Aerodynamic Simulation of Small Airway Resistance: A New Imaging Biomarker for Chronic Obstructive Pulmonary Disease.

Purpose: To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD. Patients and Methods: 24 subjects who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively. Subjects were divided into three groups: normal (10), high-risk (6), and COPD (8). The airway from the trachea down to the sixth generation of bronchioles was reconstructed by a 3D slicer. The small airway resistance (RSA) and RSA as a percentage of total airway resistance (RSA%) were calculated by CFD combined with airway resistance and FEV1 measured by pulmonary function test. A correlation analysis was conducted between RSA and pulmonary function parameters, including FEV1/FVC, FEV1% predicted, MEF50% predicted, MEF75% predicted and MMEF75/25% predicted. Results: The RSA and RSA% were significantly different among the three groups (p<0.05) and related to FEV1/FVC (r = -0.70, p < 0.001; r = -0.67, p < 0.001), FEV1% predicted (r = -0.60, p = 0.002; r = -0.57, p = 0.004), MEF50% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001), MEF75% predicted (r = -0.71, p < 0.001; r = -0.60, p = 0.002) and MMEF 75/25% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001). Conclusion: Airway CFD is a valuable method for estimating the small airway resistance, where the derived RSA will aid in the early diagnosis of COPD.

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.

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.

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.

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.

Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images.

Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019-9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.

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.

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.

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.

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.

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.

Ralstonia mannitolilytica-Induced Septicemia and Homology Analysis in Infected Patients: 3 Case Reports.

BACKGROUND: Ralstonia mannitolilytica is an emerging opportunistic pathogen. Hospital outbreaks of Ralstonia spp. are mainly associated with contaminated treatment water or auxiliary instruments. OBJECTIVES: In this report, we summarize the clinical infection characteristics of R. mannitolilytica, the drug-susceptibility testing of the bacterial strains, and the results of related infection investigations. PATIENTS AND METHODS: We retrospectively analyzed the clinical information of 3 patients with R. mannitolilytica. RESULTS: The patients' primary-onset symptoms were chills and fever. The disease progressed rapidly and septic shock symptoms developed. Laboratory tests indicated progressively decreased white blood cells and platelets, as well as significant increases in certain inflammation indicators. The effect of treatment with Tazocin was good. The growth period of R. mannitolilytica in sterile distilled water was > 6 months. The pulsed-field gel electrophoresis (PFGE) results revealed that the infectious strains from these 3 patients were not the same clonal strain. This bacterium was not detected in the nosocomial infection samples. CONCLUSIONS: Our results suggest that R. mannitolilytica-induced septicemia had an acute disease onset and rapid progression. The preferred empirical antibiotic was Tazocin. In these 3 cases, the R. mannitolilytica-induced septicemia was not due to clonal transmission.

Clinical Neuropathological Analysis of 10 Cases of Cerebral Amyloid Angiopathy-Related Cerebral Lobar Hemorrhage.

OBJECTIVE: The clinical and pathological characteristics of 10 cases of cerebral amyloid angiopathy (CAA)-related cerebral lobar hemorrhage (CLH) that was diagnosed at autopsy were investigated to facilitate the diagnosis of this condition. METHODS: The clinical characteristics of 10 cases of CAA-related CLH were retrospectively reviewed, and a neuropathological examination was performed on autopsy samples. RESULTS: The 10 cases included two with a single lobar hemorrhage and eight with multifocal lobar hemorrhages. In all of the cases, the hemorrhage bled into the subarachnoid space. Pathological examinations of the 10 cases revealed microaneurysms in two, double barrel-like changes in four, multifocal arteriolar clusters in five, obliterative onion skin-like intimal changes in four, fibrinoid necrosis of the vessels in seven, neurofibrillary tangles in eight, and senile plaques in five cases. CONCLUSION: CAA-related CLHs were located primarily in the parietal, temporal, and occipital lobes. These hemorrhages normally consisted of multiple repeated CLHs that frequently bled into the subarachnoid space. CAA-associated microvascular lesions may be the pathological factor underlying CLH.

