved diagnostic accuracies of 0.84 (A), 0.89 (B), 0.87 (C), and 0.90 (D), with the assistance of DL.

CONCLUSION

Use of DL algorithms on musculoskeletal dynamic contrast-enhanced MRI improved the benign-malignant differentiation accuracy of radiologists to a level comparable to that of pre-surgical biopsies. The developed DL algorithms have a potential to lower the risk of miss-diagnosing malignancy in radiological practice.

Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models

Purpose

To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model.

Patients and Methods

A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COPD patients from the normal, 80 thresholds from 50% to 129% were taken with a partition of 1% to establish a random forest classification model under each threshold, such that novel PFT-parameter-based high-risk criteria would be more consistent with the PRM-based machine learning classification model.

Results

Machine learning-based PRM showed that consistency between PRM parameters and PFT was better able to distinguish high-risk COPD from the normal, with an AUC of 0.84 when the threshold was 72%. When the threshold was 80%, the AUC was 0.72 and when the threshold was 95%, the AUC was 0.64.

Conclusion

Machine learning-based PRM is feasible for redefining high-risk COPD, and setting the optimal FEV1% predicted value lays the foundation for redefining high-risk COPD diagnosis.

A new AI-assisted scoring system for PD-L1 expression in NSCLC

BACKGROUND

Artificial intelligence (AI) analysis may serve as a scoring tool for programmed cell death ligand-1 (PD-L1) expression. In this study, a new AI-assisted scoring system for pathologists was tested for PD-L1 expression assessment in non-small cell lung cancer (NSCLC).

METHODS

PD-L1 expression was evaluated using the tumor proportion score (TPS) categorized into three levels: negative (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). In order to train, validate, and test the Aitrox AI segmentation model at the whole slide image (WSI) level, 54, 53, and 115 cases were used as training, validation, and test datasets, respectively. TPS reading results from five experienced pathologists, six inexperienced and the Aitrox AI model were analyzed on 115 PD-L1 stained WSIs. The Gold Standard for TPS was derived from the review of three expert pathologists. Spearman's correlation coefficient was calculated and compared between the results.

RESULTS

Aitrox AI Model correlated strongly with the TPS Gold Standard and was comparable with the results of three of the five experienced pathologists. In contrast, the results of four of the six inexperienced pathologists correlated only moderately with the TPS Gold Standard. Aitrox AI Model performed better than the inexperienced pathologists and was comparable to experienced pathologists in both negative and low TPS groups. Despite the fact that the low TPS group showed 5.09% of cases with large fluctuations, the Aitrox AI Model still showed a higher correlation than the inexperienced pathologists. However, the AI model showed unsatisfactory performance in the high TPS groups, especially lower values than the Gold Standard in images with large regions of false-positive cells.

CONCLUSION

The Aitrox AI Model demonstrates potential in assisting routine diagnosis of NSCLC by pathologists through scoring of PD-L1 expression.

[Research advances on the techniques for diagnosing burn wound depth]

The accurate diagnosis of burn wound depth is particularly important for evaluating the disease prognosis of burn patients. In the past, the diagnosis of burn wound depth often relied on the subjective judgment of doctors. With the continuous development of diagnostic technology, the methods for judging the depth of burn wound have also been updated. This paper mainly summarizes the research progress in the applications of indocyanine green angiography, laser Doppler imaging, laser speckle contrast imaging, and artificial intelligence in the diagnosis of burn wound depth, and compares the advantages and disadvantages of these techniques, so as to provide ideas for accurate diagnosis of burn wound depth.

An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.

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aHVPG is an automated HVPG quantitative estimation model based on CT

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aHVPG has the potential to assess HVPG and outperforms other non-invasive tools

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Non-invasive tools may help PHT monitoring when invasive HVPG is not available

Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death.

PURPOSE

To improve the diagnostic efficacy of musculoskeletal tumors by developing deep learning (DL) models based on contrast-enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models.

STUDY TYPE

Retrospective.

POPULATION

Three hundreds and four musculoskeletal tumors, including 212 malignant and 92 benign lesions, were randomized into the training (n = 180), validation (n = 62) and testing cohort (n = 62).

FIELD STRENGTH/SEQUENCE

A 3 T/T₁ -weighted (T₁ -w), T₂ -weighted (T₂ -w), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted (CET₁ -w) images.

ASSESSMENT

Three DL models based, respectively, on the sagittal, coronal, and axial MR images were constructed to predict the malignancy of tumors. Blinded to the prediction results, a group of specialists made independent initial diagnoses for each patient by reading all image sequences. One month after the initial diagnoses, the same group of doctors made another round of diagnoses knowing the malignancy of each tumor predicted by the three models. The reference standard was the pathological diagnosis of malignancy.

STATISTICAL TESTS

Sensitivity, specificity, and accuracy (all with 95% confidential intervals [CI]) corresponding to each diagnostic test were computed. Chi-square tests were used to assess the differences in those parameters with and without DL models. A P value < 0.05 was considered statistically significant.

RESULTS

The developed models significantly improved the diagnostic sensitivities of two oncologists by 0.15 (95% CI: 0.06-0.24) and 0.36 (95% CI: 0.24-0.28), one radiologist by 0.12 (95% CI: 0.04-0.20), and three of the four orthopedists, respectively, by 0.12 (95% CI: 0.04-0.20), 0.29 (95% CI: 0.18-0.40), and 0.23 (95% CI: 0.13-0.33), without impairing any of their diagnostic specificities (all P > 0.128).

DATA CONCLUSION

The DL models developed can significantly improve the performance of doctors with different training and experience in diagnosing musculoskeletal tumors.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Nomogram to predict haemorrhagic transformation after stroke thrombolysis: a combined brain imaging and clinical study

AIM

To construct a novel nomogram by integrating computed tomography perfusion (CTP) and clinical parameters for individualised prediction of haemorrhagic transformation (HT) in intravenous thrombolysis (IVT)-treated acute ischaemic stroke (AIS) patients.

