Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model

OBJECTIVES

To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model.

MATERIALS AND METHODS

Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed.

RESULTS

A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91.

CONCLUSION

The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model.

CLINICAL RELEVANCE STATEMENT

This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods.

KEY POINTS

Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

BACKGROUND

Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001).

INTERPRETATION

Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor.

FUNDING

Boehringer Ingelheim and Eli Lilly.

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

BACKGROUND

The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1).

INTERPRETATION

In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease.

FUNDING

Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.

Improvement of semantic segmentation through transfer learning of multi-class regions with convolutional neural networks on supine and prone breast MRI images

Semantic segmentation of breast and surrounding tissues in supine and prone breast magnetic resonance imaging (MRI) is required for various kinds of computer-assisted diagnoses for surgical applications. Variability of breast shape in supine and prone poses along with various MRI artifacts makes it difficult to determine robust breast and surrounding tissue segmentation. Therefore, we evaluated semantic segmentation with transfer learning of convolutional neural networks to create robust breast segmentation in supine breast MRI without considering supine or prone positions. Total 29 patients with T1-weighted contrast-enhanced images were collected at Asan Medical Center and two types of breast MRI were performed in the prone position and the supine position. The four classes, including lungs and heart, muscles and bones, parenchyma with cancer, and skin and fat, were manually drawn by an expert. Semantic segmentation on breast MRI scans with supine, prone, transferred from prone to supine, and pooled supine and prone MRI were trained and compared using 2D U-Net, 3D U-Net, 2D nnU-Net and 3D nnU-Net. The best performance was 2D models with transfer learning. Our results showed excellent performance and could be used for clinical purposes such as breast registration and computer-aided diagnosis.

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma

OBJECTIVE

To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets.

RESULTS

Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set.

CONCLUSION

The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

OBJECTIVES

Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients.

METHODS

A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients.

RESULTS

Reproducibility was excellent for ADC and CBV features (ICC, 0.82-0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64-0.99] vs. AUC, 0.81 [0.60-1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61-0.95] vs. AUC, 0.65 [0.46-0.84], p = 0.23).

CONCLUSION

DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis.

KEY POINTS

• Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers. • DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma.

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status

We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

Machine Learning Approach to Identify Stroke Within 4.5 Hours

Background and Purpose- We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging to identify patients within the recommended time window for thrombolysis. Methods- We analyzed DWI and FLAIR images of consecutive patients with acute ischemic stroke within 24 hours of clear symptom onset by applying automatic image processing approaches. These processes included infarct segmentation, DWI, and FLAIR imaging registration and image feature extraction. A total of 89 vector features from each image sequence were captured and used in the ML. Three ML models were developed to estimate stroke onset time for binary classification (≤4.5 hours): logistic regression, support vector machine, and random forest. To evaluate the performance of ML models, the sensitivity and specificity for identifying patients within 4.5 hours were compared with the sensitivity and specificity of human readings of DWI-FLAIR mismatch. Results- Data from a total of 355 patients were analyzed. DWI-FLAIR mismatch from human readings identified patients within 4.5 hours of symptom onset with 48.5% sensitivity and 91.3% specificity. ML algorithms had significantly greater sensitivities than human readers (75.8% for logistic regression, P=0.020; 72.7% for support vector machine, P=0.033; 75.8% for random forest, P=0.013) in detecting patients within 4.5 hours, but their specificities were comparable (82.6% for logistic regression, P=0.157; 82.6% for support vector machine, P=0.157; 82.6% for random forest, P=0.157). Conclusions- ML algorithms using multiple magnetic resonance imaging features were feasible even more sensitive than human readings in identifying patients with stroke within the time window for acute thrombolysis.

Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma

We aimed to establish a high-performing and robust classification strategy, using magnetic resonance imaging (MRI), along with combinations of feature extraction and selection in human and machine learning using radiomics or deep features by employing a small dataset. Using diffusion and contrast-enhanced T1-weighted MR images obtained from patients with glioblastomas and primary central nervous system lymphomas, classification task was assigned to a combination of radiomic features and (1) supervised machine learning after feature selection or (2) multilayer perceptron (MLP) network; or MR image input without radiomic feature extraction to (3) two neuro-radiologists or (4) an end-to-end convolutional neural network (CNN). The results showed similar high performance in generalized linear model (GLM) classifier and MLP using radiomics features in the internal validation set, but MLP network remained robust in the external validation set obtained using different MRI protocols. CNN showed the lowest performance in both validation sets. Our results reveal that a combination of radiomic features and MLP network classifier serves a high-performing and generalizable model for classification task for a small dataset with heterogeneous MRI protocols.

Two-layer compression hosiery for patients with venous leg ulceration

Patients may experience a recurrence of leg ulceration if they do not wear prescribed compression hosiery when ulcers have healed. This article examines the use of compression hosiery kits, which can be used for the treatment of active ulceration as well as prevention of recurrence. For some patients, the stockings may be easier to apply than traditional compression hosiery, thereby enabling them to benefit from continued wear.