Virulence and transferability of resistance determinants in a novel Klebsiella pneumoniae sequence type 1137 in China.

A study was designed to characterize three carbapenemase-producing Klebsiella pneumoniae isolated from pediatric patients in China. Molecular characterization was done using polymerase chain reaction and sequencing for blaVIM, blaNDM, blaIMP, blaKPC, blaCTX-Ms, blaOXAs, blaTEMs, and blaSHV; plasmid-mediated quinolone resistance determinants; aminoglycoside resistance determinants; multilocus sequencing typing; plasmid replicon typing; addiction; and virulence factors. Kp32 belonged to the newly described sequence type 1137, were positive for aac(6')-Ib-suzhou, qnrA1, qnrB4, qnrS1, aac(6')-Ib, rmtB, armA, blaSHV-12, blaCTX-M-15, blaKPC-2, and blaIMP-4; contained IncA/C plasmids that tested positive for K1 capsular antigens, the ccdAB (coupled cell division locus) addiction system and the wabG, ureA, rmpA, magA, allS, fimH, and the aerobactin virulence factors. However, the others belonged to clone ST11, and were positive for aac(6')-Ib-cr, qnrB4, blaCTX-M-14, blaSHV-11, aac(6')-Ib, rmtB, and blaKPC-2; contained IncFIA plasmids that tested positive for K2 capsular antigens, the vagCD addiction system and the uge, wabG, ureA, kfuBC, rpmA, and fimH virulence factors. ST1137 had more virulence factors than the comparative strains ST11. The blaKPC-2 gene was located on the IncFIA and IncA/C replicon groups of plasmids. An analysis of the genetic environment of blaKPC-2 gene has demonstrated that the blaKPC-2 gene was always associated with one of the Tn4401 isoforms (a or b). Our study suggested that K. pneumoniae carbapenemases being found in virulent K. pneumoniae should be emphasized, as this will eventually become a global health threat.

Efflux system overexpression and decreased OprD contribute to the carbapenem resistance among extended-spectrum beta-lactamase-producing Pseudomonas aeruginosa isolates from a Chinese university hospital.

The aim of this study was to investigate, for the first time, the combinations of carbapenem resistance mechanisms in clinical isolates of extended-spectrum beta-lactamase (ESBL)-producing Pseudomonas aeruginosa in a Chinese hospital. Pulsed-field gel electrophoresis revealed the presence of eight clonal types among the 15 ESBL producers. Multilocus sequence typing of two isolates harboured blaIMP-1 identified the clonal strain as ST325. All these genes were found either alone or simultaneously in the strains in the following five different arrangements:; ; ; ; . Regarding mutation-driven resistance, all, but four of the isolates had a relevant decrease of oprD expression. In addition, 73.3% of the isolates overexpressed mexB, 40% mexD, and 33.3% mexY. A specific combination of overexpressed mexB or mexY and alteration in loop L710 of OprD were significantly associated with meropenem resistance. In conclusion, combination of several mutation-driven mechanisms leading to OprD inactivation and overexpression of efflux systems was the main carbapenem resistance mechanism among the ESBL-producing P. aeruginosa isolates, but acquisition of a transferable resistance determinant such as metallo-ß-lactamase could be problematic in clinical settings in China.

Acquisition of carbapenem resistance in multiresistant Klebsiella pneumoniae isolates of sequence type 11 at a university hospital in China.

Four closely related KPC-producing Klebsiella pneumoniae strains, which were isolated from the patients with neonatal sepsis, harbored bla(CTX-M-14), bla(TEM-1), bla(CTX-M-15), bla(SHV-11),bla(SHV-12), class 1 integron, qnrS1, acc(6')-Ib-cr, and rmtB genes. Multilocus sequence typing experiments showed that all isolates but Kp122 were proven to share the same sequence type (ST), ST11. These isolates have not yet been previously reported in a university-affiliated children's hospital or in the city of Wenzhou.