METHODS

Anterior circulation AIS patients who underwent IVT at a single centre from January 2018 to June 2020 were reviewed retrospectively. The CTP parameters of two regions of interest (ROI), the entire perfusion lesion areas, and the infract core areas, were assessed. HT was documented by follow-up CT 24 ± 2 h after IVT. Multivariable logistic regression was conducted by including clinical variables and CTP parameters to identify the independent predictors of HT. A nomogram was developed based on the independent predictors. The discriminative value and calibration of the nomogram were tested by concordance indexes (C-indexes) and calibration plots. Internal validation was performed using fivefold cross-validation.

RESULTS

The nomogram was generated using the complete data from 341 patients. Seven variables were included in the final nomogram, including: the relative cerebral blood volume (rCBV), permeability surface (PS), and relative PS (rPS) in infract core areas, the relative time to maximum (rTmax) and rPS in entire perfusion lesion areas, the National Institutes of Health Stroke Scale (NIHSS), and atrial fibrillation (AF). The C-indexes were 0.815 and 0.817 for the nomogram and internal validation. The calibration plots showed excellent agreement.

CONCLUSION

This is the first study establishing a nomogram based on CTP and clinical parameters to predict HT after stroke thrombolysis.

Stability of pacemaker parameters in cardiac amyloidosis patients

 

Cardiac amyloidosis is a rare disease due to the deposition of amyloid fibrils in the heart. Clinical manifestation of the heart includes restrictive heart failure and arrhythmia, when myocardium and conduction system are involved respectively. Arrhythmia can present as atrioventricular block (AVB), and sick sinus syndrome (SSS), therefore cardiac amyloidosis may require permanent pacemaker implantation. However, the stability of pacemaker parameters in cardiac amyloidosis patients remains unknown.

We conducted an observational retrospective study of patients diagnosed with cardiac amyloidosis who underwent permanent pacemaker implantation from January 1, 2011 to December 31, 2019. Base-line data were obtained via Medical Record System. Data of pacemaker parameters were obtained via pacemaker programming at the time of implantation and last follow-up at the clinic.

A total of 10 patients were included in our study, among which 5 patients underwent cardiac biopsy and showed positive result, 4 patients showed positive biopsy result of other tissue and characteristic echocardiography result of restrictive diastolic dysfunction, 1 patient was diagnosed with primary systematic amyloidosis (AL) involving kidney and advanced atrioventricular block. Amyloidosis type of the 10 patients were primary systemic amyloidosis (AL). Among the 10 patients, 3 were female (30.0%), and the mean age was 61.3±3.9. All patients met the indication for permanent pacemaker implantation, including 9 SSS and 1 advanced AVB. 9 patients were implanted with DDD, and 1 with VVI. The middle survival time was 446 (331, 728) days from the time of implantation to last follow-up (June 30, 2020). Two patients died due to disease progression. As for the 7 patients whose baseline and follow-up data were both obtained, pacemaker parameters at baseline were as followed: Atrial Impedance 477.8±115.0 Ω, Atrial P Wave 1.30±0.70 mV, Atrial Threshold 0.75±0.16 V@0.4ms, Ventricular Impedance 551.3±233.4 Ω, Ventricular R Wave 7.99±4.66 mV, Ventricular Threshold 0.76±0.15 V@0.4ms. Pacemaker parameters at follow-up were as followed: Atrial Impedance 426.2±93.2 Ω, Atrial P Wave 1.34±0.71 mV, Atrial Threshold 1.59±1.51 V@0.4ms, Ventricular Impedance 405.8±41.6 Ω, Ventricular R Wave 10.69±6.53 mV, Ventricular Threshold 1.80±1.88 V@0.4ms.

Most patients relieved from cardiac symptoms and severe cardiac complications. A relatively short-term follow-up indicated elevation of Ventricular Threshold (P=0.028), and analysis of other parameters showed insignificant results. Elevation of Ventricular Threshold may be explained by the progression of amyloid fibrils deposition in the heart. Ventricular Threshold of one patient significantly increased from 1.0 V to 6.0 V at 3-month follow-up. Since all patients underwent chemotherapy for the primary amyloidosis, stability of pacemaker parameters may be another way for evaluation. Long-term follow-up is needed for further evaluation.

Funding Acknowledgement

Type of funding sources: None.

[Epidemiological characteristics of local outbreak of COVID-19 caused by SARS-CoV-2 Delta variant in Liwan district, Guangzhou]

Objective: To analyze the epidemiological characteristics of a local outbreak of COVID-19 caused by SARS-CoV-2 B.1.617.2(Delta) variant in Liwan district, Guangzhou, and provide evidence for the further prevention and control of the Delta variant of COVID-19. Methods: From May 21 to June 18, 2021, the incidence data of COVID-19 caused by Delta variant were obtained from National Notifiable Disease Report System of Chinese Disease Prevention and Control Information System and Liwan District Center for Disease Control and Prevention of Guangzhou.Frequency analysis (proportions), histograms, and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. The incubation period and time-varying reproduction numbers (R_t) estimations were used for the further analysis. Results: By June 18, 2021, a total of 127 COVID-19 cases caused by Delta variant was reported in Liwan district. The youngest case was aged 2 years and the oldest was aged 85 years. There were 18.9% (24/127) aged <18 years, 43.3% (55/127) aged 18-59 years, and 37.8% (48/127) aged ≥60 years, the male to female ratio of the cases was 1∶1.35 (54∶73). The cases were mainly retired people (32.3%, 41/127), the jobless or unemployed (18.1%, 23/127), and students (16.5%, 21/127). The infections mainly occurred in Baihedong (70.1%, 89/127) and Zhongnan street (23.6%, 30/127) communities in the southern area of Liwan district. The median incubation period of the Delta variant infection was 6 days (range: 1-15 days). The clinical classification were mainly common type (64.6%, 82/127). The basic reproduction number (R₀) was 5.1, R_t which once increased to 7.3. The transmissions mainly occurred in confined spaces, such as home (26.8%), restaurant (29.1%), neighborhood (3.9%), and market (3.1%), the household clustering was predominant. Close contacts tracing (66.1%) and community screening (33.1%) were the main ways to find the infections. Conclusion: The COVID-19 outbreak caused by Delta variant in Liwan district of Guangzhou was highly contagious, with the obvious characteristics of household clustering and high proportions of cases in adults aged 18-59 years and elderly people aged ≥60 years.

