Socio-economic factors and medical conditions affecting regular stomach cancer screening in Korea: a retrospective longitudinal study using national public health data for 11 years

OBJECTIVE

This study aimed to explore socio-economic factors and medical conditions that affect regular stomach cancer (SC) screening among Korean adults.

STUDY DESIGN

This was a retrospective observational study.

METHODS

Study subjects were 5545 adults aged ≥40 years who participated in the 2007-2012 Korean National Health and Nutrition Examination Survey and were followed up to year 2017 based on data linking to the Korean National Health Insurance Service and Korean Health Insurance Review and Assessment. Socio-economic factors included sex, age, residential area, education, occupation, marital status, disability, public and private health insurance, service through local public health organizations, history of cancer except for SC, and family history of SC. Medical factors included six gastric lesions with the possibility of facilitating SC screening, including benign gastric neoplasm, chronic atrophic gastritis, gastric polyp, Helicobacter pylori infection, intestinal metaplasia, and peptic ulcers. The outcome was adherence to SC screening, which was divided into non-adherence, irregular adherence, and regular adherence.

RESULTS

After adjusting for the effects of socio-economic factors, multivariate ordinal logistic regression revealed that participants with a history of four types of gastric lesions were more likely to regularly participate in SC screening: chronic atrophic gastritis (odds ratio [OR] 1.567; 95% confidence interval [CI] = 1.276-1.923), gastric polyps (OR 1.565; 95% CI = 1.223-2.003), H. pylori infection (OR 1.637; 95% CI = 1.338-2.003), and peptic ulcer (OR 2.226; 95% CI 1.750-2.831).

CONCLUSIONS

To improve participation in SC screening, it is necessary to implement personalized strategies for individuals at risk for gastric cancer in addition to population-based strategies for vulnerable groups.

The effect of intensive statin therapy in non-symptomatic intracranial arteries: The STAMINA-MRI sub-study

Background and aims

Pleiotropic effects of statins result in the stabilization of symptomatic intracranial arterial plaque. However, little is known about the effect of statins in non-symptomatic cerebral arteries. We hypothesized that intensive statin therapy could produce a change in the non-symptomatic cerebral arteries.

Methods

This is a sub-study of a prospective observational study under the title of "Intensive Statin Treatment in Acute Ischemic Stroke Patients with Intracranial Atherosclerosis: a High-Resolution Magnetic Resonance Imaging (HR-MRI) study." Patients with statin-naive acute ischemic stroke who had symptomatic intracranial artery stenosis (above 50%) were recruited for this study. HR-MRI was performed to assess the patients' cerebral arterial status before and 6 months after the statin therapy. To demonstrate the effect of statins in the non-symptomatic segment of intracranial cerebral arteries, we excluded symptomatic segments from the data to be analyzed. We compared the morphological changes using cerebrovascular morphometry.

Results

A total of 54 patients (mean age: 62.9 ± 14.4 years, 59.3% women) were included in this study. Intensive statin therapy produced significant morphological changes of overall cerebral arteries. Among the morphological features, the arterial luminal area showed the highest number of significant changes with a range from 5.7 and 6.7%. Systolic blood pressure (SBP) was an independent factor associated with relative changes in posterior circulation bed maximal diameter percentage change (beta -0.21, 95% confidence interval -0.36 to -0.07, p = 0.005).

Conclusion

Intensive statin therapy produced a favorable morphological change in cerebral arteries of not only the target arterial segment but also non-symptomatic arterial segments. The change in cerebral arterial luminal diameter was influenced by the baseline SBP and was dependent on the topographic distribution of the cerebral arteries.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02458755.

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the 'segmentation-stacking' method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image's 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

OBJECTIVE

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast.

METHODS

We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach.

RESULTS

Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set.

CONCLUSION

Performance gains were replicated in the validation cohort.

SIGNIFICANCE

We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement-Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

Robust multimodal fusion network using adversarial learning for brain tumor grading

BACKGROUND AND OBJECTIVE

Gliomas are graded using multimodal magnetic resonance imaging, which provides important information for treatment and prognosis. When modalities are missing, the grading is degraded. We propose a robust brain tumor grading model that can handle missing modalities.

