Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery

PURPOSE

To investigate whether steep Trendelenburg in a major urologic surgery is associated with postoperative delirium, and to examine other potential clinical and radiologic factors predictive of postoperative delirium.

METHODS

182 patients who received a major urologic surgery and underwent a 3.0-T brain MRI scan within 1 year prior to the date of surgery were retrospectively enrolled. Preoperative brain MRIs were used to analyze features related to small vessel disease burden and mesial temporal atrophy. Presence of a significant mesial temporal atrophy was defined as Scheltens' scale ≥ 2. Patients' clinico-demographic data and MRI features were used to identify significant predictors of postoperative delirium using the logistic regression analysis. Independent predictors found significant in the univariate analysis were further evaluated in the multivariate analysis.

RESULTS

Incidence of postoperative delirium was 6.0%. Patients with postoperative delirium had lower body mass index (21.3 vs. 25.0 kg/m2, P = 0.003), prolonged duration of anesthesia (362.7 vs. 224.7 min, P < 0.001) and surgery (302.2 vs. 174.5 min, P < 0.001), and had more significant mesial temporal atrophy (64% vs. 30%, P = 0.046). In the univariate analysis, female sex, type of surgery (radical prostatectomy over cystectomy), prolonged duration of anesthesia (≥ 6 h), and presence of a significant mesial temporal atrophy were significant predictors (all P-values < 0.050), but only the presence of significant mesial temporal atrophy was significant in the multivariate analysis [odds ratio (OR), 3.69; 95% CI 0.99-13.75; P = 0.046].

CONCLUSION

Steep Trendelenburg was not associated with postoperative delirium. Significant mesial temporal atrophy (Scheltens' scale ≥ 2) in preoperative brain MRI was predictive of postoperative delirium.

TRIAL REGISTRATION

Not applicable.

Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults

PURPOSE

To compare the clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics of midline-located IDH-wildtype glioblastomas (GBMs) and H3 K27-altered diffuse midline gliomas (DMGs) in adults.

METHODS

Preoperative MRI data of 55 adult patients with midline-located IDH-wildtype GBM or H3 K27-altered DMG (32 IDH-wildtype GBM and 23 H3 K27-altered DMG patients) were included. Qualitative imaging assessment was performed. Quantitative imaging assessment including the tumor volume, normalized cerebral blood volume, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen values, and mean ADC value were performed from the tumor mask via automatic segmentation. Univariable and multivariable logistic analyses were performed.

RESULTS

On multivariable analysis, age (odds ratio [OR] = 0.92, P = 0.015), thalamus or medulla location (OR = 10.48, P = 0.013), presence of necrosis (OR = 0.15, P = 0.038), and OEF (OR = 0.01, P = 0.042) were independent predictors to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.88 (95 % confidence interval: 0.77-0.95), 81.8 %, 82.6 %, and 81.3 %, respectively.

CONCLUSIONS

Along with younger age, tumor location, less frequent necrosis, and lower OEF may be useful imaging biomarkers to differentiate H3 K27-altered DMG from midline-located IDH-wildtype GBM. Tumor oxygenation imaging biomarkers may reflect the less hypoxic nature of H3 K27-altered DMG than IDH-wildtype GBM and may contribute to differentiation.

Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization

OBJECTIVES

This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility.

MATERIALS AND METHODS

Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed.

RESULTS

Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001).

CONCLUSIONS

The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation.

CLINICAL RELEVANCE STATEMENT

Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients.

KEY POINTS

• Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques

OBJECTIVES

To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).

METHODS

This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set.

RESULTS

A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657).

CONCLUSIONS

The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set.

CLINICAL RELEVANCE STATEMENT

Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance.

KEY POINTS

• Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

MRI-Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic-Clonic Seizures Alone

BACKGROUND

The clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate.

PURPOSE

To develop and validate an MRI-based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups.

STUDY TYPE

Retrospective.

POPULATION

164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets.

FIELD STRENGTH/SEQUENCE

3T; 3D T1-weighted spoiled gradient-echo.

ASSESSMENT

A total of 17 region-of-interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty-eight machine-learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set.

STATISTICAL TESTS

Model performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature.

RESULTS

The AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591-0.943) and 0.717 (0.563-0.871), respectively. SHAP analysis revealed that the first-order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model.