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics

RATIONALE AND OBJECTIVES

Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance.

MATERIALS AND METHODS

920 patients (mean age 61.2, range, 17-87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively.

RESULTS

The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in ADC, SCC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in ADC, SCC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively.

CONCLUSION

The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.

Hydroxytyrosol inhibits apoptosis in ischemia/reperfusion-induced acute kidney injury via activating Sonic Hedgehog signaling pathway

OBJECTIVE

Acute kidney injury (AKI) is a common critical illness in clinic, which seriously threatens the life of patients. The aim of this study was to validate the anti-apoptotic effect of hydroxytyrosol (HT) in ischemia/reperfusion (I/R)-induced AKI.

MATERIALS AND METHODS

The cell model of AKI was established by hypoxia/reoxygenation (H/R), and the animal model of AKI was established by I/R. The apoptosis was observed by Caspase-3 activity assay, flow cytometry and terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick end labeling (TUNEL) staining. Cell viability was detected by cell counting kit-8 (CCK-8) assay. Protein expression was measured by Western blot and mRNA level was analyzed by quantitative real-time polymerase chain reaction (RT-PCR). Renal function was assessed by measuring serum creatinine (Cr) and blood urea nitrogen (BUN).

RESULTS

H/R induced apoptosis of HK-2 cells and reduced cell viability. When HK-2 cells were pretreated with HT, apoptosis was markedly inhibited, and cell viability was greatly increased. In addition, HT could inhibit I/R-induced apoptosis of rat kidney cells and could notably improve rat kidney function. H/R promoted Sonic Hedgehog (SHH) expression in HK-2 cells, while HT treatment further enhanced SHH expression. Similarly, I/R induces SHH expression in kidney tissue, and HT could further promote SHH expression.

CONCLUSIONS

These results indicated that HT could inhibit apoptosis in I/R-induced AKI via activating SHH signaling pathway.

MiR-1231 enhances docetaxel sensitivity to gallbladder carcinoma cells by downregulating FOXC2

OBJECTIVE

To illustrate the role of microRNA-1231 (miR-1231) in regulating malignant proliferative potential and DTX sensitivity to gallbladder carcinoma (GBC) by regulating FOXC2 level.

PATIENTS AND METHODS

Expression levels of miR-1231 in GBC tissues and paracancerous ones were detected. The relationship between miR-1231 level and clinical parameters of GBC patients was analyzed. After overexpression of miR-1231, changes in proliferative and apoptotic potentials in GBC-SD and NOZ cells were examined by Cell Counting Kit-8 (CCK-8), colony formation assay and flow cytometry, respectively. Regulatory effects of miR-1231 on its downstream gene FOXC2 were determined by Luciferase assay. Finally, the role of miR-1231 in regulating DTX sensitivity to GBC cells was assessed.

RESULTS

MiR-1231 was downregulated in GBC tissues compared to paracancerous ones. GBC patients expressing lower level of miR-1231 had worse tumor staging and larger tumor size. Overexpression of miR-1231 attenuated proliferative potential, and induced apoptosis in GBC cells. FOXC2 was upregulated in GBC and negatively linked to miR-1231. Luciferase activity confirmed that FOXC2 was the target gene binding miR-1231. DTX treatment dose-dependently suppressed viability in GBC cells and overexpression of miR-1231 could enhance DTX sensitivity in GBC. Notably, overexpression of FOXC2 abolished regulatory effects of overexpressed miR-1231 on proliferative and apoptotic potentials in GBC cells.

CONCLUSIONS

MiR-1231 is downregulated in GBC species. Its level is closely linked to tumor staging and tumor size in GBC patients. By downregulating FOXC2, miR-1231 enhances DTX sensitivity to GBC cells and thus alleviates the malignant development of GBC.

Effect and safety of Chinese herbal medicine granules in patients with severe coronavirus disease 2019 in Wuhan, China: a retrospective, single-center study with propensity score matching

BACKGROUND

Chinese herbal medicine (CHM) has been used for severe illness caused by coronavirus disease 2019 (COVID-19), but its treatment effects and safety are unclear.

PURPOSE

This study reviews the effect and safety of CHM granules in the treatment of patients with severe COVID-19.

METHODS

We conducteda single-center, retrospective study on patients with severe COVID-19 in a designated hospital in Wuhan from January 15, 2020 to March 30, 2020. The propensity score matching (PSM) was used to assess the effect and safety of the treatment using CHM granules. The ratio of patients who received treatment with CHM granules combined with usual care and those who received usual care alone was 1:1. The primary outcome was the time to clinical improvement within 28 days, defined as the time taken for the patients' health to show improvement by decline of two categories (from the baseline) on a modified six-category ordinal scale, or to be dischargedfrom the hospital before Day 28.

RESULTS

Using PSM, 43 patients (45% male) aged 65.6 (57-70) yearsfrom each group were exactly matched. No significant difference was observed in clinical improvement of patients treated with CHM granules compared with those who received usual (p = 0.851). However, the use of CHM granules reduced the 28-day mortality (p = 0.049) and shortened the duration of fever (4 days vs. 7 days, p = 0.002). The differences in the duration of cough and dyspnea and the difference in lung lesion ratio on computerized tomography scans were not significant.Commonly,patients in the CHM group had an increased D-dimer level (p = 0.036).