METHODS

Our method was developed and tested on Brain Tumor Segmentation Challenge 2017 dataset (n = 285) via nested five-fold cross-validation. Our method adopts adversarial learning to generate the features of missing modalities relative to the features obtained from a full set of modalities in the latent space. An attention-based fusion block across modalities fuses the features of each available modality into a shared representation. Our method's results are compared to those of two other models where 15 missing-modality scenarios are explicitly considered and a joint training approach with random dropouts is used.

RESULTS

Our method outperforms the two competing methods in classifying high-grade gliomas (HGGs) and low-grade gliomas (LGGs), achieving an area under the curve of 87.76% on average for all missing-modality scenarios. The activation maps derived with our method confirm that it focuses on the enhancing portion of the tumor in HGGs and on the edema and non-enhancing portions of the tumor in LGGs, which is consistent with prior expertise. An ablation study shows the added benefits of a fusion block and adversarial learning for handling missing modalities.

CONCLUSION

Our method shows robust grading of gliomas in all cases of missing modalities. Our proposed network might have positive implications in glioma care by learning features robust to missing modalities.

Overview of radiomics in prostate imaging and future directions

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.

Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients

Introduction

To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs).

Methods

We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots.

Results

Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]).

Conclusion

In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

Are radiomics features universally applicable to different organs?

BACKGROUND

Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments.

METHODS

Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated.

RESULTS

Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified.

CONCLUSION

Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.

Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance

We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis ("heterogenous enhancing", "rim-enhancing necrotic", and "cystic" subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation.

Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer

Background

Radiomics suffers from feature reproducibility. We studied the variability of radiomics features and the relationship of radiomics features with tumor size and shape to determine guidelines for optimal radiomics study.

Methods

We dealt with 260 lung nodules (180 for training, 80 for testing) limited to 2 cm or less. We quantified how voxel geometry (isotropic/anisotropic) and the number of histogram bins, factors commonly adjusted in multi-center studies, affect reproducibility. First, features showing high reproducibility between the original and isotropic transformed voxel settings were identified. Second, features showing high reproducibility in various binning settings were identified. Two hundred fifty-two features were computed and features with high intra-correlation coefficient were selected. Features that explained nodule status (benign/malignant) were retained using the least absolute shrinkage selector operator. Common features among different settings were identified, and the final features showing high reproducibility correlated with nodule status were identified. The identified features were used for the random forest classifier to validate the effectiveness of the features. The properties of the uncalculated feature were inspected to suggest a tentative guideline for radiomics studies.

Results

Nine features showing high reproducibility for both the original and isotropic voxel settings were selected and used to classify nodule status (AUC 0.659–0.697). Five features showing high reproducibility among different binning settings were selected and used in classification (AUC 0.729–0.748). Some texture features are likely to be successfully computed if a nodule was larger than 1000 mm³.

Conclusions

Features showing high reproducibility among different settings correlated with nodule status were identified.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0239-z) contains supplementary material, which is available to authorized users.

Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores

To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers.

Classification of the glioma grading using radiomics analysis

Background

Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading.

Methods

We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort.

Results

Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts.

Discussion

Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.

Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer

Purpose: To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings.Experimental Design: We identified 294 patients with invasive breast cancer who underwent preoperative MRI. Patients were randomly divided into training (n = 194) and validation (n = 100) sets. A radiomics signature (Rad-score) was generated using an elastic net in the training set, and the cutoff point of the radiomics signature to divide the patients into high- and low-risk groups was determined using receiver-operating characteristic curve analysis. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of the radiomics signature, MRI findings, and clinicopathological variables with DFS. A radiomics nomogram combining the Rad-score and MRI and clinicopathological findings was constructed to validate the radiomic signatures for individualized DFS estimation.Results: Higher Rad-scores were significantly associated with worse DFS in both the training and validation sets (P = 0.002 and 0.036, respectively). The radiomics nomogram estimated DFS [C-index, 0.76; 95% confidence interval (CI); 0.74-0.77] better than the clinicopathological (C-index, 0.72; 95% CI, 0.70-0.74) or Rad-score-only nomograms (C-index, 0.67; 95% CI, 0.65-0.69).Conclusions: The radiomics signature is an independent biomarker for the estimation of DFS in patients with invasive breast cancer. Combining the radiomics nomogram improved individualized DFS estimation. Clin Cancer Res; 24(19); 4705-14. ©2018 AACR.

Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study

Attention deficit and hyperactivity disorder (ADHD) is a disorder characterized by behavioral symptoms including hyperactivity/impulsivity among children, adolescents, and adults. These ADHD related symptoms are influenced by the complex interaction of brain networks which were under explored. We explored age-related brain network differences between ADHD patients and typically developing (TD) subjects using resting state fMRI (rs-fMRI) for three age groups of children, adolescents, and adults. We collected rs-fMRI data from 184 individuals (27 ADHD children and 31 TD children; 32 ADHD adolescents and 32 TD adolescents; and 31 ADHD adults and 31 TD adults). The Brainnetome Atlas was used to define nodes in the network analysis. We compared three age groups of ADHD and TD subjects to identify the distinct regions that could explain age-related brain network differences based on degree centrality, a well-known measure of nodal centrality. The left middle temporal gyrus showed significant interaction effects between disease status (i.e., ADHD or TD) and age (i.e., child, adolescent, or adult) (P < 0.001). Additional regions were identified at a relaxed threshold (P < 0.05). Many of the identified regions (the left inferior frontal gyrus, the left middle temporal gyrus, and the left insular gyrus) were related to cognitive function. The results of our study suggest that aberrant development in cognitive brain regions might be associated with age-related brain network changes in ADHD patients. These findings contribute to better understand how brain function influences the symptoms of ADHD.

Theoretical and experimental studies for high-repetition-rate disordered crystal lasers with harmonic self-mode locking

A harmonically self-mode-locked Nd:Sr3Y2/(BO3)4 disordered crystal laser with subpicosecond pulse duration is demonstrated. We exploit the damped harmonic oscillator model to numerically verify that the mode spacing of the laser cavity can be modified to be the harmonics of the free spectral range of the Fabry-Perot cavity when the optical length of the laser cavity is close to a commensurate ratio of the optical length of the Fabry-Perot cavity. In experiment, the Fabry-Perot cavity can be formed by the pump facet of the disordered crystal and the front mirror. A 110 GHz single-pulse harmonically mode-locked pulse train with pulse duration of 857 fs is experimentally achieved under optical lengths of 27.19 and 4.08 mm for the laser cavity and Fabry-Perot cavity respectively, corresponding to a fractional number of 20/3. A maximum output power of 162 mW is obtained at an incident pump power of 3.1 W.

Reconstruction algorithm for nasal basal cell carcinoma with skin involvement only: analysis of 221 cases repaired by minor surgery

BACKGROUND

Basal cell carcinoma (BCC) often occurs on the nose. Reconstruction of the nose should yield excellent aesthetic and functional outcomes.

AIM

We propose a technical algorithm for the reconstruction of surgical defects, based on our analysis of 221 cases of nasal BCC with skin involvement only, which could be repaired by minor surgery.

METHODS

The aesthetic and functional outcomes for various reconstruction techniques were analysed according to defect location and size. A reconstruction algorithm was proposed with the aim of obtaining the best surgical results.

RESULTS

Defect location and size were key considerations. Primary closure was the first option for small defects (< 10 mm), with scores of 3.4 for objective aesthetic outcome (OAO), 3.2 for subjective aesthetic outcome (SAO) and 3.3 for subjective functional outcome (SFO). The first option for medium defects (1-20 mm) was the island pedicle flap, with scores of 3.5 for OAO, 3.2 for SAO and 3.7 for SFO. The first option for large defects (> 20 mm) was the transposition flap for the upper nose (scores of 2.0 for OAO and SAO and 3.0 for SFO) and the interpolation flap for the lower nose (2.8 for OAO and 2.9 for SAO and SFO).

CONCLUSIONS

We have proposed an algorithm to select the optimal technique for repairing nasal BCC surgical defects according to their size and location.