CONCLUSION

The proposed MRI-based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test

BACKGROUND

Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-center visual Turing test.

METHODS

Using six chest radiograph datasets, (MIMIC, CheXPert, CXR8, JSRT, VBD, and OpenI), starGAN v2 generated chest radiographs with specific pathologies. Five board-certified radiologists from three university hospitals, each with at least five years of clinical experience, evaluated the image quality through a visual Turing test. Further evaluations were performed to investigate whether GAN augmentation enhanced the convolutional neural network (CNN) classifier performances.

RESULTS

In terms of identifying GAN images as artificial, there was no significant difference in the sensitivity between radiologists and random guessing (result of radiologists: 147/275 (53.5%) vs result of random guessing: 137.5/275, (50%); p = .284). GAN augmentation enhanced CNN classifier performance by 11.7%.

CONCLUSION

Radiologists effectively classified chest pathologies with synthesized radiographs, suggesting that the images contained adequate clinical information. Furthermore, GAN augmentation enhanced CNN performance, providing a bypass to overcome data imbalance in medical AI training. CNN based methods rely on the amount and quality of training data; the present study showed that GAN augmentation could effectively augment training data for medical AI.

A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study

Background

A deep convolutional neural network (DCNN) model that predicts the degree of arteriovenous fistula (AVF) stenosis and 6-month primary patency (PP) based on AVF shunt sounds was developed, and was compared with various machine learning (ML) models trained on patients' clinical data.

Methods

Forty dysfunctional AVF patients were recruited prospectively, and AVF shunt sounds were recorded before and after percutaneous transluminal angioplasty using a wireless stethoscope. The audio files were converted to melspectrograms to predict the degree of AVF stenosis and 6-month PP. The diagnostic performance of the melspectrogram-based DCNN model (ResNet50) was compared with that of other ML models [i.e. logistic regression (LR), decision tree (DT) and support vector machine (SVM)], as well as the DCNN model (ResNet50) trained on patients' clinical data.

Results

Melspectrograms qualitatively reflected the degree of AVF stenosis by exhibiting a greater amplitude at mid-to-high frequency in the systolic phase with a more severe degree of stenosis, corresponding to a high-pitched bruit. The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis. In predicting the 6-month PP, the area under the receiver operating characteristic curve of the melspectrogram-based DCNN model (ResNet50) (≥0.870) outperformed that of various ML models based on clinical data (LR, 0.783; DT, 0.766; SVM, 0.733) and that of the spiral-matrix DCNN model (0.828).

Conclusion

The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis and outperformed ML-based clinical models in predicting 6-month PP.

Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty

OBJECTIVE

To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA).

MATERIALS AND METHODS

Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions.

RESULTS

Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram.

CONCLUSION

Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

A Radiomics Approach for the Classification of Fibroepithelial Lesions on Breast Ultrasonography

A radiomics-based classifier to distinguish phyllodes tumor and fibroadenoma on gray-scale breast ultrasonography was developed and validated. A total of 93 radiomics features were extracted from representative transverse plane ultrasound images of 182 fibroepithelial lesions initially diagnosed by core needle biopsy. High-throughput radiomics features were selected using the intra-class correlation coefficient between two radiologist readers and the Least Absolute Shrinkage and Selection Operator regression through 10-fold cross-validation. When applied to the validation set, the radiomics classifier for the differentiation of phyllodes tumors and benign/fibroadenomas achieved an area under the receiver operating characteristic curve of 0.765 (95% confidence interval [CI]: 0.597-0.888) with an accuracy of 0.703 (sensitivity: 0.857; specificity: 0.5). Our radiomics signature-based classifier may help predict phyllodes tumors among fibroepithelial lesions on breast ultrasonography.

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

PURPOSE

The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model.

METHODS

A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression.

RESULTS

In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model.

CONCLUSION

A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.

Immunoglobulin G4-Related Disease of the Ovary Mimicking Bilateral Ovarian Malignancies

Immunoglobulin G4-related disease (IgG4-RD) is a fibro-inflammatory condition characterized by several pathological features that can theoretically involve all organs. Ovarian involvement in IgG4-RD has been reported by two studies only. Herein, we report a pathologically confirmed case of ovarian involvement of IgG4-RD, which mimicked bilateral ovarian malignancies on computed tomography and magnetic resonance imaging.