CONCLUSION

Forpatients with severe COVID-19, CHM granules, combined with usual care, showed no improvement beyond usual care alone. However, the use of CHM granules reduced the 28-day mortality rate and the time to fever alleviation. Nevertheless, CHM granules may be associated with high risk of fibrinolysis.

Hsa-circ-0068566 inhibited the development of myocardial ischemia reperfusion injury by regulating hsa-miR-6322/PARP2 signal pathway

OBJECTIVE

In recent years, studies have shown that noncoding RNA (circRNA) is an important regulatory molecule involved in cell physiology and pathology. Herein, we analyzed the role of circRNA-68566 in the regulation of myocardial ischemia-reperfusion (I/R) injury by regulating miR-6322/PARP2 signaling pathway.

MATERIALS AND METHODS

Cell viability was checked by CCK-8; LDH concentration, ROS production, MDA, SOD and GSH-Px were measured by corresponding kits; QPCR was used to inspect the expression of circRNA-0068566 and miR-6322 in I/R injury and H9C2 cells; luciferase reporter assay confirmed the direct target effect of circRNA-0068566 and miR-6322; Western blot was used to investigate PARP2 protein expression in I/R injury and H9C2 cells.

RESULTS

We analyzed the regulatory effect of circRNA-68566 on I/R injury and found that circRNA-68566 promoted the proliferation of injured cardiomyocytes in vitro and in vivo. circRNA-68566 and miR-6322 were directly combined to regulate the development of I/R injury. We also confirmed that PARP2 was the target of miR-6322 in I/R injury.

CONCLUSIONS

We believed that circRNA-68566 participated in myocardial ischemia-reperfusion injury by regulating miR-6322/PARP2 signaling pathway, which provided a new possible strategy for the treatment of I/R injury.

Purification of Plasmodium and Babesia- infected erythrocytes using a non-woven fabric filter

The purification of parasite-infected erythrocytes from whole blood containing leucocytes is crucial for many downstream genetic and molecular assays in parasitology. Current methodologies to achieve this are often costly and time consuming. Here, we demonstrate the successful application of a cheap and simple Non-Woven Fabric (NWF) filter for the purification of parasitized red blood cells from whole blood. NWF filtration was applied to the malaria-parasitized blood of three strains of mice, and one strain of rat, and to Babesia gibsoni parasitized dog blood. Before and after filtration, the white blood cell (WBC) removal rates and red blood cell (RBC) recovery rates were measured. After NWF filter treatment of rodent malaria-infected blood, the WBC removal rates and RBC recovery rates were, for Kunming mice: 99.51%±0.30% and 86.12%±8.37%; for BALB/C mice: 99.61%±0.15% and 80.74%±7.11%; for C57 mice: 99.71%±0.12% and 84.87%±3.83%; for Sprague-Dawley rats: 99.93%±0.03% and 83.30%±2.96%. Microscopy showed WBCs were efficiently removed from infected dog blood samples, and there was no obvious morphological change of B. gibsoni parasites. NWF filters efficiently remove leukocytes from malaria parasite-infected mouse and rat blood, and are also suitable for filtration of B. gibsoni-infected dog blood.

Rectifying Supporting Regions With Mixed and Active Supervision for Rib Fracture Recognition

Automatic rib fracture recognition from chest X-ray images is clinically important yet challenging due to weak saliency of fractures. Weakly Supervised Learning (WSL) models recognize fractures by learning from large-scale image-level labels. In WSL, Class Activation Maps (CAMs) are considered to provide spatial interpretations on classification decisions. However, the high-responding regions, namely Supporting Regions of CAMs may erroneously lock to regions irrelevant to fractures, which thereby raises concerns on the reliability of WSL models for clinical applications. Currently available Mixed Supervised Learning (MSL) models utilize object-level labels to assist fitting WSL-derived CAMs. However, as a prerequisite of MSL, the large quantity of precisely delineated labels is rarely available for rib fracture tasks. To address these problems, this paper proposes a novel MSL framework. Firstly, by embedding the adversarial classification learning into WSL frameworks, the proposed Biased Correlation Decoupling and Instance Separation Enhancing strategies guide CAMs to true fractures indirectly. The CAM guidance is insensitive to shape and size variations of object descriptions, thereby enables robust learning from bounding boxes. Secondly, to further minimize annotation cost in MSL, a CAM-based Active Learning strategy is proposed to recognize and annotate samples whose Supporting Regions cannot be confidently localized. Consequently, the quantity demand of object-level labels can be reduced without compromising the performance. Over a chest X-ray rib-fracture dataset of 10966 images, the experimental results show that our method produces rational Supporting Regions to interpret its classification decisions and outperforms competing methods at an expense of annotating 20% of the positive samples with bounding boxes.

[Immune characteristics of Plasmodium reinfections in mice following chloroquine cure of primary Plasmodium infections]

OBJECTIVE

To investigate the disease progression and immunoprotective characteristics in mice re-infected with homogeneous/heterogeneous Plasmodium strains following cure of Plasmodium infections with chloroquine at the peak of parasitemia.

METHODS

C57BL/6 mice were infected with the non-lethal P. yoelii 17XNL strain, and half of mice were given treatment with chloroquine at the peak of parasitemia (9 days post-infection), while the other mice were self-cured naturally. Then, all cured mice were re-infected with the equivalent lethal P. yoelii 17XL or P. berghei ANKA strain 90 days following primary Plasmodium infections. The parasitemia levels during primary infections and reinfections were measured by microscopic examinations of Giemsa-stained thin blood films, and the levels of the IgG antibody in sera and the percentages of memory T cell subsets in spleen cells were detected in mice using ELISA and flow cytometry before and after parasite reinfections, respectively.