Association between pelvic organ prolapse and bone mineral density in postmenopausal women

Both pelvic organ prolapse (POP) and osteoporosis are age-related diseases in older aged women. Both POP and bone metabolism may be associated with collagen metabolism. Our study determined the relationship between POP and bone mineral density (BMD) of the lumbar spine and femur neck in postmenopausal women. We selected 554 postmenopausal women (aged 50-79 years) and divided them into two groups (moderate to severe POP and absent to mild POP). We compared the BMDs of the lumbar spine and femur neck between the moderate to severe POP and absent to mild POP groups. Lumbar spine BMD was inversely correlated with POP severity (p = 0.001). However, after adjusting for age, time since menopause, height, weight, body mass index (BMI), and vaginal delivery, the BMDs of both the lumbar spine and femur neck were not significantly different between the moderate to severe POP and absent to mild POP groups (p = 0.583 and p = 0.305, respectively). A lower BMD is associated with increased fracture risk and we postulated that women with severe POP would have an increased risk of osteoporotic fracture.

Immunohistochemical changes of adenomyosis after heat therapy: comparison of radiofrequency myolysis and endoablation

PURPOSE

To check the pathologic changes of focal adenomyosis after heat therapy using radiofrequency and to evaluate which approach--endometrial ablation or direct heat therapy--is better for adenomyosis. To evaluate whether the timing of the procedure and the menstrual cycle are related to pathologic outcomes after heat therapy.

METHODS

This study included nine women who underwent total hysterectomy for adenomyosis (diameter, > or = 6 cm). Six fresh uteri were excised in the midline and subjected to radiofrequency heat therapy at the center of the adenomyomas (direct heat therapy) and three uteri were subjected to endometrial ablation. Thereafter, 1 cm(3) myometrial tissue was obtained at 1 cm, 2 cm, and 3 cm away from the endometrium. Tissue sections were stained with hematoxylin and eosin. Immunohistochemical analysis using antibodies against cytokerain-19 (CK-19), actin, and estrogen receptor/progesterone receptor (ER/PR) was performed to evaluate CK-19 (endometrial epithelium marker), actin (myometrial marker) and ER/PR (checking the state of the menstrual cycle), respectively.

RESULTS

After endometrial ablation, cauterized tissues were not noted 2 cm away from the endometrium. All tissues between the endometruim and center of adenomyosis were cauterized after direct heat therapy. During the uterine proliferative phase, unlike the secretory phase, subendometrial layers were cauterized 10 min after direct cauterization.

CONCLUSION

Direct heat therapy is more effective than endometrial ablation in adenomyosis, and heat is conducted effectively when the patients are in the proliferative phase.

Analysis of particle laden flow and heat transfer in cascade and rocket nozzle

This paper presents results for the calculation of particle trajectories in a cascade and a rocket nozzle using a Lagrangian method. When the floating particles collide to the components, the component surface is damaged severely. The surface erosion rate is strongly dependent on a particle size, a particle impact angle and a surface material. For a compressor cascade, the particle impact rate increases proportionally with the flow inlet angle and the erosion rate on the pressure side surface of blade are related to the surface or coating materials. For a solid rocket nozzle, the particle free zone in the nozzle divergent section increases quickly with increasing particle size and the maximum heat transfer density occurs at the starting region of nozzle convergent section. The Al2O3 droplet breaks up around the nozzle throat due to the high velocity difference between the droplet and gas stream, resulting in the big change of particle free zone.

Inhibitory effect of mast cell-mediated immediate-type allergic reactions in rats by spirulina

We investigated the effect of spirulina on mast cell-mediated immediate-type allergic reactions. Spirulina dose-dependently inhibited the systemic allergic reaction induced by compound 48/80 in rats. Spirulina inhibited compound 48/80-induced allergic reaction 100% with doses of 100-1000 microg/g body weight, i.p. Spirulina (10-1000 microg/g body weight, i.p.) also significantly inhibited local allergic reaction activated by anti-dinitrophenyl (DNP) IgE. When rats were pretreated with spirulina at a concentration ranging from 0.01 to 1000 microg/g body weight, i.p., the serum histamine levels were reduced in a dose-dependent manner. Spirulina (0.001 to 10 microg/mL) dose-dependently inhibited histamine release from rat peritoneal mast cells (RPMC) activated by compound 48/80 or anti-DNP IgE. The level of cyclic AMP in RPMC, when spirulina (10 microg/mL) was added, transiently and significantly increased about 70-fold at 10 sec compared with that of control cells. Moreover, spirulina (10 microg/mL) had a significant inhibitory effect on anti-DNP IgE-induced tumor necrosis factor-alpha production. These results indicate that spirulina inhibits mast cell-mediated immediate-type allergic reactions in vivo and in vitro.