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.

Plasma levels of carboxypeptidase U (CPU, CPB2 or TAFIa) are elevated in patients with acute myocardial infarction

BACKGROUND

Two decades after its discovery, carboxypeptidase U (CPU, CPB2 or TAFIa) has become a compelling drug target in thrombosis research. However, given the difficulty of measuring CPU in the blood circulation and the demanding sample collecton requirements, previous clinical studies focused mainly on measuring its inactive precursor, proCPU (proCPB2 or TAFI).

OBJECTIVES

Using a sensitive and specific enzymatic assay, we investigated plasma CPU levels in patients presenting with acute myocardial infarction (AMI) and in controls.

METHODS

In this case-control study, peripheral arterial blood samples were collected from 45 patients with AMI (25 with ST segment elevation myocardial infarction [STEMI], 20 with non-ST segment elevation myocardial infarction [NSTEMI]) and 42 controls. Additionally, intracoronary blood samples were collected from 11 STEMI patients during thrombus aspiration. Subsequently, proCPU and CPU plasma concentrations in all samples were measured by means of an activity-based assay, using Bz-o-cyano-Phe-Arg as a selective substrate.

RESULTS

CPU activity levels were higher in patients with AMI (median LOD-LOQ, range 0-1277 mU L(-1) ) than in controls (median < LOD, range 0-128 mU L(-1) ). No correlation was found between CPU levels and AMI type (NSTEMI [median between LOD-LOQ, range 0-465 mU L(-1) ] vs. STEMI [median between LOD-LOQ, range 0-1277 mU L(-1) ]). Intracoronary samples (median 109 mU L(-1) , range 0-759 mU L(-1) ) contained higher CPU levels than did peripheral samples (median between LOD-LOQ, range 0-107 mU L(-1) ), indicating increased local CPU generation. With regard to proCPU, we found lower levels in AMI patients (median 910 U L(-1) , range 706-1224 U L(-1) ) than in controls (median 1010 U L(-1) , range 753-1396 U L(-1) ).

CONCLUSIONS

AMI patients have higher plasma CPU levels and lower proCPU levels than controls. This finding indicates in vivo generation of functional active CPU in patients with AMI.

Do laparoscopic colectomy techniques compromise oncologic principles?

BACKGROUND

Benefits of laparoscopic techniques over traditional open techniques in colon surgery are well recognized. Although both hand-assisted laparoscopic colectomy (HALC) and laparoscopic-assisted colectomy (LAC) can beeffective in the treatment of colon cancer, the superiority of either technique has yet to be determined for oncologic procedures.

MATERIALS AND METHODS

A five-year retrospective study comparing outcomes of hand-assisted laparoscopic and laparoscopic-assisted colectomies for cancer was conducted at our community-based teaching hospital. Demographic data, tumor location and stage of the disease were analyzed. Outcomes compared between the two procedures included number of lymph nodes retrieved, presence of positive margins, operative time, length of stay, and number of early postoperative complications.

RESULTS

Fifty patients underwent HALC, while 23 underwent LAC during the study period. Demographic data were similar between the two groups. Operative time was longer for LAC, compared with HALC (178 vs. 125 min., p < 0.05), however, the average number of lymph nodes retrieved was significantly higher in LAC compared with HALC (14 vs. 10, p < 0.05). No significant differences were recorded for positive margins, postoperative complications, or the length of hospital stay.

CONCLUSIONS

While HALC was more prevalent at our institution and proved to be associated with decreased operative times, the number of lymph nodes retrieved was sub-optimal and compared less favorably with LAC. Above all, oncologic principles should be respected and achieved regardless of the operative technique used.

Carboxypeptidase U (TAFIa) prevents lysis from proceeding into the propagation phase through a threshold-dependent mechanism