RESULTS

Following primary infections with the P. yoelii 17XNL strain, the serum IgG antibody levels were (5.047 ± 0.924) pg/mL in the selfcured mice and (4.429 ± 0.624) pg/mL in the chloroquine-treated mice, respectively (t = 0.437, P > 0.05), which were both significantly higher than that in the uninfected mice (1.624 pg/mL ± 0.280 pg/mL) (F = 22.522, P < 0.01). There was no significant difference in the serum IgG antibody level among self-cured and chloroquine-treated mice re-infected with the P. yoelii 17XL strain or the P. berghei ANKA strain (F = 0.542, P > 0.05); however, the serum IgG antibody levels were all significantly higher in selfcured and chloroquine-treated mice re-infected with the P. yoelii 17XLstrain[(15.487±1.173)pg/mLand(15.965±1.150)pg/mL] or the P. berghei ANKA strain [(14.644 ± 1.523) pg/mL and (15.185 ± 1.333) pg/mL] relative to primary infections (F = 67.383, P < 0.01). There was no significant difference in the proportion of CD4⁺ [(34.208 ± 2.106), (32.820 ± 1.930), (34.023 ± 2.289), (35.608 ± 1.779) pg/mL] or CD8⁺ T memory cells [(17.935 ± 2.092), (18.918 ± 2.823), (17.103 ± 1.627), (17.873 ± 1.425) pg/mL] in self-cured and chloroquine-treated mice with primary infections with the P. yoelii 17XNL strain followed by re-infections with the P. yoelii 17XL strain or the P. berghei ANKA strain (F = 0.944 and 0.390, both P > 0.05); however, the proportions of the CD4⁺ or CD8⁺ T memory cells were significantly greater in self-cured and chloroquine-treated mice with primary infections with the P. yoelii 17XNL strain followed by re-infections with the P. yoelii 17XL strain or the P. berghei ANKA strain than in mice with primary infections (F = 50.532 and 21.751, both P < 0.01).

CONCLUSIONS

The cure of murine Plasmodium infections with chloroquine does not affect the production of effective immune protections in mice during parasite re-infections. Following a primary infection, mice show a protection against re-infections with either homogeneous or heterogeneous Plasmodium strains, and a higher-level resistance to re-infections with homogeneous parasite strains is found than with heterogeneous strains.

[Analysis of factors related to recanalization of intramural hematoma-type carotid artery dissection]

Objective: To investigate the factors related to recanalization of intramural hematoma-type carotid artery dissection (CAD). Methods: Retrospective analysis was performed on 56 patients (61 CADs) with intramural-hematoma type CAD confirmed by multimodal imaging examination based on cervical vascular ultrasound (CDU) in the Stroke Center of the First Affiliated Hospital of Suzhou University from August 2015 to May 2019. The clinical and imaging data were collected, and the time from onset to visit is bounded by 14 days. CDU follow-up was performed at 3, 6, and 12 months after the onset. According to the results of the 12-month follow-up, patients were divided into complete recanalization group and incomplete recanalization group. The clinical data, ultrasonic manifestations and drug treatment of patients between the two groups were compared. Multivariate logistic regression analysis was used to analyze the related factors affecting vascular recanalization. Results: Vascular recanalization: the rates of complete recanalization at 3, 6 and 12 months were 42.6% (26/61), 55.7% (34/61) and 59.0% (36/61), respectively. While among the 25 vessels in the incomplete recanalization group, 26.2% (16/61) showed residual stenosis and 14.8% (9/61) showed persistent occlusion. Comparison between the complete recanalization group and the incomplete recanalization group: the differences in the proportion of time from onset to visit ≤ 14 days, the echo type of intramural hematoma, and the proportion of vascular occlusion were statistically significant (all P<0.05). Multivariate logistic regression analysis showed that the time from onset to visit ≤14 days (OR=5.625, 95%CI: 1.302-24.293, P=0.021), and the hypoechoic intramural hematoma (OR=4.888, 95%CI: 1.304-18.320, P=0.019) were positively correlated with complete recanalization, while the dissection vascular occlusion (OR=0.234, 95%CI: 0.059-0.932, P=0.039) was negatively correlated with complete recanalization. Conclusions: CDU showed that hypoechoic intramural hematoma-type CAD treated with standard medications in the acute phase had a higher complete recanalization rate, while the recanalization rate of patients with dissecting vessel occlusion decreased. Early evaluation can provide a basis for clinical individualized treatment.

Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics

Background

Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs.

Methods

This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance.

Results

The AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05).

Conclusions

Deep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.

Big Data and Artificial Intelligence： New Insight into the Estimation of Postmortem Interval

Abstract

The estimation of postmortem interval （PMI） is a core issue in forensic practice. A large amount of time-dependent data can be produced in the decomposition process of a body, however, such multidimensional data cannot be comprehensively and effectively analyzed and utilized by any existing conventional PMI estimation method. As a rapidly developing information technology, artificial intelligence （AI） has significant advantages in big data processing, due to it's comprehensiveness, efficiency and automation. Some scholars have already applied it to researches on the estimation of PMI, showing it's significant advantages in terms of accuracy and development prospect. This article reviews the significance, mode and progress of application of AI in PMI estimation and provides some suggestions and prospects for future study.

Chromosomal abnormalities detected by karyotyping and microarray analysis in twins with structural anomalies

OBJECTIVES

To evaluate the incidence and types of chromosomal abnormalities detected in twins with structural anomalies and compare their distribution according to chorionicity and amnionicity and by structural-anomaly type. The added value of chromosomal microarray analysis (CMA) over conventional karyotyping in twins was also estimated.

METHODS

This was a single-center, retrospective analysis of 534 twin pregnancies seen over an 11-year period, in which one or both fetuses were diagnosed with congenital structural anomalies on ultrasound. The ultrasound findings and invasive prenatal diagnostic results were reviewed. Twin pregnancies were categorized as monochorionic monoamniotic (MCMA), monochorionic diamniotic (MCDA) or dichorionic diamniotic (DCDA). Chromosomal abnormalities detected by G-banding karyotyping and/or CMA were analyzed by chorionicity and amnionicity and by structural-anomaly type.