Experimental and theoretical studies on the thermal decomposition of heterocyclic nitrosimines

A series of substituted 2-nitrosiminobenzothiazolines (2) were synthesized by the nitrosation of the corresponding 2-iminobenzothiazolines (6). Thermal decomposition of 2a--f and of the seleno analogue 7 in methanol and of 3-methyl-2-nitrosobenzothiazoline (2a) in acetonitrile, 1,4-dioxane, and cyclohexane followed first-order kinetics. The activation parameters for thermal deazetization of 2a were measured in cyclohexane (Delta H(++) = 25.3 +/- 0.5 kcal/mol, Delta S(++) = 1.3 +/- 1.5 eu) and in methanol (Delta H(++) = 22.5 +/- 0.7 kcal/mol, Delta S(++) = -12.9 +/- 2.1 eu). These results indicate a unimolecular decomposition and are consistent with a proposed stepwise mechanism involving cyclization of the nitrosimine followed by loss of N(2). The ground-state conformations of the parent nitrosiminothiazoline (9a) and transition states for rotation around the exocyclic C==N bond, electrocyclic ring closure, and loss of N(2) were calculated using ab initio molecular orbital theory at the MP2/6-31G* level. The calculated gas-phase barrier height for the loss of N(2) from 9a (25.2 kcal/mol, MP4(SDQ, FC)/6-31G*//MP2/6-31G* + ZPE) compares favorably with the experimental barrier for 2a of 25.3 kcal/mol in cyclohexane. The potential energy surface is unusual; the rotational transition state 9a-rot-ts connects directly to the orthogonal transition state for ring-closure 9aTS. The decoupling of rotational and pseudopericyclic bond-forming transition states is contrasted with the single pericyclic transition state (15TS) for the electrocyclic ring-opening of oxetene (15) to acrolein (16). For comparison, the calculated homolytic strength of the N--NO bond is 40.0 kcal/mol (MP4(SDQ, FC)/6-31G*//MP2/6-31G* + ZPE).

Mechanisms of female urinary continence under stress: frequency spectrum analysis

Intravesical and urethral pressure signals during cough and Valsalva maneuvers for 15 continent women were analyzed with frequency spectrum analysis. Clear modulation of the urethral pressure changes by the intravesical pressure rise during stress maneuvers was demonstrated in the frequency bands of 14 and 7 Hz for cough and Valsalva, respectively. The linearity between the urethral and intravesical pressure signals was strong for cough, but relatively weaker for Valsalva. The observed linearity lead to the formulation of a modified continence equation to mathematically quantify stress leak point pressure (sLPP): sLPP=MUCP/(1-alpha1)+RBP. This algebraic equation demonstrated that sLPP depends on pressure transmission, resting bladder pressure, and maximum urethral closure pressure. The equation was validated with excellent theoretical predictions for the 15 continent subjects (R(2)=0.98 and 0.97 for cough and Valsalva leak point pressure, respectively) and good but somewhat weaker predictions for 46 stress incontinent women (R(2)=0.79 and 0.48, respectively). It has been shown that pressure transmission plays the most important role in female continence function, while it may be attributable to passive structural origin as evidenced by the minimal time delay between the two pressure signals, in the order of a few milliseconds. It can be concluded that coughing seems to have a more mechanical, rather than neuromuscular basis for its signal dynamics. This study suggests that a complete assessment of female stress continence function requires comprehensive urodynamic information in terms of pressure transmission, maximum urethral closure pressure, and resting bladder pressure.

Physical activity surveillance in the 21(st) century

PURPOSE: The 1996 Surgeon General's Report on Physical Activity and Health emphasized the health-related benefits of moderate-intensity physical activities, especially everyday activities. Unfortunately most surveillance systems in the U.S. primarily measure sports-related activities, particularly vigorous intensity sports. This report describes a new physical activity surveillance instrument designed to go beyond our current measures and include moderate-intensity everyday activities.METHODS: Data were collected from a nationally representative sample of adults (n = 5010). Questions were asked about occupational activity (mostly sitting; mostly walking; mostly heavy labor); walking (for exercise, transportation, or any other reason); moderate-intensity activities (brisk walking, yard work, vacuuming); vigorous-intensity activities (running, aerobics, heavy yard work); and strengthening activities (lifting weights, pull-ups, sit-ups). Questions included frequency and duration of activities. Respondents were defined as recommended if they participated in either moderate-intensity activities >/=30 min/day for >/=5 days/wk OR vigorous-intensity activities >/=20 min/day for >/=3 days/wk. Insufficient was defined as not meeting recommended levels while inactive was defined as no leisure-time activity.RESULTS: Overall 40% of adults were in the recommended group, 44% were in the insufficient group and 16% were inactive. Among working adults 37% had jobs that involved mostly walking or heavy labor and about 50% of those also were in the recommended group. Among the 63% of working adults who report mostly sitting at work, 39% were in the recommended group.CONCLUSIONS: These results suggest that measuring only leisure-time physical activity may under represent the physical activity experience of many U.S. adults. Broadening the concept of physical activity beyond traditional sports-related vigorous "exercise" may provide a more accurate picture of the prevalence of health-related physical activity.