In an in vitro clot lysis model in human plasma, carboxypeptidase U (CPU) is generated by thrombin following the coagulation and by plasmin at the later stage of clot lysis. CPU is able to slow down clot lysis by suppressing the cofactor activity of partially degraded fibrin in the plasminogen activation by tissue-type plasminogen activator (t-PA). Making use of thrombomodulin and a thrombin inhibitor, the generation of CPU during the in vitro clot lysis can be manipulated both in terms of magnitude and time course. The data obtained demonstrate that CPU affects the clot dissolution through a threshold-dependent mechanism: as long as the CPU activity remains above the threshold value, lysis is prevented from proceeding into the propagation phase. From the moment the CPU activity drops below this threshold value, the rate of lysis accelerates. This threshold value for CPU activity is dictated by the t-PA concentration: increasing the t-PA concentration increases the CPU threshold and vice versa. This implies that the effect of the CPU pathway will become more apparent at a lower fibrinolytic capacity. Our threshold-based hypothesis indicates that the time course of proCPU activation, the stability of CPU and the t-PA concentration all play a crucial role in determining the result of the in vitro clot lysis experiment. Furthermore, this hypothesis provides us with new insights into previously published data on the effects of CPU on in vitro clot lysis by high and low t-PA concentrations.

Anti-acne effects of Oriental herb extracts: a novel screening method to select anti-acne agents

The acne-therapeutic effects of Oriental herb extracts were investigated in terms of antichemotactic effect on polymorphonuclear leucocytes, antilipogenic actions, antibacterial activity against Propionibacterium acnes and resistance induction potency in the bacteria. The ethanol extract (0.01%) of Angelica dahurica markedly suppressed neutrophil chemotaxis, comparable to the effect of erythromycin (0.01%), whereas a strong antilipogenic effect was obtained with rhizoma coptidis (Coptis chinensis) extract (0.01%), leading to a higher efficacy than that of retinoic acid (0.01%). Interestingly, only Glycyrrhiza glabra showed a remarkable antibacterial activity against P. acnes, resulting in negligible induction of resistance, in comparison with a marked development of resistance in the bacteria treated with erythromycin. We suggest that an appropriate formulation containing A. dahurica, rhizoma coptidis and G. glabra could be helpful for the prevention and treatment of acne lesions.

Condylar adaptation after alteration of vertical dimension in adult rhesus monkeys, Macaca mulatta

Remodeling in the cartilage of the mandibular condyle was investigated in young adult monkeys after an increase in vertical dimension of the midface through the use of a tooth-borne intraoral appliance. Six young adult male rhesus monkeys had bite-splints of 5 mm, 10 mm or 15 mm cemented to their maxillary dentition for 48 weeks. Five age- and sex-matched monkeys were used as controls. The thickness of the articular tissue and of the prechondroblastic and chondroblastic layers of the condylar cartilage in the superior, posterosuperior and posterior regions was measured from parasagittal sections of the temporomandibular joint (TMJ). It was found that articular tissue thickness was reduced in the superior region; the prechondroblastic layer, absent in control animals, was very distinctive (30-75 microns) in experimental animals; and there was a 62% increase in the thickness of the chondroblastic layer in the experimental animals. These findings indicate that chronic alteration of mandibular posture via increase in vertical dimension stimulates progressive remodeling of the mandibular condyle in young adult monkeys.

Inflammatory mediator profile in urine and bladder wash fluid of patients with interstitial cystitis

Interstitial cystitis is a syndrome of urinary urgency, frequency and suprapubic pain. We investigated the role of inflammatory mediators in 96 patients with histories and symptoms consistent with interstitial cystitis, and 13 controls from The New York Hospital-Cornell Medical Center, University of Washington and University of California at San Diego. Patients were classified into either group A (meets all criteria of the National Institute of Arthritis, Diabetes, Digestive and Kidney Diseases for inclusion in research studies), group B (meets all of these criteria but without glomerulations) or an "other" group. A small number of group A patients had detectable interleukin-6 in the urine. Urinary concentrations of tumor necrosis factor, prostaglandins E2, D2 and F2 alpha, and thromboxane B2 were not different among either patient groups or controls. Urine specimens contained inhibitors of the bioactivity of interleukin-6 and tumor necrosis factors but no differences between patients or controls were found. No factors chemotactic for human neutrophils were detected in a small patient sample. Bladder wash fluid concentrations of prostaglandins E2, D2 and F2 alpha, and thromboxane were much lower than urinary levels. Bladder wash fluid interleukin-6 and tumor necrosis factor were not detectable. The results suggest that while a small subset of patients may have elevated levels of interleukin-6 the majority of patients do not appear to have elevated levels of inflammatory mediators in the urine or bladder wash fluid. Evaluation of patient bladder tissue may indicate changes not detectable in urine or bladder wash fluid. Alternatively, other etiologies must be considered in those patients.