RESULTS

The 534 twin pairs analyzed comprised 25 pairs of MCMA, 112 pairs of MCDA and 397 pairs of DCDA twins. Of the 549 fetuses affected by structural anomalies, 432 (78.7%) underwent invasive prenatal testing and cytogenetic results were obtained. The incidence of overall chromosomal abnormalities in the DCDA fetuses (25.4%) was higher than that in the MCMA (3.7%) and MCDA (15.3%) fetuses. The incidence of aneuploidy was significantly higher in the DCDA group (22.8%) than in the MCMA (0.0%) and MCDA (12.4%) groups. The incidence of chromosomal abnormalities detected in fetuses, with anomalies of the cardiovascular, faciocervical, musculoskeletal, genitourinary and gastrointestinal systems, was higher in the DCDA group than in the MCDA group. In both the DCDA and MCDA groups, hydrops fetalis was associated with the highest incidence of chromosomal abnormality; of these fetuses, 67.6% had Turner syndrome (45,X). Pathogenic copy-number variations (CNVs) undetectable by karyotyping were identified by CMA in five (2.0%; 95% CI, 0.3-3.7%) DCDA fetuses. No pathogenic CNVs were found in MCMA and MCDA twins.

CONCLUSIONS

Dichorionic twins with structural anomalies have a higher risk of chromosomal abnormalities, especially aneuploidies, than do monochorionic twins. The incremental diagnostic yield of CMA over karyotyping seems to be lower (2.0%) in twins than that reported in singleton pregnancy. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

[Clinical application of HRCT three-dimensional reconstruction in traumatic ossicular chain interruption]

Objective:To investigate the clinical value of HRCT three-dimensional reconstruction technique in traumatic auditory chain traumatic fracture. Method:The clinical data of 14 patients with traumatic ear ossicular chain interruption were analyzed retrospectively. To evaluate the injury site and degree of the auditory chain before surgery, all the 14 patients underwent,HRCT scanning and three-dimensional reconstruction. The reconstructed auditory chain was observed from multiple angles and compared with the surgical exploration results under microscope. Result:The coincidence rate between ossicular chain injury observed by temporal bone HRCT scan before operation and ossicular chain injury observed during surgery was only 28.57%, the coincidence rate between ossicular chain injury observed during surgery and ossicular chain injury observed by three-dimensional reconstruction is 85.71%.Therefore, three-dimensional reconstruction imaging technique could give the doctor more clearly and stereoscopic images for the destruction of ossicular chain. Conclusion:Preoperative three-dimensional reconstruction can display and diagnosis of auditory ossicular chain destruction more clearly. It can be used to accurately evaluate auditory ossicular chain pathological changes, to develop individualized surgical plans and assess the risk of surgery.

Relationship between CT angiography-derived collateral status and CT perfusion-derived tissue viability

AIM

To explore the relationship between computed tomography (CT) angiography (CTA)-derived collateral status and CT perfusion (CTP)-derived tissue viability.

MATERIALS AND METHODS

Patients having middle cerebral artery (MCA) M1/M2 segment and/or internal carotid artery (ICA) occlusion and within 12 hours of onset were included. Collateral was graded from 0 to 3 on maximum intensity projection (MIP) images of CTA. The area with relative cerebral blood flow (rCBF) <30% or time-to-maximum (Tmax) >10 or >12 or >14 seconds was defined as the infarct core, and Tmax >6 seconds as the penumbra. Kruskal-Wallis and Spearman's correlation tests were performed to assess the correlation between collateral grade and infarct size or mismatch ratio.

RESULTS

Eighty-three patients were enrolled and 52 of them met the inclusion criteria. Infarct size defined by rCBF <30% or Tmax >10 or >12 or >14 seconds and mismatch ratios were significantly different among the four groups. The correlation between collateral grades and infarct core using rCBF <30% (ρ=-0.814, p<0.01) was better than that defined by Tmax >10s, >12s or >14s. Mismatch ratio for the infarct core defined by rCBF <30% (ρ=0.945, p<0.01) had the best correlation with collateral grades.

CONCLUSION

Patients with good collaterals show a smaller infarct core and higher mismatch ratio. Infarct size defined by rCBF <30% and mismatch ratio defined by rCBF <30% and Tmax >6 seconds appear to be more correlated with collaterals in AIS patients.

Decrease of FGF19 contributes to the increase of fasting glucose in human in an insulin-independent manner

PURPOSE

The ileum-derived fibroblast growth factor 19 (FGF19) plays key roles in hepatic glucose homeostasis in animals in an insulin-independent manner. Here, we analyzed the association of FGF19 with glucose effectiveness (GE, the insulin-independent glucose regulation), as well as hepatic glucose production (HGP) in Chinese subjects.

METHODS

GE was measured by frequently sampled intravenous glucose tolerance test (FSIVGTT) in normal glucose tolerance (NGT), isolated-impaired glucose tolerance (I-IGT), and isolated-impaired fasting glucose (I-IFG) subjects. The oral glucose tolerance test-derived surrogate of GE (oGE) was determined in NGT, I-IFG, combined glucose intolerance (CGI), and type 2 diabetes (T2DM) subjects. HGP was assessed by labeled ([3-³H]-glucose) hyperinsulinemic-euglycemic clamp in NGT subjects. Insulin secretion and sensitivity were calculated by the hyperglycemic and hyperinsulinemic-euglycemic clamps in a subgroup of NGT, I-IGT, and I-IFG subjects. Serum FGF19 levels were determined by ELISA.

RESULTS

FGF19 positively correlated with GE (r = 0.29, P = 0.004) as determined by FSIVGTT. The result was further confirmed by oGE (r = 0.261, P < 0.001). FGF19 was negatively associated with FPG (r = - 0.228, P = 0.025), but the association no longer existed after adjusting for GE (r = - 0.177, P = 0.086). FGF19 was negatively associated with basal HGP (r = - 0.697, P = 0.006). However, the correlation between FGF19 and insulin secretion and sensitivity were not found.