Kaolin-induced hydrocephalus in the hamster: temporal sequence of changes in intracranial pressure, ventriculomegaly and whole-brain specific gravity

The development of kaolin-induced hydrocephalus in adult hamsters was monitored by measuring changes in intracranial pressure (ICP), ventriculomegaly (VG) and whole-brain specific gravity (SG). Controls were intact or sham operated animals. Relative to controls, ICP of experimental animals increased at 24 h post intracisternal kaolin injection (by approximately 7-fold), reached a maximum on day 6 (by approximately 12-fold) and remained markedly elevated through day 15 (by approximately 5-fold). Ventricles differed in time of onset of distension (third: day 1, lateral: day 2, fourth: day 4) and in time of maximum ventriculomegaly (fourth: day 6; third: day 7; and lateral: day 9). Ventricular distension resulted in alterations in the ependyma; cilia were lost and apical cell surfaces were distorted. The ependyma was ruptured and the subjacent neuropil was exposed to the cerebrospinal fluid in some regions. Whole-brain SG remained constant in controls but declined in hydrocephalic hamsters after day 3 post-kaolin injection and reached its nadir on day 9 when whole-brain water content was 18% greater than in controls. Consistent with the fact that causal relationships exist between increased ICP, ventricular distension and brain edema, the alterations in each parameter occurred sequentially rather than simultaneously, and the time-course of each manifestation of hydrocephalus differed. The data suggest that the pathophysiology of kaolin-induced hydrocephalus in the hamster is tri-phasic: an initial period of rapid change, a brief interval of maximum alteration, and a subsequent period of compensation.

Chemical meningitis from bile reflux in a lumbar-gallbladder shunt

OBJECTIVE AND IMPORTANCE

The gallbladder is used to divert cerebrospinal fluid (CSF) in patients with hydrocephalus when all other sites have been exhausted. This is seen in hydrocephalic patients who have reached teenage years but have undergone multiple shunt revisions, abdominal operations and repeated neck vein cannulations during childhood. One complication of the lumbar-gallbladder shunt is discussed as well as its pathophysiologic theory and management.

CLINICAL PRESENTATION

A case of a patient with a lumbar-gallbladder shunt who developed chemical meningitis from reflux of bile into the CSF space is presented. The patient presented with generalized seizures.

INTERVENTION

Included: ventilatory support, externalization of the shunt, correction of the metabolic abnormalities and administration of anticonvulsants and steroids.

CONCLUSION

This case illustrates an unusual occurrence of reflux of bile into the CNS through a lumbar-gallbladder shunt in a patient with long-term complex communicating hydrocephalus. It also demonstrates its mode of presentation and successful management. To our knowledge, this is the first case report of its kind.

Rumen motility during induced hyper- and hypocalcaemia

Rumen motility was recorded on an experimental cow by means of telemetric signal transfer from strain gauge force transducers fixed surgically on the peritoneal surface of the rumen wall in the left flank. The normocalcaemic cow was given a standard milk fever treatment with calcium borogluconate (400 ml with 14 mg Ca/ml) intravenously. Transient clinical signs were: decreased rumination, muscle ticks, salivation and a heart rate reduction of 20%. Rectal temperature remained unaltered. Frequency of rumen contractions was reduced up to 40% whereas amplitude of contractions did not deviate from baseline values. Hypocalcaemia was induced in a second experiment by iv infusion of Na2EDTA. At 0.60 mmol/l ionized blood calcium periods of no motility were recorded whereas inactivity of rumen activity was persistent at 0.55 mmol/l ionized blood calcium. The cow went down at 0.45-0.48 mmol/l ionized blood calcium at which point the heart rate was increased by 40%. The high sensitivity of the method employed allowed the conclusion that already at a concentration of ionized blood calcium at 1.0 mmol/l both frequency and amplitude of rumen contractions decreased rapidly although eating behaviour and rumination appeared unaffected during the short term observation periods. Implications of this finding towards health and production in transition cows are discussed.

Scanning electron microscopy of central nervous system cerebrospinal-fluid-contacting surfaces: a bibliography (1963-1995)

This bibliography is compiled to assist in locating papers related to the application of scanning electron microscopy (SEM) to cerebrospinal-fluid-contacting surfaces in vertebrates. The use of SEM by neuroscientists has continued apace since the publication of the first bibliography in 1980. SEM studies now include more than 50 species of vertebrates and range from cyclostomes to humans; they encompass development from embryo to senescence and concern both normal and pathologic morphology. Although remarkable strides have been made toward a greater understanding of many aspects of the structure and function of cerebrospinal-fluid-contacting surfaces, many significant problems await the judicious application of scanning electron microscopy.

Personality and scientific promise

This study examined personality characteristics of 21 boys and 11 girls, children gifted in science and attending a junior high school. Differences between these boys and girls in Grade 6 were noted. Implications for teaching and directions for future research are noted.