CONCLUSIONS

FGF19 levels are associated positively with GE and negatively with HGP. The increase of FPG in human is at least partially due to the decrease of FGF19 in an insulin-independent manner.

Effect of NT-3 on infection-induced memory impairment of neonatal rats

OBJECTIVE

To explore the effect of neurotrophin-3 (NT-3) messenger ribonucleic acid (mRNA) in the hippocampus on infection-induced memory impairment of neonatal rats.

MATERIALS AND METHODS

80 female Sprague-Dawley (SD) rats in the neonatal stage were selected to establish memory impairment model by bacterial meningitis infection. Rats were randomly divided into experimental group (n=40) and control group (n=40). Rats in experimental group were injected with β-amyloid precursor protein 319-335 peptide APP17p into brain tissue to up-regulate the expression of NT-3, and the rats in control group didn't receive treatment. Behavioral changes of rats were observed in Morris water maze and passive avoidance experiment. Apoptosis of nerve cells was detected by terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick end labeling (TUNEL) method and Fluoro-Jade B method. NT-3 mRNA expression level was measured via reverse transcription polymerase chain reaction (RT-PCR).

RESULTS

NT-3 expression level in experimental group was higher than that in control group (p<0.05). Apoptosis rate of nerve cells in experimental group was lower than that in control group, but the learning and memory ability of rats in experimental group was better than that in control group (p<0.05).

CONCLUSIONS

Reduced NT-3 expression level may be correlated with the occurrence of meningitis because NT-3 can suppress nerve cell apoptosis and ameliorate learning and memory impairment to a certain extent to exert neuroprotective effects.

[Efficacy and mechanism of loading dose clopidogrel in patients with transient ischemic attack and minor stroke]

Objective: To assess outcome, safety and possible mechanism of loading dose clopidogrel in patients with transient ischemic attack (TIA) and minor stroke. Methods: We reviewed patients with confirmed TIA and minor stroke admitted between July 2016 and December 2017 into the First Affiliated Hospital of Soochow University. Loss-of-function allele carriers of CYP2C19 were included and randomly divided into loading dose group (first dose of 300 mg clopidogrel) and standard dose group (first dose of 75 mg clopidogrel), 100 mg aspirin was gave at the same time, followed by aspirin 100 mg/d plus clopidogrel 75 mg/d maintaining for 20 days. Platelet aggregation (maximum aggregation ratio, MAR) induced by Adenosine diphosphate (ADP) was examined before and 3 days after administration. The National Institutes of Health Stroke Scale (NIHSS) score method was employed to assess the NIHSS scores before and after treatment in each group of patients; the modified Rankin Scale (mRS) was used to assess the 3-month functional outcome. Results: There was no significant difference in baseline data between the two groups (P>0.05).The proportion of early neurological function improvement in the two groups was 75.0% and 54.8%, and the difference was statistically significant (χ(2)=4.498, P=0.034). The 3-month prognosis was 79.5% and 61.3%, and the difference was statistically significant (χ(2)=4.000, P=0.045). Adverse events: 1 case in the loading dose group, 1 case in the standard dose group, the difference was not statistically significant (2.3% vs 1.6%, χ(2)=0.061, P=0.806). After 3 days of antiplatelet therapy, the MAR of the loading dose group decreased (11%±8%), and the MAR of the standard dose group decreased (9%±4%), the difference was statistically significant (P=0.013).In the loading dose group, there were 32 (72.7%)CYP2C19*2 carriers and 42 (95.5%)CYP2C19*2+*3 carriers; early neurological function improvement in 33 cases, accounting for 93.8% and 76.2%, respectively, and the difference was statistically significant (χ(2)=4.122, P=0.042). There were 35 patients with good prognosis in 3 months, accounting for 96.9% and 81.0%, respectively. The difference was statistically significant (χ(2)=4.310, P=0.038); MAR of CYP2C19*2 carrier was decreased (15%±5%), and MAR of CYP2C19*2+*3 carrier was decreased (12%±8%). The difference was statistically significant (P=0.039). Conclusions: Loading dose clopidogrel can improve the clinical prognosis of minor stroke/TIA without increasing the risk of bleeding. Loading dose clopidogrel may improve the prognosis of minor stroke/TIA by decreasing MAR of CYP2C19*2 carriers.

[Association between the ratio of early diastolic transmitral velocity to early diastolic mitral annular velocity and invasive measured left atrial pressure in patients with atrial fibrillation and preserved left ventricular ejection fraction]

Objective: To evaluate the association between the ratio of early diastolic transmitral velocity to early diastolic mitral annular velocity (E/E') and left atrial pressure (LAP) estimated from invasive catheter measurements in patients with atrial fibrillation (AF). Methods: A total of 46 consecutive patients with non-valvular AF and preserved left ventricular ejection fraction (LVEF) admitted in our department to receive the first radiofrequency ablation from May to July 2017 were included. All patients underwent echocardiography at 24-48 hours before radiofrequency ablation, and LAP was invasively measured during the ablation procedure. According to mean LAP, patients were divided into 2 groups of normal LAP (LAP≤12 mmHg(1 mmHg=0.133 kPa, n=31) and elevated LAP (LAP>12 mmHg, n=15). Linear correlation analysis was used to evaluate the relationship between E/E' and LAP. Results: E/E' correlated well with LAP (septal E/E' (E/E'(sep)), r= 0.397, P=0.006; lateral E/E' (E/E'(lat)), r=0.433, P=0.003; mean E/E' (E/E'(mean)), r=0.431, P=0.003). Using receiver operating characteristic analysis, the optimal cut-off for E/E'(sep) was 12.5 (sensitivity 73.3%, specificity 67.7%), E/E'(lat) was 10.8 (sensitivity 80.0%, specificity 77.4%), E/E'(mean) was 11.0 (sensitivity 86.7%, specificity 64.5%) to predict mean LAP>12 mmHg. Conclusion: E/E', especially the E/E'(lat), is positively correlated with LAP in patients with AF and preserved LVEF, and may be used to estimate the diastolic function in AF patients with preserved LVEF.

Polydopamine–polyethylene glycol–albumin antifouling coatings on multiple substrates

Polydopamine–PEG coatings on different substrates: effects of PDA layer properties on PEG grafting and anti-biofouling properties.

Describing the quality of life of boys with haemophilia in China: Results of a multicentre study using the CHO-KLAT

INTRODUCTION

The treatment of haemophilia varies across countries and across regions within some countries. Similar variation has been observed in health-related quality of life (HR-QoL). Relatively little is known about the HR-QoL of boys with haemophilia in China.

AIM

The aim of this study was to describe the HR-QoL of boys with haemophilia in China using the Canadian Haemophilia Outcomes-Kids Life Assessment Tool (CHO-KLAT).

METHODS

Boys (4-18 years of age) with haemophilia and their parents were enroled in a cross-sectional study. All parents/guardians of study subjects were requested to complete a CHO-KLAT questionnaire during a clinic visit, and report on several other clinical and socioeconomic factors in the past year. Boys who were > 7 years also completed the CHO-KLAT.

RESULTS

A total of 269 parents of boys with haemophilia, from 13 hospitals in 12 provinces, were enroled during 2014. The boys ranged from 4.0 to 17.9 years of age; 91% had haemophilia A, most had moderate (52%) or severe (36%) disease, and most were receiving sub-optimal on-demand therapy or low-dose prophylactic therapy. Child self-report CHO-KLAT scores were available for 171 boys ≥7 years of age and ranged from 24.2 to 85.3 with a mean of 57.6 (n = 171). Parent proxy-reported CHO-KLAT scores ranged from 25.0 to 88.7 with a mean of 55.1 (n = 269).

CONCLUSION

HR-QoL scores in boys with haemophilia in China were substantially lower than reported from Canadian and European boys with haemophilia. Longer term prospective studies are required to examine the factors impacting the HR-QoL for boys with haemophilia in China.

[Effect of 21-gene recurrence score on chemotherapy decisions for patients with estrogen receptor-positive, epidermal growth factor receptor 2-negative and lymph node-negative early stage-breast cancer]

Objective: To investigate the effect of 21-gene recurrence score on adjuvant chemotherapy decisions for patients with estrogen receptor (ER)-positive, epidermal growth factor receptor 2 (HER-2)-negative and lymph node (LN)-negative early stage-breast cancer. Methods: One hundred and forty-eight patients with ER+ , HER-2- and LN- early stage breast cancer were recruited in the Ruijin hospital, Shanghai Jiao Tong University School of Medicine. The 21-gene recurrence score (RS)assay was performed and systemic therapeutic decisions were made before and after knowing the RS results under multidisciplinary discussion. The effects of RS assay and the other influential factors on adjuvant chemotherapy decision were further analyzed. Results: After knowing the RS results, treatment decisions were changed in 26 out of 148 patients(17.6%). Among them, 9 out of 26 patients were not recommended for chemotherapy; 16 of 26 had treatment recommendation changed to chemotherapy, and chemotherapy regimen was changed in the last one patient. Multivariate analysis showed that RS, age and histological grade were independent factors of decision-making for adjuvant chemotherapy. Conclusion: Our results suggest that 21-gene recurrence score significantly influences decision making for adjuvant chemotherapy in patients with ER+ , HER-2- and LN- early stage breast cancer.

[Analysis of the factors influencing adjuvant chemotherapy decisions for triple negative breast cancer]

Objective: To analyze adjuvant chemotherapy decisions for triple negative breast cancer (TNBC), and explore the influencing factors in the multidisciplinary treatment (MDT) modality. Methods: A retrospective analysis was performed. The cases with invasive TNBC who underwent surgery and MDT discussion for adjuvant treatment in Ruijin Hospital, from April 2013 to June 2015, were recruited. The patients' clinicopathological characteristics were analyzed and adjuvant treatment suggestions from MDT were obtained. Here the chemotherapy decision alteration was defined as a disagreement in chemotherapy or not, or inconsistence in regimens between the attending doctor and the multidisciplinary team. Results: A total of 194 patients aged ≤70 years old were enrolled in the multidisciplinary discussion, and 187 patients (96.4%) were suggested to receive chemotherapy. When compared the opinions of the attending doctor to suggestions of the multidisciplinary team, we found that the percentage of chemotherapy decision alteration reached 22.7% (39/172), of which 94.9% (37/39) were inconsistence in chemotherapy regimens. There were 119 patients who were recommended to receive epirubicin plus cyclophosphamide (EC) followed by docetaxel (T) or weekly paclitaxel (wP) regimens. Before the announcement of results for the E1199 trial, EC-T accounted for 62.5% (55/88), and EC-wP accounted for 37.5% (33/88) for this group of patients. After that, the proportion of EC-T was decreased to 22.6% (7/31) and proportion of EC-wP increased to 77.4%(24/31) (P<0.001). In addition, a total of 20 patients were suggested to receive platinum based chemotherapy. The proportions were 9.3% in cases with invasive ductal carcinoma, and 33.3% in cases with metaplastic carcinoma, respectively (P=0.016). Conclusions: The adjuvant chemotherapy decision for TNBC patients is altered in 22.7% of the patients after MDT discussion. After the announcement of SABCS E1199 results, more patients are suggested to receive EC followed by weekly paclitaxel. There is a lack of detailed evidence for platinum based adjuvant chemotherapy for TNBC, and more patients with metaplastic carcinoma receive platinum based adjuvant chemotherapy.